AN EMPIRICAL STUDY ON REVEALING THE FACTORS INFLUENCING ONLINE SHOPPING INTENTION AMONG MALAYSIAN CONSUMERS’

All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of ORICPublications, www.oricpub.com.

Journal of Human and Social Science ResearchVol. 1, No. 1 (2013), 9-18

Online available since 2013/Mar/30 at www.oricpub.com© (2013) Copyright ORIC Publications

http://www.oricpub.com/hssr-journalORICPublications

Received: 12 Mar 2013Accepted: 19 Mar 2013

Muhammad Sabbir RahmanGraduate School of Management;Multimedia University, MalaysiaPersiaran Multimedia, 63100CyberjayaSelangor Darul Ehsan, MalaysiaAbdul Highe KhanGraduate Research Student, Facultyof Economics and ManagementSciences,International Islamic UniversityMalaysiaNazrul IslamFaculty of Business Administration,Eastern University, Dhaka,Bangladesh

Correspondence:Muhammad Sabbir RahmanGraduate School of Management;Multimedia University, MalaysiaPersiaran Multimedia, 63100Cyberjaya Selangor Darul Ehsan,Malaysia.

AN EMPIRICAL STUDY ON REVEALING THE FACTORS INFLUENCINGONLINE SHOPPING INTENTION AMONG MALAYSIAN CONSUMERS’

AbstractThe growth of online shopping is increasing at a rapid rate in Malaysia by replacingtraditional store shopping habit of the substantial number of consumers. The purposeof the research is to investigate the factors that are influencing consumer’s onlineshopping intention in Malaysia. This research proposes a research framework on onlineshopping by refereeing various influential factors which are antecedents of onlineshopping in Malaysia. The framework of this study was based on the constructsgenerated from Technology Acceptance Model (TAM). In total 250 valid respondent’sresponse were gathered through self-administered survey. Exploratory factor analysis(EFA) and multiple regression analysis were employed to identify the factors that areinfluencing online shopping under Malaysian context. From the statistical results it wasdiscovered that perceived usefulness (PU) and perceived ease of use (PEOU) havepositive influenced on online shopping in Malaysia. It is expected that result from thisstudy can help online vendors to draw the further strategy to attract more consumersthrough online shopping under Malaysian context.

Keywords: Online Shopping, Perceived Usefulness (PU), Perceived Ease of Use(PEOU), Perceived Risk, Technology Acceptance Model (TAM), Malaysia.

1. INTRODUCTIONOnline shopping also called internet shopping or electronic shopping,

defined as the shopping process where consumers purchase goods orservices from any vendors through online (Mastercard WorldwideInsights, 2008). In addition online shopping also defined as observing,searching, and browsing internet for purchasing any product or service(Kim, 2004). Due to the technological advancement and increasing thenumber of internet users, Malaysian consumers’ are interested to do onlineshopping. It is estimated that, in 2014 Malaysia's online shopping marketwill be RM 5 billion where as it was only RM 1.8 billion in 2010. As aresult in Malaysia more than 70% of the total online shopping spends bythe middle income consumers and on average they spend more than RM4,000. Out of the RM 1.8 billion, RM 825 million or more was spent onlocal websites, local merchants. However still there is significant researchgap exist to identify the significant factors to stimulate online shoppingbehavior among Malaysian consumers. In Technology Acceptance Model(TAM) Davis (1989) explained perceived usefulness and perceived easeof use are influenced significantly to use any technology related business.In was also revealed that perceived usefulness and perceived ease of useinfluence consumers’ to perform online shopping. Beside that in previousresearch in different western countries under specific sample it was foundthat perceived risk is related with consumer trust on technology relatedtransaction Bahmanziari et al. 2003; Ng, 2003; Pavlou, 2003. For thisreason this research would like to test how perceived usefulness,perceived ease of use, and perceived risk affect online shopping behaviorunder Malaysian consumer’s perspective.

