Security robots increasingly assume authoritative roles, but the underlying mechanisms for why humans cooperate with them is not well understood. This study proposed and tested a cooperation model based on legitimacy theory, focusing on how distributive fairness (outcome equity) and interactional fairness (treatment equity) influence robot legitimacy and cooperation. Using a 2 × 2 online video-based experiment with 372 U.S. participants, the authors found that both fairness types promote cooperation through value alignment, with a non-significant path through obligation to obey; meanwhile, perceived effectiveness was strongly associated with both value alignment and obligation to obey. These findings extend legitimacy theory to human–robot interaction in a U.S. context, emphasizing fairness and perceived effectiveness as key to fostering cooperation and informing ethical robot design.

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.

A particular concern with SAE Level 3 automation is the takeover transition from the automated vehicle to the human driver. In response, research has focused on investigating this transition. However, researchers have used a wide range of metrics to measure takeover performance. The lack of consistency in these metrics poses challenges for synthesizing findings. To address this issue, we conducted a systematic literature review of studies published between January 2009 and December 2019, focusing on the takeover performance metrics. Following prior research, we categorize these metrics into two dimensions: timeliness and quality. Additionally, we summarize the scenarios used to elicit takeover requests and analyze the corresponding maneuvers (braking, lane changing, and lane keeping). The results have shown inconsistencies in calculation and naming conventions of takeover performance metrics. Based on these findings, this study proposes several directions for standardizing definitions and terminology, and advancing toward a unified measure of takeover performance.

Anthropomorphism—the attribution of human-like characteristics to non-human entities— has been widely studied for its influence on user interaction and technology acceptance. However, much of the existing research focuses on functional design elements or surface-level outcomes, resulting in fragmented understanding of how anthropomorphism shapes psychological and behavioral outcomes. As a result, a comprehensive integration and cohesive understanding of its strategic role in technology development, integration, and use remains limited. This assessing review synthesizes Information Systems (IS) research on anthropomorphism to clarify its implications for technology design and adoption. We categorize types of anthropomorphic design stimuli, examine the cognitive mechanisms that trigger anthropomorphic perception, and analyze user responses across a spectrum of perceptual and behavioral outcomes. We also identify key variables that moderate the effectiveness of anthropomorphic design, offering insights into how organizations can strategically leverage or mitigate its effects. Building on these insights, we propose a conceptual framework and research agenda to guide IS scholars and industry leaders in optimizing anthropomorphism for enhanced technology adoption.

Trust is a very popular concept in Human–Robot Interaction (HRI) to explain why and how people interact with robots. However, the definition of trust and the methods used to study the concept vary widely, often leading to confusion instead of insight. In this position paper, we discuss possible reasons for the confusion and disagreement by reviewing theory and methods. Our main criticism is that HRI researchers have recently taken an oversimplified approach by not adhering to the process model of trust. Instead, we have primarily measured perceived trustworthiness (often as a proxy for trust) and assumed that it accurately predicts behavior or is satisfactory as an end goal. In addition, many experimental paradigms fail to account for the critical elements of risk and vulnerability that are essential for trust to guide behavior. With this position paper, we aim to shed light on these “trust issues” in HRI and to improve the HRI community’s approach by providing suggestions for enhancing the quality of future research.

This study examined an important debate in Human-Robot Interaction (HRI) research: the suitability of non-physically non-collocated robots instead of physically collocated robots for HRI research. This meta-analysis ($\mathrm{N}=34$studies) examined the equivalence of physically and non-physically collocated robots in HRI research, focusing on anthropomorphism, social presence, and user engagement. No significant differences were found, suggesting that non-physical representations are viable alternatives. However, observed heterogeneity indicates potential moderating factors (e.g., task complexity, user characteristics, design features) warranting further investigation. These findings inform choices in resource-constrained environments.

As robots become more integrated into various sectors, understanding human-robot interaction (HRI) dynamics, particularly trust repair, is crucial for successful collaboration. For this paper, the authors conducted a meta-analysis of 22 HRI trust repair studies with 3,763 participants to evaluate the effectiveness of strategies for restoring trust after breaches relative to offering no repair. The analysis identified three key findings: (1) strategies are differentially effective, showing limited success in restoring trustworthiness; (2) the overall impact on repairing trust is marginal, with a small effect size; and (3) apologies and explanations are the most effective strategies for trust repair. These insights enrich HRI literature by providing a comprehensive evaluation of trust repair mechanisms, offering valuable guidance for future research and practical improvements in human-robot collaboration.

The increasing deployment of robots in authority roles, such as security, necessitates understanding public acceptance of robot-exercised power. This study investigated the relationship between perceived power bases (expert, legitimate, referent, reward, coercive) and public acceptance of security robots. One hundred participants viewed videos depicting robot- citizen interactions. Results revealed positive correlations between perceived expert and legitimate power and acceptance and a negative correlation between perceived coercive power and acceptance; reward power showed no significant relationship. These findings contribute to the human-robot interaction (HRI) literature by demonstrating the influence of perceived power on public acceptance and offering design guidelines for enhancing the acceptance of security robots by emphasizing expertise and legitimate authority while minimizing coercive tactics.

