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系統識別號 U0026-2106201109525500
論文名稱(中文) 以創新擴散理論之觀點探討科技商品採用後之轉換行為
論文名稱(英文) Post-Adoption Switching Behavior of Technological Products: Diffusion-of-Innovation Perspective
校院名稱 成功大學
系所名稱(中) 企業管理學系碩博士班
系所名稱(英) Department of Business Administration
學年度 99
學期 2
出版年 100
研究生(中文) 蔡鈞鋒
研究生(英文) Jiun-Feng Tsai
學號 r46981320
學位類別 碩士
語文別 英文
論文頁數 66頁
口試委員 指導教授-張心馨
口試委員-陳素雯
口試委員-林清河
中文關鍵字 創新擴散  採用後  轉換行為  習慣 
英文關鍵字 Diffusion of innovation (DOI)  Post-adoption  Switching behavior  Habit 
學科別分類
中文摘要 使用者對於科技商品採用後行為之研究在資訊系統(IS)領域上已日漸增加。然而,過去研究者對於採用後行為大多數局限於探討科技商品的持續使用而非轉換使用。因此,本研究以創新擴散理論 (Diffusion of Innovation, DOI)之觀點來探討當使用者面臨近似於完全替代品時的轉換抉擇。對於過去有關持續使用的文獻中發現使用者之習慣會影響人們做出最終的行為決策,但在轉換行為中鮮少有學者將其納入考量。因此,本研究將「習慣」作為轉換意圖與轉換行為中的干擾變項,探討使用者習慣是否會對科技商品之轉換行為產生影響。本研究在網路上進行為期一個月的問卷調查,抽樣對象為曾經使用Mozilla Firefox 或 Google Chrome瀏覽器的使用者,有效問卷共283份。資料分析採用線性與二元羅吉斯回歸。分析結果顯示相對優勢與意見領袖對使用者的轉換意圖有正向的影響,轉換意圖與轉換行為間亦有正向的影響,而複雜度與使用者的轉換意圖有負向的影響。本研究發現在科技商品轉換行為中,使用者將「習慣」與「轉換意圖」視為同等重要的考量因素,因而造成使用者的「習慣」在轉換行為當中並未顯著產生干擾效果,而是對轉換行為與轉換意圖皆有正向影響。由此可知並建議提供科技商品的業者應該要專注於商品本身的相對優勢,為自己的商品建立良好口碑,並且試著教育使用者對所提供的科技商品產生習慣性,以避免顧客心生轉換念頭。
英文摘要 Recently, information systems (IS) researchers have started to focus on post-adoption behavior instead of initial adoption. However, few of them have investigated the issue of personal technology product switching behavior. This study considers technology switching behavior as a special form of post-adoption and adopts diffusion of innovation to establish a conceptual framework that discusses user’s decision making on the switching behavior for a total substitution. In addition, this study also purposed habit as a moderator between switching intention and switching behavior because of the lack of discussion on the topic in previous studies. A web-based survey was employed to collect empirical data during a month. 283 questionnaires were collected from users on their decision to switch from Microsoft Internet Explorer (IE) to Mozilla Firefox or Google Chrome Web browser, and linear and binary logistic regression methods were used for data analyses. Results show that relative advantage and opinion leader positively but complexity negatively influenced switching intention. Furthermore switching intention positively influenced switching behavior. Surprisingly, this study found that habit has a positive and direct effect both on switching intention and behavior instead of a moderating effect on the relationship between them, because users perceive habit as a factor that is as important as switching intention. In conclusion, this study suggests that IT vendors should concentrate on their product’s relative advantage, non-complexity usage, and should establish a good reputation and try to train their existing users to become habitual users in order to avoid them switching to substitutes.