Muhammad Sabbir Rahman, Abdul Highe Khan, Nazrul Islam | 10P a g e

Vol. 1, No. 1 (2013), 9 - 18ORICPublicationswww.oricpub.com

It is fact that there are some other factors also that might be influenced consumer’s online shoppingbehavior, for instance subjective norm, consumer technology using experience, gender, race so on so forthwhich could be suggested for future research. As whole the proposed research try to answer what are thesignificant factors are influencing online shopping behavior among Malaysian consumers? As a result theobjective of this study is to empirically test the factors that are influencing consumer’s online shoppingintention under Malaysian consumer’s perspective.

2. LITERATURE REVIEW

Davis (1989) developed Technology Acceptance Model (TAM). This model has been widely employed byresearchers to explain user acceptance of technology in different types (Bahmanziari et al. 2003; Ng, 2003;Pavlou, 2003). Technology Acceptance Model (TAM) theorizes that Perceived Usefulness (PU) is asignificant factor-affecting acceptance of an information system (Davis et al., 1989).There are five parts in thismodel for instance Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude towards Using (ATU),Behavioral Intention to Use (BI), and Actual Usage (AU). Technology Acceptance Model (TAM) considerthat consumers do transaction through technology mainly for two reasons (1) Perceived Ease of Use (PEOU)and (2) Perceived Usefulness (PU). Consumers prefer online shopping then traditional store shopping forvarious reasons. In particular there is no time constraint of online shopping, consumers can order for anyproducts or services whenever they want. For this reason consumers get sufficient time to think and then givethe order (Morganosky and Cude 2000; Kim & Kim, 2004; McKinney, 2004; Harn et al., 2006; Delafrooz etal., 2009). Not only consumer can order from any place where they can use internet (home, office, café etc.)but also they can save their time. In fact in online shopping context there vendors can display thousands ofproducts on website and for this reason consumers can buy product from lots of choice and with less price thantraditional store shopping (Khatibi et al. 2006; Delafrooz &Paim, 2011). Thus, based on the above discussionfollowing hypothesis can be proposed:

H1. Perceived Usefulness is Positively Related with Online Shopping

Perceived Ease of Use (PEOU) is defined as the degree to which a person believes that using a particularsystem would be free of effort (Davis, 1989). Ease of use is also defined as any person’s perception that usingthe new technology will be free of effort (Davis, 1989, 1993). Perceived Ease of Use (PEOU) has a positiveand significant influence on consumers’ attitude on online shopping (Bisdee, 2007; Yulihasri & Daud,2011).The component of Perceived Ease of Use (PEOU) are the process, easy system, elastic and easy tobecome skillful. Selamat et al. (2009) mentioned that easier technology is always accepted by the consumersrather using complex technology for online shopping. Usually consumers do not like to use complextechnology for buying any product or service from online. Meanwhile website characteristics play animportant role in determining perceived ease of use in online shopping (Zeithaml et al., 2002; Beldona et al.2005). Thus, based on the above discussion following hypothesis can be proposed:

H2. Perceived Ease of Use is Positively Related with Online Shopping

Perceived risk is defined as the consumers’ subjective belief of suffering a loss in pursuit of a desiredoutcome. It has a stronger correlation with willingness to shop online than convenience (Zhou et al., 2007:Van Noort et al. 2007). Kim et. al. (2007) defines perceived risk as a consumer’s belief about potentialuncertain negative outcomes from anyelectronic transaction.Wang et al. (2006) indicated on their study thatpersonal privacy, safety, and product or service quality are the necessary concerns of the consumer who like todo on-line transaction. Liao and Shi (2009) stated that perceived risk has a significant and negative influenceon consumer attitude towards e-shopping, for instance e-retailing. Li & Zhang (2002) mentioned perceivedrisk is an important element of consumer attitude towards online shopping behavior. There are two elementsof perceived risk related to the process of online shopping: risk associated with the product or service and riskassociated with the context of online transaction. According to Liebermann & Stashevsky (2002) there are twoelements of perceived risk related to the process of online shopping: Internet credit card stealing andconfidentiality of consumer’s personal information. Consumer perceived risk cannot be eliminated becausethere is no physical interaction but it can be reduced by the association with transaction processes (Pavlou,

