1. NCCU Academic Hub
  2. Publications
  3. College of Commerce
  4. Theses
  5. Item

Publications-Theses

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 數位金融時代下行動銀行app持續採用行為研究
Understanding Consumers’ Continuance Intention toward Mobile Banking in the Fintech Era: A Qualitative and Quantitative Study
作者 梁榕修
Liang, Jung Hsiu
貢獻者 白佩玉
Pai, Pei Yu
梁榕修
Liang, Jung Hsiu
關鍵詞 持續採用行為
設計美感
知覺有用性
知覺易用性
複雜性
知覺風險
品牌聲望
Continuance intention
Design aesthetics
Perceived usefulness
Perceived ease of use
Complexity
Perceived risk
Brand reputation
日期 2017
上傳時間 1-Dec-2017 12:10:08 (UTC+8)
摘要 本研究從金融科技創新應用之觀點，舉行動銀行app之應用為例，整合過去行銷與科技採用之相關文獻，並呼應金融科技時代的創新元素，據此探究使用者對於行動銀行app持續採用行為、與提供未來創新發展上之建議。首先以質化研究的方式，了解行動銀行app使用者的使用原因、使用經驗、對app的整體評價與建議；其次發展出量化研究模型，找出各種影響消費者持續使用意願的因素。

本研究針對「僅使用行動銀行app者」、與「行動銀行app和網路銀行皆有使用者」發放網路問卷調查，在量化研究的部分，首先根據Fintech重要核心價值中的差異化與利基型專業產品，提出競業差異作為研究模型之第一層探討面，結果顯示:

1. 設計美感對使用者能產生正向的情感品質知覺，提升對科技使用的知覺有用性、知覺易用性與降低知覺風險。
2. 品牌聲望有助於提升消費者對於業者所提供之產品與服務的相對優勢。

其次，結合過去創新擴散理論、科技接受模式以及個人知覺風險，作為研究模型之第二層探討面，結果顯示: 複雜性、知覺有用性、知覺風險能顯著影響消費者對於行動銀行app的採用意願。

最後，整合質化訪談發現與量化結果分析，給予結論與建議:

1. 業者可從設計美感加強消費者對於新科技使用的知覺有用性與降低知覺風險
2. 品牌聲望為輔，實質創新為主，首先降低複雜性
3. 從知覺有用性方面創造創新競爭優勢、同時兼顧知覺風險
4. 持續推廣行動銀行app，作為創新發展基礎後盾、與開拓市場之契機。
This paper takes mobile banking application as an example in the view of FinTech innovation. Combined with findings from marketing and information system research, this study adopts key elements of FinTech innovation to arrive at a more complete understanding of consumers’ continuance intention toward mobile banking. By first taking the qualitative method and conducting semi-structured interviews, we look into consumers’ motivations, experiences, and evaluations of using mobile banking.
For the quantitative part our empirical tests involve structural equation modeling. In addition, with the reference to one of main core values of FinTech innovation: differentiation and niche, specialized products, we propose competitive differences among competitors to form our first layer research model, the results demonstrate that:
1. Design aesthetics can increase one’s perceived affective quality of system usage, which in turn, had a significant positive impact on perceived usefulness, perceived ease of use and lower perceived risk
2. Brand reputation can positively affect consumers’ sense of relative advantage in terms of the product and service provided by specific vendor.

Meanwhile, our research integrates the concepts of Rogers’ innovation diffusion model, technology acceptance model, and personal perceived risk to further propose our second layer research model, and the result shows that: complexity, perceived usefulness, and perceived risk emerge as important antecedents of consumers’ continuance intention toward mobile banking.
Lastly, we conclude our analysis of both qualitative and quantitative survey and make suggestions as below:
1. Placing a high value on the influence of design beauty, could increase consumers’ perceived usefulness and reduce perceived risk of new technology.
2. Focusing mainly on innovation while brand reputation subsidiary, and take complexity as priority.
3. Creating competitive advantage of innovation based on perceived usefulness, without overlooking the significant influence of perceived risk.
4. Keeping giving an impetus actively to the usage of mobile banking to solidify foundations of innovation development and increase opportunities in the market.
參考文獻 西文部分
1. Aboelmaged, M.G., & Gebba, T.R. (2013). Mobile banking adoption: an examination of technology acceptance model and theory of planned behavior. International Journal of Business Research and Development 2 (1), 35-50.
2. Al-Jabri, I.M., & Sohail, M.S. (2012). Mobile banking adoption: application of diffusion of innovation theory. Journal of Electronic Commerce Research 3 (4), 379-391.
3. Anderson, J.C., & Gerbing, D.W. (1988). Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin 103 (3), 411-423.
4. Baptista, G., & Oliveira T. (2016). A weight and a meta-analysis on mobile banking acceptance research. Computers in Human Behavior 63, 480-489.
5. Barrett, P. (2007). Structural equation modelling: Adjuging model fit. Personality and Individual Differences 42, 815-824.
