團購網站持續使用意圖之研究

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蔣玫霞(Mei-Hsia Chiang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

團購網站持續使用意圖之研究
(A Study on Continuous Usage Intention of Group-Buying Website)

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摘要(中)

本研究從資訊系統持續使用預期驗證模式的觀點，試圖辨識出影響團購網站持續使用意圖的顯著前因，與這些因子如何影響團購網站的持續使用意圖。因為趣味性是消費者參加網路團購的主要驅動力之一，所以本研究模式是建構在網路團購情境中，將趣味性感知(perceived playfulness)合併到資訊系統持續使用模式中。並採用網路問卷調查，其資料收集自有團購網站團購經驗的消費者。總共有1514份完整及有效的受訪者資料被進一步分析。
本研究在學術上的貢獻如下: 一、大多數過去的網路團購文獻主要聚焦於消費者團購行為、團購定價與團購機制設計的議題，其僅檢視消費者網路團購的採用(adoption)，本研究為彌補此知識缺口，辨識出影響團購網站持續使用意圖的顯著前因。二、本研究延伸預期驗證理論，結合趣味性感知於網路團購情境中，在此延伸模式中，滿意、趣味性感知與有用性感知(perceived usefulness)，三者共同良好的解釋了消費者再次造訪團購網站的意圖。三、消費者對預期的驗證(confirmation of expectation)較有用性感知是滿意更強的前因，然而趣味性感知對滿意並沒有顯著的影響。四、消費者內在的動機(趣味性感知)比外在的動機(有用性感知)，對持續使用團購網站的意圖有更大的影響。
本研究對實務的貢獻如下: 一、受訪者的人口統計輪廓顯示，他們主要是女性、年輕及價格敏感的消費者。二、團購網站的管理者，應該定位適當的商品組合，例如大量的優惠商品；以及發展合適的行銷策略以超越消費者的價格預期，並吸引他們的持續意圖去造訪團購網站，例如口碑與低價促銷。三、使消費者滿意，有助於消費者與團購網站建立長期的關係，且確保他們持續造訪團購網站。四、團購網站的管理者與設計者，應該提供消費者更佳的消費體驗，亦即使他們能趣味性的沉浸其中。

摘要(英)

In this study, we seek to identify the salient antecedents and how they influence group buying website (GBW) continuance intention from an expectation-confirmation model of information systems (IS) continuance perspective. Playfulness is one of the key drivers in consumers participating in online group buying (GB). Our research model is constructed by incorporating perceived playfulness with the IS continuance model in online GB contexts. The data was collected through a web-based survey of experienced GBW consumers. A total of 1514 complete and valid responses were analyzed further.
The contributions of our study to academics include the following: First, most previous online GB literature focuses mainly on consumer GB behavior, GB pricing-related topics, and GB mechanism design, and only examines consumer adoption of online GB. To fill this knowledge gap, our research identifies the salient antecedents of GBW continuance intention. Second, our study extends the expectation-confirmation theory by integration of perceived playfulness in the online GB context. In our extended model, satisfaction, perceived playfulness, and perceived usefulness together explain well consumer intent to revisit the GBW. Third, the consumer confirmation of expectation is a stronger predictor of satisfaction than perceived usefulness, but perceived playfulness does not significantly impact satisfaction. Fourth, the intrinsic motive (perceived playfulness) has a greater influence than the extrinsic motive (perceived usefulness) on consumer continued intention to use GBW.
The contributions of our study to practitioners include the following: First, the respondents’ demographic profile demonstrates that they are female, young, and price-sensitive consumers. Second, GBW managers should position the proper assortment of merchandise – e.g., a large quantity of beneficial merchandise – and develop fitting marketing strategies to beat consumer price expectations and attract their continued intention to revisit the GBW, using for example word-of-mouth or low-price promotions. Third, keeping customers satisfied aids the establishment of longer-term relationships with GBWs and ensures their continued revisiting of the GBW. Four, GBW managers and designers should provide customers with more good consumption experiences, i.e., their ability to produce a playful “flow”.

