:::

詳目顯示

回上一頁
題名:應用模糊決策實驗室分析法於科技接受模式之分析
作者:李美蘭
作者(外文):Mei-Lan Li
校院名稱:中華大學
系所名稱:科技管理學系碩士班
指導教授:李友錚
黃廷合
學位類別:博士
出版日期:2010
主題關鍵詞:科技接受模式決策實驗室分析法模糊理論高科技產業產品生命週期管理系統Technology Acceptance Model, TAMDecision Making Trial and Evaluation Laboratory, DEMATELFuzzy TheoryHigh Technology IndustryProduct Life Cycle Management, PLM
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:35
傳統科技接受模式(Technology Acceptance Model, TAM)的研究多以因素分析或結構方程模式等方法建立及驗證變數之間的因果關係,可是有些科技系統專業度與複雜度高,並非所有的受測者都完整地瞭解科技系統,而且某些變數不一定符合獨立性的假設,加上當大量樣本取得困難時,便無法正確地分析,而導致錯誤的結論。本研究為解決上述問題與假設,創新採用模糊決策實驗室分析法(Fuzzy Decision Making Trial and Evaluation Laboratory, Fuzzy DEMATEL)驗證TAM變數之間的因果關係,其亦包含計算出變數之間的相互影響程度。此方法不受限於統計方法獨立性的假設與樣本數的限制,可避免變數之間因具因果關係或樣本蒐集困難時所導致的推論偏誤,另外,Fuzzy DEMATEL採用專家意見法,可避免受測者對科技系統不完全瞭解或無完整使用經驗的問題,此外,亦可解決傳統問卷調查沒有考量人類主觀認知判斷上所具有之模糊性,而造成錯誤的判斷。本研究藉由台灣光電產業之龍頭-友達光電應用產品生命週期管理系統(Product Lifecycle Management, PLM)的個案為例,說明本方法應用於TAM的效益,研究成果將提供管理者管理以及改善解決實務上複雜難解問題的參考。
本研究發現個案Fuzzy DEMATEL方法論所建構的TAM2 模式中,除補足傳統方法之不能計算變數之間的相互影響程度外,其變數相互影響關係與傳統TAM2模式變數相互影響關係之主要差異為,主觀規範(X5)並未對印象(X8)造成影響,經驗(X6)會直接影響認知有用性(X1)、使用意圖(X3);自願性(X7)亦會直接影響使用意圖(X3);以及使用意圖(X3)會影響認知有用性(X1)。
Traditional TAM studies establish and verify the model of causal relationship between variables by factor analysis or structural equation modeling. However, some technology is highly complicated, not all respondents have thorough comprehension. Certain variables are not compatible with assumption of independence, and causal relationship cannot be analyzed accurately if mass samplings are difficult to obtain, resulting in mistaken conclusions. The study establishes TAM through the DEMTEL method, which considers the influences of inconformity between variables. Respondents may completely understand the technology, but may not adequately express it through limitations of mass sampling. Score quantification through traditional investigation asks respondents to make a choice from limited wordings in order to stress maximum attribution without considering the fuzzy thinking of humans, resulting in an imprecise summary. This study adopts Fuzzy DEMATEL to calculate the causal relationship and level of mutual effect, building on the technology acceptance model by applying the PLM system, providing administrator references to improve promotion of new technology to solve complicated and difficult problems in practice. The example of product life cycle management adopted by the Taiwan optronics manufacturing industry is used to explain the application and effect of this theory. The research found that the influence is similar to the TAM2 model based on Fuzzy DEMATEL theory. The major difference is the subjective standard (X5) did not affect the impression (X8), while the experience (X6) directly affects the purpose of use (X1) and the purpose of use (X3) which also affects useful knowledge (X1),and voluntariness (X7) directly affects the purpose of use (X3).
Al-Najjar, B. & Alsyouf, I. (2003). Selecting the most efficient maintenance approach using Fuzzy multiple criteria decision making. International Journal of Production Economics, 84(1), 85-100.

Bajaj, A. & Midumolu, S. R. (1998). A feedback model to understand information system usage. Information and Management, 33(4), 213-224.

Davis, F. D. (1986). A technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Unpublished doctoral dissertation, MT Sloan School of Management.

Davis, F. D. (1989). Percieved usefulness, perceived ease of use, & user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

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. (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. & Venkatesh, V. A. (1996). Critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19-45.

Fishbein, M. & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: A Introduction to Theory and Research. MA: Addison-Wesley.

Fontela, E. & Gabus, A. (1976). The DEMATEL Observer (DEMATEL 1976 Report). Switzerland, Geneva: Battelle Geneva Research Center.

Gabus, A. & Fontela, E. (1973). Perceptions of the World Problematique: Communication Procedure, Communicating with Those Bearing Collective Responsibility (DEMATEL Report No. 1). Switzerland, Geneva: Battelle Geneva Research Center.

Gallego, M. D., Luna, P., & Bueno, S. (2008). User acceptance model of open source software. Computers in Human Behavior, 24(5), 2199-2216.

Hajime, Y., Kenichi, I., & Hajime, M. (2005). An innovative product development process for resolving fundamental conflicts. Journal of the Japan Society for Precision Engineering, 71(2), 216-222.

Hartwick, J. (1994). Explaining the role of user participation information system use. Management science, 40(4), 440-465.

