研究生: |
林怡凡 |
---|---|
論文名稱: |
基於決策實驗室之網路流程法預測科技產品接受模式 Predictions of the Acceptance Model of Technology Products by Using the DEMATEL based Network Process |
指導教授: |
黃啟祐
Huang, Chi-Yo |
學位類別: |
碩士 Master |
系所名稱: |
科技應用與人力資源發展學系 Department of Technology Application and Human Resource Development |
論文出版年: | 2011 |
畢業學年度: | 99 |
語文別: | 英文 |
論文頁數: | 110 |
中文關鍵詞: | 智慧型手機 、先驅使用者(LUM) 、作業系統 、科技接受模式(TAM) 、使用者接受率 、決策實驗室分析法(DEMATEL) 、決策研究室分析法之網路流程(DNP) 、結構方程模型(SEM) 、多準則決策分析(MCDM) |
英文關鍵詞: | Smart Phone, Operating System, Technology Acceptance Model (TAM), Decision Making Trial and Evaluation Laboratory (DEMATEL), Lead User Method (LUM), Structural Equation Modeling (SEM), DEMATEL Network Process (DNP), Multiple Criteria Decision Making (MCDM) |
論文種類: | 學術論文 |
相關次數: | 點閱:192 下載:76 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
智慧型手機近年來於市場中嶄露頭角,並躍升為最受歡迎的消費電子產品之一,因此,分析和預測消費者對智慧型手機的購買行為並滿足消費者的需求,已成為高科技產業中行銷經理人的重要任務。然而,由於消費性電子產品的技術發展快速,使得預測更加困難。在智慧型手機市場中,主要的市場領導者包含Apple,宏達電,諾基亞,RIM…等。這些市場領導者於同一市場區隔中提供類似的產品使得競爭環境更為激烈。此外,作業系統為影響消費者購買智慧型手機的主要考量,未來也將由一至兩種作業系統瓜分市場。因此,預測消費者對智慧型手機與作業系統的接受率成為近年來重要且困難的工作。為了準確分析影響消費者接受智慧型手機之因素與接受率,本研究將以基於先驅使用者法的科技接受模式(Technology Acceptance Model, TAM)為基礎進行分析與預測,首先以並比較先驅使用者(Lead User)與一般大眾需求的差異。本研究將以決策實驗室網路流程法(DEMATEL-based Network Process, DNP)與結構方程模型(Structural Equation Model, SEM)分析先驅使用者與一般大眾之意見。首先,藉由文獻探討蒐集影響消費者之準則,並透過修正式德菲法取得專家之意見,再以決策研究室分析法之網路流程(SEM)預測影響先驅使用者對科技產品接受率之因子,透過決策研究室分析法(DEMATEL)導出要素間的因果關係與架構,並利用決策研究室分析法之網路流程(DNP)計算出準則要素的權重。同時,本研究亦將導入結構方程模式(SEM)分析影響一般大眾接受科技產品時其考量因素之路徑係數與因素負荷量,加以分析比較先驅使用者與一般大眾間之差異,最後,並比較兩大作業系統(Android與iOS)使用者之差異。本研究將以智慧型手機為實證分析驗證決策實驗室網路流程法(DNP)與結構方程模型(SEM)之可行性。本研究之結果指出一般大眾(包含Android與iOS)與使用iOS之先驅使用者皆將知覺易用性視為重要準則,使用Android之先驅使用者則將知覺有用性視為重要準則,同時,Android之使用者較能代表先驅使用者之偏好。本研究之研究方法可做為高科技產業行銷經理人擬訂策略之基準,並可用於分析預測其他高科技產品之消費者行為。
The Smart Phone emerged recently as one of the most popular consumer electronics devices. Consequently, analyzing and predicting the consumer purchasing behaviors of Smart Phones for fulfilling customers‟ needs has become an indispensable task for marketing managers of IT (information technology) firms. However, the predictions are not easy. The consumer electronics technology evolved rapidly. Market leaders including Apple, HTC, Nokia, RIM, Samsung etc. are also competing in the same segmentation by providing similar products which further complicated the competitive situation. Besides, the key feature of Smart Phones is the operating system, the market ultimately settling on one or two dominant systems. Consequently, how the consumers‟ acceptance of novel Smart Phones and the operating system can be analyzed and predicted have become an important but difficult task. In order to accurately analyze the factors influencing consumers‟ acceptance of Smart Phones and predict the consumer behavior, the Technology Acceptance Model (TAM) and the Lead User Method will be introduced. Further, the differences in the factors being recognized by both lead users as well as mass users will be compared. Afterwards, the differences between Android and iOS users will also be compared. The possible customers‟ needs will first be collected and summarized by reviewing literature on the TAM. Then, the causal relationship
iii
