Sự chấp nhận ứng dụng AI trong lĩnh vực tài chính cá nhân
Ngày xuất bản:
25/02/2025
Ngày xuất bản Online:
25/02/2025
Chuyên mục:
Quản trị kinh doanh, Marketing, Thương mại, Du lịch
Nội dung chính của bài viết
Tóm tắt
Mục đích của nghiên cứu này là đánh giá các nhân tố tác động đến sự chấp nhận ứng dụng AI trong lĩnh vực tài chính của đối tượng Gen Z với sự kết hợp giữa 3 mô hình lý thuyết. Nghiên cứu này đo lường sự ảnh hưởng của mô hình Tính phù hợp với tác vụ (TTF) đến mô hình chấp nhận công nghệ (TAM) và mô hình hệ thống thông tin thành công (ISS). Phương pháp định lượng được nghiên cứu này sử dụng với việc thu thập dữ liệu dựa trên ứng dụng Google biểu mẫu và bảng câu hỏi được thiết lập bằng việc kế thừa từ các nghiên cứu trước liên quan. Theo kết quả nghiên cứu, người dùng cảm nhận tính dễ sử dụng và tính hữu ích nhờ vào sự phù hợp với tác vụ của họ mà ứng dụng ngân hàng AI mang lại, qua đó tác động tích cực đến sự chấp nhận. Tuy nhiên, ứng dụng chưa thực sự mang lại sự hài lòng đáng kể cho người dùng. Nghiên cứu được mong đợi khi có thể đóng góp tích cực về mặt lý thuyết đối với các nghiên cứu trong tương lại về lĩnh vực công nghệ ngân hàng, nghiên cứu này cũng khuyến nghị các nhà phát triển ứng dụng cần tích hợp nhiều tính năng công nghệ hơn nhằm gia tăng sự hài lòng và sự chấp nhận ứng dụng ngân hàng AI của người dùng. Nghiên cứu này cũng đề xuất các nghiên cứu tương lai đánh giá và đo lường thêm các khái niệm giả thuyết để có thể đánh giá toàn diện về sự chấp nhận của người dùng đối với ứng dụng AI trong lĩnh vực tài chính cá nhân.
Abstract
This study aims to evaluate the factors affecting the adoption of AI applications in the financial sector of Generation Z with the combination of 3 theoretical models. This study measures the influence of the Technology Task Fit (TTF) on the Technology Acceptance Model (TAM) and the Information Success System (ISS). The quantitative methodology was used in this study and data collection was based on Google Forms while questionnaires were established by inheriting from previous related studies. According to the results, users perceive the ease of use and usefulness thanks to the relevance to their tasks that AI banking applications bring, thereby positively impacting their adoption. However, the application has not brought significant satisfaction to users. The study is expected to make a positive theoretical contribution to future research in banking technology. This study recommends that application developers integrate many features of AI technology to increase user satisfaction and adoption of AI banking applications. This study also suggests that future studies should evaluate and measure more hypothetical concepts to comprehensively assess user acceptance of AI applications in the finance sector for personal customers.
Từ khóa
Sự chấp nhận sử dụng; Sự hài lòng; Tính dễ sử dụng; Tính hữu ích; Tính phù hợp với tác vụ công nghệ
Chi tiết bài viết
Lĩnh vực kinh tế (JEL Codes)
L81 - Retail and Wholesale Trade • e-Commerce - L84 - Personal, Professional, and Business Services - Industry Studies: Services, M15 - IT Management - Business Administration
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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
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Gupta, A., & Arora, N. (2017). Consumer adoption of m-banking: a behavioral reasoning theory perspective. International Journal of Bank Marketing, 35(4), 733-747. https://doi.org/10.1108/ijbm-11-2016-0162
Gupta, K. P., Manrai, R., & Goel, U. (2019). Factors influencing adoption of payments banks by Indian customers: extending UTAUT with perceived credibility. Journal of Asia Business Studies, 13(2), 173-195. https://doi.org/10.1108/jabs-07-2017-0111
Ha, H. (2020). The cashless economy in Vietnam - The situation and policy implications. Journal of Reviews on Global Economics, 9(1), 216-223. https://doi.org/10.6000/1929-7092.2020.09.20
Hair, J. F., Hult, G. T. M., Ringle, C. M. & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). 2nd ed. California: SAGE. https://www.amazon.com/Advanced-Partial-Structural-Equation-Modeling/dp/1071862502
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
Hassan, H. E., & Wood, V. R. (2020). Does country culture influence consumers' perceptions toward mobile banking? A comparison between Egypt and the United States. Telematics and Informatics, 46, 101312. https://doi.org/10.1016/j.tele.2019.101312
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Islam, J. U., Shahid, S., Rasool, A., Rahman, Z., Khan, I., & Rather, R. A. (2020). Impact of website attributes on customer engagement in banking: a solicitation of stimulus-organism-response theory. International Journal of Bank Marketing, 38(6), 1279-1303. https://doi.org/10.1108/ijbm-12-2019-0460
Kaplan, D. (2008). Structural Equation Modeling: Foundations and Extensions (Vol. 10). Thousand Oaks, CA.: Sage. https://doi.org/10.4135/9781452226576
Kelly, A. E., & Palaniappan, S. (2019). Survey on customer satisfaction, adoption, perception, behaviour, and security on mobile banking. Journal of Information Technology & Software Engineering, 9(2), 1-15. https://www.longdom.org/open-access-pdfs/survey-on-customer-satisfaction-adoption-perception-behaviour-and-security-on-mobile-banking.pdf
Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling. London: Guilford Publications. https://www.amazon.com/Principles-Practice-Structural-Equation-Methodology/dp/146252334X
Kwateng, K. O., Atiemo, K. A. O., & Appiah, C. (2018). Acceptance and use of mobile banking: an application of UTAUT2. Journal of Enterprise Information Management, 32(1), 118-151. https://doi.org/10.1108/jeim-03-2018-0055
Lee, J. C., & Chen, X. (2023). Exploring users' adoption intentions in the evolution of artificial intelligence mobile banking applications: the intelligent and anthropomorphic perspectives. International Journal of Bank Marketing, 40(4), 631-658. https://doi.org/10.1108/ijbm-08-2021-0394
Li, Y., & Shang, H. (2020). Service quality, perceived value, and citizens’ continuous-use intention regarding e-government: Empirical evidence from China. Information & Management, 57(3), 103197. https://doi.org/10.1016/j.im.2019.103197
Lin, T. C., & Huang, C. C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410-417. https://doi.org/10.1016/j.im.2008.06.004
Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129, 911-926. https://doi.org/10.1016/j.jbusres.2020.11.001
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Trích dẫn bài báo
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