AJBR

ISSN 1178-8933

Volume 1 Number 1 2011

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AJBR

ISSN1178‐8933

Volume1Number12011

Asian Journal of Business Research

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Copyright © 2011 Asia Business Research Corporation Limited

All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording or any information storage and retrieval system, without permission in writing from the publisher. The work published is the sole responsibility of the author/s. Founding Editor : Professor Kim-Shyan Fam, Victoria University of Wellington,

New Zealand Editor : Associate Professor Zhilin Yang, City University of Hong Kong,

Hong Kong Managing Editor : Dr Kamal Ghose, Lincoln University, New Zealand Published by Asia Business Research Corporation (ABRC) Limited PO Box 5257, Lambton Quay, Wellington 6145, New Zealand

Volume 1 Number 1, 2011

ISSN 1178-8933 First published in 2011 Printed in New Zealand

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Editorial Board

Founding Editor Professor Kim-Shyan Fam

Victoria University of Wellington, New Zealand

Editor Associate Professor Zhilin Yang

City University of Hong Kong, Hong Kong

Managing Editor Dr Kamal Ghose

Lincoln University, New Zealand

Editorial Advisory Board

Professor Russell Belk York University, Canada

Professor Susan Hart University of Strathclyde, UK

Professor John Dawson University of Stirling, UK

Professor Leslie de Chernatony University of Birmingham, UK

Professor Michael Hyman New Mexico State University, USA

Professor Phil Harris University of Chester, UK

Professor László Józsa Szechenyi Istvan University, Hungary

Professor Zuohao Hu Tsinghua University, China

Professor József Berács Corvinus University of Budapest, Hungary

Professor Kara Chan Hong Kong Baptist University, Hong Kong

Professor Samsinar Md. Sidin Universiti Putra Malaysia, Malaysia

Professor Datuk Md Zabid Abdul Rashid Universiti Tun Abdul Razak, Malaysia

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Editorial Review Board

Professor Ashish Sinha University of New South Wales, Australia

Professor Michael Basil University of Lethbridge, Canada

Dr David Waller University of Technology Sydney, Australia

Associate Professor Fang Wan University of Manitoba, Canada

Professor Nelson Ndubisi Nottingham University Malaysia, Malaysia

Professor David Ackerman California State University, Northbridge, USA

Dr Song Yang University of South Australia, Australia

Professor Sanjay K. Jain University of Delhi, India

Dr Fang Liu University of Western Australia, Australia

Associate Professor Palanisamy Ganesan VIT University, India

Professor Kenneth Alan Grossberg Waseda University, Japan

Dr Shankar Lal Gupta Birla Institute of Technology, India

Professor Yong Ki Lee Sejong University, Korea

Dr Paul Wang University of Technology Sydney, Australia

Dr Pedro Brito Universidade do Porto, Portugal

Associate Professor Ernest de Run Universiti Malaysia Sarawak, Malaysia

Professor José Luis Vázquez-Burguete Universidad de León, Spain

Dr Venugopal Shetty Multi Media University, Malaysia

Assistant Professor Andreas Petrou Cyprus International Institute of Management, Cyprus

Dr Anizah Hj Zainuddin Universiti Teknologi MARA Malaysia, Malaysia

Associate Professor Tho Nguyen University of Economics, HCM City, Vietnam

Dr Boo Ho Voon Universiti Teknologi MARA Sarawak, Malaysia

Professor Syed Anwar Hamdan Bin Mohammed University, UAE

Associate Professor Margaret Craig-Lees AUT University, New Zealand

Dr Paurav Shukla University of Brighton, UK

Dr Henry Chung Massey University, New Zealand

Dr Amy Rungpaka Tiwsakul Surrey University, UK

Associate Professor David Duval University of Otago, New Zealand

Dr Li-Wei Mai University of Westminster, UK

Dr Mathew Parackal University of Otago, New Zealand

Assistant Professor Kawpong Polyorat Khonkaen University, Thailand

Associate Professor Michele Akoorie University of Waikato, New Zealand

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Asian Journal of Business Research

Volume 1 Number 1 2011

Editorial 6 Kim-Shyan Fam, Zhilin Yang and Kamal Ghose Rankings in the Eyes of the Beholder: A Vox Populi Approach to Academic Journal Ranking

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Kim-Shyan Fam, Paurav Shukla, Ashish Sinha, Chung-Leung Luk, Mathew Parackal and Joe Choon Yean Chai

Examining the Inter-relationships among the Dimensions of Relationship Marketing

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Celine Marie Capel and Nelson Oly Ndubisi Assessing Productivity of Personal Selling Effort in India: An Econometric Approach

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Mehir Kumar Baidya, Bipasha Maity and Kamal Ghose The Influence of Brand Personality Dimensions on Brand Identification and Word-of-Mouth: The Case Study of a University Brand in Thailand

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Kawpong Polyorat Intention to Adopt Mobile Marketing: An Exploratory Study in Labuan, Malaysia

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Geoffrey Harvey Tanakinjal, Kenneth R. Deans and Brendan J. Gray Factors Differentiating Green Power Electricity User/Non-user Status in Australia

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Yiming Tang and Milind Medhekar An Exploratory Study about Culture and Marketing Strategy Adam Acar, Jeevan Madhusanka Premasara and Joshua Smith Glen

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Editorial Looking to Asia and beyond The Asian region contains some of the fastest growing economies in the world. With smaller families, increasing household income and higher education, and expanding mid-to-high income classes have provided a huge opportunity for businesses around the world. These emerging markets nevertheless have different business dynamics and practices. Primarily the aim of the Asian Journal of Business Research is to provide a platform for researchers to share their findings on Asian business practices and issues with fellow researchers worldwide. The emerging economies of some Asia countries remain stronger than most developed economies. China has recently overtaken Japan to become the world’s second largest economy. With an average GDP growth of 6-8% per annum, India is fast becoming another economic powerhouse. Other Asian countries such as Indonesia and Vietnam are being seen as business friendly. These countries have large population and a high saving rate. As most Asian governments become pro-business, large organisations begin to relocate to Asia, bringing with them knowledge, experience and understanding of consumer behaviour market research. These organizations soon develop distinct capabilities by refining and abstracting lessons from their day-to-day activities. They then standardize and document these lessons, which can form the basis of transferable business models that help them develop, source, make, and sell products across a number of geographic and product markets (Brown and Hagel, 2005). For the inaugural issue of AJBR, we present seven selected papers. The first paper ‘Rankings in the eyes of the beholder: A vox populi approach to academic journal ranking’ examines the contentious issue of journal ranking. The study establishes a top 100 business journals ranking list which is statistically significant with the SCOPUS list. The second paper, based in Malaysia surveys 400 bank customers and explores inter-relationships among the dimensions of relationship marketing. The authors find strong evidence of the inter-relatedness of trust, commitment, communication and conflict handling. The third paper explores productivity of personal selling effort in FMCG sector considering two brands of two firms in India, while the fourth paper is based on a study conducted in Thailand for a university brand and shows that the brand personality dimensions of sincerity and competence have more influence on brand identification and word-of-mouth than the dimensions of excitement and sophistication.

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The fifth paper integrates innovation characteristics of the Innovation-Diffusion Theory (IDT), perceived risk, trustworthiness, and permissibility constructs to investigate what determines user intention to adopt mobile marketing in the Malaysian markets. In the sixth paper, Yiming Tang and Milind Medhekar identify factors differentiating users and non-users of GPE (Green Power Electricity) in Australia while the seventh paper explores various dimensions of culture and marketing strategy using Hofstede’s culture dimensions, to establish a connection between cultural values and marketing strategy preferences. The researchers go on to show that managers from individualistic cultures tend to chose differentiation and niche marketing strategies over mass marketing strategies. Finally with Asia soon becoming the growth engine of the world, the editorial team at AJBR encourages academic and industry-based researchers to contribute research papers and case studies for its peer-reviewed publication. Kim-Shyan Fam Zhilin Yang Kamal Ghose

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Asian Journal of Business Research Volume 1 Number 1 2011

Rankings in the Eyes of the Beholder: A Vox Populi Approach to Academic Journal Ranking Kim-Shyan Fam Victoria University of Wellington Paurav Shukla University of Brighton Ashish Sinha University of New South Wales Chung-Leung Luk City University of Hong Kong Mathew Parackal University of Otago Joe Choon Yean Chai University of Otago

Abstract

The ranking of academic journals is a contentious issue in the current higher education environment. Across the world, peers judge academics for tenure and promotion on the basis of the quality or prestige of the journals in which they publish. This research proposes a new metric (i.e., the MAG score) to assess journal impact and ranking in the field of marketing using the vox populi approach. The findings show that the vox populi approach provides a more comprehensive measure of journal impact than other impact factor metrics from the perspective of academics.

Keywords: Journal rankings, Perceptions, Impact factor, Marketing journals, Vox populi

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Introduction

Most organizations, be they academic or corporate, use various kinds of metrics to assess their progress. However, during the past two decades, the academic discipline has experienced a phenomenal increase in the use of and reliance on various ranking metrics for recruiting and promoting academics and researchers. A growing competition exists among researchers, academic institutions, and journals to achieve high rankings (Bakir et al., 2000; Dibb and Simkin, 2005; Macdonald and Kam, 2007; Mort et al., 2004). As Rynes (2007) observes, publication in major journals significantly influences academic hiring, tenure, and promotion decisions. Citation impact has also become an important measure for evaluating senior academics in academic institutions (Adler and Harzing, 2009; Saad, 2009).

Such measures have a long-term impact on individual and institutional policy for research. For example, as Rynes (2007) notes, individuals and institutions focusing too heavily on these metrics have a very narrow definition of research productivity, which focuses on counting publications in high-impact-factor journals along with citations in the limited set of journals that such ranking systems recognize. Segalla (2008) further observes that such narrow definitions slow the diffusion of ideas and stifle academic dialogue because academics tend not to focus on other specialist journals with lower citation impact.

Rynes (2007) emphasizes that journal publications count highly toward academic promotions in the United States, and a similar trend occurs in the Asian context. Lawrence (2003, p. 259) observes, “scientists are increasingly desperate to publish in a few top journals and are wasting time and energy manipulating their manuscripts and courting editors. As a result, the objective presentation of work, the accessibility of articles, and the quality of research itself are being compromised.”

Adler and Harzing (2009) opine that many academic researchers are involved in developing highly esoteric manuscripts that have questionable contribution in advancing the overall social understanding (Perren et al., 2001).

In their examination of international business journal rankings, Adler and Harzing (2009) find that competing rankings were arbitrary and no consensus exists among researchers on the superiority of any specific ranking approach. Therefore, Adler and Harzing called for an immediate examination of existing ranking systems, suggesting that the current academic assessment systems undermine rather than foster or reward scholarship that matters. To address the calls for research in this area, this article attempts to fill the gap in the literature. A distinguishing feature of this work is the use of the vox populi (Latin for “voice of the people”) approach to address the issues raised in the context of academic journal rankings.

Surowiecki (2004) argues that a diverse collection of independently deciding individuals is likely to make certain types of decisions and predictions better than individuals or even experts. Surowiecki outlines four important elements for outlining a wise crowd: (1) diversity of opinion, (2) independence, (3) decentralization, and (4) aggregation. Many scholars believe that the academic community in general possesses

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these elements, and therefore the vox populi approach may capture the “wisdom of the crowds.” To enhance the overall approach, the current research also uses the “top-of-the-mind” recall method, which the field of advertising research frequently employs.

This study contributes to the understanding of how marketing academics across the academic spectrum (i.e., from lecturers to professors) rank academic journals and also demonstrates the prevalent trend in publishing among marketing scholars. The study also examines the range of marketing academic publications and highlights whether marketing academics’ approach to publication is becoming significantly marginalized within marketing-related journals. In addition, the study helps identify whether regional differences exist in relation to publication preference. Prior research has observed that the elite of the academic profession, who may not represent the total academic population, created most of the other ranking indices (Theoharakis and Hirst, 2002; Van Fleet et al., 2000). By taking into consideration views from a wide spectrum of academics, this study provides a much more balanced view related to academic journal rankings. Finally, this study examines whether perceptions of journal quality match independent and more objective assessments of the impact of journals.

Background

Several methods that evaluate and rank journals exist; however, each has its own merits and demerits. For example, the scientific community widely uses the impact factor method (Rynes, 2007; Saad, 2009; Segalla, 2008). However, quite a few articles and commentaries have identified inherent problems with the impact factor (Hascall et al., 2007; Kirkpatrick and Locke, 1992; Lange, 2006; Perren et al., 2001). For example, (1) long articles collect many citations and, on average, receive a higher journal impact factor than short articles; (2) journals with a short publication lag allow many short-term journal citations and thus receive a higher journal impact factor; (3) new and specialized journals tend to be excluded from the impact factor; (4) no correlation exists between an individual’s journal articles and the journal impact factor; and (5) authors tend to over-cite themselves (Guerrero, 2002).

Kacmar and Whitfield (2000) identify several inherent problems with journal rankings. They suggest that the problem arises because of the generalization in rankings, as each citation is awarded equal weight. This means that an article cited in a “laundry list of references would be given the same impact score as an article that develops a complete research sty around the previous research” (Kacmar and Whitfield, 2000, p. 395). The impact factor does not include a negative citation measure, in which the work has been criticized for the shortcomings. The citation index can also be skewed depending on the journal in which an academic wishes to publish and the popularity of certain authors in the field.

Researchers suggest the use of various methods to avoid the inherent bias in journal rankings. The “perceived quality” of journals is one of the methods used to assess the

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merit of articles published by academics (Theoharakis and Hirst, 2002). Although this method is widely accepted, a panel of senior academics collates perceived quality journal lists, as in the case of the Australian Business Deans Journal List or the Association of Business Schools’ Academic Journal Quality Guide. The intent of both these lists is to assist promotion/hiring committees and allow review teams to evaluate the quality of the research output of academics.

In New Zealand, although no such “official” list exists, the panel members of the last two PBRF (Performance Based Research Fund) exercises used a combination of recognized perceived quality lists to evaluate academics’ research output. However, a point of contention raised against the perceived quality method is that the list is heavily biased toward those on the panel, as Cudd and Morris (1988) show in their study; here, they observe that respondents who published in top journals tended to give low merit points to low-ranked journals. The tendency also exits to grant a higher grade to journals in which a person him- or herself has published (see Extejt and Smith, 1990; Jobber and Simpson, 1988; Todorov and Glanzel, 1988). Van Fleet et al., (2000) claim that if a panel comprises professors and senior academics that have considerable top journal publications, new and upcoming journals are disadvantaged from developing into quality journals because they are ignored. This disadvantageous positioning may then extend to academics who publish in such journals. Thus, the use of panels to create journal rating lists may be subject to self-preservation and predisposition bias.

Another method involves collecting rating scores from a sample of academics using survey research. The biases discussed when using a panel could still surface in this method, but with a lesser magnitude. Anchors used in the survey questionnaire, which may not reflect the content quality of the journals, introduce a serious bias. Other biases include item order effect and respondent fatigue. Although item order effect can be controlled through randomization, respondent fatigue may be difficult to control because respondents must rate a large number of journals at the same time. For example, in their study, Mort et al. (2004) use a list of 73 journals.

Another concern is that the journal list used in such rating exercises often is recycled from similar studies or contained journals that the researchers believe are appropriate. In either case, the respondents have no input in the creation of the journal list. For institutions, the journal ranking list affects the funding that the institution receives, and for academics, the ranking list plays a major role in their career advancement. Therefore, producing a rating list that is free of bias is imperative.

Methodology

The current study approach is to allow academics the freedom to nominate as many journals as they can recall in a given category. Thus, through unaided recall, top-of-the-mind awareness of a journal among the academics was produced. This established method is used extensively in measuring advertisement effectiveness and brand recall. In the current case, this method is used to assess the awareness of A-, B-, and C-grade

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journals among academics. The advantage of unaided recall lies in the requirement for mental processing of retrieving the stored encoded information without any cues (see Bagozzi and Silk, 1983; Finn, 1992; Krugman, 1986; Shapiro and Krishnan, 2001; Stapel, 1998; Till and Baack, 2005). Such an approach would embody more credibility to the journal ranking list given that academics are responsible for generating and disseminating knowledge and, in the process, have seen, read, and/or published in the journals they nominate.

Sampling Approach

The sampling approach used was based on the vox populi notion of Galton (1907), which suggests that the intelligence of the masses far exceeds that of any single individual or of experts. According to the vox populi theory, any judgment respondents make will be free of passion and uninfluenced by rhetoric. Galton (1907, p. 451) claims that the result derived from vox populi is “correct to within 1 percent of the true value and more creditable to the trustworthiness of a democratic judgment than might have been expected.” To implement the vox populi sampling approach, this study developed a sampling frame of academics by scanning marketing, tourism, and international business departmental web sites of universities across the five continents.

For countries such as Singapore, Taiwan, Korea, Malaysia, Canada, the United Kingdom, the Republic of Ireland, Australia, New Zealand, and Hong Kong, the sample included academics in marketing/tourism/international business departments of all the universities in the respective countries. For the United States, in addition to including the top 500 universities based on the Shanghai Jiao Tong Academic Ranking of World Universities List 2007 (ARWU), the sample included another 200 universities not in this list. For continental Europe, Japan, India, Africa, the Middle East, and South America, the sample included universities that were listed in both the Shanghai Jiao Tong ARWU and THES-QS World University Rankings 2007, plus an additional 200 universities not listed in either of these two lists.

The survey included academics from all levels (lecturers, senior lecturers, assistant professors, associate professors, professors, and chair professors). In total, 5336 academics from the five continents were approached through their e-mail address. Of these, 425 out-of office auto-generated messages, 390 “undeliverable” e-mails (e.g., invalid e-mail addresses), and 232 responses deemed incomplete could not be used. The survey successfully captured data from 538 academics who indicated the journals they believed were A, B, and C grade. The collective data of the 538 academics were compiled to produce a list titled “The MAG Scholar List.” MAG Scholar is the abbreviation for the Marketing in Asia Group (www.magscholar.com), which initiated the study. This group is an association of researchers from all walks of academia with research interest in Asia and residing in 35 countries.

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Questionnaire Design

The questionnaire was divided into two sections. In the first section, respondents nominated up to 10 perceived A-grade journals, including the journals the respondents had published in between 2003 and 2008. Following the nomination of A-grade journals, the next step was to nominate the B-grade and C-grade journals in which the respondents had published. A six-year time frame was deemed appropriate in an attempt to adequately capture the breadth of the respondents’ publications. The second section captures the demographic profile of the respondents. This profile includes respondents’ highest academic qualification; the country in which they obtained their degree; the percentage of teaching and research activities devoted to marketing, tourism, and international business; affiliation; and gender.

Results

Demographic Profile

The total number of respondents who completed the online survey was 538. The majority (73.4%) of the respondents was male, and roughly 74% of the respondents held a doctoral degree. On the question of teaching and research activities, most of the respondents claimed marketing (76%), followed by international business (15%) and tourism (9%). Respondent affiliation profiles showed that the highest number of respondents were affiliated with the American Marketing Association (36.6%), followed by the European Marketing Academy (18.4%). The smaller groups such as the Australia New Zealand Marketing Academy, Industrial Marketing and Purchasing, and MAG Scholar registered less than 10% of respondents. The majority of the respondents were from U.S./Canada (41.3%), U.K./Europe (26.3%), Australia/New Zealand (21.2%), and Asia/Africa (11.2%). In total, 30 countries were represented in this study.

Perceived A-grade Refereed Journals

The 538 respondents recalled 364 different so-called A-grade journals. The range in number of unaided recalls was from a low of one to a high of 10 journals. Six journals had more than 100 unaided recalls, eight journals had between 50 and 99 unaided recalls, and 16 journals had between 25 and 49 unaided recalls. The remaining journals had less than 25 unaided recalls. Journal of Marketing had the highest number of unaided recalls, occupying 9% of the share of voice. Journal of Marketing Research (347) was next, with a share of voice of 8%. Of the 364 different A-grade journals, the respondents had published in only 20.4% of them since 2003. The low publication rate would further support this study’s unbiased results compared with other similar studies in which responses were usually heavily contaminated by respondents who had an interest in the journals.

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Regionally, Journal of Marketing and Journal of the Academy of Marketing Science were the most-published outlet for academics from the United States and Canada (frequency = 7.7%). The academics from Australia and New Zealand published mainly in Journal of Marketing (5.9%), Marketing Science (5.9%), and Journal of Business Research (5.5%). European and U.K. academics published in Journal of Marketing (7.1), Journal of Consumer Research (5.7), and Journal of Business Research (5.3%). Finally, Asian academics preferred journal outlets such as Journal of Business Research (7.8%) and Journal of the Academy of Marketing Science (7.8%), followed by European Journal of Marketing (6.5%).

The journals in which respondents published the most are primarily marketing related; however, the journals in which respondents published the least show an interesting trend. Among the U.S. and Canadian academics, the least-published outlets were Academy of Management Journal (frequency = 0.4%), Academy of Management Review (0.4%), Harvard Business Review (0.4%), and Administrative Science Quarterly (0.4%). A similar trend occurred across the sample. Although the aforementioned journals are highly ranked according to their impact factor scores, marketing academics did not publish in them. This trend also highlights the increasing divide between marketing- and non-marketing-oriented journals. With a fewer number of academics preferring to publish in these journals, the divide may grow larger in the future.

Determining MAG Score and Index

To determine whether the perceived journal quality list matches the more abstract bibliometric indices, this study undertook compilation of the MAG Scholar Journal List. The list included the total unaided recalls of A-, B-, and C-grade journals. This study combined all three grade journals because most of the journals in the A-grade list included B- and C-grade journals. In addition, this study intended to produce a list generated by the respondents based on the total number of unaided recalls for each journal. A combined high recalls reflect the importance of the journal. After “clean-up,” which included deleting repeated entries for the same journal in each unaided recall and journals that were marginally unrelated to the business and management discipline, the total number of journals dropped to a manageable list. Because each journal has own features and merits, the first unaided recalled journal was allocated more weight than the second, third, fourth, and so on, until the tenth position. The study designed a formula to capture the relative standing of these journals. The sum of each journal’s value was labeled MAG score, and this score was used to rank the journal relative to the others.

MAG score = ∑n(Xin /Tn) × (1/n),

Where x is the number of unaided recalls, i is the type of journal, n is the order of journal recalls (n = 1, 2, 3, …,10), and T is the total number of journal recalls.

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As Table 1, Column 4, shows, Journal of Marketing has a MAG score of 0.4168 and a MAG index of 100. Journal of Marketing Research is next with a MAG index of 60.94 (0.2540/0.4168). The “Difference” column indicates the MAG score difference between Journal of Marketing (0.4168) and Journal of Marketing Research (0.2540) multiplied by 1000. The use of 1000 is to amplify the difference. Using the figures in the “Difference” column (Column 6), the study scientifically identified a “break” between a group of high AAA-grade journals and the second-best group. For example, a significant break occurs between Journal of the Retailing (11.30) and Journal of International Business Studies, between Academy of Management Journal (9.78) and Academy of Management Review (0.05), and between Journal of Travel Research (2.10) and Journal of International Marketing (0.80). Overall, the study identified six AAA-rated, eight AAB-rated, and 15 ABB-rated journals. This statistically derived difference suggests a large distinction among the AAA-, AAB-, ABB-, and BBB-ranked journals.

