Customer Mistreatment and Venting to Conversational AI: Emotional Exhaustion as Mediator and Trust in Conversational AI as Moderator
Jialin Cheng, Jingxuan Jiang · Behavioral Sciences · 2026
Artificial intelligence (AI) technologies, such as service robots, substantially influence frontline employees in the hospitality sector. This study highlights that conversational AI (CAI) may function as a viable outlet for hospitality workers to vent negative work-related issues. This function is particularly relevant because employees in this industry frequently experience customer mistreatment. Grounded in conservation of resources theory, we conceptualize venting to CAI as a resource-replenishing coping strategy triggered by customer mistreatment. Further, we theorize that this relationship is mediated by emotional exhaustion and moderated by trust in CAI, thereby strengthening the indirect effect. We collected and analyzed two-wave data from 394 frontline employees with CAI experience in the hospitality industry. The results indicate that customer mistreatment indirectly impacted frontline employees' venting behaviors towards CAI, with emotional exhaustion functioning as the mediating mechanism. This indirect effect is particularly pronounced when employees exhibit high levels of trust in CAI. These findings offer practical insights for hospitality organizations aiming to leverage CAI as an accessible, low-risk tool for supporting employee emotional well-being and mitigating the negative consequences of customer mistreatment.