Service robots are widely deployed to address labor shortages and improve service quality, yet many still fail in practice. Most research has focused on the positive, overlooking why they fail. To address this gap, our study examines service robot failures from a service design perspective, focusing on design features. To validate this, we conducted a systematic literature review of 863 studies and selected 34 as our data sample. We identified 17 robot design features across three service design aspects and proposed an integrated framework that explains how these features lead to service failures. Theoretically, this study provides a framework for explaining service robot failures from a service design (S-D Logic) perspective, addressing a gap in HRI and service design research. In practice, this study offers actionable guidance for practitioners on improving the design of service robots, their human-robot interaction, and their system integration in real-world service environments.

ABSTRACT This study explores the impact of remote human–robot interaction (HRI) configurations on collaborative performance in construction robots. As remote‐based operation of construction robots becomes increasingly common in construction environments, understanding how operator configurations and tool characteristics influence task efficiency is growing in importance. Focusing on the Pick‐and‐Place (P‐A‐P) task—one of the most fundamental and widely used operational patterns—we examined two experimental factors: (1) operator configuration (single operator vs. three‐operator collaboration) and (2) tool type (gripper, coupler press, or concrete vibrator). Experiments were conducted in a controlled construction environment, with the P‐A‐P performance measured by task completion time. Hypothesis 1, that single‐operator tasks outperform collaborative tasks, was tested using Welch's t ‐test; Hypothesis 2, regarding tool effects and their interaction with operator configuration, was examined using two‐way ANOVA. To account for the nested and repeated‐measures structure of the data (trials within robots and robots within operators), linear mixed‐effects models (LMM) were additionally conducted. Results consistently showed that single‐operator tasks were faster, supporting the Ringelmann effect by revealing efficiency losses with increasing team size. Moreover, tool type exerted a significant influence, with complexity increasing task duration, particularly under collaborative conditions. Theoretically, the study advances the IMOI framework for remote multi‐robot collaboration by specifying mediating mechanisms such as coordination overhead and monitoring delay, and by operationalising tool complexity through the Tool Difference Index (TDI). Practically, the findings highlight that designing low‐complexity tools and minimising coordination bottlenecks are critical for enhancing the efficiency of remote construction systems. Overall, this study provides empirical evidence and conceptual guidance for developing more effective multi‐robot strategies in dynamic construction environments.

Enterprise social media (ESM) is a critical sociotechnical platform for organizational knowledge sharing. While research has extensively studied the antecedents of knowledge contribution on ESM, the consequences for the contributor have received little attention. We address this by investigating how knowledge-sharing behaviors on ESM affect the psychological well-being of contributors. Our study draws on data from surveys and uses digital trace data from a corporate ESM to supplement the analysis. Crucially, we distinguish the effects of work-related knowledge contributions (e.g., help with a work project) from those of nonwork-related contributions (e.g., advice on recovering after running a semi-marathon). This study offers a new, contributor-centric perspective on ESM, demonstrating its value beyond knowledge seeking. We also show that nonwork-related knowledge contributions are a significant positive trigger for employee well-being. Finally, we reveal that contributing knowledge on ESM is positively associated with the contributor’s own learning, a process that goes beyond vicarious learning.

Abstract Human–robot teams represent a challenging work application of artificial intelligence (AI). Building strong emotional bonds with robots is one solution to promoting teamwork in such teams, but does this come at a cost in the form of subgroups? Subgroups—smaller divisions within teams—in all human teams can undermine teamwork. Despite the importance of this question, it has received little attention. We employed a mixed‐methods approach by conducting a lab experiment and a qualitative online survey. We (a) examined the formation and impact of subgroups in human–robot teams and (b) obtained insights from workers currently adapting to robots in the workplace on mitigating impacts of subgroups. The experimental study (Study 1) with 44 human–robot teams found that robot identification (RID) and team identification (TID) are associated with increases and decreases in the likelihood of a subgroup formation, respectively. RID and TID moderated the impacts of subgroups on teamwork quality and subsequent performance in human–robot teams. Study 2 was a qualitative study with 112 managers and employees who worked collaboratively with robots. We derived practical insights from this study that help situate and translate what was learned in Study 1 into actual work practices.

We present a conceptual framework for socio-technical affordances for stigmergic coordination, that is, coordination supported by a shared work product. Based on research on free/libre open source software development, we theorize that stigmergic coordination depends on three sets of socio-technical affordances: the visibility and combinability of the work, along with defined genres of work contributions. As a demonstration of the utility of the developed framework, we use it as the basis for the design and implementation of a system, MIDST, that supports these affordances and that we thus expect to support stigmergic coordination. We describe an initial assessment of the impact of the tool on the work of project teams of three to six data-science students that suggests that the tool was useful but also in need of further development. We conclude with plans for future research and an assessment of theory-driven system design.