between the factors influencing the consumer behaviors being recognized by both the lead users as well as the mass customers will be derived by the DEMATEL based network process (DNP) and the Structural Equation Modeling (SEM) respectively. An empirical study based on the Taiwanese Smart Phone users will be leveraged for comparing the results being derived by the DNP and the SEM. By and large, the empirical indicate that both of iOS lead users and mass users (including Android and iOS) regard the ease of use as an important factor. Contrarily, the Android lead users emphasize on usefulness, it‟s also could be the representative of lead users. The research results can serve as a basis for IT marketing managers‟ strategy definitions. The proposed methodology can be used for analyzing and predicting customers‟ preferences and acceptances of high technology products in the future.
Agarwal, R. & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28 (3), 557–582.
Agarwal, R. &Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies. Decision Sciences, 30 (2), 361–391.
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Actions-control: From cognition to behavior, Heidelberg, 11-39.
Ajzen, I. (Ed.) (1987). Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology. Advances in experimental social psychology. In L. Berkowitz, San Diego, CA: Academic Press, 20, 1-63.
Ajzen, I. (1989). Attitudes, personality, and behavior. Milton Keynes: Open University Press.
Ajzen, I. (1991). The Theory of Planned Behavior. Organizational behavior and human decision processes, 50 (2), 179-211.
Ajzen, I., Andm & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, NJ.
Ajzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
Asher, H. B. (1976). Causal modeling. Berverly Hills, CA: Sage.
Bagozzi, R. P. (1981). Attitudes, intentions, and behavior: A test of some key hypotheses. Journal of Personality and Social Psychology, 41(4), 607-627.
Bagozzi, R. P. (1982). A Field Investigation of Causal Relations among Cognitions, Affect, Intentions and Behavior. Journal of Marketing Research, 19(11), 562-584.
Bajaj, A. & Nidumolu, S. R. (1998). A feedback model to understand information system usage. Information and Management, 33 (4), 213–224.
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.
Campbell, D. T. (Eds.) (1963). Social attitudes and other acquired behavioral dispositions. In S. Koch, Psychology: A study of a science, New York: McGraw-Hill, 94-172.
Carlsson, C., Hyvonen, K., Repo, P., 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.
Chau, P. Y. K. (1996). An empirical investigation on factors affecting the acceptance of case by systems developers. Information and Management, 30(6), 269–280.
Chismar, W. G. & Patton, S. W. (2003). Does the Extended Technology Acceptance Model Apply to Physicians. In 36th Hawaii International Congress on System Sciences (HICSS'03), IEEE Computer Society, Big Island, Hawaii.
Chiu, Y. J., Chen, H.C., Shyu, J.Z. & Tzeng, G.H. (2006). Marketing strategy based on customer behavior for the LCD-TV. International Journal of Management and Decision Making, 7 (2/3), 143-165.
Chuttur, M.Y. (2009). Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. Sprouts: Working Papers on Information Systems, 9(37). http://sprouts.aisnet.org/9-37.
Cohen, J., & Cohen, P. (1975). Applied multiple regression correlation analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technologies. MIS Quarterly, 13 (3), 319–340.
