||The Determinants of Consumer Resistance toward
Mobile Value-Added Service: A Combination of Task-Technology Fit Model and Social Cognitive Theory.
||Department of Business Administration
Task-Technology Fit Model
Social Cognitive Theory
Resistance to Use
Mobile Value-added Service
本文的研究對象為未曾使用加值服務者，以便利抽樣法進行網路問卷調查，使用Structural Equation Model (SEM) 檢定研究變數之因果關係，資料分析結果顯示消費者對行動任務與科技配適的程度及自我使用能力的評估會同時影響消費者價值判斷的過程，最終會影響消費者拒絕使用行動加值服務之意願。在影響拒絕意圖的因素中，發現兩項有趣的結果:(1)知覺成本不但可以直接影響拒絕使用意圖，也可透過對知覺價值間接地影響拒絕使用意圖，由此可知知覺成本會在影響拒絕使用意圖中扮演很重要的因素；(2)自我效能與結果預期會受到任務科技配適的程度高低所干擾。為比較在不同使用經驗與不同類型的行動加值服務對整體研究架構之差異，由Multiple Group Analysis的結果顯示: (1)使用者過去所累積的經驗越多，則對行動加值服務的態度越正面；(2)消費者面對不同類型之行動加值服務時，所考量的因素也不相同，例如:消費者在使用任務導向型的服務時，較重視任務的績效；而在使用娛樂導向型的服務時，較重視個人的績效。此外，當消費者在使用任務導向與人員互動型服務時，較重視價值評估的過程；而在使用娛樂導向與人機互動型服務時，只注重娛樂的利益及試用過程是否繁複。
The mobile value-added service market has seen significant growth in the past few years. The invention of a mobile value-added service has become a new opportunity for mobile service providers to create revenue. The most prominent obstacle for a new technology is getting customers to try it for the first time, because it requires a significant behavioral change. Therefore, this study extended Task-Technology Fit (TTF) with social cognitive theory (SCT) to investigate the relationships among those extrinsic factors (task-technology fit and self-efficacy), the intrinsic value of the assessment mechanism (perceived benefits, perceived costs and perceived value) and consumer behavioral intention (resistance to use). In addition, various mobile value-added services have several different characteristics, which lead to different consumers’ motives and perceptions. Thus, this study conducted four individual surveys including text messaging, contact service, payment service and gaming service to demonstrate the cross-service differences in the proposed model.
The research model was validated through an online survey of 839 respondents by a convenient sampling method. In the empirical results, this study adopted a Structural Equation Model (SEM) to test the interrelationships among all the research constructs and found that self-efficacy and task-technology fit have a simultaneous impact on perceived benefits and perceived costs, ultimately influencing perceived value and resistance to use. Among the predictors of resistance, this study also found two interesting consequences as follows: First, the perceived costs influenced user resistance both directly and indirectly through their effect on perceived value, which implied the important role of perceived costs in determining user resistance. Second, the mediating effect of task-technology fit on the relationship between self-efficacy and outcome expectancies was demonstrated. Furthermore, this study conducted Multiple Group Analysis to investigate the moderating effect of users’ prior experiences and mobile value-added service types on the research model and found two consequences. First, people with higher usage experience presented a positive attitude toward mobile value-added services than those with a lower one. Second, we found that there were notable differences among the determinants of resistance in diverse mobile value-added services. For example, people who conduct a goal-oriented task emphasized the performance-related outcome expectancies. But those people who conducted an experiential-oriented task paid more attention to personal outcome expectancies. People who completed a person-interactive service to complete their goal-oriented task paid attention to the value of the evaluation process. However, people who performed a machine-interactive service to finish their experiential-oriented task focused on the benefit of entertainment and the simple use process.
This study offered two important contributions to research and management. First, prior research applied TTF and SCT to explain the tasks’ performance of organization members. However, this study also provided the first empirical examination of both theories to understand when consumers consider about switching to the new services, how to evaluate the value of services and how they form their resistance intention. Moreover, previous research about mobile value-added service adoption has always investigated a single service or a whole service, but neglected the difference characteristics between services. Therefore, this study compared the customers’ behavior in different kinds of mobile services to provide insight into the issue, including the development of service context, the design of service interface, the improvement of consumer ability and perceived value about the service.