6. Beldad, A., de Jong, M., & Steehouder, M. (2010). How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust, Computers in Human Behavior 26 (5), 857-869.
7. Benamati, J.S., Fuller, M.A., Serva, M.A., & Baroudi, J.A. (2010). Clarifying the integration of trust and TAM in e-commerce environments: implications for systems design and management. IEEE Transactions on Engineering Management 57 (3), 380–393.
8. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation confirmation model, MIS Quarterly 25 (3), 351-370.
9. Bidgoli, H. (1990). Designing a user-friendly interface for a decision support system. Information Technology 12 (3), 148-154.
10. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. K. A. Bollen & J. S. Long (Eds.), Testing structural equation models, Beverly Hills, CA: Sage, 136-162.
11. Chandra, S., Srivastava, S.C., & Theng, Y.-L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: an empirical analysis. Communications of the Association for Information Systems 27, 561-588.
12. Chau, P.Y.K., & 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.
13. Chen, C. (2013). Perceived risk, usage frequency of mobile banking services. Managing Service Quality 23(5), 410-436.
14. Chen. L.-D. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications 6 (1), 32-52.
15. Choi, H., Kim, Y., & Kim, J. (2011). Driving factors of post adoption behaviour in mobile banking data services. Journal of Business Research 64, 1212-1217.
16. Cyr, D., Bonanni, C. & Ilsever, J. (2004). Design and e-loyalty across cultures in electronic commerce. Proceedings for the Sixth International Conference on Electronic Commerce (ICEC04), The Association for Computing Machinery (ACM).
17. Cyr, D., Hea, M., & Ivanov A. (2006). Design aesthetics leading to m-loyalty in mobile commerce. Elsevier Science Information & Management 43, 950-963.
18. Davis, F. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral Dissertation, Sloan School of Management, Massachusetts Institute of Technology.
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, 982-1003.
20. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technologies. MIS Quarterly 13 (3), 319-340.
21. DeLone, W.H., & McLean E.R. (2004). Measuring e-Commerce success: applying the DeLone & McLean information systems success model. International Journal of Electronic Commerce 9 (1), 31-47.
22. Dennis L. Jackson, J. Arthur Gillaspy, Jr. (2009). Reporting Practices in Confirmatory Factor Analysis: An Overview and Some Recommendations. Psychological Methods 14 (1), 6-23.
23. Dion, K., Bersheid, E., & Walster, E. (1972). What is beautiful is good. Journal of Personality and Social Psychology 24 (3), 285-290.
24. Ferreira, J. B., da Rocha, A., & da Silva, J. F. (2014). Impacts of technology readiness on emotions and cognition in Brazil. Journal of Business Research 67(5), 865-873.
25. Flavia´n, C., Guinalı´u, M., & Gurrea, R. (2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information & Management 43 (1), 1-14.
26. Fornell, C. & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18 (1), 39-50.
27. Forsythe, S. M., & Shi, B. (2003). Consumer patronage and risk perceptions in internet shopping. Journal of Business Research 56, 867-875.
28. Gatignon, H., & Robertson, T. S. (1989). Technology diffusion: An empirical test of competitive effects. Journal of Marketing 53, 35-49.
29. Gefen, D. & Straub, D.W. (2003). Managing user trust in B2C e-services. e-Service Journal 2 (2), 7–24.
30. Gefen, D., Straub, D.W., & Boudreau, M.C. (2000). Structural equation modeling and regression: guidelines for research practice. Communications of the Association for Information Systems 4 (7), 1-70.
31. Gu, J.-C., Lee, S.-C., & Suh, Y.-H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications 36(9), 11605-11616.
32. Ha, K.-H., Canedoli, A., Baur, A.W., & Bick, M. (2012). Mobile banking - insights on its increasing relevance and most common drivers of adoption. Electronic Markets 22(4), 217-227.
33. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Boston, MA: Pearson Education Inc.
34. Hausman, A.V. & Siekpe, J.S. (2009). The effect of web interface features on consumer online purchase intentions, Journal of Business Research 62 (1), 5-13.
35. Hu, L.-T., & Bentler, P. M. (1999). Cut-off criteria for fit indexes in covariance structure analysis. Conventional criteria versus new alternatives. Structural Equation Modeling 6, 1-55.
36. 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.
37. Jiang, Z. & Benbasat, I. (2003). The effects of interactivity and vividness of functional control in changing web consumers’ attitudes. Proceedings for the 24th International Conference on Information Systems, Seattle, USA.
38. Jung, Y., Perez-Mira, B. & Wiley-Patton, S. (2009). Consumer adoption of mobile TV: examining psychological flow and media content. Computers in Human Behavior 25 (1), 123-129.
39. Karvonen, K. (2000). The beauty of simplicity. ACM Proceedings on the Conference on Universal Usability, 85-90.
40. Kiljander, H., & Jarnstrom, J. (2003). User interface styles. C. Lindholm, T. Keinonen, H. Kiljander (Eds.), Mobile Usability: How Nokia Changed the Face of the Mobile Phone, McGraw Hill, 15-44.
41. Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior 26 (3), 310-322.
42. Kim, D.J., Ferrin, D.L., & Rao, H.R. (2009). Trust and satisfaction, two stepping stones for successful e-commerce relationships: a longitudinal exploration. Information Systems Research 20 (2), 237-257.
43. Kim, G., Shin, B., & Lee, H.G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal 19 (3), 283-311.
44. Kleijnen, M., Lee, N., & Wetzels, M. (2009). An exploration of consumer resistance to innovation and its antecedents. Journal of Economic Psychology 30, 344-357.
45. Kuo, Y.-F., Wu, C.-M., & Deng, W.-J. (2009). The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in Human Behavior 25 (4), 887-896.
46. Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking. Journal of Business Research 69, 2432-2439.
47. Laukkanen, T., & Kiviniemi, V. (2010). The role of information in mobile banking resistance. The International Journal of Bank Marketing 28 (5), 372-388.
48. Laukkanen, T., Sinkkonen, S., Kivijärvi, M., & Laukkanen, P. (2007). Innovation resistance among mature consumers. Journal of Consumer Marketing 24 (7), 419-427.
49. Lavie, T., & Tractinsky, N. (2004). Assessing dimensions of perceived visual aesthetics of web sites, International Journal of Human–Computer Studies 60 (3), 269-298.
50. Lee, T. (2005). The impact of perceptions of interactivity on customer trust and transaction intentions in mobile commerce. Journal of Electronic Commerce Research 6 (3), 165-180.
51. Lee, Y. -K., Park, J. -H., Chung, N., & Blakeney, A. (2012). A unified perspective on the factors influencing usage intention toward mobile financial services. Journal of Business Research 65 (11), 1590-1599.
52. Lee, Y.E., & Benbasat, I. (2004) A framework for the study of customer interface design for mobile commerce. International Journal of Electronic Commerce 8 (3), 79-102.
53. Legris, P. et al. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management 40, 191-204.
54. Legrisa, P., Inghamb, J., & Collerette, P. (2001). Why do people use information technology? A critical review of the technology acceptance model. Information & Management 40, 191-204.
55. Lincoln, Y.S., & Guba, E.G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage.
56. Liu, C.T., Guo, Y.M., & Lee, C.H. (2011). The effects of relationship quality and switching barriers on customer loyalty. International Journal of Information Management 31(1), 71-79.
57. Lu, Y., Yang, S., Chau, Patrick Y.K., & Cao, Yuzhi (2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information & Management 48, 393–403.
58. Luarn, P., & Lin H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior 21, 873-891.
59. Mallat, N. (2007). Exploring consumer adoption of mobile payments — a qualitative study. The Journal of Strategic Information Systems 16 (4), 413-432.
60. Marsh, H. W., Balla, J. R., & Hau, K. (1996). An evaluation of incremental fit indices: A clarification of mathematical and empirical properties. InG. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques, 315-353.
61. Martins, C., Oliveira, T., & Popovic, A. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management 34 (1), 1-13.
62. McCracken, G. (1988). The long interview. Newbury Park. California: SAGE Publications.
63. Minichiello, V., Aroni, R., Timewell, E., & Alexander, L. (1995). In-depth interviewing: Principles, techniques, analysis. Melbourne: Addison Welsey Longman.
64. Nunnally, J.C. (1978). Psychometric Theory, McGraw-Hill, New York.
65. Nysveen, H., & Pedersen, P.E., & Thorbjørnsen H. (2005). Intentions to use mobile services: antecedents and cross-service comparisons. Journal of the Academy of Marketing Science 33 (3), 330-346.
66. O`Cass, A. & Carlson, J. (2010). Examining the effects of website induced flow in professional sporting team websites, Internet Research 20 (2), 115-134.
67. Ram, S. (1989). Successful innovation using strategies to reduce consumer resistance: An empirical test. Journal of Product Innovation Management 6 (1), 20-34.
68. Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing 6 (2), 5-14.
69. Rist, T., & Brandmeier P. (2001). Customizing graphics for tiny displays of mobile devices. M.D. Dunlop, S.A. Brewster (Eds.) Proceedings of Mobile HCI 2001: Third International Workshop on Human Computer Interaction with Mobile Devices, Lille, France, 260-268.
70. Rogers, E.M. (1983). Diffusion of innovations (3rd ed.). New York: Free Press, 163-238.
71. Rosen, D.E., & Purinton, E. (2004). Website design: viewing the web as a cognitive landscape. Journal of Business Research 57, 787-794.
72. Rubin, H.J., & Rubin, I.S. (1995). Qualitative interviewing: The art of hearing data. Thousand Oaks, CA: Sage.
73. Sarker, S., & Wells, J. (2003). Understanding mobile handheld device and adoption. Communications of the ACM 46 (12), 35-40.
74. Schierz, P.G., Schilke, O., & Wirtz, B.W. (2010). Understanding consumer acceptance of mobile payment services: an empirical analysis. Electronic Commerce Research and Applications 9 (3), 209-216.
