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研究生:黃春慧
研究生(外文):DelfiaWagiri
論文名稱:A Study of Factors Influencing the Adoption of Online Shopping in Indonesia
論文名稱(外文):A Study of Factors Influencing the Adoption of Online Shopping in Indonesia
指導教授:偉耶倫
指導教授(外文):Dr. Alan Webb
學位類別:碩士
校院名稱:國立成功大學
系所名稱:國際經營管理研究所碩士班
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:120
中文關鍵詞:Technology acceptance modelPerceived ease of usePerceived usefulnessE-shopping qualityPerceived riskTrustIntention to e-shopIndonesia
外文關鍵詞:Technology acceptance modelPerceived ease of usePerceived usefulnessE-shopping qualityPerceived riskTrustIntention to e-shopIndonesia
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With the rapid pace of adoption, e-commerce has become an integral part of people's life and seems to have a bright business future to be a dominant alternative shopping channel. Therefore, consumer's intention to adopt e-shopping has become an important issue for many e-tailers. This study examines factors that affect intention to e-shop by Indonesians, using the structural equation model and hierarchical multiple regression. The model is developed based on technology acceptance model (TAM) which includes perceived usefulness, perceived ease of use and intention to use, combined with e-shopping quality as a predictor and perceived risk and trust as moderators. The results shows that e-shopping quality tends to have positive impact on perceived usefulness and perceived ease of use. The study outcome supports TAM which means perceived ease of use strongly affects perceived usefulness, perceived ease of use and perceived usefulness have significant impact on intention to e-shop. Additionally, perceived risk tends to negatively moderate the relationship between perceived usefulness and intention to e-shop, as well as the relationship between perceived ease of use and intention to e-shop. However, it is found that trust has no moderating effect. A throughout understanding of the findings can assist business practitioners’ including e-tailing and supportive industry (e.g., web designer) to understand Indonesian consumer adoption to e-shop.
With the rapid pace of adoption, e-commerce has become an integral part of people's life and seems to have a bright business future to be a dominant alternative shopping channel. Therefore, consumer's intention to adopt e-shopping has become an important issue for many e-tailers. This study examines factors that affect intention to e-shop by Indonesians, using the structural equation model and hierarchical multiple regression. The model is developed based on technology acceptance model (TAM) which includes perceived usefulness, perceived ease of use and intention to use, combined with e-shopping quality as a predictor and perceived risk and trust as moderators. The results shows that e-shopping quality tends to have positive impact on perceived usefulness and perceived ease of use. The study outcome supports TAM which means perceived ease of use strongly affects perceived usefulness, perceived ease of use and perceived usefulness have significant impact on intention to e-shop. Additionally, perceived risk tends to negatively moderate the relationship between perceived usefulness and intention to e-shop, as well as the relationship between perceived ease of use and intention to e-shop. However, it is found that trust has no moderating effect. A throughout understanding of the findings can assist business practitioners’ including e-tailing and supportive industry (e.g., web designer) to understand Indonesian consumer adoption to e-shop.
ABSTRACT I
ACKNOWLEDGEMENTS II
TABLE OF CONTENTS III
LIST OF TABLES VII
LIST OF FIGURES IX
CHAPTER ONE INTRODUCTION 1
1.1 Research Background and Motivation. 1
1.2 Research Objectives and Contributions. 11
1.3 Research Scope. 12
1.4 Research Procedures. 13
1.5 Research Structure. 14
CHAPTER TWO LITERATURE REVIEW 16
2.1 Theoretical Background: Technology Acceptance Model. 16
2.2 Definition of Independent Variable: E-Shopping Quality. 23
2.3 Definition of Dependent Variables. 25
2.3.1 Perceived Usefulness. 25
2.3.2 Perceived Ease of Use. 27
2.3.3 Intention to E-Shop. 28
2.4 Definition of Moderating Variables. 29
2.4.1 Perceived Risk. 29
2.4.2 Trust. 31
2.5 The Relationships of the Research Constructs and Hypotheses. 33
2.5.1 Interrelationship between E-Shopping Quality and Perceived Usefulness. 33
2.5.2 Interrelationship between E-Shopping Quality and Perceived Ease of Use. 34