TABLE OF CONTENTS
CHAPTER ONE INTRODUCTION 1
1.1 Research Background and Motivations 1
1.2 Research Objectives 4
1.3 Research Procedure 5
1.4 Research Structure 6
CHAPTER TWO LITERATURE REVIEW 8
2.1 Definition of Theory and Research Constructs 8
2.1.1 Task-Technology Fit Model 8
2.1.2 Social Cognitive Theory 13
2.1.3 Perceived Costs 17
2.1.4 Perceived Value 19
2.1.5 User Resistance 20
2.1.6 The Classification of Mobile Service 22
2.2 Research Hypotheses Development 23
2.2.1 Task-Technology Fit and Outcome Expectations 23
2.2.2 SCT: Self-efficacy and Perceived Benefits 25
2.2.3 Self-efficacy and Perceived Costs 27
2.2.4 Perceived Benefits, Costs, Value and User Resistance 28
2.2.5 Perceived Value and User Resistance 30
2.2.6 The Moderating Effect of Mobile Service Type 31
CHAPTER THREE RESEARCH DESIGN AND METHODOLOGY 32
3.1 The Conceptual Model 32
3.2 Construct Measurement 34
3.2.1 Task-Technology Fit 34
3.2.2 Self-efficacy 35
3.2.3 Perceived Benefits (Performance-Related Outcome Expectancies and Personal Outcome Expectancies) 37
3.2.4 Perceived Costs (Procedural Costs and Financial Costs) 38
3.2.5 Perceived Value 39
3.2.6 Resistance to Use 39
3.2.7 Measurement of Type of Mobile Service 40
3.2.8 Prior Experience of Mobile and Internet Services 40
3.2.9 Personal Demographic Information 41
3.3 Questionnaire Design and Pilot Test Procedures 42
3.4 Manipulation Check 43
3.4.1 Manipulation Check of Mobile Value-added Service Types 43
3.4.2 Manipulation Check of Simulations 48
3.5 Sample Plan 50
3.6 Data Analysis Procedure 51
3.6.1 Descriptive Statistic Analysis 51
3.6.2 Reliability and Validity of Measurement Constructs 51
3.6.3. Interrelationships among Research Variables 53
CHAPTER FOUR DATA ANALYSIS AND RESULTS 55
4.1 Descriptive Analysis 55
4.1.1 Data Collection 55
4.1.2 Characteristics of Respondents 58
4.1.3 Measurement Results for Research Variables 59
4.2 Reliability and Validity Tests 62
4.3 Common Method Bias 65
4.4 Structural Equation Model (SEM) 66
4.4.1 The Full Model 66
4.4.2 The Direct, Indirect and Total Effects 73
4.4.3 The Mediating Effect 75
4.5 Multiple Group Analysis 77
4.5.1 Testing of the Respondents’ Characteristics 77
4.5.2 Competing Models for Respondents with Different Experiences of Mobile Value-added Services 82
4.5.3 Testing of the moderating effect of mobile value-added service’s type 88
CHAPTER FIVE CONCLUSIONS AND RESEARCH CONTRIBUTIONS 97
5.1 Discussions and Conclusions 97
5.1.1 The Results of the Structural Equation Model 97
5.1.2 The Results of Multiple Group Analysis 102
5.2 Contribution and Implications 107
5.2.1 Research Implications and Contributions 107
5.2.2 Managerial Implications and Contributions 110
5.3 Limitations and Future Research 114
LIST OF TABLES
Table 3-1 Operational definition of constructs in this study 41
Table 3-2 Manipulations of mobile value-added service types 45
Table 3-3 Manipulation check of mobile value-added service type 47
Table 4-1 Characteristics of respondents 59
Table 4-2 Descriptive analysis for questionnaire items 61
Table 4-3 Reliability and validity test 63
Table 4-4 Discriminant validity 65
Table 4-5 The Fit Indices for the Structural Model 67
Table 4-6 The results of the measurment model 67
Table 4-7 The results of the structural model 72
Table 4-8 Direct, indirect and total effects 74
Table 4-9 Testing for mediation effects on the relationship between SE and PROE 76
Table 4-10 Testing for mediation effects on the relationship between SE and POE 77
Table 4-11 Sample demographics among types of mobile value-added service 78
Table 4-12 Research constructs among time of Internet Use 79
Table 4-13 Research constructs among frequency of using Internet services 80
Table 4-14 Research constructs among time of using Internet services 80
Table 4-15 Differences among type of mobile value-added service on research constructs 82
Table 4-16 Clusters of respondents with different Internet service experiences 83
Table 4-17 Differences among different Internet service experiences on research constructs 83
Table 4-18 The competing model for respondents with different Internet service experiences 86
Table 4-19 Testing for types of mobile value-added services as a moderator in the research model 94
Table 4-20 Comparison of different types of mobile value-added services 95
Table 5-1 A summary of the result of the structural model 99
Table 5-2 A summary of the path estimates among four types of mobile value-added services 105
LIST OF FIGURES
Figure 1-1 Flow Chart for this Research 6
Figure 3-1 The Conceptual Framework 33
Figure 3-2 The Scenario-Based Simulation for a Mobile Service Experience 49
Figure 3-3 The Interaction-Based Simulation for a Mobile Service Experience 49
Figure 4-1 The Process of Data Collection used in this study 57
Figure 4-2 The Structural Equation Model of the Full Model 71
Figure 4-3 The structural equation model for groups with different prior experience 87
Figure 4-4 The structural equation model for groups with different types of mobile value-added service 96
1. Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(2), 665-694.
2. Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-301.
3. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
4. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Dnglewood Cliffs, NJ: Prentice-Hall.
5. Alwitt, L. F., & Prabhaker, P. R. (1992). Functional and belief dimensions of attitudes to television. Journal of Advertising Research, 32(5), 30-42.
