Increasing Reliance on Financial Advice with Avatars: The Effects of Competence and Complexity on Algorithm Aversion

We find that avatar design can reduce algorithm aversion, which is the tendency of decision makers to ignore advice received from an algorithm after the algorithm makes an error. When the facial features of an avatar exhibit high levels of competence, algorithm aversion can be reduced relative to no avatar or a less competent-looking avatar. Humanizing the financial advice from an algorithm with an avatar that promotes the perception of competence effectively reduces algorithm aversion and can enhance reliance on the financial advice of robo-advisors.