ISSN 2226-6577 ISBM-Fall 2016 October 7-9, 2016, Taipei ... · PDF fileISBM-Fall 2016 October 7-9, 2016, Taipei 1 ISSN 2226-6577 1 Factors Affecting E-commerce Taxation Based on Tax-base

Table of Contents

Factors Affecting E-commerce Taxation Based on Tax-base Extension Policy of Tourism In-dustry in Bangkok and VicinityDoungkamol Duchdara, Titaporn Sincharoonsak, Amara Tirasriwat, Phassawan Suntraruk .......1

Optimizing Unique Supply Chains: A Case of Automobile Business Continuity ManagementAbednico Lopang Montshiwa, Akio Nagahira .............................................................................13

The Relative Study of Motivation for Participation, Job Engagement, Social Capital and Per-ceived Benefits of the International Club Member - Take the District of 300G2 Lions Clubs International as the Proof ObjectsTsan-Yin Chiu, Hung-Hui Li, Yueh-Ysen Lin ..............................................................................41

The Influence of Greenwash on Purchase Intention: the Mediation Effects of Environmental Attitude and Environmental LoyaltyShan Yu Chen, Fen A Huang .........................................................................................................69

The Impacts of Open Innovation on NPD Project Success and FFE Activities: An Empirical Study in Japanese Manufacturing FirmsKento Shimizu, Akio Nagahira, Sumie Ishihara, Shuichi Ishida ..................................................87

The Impact of Fuzzy Front End Activities and Changes in Project Execution on New Product Development Project Success : Comparative Study between B2B and B2C ProjectsKo Yamasaki, Akio Nagahira, Sumie Ishihara ..............................................................................110

What Is the Impact of Cultural and Creative Parks on Housing Price?Nien-Jin Tsai, Pin-Yang Yeh, Yi-Ting Huang, Yu-Ting Huang, Zone-Han Lin ............................135

The Impact of Night Market Opening on the Local EconomyJia-Hao Ke, Jui-Chi Chiang, Kai-Jie Huang, Ming-Han Tsai, Yu-Jia Liu ....................................136

Entrepreneurial Psychological Capital and Intentions: A Multiple Mediation ModelChen-Yi Peng, Hui-Chen Chang, Kuen-Hung Tsai ......................................................................137

Business Cycle Phases and Their Transitional Dynamics: Does the Financial Condition Mat-ter?Frankie Ho Chi Chau, Rataporn Deesomsak ................................................................................161

Factors Affecting Talent Employee Retention: A Case Study of Automotive Parts Manufactur-ing Company in ThailandNuttawut Rojniruttikul ..................................................................................................................162

ISBM-Fall 2016 October 7-9, 2016, Taipei

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Human Resource Management and Employee Satisfaction Towards Policy and Management: A Case Study of Thermoplastic Injection Manufacturing in ThailandWittaya Saengsaikaew, Nuttawut Rojniruttikul ............................................................................168

Is Your Governance Big Enough for Your Data Challenge?Janne J. Korhonen, Kari Hiekkanen .............................................................................................175

Is Your Information System Secure? An Exploratory StudySohail S Chaudhry, Peggy E Chaudhry ........................................................................................195


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Factors Affecting E-commerce Taxation Based on Tax-base Extension Policy of Tourism Industry in Bangkok and Vicinity

Doungkamol Duchdaraa, Titaporn Sincharoonsaka, Amara Tirasriwatb, Phassawan Suntrarukc*

aSchool of Accountacy, Sripatum University, Bangkok, Thailand

bMartin de Tours School of Management and Economics, Assumption University Bangkok, Thailand

cMahidol University International College, Mahidol University Nakhonpathom, Thailand

*Corresponding author: [email protected]

ABSTRACT

The primary purpose of this study is to investigate the factors influencing E-commerce taxation based on the policy of tax-based extension in tourism industry in Bangkok, Thailand and its vicinity. The research samples were E-commerce business enterpreneurs from 188 firms in tourism industry in Bangkok and its vicinity. The research findings showed that working experience of enterpreneurs, knowledge and undertanding regarding E-commerce taxes and recognition of enterpreneurs’s right in terms of tax filing are the main factors affecting the willingness of enterpreneurs that comply with E-commerce taxation policy. However, enterpreneurs’ educational level, age of the firms, size of the firms, communication channels, public relations and enterprenuers’ duties are insignificant factors. Therefore, it suggested that the policy maker should attempt to encourage awareness and knowledge of entrepreneurs and develop handy E-commerce taxation systems to boost E-commerce tax compliance and increase effectiveness of E-commerce enterprises’ tax collection. Keywords : E-commerce, taxation, tourism industry, Thailand

1. Introduction

The Internet network has been using worldwide, particularly in commercial trading. Thousands of firms have been doing their own business on the Internet network in order to trade goods or services or even transfer funds or data to their stakeholders. This business electronic form is called an electronic commerce (E-commerce). E-commerce firstly began in 1970. At that time, Electronic Fund Transfer (EFT) was firstly used; however, it was popular in only big companies and some financial institutions. Soon after EDI was launched. There was an exapnsion from the former data transfer to only financial data transfer such as transferring data between financial institutions and manufacturers or between wholesalers and retailers, etc. Afterwards there was an establishment of other communication systems or programs from stock sale system for accommodation reservation system. It presented that this world has stepped into communication era. When the era of Internet was appeared in 1990, there was a quick increase of Internet users, which encouraged the emergence of E-commerce. The main reason of rapid growth of E-commerce was supportive programs, including networks. From 1994 to 1999, the E-commerce was really popular due to the number of companies in America emphasizing and joining the E-commerce system (Pongwittayapanu, 2008). E-commerce was firstly emerged in Thailand in 1997. It was firstly launched in government organizations and other educational organiations later. Recently, many Thai government


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regulatory authorities have adopted E-commerce systems. For example, Department of Customs, Ministry of Finance, firstly used Electronic Data Interchange (EDI) for product export from Donmuang port, and later expanded to other ports (Deparment of Customs, 2012). In addition, Department of Business Economics, Ministry of Commerce, has used the E-commerce for product export sending to their members with free of charge and has acted as the Center receiving recommendations and suggestions for organizational certification setting (Ministry of Commerce, 2015). Moreover, Office of Investment Boards provides activities for the enterpreneurs to support and utilize the E-commerce benefits (Department of Export Promotion, 2008). Next, National Electronics and Computer Technology Center (NECTEC), Ministry of Science and Technology arranged activities of Information Technology Law Development Project to serve six E-commerce laws such as E-signature law and E-transfer law, etc. (NECTEC, 2011). Furthermore, Thai International Trade Law Adjustment Subcommittee regarding to E-commerce, Ministry of Justice set a draft of E-commerce law, specifically E-data exchange law (Department of Justice, 2013). Department of Industrial Promotion has prepared database of investment in each type (Department of Industrial Promotion, 2015). Besides, Bank of Thailand and Trade Siam Co., Ltd., have cooperatively joined the development project using EDI in finance (Ministry of Finance, 2015). In addition, Thai Airways Co., Ltd., and Krung Thai Bank have a joined trial project towards e-ticket booking (Thai Airways Public Co., Ltd., 2006). Last but not least, Royal Thai Police have implemented a website to get complaints towards E-commerce crime (Royal Thai Police, 2013). E-commerce on the Internet is a great technological and economic innovation that influences people’s way of life, private business management and government policy declaration, including law, politics, society, culture and public concepts towards products, currency and commerce, etc. In terms of public economy, E-commerce affects the taxation system in Thailand. Recently, the Revenue Department has a policy to expand E-commerce tax base. The reason behind is that in 2012 the Revenue Department was assigned to collect tax for 1.625 trillion baht, 82% of total income (1.98 trillion baht). Normally, the Revenue Department gets higher amount of collected tax as estimated, but in this particular year, the department got 1.616 trillion baht, which was lower than estimation for 8,624 million baht or 0.5%. Moreover, in 2013, the target of taxation was up to 1.774 trillion baht, 84.5% of total income, 2.1 trillion baht, which was 9.8-percent increase comparing with the target in 2012. The increasing amount of target taxation was based on the decrease of income tax rate that lowered from 30% to 23% and finally 20% of net profit. Besides, there was a taxing measure helping the businesess affected by BOI, flood at the end of 2011, which already became effective. As predicted, it caused the loss of income for at least 70,000 million baht per year (Revenue Department, 2015). Since the Revenue Department is an organization that is responsible for cashing up the main income of the government by collecting taxes from the citizen, whenever the government desires more budget for administration, the Revenue Department is ususally forced to oversee the tax payment of taxpayers (Revenue Department, 2013). In terms of e-commerce taxation, Thailand has not still had specific E-commerce taxation law. E-commerce enterpreneurs have to pay for tax based on the Revenue Code as other business does, even income tax, corperate income tax and value added tax becoming effective with all types of business, including E-commerce. Therefore, whenever the customers make an order, they have to pay for income tax (Sirikajronkit, 2010). Moreover, the system of Revenue Department showed that there are several causes: installment of program and operating system of some versions of Windows, data recovery, database backup and data


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transfer, imported data that is different from details of withholding tax, income data details and unrelative write-offs. Recently, a number of studies investigate how e-commerce business affects taxation. Firstly, Carla et al. (2003) studied E-commerce and international tax planning. The findings showed that flexibility of E-commerce enabled the companies to be able to arrange their own tax planning activities, the government had positive motivation conforming to E-commerce, doing E-commerce activities is the first significant step in making understanding towards main effects of E-commerce on international tax planning performance. Moreover, Donal et al. (2000) investigated the E-commerce as public tax base context. The quantitative analysis of income loss of E-commerce showed that the E-commerce is only a factor causing decrease of total collected tax. Memphis (2011) studied the effects of E-commerce output tax loss in Tannessy. It found that there was no conclusion indicating that online business makes more profit than retailing business, local shops, and E-commerce tax has not still been collected completely. Moreover, there were economic effects in Tannessy, which were decrease of full-time jobs, decrease of wage rate, consumers’ expenses, which affect taxation and decrease of output tax and value added tax. In addition,Viboonthanakul (2009) examined the effects of E-commerce smugglers on income tax collection. The findings showed that economists have investigated effects of E-commerce; there are import smuggling and loss of income tax with evidences linking to income tax loss. Although the E-commerce contributes many benefits in terms of taxation, the E-commerce policies and regulations in Thailand remain unclear. A policy of the Revenue Department of Thailand to expand tax base on E-commerce enterprises might not be effective. Therefroe, this research study aimed at investigating the factors affecting E-commerce taxation in Thailand. Samples of this research study were E-commerce firms in tourism industry located in Bangkok Metropolitan. Tourism industry is important because it accommodates travelers from all around the world. In addition, the tourists are also able to book hotels and tours traveling to their favorite tourist attractions online, so the enterpreneurs have opportunities to ulilize cyberspace. These online activities brings a big sum of revenue to the country (Ministry of Tourism and Sport, 2014). Moreover, many factors affecting E-commerce taxation are examined in this study, and it is categorized into three different areas: (i) charcteristics of enterpreneurs, (ii) knowledge, understanding and public relations regarding E-commerce taxes, and (iii) reconition of entrepreneurs’ right and duty. The findings of this research study help the policy makers understand factors affecting effectiveness of E-commerce taxation, which leads to an increase of E-commerce tax compliance and the development of E-commerce taxation systems. Operational results enable the enterpreneurs to be aware of and get more knowledge and understanding towards E-commerce taxation and tax planning.

2. Literature Review

2.1 E-commerce Taxation in Thailand E-commerce entrepreneurs run business related to goods and services purchasing via internet networks and telecommunication systems or electronic media (Revenue Department, 2015). There is a right and duty to pay tax as a general mercantile. Income tax statements have to be calculated with revenue and registered based on E-commerce Enterpreneurs Act prescription. However, the Revenue Department does not levy a new category of E-commerce enterpreneurs.

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regulatory authorities have adopted E-commerce systems. For example, Department of Customs, Ministry of Finance, firstly used Electronic Data Interchange (EDI) for product export from Donmuang port, and later expanded to other ports (Deparment of Customs, 2012). In addition, Department of Business Economics, Ministry of Commerce, has used the E-commerce for product export sending to their members with free of charge and has acted as the Center receiving recommendations and suggestions for organizational certification setting (Ministry of Commerce, 2015). Moreover, Office of Investment Boards provides activities for the enterpreneurs to support and utilize the E-commerce benefits (Department of Export Promotion, 2008). Next, National Electronics and Computer Technology Center (NECTEC), Ministry of Science and Technology arranged activities of Information Technology Law Development Project to serve six E-commerce laws such as E-signature law and E-transfer law, etc. (NECTEC, 2011). Furthermore, Thai International Trade Law Adjustment Subcommittee regarding to E-commerce, Ministry of Justice set a draft of E-commerce law, specifically E-data exchange law (Department of Justice, 2013). Department of Industrial Promotion has prepared database of investment in each type (Department of Industrial Promotion, 2015). Besides, Bank of Thailand and Trade Siam Co., Ltd., have cooperatively joined the development project using EDI in finance (Ministry of Finance, 2015). In addition, Thai Airways Co., Ltd., and Krung Thai Bank have a joined trial project towards e-ticket booking (Thai Airways Public Co., Ltd., 2006). Last but not least, Royal Thai Police have implemented a website to get complaints towards E-commerce crime (Royal Thai Police, 2013). E-commerce on the Internet is a great technological and economic innovation that influences people’s way of life, private business management and government policy declaration, including law, politics, society, culture and public concepts towards products, currency and commerce, etc. In terms of public economy, E-commerce affects the taxation system in Thailand. Recently, the Revenue Department has a policy to expand E-commerce tax base. The reason behind is that in 2012 the Revenue Department was assigned to collect tax for 1.625 trillion baht, 82% of total income (1.98 trillion baht). Normally, the Revenue Department gets higher amount of collected tax as estimated, but in this particular year, the department got 1.616 trillion baht, which was lower than estimation for 8,624 million baht or 0.5%. Moreover, in 2013, the target of taxation was up to 1.774 trillion baht, 84.5% of total income, 2.1 trillion baht, which was 9.8-percent increase comparing with the target in 2012. The increasing amount of target taxation was based on the decrease of income tax rate that lowered from 30% to 23% and finally 20% of net profit. Besides, there was a taxing measure helping the businesess affected by BOI, flood at the end of 2011, which already became effective. As predicted, it caused the loss of income for at least 70,000 million baht per year (Revenue Department, 2015). Since the Revenue Department is an organization that is responsible for cashing up the main income of the government by collecting taxes from the citizen, whenever the government desires more budget for administration, the Revenue Department is ususally forced to oversee the tax payment of taxpayers (Revenue Department, 2013). In terms of e-commerce taxation, Thailand has not still had specific E-commerce taxation law. E-commerce enterpreneurs have to pay for tax based on the Revenue Code as other business does, even income tax, corperate income tax and value added tax becoming effective with all types of business, including E-commerce. Therefore, whenever the customers make an order, they have to pay for income tax (Sirikajronkit, 2010). Moreover, the system of Revenue Department showed that there are several causes: installment of program and operating system of some versions of Windows, data recovery, database backup and data


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In general, the companies need to register for E-commerce with Department of Business Development, a commercial trade affairs, based on the announcement of Ministry of Commerce towards commercial enterpreneurs (section 11) since 10th November 1937. The companies register as an E-commerce organization must possess as follows (Department of Business Development, 2016): having goods and services trading via electronic media on the Internet network, Internet Service Provider (ISP), Web Hosting, E-marketplace, and E-commerce enterpreneur’ s taxpayer identification number. E-commerce enterpreneurs are responsible for combining E-commerce revenues with other existing revenues. It is a calculation process to give information towards personal income tax and corportate income tax. It will be use for VAT registration in the case that goods and services revenues exceed 1.8 million baht per year (Revenue Department, 2015). Since 2012, the Revenue Department of Thailand has collected lower amount of tax than what was planned. Therefore, the Revenue Department has ideas to oversee the enterpreneur’s tax payment so that it will not be tough task for them in the future. The Revenue Department has planned to increase effectiveness of taxation in number of ways. For example, it has plan to expanding tax base to new innovative business such as online business or E-commerce. Moreover, it tends to oversee business having high performance that is able to pay for tax that tends to do some mistakes in terms of paying tax. Besides, it attempts to expand tax base by forcing tax reimbursement, specifically personal income tax, corporate income tax and value added tax. Next the Revenue Department tries to link tax data in both internal and external networks to be in database for the lowest-rate tax analysis. Last but not least, it attempts to extend for tax from declaration and increasing tax payment channels: Counter Services, credit card, ATM, E-payment, Tax Smart Card, for corporate tax payment.

2.2 Factors Influencing the Effectiveness of E-commerce Taxation E-Commerce Tax Knowledge and Understanding E-commerce enterpreneurs who purchase products and services have right and duties towards taxation as same as other enterpreneurs in Thailand. E-commerce enterpreneurs have to declare personal income tax if they are natural person, juristic person or ordinary partnership. They have to calculate E-commerce income with income from other sources (salary, interests, etc.) and send the Income Declaration in Temporary Development Area to be used with P.N.D 94 from Janurary to September each year, including Income Declaration in Temporary Development Area to be used with P.N.D 90 from January to March of the next year. However, if E-commerce enterpreneurs are corporate (juristic persons who register in Thailand or abroad having branches in Thailand, who gain income from products and services purchase), they have to file corporate tax return. They have to calculate income and net profit based on description in Secion 65 of Revenue Code. If they have deficit, they do not need to pay for tax. However, they have to declare peronal income tax within two months counted from the last day of their first six months of accounting round and Income Tax Return for Companies or Juristic Partnerships (P.N.D.50) within 50 days since the last day of accounting round. The highest rate of tax is 20 percent. Besides, the e-commerce enterpreneurs who sell products and services that earn more than 1,800,000 baht a year have to register for value added tax registration enterpreneurs that report sale tax, purchasing tax and declare value added tax list, P. P. 30 within 15th of the next month even have income or not (Revenu Department, 2015). There are fundamental knowledge the the e-commerce enterpreneurs should realize and understand.


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Nevertheless, several studies have found that many tax payers had less knowledge about these tax laws. For instance, Tragkunmaipol (1998) examined knowledge and attitude of taxpayers towards personal income tax payment. The findings of this study showed that the taxpayers had less knowledge towards personal income tax payment than the standard, but they had positive attitude towards personal income tax payment. In addition, Indee (2007) investigated people’s conditions and problems of tax payment to the local administrative organizations, a case study of Lablae Municipality in Uttaradit. The research finding showed that there was moderate condition towards tax payment to the local administrative organizations. People also have knowledge and understanding towards tax at moderate level. The problems found in this research study are lack of interests in tax payment and tax knowledge and understanding, and people do not know and understand paid tax in detail. Public Relations Communication and E-commerce Public relations communication is a communicating facts news review to the public. The language use to public relations communication can be into two categories: spoken language and written language depending on media or channel suitability in the communication Chaisumred (2006). According to Marston (2012), public relations communication is a communication planned. The persuasive communication and influence by setting up forms, in order to have influence over the people. This is important and meaningful to the organization. Cutlip et al. (2006) also stated that public relations communication create a relationship between individual, organization, institution and group. It also creates quality and status relations and creates the Institute for organization relationships. The strengthens relationship and better understanding between the agencies, group of people, institutions, organization and people involved can help achieve the cooperation, support and reinforce a positive image to the authority and institute. Reduce and eliminate the sources of problem to clear misunderstanding (Suebsor, 2010). As public relations communication is important, the effectiveness of e-commece taxation should depend on this factor. Janhong (2007) examined problems of local maintenance tax payment in Santor Subdistrict Administrative Office at Muang District in Uttaradit province. He found problems of information notification, appropriateness of regulations and principles, service channels, personnel and service places; however, it was at low level. Therefore, it was recommended to increase tax payment public relations channels. In addition, Jorgenson and Vu (2003) studied E-commerce and taxation, California plan. They found that occurred problems and handicaps affected most E-commerce taxation: operational strategies, communication strategies and taxation structure management. Rights and Duties of E-commerce Entrepreneurs

Types of taxation were determined by the status of taxpayers. Income tax is collected from individuals, committees and corporations. Personal income tax is collected from individuals and special tax while corporate income tax is collected from corporate income. Some types of taxes are collected from the consumption of goods and services, including value added tax, specific business tax, etc. The Revenue Department has managed tax efficiently by using computer system for data management. To log on to all tax categories of the Revenue Department. Taxpayers must use taxpayer identification number. In case of selling goods or services that have to pay value added tax services. Based on the law, the entrepreneur is responsible for specific business tax and value added tax registration (Revenue Department, 2015).

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In general, the companies need to register for E-commerce with Department of Business Development, a commercial trade affairs, based on the announcement of Ministry of Commerce towards commercial enterpreneurs (section 11) since 10th November 1937. The companies register as an E-commerce organization must possess as follows (Department of Business Development, 2016): having goods and services trading via electronic media on the Internet network, Internet Service Provider (ISP), Web Hosting, E-marketplace, and E-commerce enterpreneur’ s taxpayer identification number. E-commerce enterpreneurs are responsible for combining E-commerce revenues with other existing revenues. It is a calculation process to give information towards personal income tax and corportate income tax. It will be use for VAT registration in the case that goods and services revenues exceed 1.8 million baht per year (Revenue Department, 2015). Since 2012, the Revenue Department of Thailand has collected lower amount of tax than what was planned. Therefore, the Revenue Department has ideas to oversee the enterpreneur’s tax payment so that it will not be tough task for them in the future. The Revenue Department has planned to increase effectiveness of taxation in number of ways. For example, it has plan to expanding tax base to new innovative business such as online business or E-commerce. Moreover, it tends to oversee business having high performance that is able to pay for tax that tends to do some mistakes in terms of paying tax. Besides, it attempts to expand tax base by forcing tax reimbursement, specifically personal income tax, corporate income tax and value added tax. Next the Revenue Department tries to link tax data in both internal and external networks to be in database for the lowest-rate tax analysis. Last but not least, it attempts to extend for tax from declaration and increasing tax payment channels: Counter Services, credit card, ATM, E-payment, Tax Smart Card, for corporate tax payment.

2.2 Factors Influencing the Effectiveness of E-commerce Taxation E-Commerce Tax Knowledge and Understanding E-commerce enterpreneurs who purchase products and services have right and duties towards taxation as same as other enterpreneurs in Thailand. E-commerce enterpreneurs have to declare personal income tax if they are natural person, juristic person or ordinary partnership. They have to calculate E-commerce income with income from other sources (salary, interests, etc.) and send the Income Declaration in Temporary Development Area to be used with P.N.D 94 from Janurary to September each year, including Income Declaration in Temporary Development Area to be used with P.N.D 90 from January to March of the next year. However, if E-commerce enterpreneurs are corporate (juristic persons who register in Thailand or abroad having branches in Thailand, who gain income from products and services purchase), they have to file corporate tax return. They have to calculate income and net profit based on description in Secion 65 of Revenue Code. If they have deficit, they do not need to pay for tax. However, they have to declare peronal income tax within two months counted from the last day of their first six months of accounting round and Income Tax Return for Companies or Juristic Partnerships (P.N.D.50) within 50 days since the last day of accounting round. The highest rate of tax is 20 percent. Besides, the e-commerce enterpreneurs who sell products and services that earn more than 1,800,000 baht a year have to register for value added tax registration enterpreneurs that report sale tax, purchasing tax and declare value added tax list, P. P. 30 within 15th of the next month even have income or not (Revenu Department, 2015). There are fundamental knowledge the the e-commerce enterpreneurs should realize and understand.


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In general, taxpayers have legal right (Revenue Department, 2015) as follows: paying tax by installments, suppliance of tax estimation obstruction, asking for stop or decrease tax fine and additional money, making copies of documents. To pay the tax, the taxpayers have to follow these steps; submiting correct payment form and and pay complete related tax, asking for taxpayer’s identification card number and legally register, preparing for documents and legal accounts,co-working with staff, including observe the staff’s summons, paying for tax based on the staff’s evaluation, and not observing tax law will be admitted both criminal and civil punishment. If taxpayers follow these rights and duties, the effective of taxation can be enhanced. Samaejai (2009) studied factors influencing effectiveness of taxation at Dongmada Subdistrict Municipality at Maelao District in Chiang Rai. The research findings showed that the factors influencing the most on effectiveness of taxation were self-realization towards their own duties, pay for tas annually, punctually with readiness and willingness in paying it to Dongmada Subdistrict Municipality. The Personnel is related to tax payment and willing to give tax payment service. Inferior to it are taxation and its law and regulations. The factor that affects on taxation the least is public relations. Moreover, Thammarat (2007) investigated the factors influencing an increase of taxation effectiveness of Nonghan Subdistrict Municipality at San Sai District in Chiang Mai. The results presented that different knowledge, understanding, conscious, cooperation and frequencies of tax payment of people influence the effectiveness of tax payment significantly.

3. Conceptual Framework and Research Methodology

3.1 Conceptual Framework The effectiveness of E-commerce taxation depends mainly on three factors: (i) the characteristics of entrepreneurs, (ii) knowledge, understanding, and public relations, and (iii) the right and duty of entrepreneurs. Figure 1 below presents the conceptual framework.

Inependent Variables Dependent Variable

Figure 1: The conceptual framework

Characteristics of Entrepreneurs Educational level Working experience Age of firms Communication channels Number of employees

Effectiveness in

E-commerce Taxation

Right and Duty of Entrepreneurs Right Roles

Knowledge, Understanding and Public Relations

Knowledge and understanding Communication and public relations


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3.2 Data and Methodology E-commerce enterpreneurs of tourism industry in Bangkok and vinicity from 254 companies were participants of this study. The research instrucment is a set of questionnaire designed based on objectives and conceptual framework, which can be categorized in the following sections: (i) demographic information, (ii) company or organization, (iii) Knowledge and understanding towards tax and towards the public relations of E-commerce enterpreneurs, (iv) E-commerce Right and Roles of Enterpreneurs. The questionnaire is in Likert Scale in which (5) is “extremely affect” and (1) is “least affect”. There were 254 questionnaires sent by post and e-mail, but only 188 questionnaires (74%) were sent back. As a result, these 188 questionnaires were used to analyze the data. Descriptive Statistices were used to analyze data towards the factors influencing on E-commerce taxatin based on tax-base extension in Thailand while Inferential Statistics were used to examine effects of factors influencing on E-commerce taxatin based on tax-base extension selection of E-commerce enterpreneurs and Multiple Regression Analysis.

4. Results

4.1 Descriptive Statistic

According to Table 1 below, the information from 188 respondents can be described as follows. There are 115 males (61.2%) and 73 females (38.8%) responded. Most of them, 145 people, are 31-40 years old (77.1%). Inferior to the majority is people who are more than 40 years old, 33 people (17.6%). The youngest respondents are 25-30 years old, 10 people (5.3%) respectively. In addition, most respondens, 171 people (91.0%), graduated bechalor’s degree and there are 17 master-degree graduate (9.0%). In terms of working experience: most respondents, 96 people (51%), have at least seven-year of experience, and 92 of them (48.9%) have 3-7 years of working experience. For the enterprises, all of them are compamy limited. Enterprises of most respondents, 141 people (75%), have run their business for 6-10 years. Inferior to it is 11-15 year span, 39 people (20.7%). The companies or organization that have run their business for 1-5 years are the least; there are 8 people (4.3%) work here. Company’s authorized capital of most companies is at less than 5 million Baht; 186 respondents (98.9%) work there, and 2 people (1.1%) work at the company or organization having 5-10 million Baht authorized capital. The organizations of majority (136 people, 72.3%) have less than 15 staff. There were 51 people (27.1%) work with the organization having 15-30 staff and 1 person (0.5%) work at the organization having more than 30 staff respectively. For the people who are responsible for arrange and propose the financial budget and tax documents to the government organizaitons, most respondents are 105 full-time staff in the Accounting Department (55.9%). Inferior to it is that they use some services o external accountability office, 55 people (29.3%) while they use all services of the accountability office the least, 28 people (14.9%) respectively. People who arrange and propose the financial budget and tax declaration form mostly have accountability office to manage the tasks, 118 respondents (62.8%) work here and 70 of them (37.2%) are accountants. In terms of business channels, most of them (183 people, 40.8%) use Catalog Website as a business channel; then Google AdSense (158 people, 35.2%) and Community Web (108 people, 24.1%) respectively.

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In general, taxpayers have legal right (Revenue Department, 2015) as follows: paying tax by installments, suppliance of tax estimation obstruction, asking for stop or decrease tax fine and additional money, making copies of documents. To pay the tax, the taxpayers have to follow these steps; submiting correct payment form and and pay complete related tax, asking for taxpayer’s identification card number and legally register, preparing for documents and legal accounts,co-working with staff, including observe the staff’s summons, paying for tax based on the staff’s evaluation, and not observing tax law will be admitted both criminal and civil punishment. If taxpayers follow these rights and duties, the effective of taxation can be enhanced. Samaejai (2009) studied factors influencing effectiveness of taxation at Dongmada Subdistrict Municipality at Maelao District in Chiang Rai. The research findings showed that the factors influencing the most on effectiveness of taxation were self-realization towards their own duties, pay for tas annually, punctually with readiness and willingness in paying it to Dongmada Subdistrict Municipality. The Personnel is related to tax payment and willing to give tax payment service. Inferior to it are taxation and its law and regulations. The factor that affects on taxation the least is public relations. Moreover, Thammarat (2007) investigated the factors influencing an increase of taxation effectiveness of Nonghan Subdistrict Municipality at San Sai District in Chiang Mai. The results presented that different knowledge, understanding, conscious, cooperation and frequencies of tax payment of people influence the effectiveness of tax payment significantly.

3. Conceptual Framework and Research Methodology

3.1 Conceptual Framework The effectiveness of E-commerce taxation depends mainly on three factors: (i) the characteristics of entrepreneurs, (ii) knowledge, understanding, and public relations, and (iii) the right and duty of entrepreneurs. Figure 1 below presents the conceptual framework.

Inependent Variables Dependent Variable

Figure 1: The conceptual framework

Characteristics of Entrepreneurs Educational level Working experience Age of firms Communication channels Number of employees

Effectiveness in

E-commerce Taxation

Right and Duty of Entrepreneurs Right Roles

Knowledge, Understanding and Public Relations

Knowledge and understanding Communication and public relations


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Table 1: Number and percentage of respondents regarding enterprenuers’ characteristics

Items Number Percentage Sex Male 115 61.2 Female 73 38.8 Age 25-30 years 10 5.3 31-40 years 145 77.1 Older than 40 years 33 17.6 Educational level Bachelor Degree 171 91 Master Degree 17 9 Working experience 3-7 years 92 48.9 Longer than 7 years 96 51.1 Business types Company Limited 188 100 Age of company 1-5 years 8 4.3 6-10 years 141 75 11-15 years 39 20.7 Company’s authorized capital Less than 5 million Baht 186 98.9 5-10 million Baht 2 1.1 People who are responsible for tax function Accounting Department and some full-time staff 105 55.9 Using all services of external accountabitlity office 28 14.9 Using some services of external accountabitlity office 55 29.3 People who arrange tax declaration and payment Accountants 70 37.2 Accountability Office 118 62.8 Types of business management channels* Catalog Website 183 40.8 Community Web 108 24.1 Google AdSense 158 35.2 Number of staff in the organization Less than 15 people 136 72.3 15-30 people 51 27.1 More than 30 people 1 0.5

*Be able to choose more than one answer 4.2 Regression Results The results of regression analysis as showed in Table 2 below indicated that different experience affects E-commerce taxation based on tax-base extension policy of E-commerce enterpreneurs in tourism industry differently (Sig. = 0.027) and statistically significant at 0.05. Working experience is positively significant for E-commerce taxation. This signals that the more working experience the enterpreneurs have, the more increase of government taxation


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effectiveness. However, educational level, age of firms, communication channels and number of employees do not affect the government taxation effectiveness. Table 2: Regression results of the relationship between the characteristics of entrepreneurs and the effectiveness in E-commerce taxation

Independent variables Coefficients t-stat Sig. Intercept 4.122* 16.519 0.000 Educational degree 0.009 0.137 0.891 Working experience 0.093* 2.231 0.027 Age of firms 0.055 1.248 0.214 Communication channels 0.061 0.325 0.746 Number of employees 0.042 1.029 0.305 R-square 0.038 Adjusted R-Square 0.011 F-Stat 1.396*

*significant at 0.05 level In terms of knowledge and understanding, Table 3 below indicates that different knowledge and understanding differently affect E-commerce taxation based on tax-base extension policy of E-commerce enterpreneurs in tourism industry (Sig. =0.000) and statistically significant at 0.05. Knowledge and understanding of the enterpreneurs has positive relationship with E-commerce taxation, specifically knowledge and understanding towards the laws and regulations of E-commerce Taxation Operation Act. These findings support the prior findings, that is, the more working experience the enterpreneurs have, the more knowledge and understanding towards E-commerce taxation, which positively affects more government taxation effectiveness. It conforms to the study of Thammarat (2007) investigating the factors that affect an increase of income attribution of Nonghan Subdistrict Municipality at San Sai District in Chiang Mai. It found that different knowledge, understanding, cooperation and tax payment frequency of people affect taxation effectiveness significantly. However, it presented that communication and public relations do not influence the government’s taxation effectiveness. Table 3: Regression results of the relationship between knowledge, understanding and public relations and effectiveness in E-commerce taxation

Independent variables Coefficients t-stat Sig. Intercept 2.667* 14.144 0.000 Knowledge and understanding 0.287* 7.823 0.000 Communication and public relations 0.067 1.305 0.194 R-square 0.287 Adjusted R-Square 0.279 F-Stat 1.167*

*significant at 0.05 level Moreover, the results of relationship of all variables presented in Table 4 that different right of E-commerce tax payment has positive significant effect on E-commerce taxation based on tax-base extension policy of E-commerce enterpreneurs in tourism industry (Sig. = 0.004) at 0.05 significant level. This positive relationship indicates that the enterpreneurs’ awareness of right towards E-commerce taxation positively affects taxation effectiveness. However, this research study found that duty is a factor what does not significantly affect taxation at 0.05. It conforms to the research study of Samerjai (2009) that investigating the factors affecting

8

Table 1: Number and percentage of respondents regarding enterprenuers’ characteristics

Items Number Percentage Sex Male 115 61.2 Female 73 38.8 Age 25-30 years 10 5.3 31-40 years 145 77.1 Older than 40 years 33 17.6 Educational level Bachelor Degree 171 91 Master Degree 17 9 Working experience 3-7 years 92 48.9 Longer than 7 years 96 51.1 Business types Company Limited 188 100 Age of company 1-5 years 8 4.3 6-10 years 141 75 11-15 years 39 20.7 Company’s authorized capital Less than 5 million Baht 186 98.9 5-10 million Baht 2 1.1 People who are responsible for tax function Accounting Department and some full-time staff 105 55.9 Using all services of external accountabitlity office 28 14.9 Using some services of external accountabitlity office 55 29.3 People who arrange tax declaration and payment Accountants 70 37.2 Accountability Office 118 62.8 Types of business management channels* Catalog Website 183 40.8 Community Web 108 24.1 Google AdSense 158 35.2 Number of staff in the organization Less than 15 people 136 72.3 15-30 people 51 27.1 More than 30 people 1 0.5

*Be able to choose more than one answer 4.2 Regression Results The results of regression analysis as showed in Table 2 below indicated that different experience affects E-commerce taxation based on tax-base extension policy of E-commerce enterpreneurs in tourism industry differently (Sig. = 0.027) and statistically significant at 0.05. Working experience is positively significant for E-commerce taxation. This signals that the more working experience the enterpreneurs have, the more increase of government taxation


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taxation effectiveness of Dongmada Subdistrict Municipality at Maelao District in Chiang Rai. The findings showed that awareness towards duty was the factor that affects taxation effectiveness the most. People punctually pay for annual tax with readiness and willingness to pay for tax to Dongmada Subdistrict Municipality. Table 4: Regression results of the relationship between the right and duty of entrepreneurs and the effectiveness in E-commerce taxation

Independent variables Coefficients t-stat Sig. Intercept 3.483 15.564 0.000 Right 0.125* 2.929 0.004 Duty 0.015 0.361 0.718 R-square 0.045 Adjusted R-Square 0.034 F-Stat 4.319

*significant at 0.05 level

5. Conclusion

E-commerce influences free trade and services, taxation policy, financial policy, intellectual property rights and information infrastructures. In addition, E-commerce also influences the sectoral economy, especially in services such as bank, property, security, retail and tourism (National Assembly, 2014). Therefore, this research study aims at investigating factors affecting E-commerce taxation based on tax-base extension policy of E-commerce enterpreneurs. The population of this research study was E-commerce enterpreneurs of tourism industry in Bangkok and vinicity. There were 254 questionnaires sent to the respondents, and only 188 questionnaires (74%) were returned. Research findings showed that different experience affects different knowledge and understanding towards E-commerce tax. In addition, different right affects E-commerce taxation based on tax-base extension policy of E-commerce enterpreneurs in tourism industry. It is then suggested that the government has to take action to build up competitive capacity of E-commerce clearly, since there has still been the lack of knowledge and accurate understanding, including clear policy in buiding up business capacities related to E-commerce, etc. Moreover, results from this study indicated that there should be an adjustment of time duration of all steps of E-commerce taxation, which conveniently practice. To increase effectiveness of given services, there should be instructions and suggestions for practical steps of E-commerce taxation, which enable the staff in organization to be able to properly manage problems and handicaps of E-commerce taxation. Moreover, the E-commerce taxation, news, and related plans should be publicized. E-commerce model scheme and national economic and social development plan must be understandable, and online information should be launched since there have rarely been E-commerce taxation information. Furthermore, there should be public relations for the enterpreneurs to be aware of more E-commerce taxation information. Although the E-commerce contributes many benefits to the country, a lot of cybercrime (harmful data, information insecurity, etc.) would probably occur due to its distortion. At present, there are international investments in Thailand that cause unsuccessfulness of E-commerce because of the protection of business data or personal data of consumers. The


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government thus strictly regulates operational characteristics and guidelines, even technological capacities and E-commerce law responsibilities, and tax law.

REFERENCES

Association of Electronic Commerce Thailand. (2012).Thai e-Commerce Association. Retrieved from www.nso.go.th

Customs Department. (2012).Taxation, Customs, inbound – outbound, and fees Including the IRS Tax Department. Retrieved from www.customs.go.th

Cutlip et al., (2006). Employee/organizational communications refer to communications and interactions among employees or members of an organization. Retrieved from. http://www.instituteforpr.org/employee-organizational-communications

Department of Business Development, Ministry of Commerce. (2016). e-Commerce Business Development. Wholesale &Retail.Franchise.LogisticsService.ServiceSector. Stat&Documents. Business Quality.Retrieved fromwww.dbd.go.th/dbdweb_en

Department of Justice. (2013).The law on electronic commerce UNCITRAL Model Law on Electronic.Retrieved from www.moj.go.th

Department of Industrial Promotion. (2015). The project promotes knowledge of online marketing and to expand the channel for online goods, OTOP. Policies of industrial development agency community.Retrieved from www.industry.go.th

Janhong, A. (2007). A Stady of problems in connection with municipal tax payment of people case study : Subdistrict administrative organization santoa amphoemueang changwatuttaradit. Retrieved from www.tci-thaijo.org/index.php/Veridian-E-Journal/article/download/31568/30101

Jorgenson & Vu. (2003). The Impact of E-Business on the Economy. Retrieved from digitalcommons.pepperdine.edu/cgi/viewcontent.cgi?article=1007.

Ketsada, S. (2009). Factors affecting the efficiency of the tax administration Chang Rai. Retrieved from www.e-article.plu.ac.th/files/2556/PP/PP560219.pdf

Kitiwiboon, T. (2007). Factors that affect the storage optimization of revenue administration. Retrieved from e-article.plu.ac.th/files/2556/PP/PP560219.pdf

Marston, J.E. (2012). Public relations is planned, persuasive communication designed to influencesignificant public. Creating a mutual understanding between a corporate and its publics; Building the corporate image; Building a favourablepublic .Source: SURYA, 13 & 27.

Memphis, J. (2011).The Impact of Sales Tax Loss to E-Commerce in the State of Tennessee. Younger Associates, September 2011.

Ministry of Commerce. (2015) .The Ministry of Commerce has launched e-commerce to support export. Retrieved from https://www.moc.go.th Ministry of Finance. (2015).The Future of the Postal Sector in a Digital World. Retrieved

from www.mof.go.th/home/index.php Ministry of Tourism and Sport. (2014).Thailand's Best Friends Forever Mega Farm Trip

2014. Retrieved from www.mots.go.th/ewtadmin/ewt/mots_eng/main.php?filename. National Assembly. (2014).Tourist Safety and Security: Practical Measures.Retrieved from

www.na.go.th. NECTEC. (2011). Ethical, legal and social implications of information technology. Retrieved

from www.nstda.or.th Pensri, J. &Supot, D. (2015). Development of e-Commerce Competencies Among

Community Enterprise and SMEs in the Northeastern Region of Thailand. GMSARN International Journal, 9, 993– 100.

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taxation effectiveness of Dongmada Subdistrict Municipality at Maelao District in Chiang Rai. The findings showed that awareness towards duty was the factor that affects taxation effectiveness the most. People punctually pay for annual tax with readiness and willingness to pay for tax to Dongmada Subdistrict Municipality. Table 4: Regression results of the relationship between the right and duty of entrepreneurs and the effectiveness in E-commerce taxation

Independent variables Coefficients t-stat Sig. Intercept 3.483 15.564 0.000 Right 0.125* 2.929 0.004 Duty 0.015 0.361 0.718 R-square 0.045 Adjusted R-Square 0.034 F-Stat 4.319

*significant at 0.05 level

5. Conclusion

E-commerce influences free trade and services, taxation policy, financial policy, intellectual property rights and information infrastructures. In addition, E-commerce also influences the sectoral economy, especially in services such as bank, property, security, retail and tourism (National Assembly, 2014). Therefore, this research study aims at investigating factors affecting E-commerce taxation based on tax-base extension policy of E-commerce enterpreneurs. The population of this research study was E-commerce enterpreneurs of tourism industry in Bangkok and vinicity. There were 254 questionnaires sent to the respondents, and only 188 questionnaires (74%) were returned. Research findings showed that different experience affects different knowledge and understanding towards E-commerce tax. In addition, different right affects E-commerce taxation based on tax-base extension policy of E-commerce enterpreneurs in tourism industry. It is then suggested that the government has to take action to build up competitive capacity of E-commerce clearly, since there has still been the lack of knowledge and accurate understanding, including clear policy in buiding up business capacities related to E-commerce, etc. Moreover, results from this study indicated that there should be an adjustment of time duration of all steps of E-commerce taxation, which conveniently practice. To increase effectiveness of given services, there should be instructions and suggestions for practical steps of E-commerce taxation, which enable the staff in organization to be able to properly manage problems and handicaps of E-commerce taxation. Moreover, the E-commerce taxation, news, and related plans should be publicized. E-commerce model scheme and national economic and social development plan must be understandable, and online information should be launched since there have rarely been E-commerce taxation information. Furthermore, there should be public relations for the enterpreneurs to be aware of more E-commerce taxation information. Although the E-commerce contributes many benefits to the country, a lot of cybercrime (harmful data, information insecurity, etc.) would probably occur due to its distortion. At present, there are international investments in Thailand that cause unsuccessfulness of E-commerce because of the protection of business data or personal data of consumers. The


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Pongwittayapanu, P. (2008). Development of e-Commerce Competencies Among Community. Retrieved from http://www.pawoot.com/what- is-ecommerce

Revenue Department. (2013).Tax electronic commerce, E-Commerce: Knowledge about e-commerce.Retrieved from www.rd.go.th/publish/53537.0.htm

Royal Thai Police. (2013). Cybersecurity: Thailand's Priorities. Retrieved from www.nisc.go.jp/security-site/campaign2013/ajsympo2013/.../lecture3.pdf

Rungrat, C. (2006). Language for Public Relations.Bangkok, Thailand. Chulalongkorn University Press.

Sirikajronkit, V. (2010). Electronic Commerce: A Managerial and Social Networks Perspective. Issue of E-Commerce Magazines in the March 2010.

Suebsor, S. (2010). The Study the behaviour of electronic commerce shopping via social media student's Rajamangala University of Technology Rattanakosin. TNRR:Thai National Research, 48-52.Retrieved from www.tnrr.in.th/2558/?page=result_search&record_ id=10067473

Thai Airways International Public Company Limited. (2006). How To Buy & Payment. Retrieved from www.thaiairways.com/th_TH/corporate_booking/FAQs_Corporate

The Export Promotion Department. (2008). Knowledge about e-commerce, exports. Retrieved from http://www.dcpthai.go.th march

The Ministry of finance. (2010). Electronic services. Retrieved from www.mof.go.th Viboonthanakul, S. (2009). Smuggling via e-commerce: effect on tax revenue. Journal of

International Trade Law and Policy, 8(3), pp.272 – 290. World Intellectual Property Organization. (2012). Copyright in the Digital Era: Building

Evidence for Policy. Retrieved from https://books.google.co.th/books?isbn=0309278953


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Optimizing unique supply chains; a case of automobile business continuity Management

a*Abednico Lopang Montshiwa, bAkio Nagahira and C Shuichi Ishida a* b Department of Management Science and Technology, Graduate School of Engineering,

Tohoku University, 661180Aza- Aoba-ku, Sendai, Miyagi, Japan c Graduate School of Technology management, Ritsumeikan University

a*Corresponding Author: [email protected]

ABSTRACT

This paper presents an optimized diamond structured automobile supply chain network towards a robust Business Continuity Management model. The model is necessitated by the nature of the automobile supply chain. Companies in tier two are centralized and numerically limited and have to supply multiple tier one companies with goods and services. The challenge with this supply chain structure is the inherent risks in the supply chain. Once supply chain disruption takes place at tier 2 level, the whole supply chain network suffers huge loses. To address this challenge, the paper replaces Risk Analysis with Risk Ranking and it introduces Supply Chain Cooperation (SCC) to the traditional Business Continuity Plan (BCP) concept. The paper employed three statistical analysis techniques (correlation analysis, regression analysis and Smart PLS 3.0 calculations). In this study, correlation and regression analysis results on risk rankings, SCC and Business Impact Analysis were significant, ascertaining the value of the model. The multivariate data analysis calculations demonstrated that SCC has a positive total significant effect on risk rankings and BCM while BIA has strongest positive effects on all BCP factors. Finally, sensitivity analysis demonstrated that company size plays a role in BCM.

Keywords: Business Continuity Plan, Diamond supply chain, Automobile supply chain network, Supply Chain cooperation, Risk ranking, Competitive Advantages, Recovery time

1. INTRODUCTION The 21st century has presented the automobile industry with unprecedented operational challenges. According to the National Highway Traffic Safety Administration (2013), in 2012 the auto industry recalled close to a third more vehicles in the U.S. The report further states that recalls were up by 25 percent from year 2012 to 2013, which is the industry’s highest rate since 2004 when 30.8 million vehicles were involved in such campaigns. Apart from recalled cars, we note that the global production system has become a complex, networked system that has operated efficiently under normal conditions (Kazemi et al, 2015; Mohamadi et al, 2015; Darvish et al, 2014; Poorbagheri, and Akhavan, 2014). Nevertheless, disasters remain the biggest threat to the gains made over the years. Recent mega disasters around the world have revealed the networked world’s vulnerability to major disasters. Companies in disaster-affected areas are part of the network and therefore crucial in the flow of information, goods and services within the supply chain network. Therefore, any supply chain disruption may force manufacturers across the world to suspend their operations. Morris et al. (2011) concluded that these operational


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challenges are very complicated to address, as there are both internal and external. Thus, a broad and detailed business appreciation of the sector is a requirement. Harryson (2006, p.91) states that, enterprises have to be up to date with latest knowledge and technology (which are dynamic) for significant benefits. Consistent improvement is crucial for innovative, scientific and engineering breakthrough towards competitive advantages. Externally, enterprises are to prepare for ‘the unknowns’ and out of enterprises’ power, (sometimes faults of others within the supply chain network) a task that has proven particularly difficult. Morris et al. (2011) states that due to increasingly turbulent external environment, companies are to continually adapt, adjust and redefine their value proposition. Scholars and industry professionals pay attention to finding or establishing the most effective and efficient ways to address these critical challenges. 1.1 Background of the study

The automobile industry is one of the complicated supply chains. There are so many companies involved in the production of a single automobile unit and as a result, network players cannot usually identify all companies in their network. On average, an automobile unit consists of about 35,000 parts, from companies trading in completely different industries. Some of the industries include electrical and electronic, computer programming and software engineering, smith and upholstery, chemical technology and engineering, thermos fluid dynamics and mechanical engineering, fuel control system etc., among others. In this regard, producing a single unit involves efficiently and chronologically bringing all the parts together. To simplify this extensive supply chain network, the supply chain is made of up of four tiers and a dozen companies under each tier and comprises of small, medium and large companies. Understanding this supply chain network is crucial for smooth manufacturing processes as the supply chain structure presents both challenges and opportunities to the sector. Fujimoto et al. (2014) discussed the Great East Japan Earthquake effects on the Japanese Automobile sector particularly Toyota Motors. They explained that certain consumables did not remain in the final product such as detergents or catalysts in the parts manufacturing process, as a result, when the 2011 Tohoku earthquake struck, such supplier were badly damaged and halted operations and the entire production process stopped. Mackenzie et al. (2012) observed that these invisible suppliers were not listed in the engineering bill of materials. Fujimoto et al. (2014) recorded that it took Toyota a week to list 500 parts sourced from 200 locations, which would be difficult to secure and recover to the normal production level. As if that was not enough, an excessively long time (about 1 month) to totally grasp locations and situations of the damaged suppliers at the second tier and lower during the Great East Japan Earthquake.

2. REVIEW OF LITERATURE

2.1 Automobile diamond structured supply chain network

To respond to this unique supply chain network, automobile companies throughout the world have adopted a diamond structured supply chain network. The need for high and trusted quality coupled with complex nature of the supply chain informed this supply chain structure. As Tokuda (2011) explained, for the industry to be content with the quality of parts makers, a few companies (highly specialized and manufacturers of the best quality parts) can only manufacture some specialized car parts. While quality is important for the industry, He recorded a situation in


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which major Japanese car assemblers were found to be sourcing their parts from the same MCUs semiconductor company, Renesas Electronics and even worst, from the same factory of Naka Plant, which was severely damaged by the earthquake, in the process making automobile production to come to a stoppage. Another related issue is that of non-transferable nature of embedded software encoded in the automotive MCUs. There have been efforts for standardizing embedded software and coding in the industries using MCUs (Tokuda, 2011). However, the company-specific coding is necessary to the extent that a buyer of MCUs cannot expend cost and efforts to develop two different software programs to replicate the same functions. These unique cases resulted in this specialized type of supply chain.

Figure 1 shows how goods and information flows through the supply chain network, typical of the automobile industry. Procurement from tier two companies to tier one is very crucial because of some risks in the supply chain. Centralized tier two companies, makes the flow of goods and information to tier one vulnerable, in the event these few specialized, companies experience any disruption the whole production process halts, not only in one car assembler but also in multiple ones.

Figure 1. DIAMOND STRUCTURED SUPPLY CHAIN NETWORK

2.2 Globalization

While the automobile supply chain structure shows how distinct the sector is, in terms of coordination, this supply chain network is made even more complicated by globalization. This concept has been discussed extensively by a number of scholars among them (Aizenman and Sun, 2010; Curtis, 2009; Ferrer- Figueras and Caselles- Moncho, 2010; Leidner, 2010; Milanovic,

Car assembler

Tier 2

Tier 1

Supply Chain Risk

Car assembler

Tier 1 Tier 1 Tier 1

Sales Sales Sales Sales

Procurement/ Production

Procurement/ Assembling

Procurement/ Production

challenges are very complicated to address, as there are both internal and external. Thus, a broad and detailed business appreciation of the sector is a requirement. Harryson (2006, p.91) states that, enterprises have to be up to date with latest knowledge and technology (which are dynamic) for significant benefits. Consistent improvement is crucial for innovative, scientific and engineering breakthrough towards competitive advantages. Externally, enterprises are to prepare for ‘the unknowns’ and out of enterprises’ power, (sometimes faults of others within the supply chain network) a task that has proven particularly difficult. Morris et al. (2011) states that due to increasingly turbulent external environment, companies are to continually adapt, adjust and redefine their value proposition. Scholars and industry professionals pay attention to finding or establishing the most effective and efficient ways to address these critical challenges. 1.1 Background of the study

The automobile industry is one of the complicated supply chains. There are so many companies involved in the production of a single automobile unit and as a result, network players cannot usually identify all companies in their network. On average, an automobile unit consists of about 35,000 parts, from companies trading in completely different industries. Some of the industries include electrical and electronic, computer programming and software engineering, smith and upholstery, chemical technology and engineering, thermos fluid dynamics and mechanical engineering, fuel control system etc., among others. In this regard, producing a single unit involves efficiently and chronologically bringing all the parts together. To simplify this extensive supply chain network, the supply chain is made of up of four tiers and a dozen companies under each tier and comprises of small, medium and large companies. Understanding this supply chain network is crucial for smooth manufacturing processes as the supply chain structure presents both challenges and opportunities to the sector. Fujimoto et al. (2014) discussed the Great East Japan Earthquake effects on the Japanese Automobile sector particularly Toyota Motors. They explained that certain consumables did not remain in the final product such as detergents or catalysts in the parts manufacturing process, as a result, when the 2011 Tohoku earthquake struck, such supplier were badly damaged and halted operations and the entire production process stopped. Mackenzie et al. (2012) observed that these invisible suppliers were not listed in the engineering bill of materials. Fujimoto et al. (2014) recorded that it took Toyota a week to list 500 parts sourced from 200 locations, which would be difficult to secure and recover to the normal production level. As if that was not enough, an excessively long time (about 1 month) to totally grasp locations and situations of the damaged suppliers at the second tier and lower during the Great East Japan Earthquake.

2. REVIEW OF LITERATURE

2.1 Automobile diamond structured supply chain network

To respond to this unique supply chain network, automobile companies throughout the world have adopted a diamond structured supply chain network. The need for high and trusted quality coupled with complex nature of the supply chain informed this supply chain structure. As Tokuda (2011) explained, for the industry to be content with the quality of parts makers, a few companies (highly specialized and manufacturers of the best quality parts) can only manufacture some specialized car parts. While quality is important for the industry, He recorded a situation in


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2003; Moshirian, 2003; Wilpert, 2009). This study takes particular interest on how globalization shaped the supply chain and risk management. Estimations are that about one-fourth of all auto parts used in the United States are imported from Asia or Europe (Klier and Rubenstein, 2008). We also note that in Asia, Japanese automobile parts makers are most preferred dealers as they supply Europe and North America with high quality car parts due of their advanced production systems and affordable prices, therefore any disruption in these car parts makers can bring the automobile industry to its knees. Even though opportunities and benefits from globalization are realized, the supply chain network has become more fragile in the process.

Because of the above challenges, there have been increased research activities regarding effective and efficient crisis and risk management strategies. Many scholars like (Harris, 2010; S.A. Torabi, 2015; S. Okabe and A. Nagahira, 2014; Q.Y. Sun and X.Y. Li, 2014; V. Cerullo and J.M. Cerullo, 2004; B. Herbane et al, 2010; J. Sharp, 2008; P.Skidar, 2011) are paying particular attention to BCM. BCM is identified as a viable option for organizations seeking to add value, creating competitive advantages, saving money, time and resources. According to Herbane (2010), BCM has evolved as a form of crisis management since the 1970’s in response to the technical and operational risks that threaten an organizations’ recovery from hazards and interruptions. Gibb and Buchanan (2006) also note that during the last 2 decades, BCM has evolved as an effective tool for ensuring the delivery of organization’s key products and or services. ISO 22301 (2012) defines BCM as a management process which identifies possible internal and external threats and or risks and their impact to business processes and provides a framework for organizational resilience. Bhamra et al. (2011) noted that the level of organizational business continuity directly relates to its resilient ability. For an effective BCM, BCP needs to be reviewed, modified and improved regularly. This is particularly essential in more challenging sectors like the automobile industry.

2.3 Aim of study

This study intends to:

(1) Presents a BCM framework suited for a complex automobile supply chain by modifying the BCP concept to accommodate globalization. (2) Evaluate each BCP component and reveal its interaction with other crucial BCM framework factors.

3 RESEARCH METHODOLOGY

The study employed correlation, regression and Smart PLS 3.O analyses. These analysis techniques have complementary properties. While correlation analysis establishes association among individual variables, regression seeks to identify a causal relationship of these variables and Smart PLS 3.0 finds out the direct and total effect multiple variables have on each other. More details are given below;

3.1 Correlation Analysis


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It is crucial to note that two variables may be associated without having a causal relationship. Even though correlation has a limited value of causative inference, it is important because even if correlation does not imply causation, causation does imply correlation. That is, although a correlational study cannot definitely prove a causal hypothesis, it may rule one out (Stanovich, 2007). Once correlation is known, it can be used to make predictions. When we know a score on one measure, we can make a more accurate prediction of another measure that is highly related to it.

3.2 Regression Analysis

Regression analysis is one of the most important statistical techniques used in scientific and business applications. It helps estimate the strength and direction of the relationship between two or more variables. This includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. This technique can employed because of its strength, which are as below;

One of the primary advantages of regression-based forecasting techniques is that they the analysis to predict what is likely to happen in the future, according to Studenmund (2011) regression and forecasting techniques can lend a scientific angle to management of small businesses, reducing large amounts of raw data to actionable information. Large data sets have the potential to yield valuable new information about small businesses and their operations. Regression and forecasting techniques can yield new insight for managers by uncovering patterns and relationships that they had not previously noticed or considered.

3.3 Smart PLS calculations

Smart PLS 3.0 was used to analyze the data. Considering the data and model characteristics, the algorithmic properties and model evaluation issues the Partial least squares structural equation modeling (PLS-SEM) approach (Smart PLS 3.0 statistical software package) was employed over Covariance- based structural equation modeling (CB- SEM) approach. This is so because of several benefits PLS- SEM offers benefits in terms of data size, distribution and algorithm properties. For instance, Chin (1998) stated that small numbers of minimum observations are applicable for analysis and was ideal for a study, which had 75 number of observations valid for analysis. Smart PLS 3.0 do not account for any distribution, thus bootstrapping resampling technique was used to get t values (Efron and Tibshirani, 1986). Missing data in a questionnaire exceeding 15% was removed, while observations containing less than 15% of missing data was treated using a group average method. The questionnaire adopted a 5 point Likert- type scales ranging from 1= strongly disagree to 5= strongly agree.

3.4 Data collection policy and procedure

Since the questionnaire was in Japanese, questionnaires that were sent to Europe, the US and other Asian countries were translated to English language and a batch sent to Chinese companies were translated into Chinese language and the translations were verified. The pilot study was done by sending questionnaires to some Japanese companies. Some modifications were introduced to the initial questionnaire before large-scale data collection.

2003; Moshirian, 2003; Wilpert, 2009). This study takes particular interest on how globalization shaped the supply chain and risk management. Estimations are that about one-fourth of all auto parts used in the United States are imported from Asia or Europe (Klier and Rubenstein, 2008). We also note that in Asia, Japanese automobile parts makers are most preferred dealers as they supply Europe and North America with high quality car parts due of their advanced production systems and affordable prices, therefore any disruption in these car parts makers can bring the automobile industry to its knees. Even though opportunities and benefits from globalization are realized, the supply chain network has become more fragile in the process.

Because of the above challenges, there have been increased research activities regarding effective and efficient crisis and risk management strategies. Many scholars like (Harris, 2010; S.A. Torabi, 2015; S. Okabe and A. Nagahira, 2014; Q.Y. Sun and X.Y. Li, 2014; V. Cerullo and J.M. Cerullo, 2004; B. Herbane et al, 2010; J. Sharp, 2008; P.Skidar, 2011) are paying particular attention to BCM. BCM is identified as a viable option for organizations seeking to add value, creating competitive advantages, saving money, time and resources. According to Herbane (2010), BCM has evolved as a form of crisis management since the 1970’s in response to the technical and operational risks that threaten an organizations’ recovery from hazards and interruptions. Gibb and Buchanan (2006) also note that during the last 2 decades, BCM has evolved as an effective tool for ensuring the delivery of organization’s key products and or services. ISO 22301 (2012) defines BCM as a management process which identifies possible internal and external threats and or risks and their impact to business processes and provides a framework for organizational resilience. Bhamra et al. (2011) noted that the level of organizational business continuity directly relates to its resilient ability. For an effective BCM, BCP needs to be reviewed, modified and improved regularly. This is particularly essential in more challenging sectors like the automobile industry.

2.3 Aim of study

This study intends to:

(1) Presents a BCM framework suited for a complex automobile supply chain by modifying the BCP concept to accommodate globalization. (2) Evaluate each BCP component and reveal its interaction with other crucial BCM framework factors.

3 RESEARCH METHODOLOGY

The study employed correlation, regression and Smart PLS 3.O analyses. These analysis techniques have complementary properties. While correlation analysis establishes association among individual variables, regression seeks to identify a causal relationship of these variables and Smart PLS 3.0 finds out the direct and total effect multiple variables have on each other. More details are given below;

3.1 Correlation Analysis


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According to Cha et al (2008) and Fasolis et al (2013), traditionally BCP consists of two main aspects, being Business Impact Analysis (BIA) and Risk Analysis (RA) figure 2. However, this approach does not seem to be sufficiently addressing the complex and elaborate nature of supply chain network in the automobile industry. To address this insufficiency, we replace RA with Risk Ranking (RR) and introduce a new term Supply Chain Cooperation (SCC) to our BCP figure 3

Figure 2. Traditional BCM framework

In order to provide an in-depth analysis, this section defines and discusses operationalization of the factors in our conceptual model. The proposed conceptual model provides a holistic approach towards BCM and covers four phases in the process, being contextual factors, Business continuity plan (BCP), BCM and success evaluation factors. The conceptual model is adopted from ISO 22301 (2012), but strikes a significant variation from the standard. It also has limited similarity with the one by (Torabi et al, 2014).

Company size,

Product type etc

Business Impact

Analysis

BCM

Recovery Time

Competitive Advantage

Risk Analysis

Business Impact

Analysis

4 Profile of surveyed companies

A total number of 151 survey questionnaire were sent to companies and 92 companies replied making a response rate of 61%.Majority of surveyed companies were from Asia, accounting for 60% whereas 40% of the companies were from North America; Canada, US and Mexico. The upper part of Table 1 shows companies operating in a single tier in which 18% of the companies were tier 1 companies, 9% in tier 2 and 12% in tier 3. The lower part shows companies dealing in more than one tier, with 9% representing tiers 1 and 2, 15% accounted for companies in tiers 1 through 3 while 19% noted companies in tiers 1 and 3, the remain 18% were unknown. The figures demonstrate the diamond structure of the automobile supply chain network as discussed above in 1.2.

Table 1. Companies’ tiers Tiers Percentage (%)

1 18 2 9 3 12

1 and 2 9 1,2 and 3 15

1and3 19 Unknown 18

Total 100

Table 2 demonstrates that the Automobile industry is a manufacturing dominant industry. In this survey, 96% of the companies were manufacturing companies and services represented by 3% while import/ export companies represented only a percent.

