Robo-advisor enablers and inhibitors: A dual-factor framework and a benefit-risk model integration for understanding customer acceptance

In recent years, the rapid strides made by artificial intelligence (AI) in the financial services industry have not only reshaped its landscape but have also brought along disruptive innovations. Prominent among them are the robo-advisors (RAs), which have brought in catalytic changes in efficiency, scalability, and personalization in the fintech space. Despite these inherent strengths, the penetration of these algorithm-driven platforms has continued to be suboptimal. The main hindrance to their adoption has been concerns over trust, perceived risks, and lack of human interaction. Against this backdrop, the current study presents an integrative framework of dual-factor theory and the benefit-risk model to identify and focus on the interplay between the push-pull factors, both positive (enablers) and negative (inhibitors), that determine the perceived benefit and perceived risk of RAs, resulting in the willingness and objection to RA usage. By deploying the PLS-SEM analysis on a diverse Indian sample, the study identified and empirically validated perceived anthropomorphism, social influence, and trust as the three distinct enablers that lead to the perceived benefit of employing RAs. The study further highlighted the pivotal role of poor interaction quality and lack of awareness in driving the perceived risk of RAs. By incorporating robo-design features, customer-centric and service factors as antecedents, this study not only contributes to the theoretical discourse on RA adoption research but also provides actionable insights for financial institutions, RA platform developers, and policymakers to improve increased market penetration of these AI-driven financial services in India's rapidly evolving fintech landscape.