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Role-taking is a basic social process underpinning much of the structural social psychology paradigm – a paradigm built on empirical studies of human interaction. Yet today, our social worlds are occupied by bots, voice assistants, decision aids, and other machinic entities collectively referred to as artificial intelligence (AI). The integration of AI into daily life presents both challenges and opportunities for social psychologists. Through a vignette study, the authors investigate role-taking and gender in human-AI relations.

Participants read a first-person narrative attributed to either a human or AI, with varied gender presentation based on a feminine or masculine first name. Participants then infer the narrator's thoughts and feelings and report on their own emotions, producing indicators of cognitive and affective role-taking. The authors supplement results with qualitative analysis from two open-ended survey questions.

Participants score higher on role-taking measures when the narrator is human versus AI. However, gender dynamics differ between human and AI conditions. When the text is attributed to a human, masculinized narrators elicit stronger role-taking responses than their feminized counterparts, and women participants score higher on role-taking measures than men. This aligns with prior research on gender, status, and role-taking variation. When the text is attributed to an AI, results deviate from established findings and in some cases, reverse.

This first study of human-AI role-taking tests the scope of key theoretical tenets and sets a foundation for addressing group processes in a newly emergent form.

The purpose of this study is to provide insights and guidance for practitioners in terms of ensuring rigorous ethical and moral conduct in artificial intelligence (AI) hiring and implementation.

The research employed two experimental designs and one pilot study to investigate the ethical and moral implications of different levels of AI implementation in the hospitality industry, the intersection of self-congruency and ethical considerations when AI replaces human service providers and the impact of psychological distance associated with AI on individuals' ethical and moral considerations. These research methods included surveys and experimental manipulations to gather and analyze relevant data.

Findings provide valuable insights into the ethical and moral dimensions of AI implementation, the influence of self-congruency on ethical considerations and the role of psychological distance in individuals’ ethical evaluations. They contribute to the development of guidelines and practices for the responsible and ethical implementation of AI in various industries, including the hospitality sector.

The study highlights the importance of exercising rigorous ethical-moral AI hiring and implementation practices to ensure AI principles and enforcement operations in the restaurant industry. It provides practitioners with useful insights into how AI-robotization can improve ethical and moral standards.

The study contributes to the literature by providing insights into the ethical and moral implications of AI service robots in the hospitality industry. Additionally, the study explores the relationship between psychological distance and acceptance of AI-intervened service, which has not been extensively studied in the literature.

An emerging body of literature has pinpointed the role of supply chain structure in influencing the extent to which supply chain members disclose information about their internal practices and performance. Nevertheless, empirical research investigating the effects of firm-level relational embeddedness on network-level transparency still lags. Drawing on social network analysis, this research examines the effect of relational embeddedness on supply chain transparency and the contingent role of digitalization in the context of environmental, social and governance (ESG) information disclosure.

In their empirical analysis, the authors collected secondary data from the Bloomberg database about 2,229 firms and 14,007 ties organized in 107 extended supply chains. The authors employed supplier and customer concentration metrics to measure relational embeddedness and performed multiple econometric models to test the hypothesis.

The authors found a positive effect of supplier concentration on supply chain transparency, but the effect of customer concentration was not significant. Additionally, the digitalization of focal firms reinforced the impact of supplier concentration on supply chain transparency.

The study findings contribute by underscoring the critical effect of relational embeddedness on supply chain transparency, extending prior literature on social network analysis, providing compelling evidence for the intersection of digitalization and supply chain management, and drawing important implications for practices.

Artificially intelligent (AI) assistant-enabled smart speaker not only can provide assistance by navigating the massive amount of product and brand information on the internet but also can facilitate two-way conversations with individuals, thus resembling a human interaction. Although smart speakers have substantial implications for practitioners, the knowledge of the underlying psychological factors that drive continuance usage remains limited. Drawing on social response theory and the technology acceptance model, this study aims to elucidate the adoption process of smart speakers.

A field survey of 391 smart speaker users were obtained. Partial least squares structural equation modeling was used to analyze the data.

Media richness (social cues) and parasocial interactions (social role) are key determinants affecting the establishment of trust, perceived usefulness and perceived ease of use, which, in turn, affect attitude, continuance usage intentions and online purchase intentions through AI assistants.

AI assistant-enabled smart speakers are revolutionizing how people interact with smart products. Studies of smart speakers have mainly focused on functional or technical perspectives. This study is the first to propose a comprehensive model from both functional and social perspectives of continuance usage intention of the smart speaker and online purchase intentions through AI assistants.

The aim of this paper is twofold. First, it seeks to understand how different forms of anthropomorphism, namely verbal and visual, can enhance or detract from the subjective well-being of consumers and their co-creation behaviors whilst collaborating with artificial intelligence (AI) service agents. Second, it seeks to understand if AI anxiety and trust in message, function as primary and secondary consumer appraisals of collaborating with AI service agents.

A conceptual model is developed using the theories of the uncanny valley and cognitive appraisal theory (CAT) with three hypotheses identified to guide the experimental work. The hypotheses are tested across three experimental studies which manipulate the level of anthropomorphism of AI.