論文目次 摘要 I
Abstract II
Contents III
List of Tables V
List of Figures VI
CHAPTER 1 1
INTRODUCTION 1
1.1 Research Background and Motivation 1
1.2 Research Objectives 3
CHAPTER 2 5
LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT 5
2.1. Diffusion of Innovation 6
2.2. Perceived Characteristics 10
2.3. Opinion leaders 13
2.4. Switching Intention and Switching Behavior 15
2.5. Habit 16
2.6. Hypotheses Development 19
2.6.1. Perceived Characteristic & Switching Intention 19
2.6.2. Opinion leader & Switching Intention 21
2.6.3. Switching Intention & Switching Behavior 22
2.6.4. Moderating effect: Habit 23
2.6.5. Control variables 25
CHAPTER 3 28
RESEARCH DESIGN AND METHODOLOGY 28
3.1. Research Framework 28
3.2. Measurement Developments 29
3.3. Pilot Test 32
3.3.1. Pilot Test Results 33
CHAPTER 4 35
RESULTS OF DATA ANALYSIS 35
4.1. Sample Demographics 35
4.2. Measurement Assessment 36
4.2.1. Confirmation Factor Analysis 37
4.2.2. Reliability and Validity 37
4.2.3. Measurement of Model Fit 41
4.3. Hypotheses Test 42
4.3.1 Multiple Regression 42
4.3.2 Binary Logistic Regression 43
CHAPTER 5 47
CONCLUSIONS AND RECOMMENDATIONS 47
5.1. Summary of Findings 47
5.2. Theoretical Implications 50
5.3. Managerial Implications 51
5.4. Limitations and Suggestions for Future Research 52
References 55
Appendix 63

參考文獻 Aarts, H., Paulussen, T., & Schaalma, H. (1997). Physical exercise habit: On the conceptualization formation of habitual health behaviours. Health Education Research, 12(3), 363-374.
Aarts, H., Verplanken, B., & Knippenberg, A. V. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology, 28(15), 1355-1374.
Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research 9(2), 204-215.
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In j. Kuhl, & j. Beckmann (eds.). Heidelberg Germany: Springer.
Ajzen, I. (1991). The theory of planned behavior. . Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Ajzen, I. (2002). Residual effects of past on later behavior: Habituation and reasoned action perspectives. Personality of Social Psychology Review, 6(2), 107-122.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.
Bansal, H. S., & Taylor, S. F. (1999). The service provider switching model (spsm): A model of consumer switching behavior in the services industry. Journal of Service Research, 2(2), 200-218.
Bansal, H. S., & Taylor, S. F. (2002). Investigating interactive effects in the theory of planned behavior in a service-provider switching context. Psychology and Marketing, 19(5), 407-425.
Bearden, W. O., Calcich, S. E., Netemeyer, R., & Teel, J. E. (1986). An exploratory investigation of consumer innovativeness and interpersonal influences. Advances in Consumer Research, 13, 77-82.
Bennis, W., & Goldsmith, J. (2003). Learning to lead: A workbook on becoming a leader (3th ed.). New York: Basic Books.
Bergeron, F., Raymond, L., Rivard, S., & Gara, M.-F. (1995). Determinants of eis use: Testing a behavioral model. Decision Support Systems, 14(2), 131-146.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370.
Bradford, M., & Florin, J. (2003). Examining the role of innovation diffusion factors on the implementation success of enterprise resource planning systems. International Journal of Accounting Information Systems, 4(3), 205-225.
Brown, S. A., & Venkatesh, V. (2005). A model of adoption of technology in the household: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399-426.
Burnham, T. A., Frels, J. K., & Mahajan, V. (2003). Consumer switching costs: A typology, antecedents, and consequences. Journal of the Academy of Marketing Science 31(2), 109-126.
Burnkrant, R. E., & Cousineau, A. (1975). Informational and normative social influence on buyer behavior. [Article]. Journal OF consumer Research, 2(3), 206-215.
Charng, H.-W., Piliavin, J. A., & Callero, P. L. (1988). Role identity and reasoned action in the prediction of repeated behavior. Social Psychology Quarterly, 51(4), 303-317.
Childers, T. L. (1986). Assessment of the psychometric properties of an opinion leadership scale. Journal of Marketing Research, 23(2), 184-188.
Chiu, C.-M., Hsu, M.-H., Lai, H., & Chang, C.-M. (2010). Exploring online repeat purchase intentions: The role of habit. Paper presented at the Pacific Asia Conference on Information Systems. http://aisel.aisnet.org/pacis2010/63
Choi, N. (2009). How loyal are you? Continuance intention and word of mouth in free/libre open source software. Paper presented at the Americas Conference on Information Systems. http://aisel.aisnet.org/amcis2009_dc/15
Choudhury, V., & Karahanna, E. (2008). The relative advantage of electronic channels: A multidimensional view. MIS Quarterly, 32(1), 179-200.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189 - 211
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.
