The impact of chatbots’ artificial intelligence type on customer engagement: moderating role of tourism service context

Purpose As chatbots become increasingly prevalent, the imperative to enhance customer engagement with them grows ever more crucial. This study, drawing from social response theory and common ground theory, aims to investigate how the interaction between the types of AI chatbot intelligence and the tourism service context affects customer engagement. By integrating these theoretical lenses, the study highlights both fit effects and the underlying relational mechanism of common ground. Design/methodology/approach This study conducted a scenario-based experiment with a 2 × 2 between-subjects design, manipulating chatbot intelligence (thinking intelligence vs. feeling intelligence) and tourism service context (utilitarian-dominant vs. hedonic-dominant). Findings The results reveal that in utilitarian-dominant contexts, customers prefer chatbot conversations with thinking intelligence for more effective engagement, whereas in hedonic-dominant contexts, feeling intelligence leads to greater engagement. Moreover, customer-perceived common ground is identified as a key psychological mechanism that explains why congruence between chatbot intelligence type and service context strengthens customer engagement, thereby enriching theoretical accounts of engagement formation in AI-mediated services. Practical implications This study offers managerial implications for effectively managing customer-AI chatbot service interactions in the hospitality and tourism industry, which can lead to more positive customer engagement. Originality/value This study moves beyond the incremental application of existing typologies by pioneering a theoretical integration of social response theory and common ground theory. It contributes original insights by demonstrating that customer engagement depends not only on the alignment between chatbot intelligence type and service context but also on the establishment of perceived common ground as a mediating process. By advancing this dual-theory perspective, the study extends research on AI-mediated service engagement and highlights a relational pathway for understanding how anthropomorphized AI can sustain customer engagement in tourism services.