Effects of the Advisor and Environment on Requesting and Complying With Automated Advice

Given the rapid technological advances in our society and the increase in artificial and automated advisors with whom we interact on a daily basis, it is becoming increasingly necessary to understand how users interact with and why they choose to request and follow advice from these types of advisors. More specifically, it is necessary to understand errors in advice utilization. In the present study, we propose a methodological framework for studying interactions between users and automated or other artificial advisors. Specifically, we propose the use of virtual environments and the tarp technique for stimulus sampling, ensuring sufficient sampling of important extreme values and the stimulus space between those extremes. We use this proposed framework to identify the impact of several factors on when and how advice is used. Additionally, because these interactions take place in different environments, we explore the impact of where the interaction takes place on the decision to interact. We varied the cost of advice, the reliability of the advisor, and the predictability of the environment to better understand the impact of these factors on the overutilization of suboptimal advisors and underutilization of optimal advisors. We found that less predictable environments, more reliable advisors, and lower costs for advice led to overutilization, whereas more predictable environments and less reliable advisors led to underutilization. Moreover, once advice was received, users took longer to make a final decision, suggesting less confidence and trust in the advisor when the reliability of the advisor was lower, the environment was less predictable, and the advice was not consistent with the environmental cues. These results contribute to a more complete understanding of advice utilization and trust in advisors.