Determinants of retail investors' satisfaction and behavioural intention towards artificial intelligence-supported stock trading apps in India

Purpose This study aims to examine the antecedents of retail investors' satisfaction and behavioural intention to use AI-enabled stock trading apps in India. Design/methodology/approach An online survey was conducted using a structured questionnaire administrated through Google Forms, collecting data from 314 investors who regularly use AI-driven stock trading apps in their investment decisions. Confirmatory Factor Analysis (CFA) was used to assess the constructs' validity and reliability, and, Structural Equation Modelling (SEM) was employed to test proposed hypothesised relationships in the conceptual framework. Findings The results of the study reveal that performance expectancy (PE), social influence (SI), facilitating conditions (FC), self-efficacy (SEF) and personal innovativeness (PI) positively influence retail investor satisfaction (SAT) with AI-enabled stock trading apps, whereas effort expectancy (EE) and information quality (IQ) have significant adverse effects on satisfaction. Furthermore, Information Quality and self-efficacy have a significant positive effect on behavioural intention (BI). In addition, satisfaction is found to have a significant effect on investors' intention to use stock trading apps. Practical implications The findings suggest that, AI-based stock trading developers may prioritise intuitive application design, high-quality information delivery and user-focused interactions to boost potential investors' adoption. Additionally, regulatory and policymakers may use results to promote sustained usage of AI-based trading apps. Originality/value This study strengthens the literature on AI-based stock trading by examining the retail investors' behaviour in an emerging market context, specifically India, where limited empirical studies explored this contemporary research area. The study employed Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating three additional constructs, such as self-efficacy, information quality and personal innovativeness, to better explore adoption behaviour, which was less explored in earlier literature from the retail investors' perspective.