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Author: 劉又維
YuWei Liu
Thesis Title: 利用決策實驗室網路流程法推導產品生命週期 各階段影響技術接受之因素
Derivations of Factors Influencing the Technology Acceptance Under Various Stages of a Product Life Cycle by Using DEMATEL-Based Analytic Network Process
Advisor: 黃啟祐
Huang, Chi-Yo
Degree: 碩士
Master
Department: 科技應用與人力資源發展學系
Department of Technology Application and Human Resource Development
Thesis Publication Year: 2012
Academic Year: 100
Language: 英文
Number of pages: 140
Keywords (in Chinese): 科技接受模式消費者行為預測決策實驗室分析法結構方程式產品生命週期先驅使用者
Keywords (in English): Technology Acceptance Model (TAM), Consumer Behavior Prediction, Decision Making Trial and Evaluation Laboratory (DEMATEL), Structural Equation Modeling (SEM), Product Life Cycle, Lead User Theory
Thesis Type: Academic thesis/ dissertation
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  • 從市場發展以及科技進步的角度來講,資訊產品扮演不可或缺的腳色。然而,預測資訊產品的科技接受因素一直是行銷以及設計人員的重大挑戰。更重要的是,在不同的產品生命週期裡產品的特色以及行銷策略也不同。故消費者的偏好以及需求也會在不同的生命週期裡有所改變。過去很少研究試著去探索在不同的產品生命週期的科技接受因素,但這個議題是重要的。因此,本論文利用模糊決策實驗室網路流程法調查先驅使用者,影響不同生命周期的科技接受因素; 再使用結構方程模型結合科技接受模式調查大眾消費者的意見。本論文將以智慧型電視作為導入期; 智慧型手機作為成長期; 筆記型電腦作為成熟期進行實證研究。最後,先驅使用者以及大眾消費者在不同產品生命週期的實證分析結果將會作比較。實證結果顯示從產業專家的角度而言,知覺有用性是最重要的準則;而大眾消費者最重視的卻是知覺易用性。本研究結果可供產業界的行銷以及研發人員,在未來的產品開發以及行銷策略上作為參考。

    Information technology (IT) products have played significant roles from both aspects of market development as well as technology growth. However, predicting technology acceptance toward the IT products is always challenging for marketers and designers. Also, the characteristics of and strategies for IT products at different stages of product life cycle are different. Therefore, the consumers’ preference and needs change rapidly at different stages in product life cycle. This research aims to propose a novel DEMATEL based approach for uncovering the factors influencing the technology acceptance of IT products in different stages in product life cycle based on lead users’ opinions. Further, the factors will also be summarized by consumers’ opinions by using the by using the structural equation modeling (SEM) method based Technology Acceptance Model (TAM) for serving as the basis of comparisons. An empirical study based on the smart TVs for the introduction stage; smart phones for the growth stage; the notebook computers for the maturity stage will be used for verifying the feasibility of this framework. Further, differences based on two analytic frameworks will be compared.

    中文摘要 i Abstract ii List of Tables iii List of Figures iv Chapter 1 Introduction 1 1.1 Research Background and Motivation 1 1.2 Research Objectives and Limitations 5 1.3 Research Methods and Framework 6 1.4 Research Process and Structure 7 Chapter 2 Literature Review 10 2.1 Theory of Reasoned Action (TRA) 10 2.2 Technology Acceptance Model (TAM) 12 2.3 The Extension of TAM Model 15 2.3.1 TAM2 15 2.3.2 E-TAM 17 2.3.3 UTAUT 18 2.3.4 TAM3 20 2.4 Lead User Theory 22 2.5 Product Life Cycle Theory 25 Chapter 3 Research Method 28 3.1 Structural Equation Modeling (SEM) 28 3.1.1 Multiple Regression 31 3.1.2 Path Analysis 33 3.1.3 Factor Analysis 36 3.1.4 Goodness of Fit Criteria 39 3.1.5 Computer Program and Software 43 3.2 Decision Making Trial and Evaluation Laboratory (DEMATEL) 45 3.3 Analytic Network Process (ANP) 50 3.4 DEMATEL based Network Process (DNP) Technique 56 Chapter 4 Empirical Study 61 4.1 Background 61 4.1.1 Smart TV 61 4.1.2 Tablet PC 62 4.1.3 Laptop PC 63 4.2 Acceptance Requirements Derivations 64 4.3 Empirical Study on Based Modified Delphi Method 67 4.4 Empirical Study on Lead User Based DNP and Mass User Based SEM Methods 69 4.4.1 Empirical Study on Smart TV 70 4.4.2 Empirical Study on Tablet PC 77 4.4.3 Empirical Study on Laptop 83 Chapter 5 Discussion 89 5.1 Practical Implication 89 5.1.1 Smart TV 89 5.1.2 Tablet PC 91 5.1.3 Laptop PC 93 5.2 Managerial Implication 95 5.2.1 Smart TV 96 5.2.2 Tablet PC 97 5.2.3 Laptop PC 99 Chapter 6 Conclusions 101 References: 103 Appendix A: Questionnaire of Lead Users for Smart TV 114 Appendix B: Questionnaire of Lead Users for Tablet PC 119 Appendix C: Questionnaire of Lead Users for Laptop PC 124 Appendix D: Questionnaire of Mass Users for Smart TV 129 Appendix E: Questionnaire of Mass Users for Tablet PC 133 Appendix F: Questionnaire of Mass Users for Laptop PC 137

    Ajzen, I. (1991). The Theory of Planned Behavior. Organizational behavior and human decision processes, 50(2), 179-211.
