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系統識別號 U0026-2507201500584400
論文名稱(中文) 智慧型手機使用者持續使用行動銀行服務之意圖:科技接受模式觀點
論文名稱(英文) Continued Usage Intention on Smart Phone Mobil Banking Services: Perspective of Technology Acceptance Model
校院名稱 成功大學
系所名稱(中) 工程管理碩士在職專班
系所名稱(英) Institute of Engineering Management (on the job class)
學年度 103
學期 2
出版年 104
研究生(中文) 徐淑芬
研究生(英文) Shu-Fen Hsu
學號 N07011402
學位類別 碩士
語文別 中文
論文頁數 91頁
口試委員 指導教授-蔡明田
口試委員-莊立民
口試委員-李國瑋
中文關鍵字 相容性  認知易用性  認知有用性  熟悉性  自我效能 
英文關鍵字 Compatibility  Perceived ease of use  Perceived usefulness  Familiarity  Self-efficacy 
學科別分類
中文摘要 有關智慧型手機使用者採用行動銀行服務意圖與認知,一直是研究學者們所熱中探討的主題。本研究為深入瞭解與觀察智慧型手機使用於採用行動銀行服務來進行其平常之金融服務活動的情形,進而提出一項創新性的結合科技接受模式與創新擴散理論構面的相容性,再加入資訊服務系統中,新近被提出的熟悉性因素,來推導出本研究的研究架構以用來分析智慧型手機使用者的內在持續使用行動銀行服務的意圖與認知。本研究為求有效驗證所提出之理論模式是否適用於智慧型手機使用者,而採用問卷調查方式來蒐集受訪者的使用意圖與傾向。過程中,總共回收了285份問卷,其中有效問卷為257份。本研究接著運用統計軟體對所回收之問卷進行後續之分析。
研究結果顯示,相容性在所有影響持續使用意圖的因素中,佔有最大的影響力。這表示了智慧型手機使用者對行動銀行這項新式服務,認為與個人過往的使用經驗、自信能否有效操作及需求的相容程度高,決定了日後是否持續使用行動銀行服務的意圖。而認知有用性、認知易用性與熟悉性也呈現正向顯著影響,這表示當智慧型手機使用者感覺行動銀行是容易操作和對生活上有幫助,就會更增強持續使用行動銀行服務的意圖。而在熟悉性與自我效能二項因素對持續使用意圖也都有顯著正向的影響。在未來銀行業在推廣行動銀行服務活動上,可以凸顯行動銀行在增進人們生活便利與助益的特性,以吸引更多智慧型手機使用者的持續使用意圖,以提升銀行業的服務績效。
英文摘要 The intention to usage mobile banking services of smart phone users has been important topic for scholars’ interesting. This study to better understand smart phone user using services of mobile banking activities, and proposed an integrate technology acceptance model and factor (compatibility) of innovation diffusion theory and familiarity factor. To infer this study framework for analysis the internal smart phone user intentions and cognitive action continued usage of mobile banking services.

Whether for the sake of this study, the effective verification of the proposed theoretical model applies to Smart phone users, and the use of questionnaire survey to collect the intended usage of the tendency of the respondents. A total of 285 questionnaires were received, of which 257 valid sample data. In this study, statistical software SPSS were applied to analyze collected questionnaire for follow-up analysis.

The research results showed that the compatibility has the largest influence intention to continuance usage of mobile banking service. This means that the smart phone users consider the mobile banking service regard to personal past and personal experience, confidence and ability to effectively operate a high degree of compatibility requirements, determine whether the intention to continuance usage of the mobile banking service in the future.

The perceived usefulness, perceived ease of use and familiarity is also showing a positive significant effect intention to continuance usage of the mobile banking service. This means that when the user feels mobile banking service is easy to use and helpful for life, it will also enhance intention to continuance usage of the mobile banking service. The factors of familiarity and self-efficacy also present positive significant to intention to continuance usage of the mobile banking service. In the future, the banking in the promotion of mobile banking service activities can be focus on the promotion of people’s lives convenient and useful features to attract more smart phone user’s intention to continuance usage of the mobile banking service for improve the business performance of the banking sector.