Desouza, K. C., Awazu, Y., & Ramaprasad, A. (2007). Modifications and innovations to technology artifacts. Technovation, 27(4), 204-220.
Dwivedi, Y., & Irani, Z. (2009). Understanding the adopters and non-adopters of broadband. Communications of the ACM - Rural engineering development 52.
Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The mode model as an integrative framework. Advances in Experimental Social Psychology, 23, 75-109.
Festinger, L. (1957). A theory of cognitive dissonance. Stanford: Stanford University Press.
Fishbein, M., & Ajzen, I. (1980). Understanding attitudes and predicting social behavior. New York: Prentice-Hall.
Fiske, S. T., & Taylor, S. E. (1991). Social cognition (International edition ed.). New York: McGraw-Hill.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. journal of Marketing Research, 18(1), 39-50.
Ganesh, J., Arnold, M. J., & Reynolds, K. E. (2000). Understanding the customer base of service providers: An examination of the differences between switchers and stayers. Journal of Marketing, 64(3), 65-87.
Gefen, D. (2003). Tam or just plain habit: A look at experienced online shoppers. Journal of End User Computing, 15(3), 1-13.
Goldenberg, J., Han, S., Lehmann, D. R., & Hong, J. W. (2009 ). The role of hubs in the adoption process. Journal of Marketing, 73(2), 1-13.
Grewal, R., Mehta, R., & Kardes, F. R. (2000). The role of the social-identity function of attitudes in consumer innovativeness and opinion leadership. Journal of Economic Psychology, 21(3), 233-252.
Griffith, T. L. (1999). Technology features as triggers for sensemaking. Academy of Management Review, 24(3), 472-488
Guinea, A. O. d., & Markus, M. L. (2009). Why break the habit of a lifetime? Rethinking the roles of intention, habit, and emotion in continuing information technology use. MIS Quarterly, 33(3), 433-444.
Hackbarth, G., Grover, V., & Yi, M. Y. (2003). Computer playfulness and anxiety: Positive and negative mediators of the system experience effect on perceived ease of use Information & Management, 40(3), 221-232
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2006). Multivariate data analysis (7th ed.). London: Pearson: A Global Perspective.
Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440-465.
Hebert, M., & Benbasat, I. (1994). Adopting information technology in hospitals: The relationship between attitudes/expectations and behavior. Hospital & Health Services Administration, 39(3), 369-383.
Hu, P. J.-H., Lin, C., Chen, H., & (2005). User acceptance of intelligence and security informatics technology: A study of coplink. Journal of the American Society for Information Science and Technology 56(3), 235-244.
Jasperson, J., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems. MIS Quarterly 29(3), 525-557
Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 30(4), 781-804.
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.
Katz, E., & Lazarsfeld, P. F. (2006). Personal influence; the part played by people in the flow of mass communications. New Jersey: Transaction Publishers
Keaveney, S. M., & Parthasarathy, M. (2001). Customer switching behavior in online services: An exploratory study of the role of selected attitudinal, behavioral, and demographic factors. Journal of the Academy of Marketing Science 29(4), 374-390.
Keleman, H. C. (1961). Process of opinion change. Public Opinion Quarterly, 25(1), 57-78.
Khalifa, M., & Liu, V. (2007). Online consumer retention: Contingent effects of online shopping habit and online shopping experience. European Journal of Information Systems, 16(6), 780-792.
Kim, G., Shin, B., & Lee, H. G. (2006). A study of factors that affect user intentions toward email service switching. Information & Management, 43(7), 884-893. doi: DOI: 10.1016/j.im.2006.08.004
Kim, M.-K., Park, M.-C., & Jeong, D.-H. (2004). The effects of customer satisfaction and switching barrier on customer loyalty in korean mobile telecommunication services. Telecommunications Policy, 28(2), 145-159.
Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Two competing perspectives on automatic use: A theoretical and empirical comparison. [Article]. Information Systems Research, 16(4), 418-432. doi: 10.1287/isre.1050.0070
Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2006). Two competing perspectives on automatic use: A theoretical and empirical comparison. Information System Research, 16(4), 418-432.
