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研究生: 詹丕宗
Pi-tzong Jan
論文名稱: 社會影響對電子化服務之採用研究
The Study of Social Influence on e-Services Adoption
指導教授: 盧希鵬
Hsi-Peng Lu
周子銓
Tzu-Chuan Chou
口試委員: 李國光
none
羅天一
none
黃如玉
none
學位類別: 博士
Doctor
系所名稱: 管理學院 - 資訊管理系
Department of Information Management
論文出版年: 2012
畢業學年度: 100
語文別: 英文
論文頁數: 118
中文關鍵詞: 數位匯流網路電視二維品質模式社會判斷理論鏡射模式數位學習制度理論科技採用
外文關鍵詞: digital convergence, IPTV, Kano model, social judgment theory, lens model, e-learning, institutional theory, technology adoption
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  • 近年來電子化服務的快速發展,不但為消費者在生活上帶來革命性的便利,也為企業帶來新的商機。然而對於消費者採用電子化服務行為的瞭解不甚完整,導致電子化服務業者面臨獲利不符預期的困境,因此,瞭解影響消費者對於電子化服務採用的因素,對於電子化服務業者而言是極為重要的研究議題。
    本論文提出二個研究問題,第一,社會因素是否影響電子化服務提供與採用之決策,並造成供需間對於品質需求認知落差?第二,影響消費者採用電子化服務之社會因素為何?藉由二個獨立的實證研究,本論文探討影響消費者採用電子化服務之社會行為,研究結果可使電子化服務業者對於消費者採用行為有更完整的瞭解,作為制定經營策略時的參考依據。
    第一個實證研究,探討IPTV服務品質,研究中首先發展出一個量測模型,確認出可用於衡量IPTV服務品質之構面,作為衡量IPTV服務品質之基礎,協助業者評估提升客戶滿意度之投資效益。藉由771份問卷調查及探索性因素分析,第一篇研究發展出包括系統品質、資訊品質、服務品質、視訊品質及適性化頻道品質等構面,適用於衡量IPTV服務品質之的量測模型,結合這個測量模型及Kano二維服務品質模型,本研究探討服務提供者與消費者之服務品質認知落差。研究結果顯示,服務品質需求之認知在供與需之間確實存在差異,提供了不同社會經驗背景個體得出不同決策結果之社會影響實證。
    第二個實證研究,探討影響企業員工使用數位學習之社會影響因素,研究中以制度理論為基礎提出一研究模式,探討強制力、規範力及模仿力等社會環境因素對於數位學習採用之行為影響,本研究以172位企業員工為調查對象,並利用淨最小平方法(Partial least squares,PLS)驗證該模式,研究結果發現,規範力與模仿力等社會因素顯著影響企業員工對於數位學習採用的態度與意圖,強制力則不影響,研究結果建議,企業可加強對員工之規範,並運用標竿模仿,以提高員工對於數位學習之使用意圖。
    最後,本論文針對二個研究結果,對實務界提出建議,協助其訂定更臻完善的經營策略,另外本文也提出對學術界未來研究方向的建議。


    The proliferation of e-services has not only created digital revaluation of our lives but has also provided the new business activities and opportunities. However, the environment of e-services is full of uncertainty. The uncertainty is that enterprises and consumers know so little about how to respond to the progress and expanding of e-services. Furthermore, enterprises hardly know how the consumers acquire, adopt, and experience the e-service; consumers also regard it hard to comprehend the ideas and considerations of enterprises in designing and promoting new e-services. Hence, this dissertation conducted two studies to provide solid models to examine and measure the perception of consumers on acquiring, adopting, and experiencing e-services.
    In the first study, focusing on the hedonic e-service, a measurement model was proposed to measure the perception discrepancy of IPTV service quality between the service provider and customers. The model, developed by exploratory factor analysis of survey data from 771 respondents, suggests adopting system quality, information quality, service quality, video quality, and adaptive channel quality to measure the IPTV service quality. Using this measurement model, this study adopts the Kano model to explore the perception of service quality between service providers and consumers. The results indicated a significant discrepancy between these two parties, and provide evidence for social factors influencing the perception discrepancy of e-service adoption between service provider and customers.
    The second study focused on the utilitarian e-service. A research model was proposed to examine three social environmental factors of coercive, normative and mimetic pressures within the e-learning context. An empirical study involving 172 subjects and the partial least square method was conducted to test this model. The results of the second study indicated that normative and mimetic pressures significantly influence the attitude and intention of adopting e-learning, while coercive pressures appear the opposite. Attitude plays a mediating role between both normative and mimetic institutional pressures and e-learning adoption. The second study contributes to a deeper understanding of the social factors that promote the use of e-learning in on-the-job training.
