Empirical Partial Least Squares Structural Equation Modeling Study on the Continued Use of Generative AI Assistants in Digital Banking: The Moderating Role of Digital Financial Literacy in Vietnam

The rapid integration of generative artificial intelligence (GenAI) assistants into digital banking services has transformed customer interactions; however, sustaining users’ continued use of these technologies remains a critical challenge, particularly in emerging markets. Drawing on the perspectives of trustworthy AI and technology continuance, this study investigates the technological and behavioral drivers of the continued use of generative AI assistants in digital banking while examining the moderating role of digital financial literacy in Vietnam. A mixed-method research design was employed. First, a qualitative phase involving in-depth discussions with 35 experts in digital banking was conducted to refine and validate the measurement scales. A large-scale quantitative survey was administered to customers who regularly use digital banking services. From 900 distributed questionnaires, 845 valid responses were collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The results demonstrate that explainable AI, AI service quality, perceived cybersecurity assurance, perceived AI accuracy and reliability, and perceived privacy protection significantly influence users’ continuance intention and trust in generative AI assistants. AI service quality emerged as the strongest predictor of trust ( = 0.548) and continuance intention ( = 0.281). Trust plays a central mediating role, significantly enhancing the intention of users to continue using GenAI assistants in digital banking. Furthermore, the findings reveal a significant moderating effect of digital financial literacy on the relationship between trust and continuance intention, indicating that users with higher levels of digital financial literacy are better able to translate trust into sustained usage behavior. This study contributes to the literature on continuance intention and AI trust by extending existing AI adoption frameworks from initial acceptance to sustained use of GenAI assistants in digital banking. It offers an integrated model that combines trustworthy AI attributes, trust, and digital financial literacy, thereby clarifying how technological quality and user capability jointly shape continuance intention in an emerging market. The findings provide practical implications for banks and policymakers by emphasizing AI service quality, transparency, cybersecurity assurance, privacy protection, and digital financial education as key levers for GenAI adoption.

Continuance intention; Digital banking; Digital financial literacy; Generative artificial intelligence; Trust

Abad-Segura, E., Gonz´alez-Zamar, M., L´opez-Meneses, E., & V´azquez-Cano, E. (2020). Financial technology: Review of trends, approaches and management. Mathematics, 8(6), 1–36. https://doi.org/10.3390/math8060951

Abis, D., Pia, P., & Limbu, Y. (2025). FinTech and consumers: A systematic review and integrative framework. Management Decision, 63(1), 49–75. https://doi.org/10.1108/MD-07-2023-1136

Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427–445. https://doi.org/10.1007/s12525-020-00414-7

Ahsan, T., Mirza, S., Al-Gamrh, B., Bin-Feng, C., & Rao, Z. (2021). How to deal with policy uncertainty to attain sustainable growth: The role of corporate governance. Corporate Governance, 21(1), 78–91. https://doi.org/10.1108/CG-04-2020-0121

Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/10.1002/hbe2.195

Al, P. (2023). (e)-trust and its function: Why we shouldn’t apply trust and trustworthiness to human–AI relations. Journal of Applied Philosophy, 40(1), 95–108. https://doi.org/10.1111/japp.12613

Al-Dosari, K., Fetais, N., & Kucukvar, M. (2024). Artificial intelligence and cyber defense system for banking industry: A qualitative study of AI applications and challenges. Cybernetics and Systems, 55(2), 302–330. https://doi.org/10.1080/01969722.2022.2112539

Al-Halbusi, H., Soto-Acosta, P., & Popa, S. (2023). Analysing e-entrepreneurial intention from the theory of planned behaviour: The role of social media use and perceived social support. International Entrepreneurship and Management Journal, 19(4), 1611–1642. https://doi.org/10.1007/s11365-023-00866-1

Alyoubi, H., & Alkhayat, H. (2025). Mapping the governance landscape of artificial intelligence through OECD policy analysis. Artificial Intelligence in Learning, 1(4), 329–345. https://doi.org/10.63913/ail.v1i4.45

Ashraf, B., Zhang, Q., Mahmood, A., & Aslam, N. (2025). Impact of information and communication technology on financial development: An international view. Journal of Global Information Technology Management, 28(3), 234–264. https://doi.org/10.1080/1097198X.2025.2520713

Bedu´e, P., & Fritzsche, A. (2022). Can we trust AI? an empirical investigation of trust requirements and guide to successful AI adoption. Journal of Enterprise Information Management, 35(2), 530–549. https://doi.org/10.1108/JEIM-06-2020-0233