We adoptedone1 factor for categorizing company size from the Center for Strategy and Evaluation Services report (2012), which was a recommendation to EU member states, the European Investment bank (EIB) and European Investment Fund (EIF). The report provided framework for statistical definitions on EU policies that supports SMEs (structural fund, framework program for research and development, competitiveness and innovation program etc.) in the role governing state aid, being; net turnover (annual sales). From our survey, 78% represented large companies, medium companies not represented while small companies were accounted for by 20% and the remaining 2% represented only micro companies by annual sales.

5 CONCEPTUAL FRAMEWORK

Table 2. Industry type Industry type Percentage (%) Manufacturing 96 import/export 1 Services 3 total 100


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According to Cha et al (2008) and Fasolis et al (2013), traditionally BCP consists of two main aspects, being Business Impact Analysis (BIA) and Risk Analysis (RA) figure 2. However, this approach does not seem to be sufficiently addressing the complex and elaborate nature of supply chain network in the automobile industry. To address this insufficiency, we replace RA with Risk Ranking (RR) and introduce a new term Supply Chain Cooperation (SCC) to our BCP figure 3

Figure 2. Traditional BCM framework

In order to provide an in-depth analysis, this section defines and discusses operationalization of the factors in our conceptual model. The proposed conceptual model provides a holistic approach towards BCM and covers four phases in the process, being contextual factors, Business continuity plan (BCP), BCM and success evaluation factors. The conceptual model is adopted from ISO 22301 (2012), but strikes a significant variation from the standard. It also has limited similarity with the one by (Torabi et al, 2014).

Company size,

Product type etc

Business Impact

Analysis

BCM

Recovery Time


Risk Analysis

Business Impact

Analysis

4 Profile of surveyed companies

A total number of 151 survey questionnaire were sent to companies and 92 companies replied making a response rate of 61%.Majority of surveyed companies were from Asia, accounting for 60% whereas 40% of the companies were from North America; Canada, US and Mexico. The upper part of Table 1 shows companies operating in a single tier in which 18% of the companies were tier 1 companies, 9% in tier 2 and 12% in tier 3. The lower part shows companies dealing in more than one tier, with 9% representing tiers 1 and 2, 15% accounted for companies in tiers 1 through 3 while 19% noted companies in tiers 1 and 3, the remain 18% were unknown. The figures demonstrate the diamond structure of the automobile supply chain network as discussed above in 1.2.

Table 1. Companies’ tiers Tiers Percentage (%)

1 18 2 9 3 12

1 and 2 9 1,2 and 3 15

1and3 19 Unknown 18

Total 100

Table 2 demonstrates that the Automobile industry is a manufacturing dominant industry. In this survey, 96% of the companies were manufacturing companies and services represented by 3% while import/ export companies represented only a percent.

We adoptedone1 factor for categorizing company size from the Center for Strategy and Evaluation Services report (2012), which was a recommendation to EU member states, the European Investment bank (EIB) and European Investment Fund (EIF). The report provided framework for statistical definitions on EU policies that supports SMEs (structural fund, framework program for research and development, competitiveness and innovation program etc.) in the role governing state aid, being; net turnover (annual sales). From our survey, 78% represented large companies, medium companies not represented while small companies were accounted for by 20% and the remaining 2% represented only micro companies by annual sales.

5 CONCEPTUAL FRAMEWORK

Table 2. Industry type Industry type Percentage (%) Manufacturing 96 import/export 1 Services 3 total 100


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Figure 3. Study’s original BCM framework

In this conceptual model, the first thing to consider is what we term as contextual factors. These are factors unique to every companies and usually influenced by the companies’ culture, mission and vision. Once the contextual factors are identified and established, the model introduces the second phase, which is business continuity plan (BCP). The logic is that, no effective plan can be realized until a thorough command of the ‘contextual factors’ is established. Under this phase, we established that Cha et al. (2008) found that the relationship between Business Impact Analysis (BIA) and Risk Analysis (RA) was crucial because the results of BIA and RA are merged to develop a suitable BCP. This significantly shaped what constitute our BCP, being BIA, risk ranking and supply chain cooperation (SCC). BIA was adopted directly from Cha et al. (2008) and RA was modified into risk ranking. The reason for this modification was that, while we appreciate the value of RA in BCP, we realized it might be made cost effective. We are of the view that risk ranking can be the common ground between companies’ ambitions of maximizing profit and inventory consideration to ensure continued supply (customer satisfaction) even during and after disruption. For instance, a company’s risk are to be ranked by taking a number of factors, which exposes it to risks like place of operation, complexity of supply chain, size of the company, and the company product. Risk ranking was further divided into 2 (manmade and natural risk ranking) for compatibility issues. Refer to 6.3.1 Model testing and results for a detailed explanation. The last component under this phase is SCC. This is term possibly has great potential in informing the final BCP outcome. As is discussed earlier, companies are part of a huge supply chain networks and developing an effective BCP should take into consideration this view. The studies by Fujimoto et al. (2014) and MacKenzie (2012) highlighted the integral significance of supply chain network during disruption. We, therefore, reference made was to that effect in our BCP. Given the importance of supply chain network in the flow of goods, services and information through the network in the automobile industry the study is of the view that introducing SCC is pivotal in BCP.

5.4 Measures and Hypotheses

Company

size, Product type etc

BCM

Recovery Time


Man-made RR Ranking Natural

RR

Supply Chain. Co


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5.4.1 Recovery time

When a disaster strike an area, rescue mission is dispatched immediately to save lives and salvage whatever possible, this is done to mitigate the severity of the disaster in the immediate aftermath. However, this stage marks just, but the beginning of a tedious and usually long process of recovery. It must be understood that in this regard, recovery will not be an isolated effort by each company, but an integrated process by all stakeholders, through which the affected community thrive to bring its state to pre- disaster conditions. This will not only be limited to facilities restoration, human resource accountability, financial capability etc. in a business enterprise, but road networks, water supply, electricity restoration and access to business suppliers and or consumers. In a sense, the recovery effort starts within an enterprise and reaches out to the entire community. Herbane at el. (2004) recorded that the concept of time as a resource to organizational advantage is long held in management field and the ability of an organization to achieve more quickly than rivals has been long considered a source of competitive advantage. Equally valid is the ability to recover more quickly than rivals who face the same crisis or interruption. The logic is that quick recovery time is likely to impress customers and investors and establishing trust in them, in the process an organization will hugely benefit from such scenario.

5.4.2 Competitive Advantages

Another evaluation factor is competitive advantages. In this study, these are defined as a number of circumstances/ conditions that puts a company in a favorable or superior business position relative to its competition. Such conditions includes quick recovery time after disaster strikes (as discussed above), increase of sales share and profits before, during and after a disaster event and maintaining an intact company image during uncertain times. Herbane et al. (2004) concluded that BCM’s potential contribution to the firm is that of value preservation, of which competitive advantage is crucial towards the desired value preservation.

n

Big

Company size

Man-made RR

Ranking

BCM

Recovery Time

Business Impact

Analysis

H1(+) H3(+)

H10(+)

H14(+)

H16(+)

H18(+)

H19(+) H12(+) H17(+)

Figure 3. Study’s original BCM framework

In this conceptual model, the first thing to consider is what we term as contextual factors. These are factors unique to every companies and usually influenced by the companies’ culture, mission and vision. Once the contextual factors are identified and established, the model introduces the second phase, which is business continuity plan (BCP). The logic is that, no effective plan can be realized until a thorough command of the ‘contextual factors’ is established. Under this phase, we established that Cha et al. (2008) found that the relationship between Business Impact Analysis (BIA) and Risk Analysis (RA) was crucial because the results of BIA and RA are merged to develop a suitable BCP. This significantly shaped what constitute our BCP, being BIA, risk ranking and supply chain cooperation (SCC). BIA was adopted directly from Cha et al. (2008) and RA was modified into risk ranking. The reason for this modification was that, while we appreciate the value of RA in BCP, we realized it might be made cost effective. We are of the view that risk ranking can be the common ground between companies’ ambitions of maximizing profit and inventory consideration to ensure continued supply (customer satisfaction) even during and after disruption. For instance, a company’s risk are to be ranked by taking a number of factors, which exposes it to risks like place of operation, complexity of supply chain, size of the company, and the company product. Risk ranking was further divided into 2 (manmade and natural risk ranking) for compatibility issues. Refer to 6.3.1 Model testing and results for a detailed explanation. The last component under this phase is SCC. This is term possibly has great potential in informing the final BCP outcome. As is discussed earlier, companies are part of a huge supply chain networks and developing an effective BCP should take into consideration this view. The studies by Fujimoto et al. (2014) and MacKenzie (2012) highlighted the integral significance of supply chain network during disruption. We, therefore, reference made was to that effect in our BCP. Given the importance of supply chain network in the flow of goods, services and information through the network in the automobile industry the study is of the view that introducing SCC is pivotal in BCP.

5.4 Measures and Hypotheses

Company

size, Product type etc

BCM

Recovery Time


Man-made RR Ranking Natural

RR

Supply Chain. Co


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Figure 4. Hypotheses

5.5 Literature informing “size of company” hypotheses formulation

Previous studies by Jayaram et al. (2014) and Akkiraju at al. (2004) provided an insight into these set of hypotheses. In their study Jayaram et al. (2014) concluded that most of the SMEs are family owned and as such limited in growth as the family has total control in regards to strategic decisions, control and operation management. In this scenario, Sharma (2004) explained that due to the nature of their operations, small businesses tend to have limited supply chain capabilities and exposed to major damage in the event of supply chain disruption. On the other hand, large companies are thought to have strong supply chain capabilities and in the process eliminate many risks associated with the supply chain network. From these studies, we were able to formulate hypotheses, which show how the company size affects BCP factors. The hypotheses are as below;

Hypothesis 1: Big company size has positive effects on manmade risk ranking

Hypothesis 2: Big company size has positive effects on natural risk ranking

Hypothesis 3: Big company size has positive effects on BIA

Hypothesis 4: Big company size has positive effects on supply chain cooperation

5.6 Literature informing “supply chain cooperation” hypotheses formulation

As Andersson (1998, p64) observed, the main message of the network view is that cooperation is more efficient than competition for the firms development. Andersson (1998, p 91) continues to state that If companies trust each other and develop bonds and communication channel between the different actors in the network, the resources and activities in the network can be organized in a more efficient way. From this observation, we were able to note that previous studies did not make mention SCC at all in their BCM framework and models even though its benefit to the supply chain network is thought to be more efficient and promote companies’ development. From this literature, were able to form the following sets of hypotheses.

Hypothesis 5: Supply chain cooperation has positively effects on manmade risk ranking

Hypothesis 6: Supply chain cooperation has positively effects on natural risk ranking

Hypothesis 7: Supply chain cooperation has positive effects on recovery time

Hypothesis 8: Supply chain cooperation has positive effects on BCM

Supply Chain. Co

Competitive Advantage Natural

RR H2(+)

H4(+) H7(+)

H8(+) H11(+)

H9(+) H5(+)

H6(+) H13(+)

H15(+)


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Hypothesis 9: Supply chain cooperation has positive effects on competitive advantage

5.7 Literature informing ‘BIA’ Hypotheses formulation

The British standard BS25999 (2006) defines BIA as the process of analyzing business functions and the effect that a business disruption might have on them. This definition relates very well with the American National Fire Protection Agency (NFPA) 1600 (2010), which defines BIA as an analysis which measures the effect of resource loss and escalating losses over time in order to provide the entity with reliable data upon which to base decisions concerning hazard mitigation, recovery strategies and continuity planning. The good practice guidelines (GPG) 2007 of the Business continuity institute (BCI) (2010), which is a management guidelines for implementing BCM following the lifecycle of BS 25999 mentioned that BIA identifies, qualifies and quantifies the business impacts of loss, interruption and disruption of business processes on an organization and provides data from which appropriate continuity strategies can be determined. This then suggest that BIA forms the core of BCP as this plan (BCP) significantly relies on the BIA outcome. From, this literature, the following hypotheses we formulated;

Hypothesis 10: BIA has positive effects on recovery time

Hypothesis 11: BIA has positive effects on competitive advantages

Hypothesis 12: BIA has positive effects on manmade risk ranking

Hypothesis 13: BIA has positive effects on natural risk ranking

Hypothesis 14: BIA has positive effects on BCM

Hypothesis 15: BIA has positive effects on supply chain cooperation

5.8 Literature informing “risk ranking” Hypotheses formulation

This set of hypotheses were informed by Tjoa et al. (2008) who stated that identifying scenarios leading to severe impacts on the company’s reputation, assets or financial position is very important. However, despite this observation, no practical solutions have been offered to effectively identify scenarios that lead to severe impacts on the company. Existing solutions, which discussed disaster preparation methods like virtual dual sourcing by Fujimoto and Park (2014) are expensive to conduct and as such discourages companies to employ BCM as a form of preparedness. In fact, a survey by Woodman (2011) revealed that despite the known benefits of BCM, its adoption is still lagging behind mainly due to the cost of implementation. This study introduces risk ranking as a practical solution in identifying scenarios leading to severe impacts while at the same time providing a common ground to optimize both inventory and profit making ambitions. From this literature, the study proposes the following sets of hypotheses.

Hypothesis 16: Manmade risk ranking has positive impact on BCM

Hypothesis 17: Natural risk ranking has positive impact on BCM

Hypothesis 18: Manmade risk ranking has positive impact recovery time

Hypothesis 19: Natural risk ranking has positive impact recovery time

Figure 4. Hypotheses

5.5 Literature informing “size of company” hypotheses formulation

Previous studies by Jayaram et al. (2014) and Akkiraju at al. (2004) provided an insight into these set of hypotheses. In their study Jayaram et al. (2014) concluded that most of the SMEs are family owned and as such limited in growth as the family has total control in regards to strategic decisions, control and operation management. In this scenario, Sharma (2004) explained that due to the nature of their operations, small businesses tend to have limited supply chain capabilities and exposed to major damage in the event of supply chain disruption. On the other hand, large companies are thought to have strong supply chain capabilities and in the process eliminate many risks associated with the supply chain network. From these studies, we were able to formulate hypotheses, which show how the company size affects BCP factors. The hypotheses are as below;

Hypothesis 1: Big company size has positive effects on manmade risk ranking

Hypothesis 2: Big company size has positive effects on natural risk ranking

Hypothesis 3: Big company size has positive effects on BIA

Hypothesis 4: Big company size has positive effects on supply chain cooperation

5.6 Literature informing “supply chain cooperation” hypotheses formulation

As Andersson (1998, p64) observed, the main message of the network view is that cooperation is more efficient than competition for the firms development. Andersson (1998, p 91) continues to state that If companies trust each other and develop bonds and communication channel between the different actors in the network, the resources and activities in the network can be organized in a more efficient way. From this observation, we were able to note that previous studies did not make mention SCC at all in their BCM framework and models even though its benefit to the supply chain network is thought to be more efficient and promote companies’ development. From this literature, were able to form the following sets of hypotheses.

Hypothesis 5: Supply chain cooperation has positively effects on manmade risk ranking

Hypothesis 6: Supply chain cooperation has positively effects on natural risk ranking

Hypothesis 7: Supply chain cooperation has positive effects on recovery time

Hypothesis 8: Supply chain cooperation has positive effects on BCM

Supply Chain. Co

Competitive Advantage Natural

RR H2(+)

H4(+) H7(+)

H8(+) H11(+)

H9(+) H5(+)

H6(+) H13(+)

H15(+)


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6 DATA ANALYSIS

6.1 Correlation Analysis results

We applied this correlation technique to investigate the interrelationship among BCP Factors (Risk ranking, supply chain cooperation and BIA) we carried a correlation analysis. Tables 3 and 4 present the results

The questionnaire asked 7 question groups of 34 questions, all written in 5 point Likert scale format except for company profile. However, we narrow our analysis only to BCP factors. The correlations analysis was carried at 5% significant level. Table 3 demonstrates correlation of risk ranking (manmade and natural) and supply chain cooperation. In this study manmade risk ranking questions (Q18-2 and 18-3) investigated degree of implementing earthquake resistant strengthening and enforcement measures while natural risk ranking questions (18-6, 18-9, 18-10, and 18-11) investigated degree of flooding, snow damage and cold wave, thunderbolt and heatwave respectively. Supply chain cooperation questions (Q3, 14, 15 and 16) investigated sufficient planning in the supply chain network, sufficient disaster and damage risks surveys, sufficient implementation of countermeasure at suppliers and measures in place to guarantee supplies to customers respectively. Q 18-3 demonstrated strong correlation with all supply chain cooperation questions. This means that companies which have strengthened their earthquake resistant measures enjoyed more cooperation within the supply chain network. It could be because once companies make their infrastructure earthquake resistant, such companies does not suffer more damage during disruption and as a result continues to operate, promoting smooth flow of goods and information within the network during disruption. Also Q 15 (countermeasures against risks at suppliers had strong correlation with both natural and manmade risk ranking (Q.18-3, 18-6, and 18- 11). Other correlations were weak (Q18-9, 18-6 1nd 18-10). This shows that the level of risk a company faces may be transferred from risks at suppliers and other stakeholders within the supply chain. The correlation results between these two factors (risk ranking and Supply chain cooperation) confirm the importance of ranking risks in developing BCP because majority of companies, which ignore the risk often, suffer more supply chain disruption as the 2 are correlated.

Table 3. Correlation Analysis (Risk ranking and Supply chain cooperation) Q18-2 Q18-3 Q18-6 Q18-9 Q18-10 Q18-11

Q14

*** 0.371

Q15

*** 0.556 *** 0.362 ** 0.339 *** 0.353

*** 0.519 ** 0.343


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Table 4 analyses the correlation between risk ranking and BIA. As was expected, Q 18-3 (degree of implementing earthquake resistant strengthening and enforcement measures) had correlation properties with BIA questions (Q2, 4 and 5), which investigated sufficient plans of securing electricity, gas, water, communication etc., sufficient priority plans for operation restoration and management resource, securing and sufficiently establishing crisis management system respectively. This correlation was expected, as Q 18-3 is part of BIA. Other questions which demonstrate a similar correlation are Q 18-10 (risk of thunderbolt) and BIA questions (Q2, 5 and 6), which also focuses on sufficient pre disaster planning. The correlation suggests that degree of implementing earthquake resistant strengthening and enforcement measures is an important aspect of pre disaster planning. The results also provide us with a crucial finding because we realized that most of the companies, which are not satisfied with their BIA, tend to pay less attention to risk and vice versa. This is so because risk ranking is instrumental to pre disaster planning, which constitutes BIA largely.

6.2 Regression Analysis 6.2.1 Business Impact Analysis

Business Impact analysis strongly and positively affected BCM with Q.1 recording a (***) with Q.7, 8 and 9 of BCM (which investigated level of drills implementation by a company in case of disaster, opinion on BCM performance and ability of emergency control system to be effective respectively). Q.1 recorded a (**) significant level with Q. 11, 12 and 13 of BCM (opinion on risk reduction by BCM, implementation of proactive countermeasures to avoid parts supply risks and implementation of proactive countermeasures with production equipment against disasters respectively). Another interesting relationship is between BIA and recovery time. While Q.1 of BIA recorded a weak positive relation with recovery time Q.19 and 21 with a (*) significant level, Q. 27 and 29 recorded a moderate significant level of (**) with recovery time Q. 21 and 23. It can also be observed that Q. 27 recorded a moderate negative relationship with Q. 19 at a (**) significant level. From Table. 5 we understood that BIA has more strong correlation with other

Q16 Q3

*** 0.355 *** 0.462 ** 0.304

Note; *p˂0.05, **p˂0.01, ***p˂0.001

Table 4. Correlation Analysis (risk ranking and BIA)

Q18-2 Q18-3 Q18-9 Q18-10 Q18-11

Q 1 ** 0.346 Q 2 ** 0.349 ** 0.332 Q 4 ** 0.327 Q 5 ** 0.330 ** 0.329 Q6 ** 0.337 *** 0.402 ** 0.303 Q 21 * 0.241 Q 28 Q29

* 0.203

Q 31

** 0.320

Note; *p˂0.05, **p˂0.01, ***p˂0.001

6 DATA ANALYSIS

6.1 Correlation Analysis results

We applied this correlation technique to investigate the interrelationship among BCP Factors (Risk ranking, supply chain cooperation and BIA) we carried a correlation analysis. Tables 3 and 4 present the results

The questionnaire asked 7 question groups of 34 questions, all written in 5 point Likert scale format except for company profile. However, we narrow our analysis only to BCP factors. The correlations analysis was carried at 5% significant level. Table 3 demonstrates correlation of risk ranking (manmade and natural) and supply chain cooperation. In this study manmade risk ranking questions (Q18-2 and 18-3) investigated degree of implementing earthquake resistant strengthening and enforcement measures while natural risk ranking questions (18-6, 18-9, 18-10, and 18-11) investigated degree of flooding, snow damage and cold wave, thunderbolt and heatwave respectively. Supply chain cooperation questions (Q3, 14, 15 and 16) investigated sufficient planning in the supply chain network, sufficient disaster and damage risks surveys, sufficient implementation of countermeasure at suppliers and measures in place to guarantee supplies to customers respectively. Q 18-3 demonstrated strong correlation with all supply chain cooperation questions. This means that companies which have strengthened their earthquake resistant measures enjoyed more cooperation within the supply chain network. It could be because once companies make their infrastructure earthquake resistant, such companies does not suffer more damage during disruption and as a result continues to operate, promoting smooth flow of goods and information within the network during disruption. Also Q 15 (countermeasures against risks at suppliers had strong correlation with both natural and manmade risk ranking (Q.18-3, 18-6, and 18- 11). Other correlations were weak (Q18-9, 18-6 1nd 18-10). This shows that the level of risk a company faces may be transferred from risks at suppliers and other stakeholders within the supply chain. The correlation results between these two factors (risk ranking and Supply chain cooperation) confirm the importance of ranking risks in developing BCP because majority of companies, which ignore the risk often, suffer more supply chain disruption as the 2 are correlated.

Table 3. Correlation Analysis (Risk ranking and Supply chain cooperation) Q18-2 Q18-3 Q18-6 Q18-9 Q18-10 Q18-11

Q14

*** 0.371

Q15

*** 0.556 *** 0.362 ** 0.339 *** 0.353

*** 0.519 ** 0.343


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BCP factors and by far the most BCM influencing factor while also significantly implicating recovery time. 6.2.2 Risk ranking

Man-made risk ranking has recorded more significant relationships than natural risk ranking. This is an interesting trend. Q. 18-2 positively affected BCM Q. 10 (*), Q. 11 (**) and recovery time Q. 19 (*), whereas Q. 18-3 positively affected BCM Q. 7 (**), Q. 8 (*) and Q. 13 (**) Table. 5 (continuation) On the contrary, natural risk ranking recorded only one relationship, in which Q. 18-11 has a positive effect on BCM Q. 12 (*), Table. 5. This observation suggests that even if a company may rank very high in terms of risks, the effect of such a risk is significantly associated with BCM. Perhaps, this underscores the importance of management in averting some risk effects. For instance, Japan is located in a highly risk region frequently hit by earthquakes, tsunamis, volcanic eruptions and flooding’s but have a flourishing automobile sector mainly because of risk management through BCM. A robust BCM can be a very powerful risk averting management engagement.

6.2.3 Business Continuity Management & Recovery time

BCM demonstrated a positive relationship with recovery time as BCM Q.8 positively affected recovery time Q. 19 (*) and Q. 23 (**). Recovery time Q. 23 established (*) negative relationship with comparative advantage Q. 32. Table. 5 (continuation)


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Table 5. Regression analysis

Dependent variables

Independent variables Man- made

risk ranking Natural manmade risk

ranking Business Continuity Management Recovery time

Specific question 18-2 18-3 18-9 18-10 18-11 7 8 10 11 12 13 2 3

Supply Chain cooperation

3 **2.516 **3.409 **2.904 **2.518 ***3.598 14 **3.492 *2.022 15 *2.482 *2.335 *2.392 *2.079 *2.008 *2.159 *2.406 **3.349 16

R 0.582 0.363 0.5 0.357 0.606 0.752 0.6 0.675 0.566 0.664 0.485 0.512 R-square 0.338 0.131 0.25 0.128 0.368 0.565 0.36 0.456 0.32 0.44 0.235 0.262 Adj.R-square 0.3 0.082 0.207 0.078 0.332 0.54 0.323 0.425 0.281 0.408 0.191 0.22

F-value 8.941 2.649 5.819 2.564 10.183 22.75 9.845 14.659 8.235 13.76 5.377 6.21 n 75 75 75 75 75 75 75 75 75 75 75 75 Note; *p˂0.05, **p˂0.01, ***p˂0.001

BCP factors and by far the most BCM influencing factor while also significantly implicating recovery time. 6.2.2 Risk ranking

Man-made risk ranking has recorded more significant relationships than natural risk ranking. This is an interesting trend. Q. 18-2 positively affected BCM Q. 10 (*), Q. 11 (**) and recovery time Q. 19 (*), whereas Q. 18-3 positively affected BCM Q. 7 (**), Q. 8 (*) and Q. 13 (**) Table. 5 (continuation) On the contrary, natural risk ranking recorded only one relationship, in which Q. 18-11 has a positive effect on BCM Q. 12 (*), Table. 5. This observation suggests that even if a company may rank very high in terms of risks, the effect of such a risk is significantly associated with BCM. Perhaps, this underscores the importance of management in averting some risk effects. For instance, Japan is located in a highly risk region frequently hit by earthquakes, tsunamis, volcanic eruptions and flooding’s but have a flourishing automobile sector mainly because of risk management through BCM. A robust BCM can be a very powerful risk averting management engagement.

6.2.3 Business Continuity Management & Recovery time

BCM demonstrated a positive relationship with recovery time as BCM Q.8 positively affected recovery time Q. 19 (*) and Q. 23 (**). Recovery time Q. 23 established (*) negative relationship with comparative advantage Q. 32. Table. 5 (continuation)


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6.3 Smart PLS

6.3.1Model testing and results

Common criteria to evaluate reflective measures of PLS path models are the average variance extracted, the composite reliability and the communality (Stone- Geissers Q2) (Chin, 1998). The results of these measures are in table 5. A major challenge we faced had to do with the risk-ranking construct. As mention earlier, this factor is an original idea, meaning that it has not been discussed before in this regard, and as such, there was no literature to get insight from. Therefore, during model formulation and testing, we realized that risk-ranking construct has to further be divided as we realized that including all the indicators in one constructs significantly reduced internal consistency reliability and validity. In this regard, risk ranking was divided into manmade risk ranking and natural risk ranking. Further to that, some indicators were removed entirely to enhance internal consistency. Measurement models assessment was done by evaluating internal reliability and validity. The two traditional criteria for evaluating the two are Cronbach’s alpha and composite reliability. All latent variables have values above suggested thresholds of 0.7 of Cronbach’s alpha and above 0.7 of composite reliability (Jayaram et al, 2014).

Average variance extracted (AVE) value of 0.5 or higher is acceptable as it indicates that the latent variable explain more than half of its indicator variance (Hair Jr et al, 2014). Considering the values in table 5, we can conclude that all the measures are well above the required minimum thresholds and acceptable. Fornell- Larcker criterion was used for evaluation of

Table 6. Results for Measurement Model Evaluation Criteria

AVE Composite Reliability

R

Square Cronbach’s

Alpha Communality Business Continuity Management 0.667 0.9231

0.7671 0.8998 0.667

Business Impact Analysis 0.6044 0.9131

0.8874 0.6044 Comparative Advantage 0.8688 0.9298

0.2934 0.8489 0.8688 Company Size 0.8046 0.925

0.8788 0.8046

Manmade risk ranking 0.7884 0.8817

0.3405 0.7319 0.7884 Natural risk ranking 0.5525 0.8595

0.2074 0.8022 0.5525

Recovery time 0.6725 0.8911

0.3858 0.8383 0.6725

Supply Chain cooperation 0.7617 0.9274

0.5345 0.8952 0.7617

Dependent variables

Independent variables Business Continuity Management Recovery time Specific question 7 8 10 11 12 13 1 2 3

Business Impact Analysis

1 ***9.686 ***4.374 ***3.796 **2.736 **2.584 **2.829 *2.099 *2.446 2 **3.071 5 *2.008 *2.425

27 **-2.525 29 *2.032 **3.352 **2.573 **2.574

R 0.846 0.855 0.79 0.782 0.621 0.657 0.575 0.539 0.494 R-square 0.716 0.732 0.623 0.612 0.385 0.431 0.33 0.291 0.244 Adj.R-square 0.687 0.704 0.584 0.571 0.321 0.372 0.26 0.216 0.166 F-value 24.752 26.09 15.84 15.083 5.993 7.256 4.719 3.921 3.098 n 75 75 75 75 75 75 75 75 75

Man- made risk ranking

18- 2 *2.102 **2.768 *2.119

18- 3 **2.869 *2.239 **2.673

R 0.42 0.3 0.407 0.438 0.458 0.306 R-square 0.176 0.09 0.166 0.192 0.21 0.093 Adj.R-square 0.154 0.051 0.143 0.169 0.188 0.068 F-value 7.712 2.335 7.153 8.528 9.571 3.709 n 75 75 75 75 75 75

Natural risk ranking 18- 11 *2.266

R 0.463 R- square 0.214 Adj.R-square 0.157 F-value 3.763 n 75 Note; *p˂0.05, **p˂0.01, ***p˂0.001


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6.3 Smart PLS

6.3.1Model testing and results

Common criteria to evaluate reflective measures of PLS path models are the average variance extracted, the composite reliability and the communality (Stone- Geissers Q2) (Chin, 1998). The results of these measures are in table 5. A major challenge we faced had to do with the risk-ranking construct. As mention earlier, this factor is an original idea, meaning that it has not been discussed before in this regard, and as such, there was no literature to get insight from. Therefore, during model formulation and testing, we realized that risk-ranking construct has to further be divided as we realized that including all the indicators in one constructs significantly reduced internal consistency reliability and validity. In this regard, risk ranking was divided into manmade risk ranking and natural risk ranking. Further to that, some indicators were removed entirely to enhance internal consistency. Measurement models assessment was done by evaluating internal reliability and validity. The two traditional criteria for evaluating the two are Cronbach’s alpha and composite reliability. All latent variables have values above suggested thresholds of 0.7 of Cronbach’s alpha and above 0.7 of composite reliability (Jayaram et al, 2014).

Average variance extracted (AVE) value of 0.5 or higher is acceptable as it indicates that the latent variable explain more than half of its indicator variance (Hair Jr et al, 2014). Considering the values in table 5, we can conclude that all the measures are well above the required minimum thresholds and acceptable. Fornell- Larcker criterion was used for evaluation of

Table 6. Results for Measurement Model Evaluation Criteria

AVE Composite Reliability

R

Square Cronbach’s

Alpha Communality Business Continuity Management 0.667 0.9231

0.7671 0.8998 0.667

Business Impact Analysis 0.6044 0.9131

0.8874 0.6044 Comparative Advantage 0.8688 0.9298

0.2934 0.8489 0.8688 Company Size 0.8046 0.925

0.8788 0.8046

Manmade risk ranking 0.7884 0.8817

0.3405 0.7319 0.7884 Natural risk ranking 0.5525 0.8595

0.2074 0.8022 0.5525

Recovery time 0.6725 0.8911

0.3858 0.8383 0.6725

Supply Chain cooperation 0.7617 0.9274

0.5345 0.8952 0.7617


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discriminant validity of the latent variables (uniqueness of the latent variable), following recommendations by Hair Jr et al 2014), we performed Fornell- Lacker analysis Table 7.

7 DISCUSSIONS

7.1 Hypothesis 1& 2 [Partly Supported]

As expected, big company size has positive effects on both man-made and natural risk ranking. However, these positive effects are not significant. Both the correlation and regression analysis could also not prove the significance of these relationships. This could be because both the big and small companies face their own unique challenge in as far as risks are concerned and as such, no significant relationship observed.

7.2. Hypothesis 3 [Partly Supported]

Indeed, this hypothesis proved to be positive but was not significant, further to that correlation and regression analysis could not establish the significance of this relationship. This could be because a well-assembled management team can develop a very good and BIA policy regardless of the size of the company. Some small companies are known to be more efficient because of little bureaucratic administration involved, while some big companies are known to be inefficient and usually take long to change due to the bureaucracy involved.


As expected, big company size has positive effects on supply chain cooperation, it can also be observed that correlation and regression analysis did not establish the significance of this relationship, and as such, this hypothesis is not significant. A possible explanation could be that large companies face more challenges due to their extensive and complicated supply chain

Table 7. Latent variable correlations (calculation with Smart PLS 3.0)

Business Continuity

Management

Business Impact

Analysis Comparative Advantages

Company size

Manmade Risk

Ranking

Natural Risk

Ranking Recovery

time

Supply chain cooperation

Business Continuity Management 1

Business Impact Analysis 0.8409 1

Comparative Advantages 0.4019 0.2292 1

Company size 0.0943 0.0043 0.3264 1 Manmade Risk

Ranking 0.4902 0.3833 0.3097 0.2053 1 Natural Risk

Ranking 0.374 0.3705 0.2027 0.1163 0.2377 1 Recovery time 0.6014 0.4997 0.245 0.1058 0.2734 0.258 1

Supply chain cooperation 0.7606 0.7209 0.1997 0.1202 0.5661 0.4449 0.5371 1


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network. Once a company has many companies in their supply chain network, its risk of disruption significantly increases making it highly vulnerable.

7.4. Hypothesis 5 and 6 [Supported]

As expected, supply chain cooperation has a significant positive effects on risk ranking (both manmade*** and natural**) in both the direct and indirect effects. The correlation and regression analysis also established the significance of this relationships, see table 3 and 5. These results are pivotal in understanding the most effective and efficient way to handle risks, be they man-made or of nature. In this regard, we refer to the works of Andersson (1998) who concluded that a cooperative network will not only eliminate risks but promotes companies’ development.

7.5. Hypotheses 7 and 9 [Partly Supported]

Supply chain cooperation’s total effects on recovery time and competitive advantages indicated a positive relationship, though not significant. However, the regression analysis results showed a direct significant relationship between supply chain cooperation and recovery time. The difference in these results is interesting and justifies why it is important to employ different statistical analysis to further probe relationships. An explanation to these differences could be that the regression analysis calculated direct relationships between these variables but Smart PLS 3.0 calculated total relationships, taking into account all factors in the model in the process reducing significance of these relationships.

7.6. Hypothesis 8 [Supported]

We also realize that Supply chain cooperation has a significant positive effect on BCM. The total effects showed a strong significant level of (***). This significant relationship was also proved by the regression analysis results in table 5. A possible explanation to this significant relationship could be because, normally a cooperative supply chain network takes care of the risk- as in hypotheses 5 and 6, which is a crucial aspect to be handled in order to develop a BCM that can withstand a serious or intense disruption.

7.7. Hypotheses 10 and 11 [Supported]

BIA has a positive and significant total effects on both recovery time (***) and competitive advantages (*), the same results were also calculated by the regression analysis in table 5 (continuation). This suggests that BIA has more effects than supply chain cooperation in as far as recovery time and competitive advantages are concerned. We also note that BIA has stronger effects on recovery time than competitive advantages. This could be because competitive advantages has more factors constituting it whereas recovery time is a single factor concerned with the aspect of time, thus easy to establish recovery time association with other factors than it is with competitive advantages.

7.8. Hypotheses 12 and 13 [Supported]

The total effects of BIA on both manmade and natural risk ranking is positive and significant (**) for manmade and (***) for natural risk ranking. Indeed correlation (table 4) and regression

discriminant validity of the latent variables (uniqueness of the latent variable), following recommendations by Hair Jr et al 2014), we performed Fornell- Lacker analysis Table 7.

7 DISCUSSIONS

7.1 Hypothesis 1& 2 [Partly Supported]

As expected, big company size has positive effects on both man-made and natural risk ranking. However, these positive effects are not significant. Both the correlation and regression analysis could also not prove the significance of these relationships. This could be because both the big and small companies face their own unique challenge in as far as risks are concerned and as such, no significant relationship observed.


Indeed, this hypothesis proved to be positive but was not significant, further to that correlation and regression analysis could not establish the significance of this relationship. This could be because a well-assembled management team can develop a very good and BIA policy regardless of the size of the company. Some small companies are known to be more efficient because of little bureaucratic administration involved, while some big companies are known to be inefficient and usually take long to change due to the bureaucracy involved.


As expected, big company size has positive effects on supply chain cooperation, it can also be observed that correlation and regression analysis did not establish the significance of this relationship, and as such, this hypothesis is not significant. A possible explanation could be that large companies face more challenges due to their extensive and complicated supply chain

Table 7. Latent variable correlations (calculation with Smart PLS 3.0)

Business Continuity

Management

Business Impact

Analysis Comparative Advantages

Company size

Manmade Risk

Ranking

Natural Risk

Ranking Recovery

time

Supply chain cooperation

Business Continuity Management 1

Business Impact Analysis 0.8409 1

Comparative Advantages 0.4019 0.2292 1

Company size 0.0943 0.0043 0.3264 1 Manmade Risk

Ranking 0.4902 0.3833 0.3097 0.2053 1 Natural Risk

Ranking 0.374 0.3705 0.2027 0.1163 0.2377 1 Recovery time 0.6014 0.4997 0.245 0.1058 0.2734 0.258 1

Supply chain cooperation 0.7606 0.7209 0.1997 0.1202 0.5661 0.4449 0.5371 1


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analysis table 5 (continuation) also established this relationships. A possible explanation could be that BIA report informs organization about potential risks and the organization responds accordingly. We think that risk ranking is an excellent way to accomplish this mission. We also note that BIA significance is relatively weak in manmade risk ranking than in natural risk ranking. A possible explanation could be that most of the companies avert any potential risks facing their organization, hence BIA having a moderate impact on manmade risk. However, BIA strongly affects natural risk ranking because such risks are usually outside the companies control in the process been affected more by BIA.


One of the strongest positive relationship in this study is that BIA has positive total effects on BCM at (***). Also, see this relationship from the regression analysis table 5 (continuation) BIA report is very crucial in the BCM formulation as it the foundation of a relevant BCM and this relationship confirms what we have expected in this study.


BIA has a strong (***) positive impact on supply chain cooperation in the total effects as expected. Indeed regression analysis established this relationship in Table 5 (continuation). We are of the view that BIA report identifies any impacts within and outside the company. Therefore, supply chain cooperation is analyzed by this report as an ‘outside’ the company factor, making this relationship very significant.


Both risks (manmade and natural) have very important contribution but insignificant to BCM as indicated by the results in Table 9. However, the regression analysis results showed a direct significant relationship between both risks (manmade and natural) and BCM Table 5 (continuation. The same reason as in hypothesis 7 and 9 can be given for this differences. This results suggest that even though some companies with high risk ranking usually develops a good BCM program suited to their conditions, such a good BCM program is not only limited to high risk ranking companies as some companies with lower risk ranking can develop a good BCM well suited to their conditions.

7.12. Hypothesis 18 and 19 [Not supported]

Contrary to our expectations both natural and manmade risk ranking has negative total effects on recovery time, but a weak positive significance (*) from the regression analysis results, this positive significant relationship seems to have been lost during Smart PLS 3.0 calculations. It can be reasoned that when risks are high frequency of disruption will be high resulting in constant challenges to recovery time and possible delay.


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Smart PLS 3.0 results analysis for both the direct and total effects are as shown in Table 8. The results are discussed below (4.0)

Table 8. Smart PLS 3.0 Direct and total effect Analysis results Direct effects Total effects

Hypotheses Original Sample

Sign level

Standard Deviation

Standard Error

T Statistics

Original Sample

Sig Level

Standard Deviation

Standard Error

T Statistics

1 0.1381 0.0932 0.0932 1.4816 0.2081 0.1066 0.1066 1.9523 2 0.0701 0.1234 0.1234 0.5679 0.1048 0.1383 0.1383 0.7573 3 0.0044 0.142 0.142 0.031 0.005 0.1397 0.1397 0.0358 4 0.1222 0.1436 0.1436 0.8511 0.1222 0.1436 0.1436 0.8511 5 0.5707 *** 0.155 0.155 3.6811 0.5706 *** 0.1669 0.1669 3.4195 6 0.3558 ** 0.1425 0.1425 2.5977 0.349 ** 0.1363 0.1363 2.5602 7 0.255 0.2309 0.2309 1.1043 0.3463 0.1979 0.1979 1.7503 8 0.2479 ** 0.0943 0.0943 2.6295 0.3069 *** 0.0904 0.0904 3.3968 9 -0.2387 0.1347 0.1347 1.7719 0.0073 0.1504 0.1504 0.0484 10 -0.0736 0.279 0.279 0.2638 0.5016 *** 0.1066 0.1066 4.7035 11 -0.2343 0.153 0.153 1.5309 0.2272 * 0.1065 0.1065 2.1323 12 -0.0296 0.1735 0.1735 0.1706 0.3818 ** 0.1091 0.1091 3.499 13 0.1116 0.1489 0.1489 0.7499 0.3685 *** 0.1023 0.1023 3.6027 14 0.623 *** 0.0741 0.0741 8.4053 0.8405 *** 0.0391 0.0391 21.5247 15 0.7215 *** 0.0607 0.0607 11.891 0.7208 *** 0.0649 0.0649 11.1042 16 0.1029 0.0713 0.0713 1.443 0.103 0.0687 0.0687 1.4996 17 0.0154 0.0763 0.0763 0.2015 0.0154 0.0763 0.0763 0.2015 18 -0.1093 0.1266 0.1266 0.8637 -0.0552 0.131 0.131 0.4212 19 -0.0048 0.1187 0.1187 0.0402 -0.013 0.1209 0.1209 0.1077 Note; *p˂0.05, **p˂0.01, ***p˂0.001

7.13 Sensitivity Analysis

As it si shown in Table 9 below are the results; the change in the values of parameters may happen due to uncertainties induced by forces beyond the management’s control. In order to examine the implications of these changes, the sensitivity analysis was carried out. We now study how changes in the company size affect our model. Generally, the analysis indicates that an increase of 20% to company size yields six strong (***) significance levels to hypotheses 5, 10, 12, 13, 14, and 15 compared to four strong (***)

analysis table 5 (continuation) also established this relationships. A possible explanation could be that BIA report informs organization about potential risks and the organization responds accordingly. We think that risk ranking is an excellent way to accomplish this mission. We also note that BIA significance is relatively weak in manmade risk ranking than in natural risk ranking. A possible explanation could be that most of the companies avert any potential risks facing their organization, hence BIA having a moderate impact on manmade risk. However, BIA strongly affects natural risk ranking because such risks are usually outside the companies control in the process been affected more by BIA.


One of the strongest positive relationship in this study is that BIA has positive total effects on BCM at (***). Also, see this relationship from the regression analysis table 5 (continuation) BIA report is very crucial in the BCM formulation as it the foundation of a relevant BCM and this relationship confirms what we have expected in this study.


BIA has a strong (***) positive impact on supply chain cooperation in the total effects as expected. Indeed regression analysis established this relationship in Table 5 (continuation). We are of the view that BIA report identifies any impacts within and outside the company. Therefore, supply chain cooperation is analyzed by this report as an ‘outside’ the company factor, making this relationship very significant.


Both risks (manmade and natural) have very important contribution but insignificant to BCM as indicated by the results in Table 9. However, the regression analysis results showed a direct significant relationship between both risks (manmade and natural) and BCM Table 5 (continuation. The same reason as in hypothesis 7 and 9 can be given for this differences. This results suggest that even though some companies with high risk ranking usually develops a good BCM program suited to their conditions, such a good BCM program is not only limited to high risk ranking companies as some companies with lower risk ranking can develop a good BCM well suited to their conditions.

7.12. Hypothesis 18 and 19 [Not supported]

Contrary to our expectations both natural and manmade risk ranking has negative total effects on recovery time, but a weak positive significance (*) from the regression analysis results, this positive significant relationship seems to have been lost during Smart PLS 3.0 calculations. It can be reasoned that when risks are high frequency of disruption will be high resulting in constant challenges to recovery time and possible delay.


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significance levels to hypotheses 5, 13, 14 and 15 when reducing company size by 20%. Overall, an increase of 20% yielded 10 significant levels of varying degree while the 20% reduction yielded 8 significant levels of varying degree. Comparing this with our data, an increase of 20% seems to be more effective as it yields 10 significance levels while the unaltered (study) data yielded 9 significant correlation of varying degree. A reduction of 20% of company size is not effective as it lower than unaltered data.

Table 9. Sensitivity Analysis results at minus (-) 20% and plus (+) 20% of Company size Decease of company size by 20% Increase of company size by 20%

Hypotheses (H)

Sign level

Original Sample

(O)

Sample Mean (M)

Standard Deviation (STDEV)

Standard Error

(STERR) T Statistics

(|O/STERR|)

Original Sample

(O)

Sign level

Sample Mean (M)

Standard Deviation (STDEV)

Standard Error

(STERR) T Statistics

(|O/STERR|) 1 0.12 0.11 0.1 0.0949 1.2373 0.1911 * 0.19 0.109 0.11 1.7495 2 0.1 0.11 0.1 0.1409 0.7445 0.14 0.15 0.134 0.13 1.0428 3 0.02 0.03 0.1 0.1395 0.131 -0.0041 0 0.144 0.14 0.0288 4 * 0.13 0.14 0.1 0.0864 1.5514 0.0842 0.1 0.155 0.15 0.5437 5 *** 0.57 0.56 0.2 0.1576 3.6251 0.5775 *** 0.58 0.149 0.15 3.8718 6 0.02 0.04 0.1 0.1401 0.131 -0.0041 0 0.139 0.14 0.0388 7 0.25 0.23 0.2 0.2233 1.1358 0.3565 * 0.34 0.19 0.19 1.8787 8 * 0.24 0.23 0.1 0.0983 2.4295 0.306 ** 0.3 0.089 0.09 3.4523 9 * -0.25 -0.2 0.1 0.139 1.7897 0.0139 0.02 0.151 0.15 0.0919 10 -0.08 -0.1 0.3 0.2628 0.32 0.5029 *** 0.53 0.111 0.11 4.5424 11 -0.23 -0.2 0.2 0.1608 1.405 0.2318 * 0.24 0.115 0.12 2.0158 12 -0.03 -0 0.2 0.1719 0.1847 0.3836 *** 0.39 0.101 0.1 3.7871 13 *** 0.37 0.39 0.1 0.1022 3.6247 0.373 *** 0.39 0.103 0.1 3.6283 14 *** 0.62 0.63 0.1 0.0775 8.004 0.8414 *** 0.84 0.038 0.04 22.2493 15 *** 0.72 0.72 0.1 0.0679 10.6 0.7223 *** 0.72 0.065 0.06 11.1841 16 * 0.1 0.1 0.1 0.0691 1.5126 0.106 0.11 0.073 0.07 1.4439 18 -0.1 -0.1 0.1 0.119 0.8448 -0.0509 -0.04 0.122 0.12 0.4159 17 0.02 0.03 0.1 0.0718 0.2674 0.0188 0.02 0.07 0.07 0.2679 19 -0.02 -0 0.1 0.1169 0.135 -0.0114 -0 0.125 0.13 0.0914

Note; *p˂0.05, **p˂0.01, ***p˂0.001


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8. CONCLUSIONS

Unique contributions of the paper

The study adopted the application of 3 statistical analysis techniques, which proved to be very helpful as the techniques complemented one another and in the process reveal relationships that cannot be revealed by any one technique. For instance, correlation revealed associations among variables while regression established the relationships’ significance and direction (dependent and independent) of variables and Smart PLS 3.0 gave direct and total effects of variables in the whole model

To address the challenge of an elaborate and complicated automobile supply chain network (diamond format), the paper introduces a different school of thought to the traditional BCP by replacing Risk Analysis with Risk Ranking and adding a new term (Supply Chain Cooperation). This was done in an effort to further strengthen the resilience of the vulnerable networked automobile supply chain in a more globalized world.

The study established that if the company size increases by 20%, more significant levels of varying degrees are achieved while the 20% reduction of company size reduces the number of significant relationships.

Theoretical and managerial implications

Companies, which have strengthened their earthquake resistant measures, enjoyed more cooperation within the supply chain network. It could be because once companies make their infrastructure earthquake resistant; such companies do not suffer more damage during and after disruption, promoting smooth flow of goods and information within the network during disruption. Disaster recovery or risk managers can take heed to these findings in making their plans more resilient.

The study also established that the degree of implementing earthquake resistant strengthening and enforcement measures is an important aspect of pre disaster planning. Most of the companies, which are not satisfied with their BIA, tend to pay less attention to risk and vice versa. This is so because risk ranking is instrumental to pre disaster planning, which constitutes BIA largely.

Even if a company may rank very high in terms of risks, the effect of such a risk is significantly associated with BCM. Perhaps, this underscores the importance of management in averting some risk effects. For instance, Japan is located in a highly risk region frequently hit by earthquakes, tsunamis, volcanic eruptions and flooding’s but have a flourishing automobile sector mainly because of risk management through BCM. A robust BCM can be a very powerful risk averting management engagement.

BIA report identifies any impacts within and outside the company. Therefore, supply chain cooperation is analyzed by this report as an ‘outside’ the company factor, making it a crucial BCP factor.

BIA’s contribution to the development of BCP, BCM and the outcome is the most significant among all BCP factors. BIA has strong positive total significant impacts on Evaluation factors


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recovery time (t- statistic, 4.7035)*** and competitive advantages (t- statistic, 2.1323)*, BCP factors (natural (t- statistic, 3.6027)*** and manmade (t- statistic, 3.499)** risk ranking & supply chain cooperation (t- statistic, 11.1042) *** and BCM (t- statistic, 21.5247)***

A robust BCM can be a very powerful risk averting management engagement.

Managers should always mind the size of their companies as it significantly affects the efficiency of their business continuity agenda

Limitations of the research and

The study encountered hurdles during data collection, as it covered regions thousands of miles from each other. The coordination of the exercise was particularly cumbersome and it took an excessively long time to get feedback from companies, which took part in the survey.

Future research directions

If the automobile industry is to be profitable, resilient and relevant, more BCM theoretical frameworks, model and approaches should be proposed and applied. This will advance the recovery and risk management profession.

Acknowledgement

This paper has been produced by the Grants-in-Aid for the Scientific Research program (KAKENHI), which is supported by the Japan Society for the Promotion of Science (JSPS). We express our gratitude to JSPS.

REFERENCES

Aizenman, J., and Sun, Y. 2010. Globalisation and the sustainability of large current account balances: Size matters, Journal of microeconomics, 32, 35- 44 Akkiraju, R., Bhattacharjya, D., and Gupta, S. 2011. Towards effective business process availability management. Annual SRII global conference, 36, 242- 251 Andersson, S. 1998. The network perspective- Its origin and differences to the marketing management approach, pp 91, ESBRI Bhamra, R., Dani, S., and Burnard, K. 2011. Resilience; the concept, a literature review and future direction. International Journal of Production Research, 49 (18), 5375- 5393 BS 25999-1. 2006. Business continuity management- part 1 code of practices. British Standard Centre for strategy and Evaluation. 2012. Evaluation of the SME definition report. Sevenoaks, United kingdom,


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Business Continuity Institute (BCI). 2010. Good Practice Guidelines 2010 – Global Edition, BCI, Caversham. Cerullo, V., and Cerullo, J.M. 2004. Business continuity planning: A comprehensive approach. Information systems management, 21, 70- 78 Cha, S. C., Juo, P.W., Liu L.T., and Chen, W.N. 2008. Riskpatrol: a risk management system considering the integration risk management with business continuity process, IEEE. Int. Conf. Intell. Secur. Inform. 110- 115 Chin, W. W. 1998. The partial least squares approach to structural equation modeling. Modern methods for business research, 295 (2), 295-336 Curtis, F. 2009. Peak globalization, climate change, oil depletion and global trade. Ecological economics, 69, 427- 434

Darvish, M., Seifabrghy, M., Saniei Monfared, M. A., & Akbari, F. 2014. Analyzing bullwhip effect in supply networks under exogenous uncertainty. International Journal of Supply and Operations Management, 1(1), 81-107. Efron, B., and Tibshirani, R. 1986. Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Statist. Sci, 1, 54-75. Ferrer- Figueras, L., and Caselles- Moncho, A. 2010. Contribution to the design of world model- globalization- sustainability. Revista Internacional de sistemas, 17, 01- 10 Fujimoto, T., Park, Y.W., and Oh, J.W. 2012. Global expansion and supply chain integration: Case study of Korean firms. International journal of procurement management, 5(4), 470-485 Fujimoto T., Gunasekaran, A., and Hong, P. 2014. Building supply chain capabilities in the age of global complexity: Emerging theories and practices. International journal of production economics, 147, 189- 197 Gibb, F., and Buchanan, S. 2006. A framework for business continuity management. International Journal of information management, 26 (2), 128- 141 Hair Jr., Joseph, F., and Bryan Lukas. 2014. Marketing research. McGraw-Hill Education Australia

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recovery time (t- statistic, 4.7035)*** and competitive advantages (t- statistic, 2.1323)*, BCP factors (natural (t- statistic, 3.6027)*** and manmade (t- statistic, 3.499)** risk ranking & supply chain cooperation (t- statistic, 11.1042) *** and BCM (t- statistic, 21.5247)***

A robust BCM can be a very powerful risk averting management engagement.

Managers should always mind the size of their companies as it significantly affects the efficiency of their business continuity agenda

Limitations of the research and

The study encountered hurdles during data collection, as it covered regions thousands of miles from each other. The coordination of the exercise was particularly cumbersome and it took an excessively long time to get feedback from companies, which took part in the survey.

Future research directions

If the automobile industry is to be profitable, resilient and relevant, more BCM theoretical frameworks, model and approaches should be proposed and applied. This will advance the recovery and risk management profession.

Acknowledgement

This paper has been produced by the Grants-in-Aid for the Scientific Research program (KAKENHI), which is supported by the Japan Society for the Promotion of Science (JSPS). We express our gratitude to JSPS.

REFERENCES

Aizenman, J., and Sun, Y. 2010. Globalisation and the sustainability of large current account balances: Size matters, Journal of microeconomics, 32, 35- 44 Akkiraju, R., Bhattacharjya, D., and Gupta, S. 2011. Towards effective business process availability management. Annual SRII global conference, 36, 242- 251 Andersson, S. 1998. The network perspective- Its origin and differences to the marketing management approach, pp 91, ESBRI Bhamra, R., Dani, S., and Burnard, K. 2011. Resilience; the concept, a literature review and future direction. International Journal of Production Research, 49 (18), 5375- 5393 BS 25999-1. 2006. Business continuity management- part 1 code of practices. British Standard Centre for strategy and Evaluation. 2012. Evaluation of the SME definition report. Sevenoaks, United kingdom,


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Herbane, B., Elliot, D., and Swartz E.M. 2004. Business Continuity Management. Time for strategic role? Long range planning, 37, 435- 457 Herbane, B. 2010. The evolution of business continuity management. A historical review of practices and drivers. Business History, 52, 978- 1002 ISO, B. 2012. 22301: 2012. Societal security. Business continuity management systems. Requirements. British Standards Institute, London. Jayaram, J., Mita D., and Jaideep .M. 2014. Supply chain management capability of small and medium sized family business in India: A multiple case study approach. International Journal of production economics, 147, 472- 485 Kazemi, A., Khezrian, V., Oroojeni Mohammad Javad, M., & Alinezhad, A. 2015. Presenting a bi-objective integrated model for production-distribution problem in a multi-level supply chain network. International Journal of Supply and Operations Management, 1(4), 507-531. Klier, T. H., & Rubenstein, J. M. 2008. Who really made your car?: restructuring and geographic change in the auto industry. WE Upjohn Institute. Leidner D.E. 2010. Globalisation, culture and information: Towards global; knowledge transparency. Journal of strategic information systems, 19, 69- 77 MacKenzie C.A., Santos J.R., and Baker. K. 2012. Measuring changes in international production from disruption: Case study of Japanese earthquake and tsunami. International Journal of economics, 138, 293- 302 Milanovic, B. 2003. The two faces of globalization: Against globalization as we know it. World development, 31 (4), 667- 683

Mohamadi, A., Yaghoubi, S., & Derikvand, H. 2015. A credibility-based chance-constrained transfer point location model for the relief logistics design (Case Study: earthquake disaster on region 1 of Tehran city). International Journal of Supply and Operations Management, 1(4), 466-488.

Morris M. H., Kuratko D.F., and Covin J.G. 2011. Cooperate entrepreneurship and innovation. Cincinatti, OH: South western/ Thomson publishers Moshirian, F. 2003. Globalisation and financial market integration. Journal of Multi. Financial management, 13, 289- 302 National Fire Protection Association. 2007. NFPA 1600: Standard on disaster/emergency management and business continuity programs. InNFPA 1600: Standard on Disaster/Emergency Management and Business Continuity Programs. Nfpa.


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National Highway Traffic Safety Administration. 2013. Traffic safety facts 2011 data--pedestrians. Annals of emergency medicine, 62(6), 612. Nunnally, J. C., & Bernstein, I. H. 1994 Psychometric theory (3rd Ed.). New York, NY: McGraw-Hill, Inc. Okabe, S., & Nagahira, A. 2014. Organizational Promoting Factors for SME BCP. Journal of disaster research, 9(5), 849-857.

Poorbagheri, T., & Akhavan Niaki, S. T. 2014. Vendor Managed Inventory of a Single-vendor Multiple-retailer Single-warehouse Supply Chain under Stochastic Demands. International Journal of Supply and Operations Management, 1(3), 297-313. Sharma, P. 2004. An overview of the field of family business studies: current status and directions for the future. Family business review, 17 (1), 1- 36 Sharp, J. 2008. The route map to business continuity management: Meeting the requirements of BS 25999 BSI. Skidar, P. 2011. Alternate approach to Business Impact Analysis. Information security journal. A global perspective, 20, 128- 134 Stanovich, K. E., & West, R. F. 2007. Natural myside bias is independent of cognitive ability. Thinking & Reasoning, 13(3), 225-247. Studenmund, A. H. 2011. Using econometrics: A practical guide. Sun, Q.Y., and Li, X.Y. 2014. The fault tolerant framework of Large- scale disasters based on continuity management: A case of power network infrastructure. International conference on management science and engineering, 21st.Helsinki. Finland Tjoa, S., Jakoubi, S., and Quirchmayr, G. 2008. Enhancing business impact analysis and risk assessment applying a risk- aware Business process modelling and simulation methodology. In: The third international conference on availability, reliability and security. Tokuda, A. 2011. "International Framework for Collaboration between European and Japanese Standard Consortia: The Case of the Automotive LAN Protocol."Information Communication Technology Standardization for E-Business Sectors: Integrating Supply and Demand Factors: Integrating Supply and Demand Factors 63. Torabi, S.A., Rezaei, S.H., and Sahebjamnia, N. 2014. A new approach for business impact assessment in business continuity management (with a case study). Safety science, 68, 309- 323

Herbane, B., Elliot, D., and Swartz E.M. 2004. Business Continuity Management. Time for strategic role? Long range planning, 37, 435- 457 Herbane, B. 2010. The evolution of business continuity management. A historical review of practices and drivers. Business History, 52, 978- 1002 ISO, B. 2012. 22301: 2012. Societal security. Business continuity management systems. Requirements. British Standards Institute, London. Jayaram, J., Mita D., and Jaideep .M. 2014. Supply chain management capability of small and medium sized family business in India: A multiple case study approach. International Journal of production economics, 147, 472- 485 Kazemi, A., Khezrian, V., Oroojeni Mohammad Javad, M., & Alinezhad, A. 2015. Presenting a bi-objective integrated model for production-distribution problem in a multi-level supply chain network. International Journal of Supply and Operations Management, 1(4), 507-531. Klier, T. H., & Rubenstein, J. M. 2008. Who really made your car?: restructuring and geographic change in the auto industry. WE Upjohn Institute. Leidner D.E. 2010. Globalisation, culture and information: Towards global; knowledge transparency. Journal of strategic information systems, 19, 69- 77 MacKenzie C.A., Santos J.R., and Baker. K. 2012. Measuring changes in international production from disruption: Case study of Japanese earthquake and tsunami. International Journal of economics, 138, 293- 302 Milanovic, B. 2003. The two faces of globalization: Against globalization as we know it. World development, 31 (4), 667- 683

Mohamadi, A., Yaghoubi, S., & Derikvand, H. 2015. A credibility-based chance-constrained transfer point location model for the relief logistics design (Case Study: earthquake disaster on region 1 of Tehran city). International Journal of Supply and Operations Management, 1(4), 466-488.

Morris M. H., Kuratko D.F., and Covin J.G. 2011. Cooperate entrepreneurship and innovation. Cincinatti, OH: South western/ Thomson publishers Moshirian, F. 2003. Globalisation and financial market integration. Journal of Multi. Financial management, 13, 289- 302 National Fire Protection Association. 2007. NFPA 1600: Standard on disaster/emergency management and business continuity programs. InNFPA 1600: Standard on Disaster/Emergency Management and Business Continuity Programs. Nfpa.


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Torabi, S.A., Sahebjamnia, N., and Mansouri, S.A. 2015. Integrated business continuity and disaster recovery planning: Towards organizational resilience. European journal of operational research, 242, 261- 273 Wilpert, B. 2009. Impact of globalization on human work. Safety science, 47, 727- 732 Woodman, P. 2011. Managing threats in a dangerous world: the 2011 business continuity management survey.


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The relative study of motivation for participation, job engagement, social capital and perceived benefits of the international club member

- take the District of 300G2 Lions Clubs International as research subjects

Hung-Hui Lia, Yueh-Ysen Lina and Tsan-Yin Chiua*

aCollege of Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li Dist., Taoyuan City, Taiwan 32003, R.O.C

*Corresponding Author: [email protected]

ABSTRACT In recent years, the management and development of international clubs are facing bottlenecks and crisis due to members’ participation situation. Therefore, breaking the current management difficult position is the most important task for international clubs. The purpose of this study was to explore relationship between participation motivation, job engagement, social capital and perceived benefits of the international club members. This study employed questionnaire survey as research method. A total of 300 questionnaires were collected, there were 176 valid responds and the valid respond rate was 68.67%. The results found that members of international clubs involved in altruistic motivation as the most obvious motivation in this study, followed by the social motivation and motivation to meet demand. Secondly, the members’ age, education, occupation, marriage, and seniority of international clubs has no significant effect on members’ participation motivation, job engagement, social capital and perceived benefits. Finally, the study proposed limitations and suggestions for future researchers and relative organizations as a reference for future development. Keyword: international club, motivation for participation, job engagement, social capital, perceived benefits, Lions Clubs International.