關鍵字(中)

★ 資訊系統持續使用預期驗證模式
★ 網路團購
★ 預期驗證理論
★ 趣味性感知

關鍵字(英)

★ online group-buying
★ perceived playfulness
★ expectation-confirmation theory
★ expectation-confirmation model of IS continuance

論文目次

論文提要 i
Abstract iii
致謝辭 v
Contents vi
Tables viii
Figures ix
Chapter 1. Introduction 1
1.1 Research Motivations 1
1.2 Research Questions 4
1.3 Organization of This Study 5
Chapter 2. Conceptual Background 7
2.1 GB and Online GB 7
2.2 GB and Online GB Related Literature 10
2.3 Expectation-Confirmation Theory 18
2.4 Playfulness 22
Chapter 3. Research Methodology 25
3.1 Research Model 25
3.2 Hypotheses Development 26
3.2.1 Confirmation and Perceived Playfulness 26
3.2.2 Perceived Playfulness and Satisfaction 27
3.2.3 Perceived Playfulness and Continuance Intention 28
3.2.4 Confirmation and Perceived Usefulness 28
3.2.5 Confirmation and Satisfaction 29
3.2.6 Perceived Usefulness and Satisfaction 29
3.2.7 Perceived Usefulness and Continuance Intention 30
3.2.8 Satisfaction and Continuance Intention 30
3.3 Sampling Design and Data Collection 31
3.4 Instrument Development 32
3.5 Method of Data Analysis 37
3.5.1 Reliability Analysis and Construct Validity 37
3.5.2 Assessment of Model Fit 39
Chapter 4. Data Analysis and Results 40
4.1 Respondents Characteristics 40
4.2 Measurement Model Assessment 47
4.3 Structural Model Assessment 51
4.3.1 Assessment of Model Fit 51
4.3.2 Evaluation of Hypotheses 52
Chapter 5. Conclusion 57
5.1 Key Findings and Discussion 57
5.2 Implications for Academics 59
5.3 Implications for Practitioners 60
5.4 Limitations and Directions for Future Research 62
References 63
Appendix 73
A. Questionnaire 73

