INNOVATIVENESS, TECHNOSTRESS, PERCEIVED EASE OF USE, AND PERCEIVED RISK IN DIGITAL BANKING ADOPTION AMONG AN ETHNIC MINORITY COMMUNITY IN CHINA
Article Sidebar
Main Article Content
Abstract
The adoption of digital services in ethnically distinct, resource-constrained communities reveals how technology interacts with cultural identity, cognitive stress, and institutionalized trust in ways that standard adoption models inadequately capture. This study examines antecedents of D-banking adoption intention among residents of Ganzi Tibetan Autonomous Prefecture—one of China's most financially underserved ethnic minority regions—by extending the Technology Acceptance Model with Technostress, Perceived Risk, Innovativeness, and Trust. Drawing on 394 valid survey responses and a moderated mediation model tested via PLS-SEM, Perceived Usefulness mediated the effects of Innovativeness, Technostress, and Perceived Ease of Use on Intention to Use D-banking; Perceived Risk negatively moderated the Perceived Usefulness–Intention relationship; and Trust emerged as one of the strongest direct predictors, with all ten hypotheses supported. These findings demonstrate that in financially excluded, culturally distinct settings, adoption intention is governed as much by community-mediated trust, affective responses to interface design, and culturally embedded risk perception as by cognitive utility evaluation, with direct implications for inclusive fintech design and digital inclusion policy.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของคณะวิทยาการจัดการ มหาวิทยาลัยราชภัฏอุดรธานี
ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้ ไม่ใช่ความคิดเห็นและความรับผิดชอบของผู้จัดทำ บรรณาธิการ กองบรรณาธิการ และคณะวิทยาการจัดการ มหาวิทยาลัยราชภัฏอุดรธานี ความรับผิดชอบด้านเนื้อหาและการตรวจร่างบทความแต่ละเรื่องเป็นความคิดเห็นของผู้เขียนบทความแต่ละท่าน
References
Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215. https://doi.org/10.1287/isre.9.2.204
Ahmad, S., Bhatti, S. H., & Hwang, Y. (2023). Technostress and employees' digital work behavior. Internet Research, 33(5), 1929–1958. https://doi.org/10.1108/INTR-05-2022-0329
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Al-Adwan, A. S., Al-Adwan, A., & Berger, H. (2023). Unlocking the black box of technology adoption. Technology in Society, 73, Article 102250. https://doi.org/10.1016/j.techsoc.2023.102250
Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
Arner, D. W., Barberis, J., & Buckley, R. P. (2020). FinTech, RegTech, and the reconceptualization of financial regulation. Northwestern Journal of International Law & Business, 40(1), 1–38.
Chen, Y., & Hu, X. (2023). Consumer innovativeness, perceived value, and smart wearable device adoption. Frontiers in Psychology, 14, Article 1172004. https://doi.org/10.3389/fpsyg.2023.1172004
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human–Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
Ganzi Tibetan Autonomous Prefecture Bureau of Statistics. (2023). Ganzi Prefecture 2023 statistical yearbook. Ganzi Bureau of Statistics.
Goldsmith, R. E., & Hofacker, C. F. (1991). Measuring consumer innovativeness. Journal of the Academy of Marketing Science, 19(3), 209–221. https://doi.org/10.1007/BF02726497
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based SEM. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Kaur, P., Dhir, A., Singh, N., Sahu, G., & Almotairi, M. (2020). An innovation resistance theory perspective on mobile payment solutions. Journal of Retailing and Consumer Services, 55, Article 102059. https://doi.org/10.1016/j.jretconser.2020.102059
Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Press.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce. Information Systems Research, 13(3), 334–359. https://doi.org/10.1287/isre.13.3.334.81
Norman, D. A. (2013). The design of everyday things (Rev. ed.). Basic Books.
People's Bank of China. (2022). Fintech development plan (2022–2025). People's Bank of China.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569. https://doi.org/10.1146/annurev-psych-120710-100452
Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostress for end users in organizations: Conceptual development and empirical validation. Information Systems Research, 19(4), 417–433. https://doi.org/10.1287/isre.1070.0165
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Tarafdar, M., Maier, C., Laumer, S., & Weitzel, T. (2019). Explaining the link between technostress and technology addiction. Information Systems Journal, 29(6), 1279–1310.
Tran, L. T. T., Nguyen, P. T. H., & Nguyen, N. D. (2023). Perceived risk and mobile payment adoption in Southeast Asia. Finance Research Letters, 57, Article 104236. https://doi.org/10.1016/j.frl.2023.104236
van Dijk, J. A. G. M. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4–5), 221–235. https://doi.org/10.1016/j.poetic.2006.05.004
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Wang, J., & Yang, S. (2024). Infrastructure constraints and digital service access in remote regions. China Rural Survey, 42(1), 98–114.
Warschauer, M. (2004). Technology and social inclusion: Rethinking the digital divide. MIT Press.
Yu, L., Li, H., He, W., Wang, F. K., & Jiao, S. (2020). A meta-analysis to explore privacy cognition and information disclosure of internet users. International Journal of Information Management, 51, Article 102015. https://doi.org/10.1016/j.ijinfomgt.2019.09.011