    Finally, the implications of these findings for service providers and the directions for future research are discussed herein.

    1. Introduction 1 1.1 Background and Motivation 1 1.2 Research Questions 3 1.3 Research Purposes 4 1.4 Organization of the Dissertation 6 2. Provision of e-service 7 2.1 Conception and Definition of e-services 7 2.2 Empirical Studies Related to e-services 11 3. Study 1: Measuring the Perception Discrepancy of the Service Quality between Providers and Customers in the Internet Protocol Television Industry 15 3.1 Introduction 15 3.2 Definition of and Current Developments in IPTV 17 3.3 Theoretical Background and Research Framework 20 3.4 Methodology 26 3.5 Data Analysis and Results 29 3.6 Findings and Discussions 37 3.7 Conclusions 41 3.8 Limitation and Future Works 43 4. Study 2: The Adoption of e-learning----An Institutional Theory Perspective 44 4.1 Introduction 44 4.2 Theoretical Background and Conceptual Model 48 4.3 Research Methodology 61 4.4 Data Analyses and Results 65 4.5 Discussion and Conclusions 74 5. Conclusions 77 5.1 Implications for e-service Practitioners 79 5.2 Implications for Academic Researchers 80 5.3 Limitation and Future Research 81 6. References 82 7. Appendix 104

    [1] Abrahamson, E. (1991). Managerial fads and fashions: The diffusion and rejection of innovations. Academy of Management Review, 16, 586-612.
    [2] Abrahamson, E., & Rosenkopf, L. (1993). Institutional and competitive bandwagons: Using mathematical modeling as a tool to explore innovation diffusion. Academy of Management Review, 18(3), 487-517.
    [3] Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication. MIS Quarterly, 15(2), 227-250.
    [4] Agarwal, R., & Karahanna, E. (2000). Time Flies When You’re Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage. MIS Quarterly, 24(4), 665-694.
    [5] Agarwal, R., & Prasad, J. (1999). Are Individual Differences Germane to the Acceptance of New Information Technologies. Decision Science, 30(2), 361-391.
    [6] Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. Action Control: From Cognition to Behavior, 2, 11-39.
    [7] Ajzen, I. (1988). Attitudes, personality, and behavior. Chicago, IL: Dorsey Press.
    [8] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2),179-211.
    [9] Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Upper Saddle River, NJ: Prentice-Hall.
    [10] Ajzen, I., & Madden, T. J., (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22(5), 453-474.
    [11] Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103 (3), 411-423.
    [12] Ang, S., & Cummings, L. L. (1997). Strategic response to institutional influences on information systems outsourcing. Organization Science, 8(3), 235-256.
    [13] Atack, L. (2003). Becoming a web-based learner: registered nurses' experiences. Journal of Advanced Nursing, 44, 289-297.
    [14] Alfonsi, B. (2005). I Want My IPTV: Internet Protocol Television Predicted a Winner. IEEE Distributed Systems Online, 6(2), 1541-4922.
    [15] Arkes, H. R., & Hammond, K. R. (1986). General Introduction. In judgment and decision making: An interdisciplinary Reader: Cambridge: Cambridge University Press.
    [16] Babin, B.J., Darden, W.R. & Griffin, M. (1994). Work and/or Fun: measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4), 644-656.
    [17] Barclay, D., Higgins, C., & Thomson, R. (1995). The partial least squares approach to causal modeling, personal computer adoption and use as an illustration. Technology Studies, 2(2), 285-309.
    [18] Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1182.
    [19] Bernardi, D., Calvanese, D., De Giacomo, G., Lenzerini, M., & Mecella, M. (2003b). A Foundational Vision of e-Services, in Proceedings of Web Services, E-Business, and the Semantic Web, Second International Workshop, WES 2003, Klagenfurt, Austria, 28-40.
    [20] Bernardi, D., Calvanese, D., De Giacomo, G., Lenzerini, M., & Mecella, M. (2003b). Reasoning about actions for e-service composition, in ICAPS 2003 Workshop on Planning for Web Services, Trento, Italy.
    [21] Bersin, J. (2007). Enterprise Learning and Talent Management 2007 Trends, Areas of Focus and Predictions for 2007. Oakland, CA: Bersin & Associates.
    [22] Bhattacherjee, A. (2001a). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201-214.
    [23] Bhattacherjee, A. (2001b). Understanding information systems continuance. An expectation-confirmation model. MIS Quarterly, 25(3), 351-370.
    [24] Bilgehan, E., & Elissa, P. M. (2008). Analysis and Realization of IPTV Service quality, Bell Labs Technical Journal, 12(4), 195-212.