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions, and behavioral impacts. International Journal of Man Machine Studies, 38 (3), 475–487.
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.
Davis, F. D. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results, Doctoral dissertation, MIT Sloan School of Management.
Dishaw, M. T. & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information and Management, 36(1), 9–21.
Engel, J. F., Blackwell, R. D. & Miniard, P. W. (1995). Consumer Behavior, 10th ed., FortWorth, T.X.: Dryden Press.
Epstein, S. (1983). Aggregation and beyond: Some basic issues on the prediction of behavior, Journal of Personality, 51(3), 360-392.
Fishbein, M. & Ajzen, I. (1974). Attitudes toward objects as predictors of single and multiple behavioral criteria. Psychological Review, 81(1), 59-74.
Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research, Reading, MA: Addison-Wesley.
Franke, N., von Hippel, E. & Schreier, M. (2006). Finding Commercially Attractive User Innovations: A Test of Lead-User Theory. Journal of Product Innovation Management, 23, 301–315.
Reisinger, D (2011). Gartner: Android leads, Windows Phone lags in Q1. Gartner, Inc.. Retrieved from http://news.cnet.com/8301-13506_3-20064223-17.html.
Gefen, D. & Straub, D.W. (2007). Gender differences the perception and use of E-mail: An extension to the Technology Acceptance Model. MIT Quarterly, 21(4), 389–400.
Guo, C., Wang, H. J. & Zhu, W. (2004). Smart-phone attacks and defenses. In Proceedings of HotNets III.
Hauser, J. R. & Shugan, S. M. (1980). Intensity Measures of Consumer Preference. Operational Research, 28(2), 279-320.
Herstatt, C. & von Hippel E. (1992). From experience: Developing new product concepts via the lead user method: A case study in a low tech field. Journal of Product Innovation Management, 9 (3), 213-221.
Hori, S. & Shimizu, Y. (1999). Designing methods of human interface for supervisory control systems. Control Engineering Practice, 7(11), 1413-1419.
Houston, S. R., & Bolding, J. T., Jr. (1974). Part, partial, and multiple correlation in commonality analysis of multiple regression models. Multiple Linear Regression Viewpoints, 5, 36-40.
Hsu, C. Y., Chen, K.T. & Tzeng, G.H. (2007). FMCDM with Fuzzy DEMATEL Approach for Customers’ Choice Behavior Model. International Journal of Fuzzy Systems, 9(4), 236-246.
Huang, C. Y. & Tzeng, G. H. (2007). Reconfiguring the Innovation Policy Portfolios for Taiwan's SIP Mall Industry. Technovation, 27 (12), 744-765.
Hu, L.T. & Bentler, P.M. (1999). Cut off criteria for fit indexes in covariance. Structural equation modeling, 6(1), 1-55.
Jackson, C. M., Chow, S. & Leitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision Sciences, 28 (2), 357–389.
Jurgen, B.&Julius, K. (1985). Action-control: From cognition to behavior, Heidelberg: Springer, 11-39.
Kamins, M. A., Alpert, F. & Perner, L. (2007). How do consumers know which brand is the market leader or market pioneer? Consumers inferential processes, confidence and accuracy. Journal of Marketing Management, 7(8), 590-611.
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.
Karen, M. G. & Susan, L. N. (2003). Consumer Behavior Applications to Real Estate Education. Journal of Real Estate Practice and Education, 6(1), 63-83.
Keil, M., Beranek, P.M. & Konsynski, B.R. (1995). Usefulness and ease of use: a field study evidence regarding task considerations. Decision Support Systems, 1375–1391.
Kim, J., & Mueller, C. W. (1978). Introduction to factor analysis: What it is and how to do it. Beverly Hills, CA: Sage.
Larcker, D. F. & Lessig, V. P. (1997). Perceived Usefulness of Information: A Psychometric Examination. Decision Science, 21(1), 389-400.
Lee, Y. T., Wu, W. W., & Tzeng, G. H. (2006). Combining the DEMATEL with the ANP and ZOGP for selecting IT projects. Information Science, 3(2), 175-189.