Table 1 MAG Score and Index of Top 30 Journals

Rank Journal

Total Recalls

MAG Scores

MAG Index

Difference Group

1 Journal of Marketing

394

0.4168

100.00

162.78

AAA

2 Journal of Marketing Research

347

0.2540

60.94

47.92

AAA

3 Journal of Consumer Research

305

0.2061

49.44

64.68

AAA

4 Marketing Science

242

0.1414

33.92

61.33

AAA

5 Journal of the Academy of Marketing Science

178

0.0800

19.20

22.09

AAA

6 Journal of Retailing

125

0.0580

13.91

11.30

AAA

7 Journal of International Business Studies

67

0.0467

11.19

2.69

AAB

8 European Journal of Marketing

76

0.0440

10.55

1.22

AAB

9 Journal of Business Research

82

0.0427

10.26

1.77

AAB

10 International Journal of Research in Marketing

89

0.0410

9.83

1.40

AAB

11 Management Science

74

0.0396

9.50

0.96

AAB

12 Annals of Tourism Research

43

0.0386

9.27

0.83

AAB

13 Journal of Advertising

67

0.0378

9.07

0.68

AAB

14 Academy of Management Journal

51

0.0371

8.90

9.78

AAB

15 Academy of Management Review

38

0.0273

6.56

0.05

ABB

16 Journal of Consumer Psychology 53 0.0273 6.55 0.51 ABB

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Rank Journal

Total Recalls

MAG Scores

MAG Index

Difference Group

17 International Marketing Review

34

0.0268

6.42

0.09

ABB

18 Tourism Management

35

0.0267

6.40

0.99

ABB

19 Journal of Consumer Behaviour

28

0.0257

6.17

0.07

ABB

20 Marketing Letters

28

0.0256

6.15

0.76

ABB

21 Journal of Advertising Research

47

0.0249

5.96

1.27

ABB

22 Journal of Services Marketing

30

0.0236

5.66

0.50

ABB

23 Strategic Management Journal

40

0.0231

5.54

1.62

ABB

24 Harvard Business Review

34

0.0215

5.15

0.90

ABB

25 Industrial Marketing Management

37

0.0206

4.94

0.33

ABB

26 Administrative Science Quarterly

30

0.0202

4.86

0.20

ABB

27 Journal of Service Research

40

0.0200

4.81

0.61

ABB

28 Journal of Marketing Management

33

0.0194

4.66

2.10

ABB

29 Journal of Travel Research

24

0.0173

4.16

2.10

ABB

30 Journal of International Marketing

30

0.0152

3.65

0.80

BBB

Note: For a full listing, please see www.magscholar.com.

Validation of MAG Score

To further validate the MAG score, this study carried out a correlation test with SSCI and Scopus indices. Primarily, the goal of the comparison is to identify whether similarity exits between the MAG score and the more widely known and established assessments of the impact of journals. Thomson’s ISI Web of Knowledge (SSCI) and Elsevier’s Scopus database have approximately 2470 and 2850 social sciences titles, respectively. The citation index measures the number of times authors cite an average article in a journal in related journals. Both indices provide a proxy for the quality of an article/journal.

To compare the MAG score with the SSCI and Scopus indices, the study compiled a MAG Scholar List of the top 100 journals. This list was used to identify similar journals indexed in SSCI and Scopus. In total, SSCI and Scopus have 44 and 74 journals that appeared in the top 100 MAG Scholar List, respectively. Then, all the 2009 articles (N) that appeared in each of these journals were counted. Also identified

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was the impact article citation index (I) for each journal. Next, the number of articles was multiplied by the citation index (N × I) to estimate the total annual citation impact factor (TIF) for each journal. After identifying the total impact factor (TIF) for each journal, the study conducted a correlation analysis to find the overall similarity among SSCI, Scopus, and the MAG score. The correlation between Scopus and the MAG score was significant (r = 0.488, p < 0.001). A strong and significant correlation was found for the top 20 journals (r = 0.420, p < 0.001). This analysis indicates that the MAG Scholar List is similar to the broadly defined Scopus index. The significant correlation implies that perceptions match independent, more objective assessments of the impacts of journals. However, no significant correlation appeared between the SSCI and the MAG score. This could be due to SSCI’s narrower business and management database.

Table 2 Comparison between MAG Score, SSCI and SCOPUS Indices

No. Journal Name

2009 Publn

Scopus IF

Scopus TIF

Scopus Rank -ing

SSCI IF

SSCI TIF

SSCI Rank -ing

MAG Score

MAG Rank -ing

1 Journal of Marketing 62 4.23 262.36 10 4.10 253.89 4 0.417 1

2 Journal of Marketing Research

77 2.58 198.65 6 2.57 198.20 22 0.254 2

3 Journal of Consumer Research

78 1.60 124.55 25 1.59 124.18 61 0.206 3

4 Marketing Science 88 2.41 212.12 19 3.31 291.19 9 0.141 4

5 Journal of the Academy of Marketing Science

41 2.25 92.39 75 1.29 52.85 76 0.080 5

6 Journal of Retailing 45 3.91 175.91 49 4.10 184.28 4 0.058 6

7 Journal of International Business Studies

93 2.62 243.70 41 2.99 278.26 12 0.047 7

8 European Journal of Marketing

76 1.04 79.17 283 0.71 54.11 132 0.044 8

9 Journal of Business Research

172 1.28 220.34 127 0.94 162.20 108 0.043 9

10 International Journal of Research in Marketing

39 1.81 70.60 71 1.61 62.83 60 0.041 10

11 Management Science 141 2.69 379.89 31 2.35 331.91 29 0.040 11

12 Annals of Tourism Research

40 1.70 68.00 126 1.10 44.16 Z 0.039 12

13 Journal of Advertising 34 0.94 32.07 154 1.00 34.00 102 0.038 13

14 Academy of Management Journal

63 5.50 346.31 9 6.08 382.98 2 0.037 14

15 Academy of Management Review

43 6.19 266.13 21 N/A N/A X 0.027 15

16 Journal of Consumer Psychology

69 2.26 156.07 Z 2.84 196.03 16 0.027 16

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No. Journal Name

2009 Publn

Scopus IF

Scopus TIF

Scopus Rank -ing

SSCI IF

SSCI TIF

SSCI Rank -ing

MAG Score

MAG Rank -ing

17 International Marketing Review

32 1.33 42.50 246 1.16 37.25 84 0.027 17

18 Tourism Management 91 1.81 164.47 134 1.27 115.93 77 0.027 18

19 Journal of Consumer Behaviour

28 N/A N/A Z N/A N/A X 0.026 19

20 Marketing Letters 22 0.74 16.38 101 0.70 15.36 135 0.026 20

21 Journal of Advertising Research

34 0.54 18.48 235 0.61 20.81 144 0.025 21

22 Journal of Services Marketing

46 0.86 39.66 272 N/A N/A X 0.024 22

23 Strategic Management Journal

72 3.32 239.12 32 3.34 240.77 8 0.023 23

24 Harvard Business Review

286 0.81 230.85 38 1.79 512.80 47 0.021 24

25 Industrial Marketing Management

107 1.77 189.86 133 1.40 150.12 70 0.021 25

26 Administrative Science Quarterly

14 1.83 25.63 34 2.85 39.94 14 0.020 26

27 Journal of Service Research

27 N/A N/A X N/A N/A X 0.020 27

28 Journal of Marketing Management

30 N/A N/A X N/A N/A X 0.019 28

29 Journal of Travel Research

39 1.12 43.77 Z N/A N/A X 0.017 29

30 Journal of International Marketing

20 1.97 39.49 189 1.67 33.34 58 0.015 30

31 Journal of Economic Surveys

33 1.44 47.47 Z 0.73 24.16 Z 0.014 31

32 Australasian Marketing Journal

22 N/A N/A X N/A N/A X 0.014 32

33 Journal of Public Policy and Marketing

24 0.93 22.22 13 N/A N/A X 0.014 33

34 Journal of Business and Industrial Marketing

51 0.59 30.28 214 N/A N/A X 0.013 34

35 Journal of Marketing Theory and Practice

28 0.78 21.78 386 N/A N/A X 0.013 35

36 Journal of Product Innovation Management

50 2.48 124.07 67 2.65 132.50 19 0.012 36

37 Marketing Intelligence and Planning

51 0.57 28.92 308 N/A N/A X 0.012 37

38 International Journal of Wine Marketing

0 N/A N/A X N/A N/A X 0.012 38

39 Advances in Consumer Research

0 0.10 0.00 531 N/A N/A X 0.012 39

40 Journal of Business Ethics

415 1.18 487.75 249 1.02 424.55 99 0.012 40

41 Psychology and Marketing

39 1.27 49.71 91 N/A N/A X 0.011 41

19

No. Journal Name

2009 Publn

Scopus IF

Scopus TIF

Scopus Rank -ing

SSCI IF

SSCI TIF

SSCI Rank -ing

MAG Score

MAG Rank -ing

42 Journal of Consumer Marketing

49 0.66 32.32 234 N/A N/A X 0.011 42

43 Journal of Finance 81 4.08 330.63 5 4.02 325.46 5 0.011 43

44 Tourism Economics 51 0.51 26.12 312 N/A N/A X 0.011 44

45 Journal of Personality and Social Psychology

154 5.15 792.77 Z 5.04 775.39 Z 0.010 45

46 Organization Science 50 2.68 133.93 29 2.58 128.75 21 0.010 46

47 Journal of Product and Brand Management

53 0.59 31.49 275 N/A N/A X 0.010 47

48 Event Management 0 0.33 0.00 510 N/A N/A X 0.010 48

49 Journal of Marketing Education

27 0.72 19.50 149 N/A N/A X 0.010 49

50 Journal of Interactive Marketing

0 1.66 0.00 53 0.91 0.00 112 0.009 50

51 Asia Pacific Journal of Marketing and Logistics

26 N/A N/A X N/A N/A X 0.009 51

52 Journal of Hospitality and Tourism Research

25 N/A N/A X N/A N/A X 0.009 52

53 Business Horizons 63 0.67 42.37 274 N/A N/A X 0.009 53

54 International Journal of Advertising

38 0.59 22.35 610 0.79 30.06 124 0.009 54

55 Journal of Brand Management

52 N/A N/A X N/A N/A X 0.008 55

56 Logistics Quarterly 0 N/A N/A X N/A N/A X 0.008 56

57 International Journal of Retail and Distribution Management

57 0.77 43.85 267 N/A N/A X 0.008 57

58 Journal of Business and Psychology

28 0.62 17.38 204 0.41 11.59 163 0.008 58

59 International Journal of Service Industry Management

28 1.53 42.82 87 0.87 24.22 117 0.008 59

60 Journal of Consumer Behavior

22 N/A N/A X N/A N/A X 0.008 60

61 Journal of Business Logistics

16 N/A N/A X N/A N/A X 0.008 61

62 Journal of Personal Selling and Sales Management

17 0.88 15.04 160 N/A N/A X 0.008 62

63 Qualitative Market Research

25 0.56 14.00 373 N/A N/A X 0.007 63

64 Health Marketing Quarterly

24 0.03 0.65 63 N/A N/A X 0.007 64

65 Journal of Relationship Marketing

18 0.21 3.83 497 N/A N/A X 0.007 65

66 Journal of Business 0 N/A N/A X N/A N/A X 0.007 66

20

No. Journal Name

2009 Publn

Scopus IF

Scopus TIF

Scopus Rank -ing

SSCI IF

SSCI TIF

SSCI Rank -ing

MAG Score

MAG Rank -ing

Ethics Education

67 Marketing Theory 32 N/A N/A X N/A N/A X 0.007 67

68 Journal of Euro-Marketing

11 N/A N/A X N/A N/A X 0.007 68

69 Journal of Retailing and Consumer Services

57 N/A N/A 271 N/A N/A X 0.007 69

70 International Journal of Hospitality Management

107 N/A N/A X N/A N/A X 0.007 70

71 Journal of Business to Business Marketing

9 0.76 6.81 X N/A N/A X 0.006 71

72 International Economic Review

45 1.15 51.62 Z 1.15 51.75 Z 0.006 72

73 Sloan Management Review

25 N/A N/A X N/A N/A X 0.006 73

74 Journal of Selling and Major Account Management

0 N/A N/A X N/A N/A X 0.006 74

75 Sustainable Tourism 0 N/A N/A X N/A N/A X 0.006 75

76 Journal of Occupational Health Psychology

34 2.53 86.13 Z 1.91 65.08 Z 0.006 76

77 American Economic Review

224 2.27 508.35 Z 2.29 511.84 Z 0.006 77

78 Management Review 0 N/A N/A X N/A N/A X 0.006 78

79 Journal of World Business

40 1.58 63.19 171 1.52 60.96 62 0.006 79

80 Journal of Media and Economics

13 0.48 6.24 X N/A N/A X 0.006 80

81 Journal of Current Issues and Research in Advertising

0 N/A N/A X N/A N/A X 0.006 81

82 Journal of Global Marketing

23 0.10 2.30 377 N/A N/A X 0.006 82

83 Journal of Promotion Management

25 0.12 2.94 398 N/A N/A X 0.006 83

84 Journal of Applied Psychology

117 4.27 499.48 Z 3.77 440.97 Z 0.006 84

85 British Food Journal 78 0.64 50.27 Z N/A N/A X 0.005 85

86 International Business Review

48 1.07 51.20 184 1.20 57.60 82 0.005 86

87 Journal of Management

60 3.82 229.46 45 3.08 184.80 10 0.005 87

88 Journal of Management Studies

58 2.92 169.60 68 2.56 148.36 23 0.005 88

89 Quantitative Marketing and Economics

11 1.23 13.48 298 N/A N/A X 0.005 89

21

No. Journal Name

2009 Publn

Scopus IF

Scopus TIF

Scopus Rank -ing

SSCI IF

SSCI TIF

SSCI Rank -ing

MAG Score

MAG Rank -ing

90 Journal of Sustainable Tourism

42 1.23 51.61 216 N/A N/A X 0.005 90

91 Service Industries Journal

107 0.50 53.50 352 0.45 48.36 160 0.005 91

92 Tourism Review 12 N/A N/A X N/A N/A X 0.005 92

93 Journal of Vacation Marketing

20 N/A N/A X N/A N/A X 0.005 93

94 Marketing Education Review

13 N/A N/A X N/A N/A X 0.005 94

95 Journal of Business Venturing

40 2.68 107.16 59 2.14 85.72 35 0.005 95

96 Management International Review

32 0.79 25.17 303 N/A N/A X 0.004 96

97 Consumption Markets and Culture

12 N/A N/A X N/A N/A X 0.004 97

98 Current Issues in Tourism

30 0.98 29.46 371 N/A N/A X 0.004 98

99 The Marketing Review

18 N/A N/A X N/A N/A X 0.004 99

100 Journal of Nonprofit and Public Sector Marketing

15 0.17 2.57 483 N/A N/A X 0.004 100

Note: Z = Journals not in Business Category; X: No information is available for this journal; Subject Category Analyzed: Business and Management; IF: Impact Factor; TIF: Total Impact Factor

Conclusions

Across the world, researchers debate the issue of journal rankings and the measurement of academic performance based on such rankings. This debate has led researchers to question the measures used in such rankings (Adler and Harzing, 2009; Rynes, 2007; Segalla, 2008). This study used the vox populi approach to explore how marketing academics across the spectrum rank academic journals. The findings highlight several important aspects related to (1) the development of the MAG score, which captures how academics themselves rank journals; (2) top-of-the-mind recall of journals academics want to publish in; (3) regional preferences among academics regarding the top journals and their publications pattern; and (4) comparison of the MAG score with other established measures (i.e., SSCI and Scopus) to augment the current assessment of journal rankings. The findings should help active researchers identify journals that are highly ranked among their peer groups and, in turn, increase their publication visibility. The findings should also help university managers, administrators, and policy makers understand the “collective psyche” of marketing academics across the globe and make better judgments about tenure, grants, and employment decisions.

The commonalities and distinctions between the MAG score and the other measures suggest the need for caution in employing any single journal ranking list in isolation

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for decision making. Scopus focuses on broader management journals, while the MAG score focuses highly on the marketing, tourism, and international business journals. However, the highly significant correlation between overall as well as the top 20 journals in Scopus and the MAG score suggests that decision makers can use these lists as complementary to each other. Such complementarity will assist more informed decision making in evaluating marketing scholarship among academics around the world.

Researchers who seek visible reputation and financial rewards in their academic careers experience pressures to follow the rules of research committees, many of which use the journal rankings as a barometer of an individual’s success. Many scholars have identified such rankings as arbitrary and have criticized them for their top-heavy approach (e.g., Adler and Harzing, 2009), in which only senior academics are involved in deciding the ranking of the journals (Mort et al., 2004). The approach this study uses captures a wide spectrum of academics (see Mort et al., 2004; Perren et al., 2001) and, as such, provides a balanced ranking. Although the vox populi approach has not been used previously to examine journal rankings, the significant correlation with Scopus suggests that the MAG score is a robust measure for journal rankings. The strong correlation between the measures also provides the evidence that results developed from academic perceptions about journal rankings are as good an indicator as the bibliometrics that Scopus or SSCI provides.

The comparative findings from Scopus and the MAG score show the popularity of U.S. journals over others. As both measures are global in nature, this study can conclude that the top journals with global influence in academic circles are primarily of U.S. origin. Of the top 10 journals, eight are U.S. based and two are European based. This demonstrates the significant influence of U.S. academic and professional bodies on the marketing discipline on a global scale. As for the European journals among the top 10, the International Journal of Research in Marketing is the official journal of the European Marketing Academy, which represents the largest body of European marketing academics. However, European Journal of Marketing is not tied to an association and is the only journal with a regional association title.

Among the top 10 Scopus journals in the management field, five relate to the field of marketing, of which four also are represented in the MAG score. Journal of Public Policy & Marketing is ranked 33 in the MAG score list. In addition, only one general business–related journal appears in the top 10 (i.e., Journal of Business Research) of the MAG score. The other nine journals strongly focus on the marketing discipline. The study results show a clear preference among marketing academics toward journals that focus on the marketing discipline only. This lower recall as well as publication preference toward broader management journals is alarming. Most journal rankings regard journals such as Academic of Management Journal, Academy of Management Review, and Administrative Science Quarterly highly. The lower inclination to publish in management journals may be detrimental to the marketing discipline at large. Marketing academics need to change their mindset toward management journals if the field is to obtain broader acceptance among academics from other disciplines as well as practitioners. Changing the current mindset will

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encourage cross-fertilization of ideas that are necessary for a healthy and impactful academic dialogue.

Three issues need further attention. First, according to the journal ranking lists, the top 30 journals are highly established in their respective fields, and following these lists may create a bias among academics toward not publishing in low-rated journals. In this sense, newer journals are particularly disadvantaged (Adler and Harzing, 2009; Lawrence, 2003; Mort et al., 2004). Therefore, future research needs to develop further measurement that focuses on new journals appearing in the field. Second, journal ranking lists themselves are dynamic; they change according to the debate in the field as well as journals’ overall impact. In turn, this may affect academic perceptions as well, and therefore perceptual studies should be undertaken on a regular basis. Third, caution should be exercised in following the journal ranking lists alone as a measure for decisions regarding promotion, tenure, and other significant decisions because publication in a high-ranking journal does not specify the quality of the article. As mentioned previously, measures such as citations of an article, peer evaluation, and industry impact should also be considered when making judgments about an academic’s scholarship.

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Asian Journal of Business Research Volume 1 Number 1 2011

Examining the Inter-relationships among the Dimensions of Relationship Marketing Celine Marie Capel University of Nottingham Malaysia, Malaysia Nelson Oly Ndubisi University of Nottingham Malaysia, Malaysia

Abstract

The purpose of this paper is to examine the interrelationship among the dimensions of relationship marketing namely, trust, commitment, communication and conflict handling. Little is understood about the inter-relationship among these factors as all prior efforts in the area have focused on their impact on relationship outcomes such as satisfaction, loyalty and word-of-mouth communication. Some 400 bank customers in Malaysia were surveyed, with 220 usable responses on which analysis was based. The research found that there is strong evidence for the interrelatedness of trust, commitment, communication and conflict handling. Important managerial and marketing implications of the findings are discussed.

Keywords: Trust, Commitment, Communication, Conflict handling, Banking, Malaysia

Introduction

The intense competitive nature of today’s business environment among other factors has resulted in the building of long lasting relationships between organizations and customers. Transactional marketing, which has been referred to as a hit and run strategy is increasingly giving way to a more customer centric approach known as relationship marketing. To achieve success in today’s complex and competitive market, researchers have prescribed a number of key areas that need to be considered if the customer is to be served satisfactorily. One of these key areas is leveraging firm-customer relationship to gain privileged information about customers and

27

thereby better understand their needs. Relationship marketing therefore enables the firm to get closer to its customers in order to correctly sense and serve their expectations in a manner superior to competition.

Marketing studies have documented the following dimensions of relationship marketing namely, trust (Morgan and Hunt, 1994; Ndubisi and Chan, 2005), commitment (Morgan and Hunt, 1994; Ndubisi, 2006; Wong and Sohal, 2002), conflict handling (Ndubisi, 2006), communication or sharing of secrets (Crosby et al., 1990; Morgan and Hunt, 1994). Many have also investigated the impact of these constructs on overall relationship quality, customer satisfaction and customer loyalty. However, investigations of the inter-relatedness of these key relationship marketing variables have been ignored, even though such studies are necessary for understanding how these factors support one another as antecedents of customer satisfaction and customer loyalty, the goals of most organisations.

The aims of this study are twofold. The first is to better understand the murky area of the interrelationship or interconnectivity of the key dimensions of relationship marketing. Due to dearth of research in this area, there is a very poor understanding of how these factors interplay. So far, works in this area have tried to link selected RM dimensions to higher order constructs such as relationship quality, customer satisfaction and customer loyalty, ignoring the possible relationships among these dimensions. The understanding gained by testing such inter-relationships will show how one dimension (or virtue) can grow by growing others. The second impetus for the study is to unveil the actual influences of the RM constructs on customer loyalty. Although there are few existing studies focusing on this relationship, the contextual nature and the mixed findings of these efforts created a need for the Malaysian perspective to be documented. As mentioned earlier, Malaysia has graduated to become an important unit of the world economy that cannot be ignored today because of its impact on the global economic system. The outcome of this research holds significant benefits for marketing researchers and practitioners, as well as banks (and other service organizations by extension) who are interested in the subjects of customer relationship management and customer loyalty.

Literature Review

Interest in the economics of long-lasting customer relationship has been growing since the last few decades. Some of the motives for organizational investment in building relationship with customers include, access to privileged information on customers’ needs and wants (Ndubisi, 2004), mutual rewards (Rapp and Collins, 1990), cost reduction and increase in profitability (Ndubisi, 2004). Reichheld (1993) reported that a 5 percent increase in customer retention grew the company’s profit by 60 percent by the fifth year. It has been argued that long-term relationships where both parties over time learn how best to interact with each other lead to decreasing relationship costs for the customer as well as for the supplier or service provider.

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Relationship marketing has been defined as a strategy to attract, maintain and enhance customer relationships (Berry, 1983). It was argued that the goals of relationship marketing are to create and maintain lasting relationships between the firm and its customers that are rewarding for both sides (Rapp and Collins, 1990). Gronroos (1994) reasoned that relationship marketing is to establish, maintain, and enhance relationships with customers and other partners, at a profit, so that the objectives of the parties involved are met This is achieved by a mutual symbiosis and fulfillment of promises (Ndubisi, 2004). Gummesson (1993) concluded that relationship marketing is a strategy where the management of interactions, relationships and networks are fundamental issues. The interaction and network approach of industrial marketing and modern service marketing approaches, clearly views marketing as an interactive process in a social context where relationship building and management are a vital underpinning (Bagozzi, 1975; Webster, 1992). Kotler (1992) prescribed that companies must move from short-term transaction-oriented goal to long-term relationship-building goal. In an analysis of the current developments in business and in marketing, Webster (1992, p. 14) reported that “there has been a shift from a transaction to a relationship focus.”

Kavali et al. (1999) indicated that relationship marketing is about healthy relationships characterized by trust, equity, and commitment. Key virtues that have been conceptualized in the relationship marketing literature include trust (Morgan and Hunt, 1994; Ndubisi and Chan, 2005), commitment (Morgan and Hunt, 1994; Wong and Sohal, 2002), conflict handling (Ndubisi, 2006), and communication (Crosby et al., 1990). This study conceptualizes that these concepts are interrelated (Figures 1- 4).

Figure 1 Predictors of Trust (Model 1)

COMMIT

CONFLICT

TRUST

COMM

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Figure 2 Predictors of Commitment (Model 2)

Figure 3 Predictors of Communication (Model 3)

COMMIT

CONFLICT

TRUST

COMM

COMMIT

CONFLICT

COMM

TRUST

30

Figure 4 Predictors of Conflict Handling (Model 4)

Trust and its role in fostering firm-customer relationship quality and customer satisfaction have been studied significantly. However, relatively little attention has gone into the investigation of its impact on customer loyalty. Abratt and Russell (1999) remarked that partners in a relationship need to have trust and good intentions among themselves. Schurr and Ozanne (1985) defined trust as the belief that a partner’s word or promise is reliable and a party will fulfill his/her obligations in the relationship. Moorman et al. (1993) defined the term as “…a willingness to rely on an exchange partner in whom one has confidence. Other scholars have defined trust in terms of opportunistic behavior (Dwyer et al., 1987), shared values (Morgan and Hunt, 1994), mutual goals (Wilson, 1995), making and keeping promises (Bitner 1995), uncertainty (Crosby et al., 1990), and actions with positive outcomes (Anderson and Narus, 1984). A betrayal of this trust (by the supplier or service provider) can lead to customer dissatisfaction and defection. Gronroos (1990) asserted that the resources of the seller - personnel, technology and systems – have to be used in such a manner that the customer’s trust in the resources involved and, thus, in the firm itself, is maintained and strengthened. Indeed, one would expect a positive outcome from a partner on whose integrity one can rely on confidently (Morgan and Hunt, 1994). Pearce (1974) differentiated this trust (i.e. cognitive trust – the subjective probability that the other will behave trustworthily) from trusting behavior. According to Pearce, it would be possible for a person to engage in trusting behavior without having reached “a cognitive state of trust”. Trust is expected to lead to customer loyalty.

