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Social robots increasingly adopt service roles in the marketplace. While service research is beginning to unravel the implications for theory and practice, other scientific disciplines have amassed a wealth of empirical data of robots assuming such service roles. The purpose of this paper is to synthesize these findings from a role theory perspective with the aim of advancing role theory for human–robot service interaction (HRSI).

A systematic review of more than 10,000 articles revealed 149 empirical HRSI-related papers across scientific disciplines. The respective articles are analyzed employing qualitative content analysis through the lens of role theory.

This review develops an organizing structure of the HRSI literature across disciplines, delineates implications for role theory development in the age of social robots, and advances robotic role theory by providing an overarching framework and corresponding propositions. Finally, this review introduces avenues for future research.

This study pioneers a comprehensive review of empirical HRSI literature across disciplines adopting the lens of role theory. The study structures the body of HRSI literature, adapts traditional and derives novel propositions for role theory (i.e. robotic role theory), and delineates promising future research opportunities.

Besides the direct physical health consequences, through social isolation COVID-19 affects a considerably larger share of consumers with deleterious effects for their psychological well-being. Two vulnerable consumer groups are particularly affected: older adults and children. The purpose of the underlying paper is to take a transformative research perspective on how social robots can be deployed for advancing the well-being of these vulnerable consumers and to spur robotic transformative service research (RTSR).

This paper follows a conceptual approach that integrates findings from various domains: service research, social robotics, social psychology and medicine.

Two key findings advanced in this paper are (1) a typology of robotic transformative service (i.e. entertainer, social enabler, mentor and friend) as a function of consumers' state of social isolation, well-being focus and robot capabilities and (2) a future research agenda for RTSR.

This paper guides service consumers and providers and robot developers in identifying and developing the most appropriate social robot type for advancing the well-being of vulnerable consumers in social isolation.

This study is the first to integrate social robotics and transformative service research by developing a typology of social robots as a guiding framework for assessing the status quo of transformative robotic service on the basis of which it advances a future research agenda for RTSR. It further complements the underdeveloped body of service research with a focus on eudaimonic consumer well-being.

With the advent of increasingly sophisticated AI, the nature of work in the service frontline is changing. The next frontier is to go beyond replacing routine tasks and augmenting service employees with AI. The purpose of this paper is to investigate whether service employees augmented with AI-based emotion recognition software are more effective in interpersonal emotion regulation (IER) and whether and how IER impacts their own affective well-being.

For the underlying study, an AI-based emotion recognition software was developed in order to assist service employees in managing customer emotions. A field study based on 2,459 call center service interactions assessed the effectiveness of the AI in augmenting service employees for IER and the immediate downstream consequences for well-being relevant outcomes.

Augmenting service employees with AI significantly improved their IER activities. Employees in the AI (vs control) condition were significantly more effective in regulating customer emotions. IER goal attainment, in turn, mediated the effect on employee affective well-being. Perceived stress related to exposure to the AI augmentation acted as a competing mediator.

Service firms can benefit from state-of-the-art AI technology by focusing on its capacity to augment rather than merely replacing employees. Furthermore, signaling IER goal attainment with the help of technology may provide uplifting consequences for service employee affective well-being.

The present study is among the first to empirically test the introduction of an AI-fueled technology to augment service employees in handling customer emotions. This paper further complements the literature by investigating IER in a real-life setting and by uncovering goal attainment as a new mechanism underlying the effect of IER on the well-being of the sender.

A vast body of literature has documented the negative consequences of stress on employee performance and well-being. These deleterious effects are particularly pronounced for service agents who need to constantly endure and manage customer emotions. The purpose of this paper is to introduce and describe a deep learning model to predict in real-time service agent stress from emotion patterns in voice-to-voice service interactions.

A deep learning model was developed to identify emotion patterns in call center interactions based on 363 recorded service interactions, subdivided in 27,889 manually expert-labeled three-second audio snippets. In a second step, the deep learning model was deployed in a call center for a period of one month to be further trained by the data collected from 40 service agents in another 4,672 service interactions.

The deep learning emotion classifier reached a balanced accuracy of 68% in predicting discrete emotions in service interactions. Integrating this model in a binary classification model, it was able to predict service agent stress with a balanced accuracy of 80%.

Service managers can benefit from employing the deep learning model to continuously and unobtrusively monitor the stress level of their service agents with numerous practical applications, including real-time early warning systems for service agents, customized training and automatically linking stress to customer-related outcomes.

The present study is the first to document an artificial intelligence (AI)-based model that is able to identify emotions in natural (i.e. nonstaged) interactions. It is further a pioneer in developing a smart emotion-based stress measure for service agents. Finally, the study contributes to the literature on the role of emotions in service interactions and employee stress.

Service robots are now an integral part of people's living and working environment, making service robots one of the hot topics for service researchers today. Against that background, the paper reviews the recent service robot literature following a Theory-Context-Characteristics-Methodology (TCCM) approach to capture the state of art of the field. In addition, building on qualitative input from researchers who are active in this field, the authors highlight where opportunities for further development and growth lie.

The paper identifies and analyzes 88 manuscripts (featuring 173 individual studies) published in academic journals featured on the SERVSIG literature alert. In addition, qualitative input gathered from 79 researchers who are active in the service field and doing research on service robots is infused throughout the manuscript.