11 | AN EMPIRICAL STUDY ON REVEALING THE FACTORS INFLUENCING ONLINE SHOPPING INTENTION AMONG MALAYSIAN CONSUMERS’P a g e

HSSR Journal of Human and Social Science Research ORICPublicationswww.oricpub.com

2003; Koufaris and Hampton-Sosa, 2004) or consumer online trust (Culnan and Armstrong, 1999; Warrington,et al., 2000; Park and Jun, 2003; Koufaris and Hampton-Sosa, 2004; Gefen and Pavlou, 2006; Mukherjee andNath, 2007). Thus, based on the above discussion following hypothesis can be proposed:

H3. Perceived Risk is Positively Related with Online Shopping

3. MATERIALS AND METHODS

To collect the data this research used convenient sampling procedure on various places from Klang Valleyarea in Malaysia. In this study, primary data were collected through distribution of survey questionnaires.Self-structured questionnaire was developed to collect the required primary data. As self-structuredquestionnaire is convenient for the target respondents. In addition self-structured questionnaire also easy todistribute to the respondents. The form of self-structured questionnaire is shorter and easier to follow by theresearcher’s point of view.

The principal component analysis was comprised with 250 respondents. Apart from that demographicinformation, respondents were also asked to rate their opinion according to five point rating scale, with arating five being “Strongly Agree” and one being “Strongly Disagree”. Figure 1 below shows the conceptualmodel proposed for hypothesis testing.

Figure 1: Proposed Research and measurement models

3.1 Research SampleIn order to reach the actual online shoppers and test the hypothesized research model shown in Figure 1, a

self-completed questionnaire was employed. Questionnaires were distributed to 350 online shoppers. A totalof 250 effective responses were obtained which represents more than 71 percent response rate. The samplecharacteristics are provided in Table 1. The demographic information of the respondents was analyzedthrough frequency distribution. Among the respondents 54 percent were male and 46 percent were female.Largest number of participants 36 percent, ranged in age 25-34. Majority of online user are married whichaccounted for 56 percent. Among the participants Malays accounted for 44 percent followed by Chinese 32percent and Indians 24 percent. This is shown in table 1 below. Out of total respondents 38 percent of theparticipants had a college graduate degree. Last but not least, the monthly personal income of 38 percent of theparticipants was between RM 1000- RM 2999. For online product purchase behavior the results shows thatmajority of respondents buy air ticket from online and their percentage is 36 percent followed by 26 percentand 22 percent of the participants buy computer/electronics items and movie ticket respectively.

3.2 Data Analysis and Results

Cronbach’s Alpha was chosen to analyze the degree of consistency among the items in a construct. Thestudy by Hair et al., (2010) indicates that the cronbach alpha must be at least 0.70 for the results to be deemedas reliable. The value for each variable is above 0.70 and it indicates that the variables are reliable. The resultof reliability analysis is given below.

Perceived Usefulness

Perceived Ease of Use

Perceived Risk

Online Shopping



Table 1: Demographic Information of the RespondentsDescriptions Frequency Percentage

Gender:Male 135 54%

Female 115 46%Age:24 and below 70 28%25-34 90 36%35-44 60 24%45 and above 30 12%Marital Status:Single 110 44%Married 140 56%Ethnic Group:Malay 110 44%Chinese 80 32%Indian 60 24%

Table 2: Demographic Information of the RespondentsDescriptions Frequency Percentage

Academic Qualification:Certificate/ Diploma 70 28%College Graduate (Bachelor) 95 38%Master’s Degree 65 26%Doctoral Degree 20 08%Profession:Student 75 30%

Job Holder 90 36%Business Owner 55 22%Academician 30 12%

Monthly Income:Below RM 1000 45 18%RM 1000-RM 2999 95 38%RM 3000-RM 4999 80 32%RM 5000 and Above 30 12%Online Product Purchase ExperienceAir ticket 90 36%Computer/Electronics items 65 26%Movie ticket 55 22%Book/DVD/CD 25 10%Others 15 06%

