Robo-advisors and investment intention: A perspective of value-based adoption

Abstract

Financial robo-advisors have been widely used to assist individuals in their investment decisions, making it important to reduce uncertainties in the assistance process. Existing empirical studies rarely explore uncertainty reduction strategies and their implications on users’ investment intentions in the context of financial robo-advisors; our study attempts to address this gap. We construct a model to explain how uncertainty reduction strategies affect users’ investment intention in using financial robo-advisors. By collecting and analyzing a sample of 307 financial robo-advisor users, we find that algorithmic interpretability, structural assurance, and interactivity as uncertainty reduction strategies are positively related to users’ investment intention through the value-based adoption mechanism. Our research extends the value-based adoption model and uncertainty reduction theory in the financial robo-advisor context. We provide insights to financial robo-advisor service providers about focusing on improving algorithmic transparency, third-party assurance, and interactivity of financial robo-advisors to enhance perceived value and investment intention.