    Ajzen, I. and Fishbein, M. (1980). Understanding attitudes and predicting behavior. Englewood Cliffs, NJ: Prentice Hall.
    Boyle, R. P. (1970). Path analysis and ordinal data. American Journal of Sociology, 75 (4), 461-480.
    Brinberg, D. (1979). An Examination of the Determinants of Intention and Behavior: A Comparison of Two Models. Social Psychology, 9(6), 560-575.
    Carlsson, C., Hyvonen, K., Repo, P. and Walden, P. (2005). Asynchronous Adoption Patterns of Mobile Services. Proceedings of the 38th Hawaii International Conference on System Sciences (HICSS-38), Island of Hawaii, USA.
    Chen, Y. C., Lien, H. P. and Tzeng, G. H. (2010). Measures and Evaluation foe environment watershed plans using a novel hybrid MCDM model. Expert System with Applications, 37(2), 926-938.
    Chiu, W. Y., Tzeng, G. H. and Li, H. L. (2010). Advances in Intelligent Decision Technologies: Proceedings of the second KES International Symposium IDT 2010, Scientific Publishing Services Pvt. Ltd, Chennai, India.
    Cohen, J. and Cohen, P. (1975). Applied multiple regression correlation analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.
    Custer, R. L., Scarcella, J. A. and Stewart, B. R. (1999). The Modified Delphi Technique--A Rotational Modification, Journal of Vocational and Technical Education, 15(2), 50-58.
    Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical. Management Science, 35(8), 982-1003.
    Day, G. S. (1981). The product life cycle: analysis and applications issues. Journal of Marketing, 45(4), 60-67.
    Fell, D. R., Hansen, E. N. and Becker, B. W. (2003). Measuring innovativeness for the adoption of industrial products. Industrial Marketing Management, 32(4), 347-353.
    Fishbein, M. (1980). A theory of reasoned action: Some applications and implications. In H. Howe and M. Page (Eds.), Nebraska Symposium on Motivation, 27, 65–116. Lincoln, NE: University of Nebraska Press.
    Fishbein, M. and Ajzen, I. (1974). Attitudes toward objects as predictors of single and multiple behavioral criteria. Psychological Review, 81(1), 59-74.
    Fishbein, M. and Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
    Foxall, G. (1997). Marketing psychology: The paradigm in the wings. London: Macmillan.
    Franke N., von Hippel E. and Schreier M. (2006). Finding Commercially Attractive User Innovations: A Test of Lead-User Theory. Journal of Product Innovation Management,12(4), 301-315.
    Gabus, A. and Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL battelle institute. Geneva Research Centre, Geneva, Switzerland.
    Gort, M. and Kleepper, S. (1982). Time paths in the diffusion of product innovations. The Economic Journal. 92(367), 630-653.
    Guo, C., Wang, H. J. and Zhu, W. (2004). Smart-phone attacks and defenses. In Proceedings of HotNets III.
    Hauser, J. R. and Shugan, S. M. (1980). Intensity Measures of Consumer Preference. Operational Research, 28(2), 279-320.
    Hippel, E. V. (1986). Lead Users: A Source of Novel Product Concepts. Management Science, 32(7), 791-805.
