Although autonomous vehicles have revolutionized the transportation landscape by enabling driving without direct human intervention, it is not yet perfect. For this reason, it is critically important for users to quickly respond to takeover requests from autonomous driving agents. Based on literature on framing effects in persuasion, this study focused on the efficacy of message framing and construal level theory. An experiment (N = 78 participants) was conducted using a driving simulator, employing a 2 (message framing: gain vs. loss) × 2 (temporal distance: distant vs. close) between-subjects design. The key findings indicate that gain framing led to higher levels of perceived benefit as well as compliance and behavioral intention. In contrast, loss framing resulted in higher levels of perceived risk related to danger and prompted quicker behavioral changes, such as lower levels of distraction and faster responses to takeover requests. Conversely, construal level in the messages did not show significant differences and had an impact only on perceived risk and distraction as a moderator. Discussion and implications are provided emphasizing the importance of the messages that autonomous car agents provide.

The global population is experiencing a significant rise in cases of depressive disorders, which have been exacerbated by the COVID-19 pandemic. However, having limited resources and fear of social stigma have discouraged individuals from seeking professional psychological counseling or visiting hospitals. In response to this issue, psychiatrists have attempted the use of chatbots as a therapeutic aid. Therefore, in this study, users’ choice data about the mental healthcare AI chatbot are collected through conjoint analysis, and the collected data is analyzed using the mixed logit method to derive users’ preferences for the mental healthcare AI chatbot. Findings highlight a consistent preference among users for certain factors in both psychological counseling chatbots and traditional psychological counseling. At first, the findings indicate that users place the highest priority on pricing and the ability to connect with a professional counselor. Furthermore, users prefer chatbots that have a more human-like appearance and characteristics. By incorporating these preferences, chatbot developers can create a more user-centric mental healthcare AI chatbot.

While human-like social interactions can enhance trust in and acceptance of automated vehicles (AVs), overuse may hinder these benefits, reflecting the “uncanny valley of mind” effect. We hypothesized that the AV agent’s human-like features—calling drivers by their name (Name) and expressing emotions (Emotion)—enhance trust and acceptance individually but may have adverse effects when combined. A 2 × 2 between-subjects experiment (N = 84) examined these effects. Participants in the Name and Emotion combination were more likely to perceive the experiential mind in the AV compared to the Name or Emotion conditions. However, they were less likely to show behavioral trust in the AV than in the Emotion condition, to perceive the AV as useful than in either the Name or Emotion condition, and to show intention to use the AV than in the Name condition. These findings highlight potential trade-offs in designing social AV interactions.