Kim, S. S., & Son, J.-Y. (2009). Out of dedication or constraint? A dual model of post-adoption phenomena and its empirical test in the context of online services. MIS Quarterly, 33(1), 49-70.
Kouzes, J. M., & Posner, B. Z. (2008). The leadership challenge (4th ed.). San Francisco: Jossey-Bass.
Krause, D. E. (2004). Influence-based leadership as a determination of the inclination to innovate and of innovation-related behaviors: An empirical investigation. Leadership Quarterly, 15(1), 79-103.
Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2007). Understanding user acceptance of multimedia messaging services: An empirical study. Journal of the American Society for Information Science and Technology 58(13), 2066-2077.
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.
Liao, C., Palvia, P., & Lin, H.-N. (2006). The roles of habit and web site quality in e-commerce. International Journal of Information Management, 26(6), 469-483
Limayem, M., & Cheung, C. M. K. (2008). Understanding information systems continuance: The case of internet-based learning technologies Information & Management, 45(4), 227-232.
Limayem, M., Cheung, C. M. K., & Chan, G. W. W. (2003). Explaining information systems adoption and post-adoption: Toward an integrative model. Paper presented at the International Conference on Information Systems. http://aisel.aisnet.org/icis2003/59
Limayem, M., & Hirt, S. G. (2003). Force of habit and information systems usage: Theory and initial validation. Journal of the Association for Information Systems, 4(1), 65-97.
Limayem, M., Hirt, S. G., & 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.
Lindbladh, E., & Lyttkens, C. H. (2002). Habit versus choice: The process of decision-making in health-related behaviour. Social Science & Medicine, 55(3), 451-465.
Locock, L., Dopson, S., Chambers, D., & Gabbay, J. (2001). Understanding the role of opinion leaders in improving clinical effectiveness. Social Science and Medicine, 53(6), 745–757.
Mantymaki, M., & Merikivi, J. (2010). Investigating the drivers of the continuous use of social virtual worlds. Paper presented at the Hawaii International Conference on System Sciences.
Marsan, C. D. (2009). The web browser turns 15: A look back, from http://www.pcworld.com/article/173288/
Mittal, B. (1998). Achieving higher seat belt usage: The role of habit in bridging the attitude-behavior gap. Journal of Applied Social Psychology, 18(12), 993-1016.
Mittman, B., Tonesk, X., & Jacobson, P. (1992). Implementing clinical practice guidelines: Social influence strategies and practitioner behavior change. Quality Review Bulletin, 18(12), 413-422.
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.
Mozilla. (2011). History of the mozilla project, from http://www.mozilla.org/about/history.html
Nelson, R. R., & Cheney, P. H. (1987). Training end users: An exploratory study. MIS Quarterly, 11(4), 547-559.
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill
Orbell, S., Blair, C., Sherlock, K., & Conner, M. (2001). The theory of planned behavior and ecstasy use: Roles for habit and perceived control over taking versus obtaining substances. Journal of Applied Social Psychology, 31(1), 31-47.
Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124(1), 54-74.
Parthasarathy, M., & Bhattacherjee, A. (1998). Understanding post-adoption behavior in the context of online services. Information Systems Research, 9(4), 362–379.
Peres, R., Muller, E., & Mahajan, V. (2010). Innovation diffusion and new product growth models: A critical review and research directions. International Journal of Research in Marketing, 27(2), 91-106. doi: 10.1016/j.ijresmar.2009.12.012
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet research. Internet Research, 14(3), 224-235.
Rashotte, L. (2007). Social influence. Malden: Blackwell Publishing.
Robinson, L. (2009, Jan). A summary of diffusion of innovations
Roch, C. H. (2005). The dual roots of opinion leadership. The Journal of Politics 67(1), 110-131.
Rogers, E. M. (1962). Diffusion of innovations (1th ed.). New York: Free Press.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.
Ronis, D. L., Yates, J. F., & Kirscht, J. P. (1989). Attitudes, decisions, and habits as determinants of repeated behavior,” in attitude, structure and function. Hilldale, NJ,: Lawrence Erlbaum Associates.
Saga, V. L., & Zmud, R. W. (1994). The nature and determinants of it acceptance, routinization, and infusion. Paper presented at the Diffusion, Transfer and Implementation of Information Technology, Amsterdam.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103.