Results demonstrate that verbal and visual anthropomorphism can assist consumer well-being and likelihood of co-creation. Further, this relationship is explained by the mediators of anxiety and trust.

The empirical results and theorizing suggest verbal anthropomorphism should be present (absent) and paired with low (high) visual anthropomorphism, which supports the “uncanny valley” effect. A moderated mediation relationship is established, which confirms AI anxiety and trust in a message as mediators of the AI service agent anthropomorphism-consumer subjective well-being/co-creation relationship. This supports the theorizing of the conceptual model based on the “uncanny valley” and CAT.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”

Artificial intelligence (AI) has gained significant momentum in recent years. Among AI-infused systems, one prominent application is context-aware systems. Although the fusion of AI and context awareness has given birth to personalized and timely AI-powered context-aware systems, several challenges still remain. Given the “black box” nature of AI, the authors propose that human–AI collaboration is essential for AI-powered context-aware services to eliminate uncertainty and evolve. To this end, this study aims to advance a research agenda for facilitators and outcomes of human–AI collaboration in AI-powered context-aware services.

Synthesizing the extant literature on AI and context awareness, the authors advance a theoretical framework that not only differentiates among the three phases of AI-powered context-aware services (i.e. context acquisition, context interpretation and context application) but also outlines plausible research directions for each stage.

The authors delve into the role of human–AI collaboration and derive future research questions from two directions, namely, the effects of AI-powered context-aware services design on human–AI collaboration and the impact of human–AI collaboration.

This study contributes to the extant literature by identifying knowledge gaps in human–AI collaboration for AI-powered context-aware services and putting forth research directions accordingly. In turn, their proposed framework yields actionable guidance for AI-powered context-aware service designers and practitioners.

This study investigates the determinants of effective human and artificial intelligence (AI) relationship-building strategies for brands. It explores the antecedents and consequences of consumers' interactant satisfaction with communication and identifies ways to enhance consumer purchase intention via AI chatbot promotion.

Microsoft Xiaoice served as the focal AI chatbot, and 331 valid samples were obtained. A two-stage structural equation modeling-artificial neural network approach was adopted to verify the proposed theoretical model.

Regarding the IQ (intelligence quotient) and EQ (emotional quotient) of AI chatbots, the multi-dimensional social support model helps explain consumers' interactant satisfaction with communication, which facilitates affective attachment and purchase intention. The results also show that chatbots should emphasize emotional and esteem social support more than informational support.

Brands should focus more on AI chatbots' emotional and empathetic responses than functional aspects when designing dialogue content for human–AI interactions. Well-designed AI chatbots can help marketers develop effective brand promotion strategies.

This research enriches the human–AI interaction literature by adopting a multi-dimensional social support theoretical lens that can enhance the interactant satisfaction with communication, affective attachment and purchase intention of AI chatbot users.

With the increase in the adoption of artificial intelligence (AI)-based decision-making, organizations are facilitating human–AI collaboration. This collaboration can occur in a variety of configurations with the division of labor, with differences in the nature of interdependence being parallel or sequential, along with or without the presence of specialization. This study intends to explore the extent to which humans express comfort with different models human–AI collaboration.

Situational response surveys were adopted to identify configurations where humans experience the greatest trust, role clarity and preferred feedback style. Regression analysis was used to analyze the results.

Some configurations contribute to greater trust and role clarity with AI as a colleague. There is no configuration in which AI as a colleague produces lower trust than humans. At the same time, the human distrust in AI may be less about human vs AI and more about the division of labor in which human–AI work.

The study explores the extent to which humans express comfort with different models of an algorithm as partners. It focuses on work design and the division of labor between humans and AI. The finding of the study emphasizes the role of work design in human–AI collaboration. There is human–AI work design that should be avoided as they reduce trust. Organizations need to be cautious in considering the impact of design on building trust and gaining acceptance with technology.

The paper's originality lies in focusing on the design of collaboration rather than on performance of the team.

Artificial intelligence (AI) has a large number of applications at the industry and user levels. However, AI's uniqueness neglect is becoming an obstacle in the further application of AI. Based on the theory of innovation resistance, this paper aims to explore the effect of AI's uniqueness neglect on consumer resistance to AI.

The authors tested four hypothesis across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI's uniqueness neglect leads to consumer resistance to AI; Studies 2 focused on the role of human–AI interaction trust as an underlying driver of resistance to medical AI. Study 3–4 provided process evidence by way of a measured moderator, testing whether participants with a greater sense of non-verbal human–AI communication are more reluctant to have consumer resistance to AI.

The authors found that AI's uniqueness neglect increased users' resistance to AI. This occurs because the uniqueness neglect of AI hinders the formation of interaction trust between users and AI. The study also found that increasing the gaze behavior of AI and increasing the physical distance in the interaction can alleviate the effect of AI's uniqueness neglect on consumer resistance to AI.

This paper explored the effect of AI's uniqueness neglect on consumer resistance to AI and uncovered human–AI interaction trust as a mediator for this effect and gaze behavior and physical distance as moderators for this effect.