The Effect of Humanizing Robo-Advisors on Investor Judgments

Using an experiment, we examine the effect of humanizing robo-advisors (computer algorithms that provide financial advice) on investor judgments. We find that investors are more likely to follow the advice of a robo-advisor when the advisor exhibits fewer human characteristics, whereas they are more likely to follow the advice of a human advisor when the advisor exhibits more human characteristics. We also find that after learning that the advisor’s recommendation underperformed other investment options, investors decrease their reliance on subsequent recommendations the most in two conditions: when the advisor is a robo-advisor with fewer human characteristics, and when the advisor is a human advisor with more human characteristics. Finally, in support of our theory, we find that advisor credibility mediates the relationship between the likelihood of following the advisor’s recommendation and the interaction between the type of advisor and level of humanization. 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 the use of robo-advisors. Lastly, our study has practical implications for wealth management firms as they increase their use of robo-advisors.