Trusting AI in Financial Advisory: Examining the Impact of AI-Based Financial Advisory Services on Retail Investors' Adoption Intentions through the Mediating Roles of Trust and Perceived Risk and the Moderating Role of Financial Literacy

We created and validated a controlled dual mediation model for the adoption intention of AI-based financial advising services among retail investors in this research. So it included TAM, Trust Theory, Perceived Risk Theory and Innovation Diffusion Theory. The study, using PLS-SEM analysis of 450 individual investors, demonstrates that perceived utility of AI advising services increases trust (β = 0.546) and decreases perceived risk (β = −0.483). Therefore, these two factors affect the AI adoption plans of people differently. The trust channel (H6 indirect 2 = 0.247) is statistically larger than the risk channel (H7 indirect 2 = 0.156), which proves that trust is the major factor that influences the acceptance of AI guidance. It is also true that perceived efficiency has direct impact on adoption intention (² = 0.177) which is in line with the direct utilitarian value assessment route that TAM found . Financial knowledge does not appear to influence much for the link between trust and uptake. That means it’s involved in more scenarios and more parts of the game than we realized. As we alter the investment process with AI-enabled financial advising services, it is even more important to understand the mental processes investors go through to evaluate, trust and eventually accept new technology. The two-fold mediation approach of the research offers a theoretically sound and empirically proven direction for FinTech designers, financial institutions, investor education programs and regulatory bodies to foster responsible, inclusive and effective use of AI in retail financial markets.

1.                Allied Market Research. (2022). Robo-advisory market size, share, competitive landscape and trend analysis report. Allied Market Research. 2.                Bauer, R. A. (1960). Consumer behavior as risk taking. In R. S. Hancock (Ed.), Dynamic marketing for a changing world (pp. 389–398). American Marketing Association. 3.                Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial intelligence in FinTech: Understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411–1430. https://doi.org/10.1108/IMDS-08-2018-0367 4.                Calcagno, R., & Monticone, C. (2015). Financial literacy and the demand for financial advice. Journal of Banking & Finance, 50, 363–380. https://doi.org/10.1016/j.jbankfin.2014.03.013 5.                Cao, X., & Niu, Q. (2019). Research on acceptance and usage behavior of mobile payment: An empirical analysis. IEEE Access, 7, 174220–174235. 6.                Cheng, X., Fu, S., & de Vreede, G.-J. (2019). A mixed method investigation of sharing economy driven car-hailing services: Online and offline perspectives. International Journal of Information Management, 44, 1–10. 7.                Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications. 8.                D'Acunto, F., Prabhala, N., & Rossi, A. G. (2019). The promises and pitfalls of robo-advising. The Review of Financial Studies, 32(5), 1983–2020. https://doi.org/10.1093/rfs/hhz014 9.                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 10.             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