Purpose This study aims to decode investor behavior in the era of financial artificial intelligence (AI) by examining the cognitive, emotional and social drivers influencing AI adoption and resistance in portfolio management. Design/methodology/approach Drawing upon the AI decision use acceptance framework, this study uses structural equation modeling to analyze survey data from AI-adopting investors. The model tests the sequential pathway from cognition to emotion to behavioral intention, integrating constructs such as social influence, hedonic motivation, anthropomorphism, performance expectancy, effort expectancy and emotional engagement. Findings Results reveal that social influence, hedonic motivation and anthropomorphism significantly enhance performance and effort expectancy. Social endorsement improves perceptions of AI accuracy and ease of use; enjoyment in interaction strengthens perceived benefits and reduces complexity concerns; and anthropomorphic features foster trust and intuitive engagement. Positive cognitive evaluations trigger emotions such as satisfaction, hope and confidence, which, in turn, strengthen willingness to use AI and diminish resistance rooted in human preference or perceived empathy gaps. Mediation analysis indicates that emotions play a dominant role in behavioral outcomes, preceding cognition in influencing adoption. Practical implications The findings provide actionable insights for financial institutions and AI developers on how to design emotionally intelligent, socially endorsed and user-friendly AI systems that foster long-term investor trust and sustained adoption. Originality/value To the best of the authors’ knowledge, this study offers one of the first integrative examinations of cognitive–emotional–social mechanisms shaping investor trust in AI. It bridges the gap between technology adoption theory and behavioral finance by highlighting emotion’s primacy in AI decision acceptance.
Tiwari et al. (2026) studied this question.