2.5.3 Interrelationship between Perceived Ease of Use and Perceived Usefulness. 35
2.5.4 Interrelationship between Perceived Usefulness and Intention to E-Shop. 37
2.5.5 Interrelationship between Perceived Ease of Use and Intention to E-Shop. 38
2.5.6 The Influence of the Moderating Effect of Perceived Risk on the Relationship among Perceived Usefulness, Perceived Ease of Use and Intention to E-Shop. 39
2.5.7 The Influence of the Moderating Effect of Trust on the Relationship among Perceived Usefulness, Perceived Ease of Use and Intention to E-Shop. 40
CHAPTER THREE RESEARCH DESIGN AND METHODOLOGY 42
3.1 The Conceptual Model. 42
3.2 Construct Measurement. 43
3.2.1 E-shopping Quality (ESQ). 43
3.2.2 Perceived Usefulness (PU). 44
3.2.3 Perceived Ease of Use (PEOU). 45
3.2.4 Perceived Risk (PR). 45
3.2.5 Trust (TR). 46
3.2.6 Intention to E-Shop (INT). 47
3.3 Hypotheses to be Tested. 47
3.4 Questionnaire Design. 48
3.5 Sampling Design and Data Collection. 49
3.6 Data Analysis Procedure. 50
CHAPTER FOUR RESEARCH RESULTS 52
4.1 Descriptive Analysis. 52
4.1.1 Sample and Data Collection Procedures. 52
4.1.2 Characteristics of Respondents. 53
4.2 Measurement Result of the Research Variables. 56
4.3 Confirmatory Factor Analysis (CFA) and Reliability. 61
4.3.1 E-Shopping Quality. 63
4.3.2 Perceived Usefulness. 64
4.3.3 Perceived Ease of Use. 64
4.3.4 Perceived Risk. 64
4.3.5 Trust. 65
4.3.6 Intention to E-Shop. 65
4.4 Structural Equation Model (SEM). 66
4.4.1 Confirmatory Factor Analysis (CFA). 66
4.4.2 Structural Equation Model (SEM) Analysis. 71
4.5 Hierarchical Multiple Regression Analysis. 76
CHAPTER FIVE CONCLUSION AND SUGGESTIONS 84
5.1 Discussion and Conclusion. 84
5.2 Managerial Implications. 89
5.3 Research Limitation. 92
5.4 Suggestions for Further Research. 93
REFERENCES 94
APPENDICES 101
Appendix A: Questionnaire for experiment English version 101
Appendix B: Questionnaire for experiment Indonesian version 108
Appendix C: The Result of CFA and SEM 116

Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361-391.
Aguiar, M., Boutenko, V., Michael, D., Rastogi, V., Subramanian, A., & Zhou, Y. (2010). Digital consumers in Brazil, Russia, India, China, and Indonesia. Boston, USA: The Boston Consulting Group.
Ahn, J., Park, J., & Lee, D. (2001). Risk focused e–commerce adoption model: A cross country study. Information and Decision Sciences Department, 1-34.
Ahn, T., Ryu, S., & Han, I. (2004). The impact on the online and offline features on the user acceptance of internet shopping malls. Electronic Commerce Research and Applications, 3, 405-420.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior (Vol. 278): Prentice-Hall.
Brown, M., Pope, N., & Voges, K. (2003). Buying or browsing? An exploration of shopping orientations and online purchase intention. European Journal of Marketing, 37(11/12), 1666-1684.
Bruner, G. C., & Kumar, A. (2005). Explaining consumer acceptance of handheld internet devices. Journal of Business Research, 58(5), 553-558.
Bughin, J. (2001). E-push or e-pull? Laggards and first-movers in European on-line banking. Journal of Computer-Mediated Communication, 7(1), 1-19.
Byrne, B. M. (2006). Structural equation modeling with EQS: Basic concepts, applications, and programming (Vol. 6): Lawrence Erlbaum Associates.
Celik, H. E., & Yilmaz, V. (2011). Extending the technology acceptance model for adoption of e-shopping. Journal of Electronic Commerce Research, 12(2), 152-164.
Chau, P. Y. K., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699-719.
Chau, P. Y. K., & Lai, V. S. K. (2003). An empirical investigation of the determinants of user acceptance of internet banking. Journal of Organizational Computing and Electronic Commerce, 13(2), 123-145.
Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information & Management, 39(8), 705-719.
Cheng, K. C. (2011). The antecedents of consumer adoption of the internet banking service technology. National Cheng Kung University, Tainan City, Taiwan.
Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511-535.
Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), 7-16.
Citrin, A. V., Stem, D. E., Spangenberg, E. R., & Clark, M. J. (2003). Consumer need for tactile input: An internet retailing challenge. Journal of Business Research, 56(11), 915-922.
Cox, D. F. (1967). Risk taking and information handling in consumer behavior: Division of Research, Graduate School of Business Administration, Harvard University.
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132.
Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of B2C channel satisfaction and preference: Validating e-commerce metrics. Information systems research, 13(3), 316-333.
Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119-134.
Featherman, M., & Fuller, M. (2002, 6- 9 January 2003). Applying TAM to e-services adoption: The moderating role of perceived risk. Paper presented at the Proceedings of the 36th Annual Hawaii International Conference on System Sciences.
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451-474.
Gefen, D. (2002). Reflections on the dimensions of trust and trustworthiness among online consumers. Database for Advances in Information Systems, 33(3), 38-53.
Gefen, D., Benbasat, I., & Pavlou, P. A. (2008). A research agenda for trust in online environments. Journal of Management Information Systems, 24(4), 275-286.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 7(1), 51-90.
Gefen, D., & Straub, D. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the Association for Information Systems, 1(1), 1-28.
Gefen, D., & Straub, D. (2003). Managing user trust in B2C e-services. E-service Journal, 2(2), 7-24.
Gefen, D., & Straub, D. W. (2004). Consumer trust in B2C e-commerce and the importance of social presence: Experiments in e-products and e-services. Omega, 32(6), 407-424.
Giffin, K. (1967). The contribution of studies of source credibility to a theory of interpersonal trust in the communication process. Psychological Bulletin, 68(2), 104.
Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565-571.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th edtion). London: Prentice Hall.
Harn, A. C. P., Khatibi, A., & Ismail, H. B. (2006). E-Commerce: A study on online shopping in Malaysia. Journal of Social Sciences, 15(5), 232-242.
Hoffman, D. L., Novak, T. P., & Peralta, M. (1999). Building consumer trust online. Communications of the ACM, 42(4), 80-85.
Hsu, C.-L., & Lu, H.-P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41(7), 853-868.
Internet World Stats. (2011). Asia top internet countries. Retrieved November, 29, 2011, from http://www.internetworldstats.com/stats3.htm
Javalgi, R. G., Wickramasinghe, N., Scherer, R. F., & Sharma, S. K. (2005). An assessment and strategic guidelines for developing e-commerce in the Asia-Pacific region. International Journal of Management, 22(4), 523-531.
Kanungo, S., & Jain, V. (2004). Relationship between risk and intention to purchase in an online context: A role of gender and product category. Paper presented at the Proceedings of the 13th European Conference, Genoa, Italy.
Kim, J. S., Kaye, J., & Wright, L. K. (2001). Moderating and mediating effects in causal models. Issues in Mental Health Nursing, 22(1), 63-75.
Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Information Technology Learning and Performance Journal, 22(1), 35-48.
Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205-223.
Krauter, S. G., & Kaluscha, E. A. (2003). Empirical research in on-line trust: A review and critical assessment. International Journal of Human-Computer Studies, 58(6), 783-812.
Kripanont, N. (2007). Examining a technology acceptance model of internet usage by academics within Thai business schools. Victoria University Melbourne, Australia, Melbourne, Australia.
Kurnia, S. (2006). E-commerce adoption in developing countries: An Indonesian study, Information Systems (pp. 14-16). Melbourne, Australia: University of Melbourne.
Lee, D., Park, J., & Ahn, J. (2001). On the explanation of factors affecting e-commerce adoption. Information and Decision Sciences, 6(14), 109-120.
Lee, G. G., & Lin, H. F. (2005). Customer perceptions of e-service quality in online shopping. International Journal of Retail & Distribution Management, 33(2), 161-176.
Li, D., Lou, H., & Day, J. (2003). The role of affiliation motivation on the use of groupware in a MBA program: A pilot study. Information Technology and Organization, 373-375.
Li, Y. H., & Huang, J. W. (2009). Applying theory of perceived risk and technology acceptance model in the online shopping channel. World Academy of Science, Engineering and Technology, 53, 919-925.
Liao, Z., & Cheung, M. T. (2001). Internet-based e-shopping and consumer attitudes: An empirical study. Information & Management, 38(5), 299-306.
Liaw, S. S. (2002). Understanding user perceptions of world-wide web environments. Journal of Computer Assisted Learning, 18(2), 137-148.
Lim, K. H., Leung, K., Sia, C. L., & Lee, M. K. (2004). Is e-commerce boundary-less? Effect of individualism-collectivism and uncertainty avoidance on internet shopping. Journal of International Business Studies, 35, 545-559.
Lim, K. S., Lim, J. S., & Heinrichs, J. H. (2005). Structural model comparison of the determining factors for e-purchase. Journal of Business, 11(2), 119-143.