3.3 Exploratory Factor Analysis

Applying Statistical Package for the Social Sciences (SPSS) 16 version, the principal component analysis(PCA) was carried out to explore the underlying factors associated with the items. The construct validity wastested through Bartlett’s Test of Sphericity and The Kaiser-Mayer-Olkin Measure of sampling adequacy. TheKaiser-Mayer-Olkin measures of sampling adequacy (KMO) were first computed to determine the suitabilityof using factor analysis. The value of KMO varies from 0 to 1, and KMO overall should be 0.60 or higher. Infactor analysis, the KMO value is quite satisfactory which 0.865 is. The result shows five (3) significantcomponents in initial Eigen values. A first component is 7.333 and its variance is 28.203 and second



component of total is 2.869 with 11.033 of variance. The cumulative percentage is 58.069. Exploratory factoranalysis also confirmed the construct validity of the scales. The factor loading for all items exceeding theminimum value of 0.50 were considered in this study.

Table 3: Reliability AnalysisVariables/ Dimensions Mean Standard Deviations Cronbach alpha

Perceived Usefulness 4.76 0.776 0.773

Perceived Ease of Use 4.57 0.798 0.834

Perceived Risk 4.35 0.922 0.888

Table 4: KMO and Bartlett’s TestParameters Value

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .829Bartlett's Test of Sphericity: Approx. Chi-Square 1836.209

df 186Sig .000

3.4 Hypothesis testing

The results from the analysis reflected that association between perceived usefulness and online shoppingare positive. The multiple regression result also shows perceived usefulness has a beta of 0.106 and significantp value is .047. The finding of this research shows that perceive usefulness has positive relationship towardsonline shopping. The results from this research also reflected that the association between perceived ease ofuse and online shopping is positive. The multiple regression result shows perceived ease of use has a beta of0.154 and significant p value is 0.007. The significant value is smaller than alpha at 0.05 and hypothesis isaccepted. As supported by previous studies (Bisdee, 2007; Yulihasri & Daud, 2011). The result of this studyindicates that Standardized coefficient (Beta) of H3 is -0.356and significance p-value is 0.000. It indicates thatour proposed hypothesis is not supported. It means there is a negative relationship between perceived riskand online shopping. Studies also identified the negative relation between perceived risk and Online shopping(Liao & Cheung, 2001; Parks et al., 2004; Pires et al., 2004).

4. CONCLUSION AND MANAGERIAL IMPLICATIONS

Effectiveness of online shopping can be maintained by increasing Malaysian consumers’ perceivedusefulness. Previous studies identified perceived usefulness have an impact on attitude towards onlineshopping (Lu et al. 2005; Hassanein & Head, 2004). This research developed to create a deeper understandingof the Malaysian consumer perception of using online shopping by adopting Technology acceptance model(TAM). The result of this research indicates that almost all the hypothesized relationships in the model wereaccepted except relationship of perceived risk and online shopping.

It is necessary to recognize the limitations of this study. First of all, survey of this research was conductedmostly in Klang valley area which can impact the generalizability of the findings from this research onMalaysian consumers’ context. Secondly the sample size of the survey was comparatively lesser. It’s better tohave larger sample size to get better output. A more detailed empirical analysis can be done on consumersatisfaction on online shopping, by using gender, race, and educational level as a mediating role. Furtherresearch can be examine the causal relationships among the factors for instance: perceived ease of use,perceived usefulness, trust, perceived risk, online shopping. In terms of managerial implication, the findingsof this research do provide feedback for the online shopping vendors in drafting various managerial policieson to increase the shoppers online purchase intention. Vendors should plan to increase the perceivedusefulness of online shopping and make online shopping easy for all level of consumers (easy website surfing,provide necessary information of product or service). Another direction for vendors that they have tominimize online shopping perceived risk (enhancing privacy and protection of consumer data , increase truston online shopping for the purpose of increasing the likelihood of shopper online purchase intention.



