Benefit versus risk: A behavioral model for using robo-advisors

Abstract

This research aims to propose and analyze a novel behavioral model for using robo-advisors grounded on stimulus–organism–response and decision theory. Data (n = 596) were collected from a panel of US participants. The findings contribute to the financial services arena by demonstrating the relevance of customers’ perceptions of robo-advisors’ benefits and risks, particularly fear of losing money and wasting time. Greater or lesser ease in learning to use the robo-advisor and the perception of safety are the stimuli for customers to cognitively assess the balance between the risks and benefits of using the robo-advisor. Younger customers are more likely than older customers to recommend the robo-advisor to others, and male users tend to have more confidence than female users in their use of the service. Thus, robo-advisors need to learn how to adapt to different customer profiles to customize the service and to increase the perception of security and ease of use.