Search

How Trust, Experience, and Expectations Drive Fintech Adoption through Technology Acceptance

Author(s)

Jiangping Li1,*, Yanqi Huang2, Chen Ning3, Yuxin Wu1

Affiliation(s)

1School of Economics and Management, North China University of Science and Technology, Tangshan, Hebei, China 2College of Science, North China University of Science and Technology, Tangshan, Hebei, China 3Yi Sheng Collage, North China University of Science and Technology, Tangshan, Hebei, China *Corresponding Author.

Abstract

While financial technology (fintech) reshapes the landscape of financial services, user adoption remains an elusive goal. This study unpacks the drivers of fintech uptake in China by extending the Technology Acceptance Model (TAM) to examine the roles of user experience, trust, and expectations. Drawing on partial least squares structural equation modeling (PLS-SEM), we analyzed survey data from 300 users to illuminate these intricate relationships. The results obtained reveal that user experience exerts a direct influence on adoption through TAM's mediating role, thereby underscoring the importance of cognitive processing in determining its functional benefits. Trust operates via dual pathways, directly boosting adoption intent and indirectly enhancing it through TAM, thus highlighting its pivotal role in alleviating risk and fostering confidence. User expectations shape adoption in both a direct and indirect manner, reflecting their nuanced influence on perceived performance and security. These findings position TAM as a critical bridge linking user experience to behavior, while trust and expectations enrich adoption dynamics through multi-path effects. This research contributes to fintech adoption theory and offers strategic insights for promoting fintech products in practice.

Keywords

Fintech; Experience; Trust; Expectations; Technology Acceptance Model; Partial Least Squares

References

[1]Palmié M, Wincent J, Parida V, et al. The evolution of the financial technology ecosystem: An introduction and agenda for future research on disruptive innovations in ecosystems. Technological Forecasting and Social Change, 2020, 151. [2]Chawla U, Mohnot R, Singh H V, et al. The Mediating Effect of Perceived Trust in the Adoption of Cutting-Edge Financial Technology among Digital Natives in the Post-COVID-19 Era. Economies, 2023, 11(12): 286. [3]Marangunic N, Granic A. Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 2015, 14(1): 81-95. [4]Davis F D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 1989, 13(3): 319-340. [5]Venkatesh V, Davis F. D. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 2000, 46(2). [6]Venkatesh V, Morris M G, Davis G B, et al. User acceptance of information technology: toward a unified view. MIS Quarterly, 2003, 27(3): 425–478. [7]Venkatesh V, Bala H. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 2008, 39(2): 273-315. [8]Norman D. The Design of Everyday Things. MIT Press (Original work published 1988), 2013. [9]Davis F D, Bagozzi R P, Warshaw P R. User acceptance of computer technology: a comparison of two theoretical models. Management Science, 1989, 35(8): 982–1003. [10]Partala T, Saari T. Understanding the most influential user experiences in successful and unsuccessful technology adoptions. Computers in Human Behavior, 2015, 53: 381-395. [11]Singh S, Sahni M M, Kovid R K. What drives FinTech adoption? A multi-method evaluation using an adapted technology acceptance model. Management Decision, 2020, 58(8): 1675-1697. [12]Nugraha D P, Setiawan B, Nathan R J, et al. Fintech Adoption Drivers for Innovation for SMEs in Indonesia. Journal of Open Innovation: Technology, Market, and Complexity, 2022, 8(4): 208. [13]Hu Z, Ding S, Li S, et al. Adoption Intention of Fintech Services for Bank Users: An Empirical Examination with an Extended Technology Acceptance Model. Symmetry, 2019, 11(3): 340. [14]Albayati H, Kim S K, Rho J J. Accepting financial transactions using blockchain technology and cryptocurrency: A customer perspective approach. Technology in Society, 2020, 62: 101320. [15]Luarn P, Lin H. Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 2005, 21: 873-891. [16]Hamakhan Y T M. The effect of individual factors on user behavior and the moderating role of trust: an empirical investigation of consumers’ acceptance of electronic banking in the Kurdistan Region of Iraq. Financial Innovation, 2020, 6(1): 43. [17]Pathak A, Bansal V. AI as decision aid or delegated agent: The effects of trust dimensions on the adoption of AI digital agents. Computers in Human Behavior: Artificial Humans, 2024, 2(2): 100094. [18]Malaquias R F, Hwang Y. An empirical study on trust in mobile banking: A developing country perspective. Computers in Human Behavior, 2016, 54: 453-461. [19]Bhattacherjee A. Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 2001, 25(3): 351-370. [20]Gupta A, Yousaf A, Mishra A. How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model. International Journal of Information Management, 2020, 52. [21]Idrees M A, Ullah S. Comparative analysis of FinTech adoption among Islamic and conventional banking users with moderating effect of education level: A UTAUT2 perspective. Journal of Open Innovation: Technology, Market, and Complexity, 2024, 10(3): 100343. [22]Wen-Lung S, Ye Y, Pu X, et al. Understanding fintech continuance: perspectives from self-efficacy and ECT-IS theories. Industrial Management & Data Systems, 2020, 120(9): 1659-1689. [23]Davis F D, Venkatesh V. A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International Journal of Human-Computer Studies, 1996, 45(1): 19-45. [24]Parasuraman A, Zeithaml V A, Malhotra A. ES-QUAL: A multiple-item scale for assessing electronic service quality. Journal of service research, 2005, 7(3): 213-233. [25]McKnight D H, Choudhury V, Kacmar C. Developing and validating trust measures for e-commerce: An integrative typology. Information systems research, 2002, 13(3): 334-359. [26]Moore G C, Benbasat I. Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 1991, 2(3): 192-222. [27]Nunnally J C. Psychometric theory. New York, NY, S: McGraw-Hill, 1967. [28]Fornell C, Larcker D F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 1981, 18(1): 39-50. [29]Hair J F, Ringle C M, Sarstedt M. Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 2013, 46(1): 1-12. [30]Featherman M S, Pavlou P A. Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies, 2003, 59(4): 451-474.