Evaluating the Impact of Emotional Apology on Human-Robot Trust

Previous research has shown that robot mistakes or malfunctions have a significant negative impact on people's trust. One way to mitigate the negative impact of trust violation is through trust repair. Although trust repair has been studied extensively, it is still not known which strategy is effective in repairing trust in a time-sensitive driving scenario. Additionally, prior research on trust repair has not dealt with the effects of expressing emotion in attempting trust repair. In this paper, we presented the development of a variety of trust repair methods for a time-sensitive scenario using a simulated driving environment as a testbed for validation. These trust repair methods included baseline apology, emotional apology, and explanation. We conducted an experiment to compare the impact of these trust repair methods on human-robot trust. Experimental results indicated that the emotional apology positively affected more participants than the no-repair, baseline apology, and explanation. Furthermore, this study identified emotional apology as the most effective method for the time-sensitive driving scenario.