Purpose: With the rapid application of artificial intelligence (AI) in smart mobility, Robotaxi—driverless taxi services—has emerged as a new AI-driven service form that is reshaping user behavior. However, the mechanisms underlying users’ acceptance and continued use of Robotaxi services remain underexplored. This study aims to investigate how the quality of AI services influences users’ reuse intention, based on the Technology Acceptance Model (TAM), while integrating key dimensions of AI service quality.Research design, data, and methodology: A structural model was developed integrating AI service quality dimensions (Reliability, Emotional Warmth, Responsiveness, and Information Quality) with TAM constructs (Perceived Usefulness, Perceived Ease of Use, and Reuse Intention). Data were collected via an of f line survey of 242 Apollo Go users in Wuhan, China (June 2025). Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) were conducted using AMOS 24.0 to assess model validity and reliability.Results: The findings show that among the service quality dimensions, Reliability and Information Quality have significant positive effects on Perceived Usefulness, while Responsiveness and Information Quality significantly influence Perceived Ease of Use. All core TAM pathways were supported: Perceived Ease of Use positively affects both Perceived Usefulness and Reuse Intention, while Perceived Usefulness emerges as the strongest predictor of Reuse Intention.Conclusions: This study confirms the theoretical feasibility of integrating AI service quality into the TAM framework and highlights the critical role of “practical value” and “clarity of information” in shaping user decisions. The results provide theoretical insights and empirical guidance for Robotaxi platforms aiming to improve user experience and service design. Additionally, the findings offer strategic implications for the deployment of AI-based mobility services in Korea and other markets.
Li et al. (Sun,) studied this question.