1. Introduction 1.1 Motivation and Background International clubs are a stage of self-cultivation and growth in society for individuals. After work, people invest time and money, take a learning attitude, adopt a self-affirming mindset, make their personality more satisfactory and mature, improve themselves and help others to complete all kinds of activities through various activities and training programs of international clubs. However, some members have left clubs for job change, low activity participation rate, insufficient innovation of activities in clubs or personal reasons over the years, which leads to the bottleneck


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and crisis of operation and development of international clubs at the present stage. Therefore, how to solve business difficulties and prevent the loss of members is really a challenge and subject of promoting the activities of international clubs at present. Studying motivation for participation can be taken as a basis of learning about the dynamic foundation of a person behind his behavior by international clubs and provide important indicators of recruiting new members. Why are successful people of all walks of life willing to invest money, energy and time in participating in international clubs? The initial affection and motivation for participation are one of motivations of this research. People are the most valuable resources in an organization. Identifying with the missions and goals of organizations, most members of international clubs join the organizations and pay little attention to material rewards. (Jiantao, Jinhua, & Chen, 2014). Asking for nothing in return, they are more eager to gain a sense of achievement from club activities and get satisfaction in the selfless process (Bakker, Van Der Zee, Lewig, & Dollard, 2006). When a job can satisfy the need of club members for a sense of achievement, belonging and self-esteem, club members will show a high degree of job engagement. Therefore, organization members will have a stronger sense of commitment to organizations, more identify with organizations and be more willing to work for organizations when actively engaged in the club work, which can avoid the loss of members and achieve the goal of retaining members (Beirne & Lambin, 2013). Therefore, this research will probe into the situation of members of international clubs in engaging in various club activities, learn about the importance and values of job engagement and find ways to help organization members to make full efforts for their clubs, throw themselves into work and take delight in working. It is the second motivation of this research. People cannot live outside the society. To live, people need to depend on all kinds of manpower and material resources. When various countries in the world pay universal attention to economic development, economic capital and human capital have been the focus of attention. However, the importance of social capital is realized now. Social capital can not only negatively reduce trading losses, but also positively promote the achievement of social goals. Social capital is gradually accumulated by means of closely-linked relationship network and mutually-beneficial communication pattern. Social capital is applied to achieve more goals of individuals and organizations (Leung, Kier, Fung, Fung, & Sproule, 2013). This research aims to learn about the actual feeling of members of international clubs in participating in clubs and the situation of their interaction with other members, find out solutions, build the consensus of members through resource value derived by interaction and communication between people and help themselves or organizations to gain


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recognition and trust and realize the common goal of organization members. It is the third motivation of this research. Today, society is free and open and witnesses diverse development. The rise of international clubs is an inevitable trend. However, international clubs will certainly face mutual competition like enterprise organizations in the case of constant development (Lough, 2013). How to recruit new members and make organizations grow constantly and healthily becomes the most important subject. To make the recruitment plan of new members and have everyone understood the benefits of joining in international clubs, the gain of joining in international clubs and the reason for obtaining benefits are the fourth motivation of this research. 1.2 Research Purposes Based on the above research motivations, this research will learn about the correlation among motivation for participation, job engagement, social capital and perceived benefit of members of Lions Club International District 300G2. Therefore, below are the purposes of this research: 1. Learn about the current situation of members of Lions Club International in the

aspect of motivation for participation, job engagement, social capital and perceived benefit.

2. Learn about the difference of members of Lions Club International with different background variables in motivation for participation, job engagement, social capital and perceived benefit.

3. Study the correlation among motivation for participation, job engagement, social capital and perceived benefit of members of Lions Club International.

4. Probe into the predictive power of members of Lions Club International in the aspect of motivation for participation, job engagement, social capital and perceived benefit.

As a result, this research will put research results together and give feedback to Lions Club International for its reference of developing organizations, retaining members and recruiting new members. It is the goal this research hopes to achieve.

2. Literature Review This research mainly sorts out concepts according to the theoretical and empirical research of domestic and foreign scholars into motivation for participation, job engagement, social capital and perceived benefit and probes into the influence of motivation for participation, job engagement, social capital and perceived benefit on this basis to establish the research framework and take it as the theoretical foundation. This chapter is divided into five sections. Section One probes into the related theory

and crisis of operation and development of international clubs at the present stage. Therefore, how to solve business difficulties and prevent the loss of members is really a challenge and subject of promoting the activities of international clubs at present. Studying motivation for participation can be taken as a basis of learning about the dynamic foundation of a person behind his behavior by international clubs and provide important indicators of recruiting new members. Why are successful people of all walks of life willing to invest money, energy and time in participating in international clubs? The initial affection and motivation for participation are one of motivations of this research. People are the most valuable resources in an organization. Identifying with the missions and goals of organizations, most members of international clubs join the organizations and pay little attention to material rewards. (Jiantao, Jinhua, & Chen, 2014). Asking for nothing in return, they are more eager to gain a sense of achievement from club activities and get satisfaction in the selfless process (Bakker, Van Der Zee, Lewig, & Dollard, 2006). When a job can satisfy the need of club members for a sense of achievement, belonging and self-esteem, club members will show a high degree of job engagement. Therefore, organization members will have a stronger sense of commitment to organizations, more identify with organizations and be more willing to work for organizations when actively engaged in the club work, which can avoid the loss of members and achieve the goal of retaining members (Beirne & Lambin, 2013). Therefore, this research will probe into the situation of members of international clubs in engaging in various club activities, learn about the importance and values of job engagement and find ways to help organization members to make full efforts for their clubs, throw themselves into work and take delight in working. It is the second motivation of this research. People cannot live outside the society. To live, people need to depend on all kinds of manpower and material resources. When various countries in the world pay universal attention to economic development, economic capital and human capital have been the focus of attention. However, the importance of social capital is realized now. Social capital can not only negatively reduce trading losses, but also positively promote the achievement of social goals. Social capital is gradually accumulated by means of closely-linked relationship network and mutually-beneficial communication pattern. Social capital is applied to achieve more goals of individuals and organizations (Leung, Kier, Fung, Fung, & Sproule, 2013). This research aims to learn about the actual feeling of members of international clubs in participating in clubs and the situation of their interaction with other members, find out solutions, build the consensus of members through resource value derived by interaction and communication between people and help themselves or organizations to gain


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of motivation for participation; Section Two probes into the related theory of job engagement; Section Three probes into the related theory of social capital; Section Four probes into the related theory of perceived benefit; Section Five probes into the concept of international clubs. 2.1 Motivation for Participation Motivation is the response of a person hoping to satisfy some needs, namely the power of related direction, quality and strength of maintaining and changing behaviors (Kelley, 1974). Motivation includes three elements, namely degree of efforts, organizational goals and employee needs (Robbins, 1992). As an internal psychological process, motivation propels an individual to engage in activities and move towards a goal till he feels satisfied. Therefore, every member participating in international clubs may have different expectations and goals in the aspect of their motivation and mindset. Whether these expectations and goals can be satisfied is very important for international clubs to formulate the direction and method of expanding the number of members. A number of domestic and foreign studies have also probed into volunteers’ motivation for participation. For instance, Liao, Chang, and Tsai (2012) conducted an empirical analysis on volunteers’ motivation for participation in sports events. According the research result, the age, educational background and income level of volunteers had a significant impact on their motivation for participation. In addition, motivation for participation exerted a significant influence on their future participation. Chen (2012) probed into the relationship between Big Five Personality and volunteers’ motivation for participation. In the eyes of Wang Liru (2004) and Zhang Zhiwen (2007), motivation for participation would change with the change of time, space and environment and might vary from person to person or place to place. Xu Guoquan (2008) also pointed it out that motivation for participation was related to perceived benefit and had significant ability in predicting perceived benefit. Throughout domestic and foreign scholars’ theories of motivation for participation, need, requirement, achievement and power were taken as the motive power of participation. When it involved probing into the dimension of volunteers’ motivation for participation, most of domestic and foreign scholars probed into three motivations including altruism, need satisfaction and social relations (Lin Qingshou & Hong Jili, 2015; Hong Kunhao, Chen Wanling, Huang Xiuqing, & Zhang Jiaming, 2015). This research adopts humanistic psychologist Maslow’s hierarchy of needs and American psychologist McClelland’s achievement motivation theory (1961), cooperates with Liao Qinqing (1987) and Wang Xunhe (2004) to make the measurement dimension of motivation for participation and summarizes three dimensions including


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psychological need, social need and self-actualization need as its measurement dimensions. Thus, this research plans to further study the influence of different variables on motivation for participation of members of international clubs from three dimensions of motivation for participation including [motivation of altruism], [motivation of need satisfaction] and [motivation of social relations] and hopes to analyze and learn about the initial motivation of members of international clubs for participation so as to develop more perfect plan of expanding membership and raise the public’s willingness of joining in international clubs. 2.2 Job Engagement Job engaged was first raised by Lodahl and Kejner in 1965. They defined job engagement as individuals’ degree of identification with work and further pointed out that job engagement referred to the degree to which job performance affected personal self-esteem. Job engagement measures an individual’s degree of identifying with his work psychologically and shows the importance degree of his job performance to self-value. Employees with a high degree of job engagement have a strong sense of identity with their work and really care about their working conditions (Robbins, 2006). Employees’ degree of engagement in their work not only involves the psychological and behavioral level of organization members, but also is closely related to organizational performance. When devoting to work, employees can be happy to make their contributions and give play to their knowledge and wisdom in work (Jiang & Chen, 2014). Since Lodahl and Kejner (1965) proposed the theory of job engagement, numerous scholars have studied this concept in succession. Many scholars attempted to apply related theories to build new theoretical models of job engagement. Some new theoretical models were established by means of different meanings and explanations, such as expectance model of Vroom, motivation framework raised by Kanungo in 1979 and comprehensive theoretical model of job engagement raised by Rabinowitz and Hall. Job engagement can bring unlimited energy to employees and effectively connect with their work activities. Therefore, [job engagement] refers to an individual’s identity with his work or the importance of work to an individual. An individual’s attitude towards his work shows the importance and values of work to himself. The more an individual identifies with a job, the more he will make full efforts for his work, devote to his work and take delight in working and the stronger his willingness of staying will be. The higher volunteers’ degree of identifying with an organization, the higher degree of job engagement they will show to the organization. In addition, volunteers’ degree of job engagement is higher than that of enterprise employees.

of motivation for participation; Section Two probes into the related theory of job engagement; Section Three probes into the related theory of social capital; Section Four probes into the related theory of perceived benefit; Section Five probes into the concept of international clubs. 2.1 Motivation for Participation Motivation is the response of a person hoping to satisfy some needs, namely the power of related direction, quality and strength of maintaining and changing behaviors (Kelley, 1974). Motivation includes three elements, namely degree of efforts, organizational goals and employee needs (Robbins, 1992). As an internal psychological process, motivation propels an individual to engage in activities and move towards a goal till he feels satisfied. Therefore, every member participating in international clubs may have different expectations and goals in the aspect of their motivation and mindset. Whether these expectations and goals can be satisfied is very important for international clubs to formulate the direction and method of expanding the number of members. A number of domestic and foreign studies have also probed into volunteers’ motivation for participation. For instance, Liao, Chang, and Tsai (2012) conducted an empirical analysis on volunteers’ motivation for participation in sports events. According the research result, the age, educational background and income level of volunteers had a significant impact on their motivation for participation. In addition, motivation for participation exerted a significant influence on their future participation. Chen (2012) probed into the relationship between Big Five Personality and volunteers’ motivation for participation. In the eyes of Wang Liru (2004) and Zhang Zhiwen (2007), motivation for participation would change with the change of time, space and environment and might vary from person to person or place to place. Xu Guoquan (2008) also pointed it out that motivation for participation was related to perceived benefit and had significant ability in predicting perceived benefit. Throughout domestic and foreign scholars’ theories of motivation for participation, need, requirement, achievement and power were taken as the motive power of participation. When it involved probing into the dimension of volunteers’ motivation for participation, most of domestic and foreign scholars probed into three motivations including altruism, need satisfaction and social relations (Lin Qingshou & Hong Jili, 2015; Hong Kunhao, Chen Wanling, Huang Xiuqing, & Zhang Jiaming, 2015). This research adopts humanistic psychologist Maslow’s hierarchy of needs and American psychologist McClelland’s achievement motivation theory (1961), cooperates with Liao Qinqing (1987) and Wang Xunhe (2004) to make the measurement dimension of motivation for participation and summarizes three dimensions including


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Therefore, an individual will show a higher degree of job engagement and be more willing to work for an organization if his degree of identifying with the organization is improved (Liao, 2012). Alfes, Shantz and Bailey (2015) called job engagement related to volunteers as volunteer engagement and found out the related motivation of volunteer engagement through research. The research conducted an investigation on 1,064 British volunteers. According to the research result, the higher the degree of volunteer engagement was, the lower their motivation of leaving their organization would be. Though the nature of volunteers in nonprofit organizations was different from the jobs of salary system, volunteers’ degree of organization participation was regarded as a part of their work life. The difference only lied in whether there was salary. This point was also proved by the studies of Liao (2012). In this research, most of members join in international clubs because they identify with the missions and goals of organizations and pay little attention to material rewards. Asking for nothing in return, they need to gain a sense of achievement from organizational work and obtain satisfaction in the selfless process. After combining with various scholars’ opinions, this research holds the opinion that job engagement is an individual’s psychological identification with his work, value cognition of his work and satisfaction from his work. When an individual shows a high degree of job engagement, he will put life interests and focus on his work, feel the importance and influence of work experience easily, take delight in making his contributions, give play to his knowledge and wisdom in work and bring positive influence to the organization. Organization members’ degree of job engagement is closely linked with organizational performance. It is necessary to guide the job engagement of members, stimulate team cohesion, promote the improvement of job performance and form the competitive advantages of the organization through learning about the psychological state of people. So to speak, job engagement is the result of interaction between value cognition and personal traits. Hence, job engagement of members of international clubs is defined as individual work attitude and behavior of cognizing work value, producing a sense of identity and responsibility, willingness and selfless contribution to work, firmly believing in the intrinsic value of work and satisfying needs consciously. 2.3 Social Capital Social capital is the sum of a kind of real or potential resources. Resources of this kind come from a long-term and stable network relationship. Social capital is the aggregation of real or potential resources in the long-lasting network formed by system relationship between people (Bourdieu, 1986). Bourdieu first introduced social capital into the field of sociology. Social capital, along with economic capital and


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cultural capital, became three kinds of basic capital patterns which were of equal importance. Bourdieu treated social capital from an economic point of view and thought that a lot of resources and information in life were passed through social human network. Social capital just referred to the sum of social relations owned by individuals or groups and aimed to contributing to creating the social assets and wealth of individuals through the lever utilization of relative relationship between people. From the perspective of Coleman (1988), social capital was the assets of people. Resources and information were exchanged through the relationship, trust and interaction between people. Social capital refers to applying the relationship network owned by individuals or social groups or obtaining real and potential resources (Nahapiet & Ghoshal, 2000). Social capital mainly develops trust, cooperative and collective behavior from the social network of groups, explains social capital through three main dimensions and distinguishes structural dimension, cognition dimension and relationship dimension (Nahapiet & Ghoshal, 1998). The three main dimensions also show that the essence of social capital is creating existing or potential resources through the relationship network owned by individuals or social groups (Ke, 2008). According to the studies of Leung et al. (2013), happiness and a sense of achievement could be searched for people through social capital, which was also many people’s motivation for participation in voluntary activities. In the participation process, they gained a sense of achievement and happiness. Therefore, this research thinks social capital means that an individual assists in taking some actions in groups or organizations and completes common goals and visions through specific factors including specification, mutual benefit and trust and individuals share trust, create and obtain tangible and intangible resources through the interaction of members in groups or organizations. 2.4 Perceived Benefit The word benefit means the achievement of a goal, including the goals achieved by participating in activities. Participants believe that participating in activities can help them to achieve a goal (Ajzen, 1991). Cognition refers to the consistency of those images synthesized from matters related to intuition and meanings of things which are thought. The characteristic of cognition is that things are truly given in the consistency of images and meanings (Max Scheler, 1973). Stebbins summarized volunteers as serious leisure in 2001. Therefore, this research takes the benefit framework of serious leisure proposed by Stebbins in 1992 as a basis. Stebbins (1992) pointed out that participants of serious leisure could gain a sense of achievement and psychological benefits in activities. Participants of serious leisure need to invest firm determination and personal efforts. They may face different

Therefore, an individual will show a higher degree of job engagement and be more willing to work for an organization if his degree of identifying with the organization is improved (Liao, 2012). Alfes, Shantz and Bailey (2015) called job engagement related to volunteers as volunteer engagement and found out the related motivation of volunteer engagement through research. The research conducted an investigation on 1,064 British volunteers. According to the research result, the higher the degree of volunteer engagement was, the lower their motivation of leaving their organization would be. Though the nature of volunteers in nonprofit organizations was different from the jobs of salary system, volunteers’ degree of organization participation was regarded as a part of their work life. The difference only lied in whether there was salary. This point was also proved by the studies of Liao (2012). In this research, most of members join in international clubs because they identify with the missions and goals of organizations and pay little attention to material rewards. Asking for nothing in return, they need to gain a sense of achievement from organizational work and obtain satisfaction in the selfless process. After combining with various scholars’ opinions, this research holds the opinion that job engagement is an individual’s psychological identification with his work, value cognition of his work and satisfaction from his work. When an individual shows a high degree of job engagement, he will put life interests and focus on his work, feel the importance and influence of work experience easily, take delight in making his contributions, give play to his knowledge and wisdom in work and bring positive influence to the organization. Organization members’ degree of job engagement is closely linked with organizational performance. It is necessary to guide the job engagement of members, stimulate team cohesion, promote the improvement of job performance and form the competitive advantages of the organization through learning about the psychological state of people. So to speak, job engagement is the result of interaction between value cognition and personal traits. Hence, job engagement of members of international clubs is defined as individual work attitude and behavior of cognizing work value, producing a sense of identity and responsibility, willingness and selfless contribution to work, firmly believing in the intrinsic value of work and satisfying needs consciously. 2.3 Social Capital Social capital is the sum of a kind of real or potential resources. Resources of this kind come from a long-term and stable network relationship. Social capital is the aggregation of real or potential resources in the long-lasting network formed by system relationship between people (Bourdieu, 1986). Bourdieu first introduced social capital into the field of sociology. Social capital, along with economic capital and


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challenges. Therefore, participants of serious leisure can gain a sense of achievement and related psychological benefits such as self-satisfaction, self-actualization, self-expression, positive social identity, social belonging and contribution to groups in the participation process. In other words, serious leisure can be considered as a field in which activity participants project the sense of identity they want. Such a sense of identity cannot be gained in daily work situation. Stebbins summarized volunteers as serious leisure in 2001. Thus, adopting the benefit theory of serious leisure has its basis. This research summarizes perceived benefit as gains and benefits obtained by members after they participate in international clubs. Members of international clubs are professional leaders of various local causes and a group of people that aspire to serve their society and world regardless of gender. International clubs provide members with opportunities of developing a friendship with like-minded people. Additionally, serving poor people is their common goal. Through the great mind of caring for others, members can improve the life of local society and communities around the world and develop personal and vocational skills. Therefore, this research divides perceived benefit into two dimensions including a sense of achievement and a sense of fulfillment based on the benefit theory of serious leisure raised by Stebbins in 1992. This research is going to further probe into the impact of different variables on the perceived benefit of members of international clubs and also hopes to analyze and learn about the gains and benefits of members after participation in international clubs so as to make more perfect plan of member recruitment, let everyone know the benefits of joining in international clubs and improve the public’s willingness of participating in international clubs. 2.5 International Club When it comes to international clubs in Taiwan, there are Rotary Club, Lions Club, Free-Mason and Youth Chamber of Commerce. In the eyes of ordinary people, these international clubs are fellowship groups in which the rich get together, idle away in seeking pleasure and provide some social services. As a matter of fact, the four clubs make great contributions which ordinary people do not know to Taiwan society. They are international non-governmental organizations and service clubs. According to the number of members, the sequence of four clubs is Lions Club, Rotary Club, Free-Mason and Youth Chamber of Commerce. According to the number published by these clubs on the website, Lions Club has 46,000 branches and 1,380,000 members in the world; Rotary Club possesses 35,000 branches and 1,200,000 members around the world; Free-Mason owns about 600,000 members worldwide;


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Youth Chamber of Commerce is distributed in 119 countries around the world and has about 200,000 members. In Taiwan, the total number of people in the four international service clubs is around 100,000. Even if members of the clubs are full of enthusiasm, ordinary Taiwanese people outside the clubs cannot really understand them. The academic circles show little interest in studying international service clubs. Few people know their social significance and contributions to Taiwan. In fact, four international service clubs had entered Taiwan with the power of international business when it was during the martial law period and people did not have the freedom of establishing clubs, which not only facilitated the contact and cooperation of the industrial and commercial circles, but also made undeniable contributions to the economic miracle of Taiwan. With the gentrification of clubs, the culture of the middle class in Taiwan had been gathered and formed. The behavior of establishing clubs kept a window of civilian force for Taiwan and indirectly played a role in promoting the road of Taiwan to democracy. When it comes to four international service clubs, Rotary Club has to be mentioned. Rotary Club is the first ancestor of this kind of clubs. The basic operation modes of the subsequent three clubs are based on Rotary Club. Rotary Club was founded by Paul Harris, a lawyer in Chicago, America in 1905. Coming to Chicago from the countryside of Vermont, Paul Harris missed the human interest of his hometown very much. Trust between people disappeared due to the vicious competition of business in the metropolis. Then, Paul Harris wanted to set up a club for the purpose of mutual cooperation. He found a coal merchant, an engineer and a tailor. Four of them founded the world’s first Rotary Club—Rotary Club of Chicago. The reason why it was named after Rotary was that they would take turns to get together in each other’s office. After its establishment, Rotary Club grew at a surprising speed. In just five years after its establishment, namely in 1910, there were 16 Rotary Clubs across America. In 1912, Rotary Club started to develop in foreign countries outside America, like Canada. Therefore, it was renamed as Rotary Club International. In 1922, it was renamed as Rotary International. It only took Rotary Club 18 years to develop from America to the world. Similar bourgeois service clubs were successively established in America after the establishment of Rotary Club. Youth Chamber of Commerce founded in St. Louis, Missouri and Free-Mason founded in Detroit, Michigan were established in 1915; Lions Club founded in Chicago, Illinois was established in 1917. The organization rule of the four clubs was similar to Rotary Club which was founded first. Their common characteristics involve classification through occupation, expanding the friendship of members through intensive gatherings, serving

challenges. Therefore, participants of serious leisure can gain a sense of achievement and related psychological benefits such as self-satisfaction, self-actualization, self-expression, positive social identity, social belonging and contribution to groups in the participation process. In other words, serious leisure can be considered as a field in which activity participants project the sense of identity they want. Such a sense of identity cannot be gained in daily work situation. Stebbins summarized volunteers as serious leisure in 2001. Thus, adopting the benefit theory of serious leisure has its basis. This research summarizes perceived benefit as gains and benefits obtained by members after they participate in international clubs. Members of international clubs are professional leaders of various local causes and a group of people that aspire to serve their society and world regardless of gender. International clubs provide members with opportunities of developing a friendship with like-minded people. Additionally, serving poor people is their common goal. Through the great mind of caring for others, members can improve the life of local society and communities around the world and develop personal and vocational skills. Therefore, this research divides perceived benefit into two dimensions including a sense of achievement and a sense of fulfillment based on the benefit theory of serious leisure raised by Stebbins in 1992. This research is going to further probe into the impact of different variables on the perceived benefit of members of international clubs and also hopes to analyze and learn about the gains and benefits of members after participation in international clubs so as to make more perfect plan of member recruitment, let everyone know the benefits of joining in international clubs and improve the public’s willingness of participating in international clubs. 2.5 International Club When it comes to international clubs in Taiwan, there are Rotary Club, Lions Club, Free-Mason and Youth Chamber of Commerce. In the eyes of ordinary people, these international clubs are fellowship groups in which the rich get together, idle away in seeking pleasure and provide some social services. As a matter of fact, the four clubs make great contributions which ordinary people do not know to Taiwan society. They are international non-governmental organizations and service clubs. According to the number of members, the sequence of four clubs is Lions Club, Rotary Club, Free-Mason and Youth Chamber of Commerce. According to the number published by these clubs on the website, Lions Club has 46,000 branches and 1,380,000 members in the world; Rotary Club possesses 35,000 branches and 1,200,000 members around the world; Free-Mason owns about 600,000 members worldwide;


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themselves and the society and developing from America to the international community (Zheng, 2013).

3. Methodology 3.1 Research Framework The principal axis of this research involves motivation for participation, job engagement, social capital and perceived benefit of members of international clubs. Therefore, the research framework is designed according to research purposes, the result of literature review and arrangement and research need in the literature of Chapter Two. The research framework is divided into four parts. (1) Motivation for participation is classified into three different types, namely altruism, need satisfaction and social relations; (2) Job engagement is categorized into three different types, namely vigor, dedication and concentration; (3) Social capital is divided into three different types, namely structure, relationship and cognition; (4) Perceived benefit is split into two different types, namely a sense of achievement and a sense of fulfillment. According to research purposes, this research intends to learn about the key concepts of motivation for participation, job engagement, social capital and perceived benefit. Difference situation and degree of correlation are also presented in the research framework. Please refer to Fig. 3-1.

Fig. 3-1 Research Framework

Motivation for Participation

1 Altruism

2 Need satisfaction

3 Social relations

Benefit Cognition

1. Sense of achievement

2. Sense of fulfillment

Job Engagement

1. Vigor

2. Dedication

3. Concentration

Social Capital

1. Structural dimension

2. Cognitive dimension

3. Relationship dimension

H2

H3

H4

H1


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This research proposes the following research hypotheses in line with research motivations, research purposes, result of literature analysis and research framework: H1: Motivation for participation, job engagement, social capital and perceived benefit of members of international clubs with different variables show significant differences. H2: Motivation for participation of members of international clubs is significantly and positively correlated with perceived benefit of members of international clubs. H3: Job engagement of members of international clubs is significantly and positively correlated with perceived benefit of members of international clubs. H4: Social capital of members of international clubs is significantly and positively correlated with perceived benefit of members of international clubs. 3.2 Research Tools This research adopts the quantitative and empirical method to conduct studies and has four research variables including motivation for participation, job engagement, social capital and perceived benefit. In addition, this research adopts the questionnaire method, takes questionnaire data as research basis to examine whether research hypotheses are correct and probes into the relationship among motivation for participation, job engagement, social capital and perceived benefit. The adopted measurement tools include motivation for participation scale, job engagement scale, social capital scale, perceived benefit scale. Now, below are explanations for the operational definitions and measurement tools of various variables. 3.2.1 Motivation for Participation Regarding the definition of motivation for participation in this research, motivation for participation refers to an internal psychological process and a kind of internal strength dominating behaviors, namely driving force from the inside of individuals. Motivation for participation propels an individual to move towards a goal till he gets satisfaction. From domestic and foreign scholars’ theory and research of motivation for participation, volunteers’ motivation for participation can be generally divided into expanding social relations, acquiring knowledge and skills, gaining self-growth and self-development, reflecting others’ expectations, seeking for future returns, making life more meaningful, getting involved in interpersonal interaction, gaining the recognition of friends and family members and a sense of achievement, including altruism, egoism and other factors. This research adopts motivation for participation scale of Saleh & Wood (1998). Cronbach’s α value of internal consistency reliability is from 0.77 and 0.83. The whole value of Cronbach’s α is 0.80. Containing three dimensions including altruism

themselves and the society and developing from America to the international community (Zheng, 2013).

3. Methodology 3.1 Research Framework The principal axis of this research involves motivation for participation, job engagement, social capital and perceived benefit of members of international clubs. Therefore, the research framework is designed according to research purposes, the result of literature review and arrangement and research need in the literature of Chapter Two. The research framework is divided into four parts. (1) Motivation for participation is classified into three different types, namely altruism, need satisfaction and social relations; (2) Job engagement is categorized into three different types, namely vigor, dedication and concentration; (3) Social capital is divided into three different types, namely structure, relationship and cognition; (4) Perceived benefit is split into two different types, namely a sense of achievement and a sense of fulfillment. According to research purposes, this research intends to learn about the key concepts of motivation for participation, job engagement, social capital and perceived benefit. Difference situation and degree of correlation are also presented in the research framework. Please refer to Fig. 3-1.

Fig. 3-1 Research Framework

Motivation for Participation

1 Altruism

2 Need satisfaction

3 Social relations

Benefit Cognition

1. Sense of achievement

2. Sense of fulfillment

Job Engagement

1. Vigor

2. Dedication

3. Concentration

Social Capital

1. Structural dimension

2. Cognitive dimension

3. Relationship dimension

H2

H3

H4

H1


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(external), need satisfaction (internal) and social relations, this scale revises some semantic words and phrases of the original questionnaire according to the tenet of Lions Club to really test the initial motivation of members of Lions Club in joining in Lions Club. The above three operational definitions of motivation for participation are expressed as follows: 1. Altruism refers to the cognition degree of being willing to serve others in the case of asking for nothing in return. 2. Need satisfaction is locked in the degree of self-affirmation reached by participating in club activities. 3. Social relations refer to the degree of social status or knowledge learning by participating in club activities. This research has 11 questions in the questionnaire of motivation for participation and uses Likert six-point scale to measure it. 3.2.2 Job Engagement Regarding the definition of job engagement in this research, job engagement refers to an individual’s degree of identification with and engagement in his work and a kind of value cognition of his work. Organization members’ degree of engagement in their work not only involves the psychological and behavioral level of members, but also is closely related to organizational performance. When devoting to their work organization members will be happy to make their contributions and give play to their knowledge and wisdom in work. In the meanwhile, turnover intention will be lowered. This research adopts the job engagement scale of Schaufeli, Bakker and Salanova (2006). Cronbach’s α value of internal consistency reliability is from 0.85 to 0.92. The whole value of Cronbach’s α is 0.92. Containing three dimensions including vigor, dedication and concentration, this scale revises some semantic words and phrases of the original questionnaire in line with the tenet of Lions Club to really test the situation of members of Lions Club in engaging in various activities. The above three operational definition of job engagement are expressed as follows: 1. Vigor refers to physical or spiritual strength or energy. In working hours, people are energetic and elastic and show a quick mind, high efficiency, confidence and strong motivation. 2 Dedication refers to serving others and making contributions passionately, giving regardless of pay and offering service for free. Dedication is important, zealous, motivational, proud and challenging feeling. 3 Concentration refers to being focused, absorbed and attentive, investing time, energy and wisdom into the work to be done, feeling the quick loss of time and finding it extremely difficult to get away from work. This research has 17 questions in the questionnaire of job engagement and uses Likert six-point scale to measure it.


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3.2.3 Social Capital The definition of social capital in this research refers to individuals can help to achieve certain actions in groups or organizations and complete the common goals and visions via some specific factors such as regulation, reciprocity, trust, etc., and co-create the obtained tangible and intangible resources through interaction between groups or organization members, shared trust and co-creation. Embedded in interpersonal interaction relationship, social capital is also a kind of overall resources of social organizations, social network, social regulation, social participation, action, trust and authority. The resources can help to improve the cooperation between people and raise the efficiency of the society and the degree of social integration. This research adopts three dimensions of social capital raised by Nahapiet & Ghoshal in 1998 as a basis and refers to the social capital scale made in the doctoral thesis of Ke Yuanda (2008). Cronbach's α value of internal consistency reliability is from 0.85 to 0.92. The whole value of Cronbach's α is 0.92. Containing three dimensions including structure, relationship and cognition, this scale revises some semantic words and phrases of the original questionnaire and is made through modification and compilation according to the tenet of Lions Club. The above three operational definitions of social capital are expressed as follows: 1 Structure refers to social network, elements of social participation and the overall pattern of interlink between network members. 2 Relationship refers to personal relationship caused by network members through interaction, including characteristics like respect and friendship which will affect each other’s behavior. 3 Cognition refers to subjective and abstract relationship elements, including elements such as generally acceptable attitudes, codes of conduct, shared visions, mutual benefit and trust. This research has 13 questions in the questionnaire of social capital and uses Likert six-point scale to measure it. 3.2.4 Perceived Benefit Regarding the definition of perceived benefit in this research, perceived benefit refers to the benefits obtained by an individual through participating in activities and also comparison of efforts and achievements. An individual can gain a sense of achievement and related psychological benefits such as self-satisfaction, self-actualization, self-expression, positive social identity, social belonging and contribution to the group in the participation process, which not only enriches personal life, but also affirms the value and meaning of life and helps to maintain lasting happiness. This research takes the benefit theory of serious leisure raised by Stebbins in 1992 as a basis. Stebbins summarized volunteers as serious leisure in 2001. Therefore,

(external), need satisfaction (internal) and social relations, this scale revises some semantic words and phrases of the original questionnaire according to the tenet of Lions Club to really test the initial motivation of members of Lions Club in joining in Lions Club. The above three operational definitions of motivation for participation are expressed as follows: 1. Altruism refers to the cognition degree of being willing to serve others in the case of asking for nothing in return. 2. Need satisfaction is locked in the degree of self-affirmation reached by participating in club activities. 3. Social relations refer to the degree of social status or knowledge learning by participating in club activities. This research has 11 questions in the questionnaire of motivation for participation and uses Likert six-point scale to measure it. 3.2.2 Job Engagement Regarding the definition of job engagement in this research, job engagement refers to an individual’s degree of identification with and engagement in his work and a kind of value cognition of his work. Organization members’ degree of engagement in their work not only involves the psychological and behavioral level of members, but also is closely related to organizational performance. When devoting to their work organization members will be happy to make their contributions and give play to their knowledge and wisdom in work. In the meanwhile, turnover intention will be lowered. This research adopts the job engagement scale of Schaufeli, Bakker and Salanova (2006). Cronbach’s α value of internal consistency reliability is from 0.85 to 0.92. The whole value of Cronbach’s α is 0.92. Containing three dimensions including vigor, dedication and concentration, this scale revises some semantic words and phrases of the original questionnaire in line with the tenet of Lions Club to really test the situation of members of Lions Club in engaging in various activities. The above three operational definition of job engagement are expressed as follows: 1. Vigor refers to physical or spiritual strength or energy. In working hours, people are energetic and elastic and show a quick mind, high efficiency, confidence and strong motivation. 2 Dedication refers to serving others and making contributions passionately, giving regardless of pay and offering service for free. Dedication is important, zealous, motivational, proud and challenging feeling. 3 Concentration refers to being focused, absorbed and attentive, investing time, energy and wisdom into the work to be done, feeling the quick loss of time and finding it extremely difficult to get away from work. This research has 17 questions in the questionnaire of job engagement and uses Likert six-point scale to measure it.


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adopting the benefit theory of serious leisure has its basis. In the meanwhile, this research refers to the benefit scale of serious leisure made in the doctoral thesis of Xu Qinxiang (2010). Cronbach's α value of internal consistency reliability is from 0.877 to 0.888. The whole value of Cronbach's α is 0.927. Containing two dimensions including a sense of achievement and a sense of fulfillment, this scale revises some semantic words and phrases of the original questionnaire and is made through modification and compilation according to the tenet of Lions Club. The above two operational definitions of perceived benefit are expressed as follows: 1 A sense of achievement refers to a pleasant or successful feeling obtained after achieving goals or accomplishing tasks. 2 A sense of fulfillment refers to being rich and colorful and a state of keeping busy within a certain time and feeling fulfilling. 3.2.5 Demographic Data In order to understand the correlations among the motivation for participation, job engagement, social capital and perceived benefit of different demographic background data, the demographic variables of this study includes gender, age, educational background, occupation, marital statuses, current posts in Lions Club, and seniority of joining in Lions Club. 1. Gender: Two groups including males and females. 2. Age: Five groups including 30 years old (and below), 31 to 40 years old, 41 to 50

years old, 51 to 60 years old and 61 (and above). 3. Educational background: Three groups including general and vocational high

schools (and below), junior colleges/universities and graduate schools (and above).

4. Occupation: Six groups including soldiers/policemen/civil servants/education, agriculture and forestry, manufacturing industry/industry, business/financial industry/cultural industry/information industry/service industry, housekeeping or retirement and other.

5. Marital statuses: Two groups including unmarried and married. 6. Current posts in Lions Club: Four groups including leading lions (including

director/deputy director/former director/district cabinet/aide/district strategic consultant/special district chairman/sub-district chairman/committee chairman), present president/former president, branch cadres (including vice president/secretary/accountant/liaison man/manager/director/supervisor) and general lions.

7. Seniority of joining in Lions Club: Six groups including 2 years (and below), 3 to 5 years, 6 to 10 years, 11 to 15 years, 16 to 20 years and 21 years (and above).


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3.3 Scope, Subject and Data Collection This research takes Lions Clubs in Lions Club International District 300G2 as the research scope. The distribution scope includes 13 districts in Taoyuan City. The researcher dwells at Taoyuan District for a long time, which provides convenience for this research. This research takes 163 Lions Clubs and 5,906 lions (Lions Club International District 300G2, 2016) within the jurisdiction of Lions Club International District 300G2 as study population to conduct a questionnaire survey. Limited by the large number of people, the method of convenience sampling is adopted to issue questionnaires. Questionnaires are issued in three ways including regular meetings held by various branches, personal request of postgraduates and entrusting various branch presidents to issue and test questionnaires. 300 questionnaires are expected to be issued.

3.4 Research Process This research takes members of Lions Club International District 300G2 as research objects in the light of the above-mentioned research motivations and research purposes. This research firstly analyzes the meaning, connotation, related theory, measuring method and previous empirical result of motivation for participation, job engagement, social capital and perceived benefit through domestic and foreign documents, uses them as the theoretical basis and establishes the research framework. In addition, this research develops corresponding scales through the discussion of related documents and theories proposed by various scholars, selects suitable statistical software for collation and data analysis, presents a conclusion and puts forward suggestions finally for the reference of all circles and Lions Club International District 300G2. 3.5 Data Analysis Method This research adopts the quantitative method to conduct data analysis, designs a questionnaire on the correlation among motivation for participation, job engagement, social capital and perceived benefit of members of international clubs, rejects invalid questionnaires after collecting and recovering questionnaires, inputs each valid questionnaire into the computer, applies SPSS 21 version of statistical package to conduct statistical analysis and verifies each hypothesis one by one. Below are analytical methods adopted by this research. 1. Descriptive Statistics Analysis: Descriptive statistics refers to preliminarily

sorting data after collecting data and firstly learning about various research variables and the basic data of samples, including gender, age, educational background, occupation, marital status, post, seniority of joining in the club and

adopting the benefit theory of serious leisure has its basis. In the meanwhile, this research refers to the benefit scale of serious leisure made in the doctoral thesis of Xu Qinxiang (2010). Cronbach's α value of internal consistency reliability is from 0.877 to 0.888. The whole value of Cronbach's α is 0.927. Containing two dimensions including a sense of achievement and a sense of fulfillment, this scale revises some semantic words and phrases of the original questionnaire and is made through modification and compilation according to the tenet of Lions Club. The above two operational definitions of perceived benefit are expressed as follows: 1 A sense of achievement refers to a pleasant or successful feeling obtained after achieving goals or accomplishing tasks. 2 A sense of fulfillment refers to being rich and colorful and a state of keeping busy within a certain time and feeling fulfilling. 3.2.5 Demographic Data In order to understand the correlations among the motivation for participation, job engagement, social capital and perceived benefit of different demographic background data, the demographic variables of this study includes gender, age, educational background, occupation, marital statuses, current posts in Lions Club, and seniority of joining in Lions Club. 1. Gender: Two groups including males and females. 2. Age: Five groups including 30 years old (and below), 31 to 40 years old, 41 to 50

years old, 51 to 60 years old and 61 (and above). 3. Educational background: Three groups including general and vocational high

schools (and below), junior colleges/universities and graduate schools (and above).

4. Occupation: Six groups including soldiers/policemen/civil servants/education, agriculture and forestry, manufacturing industry/industry, business/financial industry/cultural industry/information industry/service industry, housekeeping or retirement and other.

5. Marital statuses: Two groups including unmarried and married. 6. Current posts in Lions Club: Four groups including leading lions (including

director/deputy director/former director/district cabinet/aide/district strategic consultant/special district chairman/sub-district chairman/committee chairman), present president/former president, branch cadres (including vice president/secretary/accountant/liaison man/manager/director/supervisor) and general lions.

7. Seniority of joining in Lions Club: Six groups including 2 years (and below), 3 to 5 years, 6 to 10 years, 11 to 15 years, 16 to 20 years and 21 years (and above).


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region in the background variables of samples through the frequency distribution and percentage analysis of descriptive statistics, then working out the average value and standard deviation of various variables, sorting and analyzing them through a figure or table, and knowing about the score of subjects on each scale and sample structure.

2. Independent-Samples t-test: Independent-samples t-test compares whether average values of two variables are different and is used to test the difference of average values of two groups of data and the significant level of t value. This research uses independent-samples t-test to analyze the difference between different genders, ages, educational backgrounds, occupations, marital status, posts, seniorities of joining in the club and regions of members of international clubs and motivation for participation, job engagement, social capital and perceived benefit.

3. One-way ANOVA: As one of most frequently used statistical methods of social and behavioral sciences, one-way ANOVA mainly tests the significance difference of average values of three or more parent populations and whether there is significant difference. One-way ANOVA is also known as F statistical method. This research applies one-way ANOVA to test the difference of different genders, ages, educational backgrounds, occupations, marital statuses, posts, seniorities of joining in the club and regions of members of international clubs in motivation for participation, job engagement, social capital and perceived benefit. If F value reaches the significant level of statistics according to the analytical result, Scheff'e method will be further applied to make post-hoc comparison.

4. Pearson Correlation Analysis: The related strength of various dimensions and layered dimensions of motivation for participation, job engagement, social capital and perceived benefit is analyzed. Pearson coefficient is used to show the measurement of relationship between two randomly pairing variables. The value is between -1 and 1. -1 stands for absolute negative correlation while 1 means absolute positive correlation. 0 means that the two are not correlated with each other. In this way, the correlation among motivation for participation, job engagement, social capital and perceived benefit is tested.

5. Regression Analysis: It analyzes the explanatory power of motivation for participation, job engagement and social capital to perceived benefit, namely predictable degree. The explanatory power is between 0 and 1. The closer to 1 the value is, the higher the explanatory power will be.

4. Data Analysis and Result This chapter analyzed and discussed the relationship among the motivation for


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participation, job engagement, social capital, and perceived benefit of international club's members, and then verified whether various research hypotheses which were tested by the model constructed in accordance to this relationship can be established. The analysis result was investigated in the various sections of this chapter. 4.1 Questionnaire Recovery and Sample Structure This research issued 300 questionnaires to members of Lions Clubs in Lions Club International District 300G2 and recovered 258 questionnaires. The recovery rate reached 86%. There were 176 valid recovered samples after recovered samples were screened. The valid recovery rate was 68.67%. This research recovered 176 valid questionnaires and conducted descriptive statistics and analysis from population variables including gender, age, educational background, occupation category, marital status, current post in Lions Club and seniority of joining in Lions Club. In the aspect of gender, males were in the majority and took up 70.5% while females occupied 29.5% in this research. In the aspect of age, people at the age of 51 to 60 took up the highest proportion, namely 40.9%, followed by 27.8% occupied by people at the age of 41 to 50. Young people at the age of 30 and below took up the lowest proportion, namely 2.8%. According to the result, the age of members of Lions Clubs was mostly between 41 and 60. With a stable job and family, these members had certain social and economic status. In the aspect of educational background, subjects receiving the education of general and vocational high schools and below took up the highest proportion, namely 50.6%, followed by 42% occupied by subjects receiving the education of junior colleges and universities. Members of Lions Clubs with the educational background of general and vocational high schools and below reached up to 50.6%. According to the result, members of Lions Clubs had ordinary educational background. In the aspect of occupation category, business/financial industry/cultural industry/information industry/service industry were in the majority and occupied 56.8%, followed by manufacturing industry/industry taking up 20.5%. Agriculture and forestry took up the lowest proportion, namely 1.7%. In the aspect of marital status, most of members were married and occupied 90.9%. In addition, present president/former president were in the majority and took up 33.5% in the aspect of current post in Lions Club, followed by general lions taking up 27.8%. Leading lions (including director/deputy director/former director/district cabinet/aide/district strategic consultant/special district chairman/sub-district chairman/committee chairman) took up the lowest proportion, namely 15.9%. Finally, members joining in Lions Club for 3 to 5 years and 6 to 10 years were in the majority and took up 26.1% respectively in the aspect of respondents’ seniority of joining in Lions Club, followed by 17.6% occupied by members joining in Lions Club for 2 years and below.

region in the background variables of samples through the frequency distribution and percentage analysis of descriptive statistics, then working out the average value and standard deviation of various variables, sorting and analyzing them through a figure or table, and knowing about the score of subjects on each scale and sample structure.

2. Independent-Samples t-test: Independent-samples t-test compares whether average values of two variables are different and is used to test the difference of average values of two groups of data and the significant level of t value. This research uses independent-samples t-test to analyze the difference between different genders, ages, educational backgrounds, occupations, marital status, posts, seniorities of joining in the club and regions of members of international clubs and motivation for participation, job engagement, social capital and perceived benefit.

3. One-way ANOVA: As one of most frequently used statistical methods of social and behavioral sciences, one-way ANOVA mainly tests the significance difference of average values of three or more parent populations and whether there is significant difference. One-way ANOVA is also known as F statistical method. This research applies one-way ANOVA to test the difference of different genders, ages, educational backgrounds, occupations, marital statuses, posts, seniorities of joining in the club and regions of members of international clubs in motivation for participation, job engagement, social capital and perceived benefit. If F value reaches the significant level of statistics according to the analytical result, Scheff'e method will be further applied to make post-hoc comparison.

4. Pearson Correlation Analysis: The related strength of various dimensions and layered dimensions of motivation for participation, job engagement, social capital and perceived benefit is analyzed. Pearson coefficient is used to show the measurement of relationship between two randomly pairing variables. The value is between -1 and 1. -1 stands for absolute negative correlation while 1 means absolute positive correlation. 0 means that the two are not correlated with each other. In this way, the correlation among motivation for participation, job engagement, social capital and perceived benefit is tested.

5. Regression Analysis: It analyzes the explanatory power of motivation for participation, job engagement and social capital to perceived benefit, namely predictable degree. The explanatory power is between 0 and 1. The closer to 1 the value is, the higher the explanatory power will be.

4. Data Analysis and Result This chapter analyzed and discussed the relationship among the motivation for


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Members joining in Lions Club for 21 years and above occupied the lowest proportion, namely 7.4%. 4.2 Analysis on the Current Situation of Motivation for Participation of Members of International Clubs In this dimension, the average score and standard deviation of members of international clubs were 5.00 and 0.65, which belonged to an above-average degree. At all levels, the average score of altruism was the highest, namely 5.30, followed by social relations with average score 5.15 and [need satisfaction] with average score 4.73 in the end. As displayed from analysis on various dimensions of motivation for participation, the average score of the dimension altruism was the highest, namely 5.4 by virtue of the question [A6 I think Lions Club is a club which can expand interpersonal relationship]; the average score of the dimension need satisfaction was the highest, namely 5.22 by virtue of the question [A7 I think I can be better after joining in Lions Club]; the average score of the dimension [social relations] was the highest, namely 5.26 by virtue of the questions [A5 Participating in the activities of Lions Club can maintain my relationship with the club]; the average score of the overall dimension was the lowest, namely 3.6 by virtue of the question [A9 My company will earn more money through participating in the activities of Lions Club]. Members of international clubs showed a good state in motivation for participation. Obviously, members of international clubs presented an above-average degree in perception degree and performance of motivation for participation. According to the research result, members of Lions Club thought that Lions Club was a club which could expand interpersonal relationship and joining in Lions Club could make them better and maintain their relationship with the clubs they belonged to. 4.3 Analysis on the Current Situation of Job Engagement of Members of International Clubs In this dimension, the average score and standard deviation of members of international clubs were 4.83 and 0.71, which belonged to an above-average degree. At all levels, the average score of dedication was the highest, namely 4.90, followed by vigor with average score 4.83 and concentration with average score 4.76 in the end. As displayed from various dimensions of job engagement, the average score of the dimension vigor was the highest, namely 5.13 by virtue of the question [B8 I will be willing to attend the regular meeting once a month]; the average score of the dimension dedication was the highest, namely 5.01 by virtue of the question [B10 I


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feel proud of participating in the activities of Lions Club]; the average score of the dimension concentration was the highest, namely 5.11 by virtue of the question [B9 I feel very happy when I actively participate in activities]; the average score of the overall dimension was the lowest, namely 3.93 by virtue of the question [B16 Lions Club is the focus of my life]. Members of international clubs presented a good state in job engagement. Obviously, members of international clubs presented an above-average degree in perception degree and performance of job engagement. According to the research result, members of Lions Club were very willing to attend the regular meeting once a month and felt proud and happy to participate in the activities of Lions Club. 4.4 Analysis on the Current Situation of Social Capital of Members of International Clubs In this dimension, the average score and standard deviation of members of international clubs were 4.80 and 0.80, which belonged to an above-average degree. At all levels, the average score of the dimension was the highest, namely 4.86, followed by cognition with average score 4.81 and with average score 4.76 in the end. As displayed from various dimensions of social capital, the average score of the dimension was the highest, namely 5.02 by virtue of the question [C1 Every member of our Lions Club will contact with each other and provide information and opinions]; the average score of the dimension was the highest, namely 5.02 by virtue of the question [C5 Members of our Lions Club respect each other’s talent]; the average score of the dimension was the highest, namely 4.86 by virtue of the question [C12 Members of our Lions Club will spare no pains to achieve our goals]; the average score of the overall dimension was the lowest, namely 4.64 by virtue of the question [C8 Members of our Lions Club believe that all of us will keep our promises]. Members of international clubs presented a good state in social capital. Obviously, members of international clubs showed an above-average degree in perception degree and performance of social capital. According to the research result, members of Lions Club would contact with each other, provide information and opinions, respect each other’s talent and spare no paints to achieve goals. 4.5 Analysis on the Difference of Different Background Variables in Motivation for Participation, Job Engagement, Social Capital and Perceived Benefit of Members of International Clubs Male and female members showed significant differences only in the dimension (t=2.026, p<0.05) of motivation for participation and presented no significant differences in other dimensions. Therefore, the hypothesis 1-1 of this research got

Members joining in Lions Club for 21 years and above occupied the lowest proportion, namely 7.4%. 4.2 Analysis on the Current Situation of Motivation for Participation of Members of International Clubs In this dimension, the average score and standard deviation of members of international clubs were 5.00 and 0.65, which belonged to an above-average degree. At all levels, the average score of altruism was the highest, namely 5.30, followed by social relations with average score 5.15 and [need satisfaction] with average score 4.73 in the end. As displayed from analysis on various dimensions of motivation for participation, the average score of the dimension altruism was the highest, namely 5.4 by virtue of the question [A6 I think Lions Club is a club which can expand interpersonal relationship]; the average score of the dimension need satisfaction was the highest, namely 5.22 by virtue of the question [A7 I think I can be better after joining in Lions Club]; the average score of the dimension [social relations] was the highest, namely 5.26 by virtue of the questions [A5 Participating in the activities of Lions Club can maintain my relationship with the club]; the average score of the overall dimension was the lowest, namely 3.6 by virtue of the question [A9 My company will earn more money through participating in the activities of Lions Club]. Members of international clubs showed a good state in motivation for participation. Obviously, members of international clubs presented an above-average degree in perception degree and performance of motivation for participation. According to the research result, members of Lions Club thought that Lions Club was a club which could expand interpersonal relationship and joining in Lions Club could make them better and maintain their relationship with the clubs they belonged to. 4.3 Analysis on the Current Situation of Job Engagement of Members of International Clubs In this dimension, the average score and standard deviation of members of international clubs were 4.83 and 0.71, which belonged to an above-average degree. At all levels, the average score of dedication was the highest, namely 4.90, followed by vigor with average score 4.83 and concentration with average score 4.76 in the end. As displayed from various dimensions of job engagement, the average score of the dimension vigor was the highest, namely 5.13 by virtue of the question [B8 I will be willing to attend the regular meeting once a month]; the average score of the dimension dedication was the highest, namely 5.01 by virtue of the question [B10 I


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partial support. Different ages showed no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. It showed that the above factors were not affected by age. Therefore, different ages presented no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. As a result, the hypothesis 1-2 of this research failed to get support. Different educational backgrounds showed no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. It showed that the above factors were not affected by educational background. Therefore, different educational backgrounds presented no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. As a result, the hypothesis 1-3 of this research failed to get support. Different occupation categories showed no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. It showed that the above factors were not affected by occupation category. Therefore, different occupation categories presented no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. As a result, the hypothesis 1-4 of this research failed to get support. Different marital statuses showed no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. It showed that the above factors were not affected by marital status. Therefore, different marital statuses presented no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. As a result, the hypothesis 1-5 of this research failed to get support. In the aspect of subjects’ post, additionally, branch cadres and general lions were significantly higher than leading lions and present president/former president apart from showing significant differences in two dimensions including relationship of social capital. Other dimensions presented no significant differences. Different posts showed no significant differences in the dimensions including motivation for participation, job engagement and perceived benefit. It indicated that the above factors were not affected by occupation category. Different occupation categories presented no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. Therefore, the hypothesis 1-6 of this research got partial support. Different seniorities of joining in the club showed no significant differences in the dimensions including motivation for participation, job engagement, social capital and


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perceived benefit. It indicated that the above factors were not affected by the seniority of joining in the club. Therefore, different seniorities of joining in the club presented no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. As a result, the hypothesis 1-7 of this research failed to get support. 4.6 Analysis on the Correlation among Motivation for Participation, Job Engagement, Social Capital and Perceived Benefit of Members of International Clubs Correlation coefficient between the overall and all levels of motivation for participation and perceived benefit showed significant positive correlation. The correlation coefficient was between 0.55 and 0.71. Need satisfaction of motivation for participation was significantly, highly and positively correlated with the dimension sense of achievement of perceived benefit. Other dimensions presented significant, moderate and positive correlation. It showed that members of international clubs would display a higher cognition degree of overall perceived benefit, sense of achievement and sense of fulfillment when they presented a higher cognition degree of overall motivation for participation, altruism, need satisfaction and social relations. The hypothesis 2 of this research got support. Correlation coefficient between the overall and all levels of motivation for participation and perceived benefit showed significant positive correlation. The correlation coefficient was between 0.73 and 0.82. The dimensions including vigor] dedication and concentration of job engagement were significantly, highly and positively correlated with the dimension sense of achievement of perceived benefit. The dimensions including vigor, dedication and concentration of job engagement were significantly, highly and positively correlated with the dimension sense of fulfillment of perceived benefit. It indicated that members of international clubs presented a relatively higher cognition degree of sense of achievement and sense of fulfillment of overall perceived benefit when they displayed a higher cognition degree of overall job engagement, vigor dedication and concentration. The hypothesis 3 of this research got support. Correlation coefficient between the overall and all levels of social capital and perceived benefit showed significant positive correlation. The correlation coefficient was between 0.59 and 0.75. Cognition of social capital was significantly, highly and positively correlated with the dimension sense of achievement of perceived benefit. Other dimensions presented significant, moderate and positive correlation. It indicated that members of international clubs displayed a relatively higher cognition degree of sense of achievement and sense of fulfillment of overall perceived benefit when they

partial support. Different ages showed no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. It showed that the above factors were not affected by age. Therefore, different ages presented no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. As a result, the hypothesis 1-2 of this research failed to get support. Different educational backgrounds showed no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. It showed that the above factors were not affected by educational background. Therefore, different educational backgrounds presented no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. As a result, the hypothesis 1-3 of this research failed to get support. Different occupation categories showed no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. It showed that the above factors were not affected by occupation category. Therefore, different occupation categories presented no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. As a result, the hypothesis 1-4 of this research failed to get support. Different marital statuses showed no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. It showed that the above factors were not affected by marital status. Therefore, different marital statuses presented no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. As a result, the hypothesis 1-5 of this research failed to get support. In the aspect of subjects’ post, additionally, branch cadres and general lions were significantly higher than leading lions and present president/former president apart from showing significant differences in two dimensions including relationship of social capital. Other dimensions presented no significant differences. Different posts showed no significant differences in the dimensions including motivation for participation, job engagement and perceived benefit. It indicated that the above factors were not affected by occupation category. Different occupation categories presented no significant differences in the dimensions including motivation for participation, job engagement, social capital and perceived benefit. Therefore, the hypothesis 1-6 of this research got partial support. Different seniorities of joining in the club showed no significant differences in the dimensions including motivation for participation, job engagement, social capital and


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presented higher cognition degree of social capital, structure, relationship and cognition. The hypothesis 4 of this research got support. 4.7 Explanatory Power of Motivation for Participation, Job Engagement and Social Capital of Members of International Clubs to Perceived Benefit According to the result of regression analysis of motivation for participation, job engagement and social capital to perceived benefit in this research, R2 was 0.690. The adjusted R2 was 0.685. It showed that the explanatory power of the research model in this research to perceived benefit reached 69%. In addition, beta value and p value of motivation for participation to perceived benefit were -0.035 and 0.739, which was not significant. Beta value and p value of job engagement to perceived benefit were 0.799 and 0.000, which was significant. It indicated that job engagement had a positive and significant impact on perceived benefit. Finally, beta value and p value of social capital to perceived benefit were 0.206 and 0.005, which was significant. It indicated that social capital had a positive and significant impact on perceived benefit.

5 Conclusions and Suggestions This chapter summarizes the comprehensive research findings and conclusions of this research according to the research result, makes suggestions for international clubs and future researchers and provides reference for other related studies. 5.1 Research Findings In the aspect of current situation of members of international clubs, members of international clubs showed an above-average degree in the current situation of motivation for participation, presented an overall and good equilibrium in the dimensions including [altruism], [social relations] and [need satisfaction] and displayed the highest satisfaction degree in [altruism]. Members of international clubs showed a better-than-average degree in the current situation of job engagement, presented an overall and good equilibrium in the dimensions including [dedication], [vigor] and [concentration] and displayed the highest cognition degree in [dedication]. Members of international clubs showed an above-average degree in the current situation of social capital, presented an overall and good equilibrium in the dimensions including [cognition], [structure] and [relationship] and displayed the highest degree in [structure]. Members of international clubs showed a better-than-average degree in the current situation of perceived benefit, presented an overall and good equilibrium in the dimensions including [a sense of achievement] and [a sense of fulfillment] and displayed the highest cognition degree in [a sense of achievement].


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Regarding the difference analysis of different background variables on members of international clubs, members of international clubs with different educational backgrounds, occupation categories, marital statuses and seniorities of joining in the club showed no significant differences in motivation for participation, job engagement, social capital and perceived benefit. Male and female members showed significant differences in the dimension [altruism] of motivation for participation and presented no significant differences in other dimensions. Members of international clubs with different posts displayed significant differences in [structure] and [relationship] of social capital. Branch cadres and general lions were significantly higher than leading lions and present president/former president. In the aspect of relationship among motivation for participation, job engagement, social capital and perceived benefit of members of international clubs, this research found that [need satisfaction] of motivation for participation was significantly, highly and positively correlated with [a sense of achievement] of perceived benefit. Other dimensions showed significant, moderate and positive correlation. The dimensions including [vigor], [dedication] and [concentration] of job engagement were significantly, highly and positively correlated with [a sense of achievement] of perceived benefit. The dimensions including [vigor], [dedication] and [concentration] of job engagement were significantly, highly and positively correlated with [a sense of fulfillment] of perceived benefit. Additionally, [cognition] of social capital was significantly, highly and positively correlated with the dimension [a sense of achievement] of perceived benefit. Other dimensions presented significant, moderate and positive correlation. Moreover, job engagement and social capital of members of international clubs exerted a significant and positive impact on the perceived benefit of their international clubs in the whole framework of this research and had 69% of explanatory power to perceived benefit. 5.2 Conclusions This section comprehensively summarizes the following conclusions according to research findings and research purposes. In motivation for participation of members of international clubs, motivation of altruism is the most obvious, followed by motivation of social relations and motivation of need satisfaction. Members present a good state in motivation for participation and hold the opinion that Lions Club is a club expanding interpersonal relationship and joining in Lions Club can make them better and maintain their relationship with their clubs. This research tests the current situation of members in the aspect of motivation for participation through gender, age, educational

presented higher cognition degree of social capital, structure, relationship and cognition. The hypothesis 4 of this research got support. 4.7 Explanatory Power of Motivation for Participation, Job Engagement and Social Capital of Members of International Clubs to Perceived Benefit According to the result of regression analysis of motivation for participation, job engagement and social capital to perceived benefit in this research, R2 was 0.690. The adjusted R2 was 0.685. It showed that the explanatory power of the research model in this research to perceived benefit reached 69%. In addition, beta value and p value of motivation for participation to perceived benefit were -0.035 and 0.739, which was not significant. Beta value and p value of job engagement to perceived benefit were 0.799 and 0.000, which was significant. It indicated that job engagement had a positive and significant impact on perceived benefit. Finally, beta value and p value of social capital to perceived benefit were 0.206 and 0.005, which was significant. It indicated that social capital had a positive and significant impact on perceived benefit.

5 Conclusions and Suggestions This chapter summarizes the comprehensive research findings and conclusions of this research according to the research result, makes suggestions for international clubs and future researchers and provides reference for other related studies. 5.1 Research Findings In the aspect of current situation of members of international clubs, members of international clubs showed an above-average degree in the current situation of motivation for participation, presented an overall and good equilibrium in the dimensions including [altruism], [social relations] and [need satisfaction] and displayed the highest satisfaction degree in [altruism]. Members of international clubs showed a better-than-average degree in the current situation of job engagement, presented an overall and good equilibrium in the dimensions including [dedication], [vigor] and [concentration] and displayed the highest cognition degree in [dedication]. Members of international clubs showed an above-average degree in the current situation of social capital, presented an overall and good equilibrium in the dimensions including [cognition], [structure] and [relationship] and displayed the highest degree in [structure]. Members of international clubs showed a better-than-average degree in the current situation of perceived benefit, presented an overall and good equilibrium in the dimensions including [a sense of achievement] and [a sense of fulfillment] and displayed the highest cognition degree in [a sense of achievement].