參考文獻

1. Albert, T. C., Goes, P. B. and Gupta, A. (2004), “GIST: A model for design and management of content and interactivity of customer-centric web sites,” MIS Quarterly, 28(2), 161–182.
2. Anand, K. and Aron, R. (2003), “Group buying on the web: A comparison of price-discovery mechanisms,” Management Science, 49(11), 1546–1562.
3. Anderson, J. C. and Gerbing, D. W. (1988), “Structural equation modeling in practice: a review and recommended two-step approach,” Psychological Bulletin, 103(3), 411–423.
4. Ausubel, L. M. (2004), “An efficient ascending-bid auction for multiple objects,” American Economic Review, 94(5), 1452–1475.
5. Bagozzi, R. P., Yi, Y. and Phillips, L. (1991), “Assessing construct validity in organizational research,” Administrative Science Quarterly, 36(3), 421–458.
6. Bakos, Y. (1997), “Reducing buyer search costs: implications for electronic marketplaces,” Management Science, 43(12), 1676–1692.
7. Bentler, P. M. and Wu, E. J. C. (1993), EQS/Windows User’s Guide. Los Angeles: BMDP Statistical Software.
8. Berlyne, D. E. (1969), “Laughter, humor and play,” in G. Lindzey, E. Aronson (2nd ed.), The Handbook of Social Psychology, Addison-Wesley, New York, 795–852.
9. Bhattacherjee, A. (2001a), “An empirical analysis of the antecedents of electronic commerce service continuance,” Decision Support Systems, 32, 201–214.
10. Bhattacherjee, A. (2001b), “Understanding information systems continuance: An expectation-confirmation model,” MIS Quarterly, 25(3), 351–370.
11. Bhattacherjee, A. and Premkumar, G. (2004), “Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test,” MIS Quarterly, 28(2), 229–254.
12. Biyalogorsky, E., Gerstner, E. and Libai, B. (2001), “Customer referral management: Optimal reward programs,” Marketing Science, 20(1), 82–95.
13. Bollen, K. A. and Stine, R. A. (1992), “Bootstrapping goodness-of-fit measures in structural equation models,” Sociological Methods and Research, 21, 205–229.
14. Bye, S. J. (2011), “Group buyers are crazy about tourism and home delivery,” CommonWealth Magazine, 488, 286–287.
15. Campbell, D. T. and Fiske, D. W. (1959) “Convergent and discriminant validation by the multitrait-multimethod matrix”, Psychological Bulletin, 56(2), 81–105.
16. Castañeda, J. A., Muñoz-Leiva, F. and Luque, T. (2007), “Web acceptance model (WAM): Moderating effects of user experience,” Information & Management, 44. 384–396.
17. Chang, H. H. and Chen, S. W. (2008), “The impact of customer interface quality, satisfaction and switching costs on e-loyalty: Internet experience as a moderator,” Computers in Human Behavior, 24, 2927–2944.
18. Chau, P. Y. K. (1997), “Reexamining a model for evaluating information center success using a structural equation modeling approach,” Decision Sciences, 28(2), 309–334.
19. Chea, S. and Luo, M. M. (2008), “Post-adoption behaviors of E-service customers: The interplay of cognition and emotion,” International Journal of Electronic Commerce, 12(3), 29–56.
20. Chen, J., Chen, X., Kauffman, R. J. and Song, X. (2009), “Should we collude? Analyzing the benefits of bidder cooperation in online group-buying auctions,” Electronic Commerce Research and Applications, 8(4), 191–202.
21. Chen, J., Chen, X. and Song, X. (2002), “Bidder’s strategy under group-buying auction on the Internet,” IEEE Transaction on Systems, Man and Cybernetics: Part A, 32(6), 680–690.
22. Chen, J., Chen, X. and Song, X.. (2007), “Comparison of the group-buying auction and the fixed pricing mechanism,” Decision Support Systems, 43(2), 445–459.
23. Chen, J., Kauffman, R. J., Liu, Y. and Song, X. (2010), “Segmenting uncertain demand in group-buying auctions,” Electronic Commerce Research and Applications, 9(2), 126–147.
24. Chen, R. R. and Roma, P. (2011), “Group buying of competing retailers,” Production and Operations Management, 20(2), 181–197.
25. Chiu, C. M., Chiu, C. S. and Chang, H. C. (2007), “Examining the integrated influence of fairness and quality on learners’ satisfaction and Web-based learning continuance intention,” Information Systems Journal, 17(3), 271–287.
26. Chiu, C. M., Hsu, M. H., Sun, S. Y., Lin, T. C. and Sun, P. C. (2005), “Usability, Quality, Value and E-Learning Continuance Decisions,” Computers & Education, 45(4), 399–416.
27. Chiu, C. M. and Wang, Eric T. G. (2008), “Understanding Web-based learning continuance intention: The role of subjective task value,” Information & Management, 45(3), 194–201.
