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Service robotics, a branch of robotics that entails the development of robots able to assist humans in their environment, is of growing interest in the hospitality industry. Designing effective autonomous service robots, however, requires an understanding of Human–Robot Interaction (HRI), a relatively young discipline dedicated to understanding, designing, and evaluating robotic systems for use by or with humans. HRI has not yet received sufficient attention in hospitality robotic design, much like Human–Computer Interaction (HCI) in property management system design in the 1980s. This article proposes a set of introductory HRI guidelines with implementation standards for autonomous hospitality service robots.

A set of key user-centered HRI guidelines for hospitality service robots were extracted from 52 research articles. These are organized into service performance categories to provide more context for their application in hospitality settings.

Based on an extensive literature review, this article presents some HRI guidelines that may drive higher levels of acceptance of service robots in customer-facing situations. Deriving meaningful HRI guidelines requires an understanding of how customers evaluate service interactions with humans in hospitality settings and to what degree those will differ with service robots.

Robots are challenging assumptions on how hospitality businesses operate. They are being increasingly deployed by hotels and restaurants to boost productivity and maintain service levels. Effective HRI guidelines incorporate user requirements and expectations in the design specifications. Compilation of such information for designers of hospitality service robots will offer a clearer roadmap for them to follow.

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 purpose of this study is to review the extant literature on chatbots and stakeholder interactions to identify major trends and shed light on knowledge gaps.

A systematic literature review was conducted combining qualitative and quantitative approaches. A code book based on early systematic literature reviews was developed and used to extract information from 62 discrete peer-reviewed English articles. An inductive approach was used to analyse definitions of chatbots, topics, metrics, perspectives and implications.

Chatbots have been studied by many different disciplines, but not much from organizational, stakeholder and corporate communication perspectives. Existing studies focus on the technical developments of chatbots and chatbot language and conversations skills. Research has remained anchored at the micro-level understanding of the phenomenon, that is, the nature of chatbots, but has not yet taken into consideration the meso (organizational) or macro (societal) levels.

This study focused only on academic peer-reviewed papers in English and excluded conference proceeding, books, book chapters and editorials that may have offered other important and relevant reflections. The limited number of studies in communication-related disciplines shows that corporate communication scholars could contribute more to the discussion of chatbot–stakeholder interactions.

This is the first research in the field of corporate communication that examines organizational chatbot–stakeholder interactions. Results of this review offer important information on chatbots' organizational capabilities and affordances, which, arguably, must be taken into consideration when stakeholder engagement strategies are set.

The purpose of this paper is to clarify the conceptual issues of information behaviour research by reviewing the approaches to information interaction in the context of information seeking and retrieval (IS&R).

The study uses the conceptual analysis focussing on four pioneering models for interactive IS&R proposed by Belkin, Ingwersen and Ingwersen and Järvelin.

A main characteristic of models for information interaction is the tripartite setting identifying information resources accessible through information systems, intermediary/interface and user. Dialogue is a fundamental constituent of information interaction. Early models proposed by Belkin and Ingwersen focussed on the dialogue occurring in user-intermediary interaction, while more recent frameworks developed by Ingwersen and Järvelin devote more attention to dialogue constitutive of user-information system interaction.

As the study focusses on four models developed within the period of 1984-2005, the findings cannot be generalised to depict the phenomena of information interaction as a whole. Further research is needed to model the specific features of information interaction occurring in the networked information environments in particular.

The study pioneers by providing an in-depth analysis of the ways in which pioneering researchers have conceptualised the phenomena of interaction in the context of IS&R. The findings contribute to the elaboration of the conceptual space of information behaviour research.

Chatbots represent an innovative channel for retailers to meet young customers' needs anywhere and at any time. Being an emergent technology, however, it is important to investigate more thoroughly how users perceive it, and which are the variables that enhance a positive attitude towards this technology. On this premise, this study applies a social relationship perspective to the design of chatbots addressed to younger consumers.

The study adopts a between-participants factorial design to investigate the effects of visual cues (avatar presence vs avatar absence) and interaction styles (social-oriented vs task-oriented) on social presence and how this, in turn, enhances millennials' perceived enjoyment, trust and, ultimately, attitude towards the chatbot. A survey experiment was employed to conduct the study on data collected from 193 Italian millennials.

The results show that applying a social-oriented interaction style increases users' perception of social presence, while an insignificant effect was found for avatar presence. The partial least square structural equation modeling (PLS-SEM) analysis further confirms the hypothesised model.

The adoption of new digital technologies such as chatbots is likely to have a far reaching effect on retailers, consumers, employees and society. For this reason, a broad understanding of the phenomenon is needed. To the best of our knowledge, this is the first study to provide results from an experimental design in which both interaction style (social- vs task-oriented) and avatar (presence vs absence) of a chatbot are manipulated to directly explore social presence and its effect on trust, perceived enjoyment and millennials' attitude towards a chatbot applied for retailing purposes.

Artificial intelligence (AI)-based chatbots have brought unprecedented business potential. This study aims to explore consumers' trust and response to a text-based chatbot in e-commerce, involving the moderating effects of task complexity and chatbot identity disclosure.

