Decoding Investment Behavior in The Age of AI: An Extended Theory of Planned Behavior Model Integrating Financial and AI Literacy
Keywords:
Theory of Planned Behavior, AI Literacy, Financial Literacy, Behavioral Intention, Actual Investment Behavior, Retail Investors, SmartPLS 4.Abstract
Purpose: This study investigates retail investment decision-making in the era of financial automation by extending Ajzen’s Theory of Planned Behavior (TPB). The proposed framework integrates domain-specific capabilities—specifically Financial Literacy and AI Literacy—alongside traditional psychological determinants (Attitude, Subjective Norms, and Perceived Behavioral Control) to explain Behavioral Intention toward investment and its subsequent impact on Actual Investment Behavior.
Methodology: A quantitative, cross-sectional research design was employed. Primary data were gathered via a structured questionnaire on a 7-point Likert scale from 550 retail investors across four major urban hubs in Gujarat, India (Ahmedabad, Vadodara, Surat, and Rajkot). Data analysis and structural equation modeling were executed using Partial Least Squares SEM (PLS-SEM) in SmartPLS 4, evaluating measurement validity and structural paths via bootstrapping (5,000 resamples).
Findings: All six hypothesized structural relationships were statistically supported (p < 0.05). Attitude, Subjective Norms, Perceived Behavioral Control, Financial Literacy, and AI Literacy exert significant direct positive effects on Behavioral Intention toward investment. Attitude emerged as the strongest direct psychological antecedent, while Financial Literacy and AI Literacy provided key functional capabilities. Furthermore, Behavioral Intention demonstrated a strong direct effect on Actual Investment Behavior. The extended framework accounts for 58.2% of the variance in Behavioral Intention (R2 = 0.582) and 29.4% of the variance in Actual Investment Behavior (R2 = 0.294).
Practical & Theoretical Implications: Theoretically, the study bridges the intention–behavior gap and proves that incorporating technological competency (AI Literacy) alongside financial literacy provides superior explanatory power in digital market settings. Practically, the findings guide FinTech platform developers to design transparent, user-centered robo-advisory tools and advise policymakers to embed AI capability into public financial education programs.
