To serve and protect: a typology of service robots and their role in physically safe services

Beyond Human Detection: A Benchmark for Detecting Common Human Posture

Human detection is the task of locating all instances of human beings present in an image, which has a wide range of applications across various fields, including search and rescue, surveillance, and autonomous driving. The rapid advancement of computer vision and deep learning technologies has brought significant improvements in human detection. However, for more advanced applications like healthcare, human-computer interaction, and scene understanding, it is crucial to obtain information beyond just the localization of humans. These applications require a deeper understanding of human behavior and state to enable effective and safe interactions with humans and the environment. This study presents a comprehensive benchmark, the Common Human Postures (CHP) dataset, aimed at promoting a more informative and more encouraging task beyond mere human detection. The benchmark dataset comprises a diverse collection of images, featuring individuals in different environments, clothing, and occlusions, performing a wide range of postures and activities. The benchmark aims to enhance research in this challenging task by designing novel and precise methods specifically for it. The CHP dataset consists of 5250 human images collected from different scenes, annotated with bounding boxes for seven common human poses. Using this well-annotated dataset, we have developed two baseline detectors, namely CHP-YOLOF and CHP-YOLOX, building upon two identity-preserved human posture detectors: IPH-YOLOF and IPH-YOLOX. We evaluate the performance of these baseline detectors through extensive experiments. The results demonstrate that these baseline detectors effectively detect human postures on the CHP dataset. By releasing the CHP dataset, we aim to facilitate further research on human pose estimation and to attract more researchers to focus on this challenging task.

Can robots recover a service using interactional justice as employees do? A literature review-based assessment

Interactional justice (e.g., empathy) plays a crucial role in service recovery. It relies on human social skills that would prevent it from automation. However, several considerations challenge this view. Interactional justice is not always necessary to recover service, and progress in social robotics enables service robots to handle social interactions. This paper reviews service recovery and social robotics literature and addresses whether service robots can use interactional justice as frontline employees do during service recovery. Results show service robots can replicate interactional justice norms, although with some considerations. Accordingly, we propose a research agenda for future studies.

Contexts of Consumption and Their Evolution in the Digital Age: Beyond the Service-Dominant Logic

Starting from the observation of a conceptual gap regarding the association between consumption and the contexts in which it occurs, the paper has two objectives. The first is to fill this gap by developing a framework that includes: the identification of consumption contexts based on their building blocks (actors, goods, relationships), the basic classification of their variety, and a knowledge-based reading of consumption contexts capable of explaining their functioning. The second aim is to show that the framework allows the understanding of the digital transformation of consumption contexts. We show that services are produced in two contexts: in the first type, consumers interact directly with goods; in the second, the intermediation of frontline personnel comes into play. Actors and goods present in the consumption contexts are knowledge-holders, and the relationships between them are learning relationships. The shift from traditional consumption contexts to contexts based on artificial intelligence and the internet of things introduces a major change in that learning relationships are no longer the domain of only (human) actors who learn by interacting with each other and using goods. Both types of contexts are in fact powered by smart goods capable of interacting with each other and with humans within a given context and endowed with structural cognitive connections outside that context.

Frontline employee expectations on working with physical robots in retailing

Purpose

Service robots have increasingly been utilized in retail settings, yet empirical research on how frontline employees (FLEs) might deal with this new reality remains scarce. This mixed-methods study aims to examine how FLEs expect physical service robots to impact job characteristics and affect their job engagement and well-being.

Design/methodology/approach

First, explorative interviews (Study 1; N = 32) were conducted to investigate how FLEs currently experience job characteristics and how they believe robots might impact these job characteristics and job outcomes. Next, a survey (Study 2; N = 165) examined the relationship between job characteristics that retail FLEs expect to be impacted by robots and their own well-being and job engagement.

Findings

While the overall expectations for working with robots are mixed, retail FLEs expect that working with robots can alleviate certain job demands, but robots cannot help to replenish their job resources. On the contrary, most retail FLEs expect the pains and gains associated with robots in the workspace to cancel each other out, leaving their job engagement and well-being unaffected. However, of the FLEs that do anticipate that robots might have some impact on their well-being and job engagement, the majority expect negative effects.

Originality/value

This study is unique in addressing the trade-off between expected benefits and costs inherent to job demands-resources (JD-R) theory while incorporating a transformative service research (TSR) lens. By integrating different streams of research to study retail FLEs' expectations about working with robots and focusing on robots' impact on job engagement and well-being, this study offers new insights for theory and practice.

Experiential AR/VR: a consumer and service framework and research agenda

Purpose

The paper focuses on extended reality technologies and their potential contribution to the improvement of services. First, it identifies extended reality technologies (AR/VR) as the most promising interfaces to enable an experiential consumption of the services. It then summarises their properties and discusses similarities and differences. Last, it maps these technologies onto a consumer psychology framework of experience to derive possible areas of future research.

Design/methodology/approach

The authors conduct a literature review and present a conceptual framework of AR/VR contributions on experience.

Findings

The study provides an up-to-date literature review including AR and VR applications for consumer and service experience, as well as recommendations for possible research directions.

Originality/value

Whereas previous contributions adopted the same, experiential approach but focused on different technology (e.g. AI) or considered multiple interfaces and their impact on the consumer journey (mostly transactions), this paper aims at digging deeper into AR/VR, while retaining an experiential view on consumption that best serves the contextualisation of AR/VR.