Legris, P., Ingham, J. & Collerette, P. (2003). Why do people use information technology? a critical review of the acceptance model. Information and Management, 40 (3), 191-204.
Lilien, G., Morrison, P. D., Searls, K., Sonnack, M. & von Hippel, E. (2002). Performance Assessment of the Lead User Generation Process for New Product Development. Management Science, 48 (8), 1042-1059.
Liou, J. J. H., Tzeng, G. H. & 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.
Lucas, H.C. & Spitler, V.K. (1999). Technology use and performance: a field study of broker workstations. Decisions Sciences, 30 (2), 291–311.
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.
Mathieson, K. (1999). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2 (3), 173–191.
Olson, E. L. & Bakke G. (2001). Implementing the Lead User Method in a High Technology Firm: A Longitudinal Study of Intentions versus Actions. Journal of Product Innovation Management, 18 (2), 388-395.
Open handset alliance. (2007, 11). Open handset alliance. Retrieved from http://www.openhandsetalliance.com/oha_members.html
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.
Pedhazur, E. J. (1982). Multiple regression in behavioral research: Explanation and prediction (2nd ed,). New York: Holt, Rinehart & Winston.
Rogers, E.M. & Shoemaker, F. F. (1971). Communication of Innovations: A Cross-Cultural Approach, Free Press, New York.
Rogers, E. M. (1962). Diffusion of innovation. Free Press, New York.
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. & Lomax, R. G. (1996). A Beginner’s Guide to Structural Equation Modeling, Mahwah, N.J.: Lawrence Erlbaum Associates, Publishers.
Sherman, S. J. & Fazio, R. H. (1983). Parallels between attitudes and traits as predictors of behavior. Journal of Personality, 51(3), 308-345.
Specht, D. A. (1975), On the evaluation of causal models. Social Science Research, 4(2), 113-133. SPSSX users’ guide (3rd ed,). New York: McGraw-Hill.
Swanson, E. B. (1987). Information Channel Disposition and Use. Decision Science, 18(1), 131-145.
Subramanian, G. H. (1994). A replication of perceived usefulness and perceived ease of use measurement. Decision Sciences, 25 (6), 863–874.
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42 (1), 85–92.
Taylor, S. & Todd, P. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6 (2), 144–176.
Taylor, S. & Todd, P. (1995). Assessing IT usage: the role of prior experience. MIS Quarterly, 19(4), 561–570.
Tamura, H., Akazawa, K. & Nagata, H. (2002). Structural modeling of uneasy factors for creating safe, secure and reliable society. SICE System Integration Division Annual Conference, 330-340.
Tamura, M., Nagata, H. & Akazawa, K. (2002). Extraction and systems analysis of factors that prevent safety and security by structural models. 41st SICE annual conference, Osaka, Japan, 3, 1752-1759.
Taylor, S. & Todd, P. A. (1995). Assesing IT Usage: the Role of Prior Experience. MIT Quarterly, 19(4), 561-570.
Triandis, H. C. (1977). Interpersonal behavior, Monterey, CA: Brooks/Cole.
Tzeng, G. H., Chiang, C.H. & 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. L. & J. R. Hauser (1980). Design and Marketing of new Products, Prentice-Hall, Englewood Cliffs N.J.
Urban, G. & von Hippel, E. (1988). Lead User Analyses for the Development of New Industrial Products. Management Science, 34 (5), 569-582.
Vaughan-Nichols, S.J. (2003). OSs battle in the smart-phone market. Computer, 36(6), 10-12.
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. & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences, 27 (3), 451–481.
Venkatesh, V. & 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. & Davis, F. (2003). User Acceptance of Information Technology: towards a Unified View, MIS Quarterly, 27(3), 479-501.
Venkatesh, V. & Morris, M. G. (2000). Why do not 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.
von Hippel, E. (1986). Lead User: A source of novel product concepts. Management Science, 32(7), 791- 805.
