Despite the advancement in autonomous vehicles, public trust and acceptance are crucial for AV’s widespread adoption. This study examines how different collaboration levels between human drivers and artificial intelligence influence users’ trust and acceptance of AVs. Using an extended Technology Acceptance Model, this study incorporates psychological factors and technological attitudes such as perceived safety, perceived risk, AI literacy, and AI technophobia. Data collected from 392 vehicle owners across 11 Chinese cities were analyzed using Partial Least Squares Structural Equation Modeling and Multi-Group Analysis. The findings reveal that at the fully manual level, perceived ease of use significantly influences perceived usefulness, while trust remains grounded in mechanical reliability rather than AI systems. In contrast, as AI assumes driving responsibilities at collaborative automation levels, the findings show that AI literacy significantly increases perceived trust and ease of use, while AI technophobia decreases them, with these effects varying across different driving automation levels. As AI takes on greater driving responsibilities, perceived ease of use becomes less critical, and perceived trust increasingly influences users’ acceptance. These findings highlight the need for targeted public education and phased automation strategies, offering guidance for AV developers to address user concerns and build trust in autonomous technologies. By enhancing public trust and acceptance, this study contributes to sustainable development by promoting safer roads and enabling more efficient, resource-conscious transportation systems. Gradually integrating AVs into urban mobility also supports smart city initiatives, fostering more sustainable urban environments.

As social robots become increasingly integrated into everyday life, understanding the factors that influence users’ long-term continuance intention is essential. This study investigates how various features of MOCCA, a social robot equipped with episodic memory, affect users’ continuance usage intention through perceived trust and parasocial interaction, within the framework of the Stimulus–Organism–Response (SOR) theory. A structural model incorporating key perceived features (intimacy, morality, dependency, and information privacy risk) was tested with survey data from 285 MOCCA users and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that intimacy and morality positively influence both trust and parasocial interaction, while information privacy risk exerts a negative effect. Dependency significantly reduces parasocial interaction but does not significantly impact trust. These findings highlight the importance of balancing human-like qualities, ethical responsibility, perceived autonomy, and privacy protection in social robot design to foster trust, enhance user engagement, and support long-term adoption. This study provides theoretical, managerial, and practical insights into the field of human–robot interaction (HRI) and contributes to the broader acceptance of social robots in everyday life.