6. Ambra, J. D., & Wilson, C. S. (2004). Use of the world wide web for international travel: integrating the construct of uncertainty in information seeking and the task-technology fit (TTF) model, Journal of the American Society for Information Science and Technology, 55(8), 731-742.
7. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommendes two-step approach. Psychological Bulletin, 103, 411-423.
8. Angel, H. C., & Ignacio, R. B. (2008). The effect of innovativeness on the adoption of B2C e-commerce: a model based on the theory of planned behaviour. Computers in Human Behavior, 24 (6), 2830-2847.
9. Arbuckle, J. L. (2003). AMOS 5.0 update to the AMOS user's guide. Chicago: SPSS.
10. Arning, K., & Ziefle, M. (2007). Understanding differences in PDA acceptance and performance. Computers in Human Behaviour, 23(6), 89-93.
11. Arnould, E. & Price, L. (1993). River magic: extraordinary experience and the extended service encounter. Journal of Consumer Research, 20, 24-45.
12. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
13. Balasubramanian, S., Peterson, R. A., & Jarvenpaa, S. L. (2002). Exploring the implications of m-commerce for markets and marketing. Journal of the Academy of Marketing Science, 30(4), 348-361.
14. Bandura, A. (1986). Social foundations of thought and action: a social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
15. Beaudry, A., & Pinsonneault, A. (2005). Understanding user responses to information technology: A coping model of user adaptation. MIS Quarterly, 29 (3), 493-524.
16. Buchholz, T., Hochstatter, I., & Linnhoff-Popien, C. (2007). Distribution strategies for the contextualized mobile internet. Electronic Commerce Research and Applications, 6(1), 40-52.
17. Bone, S. A., & Mowen, J. C. (2006). Identifying the traits of aggressive and distracted drivers: a hierarchical trait model approach. Journal of Consumer Behaviour, 5(5), 454-464.
18. Bosnjak, M., Galesic, M., & Tuten, T. (2007). Personality determinants of online shopping: Explaining online purchase intentions using a hierarchical approach. Journal of Business Research, 60(6), 597-605.
19. Brown, S. A., Vik, P. W., & Porter, R. J. (1998). Change in alcohol effect and self-efficacy expectancies during addiction treatment. Substance Abuse, 19, 155-176.
20. Burkolter, D., Kluge, A., Sauer, J., & Ritzmann, S. (2009). The predictive qualities of operator characteristics for process control performance: The influence of personality and cognitive variables. Ergonomics, 52(3), pp.302-311.
21. Campbell, D. J. (1988). Task complexity: A review and analysis. Academy of Management Review, 13(1), 40-52.
22. Chang, H. H. (2008). Intelligent agent’s technology characteristics applied to online auctions’ task: A combined model of TTF and TAM. Technovation, 28(9), 564-577.
23. Charney, T. R., & Greenberg, B. S. (2002). Uses and gratifications of the internet, in Lin, C., Atkin, D. (Eds). Communication, Technology and Society: New Media Adoption and Uses, Hampton Press, Cresskill, NJ.
24. Chau, P. Y. K., & Hu, P. J. (2001). Examining a model of information technology acceptance by individual professionals: An exploratory study
25. Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information & Management, 39(8), 705-719.
26. Chen, Z., & Dubinsky, A. J. (2003). A conceptual model of perceived customer value in e-commerce: A preliminary Investigation. Psychology & Marketing, 20(4), 323-347.
27. Cheong, J. H., & Park, M. C. (2005). Mobile internet acceptance in Korea. Internet Research, 15(2), 125-140.
28. 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.
29. Chiu, C.M and Wang, E.T.G. (2008). Understanding web-based learning continuance intention: The role of subjective task value. Information & Management, 45 (3), 194-201.
30. Cho, C. H., Kang, J., & Cheon, H. J. (2006). Online shopping hesitation. CyberPsychology & Behavior, 9 (3), 261-273.
31. Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118-143.
32. Constantiou, I. D. (2009). Consumer behaviour in the mobile telecom- munications' market: the individual's adoption decision of innovative services. Telematics and Informatics, 26 (3), 270-281.
33. Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145-158.
34. Costa, P. T., Jr., & McCrae, R. R. (1985). The NEO-personality inventory manual, Odessa, FL, Psychological Assessment Resources.
35. Constantiou, I. D. (2009). Consumer behavirour in the mobile telecom- munications’ market: The individual’s adoption decision of innovative service. Telematics and Informatics, 26(3), 270-281.
36. Dash, S., & Saji, K. B. (2007). The role of consumer self-efficacy and website social-presence in customers’ adoption of B2C online shopping: an empirical study in the India context. Journal of International Consumer Marketing, 20(2), 33-48.
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., & 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., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace, Journal of Applied Social Psychology, 22(14), 1111-1132.
40. Dba, S. W. J., & Lin, C. P. (2008). Learning online community citizenship behavior: a socio-cognitive model, CyberPsychology & Behavior, 11(3), 367-370.
41. Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology- based self-service: moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184-201.
42. Dahlberg, T., Mallat, N., Ondrus, J. Zmijewska (2009). Past, present and future of mobile payments research: a literature review. Electronic Commerce Research and Applications, 7(2), 165-181.
43. Debowski, S., Wood, R. E., & Bandura, A. (2001). Impact of guided exploration and enactive exploration on self-regulatory mechanisms and information acquisition through electronic search. Journal of Applied Psychology, 86(6), 1129-1141.
44. Dishaw, M. T., & Strong, D. M. (1998). Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1), 9-21.
45. Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand and store information on buyers’ product evaluations. Journal of Marketing Research, 28 (3), 307-319.
46. Eastin, M. S. (2002). Diffusion of e-commerce: an analysis of the adoption of four e-commerce activities. Telematics and Informatics, 19(3), 251-267.
47. Eastin, M. S. (2005). Teen internet use: Relating social perceptions and cognitive models to behavior. CyberPsychology & Behavior, 8(1), 62-75.
48. Elliott, K. M., Meng, J., & Hall, M. C. (2008). Technology readiness and the likelihood to use self-service technology: Chinese vs. American consumers. The Marketing Management Journal, 18(2), 20-31.
49. Fang, X., Chan, S., Brzezinski, J., & Xu, S. (2006). Moderating effects of task type on wireless technology acceptance. Journal of Management Information Systems, 22(3), 123-157.
50. Fang, J., Shao, P., & Lan, G. (2009). Effects of innovativeness and trust on web survey participation. Computers in Human Behavior, 25(1), 144-152.
51. Fornell, C., & Larcker, D. F. (1981). Structural equation models with un- observable variables and measurement errors. Journal of Marketing Research, 18(2), 39-50.
52. Forseeing Innovative New Digiservices (2009), http://find.org.tw/
53. Gabbott, M., & Hogg, G. (1998) Consuming Services, John Wiley.
54. Gebauer, J. (2008). User requirements of mobile technology: A summary of research results. Information Knowledge Systems Management, 7(1, 2), 101-119.
55. Gebauer, J., & Ginsburg, M. (2009). Exploring the black box of task-technology fit. Communications of the ACM, 52 (1), 130-135.
56. Gefen, D. (2000). E-commerce: the role of familiarity and trust, Omega, 28, 725-737.
57. Gist, M. E. (1987). Self-efficacy: implications for organizational behavior and resource management. Academy of Management Review, 12(3), 472-485.
58. Gist, M. E., & Mitchell, T. R. (1992). Self-efficacy: A theoretical analysis of its determinants and malleability. Academy of Management Review, 17(2), 183-211.
59. Global Information research (2009), http://www.giichinese.com.tw/
60. Goodhue, D. L. (1998). Development and measurement validity of a TTF instrument for user evaluations of information systems. Decision Sciences, 29(1), 105-137.
61. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236.
62. Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under mobile broadband wireless access environment. Information & Management, 44(3), 276-286.
63. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2005). Multivariate data analysis (6th ed.), Englewood Cliffs, NJ: Prentice Hall.
64. Harrison, A. W., & Rainer, R. K. J. (1992). The Influence of individual differences on skill in end-user computing. Journal of Management Information Systems, 9(1) 93-111.
65. Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer- mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 80-94.
66. Holbrook, M. B. (1999). Introduction to consumer value, in: M.B. Holbrook (Ed.), Consumer Value: A Framework for Analysis and Research, Routledge, New York.
67. Hung, S. Y., Ku, C. Y., & Chang, C. M. (2003). Critical factors of WAP services adoption: an empirical study. Electronic Commerce Research and Applications, 2(1), 42-60.
68. 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.
69. InsightXplorer Limited (2009), http://www.insightxplorer.com/specialtopic/crossmedia_200901.html/
70. Johnson, R. D., & Marakas, G. M. (2000). Research report: the role of behavioral modeling in computer skills acquisition-toward refinement of the Model. Information Systems Research, 11(4), 402-417.
71. Joppe, M. (2000). The research process. Retrieved February 25, 1998, from http://www.ryerson.ca/~mjoppe/rp.htm.
72. Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8 user's reference guide, Chicago, Scientific Software International.
73. Junglas, I. A., & Watson, R. T. (2003). U-commerce: A conceptual extension of e- and m-commerce. The International Conference on Information Systems, Seattle, WA.
74. Juniper Research report (2008). http://www.juniperresearch.com/index.php
75. Kang, Y. S., Hong, S., & Lee, H. (2009). Exploring continued online service usage behavior: The roles of self-image congruity and regret. Computers in Human Behavior, 25(1), 111-122.
76. Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23 (2), 183-213.
77. Kalakota, R., & Robinson, M. (2002). M-Business. The race to mobility. McGraw-Hill, New York.
78. Kaye, B. K., & Johnson, T. J. (2004). A web for all Reasons: uses and gratifications of internet resources for political information. Telematics and Informatics, 21(3), 197-223.