Wei, P. L., Huang, J. H., Tzeng, G. H. & Wu, S. I. (2010). Causal modeling of Web-Advertising Effects by improving SEM based on DEMATEL technique. Information Technology & Decision Making, 9(5), 799-829.
Williams, J. D. (1974). Path analysis and causal models as regression techniques. Multiple Linear Regression Viewpoints, 5(3), 1-20.
Wolfle, L. M. (1977). An introduction to path analysis. Multiple Linear Regression Viewpoints, 8(1), 36-61.
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.
Yamazaki, M., Ishibe, K. & Yamashita S. (1997). An analysis of obstructive factors to welfare service using DEMATEL method. Reports of the Faculty of Engineering, 48, 25-307.
References
Agarwal, R. & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28 (3), 557–582.
Agarwal, R. &Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies. Decision Sciences, 30 (2), 361–391.
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Actions-control: From cognition to behavior, Heidelberg, 11-39.
Ajzen, I. (Ed.) (1987). Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology. Advances in experimental social psychology. In L. Berkowitz, San Diego, CA: Academic Press, 20, 1-63.
Ajzen, I. (1989). Attitudes, personality, and behavior. Milton Keynes: Open University Press.
Ajzen, I. (1991). The Theory of Planned Behavior. Organizational behavior and human decision processes, 50 (2), 179-211.
Ajzen, I., Andm & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, NJ.
Ajzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
Asher, H. B. (1976). Causal modeling. Berverly Hills, CA: Sage.
Bagozzi, R. P. (1981). Attitudes, intentions, and behavior: A test of some key hypotheses. Journal of Personality and Social Psychology, 41(4), 607-627.
Bagozzi, R. P. (1982). A Field Investigation of Causal Relations among Cognitions, Affect, Intentions and Behavior. Journal of Marketing Research, 19(11), 562-584.
Bajaj, A. & Nidumolu, S. R. (1998). A feedback model to understand information system usage. Information and Management, 33 (4), 213–224.
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.
Campbell, D. T. (Eds.) (1963). Social attitudes and other acquired behavioral dispositions. In S. Koch, Psychology: A study of a science, New York: McGraw-Hill, 94-172.
Carlsson, C., Hyvonen, K., Repo, P., 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.
Chau, P. Y. K. (1996). An empirical investigation on factors affecting the acceptance of case by systems developers. Information and Management, 30(6), 269–280.
Chismar, W. G. & Patton, S. W. (2003). Does the Extended Technology Acceptance Model Apply to Physicians. In 36th Hawaii International Congress on System Sciences (HICSS'03), IEEE Computer Society, Big Island, Hawaii.
Chiu, Y. J., Chen, H.C., Shyu, J.Z. & Tzeng, G.H. (2006). Marketing strategy based on customer behavior for the LCD-TV. International Journal of Management and Decision Making, 7 (2/3), 143-165.
Chuttur, M.Y. (2009). Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. Sprouts: Working Papers on Information Systems, 9(37). http://sprouts.aisnet.org/9-37.
Cohen, J., & Cohen, P. (1975). Applied multiple regression correlation analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technologies. MIS Quarterly, 13 (3), 319–340.
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions, and behavioral impacts. International Journal of Man Machine Studies, 38 (3), 475–487.
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.
Davis, F. D. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results, Doctoral dissertation, MIT Sloan School of Management.
Dishaw, M. T. & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information and Management, 36(1), 9–21.
Engel, J. F., Blackwell, R. D. & Miniard, P. W. (1995). Consumer Behavior, 10th ed., FortWorth, T.X.: Dryden Press.
Epstein, S. (1983). Aggregation and beyond: Some basic issues on the prediction of behavior, Journal of Personality, 51(3), 360-392.
Fishbein, M. & Ajzen, I. (1974). Attitudes toward objects as predictors of single and multiple behavioral criteria. Psychological Review, 81(1), 59-74.
Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research, Reading, MA: Addison-Wesley.
