Aligning (In)Congruent Chatbot–Employee Empathic Responses with Service-Recovery Contexts for Customer Retention
Hua Fan, Bing Han, Wangshuai Wang
Abstract
The use of artificial intelligence (AI) technologies in service recovery is transforming the frontline interfaces across the tourism industry, as AI chatbots are now being designed to show empathy. Using a multi-method approach combining survey, experimental, and field data obtained from hotel guests, this study explores the effects of chatbot–employee collaborative empathic responses on customer retention under various service-recovery contexts. It finds that congruence (vs. incongruence) and higher (vs. lower) levels of congruence in chatbot–employee empathic responses more effectively retain customers. Further, the effects of incongruence and congruence on customer retention diminish when the chatbot’s identity is disclosed but are strengthened when employees’ acceptance of a chatbot increases. Only the negative effects of empathic response incongruence correspondingly increase when chatbot efficiency and flexibility (ambidexterity) increase. These findings suggest that tourism practitioners can rely on chatbot–employee collaboration to accomplish service-recovery tasks but should pay attention to chatbot-side and employee-side uncertainties in a service triad, especially chatbot ambidexterity.