He, D., Lu, Y., & Zhou, D. (2008). empirical study of consumers' purchase intentions in C2C electronic commerce. Tsinghua Science and Technology, 13(3), 287-292.

Hess, P. & Siciliano, J. (1996). Management: Responsibility for Performance. New York: McGraw-Hill.

Huang, C. Y., Shyu, J. Z., & Tzeng, G. H. (2007). Reconfiguring the innovation policy portfolios for Taiwan’s SIP Mall Industry. Technovation, 27(12), 744-765.

Igbaria, M. & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587-605.

Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 13(1), 127-144.

Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, 21(3), 279-305.

Keil, M., Beranik, P. M., & Konsynski, B. R. (1995). Usefullness and ease of use: field study evidence regarding task considerations. Decision Support Systems, 13(1), 75-91.

Kenichi, F. & Yoshihiro, N. (2002). Study on function and failure analysis of snow melting machines. Transactions of the Japan Society of Mechanical Engineers, 68, 3447-3455.

Kim, Y. H. (2002). Study on impact mechanism for beef cattle farming and importance of evaluating agricultural information in Korea using DEMATEL, PCA and AHP. Agricultural Information Research, 15(3), 267-280.

Kim, B. G., Park, S. C., & Lee, K. J. (2007). A structural equation modeling of the Internet acceptance in Korea. Electronic Commerce Research and Applications, 6(4), 425-432.

Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online customer behavior. Information Systems Research, 13(2), 205-223.

Kwahk, K. Y. & Lee, J. N. (2008). The role of readiness for change in ERP implementation: Theoretical bases and empirical validation. Information and Management, 45(7), 474-481.

Lee, Y. C., Yen, T. M., & Tsai, C. H. (2008). Using importance-performance analysis and decision making trial and evaluation laboratory to enhance order-winner criteria: A study of computer industry. Information Technology Journal, 7(3), 396-408.

Lee, Y. C., Hu, H. Y., Yen, T. M., & Tsai, C. H. (2008). Kano’s model and decision making trial and evaluation laboratory apply to order-winners and qualifiers improvement: A study of computer industry. Information Technology Journal, 7(5), 702-714.

Lee, Y. C., Li, M. L. ,Yen, T. M., & Huang, T. H. (2010). Analysis of adopting an integrated decision making trial and evaluation laboratory on a technology acceptance model. Expert System with Applications, 37(1), 1745-1754.

Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management, 40(3), 191-204.

Li, R. J. (1999). Fuzzy method in group decision making. Computers and Mathematics with Applications, 38(1), 91-101.

Lin, C. J. & Wu, W. W. (2004). A Fuzzy Extension Of The DEMATEL Method For Group Decision-Making. The 1th Annual Meeting and Conference of ORSTW, Taipei, R.O.C, December 11, 2004.

Lin, C. J. & Wu, W. W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert System with Applications, 34(1), 205-213.

Liou, J. J. H., Yen, L., & Tzeng, G. H. (2008). Building an effective safety management for airlines. Journal of Air Transort Management, 14, 20-26.

Moore, G. C. & Benbasat, I. (1991). Development of an instrumentto measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), .192-222

Nanayo, F. & Toshiaki, T. (2002). A new method of paired comparison by improved DEMATEL method: Application to the integrated evaluation of a medical information which has multiple factors. Japan Journal of Medical Informatics, 22(2), 211-216.

Opricovic, S. & Tzeng, G. H. (2003). Defuzzification within a multicriteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(5), 635-652.

Riemenschneider, C. K., Harrison, D. A., & Mykytn Jr., P. P. (2003). Understanding IT adoption decisions in small business: Integrating current theories. Information and Management, 40(4), 269-285.

Schepers, J & Wetzels, M. (2007). A meta-analysis of technology acceptance model: Investigating subjective norm and moderation effects. Information and Management, 44(1), 90-103.

Seyed-Hosseini, S. M., Safaei, N., & Asgharpour, M. J. (2006). Reprioritization of failures in a system failure mode and effects analysis by decision making trial and evaluation laboratory technique. Reliability Engineering and System Safety, 91(8), 872-881.

Shin, D. H. (2008). Understanding purchasing behaviors in a virtual economy: Consumer behavior involving virtual currency in Web 2.0 communities. Interacting with Computers, 20(4/5), 433-446.

Subramanian, G. H. (1994). A replication of perceived usefulness and perceived ease of use measurement. Decision Science, 25(5/6), 863-874.

Szajna, B. (1996). Empirical evaluation of revised technology acceptance model. Management Science, 42(1), 85-92.

Tamura, H., Okanishi, H., & Akazawa, K. (2006). Decision support for extracting and dissolving consumers’ uneasiness over foods using stochastic DEMATEL. Journal of Telecommunications and Information Technology, 4, 91-95.

Van Raaij, E. M. & Schepers, J. J. L. (2008). The acceptance and use of virtual leaning environment in China. Computers and Education, 50(3), 838-852.

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), 115-139.

Venkatesh, V. & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Secence, 45(2), 186-204.

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.

Wang, R. C. & Chuu, S. J. (2004). Group decision-making using a fuzzy linguistic approach for evaluation the flexibility in a manufacturing system. European Journal of Operational Research, 154, 363-572

Wu, W. W. & Lee, Y. T. (2007). Developing global managers’ competencies using Fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499-507.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(2), 338-353.

 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top