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Anthropomorphism in Artificial Intelligence (AI)-powered devices is being used increasingly frequently in consumer-facing situations (e.g., AI Assistants such as Alexa, virtual agents in websites, call/chat bots, etc.), and therefore, it is essential to understand anthropomorphism in AI both to understand consequences for consumers and to optimize firms' product development and marketing. Extant literature is fragmented across several domains and is limited in the marketing domain. In this review, we aim to bring together the insights from different fields and develop a parsimonious conceptual framework to guide future research in fields of marketing and consumer behavior.

We conduct a review of empirical articles published until November 2021 in Financial Times Top 50 (FT50) journals as well as in 41 additional journals selected across several disciplinary domains: computer science, robotics, psychology, marketing, and consumer behavior.

Based on literature review and synthesis, we propose a three-step guiding framework for future research and practice on AI anthropomorphism.

Our proposed conceptual framework informs marketing and consumer behavior domains with findings accumulated in other research domains, offers important directions for future research, and provides a parsimonious guide for marketing managers to optimally utilize anthropomorphism in AI to the benefit of both firms and consumers.

We contribute to the emerging literature on anthropomorphism in AI in three ways. First, we expedite the information flow between disciplines by integrating insights from different fields of inquiry. Second, based on our synthesis of literature, we offer a conceptual framework to organize the outcomes of AI anthropomorphism in a tidy and concise manner. Third, based on our review and conceptual framework, we offer key directions to guide future research endeavors.

Purpose – The purpose of this chapter is to conduct a critical literature review that examines the origins and development of research on service robots in organizations, as well as the key emotional and cognitive issues between service employees, customers, and robots. This review provides a foundation for future research that leverages the emotional connection between service robots and humans.

Design/Methodology/Approach – A critical literature review that examines robotics, artificial intelligence, emotions, approach/avoid behavior, and cognitive biases is conducted.

Findings – This research provides six key themes that emerge from the current state of research in the field of service robotics with 14 accompanying research questions forming the basis of a research agenda. The themes presented are as follows: Theme 1: Employees have a forgotten “dual role”; Theme 2: The influence of groups is neglected; Theme 3: Opposing emotions lead to uncertain outcomes; Theme 4: We know how robots influence engagement, but not experience; Theme 5: Trust is necessary but poorly understood; and Theme 6: Bias is contagious: if the human mind is irrational…so too are robot minds.

Practical Implications – Practically, this research provides guidance for researchers and practitioners alike regarding the current state of research, gaps, and future directions. Importantly for practitioners, it sheds light on themes in the use of AI and robotics in services, highlighting opportunities to consider the dual role of the employee, examines how incorporating a service robot influences all levels of the organization, addresses motivational conflicts for employees and customers, explores how service robots influence the whole customer experience and how trust is formed, and how we are (often inadvertently) creating biased robots.

Cognitive Load Theory (CLT) is the product of over a decade of research in the instructional science domain (Chandler & Sweller, 1991; Sweller & Chandler, 1994), and its applications to other areas of inquiry continues to expand (see Cuevas, Fiore, & Oser, 2002; Paas, Renkl, & Sweller, 2003a; Paas, Tuovinen, Tabbers, & Van Gerven, 2003b; Scielzo, Fiore, Cuevas, & Salas, 2004). The core of CLT is based on two sets of what are termed cognitive load factors that are either endogenous or exogenous from the viewpoint of an operator interacting with the environment. Endogenous (or intrinsic) factors are sources of cognitive load in terms of the general amount and complexity of information with which the operator has to interact. In training environments, intrinsic load is directly proportional to the amount of materials that trainees need to acquire. As such, the more complex the information is in terms of volume and conceptual interactivity, the higher the cognitive load will be. In operational settings, high intrinsic load can occur whenever informational demands that need to be processed are high. Within the context of human–robot team environments, there is likely to be unique intrinsic load factors emerging from this hybrid teamwork interaction (e.g., information produced by synthetic team members). Another source of cognitive load comes from exogenous or extraneous factors. In training and operational settings alike, extraneous cognitive load may occur dependent upon the manner in which information needing attention is presented. Specifically, the more complex the human–robot team interface is in relation to the process by which information is displayed and/or communicated, the more extraneous cognitive load can be present. For example, the technological tools involved in the communication of information, and the associated modalities used to process information may inadvertently result in cognitive load. Simply put, high extraneous cognitive load can be produced as a result of using sub-optimal information presentation and communication. Overall, exogenous factors can stem from the added complexity of human–robot operations in terms of distinct command-and-control systems that emerge from using novel technology. Within such operations, it is particularly important to control sources of extraneous cognitive load that have been shown to produce two distinct negative effects on information processing – redundancy of information and split-attention. These have been shown to attenuate processing capacity thereby minimizing optimal information processing (e.g., Sweller, 1994; Mayer, 1999).