28. Chiu, C. M., Wang, Eric T. G., Shih, F. J. and Fan, Y. W., (2011), “Understanding knowledge sharing in virtual communities: An integration of expectancy disconfirmation and justice theories,” Online Information Review, 35(1), 134–153.
29. Churchill, G. A. (1979), “A paradigm for developing better measures of marketing construct,” Journal of Marketing Research, 16(3), 64–73.
30. Cohen, J., Cohen, P., West, S. G., and Aiken, L. S. (2003), Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd edition, Lawrence Earlbaum Associates, Mahwah, N. J.
31. Commerce Development Research Institute (CDRI)’s report (2011), The status quo and developing trend of Taiwan’s group buying Websites, available at http://ciis.cdri.org.tw/irr/content.aspx?sid=0B311565696956925447 last accessed on February 24, 2012.
32. Corbett, C. J. and Groote, X. (2000), “A supplier’s optimal quantity discount policy under asymmetric information,” Management Science, 46(3), 444–450
33. Cronbach L. J. (1951), “Coefficient alpha and the internal structure of tests,” Psychometrika, 16, 297–334.
34. Csikszentmihalyi, M. (1975), Beyond Boredom and Anxiety, Jossey-Bass, San Francisco.
35. Csikszentmihalyi, M. (2000), “The costs and benefits of consuming,” Journal of Consumer Research, 27, 267–272.
36. Dada, M. and Srikanth, K. N. (1987), “Pricing policies for quantity discounts.” Management Science, 33(10), 1247–1252.
37. Davis, F. D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly, 13(3), 319–340.
38. Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989), “User acceptance of computer technology: A comparison of two theoretical models,” Management Science, 35(8), 982–1003.
39. Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1992), “Extrinsic and intrinsic motivation to use computers in the workplace,” Journal of Applied Social Psychology, 22, 1111–1132.
40. Day, H. I. (1981), “Play: a lucid behavior,” in H. I. Day (ed.), Advances in Intrinsic Motivation and Aesthetics, Plenum Press, New York, 225–250.
41. Deci, E. L. and Ryan, R. M. (1985), Intrinsic Motivation and Self-determination in Human Behavior, Plenum Press, New York.
42. Dolan, R. (1987), “Quantity discounts: Managerial issues and research opportunities,” Marketing Science, 6(1), 1–22.
43. Doong, H. S. and Lai, H. (2008), “Exploring usage continuance of e-negotiation systems: Expectation and disconfirmation approach,” Group Decision and Negotiation, 17(2), 111–126.
44. Fay, S. and Xie, J. (2008), “Probabilistic goods: A creative way of selling products and services,” Marketing Science, 27(4), 674–690.
45. Fay, S. and Xie, J. (2010), “The economics of buyer uncertainty: Advance selling vs. probabilistic selling,” Marketing Science, 29(6), 1040–1057.
46. Festinger, L. A. (1957), A Theory of Cognitive Dissonance, Row and Peterson, Evanston, IL: Row, Peterson and Company.
47. Fornell, C. (1982), A Second Generation of Multivariate Analysis. Praeger, New York.
48. Fornell, C., and Larcker, D. F., (1981), “Evaluating equation models with unobservables and measurement error,” Journal of Marketing Research, 8(1), 39–50.
49. Ghani, J. A., Supnick, R. and Rooney, P. (1991), “The experience of flow in computer-mediated and in face-to-face group,” in DeGross, J. I., Benbasat, I. DeSanctis, Beath (eds.), Proceedings of the Twelfth International Conference on Information Systems, New York.
50. Hair, J. F., Black, B., Babin, B. and Anderson, R. E. (2010). Multivariate data analysis: A global perspective, 7th ed., Prentice-Hall, New Jersey.
51. Henshel, R. L. and Johnston W. (1987). “The emergence of bandwagon effects: A theory,” The Sociological Quarterly, 28(4), 493-511.
52. Hoffman, D. L. and Novak, T. P. (1995), Marketing in hypermedia computer-mediated environments: Conceptual foundations, available at http://sloan.ucr.edu/profiles/blogs/working-paper-hoffman-and-9, last accessed on March 30, 2012.
53. Hooper, D., Coughlan, J., and Mullen, M. R. (2008), “Structural equation modeling: Guidelines guide lines for determining model fit,” The Electronic Journal of Business Research Methods, 6(1), 53–60.
54. Igbaria, M., Parasuraman, S. and Barouli, J. J. (1996), “A motivational model of microcomputer usage,” Journal of Management Information Systems, 13(1), 127–143.
55. Jing, X. and Xie, J. (2011), “Group buying: A new mechanism for selling through social interactions,” Management Science, 57(8), 1354–1372.