Table 5: Exploratory Factor AnalysisVariables Components

1 2 3

PEOU1 Easy to do online shopping .698

PEOU 2 Quick and easy transaction .546

PEOU 3 Easy to become skillful in using internet .708

PEOU 4 Interaction through web pages clear and understandable. .717

PEOU 5 Web based online transaction easy .730

PEOU 6 Comfortable in surfing websites to shop online .685

PU1 Broader selection of products and services .566

PU2 Save time in online shopping .687

PU3 Product price is cheaper in online .645

PU4 Order product from any part of the world .777

PU5 Order product from lots of options .670

PU6 Prefer online shopping for its convenience .516

PR1 I feel confident in my ability to shop online. .595

PR2 I have trust in online shopping .773

PR3 Credit card number is secured .685

PR4 Reliability of the internet vendors .661

PR5 Personal information will not be hampered .767

PR6 Confidence that orders will be delivered on time .603

PR7 Trust on digital signature .737

Table 6: Causal Relationship of the ConstructsHypothesis Hypothesis Variables Standardized

coefficient (Beta)Significance Remarks

( p value)H1 Perceived Usefulness has a positive

effect on Online Shopping0.106 .047 Supported

H2 Perceived Ease of Use has a positiveeffect on Online Shopping

0.154 0.007 Supported

H3 Perceived Risk has a positive effect onOnline Shopping

-0.356 0.000 Notsupported



REFERENCES

Adams D A, Nelson R R, Todd P A. (1992). Perceived Usefulness, Ease of Use and Usage of InformationTechnology: A Replication. MIS Quarterly, 16(2), pp.227-247.

Alam S S, Yasin N M . (2010). An Investigation into the Antecedents of Customer Satisfaction of OnlineShopping.Journal of Marketing Development and Competitiveness, 11 (2), pp. 210-216.

Alam S S, Bakar Z, Ismail H B, Ahsan M N. (2008). Young Consumers Online Shopping: An EmpiricalStudy. Journal of Internet Business, 5, pp. 81-98.

Al-Maghrabi, T, Dennis C, Halliday S. (2011). Antecedents of Continuance Intentions TowardsE-Shopping: The Case of Saudi Arabia. Journal of Enterprise Information Management , 24 (1), pp.85-111.

Bahmanziari, T., Pearson, M. J., and Crosby, L. (2003). Is Trust Important in Technology Adoption? APolicy Capturing Approach, The Journal of Computer Information Systems, 43, (4), pp. 46-54

Becerra E P, Korgaonkar P K. (2011). Effects of Trust Beliefs on Consumers’ Online Intentions. EuropeanJournal of Marketing, 45 (6), pp. 936-962.

Beldona, S., Morrison, A.M., & O’Leary, J. (2005).Online shopping motivations and pleasure travelproducts: a correspondence analysis. Tourism Management, 26, 561-570.

Bisdee, D. (2007). Consumer Attitudes Review. Office of Fair Trading, June, 1-147.Brynjolfsson E, Smith M D. (2000). Frictionless Commerce?A Comparison of Internet and Conventional

Retailers.Management Science, 46 (4), pp. 563-85.Celik H. (2011). Influence of Social Norms, Perceived Playfulness and Online Shopping Anxiety on

Customers’ Adoption of Online Retail Shopping: An Empirical Study in the Turkish Context. InternationalJournal of Retail & Distribution Management, 39 (6), pp. 390-413.

Celuch K, Goodwin S, Taylor S. (2007). Understanding Small Scale Industrial User Internet Purchase andInformation Management Intentions: A Test of Two Attitude Models.Industrial Marketing Management, 36(1), pp. 109-20.

Chang H H, Chen S W. (2009). Consumer Perception of Interface Quality, Security and Loyalty inElectronic Commerce. Information & Management, 46 (7), pp. 411-7.

Chang H H, Wang Hsin-Wei. (2011). The Moderating Effect of Customer Perceived Value on OnlineShopping Behavior. Online Information Review, 35 (3), pp. 333-359.

Chang Man-Ling, Lai M, Wu Wann-Yih. (2010). The Influences of Shopping Motivation on AdolescentOnline-Shopping Perceptions.African Journal of Business Management, 4 (13), pp. 2728-2742.