Team identification is an important predictor of team success. As teams become more virtual, team identification is expected to become more important. Yet, the dimensions of virtuality such as geographic dispersion, reliance on electronic communications and diversity in team membership can undermine team identification. To better understand the impact of virtuality, the authors conducted a study with 248 employees in 55 teams to examine the complex and codependent effects of virtuality. Results indicate that although geographic dispersion and perceived differences can undermine team identification, reliance on electronic communications increases team identification and weakens the negative relationship between perceived differences and team identification.

Organizations now face a new challenge of encouraging their employees to work alongside robots. In this paper, we address this problem by investigating the impacts of human-robot similarity, trust in a robot, and the risk of physical danger on individuals' willingness to work with a robot and their willingness to work with a robot over a human co-worker. We report the results from an online experimental study involving 200 participants. Results showed that human-robot similarity promoted trust in a robot, which led to willingness to work with robots and ultimately willingness to work with a robot over a human co-worker. However, the risk of danger moderated not only the positive link between the surface-level similarity and trust in a robot, but also the link between intention to work with the robot and willingness to work with a robot over a human co-worker. We discuss several implications for the theory of human-robot interaction and design of robots.

Despite the benefits associated with virtual teams, many people on these teams are unsatisfied with their experience. The goal of this study was to determine how to better facilitate satisfaction through shared leadership, individual trust, and autonomy. Specifically, in this study we sought a better understanding of the effects of shared leadership, team members’ trust, and autonomy on satisfaction. We conducted a study with 163 individuals in 44 virtual teams. The results indicate that shared leadership facilitates satisfaction in virtual teams both directly and indirectly through the promotion of trust. Shared leadership moderated the relationships of individual trust and individual autonomy with satisfaction. Team‐level satisfaction was a strong predictor of virtual team performance. We discuss these findings and the implications for theory and design.

For the last decade, robots have been adopted into group work ranging from corporate offices to military operations. While robotic technology has matured enough to allow robots to act as team members, our understanding of how this alters group work is limited. In particular, little work has examined how the adoption of robots might alter group processes and outcomes. The purpose of this workshop is to bring together researchers investigating issues related to the theoretical frameworks and methodological approaches to studying human robot interactions within groups. We expect the workshop will contribute to our understanding of how to better design robots for group interactions.

Visual perception is dependent upon one's physical state. The apparent inclination of a hill is overestimated when the observer is carrying a heavy backpack. But, what if the hill is a virtual one and the user is about to navigate the virtual environment through an avatar? In a 2 (user with a backpack vs. user without the backpack) × 2 (avatar with a virtual backpack vs. avatar without a virtual backpack) × 2 (customized avatar vs. assigned avatar) between-subjects experiment (N = 121), participants estimated the hill as being steeper when using a customized avatar rather than an assigned one. When the avatar is encumbered by a heavy virtual backpack, those with a customized avatar perceived the virtual hill as being more difficult to climb. Avatar customization and the physical resources of the avatar (operationalized here in the form of a 'virtual' backpack) were found to be key predictors of embodied perception in virtual environments (VE). This has implications for the design of games and interventions that make use of VEs.

This study explores how human users respond to coordinated and uncoordinated gestures of a robot as an information deliverer. A between-subject experiment was conducted using the Wizard of Oz method, with 63 participants randomly assigned to one of four conditions (voice-only vs. no-gesture vs. coordinated gesture vs. uncoordinated gesture) taking an artwork class in a museum-like setting. The robot was explaining the information of the artworks with modalities accordingly designed to each condition. Results showed that the coordinated gesture was not aiding information delivery. However, there were notable relations between the coordinated gesture and intimacy, homogeneity, and involvement. These results have theoretical implications for cognitive load of working memory and practical implications for designing and deploying dynamic humanoid robots for museum tour guide.

This study explores the effect of the robot's appearance vs. behavior (voice and gestures) on the way it is perceived as a machine-like instead of a human-like robot. A between-subjects experiment with four conditions was conducted. Results suggest that both the robot's behavior and appearance are important but, if they are contradictory, the robot's behavior is more powerful than the robot's appearance in the perception of the robot as more machine-like or human-like.

This study explores how human users respond to feedback and evaluation from a robot. A between-subjects experiment was conducted using the Wizard of Oz method, with 63 participants randomly assigned to one of three evaluations (good evaluation vs. neutral evaluation vs. bad evaluation) following a training session. When participants attempted to reproduce the physical motion taught by the robot, they were given a verbal evaluation of their performance by the robot. They showed a strong negative response to the robot when it gave a bad evaluation, while showing positive attraction when it gave a good or neutral evaluation. Participants tended to dismiss criticism from the robot and attributed blame to the robot, while claiming credit to themselves when their performance was rated positively. These results have theoretical implications for the psychology of self-serving bias and practical implications for designing and deploying trainer robots as well as conducting user studies of such robots.