56. Jöreskog, K., and Sörbom, D. (1993), Structural equation modeling with the SIMPLIS command language, Scientiﬁc Software International, Chicago.
57. Kagel, J. and Levin, D. (2001), “Behavior in multi-unit demand auction: experiments with uniform price and dynamic Vickrey auctions,” Econometrica, 69(2), 413–454.
58. Kannan, P. K. and Kopalle, P. K. (2001), “Dynamic pricing on the Internet: importance and implications for consumer behavior,” International Journal of Electronic Commerce, 5(3), 63–83.
59. Kaplowitz, M. D., Hadlock, T. D. and Levine, R. (2004), “A comparison of web and mail survey response rate,” Public Opinion Quarterly, 68(1), 94–101.
60. Karahanna, E., Straub, D. W. and Chervany, M. L. (1999), “Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs,” MIS Quarterly, 23(2), 183–213.
61. Kauffman, R. J., Lai, H. and Ho, C. T. (2010), “Incentive mechanisms, fairness and participation in online group-buying auctions,” Electronic Commerce Research and Applications, 9(3), 249–262.
62. Kauffman, R. J., Lai, H. and Lin, H. C. (2010), “Consumer adoption of group-buying auctions: An experimental study,” Information Technology and Management, 11(4), 191–211.
63. Kauffman, R. J. and Wang, B. (2001), “New buyers’ arrival under dynamic pricing market microstructure: The case of group-buying discounts on the internet,” Journal of Management Information Systems, 18(2), 157–188.
64. Kauffman, R. J. and Wang, B. (2002), “Bid together, buy together: On the efficacy of group-buying business models in internet-based selling,” in P. B. Lowry, J. O. Cherrington and R. R. Watson (Eds.), Handbook of electronic commerce in business and society, Boca Raton, FL. CRC Press.
65. Kline, R. B. (2005). Principles and practice of structural equation modeling, 2nd ed., New York: Guilford.
66. Kohli, R. and Park, H. (1989), “A cooperative game theory model of quantity discounts,” Management Science, 35(6), 693–707.
67. Kowtha, N. R. and Choon, T. W. I. (2001), “Determinants of website development: A study of electronic commerce in Singapore,” Information and Management, 39(3), 227–242.
68. Lee, M. C. (2010), “Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model,” Computers & Education, 54(2), 506–516.
69. Leibenstein, H. (1950), “Bandwagon, snob, and veblen effects in the theory of consumers’ demand,” The Quarterly Journal of Economics, 64(2), 183-207.
70. Levy, J. (1983), Play Behavior, Robert E. Krieger, Malabar, FL.
71. Li, C., Chawla, S., Rajan, U. and Sycara, K. (2004), “Mechanism design for coalition formation and cost sharing in group-buying markets,” Electronic Commerce Research and Applications, 3(4), 341–354.
72. Li, C., Sycara, K. and Scheller-Wolf, A. (2010), “Combinatorial coalition formation for multi-item group-buying with heterogeneous customers,” Decision Support Systems, 49, 1–13.
73. Li, Y. M., Jhang-Li, J. H., Hwang, T. K. and Chen, P. W. (2012), “Analysis of pricing strategies for community-based group buying: The impact of competition and waiting cost,” Information System Frontiers, 14(3), 633-645.
74. Limayem, M., Hirt, S. G. and Cheung, C. M. K. (2007), “How habit limits the predictive power of intention: the case of information systems continuance,” MIS Quarterly, 31(4), 705–737.
75. Lin, C. S., Wu, S. and Tsai, R. J. (2005), “Integrating perceived playfulness into expectation-confirmation model for web portal context,” Information & Management, 42, 683–693.
76. Lin, J. and Lu, H. (2000), “Towards an understanding of the behavioral intention to use a web site,” International Journal of Information Management, 20, 197–208.
77. List, J. A. and Lucking-Reiley, D. (2000), “Demand reduction in multiunit auctions: Evidence from a sports card field experiment,” American Economic Review, 90(4), 961–972.
78. Molesworth, M. and Denegri-Knott, J. (2008), “The playfulness of eBay and the implications for business as a game-maker,” Journal of Macromarketing, 28(4), 369–380.
79. Monahan, J. P. (1984), “A quantity discount pricing model to increase vendor profits,” Management Science, 30(6), 720–726.
80. Moon, J. W. and Kim, Y. G. (2001), “Extending the TAM for a World-Wide-Web context,” Information & Management, 38, 217–230.
81. Nunnally, J. C. and Bernstein, I. H. (1994), Psychometric theory, 3rd ed., New York: McGraw-Hill.
82. Oliver, R. L. (1997), Satisfaction: A behavioral perspective on the consumer, New York: McGraw-Hill.
83. Oliver, R. L. (1980), “A cognitive model for the antecedents and consequences of satisfaction,” Journal of Marketing Research, 17(4), 460–469.