Cho J. (2004). Likelihood to Abort an Online Transaction: Influences from Cognitive Evaluations,Attitudes and Behavioral Variables. Information & Management, 41, pp. 827-838.

Chtourou M S, Souiden N (2010). Rethinking the TAM Model: Time to Consider Fun. Journal ofConsumer Marketing, 27 (4), pp. 336–344.

Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, andimpersonal trust: an empirical investigation. Organization Science, 10(1), 104-115.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use and User Acceptance of InformationTechnology. MIS Quarterly, 13 (3), pp. 319-39.

Davis, F.D. (1993). User Acceptance of Information Technology: System Characteristics, User Perceptionsand Behavioral Impacts. International Journal of Man-Machine Studies, 38(3),pp 475-487.

Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989). User Acceptance of Computer Technology: AComparison of Two Theoretical Models, Management Science, 35(8), pp. 982-1002.

Delafrooz N, Paim L H, Sidin S A, Khatibi A. (2009). Factors Affecting Students' Attitude toward OnlineShopping.African Journal of Business Management, 3 (5), pp. 200-209.



Delafrooz N, Paim L H. (2011). Determinants of Financial Wellness among Malaysia Workers.AfricanJournal of Business Management, 5 (24), pp. 10092-10100.

Fishbein M, Ajzen I. (1975). Belief, attitude, intention, and behaviour: An introduction to theory andresearch. Reading, MA: Addison-Wesley. As cited in Chang, Man-Ling., Lai, M., and Wu, Wann-Yih. (2010).The influences of shopping motivation on adolescent online-shopping perceptions. African Journal ofBusiness Management, 4 (13), pp. 2728-2742.

Foucault B E, Scheufele D A. (2002). Web vs Campus Store? Why Students Buy Textbooks Online.Journal of Consumer Marketing, 19 (5), pp.409-423.

Gefen, D., &Pavlou, P. A. (2006). An inverted-U theory of trust: The moderating role of perceivedregulatory effectiveness of online marketplaces. Twenty Seventh International Conference on InformationSystem, Milwaukee

Hair J F, Anderson R E, Tatham R L, Black W C. (2010).Multivariate Data Analysis. Ney Jersey:Prentice Hall.

Haque A, Khatibi A. (2006). The Study of the Behavior of Malaysian Consumers Towards OnlineShopping. Asian Journal of Information Technology, 5, pp. 12-19.

Harn A. C, Khatibi A, Ismail H. (2006). E-Commerce: A Study on Online Shopping In Malaysia. Journalof Social International Science, 15 (5), pp. 232-242.

Hassan H.H, Rahim S A. (2008). Factors Affecting Online Purchasing Behavior, Malaysian Journal ofCommunication, 24, pp. 1-19.

Hassan Y, Abdullah N F. (2000). Influencing Factors on Consumer Choice towards Online Shopping.Faculty of Business Management,1-12.

Hassanein, K. S. and Head, M. M. (2004).Building Online Trust through Socially Rich WebInterfaces.Proc. Second Annual Conference on Privacy, pp. 15-22.

http://www.malaysiacrunch.com/2012/03/malaysias-e-commerce-statistics-updated.htmlHuang Yu-feng, KuoFeng-yang. (2010). An Eye-Tracking Investigation of Internet Consumers’ Decision

Deliberateness. Internet Research, 21(5), pp. 541-561.Jun M, Yang Z, Kim D S. (2004).Customers' Perceptions of Online Retailing Service Quality and Their

Satisfaction.International Journal of Quality & Reliability Management, 21 (8), pp. 817-840.Kalakota R, Whinston A B. (1996). Frontiers of Electronic Commerce. MA: Addison-Wesley Publishing.Kalakota R, Whinston A. (1997). Electronic Commerce: A Manager’s Guide. Reading, MA:

Addison-Wesley. As cited in Fillis, I. and Wagner, B. (2005). E-business development: An exploratoryinvestigation of the small firm. International Small Business Journal, 23 (6), pp. 604-634.