75. Schouten, J.W. (1991). Personal Rites of Passage and the Reconstruction of Self. NA - Advances in Consumer Research (18 ed). Rebecca H. Holman and Michael R. Solomon, Provo, UT: Association for Consumer Research, 49-51.
76. Sheth, J. N. (1981). Psychology of innovation resistance: The less developed concept (LDC) in diffusion research. Research in Marketing 4, 273-282.
77. Shneiderman, B. (1987). User interface design and evaluation for an electronic encyclopedia. G. Salvendy (Ed.), Cognitive Engineering in the Design of Human–Computer Interaction and Expert Systems, Elsevier Science Publishers, 207-223.
78. Straub, D., Boudreau, M.-C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems 13 380-427.
79. Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Sage Publications, Inc.
80. van der Heijden, H. (2003). Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information &Management 40 (6), 541-549.
81. Williams M. (1997) Social Surveys: Design to Analysis. T. (May Ed.). Social Research Issues, Methods and Process. Buckingham: Open University Press.
82. World Economic Forum (2015). The Future of Financial Services: How disruptive innovations are reshaping the way financial services are structured, provisioned and consumed, 3-22.
83. Wu, J., & Du, H. (2012). Toward a better understanding of behavioral intention and system usage constructs. European Journal of Information Systems 21 (6), 680-698.
84. Wu, J.-J., & Chang Y.-S. (2005). Towards understanding members` interactivity, trust, and flow in online travel community. Industrial Management & Data Systems 105 (7), 937-954.
85. Yazdani, D., & Weber, G., PwC Global research team (2016). Global FinTech Report, March 2016, 3-29.
86. Yin, R.K. (1981a). The case study as a serious research strategy. Knowledge: Creation, Diffusion, Utilization 3, 97-114.
87. Yin, R.K. (1981b). Life hhistories of innovations: How new practices become routinized. Public Administration Review 41, 21-28.
88. Yin, R.K. (1984). Case study research: Design and methods (1st ed.). Beverly Hills, CA: Sage Publications.
89. Zahedi, F.M., & Song, J. (2008). Dynamics of trust revision: using health infomediaries. Journal of Management Information Systems 24 (4), 225-248.
90. Zhou, T. (2011). An empirical examination of continuance intention of mobile payment services. Decision Support Systems 54, 1085-1091.
91. Zhou, T., Li, H.X., & Liu, Y. (2010). The effect of flow experience on mobile SNS users` loyalty. Industrial Management & Data Systems 110 (5–6), 930-946.
中文部分
1. 吳明隆（2000），SPSS 統計應用實務，台北：松崗。
2. 吳萬益與林清河（2002），行銷研究，台北：華泰文化。
3. 陳順宇 (2005)，多變量分析，台北：華泰文化。
4. 文崇一、楊國樞（2000），訪問調查法，社會及行為科學研究法下冊，台北：東華。
5. 陳月娥 (2015)，社會研究法，台北：千華數位文化。
6. 潘淑滿 (2003)，質性研究：理論與應用，心理出版。
7. 財經| TechNews 科技新報。
「解密改變貨幣流通方式的新名詞－ FinTech 金融科技」(2016/05)
http://finance.technews.tw/2016/05/03/fintech-learn-more/
8. 科技橘報。
「2016 金融科技三大趨勢：電子支付、API 經濟、IOE 應用」(2016/03)
https://buzzorange.com/techorange/2016/03/09/2016-fintech-payment-api-ioe/
9. 玉山銀行網站「新聞中心」
https://www.esunbank.com.tw/bank/about/news-center
10. 工商時報。
「玉山銀攜手SAS 拚客戶滿意度」(2016/02)
http://www.chinatimes.com/newspapers/20160202000124-260205
描述 碩士
國立政治大學
企業管理研究所(MBA學位學程)
104363040
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104363040
資料類型 thesis
dc.contributor.advisor 白佩玉zh_TW
dc.contributor.advisor Pai, Pei Yuen_US
dc.contributor.author (Authors) 梁榕修zh_TW
dc.contributor.author (Authors) Liang, Jung Hsiuen_US
dc.creator (作者) 梁榕修zh_TW
dc.creator (作者) Liang, Jung Hsiuen_US
dc.date (日期) 2017en_US
dc.date.accessioned 1-Dec-2017 12:10:08 (UTC+8)-
dc.date.available 1-Dec-2017 12:10:08 (UTC+8)-
dc.date.issued (上傳時間) 1-Dec-2017 12:10:08 (UTC+8)-
dc.identifier (Other Identifiers) G0104363040en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/114976-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 企業管理研究所(MBA學位學程)zh_TW
dc.description (描述) 104363040zh_TW
dc.description.abstract (摘要) 本研究從金融科技創新應用之觀點，舉行動銀行app之應用為例，整合過去行銷與科技採用之相關文獻，並呼應金融科技時代的創新元素，據此探究使用者對於行動銀行app持續採用行為、與提供未來創新發展上之建議。首先以質化研究的方式，了解行動銀行app使用者的使用原因、使用經驗、對app的整體評價與建議；其次發展出量化研究模型，找出各種影響消費者持續使用意願的因素。

本研究針對「僅使用行動銀行app者」、與「行動銀行app和網路銀行皆有使用者」發放網路問卷調查，在量化研究的部分，首先根據Fintech重要核心價值中的差異化與利基型專業產品，提出競業差異作為研究模型之第一層探討面，結果顯示:

1. 設計美感對使用者能產生正向的情感品質知覺，提升對科技使用的知覺有用性、知覺易用性與降低知覺風險。
2. 品牌聲望有助於提升消費者對於業者所提供之產品與服務的相對優勢。

其次，結合過去創新擴散理論、科技接受模式以及個人知覺風險，作為研究模型之第二層探討面，結果顯示: 複雜性、知覺有用性、知覺風險能顯著影響消費者對於行動銀行app的採用意願。

最後，整合質化訪談發現與量化結果分析，給予結論與建議:

1. 業者可從設計美感加強消費者對於新科技使用的知覺有用性與降低知覺風險
2. 品牌聲望為輔，實質創新為主，首先降低複雜性
3. 從知覺有用性方面創造創新競爭優勢、同時兼顧知覺風險
4. 持續推廣行動銀行app，作為創新發展基礎後盾、與開拓市場之契機。		zh_TW
dc.description.abstract (摘要) This paper takes mobile banking application as an example in the view of FinTech innovation. Combined with findings from marketing and information system research, this study adopts key elements of FinTech innovation to arrive at a more complete understanding of consumers’ continuance intention toward mobile banking. By first taking the qualitative method and conducting semi-structured interviews, we look into consumers’ motivations, experiences, and evaluations of using mobile banking.
For the quantitative part our empirical tests involve structural equation modeling. In addition, with the reference to one of main core values of FinTech innovation: differentiation and niche, specialized products, we propose competitive differences among competitors to form our first layer research model, the results demonstrate that:
1. Design aesthetics can increase one’s perceived affective quality of system usage, which in turn, had a significant positive impact on perceived usefulness, perceived ease of use and lower perceived risk
2. Brand reputation can positively affect consumers’ sense of relative advantage in terms of the product and service provided by specific vendor.

Meanwhile, our research integrates the concepts of Rogers’ innovation diffusion model, technology acceptance model, and personal perceived risk to further propose our second layer research model, and the result shows that: complexity, perceived usefulness, and perceived risk emerge as important antecedents of consumers’ continuance intention toward mobile banking.
Lastly, we conclude our analysis of both qualitative and quantitative survey and make suggestions as below:
1. Placing a high value on the influence of design beauty, could increase consumers’ perceived usefulness and reduce perceived risk of new technology.
2. Focusing mainly on innovation while brand reputation subsidiary, and take complexity as priority.
3. Creating competitive advantage of innovation based on perceived usefulness, without overlooking the significant influence of perceived risk.
4. Keeping giving an impetus actively to the usage of mobile banking to solidify foundations of innovation development and increase opportunities in the market.		en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的與研究問題 2
第三節 研究流程 3
第二章 文獻回顧 4
第一節 金融科技(FINTECH)背景發展 4
第二節 FINTECH對金融產業的影響 5
第三節 金融服務的未來趨勢 5
第四節 FINTECH六大核心價值 10
第五節 創新產品採用理論 11
第三章 質化研究方法 15
第一節 質性訪談 15
第二節 深入訪談法（IN-DEPTH INTERVIEW） 15
第三節 訪談型式 15
第四節 訪談問題設計 17
第五節 訪談對象 18
第四章 質化研究情境 20
第一節 個案公司介紹 20
第二節 玉山銀行行動銀行APP介紹 25
第五章 質化研究結果分析 29
第一節 訪談摘要 29
第二節 訪談內容分析 34
第六章 量化研究設計 39
第一節 研究架構 39
第二節 研究假說 41
第三節 變數操作性定義與衡量 46
第四節 問卷設計 49
第五節 獨立樣本T檢定 52
第七章 資料分析與實證結果 54
第一節 結構方程模式 54
第二節 多母群體分析 61
第八章 結論與建議 62
第一節 結論分析 62
第二節 實務意涵 65
第三節 研究限制與未來建議 66
參考文獻 68
附錄一 訪談內容 75
附錄二 問卷 97		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104363040en_US
dc.subject (關鍵詞) 持續採用行為zh_TW
dc.subject (關鍵詞) 設計美感zh_TW
dc.subject (關鍵詞) 知覺有用性zh_TW
dc.subject (關鍵詞) 知覺易用性zh_TW
dc.subject (關鍵詞) 複雜性zh_TW
dc.subject (關鍵詞) 知覺風險zh_TW
dc.subject (關鍵詞) 品牌聲望zh_TW
dc.subject (關鍵詞) Continuance intentionen_US
dc.subject (關鍵詞) Design aestheticsen_US
dc.subject (關鍵詞) Perceived usefulnessen_US
dc.subject (關鍵詞) Perceived ease of useen_US
dc.subject (關鍵詞) Complexityen_US
dc.subject (關鍵詞) Perceived risken_US
dc.subject (關鍵詞) Brand reputationen_US
dc.title (題名) 數位金融時代下行動銀行app持續採用行為研究zh_TW
dc.title (題名) Understanding Consumers’ Continuance Intention toward Mobile Banking in the Fintech Era: A Qualitative and Quantitative Studyen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 西文部分
1. Aboelmaged, M.G., & Gebba, T.R. (2013). Mobile banking adoption: an examination of technology acceptance model and theory of planned behavior. International Journal of Business Research and Development 2 (1), 35-50.