    [25] Botwinick, J. (1973). Aging and Behavior. Springer, New York.
    [26] Boyer, K. K., Hallowell, R., & Roth, A. V. (2002). E-services: operations strategy – a case study and a method for analyzing operational benefits, Journal of Operations Management, 20(2), 175-188.
    [27] Boynton, A. D., Zmud, R. W., & Jacobs, G. C. (1994). The Influence of IT Management Practice on IT Use in Large Organizations. MIS Quarterly, 18(3), 299-318.
    [28] Brehmer, B. (1988). The Development of Social Judgment Theory. In B. Brehmer and C. R. B. Joyce(Eds.), Human Judgment: The SJT View. Amsterdam: North-Holland.
    [29] Brunswik, E. (1955). Representative Design and Probabilistic Theory in a Functional Psychology. Psychology Review, 62, 193-217.
    [30] Burgess, J. R. D., & Russell, J. E. A. (2003). The effectiveness of distance learning initiatives in organizations. Journal of Vocational Behavior, 63, 289-303.
    [31] Burkhardt, M. E. (1994). Social interaction effects following a technological change: A longitudinal investigation. Academy of Management Journal, 37(4), 868- 896.
    [32] Carley , K. M., & Kaufer, D. S. (1993). Semantic connectivity: An approach for analyzing symbols in semantic networks. Communication Theory, 3(3), 183-213.
    [33] Carlsson, C., Carlsson, J., Hyvönen, K., Puhakainen, J., & Walden, P.(2006). Adoption of Mobile Devices/Services-Searching for Answers with the UTAUT. Proceedings of the 39th Hawaii International Conference on System Science, 1-10.
    [34] Carman, J. M. (1990). Consumer Perceptions of Service quality: An Assessment of the SERVQUAL Dimensions. Journal of Retailing, 66(1), 33-55.
    [35] Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245-276.
    [36] Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students’ behavioral intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71-83.
    [37] Chatzoglou, P. D., Sarigiannidis, L., Vraimaki, E., & Diamantidis, A. (2009). Investigating Greek employees’ intention to use web-based training. Computers & Education, 53, 877-889.
    [38] Chang, S. C., & Wang, J. C. (2006). Technology and Strategies in Digital Convergence, Electronic Commerce Studies, 4(1), 75-96.
    [39] Chen, J. K., & Lee, Y. C. (2009). A new method to identify the category of the quality attribute. Total Quality Management, 20(10), 1139-1152.
    [40] Chen, L. S., Liu, C. H., Hsu, C. C., & Lin, C. S. (2010). C-Kano model: a novel approach for discovering attractive quality elements. Total Quality Management, 21(11), 1189-1214.
    [41] Chen, M. S., & Hsu, K. P. (2007). The Effects of Asynchronous E-learning Situation on Learning Perception and Usage Intention. Journal of Human Resource Management, 7(3), 25-44.
    [42] Chen, Y. H., & Su, C. T. (2006). A Kano-CKM model for customer knowledge discovery. Total Quality Management, 17(5), 589-608.
    [43] Cheng, B., Wang, M., Yang, Stephen, J. H., Kinshuk, & Peng, J. (2011). Acceptance of competency-based workplace e-learning: effects of individual and peer learning support. Computers & Education, 57, 1317-1333.
    [44] Cheng, B. W., & Chiu, W. H. (2007). Two-dimensional Quality Function Deployment: An Application for Deciding Quality Strategy Using Fuzzy Logic. Total Quality Management, 18(4), 451-470.
    [45] Chin, W. W. (1998a). The Partial Least Squares Approach to Structural Equation Modeling in Modern Methods for Business Research ( Marcoulides, G. A. ed.). Mahway, NJ: Lawrence Erlbaum Associates.
    [46] Chin, W. W. (1998b). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), 7-16.
    [47] Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic mail adoption study. Information Systems Research, 14 (2), 189-217.
    [48] Chiu, C., & Wang, E. T. G. (2008). Understanding web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201.
    [49] CHT. (2009). Chunghwa Telecom 2008 annual report. Retrieved from http://www.cht.com.tw/chtir
    [50] Churchill, G. A., & Surprenant, C. (1982). An Investigation into the Determinants of Customer Satisfaction. Journal of Marketing Research, 19 (November), 491-504.
    [51] Clemons, E.K., Gu, B. & Lang, K.R. (2002). Newly vulnerable markets in an age of pure information products: An analysis of online music and online news. Journal of Management Information Systems,19(3), 17-41.
