Human vs. machine-like representation in chatbot mental health counseling: the serial mediation of psychological distance and trust on compliance intention.

This study examined a serial mediation mechanism to test the effect of chatbots' human representation on the intention to comply with health recommendations through psychological distance and trust towards the chatbot counselor. The sample of the study comprised 385 adults from the USA. Two artificial intelligence chatbots either with human or machine-like representation were developed. Participants had a short conversation with either of the chatbots to simulate an online mental health counseling session and reported their experience in an online survey. The results showed that participants in the human representation condition reported a higher intention to comply with chatbot-generated mental health recommendations than those in the machine-like representation condition. Furthermore, the results supported that both psychological distance and perceived trust towards the chatbot mediated the relationship between human representation and compliance intention, respectively. The serial mediation through psychological distance and trust in the relationship between human representation and compliance intention was also supported. These findings provide practical guidance for healthcare chatbot developers and theoretical implications for human-computer interaction research.

Effect of AI chatbot emotional disclosure on user satisfaction and reuse intention for mental health counseling: a serial mediation model.

This study explored the effect of chatbot emotional disclosure on user satisfaction and reuse intention for a chatbot counseling service. It also examined the independent and sequential mediation roles of user emotional disclosure intention and perceived intimacy with a chatbot on the relationship between chatbot emotional disclosure, user satisfaction, and reuse intention for chatbot counseling. In total, 348 American adults were recruited to participate in a mental health counseling session with either of the two types of artificial intelligence-powered mental health counseling chatbots. These included a chatbot disclosing factual information only or a chatbot disclosing humanlike emotions. The results revealed that chatbot emotional disclosure significantly increased user satisfaction and reuse intention for a chatbot counseling service. The results further revealed that user emotional disclosure intention and perceived intimacy with a chatbot independently and serially mediates the effect of chatbot emotional disclosure on user satisfaction and chatbot counseling service reuse intention. The results indicate positive effects of artificial emotions and their disclosure in the context of chatbot moderated mental health counseling. Practical implications and psychological mechanisms are discussed.

Exploring the Impact of ChatGPT Literacy on User Satisfaction: The Mediating Role of User Motivations.

The introduction of chat generative pretrained transformer (ChatGPT), the fastest growing large language model, has changed the landscape of artificial intelligence-human interaction in everyday life. As the social influence of ChatGPT increases, competencies in it become important life skills. This study aims to explore the determinants of ChatGPT user satisfaction to provide practical implications by suggesting a significant independent variable and mediators between the independent variable and user satisfaction. To this end, this study recruited 822 college students with prior experience using ChatGPT (407 males and 415 females) and conducted an online survey. We tested the effects of ChatGPT literacy on user satisfaction and the mediating roles of different motives (i.e., information and knowledge acquisition and entertainment and leisure) in the relationship between ChatGPT literacy and user satisfaction. The results suggest that ChatGPT literacy significantly increases user satisfaction and that information and knowledge acquisition and entertainment and leisure partially mediate the relationship between the effect of ChatGPT literacy and user satisfaction. The results may have implications for large language model developers and practitioners, such as educators.

Do Anthropomorphic Chatbots Increase Counseling Satisfaction and Reuse Intention? The Moderated Mediation of Social Rapport and Social Anxiety.

The growing demand for mental health services and artificial intelligence chatbots to replace human agents have led to increased attention to chatbot anthropomorphizing. This study explored the effect of anthropomorphism on counseling satisfaction and reuse intention for chatbot-led mental health counseling and the mediating role of social rapport in the relationship. This study also examined the interaction effect of anthropomorphism and social anxiety on counseling satisfaction and reuse intention. A total of 374 U.S. adults were recruited from an online crowdsourcing company to simulate user-chatbot interactions in the context of mental health counseling. Two chatbots either with a high anthropomorphic design (i.e., human face) or low anthropomorphic design (i.e., robot face) were developed through Dialogflow-a natural language processing engine-to examine the hypotheses. The results revealed that the high anthropomorphic design produced higher counseling satisfaction and reuse intention than the low anthropomorphic design, while this relationship is mediated by the perceived social rapport between chatbot counselors and users. The results further revealed a significant interaction effect of anthropomorphism and social anxiety on counseling satisfaction and reuse intention. The findings of this study are expected to (a) enhance the understanding of the effect of anthropomorphic chatbots on counseling satisfaction and reuse intention, (b) clarify moderating effects of social rapport and social anxiety, and (c) help mental health practitioners and chatbot designers by providing a psychological mechanism of how anthropomorphism functions in the context of human-chatbot interaction.