COMMIT

CONFLICT

TRUST

COMM

31

Commitment is one of the important variables for understanding the strength of a marketing relationship, and it is a useful construct for measuring the likelihood of customer loyalty as well as for predicting future purchase frequency (Dwyer et al., 1987; Gundlach et al., 1995; Morgan and Hunt, 1994). Wilson (1995) argued that commitment is the most common dependent variable used in buyer-seller relationship studies. In sociology, the concept of commitment is used to analyze both individual and organizational behavior (Becker, 1960). Sociologists use commitment as a descriptive concept to mark out forms of action characteristic of particular kinds of people or groups (Wong and Sohal, 2002), while psychologists define commitment in terms of decisions or cognitions that fix or bind an individual to a behavioral disposition (Kiesler, 1971). In marketing, Moorman et al. (1992) defined commitment as an enduring desire to maintain a valued relationship. This implies a higher level of obligation to make a relationship succeed and to make it mutually satisfying and beneficial (Gundlach et al., 1995; Morgan and Hunt, 1994). Since commitment is higher among individuals who believe that they receive more value from a relationship, highly committed customers should be willing to reciprocate effort on behalf of a firm due to past benefits received (Mowday et al., 1982) and highly committed firms will continue to enjoy the benefits of such reciprocity.

Communication refers to the ability to provide timely and trustworthy information. Communications is now viewed as an interactive dialogue between the company and its customers that takes place during the pre-selling, selling, consuming and post-consuming stages (Anderson and Narus, 1990). Communication in relationship marketing includes providing information that can be trusted; providing timely information, and providing information when problem occurs. It is the role of communication to build awareness, build consumer preference by promoting quality, value, performance and other features, convince and encourage customers to make the purchase decision and to nurture quality customer relationship. Communication also tells a customer who is dissatisfied what the organization is doing to rectify the source of dissatisfaction. Berry (1995) remarked that buyer-seller interactions must be open, sincere and frequent because improving the quality of the relationship will encourage customer retention. Open, sincere and frequent interactions describe effective communication. When there is effective communication between the bank and the customers, customers are likely to be more loyal.

Dwyer et al. (1987) defined conflict handling as the supplier’s ability to minimize the negative consequences of manifest and potential conflicts. Conflicts handling reflects the supplier’s ability to avoid potential conflicts, solve manifest conflicts before they create problems and the ability to discuss openly solutions when problems arise. How conflicts are handled will ensure loyalty, exit or voice. Rusbult et al. (1988) concluded that the likelihood that an individual will engage in these behaviors depends on the degree of prior satisfaction with the relationship, the magnitude of the person’s investment in the relationship and an evaluation of the alternatives one has. The ability of the bank to handle conflict well will impact customer loyalty. To handle conflicts efficiently, there must be open, sincere and frequent interaction (Berry, 1995) until acceptable, satisfactory resolution is reached.

The above discussion leads to the following five main hypotheses:

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H1: There is a significant relationship between (a) commitment, (b) communication, (c) conflict handling and trust.

H2: There is a significant relationship between (a) trust, (b) communication, (c) conflict handling and commitment.

H3: There is a significant relationship between (a) trust, (b) commitment, (c) conflict handling and communication.

H4: There is a significant relationship between (a) trust, (b) commitment, (c) communication and conflict handling.

Methodology

The population for this study is the retail bank customers in Sabah, Malaysia. Sabah known in many Western circles as the Malaysia Borneo is an upcoming geographical location in the Eastern part of Malaysia. Although it is fast becoming a banking hub, very little research attention has been given to it relative to the Peninsular Malaysia. The choice of banking industry was made for two key reasons: (1) Although the concept of relationship marketing has emerged within the field of service marketing (Berry, 1983; Christopher et al., 1991; Gummesson, 1991; Jackson, 1985; Lindgreen et al., 2004), research in the field remains inconclusive as past studies reported differing results; (2) Banking is a volitional service with very low switching cost in Malaysia (Ndubisi and Tam, 2007). By choosing a low switching cost industry, we ensure that customers are not under forced loyalty (i.e. they are not retained because of lack of choice) and they could exit if and when they wish to. The participating banks were drawn from the Malaysian Banking Institute. A total of 400 customers voluntarily accepted the survey forms which were personally administered outside the bank’s premises. Of this number, 230 completed and returned the instrument. This is a 57.5% response rate. Out of this only 220 were usable as 10 were voided because of incomplete data.

Malaysian banks are highly regulated by the Central Bank and are known for their highly standardized services, which allows for aggregation of the samples. A recent study showed that switching costs among commercial banks in Malaysia were insignificant (Ndubisi and Tam, 2007). The authors demonstrated that the close proximity of these banks, uniform offerings, similarity of interest charges, among others contributed to the insignificant switching costs. Nevertheless, because certain banks can have specific characteristics, which might influence the forces driving relationship quality, we statistically tested for any differences that may potentially confound the results. To examine these differences one-way analysis of variance (ANOVA) was used similar to the procedure adopted by Veloutsou et al. (2004). To identify which bank customers have different perceptions, the Duncan test was used. This was re-confirmed using the Scheffe test and both reveal no statistical differences. Details of the ANOVA results show no statistical differences among the various banks with respect to the relationship marketing dimensions namely, trust (F = .799; sig.

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= .670), commitment (F = .815; sig. = .652), communication (F = .850; sig. = .614) and conflict handling (F = .517; sig. = .922), and overall relationship quality (F = .597; sig. = .865), hence the data can be pooled without hiding any confounding effect or loosing any important information.

The questionnaire items were adapted from different sources and summarized in Table 1. Communication, commitment and conflict handling items were adapted from Morgan and Hunt (1994) and Ndubisi (2006). Communication had 5 items, commitment included 4 items, and conflict handling had 3 items. Items for trust were adapted from Churchill and Surprenant (1982) and Ndubisi (2006). Trust had six items. The Multiple Regression Model was employed to predict the relationships in the construct. In view of the relatively small sample (n = 220) used in this study, regression analysis would be helpful (Bollen, 1989) since the statistical test of chi square in structural equations model is sensitive to sample size. The following assumptions of regression analysis were tested to ascertain non-violation before accepting the results: (1) linearity of the relationship, (2) constant variance of the error terms (homoscedasticity), (3) independence of the error terms (no autocorrelation), and (4) normality of the error term distribution.

Table 1 Items and Sources

Variables Items Summary Sources Trust Bank’s concern for security of transactions.

Bank’s promises are reliable. Bank fulfils obligation to customer. Bank provides quality service. Employees show respect to customers. Customers have confidence in the bank’s services.

Churchill and Surprenant, 1982; Ndubisi, 2006.

Commitment Bank offers personalize services. Bank’s flexibility in serving customer needs. Bank’s flexibility when services are changed. Making adjustments to suit customers’ needs.

Communication Bank provides timely information. Bank provides trustworthy information. Bank provides information if problem occurs. Bank provides information when there are new banking services. Bank gives and promises.

Conflict Handling Bank avoids potential conflicts. Bank manifest conflicts before they create problems. Bank and openly discusses problems.

Morgan and Hunt, 1994; Ndubisi, 2006.

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Results and Discussion

Respondents’ Characteristics

Table 2 is the summary of the demographic composition of the respondents. No statistical differences were observed in the mean values of the dimensions of interest based on the demographics of the respondents.

Table 2 Respondents’ Demographic Profile

No Profile Description Responses Percentage Below 20 years 7 3.2 20 – 39 years 161 73.2 40 – 59 years 50 22.7

1 Age

60 years above 2 0.9

Male 92 41.8 2 Gender Female 128 58.2 Business 11 5.0 Student/Housewife/Retiree 17 7.7

3 Occupation

Paid Employment 192 87.3 Below RM2000 90 40.9 RM2000 - RM3999 97 44.1 RM4000 - RM5999 22 10.0

4 Monthly Income

RM6000 and above 11 5.0 Primary 5 2.3 Secondary 47 21.4 HSC/Diploma 73 33.2

5 Highest Educational Qualification

Degree 82 37.3 Postgraduate 13 5.9

Apart from the descriptive statistics in the table above, information on the respondents’ relationship with the bank, based on the number of years they have been with the particular bank show that 19% had been with the bank for 5 years or less, 39% between 6 – 10 years and 42% had been with the bank for 11 years or more. These results show that respondents have a considerable level of repurchase behaviour towards their bank.

Psychometric Properties

Confirmatory factor analysis was used to test the measurement properties (Table 3). The oblique factor rotation was employed for this analysis, since the factors are conceptually linked, and oblique rotation represents the clustering of variables more accurately (Hair et al., 1998) as compared to the orthogonal rotation, which keeps factors uncorrelated throughout the rotation process. Convergent validity is established if all item loadings are equal to or above the recommended cut-off level of

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0.60 (Chin et al., 1997). An alternative is parsimonious sets of variables, guided by conceptual and practical considerations, namely acceptance of factor loadings of 0.50 and above (Hair et al., 1998). Based on Hair and colleagues (1998), all items in the study have a loading higher than the recommended threshold. Thus, overall we believe that a reasonable degree of convergent validity was established. High communality values are recorded for all the variables, indicating that the total amount of variance an original variable shares with all other variables included in the analysis is high. A total of 17 items loaded on 5 dimensions with total variance of 64 percent.

Table 3 Key Dimensions, Items, and Communalities

Items Loadings and Cross Loadings Communalities

F1 F2 F3 F4 1. The bank is concerned about the security of my transactions. 2. The bank’s promises are reliable. 3. The bank fulfils obligations to customers. 4. The bank consistently provides quality services. 5. Employees show respect to customers. 6. I have confidence in the bank’s services. (Eigenvalue = 6.564; Variance = 38.61%; Cronbach’s Alpha = 0.84)

.60 -.073 .261 -.093

.63 .043 .225 .158

.78 -.048 -.147 .090

.70 .072 .185 .026

.75 -.033 -.212 .067

.75 -.035 .178 -.178

0.54

0.60

0.63

0.56

0.56

0.65 7. The bank offers personalize services. 8. The bank is flexible in serving customer needs. 9. The bank is flexible when services are changed. 10. The bank makes adjustments to suit customers’ needs. (Eigenvalue = 1.630; Variance = 9.59%; Cronbach’s Alpha = 0.84)

.015 -.83 .074 .025

.026 -.60 .061 .304

-.081 -.80 .049 .171

.051 -.86 .024 -.226

0.76

0.62

0.72

0.71

11. The bank provides timely information. 12. The bank provides accurate information. 13. The bank makes and fills promises. 14. The bank provides information when there are new banking services. (Eigenvalue = 1.538; Variance = 9.05%; Cronbach’s Alpha = 0.78)

.127 .084 .75 -.098

.164 -.190 .61 -.088

.076 -.273 .60 .097

-.046 -.102 .78 .065

0.63

0.60

0.60

0.66

15. The bank tries to avoid potential conflicts. 16. The bank tries to solve

-.076 .066 .321 .83 0.78

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Items Loadings and Cross Loadings Communalities

manifest conflicts before they create problems. 17. The bank openly discusses problems when they arise. (Eigenvalue = 1.064; Variance = 6.26%; Cronbach’s Alpha = 0.73)

.287 -.244 -.149 .60

.258 -.210 -.117 .60

0.64

0.62

F1 – Trust; F2 – Commitment; F3 – Communication; F4 – Conflict Handling; Total Variance = 64%; KMO = .880; Chi-Square = 1629.17; df = 136.00; Sig = .000

The internal consistency of the instrument was tested via reliability analysis. Reliability estimates (Cronbach’s Alpha) for the construct’s dimensions are as follows: Trust (0.84), Communication (0.78), Commitment (0.84), and Conflict Handling (0.73), suggesting a high degree of reliability. The results very well exceed the 0.60 (Hair et al. 1998) lower limit of acceptability. The means (with standard deviation) of the construct dimensions are considered high based on the following statistics: trust (3.95; 0.53), commitment (3.67; 0.73), communication (3.90; 0.62), and conflict handling (3.73; 0.64).

Inter-relationships amongst the RM Dimensions

To examine the influences of the RM dimensions on one another, regression analyses were conducted. The results in Table 4 show both the significant and non-significant beta coefficients and the level of significance of the associations.

Table 4 Inter-relationships among RM Dimensions

Predictor Variables

Dependent Variables with Beta Coefficient

Trust Commitment Communication Conflict Handling

Trust N/a .138* .419** .329** Commitment .147* N/a .317** .388** Communication .342** .286** N/a .038 Conflict Handling

.319** .343** .045 N/a

R2 = .429 F = 53.79 Sig. F. = .000

R2 = .389 F = 45.82 Sig. F. = .000

R2 = .435 F = 54.41 Sig. F. = .000

R2 = .408 F = 49.09 Sig. F. = .000

N/a – Not applicable, * p < .05, ** p < .001

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From Table 5 above, it is observed that commitment, communication, and conflict handling contribute significantly (F = 53.79; p < 0.001) and predict 43% of the variations in trust. In other words, these dimensions predict a significant change in customer trust. It is further observed that the three dimensions are significantly associated with trust at the 5% significance level. This result validates hypotheses 1a, 1b and 1c. Therefore, the level of trust customers have on the bank depends on the level of commitment of the bank, communication efficiency, and the conflict handling ability of the bank.

Similarly, trust, communication, and conflict handling contribute significantly (F = 45.82; p < 0.001) and predict 39% of the variations in perceived commitment. These dimensions predict a significant change in perceived bank’s commitment. At the 5% significance level, the three dimensions are significantly associated with commitment. Hence, customers’ perception of the bank’s commitment is influenced by the latter’s trustworthiness, communication efficiency, and conflict handling ability. Hypotheses 2a-2c are supported the results.

Next, the influences on communication were examined. It was found that trust, commitment, and conflict handling contribute significantly (F = 54.41; p < 0.001) and predict 44% of the variation in the perceived efficiency of the banks’ communications. It was further revealed that only trust and commitment are the significant factors; conflict handling is not, hence hypotheses 3a and 3b are accepted and 3c is rejected.

Lastly, trust, commitment, and communication contribute significantly (F = 49.09; p < 0.001) and predict 41% of the variations in conflict handling. However, only trust and commitment are significantly associated with conflict handling (H4a and H4b); communication is not a significant factor (H4c). Thus, trusted and committed banks are deemed able to handle or resolve conflicts well.

Confirmatory Relationship Tests

It is important to further examine the robustness of these relationships by comparing the impacts of differing levels of the RM dimensions on one another. For these tests, different levels of trust, commitment, communication and conflict handling were categorized. Categorization of these dimensions followed the method used by Moschis and Moore (1979) and Carlson et al. (1990). The scales were summed and customers classified as low or high on each dimension by splitting at the median. The median of each dimension is as follows: trust (4.00), commitment (3.75), communication (4.00), and conflict handling (3.67). Below the median is low level, and above the median is high level. Dummy variables were created before using the two categories in the regression analysis. In creating the dummy variables, the first step was to decide on the number of dummy variables, which is simply k – 1, where k is the number of levels of the original variable. In this instance 2 – 1 = 1 dummy variable was created as follows: low level = 0 (un-coded variable) & high level = 1

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(coded variable). For all dependent dimensions, metric data were used (not the recoded categories). The results of the analyses are presented next.

The results presented in Table 5 show that the coded variables (higher level) more significantly determine the dependent variable than the un-coded variable (lower level).

Table 5 Inter-relationships among Levels of RM Dimensions

Predictor Variables Dependent Variables with Beta Coefficient Trust Commitment Communication Conflict

Handling Dummy Trust N/a .182* .430** .323** Dummy Commitment .125* N/a .246** .318** Dummy Communication

.419** .248** N/a .078

Dummy Conflict Handling

.231** .269** .074 N/a

R2 = .326 F = 34.26 Sig. F. = .000

R2 = .263 F = 25.58 Sig. F. = .000

R2 = .327 F = 34.71 Sig. F. = .000

R2 = .302 F = 31.12 Sig. F. = .000

N/a – Not applicable, * p < .05, ** p < .001 Dummy definition: Low (un-coded variable); High (coded variable).

Interestingly, the results in Table 5 buttress the findings in Table 4 to a very great extent. The outcome of this test consistently shows that higher level of the predictor dimensions is more robust than the lower level in determining the dependent dimensions. The following inferences are therefore drawn from the outcome of the above results. First, customers with greater perception of the bank’s commitment to service, reliability and efficiency of communications, and conflict handling ability, tend to trust the bank more than customers with a lower level of these impressions. Second, customers with greater perception of the bank’s trustworthiness, communication efficiency and reliability, and conflict handling ability, deem the bank to be committed to service compared to customers with a lower level of perceptions. Third, customers with greater perception of the bank’s trustworthiness, and commitment to service, tend to see the bank as a reliable and efficient communicator more than customers with a lower level of acuity. Last, customers with greater perception of the bank’s trustworthiness, and commitment to service, deem the bank more able to handle conflict than do customers with a lower level of the impressions.

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Implications and Conclusions

The implication of this study on theory is mainly in establishing the interconnectivity among the relationship marketing variables. The outcome of the study demonstrates the inter-relatedness of these variables. Using Malaysia as a research setting, this research found that trust is enhanced through commitment, communication, and better conflict handling. Perceived commitment is improved through communication, trust, and better conflict handling; and so on. The unveiling of the strong inter-relationships among the relationship marketing dimensions adds value to the body of knowledge in the field. The results provide empirical support for, and build on some of the past efforts in this area. Past research in this area (e.g. Morgan and Hunt, 1994; Ndubisi, 2006, 2007) had concentrated on the effects of relational dimensions on relational outcomes such as satisfaction, loyalty, word-of-mouth communication, and overall relationship quality; no known attention had gone into exploring the inter-linkages of these relationship constructs. As such, this study adds significant value to extant literature in the areas of relationship marketing, relational dynamics and services marketing.

This research has a number of implications on customer management by banks. Firstly, banks in particular and service organizations in general, which have keen interest in acquiring and keeping valuable customers, should try to build quality relationship with them. To build quality relationships, banks should act trust-worthily. They must give and keep promises, be concerned about the security of transactions, provide quality services, show respect to customers, fulfill obligations to customers, and continuously strive to enhance customers’ confidence in the bank. These actions will lead to increased trust in the bank and its services. Commitment is another important factor for developing quality relationship. This includes commitment to service and customer relationship. Commitment requires the bank to make adjustments to customers’ needs; to tailor-make products to customers’ requirements; to be flexible when products are changed, and when the production process is changed. Reliable and efficient communication with customers is inevitably a factor that will make customers perceive quality in the bank-customer interactions. By providing timely and trustworthy information, providing information if problem occurs, providing information on new banking services, providing accurate information and so on, the bank will be seen as a good communicator, and will be appreciated by the customers. Lastly conflict handling is another important factor. Since there is no guarantee that something may not go wrong in the course of the interactions between the bank and its customers, it becomes important that the bank puts in place effective conflict resolution mechanisms. They should also try to avoid potential conflicts and openly discuss problems with customers.

Since trust, commitment, communication, and conflict handling are indicators of relationship quality, which are inter-related, it becomes necessary and rewarding that organizations nurture these virtues. To enhance customers trust in the organization, management and staff of Malaysian banks besides the suggestions made earlier in the preceding paragraph, should strive to achieve high level of commitment to service and customer relationship, always communicate timely and reliable information, and endeavor to resolve conflicts satisfactorily before they create more problems. They

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can improve customers’ perceptions of their commitment by willingly communicating timely and reliable information, resolving conflicts decisively and satisfactorily without unreasonable losses to customers, and behaving trustworthily. Lastly, in order to be seen as excellent conflict managers, banking services providers should build trust and show commitment to the needs of customers by adopting proactive approaches and strategies. They should approach conflicts and handle them well by identifying sources of potential conflicts and forestall them before they occur.

In conclusion, this research has investigated the inter-relationship, of relationship marketing dimensions namely, trust, communication, commitment, and conflict handling. By using the data supplied by bank customers in Malaysia, some important findings were unveiled. The study found that building quality relationship with customers calls trust, commitment, communication and conflict handling. Hence, banking service providers interested in improved relationship quality should build trust, commitment, communication, and conflict handling. There is strong evidence for the inter-relationships among the RM dimensions. These findings are beneficial to researchers and practitioners interested in the subject of relationship marketing and relational dynamics.

Future Research Directions

Future research should test these relationships in other service sectors other than banking, for example, tourism, hospitality, health care and education. A study of this nature that compares different service sectors will help to understand the effects (if any) of industry/sector characteristics and industry/sector culture (Markoczy, 2000; Hodgkinson and Johnson, 1994). By controlling for these potential confounding effects, more generalizable outcomes can emerge.

Future research can also test these inter-relationships in different cultures. Hofstede (1980), Hofstede and Bond (1988) and Hofstede (1991) suggested that culture can be characterized according to five dimensions: individualism-collectivism, power distance, uncertainty avoidance, masculinity-femininity, and long term orientation. Although these dimensions have often been criticized in management and psychology for being overly simplistic (Osland and Byrd, 2000), the conceptual parsimony provided by these dimensions and the broad range of applicability across different countries and cultures provide an adequate starting place for understanding cultural differences, particularly in marketing relationships and relationship outcomes.

Moreover, future research in this area can introduce some other (less researched) relational dynamics such as empathy, competence, respect, rapport, power, and cooperation. By examining these effects, this future study will add value to the present knowledge in this area, by pushing back the frontier of knowledge in the field.

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Asian Journal of Business Research Volume 1 Number 1 2011

Assessing Productivity of Personal Selling Effort in India: An Econometric Approach Mehir Kumar Baidya IISWBM, India Bipasha Maity Future Institute of Engineering and Management, India Kamal Ghose Lincoln University, New Zealand

Abstract

There were not many studies dealt with productivity issues of personal selling effort in FMCG sector around the world. A modest attempt has been taken to assess the productivity of this effort by considering two brands (A & B) of two firms in India. Time-series data on sales and different marketing mix variables (advertising, sales force, promotion, distribution and price) in rupees have been collected for both the brands in question. Regression models (two multiplicative and one linear) are taken into consideration and fitted on data to estimate the adjusted sales response function to personal selling effort. Results suggest that the productivity of personal selling effort is higher for brand B than brand A and varies between the two brands. Findings will assist the managers to invest resources in this effort along with other elements in marketing mix more precisely.

Keywords: Investment, Time-series data, Regression, Productivity, India

Introduction

To develop, maintain and sustain a competent team of sales personnel needs a high degree of investment (money, time, energy, etc.) in all the nations in general and developing ones in particular like India (Skiera and Albers, 2008). This investment is huge and claims a lion share of marketing budget in the case of any brand belongs to any product category in any type of industry (Manchanda and Chintagunta, 2004).

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The costs of recruiting, training, developing and deploying a professional team are all-time high (Cespedes, 1990; Erevelles et al., 2004). Further, the personal selling element is the most expensive mode of communication among all the efforts in marketing mix on a straight cost-per contact basis in all sectors (Murthy and Mantrala, 2005). In addition, the sales personnel turnover ratio is continue to be high due to their low level of organizational loyalty added substantial costs to maintain a competent sales force team (Jackson et al., 1994; Sager et al., 1988, 1989).

Previous studies suggest that the managers have been investing resources in personal selling effort due to its versatility of application on different activities simultaneously (Thevaranjan and Joseph, 1999). Moreover, personal selling effort usually offers a relatively quick response of sales as compared to other efforts as well as it may not have only a short-term effect. However, the response depends on different selling approaches those have to be adopted in the contexts of dynamics environment, customers and trade-partners requirements (Cardozo and Ship, 1987; Cravens et al., 1990; Plank and Dempsey, 1980; Verbeke et al., 2004). Further, the response is intending to increase in sales sometimes but do not always profitable (Power, 1989; Person and Abeele, 1981). The selling effort is sometimes failed to generate desired response because there might a lack of adequate support from the top management, customers, situational variables as well as the difference between self-reported past performance and self-generated expectation of future performance (DeCarlo et al., 1999, 2007; Ingram et al., 1992; Jaramillo and Mulki, 2008; Krishna et al., 2002; Roberts et al., 1994).

