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研究生: 楊鎮聰
Chen-Chung Yang
論文名稱: 以整合型科技接受模式探討台灣 消費者之虛擬實境行為意圖
The study on Behavioral Intention of Using Virtual Reality – Application of the UTAUT model
指導教授: 欒斌
Pin Luarn
口試委員: 李國光
Gwo-Guang Lee
詹前隆
Chien-Lung Chan
學位類別: 碩士
Master
系所名稱: 管理學院 - 企業管理系
Department of Business Administration
論文出版年: 2016
畢業學年度: 104
語文別: 中文
論文頁數: 69
中文關鍵詞: 整合型科技接受模型擴充科技接受模式虛擬實境
外文關鍵詞: UTAUT, Extending TAM, Virtual Reality
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  • 隨著科技快速進步,虛擬實境的概念及產品已越臻成熟,也同時扮演著最新一波科技革命的重要角色。本研究的目的是以整合型科技接受模型(UTAUT)為基礎並結合擴充科技接受模式(Extending TAM)來對於虛擬實境裝置的使用者進行實證性的研究與探討。
    透過文獻的蒐集與整理,設計出一份符合使用者對於使用虛擬實境的接受度特性主題量表,量表完成後進行問卷調查,依照本研究結果顯示:影響使用者對於虛擬實境的行為意圖因素可以歸納為兩點,分別是(1)知覺娛樂性(2)預期付出 ,在不同的人口統計變數如性別、年齡、經驗及自願性條件下使用者對於歸納出的不同因素重視程度各有所差異。在這兩個構面底下,差異分析指出性別對「知覺娛樂性」及「預期付出」皆有顯著的影響,兩者均是男性比女性顯著;年齡只對這「知覺娛樂性」構面有顯著的差異,其中36歲以上使用者重視虛擬實境娛樂程度大於36歲以下年紀使用者;又不同的使用經驗會對「預期付出」產生顯著的影響,預期付出認知愈高者,其行為意圖頻率愈低。另外,自願性皆對這兩個構面沒有產生顯著性的差異。最後,依據實證結果,本研究提出管理理論上之解釋及實務上之建議。


    With the rapid development of the new technology. Virtual reality plays an important role among the emerging high-tech products. The purpose of this study is to explore the behavioral intention of the Virtual reality user from the model of the Unified Theory of Acceptance and Use of Technology (UTAUT).
    This study designed a questionnaire that regarded with the degree of the acceptance of Virtual reality according to the review of the literatures. After the research, this study indicates that there are two components that influenced the degree of behavioral intention of Virtual reality include perceived playfulness and effort expectancy. Different factors under different demography significantly such as gender, age, experience and voluntariness of use influence the intention of the user. Related to these two components, this study first indicates that gender has a significant influence in the expectation of the perceived playfulness and effort expectancy, and the result shows that male is more significant than female. Second, age only has significant influence in the expectation of perceived playfulness, which shows that user over 36 years old are more likely to show their behavioral intention under playful situation. Third, different experience level has significant influence in the component of effort expectancy, especially the user who has experience is more significant than the user does not have use experience. Forth, different voluntariness of use have no different significant influence among these two components.

    中文摘要 I Abstract II 目錄 III 圖目錄 IV 表目錄 V 第一章 緒論 1 第一節 研究背景 1 第二節 研究動機與目的 3 第三章 研究流程 6 第二章 文獻探討 7 第一節 以理性行為所延伸的科技接受模式 7 第二節 其他相關的科技接受行為模式 12 第三節 整合型科技接受模型(UTAUT) 15 第三章 研究方法 21 第一節 研究架構及研究假設 21 第二節 研究變數操作型定義與衡量 27 第三節 量表設計與測試 30 第四節 資料分析方法 37 第四章 資料分析與討論 39 第一節 樣本基本資料統計分析 39 第二節 信度、鑑別度與因素分析 40 第三節 相關性分析 46 第四節 路徑分析 47 第五節 控制變數的影響 49 第五章 結論與建議 57 第一節 研究發現 57 第二節 研究貢獻 60 第三節 研究限制與未來研究方向建議 61 參考文獻 64

    Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS quarterly, 665-694.
    Ahn, T., Ryu, S., & Han, I. (2007). The impact of Web quality and playfulness on user acceptance of online retailing. Information & management, 44(3), 263-275.
    Ajzen, I. (1985). From intentions to actions: A theory of planned behavior (pp. 11-39). Springer Berlin Heidelberg.
    Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological bulletin, 82(2), 261.
    Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological bulletin, 84(5), 888.
    Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall, Inc.
    Beck, M. W., Heck, K. L., Able, K. W., Childers, D. L., Eggleston, D. B., Gillanders, B. M., ... & Orth, R. J. (2001). The Identification, Conservation, and Management of Estuarine and Marine Nurseries for Fish and Invertebrates A better understanding of the habitats that serve as nurseries for marine species and the factors that create site-specific variability in nursery quality will improve conservation and management of these areas. Bioscience, 51(8), 633-641.
    Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, 351-370.
    Bruner, G. C., & Kumar, A. (2005). Explaining consumer acceptance of handheld Internet devices. Journal of business research, 58(5), 553-558.
    Bryman, A., & Cramer, D. (1997). Concepts and their measurement.Quantitative data analysis, with SPSS for Windows.
    Chiu, C. M., & Wang, E. T. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201.
    Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.
    Cornelissen, J. H. C., Lavorel, S., Garnier, E., Diaz, S., Buchmann, N., Gurvich, D. E., ... & Pausas, J. G. (2003). A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Australian journal of Botany, 51(4), 335-380.
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 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., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of applied social psychology, 22(14), 1111-1132.
    Durbin, R. M., Altshuler, D. L., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., ... & Egholm, M. (2010). A map of human genome variation from population-scale sequencing
    Gist, M. E., & Mitchell, T. R. (1992). Self-efficacy: A theoretical analysis of its determinants and malleability. Academy of Management review, 17(2), 183-211.
    Hartman, J. B., Shim, S., Barber, B., & O'Brien, M. (2006). Adolescents' utilitarian and hedonic Web consumption behavior: Hierarchical influence of personal values and innovativeness. Psychology & Marketing, 23(10), 813-839.
    Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: emerging concepts, methods and propositions. The Journal of Marketing, 92-101.
    Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of consumer research, 132-140.
    Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & management, 41(7), 853-868.
    Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of management information systems, 11(4), 87-114.
    Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
    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, 183-213.
    Kim, K. S., Zhao, Y., Jang, H., Lee, S. Y., Kim, J. M., Kim, K. S., ... & Hong, B. H. (2009). Large-scale pattern growth of graphene films for stretchable transparent electrodes. Nature, 457(7230), 706-710.
    Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in human behavior, 21(6), 873-891.
    Maddux, J. E., & Rogers, R. W. (1983). Protection motivation and self-efficacy: A revised theory of fear appeals and attitude change. Journal of experimental social psychology, 19(5), 469-479.
    Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191.
    Miller, G. A., & Johnson-Laird, P. N. (1976). Language and perception. Belknap Press.
    Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & management, 38(4), 217-230
    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.
    Nysveen, H., Pedersen, P. E., & Thorbjrnsen, H. (2005). Intentions to use mobile services: Antecedents and cross-service comparisons. Journal of the academy of marketing science, 33(3), 330-346.
    Pai, J. C., & Tu, F. M. (2011). The acceptance and use of customer relationship management (CRM) systems: An empirical study of distribution service industry in Taiwan. Expert Systems with Applications, 38(1), 579-584.
    Peter, J. P., Olson, J. C., & Grunert, K. G. (1999). Consumer behavior and marketing strategy (pp. 122-123). London: McGraw-Hill.
    Pfeffer, M. A., Braunwald, E., Moyé, L. A., Basta, L., Brown Jr, E. J., Cuddy, T. E., ... & Klein, M. (1992). Effect of captopril on mortality and morbidity in patients with left ventricular dysfunction after myocardial infarction: results of the Survival and Ventricular Enlargement Trial. New England journal of medicine, 327(10), 669-677.
    Rheingold, H. (1993). The virtual community: Finding commection in a computerized world. Addison-Wesley Longman Publishing Co., Inc..
    Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience.MIS quarterly, 561-570.
    Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.
    Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS quarterly, 125-143.
    Thong, J. Y., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810.
    Triandis, H. C. (1971). Attitude and attitude change (Vol. 8). New York: Wiley.
    Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
    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, 115-139.
    Venkatesh, V., & Speier, C. (1999). Computer technology training in the workplace: A longitudinal investigation of the effect of mood. Organizational behavior and human decision processes, 79(1), 1-28.
    Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
    Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model.Information & management, 41(6), 747-762.
    Zhang, J., & Mao, E. (2008). Understanding the acceptance of mobile SMS advertising among young Chinese consumers. Psychology & Marketing, 25(8), 787-805.

    【中文文獻】
    蕭文龍. (2014). 統計分析入門與應用: SPSS 中文版+ PLS-SEM (SmartPLS)‧ 台北: 碁峰.
    吳明隆. (2006). 結構方程模式: SIMPLIS 的應用. 五南圖書出版股份有限公司.
    岳玮宁, 董士海, 王悦, 汪国平, 王衡, & 陈文. (2004). 普适计算的人机交互框架研究. 计算机学报, 27(12), 1657-1664.
    邱皓政. (2006). 量化研究與統計分析. 五南圖書出版股份有限公司.
    吳明隆. (2000). SPSS 統計應用實務, 台北: 松崗電腦圖書資料股份有限公司.

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