Franke, N., von Hippel, E. & Schreier, M. (2006). Finding Commercially Attractive User Innovations: A Test of Lead-User Theory. Journal of Product Innovation Management, 23, 301–315.
Reisinger, D (2011). Gartner: Android leads, Windows Phone lags in Q1. Gartner, Inc.. Retrieved from http://news.cnet.com/8301-13506_3-20064223-17.html.
Gefen, D. & Straub, D.W. (2007). Gender differences the perception and use of E-mail: An extension to the Technology Acceptance Model. MIT Quarterly, 21(4), 389–400.
Guo, C., Wang, H. J. & Zhu, W. (2004). Smart-phone attacks and defenses. In Proceedings of HotNets III.
Hauser, J. R. & Shugan, S. M. (1980). Intensity Measures of Consumer Preference. Operational Research, 28(2), 279-320.
Herstatt, C. & von Hippel E. (1992). From experience: Developing new product concepts via the lead user method: A case study in a low tech field. Journal of Product Innovation Management, 9 (3), 213-221.
Hori, S. & Shimizu, Y. (1999). Designing methods of human interface for supervisory control systems. Control Engineering Practice, 7(11), 1413-1419.
Houston, S. R., & Bolding, J. T., Jr. (1974). Part, partial, and multiple correlation in commonality analysis of multiple regression models. Multiple Linear Regression Viewpoints, 5, 36-40.
Hsu, C. Y., Chen, K.T. & Tzeng, G.H. (2007). FMCDM with Fuzzy DEMATEL Approach for Customers’ Choice Behavior Model. International Journal of Fuzzy Systems, 9(4), 236-246.
Huang, C. Y. & Tzeng, G. H. (2007). Reconfiguring the Innovation Policy Portfolios for Taiwan's SIP Mall Industry. Technovation, 27 (12), 744-765.
Hu, L.T. & Bentler, P.M. (1999). Cut off criteria for fit indexes in covariance. Structural equation modeling, 6(1), 1-55.
Jackson, C. M., Chow, S. & Leitch, R. A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision Sciences, 28 (2), 357–389.
Jurgen, B.&Julius, K. (1985). Action-control: From cognition to behavior, Heidelberg: Springer, 11-39.
Kamins, M. A., Alpert, F. & Perner, L. (2007). How do consumers know which brand is the market leader or market pioneer? Consumers inferential processes, confidence and accuracy. Journal of Marketing Management, 7(8), 590-611.
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.
Karen, M. G. & Susan, L. N. (2003). Consumer Behavior Applications to Real Estate Education. Journal of Real Estate Practice and Education, 6(1), 63-83.
Keil, M., Beranek, P.M. & Konsynski, B.R. (1995). Usefulness and ease of use: a field study evidence regarding task considerations. Decision Support Systems, 1375–1391.
Kim, J., & Mueller, C. W. (1978). Introduction to factor analysis: What it is and how to do it. Beverly Hills, CA: Sage.
Larcker, D. F. & Lessig, V. P. (1997). Perceived Usefulness of Information: A Psychometric Examination. Decision Science, 21(1), 389-400.
Lee, Y. T., Wu, W. W., & Tzeng, G. H. (2006). Combining the DEMATEL with the ANP and ZOGP for selecting IT projects. Information Science, 3(2), 175-189.
Legris, P., Ingham, J. & Collerette, P. (2003). Why do people use information technology? a critical review of the acceptance model. Information and Management, 40 (3), 191-204.
Lilien, G., Morrison, P. D., Searls, K., Sonnack, M. & von Hippel, E. (2002). Performance Assessment of the Lead User Generation Process for New Product Development. Management Science, 48 (8), 1042-1059.
Liou, J. J. H., Tzeng, G. H. & 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.
Lucas, H.C. & Spitler, V.K. (1999). Technology use and performance: a field study of broker workstations. Decisions Sciences, 30 (2), 291–311.
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.
Mathieson, K. (1999). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2 (3), 173–191.
