Purpose This study evaluates the adoption and continuance usage intention of artificial intelligence (AI)-enabled robo-advisors among middle-class individuals in India. By integrating the diffusion of innovation (DOI) theory and financial behaviour theory, this study explores how functional and emotional factors influence user adoption. Design/methodology/approach A quantitative survey was conducted with 437 middle-class respondents from four metropolitan cities in India. Constructs such as relative advantage, perceived compatibility, trialability, perceived trust, cognitive involvement and perceived usability were evaluated using partial least squares structural equation modeling to determine their influence on psychological comfort and subsequent continuance intention. Findings The study identifies trialability as the strongest predictor of psychological comfort, followed by perceived trust and relative advantage. Psychological comfort has emerged as a critical mediator that significantly influences continuance usage intentions. The results underscore the importance of aligning robo-advisory features with user expectations, offering trialability and fostering trust for sustained adoption. Practical implications The findings suggest actionable strategies for financial technology providers, including the need to implement no-risk trials and interactive tutorials to alleviate user uncertainty. Emphasising explainable AI features can enhance trust and transparency, particularly in emerging markets. Additionally, user-friendly interfaces and multilingual support are crucial for catering to diverse middle-class demographics. Policymakers can leverage these insights to create supportive regulations that encourage the broader adoption of AI-driven financial tools and enhance financial inclusion and literacy. Originality/value This study bridges a critical research gap by emphasising psychological comfort in AI adoption for personal finance, particularly among underserved middle-class demographics in emerging markets. It extends the existing theoretical frameworks by integrating emotional and functional dimensions, providing actionable insights for technology developers, financial institutions and policymakers.
Arora et al. (Tue,) studied this question.