84. Oliver, R. L. (1981), “Measurement and evaluation of satisfaction processes in retail settings,” Journal of Retailing, 57(3), 25–48.
85. Oliver, R. L. (1993), “Cognitive, affective, and attribute bases of the satisfaction response,” Journal of Consumer Research, 20(3), 418–430.
86. Oliver, R. L. and DeSarbo, W. (1988), “Response determinants in satisfaction judgments,” Journal of Consumer Research, 14(3), 495–507.
87. Patterson, P. G., Johnson, L. W. and Spreng, R. A. (1997), “Modeling the determinants of customer satisfaction for business-to-business professional services,” Journal of the Academy of Marketing Science, 25(1), 4–17.
88. Pettit, F. A. (2002), “A comparison of world-wide web and paper-and-pencil personality questionnaires,” Behavior Research Methods, Instruments and Computers, 34, 50–54.
89. Robinson, J. P., Shaver, P. R. and Wrightsman, L. S. (1991a), “Criteria for scale selection and evaluation,” in Robinson, J. P., Shaver, P. R., and Wrightsman, L. S. (eds), Measures of Personality and Social Psychological Attitudes, Academic Press, San Diego, CA.
90. Robinson, J. P., Shaver, P. R. and Wrightsman, L. S. (1991b), Measures of personality and Social Psychological Attitudes, Academic Press, San Diego, CA.
91. Roca, J. C., Chiu, C. M. and Martinez, F. J. (2006), “Understanding e-learning continuance intention: An extension of the technology acceptance model,” International Journal of Human-Computer Studies, 64, 683–696.
92. Roca, J. C. and Gagne, M. (2008), “Understanding e-learning continuance intention in the workplace: A self-determination theory perspective,” Computers in Human Behavior, 24(4), 1585–1604.
93. Rogers, E. M. (1995), Diffusion of Innovations, 4th ed., Free Press, New York.
94. Sandelands, L. E., Ashford, S. J. and Dutton, J. E. (1983), “Reconceptualizing the over justification effect: A template-matching approach,” Motivation and Emotion, 7, 229–255.
95. Schumacker, R. E. and Lomax, R. G. (2010). A Beginner’’s Guide to Structural Equation Modeling, 3rd ed., New York: Routledge.
96. Shaffer, G. and Zhang, J. Z. (2002), “Competitive one-to-one promotions,” Marketing Science, 48(9), 1143–1160.
97. Shugan, S. M. (2004), “The impact of advancing technology on marketing and academic research,” Marketing Science, 23(4), 469–475.
98. Swan, J. E. and Trawick, I. F. (1981), “Disconfirmation of expectations and satisfaction with a retail service,” Journal of Retailing, 57, 40–67.
99. Syam, B. N., Ruan, R. and Hess, J. D. (2005), “Customized products: A competitive analysis,” Marketing Science, 24(4), 569–584.
100. Thong, J. Y. L., Hong, S. J. and Tam, K. Y. (2006), “The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance,” International Journal of Human-Computer Studies, 799–810.
101. Trevino, L. K. and Webster, J. (1992), “Flow in computer-mediated communication,” Communication Research, 19(5), 539–573.
102. Tse, D. K. and Wilton, P. C. (1988), “Models of consumer satisfaction: An extension,” Journal of Marketing Research, 25, 204–212.
103. Valenzuela, A., Dhar, R. and Zettelmeyer, F. (2008), “Contingent response to self-customization procedures: Implications for consumer choice and satisfaction,” Marketing Science, 46(6), 754–763.
104. Wang, Y. Z. (2010), “Analysis on the development trend of e-commerce market,” Market Intelligence & Consulting Institute (MIC).
105. Webster, J., Trevino, L. K. and Ryan, L. (1993), “The dimensionality and correlates of flow in human-computer interactions,” Computer in Human Behavior, 9(4), 411–426.
106. Wu, Y. H. (2012), “Business analysis of online shops in Taiwan,” Market Intelligence & Consulting Institute (MIC).
107. Wu, C. G., Gerlach, J. H. and Young, C. E. (2007), “An empirical analysis of open source software developers’ motivations and continuance intentions,” Information & Management, 44(3), 253–262.
108. Xie, J. and Shugan, S. M. (2001), “Electronic tickets, smart cards, and online prepayments: When and how to advance sell,” Marketing Science, 20(3), 219–243.
109. Zhou, S. L. (2010), “The important and big trends in software industry,” Market Intelligence & Consulting Institute (MIC).
110. Zweig, D. and Webster, J. (2003), “Personality as a moderator of monitoring acceptance,” Computers in Human Behavior, 19(4), 479–493.

指導教授

王存國、范懿文
(Eric T. G. Wang、Yi-Wen Fan)

審核日期

2012-7-17

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