系統識別號 | U0002-2506201610541800 |
---|---|
DOI | 10.6846/TKU.2016.00824 |
論文名稱(中文) | 以行為推理理論探討使用HCE手機信用卡之意願 |
論文名稱(英文) | Applying the Behavioral Reasoning Theory to Investigate the Intentions of HCE Mobile Payment. |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 資訊管理學系碩士在職專班 |
系所名稱(英文) | On-the-Job Graduate Program in Advanced Information Management |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 104 |
學期 | 2 |
出版年 | 105 |
研究生(中文) | 廖珮臻 |
研究生(英文) | Pei-Chen Liao |
學號 | 703630102 |
學位類別 | 碩士 |
語言別 | 繁體中文 |
第二語言別 | |
口試日期 | 2016-05-29 |
論文頁數 | 71頁 |
口試委員 |
指導教授
-
吳錦波(jpwu@mail.tku.edu.tw)
委員 - 梁德昭(tcliang@mail.im.tku.edu.tw) 委員 - 陳永昇(yschen@tea.ntue.edu.tw) |
關鍵字(中) |
行為推理理論 HCE手機信用卡 行動支付 創新抵制 |
關鍵字(英) |
Behavioral Reasoning Theory HCE Host Card Emulation Mobile Payment Innovation Resistance |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
近幾年來,行動裝置日趨普及,而也因此創造出許多新型態的支付方式,進而帶動了行動支付的市場。 HCE 的全名為Host Card Emulation(主機板模擬),是 Google 在 2013 年底所發表的行動支付方案,HCE 能透過雲端模擬晶片所做的事,不需要任何實體安全儲存媒介的申請,就能讓有NFC功能的智慧型手機直接綁定現有手邊的實體信用卡,也降低了一般消費者申辦手機信用卡的困難度。 本研究以行為推理理論作為主要架構,並參考理性行為、計畫行為及創新抵制等相關理論基礎,加入認知安全性、便利性、個人特質、知覺障礙等外部變數。本研究以台灣有使用信用卡及智慧手機之客群為研究對象,並以偏最小平方法(Partial Least Squares, PLS) 進行相關假說之驗證。 研究結果顯示,信念及價值觀、採用態度、個人特質、便利性對於使用HCE手機信用卡之採用意圖產生正向影響。知覺障礙對於使用HCE手機信用卡之採用意圖產生負向影響。而認知安全性對於使用HCE手機信用卡之採用態度及採用意圖則無顯著影響。本研究將討論及分析上述研究之各項影響及管理上之意涵,作為銀行業者及相關行動支付業者策略上之參考及分析依據。 |
英文摘要 |
Now with the mobile device growing, the diversity of mobile payment methods is every increasing. Host Card Emulation(HCE) is one of the mobile payment methods that Google released in 2013. Without any hardware-based secure storage tool like SIM card, HCE enables mobile applications to run on any supported operating systems via cloud. Being able to pay via mobile device with NFC functions, customers are more willing to embrace the area of mobile payment. This study employs the behavior reasoning theory as the base framework. It also refers to related theories, such as the theory of reasoned action, the theory of planned behavior, innovation resistance. Furthermore, this research incorporates external variables such as perceived safeties, Convenience factors, personal characteristics and perceived barriers into the model. This study targets at user with credit card and mobile phones in Taiwan. The Partial Least Squares was used in the data analysis process. The results show that user’s beliefs and values, attitudes, personal characteristics and convenience factors have significant positive impacts on user’s intention to use HCE mobile payment. On the other hand, perceived barriers have negative impacts on the intention to use HCE mobile payment. However, perceived safeties do not have significant impacts on the attitudes and intentions to use HCE mobile payment. The results and implications of this research would provide references for related industries to shape its strategy on mobile payment. |
第三語言摘要 | |
論文目次 |