    Hori, S. & Shimizu, Y. (1999). Designing methods of human interface for supervisory control systems. Control Engineering Practice, 7(11), 1413-1419.
    Houston, S. R. and Bolding, J. T. Jr. (1974). Part, partial, and multiple correlation in commonality analysis of multiple regression models. Multiple Linear Regression Viewpoints, 5, 36-40.
    Hu, L. T. and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance. Structural equation modeling, 6(1), 1-55.
    Huang, C. Y. and Tzeng, G. H. (2007). Reconfiguring the Innovation Policy Portfolios for Taiwan's SIP Mall Industry. Technovation, 27 (12), 744-765.
    Im, S., Bayus, B. L. and Mason, C. H. (2003). An Empirical Study of Innate Consumer Innovativeness, Personal Characteristics, and New-Product Adoption Behavior. Journal of the Academy of Marketing Science, 31, 61–73.
    Jurgen, B. and Julius K. (1985). Action-control: From cognition to behavior. 11-39. Heidelberg, Springer
    Kawakami, M. (2009). Growth of Taiwanese Notebook PC Manufacturers as Original Design Manufacturing Suppliers, China Information, 23(1), 103-128.
    Kim, J. and Mueller, C. W. (1978). Introduction to factor analysis: What it is and how to do it. Beverly Hills, CA: Sage.
    Kim, M. K. and Park, J. H. (2011). Demand forecasting and strategies for the successfully deployment of the smart TV in Korea, Advanced Communication Technology, 1475-1478.
    Kim, Y. J. and Sim, J. B. (2012). Acceptance-Diffusion Strategies for Tablet-PCs: Focused on Acceptance Factors of Non-Users and Satisfaction Factor of Users, ETRI Journal, 34(2), 245-255.
    Komninos, I. (2002). Product Life Cycle Management, Urban and Regional Innovation Research Unit, Thessaloniki.
    Larcker, D. F. and Lessig, V. P. (1997). Perceived Usefulness of Information: A Psychometric Examination. Decision Science, 21(1), 389-400.
    Legris, P., Ingham, J. and Collerette, P. (2003). Why do people use information technology? A critical review of the acceptance model. Information and Management, 40(3), 191-204.
    Lin, C. H., Shih, H. Y. and Sher, P. J. (2007). Integrating Technology Readiness into Technology Acceptance: The TRAM Model. Psychology and Marketing, 24(7), 642–657.
    Lin, C. L., Hsieh, M. S. and Tzeng G. H. (2010), Evaluating vehicle telematics system by using a novel MCDM techniques with dependence and feedback. Expert System with Application, 37(10), 6723–6736.
    Liou, J. J. H., Tzeng, G. H. and Chang, H. C. (2007). Airline safety measurement using a hybrid model. Journal of Air Transport Management, 13(4), 243-249.
    Long, J. S. (1983). Confirmatory factor analysis. Beverly Hills, CA: Sage.
    Lyons, M. (1971). Techniques for using ordinal measures in regression and path analysis. In H. L. Costner (Ed.), Sociological methodology. 147-171. San Francisco: Jossey-Bass.
    Meuter, M. L., Bitner M. J., Ostrom A. L. and 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.
    Mohr, J. (2001). Marketing of High-Technology Products and Innovations. Upper Saddle River, NJ: Prentice-Hall.
    Mohr, J. Sengupta, S. and Slater, S. (2010). Marketing of High-Technology Products and Innovations. Upper Saddle River, NJ: Prentice-Hall.
    Moore, G. A. (1999). Crossing the Chasm (revised edn), New York: HarperCollins Publishers.
    Olson, J. M. and Zanna, M. P. (1993). Attitudes and attitude change. Annual Review of Psychology, 44, 117–154.
    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.
    Patrick, M. 2011. Make Your TV Smarter. PC World, 29 (8), 58-65.
    Pedhazur, E. J. (1982). Multiple regression in behavioral research: Explanation and prediction (2nd ed), New York: Holt, Rinehart and Winston.
    Popper, E. T. and Buskirk, B. D. (1992). Technology life cycles in industrial markets. Industrial Marketing Management, 21(1), 23–31.
    Porter, C. E. and Donthu, N. (2006). Using the Technology Acceptance Model to Explain how Attitude Determine Internet Usage: The Role of Perceived Access Barriers and Demographics. Journal of Business Research, 59(6), 999-1000.
