參考文獻 |
中文文獻
[1] 中華民國銀行商業同業公會全過聯合會,銀行受理客戶以網路方式開立數位存款帳戶作業範本,中華民國銀行商業同業公會全過聯合會,台北,2024。
[2] 金融監督管理委員會:數位存款帳戶開戶統計。2024年05月21日,取自https://www.banking.gov.tw/ch/home.jsp?id=591&parentpath=0,590&mcustomize=multimessage_view.jsp&dataserno=201911270001&dtable=Disclosure
[3] 模型寶庫:科技接受模型(Technology Acceptance Model)在社會領域中學術應用。2019年3月31日,取自https://bigdatamodel.blogspot.com/2019/03/technology-acceptance-model.html
[4] 模型寶庫:理性行為理論(Theory of Reasoned Action)在社會領域中學術應用。2019年3月16日,取自https://bigdatamodel.blogspot.com/2019/03/theory-of-reasoned-action.html
英文文獻
[1] Abroud, A., Choong, Y. V., Muthaiyah, S., & Fie, D. Y. G. (2015). Adopting e-finance: decomposing the technology acceptance model for investors. Service Business, 9, 161-182.
[2] Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
[3] Al Tarawneh, M. m. A., Nguyen, T. P. L., Yong, D. G. F., & Dorasamy, M. A. P. (2023). Determinant of m-banking usage and adoption among millennials. Sustainability, 15(10), 8216.
[4] Almarzouqi, A., Aburayya, A., & Salloum, S. A. (2022). Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis. Informatics in Medicine Unlocked, 28, 100859.
[5] Cigdem Altin Gumussoy, C., Kaya, A., & Ozlu, E. (2018). Determinants of mobile banking use: an extended TAM with perceived risk, mobility access, compatibility, perceived self-efficacy and subjective norms. Industrial Engineering in the Industry 4.0 Era: Selected Papers from the Global Joint Conference on Industrial Engineering and Its Application Areas, GJCIE 2017, July 20–21, Vienna, Austria,
[6] Bahl, S. (2012). E-banking: Challenges & policy implications. International Journal of Computing & Business Research, 5(3), 84-101.
[7] Baklouti, F., & Boukamcha, F. (2023). Consumer resistance to internet banking services: implications for the innovation resistance theory. Journal of Financial Services Marketing, 1-13.
[8] Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
[9] Bhattacherjee, A., Limayem, M., & Cheung, C. M. (2012). User switching of information technology: A theoretical synthesis and empirical test. Information & Management, 49(7-8), 327-333.
[10] Blackwell, G., Gordon, B., Peña-López, I., Scott, R., Baker, A., Cooper, J., Rahimi, B., Vimarlund, V., Timpka, T., & Hackl, W. (2018). A systematic review of the technology acceptance model in health informatics. Applied clinical informatics, 9(03), 604-634.
[11] Bozic, B. (2017). Consumer trust repair: A critical literature review. European Management Journal, 35(4), 538-547.
[12] Briz-Ponce, L., & García-Peñalvo, F. J. (2015). An empirical assessment of a technology acceptance model for apps in medical education. Journal of medical systems, 39, 1-5.
[13] Carrera, A., Zoccarato, F., Mazzeo, M., Lettieri, E., Toletti, G., Bertoli, S., Castelnuovo, G., & Fresa, E. (2023). What drives patients’ acceptance of Digital Therapeutics? Establishing a new framework to measure the interplay between rational and institutional factors. BMC Health Services Research, 23(1), 145.
[14] Chai, L., & Pavlou, P. (2002). What drives electronic commerce across cultures? A cross-cultural empirical investigation of the theory of planned behavior. Journal of Electronic Commerce Research, 3(4), 240-253.
[15] Chang, C.-C., & Chang, P.-C. (2010). A study on Taiwan consumers’ adoption of online financial services.
[16] Chang, C.-T., Hajiyev, J., & Su, C.-R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128-143.
[17] Chang, H. H., & Hamid, M. R. B. A. (2010). An empirical investigation of internet banking in Taiwan. Global Journal of Business Research, 4(2), 39-47.
[18] Chang, I. C., Liu, C. C., & Chen, K. (2014). The push, pull and mooring effects in virtual migration for social networking sites. Information Systems Journal, 24(4), 323-346.
[19] 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.
[20] Chau, P. Y., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision sciences, 32(4), 699-719.
[21] Chen, J.-L. (2011). The effects of education compatibility and technological expectancy on e-learning acceptance. Computers & Education, 57(2), 1501-1511.
[22] Cheng, T. E., Lam, D. Y., & Yeung, A. C. (2006). Adoption of internet banking: an empirical study in Hong Kong. Decision support systems, 42(3), 1558-1572.