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background, occupation, marital status, post in the club and seniority of joining in the club. According to the empirical result, males are significantly higher than females in the dimension altruism of motivation for participation and other parts show no significant differences. Job engagement of members of international clubs shows members’ enthusiasm for clubs and degree of spiritual involvement. According to the research result, members of international clubs pay the most attention to the dimension dedication, followed by the dimension vigor and concentration. Members of international clubs present a good state in job engagement. Obviously, members of international clubs present an above-average degree in perception degree and performance of job engagement. Members of Lions Club are very willing to attend the regular meeting once a month and feel proud and happy to participate in the activities of Lions Club. Gender, age, educational background, occupation, marital status, post in the club and seniority of joining in the club do not have significant impact on the job engagement of members. Members of international clubs can respect each other’s talent, contact with each other, provide information and opinions and spare no pains to achieve the goals of Lions Club. According to the empirical result, members of Lions Club will contact with each other, provide information and opinions, respect each other’s talent and spare no pains to achieve goals. There is a very special phenomenon. Namely, different posts show significant differences in relationship of social capital. Branch cadres and general lions are significantly higher than leading lions and present president/former president, which indicates that branch cadres and general lions pay more attention to promise, confession and ability manifestation between club members. Gender, age, educational background, occupation, marital status and seniority of joining in the club have no significant impact on the current situation of social capital of members. According to the definition of perceived benefit in this research, perceived benefit refers to members’ evaluation on their efforts and feedback. As displayed from the empirical result, members can have a feeling of self-affirmation and make their life more fulfilling after joining in Lions Club. Gender, age, educational background, occupation, marital status, posts in the club and seniority of joining in the club do not have significant impact on the current situation of perceived benefit of members. This research finds that age, educational background, occupation, marital status and seniority of joining in the club of members of international clubs show no significant differences in motivation for participation, job engagement, social capital and perceived benefit, which indicates that members of international clubs can display their strengths in international clubs and make more contributions to the society regardless of their age, educational background, occupation, marital status and seniority of joining in the club as long as they want to engage in public welfare


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undertakings, adhere to the spirit of great love and contribute their efforts. The meaning and value of life do not only lie in maintaining one’s own integrity, but consist in contributions. The supreme happiness of life is taking from the society, giving back to the society, thinking for others and helping others. According to the empirical result, motivation for participation and job engagement show high interaction effect. In line with the definition of motivation for participation and job engagement, motivation for participation of members of international clubs is mainly related to whether members can gain psychological satisfaction in the participation process through specific factors including vigor, dedication and concentration, stimulate team cohesion, promote the improvement of job performance and form the competitive advantages of their organization. Therefore, all activities should encourage members to participate. When members raise their sense of identity, they will make full efforts, devote to their work and take delight in working. In this case, their willingness of staying will be enhanced accordingly. 5.3 Suggestions According to the above-mentioned main research findings and conclusions, this research refers to related documents and puts forward the following suggestions for the reference of units concerned and relevant future researchers. Suggestions for Lions Club International: 1. Expand the scope of social service and make lions more identify with the service

mission of the organization 2. Improve young lions to get familiar with the culture of Lions Club early and

strengthen training education and leadership courses to enhance their identity with the organization

3. Encourage lions to engage in advanced studies and raise the level of education so as to strengthen self-confidence and their identity with the club

4. Strengthen the marketing of public image and popularity to improve the honor and identity of Lions Club

Suggestions for Future Researchers: 1. Future researchers are advised to add some influence factors of organizational

commitment for discussion and analysis, start from different theoretical points and make Lions Club more mature in research into member management.

2. Some factors such as organizational climate, organizational identification, organizational effectiveness, organizational mission, organizational evaluation, organizational satisfaction and organizational happiness can be added to enrich the research content and establish more comprehensive research mode of international clubs. Furthermore, this research can further compare different

background, occupation, marital status, post in the club and seniority of joining in the club. According to the empirical result, males are significantly higher than females in the dimension altruism of motivation for participation and other parts show no significant differences. Job engagement of members of international clubs shows members’ enthusiasm for clubs and degree of spiritual involvement. According to the research result, members of international clubs pay the most attention to the dimension dedication, followed by the dimension vigor and concentration. Members of international clubs present a good state in job engagement. Obviously, members of international clubs present an above-average degree in perception degree and performance of job engagement. Members of Lions Club are very willing to attend the regular meeting once a month and feel proud and happy to participate in the activities of Lions Club. Gender, age, educational background, occupation, marital status, post in the club and seniority of joining in the club do not have significant impact on the job engagement of members. Members of international clubs can respect each other’s talent, contact with each other, provide information and opinions and spare no pains to achieve the goals of Lions Club. According to the empirical result, members of Lions Club will contact with each other, provide information and opinions, respect each other’s talent and spare no pains to achieve goals. There is a very special phenomenon. Namely, different posts show significant differences in relationship of social capital. Branch cadres and general lions are significantly higher than leading lions and present president/former president, which indicates that branch cadres and general lions pay more attention to promise, confession and ability manifestation between club members. Gender, age, educational background, occupation, marital status and seniority of joining in the club have no significant impact on the current situation of social capital of members. According to the definition of perceived benefit in this research, perceived benefit refers to members’ evaluation on their efforts and feedback. As displayed from the empirical result, members can have a feeling of self-affirmation and make their life more fulfilling after joining in Lions Club. Gender, age, educational background, occupation, marital status, posts in the club and seniority of joining in the club do not have significant impact on the current situation of perceived benefit of members. This research finds that age, educational background, occupation, marital status and seniority of joining in the club of members of international clubs show no significant differences in motivation for participation, job engagement, social capital and perceived benefit, which indicates that members of international clubs can display their strengths in international clubs and make more contributions to the society regardless of their age, educational background, occupation, marital status and seniority of joining in the club as long as they want to engage in public welfare


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clubs and regions internally and similar organizations externally in the future to learn about the situation of motivation for participation, perceived benefit, job engagement and social capital and provide reference basis for the strategic planning of developing organizations, knowing themselves and their competitors and winning victory in every fight.

5.4 Research Limitations From the investigation process of research, this research adopts the method of convenience sampling to issue questionnaires and conduct studies. The tested members of Lions Club fill in the questionnaire. However, this research still cannot master the situation of respondents. Due to different personal traits of tested members of Lions Club, their differences are still a limitation. In this case, this research cannot explain the quality of written questionnaires and respondents. Therefore, it is necessary to reevaluate whether the standard of answering the questionnaire can objectively reflect the actual situation of various members of Lions Club. This research can only try to be objective and perfect and reduce the interference of human factors in the process of the questionnaire survey. From the perspective of research object, everyone has different definitions for motivation for participation, job engagement, social capital and perceived benefit. Therefore, sampled research objects and certainty degree of their organizational learning are not uniformly stipulated. In addition, the scope of Lions Club International District 300G2 is distributed in 13 districts in Taoyuan City. Lions of Lions Club show high differences individually, which intensifies the difference of various Lions Clubs and leads to research limitations. Moreover, Lions Club is an international club which is distributed in 209 countries and regions and possesses 1,380,000 male and female members. Due to the limited ability of the researcher, samples are only limited to Lions Clubs in Lions Club International District 300G2 in Taiwan, which may have an impact on the deduction of research result. Adopting the method of convenience sampling to extract samples, this research cannot control the quality of answering the questionnaire and objects due to different traits of respondents. From the perspective of research literature, many domestic and foreign related documents have probed into motivation for participation, job engagement, social capital and perceived benefit of this research, but most of these documents are aimed at employees of profit organizations or public authorities and few of them are aimed at members of international clubs like Lions Club International. Therefore, there are few documents of comprehensive discussion, which affects the completeness of this research in the aspect of literature review.


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REFERENCES

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(1), 179-211.

Alfes, K., Shantz, A., & Bailey, C. (2015). Enhancing volunteer engagement to achieve desirable outcomes: What can non-profit employers do? VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 1-23.

Bakker, A. B., Van Der Zee, K. I., Lewig, K. A., & Dollard, M. F. (2006). The relationship between the big five personality factors and burnout: A study among volunteer counselors. The Journal of social psychology, 146(1), 31-50.

Bentham，Jeremy.（1789） An Introduction to the Principles of Morals and Legislation，Ethical Theories ： A Book of Readings，ed. A.I. Malden （Englewood Cliffs，New Jersey：Prentice–Hall，1955）.

Beirne, C., & Lambin, X. (2013). Understanding the Determinants of Volunteer Retention Through Capture ‐Recapture Analysis: Answering Social Science Questions Using a Wildlife Ecology Toolkit. Conservation Letters, 6(6), 391-401.

Bourdieu, Pierre（1986）, “The Forms of Capital,＂ in J. G. Richardson（eds.）, Handbook of Theory and Research for the Sociology of Education, pp 241-258, Westport, CT: Greenwood Press.

Chen, M.-T. (2012). The Big Five Personality Traits, Participation Motivation, and Involvement among the Pingtung County Ligang Squadron Volunteer Firefighters.

Coleman, James S.（1988）. “Social Capital and the Creation of Human Capital＂. American Journal of Sociology 94: S95-S119.

Driver, B.L. (1997). The defining moment of benefits. Parks & Recreation, 32(12), 38-41.

Jiantao, Z., Jinhua, Y., & Chen, C. (2014). Participation Motivation and Satisfaction of University Volunteers in National Games. Journal of Shenyang University (Social Science), 4, 024.

Kanungo, R.N. (1982). Measurement of job and work involvement. Journal of Applied Psychology, 67, 341-350.

Kelley, J. (1974). Organizational behavior (2nd ed). Homewood, IL: Richard D. Irwin.

Leung, A., Kier, C., Fung, T., Fung, L., & Sproule, R. (2013). Searching for happiness: The importance of social capital The exploration of happiness (pp. 247-267): Springer.

Liao, L.-C. C., Chang, H.-M., & Tsai, T.-F. (2012). Study of volunteers' participation

clubs and regions internally and similar organizations externally in the future to learn about the situation of motivation for participation, perceived benefit, job engagement and social capital and provide reference basis for the strategic planning of developing organizations, knowing themselves and their competitors and winning victory in every fight.

5.4 Research Limitations From the investigation process of research, this research adopts the method of convenience sampling to issue questionnaires and conduct studies. The tested members of Lions Club fill in the questionnaire. However, this research still cannot master the situation of respondents. Due to different personal traits of tested members of Lions Club, their differences are still a limitation. In this case, this research cannot explain the quality of written questionnaires and respondents. Therefore, it is necessary to reevaluate whether the standard of answering the questionnaire can objectively reflect the actual situation of various members of Lions Club. This research can only try to be objective and perfect and reduce the interference of human factors in the process of the questionnaire survey. From the perspective of research object, everyone has different definitions for motivation for participation, job engagement, social capital and perceived benefit. Therefore, sampled research objects and certainty degree of their organizational learning are not uniformly stipulated. In addition, the scope of Lions Club International District 300G2 is distributed in 13 districts in Taoyuan City. Lions of Lions Club show high differences individually, which intensifies the difference of various Lions Clubs and leads to research limitations. Moreover, Lions Club is an international club which is distributed in 209 countries and regions and possesses 1,380,000 male and female members. Due to the limited ability of the researcher, samples are only limited to Lions Clubs in Lions Club International District 300G2 in Taiwan, which may have an impact on the deduction of research result. Adopting the method of convenience sampling to extract samples, this research cannot control the quality of answering the questionnaire and objects due to different traits of respondents. From the perspective of research literature, many domestic and foreign related documents have probed into motivation for participation, job engagement, social capital and perceived benefit of this research, but most of these documents are aimed at employees of profit organizations or public authorities and few of them are aimed at members of international clubs like Lions Club International. Therefore, there are few documents of comprehensive discussion, which affects the completeness of this research in the aspect of literature review.


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motivation, work satisfaction and perceived value in mega-sports event: case of the 2009 world games. Paper presented at the International Conference on Innovation and Information Management. IPCSIT.

Lough, B. J. (2013). Social work perspectives on international volunteer service. British Journal of Social Work, bct001.

Maslow, A.H（1971） "The Farther Researches of Human Nature", N.Y., The Viking Press.

Max Scheler（1973）, "Idealism and Realism", in Max Scheler, Selected Philosophical Essays, (Evanston: N-W Univ. Press, 1973) p.308, "The notion of cognition can be traced back to that of knowledge."

Creation of Value in Firms. Academy of Management Review, 23(2), 242-266. Nahapiet, J., Ghoshal, S. (2000).Social capital and the organizational advantage in:

Knowledge and Social Capital. Eric Lesser (Ed), Oxford and Boston, Butterworth-Heinemann, 119-157.

Robbins, S. P. (1992). Organizational Behavior (6th ed). Upper Saddle River, NJ: Prentice-Hall Inc.

Robbins, S. P.,(2006).Organizational Behavior: Concepts, Controversies, and Applications, Englewood Cliffs, NJ: Prentice-Hall Inc. 1996.

Stebbins, R. A. (1992). Amateurs. Professions and Serious Leisure. McGill-Queens University Press.

Stebbins, R. A.（1982）, Serious leisure: A conceptual statement, Pacific Sociological Review, 25, pp. 251-272.


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The Influence of Greenwash on Purchase Intention: the Mediation Effects of Environmental Attitude and Environmental Loyalty

Yu-Shan Chen, A-Fen Huang*

Department of Business Administration, National Taipei University New Taipei City, Taiwan (R.O.C.)

*Corresponding Author:[email protected]

ABSTRACT With the rising concerns of global warming, consumers are becoming more conscious of environmental issues and looking for green products or services which are friendly to the environment. At the same time, companies address greenwash to make their images, products or services look like environmentally friendly. The study investigates the effect of greenwash on green purchase intention, as well as the mediating effects of environmental attitude and environmental loyalty on greenwash and green purchase intention. We adopt structural equation modeling (SEM) to test the research framework. A total of 513 questionnaires are valid. The results show that greenwash negatively and significantly relates to environmental attitude, greenwash negatively but insignificantly relates to green purchase intention, greenwash negatively and significantly relates to environmental loyalty, environmental attitude positively and significantly relates to green purchase intention, environmental loyalty positively and significantly relates to green purchase intention. Furthermore, the study indicates that the relationship between greenwash and green purchase intention is mediated by environmental attitude and environmental loyalty. Keyword: Greenwash, Environmental Attitude, Environmental Loyalty, Green Purchase Intention.

1. Introduction In the environmental era, green concern is indisputably an important topic for both marketing practitioners and policy makers. Not only because there are heightened concerns about the impact consumers’ buying and consumption behavior are having on the environment, but because businesses are facing increased pressure to incorporate environmental and social responsibilities into their corporate strategies. Companies are eager to find new ways to market their products and services in the environmental era. Green marketing has become an effective approach to achieve this differentiation. More companies expect to exploit green marketing to differentiate their green products, since green marketing has become a critical tool to attract consumers who are environmental concerned (Chang 2011; Chen and Chang 2013a).


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With the rising concerns of global warming, consumers are becoming more and more aware of environmental issues (Chen 2008a; Chen and Chang 2012). Nowadays, consumers are sending strong environmental signals via their purchase intensions. They are looking for green products that are good for the environment (Chen and Chang 2013b). The power of “purchase intensions” forces companies to change their marketing strategies. Greenwash is defined as the act of misleading consumers regarding the environmental practices of a company or the environmental benefits of products or services (Parguel et al., 2011). Companies usually utilize greenwash to selectively reveal the positive information about the environmental or social performance without full revealing negative information in order to build up positive corporate images (Lyon and Maxwell, 2011). Consequently consumers have an increasing suspicion about companies’ opportunistic greenwash (Pomering and Johnson 2009). In order to enhance consumer environmental attitude, green loyalty and green purchase intension, companies should decrease their greenwash behaviors and enable consumers to obtain enough information that enables them to compare with other companies’ brands or products on the basis of the environmental consideration (Hoedeman, 2002). Hence, firms have to disclose more information about their products or services, not just claim ‘greenness’. Without providing trustworthy information to customers, it is hard for green marketers to convince their customers of the excellence of their products or services such that their customers may be mistrustful of their green claims (Chen, 2008b). Although prior research has highlighted the relevant issues about purchase intension, no study discusses about environmental issues from the perspectives of greenwash, environmental attitude, and green loyalty. Therefore, this study would like to fill the research gap. Because companies have more greenwash behaviors which would cause consumer negative perception, this study proposes the novel construct, environmental attitude, and incorporates the concepts of green purchase intention and environmental loyalty proposed by Chen and Chang (2012,2013c) into an integral framework to further discuss the influence of greenwash on purchase intention in the field of green marketing. Green purchase intention is crucial for companies in the prevalent environmental trend . This paper develops a research framework which can help firms improve their customers’ green purchase intension via its three determinants: greenwash, environmental attitude, and environmental loyalty. This study would like to undertake an empirical test to explore the negative relationship between greenwash and purchase intention. In addition, this paper also investigates the mediation effects of environmental attitude and environmental loyalty on the negative relationship between greenwash and green purchase intention. This paper plans to build up a new framework of green purchase intention in compliance


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with environmental trends to help companies enhance their customers purchase intention and to further extend the literature on green marketing.

2. Literature review and hypothesis development

2.1 The negative effect of greenwash on environmental attitude Greenwash is widely applied to explain the dishonest and misleading claim of a company’s products or services as being eco, green, environmentally friendly, or sustainable, while they really aren’t (Parguel et al., 2011). Greenwash impairs the market demand by making consumers feel confused about the purchase of green products (Pomering and Johnson, 2009). Eventually, they stop purchasing the green products again. Many consumers agree that when firms call a product green it’s often just a marketing strategy and they would mistrust the green claims (Lyon and Maxwell, 2011). Perceptions of greenwash would depress consumers’ attitudes towards a firm’s environmental engagements (Peattie et al., 2009). As a result, greenwash may destroy the market by causing consumers to be suspicious of green products. Attitude is a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor( Eagly and Chaiken ,2007). Allport (1935) defined attitude as a mental and neural state of readiness, which exerts a direct influence upon the individual’s response to all objects and situations where it is related. People with greater knowledge and awareness of environmental issues are more likely to act in a sustainable manner (Arcury, 1990 and Hines et al., 1987); social and personal norms also play a role, with more salient guidelines for environmental action influencing attitudes and behavior (Bamberg and Möser, 2007). Based on the definition above, this study proposed a new concept, “environmental attitude”, and referred to Wiidegren(1998) to define it as “a personal characteristics formed for a long time, sustained attention to environmental issues, and finally to make environmental protection actions”. Environmental attitudes are logical to expect that people need to be concerned about the environment in order to get involved in environmental issues (Maloney and Ward, 1973. Greenwash behaviors sabotage consumers’ attitudes, such as green satisfaction and green word-of-mouth ( Chen , Lin and Chang, 2014) . Soongil Choi and Yoon C. Cho (2015) indicated that greenwash had a positive effect on customer attitude. Consequently, greenwash would decrease consumers’ environmental attitudes. Thus, this study argues that greenwash would negatively affect environmental attitudes and propose the following hypothesis: Hypothesis 1 (H1). Greenwash is negatively associated with environmental attitude.

With the rising concerns of global warming, consumers are becoming more and more aware of environmental issues (Chen 2008a; Chen and Chang 2012). Nowadays, consumers are sending strong environmental signals via their purchase intensions. They are looking for green products that are good for the environment (Chen and Chang 2013b). The power of “purchase intensions” forces companies to change their marketing strategies. Greenwash is defined as the act of misleading consumers regarding the environmental practices of a company or the environmental benefits of products or services (Parguel et al., 2011). Companies usually utilize greenwash to selectively reveal the positive information about the environmental or social performance without full revealing negative information in order to build up positive corporate images (Lyon and Maxwell, 2011). Consequently consumers have an increasing suspicion about companies’ opportunistic greenwash (Pomering and Johnson 2009). In order to enhance consumer environmental attitude, green loyalty and green purchase intension, companies should decrease their greenwash behaviors and enable consumers to obtain enough information that enables them to compare with other companies’ brands or products on the basis of the environmental consideration (Hoedeman, 2002). Hence, firms have to disclose more information about their products or services, not just claim ‘greenness’. Without providing trustworthy information to customers, it is hard for green marketers to convince their customers of the excellence of their products or services such that their customers may be mistrustful of their green claims (Chen, 2008b). Although prior research has highlighted the relevant issues about purchase intension, no study discusses about environmental issues from the perspectives of greenwash, environmental attitude, and green loyalty. Therefore, this study would like to fill the research gap. Because companies have more greenwash behaviors which would cause consumer negative perception, this study proposes the novel construct, environmental attitude, and incorporates the concepts of green purchase intention and environmental loyalty proposed by Chen and Chang (2012,2013c) into an integral framework to further discuss the influence of greenwash on purchase intention in the field of green marketing. Green purchase intention is crucial for companies in the prevalent environmental trend . This paper develops a research framework which can help firms improve their customers’ green purchase intension via its three determinants: greenwash, environmental attitude, and environmental loyalty. This study would like to undertake an empirical test to explore the negative relationship between greenwash and purchase intention. In addition, this paper also investigates the mediation effects of environmental attitude and environmental loyalty on the negative relationship between greenwash and green purchase intention. This paper plans to build up a new framework of green purchase intention in compliance


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2.2 The negative effect of greenwash on environmental loyalty Loyalty is the level of commitment to rebuy or to repurchase a preferred product or service consistently in the future (Oliver, 1999). Dick and Basu (1994) defined loyalty as the strength of the relationship between an individual’s relative attitude and his repeat purchases. Oliver (1999) suggested that customers first become loyal in the cognitive sense, then later in affective sense, and still later in a conative or behavioral manner. Sirdeshmukh, et al. (2002) and Flavián et al. (2006) stated that customer loyalty directly depends on the degree of customer trust. Customer trust is a significant determinant of future purchase intentions and loyalty (Garbarino and Johnson, 1999; Chow and Holden, 1997). Affective loyalty is based on quality-based satisfaction or price-based satisfaction. The outcome of satisfaction is loyalty, and in turn satisfaction has a positive impact on loyalty (Spiteri and Dion, 2004). Chen (2013) defined “green loyalty” as “the level of repurchase intentions prompted by a strong environmental attitude and sustainable commitment towards an object, such as a product, a service, a company, a brand, a group, or so on”. Enhancing green satisfaction, and green trust of customers can increase customers’ green loyalty(Chen,2013). Greenwashing often results in suspicion and skepticism about green claims (Self et al., 2010). Consumers will distrust all environmental efforts of marketers and manufacturers such that the environmental movement would lose the support of the market to make great progress towards sustainability. Greenwash does not only negatively affect green trust (Chen and Chang, 2012) , but also negatively influence green satisfaction( Chen, Lin and Chang, 2013). This study hypothesized that greenwash negatively affects environmental loyalty, and proposed the following hypothesis: Hypothesis 2 (H2). Greenwash is negatively associated with environmental loyalty. 2.3 The negative effect of greenwash on green purchase intention Morrison (1979) defines purchase intention as the likelihood that a consumer will buy a particular product resulting from his or her environmental needs. Purchase intention has always been treated as an important variable, and is extensively used in marketing academic literature because it is a good proxy of consumers’ actual purchase behavior (Chandon et al., 2005; Fishbein and Ajzen, 1975). Green purchase intention is defined as “a willingness to depend on a product or service based on the belief or expectation resulting from its credibility, benevolence, and ability about environmental performance” (Chen, 2010). Lu, Zhao and Wang (2010) indicate that consumer experience positively affects consumer purchase intention. Previous studies find that relevant issues of consumer attitude affect consumer satisfaction (Walker, Sisto and Mcbain, 2008). Chen, Lin and Chang (2013) indicate that companies should decrease


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their greenwash behaviors and enhance their consumers’ green perceived quality and green satisfaction to increase their consumers’ green word-of-mouth. If companies utilize greenwash actions to cheat their customers, their customers are unwilling to establish a long-term trust relationship with them. Hence, greenwash would negatively affect purchase intention. In the context of environmental management, this study argues that greenwash is negatively associated with purchase intention and proposes the following hypothesis: Hypothesis 3 (H3). Greenwash is negatively associated with green purchase intention. 2.4 The positive effect of environmental attitude on green purchase intention Irland (1993) found that the greater the consumers’ ability to contribute to the improvement of the quality of the environment through purchasing green products, the higher the consumers’ purchase intention toward green products is even if they need to pay more than usual. Ajzen (1991) insisted that a positive attitude toward green products strongly supports purchase intention. Environmental attitudes is logical to expect that people need to be concerned about the environment in order to get involved in environmental issues (Maloney and Ward 1973; Oskamp et al 1991). Environmental attitudes were adapted from previous literature (Dunlap and Van Liere, 1978). Results of many studies provided support for this association between environmental attitudes and environmental action (Karp 1996; Lee and Holden, 1999). Despite some mixed results regarding the attitude-behavior relationship, the present study shows that environmental attitudes are an important determinant of green purchase behavior. Consumers' ecological consumption is importantly determined by their attitudes toward environmental issues. That is, consumers with high environmental attitudes are more willing to buy ecologically considered products, since environmental consideration is more important nowadays. Thus, prior literature demonstrates that consumer attitude toward green products positively affects purchase intention (Soongil Choi and Yoon C. Cho, 2015). Hence, this study argues that environmental attitude negatively affects purchase intention, and implies the following hypothesis: Hypothesis 4 (H4). Environmental attitude is positively associated with green purchase intention. 2.5 The positive effect of environmental loyalty on green purchase intention Loyalty is the level of commitment to rebuy or to repurchase a preferred product or service consistently in the future (Oliver, 1999). Dick and Basu (1994) defined loyalty as the strength of the relationship between an individual’s relative attitude and his repeat purchases. Green purchase intention is defined as “a willingness to depend on a

2.2 The negative effect of greenwash on environmental loyalty Loyalty is the level of commitment to rebuy or to repurchase a preferred product or service consistently in the future (Oliver, 1999). Dick and Basu (1994) defined loyalty as the strength of the relationship between an individual’s relative attitude and his repeat purchases. Oliver (1999) suggested that customers first become loyal in the cognitive sense, then later in affective sense, and still later in a conative or behavioral manner. Sirdeshmukh, et al. (2002) and Flavián et al. (2006) stated that customer loyalty directly depends on the degree of customer trust. Customer trust is a significant determinant of future purchase intentions and loyalty (Garbarino and Johnson, 1999; Chow and Holden, 1997). Affective loyalty is based on quality-based satisfaction or price-based satisfaction. The outcome of satisfaction is loyalty, and in turn satisfaction has a positive impact on loyalty (Spiteri and Dion, 2004). Chen (2013) defined “green loyalty” as “the level of repurchase intentions prompted by a strong environmental attitude and sustainable commitment towards an object, such as a product, a service, a company, a brand, a group, or so on”. Enhancing green satisfaction, and green trust of customers can increase customers’ green loyalty(Chen,2013). Greenwashing often results in suspicion and skepticism about green claims (Self et al., 2010). Consumers will distrust all environmental efforts of marketers and manufacturers such that the environmental movement would lose the support of the market to make great progress towards sustainability. Greenwash does not only negatively affect green trust (Chen and Chang, 2012) , but also negatively influence green satisfaction( Chen, Lin and Chang, 2013). This study hypothesized that greenwash negatively affects environmental loyalty, and proposed the following hypothesis: Hypothesis 2 (H2). Greenwash is negatively associated with environmental loyalty. 2.3 The negative effect of greenwash on green purchase intention Morrison (1979) defines purchase intention as the likelihood that a consumer will buy a particular product resulting from his or her environmental needs. Purchase intention has always been treated as an important variable, and is extensively used in marketing academic literature because it is a good proxy of consumers’ actual purchase behavior (Chandon et al., 2005; Fishbein and Ajzen, 1975). Green purchase intention is defined as “a willingness to depend on a product or service based on the belief or expectation resulting from its credibility, benevolence, and ability about environmental performance” (Chen, 2010). Lu, Zhao and Wang (2010) indicate that consumer experience positively affects consumer purchase intention. Previous studies find that relevant issues of consumer attitude affect consumer satisfaction (Walker, Sisto and Mcbain, 2008). Chen, Lin and Chang (2013) indicate that companies should decrease


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product or service based on the belief or expectation resulting from its credibility, benevolence, and ability about environmental performance” (Chen, 2010). Sirdeshmukh et al. (2002) and Flavián et al. (2006) stated that customer loyalty directly depends on the degree of customer trust. Chen and Chang (2012) indicate that customer green purchase intentions is positively associated with customer green trust . Customer trust is a significant determinant of future purchase intentions (Garbarino and Johnson, 1999). In the context of environmental management, this study hypothesizes that Environmental loyalty affects their green purchase intentions and proposes the following hypothesis: Hypothesis 5 (H5). Environmental loyalty is positively associated with green purchase intention. Based on the above model, this research proposes that there are mediators bridging the relationship between Customer’s environmental attitude and environmental loyalty. First, this study posits that environmental loyalty reflects an experience-based dimension, thereby encouraging a relationship between greenwash and green purchase intension. Second, this study asserts that environmental attitude represents a cognition-based dimension and that Customer’s environmental attitude mediates the relationship between greenwash and green purchase intension. Hence, two other hypotheses are proposed as follows: Hypothesis 6 (H6). Customer’s environmental attitude mediates the relationship between greenwash and green purchase intension. Hypothesis 7 (H7). Customer’s environmental loyalty mediates the relationship between greenwash and green purchase intension . This study asserts that greenwash negatively affects green purchase intention, while environmental attitude and environmental loyalty mediate the negative relationship between greenwash and purchase intention. The antecedents of the research framework are greenwash, and the consequent is green purchase intention. While environmental attitude and environmental loyalty are two mediators in this study. The research framework is shown in Figure 1.

Environmental attitude

Environmental loyalty

Greenwash Green purchase intention

H1

H2

H3

H4

H5


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Figure 1. Research framework

3. Methodology and measurement

3.1 Data collection and the sample This study tests the hypotheses and research framework by means of questionnaire survey. The unit of analysis in this study is at the consumer level. The research object of this study is Taiwanese consumers who have the purchase experience of green products in Taiwan. A total of 1500 questionnaires were sent to the randomly sampled consumers. There are 513 valid questionnaires and the effective response rate is 34.2%. 3.2 The measurement of the constructs This study measures the questionnaire items by means of “seven-point Likert scale from 1 to 7” rating from strongly disagreement to strongly agreement. The definitions and measurements of the constructs in this study are as follows: Greenwash(GW). Greenwash is defined as “the act of misleading consumers regarding the environmental practices of a company or the environmental benefits of a product or service” (Parguel et al., 2011). This study refers to Chen, Lin and Chang (2014) to measure greenwash and its measurement includes five items: (1) This product misleads with words in its environmental features(GW1); (2) This product misleads with visuals or graphics in its environmental features(GW2); (3) This product possesses a green claim that is vague or seemingly un-provable(GW3); (4) This product overstates or exaggerates how its green functionality actually is(GW4); (5) This product leaves out or masks important information, making the green claim sound better than it is(GW5). Environmental attitude(EA). This study refers to Wiidegren (1998) to define environmental attitude as “a personal characteristics formed for a long time, sustained attention to environmental issues, and finally to make environmental protection actions”. In addition, this research refers to Wiidegren (1998) to measure green attitude and its measurement includes three items: (1)You like to use this product because of its environmental performance(EA1); (2) You have very good impression of the product because it is environmental friendly(EA2); (3) Overall, after using this product, you have very good experience and feeling because of its environmental concern (EA3). Environmental loyalty(EL). Environmental loyalty is defined as “the level of repurchase intentions prompted by a strong environmental attitude and sustainable

product or service based on the belief or expectation resulting from its credibility, benevolence, and ability about environmental performance” (Chen, 2010). Sirdeshmukh et al. (2002) and Flavián et al. (2006) stated that customer loyalty directly depends on the degree of customer trust. Chen and Chang (2012) indicate that customer green purchase intentions is positively associated with customer green trust . Customer trust is a significant determinant of future purchase intentions (Garbarino and Johnson, 1999). In the context of environmental management, this study hypothesizes that Environmental loyalty affects their green purchase intentions and proposes the following hypothesis: Hypothesis 5 (H5). Environmental loyalty is positively associated with green purchase intention. Based on the above model, this research proposes that there are mediators bridging the relationship between Customer’s environmental attitude and environmental loyalty. First, this study posits that environmental loyalty reflects an experience-based dimension, thereby encouraging a relationship between greenwash and green purchase intension. Second, this study asserts that environmental attitude represents a cognition-based dimension and that Customer’s environmental attitude mediates the relationship between greenwash and green purchase intension. Hence, two other hypotheses are proposed as follows: Hypothesis 6 (H6). Customer’s environmental attitude mediates the relationship between greenwash and green purchase intension. Hypothesis 7 (H7). Customer’s environmental loyalty mediates the relationship between greenwash and green purchase intension . This study asserts that greenwash negatively affects green purchase intention, while environmental attitude and environmental loyalty mediate the negative relationship between greenwash and purchase intention. The antecedents of the research framework are greenwash, and the consequent is green purchase intention. While environmental attitude and environmental loyalty are two mediators in this study. The research framework is shown in Figure 1.




H1

H2

H3

H4

H5


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commitment towards an object, such as a product, a service, a company, a brand, a group, or so on ” (Chen,2013). The measurement of environment loyalty includes four items: (1) I am willing to repurchase this product because of its (environmental) functions(EL1); (2) I prefer purchasing this product to other products because of its (environmental) performance(EL2); (3) I intend to continue buying this product because it is (environmental) friendly(EL3); (4) I seldom consider switching to other products because of this product’s (environmental) concern(EL4). Green purchase intention(GPI). Referring to Chen and Chang (2012), this study defines green purchase intentions as “the likelihood that a consumer would buy a particular product resulting from his or her environmental needs ”. The measurement of green purchase intentions includes three items: (1) You intend to purchase this product because of its environmental concern(GPI1); (2) You expect to purchase this product in the future because of its environmental performance(GPI2); (3) Overall, you are glad to purchase this product because it is environmental friendly(GPI3). 3.3 Empirical results This study used the AMOS 21.0 software to analyze structural equation modeling (SEM) to estimate parameters, test the fit of the model, and verify the hypotheses. SEM of this study examines the two levels of analysis, the measurement model and the structure model, and their results are shown as follows. 3.4 The results of the measurement model After removing Environmental loyalty (EL4) item with low scores of item-total correlation to improve the consistency and stability of each constructs, the model-fit indices of CFA were assessed to determine the model’s overall goodness of fit. The means, standard deviations, and correlation matrix are shown in Table 1. In Table 1, there are positive correlations among green purchase intention, environmental attitude, and environmental loyalty, while there are negative correlations between greenwash and the other constructs. The exploratory factor analysis of the four constructs is shown in Table 2. Every construct in this study can be classified into only one factor. The study refers to the prior research to design questionnaire items. Before mailing to the respondents, this study employs two pretests for the questionnaire revision. Therefore, the measurement of this study is acceptable in content validity. The model-fit indices of CFA were assessed to determine the model’s overall goodness of fit. All values of model-fit indices (χ2(69) = 141.324, CFI = 0.989, NFI = 0.978, IFI = 0.989, GFI = 0.961,AGFI = 0.941, RMSEA = 0.046) exceed their respective acceptance levels suggested by previous research (Hair, Tatham, Anderson, and Black ,2006). Thus, the model appears to have a fairly good fit with the data collected.


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Furthermore, there are several measures to confirm the reliability and validity of the constructs. On one hand, one measure of the reliability is to examine the loadings of each constructs’ individual items. With respect to the quality of the measurement model, the loadings (λ) of all items of the four constructs listed in Table 3 are significant. On the other hand, Cronbach’s α is the other measure of the reliability. Table 3 lists Cronbach’s α for the constructs. Generally the minimum requirement of Cronbach’s α coefficient is 0.7 (Hair et al., 1998). In Table 3, the Cronbach’s α coefficient of “greenwash” is 0.928; that of “environmental attitude” is 0.928; that of “environmental loyalty” is 0.851; that of “purchase intention” is 0.944. Because the Cronbach’s α coefficients of all constructs are more than 0.7, the measurement of this study is acceptable in reliability. In addition, it is also important to verify whether the validity of the measurement in this study is acceptable. This study applies Fornell and Larcker’s measure of average variance extracted (AVE) to evaluate the discriminant validity of the measurement (Fornell and Larcker, 1981). The AVE measures the amount of variance captured by the construct through its items relative to the amount of variance due to the measurement error. To satisfy the requirement of the discriminant validity, the square root of a construct’s AVE must be greater than the correlations between the construct and the other ones in the model. For example, the square roots of the AVEs for the two constructs, greenwash and environmental attitude, are 0.692 and 0.81 in Table 3 which are more than the correlation, 0.322, between them in Table 1. It demonstrates that there is adequate discriminant validity between the two constructs. The square roots of all constructs’ AVEs in Table 3 of this study are all more than the correlations among all constructs in Table 1. Therefore, the discriminant validity of the measurement is acceptable. Besides, if the AVE of a construct is higher than 0.5, it means that there is convergent validity for the construct. As shown in Table 3, the AVEs of the four constructs are 0.692, 0.81, 0.662, and 0.816, which are all higher than 0.5. It indicates that the convergent validity of the measurement is acceptable. According to the above results, the reliability and validity in this study are acceptable.

Table 1

Means, standard deviations and correlations of the constructs

Constructs Mean Standard Deviation

A. B. C.

A. Greenwash 3.563 1.165 B. Environmental attitude 5.124 0.971 -0.322** C. Environmental loyalty 4.937 0.953 -0.246** 0.668** D. Green purchase intention 5.304 0.966 -0.267** 0.787** 0.683**

commitment towards an object, such as a product, a service, a company, a brand, a group, or so on ” (Chen,2013). The measurement of environment loyalty includes four items: (1) I am willing to repurchase this product because of its (environmental) functions(EL1); (2) I prefer purchasing this product to other products because of its (environmental) performance(EL2); (3) I intend to continue buying this product because it is (environmental) friendly(EL3); (4) I seldom consider switching to other products because of this product’s (environmental) concern(EL4). Green purchase intention(GPI). Referring to Chen and Chang (2012), this study defines green purchase intentions as “the likelihood that a consumer would buy a particular product resulting from his or her environmental needs ”. The measurement of green purchase intentions includes three items: (1) You intend to purchase this product because of its environmental concern(GPI1); (2) You expect to purchase this product in the future because of its environmental performance(GPI2); (3) Overall, you are glad to purchase this product because it is environmental friendly(GPI3). 3.3 Empirical results This study used the AMOS 21.0 software to analyze structural equation modeling (SEM) to estimate parameters, test the fit of the model, and verify the hypotheses. SEM of this study examines the two levels of analysis, the measurement model and the structure model, and their results are shown as follows. 3.4 The results of the measurement model After removing Environmental loyalty (EL4) item with low scores of item-total correlation to improve the consistency and stability of each constructs, the model-fit indices of CFA were assessed to determine the model’s overall goodness of fit. The means, standard deviations, and correlation matrix are shown in Table 1. In Table 1, there are positive correlations among green purchase intention, environmental attitude, and environmental loyalty, while there are negative correlations between greenwash and the other constructs. The exploratory factor analysis of the four constructs is shown in Table 2. Every construct in this study can be classified into only one factor. The study refers to the prior research to design questionnaire items. Before mailing to the respondents, this study employs two pretests for the questionnaire revision. Therefore, the measurement of this study is acceptable in content validity. The model-fit indices of CFA were assessed to determine the model’s overall goodness of fit. All values of model-fit indices (χ2(69) = 141.324, CFI = 0.989, NFI = 0.978, IFI = 0.989, GFI = 0.961,AGFI = 0.941, RMSEA = 0.046) exceed their respective acceptance levels suggested by previous research (Hair, Tatham, Anderson, and Black ,2006). Thus, the model appears to have a fairly good fit with the data collected.


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Note: * p<0.05, ** p<0.01.

Table 2

Factor analysis of this study

Constructs Number of

Items Number of factors

Accumulation percentage of

explained variance Greenwash 5 1 77.7％ Environmental attitude 3 1 70.9％ Environmental loyalty 3 1 87.4％ Purchase intention 3 1 89.9％

Table 3

The items’ loadings (λ) and the constructs’ Cronbach’s α coefficients and AVEs

Constructs Items λ Cronbach’s α AVE The square root of AVE

A. Greenwash GW1 GW2 GW3 GW4 GW5

0.832 0.841*** 0.846*** 0.816*** 0.831***

0.928 0.692 0.832

B. Environmental attitude EA1 EA2 EA3

0.924 0.892*** 0.886***

0.928 0.810 0.900

C. Environmental loyalty EL1 EL2 EL3

0.816 0.836*** 0.784***

0.851 0.663 0.814

D. Green purchase intention GPI1 GPI2 GPI3

0.920 0.929*** 0.915***

0.944 0.816 0.903

Note: *** p<0.001. 3.5 The results of the structural model Figure 2 shows the results of the structural model in this study. The overall fit measures of the full model in the SEM indicates that the fit of the model is acceptable (GFI=0.909, RMSEA=0.099, AGFI=0.864,NFI=0.936, CFI=0.946). This study applies the structural model of structural equation modeling (SEM) to explore the causal relationship among constructs. The results of the full model in this study are shown in Table 4. Four paths estimated are significant. H1, H2, H4, and H5 are supported, but H3 is not supported in this study. This study demonstrates that the


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decrease of greenwash can not only meet the popular consumer environmentalism, but also enhance environmental attitude and environmental loyalty. In addition, the results indicate that environmental attitude and environmental loyalty are positively associated with green purchase intention.

GFI=0.909, RMSEA=0.099, AGFI=0.864,NFI=0.936, CFI=0.946 Note: ** p<0.01. *** p<0.001.

Figure 2. The results of the full model

Table 4

The results of the structural model

Hypothesis Proposed effect Path coefficient Results

H1 - -0.37*** H1 is supported

H2 - -0.33*** H2 is supported

H3 - 0.04 H3 is not supported

H4 + 0.70*** H4 is supported

H5 + 0.40*** H5 is supported

Note: *** p<0.001

EL1




H1

H2

H3

H4

H5

0.816

EL2

0.836** 0.784**

GPI1 GPI2 GPI3

0.920 0.929** 0.915** 0.70***

0.04

0.40***

-0.37***

-0.33***

GW1 GW2

GW3 GW4

0.832 0.841**

0.846** 0.816**

EA1 EA2 EA3

0.924 0.892** 0.886**

GW5 0.831**

EL3

Note: * p<0.05, ** p<0.01.

Table 2

Factor analysis of this study

Constructs Number of

Items Number of factors

Accumulation percentage of

explained variance Greenwash 5 1 77.7％ Environmental attitude 3 1 70.9％ Environmental loyalty 3 1 87.4％ Purchase intention 3 1 89.9％

Table 3

The items’ loadings (λ) and the constructs’ Cronbach’s α coefficients and AVEs

Constructs Items λ Cronbach’s α AVE The square root of AVE

A. Greenwash GW1 GW2 GW3 GW4 GW5

0.832 0.841*** 0.846*** 0.816*** 0.831***

0.928 0.692 0.832

B. Environmental attitude EA1 EA2 EA3

0.924 0.892*** 0.886***

0.928 0.810 0.900

C. Environmental loyalty EL1 EL2 EL3

0.816 0.836*** 0.784***

0.851 0.663 0.814

D. Green purchase intention GPI1 GPI2 GPI3

0.920 0.929*** 0.915***

0.944 0.816 0.903

Note: *** p<0.001. 3.5 The results of the structural model Figure 2 shows the results of the structural model in this study. The overall fit measures of the full model in the SEM indicates that the fit of the model is acceptable (GFI=0.909, RMSEA=0.099, AGFI=0.864,NFI=0.936, CFI=0.946). This study applies the structural model of structural equation modeling (SEM) to explore the causal relationship among constructs. The results of the full model in this study are shown in Table 4. Four paths estimated are significant. H1, H2, H4, and H5 are supported, but H3 is not supported in this study. This study demonstrates that the


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Besides the direct effect between greenwash and green purchase intention, Table 5 shows that there are two significant indirect effects between greenwash and green purchase intention. Speaking of the first indirect effect between greenwash and green purchase intention, greenwash can negatively affect green purchase intention indirectly via environmental attitude which accounts for the indirect effect coefficient, -0.2518, in Table 5. Environmental attitude have mediation effects between greenwash and green purchase intention. In regards to the second indirect effect between greenwash and green purchase intention, greenwash can negatively affect green purchase intention via environmental loyalty, which accounts for the indirect effect coefficient, -0.1610, in Table 5. Environmental loyalty has mediation effects between greenwash and green purchase intention. Therefore, environmental attitude and environmental loyalty have mediation effects between greenwash and green purchase intention . As a result, H6 and H7 are supported in this study.

Table 5

Direct and indirect effects of greenwash

Direct Effect -0.2670

Indirect Effects

greenwash →Environmental attitude → Green Purchase intention -0.2518

greenwash →Environmental loyalty → Green Purchase intention -0.1610

Total Indirect Effect -0.4128

Total Effect -0.6798

4.Conclusions and implications Greenwash is still popular in the market, though governments discourage greenwash activities. As a result, greenwash continues to threaten the progress to real sustainability. Consumers have learned about the pitfalls of greenwash, since many environmental claims of green products are neither true nor transparent in the market. This is why greenwash is a major impediment to green marketing. Prior literature is not conclusive on how trust can be built up and retained in an integral way under the context of prevalent greenwash behaviors nowadays. Therefore, this study offers a perception approach of environmental attitude and environmental loyalty as a way to stimulate the green purchase intension process in the greenwash trend. This study develops a research framework to discuss the influence of greenwash on green purchase intention and to explore the mediation roles of environmental attitude and environmental loyalty. The empirical results show that environmental attitude and environmental loyalty have mediation effects on the negative relationship between


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greenwash and green purchase intention. The results also demonstrate that greenwash is negatively associated with environmental attitude and environmental loyalty , but fails to prove negatively affect green purchase intention directly. Therefore, this study suggests that companies should reduce their greenwash behaviors in order to enhance their consumers’ green purchase intention. Besides, if companies would like to reduce the negative relationship between greenwash and purchase intention, they need to increase their consumers’ environmental attitude and environmental loyalty. Since green marketing has become an effective marketing approach nowadays , firms acknowledge that the green market is rapidly growing and see it as an opportunity to develop competitiveness with differentiated products and to accumulate loyal customer in the long term. Companies should apply the prevalent green opportunities to differentiate and to position their products in order to penetrate the green markets. The main challenge for companies is how to raise green purchase intention in the popularity of greenwash. This study combines the concepts of environmental attitude and environmental loyalty to develop a research framework of green purchase intention in the field of green marketing. The main purpose of this paper is to investigate the relationship between greenwash and green purchase intention, and to discuss the mediation effects of environmental attitude and environmental loyalty. Companies must decrease greenwash and increase environmental attitude, and environmental loyalty of their consumers to enhance their consumers’ green purchase intention in the environmental era. An effective starting point for marketers is to develop strategies for decreasing perceptions of greenwash, increasing environmental attitude and environmental loyalty, and for helping potential customers to develop sufficient green purchase intention to build up a longer-term relationship in the context of environmental management. In view of finite resources, companies need to consider how best to deploy resources to reduce the negative factors of green purchase intention. Greenwash would decrease green purchase intention, so marketers must lower the perceptions of greenwash. Environmental attitude and environmental loyalty would increase green purchase intention, so marketers must decrease the perceptions of greenwash at every chance. There are four academic contributions in this study. First, this study summarizes the notions of environmental attitude, and environmental loyalty to extend the literature on green marketing and to develop green purchase intention from the reduction of greenwash and increase environmental attitude, and environmental loyalty. Second, there is no prior study exploring the relationship between greenwash and green purchase intention. This study verifies that greenwash negatively affects green purchase intention indirectly to fill the research gap. Third, this study demonstrates that the relationship between greenwash and green purchase intention is mediated by

Besides the direct effect between greenwash and green purchase intention, Table 5 shows that there are two significant indirect effects between greenwash and green purchase intention. Speaking of the first indirect effect between greenwash and green purchase intention, greenwash can negatively affect green purchase intention indirectly via environmental attitude which accounts for the indirect effect coefficient, -0.2518, in Table 5. Environmental attitude have mediation effects between greenwash and green purchase intention. In regards to the second indirect effect between greenwash and green purchase intention, greenwash can negatively affect green purchase intention via environmental loyalty, which accounts for the indirect effect coefficient, -0.1610, in Table 5. Environmental loyalty has mediation effects between greenwash and green purchase intention. Therefore, environmental attitude and environmental loyalty have mediation effects between greenwash and green purchase intention . As a result, H6 and H7 are supported in this study.

Table 5

Direct and indirect effects of greenwash

Direct Effect -0.2670

Indirect Effects

greenwash →Environmental attitude → Green Purchase intention -0.2518

greenwash →Environmental loyalty → Green Purchase intention -0.1610

Total Indirect Effect -0.4128

Total Effect -0.6798

4.Conclusions and implications Greenwash is still popular in the market, though governments discourage greenwash activities. As a result, greenwash continues to threaten the progress to real sustainability. Consumers have learned about the pitfalls of greenwash, since many environmental claims of green products are neither true nor transparent in the market. This is why greenwash is a major impediment to green marketing. Prior literature is not conclusive on how trust can be built up and retained in an integral way under the context of prevalent greenwash behaviors nowadays. Therefore, this study offers a perception approach of environmental attitude and environmental loyalty as a way to stimulate the green purchase intension process in the greenwash trend. This study develops a research framework to discuss the influence of greenwash on green purchase intention and to explore the mediation roles of environmental attitude and environmental loyalty. The empirical results show that environmental attitude and environmental loyalty have mediation effects on the negative relationship between


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environmental attitude and environmental loyalty. Fourth, decreasing consumer perceptions about greenwash, increasing environmental attitude and environmental loyalty can help to ease customer skepticism and to raise consumer’s purchase intension for green products. This paper extends the research of consumer environmental attitude and environmental loyalty, and green purchase intention into the field of green marketing. There are four practical contributions in this study. First, this study verifies that enhancing greenwash, decreasing environmental attitude and environmental loyalty, and but also reducing green purchase intention. If companies would like to enhance their consumers’ green purchase intention for their products, they should increase their consumers’ environmental attitude and environmental loyalty. Second, in a more sophisticated marketing context, it is worth educating experienced retailers as a reliable information channel between consumers and manufacturers to decrease greenwash in order to raise green purchase intention. Third, firms need to upper environmental attitude and environmental loyalty of their consumers. Because there exists significant mediation effects of environmental attitude and environmental loyalty in this study, companies can subjoin environmental attitude and environmental loyalty from their consumers to decrease the extent of the negative relationship between greenwash and purchase intention. Fourth, this study indicates that greenwash negatively relates to environmental attitude and environmental loyalty which would negatively influence green purchase intention. It means that greenwash does not negatively affect green purchase intention directly, but negatively influence it via environmental attitude and environmental loyalty indirectly. The purpose of this paper is not to discourage green marketing and not to scare consumers away from green claims. This research would like to help marketers to build a more effective and honest green claims. Environmental awareness has recently been gaining the attention of stakeholders, which is proven by the rapidly expanding green market; thus, the firms should consider removal of greenwashing in the market. As green marketing overcomes the challenge of greenwash in the market, consumers would be able to trust green claims and green products could penetrate the market more quickly. Environmental attitude and environmental loyalty are found to be key issues in the marketplace for consumers, so product providers know that traditional messaging will not work and must find another ways of making the communications clearer and less ambiguous. Companies need to systematically identify the sources of environmental attitude and environmental loyalty, such as greenwash, and to rectify them. Increasing the sources of environmental attitude and environmental loyalty can help to subjoin the occurrence of them and to enhance green purchase intention. This study concentrates on information and electronics products in Taiwan, so future


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research can focus on the purchase experience of other products or other countries and compare with this study. This study verifies the hypotheses by means of a questionnaire survey, only providing cross-sectional data, so that this study can’t observe the dynamic change of greenwash, environmental attitude, environmental loyalty, and green purchase intention in the different stages of the environmental regulations in the world through longitudinal data. Therefore, future studies can consider a comprehensive and unified framework to provide a deeper understanding regarding the variables and processes related to consumers' green behavior, such as: the longitudinal study to find out the differences of greenwash, environmental attitude, environmental loyalty, and purchase intention in the different stages of the environmental regulations in the world. Finally, this study hopes that the research results are helpful to managers, researchers, practitioners, and policy makers, and contribute to future research as reference.

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The impacts of open innovation on NPD project success and FFE activities: An empirical study in Japanese manufacturing firms

Kento Shimizua, Akio Nagahiraa, Sumie Ishiharaa and Shuichi Ishidaa

a6-6-11-804 Aza-Aoba Aramaki, Aoba-ku, Sendai, Miyagi, 980-8579, Japan E-mail: kento. shimizu.r8@dc. tohoku.ac.jp

[email protected] [email protected]

ABSTRACT

The objective of this study is to clarify the impacts of open innovation on the FFE activities and NPD projects success in Japanese manufacturing firms. In previous studies, quantitative studies regarding the relationship between FFE and open innovation, also open innovation and NPD project success in manufacturing are poor. Thus, we reveal those impacts by using PLS-SEM analysis. We proposed 2 conceptual models, one is the model that used open innovation before FFE, and the other is the model that used it after FFE. Validity and reliability of the proposed two models were acceptable when considering NPD processes that contain open innovation. Analytical results show that NPD project success is not provided by using open innovation, but is provided by FFE activities that were enhanced by open innovation. This result suggests that the impact of open innovation may be gradual. The direct/indirect impacts of open innovation on NPD project success, which were blindly believed to be true, did not exist. This research gives new knowledge to literature of open innovation and FFE. Keyword: open innovation, new product development (NPD), fuzzy front end (FFE), Japanese manufacturing firms, PLS-SEM.

1. Introduction Recently, speeding new product development up is needed to catch up with changing customers need/wants. Moreover, reducing R&D risk is needed because there were changes in the external environment such as the bankruptcy of Lehman Brothers and the great east Japan earthquake. Thus, open innovation has attracted further attention. In short, open innovation is an important key for corporate growth and maintenance. The concept of open innovation was proposed by Chesbrough(2003)[1].There are two processes of open approaches, one is outside-in and the other is inside-out. Outside-in means firms have access to external resources and get new technology and


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knowledge, while inside-out means firms profit by licensing inside properties. He argued that the proactive use of open innovation can reduce R&D cost, increase productivity and newness, reduce time to market, and get a competitive advantage [2]. The usefulness of open innovation has been discussed in many places. Successful examples include, IBM which abandoned their closed innovation policy, they used CPU and OS made by other firms when they developed IBMPC. Furthermore, their 2.5 inch hard disk drive, which possessed great technology, was provided to Apple. Philips, which is well known as an electronics firm, extensively raised open innovation as a corporate strategy [3]. In an empirical study of Korean manufacturing, it was shown that open innovation firms had higher efficiency and effectiveness than closed firms [4]. Open innovation has been attracting attention among Japanese firms. Ajinomoto (food industry firm) was working with TORAY (fiber industry firm) to develop new fiber, and with Kao (chemical industry firm) to improve lifestyle-related disease prevention by health diagnosis as a joint venture. Ito, president of Ajinomoto said “By combining each excellent research technology, and scientific knowledge, a new way to global growth could be opened up.”(Source: Interview by Nikkei [5]) On the other hand, Cooper and Kleinschmidt (1994) found that, “the greatest differences between winners and losers were in the quality of pre-development activities” [6]. This early stage of an innovation process is called “fuzzy front end (FFE)” and this plays an extremely important role in explaining the success of the new product development (NPD) projects [7][8][9]. There are many empirical studies regarding the impacts of FFE activities on NPD projects. Verworn (2008) clarified FFE activities have positive effects on efficiency and effectiveness of NPD projects [7]. In a comparative study of Japanese and Korean projects, importance of FFE was confirmed in both countries [10]. In the recent changing business environment, interest in FFE is growing more. In manufacturing, many cases showed the importance of open innovation to be successful in NPD projects [2] [11] [12]. However, we couldn’t find empirical evidence for that importance. The quantitative actual impact of the open innovation on FFE phases and success is unclear. Therefore, we will contribute to open innovation and FFE literature by answering the following research questions, RQ1: Do open innovation activities have direct and/or indirect impacts on NPD

project success in manufacturing firms? RQ2: What factors of FFE are related to open innovation in manufacturing firms? RQ3: Does FFE have positive impacts on NPD project success in projects that

used open innovation activities? RQ4: Is there any difference of impacts on NPD project success between projects


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that used open innovation in before or after FFE phases? The aim of this study is to clarify the impacts of open innovation on FFE activities and NPD project success. Answering the above questions is expected to contribute to more efficient NPD process theory and/or practice. In order to gain insight into the open innovation and FFE activities, we suggest a conceptual model and test it with structural equation modeling (SEM). This article is organized as follows: after this brief introduction, we will start discussion about our possible research hypotheses and research framework that are founded in literature reviews. Then, data collection processes and characteristics of firms are mentioned. In the next section, we stress our empirical analysis’ methodology and result. Discussion and conclusion are shown in the final section.

2. Literature reviews and research framework In this section, we will describe the importance of FFE and open innovation that is discussed commonly. After that, we will address the novelty and validity of our research questions and hypotheses by referring to the lacking points. At an early stage of NPD crucial decisions are made (Cooper and Kleinschmidt, 1987) where the costs associated with process optimization are low while the effect is high (Smith and Reinertsen, 1991; Verganti, 1999)[13][14][15]. Consistent with it, Backman et al., (2007) argue that very early phases of NPD possess the greatest opportunities for improving the overall innovation process [16]. Smith and Reinertsen (1991) made popular a term: “fuzzy front end” for these early stages[14]. Murphy and Kumar (1997) define front end of new product development as: “sum of activities from the generation or adoption of a new product idea to its development or termination” [17]. A simplified figure of product development is shown in Fig.1 which was presented by Herstatt et al., (2004) to demonstrate a stage in which so called “fuzzy front end” of NPD is highlighted [18].

Fig. 1 The product development process [18] Fuzzy front end (FFE) phases are also known as pre-development phases (e.g.,

Phase I

idea generationand assessment

Phase II

conceptdevelopment ,planning

Phase III

development

Phase IV

prototypedevelopmentand testing

Phase V

production, marketintroduction anddiffusion

fuzzy front endfuzzy front end

knowledge, while inside-out means firms profit by licensing inside properties. He argued that the proactive use of open innovation can reduce R&D cost, increase productivity and newness, reduce time to market, and get a competitive advantage [2]. The usefulness of open innovation has been discussed in many places. Successful examples include, IBM which abandoned their closed innovation policy, they used CPU and OS made by other firms when they developed IBMPC. Furthermore, their 2.5 inch hard disk drive, which possessed great technology, was provided to Apple. Philips, which is well known as an electronics firm, extensively raised open innovation as a corporate strategy [3]. In an empirical study of Korean manufacturing, it was shown that open innovation firms had higher efficiency and effectiveness than closed firms [4]. Open innovation has been attracting attention among Japanese firms. Ajinomoto (food industry firm) was working with TORAY (fiber industry firm) to develop new fiber, and with Kao (chemical industry firm) to improve lifestyle-related disease prevention by health diagnosis as a joint venture. Ito, president of Ajinomoto said “By combining each excellent research technology, and scientific knowledge, a new way to global growth could be opened up.”(Source: Interview by Nikkei [5]) On the other hand, Cooper and Kleinschmidt (1994) found that, “the greatest differences between winners and losers were in the quality of pre-development activities” [6]. This early stage of an innovation process is called “fuzzy front end (FFE)” and this plays an extremely important role in explaining the success of the new product development (NPD) projects [7][8][9]. There are many empirical studies regarding the impacts of FFE activities on NPD projects. Verworn (2008) clarified FFE activities have positive effects on efficiency and effectiveness of NPD projects [7]. In a comparative study of Japanese and Korean projects, importance of FFE was confirmed in both countries [10]. In the recent changing business environment, interest in FFE is growing more. In manufacturing, many cases showed the importance of open innovation to be successful in NPD projects [2] [11] [12]. However, we couldn’t find empirical evidence for that importance. The quantitative actual impact of the open innovation on FFE phases and success is unclear. Therefore, we will contribute to open innovation and FFE literature by answering the following research questions, RQ1: Do open innovation activities have direct and/or indirect impacts on NPD

project success in manufacturing firms? RQ2: What factors of FFE are related to open innovation in manufacturing firms? RQ3: Does FFE have positive impacts on NPD project success in projects that

used open innovation activities? RQ4: Is there any difference of impacts on NPD project success between projects


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Cooper, 1988; Kleinschmidt and Cooper, 1991) or some researchers divided fuzzy front end phases into several phases as: pre-phase zero, phase zero and phase one (e.g., Khurana and Rosenthal, 1997; Khurana and Rosenthal, 1998)[19][20][21][22]. Several studies in this field emphasized the importance of FFE phase. Especially, Cooper and Kleinschmidt (1993) stated that “the greatest difference between new product winners and losers were found in the quality of execution of pre-development activities” [23]. Song and Parry (1996) analyzed Japanese firms for keys to successful new product development [24]. They found that the predevelopment proficiency (predevelopment planning, concept definition and evaluation) was among the important success factors for Japanese firms. Resource allocation in the NPD process was analyzed by Cooper and Kleinschmidt (1988) in successful projects and failures, the results clearly showed that in successful projects resources were spent more on the front end of NPD [25]. Recently, research regarding quantitative effect of FFE have clarified the importance of FFE [7] [10] [26]. The impact of joint innovation needs to be higher to create additional value. Henderson and Clark (1990) said that the ability to combine distant and previously unconnected resource is decisive for innovation [27]. Scholars further emphasize the relevance of a firm’s capacity to absorb external knowledge [28] [29] [30] [31]. From patent data analysis, highly relevant patents tend to be based on different organizational or technological domains [32] [33]. Literature on dynamic capabilities emphasizes that a company’s success depends on its abilities to build, adapt, integrate, reconfigure, and release resources [34] [35].Firms need to overcome local search and must explore the business environment to identify and seize new opportunities [36]. In previous studies, quantitative studies regarding the relationship between FFE and open innovation, also open innovation and NPD project success in manufacturing are poor. Drawing information and knowledge resources from outside of the firms can help reduce front-end fuzziness [37], and it can also detect problems early [11]. Furthermore, firms can reduce the risk of “cooperation failures” through previous experiences in partnerships [38]. Other scholars said early openness enables firms to produce more ideas and generate creative ideas that challenge the prevailing dominant logic of the innovation firms [39][40].Many case studies suggest that openness is the key to be successful, but there might be some failed project that used external knowledge/technic. Bae and Chang (2012) found evidence that open innovation firms have higher efficiency and effectiveness than closed innovation companies in Korean manufacturing [4]. However, they couldn’t reveal what factors of open innovation have impacts on it. In the service industry domain, there is quantitative evidence that openness has positive impacts on FFE activities and success of service [41]. However, this article fails if same impact is applied to manufacturing. In the study of Verworn et


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al. (2008), they didn’t consider if projects used open resources or not [7]. Thus, we chose the above research questions RQ1-3 for these reasons. Moreover, previous research didn’t consider NPD processes that used open innovation. The main objectives of using open innovation are “Growing new business by accessing open resources” and “Speeding NPD up” [2] [12]. It is assumed that these objectives make the phase of using open innovation different. For example, if firms want to start a new business, they may start collaboration in the idea generation phase, the first phase of FFE. However, collaboration between the process company and technology/equipment suppliers may be done after the FFE phase. Thus, we can’t use the same model for service innovation. We chose the above research questions RQ4 for these reasons.

3. Research framework

3.1 Conceptual model This study is based on the conceptual model that was used in the Verworn et al. (2008) [7]. Although recent studies have proposed a model that contains contextual factors and the project execution phase, the impacts that we want to clarify may be unclear by using that model, because it considers indirect effects[9][10]. The advantage of using the conceptual model is that the investigation of the target is the same Japanese manufacturing companies. In brief, relatively reliable results can be obtained by performing a comparison with this model. In addition, the impact of open innovation is further emphasized because relationship between FFE and success is shown clearly. We will present two models for RQ4, shown in Fig.2 and 3.