目錄 第一節 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 5 第三節 研究流程 6 第二章 文獻探討 8 第一節 手機信用卡業務 8 第二節 態度與意圖 17 第三節 行為推理理論 18 第四節 創新抵制 20 第五節 個人特質 23 第六節 便利性 25 第七節 認知安全性 26 第三章 研究方法 28 第一節 研究架構 28 第二節 研究假說 28 第三節 操作型定義與衡量問項 31 第四節 研究對象與範圍 36 第五節 資料分析方法 37 第六節 問卷設計與前測 40 第四章 研究結果 42 第一節 問卷基本與回收 42 第二節 敘述性統計分析 42 第三節 信度與效度分析 45 第五章 結論與建議 54 第一節 結論 54 第二節 研究限制 56 第三節 管理實務上的建議 57 參考文獻 59 附錄 65 圖目錄 圖 3‑1 2010-2018(f)臺灣智慧型手機普及率發展趨勢及預測 2 圖 3‑2 Gartner預估行動支付市場之交易量 3 圖 3‑3 Vpon調查行動裝置系統持有比例(2015年第三季) 4 圖 3‑4 本研究流程圖 7 圖 4‑1 TSM平台生態系統 13 圖 4‑2 TSM概念示意圖 13 圖 4‑3 TSM手機信用卡作業流程 14 圖 4‑4 HCE平台生態系統 16 圖 4‑5 HCE 概念示意圖 16 圖 4‑6 HCE手機信用卡作業流程 17 圖 4‑7 行為推理理論結構模型圖 20 圖 5‑1 本研究架構與假說示意圖 28 圖 6‑1 本研究路徑關係圖 48 表目錄 表 4‑1 手機信用卡相關名詞定義 9 表 5‑1 信念和價值觀之衡量題項 31 表 5‑2 採用態度之衡量題項 32 表 5‑3 採用意圖之衡量題項 32 表 5‑4 個人特質之衡量題項 33 表 5‑5 便利性因素之衡量題項 34 表 5‑6 認知安全性之衡量題項 34 表 5‑7 知覺障礙之衡量題項 35 表 5‑8 認知安全性因素之可靠性統計資料 41 表 5‑9 認知安全性因素之項目總計統計資料 41 表 6‑1敘述性統計樣本分布(性別、年齡、學歷、工作行業別) 43 表 6‑2敘述性統計樣本分布(平均收入、刷卡金額、信用卡張數) 44 表 6‑3敘述性統計樣本分布(手機系統、是否有使用手機信用卡、是否有打算申請HCE手機信用卡) 45 表 6‑4潛在構面之平均變異數萃取量、組合信度和 Cronbach’s α值 46 表 6‑5相關矩陣與平均變異抽取量之平方根 47 表 6‑6 模型路徑檢定表 (Bootstrapping n=2000) 49 表 6‑7 本研究假說檢定結果 50 |
參考文獻 |
[1] Visa 創新行動支付媒體說明會(民103)。VISA。取自http://www.visa.com.tw [2] 沈盈吟、顏瑄、林素珠、鄭仁富。(民103年12月。2014臺灣消費者行動裝置暨app使用行為研究調查報告。資策會FIND,未出版。 [3] 林娟娟、林禹均、王舒民(民99)。網路消費者的知覺風險對其購買態度及意願之研究-以網路購物經驗與退貨經驗為調節變數。電子商務研究,8(1),37-70。 [4] 信用卡業務機構辦理手機信用卡業務安全控管作業基準(民104年9月)。中華民國商業同業公會全國聯合會。取自http://www.ba.org.tw/ [5] 威朋(民104)。2015 quarter 3 台灣行動市場數據報告。VPON威朋大數據集團,未出版。 [6] 威朋(民104)。2015台灣行動廣告市場年終報告。VPON威朋大數據集團,未出版。 [7] 張基成、顏啟芳(民101)。以擴充的科技接受模式探討行動英語學習之接受度。Journal of e-Business,14(1),97-120。 [8] 安全(民104年4月)。維基百科。取自https://zh.wikipedia.org/wiki/ [9] Ajzen, I. (1985). In Action Control: From Cognition to Behavior. IN Kuhl, J., & Beckman, J., (Eds.), From intentions to actions: A theory of planned behavior: (pp. 11-39). Heidelberg: Springer. [10] Albert, M., & James, A. R. (1974). An approach to environmental psychology. Cambridge, Mass : The Mit Press. [11] Bandura, A. (1977). Self - efficacy - toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. [12] Bandura, A. (1994). Self - efficacy. IN, V. S. Ramachaudran (Ed.), Encyclopedia of Human Behavior, 4, 71-81. [13] Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods and Research, 21, 205-229. [14] Cheolho Yoon & Sanghoon Kim. (2007). Convenience and TAM in a ubiquitous computing environment: The case of wireless LAN. Electronic Commerce Research & Applications, 6(1), 102-112. [15] Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, , 295-358. [16] Claudy, M. C., Peterson, M., & O'Driscoll, A. (2013). Understanding the attitude-behavior gap for renewable energy systems using behavioral reasoning theory. Journal of Macromarketing. [17] Dabholkar, P. A., & Bagozzi, R. P. (1996). Consumer evaluations of new technology - based self - service options: An investigation of alternative models of service quality. International Journal of Research in Marketing, 13(1), 29-51. [18] Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology - based self - service: Moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184-201. [19] 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. [20] Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1-26. [21] Everett, M. R. (1962). Diffusion of innovations. New York: Free Press of Glencoe. [22] Fishbein, M. (1963). An investigation of the relationships between beliefs about an object and the attitude toward that object. Human Relations, 16, 233-240. [23] Fishbein, M., & Ajzen, I. (1975a). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. [24] Fishbein, M., & Ajzen, I. (1975b). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. [25] Fornell, C., & Lacker, D. F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. [26] Fornell, & Lacker. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(February), 39-50. [27] Gartner. (2013). Global mobile payment transaction volume from 2010 to 2017. Gartner. [28] Gatignon, H., & Robertson, T. S. (1985). A propositional inventory for new diffusion research. Journal of Consumer Research, 11(4), 849-867. [29] Gay, L. R. (1996). Educational research: Competencies for analysis and application. Englewood Cliffs, NJ: Merrill, Prentice-Hall. [30] Goldsmith, R., & Hofacker, C. (1991). Measuring consumer innovativeness. Journal of the Acad- Emy of Marketing Science, 19(3), 209-221. [31] Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, New Jersey: Prentice Hall. [32] Hayashi, F. (2008). Does safety matter? consumer payment choice and perceived safety of payment methods. Unpublished [33] Hayashi, F. (2012). Mobile payments: What’s in it for consumers? Economic Review, 97(1), 35-66. [34] Hirschman, E. C. (1980). Innovativeness, novelty seeking, and consumer creativity. Journal of Consumer Research, 7(3), 283-295. [35] Hsin Hsin , C., & Su Wen , C. (2009). Consumer perception of interface quality, security, and loyalty in electronic commerce. Information & Management, 46(7), 411-417. [36] Hulland, J. (1999). Use of partial least square (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204. [37] ITIF. (2010). Embracing the self-service economy. Information Technology and Innovation Foundation. [38] Khodawandi, D., Pousttchi, K., & Wiedemann, D. G. (2003). Akzeptanz mobiler bezahlverfahren in deutschland. Mobile Commerce, 3, 42-57. [39] Kotler, P. (1991). Marketing management: Analysis, planning, implementation and control (7th ed.). Upper Saddle River: Prentice-Hall. [40] Krech, D., Crutchfield, R. S., & Ballachey, E. L. (1962). Individual in society: A textbook of social psychology. New York: McGraw-Hill. [41] Kuisma, T., Laukkanen, T., & Hiltunen, M. (2007). Mapping the reasons for resistance to internet banking: A means-end approach. International Journal of Information Management, 27(2), 75-85. [42] Lew G. Brown (1990). Convenience in services marketing. Journal of Services Marketing, 4(1), 53-59. [43] Laukkanen, T., Sinkkonen, S., Kivijarvi, M., & Laukkanen, P. (2007). Innovation resistance among mature consumers. Journal of Consumer Marketing, 24(7), 419-427. [44] Leonard L., B., Kathleen, S., & Dhruv, G. (2002). Understanding service convenience. Journal of Marketing, 66(3), 1-17. [45] Muhammad Muazzem Hossain & Victor R. Prybutok (2008). Consumer acceptance of RFID technology: An exploratory study. IEEE Transactions on Engineering Management, 55(2), 316-328. [46] Mallat, N. (2007). Exploring consumer adoption of mobile payments- A qualitative study. The Journal of Strategic Information Systems, 16(4), 413-432. [47] Marion , M. (2010). The impact of mobile payments on the success and growth of micro-business: The case of M-pesa in kenya. The Journal of Language, Technology & Entrepreneurship in Africa, 2(1), 182-203. [48] Medina, M. Q., & Chaparro, J. P. (2007). The impact of the human element in the information