79. Katz, E., Blumler, J. G., & Gurevitch, M. (1974). The uses of mass com- munication. Beverly Hills, CA: Sage.
80. Kavassalis, P., Spyropoulou, N., Drossos, D., Mitrokostas, V., Gikas, G., & Hatzistamatiou, A. (2003). Mobile permission marketing - framing the market inquiry. 13th International Telecommunications Society's (ITS) European Regional Conference.
81. Khalifa, M., & Shen, K. N. (2008). Explaining the adoption of transactional B2C mobile commerce. Journal of Enterprise Information Management, 21(2), 110-124.
82. Kinzie, M. B., Delcourt, M. A, B., & Powers, S. M. (1994). Computer technologies: attitudes and self-efficacy across undergraduate disciplines. Research on Higher Education, 35(6), 745-768.
83. Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111-126.
84. Kim, B., Choi, M., & Han, I. (2009). User behaviors toward mobile data services: The role of perceived fee and prior experience. Expert Systems with Applications, 36, 8528-8536.
85. Kim, H. W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: a status quo bias perspective. MIS Quarterly, 29(3), 461-491.
86. Kim, H. W., & Pan, S. L. (2006). Towards a process model of information systems implementation: The case of customer relationship management (CRM). Data Base for Advances in Information Systems, 37 (1), 59-76.
87. Kim, G., Shin, B., & Lee, H. G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal, 19(3), 283-311.
88. Klaus, T., Gyires, T., & Wen, H. J. (2003). The use of web-based information systems for non-work activities: An empirical study. Human Systems Management, 22(3), 105-114.
89. Kleijnen, M., Lee, N., & Wetzels, M. (2009). An exploration of consumer resistance to innovation and its antecedents. Journal of Economic Psychology, 30, 344-357.
90. Kleijnen, M., Ruyter, K., & Wetzels, M. (2007). An assessment of value creation in mobile service delivery and the moderating role of time consciousness. Journal of Retailing, 83, 33-46.
91. 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), 35-49.
92. Ko, H., Cho, C. H., & Roberts, M. S. (2005). Internet uses and gratifications: a structural Equation model of Interactive Advertising. Journal of Advertising, 34(2), 57-70.
93. Koivumaki, T., Ristola, A., & Kesti, M. (2007). The effects of information quality of mobile information services on user satisfaction and service acceptance-empirical evidence from Finlan. Behaviour & Information Technology, 1-11.
94. Kwon, O., Choi, K., & Kim, M. (2007). User acceptance of context-aware services: self-efficacy, user innovativeness and perceived sensitivity on contextual pressure. Behaviour & Information Technology, 26(6), 483-498.
95. Kwon, T. H., & Zmud, R. W. (1987). Unifying the fragmented models of information systems implementation, in Boland and Hirschheim edited: Critical issue in information systems research, New York: John Wiley.
96. Kuo, Y. F., & Yen, S. N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25 (1), 103-110.
97. Kuo, Y. F., Wu, C. M., & Deng, W. J. (2009). The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in Human Behavior, 25(4), 887-896.
98. Larose, R., & Kim J. (2007). Share, Steal, or Buy? A social cognitive perceptive of music downloading. CyberPsychology & Behavior, 10(2), 267-277.
99. Lee, C. C., Cheng, H. K., & Cheng, H. H (2007). An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences. Decision Support Systems, 43(1), 95-110.
100. Lee, I., Choi, B., Kim, J., & Hong, S. (2007). Culture-technology fit: effects of cultural characteristics on the post-adoption beliefs of mobile internet users. International Journal of Electronic Commerce, 11(4), 11-51.
101. Leem, C. S., Suh, H. S., & Kim, D. S. (2004). A classification of mobile business models and its applications. Industrial Management & Data Systems, 104(1), 78-87.
102. Liang, T. P., & Wei, C. P. (2004). Introduction to the special issue: mobile commerce applications. International Journal of Electronic Commerce, 8(3), 7-17.
103. Liang, T. P., Huang, C. W., Yeh, Y. H., & Lin, B. (2007). Adopting of mobile technology in business: a fit-viability model. Industrial Management & Data Systems, 107(8), 1154-1169.
104. Liljander, V., Gillberg, F., Gummerus, J., & Riel, A. V. (2006). Technology readiness and the evaluation and adoption of self-service technologies. Journal of Retailing and Consumer Services, 13(3), 177-191.
105. Lin, C. A. (1996). Looking back: the contribution of Blumler and Katz’s ‘Uses of bass communication’ to communication research. Journal of Broadcasting & Electronic Media, 40(4), 574-581.
106. Lin, C. (1999). Online service adoption likelihood. Journal of Advertising Research, 39(2), 79-89.
107. Lin, J. S. C., & Hsieh, P. L. (2007). The influence of technology readiness on satisfaction and behavioral intentions toward self-service technologies. Computers in Human Behavior, 23, 1579-1615.