Cooper, 1988; Kleinschmidt and Cooper, 1991) or some researchers divided fuzzy front end phases into several phases as: pre-phase zero, phase zero and phase one (e.g., Khurana and Rosenthal, 1997; Khurana and Rosenthal, 1998)[19][20][21][22]. Several studies in this field emphasized the importance of FFE phase. Especially, Cooper and Kleinschmidt (1993) stated that “the greatest difference between new product winners and losers were found in the quality of execution of pre-development activities” [23]. Song and Parry (1996) analyzed Japanese firms for keys to successful new product development [24]. They found that the predevelopment proficiency (predevelopment planning, concept definition and evaluation) was among the important success factors for Japanese firms. Resource allocation in the NPD process was analyzed by Cooper and Kleinschmidt (1988) in successful projects and failures, the results clearly showed that in successful projects resources were spent more on the front end of NPD [25]. Recently, research regarding quantitative effect of FFE have clarified the importance of FFE [7] [10] [26]. The impact of joint innovation needs to be higher to create additional value. Henderson and Clark (1990) said that the ability to combine distant and previously unconnected resource is decisive for innovation [27]. Scholars further emphasize the relevance of a firm’s capacity to absorb external knowledge [28] [29] [30] [31]. From patent data analysis, highly relevant patents tend to be based on different organizational or technological domains [32] [33]. Literature on dynamic capabilities emphasizes that a company’s success depends on its abilities to build, adapt, integrate, reconfigure, and release resources [34] [35].Firms need to overcome local search and must explore the business environment to identify and seize new opportunities [36]. In previous studies, quantitative studies regarding the relationship between FFE and open innovation, also open innovation and NPD project success in manufacturing are poor. Drawing information and knowledge resources from outside of the firms can help reduce front-end fuzziness [37], and it can also detect problems early [11]. Furthermore, firms can reduce the risk of “cooperation failures” through previous experiences in partnerships [38]. Other scholars said early openness enables firms to produce more ideas and generate creative ideas that challenge the prevailing dominant logic of the innovation firms [39][40].Many case studies suggest that openness is the key to be successful, but there might be some failed project that used external knowledge/technic. Bae and Chang (2012) found evidence that open innovation firms have higher efficiency and effectiveness than closed innovation companies in Korean manufacturing [4]. However, they couldn’t reveal what factors of open innovation have impacts on it. In the service industry domain, there is quantitative evidence that openness has positive impacts on FFE activities and success of service [41]. However, this article fails if same impact is applied to manufacturing. In the study of Verworn et


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Fig. 2 Conceptual model 1


Enkel et al. (2009) proposed three core processes of open innovation activities,


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outside-in, inside-out, and coupled processes [42]. Thus, we adopted “introduction of external knowledge/technique” and “Consignment of development” as indicators of outside-in processes. “Licensing internal knowledge/technique” was used as an indicator of inside-out processes. “Joint R&D” was used as an indicator of coupled processes. For the sake of comparative study, factors of NPD project success and FFE activities were based on previous studies [7][9][10]. “Efficiency” and ”Effectiveness”

were used as a measure of NPD project success. “Efficiency” refers to reaching planned milestones, and sufficiency of planned financial and personnel resources [7][9][43], while ‘effectiveness’ refers to meeting: profit targets, sales volume targets, market share targets, customer satisfaction and competitive advantage targets [9][44][45][46]. “Reduction of market uncertainty” in this paper refers to full understanding of customers' needs, wants and specifications, integration of customer requirements into the definition of the new product concept, knowledge about size of potential market, knowledge about price sensitivity of customers, identification of unique selling propositions, and knowledge about competitors [7][46][47]. “Reduction of technical uncertainty” in this paper refers to a clear understanding of the product's specifications, clear specification of technical requirements, thorough verification of technical feasibility, anticipation of technical problems and a thorough understanding of technology prior to the start of development [7][23][24][46]. “Intensity of (initial) planning” factor in this paper refers to breaking the project into work packages, assignment of timing to work packages, assignment of resources to work packages, existence of detailed cost plan, and assignment of responsibilities of team members [7][9][24][43][48]. 3.2 Hypotheses

The objective of this study is to clarify the impacts of open innovation in Japanese manufacturing firms on the FFE activities and NPD projects success. Moreover, we reveal the impacts of FFE activities on NPD projects success during open innovation projects. Previous research said that there is a positive relationship between open innovation, FFE, and success [4] [37] [41]. Therefore, we propose following hypotheses regarding model 1(A) and model 2(B); Hypothesis 1A (H1A)：Proactive use of open innovation has direct/indirect positive effect on NPD project efficiency. Hypothesis 2A (H2A)：Proactive use of open innovation has direct/indirect positive effect on NPD project effectiveness.



Enkel et al. (2009) proposed three core processes of open innovation activities,


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Hypothesis 3A (H3A)：Proactive use of open innovation enhance the activities of reduction of market uncertainty. Hypothesis 4A (H4A)：Proactive use of open innovation enhance the activities of reduction of technical uncertainty. Hypothesis 5A (H5A)：Proactive use of open innovation enhance intensity of initial planning. Hypothesis 1B (H1B)：Proactive use of open innovation has direct/indirect positive effect on NPD project efficiency. Hypothesis 2B (H2B)：Proactive use of open innovation has direct/indirect positive effect on NPD project effectiveness. Hypothesis 3B (H3B)：Proactive use of open innovation is enhanced by the activities of reduction of market uncertainty. Hypothesis 4B (H4B)：Proactive use of open innovation is enhanced by the activities of reduction of technical uncertainty. Hypothesis 5B (H5B)：Proactive use of open innovation is enhanced by intensity of initial planning. Market and technical uncertainty reduction during planning have greater impact on innovation success than the reduction during development process [7] [9] [49]. Moreover, it was proposed that the intensive initial planning prior to development would support the reduction of market and technical uncertainty activities prior to the development [7] [9] [10] .However, whether the same impact is shown in our framework is unclear. Thus, we will reveal the impacts of FFE on NPD project success in open innovation process by investigating the following hypotheses; Hypothesis 6A (H6A): Efficiency of new product development projects is positively affected by the degree of reduction of market uncertainty in model 1. Hypothesis 7A (H7A): Efficiency of new product development projects is positively affected by the degree of reduction of technical uncertainty in model 1. Hypothesis 8A (H8A): Efficiency of new product development projects is positively affected by the intensity of initial planning in model 1. Hypothesis 9A (H9A): Effectiveness of new product development projects is positively affected by the degree of reduction of market uncertainty in model 1. Hypothesis 10A (H10A): Effectiveness of new product development projects is positively affected by the degree of reduction of technical uncertainty in model 1. Hypothesis 11A (H11A): Effectiveness of new product development projects is positively affected by the intensity of initial planning in model 1. Hypothesis 12A (H12A): The degree of reduction of market uncertainty is positively affected by intensity of initial planning that enhanced by open innovation in model 1.


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Hypothesis 13A (H13A): The degree of reduction of technical uncertainty is positively affected by intensity of initial planning that enhanced by open innovation in model 1. Hypothesis 14A (H14A): The effectiveness of NPD projects is positively affected by NPD efficiency in model 1. Hypothesis 6B (H6B)：Efficiency of new product development projects is positively affected by the degree of reduction of market uncertainty in model 2. Hypothesis 7B (H7B)：Efficiency of new product development projects is positively affected by the degree of reduction of technical uncertainty in model 2. Hypothesis 8B (H8B)：Efficiency of new product development projects is positively affected by the intensity of initial planning in model 2. Hypothesis 9B (H9B)：Effectiveness of new product development projects is positively affected by the degree of reduction of market uncertainty in model 2. Hypothesis 10B (H10B)：Effectiveness of new product development projects is positively affected by the degree of reduction of technical uncertainty in model 2. Hypothesis 11B (H11B)：Effectiveness of new product development projects is positively affected by the intensity of initial planning in model 2. Hypothesis 12B (H12B)：The degree of reduction of market uncertainty is positively affected by intensity of initial planning that enhanced by open innovation in model 2. Hypothesis 13B (H13B)：The degree of reduction of technical uncertainty is positively affected by intensity of initial planning that enhanced by open innovation in model 2. Hypothesis 14B (H14B)：The effectiveness of NPD projects is positively affected by NPD efficiency in model 2.

4. Research method 4.1 Data collection The questionnaire survey was conducted based on questionnaire entries which consists of two parts. The first part was concerned with various activities related to FFE. The second part was regarding open innovation activities. We used 7-point Likert-type scale ranging from 1 = strongly disagree to 7 = strongly agree in all questions of these two parts.

The data collection was completed in 2015. Development time for the new product was estimated on average as three years, which corresponded to the beginning of 2010s. Data from Japanese manufacturing firms that refers to the early 2010s was collected in 2015. The Japan Productivity Center (JPC) was used as a database for the collection of Japanese NPD projects. 1,088 questionnaires were sent to R&D division

Hypothesis 3A (H3A)：Proactive use of open innovation enhance the activities of reduction of market uncertainty. Hypothesis 4A (H4A)：Proactive use of open innovation enhance the activities of reduction of technical uncertainty. Hypothesis 5A (H5A)：Proactive use of open innovation enhance intensity of initial planning. Hypothesis 1B (H1B)：Proactive use of open innovation has direct/indirect positive effect on NPD project efficiency. Hypothesis 2B (H2B)：Proactive use of open innovation has direct/indirect positive effect on NPD project effectiveness. Hypothesis 3B (H3B)：Proactive use of open innovation is enhanced by the activities of reduction of market uncertainty. Hypothesis 4B (H4B)：Proactive use of open innovation is enhanced by the activities of reduction of technical uncertainty. Hypothesis 5B (H5B)：Proactive use of open innovation is enhanced by intensity of initial planning. Market and technical uncertainty reduction during planning have greater impact on innovation success than the reduction during development process [7] [9] [49]. Moreover, it was proposed that the intensive initial planning prior to development would support the reduction of market and technical uncertainty activities prior to the development [7] [9] [10] .However, whether the same impact is shown in our framework is unclear. Thus, we will reveal the impacts of FFE on NPD project success in open innovation process by investigating the following hypotheses; Hypothesis 6A (H6A): Efficiency of new product development projects is positively affected by the degree of reduction of market uncertainty in model 1. Hypothesis 7A (H7A): Efficiency of new product development projects is positively affected by the degree of reduction of technical uncertainty in model 1. Hypothesis 8A (H8A): Efficiency of new product development projects is positively affected by the intensity of initial planning in model 1. Hypothesis 9A (H9A): Effectiveness of new product development projects is positively affected by the degree of reduction of market uncertainty in model 1. Hypothesis 10A (H10A): Effectiveness of new product development projects is positively affected by the degree of reduction of technical uncertainty in model 1. Hypothesis 11A (H11A): Effectiveness of new product development projects is positively affected by the intensity of initial planning in model 1. Hypothesis 12A (H12A): The degree of reduction of market uncertainty is positively affected by intensity of initial planning that enhanced by open innovation in model 1.


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managers of Japanese manufacturing firms. A total number of 168 responses representing 15.4% of response rate were collected. 165 of these datasets were valid for analysis.

4.2 Characteristics of company and projects We present annual sales and number of employee distribution for Japanese manufacturing firms considered in the analysis. Annual sales are ranging from 1.3 billion JPY to 5.516 trillion JPY, and numbers of employees are ranging from 80 to 60 thousand, shown in Fig.4 and 5.

Fig. 4 Annual sales volume

0 16

71 60

13 0

0 10 20 30 40 50 60 70 80 90

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of fi

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Annual sales volume (Yen in billion）


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Fig. 5 Number of employee

4.3 Development time Fig.6 shows development time for the new product. Time is ranging from 6 months to 240 and average is 39.8 months. This result means that most projects started in beginning of 2010s.

Fig. 6 Development time We have to note that bankruptcy of Lehman Brothers occurred in 2008 and Japanese firms were also affected by that fear. Verworn et al. (2008) was conducted before that. Thus, we have to remove the influence of the difference of age. For this

0 1

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67

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<=12 13…36 37…120 >120

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ompa

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Development time (Month)

managers of Japanese manufacturing firms. A total number of 168 responses representing 15.4% of response rate were collected. 165 of these datasets were valid for analysis.

4.2 Characteristics of company and projects We present annual sales and number of employee distribution for Japanese manufacturing firms considered in the analysis. Annual sales are ranging from 1.3 billion JPY to 5.516 trillion JPY, and numbers of employees are ranging from 80 to 60 thousand, shown in Fig.4 and 5.

Fig. 4 Annual sales volume

0 16

71 60

13 0

0 10 20 30 40 50 60 70 80 90

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Num

ber

of fi

rms

Annual sales volume (Yen in billion）


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reason, we investigated the impacts of FFE activities on success by using the same model (Model 3) as Verworn et al. (2008) [7], shown in Fig.7.

Fig. 7 Conceptual model 3 4.4 Analysis method Smart PLS was used to analyze the data. Considering the data and model characteristics, the algorithmic properties and model evaluation issues the Partial least squares structural equation modeling (PLS-SEM) approach (Smart PLS 2.0 statistical software package) was used over Covariance-based structural equation modeling (CB-SEM) approach. This is because of several benefits PLS-SEM offers, benefits in terms of data size, distribution and algorithm properties [50]. Smart PLS doesn’t account for any distribution, thus bootstrapping resampling technique was used to get t values [51]. Missing data in a questionnaire exceeding 15% was removed while the observations containing less than 15% of missing data was treated using a group average method [50]. 4.5 Model testing and result Measurement validation was checked. We employed two traditional criteria for evaluation of internal consistency reliability－Cronbach’s alpha and composite reliability. All latent variables have values well above the suggested threshold value of 0.60 [52]. Average variance extracted (AVE) value of 0.5 or higher is considered to be


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acceptable (AVE value of 0.5 and above indicates that the latent variables explain more than half of the variance of its indicators) [50].Thus we can conclude that the measurements are reliable. These calculations are shown in Table 1, 2, and 3. Table 1. Measurement assessment of model 1

Model 1 AVE > 0.5 Composite

Reliability > 0.6

R Square Cronbach’s Alpha > 0.6

FFE_intensity of planning 0.5434 0.876 0.0345 0.8311 FFE_reduction of market uncertainty

0.6467 0.9163 0.301 0.8899

FFE_reduction of technical uncertainty

0.7397 0.9443 0.3211 0.9288

effectiveness 0.7444 0.9356 0.3214 0.9137 efficiency 0.5985 0.8172 0.2625 0.7024 use of open innovation 0.5931 0.8098 0 0.6721

reason, we investigated the impacts of FFE activities on success by using the same model (Model 3) as Verworn et al. (2008) [7], shown in Fig.7.

Fig. 7 Conceptual model 3 4.4 Analysis method Smart PLS was used to analyze the data. Considering the data and model characteristics, the algorithmic properties and model evaluation issues the Partial least squares structural equation modeling (PLS-SEM) approach (Smart PLS 2.0 statistical software package) was used over Covariance-based structural equation modeling (CB-SEM) approach. This is because of several benefits PLS-SEM offers, benefits in terms of data size, distribution and algorithm properties [50]. Smart PLS doesn’t account for any distribution, thus bootstrapping resampling technique was used to get t values [51]. Missing data in a questionnaire exceeding 15% was removed while the observations containing less than 15% of missing data was treated using a group average method [50]. 4.5 Model testing and result Measurement validation was checked. We employed two traditional criteria for evaluation of internal consistency reliability－Cronbach’s alpha and composite reliability. All latent variables have values well above the suggested threshold value of 0.60 [52]. Average variance extracted (AVE) value of 0.5 or higher is considered to be


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Table 2. Measurement assessment of model 2


Reliability > 0.6


FFE_intensity of planning 0.5434 0.876 0 0.8311 FFE_reduction of market uncertainty

0.6468 0.9163 0.3009 0.8899


0.7395 0.9442 0.3064 0.9288

effectiveness 0.7444 0.9356 0.3211 0.9137 efficiency 0.5985 0.8173 0.2624 0.7024 use of open innovation 0.595 0.8117 0.0655 0.6721



Reliability > 0.6



0.6467 0.9163 0.3022 0.8899


0.7399 0.9443 0.3071 0.9288

effectiveness 0.7444 0.9356 0.3172 0.9137 efficiency 0.5983 0.8171 0.2622 0.7024

5. SEM analysis result

The bootstrap procedure was used to obtain t-statistics in order to evaluate the significance of the parameters [51]. Table.4 and 5 summarises the results of direct and total effects by showing t-values and the corresponding levels of significance (marked with asterisks) for correlation coefficients: * p<0.05; ** p<0.01; *** p<0.001. Moreover, data for comparison of Verworn et al. (2008) is shown in Table.6.


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Table 4. Smart PLS 2.0 analysis results of model 1 Total

effects Total

effects Standard

Error T Statistics p value Sig. Level

H1A 0.0979 0.0878 1.1147 0.2666

H2A -0.012 0.0838 0.1435 0.8861

H3A 0.0978 0.0908 1.0769 0.2831

H4A 0.2225 0.0819 2.7172 0.0073 ** H5A 0.1857 0.0829 2.2393 0.0265 * H6A 0.1352 0.1462 0.925 0.3563

H7A 0.0692 0.1191 0.5814 0.5618

H8A 0.4877 0.0911 5.3542 0.0000 *** H9A 0.4527 0.1003 4.5156 0.0000 *** H10A -0.0153 0.0983 0.1552 0.8769

H11A 0.3231 0.0668 4.8346 0.0000 *** H12A 0.5494 0.0573 9.593 0.0000 *** H13A 0.5304 0.0579 9.1531 0.0000 *** H14A 0.3342 0.0886 3.7719 0.0002 *** Direct effects

Path Coeff. Standard


H1A -0.0007 0.045 0.0159 0.9873

H2A -0.068 0.0571 1.1913 0.2352

H3A -0.0042 0.0456 0.0921 0.9267

H4A 0.124 0.065 1.9064 0.0583

H5A 0.1857 0.0758 2.4481 0.0154 * H6A 0.1352 0.1242 1.0884 0.2780

H7A 0.0692 0.0791 0.8757 0.3825

H8A 0.3767 0.1207 3.1218 0.0021 ** H9A 0.4075 0.099 4.1152 0.0001 *** H10A -0.0384 0.0593 0.6477 0.5181

H11A -0.0434 0.0562 0.7727 0.4408

H12A 0.5494 0.0573 9.593 0.0000 *** H13A 0.5304 0.0579 9.1531 0.0000 *** H14A 0.3342 0.088 3.7987 0.0002 ***



Reliability > 0.6



0.6468 0.9163 0.3009 0.8899


0.7395 0.9442 0.3064 0.9288

effectiveness 0.7444 0.9356 0.3211 0.9137 efficiency 0.5985 0.8173 0.2624 0.7024 use of open innovation 0.595 0.8117 0.0655 0.6721



Reliability > 0.6



0.6467 0.9163 0.3022 0.8899


0.7399 0.9443 0.3071 0.9288

effectiveness 0.7444 0.9356 0.3172 0.9137 efficiency 0.5983 0.8171 0.2622 0.7024

5. SEM analysis result

The bootstrap procedure was used to obtain t-statistics in order to evaluate the significance of the parameters [51]. Table.4 and 5 summarises the results of direct and total effects by showing t-values and the corresponding levels of significance (marked with asterisks) for correlation coefficients: * p<0.05; ** p<0.01; *** p<0.001. Moreover, data for comparison of Verworn et al. (2008) is shown in Table.6.


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effects Total

effects Standard


H1B -0.0009 0.0735 0.0127 0.990

H2B -0.0664 0.0809 0.8204 0.413

H3B -0.1455 0.1185 1.2281 0.221

H4B 0.2527 0.1079 2.3417 0.020 * H5B 0.1858 0.0848 2.192 0.030 * H6B 0.1345 0.1425 0.9441 0.346

H7B 0.0697 0.1163 0.5997 0.550

H8B 0.4891 0.0907 5.3947 0.000 *** H9B 0.4623 0.1004 4.6064 0.000 *** H10B -0.0326 0.0975 0.3341 0.739

H11B 0.3097 0.0667 4.6464 0.000 *** H12B 0.5485 0.054 10.1595 0.000 *** H13B 0.5536 0.0549 10.0899 0.000 *** H14B 0.3343 0.0906 3.6903 0.000 *** Direct effects



H1B -0.0009 0.0448 0.0208 0.983

H2B -0.0661 0.0586 1.1286 0.261

H3B -0.1455 0.0998 1.4583 0.147

H4B 0.2527 0.1033 2.4458 0.016 * H5B 0.1257 0.0888 1.4166 0.158

H6B 0.1344 0.1241 1.0828 0.280

H7B 0.07 0.0797 0.8778 0.381

H8B 0.3768 0.1223 3.0811 0.002 ** H9B 0.4077 0.0998 4.087 0.000 *** H10B -0.0392 0.0588 0.6667 0.506

H11B -0.0435 0.0565 0.7695 0.443

H12B 0.5485 0.054 10.1595 0.000 *** H13B 0.5536 0.0549 10.0899 0.000 *** H14B 0.3343 0.0895 3.7364 0.000 ***


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Table 6. Smart PLS 2.0 analysis results of model 3

Total effects Total

effects Standard

Error T

Statistics p value

Sig. Level

FFE_initial planning -> FFE_market

0.5497 0.0549 10.0159 0.0000 ***

FFE_initial planning -> FFE_tech

0.5542 0.0547 10.1325 0.0000 ***

FFE_initial planning -> effectiveness

0.3104 0.0663 4.6848 0.0000 ***

FFE_initial planning -> efficiency

0.4889 0.0865 5.651 0.0000 ***

FFE_market -> effectiveness 0.4626 0.101 4.5821 0.0000 *** FFE_market -> efficiency 0.1357 0.1428 0.9503 0.3433

FFE_tech -> effectiveness -0.0328 0.0962 0.3404 0.7340

FFE_tech -> efficiency 0.0679 0.1138 0.5967 0.5515

efficiency -> effectiveness 0.3343 0.0892 3.7478 0.0002 ***

Direct effects Path

Coeff. Standard

Error T

Statistics p value

Sig. Level

FFE_initial planning -> FFE_market

0.5497 0.0549 10.0159 0.0000 ***

FFE_initial planning -> FFE_tech

0.5542 0.0547 10.1325 0.0000 ***

FFE_initial planning -> effectiveness

-0.0517 0.0571 0.9051 0.3667

FFE_initial planning -> efficiency

0.3767 0.1194 3.1553 0.0019 **

FFE_market -> effectiveness 0.4172 0.0999 4.1775 0.0000 *** FFE_market -> efficiency 0.1357 0.1205 1.1266 0.2615

FFE_tech -> effectiveness -0.0555 0.0617 0.8989 0.3700

FFE_tech -> efficiency 0.0679 0.0763 0.8903 0.3746

efficiency -> effectiveness 0.3343 0.0878 3.8074 0.0002 ***

6. Discussion 6.1 Difference of age The difference from Verworn et al. (2008) is that there was no impact of reduction of technical uncertainty on NPD project success. This result suggests the


effects Total

effects Standard


H1B -0.0009 0.0735 0.0127 0.990

H2B -0.0664 0.0809 0.8204 0.413

H3B -0.1455 0.1185 1.2281 0.221

H4B 0.2527 0.1079 2.3417 0.020 * H5B 0.1858 0.0848 2.192 0.030 * H6B 0.1345 0.1425 0.9441 0.346

H7B 0.0697 0.1163 0.5997 0.550

H8B 0.4891 0.0907 5.3947 0.000 *** H9B 0.4623 0.1004 4.6064 0.000 *** H10B -0.0326 0.0975 0.3341 0.739

H11B 0.3097 0.0667 4.6464 0.000 *** H12B 0.5485 0.054 10.1595 0.000 *** H13B 0.5536 0.0549 10.0899 0.000 *** H14B 0.3343 0.0906 3.6903 0.000 *** Direct effects



H1B -0.0009 0.0448 0.0208 0.983

H2B -0.0661 0.0586 1.1286 0.261

H3B -0.1455 0.0998 1.4583 0.147

H4B 0.2527 0.1033 2.4458 0.016 * H5B 0.1257 0.0888 1.4166 0.158

H6B 0.1344 0.1241 1.0828 0.280

H7B 0.07 0.0797 0.8778 0.381

H8B 0.3768 0.1223 3.0811 0.002 ** H9B 0.4077 0.0998 4.087 0.000 *** H10B -0.0392 0.0588 0.6667 0.506

H11B -0.0435 0.0565 0.7695 0.443

H12B 0.5485 0.054 10.1595 0.000 *** H13B 0.5536 0.0549 10.0899 0.000 *** H14B 0.3343 0.0895 3.7364 0.000 ***


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following possibilities. First, firms have not done high risk R&D to be more cautious. Second, the importance of other factors has increased and the impact of FFE has been weaker. On the basis of this result, we conduct discussion of hypotheses. 6.2 Hypotheses regarding relationship between open innovation and success In previous case studies and researches, the effect of open innovation on the success of the NPD project was most expected. However, these effects were not significant and Hypothesis 1 and 2 were not supported in both models. This result is different from the service innovation model [41]. This could be because other phases and factors have a large effect on success, for instance, project execution phase or product phase. 6.3 Hypotheses regarding relationship between open innovation and FFE There was not enough statistical evidence to support hypothesis 3A&B. The result shows that market uncertainty was not related to using open innovation. This could be because indicators that were used in this research scoped solutions of technical problems. If we employed solutions of market problems, results might change. On the other hand, reduction of technical uncertainty and intensity of initial planning have positive relationship with using open innovation. Hypothesis 4A, 4B, 5A, and 5B were all supported. It is said that most of the innovation fails [2], however projects in collaboration with other firms are not permitted to fail easily. Thus, FFE activities become livelier. In addition, it is presumed that the internal procedures are strict when determining the use of open resources. These activities may play an important role for the go/no go decision. 6.4 Hypotheses regarding relationship between FFE and success There was not enough evidence to support impacts of reduction of market uncertainty on project efficiency (Hypothesis 6A&B), reduction of technical uncertainty on project efficiency (Hypothesis 7A&B), reduction of technical uncertainty on effectiveness (Hypothesis 10A&B). These result showed some differences from previous studies, and it was shown that results were affected by the difference of age, as discussed in 6.1. The other FFE activities had positive impacts on the success of NPD projects. The intensity of initial planning had significant positive impact on efficiency and effectiveness (Hypothesis 8A, 8B, 11A, and 11B). The degree of reduction of market uncertainty had significant positive impact on effectiveness (Hypothesis 9A&B). The intensity of initial planning enhanced reduction of market and technical uncertainty (Hypothesis 12A, 12B, 13A, and 13B) .


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The effectiveness of NPD projects was positively affected by NPD efficiency in open innovation process (Hypothesis 14A&B). These results prove that FFE is a key to success, regardless of differences in age and process.

7. Conclusion New findings can be drawn from the analysis of structural models: 1. The direct/indirect impacts of open innovation on NPD project success, which

were blindly believed to be true, did not exist. 2. Reduction of market uncertainty during FFE was not related to open innovation

activities. In contrast reduction of technical uncertainty and intensity of initial planning during FFE were related to open innovation activities.

3. Reduction of market uncertainty and intensity of initial planning during FFE had impacts on NPD project success in projects that used open innovation. In contrast reduction of technical uncertainty did not have any impacts on NPD project success in projects that used open innovation. This is the influence of the difference of age.

4. There was no difference of result between model 1 and 2.Validity and reliability of the proposed two models were observed.

These results lead to the following conclusions. First, NPD project success is not provided by using open innovation, but is provided by FFE activities that were enhanced by open innovation. The impact of open innovation may be gradual. Both open innovation and FFE is the key to NPD project success. Second, open innovation and FFE have a positive relationship in both models. There is no difference between projects that used open innovation in different phases. Both conceptual models are acceptable when considering NPD processes that contain open innovation.Managers should take into consideration these conclusions and use open innovation to enhance FFE activities and solve technical problems.

References

[1] Chesbrough, H.W. (2003a). The era of open innovation. MIT Sloan Management Review, 44 (3), pp.35–41.

[2] Chesbrough, H.W. (2003b). Open innovation: the new imperative for creating and profiting from technology. Harvard Business School Press, Boston, Mass.

[3] Baller, T. (2011). Philips Open Innovation and High Tech Campus Eindhoven, http://www.stifterverband.de/veranstaltungen/archiv/2011_06_16_enterprising_knowledge/baller_philips_open_innovation_and_high_tech_campus_eindhoven.pdf

following possibilities. First, firms have not done high risk R&D to be more cautious. Second, the importance of other factors has increased and the impact of FFE has been weaker. On the basis of this result, we conduct discussion of hypotheses. 6.2 Hypotheses regarding relationship between open innovation and success In previous case studies and researches, the effect of open innovation on the success of the NPD project was most expected. However, these effects were not significant and Hypothesis 1 and 2 were not supported in both models. This result is different from the service innovation model [41]. This could be because other phases and factors have a large effect on success, for instance, project execution phase or product phase. 6.3 Hypotheses regarding relationship between open innovation and FFE There was not enough statistical evidence to support hypothesis 3A&B. The result shows that market uncertainty was not related to using open innovation. This could be because indicators that were used in this research scoped solutions of technical problems. If we employed solutions of market problems, results might change. On the other hand, reduction of technical uncertainty and intensity of initial planning have positive relationship with using open innovation. Hypothesis 4A, 4B, 5A, and 5B were all supported. It is said that most of the innovation fails [2], however projects in collaboration with other firms are not permitted to fail easily. Thus, FFE activities become livelier. In addition, it is presumed that the internal procedures are strict when determining the use of open resources. These activities may play an important role for the go/no go decision. 6.4 Hypotheses regarding relationship between FFE and success There was not enough evidence to support impacts of reduction of market uncertainty on project efficiency (Hypothesis 6A&B), reduction of technical uncertainty on project efficiency (Hypothesis 7A&B), reduction of technical uncertainty on effectiveness (Hypothesis 10A&B). These result showed some differences from previous studies, and it was shown that results were affected by the difference of age, as discussed in 6.1. The other FFE activities had positive impacts on the success of NPD projects. The intensity of initial planning had significant positive impact on efficiency and effectiveness (Hypothesis 8A, 8B, 11A, and 11B). The degree of reduction of market uncertainty had significant positive impact on effectiveness (Hypothesis 9A&B). The intensity of initial planning enhanced reduction of market and technical uncertainty (Hypothesis 12A, 12B, 13A, and 13B) .


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[4] Bae, Y., Chang ,H. (2012). Efficiency and effectiveness between open and closed innovation: empirical evidence in South Korean manufacturers. Technology Analysis & Strategic Management, 24(10), pp.967–980.

[5] http://www.nikkei.com/article/DGXNASGF1702O_X10C12A7TBU000/ [6] Cooper, R.G., Kleinschmidt, E.J. (1994). Screening new products for potential

winners. IEEE Transaction Engineering Management, 22(4), 24–30. [7] Verworn, B., Herstatt, C., Nagahira, A. (2008). The fuzzy front end of Japanese

new product development projects: Impact on success and differences between incremental and radical projects. R&D Management, 38 (1), pp.1-19.

[8] A. Nagahira, K.H Kim, H.J Yoon, S. Ishihara, Y. Chiba, Y. Tanaka and A. Miura (2015). Comparative study on FFE activities between Korean and Japanese NPD projects. International Journal of Technology Marketing, 10 (1), 67-94.

[9] Verworn, B. (2009). A structural equation model of the impact of the "fuzzy front end" on the success of new product development. Research Policy, 38(10), pp. 1571-1581.

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[12] Thomas, L., Kent, T., Nikola, A. (2015). Open innovation and open production: a case of technology supplier/user collaboration in the process industries. International Journal of Innovation Management, 19(2), 1550022.

[13] Cooper, R.G. and Kleinschmidt, E.J. (1987). “What makes a new product a winner: success factors at the project level.” R&D Management, 17(3), pp.175–189.

[14] Smith, P.G. and Reinertsen, D.G. (1991). Developing Products in Half the Time. New York: Van Nostrand Reinhold.

[15] Verganti, R. (1999). “Planned flexibility: Linking anticipation and reaction in product development projects.” Journal of Product Innovation Management, 16(4), pp. 363–376.

[16] Backman, M., Börjesson, S., and Setterberg, S., (2007). Working with concepts in the fuzzy front end: exploring the context for innovation for different types of concepts at Volvo Cars. R&D Management, 38 (1), pp.17–28.

[17] Murphy, S.A.and Kumar, V.(1997). “The front end of new product development: a Canadian survey.” R&D Management, 27(1), pp.5–16.

[18] Herstatt, C., Verworn, B., and Nagahira A. (2004). Reducing project related uncertainty in the 'fuzzy front end’ of innovation: a comparison of German and Japanese product innovation projects." International Journal of product development, 1, pp.43-65.

[19] Cooper, R.G. (1988). Predevelopment activities determine new product success. Industrial Marketing Management, 17 (3), pp.237–247.


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[20] Kleinschmidt, E.J., and Cooper, R.G. (1991). The Impact of Product Innovativeness on Performance. Journal of Product Innovation Management, 8 (4), pp.240–251.

[21] Khurana, A. and Rosenthal, S.R. (1997). Integrating the Fuzzy Front End of New Product Development. Sloan Management Review, 38 (2), pp.103–120.

[22] Khurana, A. and Rosenthal, S.R. (1998). Towards holistic “front ends” in new product development. Journal of Product Innovation Management, 15 (1), pp.57–74.

[23] Cooper, R.G. and Kleinschmidt, E.J. (1993). Screening new products for potential winners. Long Range Planning, 26(6), pp.74–81.

[24] Song, X.M. and Parry M.E. (1996). What separates Japanese new product winners from losers. Journal of Product Innovation Management, 13(5), pp.422–439.

[25] Cooper, R.G. and Kleinschmidt, E.J., (1988). Resource allocation in the new product process. Industrial Marketing Management. 17 (3), pp.249–262.

[26] Verworn, B. (2009). A structural equation model of the impact of the "fuzzy front end" on the success of new product development. Research Policy, 38, pp.1571-1581.

[27] Henderson, R.M., Clark, K.B. (1990). Architectural Innovation: The reconfiguration of existing product technologies and the failure of established firms, Administrative Science Quarterly, 35, 9-30.

[28] Cohen, W. and Levinthal, D. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35, 128–52.

[29] Tsai, W. (2001). Knowledge Transfer in Intraorganizational Networks: Effects of Network Position and Absorptive Capacity on Business Unit Innovation and Performance. Academy of Management Journal, 44, 996–1004.

[30] Jansen, J.J., van den Bosch, F.A. and Volberda, H. (2005). Managing Potential and Realized Absorptive Capacity: How Do Organizational Antecedents Matter? Academy of Management Journal, 48, 999–1015.

[31] Lichtenthaler, U. (2010). Absorptive Capacity, Environmental Turbulence, and the Complementarity of Organizational Learning Processes. Academy of Management Journal, 52, 822–46.

[32] Miller, D.J., Fern, M.J. and Cardinal, L.B. (2007). The Use of Knowledge for Technological Innovation within Diversified Firms. Academy of Management Journal, 50, 308–26

[33] Rosenkopf, L. and Nerkar, A. (2001). Beyond Local Search: Boundary-Spanning, Exploration, and Impact in the Optical Disk Industry. Strategic Management Journal, 22, 287–306.

[34] Helfat, C.E. and Peteraf, M.A. (2003). The Dynamic Resource-Based View: Capability Lifecycles. Strategic Management Journal, 24, 997–1010.

[4] Bae, Y., Chang ,H. (2012). Efficiency and effectiveness between open and closed innovation: empirical evidence in South Korean manufacturers. Technology Analysis & Strategic Management, 24(10), pp.967–980.

[5] http://www.nikkei.com/article/DGXNASGF1702O_X10C12A7TBU000/ [6] Cooper, R.G., Kleinschmidt, E.J. (1994). Screening new products for potential

winners. IEEE Transaction Engineering Management, 22(4), 24–30. [7] Verworn, B., Herstatt, C., Nagahira, A. (2008). The fuzzy front end of Japanese

new product development projects: Impact on success and differences between incremental and radical projects. R&D Management, 38 (1), pp.1-19.

[8] A. Nagahira, K.H Kim, H.J Yoon, S. Ishihara, Y. Chiba, Y. Tanaka and A. Miura (2015). Comparative study on FFE activities between Korean and Japanese NPD projects. International Journal of Technology Marketing, 10 (1), 67-94.

[9] Verworn, B. (2009). A structural equation model of the impact of the "fuzzy front end" on the success of new product development. Research Policy, 38(10), pp. 1571-1581.


[11] Chesbrough, H., Crowther,A.K.(2006). Beyond hightech: Early adopters of open innovation in other industries. R&D Management, 36, 229–236.

[12] Thomas, L., Kent, T., Nikola, A. (2015). Open innovation and open production: a case of technology supplier/user collaboration in the process industries. International Journal of Innovation Management, 19(2), 1550022.

[13] Cooper, R.G. and Kleinschmidt, E.J. (1987). “What makes a new product a winner: success factors at the project level.” R&D Management, 17(3), pp.175–189.

[14] Smith, P.G. and Reinertsen, D.G. (1991). Developing Products in Half the Time. New York: Van Nostrand Reinhold.

[15] Verganti, R. (1999). “Planned flexibility: Linking anticipation and reaction in product development projects.” Journal of Product Innovation Management, 16(4), pp. 363–376.

[16] Backman, M., Börjesson, S., and Setterberg, S., (2007). Working with concepts in the fuzzy front end: exploring the context for innovation for different types of concepts at Volvo Cars. R&D Management, 38 (1), pp.17–28.

[17] Murphy, S.A.and Kumar, V.(1997). “The front end of new product development: a Canadian survey.” R&D Management, 27(1), pp.5–16.

[18] Herstatt, C., Verworn, B., and Nagahira A. (2004). Reducing project related uncertainty in the 'fuzzy front end’ of innovation: a comparison of German and Japanese product innovation projects." International Journal of product development, 1, pp.43-65.

[19] Cooper, R.G. (1988). Predevelopment activities determine new product success. Industrial Marketing Management, 17 (3), pp.237–247.


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[35] Teece, D.J., Pisano, G. and Shuen, A. (1997). Dynamic Capabilities and Strategic Management. Strategic Management Journal, 18, 509–33.

[36] Teece, D.J. (2007). Explicating Dynamic Capabilities: The Nature and Microfoundations of (Sustainable) Enterprise Performance. Strategic Management Journal, 28, 1319–50.

[37] Alam, I., (2006). Removing the fuzziness from the fuzzy front-end of service innovations through customer interactions. Industrial Marketing Management, 35,468–480.

[38] Lhuillery, S.,Pfister, E.,(2009). R&D cooperation and failures in innovation projects: Empirical evidence from French CIS data. Research Policy, 38, 45–57.

[39] Björk, J.,Magnusson,M., (2009). Where do good innovation ideas come from? Exploring the influence of network connectivity on innovation idea quality. Journal Product Innovation Management, 26,662–670.

[40] Magnusson, P.R., (2009).Exploring the contributions of involving ordinary users in ideation of technology-based services. Journal Product Innovation Management, 26,578–593.

[41] Thanasopon, B., Papadopoulos, T., Vidgen, R. (2016). The role of openness in the fuzzy front-end of service innovation. Technovation, 47, pp.32-46.

[42] Enkel, E., Gassmann, O., Chesbrough, H., (2009). Open R&D and open innovation: exploring the phenomenon. R&D Management, 39,311–316.

[43] Dvir, D. and Lechler, T. (2004). Plans are nothing, changing plans is everything: the impact of changes on project success. Research Policy, 33(1), pp.1–15.

[44] Pinto, J.K. and Slevin D.P. (1988). Project success: definitions and measurement techniques. Project Management Journal, 19(1), pp.67–72.

[45] Griffin, A. and Page, A.L. (1996). PDMA success measurement project: recommended measures for product development success and failure. Journal of Product Innovation Management, 13, pp.478-496.

[46] Lynn, G.S., Reilly, R.R. and Akgun, A.E. (2000). Knowledge management in new product teams: practices and outcomes. IEEE Transactions on Engineering Management, 47(2), pp.221–231.

[47] Song, X.M. and Parry, M.E. (1997). A cross-national comparative study of new product development processes: Japan and the United States. Journal of Marketing, 61(2), pp.1–18.

[48] Langerak, F., Hultink, E.J. and Robben, S.J. (2004). The role of predevelopment activities in the relationship between market orientation and performance. R&D Management, 34(3), pp.295-309.

[49] Moenaert, R.K., De Meyer, A., Souder, W.E. and Deschoolmeester, D. (1995). R&D/Marketing communication during the fuzzy front-end. IEEE Transactions on Engineering Management, 42(3), pp.243–258.

[50] Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage Publication, Inc.,


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USA. [51] Efron, B., and Tibshirani, R. J. (1993). An introduction to the bootstrap. New

York, NY: Chapman and Hall/CRC. [52] Nunnally, J.C. and Bernstein, I.H. (1994). Psychometric theory, McGraw-Hill,

New York.

[35] Teece, D.J., Pisano, G. and Shuen, A. (1997). Dynamic Capabilities and Strategic Management. Strategic Management Journal, 18, 509–33.

[36] Teece, D.J. (2007). Explicating Dynamic Capabilities: The Nature and Microfoundations of (Sustainable) Enterprise Performance. Strategic Management Journal, 28, 1319–50.

[37] Alam, I., (2006). Removing the fuzziness from the fuzzy front-end of service innovations through customer interactions. Industrial Marketing Management, 35,468–480.

[38] Lhuillery, S.,Pfister, E.,(2009). R&D cooperation and failures in innovation projects: Empirical evidence from French CIS data. Research Policy, 38, 45–57.

[39] Björk, J.,Magnusson,M., (2009). Where do good innovation ideas come from? Exploring the influence of network connectivity on innovation idea quality. Journal Product Innovation Management, 26,662–670.

[40] Magnusson, P.R., (2009).Exploring the contributions of involving ordinary users in ideation of technology-based services. Journal Product Innovation Management, 26,578–593.

[41] Thanasopon, B., Papadopoulos, T., Vidgen, R. (2016). The role of openness in the fuzzy front-end of service innovation. Technovation, 47, pp.32-46.

[42] Enkel, E., Gassmann, O., Chesbrough, H., (2009). Open R&D and open innovation: exploring the phenomenon. R&D Management, 39,311–316.

[43] Dvir, D. and Lechler, T. (2004). Plans are nothing, changing plans is everything: the impact of changes on project success. Research Policy, 33(1), pp.1–15.

[44] Pinto, J.K. and Slevin D.P. (1988). Project success: definitions and measurement techniques. Project Management Journal, 19(1), pp.67–72.

[45] Griffin, A. and Page, A.L. (1996). PDMA success measurement project: recommended measures for product development success and failure. Journal of Product Innovation Management, 13, pp.478-496.

[46] Lynn, G.S., Reilly, R.R. and Akgun, A.E. (2000). Knowledge management in new product teams: practices and outcomes. IEEE Transactions on Engineering Management, 47(2), pp.221–231.

[47] Song, X.M. and Parry, M.E. (1997). A cross-national comparative study of new product development processes: Japan and the United States. Journal of Marketing, 61(2), pp.1–18.

[48] Langerak, F., Hultink, E.J. and Robben, S.J. (2004). The role of predevelopment activities in the relationship between market orientation and performance. R&D Management, 34(3), pp.295-309.

[49] Moenaert, R.K., De Meyer, A., Souder, W.E. and Deschoolmeester, D. (1995). R&D/Marketing communication during the fuzzy front-end. IEEE Transactions on Engineering Management, 42(3), pp.243–258.

[50] Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage Publication, Inc.,


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The impact of fuzzy front end activities and changes in project execution on new product development project success : Comparative study between B2B and B2C projects

Ko Yamasaki, Akio Nagahira and Sumie Ishihara Department of engineering, Tohoku University

6-6-11-802 Aza Aoba Aramaki, Aoba-ku, Sendai, Miyagi Japan [email protected]


ABSTRACT

The aim of this study was to investigate the direct and indirect impacts of FFE activities and changes in project execution on NPD projects success of B2B and B2C projects in Japanese manufacturing firms in the 2010s. The analysis of 245 B2B and 81 B2C new product development projects was carried out using structural equation modeling. Result suggests that FFE activities lead to project success in both B2B and B2C project in Japanese manufacturing firms in the 2010s. Considering product characteristics, differences between B2B and B2C projects are noticed in terms of the project execution phase. Regarding B2B projects, FFE activities reduce the changes in the project execution phase. Consequently, fewer changes lead to project success. As for B2C projects, changes in the project execution phase are unavoidable even if there exists qualified predevelopment activities such as the reducing uncertainties and initial planning. In addition, execution of the project in conformity to initial plans has little causal relationship between the successes. The changes in the project execution phase are unavoidable because of the market complexity. Thus, B2C projects will be successful if the projects have flexibility in the project execution and FFE activities. Keywords: new product development (NPD); fuzzy front end (FFE);

structural equation modeling (SEM); Smart PLS; project execution phase; B2B;B2C

1. Introduction

1.1 Research background Numerous Japanese firms used to enjoy well-deserved reputations for excellence in new product development (NPD) in the 1980s. NPD practices in Japanese firms were frequently mentioned as exemplars for U.S firms [1]. However, these days (2010s)


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The impact of fuzzy front end activities and changes in project execution on new product development project success : Comparative study between B2B and B2C projects

Ko Yamasaki, Akio Nagahira and Sumie Ishihara Department of engineering, Tohoku University

6-6-11-802 Aza Aoba Aramaki, Aoba-ku, Sendai, Miyagi Japan [email protected]


ABSTRACT

The aim of this study was to investigate the direct and indirect impacts of FFE activities and changes in project execution on NPD projects success of B2B and B2C projects in Japanese manufacturing firms in the 2010s. The analysis of 245 B2B and 81 B2C new product development projects was carried out using structural equation modeling. Result suggests that FFE activities lead to project success in both B2B and B2C project in Japanese manufacturing firms in the 2010s. Considering product characteristics, differences between B2B and B2C projects are noticed in terms of the project execution phase. Regarding B2B projects, FFE activities reduce the changes in the project execution phase. Consequently, fewer changes lead to project success. As for B2C projects, changes in the project execution phase are unavoidable even if there exists qualified predevelopment activities such as the reducing uncertainties and initial planning. In addition, execution of the project in conformity to initial plans has little causal relationship between the successes. The changes in the project execution phase are unavoidable because of the market complexity. Thus, B2C projects will be successful if the projects have flexibility in the project execution and FFE activities. Keywords: new product development (NPD); fuzzy front end (FFE);

structural equation modeling (SEM); Smart PLS; project execution phase; B2B;B2C

1. Introduction

1.1 Research background Numerous Japanese firms used to enjoy well-deserved reputations for excellence in new product development (NPD) in the 1980s. NPD practices in Japanese firms were frequently mentioned as exemplars for U.S firms [1]. However, these days (2010s)

Japanese manufacturing companies are experiencing the decline in the global market. Empirical study has shown that the greatest differences between winners and losers were found in the quality of execution of pre-development activities [2]. We assume that the reason for the decline in Japanese manufacturing firms exists in these activities. These pre-development activities of an innovation process is called “fuzzy front end (FFE)”. At this stage, the effort to optimize is low and effects on the whole innovation process are high [3]. As shown in Figure 1 [3], FFE is defined as including the very first activities within the NPD process(PhaseⅠ and Ⅱ),and delivering a list of specifications. At this stage, the effort to optimize is low and effects on the whole invention process are high [4]. Thus, a deeper understanding of FFE and its impact on NPD success could help firms to be more successful in their efforts to develop new products.

Figure.1 The product development process [3]

Previous studies clarify the impact of the FFE on the success and the differences regarding products newness [5] [6]. In further study, project execution phase is considered. The model which is closer to Real-world situations is employed as shown in Figure.2 [6].

Figure.2 Source Verworn 2009

Result suggests there is the evidence for the existence of indirect effect [6]. In addition to these results, researchers suggest that differences appear in the NPD process regarding product characteristics. The product characteristics could be an important factor for the deeper understanding of FFE. However, this has not been studied. Thus we need to clarify the impact of the FFE on success regarding product characteristics.


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1.2 Research objective The aim of this study is to reveal the direct and indirect impacts of FFE activities and changes in project execution on NPD project success in Japanese firms and analyze the similarities and differences of the NPD projects between B2B (Business to Business) and B2C (Business to Consumer) product. The paper is organized as follows. After this brief introduction (Section 1), we describe the literature reviews in Section 2. In section 3 we describe our conceptual model, and provide more details about our hypotheses and measures to test our conceptual model. Section4 and 5 describes our research method.Section6 and 7 include results and discussion, and the conclusion is made in Section8.

2. Literature review Researchers and practitioners in the field of innovation management have been paying attention to the so called ‘FFE’ of product development [1]. Managers have identified the FFE as being the greatest weakness in product innovation [7]. It is because it strongly determines which projects will be executed, and furthermore the quality, costs, and time frame are to large extent defined here. Regarding NPD Japanese research, Song and Parry (1996) clarified that

pre-development activity could be an important success factor in Japanese and U.S NPD projects [1]. Song and Parry (1997) applied the causal model to Japanese NPD projects in further research [8]. The Results suggest that there are three important success factors, Technical proficiency, Marketing proficiency and initial planning. Verworn et al.(2008) performed quantitative analysis on these three factors [5]. Verworn et al.(2008) analyzed the impact of the FFE of Japanese NPD on the project’s success and differences between radical and incremental product [5]. In this research, based on literature, ‘efficiency’ and ‘effectiveness’ are considered factors in NPD success at the project level [8]. The results suggest that FFE has a positive impact on project success as well as the differences between radical and incremental products. In further work Verworn (2009) proposed an analysis of the direct and indirect effects of FFE of German NPD project [6]. Verworn (2009) considered product newness as a contextual factor and the project execution phase in addition to FFE and project evaluation [6]. Different from Verworn et al.(2008), ‘overall satisfaction’ was used instead of ‘effectiveness’ as a success factor, this is because most projects in the data were effective projects [5]. The results suggest that FFE has direct and indirect impacts on project success, and product newness has an effect on NPD processes. Both studies suggest that product characteristics should be considered because the data was mostly regarding B2B


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1.2 Research objective The aim of this study is to reveal the direct and indirect impacts of FFE activities and changes in project execution on NPD project success in Japanese firms and analyze the similarities and differences of the NPD projects between B2B (Business to Business) and B2C (Business to Consumer) product. The paper is organized as follows. After this brief introduction (Section 1), we describe the literature reviews in Section 2. In section 3 we describe our conceptual model, and provide more details about our hypotheses and measures to test our conceptual model. Section4 and 5 describes our research method.Section6 and 7 include results and discussion, and the conclusion is made in Section8.

2. Literature review Researchers and practitioners in the field of innovation management have been paying attention to the so called ‘FFE’ of product development [1]. Managers have identified the FFE as being the greatest weakness in product innovation [7]. It is because it strongly determines which projects will be executed, and furthermore the quality, costs, and time frame are to large extent defined here. Regarding NPD Japanese research, Song and Parry (1996) clarified that

pre-development activity could be an important success factor in Japanese and U.S NPD projects [1]. Song and Parry (1997) applied the causal model to Japanese NPD projects in further research [8]. The Results suggest that there are three important success factors, Technical proficiency, Marketing proficiency and initial planning. Verworn et al.(2008) performed quantitative analysis on these three factors [5]. Verworn et al.(2008) analyzed the impact of the FFE of Japanese NPD on the project’s success and differences between radical and incremental product [5]. In this research, based on literature, ‘efficiency’ and ‘effectiveness’ are considered factors in NPD success at the project level [8]. The results suggest that FFE has a positive impact on project success as well as the differences between radical and incremental products. In further work Verworn (2009) proposed an analysis of the direct and indirect effects of FFE of German NPD project [6]. Verworn (2009) considered product newness as a contextual factor and the project execution phase in addition to FFE and project evaluation [6]. Different from Verworn et al.(2008), ‘overall satisfaction’ was used instead of ‘effectiveness’ as a success factor, this is because most projects in the data were effective projects [5]. The results suggest that FFE has direct and indirect impacts on project success, and product newness has an effect on NPD processes. Both studies suggest that product characteristics should be considered because the data was mostly regarding B2B

projects [5] [6]. Therefore there is a possibility that the result was B2B specific. In recent research conducted by Ruslan et al. (2016) a comparative study was done between NPD projects in Japanese firms (1980s) and Korean firms (2010s) [9]. As a result, regardless of external environment, FFE has direct positive effect on project success. Previous studies have analyzed the impact of FFE on the project execution phase and project success for all types of products [5] [6] [9]. However, a comparative study between the different characteristic of products has not been conducted. Therefore, we introduced two research questions. 1. “What difference and similarity appear in FFE if the product characteristics are considered?” 2. “What effect does FFE have in recent NPD projects in Japanese firms?” In order to consider the product characteristics, we separate the projects into B2B project and B2C project and analyze each. Through the comparative study, we aimed to revise the direct and indirect impact of FFE activities on NPD project success in recent Japanese firms in terms of the product characteristics. We compare the data of B2B with B2C projects collected in 2014-15.

3. Conceptual model and hypotheses 3.1 Construction of model The framework of this research is based on the latest work, which analyses the direct and indirect impact of the FFE factors on NPD project success [9]. The framework includes the FFE, project execution phase, and project success. In addition to the project phases, the degree of newness is considered as a contextual factor. FFE factors are: “reduction of market uncertainty” (knowledge about: customers’ needs, wants and specifications, customer requirements, size of potential market, price sensitivity, unique selling propositions and competitors), “reduction of technical uncertainty” (well-understood technology, product’s specifications, technical requirements, technical feasibility and anticipation of technical problems), and “intensity of planning” (breaking the project into work packages, timings, resource allocation, a detailed cost plan and assignment of responsibilities of team members). Seven items under the project execution phase are assigned to two factors “Conforming with specifications” (no emergence of new elements, not meeting up with surprises and unforeseen findings, no deviations from planned procedures, and no formation of new concept definitions) and “Unchanged project vision” (no changes in technical concepts, no changes in project objectives, and absence of design alteration) using a simulation method. Two factors differ greatly in terms of degree of changes. “Unchanged project vision” refers to more dramatic changes and could lead to revision


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of the whole project while the “Conforming with specifications” refers to unavoidable changes that are necessary to adapt to requirements determined by dynamic environmental changes. In the model proposed, success measures are employed [5], where three factors for NPD project success are used: ‘efficiency’ (degree of agreement between financial and personnel resources planned during the FFE and those actually required, and the accordance with milestones), ‘effectiveness’ (meeting profit targets, sales volume targets, market share targets, customer satisfaction and competitive advantage achieved), and ‘overall satisfaction’ (Satisfaction of NPD process, Overall success, Learned from project). We add the overall satisfaction as a success factor for two reasons. Firstly, R&D managers are key people in projects in the measurement and control sector. Secondly, the satisfaction of R&D managers with teamwork, the development process, and the outcomes of a project determines their future attitude toward new product development, and therefore their performance on future projects, for example, interaction with other departments. Hence, in the long run, the overall satisfaction of key individual enhances the firm’s ability to successfully develop new products. The degree of newness of a NPD project is identified as a key contextual factor [6][9].Based on literature, eleven items are used to describe the degree of newness. Some of the items could contradict each other, in which case it is proposed analysis of the item should be conducted under different categories [10]. Thus the degree of newness is analyzed under three categories: ‘degree of market newness’ (difference in target market, distribution channels, and advertisement of new product), ‘degree of technical newness’ (difference in technical components, product lines, processes and knowledge required) and ‘degree of organizational newness’ (difference in cooperate strategy, necessity for organizational changes, requirements of different competencies and skills, and difference in capital needed). The allocation of the items is performed using a simulation method [9].

Figure3.Research framework


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of the whole project while the “Conforming with specifications” refers to unavoidable changes that are necessary to adapt to requirements determined by dynamic environmental changes. In the model proposed, success measures are employed [5], where three factors for NPD project success are used: ‘efficiency’ (degree of agreement between financial and personnel resources planned during the FFE and those actually required, and the accordance with milestones), ‘effectiveness’ (meeting profit targets, sales volume targets, market share targets, customer satisfaction and competitive advantage achieved), and ‘overall satisfaction’ (Satisfaction of NPD process, Overall success, Learned from project). We add the overall satisfaction as a success factor for two reasons. Firstly, R&D managers are key people in projects in the measurement and control sector. Secondly, the satisfaction of R&D managers with teamwork, the development process, and the outcomes of a project determines their future attitude toward new product development, and therefore their performance on future projects, for example, interaction with other departments. Hence, in the long run, the overall satisfaction of key individual enhances the firm’s ability to successfully develop new products. The degree of newness of a NPD project is identified as a key contextual factor [6][9].Based on literature, eleven items are used to describe the degree of newness. Some of the items could contradict each other, in which case it is proposed analysis of the item should be conducted under different categories [10]. Thus the degree of newness is analyzed under three categories: ‘degree of market newness’ (difference in target market, distribution channels, and advertisement of new product), ‘degree of technical newness’ (difference in technical components, product lines, processes and knowledge required) and ‘degree of organizational newness’ (difference in cooperate strategy, necessity for organizational changes, requirements of different competencies and skills, and difference in capital needed). The allocation of the items is performed using a simulation method [9].

Figure3.Research framework

3.2 Hypotheses

3.2.1 Impact of FFE Phase Same hypotheses are employed for both B2B and B2C projects in order to revise the similarities and differences. We assume that there exists strong causal relationship between FFE and the project execution phase and the project success. Literature suggests that through thorough the predevelopment activities result in less change in project execution and have a positive effect on project success [5][6][10][11][12]. Thus we propose, Thorough reduction of market uncertainty activities result in NPD project success and less changes in project execution (hypothesis 1 to 5). Hypothesis 1 The efficiency of NPD projects is positively affected by the degree of

reduction of market uncertainty during the FFE. Hypothesis 2 The effectiveness of NPD projects is positively affected by the degree

of reduction of market uncertainty during the FFE. Hypothesis 3 The overall satisfaction of NPD projects is positively affected by the

degree of reduction of market uncertainty during the FFE. Hypothesis 4 The conforming with specifications is positively affected by the degree

of reduction of market uncertainty during the FFE. Hypothesis 5 The unchanged project vision is positively affected by the degree of

reduction of market uncertainty during the FFE.

Figure.4 Hypotheses 1-5 Considering the impact of reduction of technical uncertainty activities on project success and the project execution phase mentioned in literature [2][5][6][10][11][12], we propose between hypothesis 6 and 10. Hypothesis 6 The efficiency of NPD projects is positively affected by the degree of

reduction of technical uncertainty during the FFE. Hypothesis 7 The effectiveness of NPD projects is positively affected by the


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degree of reduction of technical uncertainty during the FFE. Hypothesis 8 The overall satisfaction of NPD projects is positively affected by the

degree of reduction of technical uncertainty during the FFE. Hypothesis 9 The conforming with specifications is positively affected by the

degree of reduction of technical uncertainty during the FFE. Hypothesis 10 The unchanged project vision is positively affected by the degree of

reduction of technical uncertainty during the fuzzy front end.

Figure.5 Hypotheses 6-10

Initial planning drafts the process from development to product launch [6]. Planning activities prior to development are considered to be one of the most important activities affecting project success [5][6][11][12][13][14]. We propose the following hypotheses (from H11 to H17). Hypothesis 11 The degree of reduction of market uncertainty during the FFE is

positively affected by the intensity of planning. Hypothesis 12 The degree of reduction of technical uncertainty during the FFE is

positively affected by the intensity of planning. Hypothesis 13 The efficiency of NPD projects is positively affected by the intensity

of planning. Hypothesis 14 The effectiveness of NPD projects is positively affected by the

intensity of planning. Hypothesis 15 The overall satisfaction of NPD projects is positively affected by the

intensity of planning. Hypothesis 16 The conforming with specifications is positively affected by the

intensity of planning. Hypothesis 17 The unchanged project vision is positively affected by the intensity

of planning.


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degree of reduction of technical uncertainty during the FFE. Hypothesis 8 The overall satisfaction of NPD projects is positively affected by the

degree of reduction of technical uncertainty during the FFE. Hypothesis 9 The conforming with specifications is positively affected by the

degree of reduction of technical uncertainty during the FFE. Hypothesis 10 The unchanged project vision is positively affected by the degree of

reduction of technical uncertainty during the fuzzy front end.


Initial planning drafts the process from development to product launch [6]. Planning activities prior to development are considered to be one of the most important activities affecting project success [5][6][11][12][13][14]. We propose the following hypotheses (from H11 to H17). Hypothesis 11 The degree of reduction of market uncertainty during the FFE is

positively affected by the intensity of planning. Hypothesis 12 The degree of reduction of technical uncertainty during the FFE is

positively affected by the intensity of planning. Hypothesis 13 The efficiency of NPD projects is positively affected by the intensity

of planning. Hypothesis 14 The effectiveness of NPD projects is positively affected by the

intensity of planning. Hypothesis 15 The overall satisfaction of NPD projects is positively affected by the

intensity of planning. Hypothesis 16 The conforming with specifications is positively affected by the

intensity of planning. Hypothesis 17 The unchanged project vision is positively affected by the intensity

of planning.

Figure.6 Hypothesis 11-17

3.2.2 Impact of the project execution phase and Interrelationship between success factors Previous research described that in order to adapt to requirements, changes were necessary during the project execution phase, however, they should be kept to a minimum [14]. While relationships between ‘goal changes’ and ‘plan changes’ are considered to be one way relationships, the two project execution factors are interrelated [14].Thus, we propose the following hypotheses 18 to 24. Hypothesis 18 The unchanged project vision is positively affected by the

conforming with specifications. Hypothesis 19 The efficiency of NPD projects is positively affected by the

conforming with specifications during the project execution phase. Hypothesis 20 The effectiveness of NPD projects is positively affected by the

conforming with specifications during the project execution phase. Hypothesis 21 The overall satisfaction of NPD projects is positively affected by the

conforming with specifications during the project execution phase. Hypothesis 22 The efficiency of NPD projects is positively affected by the

unchanged project vision during the project execution phase. Hypothesis 23 The effectiveness of NPD projects is positively affected by the

unchanged project vision during the project execution phase. Hypothesis 24 The overall satisfaction of NPD projects is positively affected by the

unchanged project vision during the project execution phase. Project efficiency will result in effectiveness and overall satisfaction respectively. Moreover, Effectiveness results in high overall satisfaction [5] [6] [15]. Hypothesis 25 The effectiveness of NPD projects is positively affected by the

efficiency of NPD projects. Hypothesis 26 The overall satisfaction of NPD projects is positively affected by the

efficiency of NPD projects. Hypothesis 27 The overall satisfaction of NPD projects is positively affected by the


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effectiveness of NPD projects.


3.2.3 Impact of contextual factors on FFE factors The unpredictability of time paths associated with the degree of newness makes it difficult to reduce uncertainties and to develop an effective initial plan during the FFE [5] [6] [10]. Thus we propose following hypotheses 28 to 38. Hypothesis 28 The degree of organizational newness is positively affected by the

degree of market newness. Hypothesis 29 The degree of organizational newness is positively affected by the

degree of technical newness. Hypothesis 30 The higher degree of technical newness makes it more difficult to

reduce the market uncertainty during the fuzzy front end. Hypothesis 31 The higher degree of market newness makes it more difficult to

reduce the market uncertainty during the fuzzy front end. Hypothesis 32 The higher degree of organizational newness makes it more difficult

to reduce the market uncertainty during the fuzzy front end. Hypothesis 33 The higher degree of technical newness makes it more difficult to

reduce the technical uncertainty during the fuzzy front end. Hypothesis 34 The higher degree of market newness makes it more difficult to

reduce the technical uncertainty during the fuzzy front end. Hypothesis 35 The higher degree of organizational newness makes it more difficult

to reduce the technical uncertainty during the fuzzy front end.