In above few paragraphs some issues of personal selling effort have been discussed in overall context. To be more specific, an exploratory research has been done in the hair-oil product sector in West Bengal, India. Results reveal that the sample managers are investing a huge sum of money in personal selling effort and doing so, on the basis of their experience, intuition, hunch, etc. And have no clear idea about how to assess the productivity in financial terms of this effort. However, without knowing the productivity, there might be a chance of continuation of an ineffective sales team and a risk of misallocation of funds on useless activities.

In the above backdrop, the following one basic, yet managerially important research question has been identified:

What is the impact of personal selling effort on sales? Does this impact vary between one brand and the other? What is the productivity per one rupee investment in this effort of marketing mix?

In this study, a modest attempt has been taken to address the question empirically by taking into consideration of two brands (A & B) of two firms in the hair-care product sector in West Bengal, India. At the time of this study, there were 11 firms in the defined research area. All the firms have been approached to participate but none of them except two (A & B) have agreed to share the required unpublished time-series data for the purpose of this research.

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Methodology

All the relevant models followed by types of data along with methods of analysis have been presented below:

Models

Two double-log regression models (equations 1 and 2) and a linear regression model (equation 3) have been considered and postulated as:

,43214321 ttttttt uPXXXAXY p For .24,...,2,1t (1)

,431431 tttttt vPXXBXY p For .24,...,2,1t (2)

,)( 2 ttt wXAdjustedY For .24,...,2,1t (3)

where, tY Sales in rupees in period t, tX 1 Advertising expenditures in terms of

rupees for period t; tX 2 Sales force / personal selling expenditures in terms of

rupees for period t; tX 3 Promotion expenditures in terms of rupees for period

t; tX 4 Distribution expenditures in terms of rupees for period t; tP Price of the

brand in terms of rupees for period t; Regression coefficient of personal selling effort in equation (3) and it represents productivity, which is defined as change in nominal sales for one rupee change in investment in this effort; tu Random

disturbance term for period t in equation (1); tv Random disturbance term for period

t in equation (2) and tw Random disturbance term for period t in equation (3).

These forms (equations 1 and 2) have been taken into consideration since many empirical studies support diminishing marginal return to individual marketing efforts (Basu and Batra, 1988; Simon and Arndt, 1980; White et al., 2001). However, a linear form (equation 3) has been shorted to estimate productivity of personal selling effort in this work. Since an S-shaped response curve can be treated as straight line for a limited range of and not so prominent variation (as the case may be) in individual marketing efforts (Freeland and Weinberg, 1980; Mjelde, 1983).

Data and Methods of Analysis

Quarterly time-series data on sales, price and various marketing efforts (advertising, personal selling, promotion and distribution) in rupees have been gathered for two brands (A & B) over a period 2003-2008.

The empirical analyses have been carried out in several stages. In the first stage, to identify the recurring pattern in the data, the seasonal indices have been calculated for all the variables for both the cases (Makridakis et al., 1998). For computing the adjustment factors for seasonality, the combined effects of trend and cyclical variation

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have been measured by the moving average method. Then the adjusted seasonal indices have been calculated by the appropriate averaging of the ratios of the original values to the moving averages and the use of a suitable correction factor. Moreover, a few extreme observations have been deleted by following the appropriate tests for outlier in both the cases.

In the next stage, a double-log regression model has been fitted on purified data by considering sales (in rupees) as dependent variable and advertising, sales force, promotion, distribution and price are as predictors. This defined equation is used to estimate quarter-wise sales for both the brands.

In the third stage, a multiple regression analysis with ordinary least squares (OLS) algorithm has been performed between sales and advertising, promotion, distribution with price. Again, this equation is utilized to estimate quarter-wise sales in both the cases. Then, the calculated values of sales (in stage three) have been subtracted from the calculated values of sales (in stage two) to obtain the adjusted sales for other marketing efforts except personal selling for further analysis.

Finally, a linear regression analysis with ordinary least squares (OLS) algorithm has been performed on the quarterly estimated adjusted sales (mentioned above) and personal selling effort data for both the brands. The estimated parameter has been interpreted as productivity (change in sales per one rupee change in investment) since a linear regression model has been used in this purpose.

Results and Discussion

Findings of this work have been presented and discussed in this section.

Results of regression analysis (equation 1) between sales and advertising, sales force, promotion, distribution and price have been presented in Table 1.

Table 1 Coefficients of Sales Equation (1) for A and B

Null Hypotheses of Zero Elasticity

( i = 0)

Elasticity

A A

Predictor

A B t t AD 0.750 0.116 1.85*** 1.81***

SF 1.250 1.350 7.10* 5.35*

PM 0.052 0.114 5.25* 1.70***

DIST 0.623 0.323 1.70*** 1.71***

P -0.90 -1.15 -8.46* -3.88*

Note: AD = Advertising, SF = Sales force, PM = Promotion, DIST = Distribution, P = Price, *p < 0.001(one-tail), ***p < 0.05 (one-tail).

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Findings reveal that the sales in rupees have significant positive association with individual marketing efforts such as advertising, sales force, promotion and distribution for both the brands. However, the price has significant negative relationship with sales in both the cases as expected. Validation statistics of this equation appear in Table 2.

Table 2 Validation Statistics for Equation (1)

Brand R2 F-value DW A 0.90 70.24* 2.05 B 0.97 158.50* 2.10

Note: *p < 0.001, DW = Durbin-Watson Statistic.

It is found that the R2 values are notably high for both the brands. That is, sales as a double-log function of individual marketing efforts with price provide a good fit to the observed data for both the brand. Further, the results indicate, both the values of DW statistic are insignificant, which would lead to the conclusion that there is no evidence of significant autocorrelation of the error terms in both the cases.

A second regression analysis (equation 2) has been performed between sales and advertising, promotion, distribution with price. Results are shown in Table 3.

Table 3 Coefficients of Sales Equation (2) for A and B

Null Hypotheses of Zero Elasticity ( i = 0)

Elasticity

A B

Predictor

A B t t AD 0.650 0.216 1.87*** 1.70***

PM 0.051 0.426 4.56* 1.70***

DIST 0.440 0.312 1.77*** 1.85***

P -01.10 -1.42 -7.79* -4.50*

Note: AD = Advertising, SF = Sales force, PM = Promotion, DIST = Distribution, P = Price, *p < 0.001(one-tail), ***p < 0.05 (one-tail).

Findings suggest that the sales (in rupees) have significant positive relationship with advertising, promotion and distribution except for price in both the cases. Results are shown in Table 4.

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Table 4 Validation Statistics for Equation (2)

Brand R2 F-value DW A 0.85 60.24* 2.11 B 0.89 100.50* 1.98

Note: *p < 0.001, DW = Durbin-Watson Statistic.

There is no qualitative difference between validation statistics of equation (2) in Table 4 and validation statistics of equation (2) in Table II hence claim similar interpretation as presented above.

The third regression analysis (equation 3) has been carried out between adjusted sales and personal selling effort. Results are presented in Table 5.

Table 5 Coefficients of Sales Equation (3) for A and B

Null Hypotheses of Zero Coefficient

( = 0)

Coefficient

A B

Predictor

A B t t Constant 0.91 -1.82 21.60* -15.15*

PSE 18.50 102.91 10.45* 23.75*

Note: PSE = Personal Selling Effort, *p < 0.001(one-tail).

Results reveal that the personal selling effort has a positive effect on sales for both the brands. That is, per rupee increase in expenditure on this effort would lead to increase sales for both the brands. Here, it has been observed that sales would increase by 18.50 and 102.91 rupees for an additional rupee investment in this effort for A and B respectively. To test whether the personal selling effort has a significant positive effect on adjusted sales, the following hypothesis has been formulated for both the brands with respect to equation 3.

:H Expenditure on personal selling effort would have a positive effect on adjusted sales. That is, in mathematical form, :0H 0 against :aH 0

Results in Table V have confirmed the above-mentioned hypothesis for both the brands. That is, the personal selling effort has significant positive effect on adjusted sales in both the cases. However, the effect is more rigorous (on the basis of t values) in the case of B than in the case of A.

Table 6 Validation Statistics for Equation (3)

Brand R2 F-value DW A 0.91 130.80* 1.89 B 0.95 140.25* 1.93

Note: *p < 0.001, DW = Durbin-Watson Statistic.

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Results indicate that the R2 values are notably high for both the brands. That is, adjusted sales as function of personal selling effort provide a good fit to the observed data for both the brands. Further, both the values of DW statistic claim similar qualitative interpretation as mentioned earlier in both the cases.

An alternative way to understand the differential effect of personal selling effort in generating sales is examining the productivity. Productivity has been defined as increase in revenue per one rupee investment in personal selling effort in this study. Further, productivity is basically an alternative way to represent the estimated coefficient of personal selling effort instead of elasticity since a linear form of relationship has been used. Results show that the productivity of personal selling effort is much higher in the case of B than in the case of A. That is, the manager of this brand (B) utilized this effort more efficiently than the rival brand (A).

Conclusion

This paper examines the effects of different marketing mix variables on sales in rupees with special interest in personal selling effort for two brands in India. Hence, sales have been given adjustment for other marketing efforts. Results of this study reveal that the personal selling effort has significant positive effect on adjusted sales and this effect varies from one brand to another. Coming to the productivity of personal selling effort, it has been observed that the sales personnel of brand B are more efficient than brand A.

This study ignores completely the interaction between personal selling effort and other efforts in marketing mix due to small number of observations. Nor does it include those factors, which influences the productivity of sales personnel since data were not available with both the firms.

Researchers will expand the current study from a two-brand analysis in a single sector to a multi-brand one covering a number of sectors to identify the differential effect of personal selling effort on sales from one sector to another.

Implications for Business Marketing Practice

Findings of this research might have at least two important business implications. On one hand, the managers of both the brands could allocate a fixed budget to different marketing efforts in general and personal selling effort in particular by using the estimated sales response functions more precisely. On the other hand, the managers should increase in investment in personal selling effort to boast up in sales since productivity of this element is high. That is, they should increase the number of sales personnel for better coverage in the existing markets.

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DeCarlo, T.E., Agarwal, S. and Vyas, S.B. (2007), “Performance expectations of salespeople: The role of past performance and causal attributions in independent and interdependent cultures”, Journal of Personal Selling & Sales Management, vol. 27, no. 2, pp. 133-148.

DeCarlo, T.E., Rody, R.C. and Decarlo, J.E. (1999), “A cross-national example of supervisory management practices in the sales force”, Journal of Personal Selling & Sales Management, vol. 19, no. 1, pp. 1-14.

Erevelles, S., Dutta, I. and Galantine, C. (2004), “Sales force compensation plans incorporating multidimensional sales effort and salesperson efficiency”, Journal of Personal Selling & Sales Management, vol. 24, no. 2, pp. 101-112.

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Jaramillo, F. and Mulki, J.P. (2008), “Sales effort: The intertwined roles of the leader, customers, and the salesperson”, Journal of Personal Selling & Sales Management, vol. 28, no.1, pp. 37-52.

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Roberts, J.A., Lapidus, R.S. and Chonko, L.B. (1994), “An exploratory examination of situational variables, effort and salesperson performance”, Journal of Marketing Theory and Practice, vol. 2, no. 3, pp. 70-94.

Sager, J.K., Futrell, C.M. and Varadarajan, R. (1989), “Exploring salesperson turnover: A causal model”, Journal of Business Research, vol. 8, no. 1, pp. 21-35.

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Acknowledgement

The authors gratefully acknowledge the constant support of Dey’s Medical Ltd. and Emami Ltd. as well as providing unpublished internal data for the purpose of this research.

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Asian Journal of Business Research Volume 1 Number 1 2011

The Influence of Brand Personality Dimensions on Brand Identification and Word-of-Mouth: The Case Study of a University Brand in Thailand Kawpong Polyorat Khonkaen University, Thailand

Abstract

Brand personality refers to personality traits associated with a brand. The results of the study conducted in Thailand, where academic research in this area is scant, reveal that, for a university brand, brand personality dimensions of sincerity and competence have more influence on brand identification and word-of-mouth than the dimensions of excitement and sophistication. In addition, brand identification appears to mediate the influence of these two dimensions of brand personality on word-of-mouth. Implications and future study directions conclude the study report.

Keywords: Brand personality, Brand identification, Word-of-mouth, Thailand, University

Introduction

Brand personality has attracted interests from marketing researchers for several decades (Rojas-Mendez et al., 2004). Earlier research in brand personality examined different brands in several product categories and demonstrated that consumers perceived brands in terms of five orthogonal personality dimensions: sincerity (e.g. Hallmark), competence (e.g. The Wall Street Journal), excitement (e.g. MTV), sophistication (Guess), and ruggedness (e.g. Marlboro)(Monga and Lau-Gesk, 2007). This stream of research, however, focused on brands predominantly characterized by one single dimension of brand personality (Monga and Lau-Gesk, 2007) while a brand may simultaneously possess multiple dimensions of brand personality. The relative impact of each dimension of brand personality has been largely unexplored. The current study aims to fill in this gap.

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From a managerial perspective, this study also seeks to extend brand personality research into a non-profit organization such as an academic institution. Specifically, the current study attempts to investigate the impact of different brand personality dimensions of a university on two important outcome variables: brand identification and word-of-mouth.

In addition, to contribute to the cross-cultural marketing literature, the present investigation is conducted in Thailand, an Asian country where scholarly research in brand personality is scarce (Polyorat et al., 2008). Several studies (Aaker and Maheswaran, 1997; Aaker and Schmitt, 2001; Jung et al., 2009) have documented the differences between the behaviors of Asian and those of Western consumers. As a result, more research to better understand consumer behaviors in an international context is needed. The present study is one step toward such direction.

This research article is structured as follows. First, the situation of university branding in Thailand and literature in brand personality, brand identification, and word-of-mouth are reviewed. Next, a set of research hypotheses are offered and then empirically examined using survey research. Subsequently, data are analyzed and discussed. Finally, research implications and avenues for future research are suggested.

Theoretical Background

Current Situation of University Branding in Thailand

The use of Thailand as a country of focus is suitable for the context of the present study, which is university branding, because at present more universities in Thailand have attempted to employ several marketing strategies to draw more attention and interest from their major consumers – students. A survey of local magazines and newspapers (FourP, 2007; Komchudleuk Education, 2009; Marketeer, 2007) reveals increasing competitions among universities in Thailand. For example, several foreign universities, especially from the West, have recently opened their campuses in Thailand. These foreign players tend to bring with them the marketing –oriented practices such as branding which is common in their home countries. Further, numerous public Thai universities have become more modernized or some have been privatized, resulting in higher needs to attract more students to bring in more revenue for the university. The private universities themselves also respond to this competitive intensity through such marketing gimmicks as the provision of laptops for students, heavy advertising in various media, event marketing and sponsorships in order to strengthen their university brand.

In a global context, branding in higher education has become inevitable. Several universities have allocated their resources to build and maintain their brands (Chapleo, 2007). For example, a number of UK universities have a position for marketing director to “guard” their brand (Temple, 2006). Universities in Thailand follow this trend, university branding. Cursory scanning of local newspapers and

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magazines reveals numerous examples of heightening significance of university branding. For instance, an advertisement for a university states that “... XXX is a modern, dynamic, challenging and exciting college that reflects the fast-moving world…” (FourP, 2007). Another example includes “Making You Outstanding with Supremacy” as the headline of the advertisement. In its copy is the elaboration of the claims “…offers cutting-edge education to create business leaders for today’s fast changing environment…, lectured by highly-qualified faculty members from many countries in state of the arts classrooms…, collaborates with world-class university...” (Marketeer, 2007). These marketing tactics reflect the importance of brand building for universities as they can demonstrate various personalities of the universities. From this view, the present study would provide a number of managerial implications for universities wishing to build, strengthen, or refresh their brand.

Brand Personality

Brand personality refers to personality traits associated with a brand (Aaker, 1997). Brand personality, as a characterization of a brand (e.g. youthful) is a major component of brand image, in addition to the product’s physical attributes (e.g. green in color) and the product’s benefits (e.g. cleans teeth more effectively; Diamantopoulos et al., 2005). Because brand personality is likely to be more difficult to imitate than tangible product attributes, marketing practitioners may use it to achieve a more sustainable advantage (Ang and Lim, 2006) such as in product differentiation and positioning.

Through a series of studies, Aaker (1997) has uncovered five dimensions of brand personality: sincerity (down-to-earth, honest, wholesome and cheerful), excitement (daring, spirited, imaginative and up-to-date), competence (reliable, intelligent and successful), sophistication (upper class and charming) and ruggedness (outdoorsy and tough). These five dimensions are reported to be robust across the male sub-sample, female sub-sample, younger sub-sample and older sub-sample. These five dimensions emerge from different sets of brands and different sets of product categories, thus suggesting the scale generalizability. Despite some criticisms, most of the brand personality studies conducted after 1997 is based on Aaker’s (1997) scale (Azoulay and Kapferer, 2003). Following previous studies in university brand personality (Opoku et al., 2006, 2008), the present study will use Aaker’s framework - the five brand personality dimensions - as it appears to be relevant for the marketing of higher academic institutions.

Brand Identification

Brand identification occurs when consumers believe that they belong to a particular brand (Bhattacharya et al., 1995) and use the brand for self-referencing or self-defining (Donavan et al., 2006). That is, consumers may have a strong emotional attachment with the brand and a sense of belonging to the brand. One source of social

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identity is the social categories to which consumers belong (Tajfel and Turner, 1985). In the context of the present study, these social categories can be based on organizational memberships such as a university or an academic institution. The present study attempts to examine the brand personality of the academic institution to which consumers (i.e. students) belong or with which ones identify (Pratt, 1998).

A number of research has suggested that brand identification is influenced by several factors including brand prestige, brand satisfaction, corporate communication, and brand attractiveness (Curras-Perez et al., 2009; Kuenzel and Halliday, 2009). Kim et al. (2001) indicated that the attractiveness and distinctiveness of brand personality influence brand identification and word-of-mouth. The current study attempts to extend this piece of finding by suggesting that certain dimensions of brand personality (i.e. sincerity and competence for a university brand) have more impacts on influencing brand identification and word-of-mouth. This research question is guided by Carlson et al.’s (2009) findings. Their study, in a sport context, reveals that the brand personality dimensions of imaginativeness and toughness positively influence identification while successfulness has a negative influence.

The hypothesis development in this study is based on the distinction between two consumption motives. On one hand, a utilitarian motive is primarily concerned with the functional or instrumental usefulness of the consumption which is derived from the product/service performance. On the other hand, a hedonic motive emphasizes the experiential pleasure derived from affective, esthetic, sensory and/or symbolic aspects of the consumption (Batra and Ahtola, 1991; Voss et al., 2003). The primary benefit of a university, as an educational institution, should primarily reflect the utilitarian motive where consumers (i.e. students) come to study, seek knowledge, augment their intellectual capabilities, and prepare themselves for future careers. As a consequence, the brand personality dimensions of sincerity and competence (vs. excitement and sophistication) which are relatively closer to the utilitarian motive are likely to have more influences on brand identification or students’ identification with their academic institution. This hypothesis is, in part, derived from two findings in the marketing literature. First, an experimental study by Ang and Lim (2006) reveals that brand of symbolic or hedonic products (cologne and a designer watch) are perceived to be more sophisticated and exciting, but less sincere and competent than those of utilitarian products (mineral water and toothpaste). Second, a content analytic study by Polyorat and Thaikasame (2008) reveals that the majority of advertisements for utilitarian products employ rational advertising appeals while those for hedonic products primarily employ emotional or affective advertising appeals.

H1: For a university brand, brand personality dimensions of sincerity and competence will have more influences on brand identification than the dimensions of excitement and sophistication.

Word-of-Mouth

The success of a brand depends on bonds building with its consumers who, in turn, take part in multiple social networks where they could influence one another through word-of-mouth in the formation of consumption attitudes and behaviors (Allsop et al.,

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2007). Word-of-mouth is an informal communication between consumers and consumers regarding the products or services, but not communication between consumers and marketing organizations such as complaints or promotions (Mazzarol et al., 2007). In comparison with marketer-initiated communication, word-of-mouth is less expensive (Villanueva et al., 2008), but more credible (Allsop et al., 2007). These advantages are attributable to the fact that the message is delivered personally and the message sender is not paid and often has only the best interest of the message recipient as the motivation for sharing an opinion (Etzel et al., 2007).

Given the important role of word-of-mouth as discussed above, it is pivotal to extend the current literature by examining if different dimensions of brand personality may have different impacts on word-of-mouth. The results of this research question will provide some guidelines on how to manage brand personality of a university to elicit word-of-mouth from their students.

As the utilitarian versus hedonic consumption benefit is found to have different impact on word-of-mouth (Chitturi et al., 2008), the line of reasoning similar to that of brand identification regarding the utilitarian versus hedonic motive of consumption is also used. Therefore, it is similarly expected that the brand personality dimensions of sincerity and competence (vs. excitement and sophistication) will have more influences on consumer’s word-of-mouth (or students’ positive talking about their university).

H2: For a university brand, brand personality dimensions of sincerity and competence will have more influences on word-of-mouth than the dimensions of excitement and sophistication.

Consumers who identify themselves with a particular brand are likely to experience a positively psychological outcome and, therefore, are more inclined to engage in favorable actions toward the brand (Donavan et al., 2006). Therefore, consumers’ identification with a social object could lead to positive behaviors towards that social object (e.g. a group, an organization) (Kim et al., 2001). A number of studies (Bhattacharya and Sen 2003; Kuenzel and Halliday 2008; Mael and Ashforth, 1992) have suggested that identification with a brand may elicit favorable behaviors such as word-of-mouth. As a result, it is also expected that brand identification with a university will influence consumers’ word-of-mouth. Based on H1 and H2, this impact is expected to mediate the influence of brand personality dimensions on word-of-mouth. This hypothesis regarding the mediating role of brand identification on the relationship between selected dimensions of brand personality and positive behaviors received preliminary support from Carlson et al.’s (2009) study. They report the influence of brand personality dimensions on retail spending and viewships through brand identification. The hypothesized relationships are visually displayed in Figure 1.

H3: Brand identification will mediate the influence of brand personality dimensions on word-of-mouth.

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Figure 1 Hypothesized Relationships

The dimensions of sincerity and excitement explain the largest variance in brand personality, followed by competence, sophistication, and ruggedness, respectively (Aaker, 1997). Because ruggedness accounts for the least amount of variance, it may be relevant for a smaller number of products or services in comparison with the other dimensions. As this study is exploratory, the formal hypotheses regarding the influences of the ruggedness dimension are not formulated but the data regarding this dimension will be also collected for exploratory purpose (Ang and Lim, 2006).

Methodology

Overview

A survey research was conducted to examine the relationships between independent variables (brand personality dimensions) and dependent variables (brand identification and word-of-mouth). After reliabilities of the measures being used are ascertained, a series of regression analyses were conducted to test the hypotheses following Baron and Kenny’s (1986), Punjaisri and Wilson’s (2007), and Sophonsiri and Polyorat’s (2009) approach.

Samples

Data were collected from 357 undergraduate students attending a major Northeastern university in Thailand. The sample size was within the range identified in the literature (Tuntabundit and Polyorat, 2007) and the purposive sampling procedure was used following Tuntabundit and Polyorat (2007). All respondents were Thai. They filled out the survey during regular class hours. Subjects were first informed of the study description, then asked to complete the brand personality measure, brand identification measure and word-of-mouth measure, and provided personal data at the end.

Brand Personality Dimensions

- Competence - Sincerity

Brand Identification

Word-of-Mouth

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Measures

All original scales in English were translated to Thai using a back-translation procedure (Brislin, 1980). Brand personality was measured with Aaker’s (1997) 42-item Brand Personality Scale. The psychometric property of this scale with Thai consumers has been supported (Polyorat et al., 2008). The respondents were instructed to think of the brand of the university where they were studying as if it were a person and to rate on a five-point scale (1 = not at all descriptive, 5 = extremely descriptive) the extent to which each of the 42 brand personality traits describes the brand. “Sincere”, “exciting”, “reliable”, “glamorous”, and “rugged” are examples of items used to assess the brand personality dimensions of sincerity, excitement, competence, sophistication, and ruggedness, respectively.