systems quality for decision making and user satisfaction. The Journal of Computer Information Systems, 48(2), 44-53. [49] Midgley, D. F., & Dowling, G. R. (1978). Innovativeness - concept and its measurement. Journal of Consumer Research, 4(4), 229-242. [50] Pui-Lai To, CheChen Liao & Tzu-Hua Lin (2007). Shopping motivations on internet: A study based on utilitarian and hedonic value, Technovation, 27(12), 774-787. [51] Parasuraman, A. (2000). Technology readiness index: A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 4, 307-320. [52] Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115-143. [53] Petty, R. E., Cacioppo, J. T., and Goldman, R. (1981). Personal involvement as a determinant of argument-based persuasion. Journal of Personality and Social Psychology, 41(5), 847. [54] Ram, S. (1987). A model of innovation resistance. Advances in Consumer Research, 14, 208-212. [55] Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5-14. [56] Richard R., K., & Gerard A., A. (2009). Consumer innovativeness and the use of new versus extended brand names for new products. The Journal of Product Information Management, 27(1), 23-32. [57] Ringle, C. M., Wende, S. & Will, A. (2005). SmartPLS-version 2.0. Retrieved from http://www.smartpls.de. [58] Rogers, E. M. (1983). Diffusion of innovations. (3rd ed.). New York: A Division of Macmillan Publishing Co., Inc. [59] Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York.: A Division of Macmillan Publishing Co., Inc. [60] Rogger, A. J., & Celia, I. (2004). Akzeptanz des kaufens und bezahlens mit dem mobiltelefon. Mobile Commerce, 4, 79-85. [61] Rosenberg, M. J., & Hovland, C. I. (1960). Cognitive, affective, and behavioral components of attitude. in M. J. rosenberg et al. (eds.), attitude organization and Change:An analysis of consistency among attitude components. New Haven, CT: Yale University. [62] Siau, K., Sheng, H., Nah, F., & Davis, S. (2004). A qualitative investigation on consumer trust in mobile commerce. International Journal of Electronic Business, 2(3), 283-300. [63] Szmigin, I., & Foxall, G. (1998). Three forms of innovation resistance: The case of retail payment methods. Technovation, 18(6-7), 459-468. [64] Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: The case for an augmented technology acceptance. Information and Management, 41(6), 747-762. [65] Westaby, J. D. (2005). Behavioral reasoning theory: Identifying new linkages underlying intentions and behavior. Organizational Behavior and Human Decision Processes, 98, 97-120. [66] Wise, J. B. (2007). Testing a theory that explains how self - efficacy beliefs are formed: Predicting self - efficacy appraisals across recreation activities. Journal of Social and Clinical Psychology, 26(7), 841-848. [67] Wold, H. (1966). Estimation of principal components and related models by iterative least squares (P.R. Krishnaiah(ed.) Trans.). New York: Academic Press. [68] Zaltman, G., & Wallendorf, M. (1979). Consumer behavior: Basic findings and management implications. . Hoboken, NJ: John Willey and Sons,. [69] Zhang, J., Pantula, S. G., & Boos, D. D. (1991). Robust methods for testing the pattern of a single covariance matrix. Biometrika, 78, 787-795. |
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