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.

Recently, many artificial intelligence speakers have been released on the market worldwide. However, the AI speaker thinks autonomously, which is a characteristic of AI. There seems to be no element to talk about. Even answering the question of this study and saying, “It’s not like an artificial intelligence speaker, it’s like a slightly advanced Internet-connected radio.” To determine the difference between these released technologies and users’ expectations in detail, we tried to analyze whether the quality and value of artificial intelligence speakers affect the positive (+) effect on use and user satisfaction. Finally, we tried to determine if it comes with user benefits. As a result of analyzing DeLone and McLean’s IS SUCCESS model, it was found that the information quality provided by the artificial intelligence speaker is used, but it is difficult to deliver satisfaction, but the service and system quality deliver user satisfaction. Moreover, it was found that perceived pleasure had no positive effect on the use or user satisfaction. It is necessary to upgrade to a level that can provide information providing quality and enjoyment that users can perceive. This means that it is a system with built-in voice recognition to answer questions connected to the Internet.

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.

Recently, artificial intelligence speakers have been used a lot in homes and offices. However, users say that it is an automated speaker, not an artificial intelligence speaker. Regression analysis was performed by applying the Value-Based Acceptance Model (VAM) to see if there are any improvements to the negative perceptions of users mentioned above. As a result of the regression analysis, improvements were needed for convenience and security threats, and it did not reach the level of anthropomorphism such as with humans. In addition, it is concluded that the factors that positively affect the perceived value are usefulness and enjoyment and that they are somewhat satisfied with the burden of technical difficulties, cost, and reliability of the information. In conclusion, artificial intelligence should continuously collect various data and provide information or suggest choices and alternatives through the process of analysis, learning, and inference. However, as a result of this study, it is concluded that it is similar to an automated machine that simply finds the data among many data connected to the Internet, plays music, and connects to a site where you can shop and process it non-face-to-face. The rationale for being similar to an automated machine is that it has not reached the level of anthropomorphism.