108. Lin, T. C., & Huang, C. C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410-417.
109. Lin, C. H., Shih, S. Y., & Sher, P. J. (2007). Integrating technology readiness into technology acceptance: The TRAM model. Psychology & Marketing, 24(7), 641-657.
110. Lin, H. H. & Wang, Y. S. (2006). An examination of the determinants of customer loyalty in mobile commerce contexts. Information & Management, 43(3), 271-282.
111. Liu, X., & Larose, R. (2008). Does using the internet make people more satisfied with their lives? The effects of the internet on college students’ school life satisfaction. CyberPsychology & Behavior, 11(3), 310-320.
112. Looney, C. A., Valacich, J. S., Todd, P. A., & Morris, M. G. (2006). Paradoxes of online investing: testing the influence of technology on user expectancies, Decision Sciences, 37(2), 205-246.
113. Lovelock, C., & Wirtz, J. (2004). Service marketing: People, technology, strategy (5th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
114. Lu, J., Liu, C., Yu, C. S., & Wang, K. (2008). Determinants of accepting wireless mobile data services in China. Information & Management, 45(1), 52-64.
115. 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.
116. Mallat, N. (2007). Exploring consumer adoption of mobile payments- a qualitative study. Journal of Strategic Information Systems, 16(4), 413-432.
117. Mallat, N., Rossi, M., Tuunainen, V. K., & Öörni, A. (2009). The impact of use context on mobile services acceptance: The case of mobile ticketing. Information & Management, 46(3), 190-195.
118. Mao, E., Srite, M., Thatcher, J., & Yaprak, O. (2005). A research model for mobile phone service behaviors: empirical validation in the U.S. and Turkey. Journal of Global Information Technology. Management, 8(4), 7-28.
119. Marakas, G. M., Yi, M. Y., & Johnson, R, D. (1998). The multilevel and multifaceted character of computer Self-Efficacy: Toward clarification on the construct and an integrative framework for research. Information Systems Research, 9(2), 126-163.
120. March, J. G.., & Simon, H. A. (1958). Organizations. New York, Wiley.
121. Marsh, H. W. (1994). Confirmatory factor analysis models of factorial invariance: a multifaceted approach. Structural Equation Modelin, 1(1), 5-34.
122. Marsh, H., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: first and higher order factor models and their invariance across groups. Psychological Bulletin, 95(3), 562-582.
123. Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3) 173-191.
124. Mathieson, K, Peacock, E., & Chin, W. (2001). Extending the technology acceptance model: the influence of perceived user resources. Database for Advances in Information Systems, 32(3), 86-112.
125. Mathwick, C., Malhotra, N., & Rigdon, E. (2001). Experiential Value: Conceptualization, Measurement and Application in the Catalog and Internet Shopping Environment. Journal of Retailing, 77 (1), 39-56.
126. Massey, A.P., Khatri, V., & Montoya-Weiss, M. M. (2007). Usability of online services: The role of technology readiness and context. Decision Sciences, 38 (2), 277-308.
127. McElroy, J. C., Hendrickson, A. R., Townsend, A. M., & DeMarie, S. M. (2007). Dispositional factors in internet use: personality versus cognitive style, MIS Quarterly, 31(4), 809-820.
128. McGrath, J. E. (1984). Groups: Interaction and performance. Prentice-Hall, Inc, Englewood Cliffs, N.J.
129. Melenhorst, A. S., Rogers, W. A., & Caylor, E. C. (2001). The use of communication technologies by older adults: exploring the benefits from the user's perspective. Proc. Human Factors and Ergonomics Soc., 45th Annual Meeting, Minneapolis, Minnesota, USA.
130. Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing among alternative service delivery modes: an investigation of customer trial of self-service technologies. Journal of Marketing, 69(2), 61–83.
131. Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: understanding customer satisfaction with technology-based service encounters, Journal of Marketing, 64(3), 50-64.
132. Moore, G., & Benbasat, I. (1991). Development of an instrument to measure the perception of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
133. Moores, T. T., & Chang, J. C. J. (2009). Self-efficacy, overconfidence, and the negative effect on subsequent performance: A field study. Information & Management, 46(2), 69-76.
134. Mowen, J. C. (2000). The 3M model of motivation and personality: Theory and empirical applications to consumer behavior. Norwell, MA, Kluwer Academic Press.
135. Mowen, J.C., Fang, X., & Scott, K. (2009). A hierarchical model approach for identifying the trait antecedents of general gambling propensity and of four gambling-related genres. Journal of Business Research.
136. Mowen, J. C., Park, S. & Zablah, A. (2007). Toward a theory of motivation and personality with application to word-of-mouth communications. Journal of Business Research, 60(6), 590-596.
137. Ngai, E. W. T., & Gunasekaran (2007). A review for mobile commerce research and applications, Decision Support System, 43 (1), 3-15.
138. Mowen, J.C., Park, S. & Zablah, A. (2007). Toward a theory of motivation and personality with application to word-of-mouth communications, Journal of Business Research, 60 (6), 590-596.