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effectiveness of NPD projects.


3.2.3 Impact of contextual factors on FFE factors The unpredictability of time paths associated with the degree of newness makes it difficult to reduce uncertainties and to develop an effective initial plan during the FFE [5] [6] [10]. Thus we propose following hypotheses 28 to 38. Hypothesis 28 The degree of organizational newness is positively affected by the

degree of market newness. Hypothesis 29 The degree of organizational newness is positively affected by the

degree of technical newness. Hypothesis 30 The higher degree of technical newness makes it more difficult to

reduce the market uncertainty during the fuzzy front end. Hypothesis 31 The higher degree of market newness makes it more difficult to

reduce the market uncertainty during the fuzzy front end. Hypothesis 32 The higher degree of organizational newness makes it more difficult

to reduce the market uncertainty during the fuzzy front end. Hypothesis 33 The higher degree of technical newness makes it more difficult to

reduce the technical uncertainty during the fuzzy front end. Hypothesis 34 The higher degree of market newness makes it more difficult to

reduce the technical uncertainty during the fuzzy front end. Hypothesis 35 The higher degree of organizational newness makes it more difficult

to reduce the technical uncertainty during the fuzzy front end.


3.2.4 Impact of contextual factors on project execution phase Based on the literature we propose the higher the degree of newness of a product the more changes in the project execution phase [6]. Thus, we propose hypotheses 39 to 44. Hypothesis 36 The higher degree of technical newness makes the conforming with

specifications more difficult during the project execution phase. Hypothesis 37 The higher degree of market newness makes the conforming with

specifications more difficult during the project execution phase. Hypothesis 38 The higher degree of organizational newness makes the conforming

with specifications more difficult during the project execution phase. Hypothesis 39 The higher degree of technical newness makes the unchanged project

vision more difficult during the project execution phase. Hypothesis 40 The higher degree of market newness makes the unchanged project

vision more difficult during the project execution phase. Hypothesis 41 The higher degree of organizational newness makes the unchanged

project vision more difficult during the project execution phase.


3.2.5 Impact of contextual factors on success We propose, the higher the degree of newness of a product, greater the possibility of project failure. Hypothesis 42 The higher degree of technical newness leads to lower efficiency of


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the NPD projects. Hypothesis 43 The higher degree of market newness leads to lower efficiency of the

NPD projects. Hypothesis 44 The higher degree of organizational newness leads to lower

efficiency of the NPD projects. Hypothesis 45 The higher degree of technical newness leads to lower effectiveness

of the NPD projects. Hypothesis 46 The higher degree of market newness leads to lower effectiveness of

the NPD projects. Hypothesis 47 The higher degree of organizational newness leads to lower

effectiveness of the NPD projects. Hypothesis 48 The higher degree of technical newness leads to lower overall

satisfaction of the NPD projects. Hypothesis 49 The higher degree of market newness leads to overall satisfaction of

the NPD projects. Hypothesis 50 The higher degree of organizational newness leads to overall

satisfaction of the NPD projects.


4. Research method

4.1 DATA collection procedure In this study, we have considered the data from Japanese manufacturing firms collected in 2014 and 2015(326 usable samples out of 332 respondents with a response rate of 15.6%). From the usable samples, there were 245 B2B projects and 81 B2C projects. We used 7-point Likert-type scales ranging from 1=strongly disagree to 7=objectives exceeded.


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the NPD projects. Hypothesis 43 The higher degree of market newness leads to lower efficiency of the

NPD projects. Hypothesis 44 The higher degree of organizational newness leads to lower

efficiency of the NPD projects. Hypothesis 45 The higher degree of technical newness leads to lower effectiveness

of the NPD projects. Hypothesis 46 The higher degree of market newness leads to lower effectiveness of

the NPD projects. Hypothesis 47 The higher degree of organizational newness leads to lower

effectiveness of the NPD projects. Hypothesis 48 The higher degree of technical newness leads to lower overall

satisfaction of the NPD projects. Hypothesis 49 The higher degree of market newness leads to overall satisfaction of

the NPD projects. Hypothesis 50 The higher degree of organizational newness leads to overall

satisfaction of the NPD projects.


4. Research method

4.1 DATA collection procedure In this study, we have considered the data from Japanese manufacturing firms collected in 2014 and 2015(326 usable samples out of 332 respondents with a response rate of 15.6%). From the usable samples, there were 245 B2B projects and 81 B2C projects. We used 7-point Likert-type scales ranging from 1=strongly disagree to 7=objectives exceeded.

4.2 Company and project characteristics In this section, descriptive statistics about B2B and B2C NPD projects were reported. The B2B firms participating in our study had between ranging from 600 to 131,370 employees and annual sales ranging from 12 to 60,000million yen. As for B2C firms, they had between 12 to 60,000 employees and annual sales ranging from 80 to 131,370 million yen. The histogram of number of employees in the considered project is shown in Figure .6 and 7. As for the annual sales, they are shown in Figure.8 and 9.

Figure.11 Annual Sales volume

Figure.12 Number of employees

Interviewees were asked to consider the development of the last product brought to market. This definition includes the modification of existing products. However, as shown in Figure.10 and 11, most of the products studied here are innovative or highly innovative. According to the categories of Booz,Allen,Hamilton (1982,p.9)[12], as for B2B projects, 34% of the products were new to the world, 32 % extend or new product lines, and 18% product modification. As for B2C projects, 27% of the products were new to the world, 35 % extend or new product lines, and 12% product modification.


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Figure.13 Degree of newness of the new product concept of B2B

Figure.14 Degree of newness of the new product concept of B2C

4.3 Analysis method Considering the data characteristics and model characteristics, we employed the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach (Smart PLS 2.0 statistical software package). In contrast to Covariance-Based procedures, this approach allows for the estimation of relationship between latent variables for small sample size [18]. Smart PLS does not account for any distribution, thus bootstrapping resampling technique was used to get t-value [19].

5. Model testing and results Measurement validation was checked. A common measure to establish convergent validity on the construct level－average variance extracted (AVE), R square and communality, internal consistency reliability－ Cronbach’s alpha and composite

7%

19%

12%

35%

27%

8% 8%

18%

32%

34%


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Figure.13 Degree of newness of the new product concept of B2B

Figure.14 Degree of newness of the new product concept of B2C

4.3 Analysis method Considering the data characteristics and model characteristics, we employed the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach (Smart PLS 2.0 statistical software package). In contrast to Covariance-Based procedures, this approach allows for the estimation of relationship between latent variables for small sample size [18]. Smart PLS does not account for any distribution, thus bootstrapping resampling technique was used to get t-value [19].

5. Model testing and results Measurement validation was checked. A common measure to establish convergent validity on the construct level－average variance extracted (AVE), R square and communality, internal consistency reliability－ Cronbach’s alpha and composite

7%

19%

12%

35%

27%

8% 8%

18%

32%

34%

reliability. The threshold requirements were met by almost each of the constructs [20]. The results of these calculations are shown in Table 1 and Table 2. Table.1 Measurement assessment B2B (Calculation with smart pls)

Table.2 Measurement assessment B2C (Calculation with smart pls)

6 SEM analysis result

AVE>0.5

CompositeReliability>

0.6

R Square>0.2

CronbachsAlpha>0.6

Communality>0.5

Reduction of marketuncertainty

0.6178 0.9064 0.3138 0.8759 0.6178

Reduction of technicaluncertainty

0.7062 0.923 0.2491 0.8959 0.7062

Intensity of planning 0.5235 0.8458 0 0.775 0.5235Conforming withspecifications

0.5026 0.7994 0.1436 0.6676 0.5026

Unchanged project vision 0.5002 0.7499 0.4504 0.509 0.5002

Efficiency 0.6057 0.8213 0.324 0.6836 0.6057Effectiveness 0.7223 0.9285 0.3472 0.9035 0.7223Overall satisfaction 0.7772 0.8746 0.5339 0.7138 0.7772The degree of technicalnewness

0.605 0.8594 0 0.7821 0.605

The degree of marketnewness

0.6815 0.8632 0 0.7638 0.6815

The degree oforganizational newness

0.6171 0.8651 0.4342 0.7954 0.6171

AVE>0.5

CompositeReliability>

0.6

R　Square>0.2

CronbachsAlpha>0.6

Communality>0.5

Reduction of marketuncertainty

0.5496 0.8258 0.2334 0.7299 0.5496

Reduction of technicaluncertainty

0.7206 0.9275 0.2895 0.9011 0.7206

Intensity of planning 0.5482 0.8253 0 0.7207 0.5482Conforming withspecifications

0.5175 0.8094 0.1468 0.6869 0.5175

Unchanged project vision 0.6234 0.8317 0.3208 0.7255 0.6234

Efficiency 0.7083 0.878 0.5052 0.7857 0.7083Effectiveness 0.6006 0.8822 0.2534 0.8337 0.6006Overall satisfaction 0.8068 0.893 0.5436 0.7606 0.8068The degree of technicalnewness

0.522 0.8001 0 0.7665 0.522

The degree of marketnewness

0.675 0.8606 0 0.7549 0.675

The degree oforganizational newness

0.5676 0.8394 0.3056 0.7442 0.5676


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Table3 and 4 summarizes the results by showing t-value, p-value. Moreover, path coefficients with the corresponding signs (positive or negative) are shown in the 3rd and the 6th corresponding columns.

Table.3 Measurement assessment B2B (Calculation with Smart PLS)

OriginalSample

t value p valueOriginalSample

t value p value

H1 Reduction of market uncertainty > Efficiency 0.0308 0.3226 0.748 0.0521 0.5371 0.593H2 Reduction of market uncertainty > Effectiveness 0.3129 4.3828 0.000 0.3262 4.1205 0.000H3 Reduction of market uncertainty > Overall satisfaction -0.0291 0.4462 0.657 0.095 1.2652 0.209H4 Reduction of market uncertainty > Comforming with specifications 0.0933 1.1275 0.263 0.0933 1.1275 0.263H5 Reduction of market uncertainty > Unchaged project vision -0.0011 0.0144 0.989 0.0377 0.4561 0.650H6 Reduction of technical uncertainty > Efficiency 0.0481 0.5359 0.594 0.1006 1.1081 0.271H7 Reduction of technical uncertainty > Effectiveness -0.0162 0.2404 0.811 0.0271 0.3602 0.720H8 Reduction of technical uncertainty > Overall satisfaction 0.2016 3.1474 0.002 0.2686 4.0081 0.000H9 Reduction of technical uncertainty > Comforming with specifications 0.1155 1.4466 0.152 0.1155 1.4466 0.152H10 Reduction of technical uncertainty > Unchaged project vision 0.1576 2.0025 0.049 0.2056 2.4758 0.015H11 Intensity of planning > Reduction of market uncertainty 0.4823 9.1378 0.000 0.4823 9.1378 0.000H12 Intensity of planning > Reduction of technical uncertainty 0.4727 8.876 0.000 0.4727 8.876 0.000H13 Intensity of planning > Efficiency 0.284 4.2405 0.000 0.4366 7.9856 0.000H14 Intensity of planning > Effectiveness 0.0274 0.3757 0.708 0.3297 5.6703 0.000H15 Intensity of planning > Overall satisfaction -0.0165 0.2614 0.794 0.3615 6.0195 0.000H16 Intensity of planning > Comforming with specifications 0.1328 1.549 0.125 0.2325 3.0585 0.003H17 Intensity of planning > Unchaged project vision 0.2991 4.6525 0.000 0.4696 8.2487 0.000H18 Comforming with specifications > Maintaining the project goals 0.4153 7.4955 0.000 0.4153 7.4955 0.000H19 Comforming with specifications > Efficiency 0.1607 2.3053 0.024 0.2294 4.024 0.000H20 Comforming with specifications > Effectiveness -0.0567 0.8786 0.382 0.0513 0.8404 0.403H21 Comforming with specifications > Overall satisfaction -0.0186 0.3203 0.750 0.1271 2.158 0.034H22 Unchaged project vision > Efficiency 0.1654 2.1304 0.036 0.1654 2.1304 0.036H23 Unchaged project vision > Effectiveness 0.1041 1.4829 0.142 0.1508 2.0936 0.039H24 Unchaged project vision > Overall satisfaction 0.16 2.527 0.013 0.2539 3.6165 0.001H25 Efficiency > Effectiveness 0.2824 4.1375 0.000 0.2824 4.1375 0.000H26 Efficiency > Overall satisfaction 0.2731 4.0237 0.000 0.3645 5.408 0.000H27 Effectiveness > Overall satisfaction 0.3238 5.2902 0.000 0.3238 5.2902 0.000H28 The degree of technical newness > The degree of organizational newness 0.4203 6.8946 0.000 0.4203 6.8946 0.000H29 The degree of market newness > The degree of organizational newness 0.3527 6.2972 0.000 0.3527 6.2972 0.000H30 The degree of technical newness > Reduction of market uncertainty 0.1155 1.5066 0.136 0.1132 1.7657 0.081H31 The degree of market newness > Reduction of market uncertainty -0.2435 3.5442 0.001 -0.2454 3.9055 0.000H32 The degree of organizational newness > Reduction of market uncertainty -0.0053 0.0624 0.950 -0.0053 0.0624 0.950H33 The degree of technical newness > Reduction of technical uncertainty 0.0795 0.9854 0.327 0.0173 0.243 0.809H34 The degree of market newness > Reduction of technical uncertainty 0.0184 0.2585 0.797 -0.0338 0.4888 0.626H35 The degree of organizational newness > Reduction of technical uncertainty -0.1479 1.7526 0.084 -0.1479 1.7526 0.084H36 The degree of technical newness > Comforming with specifications -0.0835 0.9355 0.352 -0.1813 2.2083 0.030H37 The degree of market newness > Comforming with specifications 0.1742 2.1028 0.039 0.0548 0.7186 0.474H38 The degree of organizational newness > Comforming with specifications -0.2626 3.4152 0.001 -0.2802 3.6316 0.000H39 The degree of technical newness > Unchaged project vision 0.1617 2.471 0.016 0.0958 1.6982 0.093H40 The degree of market newness > Unchaged project vision -0.116 1.736 0.086 -0.0926 1.6958 0.094H41 The degree of organizational newness > Unchaged project vision 0.0163 0.2313 0.818 -0.1234 1.5882 0.116H42 The degree of technical newness > Efficiency 0.0061 0.0796 0.937 -0.0306 0.4168 0.678H43 The degree of market newness > Efficiency -0.1308 1.9093 0.060 -0.1698 2.5743 0.012H44 The degree of organizational newness > Efficiency -0.0661 0.7403 0.461 -0.1388 1.5307 0.130H45 The degree of technical newness > Effectiveness 0.1106 1.4746 0.144 0.1564 2.0623 0.042H46 The degree of market newness > Effectiveness -0.1332 1.8105 0.074 -0.2709 3.8949 0.000H47 The degree of organizational newness > Effectiveness -0.0022 0.0313 0.975 -0.0376 0.476 0.635H48 The degree of technical newness > Overall satisfaction 0.1704 2.6891 0.009 0.1646 2.1809 0.032H49 The degree of market newness > Overall satisfaction -0.008 0.1397 0.889 -0.2138 3.1086 0.003H50 The degree of organizational newness > Overall satisfaction -0.1593 2.103 0.039 -0.2536 2.8681 0.005

Hypothesis

Direct effect Total effectB2B B2B


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Table3 and 4 summarizes the results by showing t-value, p-value. Moreover, path coefficients with the corresponding signs (positive or negative) are shown in the 3rd and the 6th corresponding columns.

Table.3 Measurement assessment B2B (Calculation with Smart PLS)

OriginalSample


t value p value

H1 Reduction of market uncertainty > Efficiency 0.0308 0.3226 0.748 0.0521 0.5371 0.593H2 Reduction of market uncertainty > Effectiveness 0.3129 4.3828 0.000 0.3262 4.1205 0.000H3 Reduction of market uncertainty > Overall satisfaction -0.0291 0.4462 0.657 0.095 1.2652 0.209H4 Reduction of market uncertainty > Comforming with specifications 0.0933 1.1275 0.263 0.0933 1.1275 0.263H5 Reduction of market uncertainty > Unchaged project vision -0.0011 0.0144 0.989 0.0377 0.4561 0.650H6 Reduction of technical uncertainty > Efficiency 0.0481 0.5359 0.594 0.1006 1.1081 0.271H7 Reduction of technical uncertainty > Effectiveness -0.0162 0.2404 0.811 0.0271 0.3602 0.720H8 Reduction of technical uncertainty > Overall satisfaction 0.2016 3.1474 0.002 0.2686 4.0081 0.000H9 Reduction of technical uncertainty > Comforming with specifications 0.1155 1.4466 0.152 0.1155 1.4466 0.152H10 Reduction of technical uncertainty > Unchaged project vision 0.1576 2.0025 0.049 0.2056 2.4758 0.015H11 Intensity of planning > Reduction of market uncertainty 0.4823 9.1378 0.000 0.4823 9.1378 0.000H12 Intensity of planning > Reduction of technical uncertainty 0.4727 8.876 0.000 0.4727 8.876 0.000H13 Intensity of planning > Efficiency 0.284 4.2405 0.000 0.4366 7.9856 0.000H14 Intensity of planning > Effectiveness 0.0274 0.3757 0.708 0.3297 5.6703 0.000H15 Intensity of planning > Overall satisfaction -0.0165 0.2614 0.794 0.3615 6.0195 0.000H16 Intensity of planning > Comforming with specifications 0.1328 1.549 0.125 0.2325 3.0585 0.003H17 Intensity of planning > Unchaged project vision 0.2991 4.6525 0.000 0.4696 8.2487 0.000H18 Comforming with specifications > Maintaining the project goals 0.4153 7.4955 0.000 0.4153 7.4955 0.000H19 Comforming with specifications > Efficiency 0.1607 2.3053 0.024 0.2294 4.024 0.000H20 Comforming with specifications > Effectiveness -0.0567 0.8786 0.382 0.0513 0.8404 0.403H21 Comforming with specifications > Overall satisfaction -0.0186 0.3203 0.750 0.1271 2.158 0.034H22 Unchaged project vision > Efficiency 0.1654 2.1304 0.036 0.1654 2.1304 0.036H23 Unchaged project vision > Effectiveness 0.1041 1.4829 0.142 0.1508 2.0936 0.039H24 Unchaged project vision > Overall satisfaction 0.16 2.527 0.013 0.2539 3.6165 0.001H25 Efficiency > Effectiveness 0.2824 4.1375 0.000 0.2824 4.1375 0.000H26 Efficiency > Overall satisfaction 0.2731 4.0237 0.000 0.3645 5.408 0.000H27 Effectiveness > Overall satisfaction 0.3238 5.2902 0.000 0.3238 5.2902 0.000H28 The degree of technical newness > The degree of organizational newness 0.4203 6.8946 0.000 0.4203 6.8946 0.000H29 The degree of market newness > The degree of organizational newness 0.3527 6.2972 0.000 0.3527 6.2972 0.000H30 The degree of technical newness > Reduction of market uncertainty 0.1155 1.5066 0.136 0.1132 1.7657 0.081H31 The degree of market newness > Reduction of market uncertainty -0.2435 3.5442 0.001 -0.2454 3.9055 0.000H32 The degree of organizational newness > Reduction of market uncertainty -0.0053 0.0624 0.950 -0.0053 0.0624 0.950H33 The degree of technical newness > Reduction of technical uncertainty 0.0795 0.9854 0.327 0.0173 0.243 0.809H34 The degree of market newness > Reduction of technical uncertainty 0.0184 0.2585 0.797 -0.0338 0.4888 0.626H35 The degree of organizational newness > Reduction of technical uncertainty -0.1479 1.7526 0.084 -0.1479 1.7526 0.084H36 The degree of technical newness > Comforming with specifications -0.0835 0.9355 0.352 -0.1813 2.2083 0.030H37 The degree of market newness > Comforming with specifications 0.1742 2.1028 0.039 0.0548 0.7186 0.474H38 The degree of organizational newness > Comforming with specifications -0.2626 3.4152 0.001 -0.2802 3.6316 0.000H39 The degree of technical newness > Unchaged project vision 0.1617 2.471 0.016 0.0958 1.6982 0.093H40 The degree of market newness > Unchaged project vision -0.116 1.736 0.086 -0.0926 1.6958 0.094H41 The degree of organizational newness > Unchaged project vision 0.0163 0.2313 0.818 -0.1234 1.5882 0.116H42 The degree of technical newness > Efficiency 0.0061 0.0796 0.937 -0.0306 0.4168 0.678H43 The degree of market newness > Efficiency -0.1308 1.9093 0.060 -0.1698 2.5743 0.012H44 The degree of organizational newness > Efficiency -0.0661 0.7403 0.461 -0.1388 1.5307 0.130H45 The degree of technical newness > Effectiveness 0.1106 1.4746 0.144 0.1564 2.0623 0.042H46 The degree of market newness > Effectiveness -0.1332 1.8105 0.074 -0.2709 3.8949 0.000H47 The degree of organizational newness > Effectiveness -0.0022 0.0313 0.975 -0.0376 0.476 0.635H48 The degree of technical newness > Overall satisfaction 0.1704 2.6891 0.009 0.1646 2.1809 0.032H49 The degree of market newness > Overall satisfaction -0.008 0.1397 0.889 -0.2138 3.1086 0.003H50 The degree of organizational newness > Overall satisfaction -0.1593 2.103 0.039 -0.2536 2.8681 0.005

Hypothesis

Direct effect Total effectB2B B2B

Table.4 Measurement assessment of B2C (Calculation with Smart PLS)

7. Result and discussion In this section the analysis result and discussion follow under sub group considering projects characteristics. B2B and B2C are different in terms of complexity of product, demand, size of customer base, size of purchase amount per customer and relationship between supply and demand [21]. The main differences between B2B and B2C customer are the greater degree of interdependence between buyers and sellers in former [23].We look at direct and total (direct + indirect) effects. To test hypothesis, we

OriginalSample


t value p value

H1 Reduction of market uncertainty > Efficiency -0.16 1.2699 0.208 -0.1495 1.0173 0.312H2 Reduction of market uncertainty > Effectiveness 0.3227 2.4331 0.017 0.291 2.1604 0.034H3 Reduction of market uncertainty > Overall satisfaction 0.1605 1.2869 0.202 0.199 1.5773 0.119H4 Reduction of market uncertainty > Comforming with specifications -0.016 0.1025 0.919 -0.016 0.1025 0.919H5 Reduction of market uncertainty > Unchaged project vision 0.1656 1.5083 0.135 0.1594 1.3052 0.196H6 Reduction of technical uncertainty > Efficiency -0.0033 0.0285 0.977 -0.0049 0.0396 0.969H7 Reduction of technical uncertainty > Effectiveness 0.0643 0.4843 0.629 0.0631 0.4752 0.636H8 Reduction of technical uncertainty > Overall satisfaction 0.0438 0.3977 0.692 0.0525 0.4043 0.687H9 Reduction of technical uncertainty > Comforming with specifications 0.0063 0.0492 0.961 0.0063 0.0492 0.961H10 Reduction of technical uncertainty > Unchaged project vision -0.0388 0.2716 0.787 -0.0364 0.2615 0.794H11 Intensity of planning > Reduction of market uncertainty 0.3659 3.0681 0.003 0.3659 3.0681 0.003H12 Intensity of planning > Reduction of technical uncertainty 0.4541 4.2005 0.000 0.4541 4.2005 0.000H13 Intensity of planning > Efficiency 0.5787 5.3995 0.000 0.5911 7.1691 0.000H14 Intensity of planning > Effectiveness -0.0334 0.1923 0.848 0.2724 2.188 0.032H15 Intensity of planning > Overall satisfaction 0.1519 1.0702 0.288 0.5415 5.5193 0.000H16 Intensity of planning > Comforming with specifications 0.1166 0.7425 0.460 0.1136 0.8798 0.382H17 Intensity of planning > Unchaged project vision 0.3024 2.0376 0.045 0.3889 3.5948 0.001H18 Comforming with specifications > Maintaining the project goals 0.3834 3.2265 0.002 0.3834 3.2265 0.002H19 Comforming with specifications > Efficiency 0.3065 2.3556 0.021 0.3437 2.9107 0.005H20 Comforming with specifications > Effectiveness 0.132 0.8569 0.394 0.2194 1.5296 0.130H21 Comforming with specifications > Overall satisfaction 0.0285 0.2502 0.803 0.2416 2.0875 0.040H22 Unchaged project vision > Efficiency 0.097 0.9456 0.347 0.097 0.9456 0.347H23 Unchaged project vision > Effectiveness 0.026 0.2082 0.836 0.0478 0.3778 0.707H24 Unchaged project vision > Overall satisfaction 0.1265 1.168 0.246 0.1697 1.461 0.148H25 Efficiency > Effectiveness 0.2256 1.3149 0.192 0.2256 1.3149 0.192H26 Efficiency > Overall satisfaction 0.3297 1.904 0.061 0.3824 2.7466 0.007H27 Effectiveness > Overall satisfaction 0.2338 1.5359 0.129 0.2338 1.5359 0.129H28 The degree of technical newness > The degree of organizational newness 0.3476 2.5095 0.014 0.3476 2.5095 0.014H29 The degree of market newness > The degree of organizational newness 0.342 2.6146 0.011 0.342 2.6146 0.011H30 The degree of technical newness > Reduction of market uncertainty 0.1109 0.5186 0.605 0.1128 0.5566 0.579H31 The degree of market newness > Reduction of market uncertainty -0.2758 2.316 0.023 -0.2739 2.2678 0.026H32 The degree of organizational newness > Reduction of market uncertainty 0.0056 0.0417 0.967 0.0056 0.0417 0.967H33 The degree of technical newness > Reduction of technical uncertainty -0.0176 0.1245 0.901 0.1352 0.9182 0.361H34 The degree of market newness > Reduction of technical uncertainty 0.0915 0.7588 0.450 0.0855 0.7695 0.444H35 The degree of organizational newness > Reduction of technical uncertainty 0.1413 0.8897 0.376 -0.0176 0.1245 0.901H36 The degree of technical newness > Comforming with specifications -0.2247 1.3623 0.177 -0.3209 1.7394 0.086H37 The degree of market newness > Comforming with specifications 0.1038 0.6009 0.550 0.0151 0.1022 0.919H38 The degree of organizational newness > Comforming with specifications -0.2738 1.3723 0.174 -0.274 1.3905 0.168H39 The degree of technical newness > Unchaged project vision 0.1274 0.6953 0.489 0.0212 0.1106 0.912H40 The degree of market newness > Unchaged project vision -0.0694 0.4811 0.632 -0.1088 0.8149 0.418H41 The degree of organizational newness > Unchaged project vision 0.0099 0.0693 0.945 -0.0935 0.6042 0.547H42 The degree of technical newness > Efficiency 0.0762 0.6065 0.546 -0.0325 0.281 0.779H43 The degree of market newness > Efficiency -0.1665 1.5065 0.136 -0.1228 1.2362 0.220H44 The degree of organizational newness > Efficiency 0.0176 0.1407 0.888 -0.0763 0.5875 0.559H45 The degree of technical newness > Effectiveness -0.0416 0.2758 0.783 0.0456 0.3206 0.749H46 The degree of market newness > Effectiveness -0.1341 0.8542 0.396 -0.1558 1.0272 0.307H47 The degree of organizational newness > Effectiveness 0.2625 1.7588 0.082 0.2074 1.421 0.159H48 The degree of technical newness > Overall satisfaction -0.0209 0.1595 0.874 0.0368 0.3003 0.765H49 The degree of market newness > Overall satisfaction 0.0266 0.2694 0.788 -0.0643 0.5632 0.575H50 The degree of organizational newness > Overall satisfaction 0.1157 1.1389 0.258 0.1195 0.9717 0.334

Hypothesis

Direct effect Total effectB2C B2C


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assessed path coefficients and their t-values from quantitative survey. 7.1 FFE factors (hypothesis 1-17) 7.1.1 Results of B2B projects As expected, the reduction of market uncertainty has positive direct and total effect on the effectiveness (hypothesis 2). Comparing previous research, once again the importance of the reduction of market uncertainty is reinforced [5] [6]. Contrary to expectation, the reduction of market uncertainty doesn’t have an impact on the project execution phase (hypotheses 4 and 5). The reduction of market uncertainty plays a minor role in terms of reducing the changes in the project execution phase, because of close cooperation with their customer. Consequently, reduction of market uncertainty such as, market investigation, understanding customer and competitor in FFE has little effect on the project execution phase. An expected positive direct and total impact of reduction of technical uncertainty on overall satisfaction can be noticed (hypothesis 8). This results support the previous study [6]. Same as previous research, the reduction of technical uncertainty has direct and total effect on the unchanged project vision (Hypothesis 10). Moreover, we noticed the increase of significance level in total effect compared to the direct effect. The indirect effect of the reduction of technical uncertainty contributes to the unchanged project vision. As expected, the intensity of planning has a positive effect on the reduction of market uncertainty and technical uncertainty (hypothesis 11 and 12). The assumption that intensive initial planning reduces market and technical uncertainties in FFE is reinforced. The intensity of planning has positive direct and total impact on the efficiency (hypothesis 13). This result supports the assumption that intense planning increase the efficiency [5][6][9][11]. Moreover, the intensity of planning has a positive total effect on effectiveness and overall satisfaction despite the fact that the direct effect is not statistically significant (hypotheses 14 and 15). As for the result of hypothesis 14 and 15, the intensity of planning has an indirect effect on effectiveness and overall satisfaction. A possible explanation could be uncertainties were reduced by initial planning and intense initial plan leads to efficient project execution. Thus, as a whole process, the intensity of planning has a positive effect on effectiveness and overall satisfaction.


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assessed path coefficients and their t-values from quantitative survey. 7.1 FFE factors (hypothesis 1-17) 7.1.1 Results of B2B projects As expected, the reduction of market uncertainty has positive direct and total effect on the effectiveness (hypothesis 2). Comparing previous research, once again the importance of the reduction of market uncertainty is reinforced [5] [6]. Contrary to expectation, the reduction of market uncertainty doesn’t have an impact on the project execution phase (hypotheses 4 and 5). The reduction of market uncertainty plays a minor role in terms of reducing the changes in the project execution phase, because of close cooperation with their customer. Consequently, reduction of market uncertainty such as, market investigation, understanding customer and competitor in FFE has little effect on the project execution phase. An expected positive direct and total impact of reduction of technical uncertainty on overall satisfaction can be noticed (hypothesis 8). This results support the previous study [6]. Same as previous research, the reduction of technical uncertainty has direct and total effect on the unchanged project vision (Hypothesis 10). Moreover, we noticed the increase of significance level in total effect compared to the direct effect. The indirect effect of the reduction of technical uncertainty contributes to the unchanged project vision. As expected, the intensity of planning has a positive effect on the reduction of market uncertainty and technical uncertainty (hypothesis 11 and 12). The assumption that intensive initial planning reduces market and technical uncertainties in FFE is reinforced. The intensity of planning has positive direct and total impact on the efficiency (hypothesis 13). This result supports the assumption that intense planning increase the efficiency [5][6][9][11]. Moreover, the intensity of planning has a positive total effect on effectiveness and overall satisfaction despite the fact that the direct effect is not statistically significant (hypotheses 14 and 15). As for the result of hypothesis 14 and 15, the intensity of planning has an indirect effect on effectiveness and overall satisfaction. A possible explanation could be uncertainties were reduced by initial planning and intense initial plan leads to efficient project execution. Thus, as a whole process, the intensity of planning has a positive effect on effectiveness and overall satisfaction.

The intensity of planning has a total positive effect on the conforming with specifications despite the fact that the direct effect is not statistically significant (hypothesis 16). This result suggests that the indirect impact of intensity of planning has a positive effect on the conforming with specifications. The activities of the reduction of market and technical uncertainties have a positive effect on the conforming with specifications supported by the initial planning. Expected positive direct and total effect of the intensity of planning on the unchanged project vision can be noticed (hypothesis 17). Intensity of planning leads to no changes in concept, objective, design (unchanged project vision). 7.1.2 Results of B2C projects As expected, positive direct and total effect of reduction of market uncertainty on the effectiveness can be noticed (hypothesis 2). Comparing the direct and the total effect, direct effect has a stronger effect than the total effect. Thus, reduction of market uncertainty has a negative indirect impact on effectiveness. A possible explanation could be the relationship between efficiency and effectiveness in B2C. We observed a negative effect of the reduction of market uncertainty on efficiency but this is not statistically significant. Based on this result, the effort to predict and follow the complex consumer requirements results in low efficiency. Consequently, the reduction of market uncertainty has a negative indirect effect on effectiveness. Thus, we can describe that the relationship between efficiency and effectiveness are a trade off in B2C projects. Contrary to expectation, the reduction of technical uncertainty doesn’t have an effect on the success (hypotheses 6, 7 and 8). The reason for these results is described as follow. Technical uncertainty is not so high because technical requirements are engaged in B2B firms which cooperate with B2C firms. We don’t obtain a statistical relationship between reduction of market and technical uncertainties and project execution phase (hypothesis 4, 5, 9, and 10). Considering instability of the consumer market, these results are concluded as follow. The consumer market is more likely to be affected by external circumstances. Requirements of projects were changed according to environmental changes. Thus, changes in the project execution phase were unavoidable even though uncertainties were reduced in FFE. Only intensity of planning (breaking down the project into work packages and a detailed cost planning) led to the no changes in the project objective, product concept and design (unchanged project vision). FFE factors are interrelated. As expected, the intensity of planning has a positive effect on the reduction of market uncertainty and technical uncertainty (hypothesis 11 and 12).


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The assumption that intensive initial planning reduces market and technical uncertainties in FFE is reinforced. We obtained a positive relationship for direct and total effect of the intensity of planning on efficiency (hypothesis13) and total effect on the effectiveness (hypothesis 14) and overall satisfaction (hypothesis 15). As mentioned above, intensity of planning has an indirect impact on effectiveness and overall satisfaction. As expected, the intensity of planning has positive direct and total effect on unchanged project visions (hypothesis 17). Thus, intensive initial plans reduce project revision in project execution phase.

7.2 Interrelationship between project execution phase factors. (Hypothesis 18) With regard to hypothesis 18, it is supported in both B2B and B2C projects. As expected, project execution factors are interrelated. An expected positive effect of conforming with specifications on the unchanged project visions can be noticed (hypothesis18). This result is different from the result of a previous study which analyzed all products (B2B+B2C) in the 1980s in Japan [9]. A possible explanation could be that the causal relationship between factors in the project execution phase is strengthened because in the 2010s, frequency of emergence and degree of unavoidable changes were increased by dynamic external circumstances. 7.3 Impact of the project execution phase on the success (Hypothesis 19, 20, 21, 22, 23 and 24) 7.3.1 Result of B2B projects As expected, conforming with specifications has a positive effect on the efficiency (hypothesis 19). Furthermore, we noticed the total effect has stronger coefficient and significance compared to direct effect. Thus, the indirect impact of the conforming with specifications contributes to the efficiency. The effect of conforming with specifications on the effectiveness is not significant. This could be the style of production. Most B2B projects produce the product by commissioning. Thus, there is a small relationship between process and outcome (profit, sales, and so on). The total effect of conforming with the specification on the overall satisfaction is significant (hypothesis 21). Considering the path model, we conclude that conforming with specifications supports the efficiency which results in overall satisfaction.


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The assumption that intensive initial planning reduces market and technical uncertainties in FFE is reinforced. We obtained a positive relationship for direct and total effect of the intensity of planning on efficiency (hypothesis13) and total effect on the effectiveness (hypothesis 14) and overall satisfaction (hypothesis 15). As mentioned above, intensity of planning has an indirect impact on effectiveness and overall satisfaction. As expected, the intensity of planning has positive direct and total effect on unchanged project visions (hypothesis 17). Thus, intensive initial plans reduce project revision in project execution phase.

7.2 Interrelationship between project execution phase factors. (Hypothesis 18) With regard to hypothesis 18, it is supported in both B2B and B2C projects. As expected, project execution factors are interrelated. An expected positive effect of conforming with specifications on the unchanged project visions can be noticed (hypothesis18). This result is different from the result of a previous study which analyzed all products (B2B+B2C) in the 1980s in Japan [9]. A possible explanation could be that the causal relationship between factors in the project execution phase is strengthened because in the 2010s, frequency of emergence and degree of unavoidable changes were increased by dynamic external circumstances. 7.3 Impact of the project execution phase on the success (Hypothesis 19, 20, 21, 22, 23 and 24) 7.3.1 Result of B2B projects As expected, conforming with specifications has a positive effect on the efficiency (hypothesis 19). Furthermore, we noticed the total effect has stronger coefficient and significance compared to direct effect. Thus, the indirect impact of the conforming with specifications contributes to the efficiency. The effect of conforming with specifications on the effectiveness is not significant. This could be the style of production. Most B2B projects produce the product by commissioning. Thus, there is a small relationship between process and outcome (profit, sales, and so on). The total effect of conforming with the specification on the overall satisfaction is significant (hypothesis 21). Considering the path model, we conclude that conforming with specifications supports the efficiency which results in overall satisfaction.

As expected, results suggest that the unchanged project vision has positive direct and total effect on the efficiency and the overall satisfaction (hypothesis 22 and 24). Regarding the effectiveness, total effect of the unchanged project vision is significant (hypothesis 23). Therefore, unchanged project vision has a positive indirect effect on effectiveness. A possible explanation for the indirect effect of the project execution phase is efficiency. A high efficiency, which is supported by unchanged project vision has a positive effect on effectiveness. In summarize, concerning direct and indirect effects, we can describe that fewer changes in the project execution phase will result in success. 7.3.2 Result of B2C projects As expected, conforming with specifications has positive indirect and total effect on efficiency (hypothesis 19). We noticed the increase of total effect compared to direct effect. Thus indirect effect of conforming with the specification contributes to the efficiency. The total effect of conforming with specifications on overall satisfaction is significant, same as B2B (hypothesis 21). Contrary to expectation, the effect of conforming with the specifications on effectiveness is not significant. Moreover, the unchanged project vision doesn’t have an effect on three success factors (hypothesis 22, 23, 24). These could be the dynamic environmental changes. In an instable period, projects should be executed with flexibility which makes it possible to fit the market mobility. Consequently, maintaining the initial plan in the project execution phase doesn’t have an effect on the success. 7.4 Interrelationships between success factors (hypothesis 25-27) 7.4.1 Result of B2B projects The results suggest that the success factors are strongly interrelated. The efficiency has a positive effect on the effectiveness and overall satisfaction (hypothesis 25, 26). Moreover, the effectiveness has a positive effect on the overall satisfaction (hypothesis 27). 7.4.2 Result of B2C projects As expected, the positive total effect of the efficiency on overall satisfaction is significant (hypothesis 26). Contrary to expectation, hypotheses 25 and 27 are not supported. A possible explanation


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could be the culture of Japanese firms. Most Japanese firms evaluate the projects with efficiency (reaching planned milestone, absence of additional resource allocation and so on) rather than effectiveness. Moreover, in the 2010s needs of target customers of Japanese firms are not satisfied with efficiency because there are a lot of other companies in other countries which are efficient. Consequently, efficiency doesn’t lead to effectiveness which doesn’t lead to overall satisfaction. 7.5 Interrelationships between contextual factors (Hypothesis 28 and 29) The same statistical significance in B2B and B2C can be noticed. Results suggest that contextual factors are interrelated in both projects. The degree of technical and market newness have a positive effect on the degree of organizational newness (hypotheses 28 and 29). These results suggest that organization in Japanese firms is changed according to technology and market. 7.5 Effect of the contextual factor on the FFE factors (Hypothesis 30-35) The same statistical significance can be noticed in both B2B and B2C projects. As expected, the reduction of market uncertainty is negatively affected by the degree of market newness (Hypothesis 31). Contrary to expectation, there is no statistical significance of the effect of the degree of newness on the reduction of technical uncertainty (hypotheses 33, 34, and 35). In both B2B and B2C projects, the reduction of technical uncertainty is not affected by the degree of product newness. Regardless of the product newness, activities of the reduction of technical uncertainty are conducted because the way of treating the technology is stored in Japanese firms. 7.6 Effect of the contextual factor on the project execution phase (Hypothesis 36-41) 7.6.1 Result of B2B projects A negative total effect of the degree of technical newness on conforming with specifications can be noticed despite the fact that the direct effect is not statistically significant (hypothesis 36). A possible explanation could be that technical challenges lead to organizational restructure such as employment of different competencies, and changes of corporate strategy. Consequently, the projects stability lowered. As expected, the degree of organizational newness has negative direct and total effect on unchanged project vision (hypothesis 38). The organizational changes result in


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could be the culture of Japanese firms. Most Japanese firms evaluate the projects with efficiency (reaching planned milestone, absence of additional resource allocation and so on) rather than effectiveness. Moreover, in the 2010s needs of target customers of Japanese firms are not satisfied with efficiency because there are a lot of other companies in other countries which are efficient. Consequently, efficiency doesn’t lead to effectiveness which doesn’t lead to overall satisfaction. 7.5 Interrelationships between contextual factors (Hypothesis 28 and 29) The same statistical significance in B2B and B2C can be noticed. Results suggest that contextual factors are interrelated in both projects. The degree of technical and market newness have a positive effect on the degree of organizational newness (hypotheses 28 and 29). These results suggest that organization in Japanese firms is changed according to technology and market. 7.5 Effect of the contextual factor on the FFE factors (Hypothesis 30-35) The same statistical significance can be noticed in both B2B and B2C projects. As expected, the reduction of market uncertainty is negatively affected by the degree of market newness (Hypothesis 31). Contrary to expectation, there is no statistical significance of the effect of the degree of newness on the reduction of technical uncertainty (hypotheses 33, 34, and 35). In both B2B and B2C projects, the reduction of technical uncertainty is not affected by the degree of product newness. Regardless of the product newness, activities of the reduction of technical uncertainty are conducted because the way of treating the technology is stored in Japanese firms. 7.6 Effect of the contextual factor on the project execution phase (Hypothesis 36-41) 7.6.1 Result of B2B projects A negative total effect of the degree of technical newness on conforming with specifications can be noticed despite the fact that the direct effect is not statistically significant (hypothesis 36). A possible explanation could be that technical challenges lead to organizational restructure such as employment of different competencies, and changes of corporate strategy. Consequently, the projects stability lowered. As expected, the degree of organizational newness has negative direct and total effect on unchanged project vision (hypothesis 38). The organizational changes result in

instability of the project object because, there exists communication costs. Surprisingly, the degree of market newness has a direct positive effect on conforming with specifications, however, this is not the case for the total effect. The degree of organizational newness has negative direct and total effect on conforming with specifications. Contrary to expectation, the degree of technical newness has a positive effect on the unchanged project visions however, total effect is not significant. A possible explanation for positive direct effect of the degree of newness on the project execution phase is that more technically and market challenging projects lead to more changes to organization for adjusting to new domains, however its newness will result in reducing changes of objectives for instance because the project didn’t allow the competitors to follow. 7.6.2 Result of B2C projects Results suggest that there is no statistical relationship between the degree of newness and the project execution phase. Probable explanation could be instability of external environment. Dynamic changes of the consumer market play a major role in terms of changing the initial plan. Thus, there is a small causal relationship between the degree of newness and the project execution phase. 7.7 Effect of the contextual factor on the success (Hypothesis 42-50) 7.7.1 Result of B2B projects As expected, the degree of market newness has negative total effect on efficiency despite the fact that the direct effect is not statistically significant (hypothesis 43). In addition, the degree of market newness has a negative total effect on effectiveness and overall satisfaction in spite of the fact that direct effect is not statistically significant (hypothesis46 and 49). A possible explanation could be the indirect effect of the degree of technical and market newness. As mentioned above, highly product newness leads to organizational change. Consequently, the project difficulty is heightened. Thus, on the whole, the technical and market newness have a negative effect on effectiveness and efficiency. As expected, overall satisfaction is negatively affected by organizational newness in terms of direct and total effect (hypothesis 50). Substantial changes of organization increase the communication costs, which has a negative effect on overall satisfaction. The flowing results contradict the assumption. Contrary to expectation, the degree of technical newness has a positive total effect on the effectiveness (hypothesis45).


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Moreover, contrary to expectation, the degree of newness has positive direct and total effect on the overall satisfaction (hypothesis 48). This could be that B2B projects which are technology driven, refining a technology results in the success in the market and improving the worker’s satisfaction. 7.7.2 Result of B2C projects Contrary to expectation, results suggest that there is no statistical relationship between the degree of newness and the project success. Considering the results of the questionnaire, the average value of the product newness (technical, market, organizational newness) was low in B2C. We assume that, generally, B2C projects didn’t challenge to new domains. Thus, the degree of newness doesn’t have an effect on the project success.

8. Conclusion In this study, a comparative study was carried out to investigate the direct and indirect impacts of FFE activities and changes in project execution on NPD projects success of B2B and B2C projects in Japanese manufacturing firms. The following conclusions can be described from SEM analysis. Firstly, as for FFE activities, regardless of the product characteristics, the reduction of market uncertainty has positive direct and total effect (direct and indirect effects) on the effectiveness , however as for B2C projects, we noticed negative indirect effect of reduction of market uncertainty. In addition, we found significant difference between B2B and B2C in terms of the impact of the technical uncertainty. A positive impact of technical uncertainty on project execution phase and the project success could be noticed but this is not the case for B2C. Secondly, regarding changes in the project execution phase, there is enough evidence that shows fewer changes in the project execution phase result in the project success in B2B, however, as for B2C projects, relationship between changes in the project execution phase and the project success are small. In summary, FFE activities lead to project success in both B2B and B2C project in Japanese manufacturing firms in the 2010s. Considering product characteristics, differences between B2B and B2C projects are noticed in terms of the project execution phase. Regarding B2B projects, FFE activities reduce the changes in the project execution phase. Consequently, fewer changes lead to project success. As for B2C projects, changes in the project execution phase are unavoidable even if there exists qualified predevelopment activities such as the reducing uncertainties and initial


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Moreover, contrary to expectation, the degree of newness has positive direct and total effect on the overall satisfaction (hypothesis 48). This could be that B2B projects which are technology driven, refining a technology results in the success in the market and improving the worker’s satisfaction. 7.7.2 Result of B2C projects Contrary to expectation, results suggest that there is no statistical relationship between the degree of newness and the project success. Considering the results of the questionnaire, the average value of the product newness (technical, market, organizational newness) was low in B2C. We assume that, generally, B2C projects didn’t challenge to new domains. Thus, the degree of newness doesn’t have an effect on the project success.

8. Conclusion In this study, a comparative study was carried out to investigate the direct and indirect impacts of FFE activities and changes in project execution on NPD projects success of B2B and B2C projects in Japanese manufacturing firms. The following conclusions can be described from SEM analysis. Firstly, as for FFE activities, regardless of the product characteristics, the reduction of market uncertainty has positive direct and total effect (direct and indirect effects) on the effectiveness , however as for B2C projects, we noticed negative indirect effect of reduction of market uncertainty. In addition, we found significant difference between B2B and B2C in terms of the impact of the technical uncertainty. A positive impact of technical uncertainty on project execution phase and the project success could be noticed but this is not the case for B2C. Secondly, regarding changes in the project execution phase, there is enough evidence that shows fewer changes in the project execution phase result in the project success in B2B, however, as for B2C projects, relationship between changes in the project execution phase and the project success are small. In summary, FFE activities lead to project success in both B2B and B2C project in Japanese manufacturing firms in the 2010s. Considering product characteristics, differences between B2B and B2C projects are noticed in terms of the project execution phase. Regarding B2B projects, FFE activities reduce the changes in the project execution phase. Consequently, fewer changes lead to project success. As for B2C projects, changes in the project execution phase are unavoidable even if there exists qualified predevelopment activities such as the reducing uncertainties and initial

planning. In addition, execution of the project in conformity to initial plans has little causal relationship between the successes. The changes in the project execution phase are unavoidable because of the market complexity. Thus, B2C projects will be successful if the projects have qualified FFE activities and flexible project execution and FFE activities. These results contribute to NPD project management according to characteristics of the products of firms.

References

[1]X.M. Song and M.E. Parry: What separates Japanese new product winners from losers. Journal of Product Innovation Management 13, 5 (1996), pp.422–439.

[2] R.G. Cooper and E.J. Kleinschmidt: Screening new products for potential winners. Long Range Planning, Vol. 26, No. 6 (1993), pp.74–81.

[3] Herstatt, C., Verworn, B. and Nagahira, A. (2004)Reducing project related uncertainty in the ‘‘fuzzyfront end’’ of innovation: a comparison of German and Japanese product innovation projects. International Journal of Product Development, 1, 1, 43–65.

[4]Moore WL and Pessemier EA(1993):Product planning and management :designing and delivering value;McGraw-Hill,New York et al.

[5] B. Verworn, C. Herstatt and A. Nagahira: The fuzzy front end of Japanese new product development projects: impact on success and differences between incremental and radical projects. R&D Management , Vol.38, No.1 (2008), pp.1–19.

[6] B. Verworn: A structural equation model of the impact of the "fuzzy front end" on the success of new product development. Research Policy, 38 (2009), pp.1571-1581.

[7]Khurana, A. and Rosenthal, S.R.(1997)’integrating the fuzzy front end of new product innovation projects’, R&D Management, Vol.27,No4,pp377-392.

[8] Song, X.M. and Parry, M.E. (1997a) The determinants of Japanese new product successes.Journal of MarketingResearch, 34, 1, 64–76


[10]R.K. Moenaert, A. De Meyer, W.E. Souder and D. Deschoolmeester: R&D/Marketing communication during the fuzzy front-end. IEEE Transactions on Engineering Management 42, 3 (1995), pp.243–258.

[11] Nagahira, A., Kim, K.K, Yoon, H.J. Ishihara, S., Chiba, Y., Tanaka, Y., Miura, A. (2015). Comparative study on FFE activities between Korean and Japanese NPD projects, International Journal of Technology Marketing, Vol.10, pp.67-94

[12] Bstieler, L. (2005). The Moderating Effect of Environmental Uncertainty on New Product Development and Time Efficiency, Journal of Product Innovation Management, Vol. 22, No.3, 267-284.


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[13] Ho, Y.C. and Tsai, C.T. (2011). Front end of innovation of high technology industries: The moderating effect of front-end fuzziness, Journal of High Technology Management Research, Vol.22, pp. 47–58

[14]Dvie,D.and lechler,T.(2004)’Plans are nothing,Changing plans is everything:the impact of changes on project success’, research Policy,Vol.33,No.1,pp.1-15

[15] Pinto,J.K. and Slevin,D.P.(1988b) Critical success factors across the project life cycle.project management Jounal,19,3,67-75

[16] Booz, Allen, and Hamilton (1982) New Product Management for the 1980s. New York: Booz, Allen, and Hamilton.

[17]Dvie,D.and lechler,T.(2004)’Plans are nothing,Changing plans is everything:the impact of changes on project success’,Reserch Policy,Vol.33,No.1,pp.1-15

[18] Chin, W.W. and Dibbern, J. (2010). An introduction to a permutation based procedure for multigroup PLS analysis: Results of tests of differences on simulated data and cross cultural analysis of the sourcing of information system services between Germany and the USA. In Esposito Vinzi, V., Chin, W.W., Henseler, J., Wang., H. (Eds), Handbook of partial least squares. Concept, methods and application, Berlin, Heidelberg: Springer-Verlag, pp. 171-193.

[19] Efron, B. and Tibshirani, R. J. (1993). An introduction to the bootstrap. New York, NY: Chapman and Hall/CRC.

[20] Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage Publication, Inc., USA.

[21]Philip Kotler, Waldemar Pfoertsch, and Michi(2006), “B2B Brand Management”, Springer

[22]Nakata, Cheryl, subin Im, Huengsoo Park, and Young- Won Ha . “Antecedents and Consquence of Korean and Japanese New Product Advantage,” Journal of Business Research, No.59 (February) , pp.28-36 2006.

[23]D.C.Winmalachandra,B.Frank,T.Enkawa”Enhancing NPD Operational Performance through B2B and B2C Customer Involvement for Vaying Degree of Product Technology”2013 IEEE


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What Is the Impact of Cultural and Creative Parks on Housing Price?

Nien-Jin Tsaia, Pin-Yang Yeha*, Yi-Ting Huanga, Yu-Ting Huanga, Zone-Han Lina,

Yu-Chun Lina and Chien-Yuan Shera aDepartment of Business Management, National Sun Yat-sen University,

Lianhai Rd., Kaohsiung City, Taiwan ROC *Corresponding Author: [email protected]

ABSTRACT

According to previous literature, great public construction has a positive influence on house prices of the surrounding areas. Cultural and Creative Park interrelates the value of culture, education, leisure and economy. This study thus states that this type of cultural and creative parks will increases house prices. Housing as heterogeneous good is easily affected by surrounding areas and its price has implicit attributes which are hardly observed. This study adopts Hedonic Price to construct the price mode of house attribute with seven cultural and creative parks in Taiwan. House price and quality are provided by the website of The Department of Land Administration, Ministry of the Interior (http://lvr.land.moi.gov.tw/) from August, 2012 to July, 2016. Moreover, related research reveals that the distance between house and public construction is an important variable. This study uses Quantum Geographic Information System (QGIS) to obtain related information. Through the analysis of above data, this study expects that a cultural and creative park would promote the house prices of surrounding areas and analyzes the attributes which may have positive impacts. Keyword: Cultural and Creative Park, Housing Price, Hedonic Price


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The Impact of Night Market Opening on the Local Economy

Jia-Hao Ke a, Jui-Chi Chianga, Kai-Jie Huanga, Ming-Han Tsaia*, Yu-Jia Liua, Yu-Chun Lina and Chien-Yuan Shera

aDepartment of Business Management, National Sun Yat-sen University, Lianhai Rd., Kaohsiung City, Taiwan ROC

*Corresponding Author: [email protected]

ABSTRACT Night market, with abundant local and cultural features, is a unique commercial environment in Asian or Chinese communities. Some of them have gradually become famous attractions. Past researches focus more on the discussion about the image of the night market itself or the tourists/consumers experience. However, there is almost no research discussing whether night markets bring economic benefits while it should be the issue noticed by the government and residents. Therefore, this study aims to examine whether night markets promote local economy by using Fixed Effect Model to understand 42 night markets in Taiwan, listed on the website of Ministry of Finance from 1999 to 2012. We expect that night market would increase local income whereas it may have negative effects due to other factors. Keywords: Night Market, Local Economy, Fixed Effect Model


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Entrepreneurial Psychological Capital and Intentions: A Multiple Mediation Model

Hui-Chen Changa, Kuen-Hung Tsaia and Chen-Yi Penga*

aDepartment of Business Administration, National Taipei University 151 University Rd., San Shia, Taipei 237, Taiwan *Corresponding Author: [email protected]

ABSTRACT

Drawing from literatures on positive psychology, this study extended the research on the relationship between entrepreneurial psychological capital and entrepreneurial intentions by developing a multiple mediation model. This model posits that the link between entrepreneurial psychological capital and entrepreneurial intentions is mediated by three antecedents of the theory of planned behavior, attitudes toward entrepreneurship, subjective norms, and perceived behavioral control. Based on a sample of 372 valid questionnaires from entrepreneurial training-course participants in Taiwan, a multiple mediation analysis reveals several interesting results. First, entrepreneurial psychological capital positively affects entrepreneurial intentions through attitudes toward entrepreneurship and subjective norms. Second, compared with subjective norms, attitudes toward entrepreneurship have a stronger mediating effect in linking entrepreneurial psychological capital and entrepreneurial intentions. In addition, the results indicated that subjective norms positively affect entrepreneurial intentions. These findings indicated that the model advances the use of psychological capital and the theory of planned behavior, as well as can be used to describe a person as an entrepreneur. Keyword: Entrepreneurial Psychological Capital, Entrepreneurial Intentions, Theory of Planned Behavior, Entrepreneurship


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1. Introduction As the first step in new venture creation process, entrepreneurial intentions is vital to understanding the process of becoming an entrepreneur in entrepreneurship and management research. A volume of scholarly research has identified entrepreneurial psychological capital (entrepreneurial PsyCap) as an important factor for understanding an individual’s business start-up intentions and success (e.g., Terrence C. Sebora & Sannan Tantiukoskula. 2011). These studies have made a substantial contribution to the entrepreneurial literature. Exploring the relationship between entrepreneurial psychological capital and intentions/preparation to be an entrepreneur may provide a useful contribution to training individuals in new venture formation. However, there are still unanswered issues regarding the influential process of entrepreneurial psychological capital on the intentions of starting a business. Psychological capital (PsyCap) is a core construct of Positive organizational behavior (POB) and is based in positive psychology (Luthans & Youssef, 2004). POB comprises self-efficacy, confidence, hope, and resiliency related to one’s motivation and positive attitude in a specific context (Luthans, 2002). Previous research has consistently linked psychological capital to workplace outcomes including employee attitudes, behaviors, and performance, and indicated that psychological capital is not only a state rather than trait, but also can be learned and developed (Luthans et al., 2007). Furthermore, the theory of planned behavior (TPB) suggests that personal beliefs in the ability to perform particular behaviors influences perceptions of behavioral control and attitudes toward particular behaviors, thus affecting the intention to perform those behaviors (Ajzen 1991). This view implies that there are processes mediating the entrepreneurial self-efficacy—intention relationship when perceived behavioral control and attitudes toward entrepreneurship are considered. However, so far, the entrepreneurial literature has not investigated these issues. This reinforces the necessity of further research to answer why and how entrepreneurial psychological capital affects entrepreneurial intentions. This paper fills existing knowledge gaps in the entrepreneurship and management literature by investigating this question: Do attitudes toward entrepreneurship, subjective norms, and perceived behavioral control mediate the relationship between entrepreneurial psychological capital and entrepreneurial intentions? This study contributes to the entrepreneurship literature in two respects. First, this study expands upon the relationship between entrepreneurial psychological capital and entrepreneurial intentions by investigating its mediation process. Whereas relevant studies have focused on examining the direct effect entrepreneurial psychological capital exerts on intentions (Terrence C. Sebora & Sannan Tantiukoskula. 2011), this study investigates the mediating roles of attitudes toward entrepreneurship, subjective


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norms, and perceived behavioral control in the entrepreneurial psychological capital—intention relationship. Second, this study extends the roles concerning the theory of planned behavior during the process of becoming an entrepreneur. Relevant entrepreneurship literature has adopted the theory of planned behavior, considering attitudes toward entrepreneurship, subjective norms, and perceived behavioral control as the antecedents of entrepreneurial intentions (Fayolle et al. 2006; Kolvereid and Isaksen 2006; Krueger et al. 2000). By contrast, this study suggests that these three components play mediating roles in the relationship between entrepreneurial psychological capital and entrepreneurial intentions to start a business. The remainder of this paper is organized as follows: Literature Review briefly reviews the literature linking entrepreneurial psychological capital and entrepreneurial intentions. Conceptual Framework and Research Hypotheses presents the conceptual framework that guided the literature review and the development of the research hypotheses; Methods describes the research methods, including the model, definitions of variables, measurements, and data used in this study; and Analyses and Results presents and discusses the results. Discussion summarizes and discusses the results. Finally, Conclusion presents a brief conclusion, addresses the implications of the findings for theory and managerial practice, and suggests compelling directions for future research.

2. Literature Review Psychological capital is a core construct of POB and comprises self-efficacy, hope, optimism, and resilience related to one’s motivation and positive attitude in a specific context (Luthans, 2002). The theory of planned behavior (TPB) has been widely adopted in research of behavioral intentions. This theory argues that a person’s behavioral intentions are the result of three antecedents: (a) the attitude toward behavior (personal evaluation), (b) subjective norms (social pressures), and (c) perceived behavioral control (ability to perform the behavior) (Liñán 2004). In the entrepreneurial context, personal attitude toward a behavior refers to the attractiveness of the proposed behavior or degree to which the person positively or negatively evaluates the idea of becoming an entrepreneur (Ajzen 1991, 2002; Kolvereid 1996). Perceived behavioral control refers to the perceived ease or difficulty of becoming an entrepreneur (Ajzen 1991). Subjective norms involve measuring the perceived social pressure from family, friends, or other people that are considered important (Ajzen 1991) to become an entrepreneur. Many researchers have applied the theory to analyze new firm creation and have provided empirical support in the research stream of entrepreneurial intentions.

1. Introduction As the first step in new venture creation process, entrepreneurial intentions is vital to understanding the process of becoming an entrepreneur in entrepreneurship and management research. A volume of scholarly research has identified entrepreneurial psychological capital (entrepreneurial PsyCap) as an important factor for understanding an individual’s business start-up intentions and success (e.g., Terrence C. Sebora & Sannan Tantiukoskula. 2011). These studies have made a substantial contribution to the entrepreneurial literature. Exploring the relationship between entrepreneurial psychological capital and intentions/preparation to be an entrepreneur may provide a useful contribution to training individuals in new venture formation. However, there are still unanswered issues regarding the influential process of entrepreneurial psychological capital on the intentions of starting a business. Psychological capital (PsyCap) is a core construct of Positive organizational behavior (POB) and is based in positive psychology (Luthans & Youssef, 2004). POB comprises self-efficacy, confidence, hope, and resiliency related to one’s motivation and positive attitude in a specific context (Luthans, 2002). Previous research has consistently linked psychological capital to workplace outcomes including employee attitudes, behaviors, and performance, and indicated that psychological capital is not only a state rather than trait, but also can be learned and developed (Luthans et al., 2007). Furthermore, the theory of planned behavior (TPB) suggests that personal beliefs in the ability to perform particular behaviors influences perceptions of behavioral control and attitudes toward particular behaviors, thus affecting the intention to perform those behaviors (Ajzen 1991). This view implies that there are processes mediating the entrepreneurial self-efficacy—intention relationship when perceived behavioral control and attitudes toward entrepreneurship are considered. However, so far, the entrepreneurial literature has not investigated these issues. This reinforces the necessity of further research to answer why and how entrepreneurial psychological capital affects entrepreneurial intentions. This paper fills existing knowledge gaps in the entrepreneurship and management literature by investigating this question: Do attitudes toward entrepreneurship, subjective norms, and perceived behavioral control mediate the relationship between entrepreneurial psychological capital and entrepreneurial intentions? This study contributes to the entrepreneurship literature in two respects. First, this study expands upon the relationship between entrepreneurial psychological capital and entrepreneurial intentions by investigating its mediation process. Whereas relevant studies have focused on examining the direct effect entrepreneurial psychological capital exerts on intentions (Terrence C. Sebora & Sannan Tantiukoskula. 2011), this study investigates the mediating roles of attitudes toward entrepreneurship, subjective


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3. Conceptual Framework and Hypotheses

Figure 1 illustrates the conceptual model of this study. This model was based on Psychological capital (PsyCap) and the Theory of Planned Behavior (TPB). Psychological capital is a core construct of POB and comprises self-efficacy, hope, optimism, and resilience related to one’s motivation and positive attitude in a specific context (Luthans, 2002). Previous research has consistently linked psychological capital to workplace outcomes including employee attitudes, behaviors, and performance. TPB suggests that people’s control beliefs affect their perceived behavioral control and attitudes toward behavior, thus affecting their subsequent intentions and behavior (Ajzen 1991). According to these perspectives, if psychological capital is found to be related to entrepreneurship and entrepreneurship can be taught, then adding training in psychological capital could benefit aspiring entrepreneurs. Therefore, we posited that, in addition to a direct link between entrepreneurial psychological capital and entrepreneurial intentions, entrepreneurial psychological capital is indirectly linked to entrepreneurial intentions through attitudes toward entrepreneurship, subjective norms, and perceived behavioral control of entrepreneurship. The framework was used to guide the development of the research hypotheses. The specific hypotheses, as well as their underlying rationale, are detailed in the remainder of this section.