Brand identification was measured with six items adapted from Kim et al. (2001). Using five-point Likert scale where 1 = strongly disagree and 5 = strongly agree, respondents were asked to indicate the extent to which they agree or disagree with each of the six items. “The successes of XXX university are my success” and “When someone praised XXX university, it feels like a personal compliment” were examples of this scale.

Word-of-mouth was measured with three items adapted from Kim et al. (2001) also using five-point Likert scale where 1 = strongly disagree and 5 = strongly agree. “I recommend XXX university to other people” and “I talk about my experience with XXX university” were examples of this scale.

Results

Means, standard deviations, and reliabilities of the variables are displayed in Table 1. All scales exhibit Cronbach’s alphas higher than 0.70. Further, Variation Inflation Factors were all lower than 0.36 in all three regression models which were well below the recommended cutoff point of 10 (Neter et al., 1985). This provides evidence that multicollinearity is not a problem in interpreting the results from regression parameter estimates.

Table 1 Descriptive Statistics and Reliabilities

Mean

SD α

Sincerity 3.54 0.53 0.82

Excitement 3.62 0.63 0.91

Competence 3.85 0.62 0.91 Sophistication 3.25 0.72 0.88

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Ruggedness 3.52 0.77 0.87 Brand Identification 3.95 0.70 0.80 Word-of-Mouth 4.28 0.77 0.88

Impact of dimensions of brand personality on brand identification (H1). To test this hypothesis, brand identification was regressed on sincerity, excitement, competence, sophistication, and ruggedness. The multiple regression results are shown in Table 2.

Table 2 Multiple Regression Results

(1) Brand

Identification (2)Word-of-

Mouth (3)Word-of-

Mouth Independent Variables β t-value β t-value β t-value

Sincerity .17 2.68** .14 2.21* .05 .98

Excitement -.07 -.79 -.06 -.71 -.03 -.36

Competence .24 2.93** .34 4.12*** .22 3.01**

Sophistication -.00 -.16 -.00 -.01 -.00 -.02

Ruggedness .18 3.01** .09 1.40 -.01 -.14

Brand Identification --- --- --- --- .51 11.04***

Note: * p < 0.05, ** p < 0.01; **, p < 0.001 (two-tailed). (1) F(5,351) = 17.65, p < 0.001, R2 = 0.20, Adjusted R2 = 0.19 (2) F(5,351) = 17.48, p < 0.001, R2 = 0.20, Adjusted R2 = 0.19 (3) F(6,350) = 39.87, p < 0.001, R2 = 0.41, Adjusted R2 = 0.40

As expected, sincerity (β = 0.17, t = 2.68, p < 0.01) and competence (β = 0.24, t = 2.93, p < 0.01) influenced brand identification while excitement (β = -0.07, t = -0.79, p > 0.1) and sophistication (β = -0.00, t = -0.16, p > 0.1) did not. Thus H1 is supported. For exploratory purpose, ruggedness (β = 0.18, t = 3.01, p < 0.01) was found to influence brand identification.

Impact of dimensions of brand personality on word-of-mouth (H2). To test this hypothesis, word-of-mouth was regressed on sincerity, excitement, competence, sophistication, and ruggedness. The multiple regression results are shown in Table 2.

As expected, sincerity (β = 0.14, t = 2.21, p < 0.05) and competence (β = 0.34, t = 4.12, p < 0.001) influenced word-of-mouth while excitement (β = -.06, t = -0.71, p > 0.1) and sophistication (β = -0.00, t = -0.01, p > 0.1) did not. Thus H2 is supported. For exploratory purpose, ruggedness (β = 0.09, t = 1.40, p > 0.1) was not found to influence word-of-mouth.

Mediating role of brand identification (BI) on the relationship between brand personality dimensions (BP) and word-of-mouth (WOM) (H3). To test the mediating role of brand identification, a series of regression analyses were run according to

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Baron and Kenny (1986). First, H2 was supported, indicating that sincerity and competence dimensions of brand personality had significant impact on word-of-mouth: BP WOM. Second, in a simple regression analysis, brand identification was found to influence word-of-mouth (β = 0.60, t = 14.21, p < 0.001): BI WOM. Third, H1 was supported, indicating that sincerity, competence, and ruggedness dimensions of brand personality had significant impact on brand identification: BP BI. Fourth, to test a mediating role of BI on BP-WOM relationship, word-of-mouth was regressed on both brand personality dimensions and brand identification. The results indicated that beta coefficients for BP dimensions have been reduced significantly enough to confirm mediation (F6, 350 = 39.87, Adjusted R2 = 0.40; sincerity = 0.05, t = 0.98, p > 0.1; excitement = -0.03, t = -0.36, p > 0.1; competence = 0.22, t = 3.01, p < 0.01; sophistication = -0.00, t = -0.02, p > 0.1; ruggedness = -0.01, t = -0.14, p > 0.1; BI = 0.51, t = 11.04, p < 0.001). As a result, H3 was supported.

Discussions

Summary

The study results reveal that, for a university brand, brand personality dimensions of sincerity and competence have more influences on brand identification and word-of-mouth than those of excitement and sophistication. In addition, brand identification appears to mediate the influence of brand personality dimensions on word-of-mouth. The ruggedness dimension, although not formally hypothesized, is found to influence brand identification but not word-of-mouth.

Theoretical Implications

This study provides both theoretical and managerial contributions to the areas of marketing and consumer behavior. In terms of theoretical implications, the current study suggests that each dimension of brand personality may exhibit different levels of influence on consumer behavior depending on the consumption motive or the characteristics of a product or service. For a product/service consumed primarily for utilitarian benefits such as a university in the present study, the dimensions of competence and sincerity (vs. excitement and sophistication) are found to have more impacts on brand identification and word-of-mouth. The results of the present study are, thus, in line with Ang and Lim’s (2006) study although the focal products or services (university vs. mineral water, toothpaste), as well as the methodology (survey vs. experiment), are different. Future research may examine an institution or organization where the primary consumption motive is hedonic to complement the findings of the present study. In addition, the present study supports Aaker’s (1997) view of brand personality. That is, more brand personality studies are needed at the dimension level because each dimension may have different importance or relevance for varied product/service categories. As a consequence, the present study suggests

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that, when a brand personality study is to be conducted, the focal dimensions of interest need to be specified.

Next, the study results contribute to the brand identification and word-of-mouth literature by suggesting dimensions of brand personality as one of their antecedents. Our results concerning the relationship between brand identification and word-of-mouth is consistent with those of Kim et al.’s (2001) study, therefore suggesting the external validity of the results across product categories (university and cell phone), across cultures (Thailand and Korea), and across different levels of brand personality analysis (dimension level and aggregate level).

The present study also contributes to the cross-cultural consumer behavior area by examining the brand personality construct in Thailand, an underrepresented country in the cross-cultural research literature (Polyorat et al., 2008). This study provides empirical evidence regarding the relative importance of five dimensions of brand personality in Asia in general and in Thailand in particular. The results of the current study corroborate with those of the studies conducted in the western part of the world (Opoku et al., 2008) by demonstrating the applicability of Aaker’s (1997) five brand personality dimensions for institutions of higher education.

Managerial Implications

The present study demonstrates the differential importance of each brand personality dimension in eliciting consumer’s favorable reaction toward the brand of an academic institution. Moreover, the present study provides supporting evidence that brand identification is an important component for an academic institution to achieve a distinct identity and competitive advantage (Nandan, 2005) for desirable consumer behaviors such as positive word-of-mouth.

The results, hence, suggest possible guidelines on how to increase students’ willingness to contribute to their university (Shamir, 1990). For example, in terms of fundraising, the university management may wish to focus on certain dimensions of brand personality (i.e. competence and sincerity) when approaching alumni to increase the brand identification (Mael and Ashforth, 1992). For the current students as well as alumni, the focus on certain dimensions could increase the sale of school merchandise such as gifts with university logos (Donavan et al., 2006).

With the increasing role of word-of-mouth via information communication channels such as emails, cell phones, and blogs (Allsop et al., 2007), the university management may consider putting special effort into managing their selected dimensions of university brand personality. For instance, via these channels, university marketing practitioners may want to focus on the sincerity and competence dimensions of brand personality during their interaction with students over the long-term period. This decision may encourage the consumers to increase their help to their institution by spreading more positive word-of-mouth and may even defend their university when it is negatively criticized. Because word-or-mouth is often more

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effective than traditional advertising in attitude change or formation (Hogan et al., 2004), marketing practitioners should allocate special attention to the selection of brand personality dimensions they want to focus to make the best use from their limited resources.

Study Limitations and Avenues for Future Research

As word-of-mouth could be viewed as connected to other consumer behaviors such as brand loyalty (Mazzarol et al., 2007) and complaints, future research may want to examine the impact of brand personality dimensions on these two outcomes. Furthermore, other dependent measures, especially conative ones such as symbol collecting and passing (Donavan et al., 2006) could be included because these behaviors are a mechanism through which consumers enhance their self-esteem (Burmann and Zeplin, 2005) and strengthen their sense of belonging to an academic institution. However, as more variables are included in the model, structural equation model (SEM) can be used to examine a complex set of relationship especially when the established scales are adapted in a study (Sophonsiri and Polyorat, 2009).

While the measure of brand identification exhibited Cronbach’s alpha of 0.80 which could seem relatively modest in comparison with other scales used in the current study, it still achieved the level of 0.80 suggested by Nunnally (1978) for basic research. In addition, the mean alphas reported in a meta-analysis conducted by Peterson (1994) were lower than 0.80 for a scale comparable with brand identification measure in terms of sample size (300 or more), type of sample (college students), number of scale categories (five), number of items (six), scale type (Likert), scale format (only end points labeled), nature of scale (odd number of item categories), administration mode (self), scale orientation (respondent centered), nature of construct, and type of research. The brand identification measure in the current study is therefore deemed satisfactorily reliable. Future research, however, may further refine the scale to increase its reliability, using the guideline suggested by Peterson (1994).

Further, because the data in this study was collected from a single university, future research may be conducted on a larger scale by drawing study samples from several universities. This will enhance the generalizability of the study to better represent Thailand as a whole. Moreover, future research could also replicate this study in other countries with different cultures, religions, levels of economic development, and degree of exposure to globalization. For example, in an individualist culture where consumers tend to have dominant independent self-construal, reactions to a university brand composed of several dimensions of brand personality may be less favorable (Monga and Lau-Gesk, 2007).

Other types of institutions (Bhattacharya, 1995) such as an art museum, a political party, or an institution where hedonic motive could be more pronounced warrant more studies. Different dimensions of brand personality may exhibit different levels of influence for varied types of organizations.

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In addition to a survey method, other research methods including depth interview (Blythe, 2007), experiment (Monga and Lau-Gesk, 2007) and content analysis (Opoku et al., 2006) may provide additional explanations on the relative influences of each dimension of brand personality.

Implications for Business Practice

Brand personality refers to personality traits associated with a brand (Aaker, 1997). Brand personality is composed of five dimensions (Aaker, 1997): sincerity (down-to-earth, honest, wholesome and cheerful), excitement (daring, spirited, imaginative and up-to-date), competence (reliable, intelligent and successful), sophistication (upper class and charming) and ruggedness (outdoorsy and tough). Brand identification occurs when consumers believe that they belong to a particular brand (Bhattacharya et al., 1995) and use the brand for self-referencing or self-defining (Donavan et al., 2006). Word-of-mouth is an informal communication between consumers and consumers regarding the products or services, but not communication between consumers and marketing organizations such as complaints or promotions (Mazzarol et al., 2007). The present study attempts to examine the impact of the different dimensions of brand personality on brand identification and word-of-mouth.

This study is conducted in Thailand where academic research in this area is scant. Using data from a survey research with 357 Thai undergraduate students, the results reveal that, for a university brand, brand personality dimensions of sincerity and competence have more influences on brand identification and word-of-mouth than those of excitement and sophistication. In addition, brand identification appears to mediate the influence of these two dimensions of brand personality on word-of-mouth. The utilitarian (vs. hedonic) consumption benefit of an academic institution is suggested to be the explanation of the study results.

This study demonstrates the differential importance of each brand personality dimension in eliciting consumer’s favorable reaction toward the brand of an academic institution. Moreover, the present study provides supporting evidence that brand identification is an important component for an academic institution to achieve a distinct identity and competitive advantage for desirable consumer behaviors such as positive word-of-mouth.

The results, hence, suggest possible guidelines on how to increase students’ willingness to contribute to their university (Shamir, 1990). For example, in terms of fundraising, the university management may wish to focus on certain dimensions of brand personality (i.e. competence and sincerity) to increase the brand identification which could finally result in favorable outcomes such as donations to ones’ alma mater (Mael and Ashforth, 1992). For the current students as well as alumni, the focus on certain dimensions could increase the sale of school merchandise such as gifts with university logos (Donavan et al., 2006).

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With the increasing role of word-of-mouth via information communication channels such as emails, cell phones, and blog (Allsop et al., 2007), the university management may consider putting special efforts into their selected dimensions of university brand personality. For instance, via these channels, marketing practitioners may want to focus on the sincerity and competence dimensions of brand personality during their interaction with students. This decision could result in consumers’ wish to increase their contributions to their institute by spreading more positive word-of-mouth and may even defend their university when it is negatively criticized. Because word-or-mouth is often more effective than traditional advertising in attitude change or formation (Hogan et al., 2004), marketing practitioner should allocate special attention to the selection of brand personality dimensions they want to focus to make the best use from their limited resources.

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Asian Journal of Business Research Volume 1 Number 1 2011

Intention to Adopt Mobile Marketing: An Exploratory Study in Labuan, Malaysia Geoffrey Harvey Tanakinjal Universiti Malaysia Sabah, Malaysia Kenneth R. Deans University of Otago, New Zealand Brendan J. Gray University of Otago, New Zealand

Abstract

The evolution of e-commerce also has brought with it a new marketing channel known as mobile marketing (m-marketing). Although mobile devices have been seen as a potential channel to reach consumers, effort is still needed to understand what influences intention to adopt mobile marketing. This study integrates innovation characteristics of the Innovation-Diffusion Theory (IDT), perceived risk, trustworthiness, and permissibility constructs to investigate what determines user intention to adopt mobile marketing. The proposed model in this study was empirically tested using data collected from a survey of mobile users. The research findings suggested that relative advantage of mobile marketing is the strongest influence in building consumers’ intention decision to adopt mobile marketing. All other constructs were statistically significant in influencing behavioural intent to adopt mobile marketing. This study’s findings support Rogers’ (2003) perceived characteristics of innovation attributes that form a favourable or unfavourable attitude toward the innovation.

Keywords: Innovation diffusion theory, Perceived risk, Trustworthiness, Permissibility, Mobile marketing services

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Introduction

The American Marketing Association (AMA) has defined marketing as, “an organizational function and a set of processes for creating, communicating, and delivering value to customers and for managing customer relationships in ways that benefit the organization and its stakeholders” (AMA, 2004). Based on this definition, one area that has played an important role in providing alternatives, creating choices, and delivering value for the betterment of the modern lifestyle is technology. The marketing discipline has evolved where the environment consisting of consumers and service providers co-exist. Technology has also changed the way a product or a service from a provider reaches the consumer. According to Cox and Alderson (1948, p. 151), “the most acute marketing problems are precipitated by the facts of technological change.” In this situation, the market analyst does not have the luxury of choice as to whether they will adopt a dynamic view, but at the very least he/she must take account of technological changes in marketing (Cox and Alderson, 1948). With technology change in mind, this paper will explore constructs that may influence the intention to adopt adoption an innovation (mobile marketing) from a user’s perspective. According to Leppäniemi et al. (2006, p. 10), “mobile marketing is the use of the mobile medium as a means of marketing communications.” Mobile marketing is sometimes used interchangeably with mobile advertising (Barnes and Scornavacca, 2004; Carroll et al., 2007; Tsang et al., 2004), but according to MMA (2007) the difference between mobile marketing and mobile advertising lies in the former being the vehicle for content delivery and direct response and the latter being the form of the message communicated through a consumer’s handset. Key words in the above definition are mobile medium (e.g. mobile phones) and marketing communications (e.g. information, promotions, competitions, etc.). According to Pousttchi (2006), marketing experts consider that the mobile device is an extremely promising marketing tool to overcome the major challenges of getting time and the attention of consumers. Mobile device also provides opportunities to target messages for customers in more efficient ways than the present mass media (Barwise and Strong, 2002). The importance of mobile phones to end users has certainly been recognised by marketers, who view this as a communication channel with huge potential (Kavassalis et al., 2003; Norris, 2007; Nysveen et al., 2005). Although this innovation is good for marketers, mobile marketing also creates perceived problems of privacy and security risk for consumers. For consumers who have concerns over security and privacy in mobile marketing, service providers need to reassure consumers that transmission of their personal details are safe (Leek and Cristodoulides, 2009). In terms of cost, a local newspaper (Daily Express) reported that SMS scammers managed to get close to RM1.9 million from victims throughout the state of Sabah since 2006. According to Sabah’s Commissioner of Police, Datuk Nor Rashid Ibrahim, RM26,000 was lost in 2006, while RM1.8 million went into the pockets of these conmen the following year (“RM 1.9m Sabah SMS rip-offs”, 2009).

On the other hand, there is a possibility that the more exposed one is towards a particular/similar communication technology, the more confident one would be towards using the new marketing communication channel (Kavassalis et al., 2003; Norris, 2007; Nysveen et al., 2005). Although high mobile phone penetration rates do not necessarily mean high mobile marketing use, the potential of communicating

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marketing messages through mobile phones does exist. For example, in Malaysia, although the penetration rate of mobile phones in 2008 was 93.9 per cent (26,126,000 users) (MCMC), only seven percent of mobile phone subscribers had registered for mobile banking services, and only 13.7 percent accessed the Internet through their mobile phones (MCMC, 2007). According to a study conducted by Mutz (2005), people believe that, “brick and mortar” businesses are more trustworthy than electronic businesses (e-business). This view is shared by Siau and Shen (2003), who stated that while the internet creates unprecedented opportunities for initiating customer relationships, trust between the consumer and provider is an essential ingredient for the adoption of e-business. Trust is needed most when risks are perceived to be high, and many consumers perceive e-commerce as being highly risky (Mutz and Journals, 2005). Perceived risk is important in explaining consumer’s behaviour, because consumers are more often motivated to avoid mistakes than maximising utility in purchasing (Mitchell, 1999). Despite many consumers being concerned with transaction security, merchant information, online privacy, and personal data, the problem of mistrust by consumers are often ignored by e-commerce providers (Wu and Wang, 2005). Thus, empirical investigation of privacy risk and personal data security is needed (Leppäniemi et al., 2006) in order to address consumers’ perceived risk in technological adoption perspective. One area that may help in addressing the issue of privacy and security risk in e-commerce is obtaining consumers’ permission (Kavassalis et al., 2003) to allow marketers to communicate with potential consumers and use consumers’ data. Admissibility as a consequence of being permitted is known as permissibility (The American Heritage Dictionary of the English Language, 2003). It is important to stress that in the context of mobile marketing, trust, perceived risk (privacy and data security) and permissibility have been viewed identified in this study as important variables that need to be addressed by service providers to give consumers freedom from doubt (uncertainty) or assurance in adopting mobile marketing services.

Significant of the Study

Statistically, the mobile marketing industry grew from US$4 billion to US$16 billion from 2003 to 2005, serving over 500 million users world-wide (Carroll et al., 2007). Another study by ABI Research (2008) estimated that the global mobile marketing business is currently worth $3 billion, and is projected to reach $19 billion by 2011. According to comScore M:Metrics, the downside of the high mobile phone penetration rate is the accompanying unwanted high text messaging or unsolicited Short Message Service (SMS) rate that is growing by 21.3 percent per year in the European Union (2008) alone. However, despite the increasing number of companies investing in mobile marketing campaigns, there is, as yet, little academic research on mobile marketing, and the implications of using this channel for marketing purposes are not fully understood (Bauer et al., 2005). Although mobile devices are promising marketing channels (Pousttchi and Wiedemann, 2006) mobile spam (i.e. unsolicited SMS messages) raises privacy concerns related to the utilization of the personal and location data used to personalize mobile marketing messages (Leppäniemi et al., 2006). Consumers may be reluctant to trust the innovation as a marketing

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communication channel because they perceive risk regarding the safety of their personal data and privacy. Users may also have perceived potential risks from immature technology (Wu and Wang, 2005). Hence, there is a need to conduct further research in understanding the factors that may influence the intention to adopt mobile marketing from a consumer’s perspective particularly through Rogers’ (1983) perceived innovation characteristics, perceived risk, trust, and permissibility.

Literature Review

Armstrong and Kotler (2009) define the adoption process of an innovation as the mental process through which an individual passes from first learning about an innovation to final adoption. However, this paper argues that final adoption can only be fully understood by looking into the factors that may influence intention decision to adopt mobile marketing. In recent years, researchers have begun to note the importance of gaining consumers’ trust in relation to privacy and security for the mobile marketing communication channel to be accepted. Bauer (2005) further elaborated that trust is the prime prerequisite for consumers’ willingness to permit the reception of advertising messages on their mobile phones and to provide personal data for the personalization of those messages. Risk is also related to the concept of trust (Mitchell, 1999) and in mobile marketing users tend to have concerns about data manipulation, unauthorized data access, and unwanted tracking of usage patterns (Bauer et al., 2005). In this thesis, perceived risk will be limited to personal data security and privacy risks because these are two important aspects in mobile marketing that need empirical investigation (Leppäniemi et al., 2006), and risk associated with mobile marketing is mainly perceived as one of data security (Bauer et al., 2005).

If mobile marketing is to be effective, end-user privacy must be respected (Kavassalis et al., 2003). Privacy issues are particularly sensitive with respect to mobile marketing due to the intimate nature of mobile devices (Brown, 2006). Besides worries of intrusion into one’s private space, mobile spam raises privacy concerns related to the utilization of the personal and location data used to personalise mobile marketing messages (Leppäniemi et al., 2006). Constant messaging from the service providers may irritate consumers because of the ill timing and irrelevant content of the messages. Relevancy of a marketing message can only be determined by the consumer. Therefore, mobile marketers must interact with consumers and let them decide which types of marketing messages are permissible. By seeking consumers’ inputs, marketers will not depend solely on random unsolicited messages, but can improve their targeting strategies by personalising communications.

User permission is also an important variable in mobile marketing due to consumers’ fears of high levels of spam (Barnes and Scornavacca, 2004; Carroll et al., 2007). According to Kavassalis (2003), end-users’ permission to opt-in with clear opt-out instructions must also be present if mobile marketing is to be effective. In this case, permissible mobile marketing messages may be seen as a strategy to reduce clutter

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and search costs for consumers and improving targeting precision for marketers (Krishnamurthy, 2001).

Basic Concepts and Research Model and Hypotheses

The scope of this paper was based on the Diffusion of Innovation (DoI) Theory (Rogers, 1983). According to Rogers (2003, p. 175), there are five perceived characteristics of innovation that can be used to form a favourable or unfavourable attitude toward an innovation, namely: relative advantage, compatibility, complexity, trialability, and observability. In mobile services research, although the DoI theory has been discussed in general by previous researchers, perceived characteristics of the innovation are often trimmed down based on Tornatzky and Klien’s (1982) meta-analysis research findings (Teo and Pok, 2003; Wu and Wang, 2005) that recommend that relative advantage, complexity (ease of use) and compatibility were consistently related to adoption decisions. Moore and Benbasat (1991) argued that the original construct of observability was defined in a complex manner by Rogers (1983, p. 232) in which the results of an innovation are visible and communicable to others, and it also included the idea of the innovation being visible. Moore and Benbasat (1991) further explained that based on the definition of observability, it was decided in their study to split the construct and focus on each dimension independently; one dimension was named Results Demonstrability and the other, Visibility. Another argument regarding the observability characteristic was offered by Tornatzky and Klien (1982), who emphasized that it was unclear whether observability refers to cost or compatibility.

Based on these arguments, this paper will not include “observability” as one of perceived innovation characteristics because of various interpretations of the characteristic. This research will only maintain the original four out of five perceived innovation characteristics as proposed by Rogers (1983): relative advantage, complexity, compatibility, and trialability. Therefore the current research hypothesizes:-

H1: Relative advantage has a direct influence on intention decision

H2: Compatibility has a direct influence on intention decision

H3: Complexity has a direct influence on intention decision

H4: Trialability has a direct influence on intention decision

Another area that may also contribute to understanding the adoption of mobile marketing services is trust. According to Siau and Shen (2003), trust is one of the major reasons influencing peoples’ decisions in giving service providers their personal data via an electronic medium. This view is supported by Leppäniemi (2006), who indicated the need for empirical investigations into the factors that affect consumers’ willingness to provide personal information, and granting permission to use this information in mobile marketing. Therefore the research hypothesizes:

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H5: Trustworthiness has a direct effect on intention decision.