Olson, E. L. & Bakke G. (2001). Implementing the Lead User Method in a High Technology Firm: A Longitudinal Study of Intentions versus Actions. Journal of Product Innovation Management, 18 (2), 388-395.
Open handset alliance. (2007, 11). Open handset alliance. Retrieved from http://www.openhandsetalliance.com/oha_members.html
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.
Pedhazur, E. J. (1982). Multiple regression in behavioral research: Explanation and prediction (2nd ed,). New York: Holt, Rinehart & Winston.
Rogers, E.M. & Shoemaker, F. F. (1971). Communication of Innovations: A Cross-Cultural Approach, Free Press, New York.
Rogers, E. M. (1962). Diffusion of innovation. Free Press, New York.
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. & Lomax, R. G. (1996). A Beginner’s Guide to Structural Equation Modeling, Mahwah, N.J.: Lawrence Erlbaum Associates, Publishers.
Sherman, S. J. & Fazio, R. H. (1983). Parallels between attitudes and traits as predictors of behavior. Journal of Personality, 51(3), 308-345.
Specht, D. A. (1975), On the evaluation of causal models. Social Science Research, 4(2), 113-133. SPSSX users’ guide (3rd ed,). New York: McGraw-Hill.
Swanson, E. B. (1987). Information Channel Disposition and Use. Decision Science, 18(1), 131-145.
Subramanian, G. H. (1994). A replication of perceived usefulness and perceived ease of use measurement. Decision Sciences, 25 (6), 863–874.
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42 (1), 85–92.
Taylor, S. & Todd, P. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6 (2), 144–176.
Taylor, S. & Todd, P. (1995). Assessing IT usage: the role of prior experience. MIS Quarterly, 19(4), 561–570.
Tamura, H., Akazawa, K. & Nagata, H. (2002). Structural modeling of uneasy factors for creating safe, secure and reliable society. SICE System Integration Division Annual Conference, 330-340.
Tamura, M., Nagata, H. & Akazawa, K. (2002). Extraction and systems analysis of factors that prevent safety and security by structural models. 41st SICE annual conference, Osaka, Japan, 3, 1752-1759.
Taylor, S. & Todd, P. A. (1995). Assesing IT Usage: the Role of Prior Experience. MIT Quarterly, 19(4), 561-570.
Triandis, H. C. (1977). Interpersonal behavior, Monterey, CA: Brooks/Cole.
Tzeng, G. H., Chiang, C.H. & 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. L. & J. R. Hauser (1980). Design and Marketing of new Products, Prentice-Hall, Englewood Cliffs N.J.
Urban, G. & von Hippel, E. (1988). Lead User Analyses for the Development of New Industrial Products. Management Science, 34 (5), 569-582.
Vaughan-Nichols, S.J. (2003). OSs battle in the smart-phone market. Computer, 36(6), 10-12.
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. & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences, 27 (3), 451–481.
Venkatesh, V. & 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. & Davis, F. (2003). User Acceptance of Information Technology: towards a Unified View, MIS Quarterly, 27(3), 479-501.
Venkatesh, V. & Morris, M. G. (2000). Why do not 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.
von Hippel, E. (1986). Lead User: A source of novel product concepts. Management Science, 32(7), 791- 805.
Wei, P. L., Huang, J. H., Tzeng, G. H. & Wu, S. I. (2010). Causal modeling of Web-Advertising Effects by improving SEM based on DEMATEL technique. Information Technology & Decision Making, 9(5), 799-829.
Williams, J. D. (1974). Path analysis and causal models as regression techniques. Multiple Linear Regression Viewpoints, 5(3), 1-20.
Wolfle, L. M. (1977). An introduction to path analysis. Multiple Linear Regression Viewpoints, 8(1), 36-61.
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.
Yamazaki, M., Ishibe, K. & Yamashita S. (1997). An analysis of obstructive factors to welfare service using DEMATEL method. Reports of the Faculty of Engineering, 48, 25-307.