Schwarz, A., Junglas, I. A., Krotov, V., & Chin, W. W. (2004). Exploring the role of experience and compatibility in using mobile technologies. Information Systems and E-Business Management 2(4), 337-365.
Scott, J. E., & Walczak, S. (2009). Cognitive engagement with a multimedia erp training tool: Assessing computer self-efficacy and technology acceptance. Information & Management, 46(4), 221-232.
Shen, C. C., & Chiou, J. S. (2010). The impact of perceived ease of use on internet service adoption: The moderating effects of temporal distance and perceived risk. Computers in Human Behavior, 26(1), 42-50.
Sun, T., Youn, S., Wu, G., & Kuntaraporn, M. (2006). Online word-of-mouth (or mouse): An exploration of its antecedents and consequences. . Journal of Computer-Mediated Communication, 11(4).
Sweeney, J. C. & Soutar, G. H. (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77(2), 203-220.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.
Thompson, G. N., Estabrooks, C. A., & Degner, L. F. (2006). Clarifying the concepts in knowledge transfer: A literature review. Integrative Literature Reviews And Meta-Analyses, 53(6), 691-701.
Thong, J. Y. L., Hong, W., & Tam, K. Y. (2004). What leads to user acceptance of digital libraries? Communications of the ACM, 47(11), 79-83.
Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28-45.
Triandis, H. C. (1977). Interpersonal behavior. Monterey: Brooks Cole.
Tscherning, H., & Mathiassen, L. (2010). The role of social networks in early adoption of mobile devices. Human Benefit through the Diffusion of Information Systems Design Science Research, 318, 52-70.
Tyre, M. J., & Orlikowski, W. J. (1994). Windows of opportunity: Temporal patterns of technological adaptation in organizations. Organization Science, 5(1), 98-118.
Van Slyke, C., Lou, H., & Day, J. (2002). The impact of perceived innovation characteristics on intention to use groupware. Information Resources Management Journal., 15(1), 5-12.
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.
Verplanken, B. (2006). Beyond frequency: Habit as mental construct. British Journal of Social Psychology, 45(3), 639-656.
Verplanken, B., & Aarts, H. (1999). Habit, attitude, and planned behaviour: Is habit an empty construct or an interesting case of goal-directed automaticity? (Vol. 10). London.
Verplanken, B., & Orbell, S. (2003). Reflections on past behavior: A self-report index of habit strength1. Journal of Applied Social Psychology and Marketing, 33(6), 1313-1330.
Vries, M. K. d. (2003). Doing an alexander: Lessons on leadership by a master conqueror. European Management Journal, 21(3), 370-376.
Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4), 441-459.
Wood, W., & Neal, D. T. (2007). A new look at habits and the interface between habits and goals. Psychological Review 114(4), 843-863.
Wood, W., Quinn, J. M., & Kashy, D. A. (2002). Habits in everyday life: Thought, emotion, and action. Journal of Personality and Social Psychology, 83(6), 1281-1297.
Wu, J. H., Chen, Y. C., & Lin, L. M. (2007). Empirical evaluation of the revised end user computing acceptance model. Computers in Human Behavior, 23(1), 162-174.
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.
Wu, M. C., & Kuo, F. Y. (2008). An empirical investigation of habitual usage and past usage on technology acceptance evaluations and continuance intention. The DATA BASE for Advances in Information Systems, 39(4), 48-73.
Ye, C., & Potter, R. (2007). The role of habit in post-adoption switching of personal information technologies: A push, pull and mooring model. Paper presented at the Diffusion Interest Group In Information Technology.
Ye, C., Seo, D., Desouza, K. C., Sangareddy, S. P., & Jha, S. (2008). Influences of it substitutes and user experience on post-adoption user switching: An empirical investigation. Journal of the American Society for Information Science & Technology, 59(13), 2115-2132.
Zhang, K. Z. K., Lee, M. K. O., Cheung, C. M. K., & Chen, H. (2009). Understanding the role of gender in bloggers' switching behavior. Decision Support Systems 47(4), 540-546.
Zhu, K., Dong, S., Xu, S. X., & Kraemer, K. L. (2006). Innovation diffusion in global contexts: Determinants of post-adoption digital transformation of european companies. European Journal of Information Systems, 15(6), 601-616.
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