Perceived risk was also included in the model because according to Mitchell (1999), perceived risk is a necessary antecedent for trust to be operative and an outcome of trust building is a reduction in the perceived risk of the transaction or relationship. Hence, perceived risk is essential in the intention to adopt decision, and the current study proposes the following hypothesis:

H6: Perceived risk has a direct effect on intention decision

Permissibility represents permission obtained from users to allow marketers to communicate relevant and anticipated marketing messages consumers. According to Godin (1999, p. 21), “permission marketing is an approach, which offers the consumer an opportunity to volunteer to be marketed”. Reflecting these considerations the following hypothesis can be formulated.

H7. Permissibility has a direct effect on intention decision

Figure 1 Theoretical Research Framework and Hypothesis Paths

Methodology

Previous research was reviewed to ensure that a comprehensive list of measures were included. Those of ‘relative advantage’, ‘compatibility’, ‘complexity’, and ‘trialability’ were adopted from Moore and Benbasat (1991). Items for ‘Intention decision’ was adapted from Nysveen et al. (2005). New items were also proposed in

Intention Decision

Relative Advantage

Complexity

Compatibility

Trialability

H1

Perceived Risk

Trustworthiness

Permissibility

H2

H3

H4

H5

H6

H7

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this paper to measure trustworthiness, perceived risk and permissibility. Items representing each constructs can be found in Table 3. Once the initial questionnaire was completed, two rounds of comments by expert judges, senior academic lecturers, were conducted to refine the instrument. These expert judges’ rounds enabled the researcher to gauge the clarity of the constructs, access whether the instrument was capturing the desired phenomena, and verify that important aspects had not been omitted. Some changes and amendments were made to the questionnaire. Feedback served as a basis for correcting, refining and enhancing the instruments scales. Some items were omitted from the questionnaire because they were found to represent essentially the same aspect with only slightly wording differences. Some items were modified to represent mobile marketing characteristics. The questionnaire consisted of 27 items measuring eight latent variables. Table 1 summarizes the definitions of variable.

Table 1 Definition of Constructs

Variables Definition of Construct Adapted from Permission marketing

A marketing approach that offers the consumer an opportunity to volunteer to receive marketing messages.

Godin, 1999

Perceived risk Consumers’ subjective belief of suffering a loss in pursuit of a desired outcome. Risk in this context is related to subjective assessment of potential risk (i.e. security and privacy) rather than “real world” (objective) risk.

Bauer, 1960

Trust Emerges from the identification of a need that cannot be met without the assistance of another and some assessment of risk involved in relying on the other to meet this need. Trust is a willing dependency on another’s action, but it is limited to the area of need and subject to overt and covert testing. The outcome of trust is an evaluation of the congruence between expectations of the trusted person (party) and actions.

Hupcey et al., 2001

Relative advantage

the degree to which an innovation is perceived as being better than the idea it supersedes.

Rogers, 2003, p. 229

Compatibility the degree to which an innovation is perceived as consistent with the existing values, past experiences, and the needs of potential adopters.

Rogers, 2003, p. 240

Complexity the degree to which an innovation is perceived as being relatively difficult to understand and use.

Rogers, 2003, p. 257

Trialability the degree to which an innovation may be experimented with on a limited basis.

Rogers, 2003, p. 258

Intention to Use (Decision Stage)

when an individual (or other decision –making unit) engages in activities that lead to a choice to adopt or reject an innovation.

Rogers, 2003, p. 177

The questionnaire was later pre-tested using Malaysian post-graduate candidates throughout New Zealand, the United Kingdom and Malaysia. Apart from completing

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the questionnaire, participants were also asked to comment on the language used, the accuracy of the translation and the relevance of the questions in the questionnaire. Based on their feedback some changes were made to the translation, such as using simple instructions in each of the sub-headings to help respondents easily understand the requirements of the questionnaire. A total of 87 questionnaires were distributed but only 61 questionnaires were returned, with 57 of them usable. Based on the pilot testing, several items were removed from the questionnaire to improve the reliability score. In the early stages of basic research, Nunnally (1967) suggests reliabilities of 0.50 to 0.60 would suffice and that increasing reliabilities beyond 0.80 is probably wasteful. Thus, for this paper the target level of minimum reliability was set in the 0.60 to 0.70 range.

Table 2 Reliability Analyses by Sections

Section Cronbach’s Alpha

Number of Items

Perceived Risk .654 4 Permissibility .790 4 Trust .827 4 Relative Advantage .906 3 Compatibility .887 3 Complexity .853 3 Trialability .891 3 Intention Decision .699 3 27 items

Data collection

A total of 670 questionnaires were distributed to mobile phone users in Labuan, Malaysia. The return percentage was 57.46 percent (380 questionnaires), but only 341 questionnaires were usable.

Statistical analysis

All data analysis was conducted using SPSS v.15 and AMOS 7. A descriptive analysis will be used to portray a general picture of the survey respondents. The two main types of statistical analysis used in this research were the Factor Analysis Method and Structural Equation Modeling (SEM).

Results

For this paper, the KMO measure of sampling adequacy was 0.894, indicating that the data clearly supported the use of factor analysis and suggesting that the data may be

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grouped into a smaller set of underlying factors. Eight major factors were identified, representing 58.337 percent of the total variance explained. The indicators in the model loaded highly on their constructs and were significant (factor loadings ranged from 0.53 to 0.91). Construct validity was further assessed by calculating Cronbach’s alpha for each of the scales. The Cronbach’s alpha measures included in the model ranged from 0.74 to 0.89 (Table 3). The alphas for the study constructs exceeded or equaled the threshold 0.70 (Nunnally, 1978). The composite reliability (CR) estimates the extent to which a set of latent construct indicators share in their measurement of a construct, whilst the average variance extracted (AVE) is the amount of common variance among latent construct indicators (Hair et al., 1998). The composite reliability also test evaluates the internal consistency of the measurement model (Karjaluoto et al., 2008). The CR values for all the constructs were above the recommended value of 0.70. According to Fornell and Larcker (1981) a model can be considered to have a good convergent validity if the AVE for each construct is greater than 0.50, as this indicates that more of each construct is explained by its indicator than by other external influences. The AVE of the constructs in the model ranged from 0.30 to 0.74. Perceived risk (0.49) and Decision Intention (0.30) have lower than 0.50 AVE value (Table 3).

Table 3 Factor Loadings and Assessment of Construct Reliability

ITEMS Loading Factor 1 - Complexity (CA = .89; CI=.89; AVE=.74) CPLX1 Learning to use mobile marketing services would be easy for me. .819 CPLX2 If I were to adopt mobile marketing services, it would be easy for

me to adapt. .862

CPLX3 If I were to adopt mobile marketing services, it would be easy due to my previous experience with mobile phone usage.

.705

Factor 2 - Compatibility (CA = .88; CI=.88; AVE=.72) COM1 If I were to adopt mobile marketing services, it would be

compatible with my internet searching methods. .671

COM2 If I were to adopt mobile marketing services, it would fit my product and services information gathering style.

.825

COM3 If I were to adopt mobile marketing services, it would fit well with the way I like to seek relevant product and services information.

.689

Factor 3 - Trialability (CA = .86; CI=.87; AVE=.69) TRY1 Before deciding on whether or not to adopt mobile marketing

services, I would be able to use it on a trial basis. .724

TRY2 Before deciding on whether or not to adopt mobile marketing services, I would be able to test the suitability of the services.

.812

TRY3 I would be permitted to use mobile marketing services on a trial basis long enough to see what it can do.

.719

Factor 4 - Permissibility (CA = .77; CI = .84; AVE = .63) PM2 I would consider giving my permission to receive mobile

marketing messages if the messages are relevant. .699

PM3 I would consider giving my permission to receive mobile marketing messages if I anticipate the content of the message.

.697

PM4 I would consider giving my permission to receive mobile .716

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ITEMS Loading marketing messages if the messages are personalised.

Factor 5 – Trustworthy (CA= .76; CI = .81; AVE = .59) T1 I consider mobile marketing is a reliable way to receive relevant

information. .583

T3 Mobile marketing services are a trustworthy source of personalised marketing messages.

.585

T4 Mobile marketing services are reliable because messages are up-to-date.

.631

Factor 6 - Relative Advantage (CA = .88; CI = .89; AVE = .73) RA1 If I were to adopt mobile marketing services, it would enable me

to get information more quickly. .572

RA2 If I were to adopt mobile marketing services, the quality of my information would improve.

.718

RA3 If I were to adopt mobile marketing services, it would enhance my effectiveness on information gathering.

.742

Factor 7 - Perceived Risk (CA= .74; CI = .74; AVE = .49) RISK1 It is safe to accept and reply to mobile marketing messages via

mobile phone. .682

RISK3 There is no more privacy risk involved in receiving marketing messages via mobile phone than there is when getting marketing messages via email or TV advertisement.

.573

RISK4 I do not consider mobile marketing to be a privacy risk way to receive marketing messages.

.667

Factor 8 - Decision Stage (CA = .70; CI = .56; AVE = .30) DS1 I intend to accept mobile marketing messages occasionally from

my current service provider in the next 6 months. .427

DS2 I intend to accept marketing messages from my current service provider frequently in the next 6 months.

.406

DS3 I intend to use my mobile phone to get relevant marketing messages in the next 6 months.

.843

Table 4 Correlations and Square Root of Average Variance Extracted

AVE

Per

ceiv

ed R

isk

Tru

stw

orth

ines

s

Per

mis

sib

ilit

y

Com

ple

xity

Com

pat

ibil

ity

Tri

alab

ilit

y

Rel

ativ

e A

dva

nta

ge

Dec

isio

n I

nte

nti

on

Perceived Risk .49 .700

Trustworthiness .59 .708 .768

Permissibility .63 .010 .011 .793

Complexity .74 .247 .337 .223 .860

Compatibility .72 .377 .499 .173 .339 .848

80

AVE

Per

ceiv

ed R

isk

Tru

stw

orth

ines

s

Per

mis

sib

ilit

y

Com

ple

xity

Com

pat

ibil

ity

Tri

alab

ilit

y

Rel

ativ

e A

dva

nta

ge

Dec

isio

n I

nte

nti

on

Trialability .69 .134 .325 .326 .408 .522 .830

Relative Advantage .73 .512 .587 .289 .331 .623 .445 .854

Decision Intention .30 .258 .383 .217 .301 .655 .611 .529 .547

To assess the discriminant validity of the model, tests were performed as to whether the square root of AVE for each construct is greater than the correlation with each other construct (Fornell and Larcker, 1981). Table 4 represents the square root of average variance extracted and the correlations between the constructs. As can be seen, the square root of AVE is greater than the correlation with any other construct except for Decision Intention construct. These indicate good discriminant validity of the model.

Estimates and Fit Criteria

For SEM, goodness-of-fit (GFI) indexes are used to evaluate the model in order to assess the model in terms of model fit and model parsimony (refer to Table 5). The GFI measures the percent of observed covariances explained by the covariances implied by the model, and the GFI should be equal or greater than 0.90 to accept the model (Gefen et al., 2000). For this paper the GFI is 0.907, above the recommended value of > 0.90.

The Adjusted Goodness-of-Fit Index (AGFI) is adjusted for the degrees of freedom of a model relative to the number of variables, and should be above 0.80 (Chin and Todd, 1995; Segars and Grover, 1993). For this model, the AGFI was 0.888, above the recommended value of > 0.80.

Bentler (1990) revised the NFI to consider sample size and proposed the comparative fit index (CFI). Although Bentler (1992, p. 401) stated that, “higher values indicate greater covariation accounted for, with excellent model having NFI values above 0.90 representative of a well fitting model”, a revised cut-off value close to 0.95 has recently been advised (Hu and Bentler, 1995) for CFI. The NFI value was 0.904, above the recommended value of > 0.90, and the CFI value was 0.966, above the cut-off value > 0.95, suggesting a good fit between the hypothesized model and the sample data.

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The Tucker-Lewis index (TLI) yields values ranging from zero to 1.00, with values close to 0.95 (for a large sample) being indicative of good fit (Byrne, 2001; Hu and Bentler, 1999). The TLI for the current model was 0.962.

For Root Mean Square Error of Approximation (RMSEA), a value less than 0.05 indicates good fit, and a value as high as 0.08 represents reasonable errors of approximation in the population (Browne and Cudeck, 1993). But MacCallum (1996, p. 134), “considers values in the range of 0.08 to 0.10 to indicate mediocre fit”, and Hu (1999) suggested a value of 0.06 to be indicative of good fit between the hypothesized model and the observed data. This paper reports the index value for RMSEA to be within the recommended value (specifically, < 0.05) at 0.038, which indicates a good fit between the hypothesized model and the observed data.

Table 5 Goodness-of-Fit Statistics

Hypothesis Testing

Table 6 Summary of Research Findings

Hypothesis Influence direction

Critical ratio

Findings

H1. Relative Advantage has a direct effect on intention decision H2. Compatibility has a direct effect on intention decision H3. Complexity has a direct effect on intention decision H4. Trialability has a direct effect on intention decision H5 Perceived risk has a direct effect on intention decision H6. Trustworthiness has a direct effect on intention decision H7. Permissibility has a direct effect on intention decision

+

+

+

+

+

+

+

9.776***

9.324***

7.840***

7.878***

6.362***

7.482***

6.116***

Supported

Supported

Supported

Supported

Supported

Supported

Supported

The results of structural equation modeling are standardized maximum likelihood path coefficient for the hypothesized model. * Significant at the p < 0.1 level, ** Significant at the p < 0.05 level, *** Significant at the p < 0.01 level.

Statistic Recommended criteria

Value

Goodness-of-fit index (GFI) > 0.90 0.907 Adjusted goodness-of-fit index (AGFI) > 0.80 0.888 Comparative Fit Index (CFI) > 0.95 0.966 Tucker –Lewis index (TLI) > 0.95 0.962 Normed fit index (NFI) > 0.90 0.904 Root mean square of approximation (RMSEA) < 0.05 0.038 Source: AMOS 7.0 output.

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Figure 2 presents the significant structural relationship among the variables and standardized path coefficients. All the hypotheses for this paper were strongly supported and all standardised paths were significant (>±1.96) (refer to Table 6). For hypothesis 1, the result indicated that relative advantage has a significant effect on the decision intention by consumers to adopt mobile marketing (β = 0.83). This indicates that users’ relative advantage of a new innovation is an important determinant for users’ decision intention to adopt mobile marketing. Compatibility, complexity, and trialability also have direct effects on intention decision with regression weight (β) of 0.79, 0.61 and 0.63, respectively. These findings support Rogers’ (2003) perceived characteristics of innovation attributes where the above three constructs can be used to form a favourable or unfavourable attitude toward the innovation.

Figure 2 Structural Relationship among the Variables and Standardized Path Coefficients

Trustworthiness1

T4l4 T3l3

T1l1

R1

Permissibility1

PM4 p4 .76 PM3 p3 .75 PM2 p2 .72

R3

Relative Advantage1

RA3 s3 .91 RA2 s2 RA1 s1

Compatibility1

COM3 c3 COM2 c2 COM1 c1

.86

.80

Complexity1

CPLX3 x3 CPLX2 x2 CPLX1 x1

.79

.95

Trialability1

TRY3y3 TRY2y2 TRY1y1

.78

.90

.81

R4

R5

R6

R7

.90

Decision Intention1

DS1 d1 .56

DS2 d2 .52

DS3 d3 .56

.84

.77.88

Perceived Risk

RISK1 e1 .72 RISK3 e2 .69 RISK4 e3 .69

R10

.66 .69

.68

.79

.61

.63

.52

.67

.46

.83

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Perceived risk also registered a significant direct effect towards intention decision with regression weight of 0.52 in this study. This result was consistent with Wu and Wang’s (2005) findings and they attributed their result to users’ previous experience with online services which may imply that consumers are more aware of the existence of potential risk and have a better understanding of the mobile commerce context. This result also supports Ulivieri’s (2004) argument that a consumer goes on doing something that initially seemed to be risky or dangerous but little by little she/he becomes more confident; it is a form of basic trust derived from habit and from the decreasing perceived probability of damage. According to Kim (2008), consumers are often faced with at least some degree of risk or uncertainty in using mobile technology, however risk is not the only factor consumers are sensitive to, but relates the perceived benefit that provides consumer with an incentive to use the mobile technology

Permissibility was statistically significant in influencing decision intention (c.r. = 6.116) with a regression weight of 0.46. Through users’ permission, companies can develop an iterative product development approach that can incorporate demand requirements while familiarizing the customer with the technology dimension of a mobile marketing campaign (Kavassalis et al., 2003). The significant relationship between permissibility and intention to adopt also supports Barwise and Strong’s (2002) findings that consumers’ explicit permission is essential for a high level of acceptance and satisfaction of mobile marketing.

Trustworthiness with a regression weight of 0.67 was also significant in influencing consumers’ intentions to adopt mobile marketing. The nature of the innovation determines what specific type of relative advantage is important to the adopters (Rogers, 2003). At this stage respondents may perceive that by trusting on the service, they might receive better and up-to-date information about their interest/s and relating this information within their circle of friends. If mobile marketing is to be an effective and lucrative industry, it has to deliver relevant, requested (trusted), and interactive content to customers (Kavassalis et al., 2003).

Research Limitation and Recommendation for Future Research

First, the current research only looked at the adoption decision of mobile marketing services through one type of mobile device (i.e. mobile phones) and not through other mobile devices (i.e. PDAs, Palms, etc). Other consumers using other types of mobile devices may have a different response if they had been included in the study. For example, on October 24 2008, the Oprah Winfrey Show introduced “Kendall” by built by Amazon, which is a portable, wireless electronic book that could download about 7,000 books at half the price of the shelf price. Kendall is also capable of downloading newspapers and getting definitions of words instantly from the Internet at the user’s convenience. New mobile devices will continue to be introduced; therefore this paper has limited its finding to mobile phones only. Second, the research only looked at one community (social system) to represent the adoption process by which an innovation is communicated through certain channel over time

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among members of a social system. In addition, owing to resource limitations the research did not survey respondents outside of the social system chosen for the study.

Focusing on the implications for future research data collected for this paper strongly suggest that perceived risk is more than just risk in general and should focus on data security and privacy concerns for mobile marketing research. Although a universally-agreed theoretical definition still eludes marketing academia for perceived risk, good models of perceived risk can only be judge on what the researcher is attempting to achieve by designing the model (Mitchell, 1999). Previous researchers may have used perceived risk in their models (Teo and Pok, 2003), but tend to treat perceived risk as a general construct (i.e. risk). Perceived risk was based on security and privacy risk faced by mobile phone users in m-marketing. The result suggested that low perceived risk was associated with m-marketing when questioned regarding security and privacy issues faced by potential users of m-marketing. When perceived risk is low, significant relationship was found on trustworthiness of the service. In terms of perceived risk, then, low or high perception of specific risks (i.e. security and privacy) can be seen as an active process of engagement in the adoption process.

Conclusion

This paper explored the potential factors that may influence the intention of mobile phone users to adopt mobile marketing services through seven perceived characteristics namely; relative advantage, compatibility, complexity and trialability, perceived risk, trustworthiness and permissibility that may play important roles in determining consumer decision intention to adopt mobile marketing. Although the constructs in this paper have been represented by a strong direct significant relationship towards decision intention, nonetheless, future research should incorporate the Theory of Reasoned Action (TRA) or the Theory of Planned Behaviour (TPB) to better understand the innovation-decision process, because the study’s proposed model did not allow the researcher to explore the effects of attitude and intention measurement to determine what factors amplify or disrupt the adoption process.

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Asian Journal of Business Research Volume 1 Number 1 2011

Factors Differentiating Green Power Electricity User/Non-user Status in Australia Yiming Tang Macquarie University, Australia Milind Medhekar Macquarie University, Australia Abstract

This study aims to identify factors differentiating users and non-users of GPE (Green Power Electricity) in Australia. The internet-based survey reached audience across Australia. The results show that consumers’ environmental concern and ecologically conscious behaviour are key drivers of GPE purchase. Younger consumers are also more likely to be GPE users. Findings of this study bear significant implications for both government policymakers and GPE distributors and/or resellers. This study calls for increased effort in public education especially of younger people – and in marketing campaigns to raise consumers’ awareness of and concern for the environment. Further, it recommends that in order to maximize the adoption of GPE, and promotional campaign of GPE should be combined with other activities to educate the consumers of the benefits and the availability of GPE products and to promote ecologically friendly behaviors.

Keywords: Green Power Electricity, Environmental concern, Ecologically conscious behaviour, Australia

Introduction

Climate change in recent years has caused increasing concerns and posed a significant challenge to economic development in countries around the world. In response to this challenge, consumers around the world have demanded more environmentally friendly products. Recent research findings show consumer spending on green

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products is expected to reach US$500 billion in recent years (Lander et al., 2007), clearly showing that green marketing has been gathering momentum. Among the growing list of environmentally friendly products is Green Power Electricity (GPE) – electricity which is produced from renewable sources, including solar, wind, tidal wave, biomass and geothermal energy. GPE is regarded as socially desirable electricity due to its non-polluting nature (Swezey and Bird, 2000). The renewable energy sector has been forecasted to grow between 20 – 30% annually (Tilting at Windmills, 2006). There are currently 196 certified GPE providers in Australia National Green Power Accreditation Program Status Report, 2009) and GPE option has been available to 96% of the population since 2004 (Rundle-Thiele et al., 2008). Despite the increasing demand for GPE and the availability of technology to produce it, and the wide availability of GPE in Australia, the Australian electricity market is still dominated by coal-generated power, which accounts for 80% of production, while the renewable sources account for only 8% (Australian Coal Association and Climate Institute, 2008), with an estimated retail market penetration rate slightly above 2% (CIA, 2009, National Green Power Accreditation Program Status Report, 2009).

GPE provides an equal amount of utility as conventional electricity. Yet despite GPE being more costly than conventional electricity to produce, some consumers prefer GPE. So the obvious question from a marketing viewpoint is what the key factors are that promote GPE purchase. While there has been research on GPE marketing and GPE purchase in Australia, such effort has been limited to anecdotal experience via a case study of an electricity retailer (Rundle-Thiele et al., 2008). Little systematic research via large scaled empirical studies of electricity consumers is found in the literature to cover such an important subject in Australia. This unsatisfying situation paves the rationale for us to conduct this current research, representing a first empirical study via systematic research effort to investigate this subject in Australia. Specifically, the research question for our study is to identify the key factors that are associated with GPE purchase or non-purchase, thus, differentiating users and non-users of GPE in Australia.

The rest of the paper is organized into four sections. In section II we critically review previous research literature on related topics, and in section III we develop our research hypotheses and measurements. In section IV we present our research methodology, including data collection, data analysis and the results of the hypothesis testing. In the last section V, we discuss the academic contribution, practitioner implications and our conclusions.

Literature Review

Previous studies on consumers’ engagement in their purchase of environmentally friendly products and/or services have identified a wide range of factors that influence such purchases, including ecological concern, ecological consciousness, subjective norms, perceived consumer effectiveness, consumer values, awareness and knowledge, and willingness to pay.

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Past research has found that environmental concern is positively and significantly correlated with environmentally friendly behaviour (Kinnear et al., 1974; Roberts, 1996; Roberts and Bacon, 1997), and consumers with higher levels of such concern are more likely to purchase green products (Banerjee and McKeage, 1994; Chan, 1999; Chan and Lau, 2000; Laroche et al., 2001; Straughan and Roberts, 1999). A strong positive link was also found between consumers’ environmental consciousness and their purchase of environmentally friendly products such as biodegradable detergents and paper products (Schlegelmilch et al., 1996). Suchard and Polonsky (1991) stipulated that ecologically conscious consumers will try to protect the environment in various ways, by performing such activities as purchasing green products, engaging in recycling activities and favoring packaging made of recycled materials. Straughan and Roberts (1999) found that an environmentally friendly attitude is a precursor to environmentally friendly behaviour.