This paper investigates effective human-robot collaboration (HRC) and presents implications for Human Resource Management (HRM). A brief review of current literature on HRM in the smart industry context showed that there is limited research on HRC in hybrid teams and even less on effective management of these teams. This book chapter addresses this issue by investigating factors affecting intention to collaborate with a robot by conducting a vignette study. We hypothesized that six technology acceptance factors, performance expectancy, trust, effort expectancy, social support, organizational support and computer anxiety would significantly affect a users' intention to collaborate with a robot. Furthermore, we hypothesized a moderating effect of a particular HR system, either productivity-based or collaborative. Using a sample of 96 participants, this study tested the effect of the aforementioned factors on a users' intention to collaborate with the robot. Findings show that performance expectancy, organizational support and computer anxiety significantly affect the intention to collaborate with a robot. A significant moderating effect of a particular HR system was not found. Our findings expand the current technology acceptance models in the context of HRC. HRM can support effective HRC by a combination of comprehensive training and education, empowerment and incentives supported by an appropriate HR system.

The relationship between robots and spirituality in the workplace is an interesting and evolving area of research that could provide important insights into the role of technology in promoting human well-being and personal growth. Robots are becoming increasingly common in the workplace and their functions in the business world are increasing. The use of robots in the workplace can affect people's spiritual values. Spiritual values such as being successful in their work, providing a sense of purpose and satisfaction, and feeling valued and important are important. The use of robots in the workplace may cause some people to take over many of the tasks that their jobs once did. In this case, employees may feel that their work no longer makes sense and may experience a loss of motivation. The fact that robots don't need the skills and experience of humans can make people feel inadequate in their jobs. However, the use of robots in the workplace can also support people's spiritual values. When robots work with humans, they have responsibilities such as interacting with them, showing empathy, respecting coworkers, and treating humans appropriately. This is important for people's mental and emotional health in the workplace. This approach will help people in the workplace work successfully and happily with robots. The use of robots in the workplace raises moral and ethical questions. In this section, research on the production of artificial intelligence-equipped robots and other intelligent technological machines and their use in organizations is evaluated within the framework of spirituality.

Purpose: The goal of the chapter is to define customer attitudes towards robots in travel, tourism and hospitality (TTH) and to analyse their most significant characteristics.

Design/methodology/approach: The book chapter develops a conceptual framework of attitudes towards robots in travel, tourism and hospitality, based on critical analysis of relevant publications.

Findings: The chapter provides a definition and discussion of the characteristics of customer attitudes towards robots in TTH. It elaborates the structural elements of attitudes towards robots, and the links and interactions between the elements.

Research limitations: Research limitations stem from the small number of studies on customer attitudes towards robots in TTH.

Practical implications: The theoretical analysis can be used as a starting point for empirical studies of customer attitudes towards robots in travel, tourism and hospitality.

Social implication: Combined services, based on human employee-service robot collaboration, are the optimal decision for forming favourable customer attitudes towards robotisation and automation in tourism and hospitality. In that way clients’ needs of high technological convenience, interpersonal communication and socialisation are met simultaneously.

Originality/value: This research is among the few publications that study customer attitudes towards robots in travel, tourism and hospitality. The authors develop a matrix of users’ attitudes and behaviours when using robots in travel, tourism and hospitality.

Purpose: The purpose of this chapter is to elaborate on the major conceptual and practical considerations of the use of robots, artificial intelligence and service automation (RAISA) in travel, tourism, and hospitality companies (TTH).

Design/methodology/approach: The chapter develops a conceptual framework of the major issues related to the use of RAISA in the travel, tourism and hospitality context.

Findings: The findings indicate that while there is a creeping incursion of RAISA into TTH, there are major concerns that the TTH industry has to consider in regard to automating TTH services.

Practical implications: In a practical sense, the chapter identifies the decisions that TTH industry professionals need to take when dealing with RAISA technologies. Furthermore, the chapter elaborates on the impacts RAISA have on business operations, marketing management, human resources and financial management of TTH companies. The TTH industry has to adjust its practices and communicate with its workforce in ways as not to increase Luddite tendencies and resistance among employees.

Social implications: The analysis shows that there is an upcoming era in which automation of services will be so advanced that wealthy countries may not need to import labour to make up with its own aging workforce, suggesting that RAISA and its further development has the potential for disrupting society and international relations.

Originality/value: This chapter provides a comprehensive review of the issues related to the use of RAISA in the TTH industry, including the drivers of RAISA adoption in tourism, advantages and disadvantages of RAISA technologies compared to human employees, decisions that managers need to take, and the impacts of RAISA on business processes. It shows how macroenvironmental pressures shape the microeconomic decisions to use RAISA in a TTH context.

• Service robots (SRs) in retail is an emerging topic for both scholars and practitioners.

• Most of the studies are performed in the field using observational methods.

• To date, there is a lack of using uniform frameworks in order to fully understand customer–robot interactions.

• The dark side of robots in retail is still an underexplored area.

Service robots (SRs) in retail is an emerging topic for both scholars and practitioners.

Most of the studies are performed in the field using observational methods.

To date, there is a lack of using uniform frameworks in order to fully understand customer–robot interactions.

The dark side of robots in retail is still an underexplored area.

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.