Following the Robot? Investigating Users’ Utilization of Advice from Robo-Advisors

Abstract

Companies are gradually creating new services such as robo-advisors (RA). However, little is known if users actually follow RA advice, how much the fit of RA to task requirements influences the utilization, how users perceive RA characteristics and if the perceived advisor’s expertise is influenced by the user’s expertise. Drawing on judge-advisor systems (JAS) and task-technology fit (TTF), we conducted an experimental study to measure actual advice-taking behavior in the context of RA. While the perceived advisor’s expertise is the most influential factor on task-advisor fit for RA and human advisors, integrity is a significant factor only for human advisors. However, for RA the user’s perception of the ability to make decisions efficiently is significant. In our study, users followed RA more than human advisors. Overall, our study connects JAS and TTF to predict advice utilization and supports companies in service promotion.