Relational intelligence in AI chatbots: Examining the trust–engagement-emotion–social presence model using PLS-SEM and fsQCA

Abstract

AI-driven chatbots may have emerged as the main architects for developing relationships rather than simply processing transactions. The core challenge is to understand how AI-driven chatbots can help build customer relationships. Therefore, this study investigates how AI-driven chatbot determinants, such as personalization, interactivity, and ease of use, transform customer-chatbot relationship outcomes (i.e., engagement and emotional connection) via psychological mechanisms (i.e., chatbot trust) by incorporating the moderating role of chatbot social presence in the travel industry. An online survey was used to collect responses from the Chinese tourists who regularly used AI chatbots for their trip planning and execution. This study analyzed 690 responses using PLS-SEM and fsQCA techniques. The findings revealed that AI-driven chatbot determinants, such as personalization, interactivity, and ease of use, significantly boosted chatbot trust, which in turn enhanced relational outcomes, including engagement and emotional connection. Engagement was an effective factor for improving emotional connection. In addition, chatbot social presence as a moderator significantly enhanced engagement. Finally, the fsQCA analysis revealed that personalization, chatbot ease of use, and engagement are key factors in fostering tourists’ emotional connections with AI chatbots. This study contributes to relationship theories and provides important managerial implications.