論文目次 摘 要........................................I
Extended Abstract...........................II
誌 謝........................................V
目 錄........................................VI
表 目 錄.....................................VIII
圖 目 錄..................................... X
第一章 緒論....................................1
第一節 研究背景.................................1
第二節 研究動機.................................3
第三節 研究目的.................................5
第四節 研究範圍.................................7
第五節 研究流程.................................8
第二章 文獻探討.................................10
第一節 行動銀行服務之運用.........................10
第二節 相容性...................................13
第三節 科技接受模式 ..............................18
第四節 熟悉性...................................24
第五節 自我效能.................................26
第三章 研究方法.................................28
第一節 研究架構.................................28
第二節 研究假設.................................30
第三節 研究變數之操作性定義與衡量..................37
第四節 研究設計.................................44
第五節 研究對象與問卷蒐集.........................46
第六節 資料分析方法 ..............................48
第四章 資料分析結果與討論.........................52
第一節 研究樣本與變項之描述性分析..................52
第二節 探索性因素分析與信度分析....................54
第三節 Pearson相關分析..........................63
第四節 單因子變異數分析(ANOVA)...................64
第五節 迴歸分析 (Regression Analysis; RA).......68
第五章 結論與建議................................75
第一節 實證結果與討論.............................75
第二節 研究貢獻..................................79
第三節 研究限制..................................81
第四節 後續研究建議 ...............................83
參考文獻........................................85
附錄 研究問卷....................................1
參考文獻 一、中文文獻
中央社 (2012),http://www.cna.com.tw/。
行政院主計總處 (2015),http://www.dgbas.gov.tw/mp.asp?mp=1。
吳明隆. (2005). SPSS應用學習實務:問卷分析與應用統計. 台北: 知城出版。
吳萬益, & 林清河. (2005). 企業研究方法. 台北市: 華泰。
林震岩. (2007). 多變量分析: SPSS 的操作與應用, 台北: 智勝文化。
資訊工業策進會FIND中心∕經濟部技術處(2015),http://www.iii.org.tw/Service/3_1_1_c.aspx?id=1367。
資策會「2013年行動購物調查」,http://www.iii.org.tw/Default.aspx。

二、英文文獻
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.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665-683.
Ajzen, I., & Fishbein, M. (1975). Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261-277.
Ajzen, I., & Fishein, M. (1980). Understanding attitudes and predicting social behavior: Prentice Hall, Englewood Cliffs, NJ: Prentice Hall.
Al-Jabri, I. M., & Sohail, M. S. (2012). Mobile banking adoption: Application of diffusion of innovation theory. Journal of Electronic Commerce Research, 13(4), 379-391.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
Bandura, A. (1982). Self-efficacy mechanism in human agency. American psychologist, 37(2), 122.
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational behavior and human decision processes, 50(2), 248-287.
Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (2001). Self-efficacy beliefs as shapers of children's aspirations and career trajectories. Child Development, 72(1), 187-206.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173.
Beuninger, J. V., Ruyter, K. D., Wetzels, M. & Streukens, S. (2009). Customer self-efficacy in technology-based self-service: assessing between- and within-person differences, Journal of Service Research, 11(4), 407-428.
Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, 351-370.
Black, N., A. Lockett, C. Ennew, H. Winklhofer, and S. McKechnie. (2002). Modelling Consumer Choice of Distribution Channels: An Illustration from Financial Services. The International Journal of Bank Marketing, 20(4): 161-173.
Bolton, D. J., Oser, A. H., Cocoma, G. J., Palumbo, S. A., & Miller, A. J. (1999). Integrating HACCP & TQM reduces port carcass contamination. Food Technology, 53(4), 40-43.
Casaló, L. V., C. Flavián, and M. Guinalíu. (2007). Antecedents and Effects of Participation in Virtual Brand Communities. IADIS international conference on web based communities, Salamanca.
Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students' behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71-83.
Chau, P. Y., & Hu, P. J. (2002). Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of management information systems, 18(4), 191-229.
Chen, L.-D., & Tan, J. (2004). Technology Adaptation in E-commerce: Key Determinants of Virtual Stores Acceptance. European Management Journal, 22(1), 74-86.
Chen, S.-S., Chuang, Y.-W., & Chen, P.-Y. (2012). Behavioral intention formation in knowledge sharing: Examining the roles of KMS quality, KMS self-efficacy, and organizational climate. Knowledge-Based Systems, 31, 106-118.
Colombo, R. A., & Morrison, D. G. (1989). Note-A Brand Switching Model with Implications for Marketing Strategies. Marketing Science, 8(1), 89-99.
Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS quarterly, 145-158.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.
Davis, F. D., Bagozze, R. P., & Warshaw, P. R. (1989). User acceptance of of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.
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., Lucier, J. S., & Logerfo, F. W. (1986). Organization of an organ donation network. Surgical Clinics of North America, 66(3), 641-652.
Eastin, M. S., & LaRose, R. (2000). Internet self‐efficacy and the psychology of the digital divide. Journal of Computer‐Mediated Communication, 6(1), 0-0.
Ecker, U. K. H., Zimmer, H. D., Groh-Bordin, C., & Mecklinger, A. (2007). Context effects on familiarity are familiarity effects of context—An electrophysiological study. International Journal of Psychophysiology, 64(2), 146-156.
Flavián, C., M. Guinalíu, & R. Gurrea. (2006). The Influence of Familiarity and Usability on Loyalty to Online Journalistic Services: The Role of User Experience. Journal of Retail and Consumer Services, 13(5), 363-375.
Freiden, J., Goldsmith, R., Takacs, S., & Hofacker, C. (1998). Information as a product: not goods, not services. Marketing Intelligence & Planning, 16(3), 210-220.
Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega-International Journal of Management Science, 28(6), 725-737.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Inexperience and experience with online stores: The importance of TAM and trust. Engineering Management, IEEE Transactions on, 50(3), 307-321.
Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365-374.
Guiltinan, J. P. (1999). Launch Strategy, Launch Tactics, and Demand Outcomes. The Journal Product Innovation Management Science, 16, 509-529.
Hair, J. F., Black, W. C., Anderson, R. E., & Babin, B. J. (2009). Multivariate data analysis (7th edition): Upper Saddle River: Prentice-Hall.
Ida Gobbini, M., Leibenluft, E., Santiago, N., & Haxby, J. V. (2004). Social and emotional attachment in the neural representation of faces. Neuroimage, 22(4), 1628-1635.
Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587-605.
Internet Retailer (2014). https://www.internetretailer.com/.
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.
Kelley, C. M., & Jacoby, L. L. (1998). Subjective reports and process dissociation: Fluency, knowing, and feeling. Acta Psychologica, 98(2), 127-140.
Kim, D., & Chang, H. (2007). Key functional characteristics in designing and operating health information websites for user satisfaction: An application of the extended technology acceptance model, International Journal of Medical Informatics, 76(12), 790-800.
Langerak, F., Verhoef, P. C., Verlegh, P. W., & De Valck, K. (2003). The effect of members' satisfaction with a virtual community on member participation.
LaRose, R., & Eastin, M. S. (2002). Is online buying out of control? Electronic commerce and consumer self-regulation. Journal of Broadcasting & Electronic Media, 46(4), 549-564.
Lee, H., Choi, S. and Kang, Y. (2009). Formation of e-satisfaction and repurchase intention: moderating roles of computer self-efficacy and computer anxiety. Expert Systems with Applications. 36(4), 7848-7859.
Lu, Y., Zhao, L., & Wang, B. (2010). From virtual community members to C2C e-commerce buyers: Trust in virtual communities and its effect on consumers’ purchase intention. Electronic Commerce Research and Applications, 9(4), 346-360.
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.
Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: the influence of perceived user resources. ACM SigMIS Database, 32(3), 86-112.
McKee, D., Simmers, C.S. & Licata, J. (2006). Customer self-efficacy and response to service. Journal of Service Research, 8(3), 207-220.
Miller, W. R., Sovereign, R. G., & Krege, B. (1988). Motivational interviewing with problem drinkers: II. The Drinker's Check-up as a preventive intervention. Behavioural Psychotherapy, 16(04), 251-268.
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.
Moschis, G. P., & Mitchell, L. G. (1986). Television advertising and interpersonal influences on teenagers’ participation in family consumer decisions. Advances in consumer research, 13(1), 181-186.
Murphy, C. A., Coover, D., & Owen, S. V. (1989). Development and validation of the computer self-efficacy scale. Educational and Psychological measurement, 49(4), 893-899.
Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). NY: Mc Graw-Hill.
O’cass, A., & Fenech, T. (2003). Web retailing adoption: exploring the nature of internet users Web retailing behaviour. Journal of Retailing and Consumer services, 10(2), 81-94.
