Behavioral Biases and Technology Usage In Retail Investors' Decision-Making

Purpose: This study investigates the structural relationships among behavioral biases, technology usage, and investment decision quality among retail investors in the Indian stock market, a context characterized by rapid digital transformation and expanding retail participation. Methodology: A quantitative, cross-sectional research design was employed, with primary data collected from 101 active retail equity investors using a structured 5-point Likert-scale questionnaire. Constructs encompassed overconfidence, herding, loss aversion, anchoring, disposition effect, technology usage, investor sentiment, and risk perception. Reliability was assessed via Cronbach's alpha (overall alpha = 0.843; overconfidence subscale = 0.849), and construct validity was examined through Exploratory Factor Analysis (KMO = 0.764; Bartlett's chi-square = 828.168, p < .001). Multiple regression analysis (IBM SPSS Statistics) was applied to test fifteen directional hypotheses. Findings: Risk perception emerged as the single strongest predictor of investment decision quality (beta = 0.485, p < .001), followed by technology usage (beta = 0.217, p = .008) and overconfidence (beta = 0.156, p = .048). The model explained 53.6% of variance in decision quality (R² = 0.536, F[8, 92] = 13.259, p < .001). Loss aversion, anchoring, and herding were statistically non-significant. Demographic variables (age, gender, income) exerted no significant moderating effect, while education showed partial influence. Contribution: The study integrates behavioral finance and digital finance frameworks, providing actionable insights for investors, brokerage platforms, financial advisors, and policymakers on bias mitigation and technology-augmented decision-making.