Smart Service Interactions in Hospitality: Factors Influencing Customer Switching Intention to Chatbots
DOI:
https://doi.org/10.55123/toba.v5i1.7620Keywords:
Chatbots, Conversational AI, Hospitality Technology, Human–AI Interaction, Problem-Solving, Synchronicity, Perceived Humanness, ComprehensionAbstract
The rapid development of conversational artificial intelligence (AI) has transformed customer interaction patterns in the hospitality sector, with chatbots increasingly deployed as frontline support tools across multiple service touchpoints. However, while chatbot usage continues to grow, customer reactions to automated assistance remain mixed, prompting an examination of the technological factors that shape customers’ willingness to shift from human agents to chatbots. This study investigates how four key chatbot-related variables: comprehension, perceived humanness, synchronicity, and problem-solving ability, influence customer switching intentions in hospitality contexts. Using a quantitative method, data were collected from 149 Indonesian consumers with prior experience using both chatbots and human service agents during online hospitality-related transactions. Structural Equation Modeling (SEM) via SmartPLS was employed to test the proposed hypotheses. The results show that all four variables have a significant positive influence on switching intention, with problem-solving ability being the strongest predictor, followed by synchronicity, perceived humanness, and comprehension. These findings suggest that customers are more inclined to adopt chatbot-based support when the technology demonstrates efficient problem resolution, real-time responsiveness, and a degree of human-like interaction. The study contributes to chatbot adoption literature by focusing on technological interaction attributes rather than solely psychological acceptance factors and highlights the growing relevance of AI-mediated service encounters in hospitality. Limitations include the cross-sectional design, self-reported data, and sector-specific sampling. Future research is encouraged to investigate sectoral differences, adopt longitudinal or experimental approaches, and examine moderating influences such as digital literacy, trust propensity, or cultural background.
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