Behavioral intention of Chinese users to use DeepSeek for medical tourism

This study investigates the behavioral intentions of Chinese users toward adopting DeepSeek - an open-source large language model (LLM) - in the context of medical tourism. Using structural equation modeling, we analyzed data collected from 502 Chinese users experienced with DeepSeek to explore how various psychological, social, and practical factors influence user adoption. Results highlight that utilitarian motivation, hedonic motivation, hedonic value, and price value significantly shape users' attitudes and adoption intentions. Additionally, habit and social influence emerge as critical predictors of behavioral intention. Theoretically, the findings provide a comprehensive understanding of user acceptance behavior toward AI-driven medical tourism applications. Practically, the insights guide healthcare providers, tourism businesses, and policymakers in effectively leveraging AI technologies by enhancing emotional appeal, ensuring competitive pricing, and tailoring culturally relevant digital experiences for sustainable medical tourism growth in Asia. This study contributes theoretically by integrating three established models and provides practical insights for developers and healthcare providers to enhance AI-enabled medical tourism platforms in the Asian context.