Bernards, N., & Campbell-Verduyn, M. (2019). Understanding technological change in global finance through infrastructures. Review of International Political Economy, 26(5), 773–789. https://doi.org/10.1080/09692290.2019.1625420

Butler, T., Gozman, D., & Lyytinen, K. (2023). The regulation of and through information technology: Towards a conceptual ontology for IS research. Journal of Information Technology, 38(2), 86–107. https://doi.org/10.1177/02683962231181147

Cao, Y., & Zhai, J. (2022). A survey of AI in finance. Journal of Chinese Economic and Business Studies, 20(2), 125–137. https://doi.org/10.1080/14765284.2022.2077632

Cheng, Y., & Jiang, H. (2022). Customer–brand relationship in the era of artificial intelligence: Understanding the role of chatbot marketing efforts. Journal of Product and Brand Management, 31(2), 252–264. https://doi.org/10.1108/JPBM-05-2020-2907

Chinoda, T., & Kapingura, F. (2024). Digital financial inclusion and economic growth in sub-saharan africa: The role of institutions and governance. African Journal of Economic and Management Studies, 15(1), 15–30. https://doi.org/10.1108/AJEMS-09-2022-0372

Choung, H., David, P., & Ross, A. (2023). Trust and ethics in AI. AI and Society, 38(2), 733–745. https://doi.org/10.1007/s00146-022-01473-4

Dahmani, M., Mabrouki, M., & Ben Youssef, A. (2023). The ICT, financial development, energy consumption and economic growth nexus in MENA countries. Applied Economics, 55(10), 1114–1128. https://doi.org/10.1080/00036846.2022.2096861

Dawar, S., Panwar, S., Dhaka, S., & Kudal, P. (2022). Antecedents and role of trust in chatbot use intentions: An indian perspective. Marketing and Management of Innovations, 13(4), 198–206. https://doi.org/10.21272/mmi.2022.4-18

Dvoulet´y, O., Jir´asek, M., Giglio, C., & Liguori, E. (2025). Artificial intelligence, digitalization, and new technologies in shaping business and management research. Cogent Business and Management, 12(1), 1–10. https://doi.org/10.1080/23311975.2025.2580126

Egbeleo, E., & Sodokin, K. (2025). Digital transformation, institutional quality and productivity in sub-saharan africa. Cogent Economics & Finance, 13(1), 1–21. https://doi.org/10.1080/23322039.2025.2519924

Emuron, A., van der Nest, D. P., & Coffie, C. P. K. (2024). FinTech and financial development: The role of traditional financial institutions. African Journal of Economic and Management Studies, 15(4), 704–719. https://doi.org/10.1108/AJEMS-10-2023-0406

Goli, M., Sahu, A. K., Bag, S., & Dhamija, P. (2023). Users’ acceptance of artificial intelligence-based chatbots: An empirical study. International Journal of Technology and Human Interaction, 19(1), 1–18. https://doi.org/10.4018/IJTHI.318481

Grout, P. (2021). AI, ML, and competition dynamics in financial markets. Oxford Review of Economic Policy, 37(3), 618–635. https://doi.org/10.1093/oxrep/grab014

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hien, L., & Tam, P. (2025). Impacting technology on employees’ job performance: A case study of commercial banks in vietnam. Journal of Applied Data Sciences, 6(2), 1129–1142. https://doi.org/10.47738/jads.v6i2.630

Huang, W., Gu, X., Lin, L., Alharthi, M., & Usman, M. (2023). Do financial inclusion and income inequality matter for human capital?. Borsa Istanbul Review, 23(1), 22–33. https://doi.org/10.1016/j.bir.2022.09.002

Hussain, S., Gupta, S., & Bhardwaj, S. (2025). Unraveling the dynamics of digital financial services adoption and digital divide: A systematic literature review and future research agenda. Journal of Global Information Technology Management, 28(2), 77–110. https://doi.org/10.1080/1097198X.2025.2480970

Huy, N., & Tam, P. (2025). Applied data science for analyzing the mediating role of digital transformation influencing banking business efficiency in vietnam. Journal of Applied Data Sciences, 6(3), 2031–2045. https://doi.org/10.47738/jads.v6i3.807