Khatibi A, Haque A, Karim K. (2006). E-Commerce: A Study on Internet Shopping In Malaysia. Journalof Applied Science, 6, pp. 696-705.

Kim, J. (2004). Understanding Consumers’ Online Shopping and Purchasing Behaviors, unpublished PhDthesis, Graduate College of Oklahoma State University, Stillwater, OK

Kim D j, Ferrin D L, Rao H R. (2009). Trust and Satisfaction, Two Stepping Stones for SuccessfulE-Commerce Relationships: A Longitudinal Exploration. Information System Research, 20 (2), pp. 237-257.

Kim E Y, Kim Y K. (2004). Preducting Online Purchase Intentions for Clothing Products.EuropeanJournal of Marketing, 38 (7), pp. 833-897.

Kim, D. J., Ferrin, D. L., &Rao, H. R. (2007). A Trust-Based Consumer Decision-Making Model inElectronic Commerce: The Role of Trust, Perceived Risk, and their Antecedents. Decision Support System,44, 544-564.

Koufaris, M. & Hampton-Sosa, W., (2004).The Development of Initial Trust in an Online Company byNew Customers, Information and Management, 413, pp. 377-397.

Koufaris, M., & Hampton-Sosa, W. (2004).The development of initial trust in an online company by newcustomers.Information & Management, 41, 377-397.



Leppel K, McCloskey D W. (2011). A Cross-Generational Examination of Electronic CommerceAdoption.Journal of Consumer Marketing, 28 (4), pp. 261–268.

Li N. & Zhang P., (2002). Consumer Online Shopping Attitudes and Behavior: An Assessment ofResearch, Eighth Americas Conference on Information Systems, pp. 508-817

Liao Z, Cheung M T (2001) Internet-Based E-Shopping and Consumer Attitudes an Empirical Study.Information & Management, 38 (5), pp. 299-306.

Liebermann, Y., &Stashevsky, S. (2002). Perceived Risks as Barriers to Internet and E-CommerceUsage.QualitativeMarket Research: An International Journal, 5(4), 291–300.

Lim H, Dubinsky A. (2004). Consumers' Perceptions of E-Shopping Characteristics: AnExpectancy-Value Approach. Journal of Services Marketing. 18(7), pp. 500-513.

Lin Pei-Chun, Chou Yu-Hwa. (2009). Perceived Usefulness, Ease of Use, And Usage of Citation DatabaseInterfaces: A Replication. The Electronic Library, 27 (1), pp. 31-42.

Lu, H., Hsu, C., & Hsu, H. (2005).An Empirical Study of the Effects of Perceived Risk upon Intention toUse Online Applications.Information Management & Computer Security, 13(2), 106-120

Mastercard Worldwide Insights (2008). online shopping in Asia-Pacific – patterns, trends and futuregrowth, available at:www.mastercard.com/us/company/en/insights/studies/2008/asiaonlineshopping.html

Mauricio S, Featherman M S, Miyazaki A D, Sprott D E. (2010).Reducing Online Privacy Risk toFacilitate E-Service Adoption: The Influence of Perceived Ease of Use and Corporate Credibility.Journal ofServices Marketing, 24 (3), pp. 219–229.

McKinney L N. (2004). Creating A Satisfying Internet Shopping Experience Via Atmospheric Variables.International Journal of Consumer Studies, 28 (3), pp. 268-283.

Morganosky, M.A. and Cude, B.J. (2000). Consumer Response to Online Grocery Shopping, InternationalJournal of Retail & Distribution Management, Vol. 28 (1), pp. 17-26.

Murkherjee, A., &Nath, P. (2007). Role of electronic trust in online retailing: A re-examination of thecommitment-trust theory. European Journal of Marketing, 41(9/10), 1173-1202.

Ng, C.F. (2003). Satisfying Shoppers’ Psychological Needs: From Public Market To Cyber-Mall. Journalof Environmental Psychology, 23, pp.439–455.

Ong K. (2010). Online Shopping Statistics in Malaysia. Humanwebsites.com.my [blog], 28 May.Available at: http://humanwebsite.com.my/blog/online-shopping-statistics-in-malaysia.html [Accessed 20December 2011].