Fig. 1 A multiple mediation model of the entrepreneurial psychological

capital-intention relationship 3.1 Mediating Roles of Attitudes toward Entrepreneurship, Perceived Behavioral

Control, and Subjective Norms According to the theory of planned behavior, people’s beliefs regarding the

Attitudes

Toward Entrepreneurship

Entrepreneurial

psychological capital

Entrepreneurial

Intentions

Perceived

Behavioral Control

Subjective Norms


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consequences of a particular behavior affect their attitude toward that behavior (Ajzen 1991). People’s attitudes toward entrepreneurship consist of their positive and negative evaluations of entrepreneurship. And those higher in the hope capacity are more able to derive multiple pathways to be successful in the entrepreneurial job. In addition, due to higher levels of resilience, even when they experience negative events in the specific context, high PsyCap individuals are more likely to positively adapt and bounce back from those events. Moreover, their higher levels of optimism regarding the future and confidence in their ability to succeed in their current job will always look at the bright side of the future, motivate them to take charge of their own destinies (Seligman, 1998), self-select into challenging endeavors (Bandura, 1997), engage the necessary efforts and resources, and have more perseverance of facing obstacles (Stajkovic & Luthans, 1998a). Finally, people who exhibit high levels of self-efficacy strongly believe in their ability to complete a job or a specific set of tasks (Bandura 1977; Bandura et al. 1980). Persons with high levels of confidence in their skills to start a business are likely to expect that they can establish a successful venture, and this expectation may motivate their behavior in regard to entrepreneurship. Based on this logic, entrepreneurial psychological capital positively affects entrepreneurial intentions by engendering a positive attitude toward entrepreneurship. H1: Entrepreneurial psychological capital affects entrepreneurial intentions through attitudes toward entrepreneurship. From the perspective of motivation theory, the motivation to begin a business in response to the pressures associated with perceived expectations is extrinsic in nature. By contrast, entrepreneurial motivation grounded in entrepreneurial self-efficacy, i.e., the confidence in perceived skills and abilities to begin an enterprise, is intrinsic (Amabile et al. 1994). Intrinsic motivation includes the motivation to begin a business primarily for its own sake because starting a business provides the person with a sense of accomplishment or satisfaction. People who possess skills feel more confident in starting a firm (DeNoble et al. 1999). These specific abilities can be more easily exercised when a person is an entrepreneur (Liñán 2008). (Christensen et al. 1994). A person who possesses considerable work experience, a high level of education, knowledge of the market, and business practice is likely able to identify an opportunity to start a new business (De Wit and van Winden 1989). Thus, ability can be associated with subjective norms (Scherer et al. 1991; Carsrud 1992; Boyd and Vozikis 1994). Subjective norms reflect the perceived expectations people have toward salient others (Ajzen 1991). In entrepreneurial research, subjective norms refer

3. Conceptual Framework and Hypotheses

Figure 1 illustrates the conceptual model of this study. This model was based on Psychological capital (PsyCap) and the Theory of Planned Behavior (TPB). Psychological capital is a core construct of POB and comprises self-efficacy, hope, optimism, and resilience related to one’s motivation and positive attitude in a specific context (Luthans, 2002). Previous research has consistently linked psychological capital to workplace outcomes including employee attitudes, behaviors, and performance. TPB suggests that people’s control beliefs affect their perceived behavioral control and attitudes toward behavior, thus affecting their subsequent intentions and behavior (Ajzen 1991). According to these perspectives, if psychological capital is found to be related to entrepreneurship and entrepreneurship can be taught, then adding training in psychological capital could benefit aspiring entrepreneurs. Therefore, we posited that, in addition to a direct link between entrepreneurial psychological capital and entrepreneurial intentions, entrepreneurial psychological capital is indirectly linked to entrepreneurial intentions through attitudes toward entrepreneurship, subjective norms, and perceived behavioral control of entrepreneurship. The framework was used to guide the development of the research hypotheses. The specific hypotheses, as well as their underlying rationale, are detailed in the remainder of this section.

Fig. 1 A multiple mediation model of the entrepreneurial psychological

capital-intention relationship 3.1 Mediating Roles of Attitudes toward Entrepreneurship, Perceived Behavioral

Control, and Subjective Norms According to the theory of planned behavior, people’s beliefs regarding the

Attitudes

Toward Entrepreneurship

Entrepreneurial

psychological capital

Entrepreneurial

Intentions

Perceived

Behavioral Control

Subjective Norms


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to a person’s perceptions of reference people, including family, friends, and significant others, who would or would not approve of the decision to become an entrepreneur (Liñán 2008). This definition suggests that subjective norms related to entrepreneurship may be positive or negative; the current study focuses on positive subjective norms. In other words, the influence of entrepreneurial self-efficacy on entrepreneurial intention may be mediated by subjective norms. Thus, we propose the following hypothesis: H2: Entrepreneurial psychological capital affects entrepreneurial intentions through subjective norms. Furthermore, similar to the concept of perceived behavioral control defined in the theory of planned behavior, perceived entrepreneurial control refers to a person’s perceived ease or difficulty in beginning a venture. The theory of planned behavior asserts that the control beliefs of people determine their perceived behavior control (Ajzen 1991). Based on this logic, people with strongly confident in their ability to perform entrepreneurial activities are more likely to have a low risk perception of performing these activities (Liñán 2008); thus, they may be highly willing to begin a business. Similarly, people who exhibit high levels of entrepreneurial self-efficacy may have high levels of entrepreneurial intention because they strongly believe that a venture can be established with ease. Based on these arguments, we posited the following hypotheses: H3: Entrepreneurial psychological capital affects entrepreneurial intentions through perceived behavioral control.

4. Methods 4.1 Measures of Constructs This study developed measures of constructs based on previous research. Wherever possible, existing measures were used or modified to meet the purpose of this study. To ensure accurate translation, we applied a double translation process to translate the measures between Chinese and English (Douglas and Craig 1983). To identify the subsets of measures that were unique, we submitted a list of constructs and corresponding measurement items to two panels of academic experts in various entrepreneurial fields. These panels critically evaluated each item for clarity, specificity, and representativeness. Based on the feedback from these two panels of experts, we prepared a questionnaire that included items that were determined to


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exhibit face validity and high consistency with the constructs. The final stage of measurement development consisted of one pretest of the questionnaire was performed on a sample of four academic experts working in areas pertaining to entrepreneurship development to evaluate the comprehensiveness of the constructs. The measures used in this study were based on the Entrepreneurial Intention Questionnaire (EIQ) and Entrepreneurial Psychological Capital Questionnaire (EPCQ). EIQ first developed by Liñán and Chen (2009) and later modified by Liñán et al. (2011c). The revised EIQ comprises 20 items that were used to measure the four constructs of entrepreneurial intentions, attitudes toward entrepreneurship, subjective norms, and perceived behavioral control; these four constructs are separately measured by six items (4, 6, 9, 13, 17, 19), five items (2, 10, 12, 15, 18), three items (3, 8, 11), and six items (1, 5, 7, 14, 16, 20), respectively. In addition, the Psychological Capital Questionnaire (PCQ) was developed by Luthans et al. (2007), and was originally used to measure the four dimensions of hope (Snyder et al., 1996), optimism (Scheier & Carver, 1985), resilience (Wagnild & Young, 1993), and self-efficacy (Parker, 1998) in workplace. The EPCQ comprise 24 items that were modified from PCQ to fit an entrepreneurship context by Terrence C. Sebora & Sannan Tantiukoskula (2011); these four dimensions of hope, optimism, resilience, and self-efficacy are separately measured by six items (24, 32, 36, 38, 39, 40), six items (22, 30, 37, 41, 42, 44), six items (23, 26, 27, 34, 35, 43), and six items (21, 25, 28, 29, 31, 33). All items were scored on a five-point Likert scale, where 1 = “Strongly disagree” and 5 = “Strongly agree.” The participants were asked to answer how they felt “right now”. These items were reported in the appendix. In this study, demographic variables, including age, gender, level of education, and working experience were controlled to reduce the possibility of alternative explanations. These variables were adopted to explain entrepreneurial intention and have been widely documented in the entrepreneurship literature (Dabic et al. 2012; Díaz-García and Jiménez-Moreno 2010; Díaz-Casero et al. 2012; Liñán et al. 2011a; Liñán et al. 2011b). Gender was a nominal variable for which 1=male and 0=female. Age is scored on a 5-point scale on which 1=younger than 25 years, 2=older than 25–35 years, 3=older than 35–45 years, 4= older than 45–55 years, and 5=older than 55 years. Level of education is scored on a four-point scale on which 1=junior school, 2=high or vocational school, 3=university, and 4=graduate. Working experience was scored on a 5-point scale on which 1=never, 2=less than 3 years, 3=more than 3–5 years, 4=more than 5–10 years, and 5=more than 10 years. 4.2 Statistical Methods Confirmatory factor analysis, which was used to assess reliability and validity of the

to a person’s perceptions of reference people, including family, friends, and significant others, who would or would not approve of the decision to become an entrepreneur (Liñán 2008). This definition suggests that subjective norms related to entrepreneurship may be positive or negative; the current study focuses on positive subjective norms. In other words, the influence of entrepreneurial self-efficacy on entrepreneurial intention may be mediated by subjective norms. Thus, we propose the following hypothesis: H2: Entrepreneurial psychological capital affects entrepreneurial intentions through subjective norms. Furthermore, similar to the concept of perceived behavioral control defined in the theory of planned behavior, perceived entrepreneurial control refers to a person’s perceived ease or difficulty in beginning a venture. The theory of planned behavior asserts that the control beliefs of people determine their perceived behavior control (Ajzen 1991). Based on this logic, people with strongly confident in their ability to perform entrepreneurial activities are more likely to have a low risk perception of performing these activities (Liñán 2008); thus, they may be highly willing to begin a business. Similarly, people who exhibit high levels of entrepreneurial self-efficacy may have high levels of entrepreneurial intention because they strongly believe that a venture can be established with ease. Based on these arguments, we posited the following hypotheses: H3: Entrepreneurial psychological capital affects entrepreneurial intentions through perceived behavioral control.

4. Methods 4.1 Measures of Constructs This study developed measures of constructs based on previous research. Wherever possible, existing measures were used or modified to meet the purpose of this study. To ensure accurate translation, we applied a double translation process to translate the measures between Chinese and English (Douglas and Craig 1983). To identify the subsets of measures that were unique, we submitted a list of constructs and corresponding measurement items to two panels of academic experts in various entrepreneurial fields. These panels critically evaluated each item for clarity, specificity, and representativeness. Based on the feedback from these two panels of experts, we prepared a questionnaire that included items that were determined to


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scales for measuring the constructs, was adopted because this approach can be used to test whether measures of a construct are consistent with the nature of that construct, and has been widely employed in social research (Hair et al. 2010). In addition, regression analysis was utilized to test the research hypotheses (i.e., the mediating effects of attitudes toward entrepreneurship, subjective norms, and perceived behavioral control). Essentially, a structural equations method can be considered to analyze the data in this study. However, using measures to construct the interaction terms in a structural equations model is a complex process (Ping 1996), and lacks a measure with which to evaluate the degree of multicollinearity. Thus, instead of using a structural equations model, we adopted regression analysis according to the suggestion by Aiken and West (1991) to evaluate the proposed models. In addition, we employed the bootstrap approach suggested by Zhao et al. (2010) and the method by Preacher and Hayes (2008) to assess the mediating effects. We conducted confirmatory factor analysis using LISREL 8.51; for multiple regression analysis and the bootstrap approach, we used SPSS for Windows. 4.3 Sample and data collection Most studies on entrepreneurship intentions have investigated university students rather than potential real-world entrepreneurs as research subjects. Although university students are considered a population highly inclined toward entrepreneurship (Liñán 2004; Reynolds et al. 2002a; Zhang et al. 2014), they may not be representative of the total population. McGee et al. (2009) even consider that the results based on samples of students may be associated with a bias because students could not judge whether they can be successful entrepreneurs. Researchers have suggested that entrepreneurial research should be conducted in other contexts, in addition to university students (Díaz-García and Jiménez-Moreno 2010; Liñán 2008; Naktiyok et al. 2010). Thus, this study sampled potential real-world entrepreneurs. We collected data by surveying people who were interested in attending an entrepreneurial awareness course. This course was free and designed to provide entrepreneurship knowledge to people aged 18 or older in Taiwan. The course was held twice in 2 months. Before the course commenced, the attendees were asked to complete the questionnaires anonymously. A total of 426 valid responses were obtained; 54 unqualified or incomplete questionnaires were eliminated. This sample included respondents of diverse demographic backgrounds (gender: women, 38.2 %, and men, 68.1 %; degree of education: high school or vocational degree, 45.7 %, and university or graduate degrees 52.1%; entrepreneurial experience: yes, 22.8 %, and no, 77.2 %; age, younger than 30 years, 17.5 %, 31–50 years, 69.1 %, and older than 50 years, 13.4 %). To examine the representativeness of the sample, we performed a


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multivariate analysis of variance to compare the differences between the two sets of valid responses (194 vs. 178) based on all of the antecedent variables. The results were nonsignificant at the 95 % confidence level, suggesting that there were no significant differences between the two groups. We also compared the possible differences in age, gender, and education between the first collection of response samples and the remaining response samples. The results were nonsignificant at the 95 % confidence level. In addition, we used a Kolmogorov-Smirnov test to compare the differences in the demographic variables between finished and unfinished questionnaires. The chi-square value revealed a nonsignificant difference at the 95 % confidence level. Because the data were collected using survey measures from a single source, a potential concern was contamination by common-method variance. To address this concern, we adopted the suggestion of Podsakoff et al. (2003) to lay the scale by randomly ordering the measures and including reversed items. We also adopted several statistical approaches recommended by Podsakoff et al. (2003). First, we conducted Harman’s one-factor test. We used varimax rotations and principal component analysis to conduct an exploratory factor analysis for all of the independent and dependent variables. No single major factor emerged that explained most of the variance involved in the model. This provided preliminary evidence that no substantial common-method bias existed in the data. Second, we conducted a partial correlation analysis. We performed a factor analysis on all of the variables used in this study (except for the demographics of the respondents) and partialled out the variance from the first unrotated factor. We determined that the correlation structure involving entrepreneurial intentions, entrepreneurial psychological capital, attitudes toward entrepreneurship, perceived behavioral control, and subjective norms remained because all the significant zero-order correlations remained significant after partial correlation analysis. We then conducted a post-hoc analysis. Based on the items loaded together on the first factor of the previous factor analysis, we trimmed the items that overlapped for various measures. We conducted another Harman’s one-factor test and partial correlation analysis and determined that the results remained unchanged. A severe common-method bias problem is indicated when a single latent factor accounts for all manifest variables (Podsakoff et al. 2003). Overall, these statistical evaluations suggested that common-method bias was not a severe problem in this study. Another potential concern is that the people included in the sample may have high entrepreneurial intention because they voluntarily attended a free entrepreneurship awareness course. To address this concern, we compared the mean (4.23) and variance (.59) of entrepreneurial intentions with that determined in previous research by using nonstudent samples. The cross-country evaluation of entrepreneurial

scales for measuring the constructs, was adopted because this approach can be used to test whether measures of a construct are consistent with the nature of that construct, and has been widely employed in social research (Hair et al. 2010). In addition, regression analysis was utilized to test the research hypotheses (i.e., the mediating effects of attitudes toward entrepreneurship, subjective norms, and perceived behavioral control). Essentially, a structural equations method can be considered to analyze the data in this study. However, using measures to construct the interaction terms in a structural equations model is a complex process (Ping 1996), and lacks a measure with which to evaluate the degree of multicollinearity. Thus, instead of using a structural equations model, we adopted regression analysis according to the suggestion by Aiken and West (1991) to evaluate the proposed models. In addition, we employed the bootstrap approach suggested by Zhao et al. (2010) and the method by Preacher and Hayes (2008) to assess the mediating effects. We conducted confirmatory factor analysis using LISREL 8.51; for multiple regression analysis and the bootstrap approach, we used SPSS for Windows. 4.3 Sample and data collection Most studies on entrepreneurship intentions have investigated university students rather than potential real-world entrepreneurs as research subjects. Although university students are considered a population highly inclined toward entrepreneurship (Liñán 2004; Reynolds et al. 2002a; Zhang et al. 2014), they may not be representative of the total population. McGee et al. (2009) even consider that the results based on samples of students may be associated with a bias because students could not judge whether they can be successful entrepreneurs. Researchers have suggested that entrepreneurial research should be conducted in other contexts, in addition to university students (Díaz-García and Jiménez-Moreno 2010; Liñán 2008; Naktiyok et al. 2010). Thus, this study sampled potential real-world entrepreneurs. We collected data by surveying people who were interested in attending an entrepreneurial awareness course. This course was free and designed to provide entrepreneurship knowledge to people aged 18 or older in Taiwan. The course was held twice in 2 months. Before the course commenced, the attendees were asked to complete the questionnaires anonymously. A total of 426 valid responses were obtained; 54 unqualified or incomplete questionnaires were eliminated. This sample included respondents of diverse demographic backgrounds (gender: women, 38.2 %, and men, 68.1 %; degree of education: high school or vocational degree, 45.7 %, and university or graduate degrees 52.1%; entrepreneurial experience: yes, 22.8 %, and no, 77.2 %; age, younger than 30 years, 17.5 %, 31–50 years, 69.1 %, and older than 50 years, 13.4 %). To examine the representativeness of the sample, we performed a


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intentions by Engle et al. (2010) showed that the means (and variances) of entrepreneurial intention in Bangladesh, Costa Rica, Ghana, Russia, Egypt, Spain, the USA, and France were 3.73 (.75), 3.70 (1.19), 4.10(.72), 4.07 (.62), 3.63 (.77), 2.60 (1.11), 2.63 (1.28), and 2.80 (1.21), respectively. In another study conducted in Taiwan, the mean (and variance) of entrepreneurial intentions was 3.82 (.84) (Lo and Wang 2007). Compared with these descriptive statistics, the entrepreneurial intentions in the present study was not abnormally high.

5. Analyses and Results 5.1 Reliability, Validity, and Descriptive Statistics Table 1 presents the reliability, validity, and descriptive statistics for the variables used in model estimation. Reliability was evaluated using Cronbach’s alpha (α). The α values for entrepreneurial intentions, hope, optimism, resilience, entrepreneurial self-efficacy, attitudes toward entrepreneurship, subjective norms, and perceived behavioral control were .91, .89, .76, .86, .93, .84, .88, and .77, respectively. The results indicated that the measures for the constructs were highly reliable (Nunnally 1978). This study also performed confirmatory factor analysis (CFA) to verify the reliability and validity of the scales. The fit indices indicated that the model fit the data reasonably well (χ2/d.f. =2.99, goodness-of-fit index=.78, root mean square error of approximation=.07, root of mean square error=.06, non-normed fit of index=.97, comparative fit index=.97, and incremental fit index=.97). The composite reliability (CR) of each scale was calculated using the procedures suggested by Fornell and Larcker (1981). The CR values for entrepreneurial intentions, hope, optimism, resilience, entrepreneurial self-efficacy, attitudes toward entrepreneurship, subjective norms, and perceived behavioral control were .92, .89, .77, .87, .94, .85, .89, and .79, exceeding the acceptable level of .60 suggested by Bagozzi and Yi (1988).

Table 1

Furthermore, all of the values for average variance extracted (AVE) exceed the threshold level of .50 suggested by Bagozzi et al. (1991). All of the indicators loaded significantly on their corresponding latent constructs and ranged from .54 to .73. The significance of each standardized coefficient loading indicated convergent validity (Bagozzi et al. 1991). The CFA results showed that each of the measures loaded significantly on its intended construct, indicating convergent validity for each scale. As Table 1 indicates, the diagonal elements representing the square roots of the AVE for each construct were significantly greater than the off-diagonal elements (Hulland 1999), meeting the criterion for discriminant validity suggested by Fornell and Larcker (1981). In addition, we assessed discriminant validity by running all pairs of


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constructs in a two-factor CFA model analysis (Anderson and Gerbing 1988; Fornell and Larcker 1981). The results indicated discriminant validity because all of the differences between the constraining model and the free model of each construct dimension were higher than the critical value of 3.84 (Bagozzi and Yi 1988). 5.2 Results We followed the procedure of Baron and Kenny (1986) to test the mediating effects of attitudes toward entrepreneurship, subjective norms, and perceived behavioral control. The results show that entrepreneurial psychological capital significantly affects attitudes toward entrepreneurship, subjective norms, and perceived behavioral control (β=.5807, p<.00; β=.7413, p<.00; β=.8642, p<.00). And attitudes toward entrepreneurship and subjective norms significantly affects entrepreneurial intentions (β=.637, p<.00; β=.1199, p<.00). However, perceived entrepreneurial control doesn’t have significant effects on entrepreneurial intentions (β=.0515, p<.1538). Furthermore, employing the bootstrap approach suggested by Zhao et al. (2010) to assess the mediating effects, we set the bootstrap samples to 5000 and the confidence level to 95 %. The results indicated that the confidence intervals (bias corrected confidence intervals) of the indirect effects of attitudes toward entrepreneurship, subjective norms, and perceived entrepreneurial control were .3019-.4523, .0401-.1522, and -.0195–.1052, respectively. These confidence intervals of attitudes toward entrepreneurship and subjective norms do not include 0, indicating that the indirect effects were significant and the mediations were established. These results support Hypotheses 1 and 2, indicating that attitudes toward entrepreneurship and subjective norms mediate the effect of entrepreneurial psychological capital on entrepreneurial intentions. Hypotheses 3 was not be supported, indicating that perceived behavioral control doesn’t have mediating effects between entrepreneurial psychological capital and entrepreneurial intentions.

6. Discussion

In this section, according to previous entrepreneurship research, we further discuss the results. First, many studies have demonstrated that psychological capital positively influences entrepreneurial intentions (e.g., Terrence C. Sebora & Sannan Tantiukoskula. 2011). The results of this study verify the findings of previous studies and include the mediating roles of attitudes toward entrepreneurship and subjective norms. The results suggest that the entrepreneurial psychological capital-intention relationship is mediated by attitudes toward entrepreneurship and subjective norms, whereas perceived behavioral control doesn’t play a mediating role between entrepreneurial psychological capital and entrepreneurial intentions. These findings

intentions by Engle et al. (2010) showed that the means (and variances) of entrepreneurial intention in Bangladesh, Costa Rica, Ghana, Russia, Egypt, Spain, the USA, and France were 3.73 (.75), 3.70 (1.19), 4.10(.72), 4.07 (.62), 3.63 (.77), 2.60 (1.11), 2.63 (1.28), and 2.80 (1.21), respectively. In another study conducted in Taiwan, the mean (and variance) of entrepreneurial intentions was 3.82 (.84) (Lo and Wang 2007). Compared with these descriptive statistics, the entrepreneurial intentions in the present study was not abnormally high.

5. Analyses and Results 5.1 Reliability, Validity, and Descriptive Statistics Table 1 presents the reliability, validity, and descriptive statistics for the variables used in model estimation. Reliability was evaluated using Cronbach’s alpha (α). The α values for entrepreneurial intentions, hope, optimism, resilience, entrepreneurial self-efficacy, attitudes toward entrepreneurship, subjective norms, and perceived behavioral control were .91, .89, .76, .86, .93, .84, .88, and .77, respectively. The results indicated that the measures for the constructs were highly reliable (Nunnally 1978). This study also performed confirmatory factor analysis (CFA) to verify the reliability and validity of the scales. The fit indices indicated that the model fit the data reasonably well (χ2/d.f. =2.99, goodness-of-fit index=.78, root mean square error of approximation=.07, root of mean square error=.06, non-normed fit of index=.97, comparative fit index=.97, and incremental fit index=.97). The composite reliability (CR) of each scale was calculated using the procedures suggested by Fornell and Larcker (1981). The CR values for entrepreneurial intentions, hope, optimism, resilience, entrepreneurial self-efficacy, attitudes toward entrepreneurship, subjective norms, and perceived behavioral control were .92, .89, .77, .87, .94, .85, .89, and .79, exceeding the acceptable level of .60 suggested by Bagozzi and Yi (1988).

Table 1

Furthermore, all of the values for average variance extracted (AVE) exceed the threshold level of .50 suggested by Bagozzi et al. (1991). All of the indicators loaded significantly on their corresponding latent constructs and ranged from .54 to .73. The significance of each standardized coefficient loading indicated convergent validity (Bagozzi et al. 1991). The CFA results showed that each of the measures loaded significantly on its intended construct, indicating convergent validity for each scale. As Table 1 indicates, the diagonal elements representing the square roots of the AVE for each construct were significantly greater than the off-diagonal elements (Hulland 1999), meeting the criterion for discriminant validity suggested by Fornell and Larcker (1981). In addition, we assessed discriminant validity by running all pairs of


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have not been documented in the entrepreneurship literature. Second, previous studies based on the theory of planned behavior have revealed that attitudes toward behavior (entrepreneurship) and perceived behavioral (entrepreneurial) control enhance intentions to start a business even though the magnitudes differ (e.g., Mueller 2011; Solesvik 2013; Van Gelderen et al. 2008). Again, our results verify the findings of these studies, suggesting that the effect of perceived behavioral control on entrepreneurial intentions is not significant. A possible reason for this result may be inferred from the metaanalytic review by Schlaegel and Koenig (2014) that indicated that the perceived behavioral control—intention relationship in a group of nonstudents was far lower than that in the student sample. In other words, our results may have been affected by the sample chosen for model testing. In this study, we focused on members of society instead of students. People in society may encounter a more realistic entrepreneurial environment than that perceived by students and consider more factors, such as family and time; thus, the influence of perceived behavioral control on entrepreneurial intention may diminish. Third, this study suggests that subjective norms not only positively affect entrepreneurial intentions, but also play a mediating role between entrepreneurial psychological capital and entrepreneurial intentions. Various previous studies have not verified the subjective norms—intention relationship (e.g., Garcia-Rodrigues et al. 2013; Lin 2013; Shiri et al. 2012); by contrast, the current study supports the view that subjective norms negatively affect entrepreneurial intention (Deci et al. 1999; Fu et al. 2010; Schlaegel and Koenig 2014). In Taiwan, family support and resources strongly affect a person’s perception of entrepreneurial behavior (Numazaki 1997). Since family and people with whom a person has close ties play a key role in formulating subjective norms, our finding is not surprising. Finally, in Psycap—intention relations, attitude of the indirect effects are stronger than subjective norms

7. Conclusion

7.1 Implications The findings of this study have several critical implications for entrepreneurship research and practices. First, we clarified the attitudes toward entrepreneurship and subjective norms by revealing their mediating roles. We indicated that attitudes toward entrepreneurship and subjective norms convert entrepreneurial psychological capital into entrepreneurial intentions. This new insights implies that by not considering the mediating roles of attitudes toward entrepreneurship and subjective norms, previous research may have overlooked the roles of interest and contextual


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factors when beginning a business and therefore may have portrayed a overly simplified view of the entrepreneurial psychological capital involved in entrepreneurial intentions. In addition, these findings verify the theory of planned behavior notion that beliefs are necessary but insufficient for behavioral intentions and that both attitudes toward the behavior and subject norms are the key stimulants of intentions. This study suggests that attitudes toward entrepreneurship and subject norms play critical roles in entrepreneurial intentions (as predicted by using the theory of planned behavior) by partially mediating the effects entrepreneurial psychological capital exerts on entrepreneurial intentions. In other words, the value of entrepreneurial psychological capital in regard to entrepreneurial intentions may not be completely intrinsic, but rather is realized through attitudes toward entrepreneurship and subjective norms. Second, these results suggest that theoretically exploring the entrepreneurial psychological capital—intention relationship should not be restricted to a single method, as suggested in many previous studies. Rather, the relationship can be described as direct and indirect paths that are contextually dependent. Examining the entrepreneurial psychological capital—intention relationship is crucial because much relevant research may afford an incomplete view of the relationship. The results of this study also have implications for entrepreneurial practice. First, the results suggested that entrepreneurial psychological capital remains a critical driver of entrepreneurial intentions even though its effects may be mediated by attitudes toward entrepreneurship and subjective norms. These findings suggest that governments should encourage people to become entrepreneurs by improving their entrepreneurial psychological capital (eg. hope, optimism, resilience, and self-efficacy). This can be done by implementing policy tools, such as entrepreneurial activity funds and loans, and by promoting the exchange and sharing of experiences by guest speakers and entrepreneurs with various experiences and expertise. These results also suggested that, among students, governments could improve students’ entrepreneurial intentions after graduation by increasing their entrepreneurial psychological capital. This can be done by implementing entrepreneurship courses in universities. Second, the results revealed that attitudes toward entrepreneurship and subjective norms pivot on the link between entrepreneurial psychological capital and entrepreneurial intentions. These findings imply that entrepreneurial training and education that promote hope, optimism, resilience, and self-efficacy should also focus on enhancing positive attitudes and forming subjective norms among attendees and students. 7.2 Limitations and Directions for Future Research This study yielded compelling results; however, the generalizability of these results is

have not been documented in the entrepreneurship literature. Second, previous studies based on the theory of planned behavior have revealed that attitudes toward behavior (entrepreneurship) and perceived behavioral (entrepreneurial) control enhance intentions to start a business even though the magnitudes differ (e.g., Mueller 2011; Solesvik 2013; Van Gelderen et al. 2008). Again, our results verify the findings of these studies, suggesting that the effect of perceived behavioral control on entrepreneurial intentions is not significant. A possible reason for this result may be inferred from the metaanalytic review by Schlaegel and Koenig (2014) that indicated that the perceived behavioral control—intention relationship in a group of nonstudents was far lower than that in the student sample. In other words, our results may have been affected by the sample chosen for model testing. In this study, we focused on members of society instead of students. People in society may encounter a more realistic entrepreneurial environment than that perceived by students and consider more factors, such as family and time; thus, the influence of perceived behavioral control on entrepreneurial intention may diminish. Third, this study suggests that subjective norms not only positively affect entrepreneurial intentions, but also play a mediating role between entrepreneurial psychological capital and entrepreneurial intentions. Various previous studies have not verified the subjective norms—intention relationship (e.g., Garcia-Rodrigues et al. 2013; Lin 2013; Shiri et al. 2012); by contrast, the current study supports the view that subjective norms negatively affect entrepreneurial intention (Deci et al. 1999; Fu et al. 2010; Schlaegel and Koenig 2014). In Taiwan, family support and resources strongly affect a person’s perception of entrepreneurial behavior (Numazaki 1997). Since family and people with whom a person has close ties play a key role in formulating subjective norms, our finding is not surprising. Finally, in Psycap—intention relations, attitude of the indirect effects are stronger than subjective norms

7. Conclusion

7.1 Implications The findings of this study have several critical implications for entrepreneurship research and practices. First, we clarified the attitudes toward entrepreneurship and subjective norms by revealing their mediating roles. We indicated that attitudes toward entrepreneurship and subjective norms convert entrepreneurial psychological capital into entrepreneurial intentions. This new insights implies that by not considering the mediating roles of attitudes toward entrepreneurship and subjective norms, previous research may have overlooked the roles of interest and contextual


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limited because they are based on a sample of people who only had work experience in Taiwan. The findings should be validated through further research in other contexts, because entrepreneurial activities may vary between different societies and cultures (Fernández-Serrano and Romero 2014). In addition, because the people included in the sample voluntarily attended a free entrepreneurship awareness course, they may have had high entrepreneurial intentions. Even if comparisons with the results reported in previous research suggest the concern is not serious, the generalizability of the findings may still be limited by the specificity of the sample. In addition to validating external validity in other contexts, this study also suggested interesting directions for future research. First, in relevant research, subjective norms have been referred to as injunctive norms and descriptive norms (Larimer et al. 2004; Rhodes and Courneya 2003). Previous psychological research has suggested that these two types of norm influence behavioral intentions differently (Manning 2009). Future research can extend this study by investigating how different norms mediate the relationship between entrepreneurial psychological capital and intentions. Second, recent entrepreneurship studies have examined the entrepreneurial psychological capital variable as a multidimensional construct (Terrence C. Sebora & Sannan Tantiukoskula. 2011). Researchers can examine whether attitudes toward entrepreneurship, subjective norms, and perceived behavioral control mediate the links between different dimensions of entrepreneurial psychological capital and intentions. 7.3 Conclusion In conclusion, this study augments the entrepreneurship literature by examining the mediating roles of attitudes toward entrepreneurship, subjective norms, and perceived behavioral control in the relationship between entrepreneurial psychological capital and entrepreneurial intentions. Overall, the results suggest that entrepreneurial psychological capital affects entrepreneurial intentions through attitudes toward entrepreneurship and subjective norms. Moreover, subjective norms direct influence entrepreneurial intentions. These results imply that determining the mediating roles of attitudes toward entrepreneurship and subjective norms is beneficial to understand the process involved in becoming an entrepreneur. This study enhances the understanding of the entrepreneurial process by refining extant research. More importantly, these findings underscore the requirement for TPB view to be included in the entrepreneurship theory. Without TPB view, the entrepreneurship theory is unlikely to be used to develop new insights into the role of entrepreneurial psychological capital in entrepreneurial intentions.


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drinking bebaviour and alcohol related problems among fraternity and sorority members: examining the role and injunctive norms on college student gambling. Psychology of Addictive Behaviours, 17(5), 235-243.

Liñán, F. (2004). Intention-based models of entrepreneurship education. Piccola Impresa/Small Business, 2004(3), 11-35.

Liñán, F. (2008). Skill and value perceptions: how do they affect entrepreneurial intention. International Entrepreneurship and Management Journal, 4(3), 257-272.

Liñán, F., & Chen, Y. W. (2009). Development and cross-cultural application of a specific instrument to measure entrepreneurial intentions. Entrepreneurship Theory and Practice, 33(3), 593-617.

Liñán, F., Rodríguez-Cohard, J. C., & Rueda-Cantuche J. M. (2011a). Factors affecting entrepreneurial intention levels: a role for education. International Entrepreneurship and Management Journal, 7(2), 195-218.

Liñán, F., & Santos, F. J. (2007). Does social capital affect entrepreneurial intentions? International Advances in Economic Research, 13(4), 443-453.

Liñán, F., Santos, F. J., & Fernández, J. (2011b). The influence of perceptions on potential entrepreneurs. International Entrepreneurship and Management Journal, 7(3), 373-390.

Liñán, F., Urbano, D., & Guerrero, M. (2011c). Regional variations in entrepreneurial cognitions: Start-up intentions of university students in Spain. Entrepreneurship and Regional Development, 23(3/4), 187-215.

Lin, X. (2013). Determinants of entrepreneurial intentions: applying western model to the Sri Lanka context. Journal of Entreprising Culture, 21(2), 153-174.

Lo, C.-M., & Wang, J.-R. (2007). The entrepreneurial intention under environmental uncertainty. Journal of Accounting, Finance & Management Strategy, 3(1), 21-43.

Luthans F.(2002). The need for and meaning of positive organizational behavior. Journal of Organizational behavior, 23, 695-706.

Luthans, F., Avey, J. B., & Norman, S. M. (2007). Positive psychological capital: measurement and relationship with performance and satisfaction. Personnel Psychology, 60(3), 541-572.

Luthans, F., Youssef, C.M., & Avolio, B.J. (2007). Psychological capital: Developing the human competitive edge. Oxford, UK: Oxford University Press.


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Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.

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Rhodes, R. E., & Courneya, K. S. (2003). Investigating multiple components of attitude, subjective norm, and perceived control: An examination of the theory of planned behaviour in the exercise domain. British Journal of Social Psychology, 42, 129-146.

Roper, S. & Scott, J. M. (2009). Perceived financial barriers and the start-up decision: An econometric analysis of gender differences using GEM data. International Small Business Journal, 27(2), 149-171.

Sánchez, J. C. (2011). University training for entrepreneurial competencies: Its impact on intention of venture creation. International Entrepreneurship and Management Journa, 7(2), 239-254.

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Shapero, A., & Sokol, L. (1982). Social dimensions of entrepreneurship. In C. A. Kent, D. L. Sexton, & K. H. Vesper (Eds.), Encyclopedia of entrepreneurship (pp. 72-90). Englewood Cliffs , NJ: Prentice Hall.

Shiri, N., Mohammadi, D., & Hosseini, S. M. (2012). Entrepreneurial intention of agricultural students: Effects of role model, social support, social norms, and perceived desirability. Archives of Applied Science Research, 4(2), 892-897.

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Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40 (3), 879–891.

Reynolds, P. D., Bygrave, W. D., Autio, E., Cox, L. W., & Hay, M. (2002a). Global Entrepreneurship Monitor: 2002 Executive Report. Babson College/Ewin Marion Kauffman Foundation, London Business School.

Rhodes, R. E., & Courneya, K. S. (2003). Investigating multiple components of attitude, subjective norm, and perceived control: An examination of the theory of planned behaviour in the exercise domain. British Journal of Social Psychology, 42, 129-146.

Roper, S. & Scott, J. M. (2009). Perceived financial barriers and the start-up decision: An econometric analysis of gender differences using GEM data. International Small Business Journal, 27(2), 149-171.

Sánchez, J. C. (2011). University training for entrepreneurial competencies: Its impact on intention of venture creation. International Entrepreneurship and Management Journa, 7(2), 239-254.

((Scheier & Carver, 1985)) Scheier, M. F., & Carver, C. S. (1985). Optimism, coping, and health: Assessment and implications of generalized outcome expectancies. Health Psychology, 4, 219-247.

Schlaegel, C., & Koenig, M. (2014). Determinants of entrepreneurial intent: a meta-analytic test and integration of competing models. Entrepreneurship Theory and Practice, 38(1), 291-332.

Segal, G., Borgia, D., & Schoenfeld, J. (2005). The motivation to become an entrepreneur. International Journal of Entrepreneurial Behaviour & Research, 11(1), 42-57.

Shapero, A., & Sokol, L. (1982). Social dimensions of entrepreneurship. In C. A. Kent, D. L. Sexton, & K. H. Vesper (Eds.), Encyclopedia of entrepreneurship (pp. 72-90). Englewood Cliffs , NJ: Prentice Hall.

Shiri, N., Mohammadi, D., & Hosseini, S. M. (2012). Entrepreneurial intention of agricultural students: Effects of role model, social support, social norms, and perceived desirability. Archives of Applied Science Research, 4(2), 892-897.

((Snyder et al., 1996)) Snyder, C. R., Sympson, S. C., Ybasco, F. C., Borders, T. F., Babyak, M. A., & Higgins, R. L. (1996). Development and validation of the state hope scale. Journal of Personality and Social Psychology, 70, 321–335.

Solesvik, M. Z. (2013). Entrepreneurial motivations and intentions: Investigating the


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APPENDIX The Scale Items

01. Starting a firm and keeping it viable would be easy for me. 02. A career as an entrepreneur is totally unattractive to me. 03. My friends would approve of my decision to start a business. 04. I am ready to do anything to be an entrepreneur. 05. I believe I would be completely unable to start a business. 06. I will make every effort to start and run my own business. 07. I am able to control the creation process of a new business. 08. My immediate family would approve of my decision to start a business. 09. I have serious doubts about ever starting my own business. 10. If I had the opportunity and resources, I would love to start a business. 11. My colleagues would approve of my decision to start a business. 12. Amongst various options, I would rather be anything but an entrepreneur. 13. I am determined to create a business venture in the future. 14. If I tried to start a business, I would have a high chance of being successful. 15. Being an entrepreneur would give me great satisfaction. 16. It would be very difficult for me to develop a business idea. 17. My professional goal is to be an entrepreneur. 18. Being an entrepreneur implies more advantages than disadvantages to me. 19. I have a very low intention of ever starting a business. 20. I know all about the practical details needed to start a business 21. I feel confident developing new business ideas. 22. I’m optimistic about what will happen to me in the future as it pertains to a

new business. 23. I feel I can handle many things at a time as I prepare to start a business. 24. I can think of many ways to reach my current new business goals. 25. I feel confident presenting my ideas for a new business to others. 26. I can be “on my own,” so to speak, in preparing for my new business if I

have to. 27. I usually take stressful things required in new business preparation in stride. 28. I feel confident making decisions involving uncertainty and risk. 29. I feel confident convincing others to join in pursuit of my vision for a new

business. 30. I always look on the bright side if things regarding my starting a business. 31. I feel confident assembling a well-rounded team of individuals to help me

start a new business. 32. Right now I see myself as being pretty successful at new business


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preparation. 33. I feel confident acting on a new idea for a business when others do not. 34. I usually manage difficulties I encounter in my new business preparation one

way or other. 35. I can get through difficult times during my new business preparation because

I’ve experienced difficulty before. 36. If I should find myself in a jam in my new business preparation, I could think

of many ways to get out of it. 37. When things are uncertain for me as I prepare for my new business, I usually

expect the best. 38. At this time, I am meeting the new business goals that I have set for myself. 39. There are lots of ways around any problem. 40. At the present time, I am energetically pursing my new business goals. 41. I approach my preparation for a new business as if “every cloud has a silver

lining.” 42. In my preparation for starting a business, things never work out the way I

want them to. 43. When I have a setback at new business preparation, I have trouble recovering

from it, moving on. 44. If something can go wrong for me as I prepare to start a business, it will.

The items are adopted from the revised entrepreneurial intention questionnaire (Liñán et al. 2011c) and XXX

APPENDIX The Scale Items

01. Starting a firm and keeping it viable would be easy for me. 02. A career as an entrepreneur is totally unattractive to me. 03. My friends would approve of my decision to start a business. 04. I am ready to do anything to be an entrepreneur. 05. I believe I would be completely unable to start a business. 06. I will make every effort to start and run my own business. 07. I am able to control the creation process of a new business. 08. My immediate family would approve of my decision to start a business. 09. I have serious doubts about ever starting my own business. 10. If I had the opportunity and resources, I would love to start a business. 11. My colleagues would approve of my decision to start a business. 12. Amongst various options, I would rather be anything but an entrepreneur. 13. I am determined to create a business venture in the future. 14. If I tried to start a business, I would have a high chance of being successful. 15. Being an entrepreneur would give me great satisfaction. 16. It would be very difficult for me to develop a business idea. 17. My professional goal is to be an entrepreneur. 18. Being an entrepreneur implies more advantages than disadvantages to me. 19. I have a very low intention of ever starting a business. 20. I know all about the practical details needed to start a business 21. I feel confident developing new business ideas. 22. I’m optimistic about what will happen to me in the future as it pertains to a

new business. 23. I feel I can handle many things at a time as I prepare to start a business. 24. I can think of many ways to reach my current new business goals. 25. I feel confident presenting my ideas for a new business to others. 26. I can be “on my own,” so to speak, in preparing for my new business if I

have to. 27. I usually take stressful things required in new business preparation in stride. 28. I feel confident making decisions involving uncertainty and risk. 29. I feel confident convincing others to join in pursuit of my vision for a new

business. 30. I always look on the bright side if things regarding my starting a business. 31. I feel confident assembling a well-rounded team of individuals to help me

start a new business. 32. Right now I see myself as being pretty successful at new business


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Table 1. Reliability, Validity, and Descriptive Statistics of the Variables

1 2 3 4 5 6 7 8

1. EI .75 2. ESE .48** .84 3. HP .56** .79** .76 4. RL .48** .67** .75** .76 5. OM .35** .40** .53** .47** .73 6. ATT .79** .44** .51** .42** .34** .77 7. PBC .52** .59** .61** .55** .37** .48** .75 8. SN .52** .44** .47** .43** .28** .43** .53** .85 Mean .60 .61 .58 .55 .68 .58 .68 .61 SD 4.24 4.03 4.05 4.15 3.77 4.26 3.62 3.89 CR .92 .94 .89 .87 .77 .85 .79 .89

Notes: a, p<.001, b, p<.01, c, p<.05. The figures underlined and included on the diagonal are square roots of the AVE. The figures on the triangle elements are correlations among the variables. EI = entrepreneurial intention. ESE = entrepreneurial self-efficacy. HP = hope. RL = resilience. OM = optimism. ATT = attitudes toward entrepreneurship. PBC = perceived behavioral control. SN = subjective norms. GE = gender. LE = level of education. WE = working experience. SD = standard deviation. CR = composite reliability. N.A. = not applicable.


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1

Business Cycle Phases and Their Transitional Dynamics:

Does the Financial Condition Matter?

Frankie Chau* and Rataporn Deesomsak

Durham University Business School, Mill Hill Lane, Durham DH1 3LB, UK

Abstract

In this paper we use a time-varying transition probability Markov regime-switching model

developed by Filardo (1994) to investigate the usefulness of a number of new financial

conditions indicators in anticipating phase changes in the U.S. business cycle. Specifically,

using the three-month moving average Chicago Fed National Activity Index (CFNAI-MA3)

as a proxy for the business cycle, we found that the Federal Reserve Bank of Cleveland’s

(CFSI) financial stress indicators (for the U.S. equity, debt, banking, and foreign exchange

markets) were not only statistical significant determinants of the transition probabilities but

they also considerably improved the dating of the business cycle. The evidence suggests that

financial condition (or stress) can have a particularly severe effect on the economy activity

and in particular as financial stress rises, the probability that the economy moves into the

recessionary regime also begins rising. In general, these results are robust to model

specifications and consistent with the notion that rising financial stress does play an

important role in tipping the economy into the recession. Overall, our findings are important

in understanding the role of financial development as well as the conditions in ensuring

economic growth and are of great significance to the market participants and policy makers.

JEL classification: C22, C52, E32 Keywords: Markov-switching model, time-varying transition probability, financial stress, business cycle This version: July 2016 * Correspondence: Frankie Chau Tel.: +44 (0)191 334 5298; Email: [email protected]


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Factors Affecting Talent Employee Retention: A Case Study of Automotive Parts Manufacturing Company in Thailand

Nuttawut Rojniruttikula*

aFaculty of Administration and Management, King Mongkut’s Institute of Technology Ladkrabang,

Chalongkrung Rd, Bangkok, Thailand *Corresponding Author: [email protected]

ABSTRACT

The objectives of this research were to examine the levels of talent employee retention and the factors affecting talent employee retention. The sample was 400 talent employees in an automotive parts manufacturing company in Thailand. The research instrument was questionnaire. The data were analyzed by using percentage, arithmetic mean, and standard deviation. Multiple linear regression analysis was used for hypothesis testing. The results showed that the level of talent employee retention was at moderate level. Compensation, employee support, and career opportunity could affect talent employee retention in this company at statistical significant level of 0.01. Keyword: Talent Management, Automotive Parts, Compensation, Employee Support, Career Opportunity

Introduction

Thailand automotive industry was the largest in Southeast Asia and the 12th largest in the world in 2015. Most of the vehicles built in Thailand are developed and licensed by foreign producers, mainly Japanese and American. Several brands of vehicles produced in Thailand include Ford, Mazda, Honda, Isuzu, Nissan, Suzuki, Toyota, etc. Therefore, automotive industry is accounting for approximately 12% of the Thailand’s gross domestic product (GDP). In order to develop Thailand as a global automotive production base, an automotive technology research should be promoted with strong support from both public and private sectors. To this regard, the automotive parts and components sector contributes the success of Thailand’s automotive industry. In present, there are approximately 2,400 automotive suppliers in Thailand, 709 of which are original equipment manufacturers (OEMs). Major foreign parts and components manufacturers in Thailand include Bosch, Denso, Continental, Magna International, Aisin Seiki, Johnson Controls, Faurecia, ZF Friedrichshafen, Yazaki, and Lear (Thailand Board of Investment, 2015). Therefore, it


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Factors Affecting Talent Employee Retention: A Case Study of Automotive Parts Manufacturing Company in Thailand

Nuttawut Rojniruttikula*

aFaculty of Administration and Management, King Mongkut’s Institute of Technology Ladkrabang,

Chalongkrung Rd, Bangkok, Thailand *Corresponding Author: [email protected]

ABSTRACT

The objectives of this research were to examine the levels of talent employee retention and the factors affecting talent employee retention. The sample was 400 talent employees in an automotive parts manufacturing company in Thailand. The research instrument was questionnaire. The data were analyzed by using percentage, arithmetic mean, and standard deviation. Multiple linear regression analysis was used for hypothesis testing. The results showed that the level of talent employee retention was at moderate level. Compensation, employee support, and career opportunity could affect talent employee retention in this company at statistical significant level of 0.01. Keyword: Talent Management, Automotive Parts, Compensation, Employee Support, Career Opportunity

Introduction

Thailand automotive industry was the largest in Southeast Asia and the 12th largest in the world in 2015. Most of the vehicles built in Thailand are developed and licensed by foreign producers, mainly Japanese and American. Several brands of vehicles produced in Thailand include Ford, Mazda, Honda, Isuzu, Nissan, Suzuki, Toyota, etc. Therefore, automotive industry is accounting for approximately 12% of the Thailand’s gross domestic product (GDP). In order to develop Thailand as a global automotive production base, an automotive technology research should be promoted with strong support from both public and private sectors. To this regard, the automotive parts and components sector contributes the success of Thailand’s automotive industry. In present, there are approximately 2,400 automotive suppliers in Thailand, 709 of which are original equipment manufacturers (OEMs). Major foreign parts and components manufacturers in Thailand include Bosch, Denso, Continental, Magna International, Aisin Seiki, Johnson Controls, Faurecia, ZF Friedrichshafen, Yazaki, and Lear (Thailand Board of Investment, 2015). Therefore, it

is essential for the manufacturing companies to recruit and retain talent employees in order to drive new products and innovation to meet the customers’ demand. Therefore, it is necessary to investigate what factors contribute to the retention of talent employees. The purpose of this paper is to analyze the factors affecting talent employee retention in an automotive parts manufacturing company in Thailand. The paper is structured in the following way: research framework, methodology, results, conclusion, and future research.

Research Framework

The two main objectives of this research are (1) to study the level of talent employee retention in an automotive parts manufacturing company and 2) to study the factor affecting talent employee retention in an automotive parts manufacturing company. Figure 1 depicts the theoretical research model which developed from the human resource management concept and organization theory. Independent variables in this research are compensation, employee support, career opportunity, and working environment. Hypothesis:

Compensation, Employee support, Career Opportunity, and Working Environment affect talent employee retention

Independent Variable Dependent Variable

Figure 1: Research Model

Methodology The paper analyses the results of a mail survey to talent employees in an automotive parts manufacturing company in Thailand. The data for this study was drawn from a random sample of all talent employees by using questionnaires as research instrument. The questionnaire asked the respondents to rate the retention and all factors in this study (compensation, employee support, career opportunity, and working environment) using a five-point Likert scale. Then the variables used in the empirical study to explore the research questions were operationalized according to the

Organizational Factors

1. 1. Compensation 2. 2. Employee support 3. 3. Career opportunity 4. 4. Working environment 5.

Talent Employee Retention


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description of Table I which also shows the reliability of all constructs have acceptable internal consistency reliability (Cronbach’s alpha over 0.7 according to Nunnally (1978)). Table I. Operationalization of scale variables

Variables Description Cronbach’s α Independent Variable Compensation Extent to which both intrinsic and

extrinsic reward given by the organization

0.91

Employee Support

Extent to which there is perceived autonomy, supervisor support, and collegial support

0.90

Career Opportunity

Extent to which there is the chance for employee to choose type of work or job to gain advancement in the organization

0.83

Working Environment Extent to which employees feel comfortable when they work with others in the workplace

0.88

Dependent Variable Talent Employee Retention

Extent to which there is employee engagement and quality of work-life

0.93

Results

Table II reports percentage among individual characteristics (gender, age, education). The results show that most respondents are female who are 20-30 year-old. Majority of the respondents have education background in bachelor’s degree or lower and have less than 5 years working experience in the company.

Table II. Characteristics of respondents

Characteristics Percentage Gender Male 31.8

Female 68.2 Age 20-30 year-old 67.8

31-40 year-old > 40 year-old

31.2 1.0

Education Bachelor’s degree or lower 98.2 Master’s degree 1.8

Working Experience < 5 years 6-10 years > 11 years

62.2 16.8 1.0


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description of Table I which also shows the reliability of all constructs have acceptable internal consistency reliability (Cronbach’s alpha over 0.7 according to Nunnally (1978)). Table I. Operationalization of scale variables

Variables Description Cronbach’s α Independent Variable Compensation Extent to which both intrinsic and

extrinsic reward given by the organization

0.91

Employee Support

Extent to which there is perceived autonomy, supervisor support, and collegial support

0.90

Career Opportunity

Extent to which there is the chance for employee to choose type of work or job to gain advancement in the organization

0.83

Working Environment Extent to which employees feel comfortable when they work with others in the workplace

0.88

Dependent Variable Talent Employee Retention

Extent to which there is employee engagement and quality of work-life

0.93

Results

Table II reports percentage among individual characteristics (gender, age, education). The results show that most respondents are female who are 20-30 year-old. Majority of the respondents have education background in bachelor’s degree or lower and have less than 5 years working experience in the company.

Table II. Characteristics of respondents

Characteristics Percentage Gender Male 31.8

Female 68.2 Age 20-30 year-old 67.8

31-40 year-old > 40 year-old

31.2 1.0

Education Bachelor’s degree or lower 98.2 Master’s degree 1.8

Working Experience < 5 years 6-10 years > 11 years

62.2 16.8 1.0

Table III shows the mean and the standard deviations for all factors and talent employee retention. The results show that the most rated factor was working environment (3.41), followed by employee support (3.35), career opportunity (3.08), and compensation (2.94). In addition, the level of talent employee retention was at moderate level (3.35). Table III. Descriptive statistics of factors and talent employee retention

Variable Mean S.D. Level Independent Variable Compensation 2.94 0.529 Moderate Employee support 3.35 0.571 Moderate Career opportunity 3.08 0.578 Moderate Working Environment 3.41 0.501 Moderate Dependent Variable Talent employee retention 3.35 0.564 Moderate

Table IV gives the mean and correlation for the four factors and talent employee retention (TER). All four factors are compensation (COM), career opportunity (COP), employee support (EMS), and working environment (WEN). The correlation value, which range from 0.181 to 0.681, are all statistically significant (p<0.01). Table IV. The relationship (correlation) among factors and talent employee retention Mean TER COM COP EMS WEN TER 3.35 COM 2.94 0.681** COP 3.08 0.598** 0.676** EMS 3.35 0.463** 0.275** 0.235** WEN 3.41 0.364** 0.476** 0.539** 0.181**

** p < 0.01 Table V shows the regression analysis results of research model. Compensation (COM), employee support (EMS), and career opportunity (COP) positively affects the talent employee retention at statistically significant level of 0.01. In addition, working environment (WEN) had no effect on talent employee retention in this automotive parts manufacturing company. All factors could explain the variation in talent employee retention at 61.7 percent.


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Table V: Regression of Talent Employee Retention Factors Standardized

Coefficients (Beta)

t p-value

Compensation 0.525 11.970 0.000** Career opportunity 0.176 3.906 0.000** Employee support 0.295 9.064 0.000** Working environment -0.019 -0.521 0.603** R= 0.786; R2 = 0.617; SEE = 0.351; F = 159.38 ; p-value = 0.000**

** P < 0.01

Conclusion This study has sought to provide answers to research question regarding what factors affect the talent employee retention in an automotive parts manufacturing company in Thailand. To answer this question, this research focused on two aspects: (1) to study the level of talent employee well-being in an automotive parts manufacturing company in Thailand and (2) to explore the factors affecting talent employee retention by using quantitative analysis techniques (linear regression analysis). Analysis of data from questionnaires strongly indicated that the level of talent employee retention was considered as moderate. Compensation, employee support, and career opportunity positively affect the talent employee retention. These findings support prior findings that human resource management practices in compensation and rewards can help reduce absenteeism, employee retention and better quality work (Irshad, 2011). Therefore, the management level should design appropriate monetary rewards for talent employees based on particular situation. This can be evaluated by considering both situational variables and individual variables according to particular organizational contexts. In addition, employee support plays vital role to help retain talent employees. According to Fitz-enz (1990) employee retention is not influenced by a single factor, but there are hosts of factors which are responsible for retaining employees in an organization. Management need to pay attention to factors such as compensation & rewards, job security, training & developments, supervisor support culture, work environment and organization justice etc. Finally, career opportunity positively affects the talent employee retention. According to Prince (2005) promotion and opportunities for growth as a significant reason for which employees decide to leave or stay in an organization and went further by identifying influential factors pertaining to career growth opportunities, which are: advancement plans, internal promotion and accurate career previews.


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Table V: Regression of Talent Employee Retention Factors Standardized

Coefficients (Beta)

t p-value

Compensation 0.525 11.970 0.000** Career opportunity 0.176 3.906 0.000** Employee support 0.295 9.064 0.000** Working environment -0.019 -0.521 0.603** R= 0.786; R2 = 0.617; SEE = 0.351; F = 159.38 ; p-value = 0.000**

** P < 0.01

Conclusion This study has sought to provide answers to research question regarding what factors affect the talent employee retention in an automotive parts manufacturing company in Thailand. To answer this question, this research focused on two aspects: (1) to study the level of talent employee well-being in an automotive parts manufacturing company in Thailand and (2) to explore the factors affecting talent employee retention by using quantitative analysis techniques (linear regression analysis). Analysis of data from questionnaires strongly indicated that the level of talent employee retention was considered as moderate. Compensation, employee support, and career opportunity positively affect the talent employee retention. These findings support prior findings that human resource management practices in compensation and rewards can help reduce absenteeism, employee retention and better quality work (Irshad, 2011). Therefore, the management level should design appropriate monetary rewards for talent employees based on particular situation. This can be evaluated by considering both situational variables and individual variables according to particular organizational contexts. In addition, employee support plays vital role to help retain talent employees. According to Fitz-enz (1990) employee retention is not influenced by a single factor, but there are hosts of factors which are responsible for retaining employees in an organization. Management need to pay attention to factors such as compensation & rewards, job security, training & developments, supervisor support culture, work environment and organization justice etc. Finally, career opportunity positively affects the talent employee retention. According to Prince (2005) promotion and opportunities for growth as a significant reason for which employees decide to leave or stay in an organization and went further by identifying influential factors pertaining to career growth opportunities, which are: advancement plans, internal promotion and accurate career previews.

Future Research This study used the perceptions of respondents who work as operative employees within an automotive parts manufacturing company as the data source. Therefore, the future research should involve other managerial positon in the organization. Finally, the combination of quantitative and qualitative approach is also recommended in order to provide in-depth understanding of the interaction among variables in the conceptual framework.

REFERENCES Fitz-enz, J. 1990.Getting and keeping good employees. In personnel. 67(8): 25-29. Irshad, M. 2001. Factors Affecting Employee Retention: Evidence From Literature

Review. Abasyn Journal of Social Sciences; 4(1). Nunnally, J. 1978, Psychometric Theory, McGraw-Hill, New York, NY. Olivier, L. 2013. Thailand poised to Surpass Car Production target. Thailand Business

News. Thailand Board of Investment. 2015. Thailand: Global Green Automotive Production

Base. Prince, B.J. 2005. Career-Focused Employee Transfer Processes. Career

Development International, 10, 293-309.


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Human Resource Management and Employee Satisfaction Towards Policy and Management: A Case Study of Thermoplastic Injection

Manufacturing in Thailand

Wittaya Saengsaikaewa , Nuttawut Rojniruttikulb

aFaculty of Administration and Management, King Mongkut’s Institute of Technology Ladkrabang

Chalongkrung Rd., Bangkok, Thailand bAssistant Professor, Faculty of Administration and Management,

King Mongkut’s Institute of Technology Ladkrabang Chalongkrung Rd., Bangkok, Thailand

E-mail : [email protected], [email protected]

ABSTRACT

The objectives of the research were 1) to study the level of job satisfaction towards policy and management of employees at thermoplastic injection manufacturing in Thailand and 2) to compare the level of job satisfaction towards policy and management of employees at thermoplastic injection manufacturing in Thailand. This research used sample data from 262 employees who were drawn by simple random sampling method. Data were analyzed by using a statistical program. The statistics used in the research were percentage, arithmetic mean and standard deviation. One-way ANOVA was used to test the hypothesis. The results found that the level of job satisfaction towards policy and management of employees was at the moderate level. Employees who had different opinion on human resource planning and labor relation had different job satisfaction towards policy and management at statistical significant level of 0.01 and employees who had different opinion on compensation and labor relation had different job satisfaction towards policy and management at statistical significant level of 0.05. In addition, employees who had different opinion on other human resource management practices had no different job satisfaction. Keyword: Human Resource Management, Job Satisfaction Towards Policy and Management, Human Resource Planning, Labor Relation, Compensation. Preface

Nowadays, people are a very important factor for the administration because people operate all activities in the organization. If there are no employees or there are unqualified employees working in the organization, it would be hard for the administration to achieve the objectives of the organization. In order to have a well-qualified person to work in the organization, there are many factors involved. One of the significant factors is the human resource management which is the key driver to build the competitive advantage, enhance the potentiality and ability of the organization development. People have played more important role which increases the chance for people to be trained and developed skills to work more efficiently. (Suwaluk Tripornchaisak. 1999.)

The economic and social environment for running business today has changed rapidly, severely and fluctuated over time. In order to create the competitive advantage in this globalization era, organization needs to adapt to changes in the environment. For the competitiveness and sustainability of the organization for both


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Human Resource Management and Employee Satisfaction Towards Policy and Management: A Case Study of Thermoplastic Injection

Manufacturing in Thailand

Wittaya Saengsaikaewa , Nuttawut Rojniruttikulb

aFaculty of Administration and Management, King Mongkut’s Institute of Technology Ladkrabang

Chalongkrung Rd., Bangkok, Thailand bAssistant Professor, Faculty of Administration and Management,

King Mongkut’s Institute of Technology Ladkrabang Chalongkrung Rd., Bangkok, Thailand

E-mail : [email protected], [email protected]

ABSTRACT

The objectives of the research were 1) to study the level of job satisfaction towards policy and management of employees at thermoplastic injection manufacturing in Thailand and 2) to compare the level of job satisfaction towards policy and management of employees at thermoplastic injection manufacturing in Thailand. This research used sample data from 262 employees who were drawn by simple random sampling method. Data were analyzed by using a statistical program. The statistics used in the research were percentage, arithmetic mean and standard deviation. One-way ANOVA was used to test the hypothesis. The results found that the level of job satisfaction towards policy and management of employees was at the moderate level. Employees who had different opinion on human resource planning and labor relation had different job satisfaction towards policy and management at statistical significant level of 0.01 and employees who had different opinion on compensation and labor relation had different job satisfaction towards policy and management at statistical significant level of 0.05. In addition, employees who had different opinion on other human resource management practices had no different job satisfaction. Keyword: Human Resource Management, Job Satisfaction Towards Policy and Management, Human Resource Planning, Labor Relation, Compensation. Preface

Nowadays, people are a very important factor for the administration because people operate all activities in the organization. If there are no employees or there are unqualified employees working in the organization, it would be hard for the administration to achieve the objectives of the organization. In order to have a well-qualified person to work in the organization, there are many factors involved. One of the significant factors is the human resource management which is the key driver to build the competitive advantage, enhance the potentiality and ability of the organization development. People have played more important role which increases the chance for people to be trained and developed skills to work more efficiently. (Suwaluk Tripornchaisak. 1999.)

