Value versus cost in post-experience robotaxi use: evidence from a cognitive–affective–conative model

Recent advances in autonomous driving have accelerated the deployment of robotaxi services, positioning them as a key component of future urban mobility. However, existing research has largely emphasized pre-adoption intentions, offering limited insight into users’ post-experience evaluations and continued usage behavior in fully driverless contexts. To address this gap, this study examines how perceived value and perceived risk, conceptualised as higher-order constructs, are jointly associated with continued usage intention toward robotaxi services in China. Guided by the cognitive–affective–conative (CAC) framework, perceived value and perceived risk are conceptualized as multidimensional cognitive evaluations formed through actual usage experiences. Based on survey data collected from 409 users with real-world robotaxi experience in Wuhan, China, the study employs structural equation modeling to investigate how these cognitive evaluations shape affective responses, including positive emotion, negative emotion, and satisfaction and, in turn, continued usage intention. The results show that perceived value is strongly associated with higher post-experience positive emotion and satisfaction, which in turn are positively related to continued usage intention, with satisfaction emerging as the strongest direct predictor. In contrast, perceived risk is primarily associated with higher post-experience negative emotion, while its associations with satisfaction and continued usage intention remain weak. This suggests that positive affective and evaluative mechanisms play a more dominant role in sustaining usage than cost-related concerns. By extending the CAC framework to post-experience robotaxi usage, this study deepens the understanding of continued usage behavior in autonomous mobility and offers practical implications for enhancing user acceptance and supporting the large-scale deployment of robotaxi services in China.