139. Müller-Seitz, G. , Dautzenberg, K. , Creusen, U., & Stromereder, C. (2009). Customer acceptance of RFID technology: Evidence from the German electronic retail sector. Journal of Retailing and Consumer Services, 16(1), 31-39.
140. Noe, R. A., & Wilk, S. L. (1993). Investigation of the factors that influence employees’ participation in development activities. Journal of Applied Psychology, 78(2), 291-302.
141. Nysveen, H., Pedersen, P. E., & Thorbjornsen, H. (2005). Intentions to use mobile services: antecedents and cross-service comparisons. Journal of the Academy of Marketing Science, 33(3), 330-346.
142. Okazaki, S. (2006). What do we know about mobile internet adopters? A cluster analysis. Information & management, 43(2), 127-141.
143. Ong, C. S., Lai, J. Y., & Wang, Y. S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & Management, 41(6), 795-804.
144. Overby, J. W., & Lee, E. J. (2006). The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research, 59, 1160-1166.
145. Oyedele, A., & Simpson, P. M. (2007). An empirical investigation of consumer control factors on intention to use selected self-service technologies. International Journal of Service Industry Management, 18(3), 287-306.
146. Pagain, M. (2006). Determinants of adoption of high speed data services in the business market: evidence for a combined technology acceptance model with task technology fit model. Information & Management, 43(7), 847-860.
147. Papacharissi, Z., & Rubin, A. M. (2000). Predictors of internet use. Journal of Broadcasting and Electronic Media, 44(2), 175-196.
148. Parasuraman, A. (2000). Technology Readiness Index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320.
149. Parasuraman, A., & Colby, C. L. (2001). Techno-ready marketing: How and why customers adopt technology. New York: The Free Press.
150. Park, Y., & Chen, J. V. (2007). Acceptance and adoption of the innovative use of smartphone. Industrial Management & Data, 107(9), 1349-1365.
151. Peters, C., Amato, C. H., & Hollenbeck, C. R. (2007). An exploratory investigation of consumers’ perceptions wireless advertising. Journal of Advertising, 36(4), 129-145.
152. Petre, M., Minocha, S., & Roberts, D. (2006). Usability beyond the website: an empirically grounded e-commerce evaluation instrument for the total customer experience. Behaviour & Information Technology, 25(2), 189–203.
153. Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). Research report: richness versus parsimony in modeling technology adoption decisions-understanding merchant adoption of a smart card-based payment system. Information Systems Research, 12(2), 208-222.
154. Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: problems and prospects. Journal of Management, 12(2): 531-544.
155. Poole, M. S., Siebold, D. R., & Mcphee, R. D. (1985). Group decision-making as a structurational process. Quartly Journal of Speech, 71(1), 74-102.
156. Porter, C. E., & Donthu, E. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: the role of perceived access barriers and demographics. Journal of. Business Research, 59(9), 999-1007.
157. Robinson, J. P., & Shaver, P. R. (1973). Measures of psychological attitudes, Ann Arbor, MII: Survey research center institute for social research, University of Michigan.
158. Rogers, E. M. (1995). Diffusion of innovations (4th ed.) Free Press, New York.
159. Rogers, E.M. (2003). The Diffusion of innovation (5th ed.). Free Press, New York.
160. Rubin, A. M. (1994). Media uses and effects: a uses and gratifications perspective (Bryant, J., Zillmann, D. eds.), Media effects: advances in theory and research. Hillsdale, NJ: Lawrence Erlbaum.
161. Ryu, M. H., Kim, S., & Lee, E. (2009). Understanding the factors affecting online elderly user’s participation in video UCC services. Computers in Human Behavior, 25(3), pp. 597-792.
162. Scharl, A., Dickinger, A., & Murphy, J. (2005). Diffusion and success factors of mobile marketing. Electronic Commerce Research and Applications, 4(2), pp. 159-173.
163. Schmitt, B. H. (1999), Experiential Marketing, New York: The Free Press.
164. Seltzer, V. C. (1980). Social comparison behavior of adolescents. In E. A. Pepitone (Eds), Children in cooperation and competition. Lexington, Ma: Lexington Books.
165. Severin, W. J., & Tankard, J. W. J. (1997). Communication theories: Origins, methods, and uses in the mass (4th ed.). White Plains, NY: Longman.
166. Sherry J., Lucas, K., Rechtsteiner, S. Brooks, C., & Wilson, B. (2001). Video game use and gratifications as predictors of use and game preference. Presented at the 51th convention of the International Communication Association, Washington.
167. Shin, D. H. (2009). Determinants of customer acceptance of multi-service network: An implication for IP-based technologies. Information & Management, 46(1), 16-22.
168. Siau, K., Lim, E. P., & Shen, Z. (2001). Mobile commerce: promises, challenges, and research agenda. Journal of Database Management, 12(3), 4-13.
169. Siau, K., & Shen, Z. (2003). Building customer trust in mobile commerce. Communications of the ACM, 46(4), 91-94.
170. Sievert, M. E, Albritton, R. L., Roper, P., & Clayton, N. (1988). Investigating computer anxiety in an academic library. Information Technology and Libraries, 7(9), 243-252.