The economic and social environment for running business today has changed rapidly, severely and fluctuated over time. In order to create the competitive advantage in this globalization era, organization needs to adapt to changes in the environment. For the competitiveness and sustainability of the organization for both

internal and external environment including society and culture, demographics, politics and laws, science, technology or innovation, international trades, economics, the organization must be innovative for new products, new services, and new systems to be able to quickly respond the customer needs and mostly satisfy the demand of consumers but minimize the use of the limited resource available and maximize the benefits. Those mentioned above will not happen at all if we lack the good quality human resource to think, develop, plan, manage and control the operations according to the objectives and targets aimed by the organization. The human resource problems encountered by many organizations such as the rate of resignation, rate of absences, leaves, unpunctuality, low efficiency of employees, or also the taking over of talent and knowledge workers are still increasing even though many companies have brought in new technology for the management and administration. Lately, the management concepts about the satisfaction of employees about policies and administration have been used to create sense and acceptance of the company, develop and manage the working system continuously, and also to measure the satisfactory level of employees to use as a guide to solve the problem to some extent. Recently, there is a further study to deeply understand about the satisfaction of employees about the policies and administration of plastics manufacturing company. The concept about the satisfaction of employees about the policies and administration is considered as an important factor in human resource management because the satisfaction of employees about the policies and administration is the factor which makes the organization effective and sustain. The satisfaction of employees about the policies and administration is the driving force or motivation for employees to work with dedication as they feel that they are involved in the ownership of the organization and it is also the connection between the needs of employees in accordance with the goals of the organization as well. The employees who satisfy with the jobs they are responsible see their jobs as a channel to make benefits for the organization and are able to achieve their targets successfully by working hard and willing to make an effort to be fully functional. (Booncri Plungsiri. 2007.)

According to the reasons mentioned above, the researcher has interested in the study of the human resource management and the satisfaction of employees about the policies and administration of plastics manufacturing company. The researcher expects that the research outcome would be a guideline to analyze, reform, and plan for the solutions to the occurring human resource problems especially the rate of resignation, absences, leaves, and unpunctuality of employees as well as a guideline to develop the human resource and administrative work in consistent with the needs and motivation of employees which promotes the working efficiency and happiness of employees. Research Objectives

1. To study about the satisfactory level of employees about the policies and administration of the plastics manufacturing company 2. To compare the satisfactory level of employees about the policies and administration of the plastics manufacturing company by classifying on the opinion level about the human resource management Research Assumptions

The employees who have different opinions about human resource management in the fields of human resource planning, recruitment and selection,


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training and development, performance evaluation, remuneration and labor relation will also have different satisfactory levels about the policies and administration of the plastics manufacturing company.

Research Framework

The research framework is obtained from concepts and experience of the researcher and the study of other related papers in order to make the research framework clearer and reasonable.

The concept about the satisfactory level of employees about the policies and administration of the plastics manufacturing company has been applied from the concept of Herzberg et al.(1959) in the field of policies and administration

Figure1 Research Framework

Scope of Research

1. Scope of Population Population used in this study is 760 employees in a plastics manufacturing

company with the sample size of 262 calculated from Taro Yamane formula with the confidence level of 95 percent.

2. Scope of Variables used in the Study 1. Independent variable is human resource management including 1) human

resource planning 2) recruitment and selection 3) training and development4) performance evaluation 5) remuneration 6) labor relation. 2. Dependent variable is satisfaction about policies and administration

3. Scope of Time The researcher had collected information for 2 months from September 2014

to October 2014. 4. Research Instrument Research instrument used for data collection are open-ended questionnaire and

close-ended questionnaire using Likert Scale for the field of human resource management and satisfaction of employees about policies and administration of plastics manufacturing company Data Analysis

Questionnaire are analyzed to compare the satisfaction of employees about policies and administration of plastics manufacturing company by classifying on the opinion level about the human resource management using one-way analysis of variance.

Human Resource Management - Human Resource Planning - Recruitment and Selection - Training and Development - Performance Evaluation - Remuneration - Labor Relation

Satisfaction about

policies and administration


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training and development, performance evaluation, remuneration and labor relation will also have different satisfactory levels about the policies and administration of the plastics manufacturing company.

Research Framework

The research framework is obtained from concepts and experience of the researcher and the study of other related papers in order to make the research framework clearer and reasonable.

The concept about the satisfactory level of employees about the policies and administration of the plastics manufacturing company has been applied from the concept of Herzberg et al.(1959) in the field of policies and administration

Figure1 Research Framework

Scope of Research

1. Scope of Population Population used in this study is 760 employees in a plastics manufacturing

company with the sample size of 262 calculated from Taro Yamane formula with the confidence level of 95 percent.

2. Scope of Variables used in the Study 1. Independent variable is human resource management including 1) human

resource planning 2) recruitment and selection 3) training and development4) performance evaluation 5) remuneration 6) labor relation. 2. Dependent variable is satisfaction about policies and administration

3. Scope of Time The researcher had collected information for 2 months from September 2014

to October 2014. 4. Research Instrument Research instrument used for data collection are open-ended questionnaire and

close-ended questionnaire using Likert Scale for the field of human resource management and satisfaction of employees about policies and administration of plastics manufacturing company Data Analysis

Questionnaire are analyzed to compare the satisfaction of employees about policies and administration of plastics manufacturing company by classifying on the opinion level about the human resource management using one-way analysis of variance.

Human Resource Management - Human Resource Planning - Recruitment and Selection - Training and Development - Performance Evaluation - Remuneration - Labor Relation

Satisfaction about

policies and administration

Data Analysis Result Table1 showing quantity and percentage of personal factors

Personal Factor Quantity (person) Percentage

1. Gender Male Female

91 171

34.7 65.3

Total 262 100.0 2.Age (Year) Less than or equal to 30 More than 30 – 40 More than 40 – 50 More than 50

140 81 24 17

53.4 30.9 9.2 6.5

Total 262 100.0 3.Work experience in the company (Year) Less than or equal to 5 More than 5 – 10 More than 10 – 15 More than 15

180 31 10 41

68.7 11.8 3.8 15.7

Total 262 100.0 4.Education Level

Lower than Senior High School (Vocational Certificate) Senior High School (Vocational Certificate) Diploma (Higher Vocational Certificate) Bachelor Degree Higher than Bachelor Degree

111 83 24 42 2

42.4 31.7 9.2 16.0 0.7

Total 262 100.0 5.Average Income

Less than or equal to 15,000 Baht More than 15,000 – 20,000 Baht More than20,000 – 25,000 Baht More than 25,000 – 30,000 Baht More than30,000 – 35,000 Baht More than 35,000 Baht

60 170 17 3 3 9

22.9 64.9 6.4 1.2 1.2 3.4

Total 262 100.0 From table 1, most of the employees who filled in the questionnaire are female

who have the age of less than or equal to 30, the work experience in the company of less than or equal to 5 years, the educational level of lower than senior high school level (vocational certificate), and the average income of more than 15,000 – 20,000 Baht. Table 2 Average, Standard Deviation, and Satisfactory level of Employees about policies and administration of plastics manufacturing company

Dependent variable X S.D. Level Satisfaction about policies and administration 3.250 0.436 Moderate


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From table 2, the satisfaction of employees about policies and administration of plastics manufacturing company is in moderate level and each employee has not much different satisfactory level. Table3Average, Standard Deviation, Level and Rank of the human resource management in the plastics manufacturing company

Human Resource Management

X S.D. Level Rank

Human Resource Planning 3.176 0.438 Moderate 3 Recruitment and Selection 3.139 0.506 Moderate 5 Training and Development 3.132 0.473 Moderate 6 Performance Evaluation 3.195 0.418 Moderate 2 Remuneration 3.166 0.483 Moderate 4 Labor Relation 3.235 0.414 Moderate 1

Total 3.174 0.226 Moderate From table 3, overall level of human resource management is moderate. The human resource management in the field of labor relation is the 1st rank followed by performance evaluation, human resource planning, remuneration, recruitment and selection, training and development respectively. Table 4Analysis result for the comparison of the satisfactory level of employees about policies and administration of plastics manufacturing company by classifying on the opinion level about the human resource management

Opinion level about the human resource management p-value Human Resource Planning 0.003** Recruitment and Selection 0.683 Training and Development 0.534 Performance Evaluation 0.130 Remuneration 0.029* Labor Relation 0.003** Remark * statistical significance level of 0.05 ** statistical significance level of 0.01 From table 4, the employees who have different opinions about human resource management in term of remuneration have significantly different satisfaction about policies and administration of plastics manufacturing company with the statistical significance level of 0.05. The employees who have different opinions about human resource management in term of human resource planning and labor relation have significantly different satisfaction about policies and administration of plastics manufacturing company with the statistical significance level of 0.01. For the employees who have different opinions about other human resource management practice, the satisfaction about policies and administration of plastics manufacturing company is not different. Summary and Discussion 1. The satisfactory level of employees about policies and administration of plastics manufacturing company

From the research result, the satisfactory level of employees about policies and administration of plastics manufacturing company is in moderate level because the


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From table 2, the satisfaction of employees about policies and administration of plastics manufacturing company is in moderate level and each employee has not much different satisfactory level. Table3Average, Standard Deviation, Level and Rank of the human resource management in the plastics manufacturing company

Human Resource Management

X S.D. Level Rank

Human Resource Planning 3.176 0.438 Moderate 3 Recruitment and Selection 3.139 0.506 Moderate 5 Training and Development 3.132 0.473 Moderate 6 Performance Evaluation 3.195 0.418 Moderate 2 Remuneration 3.166 0.483 Moderate 4 Labor Relation 3.235 0.414 Moderate 1

Total 3.174 0.226 Moderate From table 3, overall level of human resource management is moderate. The human resource management in the field of labor relation is the 1st rank followed by performance evaluation, human resource planning, remuneration, recruitment and selection, training and development respectively. Table 4Analysis result for the comparison of the satisfactory level of employees about policies and administration of plastics manufacturing company by classifying on the opinion level about the human resource management

Opinion level about the human resource management p-value Human Resource Planning 0.003** Recruitment and Selection 0.683 Training and Development 0.534 Performance Evaluation 0.130 Remuneration 0.029* Labor Relation 0.003** Remark * statistical significance level of 0.05 ** statistical significance level of 0.01 From table 4, the employees who have different opinions about human resource management in term of remuneration have significantly different satisfaction about policies and administration of plastics manufacturing company with the statistical significance level of 0.05. The employees who have different opinions about human resource management in term of human resource planning and labor relation have significantly different satisfaction about policies and administration of plastics manufacturing company with the statistical significance level of 0.01. For the employees who have different opinions about other human resource management practice, the satisfaction about policies and administration of plastics manufacturing company is not different. Summary and Discussion 1. The satisfactory level of employees about policies and administration of plastics manufacturing company

From the research result, the satisfactory level of employees about policies and administration of plastics manufacturing company is in moderate level because the

employees are satisfied about working and problem solving to achieve the target within the timeline, having an opportunity to responsible for the jobs suited for their ability, having the recognition from bosses and colleagues about ideas, suggestions, practices, and trust.

2. The result of the comparison of the satisfactory level of employees about policies and administration of plastics manufacturing company by classifying on the opinion level about the human resource management

From the research findings, employees who have different opinion about human resource management in term of remuneration have significantly different satisfaction about policies and administration of plastics manufacturing company with the statistical significance level of 0.05 due to the company has to pay the proper remuneration to the employees to be enough to make a living in the current economic conditions and the remuneration system must be consistent with the wisdom and experience of employees. There must be varieties of wage, compensation, and other rewards for the employees reasonably. The company must explain and give information about the remuneration to the employees and must periodically adjust the remuneration rate to be in consistent with the labor market. De Coninck and Stilwell (2004) found that fairness in the organization and roles affects the behavior of employees, satisfaction in the remuneration has the direct effect to the feeling of employees for the fairness about remuneration while the fairness about process and role is important in predicting the satisfaction about the administrators. The result obtained from research showed that when the employees acknowledge their reasonable and fair allocated shares or compensation (fairness about remuneration) and the process of allocation (fairness about process), the employees will be satisfied about the received payment and respect the executives more.

For the human resource planning, the research result showed that the employees who have different opinion about human resource management in term of human resource planning have significantly different satisfaction about policies and administration of plastics manufacturing company with the statistical significance level of 0.01 due to the company must have the suitable number of employees in each position, the proper planning in advance for recruiting new employees to meet the number of employees required by the company, the proper planning to recruit new employees to replace the retired employees, and must be able to define the job description and qualification of the new employees which is consistent with stated by Nattaphan Kecharananta (1999). Human resource planning is the process used to forecast the human resource needs of the organization which will lead to determining how to comply with the relevant personnel, and response to surrounding factors since the person joins the organization, works in the organization until retires from the organization. The organization utilizes it as practices to ensure that the organization always has adequate number of qualified persons as well as giving the quality of work life to be able to work efficiently and effectively to comply with the objectives of the organization, personnel, and society.

For the labor relation, the research result showed that the employees who have different opinion about human resource management in term of labor relation have significantly different satisfaction about policies and administration of plastics manufacturing company with the statistical significance level of 0.01 due to the company must emphasize the employees to do the activities which create solidarity in working, encourage employees to do activities which promote them to work together to achieve the objective of working by creating team-work activities to achieve the


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objectives together. For example, the activities which improve and develop the operational system to be more effective as well as the activities which promote creativity in finding a new way to work as stated by Somchai Hirunkiti (1999). For the employee and labor relations, employee is the internal environment in which the executives must highly consider because the work achievement is actually from the employees. Labor Relations is the relationship between employees (Labor Union) and employers. Recommendation

1. The company should pay the proper remuneration to the employees to be enough to make a living in the current economic conditions and the remuneration system must be consistent with the wisdom and experience of employees. There must be varieties of wage, compensation, and other rewards for the employees reasonably. The company must explain and give information about the remuneration to the employees and must periodically adjust the remuneration rate to be in consistent with the market rate. For the human resource planning, the company should have proper number of employees in each position not too many or too less, the proper planning in advance for recruiting new employees to meet the number of employees required by the company, the proper planning to recruit new employees to replace the retired employees, and must be able to define the job description and qualification of the new employees. For the labor relations, the company must emphasize the employees to do the activities which create solidarity in working, encourage employees to do activities which promote them to work together to achieve the objective of working by creating team-work activities to achieve the objectives together and promote the employees to do the activities which improve and develop the operational system to be more effective as well as the activities which promote creativity in finding a new way to work. These would allow the company to operate efficiently afterward.

2. For the further research, the researcher should bring the research result in the research to apply and should study about the variables that represent the degree of satisfaction in each area thoroughly. For example, the satisfactory in type of work, satisfaction in the promotion of the work position, and satisfaction in salary and compensation.

REFERENCES Booncri Plungsiri.2007. Website: https://ommyphuket.wordpress.com DeConinck and Stilwell. 2004. Incorporating organizational justice, role states, pay

satisfaction and supervisor satisfaction in a model of turnover intentions, Journal of Business Research, 225 – 231

Herzberg, Frederick, Bernarol and Synderman, Barbara Bloch.1959. The Motivation to Work. New York : John Wiley and Sons, lnc.

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Somchai Hirunkiti. 1999. The Complete Human Resource Management, Bangkok: Theera Film & Scitex Co., Ltd

Suwaluk Tripornchaisak. 1999.Factors affecting the organization of salesperson : Case study, Ruam Charoen Pattana Co., Ltd. (Top Charoen Optical), Master thesis, The Graduate Program of Human Resource Development, National Institute of Development Administration.

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Is your governance big enough for your data challenge?

Janne J. Korhonen*, Kari Hiekkanen

School of Science, Aalto University, Konemiehentie 2, Espoo, Finland

*Corresponding Author: {first.lastname}@aalto.fi

ABSTRACT In today’s volatile, high-velocity markets, organizational strategy focuses on developing a series of temporary advantages through non-stop organizational transformation vis-à-vis the environment. To sense and respond to constantly emerging value deficiencies and opportunities, quantitative and fact-based real-time analysis of timely and high-quality data on the organization’s external and internal environment is vital. Proper handling of large-volume, dynamic and heterogeneous data in a networked, multi-organizational setting with limited or no central control calls for a qualitatively new approach to data governance than the context in the past. In this paper, we outline a typology of four progressively complex and sophisticated governance logics: conformant, coordinative, collaborative, and co-adaptive logic. The typology helps match the governance logic with the “size” of the data challenge, the strategic logic and the complexity of the environment. Using the lens of the co-adaptive logic, we discuss the requirements for and implications of data governance in today’s competitive environment. Keyword: data governance, big data, environmental contingency, strategic logic, governance logic.

1. Introduction To rapidly reconfigure the organization to changing circumstances on an ongoing basis calls for higher quality information and more complete knowledge than ever before: what customers find valuable now and in the future, trends in the marketplace, disruptive forces that need to be avoided or incorporated into strategies and operations. On the other hand, up-to-date visibility and insight into the organization’s own resources, competencies, and operations are needed in order to provide value on a sustainable basis. Organizations need to reconsider the role of data and analytical capabilities in decision-making, innovation management, marketing, and operations management (McAfee et al., 2012; Wamba et al. 2015). Decision-time, quantitative, fact-based analysis of large pools of data enabled by recent advances in information technology


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help organizations sense and seize new opportunities for value innovation, creation, and appropriation in an agile way. However, to leverage the full potential of these new Big Data technologies, a respective organizational capability is needed to pertinently analyze, organize, govern, manage, and leverage data. A controlled evolution of related processes and organizational structures calls for a more holistic data governance. Data is increasingly digital – mobile, sensor, and social data – and needs to be identified, categorized, and retrieved for a variety of business purposes: e.g. optimization through predictive analytics or interactive decision support through real-time analytics. The collection, storage, and mining of big data will only increase. According to IDC, the number of sensors that track, monitor, and feed data is already more than 50 billion, with about trillion-sensor networks expected within 10 years. The digital universe is doubling in size every two years, and by 2020 it will reach 44 zettabytes, or 44 trillion gigabytes (Gantz and Reinsel, 2012). Data is increasingly recognized as a valuable enterprise asset that requires equal, if not higher, degree of governance than financial assets. Organizations that have adopted data-driven decision-making have output and productivity that is 5 % to 6 % higher than that of other firms, and the relationship extends to other performance measures such as asset utilization, return on equity, and market value (Brynjolfsson, Hitt and Kim, 2011). Big Data governance needs to address a number of challenges, including, but not limited to, data security, data privacy and complex regulatory environment. Data security itself is no longer an obscure discipline within information technology (IT) that is relevant primarily to financial and government institutions. It has become a topic of conversation for C-level executives and is commonly discussed by the mainstream media. The attention is justified: the financial and reputational cost that information security incidents like data breaches can have on an organization is significant. Big Data also affects data privacy by making it easier and cheaper to collect, search, store, aggregate, and market information. Concerns about personal privacy as well potentially abusive or careless use of technology by organizations are causing a significant shift on how individuals think about personal information when held by an organization. Proper governance of data is of particular importance in an Internet of Things environment, where the lines between different organizations and industries are


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dissolving and where data ownership, stewardship, and decision rights are often intricate, ill-defined and/or fleeting. In this context, data governance across value chain partners should facilitate easy, secure and timely access to each other’s data. This calls for decisions such as what information to share, what quality controls to have, and what the access control levels are for different stakeholders. Availability of information, complex discovery of information, retention requirements, privacy requirements, and portability of data are also important considerations. Traditionally, data governance has been defined as the exercise of authority and control over the management of data assets including the planning, supervision and control over data management and use. However, we view that just as today’s complex business environment calls for a new approach to strategy, governance of data in a dynamic business ecosystem setting requires a qualitatively different logic that transcends the traditional data governance focused on a single organization. In particular, it would seem to require nimble, adaptive responses to unexpected external events through real-time identification of relevant signals, synthesis of data, and quantitative analysis of possible scenarios. To explore this conjecture, we aim to respond to the following research questions:

1. How is the logic of data governance contingent on the strategic context of the organization?

2. What are the requirements for data governance in today’s competitive environment?

The structure of the paper is as follows: In Section 2, we review the literature on environmental contingency of strategy in an integrative fashion, identifying three levels of environmental complexity and respective strategic logics. Section 3 suggests our own contribution: four commensurate logics of governance applied to data and analytics. Section 4, recapitulates the paper and sums up the response to the research questions. In this section, we also discuss the observable trends from the data governance point of view, some future directions that can be extrapolated from these trends, and the relevance of the proposed typology.

2. Increasingly Complex Strategic Context In strategy research, it has long been recognized that in order to survive over time, organizations must be aligned with their environment (e.g. Chandler, 1962; Andrews, 1971; Miles and Snow, 1978). This “fit” (Venkatraman and Camillus, 1984) is consistent with the idea of requisite variety (Ashby, 1952): internal variety of the system must match or exceed the variety external to the system. From the

help organizations sense and seize new opportunities for value innovation, creation, and appropriation in an agile way. However, to leverage the full potential of these new Big Data technologies, a respective organizational capability is needed to pertinently analyze, organize, govern, manage, and leverage data. A controlled evolution of related processes and organizational structures calls for a more holistic data governance. Data is increasingly digital – mobile, sensor, and social data – and needs to be identified, categorized, and retrieved for a variety of business purposes: e.g. optimization through predictive analytics or interactive decision support through real-time analytics. The collection, storage, and mining of big data will only increase. According to IDC, the number of sensors that track, monitor, and feed data is already more than 50 billion, with about trillion-sensor networks expected within 10 years. The digital universe is doubling in size every two years, and by 2020 it will reach 44 zettabytes, or 44 trillion gigabytes (Gantz and Reinsel, 2012). Data is increasingly recognized as a valuable enterprise asset that requires equal, if not higher, degree of governance than financial assets. Organizations that have adopted data-driven decision-making have output and productivity that is 5 % to 6 % higher than that of other firms, and the relationship extends to other performance measures such as asset utilization, return on equity, and market value (Brynjolfsson, Hitt and Kim, 2011). Big Data governance needs to address a number of challenges, including, but not limited to, data security, data privacy and complex regulatory environment. Data security itself is no longer an obscure discipline within information technology (IT) that is relevant primarily to financial and government institutions. It has become a topic of conversation for C-level executives and is commonly discussed by the mainstream media. The attention is justified: the financial and reputational cost that information security incidents like data breaches can have on an organization is significant. Big Data also affects data privacy by making it easier and cheaper to collect, search, store, aggregate, and market information. Concerns about personal privacy as well potentially abusive or careless use of technology by organizations are causing a significant shift on how individuals think about personal information when held by an organization. Proper governance of data is of particular importance in an Internet of Things environment, where the lines between different organizations and industries are


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environmental selection perspective (e.g. Hannan and Freeman, 1977), this fit can only be achieved in response to external change (responsive fit), while from the adaptation perspective (Eisenhardt and Martin, 2000; Nelson and Winter, 1982) the absorptive capacity (Cohen and Levinthal, 1990) can be actively managed (proactive fit) (Ben-Menahem, Kwee, Volberda, and Van Den Bosch, 2013). The internal change of the organization must equal or exceed external rate of change (Volberda and Lewin, 2003). Organization-wide strategic renewal actions must thereby keep up with the changes in strategic context over time. However, this temporal dimension of strategic renewal remains under-researched (Ben-Menahem et al., 2013). In the following, we identify three levels of environmental complexity and respective strategic logics based on the literature: 1) the laminar environment that requires the position logic, 2) the discontinuous environment that requires the leverage logic, and 3) the turbulent environment and the respective opportunity logic. 2.1 Laminar Environment, Position Logic The laminar environment is predictable: future events can be extrapolated from the past ones. The market and the industry structure are relatively stable and disruptions are rare. As change is faster than response (Ansoff and Sullivan, 1993), strategy needs to be distinguished from tactics (cf. Emery and Trist, 1973): an organization must have knowledge of its environment to respond pertinently in line with its goals, rather than reacting to events after the fact. This environment is commensurate with the placid, clustered environment (Emery and Trist, 1965) or the changing (level 3) environment (Ansoff and Sullivan, 1993). The requisite strategy in this type of environment would appear to be a commitment to a certain competitive position that changes relatively infrequently (Sanchez, 1996). As per this position logic (Bingham and Eisenhardt, 2008), competitive advantage stems from a unique, defensible strategic position executed through an integrated system of activities. Commitment to a specific position establishes the uniqueness and value of products or services, and the integrated activity systems reinforce how well the position is executed (Porter, 1980). 2.2 Discontinuous Environment, Leverage Logic The discontinuous environment is only partially predictable; events are familiar, but discontinuous (Ansoff and Sullivan, 1993). Strategy focuses on adaptation to change,


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which typically happens through adoption of new technologies and introduction of new products or services (Sanchez, 1996). An organization in such an environment must be a purposeful system (Ackoff, 1971) that is able to change is goals and choose between a number of possible tactical options (Emery and Trist, 1973). This type or environment is equivalent to the disturbed-reactive environment (Emery, 1965) or the discontinuous (level 4) environment (Ansoff and Sullivan, 1993). While the position logic is geared to business competitiveness at any given time, it does not account for sustained competitive advantage over time. It also places little emphasis on the impact of intra-organizational idiosyncracies on competitive position and considers resources as highly mobile in factor markets. Under leverage logic (Bingham and Eisenhardt, 2008), sustained competitive advantage stems from unique combinations of core resources that are valuable, rare, inimitable and non-substitutable (Barney, 1991): asymmetries in and immobility of valuable resources of businesses in the same industry are possible sources of competitive advantage. Due to path dependence, causal ambiguity, and social complexity of developing and trading these resources and capabilities (Barney, 2001), they are inelastic in supply and thereby a source of sustained competitive advantage (Peteraf, 1993). Leverage strategy consists of identifying, building, and exploiting a portfolio of core resources and updating this portfolio as market shifts occur (Bingham and Eisenhardt, 2008). 2.3 Turbulent Environment, Opportunity Logic In the turbulent environment, change is discontinuous, unpredictable, and much faster than response (Ansoff and Sullivan, 1993), exceeding the organization's capacity for prediction and control (Emery and Trist, 1965). This type of environment is commensurate with the turbulent field (Emery and Trist, 1965), the surpriseful (turbulence level 5) environment of (Ansoff and Sullivan, 1993), the hypercompetitive environment (D’aveni, 1994), or high velocity competition (Brown and Eisenhardt, 1998). The complexity and dynamism of this environment renders obsolete strategic concepts such as fit, sustainable advantage, barriers to entry, or long-range planning (D’aveni, 1994). In the face of hypercompetition (D’aveni, 1994), defensible strategic positions (Porter, 1980) or even valuable, rare, inimitable and non-substitutable resources (Barney, 1991) no longer provide sustainable competitive advantage. Values, competencies and systems that served the company well in the past may turn to deeply embedded “core rigidities” that inhibit development of new capabilities

environmental selection perspective (e.g. Hannan and Freeman, 1977), this fit can only be achieved in response to external change (responsive fit), while from the adaptation perspective (Eisenhardt and Martin, 2000; Nelson and Winter, 1982) the absorptive capacity (Cohen and Levinthal, 1990) can be actively managed (proactive fit) (Ben-Menahem, Kwee, Volberda, and Van Den Bosch, 2013). The internal change of the organization must equal or exceed external rate of change (Volberda and Lewin, 2003). Organization-wide strategic renewal actions must thereby keep up with the changes in strategic context over time. However, this temporal dimension of strategic renewal remains under-researched (Ben-Menahem et al., 2013). In the following, we identify three levels of environmental complexity and respective strategic logics based on the literature: 1) the laminar environment that requires the position logic, 2) the discontinuous environment that requires the leverage logic, and 3) the turbulent environment and the respective opportunity logic. 2.1 Laminar Environment, Position Logic The laminar environment is predictable: future events can be extrapolated from the past ones. The market and the industry structure are relatively stable and disruptions are rare. As change is faster than response (Ansoff and Sullivan, 1993), strategy needs to be distinguished from tactics (cf. Emery and Trist, 1973): an organization must have knowledge of its environment to respond pertinently in line with its goals, rather than reacting to events after the fact. This environment is commensurate with the placid, clustered environment (Emery and Trist, 1965) or the changing (level 3) environment (Ansoff and Sullivan, 1993). The requisite strategy in this type of environment would appear to be a commitment to a certain competitive position that changes relatively infrequently (Sanchez, 1996). As per this position logic (Bingham and Eisenhardt, 2008), competitive advantage stems from a unique, defensible strategic position executed through an integrated system of activities. Commitment to a specific position establishes the uniqueness and value of products or services, and the integrated activity systems reinforce how well the position is executed (Porter, 1980). 2.2 Discontinuous Environment, Leverage Logic The discontinuous environment is only partially predictable; events are familiar, but discontinuous (Ansoff and Sullivan, 1993). Strategy focuses on adaptation to change,


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(Leonard-Barton, 1992). Strategy in this environment is about rapidly reconfiguring the organization to changing circumstances on an ongoing basis (Sanchez, 1996). Under opportunity logic (Bingham and Eisenhardt, 2008), strategy focuses on developing a series of temporary advantages. More important than resource fortification are semi-structured strategic processes that enable dynamic strategic repositioning (Eisenhardt and Brown, 1999) guided by simple rules (Eisenhardt and Brown, 1999; Eisenhardt and Sull, 2001), leaving room for spontaneous and improvisational action to flexibly seize attractive and unexpected opportunities as they arise (Bingham and Eisenhardt, 2008). Competitive advantage is achieved and sustained through dynamic capabilities (Teece, Pisano, and Shuen, 1997; Teece, 2007; Eisenhardt and Martin, 2000; Benner and Tushman, 2003) that enable the organization to sense, seize, and reconfigure opportunities as they emerge (Teece, 2007).

3. Changing Role of Data Governance Ladley (2012) defines Data Governance as “the organization and implementation of policies, procedures, structure, roles, and responsibilities which outline and enforce rules of engagement, decision rights, and accountabilities for the effective management of information assets.” It is not a function performed by those who manage information, but there is a separation of duties between those who manage data and those who govern data management. Most of the data in an enterprise is ungoverned: it is rarely defined, its quality is unknown, its business rules are nonexistent or conflict with one another, and no one is accountable for the data (Plotkin, 2014). Governed data, by contrast, is trusted and understood, and someone is accountable for the data and for addressing issues about the data (ibid.). Without appropriate governance procedures and processes, data management efforts are prone to delays, poor quality and failure. Effective data governance, by contrast, increases accountability for the organization’s data and improves decision-making and data use; increases data consistency and improves data quality; improves data safety and security, and ensures compliance with data-focused regulations (Bhansali, 2014). Ladley (2012) makes the point that regardless of the type of data or content being governed, data governance is essentially done the same way. While we agree on the agnosticism with reference to the type and content, we argue, however, that the way data shall be governed is contingent on the strategic context and logic, as outlined in


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Section 2. In the subsections below, we suggest four progressively complex and sophisticated logics of data governance – conformant, coordinative, collaborative, and co-adaptive – and discuss the manifestation of each logic in terms of 1) strategy and policy, 2) technological architecture, 3) types of decisions governed, 4) data quality, and 5) privacy, security and compliance. 3.1 Conformant Logic As per the conformant logic, a good data governance strategy enhances the organization’s activity system in line with the strategic position (Bhansali, 2014). The role of data governance is to support the systematic management and use of data resources to achieve objectives that contribute to the business strategy. In terms of architecture and integration, the development of independent business intelligence (BI) applications, each with its own users, software and data sources, is requisite to address the relatively unchanging needs. Data warehousing technologies sufficiently address challenges pertaining to the conformant logic: performance, data integration, data quality, and visual presentation of reporting. As the basic assumption is that past trends continue into the future (cf. Ansoff, 1980), no advanced analytics are required for foresight. Data is integrated in data marts that cater for a specific departmental need, and their sponsorship, approval, funding, and governance also occur at the departmental level (Watson, 2009). These data marts typically support operational reporting that informs control decisions, such as assignment of resources to workloads, occur once and are re-used many times, often in a highly-automated system that guides the decision-makers (Saxena and Srinivasan, 2013). The conformant logic requires reliable and consistent data, which is achieved through more systematic data management practices. Data management challenges in an enterprise environment with concurrent data users include maintaining accurate and current data, resolving conflicting versions, eliminating undesirable data redundancy, balancing accessibility with security, and reporting and disseminating information. Systematic data management gives rise to a data management organization, function, or practice. Standardization and formalization of data quality tend to be the first considerations (Ladley, 2012).

(Leonard-Barton, 1992). Strategy in this environment is about rapidly reconfiguring the organization to changing circumstances on an ongoing basis (Sanchez, 1996). Under opportunity logic (Bingham and Eisenhardt, 2008), strategy focuses on developing a series of temporary advantages. More important than resource fortification are semi-structured strategic processes that enable dynamic strategic repositioning (Eisenhardt and Brown, 1999) guided by simple rules (Eisenhardt and Brown, 1999; Eisenhardt and Sull, 2001), leaving room for spontaneous and improvisational action to flexibly seize attractive and unexpected opportunities as they arise (Bingham and Eisenhardt, 2008). Competitive advantage is achieved and sustained through dynamic capabilities (Teece, Pisano, and Shuen, 1997; Teece, 2007; Eisenhardt and Martin, 2000; Benner and Tushman, 2003) that enable the organization to sense, seize, and reconfigure opportunities as they emerge (Teece, 2007).

3. Changing Role of Data Governance Ladley (2012) defines Data Governance as “the organization and implementation of policies, procedures, structure, roles, and responsibilities which outline and enforce rules of engagement, decision rights, and accountabilities for the effective management of information assets.” It is not a function performed by those who manage information, but there is a separation of duties between those who manage data and those who govern data management. Most of the data in an enterprise is ungoverned: it is rarely defined, its quality is unknown, its business rules are nonexistent or conflict with one another, and no one is accountable for the data (Plotkin, 2014). Governed data, by contrast, is trusted and understood, and someone is accountable for the data and for addressing issues about the data (ibid.). Without appropriate governance procedures and processes, data management efforts are prone to delays, poor quality and failure. Effective data governance, by contrast, increases accountability for the organization’s data and improves decision-making and data use; increases data consistency and improves data quality; improves data safety and security, and ensures compliance with data-focused regulations (Bhansali, 2014). Ladley (2012) makes the point that regardless of the type of data or content being governed, data governance is essentially done the same way. While we agree on the agnosticism with reference to the type and content, we argue, however, that the way data shall be governed is contingent on the strategic context and logic, as outlined in


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As per the conformant logic, information security tends to be seen as a by-product, if not as a necessary evil (Dlamini, Eloff, and Eloff, 2007), minimally conforming to the stipulated information security requirements. Likewise, privacy considerations are applied to information systems to comply with legislation. However, these considerations are often more like a reactive afterthought than a pervasive, built-in aspect of these systems. Table 1. Data governance following the conformant logic.

Aspect Manifestation Strategy and policy Systematic data management that

enhances the organization’s activity system in line with the strategic position

Technological architecture Independent data marts that support operational reporting

Types of decisions governed Control decisions, e.g. resource allocation Data quality Standardized and formalized Privacy, security and compliance By-product, afterthought, minimum

compliance 3.2 Coordinative Logic The coordinative governance logic requires data governance across intrafirm networks (cf. Bhansali, 2014) to enable strategic repositioning of the organization as a whole vis-à-vis the evolving market. A contrived, more encompassing and design-oriented approach to data governance is called for. Business units have a degree of freedom to make their own resource allocation and data governance decisions while cooperating with other business units. Data governance formalizes and institutionalizes procedures and policies that coordinate decision-making and data sharing across the units. It is a competency that accommodates to future contingencies. The coordinative logic calls for the creation of enterprise-wide infrastructure that provides more consistent decision-support data and supports current and future BI needs (cf. Watson, 2009). The role of data warehousing remains to be a reliable factory that can deliver data into analytics solutions, but the information captured by an enterprise data warehouse is queried and reported with BI software. Such architecture only supports descriptive analytics, however: what has happened in the past rather than predicting the future.


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Data about production processes, sales, customer interactions and more is recorded, aggregated, and analyzed. This data typically supports decisions to change capacity and set targets with a mix of a few repeatable and process-driven analyses that may be automated and ad-hoc analyses conducted upon request (Saxena and Srinivasan, 2013). Staging data for inclusion in these analyses is difficult and time-consuming; much of analysts’ time is spent extracting, transforming, and loading data for analysis, and relatively little on the analysis itself. In any event, this Analytics 1.0 (Davenport, 2013) represents a real progress in gaining an objective, deep understanding of important business phenomena and giving managers the fact-based comprehension to go beyond intuition when making decisions. Executive-level decision makers view data as a strategic asset, and data management plays a critical role within an organization. All or most areas of the organization are involved with data management processes. Data quality is built into processes (Ladley, 2012). As a company matures, it receives more tangible value from consistent, accurate and reliable data, and as it consequently starts to make better decisions, data management projects begin to receive executive- and management-level approval for data management projects. As the amount of information and the number of interconnected parties increases, so do the risks and costs of security breaches. An organization’s ability to take advantage of new opportunities and to create new markets depends heavily on its ability to provide open, accessible and available network connectivity and services (Korhonen, Hiekkanen and Mykkänen, 2012). This requires diligent and proactive protection of business assets, reputation, profitability, customer confidence and economic performance. Security measures, policies and guidelines must simultaneously both protect information and enable its safe passage to interested parties (ibid.). Information security is increasingly a business issue (von Solms and von Solms, 2005) that calls for governance of its own (von Solms, 2006). Key elements to be protected include not just data itself but also organizational assets such as trust, reputation, brand, stakeholder value and customer loyalty for which security breaches could have negative effects (Korhonen et al., 2012). Table 2. Data governance following the coordinative logic.

Aspect Manifestation Strategy and policy Federated data governance that

coordinates decision-making and data

As per the conformant logic, information security tends to be seen as a by-product, if not as a necessary evil (Dlamini, Eloff, and Eloff, 2007), minimally conforming to the stipulated information security requirements. Likewise, privacy considerations are applied to information systems to comply with legislation. However, these considerations are often more like a reactive afterthought than a pervasive, built-in aspect of these systems. Table 1. Data governance following the conformant logic.

Aspect Manifestation Strategy and policy Systematic data management that

enhances the organization’s activity system in line with the strategic position

Technological architecture Independent data marts that support operational reporting

Types of decisions governed Control decisions, e.g. resource allocation Data quality Standardized and formalized Privacy, security and compliance By-product, afterthought, minimum

compliance 3.2 Coordinative Logic The coordinative governance logic requires data governance across intrafirm networks (cf. Bhansali, 2014) to enable strategic repositioning of the organization as a whole vis-à-vis the evolving market. A contrived, more encompassing and design-oriented approach to data governance is called for. Business units have a degree of freedom to make their own resource allocation and data governance decisions while cooperating with other business units. Data governance formalizes and institutionalizes procedures and policies that coordinate decision-making and data sharing across the units. It is a competency that accommodates to future contingencies. The coordinative logic calls for the creation of enterprise-wide infrastructure that provides more consistent decision-support data and supports current and future BI needs (cf. Watson, 2009). The role of data warehousing remains to be a reliable factory that can deliver data into analytics solutions, but the information captured by an enterprise data warehouse is queried and reported with BI software. Such architecture only supports descriptive analytics, however: what has happened in the past rather than predicting the future.


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sharing across intrafirm networks Technological architecture Enterprise data warehouse feeding

business intelligence software Types of decisions governed Capacity decisions Data quality Built into processes Privacy, security and compliance Balancing protection and connectivity 3.3 Collaborative Logic The collaborative governance logic requires enterprise-wide, integrated data governance that enables the organization to establish and maintain its distinctive analytics capability (cf. Davenport and Harris, 2007) and inter-enterprise data governance to continually reconfigure this co-specialization (cf. Teece et al., 1994) vis-à-vis its ecosystem as the value configuration evolves. Big Data, as opposed to small data, is not generated purely by the organization’s internal transaction systems, but is externally sourced as well. Sources such as the social media platforms, sensors of various types, captures of audio and video recordings create an unprecedented influx of new types of data. Predictive analytics models are used to forecast the future. From the technical architecture point of view, the computing resources and infrastructure are moved from within the organization to the network. The collaborative governance logic requires distributed database and computing technologies that allow for the 3V demands of Big Data (volume, velocity, and variety). Public or private cloud computing environments help store and analyze vast volumes of data. The back-end of these cloud environments rests on efficient and resilient data center architectures that abstract commodity hardware through virtualized computation and storage technologies (Kambatla et al., 2014). As Big Data cannot be analyzed fast enough on a single server, the MapReduce model allows for fast batch data processing across parallel servers. “In memory” and “in database” analytics allow for fast number crunching. A new class of databases known as NoSQL deals with relatively unstructured data. The computational and analytical skills required by this logic gives rise to the profession of Data Science, an emerging field that combines mathematical, statistical, computer science, and behavioral science to make sense of enterprise data. As analytics takes on an increasing role in shaping the very business, data scientists are moving from the back office to the front line of working on new product and service offerings. They provide in-depth ad-hoc analyses that support strategic decisions.


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These decisions have long time-frames and large impact (Saxena and Srinivasan, 2013). It is very expensive to generate the agile analytics capacity to rapidly address ad hoc needs by adding them into the “production grade” data infrastructure (ibid.). The collaborative logic requires that information assets are woven into the fabric of the organization and information management that supports business innovation (Ladley, 2012). Data management is ingrained throughout the enterprise and data-defect prevention is the primary focus across the organization. Processes are entirely (or almost entirely) automated and continuously validated. Whereas the main concern of utilizing data and information has traditionally been the access cost to information, the trade-off between cost and quality of information has increasingly sided with the quality aspect (Lukoianova and Rubin, 2013). A precondition to collaborative data governance is that the data is of high quality. As external data needs to be combined with internal data sources that both feed analytics processes, it is pronouncedly important that the data is unambiguous and free of defects. Much of the information in the cloud is such that the originator wants to protect it, such as trade secrets, customer lists, confidential memos, etc., or information requiring the highest security, such as financial transactions, personnel files, medical records, military intelligence, etc., yet much of the “digital universe” is unprotected (Gantz and Reinsel, 2012). Cloud computing comes with serious security risks, such as information loss due to deletion, unlinking, or unreliable storage media; malicious attacks, e.g. through malicious code such as Trojan horses, malware, botnets, and spam; insecure points of cloud connection, including anonymous logins and reusable passwords, unencrypted storage, verification and transmission, rigid basic access controls or false authorizations, poor monitoring and recording of logins and activity, weak application programming interfaces, or weaknesses in third-party add-ons; and flaws in virtualization hypervisors that enable access to other guest operating systems and applications in a multitenant system (Crocker, 2014). IT management will need to provide the appropriate security to allow access to data regardless of where it is stored. The same data security policies that apply to the data when it exists in databases or files must also be enforced in the Big Data context (Villars et al., 2011). This poses a challenge, as Big Data software generally do not provide the same level of security safeguards as the fine-grained access control and security measures of conventional database management systems (ibid.). Table 3. Data governance following the collaborative logic.

sharing across intrafirm networks Technological architecture Enterprise data warehouse feeding

business intelligence software Types of decisions governed Capacity decisions Data quality Built into processes Privacy, security and compliance Balancing protection and connectivity 3.3 Collaborative Logic The collaborative governance logic requires enterprise-wide, integrated data governance that enables the organization to establish and maintain its distinctive analytics capability (cf. Davenport and Harris, 2007) and inter-enterprise data governance to continually reconfigure this co-specialization (cf. Teece et al., 1994) vis-à-vis its ecosystem as the value configuration evolves. Big Data, as opposed to small data, is not generated purely by the organization’s internal transaction systems, but is externally sourced as well. Sources such as the social media platforms, sensors of various types, captures of audio and video recordings create an unprecedented influx of new types of data. Predictive analytics models are used to forecast the future. From the technical architecture point of view, the computing resources and infrastructure are moved from within the organization to the network. The collaborative governance logic requires distributed database and computing technologies that allow for the 3V demands of Big Data (volume, velocity, and variety). Public or private cloud computing environments help store and analyze vast volumes of data. The back-end of these cloud environments rests on efficient and resilient data center architectures that abstract commodity hardware through virtualized computation and storage technologies (Kambatla et al., 2014). As Big Data cannot be analyzed fast enough on a single server, the MapReduce model allows for fast batch data processing across parallel servers. “In memory” and “in database” analytics allow for fast number crunching. A new class of databases known as NoSQL deals with relatively unstructured data. The computational and analytical skills required by this logic gives rise to the profession of Data Science, an emerging field that combines mathematical, statistical, computer science, and behavioral science to make sense of enterprise data. As analytics takes on an increasing role in shaping the very business, data scientists are moving from the back office to the front line of working on new product and service offerings. They provide in-depth ad-hoc analyses that support strategic decisions.


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Aspect Manifestation Strategy and policy Enterprise-wide, integrated data

governance that enables distinctive analytics capability and inter-enterprise collaboration

Technological architecture Cloud environment, data center architectures, distributed storage and computation technologies, predictive “Big Data” analytics, Data Science tools

Types of decisions governed Strategic decisions Data quality Ingrained data-defect prevention Privacy, security and compliance Security risks pertaining to cloud

computing; Big Data security safeguards 3.4 Co-Adaptive Logic The co-adaptive governance logic requires inter-enterprise data governance that enables virtually real-time response to unfolding threats and opportunities. As discontinuities are faster than response, data governance must enable prescriptions for action based on the detection of “weak signals” (cf. Ansoff, 1980). Such governance must address “Analytics 3.0” (Davenport, 2013), wherein analytics and optimization are embedded into virtually all business decisions at the front lines of operations. Every device, shipment, and consumer leaves a digital data trail that can be analyzed to data-enrich offerings for the benefit of customers and markets. Analytics is prescriptive and provides guidance through evaluation of possible scenarios. As per this logic, the architecture and infrastructure must enable real-time analytics. To drive decisions and actions without human intervention, the analytics processes have to be automated, and to meet the speed required to enable a decision, they must be executed lightning quick (Franks, 2014). The technical architecture must deal with unstructured data discovery over very large data sets that may have very high latency, on one hand, and adaptive analytics activities that bring the analytics to a conversation level and require very low latency, on the other (Sathi, 2012). To achieve real-time, or “decision-time” (Franks, 2014), analytics, the architecture must enable stream computing: real-time identification, data synthesis, and scoring. The co-adaptive logic calls for the ability of an organization to sense unexpected external events and to respond adaptively. These events are diverse in nature, have a large impact, and are difficult to address and to dispatch to the right decision layer


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(Saxena and Srinivasan, 2013). Digital ecosystems are critically reliant upon data to control and improve processes, to monitor inventory, to optimize production, and to identify trends in costs and performance. Controls for continuous improvement of data must therefore be included in the data production process (Hazen et al., 2014) that translates raw data to data products (Wang et al., 1995). Privacy is one of the biggest governance concerns in the co-adaptive landscape of Big Data and operational analytics (Franks, 2014). More and more data about individuals is available: consumer data collected by a variety of means, sensor data from an increasing number of smart devices, location data from a smart phone, web activity logs, information harnessed from social media. It is also becoming easier and easier to link the data and run analyses that pose a risk to individuals and to the society (ibid.). The desire to safeguard personal information is nothing new, but never before has the following question been as topical as it is today: What data should the government and organizations be allowed to collect and what safeguards should be in place about how it is used? Table 4. Data governance following the co-adaptive logic.

Aspect Manifestation Strategy and policy Sense and respond Technological architecture Prescriptive analytics, stream computing Types of decisions governed Adaptive decisions in response to

unexpected external events Data quality Controlled continuous improvement Privacy, security and compliance Heightened privacy concerns

4. Conclusions and Discussion In this paper, we first outlined, based on an integrative review of literature, a typology of three types of environments of increasing complexity and dynamism and three respective strategic logics needed to cope with the environmental complexity. We then suggested our own contribution: a typology of four governance logics that would align with the strategic logics. While there is no established notion of a strategic logic beyond the opportunity logic (cf. Bingham and Eisenhardt, 2008) in the strategy literature, we identified a distinction between “collaborative” and “co-adaptive” governance, also clearly reflected in the literature, giving rise to a fourth governance logic.

Aspect Manifestation Strategy and policy Enterprise-wide, integrated data

governance that enables distinctive analytics capability and inter-enterprise collaboration

Technological architecture Cloud environment, data center architectures, distributed storage and computation technologies, predictive “Big Data” analytics, Data Science tools

Types of decisions governed Strategic decisions Data quality Ingrained data-defect prevention Privacy, security and compliance Security risks pertaining to cloud

computing; Big Data security safeguards 3.4 Co-Adaptive Logic The co-adaptive governance logic requires inter-enterprise data governance that enables virtually real-time response to unfolding threats and opportunities. As discontinuities are faster than response, data governance must enable prescriptions for action based on the detection of “weak signals” (cf. Ansoff, 1980). Such governance must address “Analytics 3.0” (Davenport, 2013), wherein analytics and optimization are embedded into virtually all business decisions at the front lines of operations. Every device, shipment, and consumer leaves a digital data trail that can be analyzed to data-enrich offerings for the benefit of customers and markets. Analytics is prescriptive and provides guidance through evaluation of possible scenarios. As per this logic, the architecture and infrastructure must enable real-time analytics. To drive decisions and actions without human intervention, the analytics processes have to be automated, and to meet the speed required to enable a decision, they must be executed lightning quick (Franks, 2014). The technical architecture must deal with unstructured data discovery over very large data sets that may have very high latency, on one hand, and adaptive analytics activities that bring the analytics to a conversation level and require very low latency, on the other (Sathi, 2012). To achieve real-time, or “decision-time” (Franks, 2014), analytics, the architecture must enable stream computing: real-time identification, data synthesis, and scoring. The co-adaptive logic calls for the ability of an organization to sense unexpected external events and to respond adaptively. These events are diverse in nature, have a large impact, and are difficult to address and to dispatch to the right decision layer


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Table 5 sums up the response to Research Question 1: How is the logic of data governance contingent on the strategic context of the organization? The conformant logic to data governance, governing systematic data management in line with the strategic position, is requisite in a laminar environment, which is relatively stable and predictable and in which the position logic of strategy is apropos. In a more complex, discontinuous environment, the strategic logic is that of leverage: leveraging idiosyncratic core resources and competencies that bring competitive advantage. This is reflected by the coordinative governance logic, whereunder federated data governance coordinates decision-making and data sharing within the organization. Under collaborative logic, enterprise-wide integrated data governance enables inter-enterprise collaboration. This is in line with the opportunity logic of strategy that calls for reconfiguring the organization in an agile way to continually changing circumstances of the unpredictable, turbulent environment. Co-adaptive governance logic goes even one step further: as per this logic, data governance must meet the very demanding requirements of real-time stream computing and prescriptive analytics. Table 5. The governance logic is contingent on the strategic context.

Environment Strategic Logic Governance Logic Turbulent Opportunity Co-adaptive

Collaborative Discontinuous Leverage Coordinative Laminar Position Conformant The progressively sophisticated governance logics reveal a number of dimensions that determine the size of the data challenge. First of all, the institutional level at which it is enacted is higher with each successive logic, ranging from the level of intra-organizational data management system under the conformant logic to global, digital ecosystems under the co-adaptive logic. Second, the degree of agency and autonomy of the constituent systems is increasing. Whereas the focus of the conformant logic is on reactive information systems, the focal agents under the co-adaptive logic are proactive, purposeful, and intentional organizations with autonomous agency. Third, each successive governance logic comes with progressive embeddedness of its aspects. Whereas, for instance, data security and privacy under the conformant logic are relatively separate considerations from the systems they are applied to, they are increasingly inseparable aspect-systems of the data/analytics (eco)system under the higher logics. Fourth, the heterogeneity of data increases by each successive logic. Whereas linking structured data across databases is rather


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trivial, analyzing combinations of different types of structured and unstructured data poses a bigger challenge – a Big Data challenge. What are the requirements for data governance in today’s competitive environment? This was Research Question 2. To answer this question, we first note that the competitive environment in the technocratic-capitalist economy has been turning turbulent for decades (cf. Emery and Trist, 1965, 1973). Data and analytics technologies and capabilities have caught up with this development, and the data/analytics practice in leading edge companies is more or less in step with the vast complexity of the environment. However, we view that whereas Davenport and Harris (2007) defined “analytics competitors” in terms that we deem commensurate with the collaborative logic, the leading edge of data management, business analytics, and their governance seems to have shifted one step further. The new co-adaptive logic of data governance comes with a number of hitherto unforeseen requirements as outlined in Section 3.4. The new logic has far-reaching implications on organizations and the society at large. Companies that manage to increase the “clock speed” of their decision processes to real time can involve their customers in dialogues about brands and gather ideas about new products and how to market them (Galbraith, 2014). To harness the full power of analytics, however, a power shift is needed from experienced and judgmental decision makers to digital decision makers that operate in a cross-functional “newsroom” fashion (ibid.). Big Data bears the potential of “informating” (Zuboff, 1985) decision-making on an entirely new level and thereby decentralizing organizations through lateral coordination mechanisms (Berner, Graupner, and Maedche, 2014). The gap between the “analytical innovators” and the “analytically challenged” (cf. Kiron, Prentice, and Ferguson, 2014) seems to be widening, and those resorting to outdated governance logics run the risk of losing touch with the competition and the ever-faster-moving embedding context, polarizing the social field maladaptively to enclaves and vortices (cf. McCann and Selsky, 1984; Babüroðlu, 1988). This “winner takes it all” phenomenon can be seen in the way how Amazon, Apple, Facebook and Google have redefined business in the “age of the platform” (Simon, 2011). These leaders seem to have embraced the complexity, created requisite capacity, and matched their data challenge with respective governance, while the laggards are struggling to swim in the breakers. Big Data and real-time analytics raise new fundamental questions pertaining to ethics.

Table 5 sums up the response to Research Question 1: How is the logic of data governance contingent on the strategic context of the organization? The conformant logic to data governance, governing systematic data management in line with the strategic position, is requisite in a laminar environment, which is relatively stable and predictable and in which the position logic of strategy is apropos. In a more complex, discontinuous environment, the strategic logic is that of leverage: leveraging idiosyncratic core resources and competencies that bring competitive advantage. This is reflected by the coordinative governance logic, whereunder federated data governance coordinates decision-making and data sharing within the organization. Under collaborative logic, enterprise-wide integrated data governance enables inter-enterprise collaboration. This is in line with the opportunity logic of strategy that calls for reconfiguring the organization in an agile way to continually changing circumstances of the unpredictable, turbulent environment. Co-adaptive governance logic goes even one step further: as per this logic, data governance must meet the very demanding requirements of real-time stream computing and prescriptive analytics. Table 5. The governance logic is contingent on the strategic context.

Environment Strategic Logic Governance Logic Turbulent Opportunity Co-adaptive

Collaborative Discontinuous Leverage Coordinative Laminar Position Conformant The progressively sophisticated governance logics reveal a number of dimensions that determine the size of the data challenge. First of all, the institutional level at which it is enacted is higher with each successive logic, ranging from the level of intra-organizational data management system under the conformant logic to global, digital ecosystems under the co-adaptive logic. Second, the degree of agency and autonomy of the constituent systems is increasing. Whereas the focus of the conformant logic is on reactive information systems, the focal agents under the co-adaptive logic are proactive, purposeful, and intentional organizations with autonomous agency. Third, each successive governance logic comes with progressive embeddedness of its aspects. Whereas, for instance, data security and privacy under the conformant logic are relatively separate considerations from the systems they are applied to, they are increasingly inseparable aspect-systems of the data/analytics (eco)system under the higher logics. Fourth, the heterogeneity of data increases by each successive logic. Whereas linking structured data across databases is rather


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The issues become about individual consent to data collection and attacks on personal privacy, and therefore concerns relating to infringes of civic liberties. Of particular consideration are the mobile and sensor-based data, because of the potentially individualized nature of the data gathered. Are the companies forced to compromise personal privacy in the face of ever-fiercer competition and the need to customize services with finer and finer granularity, or do they have ethical leeway on the matter? Motivated by the mounting security threats and by the need to protect consumers’ personal information from abuse, regulators are taking an active interest in ensuring the rights of individuals. While the Internet can be viewed as a global network of networks, many elements of Internet law remain delineated by sovereign nations, creating the potential for regulatory conflict and spillover. European Union has proactively regulated uses of personal data, and privacy is considered a fundamental human right, which the government is responsible for providing to its citizens. Recently EU approved a General Data Protection Regulation (GDPR) that will become a law in 2018 across all 28 EU member states, replacing inconsistent laws of individual EU member states. The GDPR not only stipulates increased compliance requirements, but these are backed by heavy financial penalties, enforcing organizations to adopt new ways of collecting and using personal information. This, for its part, places new demands for holistic data governance. In our view, it is important that the governance logic employed by the organization in its data and analytics endeavors is in line with the size of the data challenge. If the most challenging use of data is to stage it for periodic operational reporting, the requirements for proper data governance are substantially less stringent than when the data must be analyzed and acted upon in real-time. The typology suggested herein denotes the first step of conceptualizing the changing nature of data governance and its contingency on the strategic context and logic. We welcome further research to operationalize the presented typology. In particular, an actionable capability maturity model or assessment instrument that would help identify and articulate the current and target levels of data governance would be of great practical value to organizations striving to match their digital capability with the complexity of their strategic context.

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The issues become about individual consent to data collection and attacks on personal privacy, and therefore concerns relating to infringes of civic liberties. Of particular consideration are the mobile and sensor-based data, because of the potentially individualized nature of the data gathered. Are the companies forced to compromise personal privacy in the face of ever-fiercer competition and the need to customize services with finer and finer granularity, or do they have ethical leeway on the matter? Motivated by the mounting security threats and by the need to protect consumers’ personal information from abuse, regulators are taking an active interest in ensuring the rights of individuals. While the Internet can be viewed as a global network of networks, many elements of Internet law remain delineated by sovereign nations, creating the potential for regulatory conflict and spillover. European Union has proactively regulated uses of personal data, and privacy is considered a fundamental human right, which the government is responsible for providing to its citizens. Recently EU approved a General Data Protection Regulation (GDPR) that will become a law in 2018 across all 28 EU member states, replacing inconsistent laws of individual EU member states. The GDPR not only stipulates increased compliance requirements, but these are backed by heavy financial penalties, enforcing organizations to adopt new ways of collecting and using personal information. This, for its part, places new demands for holistic data governance. In our view, it is important that the governance logic employed by the organization in its data and analytics endeavors is in line with the size of the data challenge. If the most challenging use of data is to stage it for periodic operational reporting, the requirements for proper data governance are substantially less stringent than when the data must be analyzed and acted upon in real-time. The typology suggested herein denotes the first step of conceptualizing the changing nature of data governance and its contingency on the strategic context and logic. We welcome further research to operationalize the presented typology. In particular, an actionable capability maturity model or assessment instrument that would help identify and articulate the current and target levels of data governance would be of great practical value to organizations striving to match their digital capability with the complexity of their strategic context.

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Is Your Information System Secure? An Exploratory Study

Peggy E. Chaudhry, and Sohail S. Chaudhry*

Department of Management and Operations/International Business, Villanova School of Business, Villanova University,

800 Lancaster Avenue, Villanova, 19085, USA *Corresponding Author: [email protected]

Abstract

This paper deals with security breaches associated with enterprise information system. More, specifically the focus of this work is related to internal threats. We review the current literature on enterprise information system security and provide some insights. Furthermore, an enterprise information system security model is proposed and we provide our preliminary investigation with a variety of businesses.

Main Description

Over the past couple of decades, enterprises have become more dependent to work closely with their headquarters and divisions as well as business partners not just within their geographical boundaries but across the globe. For example, Boeing’s 787 Dreamliner which is assembled in the USA, has many of the suppliers that are based within the USA as well as overseas in countries such as Japan, Italy, Korea, Germany, the United Kingdom, Sweden, and France (Kavilanz). Also, due to enterprise globalization and the proliferation of internet connectivity, enterprises have implemented highly sophisticated information systems to conduct and manage their internal and external business operations and processes. This technological proliferation within the business environment has created a tremendous need for the protection of an enterprise’s information security system (Chaudhry et al. 2014). The current global business environment is plagued by security breaches of enterprise information systems (EIS) resulting from both external (e.g., hackers) and internal (e.g., negligent employees) problems. These security breaches are becoming more commonplace and are increasing at an alarming rate (Paoli 2015). It is quite likely that one learns about the external breaches such as Anthem Blue Cross Insurance and Target Stores, however, internal breaches are rarely reported by the breached enterprise. A principal managerial concern is to safeguard their proprietary information (Swartz 2007; Walsh 2008). However, recent studies by the Ponemon Institute and Hewlett Packard strongly assert that the majority of top-level management do not fully comprehend risks of data breaches for their firms (Ashford 2014). The ideal security module will protect a firm’s intellectual property from both nefarious outsiders and


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http://www.csoonline.com/article/2122277/application-security/the-erp-security-challenge.html

avert insider violations from its employees. In general, there has been very little research conducted that evaluates the internal security measures of the firm (Kankanhalli et al. 2003; Price-Waterhouse Coopers, 2012). According to many reports, internal threats exceed the external infiltrations of business enterprises around the world. In this paper, we provide a succinct review of the current academic literature and present an EIS security framework to assist researchers to further study this evolving area of research. We explore the relevance of this model using a case study approach by way of interviewing top-level information systems managers in a variety of different businesses. We validate the model through using key informant in-depth interviews and qualitative research methods. In general, a principal finding of this comprehensive research is that few studies have been conducted for a variety of reasons. Keywords: Information System, Security, Internal Threats.

REFERENCES

Ashford, W. (2014, October 28). Most businesses do not understand risk of data breaches, study finds. Retrieved on December 15, 2016 at http://www.computerweekly.com/news/2240233517/Most-businesses-do-not-understand-risks-of-data-breaches-study-finds

Chaudhry, P., Chaudhry, S., Clark, K., and Jones, D. (2013). Enterprise information systems security: A case study in the banking sector. Enterprise Information Systems of the Future. Springer: Berlin.

Kankanhalli, A., Teo, H.H., Tan, B.C.Y., Wei, K.K. (2003). An integrative study of information systems security effectiveness. International Journal of Information Management, 23(2), 139-154.

Kavilanz, P., Dreamliner: Where in the world its parts come from, January 18, 2013. (Retrieved on August 15, 2016 at http://money.cnn.com/2013/01/18/news/companies/boeing-dreamliner-parts/)

Paoli, C. (2015, March 24). Enterprises are losing the security breach battle. Retrieved on August 15, 2016 at https://redmondmag.com/articles/2015/04/01/lack-of-control.aspx

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