Deep mind in social responses to technologies: A new approach to explaining the Computers are Social Actors phenomena

Can social robots reduce the loneliness of older adults? -Interaction and communication between social robots and older adults

This study explored how older adults living alone interact with social robots over time, focusing on whether such engagement mitigates social and emotional loneliness. Fifteen adults (≥65 years) who had used a specific social robot for at least three months participated in in-depth interviews and observations, analyzed through a constructivist grounded theory lens. Many participants anthropomorphized the robot (e.g., "friend," "granddaughter"), resulting in deeper emotional engagement, especially when two-way communication was sustained. Others used it primarily for functional tasks yet still experienced mild relief from social isolation. Mediators, such as care workers or family members, influenced interaction depth, though older adults with higher digital literacy required less support. Over time, adherence depended on the robot's conversational abilities, accurate initial framing, and user willingness to form a "relationship." Despite these promising outcomes, repetitive dialogue or mismatched expectations led some participants to disengage, underscoring the need to refine design and introduction processes.

Assessing the impact of chatbots on health decision-making: A multifactorial experimental approach

BackgroundChatbots are increasingly integrated into healthcare, offering personalized and accessible health advice. However, the impact of factors such as chatbot authority, health information type, and interaction style on users' decision-making remains unclear.ObjectiveThis study aims to investigate how these elements influence users' willingness to adopt health advice provided by chatbots.MethodsA 2 × 2 × 2 factorial experiment was conducted with 480 university students to examine the effects of chatbot authority (authoritative vs. non-authoritative), health information type (preventive vs. treatment-related), and interaction style (formal vs. informal). Participants' willingness to adopt the health advice was measured before and after interacting with the chatbot.ResultsThe study found that a authoritative chatbot delivering treatment-related advice in a formal style significantly increased willingness to adopt the advice. Conversely, preventive information was more effective when presented informally by a non-authoritative chatbot. These results support the media evocation paradigm, which suggests that chatbots framed as authoritative figures evoke greater user engagement and trust in health contexts.ConclusionThe findings extend the media evocation paradigm by demonstrating that chatbot authority, information type, and interaction style should be aligned with the nature of health advice to maximize effectiveness. This study provides insights for designing chatbots that improve health decision-making by tailoring their communication strategies.

Expectancy Violations and Discontinuance Behavior in Live-Streaming Commerce: Exploring Human Interactions with Virtual Streamers

Virtual streamers, as a typical application of AI-enabled digital humans, are increasingly being utilized in live-streaming commerce due to technological advancements and industry innovations. Although virtual streamers present several benefits, there is potential for adverse effects when they do not align with consumer expectations. Drawing upon expectancy violations theory, this study developed a theoretical model to explore whether and how consumers' expectation violations during human-virtual streamer interactions affect consumers' discontinuance behavior. Through an online questionnaire survey of 307 Chinese consumers with prior experience interacting with virtual streamers, this study used a partial least squares structural equation model to analyze the research model. The empirical results indicated that professionalism expectation violation, empathy expectation violation, and responsiveness expectation violation positively influenced consumers' distrust and dissatisfaction, which subsequently led to discontinuance behavior. This study contributes to the literature on live-streaming commerce, human-AI interaction, and expectancy violation theory. Furthermore, the findings offer valuable insights for practitioners in the field of live-streaming commerce by enabling them to formulate preventive or remedial strategies to mitigate potential negative outcomes when implementing virtual streamers.

Social pain by non-social agents: Exclusion hurts and provokes punishment even if the excluding source is a computer

Prior research has shown that being excluded by computer-agents in experimental exclusion paradigms threatens individuals' basic needs to a similar extent as being excluded by humans. It is less clear, however, why this similarity between computer and human exclusion occurs, and whether it applies only to reactions immediately after the exclusion event (reflexive stage), or also to reactions that occur further downstream (e.g., reflective stage). Four studies (N = 1048) with three different exclusion paradigms provide several key insights: First, the similarity between computer and human exclusion is robust and pervasive, as Bayesian analyses provide consistent support that the source of exclusion (human vs. computer) does not affect reflexive need satisfaction. Second, this similarity also extends to reflective reactions and punishing behavior. Finally, the present studies extend our knowledge about the processes underlying this similarity by uncovering the role of anthropomorphism. Implications and directions for future research are discussed.