    [52] Clemons, E.K. & Lang, K.R. (2003). The decoupling of value creation from revenue: A strategic analysis of the markets for pure information goods. Information Technology and Management, 4, 259-287.
    [53] Compeau, D. R., & Higgins, C. A. (1995). Computer Self-efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189-211.
    [54] Cooper, Harris M. (1979). Statistically combining independent studies: A meta-analysis of sex differences in conformity research. Journal of personality and social psychology, 37(January), 131-146.
    [55] Cuieford‚ J. P. (1965). Fundamental Statistics in Psychology and Education. N.Y.: McGraw-Hill.
    [56] Darden, W. R., Babin, B. J., Griffin, M., & Coulter, R. (1994). Investigation of Products Liability Attitudes and Options: A Consumer Perspective. The Journal of Consumer Affairs, 28(1), 54-81.
    [57] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 318-340.
    [58] Davis, F. D.(1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral Thesis, Massachusetts Institute of Technology, Sloan School of Management, USA.
    [59] 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), 982-1002.
    [60] De Ruyte, K., Wetzels, M., & Kleijnen, M. (2001). Customer adoption of e-service: An experimental study. International Journal of Service Industry Management. 12(2), 184-207.
    [61] DeLone, W. H. (1988). Determinants of Success for Computer Usage in Small Business. MIS Quarterly, 12(1), 51-61.
    [62] DeLone, W. H., & McLean, E. R. (1992), Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 3(1), 60-95.
    [63] DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30.
    [64] DeLone, W. H., & McLean, E. R. (2004). Measuring e-Commerce Success: Applying the DeLone & McLean Information Systems Success Model. International Journal of Electronic Commerce, 9(1) 31-47.
    [65] Demet, K., Cigdem, A. G., & Fethi, C. (2011). Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Computers in Human Behavior, 27, 343-354.
    [66] DeRouin, R. E., Fritzsche, B. A., & Salas, E. (2005). Learner control and workplace e-learning: design, person, and organizational issues. In J. Martocchio Eds.), Research in personnel and human resources management( pp.181-214). New York.
    [67] DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: institutional isomorphism in organizational fields. American Sociological Review, 48, 147-160.
    [68] Dinev, T. & Hart, P. (2006). Privacy Concerns and Levels of Information Exchange: An Empirical Investigation of the Intended e-Services Use. E-Service Journal, 4(3), 25-59.
    [69] Edvardsson, B., Larsson, G., & Setterlind, S. (1997). Internal Service quality and the Psychosocial Work Environment : An Empirical Analysis of Conceptual Interrelatedness. The Service Industries Journal, 17(2), 252-263.
    [70] Ely, K., Sitzmann, T., & Falkiewicz, C. (2009). The influence of goal orientation dimensions on time to train in a self-paced training environment. Learning and Individual Differences, 19(1), 146-150.
    [71] Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, MA: Addison-Wesley Reading.
    [72] Flanagin, A. J. ( 2000 ). Social pressure on organizational website adoption. Human Communication Research, 26(4), 618-646.
    [73] Fletcher, G. H. (2004). The Future of E-Learning. Technological Horizons in Education Journal, 32(2), 4-5.
    [74] Forman, D., Nyatanga, L., & Rich, T. (2002). E-learning and educational diversity. Nurse Education Today, 22, 76-82.
    [75] Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19 (4), 440-452.
    [76] Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50.
    [77] Fuerst, W. L., & Cheney, P. H. (1982). Factors Affecting the Perceived Utilization of Computer-Based Decision Support Systems in the Oil Industry. Decision Sciences, 13(4), 554-569.
    [78] Gasco, J. L., Llopis, J., & Gonzalez, M. R. (2004). The use of information technology in training human resources: an e-learning case study. Journal of European Industrial Training, 28, 370-382.
    [79] Goldman, A., & Johansson, J. K. (1978). Determinants of search for lower prices: An empirical assessment of the economics of information theory. Journal of Consumer Research, 5(December), 176-186.
    [80] Govindasamy, T. (2002). Successful implementation of E-learning pedagogical considerations. Internet and Higher Education, 4, 287-299.
    [81] Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6) 1420-1443.
    [82] Grewal, R., & Dharwadkar, R. (2002). The role of the institutional environment in marketing channels. The Journal of Marketing, 66(3), 82-97.
    [83] Haigh, J. (2004). Information technology in health professional education: why IT matters. Nurse Education Today, 24, 547-552.
    [84] Hair, J. F., Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis, (6th ed.), Mahwah, New Jersey: Prentice-Hall International.
    [85] Hammond, K. R., Todd, F. J., Wilkins, M., & Mitchell, T. O. (1966). Cognitive Conflict between Persons: Application of the ‘Lens Model’ Paradigm. Journal of Experimental Social Psychology. 2(4), 343-360.