2. Al-Jabri, I.M., & Sohail, M.S. (2012). Mobile banking adoption: application of diffusion of innovation theory. Journal of Electronic Commerce Research 3 (4), 379-391.
3. Anderson, J.C., & Gerbing, D.W. (1988). Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin 103 (3), 411-423.
4. Baptista, G., & Oliveira T. (2016). A weight and a meta-analysis on mobile banking acceptance research. Computers in Human Behavior 63, 480-489.
5. Barrett, P. (2007). Structural equation modelling: Adjuging model fit. Personality and Individual Differences 42, 815-824.
6. Beldad, A., de Jong, M., & Steehouder, M. (2010). How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust, Computers in Human Behavior 26 (5), 857-869.
7. Benamati, J.S., Fuller, M.A., Serva, M.A., & Baroudi, J.A. (2010). Clarifying the integration of trust and TAM in e-commerce environments: implications for systems design and management. IEEE Transactions on Engineering Management 57 (3), 380–393.
8. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation confirmation model, MIS Quarterly 25 (3), 351-370.
9. Bidgoli, H. (1990). Designing a user-friendly interface for a decision support system. Information Technology 12 (3), 148-154.
10. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. K. A. Bollen & J. S. Long (Eds.), Testing structural equation models, Beverly Hills, CA: Sage, 136-162.
11. Chandra, S., Srivastava, S.C., & Theng, Y.-L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: an empirical analysis. Communications of the Association for Information Systems 27, 561-588.
12. Chau, P.Y.K., & 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.
13. Chen, C. (2013). Perceived risk, usage frequency of mobile banking services. Managing Service Quality 23(5), 410-436.
14. Chen. L.-D. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications 6 (1), 32-52.
15. Choi, H., Kim, Y., & Kim, J. (2011). Driving factors of post adoption behaviour in mobile banking data services. Journal of Business Research 64, 1212-1217.
16. Cyr, D., Bonanni, C. & Ilsever, J. (2004). Design and e-loyalty across cultures in electronic commerce. Proceedings for the Sixth International Conference on Electronic Commerce (ICEC04), The Association for Computing Machinery (ACM).
17. Cyr, D., Hea, M., & Ivanov A. (2006). Design aesthetics leading to m-loyalty in mobile commerce. Elsevier Science Information & Management 43, 950-963.
18. Davis, F. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral Dissertation, Sloan School of Management, Massachusetts Institute of Technology.
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, 982-1003.
20. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technologies. MIS Quarterly 13 (3), 319-340.
21. DeLone, W.H., & McLean E.R. (2004). Measuring e-Commerce success: applying the DeLone & McLean information systems success model. International Journal of Electronic Commerce 9 (1), 31-47.
22. Dennis L. Jackson, J. Arthur Gillaspy, Jr. (2009). Reporting Practices in Confirmatory Factor Analysis: An Overview and Some Recommendations. Psychological Methods 14 (1), 6-23.
23. Dion, K., Bersheid, E., & Walster, E. (1972). What is beautiful is good. Journal of Personality and Social Psychology 24 (3), 285-290.
24. Ferreira, J. B., da Rocha, A., & da Silva, J. F. (2014). Impacts of technology readiness on emotions and cognition in Brazil. Journal of Business Research 67(5), 865-873.
25. Flavia´n, C., Guinalı´u, M., & Gurrea, R. (2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information & Management 43 (1), 1-14.
26. Fornell, C. & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18 (1), 39-50.
27. Forsythe, S. M., & Shi, B. (2003). Consumer patronage and risk perceptions in internet shopping. Journal of Business Research 56, 867-875.
28. Gatignon, H., & Robertson, T. S. (1989). Technology diffusion: An empirical test of competitive effects. Journal of Marketing 53, 35-49.
29. Gefen, D. & Straub, D.W. (2003). Managing user trust in B2C e-services. e-Service Journal 2 (2), 7–24.
30. Gefen, D., Straub, D.W., & Boudreau, M.C. (2000). Structural equation modeling and regression: guidelines for research practice. Communications of the Association for Information Systems 4 (7), 1-70.
31. Gu, J.-C., Lee, S.-C., & Suh, Y.-H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications 36(9), 11605-11616.
32. Ha, K.-H., Canedoli, A., Baur, A.W., & Bick, M. (2012). Mobile banking - insights on its increasing relevance and most common drivers of adoption. Electronic Markets 22(4), 217-227.
33. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Boston, MA: Pearson Education Inc.
34. Hausman, A.V. & Siekpe, J.S. (2009). The effect of web interface features on consumer online purchase intentions, Journal of Business Research 62 (1), 5-13.
35. Hu, L.-T., & Bentler, P. M. (1999). Cut-off criteria for fit indexes in covariance structure analysis. Conventional criteria versus new alternatives. Structural Equation Modeling 6, 1-55.
36. 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.
37. Jiang, Z. & Benbasat, I. (2003). The effects of interactivity and vividness of functional control in changing web consumers’ attitudes. Proceedings for the 24th International Conference on Information Systems, Seattle, USA.
38. Jung, Y., Perez-Mira, B. & Wiley-Patton, S. (2009). Consumer adoption of mobile TV: examining psychological flow and media content. Computers in Human Behavior 25 (1), 123-129.
39. Karvonen, K. (2000). The beauty of simplicity. ACM Proceedings on the Conference on Universal Usability, 85-90.
40. Kiljander, H., & Jarnstrom, J. (2003). User interface styles. C. Lindholm, T. Keinonen, H. Kiljander (Eds.), Mobile Usability: How Nokia Changed the Face of the Mobile Phone, McGraw Hill, 15-44.
41. Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior 26 (3), 310-322.
42. Kim, D.J., Ferrin, D.L., & Rao, H.R. (2009). Trust and satisfaction, two stepping stones for successful e-commerce relationships: a longitudinal exploration. Information Systems Research 20 (2), 237-257.
43. Kim, G., Shin, B., & Lee, H.G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal 19 (3), 283-311.
44. Kleijnen, M., Lee, N., & Wetzels, M. (2009). An exploration of consumer resistance to innovation and its antecedents. Journal of Economic Psychology 30, 344-357.
45. Kuo, Y.-F., Wu, C.-M., & Deng, W.-J. (2009). The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in Human Behavior 25 (4), 887-896.
46. Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking. Journal of Business Research 69, 2432-2439.
47. Laukkanen, T., & Kiviniemi, V. (2010). The role of information in mobile banking resistance. The International Journal of Bank Marketing 28 (5), 372-388.
48. Laukkanen, T., Sinkkonen, S., Kivijärvi, M., & Laukkanen, P. (2007). Innovation resistance among mature consumers. Journal of Consumer Marketing 24 (7), 419-427.
49. Lavie, T., & Tractinsky, N. (2004). Assessing dimensions of perceived visual aesthetics of web sites, International Journal of Human–Computer Studies 60 (3), 269-298.
50. Lee, T. (2005). The impact of perceptions of interactivity on customer trust and transaction intentions in mobile commerce. Journal of Electronic Commerce Research 6 (3), 165-180.
51. Lee, Y. -K., Park, J. -H., Chung, N., & Blakeney, A. (2012). A unified perspective on the factors influencing usage intention toward mobile financial services. Journal of Business Research 65 (11), 1590-1599.
52. Lee, Y.E., & Benbasat, I. (2004) A framework for the study of customer interface design for mobile commerce. International Journal of Electronic Commerce 8 (3), 79-102.
53. Legris, P. et al. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management 40, 191-204.
54. Legrisa, P., Inghamb, J., & Collerette, P. (2001). Why do people use information technology? A critical review of the technology acceptance model. Information & Management 40, 191-204.
55. Lincoln, Y.S., & Guba, E.G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage.
56. Liu, C.T., Guo, Y.M., & Lee, C.H. (2011). The effects of relationship quality and switching barriers on customer loyalty. International Journal of Information Management 31(1), 71-79.
57. Lu, Y., Yang, S., Chau, Patrick Y.K., & Cao, Yuzhi (2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information & Management 48, 393–403.
58. Luarn, P., & Lin H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior 21, 873-891.
59. Mallat, N. (2007). Exploring consumer adoption of mobile payments — a qualitative study. The Journal of Strategic Information Systems 16 (4), 413-432.
60. Marsh, H. W., Balla, J. R., & Hau, K. (1996). An evaluation of incremental fit indices: A clarification of mathematical and empirical properties. InG. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques, 315-353.
61. Martins, C., Oliveira, T., & Popovic, A. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management 34 (1), 1-13.
62. McCracken, G. (1988). The long interview. Newbury Park. California: SAGE Publications.
63. Minichiello, V., Aroni, R., Timewell, E., & Alexander, L. (1995). In-depth interviewing: Principles, techniques, analysis. Melbourne: Addison Welsey Longman.
64. Nunnally, J.C. (1978). Psychometric Theory, McGraw-Hill, New York.
65. Nysveen, H., & Pedersen, P.E., & Thorbjørnsen H. (2005). Intentions to use mobile services: antecedents and cross-service comparisons. Journal of the Academy of Marketing Science 33 (3), 330-346.
66. O`Cass, A. & Carlson, J. (2010). Examining the effects of website induced flow in professional sporting team websites, Internet Research 20 (2), 115-134.