    Richard, J. H. and Ben-Tzion, K. (2010). The Technology Acceptance Model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172.
    Rijnsoever, F. J. and Oppewal, H. (2012). Predicting early adoption of successive video player generations, Technological Forecasting & Social Change, 79(3), 558-569.
    Rogers, E. M. (1995). Diffusion of Innovations (4th edn), New York: The Free Press.
    Rogers, E. M. (2003). Diffusion of Innovations, 5th ed. Free Press, New York.
    Ryan, C. and Riggs, W. E. (1996). Redefining the product life cycle: The five-element product wave. Business Horizons, 36(5), 33–39.
    Satty, T. L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill.
    Satty, T. L. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process. Pittsburgh: RWS Publication.
    Satty, T. L. (1999). Fundamentals of the Analytic Network Process. In Proceedings of International Symposium on Analytical Hierarchy Process, Japan, Kobe.
    Satty, T. L. (2005). Theory and Applications of the Analytic Network Process. Pittsburg, PA: RWS Publications.
    Schumacker, R. E. and Lomax, R. G. (1996). A Beginner’s Guide to Structural Equation Modeling, Mahwah, N.J.: Lawrence Erlbaum Associates, Publishers.
    Shun Y. L., Chiang, J. and Parasuraman A. (2008). The Effects of The Dimensions Of Technology Readiness On Technology Acceptance: An Empirical Analysis. Journal of Interactive Marketing, 22(4), 19-39.
    Specht, D. A. (1975). On the evaluation of causal models. Social Science Research, 4(2), 113-133.
    Swanson, E. B. (1987). Information Channel Disposition and Use. Decision Science, 18(1), 131-145.
    Tamura, H., Akazawa, K. and Nagata, H. (2002). Structural modeling of uneasy factors for creating safe, secure and reliable society. SICE System Integration Division Annual Conference, 330-340.
    Taylor, S. and Todd, P. (1995). Assessing IT usage: a test of competing models. Information System Research, 19(4), 144–76.
    Taylor, S. and Todd, P. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6 (2), 144–176.
    Triandis, H. C. (1977). Interpersonal behavior. Monterey, CA: Brooks/Cole.
    Tzeng, G. H., Chiang, C. H. and Li, C. W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028-1044.
    Urban, G. and von Hippel, E. (1988). Lead User Analyses for the Development of New Industrial Products. Management Science, 34 (5), 569-582.
    Utterback, J. M. (1994). Mastering the Dynamics on Innovation. Boston: Harvard Business School Press.
    Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation and Emotion into the Technology Acceptance Model. Information System Research, 11 (4), 342-365.
    Venkatesh, V. and Bala H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273-315.
    Venkatesh, V. and Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Models: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.
    Venkatesh, V., Morris, M., Davis, G. and Davis, F. (2003). User Acceptance of Information Technology: towards a Unified View. MIS Quarterly, 27(3), 479-501.
    Wei, P. L., Huang, J. H., Tzeng, G. H. and Wu, S. I. (2010). Causal modeling of Web-Advertising Effects vby improving SEM based on DEMATEL technique. Information Technology and Decision Making, 9(5), 799-829.
    Wolfle, L. M. (1977). An introduction to path analysis. Multiple Linear Regression Viewpoints, 8(1), 36-61.
    Wong, H. K. and Paul D. (2007). Ellis, Is market orientation affected by the product life cycle. Journal of World Business, 42(2), 145-156.
    Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557-585.
    Wright, S. (1934). The method of path coefficients. Annals Mathematical Statistics, 5(3), 161-215.
    Wright, S. (1960). Path coefficients and path regression: Alternative or complementary concepts. Biometrics, 16(2), 189-202.
    Yoo, C. S. (2010). Product Life Cycle Theory and the Maturation of the Internet. Northwestern University Law Review, 104(2).
    Yousafzai, S. Y., Foxall, G. R. and Palliste, J. G. (2010). Explaining Internet Banking Behavior: Theory of Reasoned Action, Theory of Planned Behavior, or Technology Acceptance Model. Journal of Applied Social Psychology, 40(5), 1172–1202.
    Yuan X., Shen L. and Ashayeri J. (2010). Dynamic simulation assessment of collaboration strategies to manage demand gap in high-tech product diffusion. Robotics and Computer-Integrated Manufacturing, 26(6), 647-657.

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