[23] Cobelli, N., Gill, L., Cassia, F., & Ugolini, M. (2014). Factors that influence intent to adopt a hearing aid among older people in I taly. Health & social care in the community, 22(6), 612-622.
[24] Cruz, P., Barretto Filgueiras Neto, L., Muñoz‐Gallego, P., & Laukkanen, T. (2010). Mobile banking rollout in emerging markets: evidence from Brazil. International Journal of bank marketing, 28(5), 342-371.
[25] Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205-219.
[26] DeYoung, R. (2001). The financial performance of pure play Internet banks. Economic Perspectives-Federal Reserve Bank of Chicago, 25(1), 60-73.
[27] Dhagarra, D., Goswami, M., & Kumar, G. (2020). Impact of trust and privacy concerns on technology acceptance in healthcare: an Indian perspective. International journal of medical informatics, 141, 104164.
[28] Ebardo, R., & Suarez, M. T. (2023). Do cognitive, affective and social needs influence mobile learning adoption in emergency remote teaching? Research and Practice in Technology Enhanced Learning, 18, 014-014.
[29] Edo, O. C., Ang, D., Etu, E.-E., Tenebe, I., Edo, S., & Diekola, O. A. (2023). Why do healthcare workers adopt digital health technologies-A cross-sectional study integrating the TAM and UTAUT model in a developing economy. International Journal of Information Management Data Insights, 3(2), 100186.
[30] Faber, S., van Geenhuizen, M., & de Reuver, M. (2017). eHealth adoption factors in medical hospitals: a focus on the Netherlands. International journal of medical informatics, 100, 77-89.
[31] Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.
[32] Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
[33] Gagnon, M.-P., Simonyan, D., Godin, G., Labrecque, M., Ouimet, M., & Rousseau, M. (2016). Factors influencing electronic health record adoption by physicians: A multilevel analysis. International Journal of Information Management, 36(3), 258-270.
[34] Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), 725-737.
[35] Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information & management, 39(8), 705-719.
[36] Guenther, P., Guenther, M., Ringle, C. M., Zaefarian, G., & Cartwright, S. (2023). Improving PLS-SEM use for business marketing research. Industrial Marketing Management, 111, 127-142.
[37] Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5, No. 3, pp. 207-219). In: Upper Saddle River, NJ: Prentice hall.
[38] Hale, J. L., Householder, B. J., & Greene, K. L. (2002). The theory of reasoned action. The persuasion handbook: Developments in theory and practice, 14(2002), 259-286.
[39] Hung, S.-Y., Tsai, J. C.-A., & Chuang, C.-C. (2014). Investigating primary health care nurses′ intention to use information technology: An empirical study in Taiwan. Decision Support Systems, 57, 331-342.
[40] Idoga, P. E., Toycan, M., Nadiri, H., & Çelebi, E. (2018). Factors affecting the successful adoption of e-health cloud based health system from healthcare consumers’ perspective. IEEE Access, 6, 71216-71228.
[41] Irimia-Diéguez, A., Velicia-Martín, F., & Aguayo-Camacho, M. (2023). Predicting FinTech innovation adoption: the mediator role of social norms and attitudes. Financial Innovation, 9(1), 36.
[42] Jeyaraj, A., Dwivedi, Y. K., & Venkatesh, V. (2023). Intention in information systems adoption and use: Current state and research directions. In (Vol. 73, pp. 102680): Elsevier.
[43] Kaabachi, S., Ben Mrad, S., & O’Leary, B. (2019). Consumer’s initial trust formation in IOB’s acceptance: The role of social influence and perceived compatibility. International Journal of bank marketing, 37(2), 507-530.
[44] Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.
[45] Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS quarterly, 781-804.
[46] Karahoca, A., Karahoca, D., & Aksöz, M. (2018). Examining intention to adopt to internet of things in healthcare technology products. Kybernetes, 47(4), 742-770.
[47] Karjaluoto, H., Koivumaki, T., & Salo, J. (2003). Individual differences in private banking: Empirical evidence from Finland. 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the,
[48] Karkonasasi, K., Cheah, Y.-N., Vadiveloo, M., & Mousavi, S. A. (2023). Acceptance of a Text Messaging Vaccination Reminder and Recall System in Malaysia’s Healthcare Sector: Extending the Technology Acceptance Model. Vaccines, 11(8), 1331.
[49] Khan, I., Xitong, G., Ahmad, Z., & Shahzad, F. (2019). Investigating factors impelling the adoption of e-health: a perspective of African expats in China. Sage Open, 9(3), 2158244019865803.
[50] Khatun, F., Heywood, A. E., Ray, P. K., Hanifi, S., Bhuiya, A., & Liaw, S.-T. (2015). Determinants of readiness to adopt mHealth in a rural community of Bangladesh. International journal of medical informatics, 84(10), 847-856.