    [86] Harcourt, M., Lam, H., & Harcourt, S. (2005). Discriminatory practices in hiring: Institutional and rational economic perspectives. International Journal of Human Resource Management, 16(11), 2113-2132.
    [87] Hirschman, E.C. & Holbrook, M.B. (1982). Hedonic consumption: emerging concepts, methods and propositions. Journal of Marketing, 46, 92-101.
    [88] Hirschman, E.C. (1983). Aesthetics, ideologies and the limits of the marketing concept. Journal of Marketing, 47(3), 45-55.
    [89] Hoffman, A. W. (1997). From Heresy to Dogma: An Institutional History of Corporate Environmentalism. San Francisco, CA: New Lexington Press.
    [90] Holt, D.B. (1995).How consumers consume: a typology of consumption practices. Journal of Consumer Research, 22(1), 1-16.
    [91] Held, G. (2007). Understanding IPTV, New York : Auerbach Publications.
    [92] Hsu, T. M. (2009). Global IPTV market and develop analysis. Taipei,Taiwan: Institute for Information Industry.
    [93] ITU-T. (2006). IPTV-Market, Regulatory Trends and Policy Options in Europe. Background Material, Recommendation, October.
    [94] IBM. (1997). Are you ready for e-business? Wall Street Journal, 7, b9-b16.
    [95] Järvinen, R., Lehtinen, U., & Vuorinen, I. (2003). Options of strategic decision making in services, tech, touch and customizations in financial services. European Journal of Marketing. 37( 5/6), 774-795.
    [96] Johnson, C., Dowd, T. J., & Ridgeway, C. L. (2006). Legitimacy as a social process. Annual Review of Sociology, 32, 53-78.
    [97] Jöreskog, K., & Sörbom, D. (1993). LISREL 8: User’s reference guide. Chicago, IL: Scientific Software International.
    [98] Juran, J. M. (1974). Quality Control Handbook(3rd ed.). New York: McGraw-Hill.
    [99] Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141-151.
    [100] Kano, N., Seraku, N., Takahashi F., & Tsuji, S. (1984). Attractive quality and must-be quality. Journal of Japanese Society for Quality Control, 14, 39-48.
    [101] Keller, C., & Cernerud, L. (2002). Students’ Perceptions of E-Learning in University Education. Journal of Educational Media, 27(1-2), 55-67.
    [102] Kettinger, W. J., & Lee, C. C. (1997). Pragmatic Perspectives on the Measurement of Information Systems Service quality. MIS Quarterly, 21(2), 223-240.
    [103] King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755.
    [104] Krassa, M. A. (1988). Social groups, selective perception, and behavioral contagion in public opinions. Social Networks, 10(1), 109-136.
    [105] Kuo, Y. F. (2003). A study on service quality of virtual community Web sites. Total Quality Management, 14, 461-473.
    [106] Kuo, Y. F. (2004). Integrating Kano’s model into web-community service quality. Total Quality Management, 15 (7), 925-939.
    [107] Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53, 1320-1329.
    [108] Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, 54, 506-516.
    [109] Lee, Y. H., Hsieh, Y. C., & Ma, C. Y. (2011). A model of organizational employees’ e-learning systems acceptance. Knowledge-Based Systems, 24, 355-366.
    [110] Leender, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: constructing the weight matrix. Social Networks, 24(1), 21-47.
    [111] Leonard-Barton, D. (1987). Implementation of Structured Software Methodologies: A Case of Innovation in Process Technology. Interfaces, 17(3),.6-17.
    [112] Leonard-Barton, D., & Deschamps, I. (1988). Managerial Influence in the Implementation of New Technology. Management Science, 35(10), 1252-1265.
    [113] Lewis, B. R. (1993). Service quality Measurement. Marketing Intelligence and Planning, 4, 4-12.
    [114] Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of Influence on Beliefs about Information Technology Use: An Empirical Study of Knowledge Workers. MIS Quarterly, 27(4), 657-678.
    [115] Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007). Assimilation of enterprise systems: The effect of institutional pressures and the mediating role of top management. MIS Quarterly, 31(1), 59- 87.
    [116] Liaw, S. S. (2007). Computers and the Internet as a job assisted tool: based on the three-tire use model approach. Computers in Human Behavior, 23, 399-414.
    [117] Liaw, S. S. (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: a case study of the Blackboard system. Computers & Education, 51(2), 864-873.
    [118] Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers and Education, 49(4), 1066-1080.
