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研究生:李婷鈺
研究生(外文):Ting-Yu Li
論文名稱:影響台灣使用者採納行動商務之因素
論文名稱(外文):Factors of Influencing the Adoption of Mobile Commerce in Taiwan
指導教授:何建達何建達引用關係
指導教授(外文):Chien-Ta Bruce Ho
口試委員:祝道松王榮祖
口試委員(外文):Dauw-Song ZhuRong-Tsu Wang
口試日期:2015-06-17
學位類別:碩士
校院名稱:國立中興大學
系所名稱:科技管理研究所
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:2015
畢業學年度:103
語文別:英文
論文頁數:56
中文關鍵詞:行動商務國家文化整合型科技接受模式行為意圖
外文關鍵詞:Mobile CommerceNational CultureUTAUTBehavioral Intention
相關次數:
  • 被引用被引用:5
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  • 收藏至我的研究室書目清單書目收藏:1
隨著智慧型手機和平板電腦的普及,根據台灣網路資訊中心(TWNIC)於2013年所作出的調查指出台灣使用無線網路(包含行動網路)的使用者已經達到一千一百萬,相較去年同期成長了 14.97個百分點,顯示在台灣行動上網的成長快速並且已經成為主要的上網方式之一,因此行動商務被認為具有巨大發展潛力。然而台灣使用者使用行動商務服務和其應用程式數量仍然有限。因此,本研究旨在調查台灣消費者採用行動商務的關鍵因素。本研究藉由使用學者Venkatesh所提出的整合型科技接受模式,及Hofestede 所提出國家文化構面的「權力距離」和「不確定性規避」為調節變數來了解台灣消費者使用行動商務的行為意圖。
本研究以量化方式在線上發放問卷,共計有效問卷435份,以SPSS 及 smartPLS 統計軟體測試各項結果與解釋數據,研究顯示努力期望、績效期望及社會影響及信任對於使用者的採納行動商務意圖具有相對高的影響程度,且權力距離與社會影響具有顯著的干擾效果。
With the highly growing popularity of smartphones and tablets, the amount of users who use wireless Internet including mobile Internet have reached 11 million in 2013 (TWNIC 2013), it increased 14.97% compared to the same period last year. Mobile commerce (m-commerce) is regarded as a tremendous market potential for businesses and customers. However, the expected benefits have not yet to be realized. The number of m-commerce services and applications accepted by the customers in Taiwan is still small. As a result, it becomes significant for researchers to understand customers’ mobile commerce adoption behavior through national culture in Taiwan. This study aims to investigate the factors that predict users’ behavioral intention to adopt m-commerce in Taiwan. Based upon the revised Unified Theory of Acceptance and Use of Technology model (UTAUT), this research used two of Hofstede’s cultural dimensions: power distance and uncertainty avoidance as moderators on the UTAUT model to enhance the understanding of influencing users’ intention or actual use of m-commerce.
In the research, the study sample consists of 435 valid respondents by using online questionnaires to collect data. The SPSS and smartPLS will be used to analyze and explain the meaning of each factor. According to the research result, it shows that effort expectation, performance expectation, social influence, and trust significantly influence the behavioral intention to use m-commerce. For moderator effects, power distance has interacting effect with social influence, and has negative relationship with social influence on behavioral intention to use m-commerce.
Table of Contents
1. Introduction 1
1.1 Research Background and Motivation 1
1.2 Research Objectives 4
1.3 Research Process 4
2. Literature Review 5
2.1 Definitions of Mobile Commerce (m-commerce) 5
2.2 Hofstede’s Cultural Dimensions Theory 9
2.2.1 Definitions of Culture 9
2.2.2 Cultural Dimensions Theory 9
2.3 The Unified Theory of Acceptance and Use of Technology Model (UTAUT) 14
3. Methodology 21