Joshi, S., Pandey, K., & Maheta, D. (2025). The role of perceived trust and technology anxiety in intention to use technology tools. Cogent Education, 12(1), 1–24. https://doi.org/10.1080/2331186X.2025.2597051

Juvina, I., Collins, C. G., Larue, H., Kennedy, L. G., Visser, E. D., & Melo, L. D. (2019). Toward a unified theory of learned trust in interpersonal and human–machine interactions. ACM Transactions on Interactive Intelligent Systems, 9(4), 1–33. https://doi.org/10.1145/3230735

Kshetri, N. (2021). The role of artificial intelligence in promoting financial inclusion in developing countries. Journal of Global Information Technology Management, 24(1), 1–6. https://doi.org/10.1080/1097198X.2021.1871273

Lee, J. C., Tang, Y., & Jiang, S. (2023). Understanding continuance intention of artificial intelligence (AI)-enabled mobile banking applications. Humanities and Social Sciences Communications, 10(1), 1–12. https://doi.org/10.1057/s41599-023-01845-1

Mfumbilwa, E. E., Mnong’one, C., Chao, E., & Amani, D. (2024). Invigorating continuance intention among users of AI chatbots in the banking industry: An empirical study from tanzania. Cogent Business & Management, 11(1), 1–19. https://doi.org/10.1080/23311975.2024.2419482

Mhlanga, D., & Mhlanga, D. (2020). Industry 4.0 in finance: The impact of artificial intelligence on digital financial inclusion. International Journal of Financial Studies, 8(3), 1–14. https://doi.org/10.3390/ijfs8030045

Milana, C., & Ashta, A. (2021). Artificial intelligence techniques in finance and financial markets: A survey of the literature. Strategic Change, 30(3), 189–209. https://doi.org/10.1002/jsc.2403

Mohammed, E. F., & Mirlam, M. S. (2026). Assessing the impact of credit score and employment stability on loan approval using logistic regression. Journal of Digital Market and Digital Currency, 3(1), 1–18. https://doi.org/10.47738/jdmdc.v3i1.53

Odei-Appiah, S., Wiredu, G., & Adjei, J. K. (2022). FinTech use, digital divide and financial inclusion. Digital Policy, Regulation and Governance, 24(5), 435–448. https://doi.org/10.1108/DPRG-09-2021-0111

Queiroz, M., Anand, A., & Baird, A. (2024). Manager appraisal of artificial intelligence investments. Journal of Management Information Systems, 41(3), 682–707. https://doi.org/10.1080/07421222.2024.2376383

Rana, N. P., Luthra, S., & Rao, H. R. (2020). Key challenges to digital financial services in emerging economies: The indian context. Information Technology & People, 33(1), 198–229. https://doi.org/10.1108/ITP-05-2018-0243

Rantelabi, E., & Hernando, C. (2025). Explainable AI (XAI) for credit scoring models in FinTech: SHAP-value interpretation within digital lending ecosystems. FinTech Innovation Journal, 1(4), 325–344. https://doi.org/10.63913/ftij.v1i4.85

Shahzad, M. F., Xu, S., An, X., Asif, M., & Javed, I. (2025a). Do generative AI technologies play a double-edged sword role in education? findings from hybrid approach using PLS-SEM and fsQCA. Education and Information Technologies, 30, 19647–19676. https://doi.org/10.1007/s10639-025-13528-2

Shahzad, M. F., Xu, S., Hanif, S., & Akram, S. (2025b). What factors affect the intention to use information and communication technology? perspectives of chinese university students. Humanities and Social Sciences Communications, 12(1), 847. https://doi.org/10.1057/s41599-025-05156-5

Subhani, F., Khan, S. A., Sandhu, M. A., & Shahzad, M. F. (2025). What factors affect the adoption intention and actual use of ChatGPT in higher education? the moderating role of academic integrity. TechTrends, 69, 1056–1071. https://doi.org/10.1007/s11528-025-01096-8

Suryadi, N., Zulfikar, R., Prasarry, Y. V., & F, M. F. I. (2025). AI product knowledge as moderator of trust and attitude in usage intention. Cogent Social Sciences, 11(1), 1–17. https://doi.org/10.1080/23311886.2025.2526799

Zhou, X., Yang, Z., Hyman, M. R., Li, G., & Munim, Z. H. (2022). Impact of artificial intelligence on business strategy in emerging markets. International Journal of Emerging Markets, 17(4), 917–929. https://doi.org/10.1108/IJOEM-04-2022-995