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Behavioral Responses to AI Financial Advisors: Trust Dynamics and Decision Quality Among Retail Investors
Open accessAbstract
This study examines trust dynamics and decision quality among retail investors interacting with AI financial advisors. Using a convergent parallel mixed methods design incorporating surveys (n=428) and interviews (n=42), we investigate how trust forms, evolves, and influences investment outcomes. Results demonstrate that institutional affiliation (β=0.68, p<0.001) and perceived competence (β=0.57, p<0.001) significantly influence initial trust formation, while system explainability features substantially impact trust sustainability (high-explainability M=4.76 vs. low-explainability M=3.28, p<0.001, d=1.78). Longitudinal analysis reveals non-linear trust trajectories with distinct investor classes showing increasing (58.2%), stable (23.7%), or degrading (18.1%) patterns over nine months. AI-advised portfolios outperformed self-directed investments by 7.2% on risk-adjusted returns (Sharpe ratio: 0.83 vs. 0.65, p<0.01) with significant reductions in overconfidence bias (41.3%) and disposition effect (37.8%). Financial literacy moderates these benefits, with high-literacy investors showing smaller performance differentials between AI-advised and self-directed conditions compared to low-literacy participants. Transparency regarding system limitations demonstrates particularly strong effects on trust calibration, with 76.4% of participants in transparent conditions exhibiting appropriate reliance patterns versus 34.2% in non-disclosure conditions. These findings advance understanding of human-AI interaction in financial contexts and provide implications for designing trustworthy advisory systems that enhance retail investor outcomes.
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“In the context of financial decision-making, calibrated trust improved appropriate reliance on AI recommendations (Trinh et al., 2025).”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Trinh et al., 2025
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Dogru, E. Ö., & Krämer, N. C. (2025) · Journal of Decision Systems · doi:10.1080/12460125.2025.2593251 · Cited by 15
“In the context of financial decision-making, calibrated trust improved appropriate reliance on AI recommendations (Trinh et al., 2025).”
Metadata
- Authors
- Trinh, T. K., Jia, G., Cheng, C., & Ni, C.
- Affiliations
- California State University, Long Beach (US)
- Rice University (US)
- University of California San Diego (US)
- The University of Texas at Dallas (US)
- Corresponding author
- Trinh, Toan Khang
- ORCID
- Jia, Guancong
- Venue
- Applied and Computational Engineering
- Publisher
- EWA Publishing
- Published
- 2025-04-07
- Volume
- 144
- Issue
- 1
- Pages
- 69-79
- ISSN
- 2755-2721 (ISSN-L)
- 2755-273X
- OpenAlex
- W4409237128
- Cited by
- 7
- References
- 0
- Citations by year
- 2026: 5 · 2025: 2
- FWCI
- 9.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Applied and Computational Engineering (publishedVersion, cc-by)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Literacy, Pension, Retirement Analysis
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- trust in AI, retail investors, investment decision quality, perceived competence, institutional affiliation, financial literacy, overconfidence, disposition effect, human-AI interaction, risk-adjusted returns
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