A survey method with 299 useable responses was conducted in this research. This study adopted the ordinary least squares regression to test the hypotheses.

First, the consumers' perception of both the empathy and friendliness of the chatbot positively impacts their trust in it. Second, task complexity negatively moderates the relationship between friendliness and consumers' trust. Third, disclosure of the text-based chatbot negatively moderates the relationship between empathy and consumers' trust, while it positively moderates the relationship between friendliness and consumers' trust. Fourth, consumers' trust in the chatbot increases their reliance on the chatbot and decreases their resistance to the chatbot in future interactions.

Adopting the stimulus–organism–response (SOR) framework, this study provides important insights on consumers' perception and response to the text-based chatbot. The findings of this research also make suggestions that can increase consumers' positive responses to text-based chatbots.

Extant studies have investigated the effects of automated bots' attributes on consumers' perceptions. However, the boundary conditions of these effects are largely ignored. This research is one of the first attempts to provide a deep understanding of consumers' responses to a chatbot.

Customization by segmenting within human–computer interaction is an emerging phenomenon. Appealing graphical elements that cater to user needs are considered progressively important, as the way a graphic is visually represented can greatly contribute to the interaction. However, aesthetic perceptions are subjective and may differ by target group. Understanding variations in user perceptions may aid in design processes; therefore, we set out to investigate the effects of demographic differences relating to perceptions of graphical user interface (GUI) element (i.e. game app icon) aesthetics.

The authors employed a vignette experiment with random participant (n = 513) assignment to evaluate 4 icons from a total of 68 pre-selected mobile game icons using semantic differential scales. This resulted in a total of 2052 individual icon evaluations. Regression analyses were performed with the effects of age, gender and time using graphical user interfaces (i.e. app stores) and the interactions of these variables relating to perceptions of GUI element aesthetics.

The results indicate that, overall, demographic factors have relatively little effect on how icons are perceived. Significant relations suggest that experienced users, younger audiences and women are more critical in their perception of aesthetic excellence, and that perceptions change for younger women. The implications of the findings are discussed via adaptive decision-making theory.

In the context of graphical user interface element aesthetics, demographic differences have received minimal attention as moderating variables regardless of their relevance in design and development. Hence, it merits further research.

While artificial intelligence (AI) has shown its promise in assisting human decision, there exist barriers to adopting AI for decision-making. This study aims to identify barriers in the adoption of AI for automated organisational decision-making. AI plays a key role, not only by automating routine tasks but also by moving into the realm of automating decisions traditionally made by knowledge or skilled workers. The study, therefore, selected respondents who experienced the adoption of AI for decision-making.

The study applied an interpretive paradigm and conducted exploratory research through qualitative interviews with 13 senior managers in South Africa from organisations involved in AI adoption to identify potential barriers to using AI in automated decision-making processes. A thematic analysis was conducted, and AI coding of transcripts was conducted and compared to the manual thematic coding of transcripts with insights into computer vs human-generated coding. A conceptual framework was created based on the findings.

Barriers to AI adoption in decision-making include human social dynamics, restrictive regulations, creative work environments, lack of trust and transparency, dynamic business environments, loss of power and control, as well as ethical considerations.

The study uniquely applied the adaptive structuration theory (AST) model to AI decision-making adoption, illustrated the dimensions relevant to AI implementations and made recommendations to overcome barriers to AI adoption. The AST offered a deeper understanding of the dynamic interaction between technological and social dimensions.

Empathy is part of what makes us human and humane, and it has become a core component of the Social Awareness competency of Social and Emotional Learning (SEL) (CASEL, 2019). SEL fosters the understanding of others’ emotions, is the basis of Theory of Mind skills and frames the development of empathy. The purpose of this paper is to trace the links between empathy development and social and emotional learning when using real versus virtual environments. Empathy is a uniquely human emotion facilitated by abstract thinking and language. Virtual play is a teaching tool for acquiring prosocial behaviors. And finally, human-mediated (traditional and virtual) play is most favorable for SEL growth. Recognition of emotions such as empathy and other socio-communication skills have been taught to children with Autism Spectrum Disorders (ASD). Therefore, technology can be a venue for acquiring empathy.

This paper uses a qualitative interpretive methodology to advocate for the use of technology with human mediation to teach Social and Emotional Learning skills, based on the premise that cognitive and social-emotional development occurs synergistically and mediated by speech and interaction with the environment.

Technology is best seen as an instrument of assessing and teaching socio-emotional skills, but not as the only means to an end, because what makes us human can only be taught within an ecology of human interaction in real-life situations.

This paper reviews previous research works (both empirical and theoretical) that bring to light the connection between socio-emotional development, specifically empathy development, and virtual environments.

Research into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.

The author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.

The author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).

As in other systematic literature review studies, the results are limited by the content of the selected papers.

The findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountability and maintaining the “social goodness” of AI. The results guide practitioners in developing metrics and standards for AI explainability, with the context of AI operation as the focus.

This study addresses polarized beliefs amongst scholars and practitioners about the benefits of AI explainability versus its counterproductive effects. It poses that there is no single best way to maximize AI explainability. Instead, the co-existence of enabling and constraining effects must be managed.