Oatley, K., & Jenkins, J. (1996). Understanding emotions: In psychology, psychiatry, and social sciences: Cambridge, MA: Blackwell.
Pavlou, P.A. and Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information Systems Research, 15(1), 37-60.
Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International journal of educational research, 31(6), 459-470.
Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). Research report: richness versus parsimony in modeling technology adoption decisions—understanding merchant adoption of a smart card-based payment system. Information systems research, 12(2), 208-222.
Ratcliffe, M. (2002). Heidegger's attunement and the neuropsychology of emotion. Phenomenology and the Cognitive Sciences, 1(3), 287-312.
Rawstorne, P., Jayasuriya, R., & Caputi, P. (2000). Issues in predicting and explaining usage behaviors with the technology acceptance model and the theory of planned behavior when usage is mandatory. Paper presented at the Proceedings of the twenty first international conference on Information systems.
Rindfleisch, A., & Inman, J. (1998). Explaining the familiarity-liking relationship: mere exposure, information availability, or social desirability? Marketing Letters, 9(1), 5-19.
Rogers, E. M. (1983). Diffusion of Innovations: Third Edition, Free Press. New York, NY.
Rothaermel, F. T., and S. Sugiyama. (2001). Virtual Internet Communities and Commercial Success: Individual and Community-Level Theory Grounded in the Atypical Case of TimeZone.com. Journal of Management, 27(3), 297-312.
Söderlund, M. (2002). Customer familiarity and its effects on satisfaction and behavioral intentions. Psychology & Marketing, 19(10), 861-879.
Sahin, I. (2006). Detailed Review of Rogers' Diffusion of Innovations Theory and Educational Technology-Related Studies Based on Rogers' Theory. Online Submission, 5(2).
Syed H. Akhter (2014). Privacy concern and online transactions: the impact of internet self-efficacy and internet involvement. Journal of Consumer Marketing. 31(2), 118-125.
Tan, M., & Teo, T. S. (2000). Factors influencing the adoption of Internet banking. Journal of the AIS, 1(1es), 5.
Taylor, S., & Todd, P. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144-176.
Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation - a meta-analysis of findings. Ieee Transactions on Engineering Management, 29(1), 28-45.
Tsai, H.-T., & Huang, H.-C. (2007). Determinants of e-repurchase intentions: An integrative model of quadruple retention drivers. Information & management, 44(3), 231-239.
Tung, F.C., Chang, S.C. and Chou, C.M. (2008). An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry, International Journal of Medical Informatics, 77(5), 324-335.
Van Slyke, C., Comunale, C. L., & Belanger, F. (2002). Gender differences in perceptions of web-based shopping. Communications of the ACM, 45(8), 82-86.
Van Slyke, C., Johnson, R. D., Hightower, R., & Elgarah, W. (2008). Implications of researcher assumptions about perceived relative advantage and compatibility. ACM SIGMIS Database, 39(2), 50-65.
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management science(46), 186-204.
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., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3).
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.
Wang, Y. S. (2003). The adoption of electronic tax filing systems: an empirical study. Government Information Quarterly, 20(4), 333-352.
Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & management, 42(5), 719-729.
Wu, M. C., & Kuo, F. Y. (2008). An empirical investigation of habitual usage and past usage on technology acceptance evaluations and continuance intention. ACM SIGMIS Database, 39(4), 48-73.
Yang, S.Q., Lu, Y.B., Zhao, L. & Gupta, S. (2011). Empirical investigation of customers’ channel extension behavior: perceptions shift toward the online channel, Computers in Human Behavior, 27(5), 1688-1696.
Yarbrough, A. K. & Smith, T. B. (2007), "Technology acceptance among physicians: A new take on TAM, Health and Nursing Journals, 64(6), 650-672.
Yi, Y. & Gong, T. (2008). The electronic service quality model: the moderating effect of customer self-efficacy. Psychology & Marketing, 25(7), 587-601.
Zhao, Y. (2002). Measuring the social capital of laid-off Chinese workers. Current Sociology, 50(4), 555-571.
Zhu, K., Dong, S., Xu, S. X., & Kraemer, K. L. (2006). Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies. European journal of information systems, 15(6), 601-616.
Zimmerman, B. J., Boekarts, M., Pintrich, P., & Zeidner, M. (2000). A Social Cognitive Perspective. Handbook of self-regulation, 13.
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