3.1 Research Framework and Model 21
3.2 Variables and Research Hypotheses 23
3.3 Questionnaire Design 25
3.4 Data Collection 26
3.5 Analysis Method 26
4. Data Analysis and Findings 27
4.1 Descriptive Statistics 27
4.1.1 Gender 27
4.1.2 Age 28
4.1.3 Level of Education 29
4.1.4 Experience 30
4.1.5 Descriptive Statistics- Mean and Standard Deviation 31
4.2 Reliability and Validity 32
4.2.1 Reliability 32
4.2.2 Construct Validity 33
4.3 Correlation Analysis 35
4.4 Structural Equation Modeling 37
5. Conclusions and Implications 43
5.1 Conclusions 43
5.2 Contribution and Practical Implication 44
5.3 Limitations 45
References 46
Appendix I. Questionnaire 53
1.Achelis, S. (2013). Technical Analysis from A to Z, 2nd Edition (2 edition). McGraw-Hill Education: McGraw-Hill Education.
2.Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Information & Management, 44(8), 681–691.
3.Alkhunaizan, A., & Love, S. (2013). Effect of Demography on Mobile Commerce Frequency of Actual Use in Saudi Arabia. In Á. Rocha, A. M. Correia, T. Wilson, & K. A. Stroetmann (Eds.), Advances in Information Systems and Technologies (pp. 125–131). Springer Berlin Heidelberg.
4.Andrews, F. M. (1991). Measures of Personality and Social Psychological Attitudes. Gulf Professional Publishing.
5.Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and Test of a Theory of Technological Learning and Usage. Human Relations, 45(7), 659–686. Bagozzi, R. P., & Yi, Y. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16(1), 74–94.
6.Benmoussa, C. (2000). Workers on the Move: New Opportunities Through Mobile Commerce.
7.Bond, M. H., Leung, K., Au, A., Tong, K.-K., Carrasquel, S. R. de, Murakami, F., Lewis, J. R. (2004). Culture-Level Dimensions of Social Axioms and Their Correlates across 41 Cultures. Journal of Cross-Cultural Psychology, 35(5), 548–570.
8.Carlsson, C., & Walden, P. (2002). Further Quests for Value Added Products and Services in Mobile Commerce. ECIS 2002 Proceedings.
9.Carter, L., & Weerakkody, V. (2008). E-government adoption: A cultural comparison. Information Systems Frontiers, 10(4), 473–482.
10.Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information & Management, 39(8), 705–719.
11.Chong, A. Y.-L. (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240–1247.
12.Chong, A. Y.-L., Chan, F. T. S., & Ooi, K.-B. (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53(1), 34–43.
13.Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
14. Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.
15.Dai, H., & Palvi, P. C. (2009). Mobile commerce adoption in China and the United States: a cross-cultural study. ACM SIGMIS Database, 40(4), 43–61.
16.Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340.
17.DL Day. (2006). Cultural Aspects of User Interface Acceptance. In International Encyclopedia of Ergonomics and Human Factors, Second Edition - 3 Volume Set (Vols. 1–0). CRC Press.
18.Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: an introduction to theory and research. Reading, Mass.: Addison-Wesley Pub. Co.