67. Ram, S. (1989). Successful innovation using strategies to reduce consumer resistance: An empirical test. Journal of Product Innovation Management 6 (1), 20-34.
68. Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing 6 (2), 5-14.
69. Rist, T., & Brandmeier P. (2001). Customizing graphics for tiny displays of mobile devices. M.D. Dunlop, S.A. Brewster (Eds.) Proceedings of Mobile HCI 2001: Third International Workshop on Human Computer Interaction with Mobile Devices, Lille, France, 260-268.
70. Rogers, E.M. (1983). Diffusion of innovations (3rd ed.). New York: Free Press, 163-238.
71. Rosen, D.E., & Purinton, E. (2004). Website design: viewing the web as a cognitive landscape. Journal of Business Research 57, 787-794.
72. Rubin, H.J., & Rubin, I.S. (1995). Qualitative interviewing: The art of hearing data. Thousand Oaks, CA: Sage.
73. Sarker, S., & Wells, J. (2003). Understanding mobile handheld device and adoption. Communications of the ACM 46 (12), 35-40.
74. Schierz, P.G., Schilke, O., & Wirtz, B.W. (2010). Understanding consumer acceptance of mobile payment services: an empirical analysis. Electronic Commerce Research and Applications 9 (3), 209-216.
75. Schouten, J.W. (1991). Personal Rites of Passage and the Reconstruction of Self. NA - Advances in Consumer Research (18 ed). Rebecca H. Holman and Michael R. Solomon, Provo, UT: Association for Consumer Research, 49-51.
76. Sheth, J. N. (1981). Psychology of innovation resistance: The less developed concept (LDC) in diffusion research. Research in Marketing 4, 273-282.
77. Shneiderman, B. (1987). User interface design and evaluation for an electronic encyclopedia. G. Salvendy (Ed.), Cognitive Engineering in the Design of Human–Computer Interaction and Expert Systems, Elsevier Science Publishers, 207-223.
78. Straub, D., Boudreau, M.-C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems 13 380-427.
79. Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Sage Publications, Inc.
80. van der Heijden, H. (2003). Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information &Management 40 (6), 541-549.
81. Williams M. (1997) Social Surveys: Design to Analysis. T. (May Ed.). Social Research Issues, Methods and Process. Buckingham: Open University Press.
82. World Economic Forum (2015). The Future of Financial Services: How disruptive innovations are reshaping the way financial services are structured, provisioned and consumed, 3-22.
83. Wu, J., & Du, H. (2012). Toward a better understanding of behavioral intention and system usage constructs. European Journal of Information Systems 21 (6), 680-698.
84. Wu, J.-J., & Chang Y.-S. (2005). Towards understanding members` interactivity, trust, and flow in online travel community. Industrial Management & Data Systems 105 (7), 937-954.
85. Yazdani, D., & Weber, G., PwC Global research team (2016). Global FinTech Report, March 2016, 3-29.
86. Yin, R.K. (1981a). The case study as a serious research strategy. Knowledge: Creation, Diffusion, Utilization 3, 97-114.
87. Yin, R.K. (1981b). Life hhistories of innovations: How new practices become routinized. Public Administration Review 41, 21-28.
88. Yin, R.K. (1984). Case study research: Design and methods (1st ed.). Beverly Hills, CA: Sage Publications.
89. Zahedi, F.M., & Song, J. (2008). Dynamics of trust revision: using health infomediaries. Journal of Management Information Systems 24 (4), 225-248.
90. Zhou, T. (2011). An empirical examination of continuance intention of mobile payment services. Decision Support Systems 54, 1085-1091.
91. Zhou, T., Li, H.X., & Liu, Y. (2010). The effect of flow experience on mobile SNS users` loyalty. Industrial Management & Data Systems 110 (5–6), 930-946.
中文部分
1. 吳明隆（2000），SPSS 統計應用實務，台北：松崗。
2. 吳萬益與林清河（2002），行銷研究，台北：華泰文化。
3. 陳順宇 (2005)，多變量分析，台北：華泰文化。
4. 文崇一、楊國樞（2000），訪問調查法，社會及行為科學研究法下冊，台北：東華。
5. 陳月娥 (2015)，社會研究法，台北：千華數位文化。
6. 潘淑滿 (2003)，質性研究：理論與應用，心理出版。
7. 財經| TechNews 科技新報。
「解密改變貨幣流通方式的新名詞－ FinTech 金融科技」(2016/05)
http://finance.technews.tw/2016/05/03/fintech-learn-more/
8. 科技橘報。
「2016 金融科技三大趨勢：電子支付、API 經濟、IOE 應用」(2016/03)
https://buzzorange.com/techorange/2016/03/09/2016-fintech-payment-api-ioe/
9. 玉山銀行網站「新聞中心」
https://www.esunbank.com.tw/bank/about/news-center
10. 工商時報。
「玉山銀攜手SAS 拚客戶滿意度」(2016/02)
http://www.chinatimes.com/newspapers/20160202000124-260205		zh_TW