Human-robot collaboration in service recovery: Examining apology styles, comfort emotions, and customer retention

Abstract

This research employs a serial mediation model to explore how different levels of human-robot collaboration, apology styles, and emotional responses affect customer intentions after a service failure, based on a scenario-based experiment with 311 participants, analyzed using MANCOVA and PROCESS Macro Model 6. Our findings reveal that human-robot collaboration configurations where robots play a significant role, either augmenting or replacing humans, are more effective. Economic apologies are more impactful when the robot leads the recovery, while social apologies work best when human staff are involved. Comfort emotions and robot continuance usage sequentially mediate the relationship between human-robot collaboration and behavioral intentions. This is the first paper to integrate frontline technology with traditional recovery methods, highlighting the effectiveness of human-robot collaboration in enhancing customer retention. Practically, this research provides essential guidance for robot and AI designs in services, enabling service managers to effectively manage human-robot task allocation and customer loyalty in a robot-mediated service recovery.