Modelling the factors influencing customer adoption of financial robo-advisors

Abstract

In this study, we examine the intention to use robo-advisors among potential users by employing an extended UTAUT model. The novelty of this model lies in its incorporation of constructs such as trust and perceived risk. Furthermore, it also builds upon artificial intelligence attributes, including perceived intelligence and anthropomorphism. To test the theoretical model, we conducted an online questionnaire survey in 2024, which yielded 249 valid responses. Structural equation modelling (CB-SEM) was applied to assess the extended model and its associated hypotheses. The findings indicate that performance expectancy and social influence exert significant effects on the intention to use robo-advisors. Among the AI attributes, perceived intelligence has an indirect impact on usage intention. The results show that fostering trust, enhancing security, and promoting digital literacy are critical for attracting potential users. Proper management of these factors is indispensable for fintech companies seeking to maximize the benefits of AI-based financial services while minimizing the associated perceived risks. The originality of this research lies in its integrated analysis of perceived intelligence and anthropomorphism within an extended UTAUT model, highlighting their combined impact in shaping the social acceptance of robot advisors.