Artificial intelligence service recovery: The role of empathic response in hospitality customers' continuous usage intention

Artificial intelligence (AI) service failures are inevitable in hospitality companies; thus, how AI service recovery retains customers is an issue that cannot be ignored. This article focuses on AI service recovery, abandoning the traditional intelligence quotient thinking and exploring the recovery effect of empathy response from the perspective of emotional intelligence. Using four experimental scenarios, the results indicate that, in service recovery, a high-empathy AI response can increase customers' continuous usage intention, and psychological distance and trust are sequential mediators in this process. Compared with mono-sensory stimulus interactions (text only), a high-empathy response that adopts multisensory stimulus interactions (text and voice) could strengthen the recovery effect of empathy responses. This paper extends the field of AI service research from a focus on time and phase to the continuing use of AI after service failure. It also moves beyond the traditional intelligence quotient improvement thinking and reveals the importance of using AI emotional intelligence to activate customer emotional response in AI service recovery. Finally, it provides a useful tool for resolving AI service failure problems autonomously in the service process, which is of great value to research and development and hospitality operators in the promotion and application of AI services.

Automated summary

This article explores the importance of AI service recovery in hospitality companies and how a high-empathy AI response can increase customers' continuous usage intention. It highlights the role of psychological distance and trust as sequential mediators in this process, and shows that using multisensory stimulus interactions can strengthen the recovery effect of empathy responses. By focusing on emotional intelligence in AI service recovery, the paper provides a useful tool for autonomously resolving AI service failure problems in the service process.