Triandis (1993) and McCarty and Shrum (1994) argue that collectivism values motivate people to transcend selfish concerns and promote the welfare of others and of nature. Therefore, collectivist people could be considered friendlier to the environment, with a tendency for restrictive conformity to social expectations. Schwartz (1992) labeled collective values as “self-transcendence”, the tendency for restrictive conformity as “conservation”, and individualist values as “self-enhancement” to reflect values motivating people to enhance their personal interests. Follows and Jobber (2000) found a positive association between self-transcendence and the likelihood of purchasing disposable diapers (an environmentally responsible product), and between consumers’ conservation value, their attitude towards the environment and their purchase of environmentally responsible products. In addition, those holding stronger self-enhancement values are more likely to purchase a product with the lowest individual consequences, irrespective of their social implications. These results support earlier findings that individualistic people tend to be less friendly to the environment (McCarty and Shrum, 1994; Triandis, 1993). Aoyagi-Usui et al. (2003) find that in Japan, with its strong collectivism culture root, traditional way of thinking, such as honoring parents and elders and family security, etc, is welcomed. And green consumption behaviour and energy-saving is viewed as “contrary to the progressive value favoring economic growth and technological innovation” (Aoyagi-Usui et al., 2003, p. 29). As such, environmental value is positively associated with green-consumption behaviour in Japan.

Perceived effectiveness in solving environmental problems has also been linked to consumers’ green products purchase. Samdahl and Robertson (1989) discovered that consumers expect intervention by regulatory bodies to deal with environmental problems. Roberts (1996) concluded that perceived consumer effectiveness was one of the strongest predictors of ecologically conscious consumer behaviour. On the other hand, consumers were unwilling to pay higher prices for green products if they believe the government and corporations, not individuals, should be responsible for solving ecological problems (Laroche et al., 2001).

Subjective norms relate to an individual’s perception of an important referent’s evaluation of his/her behaviour and the person’s motivation to behave as per these evaluations. According to Fishbein and Ajzen (1975), as subjective norms become

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more favorable, a person’s intention to perform certain behaviour increases. In another study, Ajzen and Fishbein (1980) pointed out that for co-operative behaviors, normative considerations are more important than attitude considerations. Cordano and Frieze (2000) applied Ajzen’s Theory of Planned Behaviour to understand the behavioural preferences of 295 environmental managers, and found a positive relationship between environmental managers’ assessment of subjective norms about environment regulation and their preference for implementing activities to reduce the sources of pollution. Thus, if a person believes that his/her referent groups expect him/her to contribute towards solving environmental problems via environmentally friendly behaviour, such as buying “green products”, s/he is more likely to do so.

Consumer knowledge has long been recognized as influencing all phases of a buying decision process. Rogers (1995) noted that the adoption process begins with the awareness of a problem (need), and the knowledge of the existence of an innovation (product). Consumers who identify environmental degradation as a serious social problem, and think that they need to do something about it, would be looking for green products as a solution. Batley et al. (2001) found that UK consumers’ awareness level of GPE availability and its benefits is positively associated with their behaviour. Bang et al. (2000) found that lack of GPE knowledge was one reason for its slow uptake in the US. Similar finding is reported in a business case study in Australia as well (Rundle-Thiele et al., 2008)

Vlosky et al. (1999), in their study of ecologically certified wood products, found a positive relationship between environmental consciousness and willingness to pay a premium for such products. Furthermore, Farhar (1999) also discovered that the majority respondents in his research stated they were willing to pay a premium for GPE, hinting that people who are willing to pay more for green products should more likely to purchase such products. However, other empirical research reveals that a big gap often exists between the stated willingness to pay and actual purchase. For example, in a study of GPE pricing, Wiser (1998) discovered that less than 3% of residential customers actually joined the GPE programs, although 40 - 70% claimed willingness to pay. Such findings clearly show that willingness (intent) to pay for green products is not necessarily a reliable predictor of GPE purchase (behaviour).

Previous studies have also examined the association between consumers’ demographics, their social-economic characteristics, with their ecologically conscious attitudes and/or their purchase behaviour towards green products. While age has shown equivocal associations (Anderson et al., 1974; Roberts, 1996; Samdahl and Robertson, 1989, Zimmer et al., 1994), younger people have been found to be more sensitive to environmental issues (Anderson and Cunningham, 1972; Kinnear et al., 1974; Roberts, 1995, 1996; Roberts and Bacon, 1997; Webster, 1975). A common explanation is that younger people, growing up in a time when environmental threats are increasingly seen as salient issues, are more sensitive to these issues (Straughan and Roberts, 1999). As far as gender is concerned, women are more likely than men to hold attitudes consistent with the green movement (Banerjee and McKeage, 1994; Laroche et al., 2001; Straughan and Roberts, 1999; Webster, 1975), and they are more likely to pay more for green products as well (Laroche et al., 2001). Income has also been examined as a predictor of ecologically conscious consumer behaviour, but the

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findings on the income-adoption of green products association show mixed directions (Anderson and Cunningham, 1972; Kinnear et al., 1974; Roberts, 1995, 1996; Roberts and Bacon, 1997; Zimmer et al., 1994). Several studies have also examined the relation of education levels with environmental concerns and environment friendly behaviour. Some of them found a positive association (Aaker and Bagozzi, 1982; Murphy et al., 1978; Roberts, 1996; Zimmer et al., 1994), although Samdahl and Robertson (1989) found a negative association. A possible explanation for the relationship between education level and environmentally conscious consumer behaviour is that educated people tend to have higher sensitivity to social problems, although no strong view has been found to explain the negative association.

Research Hypotheses and Measurements

Our literature review has uncovered a range of factors directly associated with purchase of green products. Yet, few of above cited studies have dealt with GPE purchase, and with only a case study (of a retailer) in Australia. As GPE belongs to the category of green products which would offer a solution to the current environmental degradation challenge, it is therefore logical to assume that findings of our literature review should also apply to GPE purchase in Australia.

Previous studies show that individual consumers’ level of ecological concern is an important factor in determining his or her purchase of green products. Studies by Banerjee and McKeage (1994), Chan (1999), Chan and Lau (2000) and Laroche et al. (2001) show that higher level of environmental concern leads to green product purchases and participation in recycling programs. This positive association between environmental concern and the actual purchase behaviour of the green products should also apply to GPE, which is one of the green products available today. Thus, our first hypothesis (H1) states that a consumer is more likely to be a GPE user, the more concerned s/he is about the environment.

Several studies have found a positive association between individuals’ environmental concern and their environmentally friendly behaviour (Kinnear et al., 1974; Roberts, 1996; Roberts and Bacon, 1997; Schlegelmilch et al., 1996). Ecologically conscious consumers will try to protect the environment via engagement in various activities and solutions, including purchasing green products, engaging in recycling activities, favoring packaging made of recycled materials, and similar activities (Suchard and Polonsky, 1991). One such solution is to purchase GPE. Thus, it is logical to believe that an environmentally conscious consumer, who is already involved in other environmentally friendly activities, is more likely to be a GPE user as well. Built on this logic, our second hypothesis (H2) states that a consumer is more likely to be a GPE user, the more engaged s/he is in ecologically conscious activities.

Collectivist people tend to be friendlier to the environment (McCarty and Shrum, 1994; Triandis, 1993). The pro-social domain of the collective values reflects the extent to which they motivate people to transcend their selfish concerns and promote the welfare of others and of nature. Schwartz (1992) labels this as “self-transcendent

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values”, and argues that people holding stronger such value are more likely to be concerned about protecting the environment. Follows and Jobber (2000) found a positive relationship between self-transcendence and the likelihood of purchasing environmentally responsible products. Such findings suggest that self-transcendent values should promote GPE consumption as well, since the use of GPE benefits society in general. It is therefore reasonable to speculate that a positive associate should exist between consumers with pro-social self-transcendence values and their likelihood of use of GPE. Hence, our third hypothesis (H3) is that a consumer is more likely to be a GPE user, the higher self-transcendence value s/he hold.

Schwartz (1992) points out that people with conservatism values have a strong need for maintaining the status quo. Follows and Jobber (2000) found that consumers with high conservatism values had a less positive attitude towards the environment and were less likely to purchase environmentally responsible products. These findings can and should apply to GPE purchase as well. Consumers with high conservative values would be less likely to abandon the use of conventional electricity for something different and new, such as GPE, which does not bring higher level of utility to purchaser. The fourth hypothesis (H4) states, that a consumer is less likely to be a GPE user, the more conservation value s/he exhibits.

Schwartz (1992) re-named individualist values from the collective-individual dichotomy as ‘self-enhancement’ to reflect the values that motivate people to enhance their own personal interests. He created domains of hedonism (reflecting pleasure or sensuous gratification of oneself), achievement and power to be included in self-enhancement. Using these domains in their study, Follows and Jobber (2000) found that those who hold stronger self-enhancement values are more likely to purchase a product with the lowest individual consequences, regardless of its environmental friendliness. Such consumers seek products that are beneficial to themselves, irrespective of the social implications of using the products, supporting the arguments made by Triandis (1993) and by McCarty and Shrum (1994) that individualistic people tend to be less friendly to the environment. As the use of GPE would increase individual consequences in terms of paying higher prices without receiving any additional functional benefits from it, individuals holding higher self-enhancement values seem less likely to be users of GPE. As such, our fifth hypothesis (H5) is that a consumer is less likely to be a GPE user, the stronger self-enhancement values s/he holds.

Subjective norms relate to an individual’s perception of an important referent’s (e.g. a friend’s or family member’s) evaluation of one’s behaviour and the person’s motivation to behave in line with these evaluations. According to Fishbein and Ajzen (1980), as subjective norms become more favorable, a person’s propensity to certain behaviour increases. For co-operative behaviors, normative considerations are more important than an individual’s attitude. If people can be influenced by their referent groups which expect them to perform according to the group’s expectations, then there is a greater probability of their fulfilling those expectations. Hence, if people believe that their referent groups, such as family, close friends or colleagues, expect them to contribute towards solving environmental problems via environmentally friendly behaviour (such as buying ‘green products’), they are therefore more likely to

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use such products. While subjective norms construct has been applied in previous consumer behavioural studies, little evidence is found in the literature to link its application to the purchase of a specific environmentally friendly product such as GPE. Given that the use of GPE benefits society in general, it is expected that it would become a social norm in future to do so. The normative influences associated with what benefits society should therefore play a very positive role in the likelihood of GPE purchase. It is based on this assumption that our six hypothesis (H6) states that a consumer is more likely to be a GPE user, if s/he thinks that her/his referent groups expect him/her to do so.

Findings from previous studies also show that perceived consumer effectiveness (PCE) is not only associated with consumers’ ecological consciousness (Ellen et al., 1991; Kinnear et al., 1974), but also linked to their purchase behaviour (Samdahl and Robertson, 1989; Roberts, 1996). Laroche et al. (2001) found that consumers who believe that it is the responsibility of governments and corporations, rather than individuals, to solve ecological problems, are not willing to pay more for green products. While the above cited studies are not related to GPE, it is logical to expect that a similar rationale should be applicable to GPE purchase as well. Given that there is no apparent additional functional advantage to consumers for choosing GPE over electricity produced from conventional sources, those consumers who strongly believe that they are individually less influential in protecting the environment, and that it is the government’s and/or corporations’ responsibility to solve the ecological problems, are less likely to be GPE users. Therefore, our seventh hypothesis (H7) states that a consumer is less likely to be a GPE user, if s/he feels that individuals cannot be effective in solving environmental problems.

Rogers (1995) seminal research on product adoption clearly demonstrates that product awareness is a critical factor in early adoption of a new product. Consumers who identify environmental degradation as a serious social problem, and think that they need to do something about it, would be looking for various solutions. Therefore, if consumers are aware of the availability of GPE in the market, and that adopting GPE can help to solve the environmental problems, they may consider GPE purchase. Finding of a business case study in Australia shows, from the opposite angle, that low awareness of GPE in Australia has been a key reason for the slow adoption and low penetration of GPE in Australia (Rundle-Thiele et al., 2008). These evidences show that awareness of the existence of a product like GPE appears to be a precursor to its use. Thus, electricity consumers who have information about GPE are most likely to be its users. Hence, our eighth hypothesis (H8) states that a consumer is more likely to be a GPE user, if s/he is aware of its availability.

Our literature survey reveals that several studies have investigated consumers’ willingness to pay a premium for environmentally friendly products, but their findings show a gap between stated intent and purchase, indicating a very unclear picture of the association between consumers’ willingness to pay and actual purchase of green products (Farhar, 1999; Suchard and Polonsky, 1991; Vlosky et al., 1999; Wiser, 1998), indicating that willingness to pay is not a reliable predictor of green consumption behaviour. As such, we have decided to exclude this factor from the current study. Many studies have also investigated the association between

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consumers’ purchase of green products and their demographic and social-economic characteristics, including age (Anderson and Cunningham, 1972; Anderson et al., 1974; Kinnear et al., 1974; Roberts, 1995, 1996; Roberts and Bacon, 1997; Webster, 1975; Zimmer et al., 1994), Gender (Banerjee and McKeage, 1994; Laroche et al., 2001; Straughan and Roberts, 1999; Webster, 1975), education (Aaker and Bagozzi, 1982; Kinnear et al., 1974; Murphy et al., 1978; Roberts, 1996; Samdahl and Robertson, 1989; Zimmer et al., 1994), and income (Anderson and Cunningham, 1972; Kassarjian, 1971; Kinnear et al., 1974; Roberts, 1995, 1996; Roberts and Bacon, 1997; Straughan and Roberts, 1999; Zimmer et al., 1994). However, these studies have produced mixed results in terms of the associations between consumers’ demographic and social-economic factors and their green products purchase. As a result, we have decided to include the various demographic characteristics as control variables in this research without formulating a specific hypothesis for each of these factors.

Methodology

In this study, the GPE purchase behaviour is measured using GPE user and non-user status. Measures capturing the other variables covered in the research hypotheses are adopted from the relevant studies covered in our literature review. Table 1 contains our research hypotheses and corresponding measures. Other demographic variables are adopted from the literature.

Table 1 Research Hypotheses and Scale Measurements

Research Hypotheses Measures H1. A consumer is more likely to be a user of GPE, the more environmentally conscious s/he is.

Environmental consciousness (EC): 12 items, 5-point scale: 5 = strongly agree, 1 = strongly disagree; Adapted, based on Straughan and Roberts (1999)

H2. A consumer is more likely to be a user of GPE, the more s/he is engaged in ecologically conscious consumer behaviour.

Ecologically conscious consumer behaviour (ECCB): 22 items, 5-point scale: 5 = Always True, 1 = Never True; Adapted, based on Straughan and Roberts (1999)

H3. A consumer is more likely to adopt GPE, the stronger self-transcendence values s/he holds.

Self-transcendence Scale (STS): 4 items, 7-point scale: 7 = Extremely Important, 1 = Not important); Adopted from Follows and Jobber (2000)

H4. A consumer is less likely to be a user of GPE, the more conservation s/he exhibits.

Conservation Scale (CS): 3 items, 7-point scale: 7 = Extremely Important, 1 = Extremely Unimportant; Adopted from Follows and Jobber (2000)

H5. A consumer is less likely to be a user of GPE, the stronger self-enhancement values s/he holds.

Self-enhancement Scale (SES): 3 items, 7-point scale: 7 = Extremely Important, 1 = Extremely Unimportant; Adopted from Follows and Jobber (2000)

H6. A consumer is more likely to be a user of GPE if s/he thinks that her/his referent groups expect him/her to do so.

Subjective Norms (SN): 4 items, 5 point scale: 5 = Strongly Agree, 1 = Strongly Disagree; Adapted, based on Ajzen and Fishbein (1980)

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H7. A consumer is less likely to be a user of GPE if s/he thinks that individuals cannot be effective in solving the environmental problems.

Perceived Consumer Effectiveness (PCE): 4 items, 5-point scale: 5 = Strongly Agree, 1 = Strongly Disagree) Straughan and Roberts (1999)

H8. A consumer is more likely to be a GPE user, if s/he is aware of its availability.

Consumer’s knowledge (CK): awareness (Yes/ No) of GPE’s availability in Australia

Prior to the main study, we conducted a pilot study to test the measures adopted from previous studies via a convenience sample of 23 households selected from the Sydney metropolitan area. Personal interviews were conducted with selected households. The pilot study results were incorporated into the survey used for the main study.

Data collection for the main study was initially planned via a mail survey among existing customers of key Australian electricity retailers. Considerable effort was taken over a six-month period in negotiating with several interested electricity retailers across the country as potential research sites. Unfortunately, such effort failed to produce an acceptable agreement from the electricity retailers, due to their proposed exclusive ownership of the research data and their legal restrictions on publishing subsequent research findings. As a result, an open online survey was carried for the main data collection as a compromise. A user-friendly web-based questionnaire was developed using the static (or scrollable) approach advocated by Dillman (2000), consisting of single HTML format, which survey respondents could easily scroll up and down. This aims to minimize invalid responses. The survey takes approximately 20 minutes for a respondent to complete the survey. A notification was posted to the websites of NSW Dept of Energy, Utilities and Sustainability (DEUS), Clean-Up Australia, and Climate Action Network Australia (CANA) groups, reaching approximately 2750 people Australia-wide via a broadcasting email newsletter. Screening questions are used to ensure the online survey is completed by our targeted research subjects, who are decision-makers of residential electricity purchases. A total of 220 surveys were returned over a four-month period, of which 210 were deemed usable. The estimated response rate is of 7.64%.

Results, Discussion, Limitations and Future Research Directions

Prior to data analysis, both reliability and validity tests were carried out on the multiple-item measurement scales used in the main survey. Reliability is an assessment of the degree of consistency between a variable and its multiple measurements, or the correlation between a measure and the construct itself (Peter, 1979). Our reliability test results show that the Cronbath’s coefficient alpha values for all the measurements scales range from 0.676 to 0.946, indicating acceptable reliability of these measures (Hair et al., 1998; Nunnally, 1978; Peter, 1979). Whereas reliability relates to the consistency of the measure(s), validity is the extent to which a scale or set of measures accurately represents the concept of interest (Hair et al.,

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1998). Confirmatory factor analysis was undertaken to establish the validity of the psychographic variables through the Structural Equation Modeling (SEM) using AMOS (Arbuckle and Wothke, 1999). Results show that CMIN/DF ratios for the scales are within the acceptable 13 range (Cheung and Rensvold, 2002), CFI and TLI index values of > 0.95 (Hu and Bentler, 1999) and of RMSEA values up to 0.10 (Cunningham et al., 2004; Wang et al., 2005), with the exception of the environmental consciousness scale. Rectification of this scale would require a deletion of five of the 12 items used within the original scale, a substantial deviation from the measurement construction and from the theoretical foundation of the scale. Consequently, it was decided to keep the scale composition unchanged.

When a dependent variable is discrete, both logit analysis and discriminant analysis can be utilized to assess the level of association between the dependent variable and the independent variables (Hair et al., 1998; Sharma, 1996). Between the two methods, logit analysis exhibits several advantages over discriminant analysis in that it does not rely on any assumption about the distribution of the independent variables, an advantage over linear regression as well, and that its output is similar to linear regression with straightforward statistical tests. Several forms of logit analysis exist, including binomial, multinomial and ordered logit (Greene, 2003; McCullagh and Nelder, 1989). When the dependent variable is dichotomous, binomial logit analysis is preferred (Afifi and Clark, 1984; Hair et al., 1998). This applies to ‘user/non-user status,’ the response variable of our study. Therefore, we applied binomial logit analysis for our hypotheses testing, and the model results are summarized in Table 2.

Table 2 Research Hypotheses Test Results

Variable (hypothesis)

B S.E. Wald df Sig. Exp(B)

EC(H1) 0.921 0.364 6.382 1 0.012** 2.511 ECCB (H2) 0.630 0.217 8.468 1 0.004*** 1.879

SELTRNSDNC(H3) 0.074 0.098 0.562 1 0.453 1.076 CONSERVTN(H4) -0.151 0.103 2.149 1 0.143 0.860 SELENHMT(H5) -0.166 0.084 3.915 1 0.048** 0.847

SN(H6) -0.014 0.058 0.058 1 0.810 0.986 PCE(H7) -0.023 0.045 0.264 1 0.608 0.977

AGE1 1.311 0.688 3.629 1 0.057* 3.709 AGE2 0.502 0.599 0.701 1 0.402 1.652

GENDER 0.109 0.421 0.067 1 0.796 1.115 EDUCAT1 0.286 0.732 0.152 1 0.696 1.331 EDUCAT2 0.309 0.457 0.455 1 0.500 1.361 INCOME1 0.331 0.522 0.401 1 0.526 1.392 INCOME2 -0.488 0.561 0.759 1 0.384 0.614

Note: * p < 0.10, ** p < 0.05, *** p < 0.01.

Our hypothesis tests have produced significant and very interesting results. First, the test results show that our first hypothesis (H1) on the association between a consumer’s environmental consciousness and his/her GPE purchase behaviour is

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supported. In other words, the more a respondent is concerned about environment, the more likely s/he is a GPE user. Such a result clearly shows that those who are concerned about the environment and its deterioration are more likely to be GPE users. This finding is consistent with previous research findings that people who exhibit strong environmental concern are more inclined to purchase green products (Banerjee and McKeage, 1994; Chan, 1999; Chan and Lau, 2000; Laroche et al., 2001; Straughan and Roberts, 1999). Furthermore, the logit output shows that the odd ratio for the EC (environmental concerns) variable is 2.51, suggesting that if other things are equal; a person exhibiting higher level of EC is 2.5 times more likely to be a GPE user than a person exhibiting a lower level of EC. Such a result clearly demonstrates that the level of EC of a consumer is a strong predictor of his/her GPE purchase behaviour.

Second, the test results show our second hypothesis (H2) with regard to the association between a consumer’s ecologically conscious consumer behaviour and his/her GPE user status is also supported. This finding demonstrates that those who have already actively practiced ecological conscious consumer behaviour are also most likely to purchase GPE as well. Such an association is logical, since undertaking environmentally friendly activities expresses a person’s environmental consciousness. Therefore, a person with such an attitude is more likely to attempt to reduce further damage to the environment, and sees purchase of GPE as another means towards this end. Again, this finding is consistent with previous studies’ results (Kinnear et al., 1974; Roberts, 1995; Roberts and Bacon, 1997; Schlegelmilch et al., 1996; Suchard and Polonsky, 1991). The logit analysis result also shows that the odds ratio for this variable is 1.88, indicating that, when other things are equal, a person already engaged in environmentally conscious consumer behaviour is 1.88 times more likely to be a user of GPE than a person who has not. Once again our study provides further supporting evidence to show that a person’s level of ECCB is another strong predictor of his/hers purchase behaviour of GPE.

Third, our test results further show that the fifth hypothesis (H5) relating to the assumed association between respondent’s self-enhancement value and his/hers GPE user status is supported as well. This finding show that those who exhibit a higher level of self-enhancement sentiment are less likely to be GPE users. In other words, the more selfish a consumer is, the less likely s/he will adopt and purchase GPE. Self-enhancement represents a self-centred orientation towards a person’s physical needs and successes. This individualistic concern tends to put individual consequences of consumption ahead of social consequences, which might explain our finding of the negative association between self-enhancement and GPE user status. Again this finding supports results of previous studies, that people with such values are inclined to maximise their individual benefit (Schwartz, 1992), and are less likely to purchase and use GPE (Follows and Jobber, 2000), since purchase of GPE does not directly provide additional benefits to these individuals. The odds ratio for the self-enhancement variable is 0.847, weaker than the above two accepted hypotheses. It suggests that, when other things are equal, a consumer exhibiting higher high self-enhancement values is 0.85 times less likely to be a GPE user than a person holding a lower such value. This result shows a consumer’s SE value is yet another significant

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predictor of his/her purchase behaviour towards GPE, but at a lesser degree as compared with the consumer’s EC and ECCB levels.

Fourth, our test results further show that the following four hypotheses are not found to be significantly associated with GPE user status. Specifically, (H3) a respondent’s self-transcendence value, (H4) his/her conservation attitude, (H6) his/her perceived reference group preferences, (H7) his/her perceived effectiveness in solving environmental problems, are not found to be significantly associated with his/her GPE user status.

Our eighth hypothesis (H8) relates to a respondent’s awareness of GPE’s availability. It is self-evident that GPE users are aware of its availability from electricity suppliers and so the dichotomous measure (Yes/No) to the question on GPE awareness is pertinent only to the non-user group. Consequently, H8 was tested not via the binomial logistic model but via a chi-square test of the association between AWARENESS and a surrogate variable of “willingness-to-subscribe” (divided into “willing” or non-willing subgroups). The results show that 83.7% respondents within the non-awareness group are willing to subscribe GPE, which is compared to 77.6% respondents within the awareness group (p = 0.400). As such, awareness of GPE availability is not found to significantly associate with GPE purchase.