[51] Kim, D., & Ammeter, T. (2014). Predicting personal information system adoption using an integrated diffusion model. Information & Management, 51(4), 451-464.
[52] Kim, S. S., & Malhotra, N. K. (2005). Predicting system usage from intention and past use: scale issues in the predictors. Decision Sciences, 36(1), 187-196.
[53] Leong, Y. P., & Wang, Q. (2006). Effects of customer beliefs on relationship marketing tactics and customer attitude on switching intention in a competitive service industry. ACR Asia-Pacific Advances.
[54] López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & management, 45(6), 359-364.
[55] Luqman, A., Masood, A., & Ali, A. (2018). An SDT and TPB-based integrated approach to explore the role of autonomous and controlled motivations in “SNS discontinuance intention”. Computers in Human Behavior, 85, 298-307.
[56] Maditinos, D., Chatzoudes, D., & Sarigiannidis, L. (2013). An examination of the critical factors affecting consumer acceptance of online banking: A focus on the dimensions of risk. Journal of Systems and information Technology, 15(1), 97-116.
[57] Matsuo, M., Minami, C., & Matsuyama, T. (2018). Social influence on innovation resistance in internet banking services. Journal of Retailing and Consumer Services, 45, 42-51.
[58] McKechnie, S., Winklhofer, H., & Ennew, C. (2006). Applying the technology acceptance model to the online retailing of financial services. International Journal of Retail & Distribution Management, 34(4/5), 388-410.
[59] McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The journal of strategic information systems, 11(3-4), 297-323.
[60] Melas, C. D., Zampetakis, L. A., Dimopoulou, A., & Moustakis, V. (2011). Modeling the acceptance of clinical information systems among hospital medical staff: an extended TAM model. Journal of biomedical informatics, 44(4), 553-564.
[61] Mobarak, A. M., Dakrory, M. I., Elsotouhy, M. M., Ghonim, M. A., & Khashan, M. A. (2024). Drivers of mobile payment services adoption: a behavioral reasoning theory perspective. International Journal of Human–Computer Interaction, 40(7), 1518-1531.
[62] 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.
[63] Mortenson, M. J., & Vidgen, R. (2016). A computational literature review of the technology acceptance model. International Journal of Information Management, 36(6), 1248-1259.
[64] Msaed, C., Al-Kwifi, S. O., & Ahmed, Z. U. (2017). Building a comprehensive model to investigate factors behind switching intention of high-technology products. Journal of Product & Brand Management, 26(2), 102-119.
[65] Mutahar, A. M., Daud, N. M., Ramayah, T., Putit, L., & Isaac, O. (2017). Examining the effect of subjective norms and compatibility as external variables on TAM: mobile banking acceptance in Yemen. Science International, 29(4), 769-776.
[66] Nezamdoust, S., Abdekhoda, M., & Rahmani, A. (2022). Determinant factors in adopting mobile health application in healthcare by nurses. BMC Medical Informatics and Decision Making, 22(1), 47.
[67] Ng, Y. M. M. (2020). Re-examining the innovation post-adoption process: The case of Twitter discontinuance. Computers in Human Behavior, 103, 48-56.
[68] Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134.
[69] Peng, X., Zhao, Y. C., & Zhu, Q. (2016). Investigating user switching intention for mobile instant messaging application: Taking WeChat as an example. Computers in Human Behavior, 64, 206-216.
[70] Pennington, R., Wilcox, H. D., & Grover, V. (2003). The role of system trust in business-to-consumer transactions. Journal of management information systems, 20(3), 197-226.
[71] Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet research, 14(3), 224-235.
[72] Puspitasari, I., Wiambodo, A. N. R., & Soeparman, P. (2021). The impact of expectation confirmation, technology compatibility, and customer’s acceptance on e-wallet continuance intention. AIP conference proceedings,
[73] Putri, G. A., Widagdo, A. K., & Setiawan, D. (2023). Analysis of financial technology acceptance of peer to peer lending (P2P lending) using extended technology acceptance model (TAM). Journal of Open Innovation: Technology, Market, and Complexity, 9(1), 100027.
[74] Raut, R. K., Kumar, R., & Das, N. (2021). Individual investors’ intention towards SRI in India: an implementation of the theory of reasoned action. Social Responsibility Journal, 17(7), 877-896.
[75] Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of bank marketing, 28(5), 328-341.
[76] Rogers, E. M. (1995). Diffusion of Innovations: modifications of a model for telecommunications. Die diffusion von innovationen in der telekommunikation, 25-38.
[77] Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in human behavior, 26(6), 1632-1640.