Park J, Lee D, Ahn J. (2004). Risk-Focused e-Commerce adoption model: A cross-country study, Journalof Global Information Management, 7, pp. 6-30.

Park, C., & Jun, J. K. (2003). A cross-cultural comparison of internet buying behavior: Effects of internetusage, perceived risks, and innovativeness. International Marketing Review, 20(5), 265-335.

Park, J., D. Lee and J. Ahn. (2004). Risk-Focused E-Commerce Adoption Model: a cross-country study,Journal of Global Information Management, 7(2), pp. 6-30.

Parsons A G. (2002). Non-Functional Motives for Online Shoppers: Why We Click. Journal of ConsumerMarketing, 19 (5), pp. 380-392.

Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with theTechnology Acceptance Model. International Journal of Electronic Commerce, 7(3), pp 101-134.

Paynter J, Lim J. (2001). Drivers and Impediments to E-Commerce in Malaysia. Malaysian Journal ofLibrary & Information Science, 6 (2), pp. 1-19.

Pires G, Stanton J, Eckford A. (2004).Influences on the Perceived Risk of Purchasing Online.Journal ofConsumer Behavior, 4 (2), pp. 118-131.

Pires, G., J. Stanton, and A. Eckford (2004.). Influences on the Perceived Risk of Purchasing Online,Journal of Consumer Behaviour, 4 (2), 118-131,



Ramayah T, Ignatius J. (2005). Impact of Perceived Usefulness, Perceived Ease of Use and PerceivedEnjoyment on Intention to Shop Online.Journal of Systems Management, 3 (3), pp. 36-51.

Schibrowsky J A, Peltier J W Nill A. (2007). The State of Internet Marketing Research.EuropeanJournal of Marketing, 41 (7/8), pp. 722-33.

Selamat, Z., Jaffar, N., &Ong, B. H. (2009).Technology Acceptance in Malaysian BankingIndustry.European Journal of Economics, Finance and Administrative Sciences, 1(17), 143-155.

Strategic Decision. (2011). Everyone's Getting the Online Habit But Don't Write Off the TraditionalActivities. Strategic Decision, 27 (8), pp. 25-27.

Syed, Bakar, Z. Ismail H B, Ahsan M N. (2008). Young Consumers Online Shopping: An EmpiricalStudy. Journal of Internet Business, (5), pp. 81-98.

Van Noort G., Kerkhof P. &Fennis B. M., 2007. Online versus Conventional Shopping: Consumers’ RiskPerception and Regulatory Focus, CyberPsychology& Behavior, 10 (5), pp. 731-733

Venkatesh V, Davis D. (2000). A Theoretical Extension of the Technology Acceptance Model: FourLongitudinal Field Studies. Information Systems Research, 46 (2), pp.186-204.

Wang, J. Ashleigh, M. and Meyer, E. (2006).Knowledge Sharing and Team Trustworthiness: It’s All aboutSocial Ties, Knowledge Management Research and Practice. 4 (3) , pp. 175-186

Warrington, T. B., Abgrab, N. J., & Caldwell, M. (2000).Building trust to develop competitive advantagein E-business relationships.Competitiveness Review, 10(2), 160-168.

Yuslihasri, I. A., &Daud, A. K. (2011).Factors that Influence Customers Buying Intention on ShoppingOnline.International Journal of Marketing Studies, 3(1), 128-143

Zeithaml V, Parasuraman A, Malhotra A. (2002a). Service Quality Delivery through Web Sites: ACritical Review of Extant Knowledge. Journal of the Academy of Marketing Science, 30 (4), pp. 362-375.

Zhou, L., Dai, L., and Zhang, D., (2007). Online Shopping Accepting Model — A Critical Survey ofConsumer factors in Online Shopping, Journal of Electronic Commerce Research, 8(1), pp. 41-62.

Documents

AN EMPIRICAL STUDY ON REVEALING THE FACTORS INFLUENCING ONLINE SHOPPING INTENTION AMONG MALAYSIAN CONSUMERS’