19.Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50.
20.Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Q., 27(1), 51–90.
21.Global Mobile Commerce Forum. (1997). Retrieved January 26, 2015, from http://cryptome.org/jya/glomob.htm
22.Gu, J.-C., Lee, S.-C., & Suh, Y.-H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36(9), 11605–11616.
23.Hinton, P. R., McMurray, I., & Brownlow, C. (2004). SPSS Explained. Routledge.
24.Hofstede, G. (1984). Cultural dimensions in management and planning. Asia Pacific Journal of Management, 1(2), 81–99.
25.Hofstede’s Cultural Dimensions cross cult comm. (2014). Retrieved June 3, 2015, from https://crosscultcomm.wordpress.com/2014/07/21/hofstedes-cultural-dimensions/


26.Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 20(2), 195–204.
27.Kontinen, K. (2001). Mobile Electronic Commerce Challenges for Global Cooperation Keynote Address. ECIS 2001 Proceedings.
28.Lee, H.-J., Lim, H., Jolly, L. D., & Lee, J. (2009). Consumer Lifestyles and Adoption of High-Technology Products: A Case of South Korea. Journal of International Consumer Marketing, 21(2), 153–167.
29.Lee, I. (2004). Cross-cultural Comparison for Cultural Aspects of Mobile Internet: Focusing on Korea and Hong Kong.
30.Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204.
31.Leong, L.-Y., Hew, T.-S., Tan, G. W.-H., & Ooi, K.-B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604–5620.
32.Liu, C., Marchewka, J. T., Lu, J., & Yu, C.-S. (2005). Beyond concern—a privacy-trust-behavioral intention model of electronic commerce. Information & Management, 42(2), 289–304.
33.Luarn, P., & Lin, H.-H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891.
34.Lundgren, H., & Walczuch, R. (2003). Moderated Trust–The Impact of Power Distance and Uncertainty Avoidance on the Consumer Trust Formation Process in E-Retailing. A Research Agenda for Emerging Electronic Markets, 31.

35.Lyytinen, K. (2001). Mobile Commerce: A New Frontier for E-business. Hawaii International Conference on System Sciences, 9, 9012.
36.Malladi, R., & Agrawal, D. P. (2002). Current and Future Applications of Mobile and Wireless Networks. Commun. ACM, 45(10), 144–146.
37.Marcoulides, G. A. (1998). Modern Methods for Business Research. Psychology Press.
38.Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1–13.
39.Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An Integrative Model of Organizational Trust. The Academy of Management Review, 20(3), 709–734.
40.Miller, J. B. (2012). Toward a New Psychology of Women. Beacon Press.
41.Mobile Commerce: Report. (1999). Durlacher Research Limited.
42.Natsuno, T. (2003). i-mode Strategy (1 edition). Chichester ; Hoboken, NJ: Wiley.
43.Norhayati Zakaria, J. M. S. (2003). Designing and implementing culturally-sensitive IT applications: The interaction of culture values and privacy issues in the Middle East. IT & People, 16, 49–75.
44.Sadeh, N. (2003). M-Commerce: Technologies, Services, and Business Models. John Wiley & Sons.
45.Sekaran, U., & Bougie, R. (2010). Research Methods for Business: A Skill Building Approach. John Wiley & Sons.
46.Sharma, S., & Gutiérrez, J. A. (2010). An evaluation framework for viable business models for m-commerce in the information technology sector. Electronic Markets, 20(1), 33–52.

47.Tarasewich, P., Nickerson, R. C., & Warkentin, M. (2002). Issues in Mobile E-Commerce. Communications of the Association for Information Systems, 8(1).
48.Taylor, S., & Todd, P. (1995). Assessing IT Usage: The Role of Prior Experience. Management Information Systems Quarterly, 19(4).
49.Tiwari, R., Buse, S., & Herstatt, C. (2006). From Electronic to Mobile Commerce: Opportunities Through Technology Convergence for Business Services (SSRN Scholarly Paper No. ID 1583445). Rochester, NY: Social Science Research Network.
50.Taiwan - Geert Hofstede. (2014). Retrieved June 1, 2015, from http://geert-hofstede.com/taiwan.html

51.Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204.
52.Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision-Making Processes. Organizational Behavior and Human Decision Processes, 83(1), 33–60.
53. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478.
54.Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology (SSRN Scholarly Paper No. ID 2002388). Rochester, NY: Social Science Research Network.
55.Venkatesh, V., & Zhang, X. (2010). Unified Theory of Acceptance and Use of Technology: U.S. Vs. China. Journal of Global Information Technology Management, 13(1), 5–27.
56.Wold, H. (1974). Causal flows with latent variables: Partings of the ways in the light of NIPALS modelling. European Economic Review, 5(1), 67–86.
57.Wold, S., Kettaneh-Wold, N., & Skagerberg, B. (1989). Nonlinear PLS modeling. Chemometrics and Intelligent Laboratory Systems, 7(1–2), 53–65.
58.Yang, K. C. C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and Informatics, (3), 257–277.
59.Yu, C.-S. (2012). Factors Affecting Individuals to Adopt Mobile Banking: Empirical Evidence from the Utaut Model. Journal of Electronic Commerce Research, 13(2), 104.
60.Zmud, R. W. (1982). Diffusion of Modern Software Practices: Influence of Centralization and Formalization. Management Science, 28(12), 1421–1431.
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