Fifth, among the respondents’ demographic and social-economic factors, a consumer’s age is found to be marginally (p < .10) associated with his/her GPE user status. Our logistic regression model shows that GPE users in our sample are slightly younger than the non-users. This evidence supports previous research findings that younger people have a higher sensitivity to environmental issues (Anderson and Cunningham, 1972; Kinnear et al., 1974; Roberts, 1995 1996; Roberts and Bacon, 1997; Straughan and Roberts, 1999; Webster, 1975). It is logical to speculate that, since younger generations are experiencing increasing environmental degradation in their times, they are naturally very concerned about the impact of such a challenge on their future. As such, they may be more likely to adopt GPE as one of their counter-actions facing the challenge of environmental degradation. Other demographic and social-economic characteristics included in the study are not found to have a significant association with his/her adoption of GPE. Despite of such findings, some test results deserve further analysis. Specifically, the coefficient for gender variable (male = 0, female = 1) is 0.109, showing increased odds that a GPE user being a female than a male, which is consistent with findings by Straughan and Roberts (1999), and by Laroche et al. (2001).

Table 3 Respondents’ GPE User Status and their Demographics

GPE user status

Sex (m/f) Average Age

Family size

Education level Family income

User 58%/42% 38.6 3-4 Trades to Diploma/Graduate

50-60k

Non-user 71%/29% 40.2 3-4 Diploma - Graduate 60k+

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This study makes several contributions. First, it fills a gap within the field of green marketing research by covering GPE in Australia. Results relating to the accepted and partially accepted hypotheses bring in additional empirical evidence from a new product and in a different geographic location. Therefore, findings of our study add knowledge to the field of green marketing research and thus, further strengthen the current theory on green marketing. Second, the study establishes a significant and positive association between a consumer’s GPE user status, his/her level of environmental concerns, and his/her level of ecologically conscious behaviour, as well as a significant but negative association between a consumer’s self-enhancement value and his/her GPE user status. Third, it shows that younger generations are more likely to be GPE users, due probably to their increased level of awareness and concerns over the impact of environmental degradation on their future.

The above contributions bear significant implications for both GPE providers and governments at various levels. First, significant effort is needed in conducting public education campaigns in order to raise consumers’ awareness and level of concern about the environment. Second, such campaigns should combine with other activities promoting ecologically friendly behaviors to maximize the impact. Third, while such campaigns should target all age groups, public education needs to start early to achieve an enduring effect, and more effort is needed in pursuing older audiences as well. Given the fact that green marketing theory has been developed and tested via empirical studies in countries other than Australia, the implications of the findings of this study to policy makers and to practitioners should be seen as having more significance to today’s society in terms of finding ways to promote and increase GPE purchase.

This study might suffer from possible sample biases. First, the proportion of GPE users in our sample is higher than the market penetration rate, which is probably due to some of the people who received notification of the online survey are directly and/or indirectly involved in environment protection related fields and/or activities. However, given that this study’s key objective is to discover factors differentiating GPE users from non-users, and given the low GPE penetration rate in the Australian electricity market and the anticipated low response rate of online survey, it was decided that we needed to target GPE users in order to obtain sufficient numbers of users for the data analysis and hypotheses testing. Second, our sample size is relatively small, which is a result of the time and budget constraints as well as the legal restrictions we faced on the potential publication of the research findings. Future studies should try to further address these issues by collecting data from a larger sample following a random sampling method.

The response rate of our study seems relatively low, although our study is the first of its kind on GPE purchase using internet survey in Australia, little previous information is available for comparison. Several factors are believed to have contributed to the relatively low response rate. First, the low penetration rate of renewable energy in Australia energy market might suggest a lower level of interest on the subject in the country, which would be a key contributor. Second, Research findings indicate that web-based surveys have lower response rate than mail survey

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(Cole, 2005), and that there has also been an overall increase of non-response rate to web-based surveys in recent years (Sax et al., 2003). Furthermore, Australia’s internet penetration rate was reported to be around 79% (Miniwatts Marketing Group, 2008). So, a good proportion of the Australian population still has no access to internet, further restricting participation of a web-based survey. And third, despite of assurances of anonymity and confidentiality in the announcement posted on the web-survey site, some people might still have doubts and therefore might have refrained from participating, further contributing to the lower return rate. Future studies should make additional effort to draw evidence from larger samples, and to utilize advance notice and follow ups in order to increase their survey return rates. Despite of these potential limitations, this study represents a first step towards systematic research effort in the field of green marketing research on GPE purchase in Australia.

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Asian Journal of Business Research Volume 1 Number 1 2011

An Exploratory Study about Culture and Marketing Strategy Adam Acar Kobe City University of Foreign Studies, Japan Jeevan Madhusanka Premasara International University of Japan, Japan Joshua Smith Glen International University of Japan, Japan

Abstract

This study has empirically examined how culture influence marketing strategy choice. By presenting opposing marketing strategies in a bi-polar continuum and correlating selected marketing strategies with country scores on Hofstede’s culture dimensions, this research established a connection between cultural values and marketing strategy preferences. The results have shown that managers from individualistic cultures tend to chose differentiation and niche marketing strategies over mass marketing strategies. Additionally, it was found that most Asians prefer Blue Ocean strategy versus direct competition strategy.

Keywords: Culture, Marketing strategy

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Introduction

“The way of the sage is to act but not to compete”

Lao-tse

After listing a number of major marketing and product strategy failures of MNCs, Kim and Mauborgne (1987) address the importance of understanding local cultures in order to succeed in the global arena. By the same token, Doole and Lowe (2003) also claim that pre-existing social and cultural complexities in local markets will always be challenging for marketing practitioners during the strategic analysis and international market planning phases. The authors base their judgments on the mere fact that people from different countries think and act differently in similar conditions mostly because of variations in religion, language, aesthetic perception, law and politics, material culture, social organizations, education, values and beliefs. While it becomes obvious that culture impacts marketing practices, the question still remains to be answered is how culture influences overall marketing strategy or what aspects of culture come into play when developing long term marketing strategies.

So far, only a few studies explored the relationship between culture and marketing strategy which can be linked to the problem with the definition of the concept. Today, there is no clear-cut definition of “marketing strategy” as each academic paper provides a different one. Since it is hard to define, it naturally becomes difficult to operationalize both strategy and marketing strategy. For instance, there is no consensus on if marketing strategy should be treated as an individual choice or an organizational decision, measured by observations or self reported measures and employ attitudinal items or behavioral measures. Despite the fact that there is some gray area in the operationalization, majority of the past studies followed the format of questionnaire survey with foreign company managers about the past marketing operations (Shah et al., 2000 (USA); Fongsuwan, 1999 (Thailand); Ekerete, 2001 (Nigeria)).We claim that contacting with business executives and asking them what type of marketing techniques they used in the past might not give us the clear picture of how culture impacts strategy or the relationship between country of origin and marketing practices. This is mostly because of the interference of several situational factors such as category characteristics (e.g. service vs. manufacturing), organizational culture (e.g. innovative vs. traditional), company structure (e.g. multinational vs. individual), company size (small vs. large) and economic conjuncture (developing vs. developed economies) during marketing managers` decision making process. Thus, instead of asking what practices have been done in the past, it might be more appropriate to present opposing marketing strategies in a bi-polar continuum and ask respondents to indicate their preference among two available options in a general situation. By addressing the past methodological problems and using a creative approach to the measurement of strategy, this study aims to fill the literature gap about the relationship between cultural dimensions and marketing strategy. With the help of this research, we can understand how an individual’s local culture predicts his/her marketing strategy decision. Additionally, the scope of this study was marketing

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strategy tendencies of Asian business executives in particular. Since most of the participants were Asian, the results provided valuable insights about what kind of marketing strategies are preferred in Asia. Given that Asia has been leading the recovery of the world out of the recession and has been gaining more economic and political power in the world stage, the results would be enormously helpful for the marketing practitioners both in and out of Asia.

Literature Review

Culture and Strategy

Culture can be summarized as a collection of morals, laws, beliefs and customs that forms the behavior or structures how a person perceives the world. Cultural norms are passed on by older members to young members of a society and shared by almost all (Carrol, 1982). Culture acts as guidance for human behavior and creates social groups which are different from each other which react differently to their environment (Adler, 2002). Culture in business organization can be considered as the shared mental software of the people in an organization. “All business today is global and those individual businesses. Firms, industries who understand the new rules of doing business in world economy will prosper; those that do not will perish” (Adler, 2002). The global competition has forced businesses to think differently. Today’s global and multinational Business practices has become very competitive and it is of utmost importance to learn about different cultures, monitor ongoing changes in culture and understand the impact of culture on business practices (Kanungo, 2006; Yip, 1995). A strong organizational culture is likely to shape the company’s strategic decisions and moves. Such companies have culture driven bias on strategy making and strategic moves (Thompson, 2001). Cultural differences are based on to what extent people`s thinking can be changed: how people dominate and take control of environment, individualistic and group behaviors, how people like doing things and how the space is seen in relation to privacy and orientation towards time such as present, past or future (Adler, 2002). These factors can be used to explain how individuals manage different situation based on a strategy influenced by their own cultures.

Culture and Marketing Strategy

A considerable amount of literature has been written on culture and consumer behavior, culture and advertising, as well as culture and market orientation (de Mooij, 2004). Much of the literature in the past has been written with goal of explaining such cultural influence on strategy and how to design different strategies to fit different cultures. Hofstede (1991) stated that culture is “the collective mental programming of the people in an environment. Culture is not a characteristic of individuals; it encompasses a number of people who were conditioned by the same education and life experience.” Hawkins et al. (1986) state that the idea of how different cultural variations influence marketing strategy state that the cultural aspects of language,

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demographics, nonverbal communication, and values influence consumer behavior, consumer behavior in turn influences the marketing strategy making process.

On top of differences in culture, Yoon and Lee (2005) pointed out in their paper that there are considerable differences in the definition of market-orientation. Yoon and Lee found that “market-oriented culture does not only affect firm performance directly, but does so indirectly by affecting the marketing strategy making process.” Menon et al. (1999) defined the marketing strategy making (MSM) process as “a complex set of activities, processes, and routines involved in the design and execution of marketing plans.” Menon et al.’s work describes the internal organizational culture and its effects on MSM as well as the MSM process and its effect on the firm performance in considerable detail. However, external influences on MSM were outside the scope of Menon et al.’s (1999) research. When the study of culture’s influence on marketing strategy is limited to Asia or Asian corporations, there is significantly less literature available on the subject. The National Identity, or NATID, Framework established by Keillor et al. (1996) went a long way in quantifying national identity differences based on a number of underlying factors. These factors include national heritage, cultural homogeneity, belief systems and consumer ethnocentrism. The practical purpose of developing such a framework is to identify and use cultural and national differences in a marketing context. Keillor et al.’s (1996) work included a comparison of Japan and Sweden. Their work was later supported by other work that had applied the NATID framework to a number of other East Asian countries including, South Korea, Taiwan, Thailand and Singapore (Phau and Chan, 2003). Phau and Chan’s (2003) findings confirmed the usefulness of the NATID framework. However, this is a stark difference from earlier findings of Japanese and European companies that had achieved success through “expanding commonalities across national boundaries rather than focusing on customer differences based on nationalities” (Kotabe, 1990, italics added). According to Kotabe (1990), continual improvements in product and process innovation by Japanese firms lead to cost advantages. Standardization would lead to economies of scale and greater cost advantages. Abbeglen and Stalk (1986) explained that Japanese firms established a “winning cycle” by focusing on domestic markets first, perfecting and standardizing their product. Once momentum was built in the domestic market, firms would use their low cost advantage in other markets, essentially using a low price strategy. By comparing companies from Japan and Germany, Shah et al. (2000) point out that firms from differing countries use differing generic strategies. While their work shows the differences in strategy based on nationality of the firm’s home country, it remains unclear how much of the difference in attributable to differences in culture or other factors.

Marketing Strategy in Asia

Business executives’ individual decisions are influenced by their own national or local cultures (Hofstede, 1991) and there is a considerable cultural variation within Asia (Hofstede, 1980; Redding, 1990) which might be associated with differences in

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business strategies. Strategic choices are inherently affected by managers’ national cultures (Hofstede, 1991), and surprisingly the Chinese managers, who have a widely noted cultural inclination to rely on informal ties, count on personal connections to succeed in achieving organizational goals (Chen, 1991). Real cultural differences exist in many Asian countries, within Asia, and between countries in Asia and elsewhere. It is presumed that national cultures influence business strategy through senior management beliefs and practices, authority and relationships, individualistic and group behavior aspects, personal exchange and finally decision-making (Fukuyama, 1995; Hamilton and Biggart, 1988; Redding, 1990; Westwood, 1997). The emergence and success of businesses such as the keiretsu in Japan, chaebol in South Korea, entrepreneurial firms in Taiwan, and Chinese family businesses in several Southeast Asian countries are examples of how culture impacts business formations.

Traditional Asian texts such as Sun Tzu’s Art of War and their modern interpreters (Wee et al., 1996) are perceived to have a greater impact on Asian managers than the modern western strategy theory and concepts. It can be argued that the Asian businesses implement strategy from a more intuitive, tradition-based and informal perspectives than the western managers, who adopt a rather contrasting systematic, scientific, and formal approaches (Chen, 2001). The underlying influence of strong and different cultures leads to form the Asian strategy to be a more manipulative, evolving, risk-seeking and results-oriented, in contrast to Western strategy, which is projected as a more planned, rigid, risk-avoiding and process-driven. Asian countries are considered to be “high-context”, while the Western countries are considered to be more “low context” (Hall, 1976). It could be reasoned out that the influences made from basic culture and related factors on strategy, appear to be multifaceted and not direct, and felt more through the impacts on structures, authority associations and decision-making processes (Hofstede and Bond, 1984). The links between cultural values particularly of Confucian origin with economic growth are indications for many of these influences (Hofstede, 1980). Erramilli (1996) tested the impact of a country’s ‘national characteristics’ on the subsidiary ownership policies of its multinational companies. According to these findings firms based in countries characterized by high power distance and high uncertainty avoidance will have a higher preference for majority or wholly owned subsidiaries. Erramilli (1996) continues that managers of multinational companies based in high power distance countries are more authoritarian, less agreeable to share decision-making with others, and therefore more likely to prefer wholly owned subsidiaries over shared-equity ventures.

Theoretical Framework

Hofstede (1980, p. 9) defines culture as “the collective programming of the mind that distinguishes the members of one group or category of people from the other groups.” According to Hofstede (1980), most of the social scientists after the 50s tried to explain different nature of societal problems with different dimensions of the culture

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ranging from economic evolution to communication context. He also came up with five unique dimensions of the culture after analyzing 100,000 responses to his measures from 50 different countries between 1970 and 1980. Follow up studies validated his dimensions and during the initial period of 1980-1993, there were 1036 academic citations referring to his dimensions and his book (Sondergaard, 1994). Hofstede’s (1980) five culture dimensions can be summarized as:

1) Power distance refers to the perception of power gap between different segments of the society such as elderly and youngsters, managers and subordinates and teachers and students. In societies where there is higher power distance, more inequality among people and less even distribution of economic wealth would be common.

2) Individualism/collectivism continuum represents the degree of individualistic versus collectivist tendencies that exist in each society. Individualistic societies put more value on achieving individual potential and personal freedom.

3) Uncertainty avoidance simply means refraining unambiguous situations. In uncertainty avoiding cultures, people cannot perform well in unstructured and unfamiliar conditions unlike some other cultures where ambiguity is part of a daily life.

4) Masculinity stands for the preference for competition and higher achievement in society. Masculine cultures tend to favor assertiveness and male dominance whereas feminine cultures value nurturing and caring.

5) Long term orientation is having future focus when making decisions. In long-term oriented societies, people put special emphasis on perseverance and frugality while saving one’s face and relationships with others come first in short-term oriented societies.

Table 1 shows some of the past research related with Hofstede’s culture dimensions and culturally driven risk taking, decision making and problem solving behaviors.

Table 1 Studies about the Hofstede’s Culture Dimensions and

Business Practices

Authors Study Content

Hayton et al., 2002 Long term orientation and high uncertainty avoidance result with reduced risk taking and entrepreneurial behavior.

Ali, 1993 Individualism is positively correlated with risk taking behavior.

Chen and Li, 2005 People from collectivist countries tend to make uncooperative business decisions in mixed-motive business environments and show low levels of cooperation with foreigners.

Smith et al., 1998 Collectivism and power distance directly impact the handling of disagreements during the problem solving phase.

Barkema et al., 1997 Uncertainty avoidance and long term orientation positively impact the longevity of international joint ventures.

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Specification of the concept: There are negative or positive relationships between certain cultural dimensions and certain types of marketing strategies. Application of the concept: Company A which is located in country X where Y cultural dimension is high, likely to use Z type of marketing strategy. Thus we hypothesize: H1: National culture influences marketing strategy decisions. More specifically, we presume that some of the Hofstede’s cultural dimensions will have a positive or negative relationship with some of the marketing strategy choices of participants from different countries. Methodology Subjects

The data was collected in February, 2010 via a password-protected online survey web site. All of the participants were graduate students at an International University in Japan majoring in MBA or E-Business programs. Approximately fifty three percent of the participants were males (n = 31, mean age = 31) and 46 % were females (n = 27, mean age = 29). There were a total of 59* participants from 20 different countries from all around the world; 17 of them being Asian (Bangladesh, 2; China, 5; Costa Rica, 1 ; Finland, 1; France, 1; India, 6; Indonesia, 5; Japan, 4; Malaysia, 1; Mongolia, 4; Mexico, 1; Myanmar, 6; Philippines, 2; Senegal, 1; Sri Lanka, 3; Thailand, 3; USA, 2; Vietnam, 6; Unknown, 2). The study was announced to all students on the campus mailing list and there was no incentive for participation.

Measures

Based on the popular marketing strategy textbook “Marketing Strategies: A Decision Based Approach” (Walker, 2003) and some Harvard Business Review case studies we have developed 10 different scenarios where the respondents had to choose between two available options. Most marketing strategy books and business cases provide information about pros and cons of available options but do not necessarily identify a correct strategy or favor one strategy over another. With this in mind, we thought that the culture that the participants are coming from should influence the selection of available alternatives. Five-point semantic differential scale was used to measure the favorability of each option on individual level (e.g. follower 1…2….3 (neutral)….4…..5 leader). Some of the scenarios that were based on the textbook were mass marketing vs. niche marketing (Chapter 2), leader versus follower in market entry decisions (Chapter 8), and preference for competitive positioning versus

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blue ocean strategy (Chapter 6). We also asked about the preference for marketing control over distribution capacity (inspired by HBR business case; Ben Jerry’s Japan Entry), focus on the local market versus overseas expansion (HBR Business Case; Hikma Pharmaceuticals) (Quelch and Root, 1997), following a farmer or hunter option when developing new technology products (McDonald, 1992).

Each respondent served as a representative of the country he/she belongs and were associated with five cultural dimension score that is available on www.geert-hofstede.com. These scores then correlated with the individual`s strategy choice for each scenario.

Analysis and Findings

Exploratory factor analysis with Varimax rotation was carried out to detect any underlying groupings among the 10 different strategy options in our study. The analysis revealed four major factors with Eigen values larger than 1, namely “competitive” (competitive strategy, differentiation, niche marketing), “opportunist” (partner capacity, opportunities), “leader” (leader, detailed research, don’t affiliate), “hunter” (hunter, extend to foreign markets). The factors explained about 60.3 % of the common variance in the data set also yielding statistically significant Bartlett`s Test of Sphericity (Chi-Square = 64.68) (see Appendix I-II).

Table 2 Factor Loadings for Strategy Variables

Item Competitive Opportunist Leader Hunter

Blue ocean Strategy vs. Competitive Strategy -0.65

Cost leader vs. Differentiation Leader 0.82

Mass marketing strategy vs. Niche Marketing Strategy

0.75

Control is important vs. Partner capacity is important

0.70

Core Strengths vs. Opportunities 0.79 Affiliate vs. Don`t affiliate 0.57 Follower vs. Leader 0.67 Basic research vs. Detailed research 0.65 Farmer vs. Hunter 0.77 Extend to foreign markets vs. domestic market -0.65

Following the factor analysis, Pearson Product Moment correlation coefficients were created by SPSS 15 (2008) for all of the strategy factors and the cultural dimensions. It was found that individualism was positively correlated with competitiveness (r = .34, p < .05) and long term orientation was negatively correlated with hunter factor (extend to foreign markets, focus on currently cool products). Thus, H1 was

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supported. Although correlation does not mean causation, these findings made sense and were in line with our literature review.

Table 3 Correlations between Strategy Variables and Hofstede’s Dimensions

Pow

er D

ista

nce

Ind

ivid

uali

sm

Mas

culi

nit

y

Un

cert

aint

y A

void

ance

Lon

g T

erm

O

rien

tati

on

Com

pet

itiv

e

Op

port

un

ist

Lea

der

Hu

nte

r

Power Distance _

Individualism -.588 _

Masculinity -.087 .270 _

Uncertainty Avoidance -.443 .317 .469 _

Long Term Orientation .125 -.497 .173 -.214 _

Competitive -.214 .336 .156 .113 -.021 _

Opportunist -.225 .140 -.087 .266 .008 .000 _

Leader .191 -.189 .110 -.005 -.134 .000 .000 _

Hunter .087 .045 -.221 .075 -.546 .000 .000 .000 _

Lastly, mean scores for each marketing strategy option were calculated to see overall strategic tendencies in Asia. Although most of the mean scores were clustered around the middle point (3) it was observed that preference for niche marketing, Blue Ocean Strategy and differentiation were higher when compared with mass marketing, competitive marketing and cost leadership.

Conclusions

In this simple yet conclusive study we have found that cultural dimensions are closely related with marketing strategy choices of business executives. It was observed that people from individualistic countries tend to choose differentiating and niche marketing strategies and people from long term orientation countries prefer focusing on the local market and take time to extend to new markets. These findings make perfect sense as long term orientation urges people to search for long term benefits

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and business practitioners from individualistic societies want to be different rather than creating a generic brand and covering the whole market.

Respondents from 17 different Asian countries did not necessarily favor any specific marketing strategy that is statistically significant. However, higher preference for Blue Ocean Strategy (Kim and Mauborghne, 2005) versus competitive strategy stood out. This should not be a surprise, given that Asian culture is believed to be heavily influenced by harmony-promoting thinkers like Lao Tse and Confucius. In societies where avoiding conflict is virtue, analyzing the details of competitive activity might not be any interest managers. Western business executives should keep in mind that when entering to new markets in Asia, their counterparts might lack the detailed information about the competitor.

Limitations

As the readers might already have noticed, this study did not measure cultural dimensions on the respondent level but just used country scores from Hofstede (1980) for each respondent. Although it might be difficult to measure culture individually, presuming that individuals from the same country would have exactly the same interpretation of their own culture and act exactly the same way in cross-cultural conditions can be misleading. Recognizing this as a limitation, we recommend future researchers to conduct similar studies with individual level measurements. Additionally, the two opposing ends of the strategy options, their definitions and available strategy choices might be perceived as subjective by some readers. Hopefully this study would be base for the future studies which can confirm reliability of our strategy choices and the definitions.

Finally, the sample size (35 subjects for the correlation analysis) and the nature of the sample are two serious limitations to this study. However, although the sample consisted of university students, the median age was about 30 and majority of the participants worked in high-level positions before joining the program. Regardless of the sample characteristics, these results should be just considered exploratory in nature and be replicated with larger samples and more rigorous analysis methods such as SEM or canonical correlation analysis.

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Appendixes Appendix I Factor Analysis Results: KMO and Bartlett’s Test of Sphericity Kaiser-Meyer-Olkin Measure of Sampling Adequacy

0.489

Bartlett’s Test of Sphericity Approx. Chi-Square 64.681 df 45 Sig. 0.029

Appendix II Total Variance Explained Component Initial Eigenvalues Rotation Sums of Squared Loadings

Total % of Variance

Cumulative %

Total % of Variance

Cumulative %

1 1.970 19.696 19.696 1.878 18.783 18.783

2 1.587 15.871 35.566 1.529 15.286 34.069

3 1.359 13.587 49.153 1.361 13.611 47.680

4 1.118 11.179 60.332 1.265 12.652 60.332

Extraction Method: Principal Component Analysis.

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