    [119] Lilja, J., & Wiklund, H. (2007). A Two-Dimensional Perspective on Attractive Quality. Total Quality Management, 18(6), 667-679.
    [120] Little, B. (2001). Achieving high performance through e-learning. Industrial and Commercial Training, 33(6), 203-207.
    [121] Liu, I. F., Chen, M. C., Sun, Y. S., David, W. C., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers & Education, 54(2), 600-610.
    [122] Lohmoller, J. B. (1989). The PLS program system: Latent variables path analysis with partial least squares estimation. Multivariate Behavioral Research, 23(1), 125-127.
    [123] Longworth, N., & Davies, W. K. (1996). Lifelong Learning, London: Kogan Page.
    [124] Lopes, A.B. & Galletta, D.F. (2006). Consumer perceptions and willingness to pay for intrinsically motivated online content. Journal of Management Information Systems. 23(2), 203-231.
    [125] Luarn, P. & Lin, H. H. (2003). A customer loyalty model for e-service context. Journal of Electronic Commerce Research, 4(4), 156-167.
    [126] Margherio L., Henry, D., Cooke, S., Montes, S., & Hughes, K. (1998). The Emerging Digital Economy. Secretariat on Electronic Commerce, US Department of Commerce, Washington, DC.
    [127] Marki, R. H., Maki, W. S., Patterson, M., & Whittaker, P. D. (2000). Evaluation of a Web-based Introductory Psychology Course: I. Learning and Satisfaction in On-line Versus Lecture Courses. Behavior Research Methods, Instruments and Computers, 32(2), 230-239.
    [128] Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Systems Research, 2(3), 173-191.
    [129] Meyer, J. W., & Rowan, B. (1977). Institutionalized organizations: formal structure as myth and ceremony. American Journal of Sociology, 83, 340-363.
    [130] Monge, P. R., Cozzens, M. D., & Contractor, N. S. (1992). Communication and Motivational Predictors of the Dynamics of Organizational Innovation. Organization Science, 3(2), 250-274.
    [131] Molla, A., & Licker, P.S. (2001). E-commerce systems success: An attempt to extend and respecify the DeLone and McLean model of IS success. Journal of Electronic Commerce Research, 2(4), 1-11.
    [132] 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 .
    [133] Moore, M. G., & Kearsley, G. (1996). Distance education: A systems view, Belmont, CA: Wadsworth Publishing.
    [134] Morris, M. G., & Dillon, A. (1997). How user perceptions influence software use. IEEE Transactions, July/August, 58-65.
    [135] Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing workforce . Personnel Psychology, 3(2), 375-403.
    [136] NCC. (2009). Cable TV subscribers statistic, Retrived from National Communications Commission
    [137] Nelson, E. A. (2003). E-Learning: A Practical Solution for Training and Tracking in Patient-Care Settings. Nursing Administration Quarterly, 27(1), 29-32.
    [138] North, D. C. (1989). Institutional change and economic history. Journal of Institutional and Theoretical Economy, 145(4), 238-245.
    [139] North, D. C. (1990). Institutions, Institutional Change and Economic Growth, Cambridge, UK: Cambridge University Press.
    [140] Nunnally, J. C. (1978). Psychometric Theory(2nd ed), McGraw-Hill Book Company, New York, NY.
    [141] Ong, C. S. (2000). A study on the consumer’s expectations of the Internet. Journal of Information Management, 5, 51-73.
    [142] Ong, C. S., & Lai, J. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22 (5), 816-829.
    [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, 95-804.
    [144] 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.
    [145] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A Conceptual Model of Service quality and Its Implications for Future Research. Journal of Marketing, 49(Fall), 41-50.
    [146] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service quality. Journal of Retailing, 64(1), 12-40.
    [147] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1990). Five Imperatives for Improving Service quality. Sloan Management Review, 31(4), 29-38.
    [148] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1991). Refinement and Reassessment of the SERVQUAL Scale. Journal of Retailing, 67, 420-450.
    [149] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1994a). Reassessment of Expectations as a Comparison Standard in Measuring Service quality: Implications for Further Research. Journal of Marketing, 58(1), 111-124.
    [150] Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1994b). Alternative Scale for Measuring Service quality: A Comparative Assessment Based on Psychometric and Diagnostic Criteria. Journal of Retailing, 70(3), 201-230.
    [151] Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning. Educational Technology & Society, 12 (3), 150-162.
    [152] Peter, J. P. (1979). Reliability: a review of psychometric basic and recent marketing practice. Journal of Marketing Research, 16(February), 6-17.
    [153] Pituch, K., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244.