[78] Santos, A. A. d., & Ponchio, M. C. (2021). Functional, psychological and emotional barriers and the resistance to the use of digital banking services. Innovation & Management Review, 18(3), 331-348.
[79] Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35.
[80] Schnall, R., Higgins, T., Brown, W., Carballo-Dieguez, A., & Bakken, S. (2015). Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Studies in health technology and informatics, 216, 467.
[81] Schofield, J. W. (1975). Effect of norms, public disclosure, and need for approval on volunteering behavior consistent with attitudes. Journal of personality and Social Psychology, 31(6), 1126.
[82] Shaikh, I. M., Qureshi, M. A., Noordin, K., Shaikh, J. M., Khan, A., & Shahbaz, M. S. (2020). Acceptance of Islamic financial technology (FinTech) banking services by Malaysian users: an extension of technology acceptance model. foresight, 22(3), 367-383.
[83] Shen, Q., & Li, Y. (2010). Explore antecedent factors of switching costs and intentions and their impact on customer loyalty. The 13th Conference on Interdisciplinary and Multifunctional Business Management,
[84] Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European journal of marketing, 53(11), 2322-2347.
[85] Song, J. (2007). Trust in health infomediaries. Decision support systems, 43(2), 390-407.
[86] Sumiati, A., Widyastuti, U., & Takidah, E. (2021). The millennials generation′s intention to invest: A modified model of the theory of reasoned action. International journal of entrepreneurship, 25(3), 1-11.
[87] Tsai, C.-H. (2014). Integrating social capital theory, social cognitive theory, and the technology acceptance model to explore a behavioral model of telehealth systems. International journal of environmental research and public health, 11(5), 4905-4925.
[88] Tu, J.-C., Luo, S. C., Lee, Y.-L., Shih, M.-F., & Chiu, S.-P. (2022). Exploring usability and patient attitude towards a smart hospital service with the technology acceptance model. International Journal of Environmental Research and Public Health, 19(10), 6059.
[89] Venkatesh, V. (2006). Where to go from here? Thoughts on future directions for research on individual‐level technology adoption with a focus on decision making. Decision Sciences, 37(4), 497-518.
[90] Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision sciences, 27(3), 451-481.
[91] 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.
[92] Walczak, R., Kludacz-Alessandri, M., & Hawrysz, L. (2022). Use of telemedicine technology among general practitioners during COVID-19: A modified technology acceptance model study in Poland. International Journal of Environmental Research and Public Health, 19(17), 10937.
[93] Wang, Y.-Y., Wang, Y.-S., & Lin, T.-C. (2018). Developing and validating a technology upgrade model. International Journal of Information Management, 38(1), 7-26.
[94] Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of Internet banking: an empirical study. International journal of service industry management, 14(5), 501-519.
[95] Wu, J.-H., Shen, W.-S., Lin, L.-M., Greenes, R. A., & Bates, D. W. (2008). Testing the technology acceptance model for evaluating healthcare professionals′ intention to use an adverse event reporting system. International Journal for Quality in Health Care, 20(2), 123-129.
[96] Wu, L., & Chen, J.-L. (2005). An extension of trust and TAM model with TPB in the initial adoption of on-line tax: an empirical study. International Journal of Human-Computer Studies, 62(6), 784-808.
[97] Yamin, M. A. Y., & Abdalatif, O. A. A. (2024). Examining consumer behavior towards adoption of quick response code mobile payment systems: transforming mobile payment in the fintech industry. Humanities and Social Sciences Communications, 11(1), 1-11.
[98] Yan, M., Zhang, M., Kwok, A. P. K., Zeng, H., & Li, Y. (2023). The Roles of Trust and Its Antecedent Variables in Healthcare Consumers’ Acceptance of Online Medical Consultation during the COVID-19 Pandemic in China. Healthcare,
[99] Yap, K. B., Wong, D. H., Loh, C., & Bak, R. (2010). Offline and online banking–where to draw the line when building trust in e‐banking? International Journal of Bank Marketing, 28(1), 27-46.
[100] Ye, C., & Potter, R. (2011). The role of habit in post-adoption switching of personal information technologies: An empirical investigation. Communications of the Association for Information Systems, 28(1), 35.
[101] Yee‐Loong Chong, A., Ooi, K. B., Lin, B., & Tan, B. I. (2010). Online banking adoption: an empirical analysis. International Journal of bank marketing, 28(4), 267-287.
[102] Yu, P., Li, H., & Gagnon, M.-P. (2009). Health IT acceptance factors in long-term care facilities: a cross-sectional survey. International journal of medical informatics, 78(4), 219-229.
[103] Zhao, J., Fang, S., & Jin, P. (2018). Modeling and quantifying user acceptance of personalized business modes based on TAM, trust and attitude. Sustainability, 10(2), 356. |