The Effect of Humanizing Robo-Advisors on Investor Judgments*

Abstract

We examine the effect of humanizing (naming) robo-advisors on investor judgments, which has taken on increased importance as robo-advisors have become increasingly common and there is currently little SEC regulation governing key aspects of their use. In our first experiment, we predict and find that investors are more likely to rely on the investment recommendation of anunnamedrobo-advisor, whereas they are more likely to rely on the investment recommendation of anamedhuman advisor. Theory suggests one reason that naming a robo-advisor may have drawbacks pertains to the complexity of the task the robo-advisor performs. We explore the importance of task complexity in our second experiment. We predict and find that investors are less likely to rely on a named robo-advisor when the advisor is perceived to be performing a relatively complex task, consistent with our first experiment, and more likely to rely on a named robo-advisor when the advisor is perceived to be performing a relatively simple task, consistent with prior research on human-computer interactions. Our findings contribute to the literature examining how technology influences the acquisition and use of financial information and the general literature on human-computer interactions. Our study also addresses a call by the SEC to learn more about robo-advisors. Lastly, our study has practical implications for wealth management firms by demonstrating the potentially negative effects of making robo-advisors more humanlike in an attempt to engage and attract users.