    [154] Point Topic. (2009). IPTV Subscribers Q1 2009. Retrived from Point Topic website:http://point-topic.com/content/bmm/reports/iptv%20subscribers%20q1%202009.xls
    [155] Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838-852.
    [156] Randy, J. (1995). Exploring the Determinants of Cable Television Subscriber Satisfaction. Journal of Broadcasting and Electronic Media, 39, 262-274.
    [157] Raymond, L. (1988). The Impact of Computer Training on the Attitudes and Usage Behavior of Small Business Managers. Journal of Small Business Management, 26(3), 8-13.
    [158] Roca, J. C., & Gagne, M. (2008). Understanding e-learning continuance intention in the workplace. A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585-1604.
    [159] Rogers, E. M. (1995). Diffusion of innovations (4th ed.), New York Free Press
    [160] Rosenberg, M. J. (2006). E-learning: Strategies For Delivering Knowledge In The Digital Age. New York: McGraw-Hill.
    [161] Rowley, J. (2006). An analysis of e-service literature: Toward a research agenda. Internet Research, 16(3), 339-359.
    [162] Rust, R. T. & Lemon, C. N.( 2001). E-Service and the Consumer. International Journal of Electronic Commerce. 5(3). 85-101.
    [163] Rust, R. T. & Kannan, P. K. (2003). E-service: A new paradigm for business in the electronic environment. Communications of the ACM, 46(6), 37-42.
    [164] Sahai, A., & Machiraju, V. (2001). Enabling of the Ubiquitious e-Service Vision on the Internet. E-Service Journal, 1(1), 5-21.
    [165] Salas, E., Kosarzycki, M. P., Burke, C. S., Fiore, S. M., & Stone, D. L. (2002). Emerging themes in distance learning research and practice: some food for thought. International Journal of Management Reviews, 4(2), 135-153.
    [166] Sanders, G. L., & Courtney, J.F. (1985). A Field Study of Organizational Factors Influencing DSS Success. MIS Quarterly, 9(1), 77-91.
    [167] Santouridis, I., Trivellas, P., & Reklitis, P. (2008). Internet service quality and customer satisfaction: Examining Internet banking in Greece. Total Quality Management, 20(2), 223-239.
    [168] Scott, W. R. (1987). The adolescence of institutional theory. Administrative Science Quarterly, 32 (4), 493-511.
    [169] Scott, W. R. (2001). Institutions and Organizations (2nd ed.). Thousand Oaks, CA: Sage.
    [170] Scott, W. R. (2004), Institutional theory. Thousand Oaks, CA: Sage.
    [171] Selim, H. M. (2003). An empirical investigation of student acceptance of course websites. Computers & Education, 40(4), 343-360.
    [172] Shachtman, N. (2000). E-Learning moves out of the office. Information Week, Oct.23, 208-210.
    [173] Shahin, A., & Zairi, M. (2009). Kano model: A dynamic approach for classifying and prioritizing requirements of airline travellers with three case studies on international airlines. Total Quality Management, 20(9), 1003-1028.
    [174] Sherif, C. M., & Nebergall, R. E. (1965). Attitude and Attitude Change: the Social Judgment Involvement Approach. Pennsylvania New Haven: Yale University Press.
    [175] Shin, D.H. (2007). Potential user factors driving adoption of IPTV. What are customers expecting from IPTV? Technological Forecasting & Social Change, 74,1446-1464.
    [176] Shin, D. H.(2009). Determinants of customer acceptance of multi-service network: an implication for IP-based technologies. Information & Management, 46.
    [177] Shin, D. H.(2009). An empirical investigation of a modified technology acceptance model of IPTV. Behaviour & Information Technology, 28(4).
    [178] Shin, Y., Jeon, H., & Choi, M. (2008). Analysis of the mobile IPTV adoption model and moderator effect using extended TAM model. In Proc. of Fourth International Conference on Networked Computing and Advanced Information Management, 2008.
    [179] Sohn, C., & Tadisina, S. K. (2008). Development of e-service quality measure for Internet-based financial institutions. Total Quality Management, 19(9), 903-918.
    [180] Stigler, G. J. (1961). The economics of information. Journal of Political Economy, 60(June), 213-225.
    [181] Stoel, L., & Lee, K. H. (2003). Modeling the effect of experience on student acceptance of Web-based courseware. Internet Research, 13(5), 364-374.
    [182] Straub, D. W. (1989). Validating Instruments in MIS Research. MIS Quarterly, 13(2), 147-169.
    [183] Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers and Education, 50(4), 1183-1202.
    [184] Tan, K. C., & Shen X. X. (2000). Integrating Kano’s model in the planning matrix of quality function deployment. Total Quality Management, 11, 1141-1151.