Lim, M. (2003). The internet, social networks, and reform in Indonesia. In N. Couldry & J. Curran (Eds.). United States of America: Rowman & Littlefield Publishers, Inc.
Limthongchai, P., & Speece, M. W. (2003). The effect of perceived characteristics of innovation on e-commerce adoption by SMEs in Thailand.
Lingyun, Q., & Dong, L. (2008). Applying TAM in B2C e-commerce research: An extended model. Tsinghua Science & Technology, 13(3), 265-272.
Loiacono, E. T., Watson, R. T., & Goodhue, D. L. (2002). WebQual: A measure of website quality. Marketing Theory and Applications, 13, 37-64.
Luo, X., Li, H., Zhang, J., & Shim, J. (2010). Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decision Support Systems, 49(2), 222-234.
MasterCard Worldwide. (2008). Online shopping in Asia/Pacific patterns, trends and future growth, Knowledge leadership: MasterCard.
McCole, P., Ramsey, E., & Williams, J. (2010). Trust considerations on attitudes towards online purchasing: The moderating effect of privacy and security concerns. Journal of Business Research, 63(9-10), 1018-1024.
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334-359.
Monsuwe, T. P. Y., Dellaert, B. G. C., & Ruyter, K. D. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1), 102-121.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
Naiyi, Y. (2004). Dimensions of consumer's perceived risk in online shopping. Journal of Electronic Science and Technology of China, 2(3), 177-182.
Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). ES-QUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213-233.
Pavlou, P. (2001). Integrating trust in electronic commerce with the technology acceptance model: Model development and validation. Paper presented at the Seventh Americas Conference on Information Systems.
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134.
Roca, J. C., Chiu, C. M., & Martínez, F. J. M. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683-696.
Samadi, M., & Nejadi, A. Y. (2009). A survey of the effect of consumers' perceived risk on purchase intention in e-shopping. Business Intelligence Journal, 2(2), 261-275.
Santos, J. (2003). E-service quality: A model of virtual service quality dimensions. Managing Service Quality, 13(3), 233-246.
Shih, H. P. (2004). An empirical study on predicting user acceptance of e-shopping on the web. Information & Management, 41(3), 351-368.
Socialbakers. (2011). Facebook statistics by country. Retrieved November 28, 2011, from http://www.socialbakers.com/facebook-statistics/
Sohn, C., & Tadisina, S. K. (2008). Development of e-service quality measure for internet-based financial institutions. Total Quality Management, 19(9), 903-918.
Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561-570.
Tempaiboolkul, J. (2011). Factor Influencing the Adoption of Internet Banking in Thailand. National Cheng Kung University, Tainan.
Teo, T. S. H. (2006). To buy or not to buy online: Adopters and non-adopters of online shopping in Singapore. Behaviour & Information Technology, 25(6), 497-509.
The Association of Internet Service Providers of Indonesia. (2011). Statistics of APJII. Retrieved November 29, 2011, from http://www.apjii.or.id/index.php?option=com_content&view=article&id=59&Itemid=53
The Nielsen Global Online Consumer. (2010). Global trends in online shopping (pp. 1-10). United States of America: Nielsen Company.
Tsai, Y. C., & Yeh, J. C. (2010). Perceived risk of information security and privacy in online shopping: A study of environmentally sustainable product. African Journal of Business Management 4(18), 4057-4066.
Tsen, S. L. (2010). A TAM approach toward online shopping: Using personality as the moderator. National Cheng Kung University, Tainan City, Taiwan.
Vehovar, V. (2003). Security concern and on-line shopping, Social Sciences (pp. 1-36): University of Ljubljana.
Venkatesh, V. (1999). Creation of favorable user perceptions: exploring the role of intrinsic motivation. MIS Quarterly, 23(2), 239-260.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. (2003). Determinants of user acceptance of internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501-519.
Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729.
Yoo, B., & Donthu, N. (2001). Developing a scale to measure the perceived quality of an internet shopping site (SITEQUAL). Quarterly Journal of Electronic Commerce, 2(1), 31-47.
Yu, J., Ha, I., Choi, M., & Rho, J. (2005). Extending the TAM for a t-commerce. Information & Management, 42(7), 965-976.
Zhou, L., Dai, L., & Zhang, D. (2007). Online shopping acceptance model-a critical survey of consumer factors in online shopping. Journal of Electronic Commerce Research, 8(1), 41-62.
Zwass, V. (1999). Structure and macro-level impacts of electronic commerce. Emerging Information Technologies: Improving Decisions, Cooperation, and Infrastructure, Sage, Beverly Hills, CA, 289-315.

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