With the rapid proliferation of the artificial intelligence (AI)-generated content and the accompanying questions of ownership of it, it is important to understand how users experience psychological ownership and what drive them to share that content. Accordingly, we examine (a) the mechanisms by which psychological ownership arises during AI-generative content creation, (b) the effect of that ownership on content sharing, and (c) the moderator effect of identity relevance on ownership and sharing intention. Two online surveys were administered: Study 1 surveyed experienced generative-AI-generated content user to assess contextual appraisals and psychological status. Study 2 employed a 2 × 2 scenario‐based survey manipulating autonomy cues and identity relevance. The result is that autonomy in AI-generated content creation increases psychological ownership and, consequently, posting intention. These findings illuminate mechanisms driving human–AI collaboration and inform strategies to foster AI-content dissemination.

With the development of natural language processing (NLP), open-domain chatbots can operate as companions. The success of open-domain chatbots depends on understanding how positive and negative expectancy violations affect both utilitarian and hedonic gratification, which in turn influences user satisfaction and continued use. Therefore, this study examines how violations of expectations influence the hedonic and utilitarian evaluations of open-domain chatbots using the expectancy-violation theory. Participants (n = 204) interacted with an improvising AI chatbot and reported their satisfaction. Results indicated that both hedonic and utilitarian satisfaction were higher when negative expectations were met compared to when expectations were violated. However, no significant difference was found in hedonic gratification between the chatbot’s expected performance and positively unexpected performance. Conversations meeting expectations elicited more utilitarian satisfaction than positively surprising interactions. These findings highlight the dynamics between value types and expectation violations in AI chatbot evaluations.

This research addresses the rising prominence of virtual influencers (VIs) by asking a crucial question: “How can we effectively use virtual influencers to not only reach audiences but also deeply resonate with them, particularly in promoting socially responsible behaviors?” We propose employing narrative messaging to enhance virtual influencers’ effectiveness in delivering prosocial messages. In a 2 (VI appearance: human-like vs. anime-like) × 2 (message style: narrative vs. non-narrative) between-subjects design, 320 Gen-Z and younger Millennials were exposed to simulated Instagram posts by a VI discussing cyberbullying. Results indicated that human-like virtual influencers led to higher supporting intent and message credibility, especially in the non-narrative condition. However, in the narrative message condition, the advantage of human-like appearance diminished. These findings highlight the significant role of VI appearance in prosocial message reception and the conditional influence of message style. Actionable insights for practitioners leveraging VIs in social marketing strategies are discussed.

This study conducted an experiment to test how the level of blame differs between an artificial intelligence (AI) and a human driver based on attribution theory and computers are social actors (CASA). It used a 2 (human vs. AI driver) x 2 (victim survived vs. victim died) x 2 (female vs. male driver) design. After reading a given scenario, participants (N = 284) were asked to assign a level of responsibility to the driver. The participants blamed drivers more when the driver was AI compared to when the driver was a human. Also, the higher level of blame was shown when the result was more severe. However, gender bias was found not to be significant when faulting drivers. These results indicate that the intention of blaming AI comes from the perception of dissimilarity and the seriousness of outcomes influences the level of blame. Implications of findings for applications and theory are discussed.

This study employed an experiment to test participants’ perceptions of an artificial intelligence (AI) recruiter. It used a 2 (Specialist AI/Generalist AI) × 2 (Sexist/nonsexist) design to test the relationship between these labels and the perception of moral violations. The theoretical framework was an integration of the Computers Are Social Actors (CASA) and Elaboration Likelihood Model (ELM) approaches. Participants (n = 233) responded to an online questionnaire after reading one of four scenarios involving an AI recruiter’s evaluation of job candidates. Results found that the concept of “mindlessness” in CASA is situational, based on whether the issue is processed with the central route or the peripheral route. Moreover, this study shows that CASA can explain the evaluation of machines with the third-person point of view. Also, there was a distinction between the perception of the AI and its decisions. Furthermore, participants were found to be more sensitive about the AI agent’s sexism – which was more anthropomorphic and emotionally engaging – than about the AI agent’s status as a specialist.

This study examines how people perceive artwork created by artificial intelligence (AI) and how presumed knowledge of an artist's identity (Human vs. AI) affects individuals’ evaluation of art. Drawing on Schema theory and theory of Computers Are Social Actors (CASA), this study used a survey-experiment that controlled for the identity of the artist (AI vs. Human) and presented participants with two types of artworks (AI-created vs. Human-created). After seeing images of six artworks created by either AI or human artists, participants ( n = 288) were asked to evaluate the artistic value using a validated scale commonly employed among art professionals. The study found that human-created artworks and AI-created artworks were not judged to be equivalent in their artistic value. Additionally, knowing that a piece of art was created by AI did not, in general, influence participants’ evaluation of art pieces’ artistic value. However, having a schema that AI cannot make art significantly influenced evaluation. Implications of the findings for application and theory are discussed.