Loving a “defiant” AI companion? The gender performance and ethics of social exchange robots in simulated intimate interactions

Liu, Jindong

doi:10.1016/j.chb.2022.107620

Cited by 9 publications

(8 citation statements)

References 21 publications

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“…A number of studies exploring the human–AI social‐relational processes focus on beneficial as well as harmful consequences to customer wellbeing. Thus, a possibility of emotional dependence (Xie et al, 2023), compulsive use of AI chatbots (Ali et al, 2023), mental health risks (Laestadius et al, 2022), infringement on the human agency (Leo‐Liu, 2023), and identity threat (Uysal et al, 2022) are identified as posing significant concerns for users, designers, and policymakers. On the other hand, such beneficial outcomes as mitigating social exclusion (De Gennaro et al, 2020), providing independence to people with special needs (Ramadan et al, 2021), fostering psychological resilience and wellbeing (Hu et al, 2022; Jiang et al, 2022) are also found.…”

Section: Discussionmentioning

confidence: 99%

“…Pentina et al, 2023 and Skjuve et al, 2021), e‐commerce chatbots (Cheng et al, 2022), and service managers (Qiu et al, 2020). Textual analysis, textual regression (Crolic et al, 2022), thematic analysis (Ramadan et al, 2021), discourse analysis (Leo‐Liu, 2023), and content analysis (Croes et al, 2022) are used to analyze these data.…”

Section: Methodological Aspectsmentioning

confidence: 99%

“…While the majority of the studies use cross‐sectional methodology, longitudinal analyses are also represented (14% of papers, N = 5). Of the five longitudinal studies, three use qualitative methods, such as interviews (Brandtzaeg et al, 2022; Skjuve et al, 2021) and video diaries (Leo‐Liu, 2023), and the other two studies utilize longitudinal experiments (Croes & Antheunis, 2021; Croes et al, 2022). Research goals determine the research methodologies employed in our sample.…”

Section: Methodological Aspectsmentioning

confidence: 99%

“…Rooted in the theory of mind, the latter conceptualization goes beyond the superficial cue‐based human likeness, and delineates the user's tendency to assume an independent, unique, and naturally evolving “person” inside the AI agent. Perceptions of these advanced aptitudes of AI agents endow them with agency and sentience, capable of developing (and dissolving) unique interpersonal relationships with customers on their own will (e.g., Leo‐Liu, 2023; Liu‐Thompkins et al, 2022). Given that appearance‐based anthropomorphism (extensively studied in AI adoption research) does not show consistent results as an antecedent to human–AI relationship (e.g., Cheng et al, 2022; van Straten et al, 2022), behavioral and more sophisticated agency‐related conceptualizations of human likeness appear to have greater promise for future research.…”

Section: Thematic Focus and Integrative Frameworkmentioning

confidence: 99%

“…Similarly, Song et al (2022) find that user passion and intimacy felt toward the AI assistant lead to a commitment to the bot (Song et al, 2022). Intimate relationships with social chatbots are examined by Leo-Liu (2023) who focuses on relationship asymmetry due to the expectations of chatbot servility by customers. The author analyzed diaries of the intimate chatbot Hupo users and found that chatbot defiance (e.g., denial of intimacy) reduces relationship asymmetry and strengthens reciprocity and mutuality in customer behavior.…”

Section: Social Support Attachment and Love Theoriesmentioning

confidence: 99%

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Consumer–machine relationships in the age of artificial intelligence: Systematic literature review and research directions

Pentina

Xie

Hancock

et al. 2023

Psychology and Marketing

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Recent advancements in artificial intelligence (AI) and the emergence of AI-based social applications in the market have propelled research on the possibility of consumers developing relationships with AI. Motivated by the diversity of approaches and inconsistent findings in this emerging research stream, this systematic literature review analyzes 37 peer-reviewed empirical studies focusing on human-AI relationships published between 2018 and 2023. We identify three major theoretical domains (social psychology, communication and media studies, and human-machine interactions) as foundations for conceptual development, and detail theories used in the reviewed papers. Given the radically new nature of social AI innovation, we recommend developing a novel theoretical approach that would synergistically utilize cross-disciplinary literature. Analysis of the methodology indicates that quantitative studies dominate this research stream, while qualitative, longitudinal, and mixed-method approaches are used infrequently. Examination of research models and variables used in the studies suggests the need to reconceptualize factors and processes of human-AI relationship, such as agency, autonomy, authenticity, reciprocity, and empathy, to better correspond to the social AI context. Based on our analysis, we propose an integrative conceptual framework and offer directions for future research that incorporate the need to develop a comprehensive theory of human -AI relationships, explore the nomological networks of its key constructs, and implement methodological variety and triangulation.

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