Adoption of AI-enabled Robo-advisors in Fintech: Simultaneous Employment of UTAUT and the Theory of Reasoned Action

Abstract

In fintech, robo-advisors are a helpful technology for users desiring to use financial services remotely; as such, robo-advisors are being used by an increasing number of users. However, service providers of this artificial intelligence (AI)-based technology still have challenges to solve, such as issues with security, privacy, and distrust. In addition to technological benefits, we argue that if the service providers were to consider the risk-sensing behavioral attitudes of users toward AI-based robo-advisors, then more users may be attracted to this technology. By simultaneously employing the unified theory of acceptance and use of technology (UTAUT) and the theory of reasoned action (TRA), we develop a conceptual model and propose a series of hypotheses related to users’ adoption of robo-advisors in fintech services. Specifically, we argue that the antecedents (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions) affect the positive attitudes that individual investors hold toward robo-advisors, and we claim that the TRA-related factors (i.e., perceived security, perceived privacy, and trust) play vital roles in encouraging the use of robo-advisors. Using large-scale survey data from 638 Chinese users having experience with robo-advisor services, we empirically tested our framework using the structural equation modeling approach. The results clearly support the proposed hypotheses concerning the direct and indirect effects of various predictors, such as performance expectancy, effort expectancy, social influence, facilitating conditions, perceived security, and perceived privacy, on user attitudes toward robo-advisors and their intention to adopt such fintech services. In addition, our results demonstrate that the majority of these relationships are indirect by virtue of the mediating roles of attitude, trust, and facilitating conditions. This study contributes to the understanding of users’ adoption of robo-advisors by combining UTAUT and TRA, which is useful for exploring the relationships between attitudes and behavioral intentions to use as well as the interrelationships among security, privacy, and trust.