171. Simon, H. (1977). The new science of management decisions. (Rev. ed). Englewood Cliffs, NJ: Prentice-Hall.
172. Sirdeshmukh, D., Singh, J., & Sabol, B. (2002). Consumer trust, value and loyalty in relational exchange. Journal of Marketing, 66 (1), 15-37.
173. Smith, H. M., & Betz, N. E. (2000). Development and validation of a scale of perceived social self-efficacy. Journal of Career Assessment, 8(3), 283-301.
174. Stafford, T. F., Stafford, M. P., & Schkade, L. L. (2004). Determining uses and gratifications for the internet. Decision Sciences, 35(2), 259-289.
175. Steuer, J. (1992). Defining virtual reality: Dimensions of determining telepresence. Journal of Communication, 42(4), 73-93.
176. Tan, M., & Teo T. S. H. (2000). Factors influencing the adoption of internet banking. Journal of the Association for Information Systems, 1(5), 1-42.
177. Tannenbaum, S. I., Mathieu, J. E., & Cannon-Bowers, J. A. (1991). Meeting trainees’ expectations: the influence of training fulfillment on the development of commitment, self-efficacy, and motivation, Journal of Applied Psychology, 76(6), 759-769.
178. Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in theory of planned behavior: a study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155.
179. Teo, T. S. H., & Pok, S. H. (2003). Adoption of WAP-enabled mobile phones among Internet users. The International Journal of Management Science, 31(6), 483-498.
180. Thatcher, J. B., & Perrewe, P. L. (2002). An Empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Quarterly, 26(4), pp.381-396.
181. Thompson, R. L., & Higgins, C. A. (1991). Personal computing: toward a conceptual model of utilization. MIS Quarterly, 15(1), 125-143.
182. Tjan, A. K. (2001). Finally, a way to put your internet portfolio in order. Harvard Business Review, 79(2), 76-85.
183. Tsikriktsis, N. (2004). A technology readiness-based taxonomy of customers: A Replication and extension. Journal of Service Research, 7(1), 42-52.
184. Tung, F. C., Chang, S. C. & Chou, C. M. (2008). An extension of trust and TAM model with IDT in the adoption of the electronic logistics information systems in HIS in the medical industry, Journal of Medical Informatics. 77(5), 324-335.
185. Turban, E. & King, D. (2003). Introduction to E-commerce. Upper Saddle River, NJ: Prentice Hall.
186. Turban, E., King, D., Lee, J., & Viehland, D. (2004). Electronic commerce and a managerial perspective. Pearson Prentice Hall, New Jersey.
187. Taiwan Network Information Center (2009). http://www.twnic.net/index2.php.
188. Uray, N., & Ayla, D. (1997). Identifying fashion clothing innovators by self-report method. Journal of Euromarketing, 6(3), 27-46.
189. Venkatesh, V., & Davis, F, D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-482.
190. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186-204.
191. 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.
192. Venkatraman, M. P. (1991). The impact of innovativeness and innovation type on adoption. Journal of Retailing, 67(1), 51-67.
193. Vessey, I. (1991). Cognitive fit: a theory-based analysis of the graphs versus tables literature, Decision Sciences, 22(2), 219-240.
194. Walczuch, R., Lemmink, L. & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206-215.
195. Wang, Y. S., & Liao, Y. W. (2007). The conceptualization and measurement of m-commerce user satisfaction. Computers in Human Behavior, 23(1), 381-398.
196. Wang, Y. S., & Wang, H. Y. (2008). Developing and validating an instrument for measuring mobile computing self-efficacy. CyberPsychology & Behavior, 11(4), 405-413.
197. Watson, R., Pitt, L., Berthon, P., & Zinkhan, G. (2002). U-commerce: expanding the universe of marketing. Journal of the Academy of Marketing Science, 30(4), 333-347.
198. Webster, J., & Martocchio, J. J. (1992). Microcomputer playfulness: development of a measure with workplace implications. MIS Quarterly, 16(2), 201-226.
199. Weise, G. (1975). Psychologische Leistungstests, Hogrefe, Gottingen, Germany.
200. Wu, J. H., & Wang, Y. M. (2006). Development of a tool for selecting mobile shopping site: A customer perspective. Electronic commerce research and applications, 5(3), 192-200.
201. 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.
202. Wu, J. H., Wang, S. C., & Lin, L. M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation mode. International Journal of Medical Informatics, 76(1), 66-77.
203. Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006), Understanding information technology acceptance by individual professionals: toward an integrative view. Information & Management, 43(3), 350-363.
204. Yoo, S. H., & Yoon, W. (2001). Development of metaphor-based interface design for VR manipulator. The 5th Asian Design Conference-International Symposium on Design Science.
205. Zinkhan, G. M., & Wallendorf, M. (1985). Service-set similarities in patterns of consumer satisfaction/dissatisfaction. International Journal of Research in Marketing, 2(4), 227-235.