The key research foci of the service robot literature to date include comparing service robots with humans, the role of service robots' look and feel, consumer attitudes toward service robots and the role of service robot conversational skills and behaviors. From a TCCM view, the authors discern dominant theories (anthropomorphism theory), contexts (retail/healthcare, USA samples, Business-to-Consumer (B2C) settings and customer focused), study characteristics (robot types: chatbots, not embodied and text/voice-based; outcome focus: customer intentions) and methodologies (experimental, picture-based scenarios).

The current paper is the first to analyze the service robot literature from a TCCM perspective. Doing so, the study gives (1) a comprehensive picture of the field to date and (2) highlights key pathways to inspire future work.

Service research offering a view of both the dark and bright sides of smart technology remains scarce. This paper embraces a critical perspective and examines the conflicting outcomes of smart services on the customer experience (CX), with a specific focus on chatbots.

This study uses empirical research methods to examine a single case study where an online retail service provider implemented a chatbot for customer service. Using discourse analysis, we analysed 7,167 conversations between customers and the chatbot over a two-year period.

The analysis identifies seven general themes related to the effects of the chatbot on CX: interaction quality, information gathering, procedure literacy, task achievement, digital trust, shopping stress and shopping journey. We illuminate both positive (i.e. having a pleasant interaction, providing information, knowing procedures, improving tasks, increasing trust, reducing stress and completing the journey) and negative outcomes (i.e. having an unpleasant interaction, increasing confusion, ignoring procedures, worsening tasks, reducing trust, increasing stress and abandoning the journey).

The paper develops a comprehensive framework to offer a clearer view of chatbots as smart services in customer care. It delves into the conflicting effects of chatbots on CX by examining them through relational, cognitive, affective and behavioural dimensions.

As a smart service, location-based advertising (LBA) integrates advanced technologies to deliver personalized messages based on a user’s real-time geographic location and needs. However, research has shown that privacy concerns threaten the diffusion of LBA. This research investigates how privacy-related factors (i.e. LBA type, privacy self-efficacy (PSE) and consumer generation) impact consumers’ value-in-use and their intention to use LBA.

This study developed and examined an LBA value-in-use framework that integrates the role of LBA type, consumers’ PSE and consumer generation into the technology acceptance model (TAM). Data were collected through two experiments in the field with a total of 374 consumers. The proposed relationships were tested using PROCESS modeling.

The results reveal that pull (vs push) LBA causes higher value-in-use in terms of perceived usefulness and perceived ease of use, leading to greater usage intention. Further, the differences in the mediated relationship between pull- and push-LBA are larger among consumers of low PSE (vs high PSE) and Generation Z (vs other generations). The findings suggest that the consumer value-in-use brought about by LBA diminishes when using push-LBA for low PSE and Generation Z consumers.

This research is the first to integrate the privacy-related interactions of LBA type and consumer characteristics into TAM to develop a TAM-based LBA value-in-use framework. This study contributes to the literature on service value-in-use, smart services and LBA by clarifying the boundary conditions that determine the effectiveness of LBA in enhancing consumers’ value-in-use.

Consumers' perceptions of product intelligence affect their willingness to accept smart offerings. This paper explores how people perceive various smart products based on their smartness profiles, composed of five distinct smartness facets. Additionally, the study investigates how these perceptions of product intelligence impact consumers' evaluation of factors that either promote or impede the adoption of smart products. These factors are examined as potential mediators in the adoption process. This paper aims to determine if the value-based adoption model can be applied to a broad range of smart service systems.

Consumers assessed one of 28 smart products in a scenario-based quantitative study. Multilevel structural equation modeling (SEM) is used to test the conceptual model, taking the nested data structure into account.

The findings show that product smartness essentially enhances usage intention via adoption drivers (enjoyment and usefulness) and reduces usage intention via adoption barriers (intrusiveness). In particular, the ability to interact in a humanlike manner increases the benefits consumers perceive, which in turn increases consumer acceptance. Only the smartness characteristic of awareness impairs usage intention, mediated by the perceived benefits of enjoyment and usefulness.

In contrast to previous research, which usually focuses on single smart products, this work examines a variety of different products, which allows for better transferability of the results to other smart offerings. Furthermore, prior research has mainly focused on single facets of product smartness or researched smartness on an aggregated level. By considering the consumer perception of each smartness facet, the authors gain deeper insights into the perceptual differences regarding product smartness and how this affects technology adoption via conflicting key acceptance drivers and barriers.

This study aims to understand how retailscape of pop-up stores could influence young Chinese tourists’ emotional response and their subsequent shopping intention in the context of luxury retailing.

A quantitative approach was chosen. Building on the theoretical framework of the stimulus–organism–response (S–O–R) theory, a pop-up store retailscape behavior model was developed to explore the effect of retailscape on young Chinese tourists’ emotional response and patronage intention in a luxury retail setting. In total, 226 structured questionnaires were collected onsite.

The multiple regression analysis reveals that a luxury pop-up store’s retailscape has a positive influence on young Chinese tourists’ emotional response, but it only has a partial influence on their patronage intention. In addition, the result suggests that young Chinese tourists’ emotional response positively influences their patronage intention in luxury pop-up stores.

The study reveals how retailscape influences behavior among the younger generation, and the results provide important references for the luxury retailers in future design and management of pop-up stores so as to attract and retain the interest of the younger generation.

This study puts retailscape effect under scrutiny in the context of luxury pop-up stores which attract young Chinese tourists, who are regarded as one of the major patrons supporting exclusive retail brands in the world. The stimuli element in the S–O–R model is, thus, reexamined in the context of luxury pop-up stores.