    [185] Teo, H. H., Wei, K. K., & Benbasa, I. (2003). Predicting intention to adopt interorganizational linkages: An institutional perspective. MIS Quarterly, 27 (1), 19-49.
    [186] Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 124-143.
    [187] Tiwana, A., & Ramesh, B. (2001). e-Services: Problems, Opportunities, and Digital Platforms, in Proceedings of the 34th Hawaii International Conference on System Sciences.
    [188] Tolman, E., & Brunswik, E. (1935). The Organism and the Casual Texture of the Environment. Psychological Review, 42, 43-77.
    [189] Tontini, G. (2007). Integrating the Kano Model and QFD for Designing New Products. Total Quality Management, 18(6), 599-612.
    [190] Triandis, H. C. (1977). Interpersonal behavior. Monterey, CA: Brooke/Cole.
    [191] Trombley, K. B., & Lee, D. (2002). Web-based learning in corporations: Who is using it and why, who is not and why not? Journal of Educational Media, 27(3), 137-146.
    [192] Turban, E., King, D., Lee, J., Warkentin, M., & Chung, H. M. (2002). Electronic Commerce: An Managerial Perspective. New York: Prentice Hall.
    [193] U.S. Department of Commerce (2011). E-commerce 2009 report. Economics and Statistics Administration, U.S. Census Bureau, U.S. available at: http://www.census.gov/econ/estats/2009/2009reportfinal.pdf
    [194] Van Den Bulte, C., & Lilien, G. L. (2001). Medical innovation revisited: Social contagion versus marketing effort. American Journal of Sociology, 106(5), 1409-1435.
    [195] Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704.
    [196] Van Dyke, T. P., Kappelman, L. A., & Prybutok, V. R. (1997). Measuring Information Systems Service quality: Concerns on the Use of the SERVQUAL Questionnaire. MIS Quarterly, 21(2), 195-208.
    [197] 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.
    [198] Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 45 (2), 186-204.
    [199] Venkatesh, V., & Morris, M. G. (2000). Why Don’t Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior. MIS Quarterly, 24(1), 115-139.
    [200] 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.
    [201] Venkatraman, M.P. & Maclnnis, D.J. (1985). The epistemic and sensory exploratory behavior of hedonic and cognitive consumers. Advances in Consumer Research, 12, 102-107.
    [202] Wang, S., & Cheung, W. (2004). E-Business adoption by travel agencies: Prime candidates for mobile e-business. International Journal of Electronic Commerce, 8(3), 43-63.
    [203] Watson, R. T., Pitt, L. F., & Kavan, C. B. (1998). Measuring information systems service quality: lessons from two longitudinal case studies. MIS Quarterly, 22, 61-79.
    [204] Weber, M. (1946). Essays in Sociology, New York: Oxford University Press.
    [205] Wilson, B. G. (2004). Designing e-learning environments for flexible activity and instruction. Educational Technology Research and Development, 52, 77-84.
    [206] Wold, H. (1985). Partial least squares. In Kotz, S., & Johnson, N. (Eds.). Encyclopedia of Statistical Sciences, New York: Wiley.
    [207] Yang, C. C. (2005). The refined Kano’s model and its application. Total Quality Management, 16(10), 1127-1137.
    [208] Yu, S., Chen, I. J., Yang, K. F., Wang, T.F., & Yen, L. L. (2007). A feasibility study on the adoption of e-learning for public health nurse continuing education in Taiwan. Nurse Education Today, 27(7), 755-761.
    [209] Yu, T. K. (2006). An Empirical Study of Web-based Learning Adoption in the Behavioral and Cognitive Styles. Journal of Education & Psychology, 29(4), 687-717.
    [210] Yu, T. K., & Yang, S. F. (2005). The Constructionalization and Comparison of Use Intention Model in Electronic Learning System. Taiwan Academy of Management Journal, 5(2), 311-337.
    [211] Zeithaml, V. A. & Bitner, M. J. (2000). Service Marketing: Integrating Customer Focus across the Firms. McGraw-Hill, New York.
    [212] Zhang, D., & Zhou, L. (2003). Enhancing e-learning with interactive multimedia. Information Resources Management Journal, 16(4), 1-14.
    [213] Zhang, D., Zhao, J. L., Zhou, L., & Nunamaker, J. F. Jr. (2004). Can e-learning replace classroom learning. Communications of the ACM, 47(5), 75-79.
    [214] Zhu, K., Kraemer, K. L., Xu, S., & Dedrick, J. (2004). Information technology payoff in e-business environments: An international perspective on value creation of e-business in the financial services industry. Journal of Management Information Systems, 21(1), 17-54.

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