After-action reviews (AARs) are professional discussions that help operators and teams enhance their task performance by analyzing completed missions with peers and professionals. Previous studies comparing different formats of AARs have focused mainly on human teams. However, the inclusion of robotic teammates brings along new challenges in understanding teammate intent and communication. Traditional AAR between human teammates may not be satisfactory for human-robot teams. To address this limitation, we propose a new training review (TR) tool, called the Virtual Spectator Interface (VSI), to enhance human-robot team performance and situational awareness (SA) in a simulated search mission. The proposed VSI primarily utilizes visual feedback to review subjects' behavior. To examine the effectiveness of VSI, we took elements from AAR to conduct our own TR, and designed a 1$\times 3$between-subjects experiment with experimental conditions: TR with (1) VSI, (2) screen recording, and (3) non-technology (only verbal descriptions). The results of our experiments demonstrated that the VSI did not result in significantly better team performance than other conditions. However, the TR with VSI led to more improvement in the subjects' SA over the other conditions.

When humans supervise multiple semi-autonomous robots while also attending to their own tasks simultaneously, they may lack the situation awareness needed to assist their robot teammates. There is a need to monitor the human’s situation awareness in real-time, so interventions can be taken to improve poor situation awareness. While prior work has developed models to estimate human situation awareness, they rely heavily on advanced machine learning models and a single source of input through eye-tracking that can pose operational challenges. We develop a real-time human situation awareness estimator based on data from a human-robot teaming experiment. The situation awareness estimator uses simple and interpretable logistic regression models that take inputs from both eye-tracking and behavioral measures. Cross-validation demonstrated the situation awareness estimator had an average accuracy of 74%. The estimator is robust to missing inputs, and can monitor human situation awareness non-intrusively in real-time.

Effective human supervision of autonomous robots in high-stakes scenarios requires efficient intervention, particularly when unmanned ground vehicles (UGVs) encounter local minima problems. This study investigates user interface designs to support human intervention in resolving such issues without a complete system takeover. We conducted a human-subjects experiment comparing two intervention methods: direct waypoint selection via mouse input and directional commands via arrow keys. Participants supervised two UGVs while simultaneously performing a secondary task, simulating real-world multitasking scenarios. Results demonstrate that mouse-based waypoint selection led to significantly more efficient UGV paths than arrow key controls and was also preferred by participants. Our findings contribute to the design of human-autonomy interfaces.

Robots are rapidly becoming more interactive and dyadic. With advancements in artificial intelligence and robotic movements, companies are shifting their corporate messaging to highlight the social and companionship features of their robots. Realbotix’s recent rebranding exemplifies a deliberate effort to carve a new path within the humanoid robotics industry. Grounded in political economy and discourse analysis, this paper examines 86 publicity interviews and press releases from Realbotix to assess the positioning of intimacy and its associated corporate power. The findings reveal a focus on the robot’s social intelligence, framing the company as a leader in humanoid robotics and reshaping human–robot interactions.

Security robots are becoming increasingly prevalent for maintaining law and order, offering cost efficiencies and safety benefits in hazardous environments. Despite these advantages, significant questions remain regarding the public acceptance of robots as replacements for human security agents. This paper presents a systematic literature review to explore whether there is a discernible public preference between human security personnel and their robotic counterparts. The review identifies a contextual pattern: individuals tend to prefer human agents in citizen-initiated interactions, and security robots in police-initiated ones. This paper offers valuable insights to guide the future design and deployment of security robots.

Improving end-users' understanding of decisions made by autonomous vehicles (AVs) driven by artificial intelligence (AI) can improve utilization and acceptance of AVs. However, current explanation mechanisms primarily help AI researchers and engineers in debugging and monitoring their AI systems, and may not address the specific questions of end-users, such as passengers, about AVs in various scenarios. In this paper, we conducted two user studies to investigate questions that potential AV passengers might pose while riding in an AV and evaluate how well answers to those questions improve their understanding of AI-driven AV decisions. Our initial formative study identified a range of questions about AI in autonomous driving that existing explanation mechanisms do not readily address. Our second study demonstrated that interactive text-based explanations effectively improved participants' comprehension of AV decisions compared to simply observing AV decisions. These findings inform the design of interactions that motivate end-users to engage with and inquire about the reasoning behind AI-driven AV decisions.

As advances in robotics continue, security robots are increasingly integrated into public and private security, enhancing protection in locations such as streets, parks, and shopping malls. To be effective, security robots must interact with civilians and security personnel, underscoring the need to enhance our knowledge of their interactions with humans. To investigate this issue, the authors systematically reviewed 47 studies on human interaction with security robots, covering 2003 to 2023. Papers in this domain have significantly increased over the last 7 years. The article provides three contributions. First, it comprehensively summarizes existing literature on human interaction with security robots. Second, it employs the Human–Robot Integrative Framework (HRIF) to categorize this literature into three main thrusts: human, robot, and context. The framework is leveraged to derive insights into the methodologies, tasks, predictors, and outcomes studied. Last, the article synthesizes and discusses the findings from the reviewed literature, identifying avenues for future research in this domain.

Advances in language modeling have paved the way for novel human-AI co-writing experiences. This paper explores how varying levels of scaffolding from large language models (LLMs) shape the co-writing process. Employing a within-subjects field experiment with a Latin square design, we asked participants (N=131) to respond to argumentative writing prompts under three randomly sequenced conditions: no AI assistance (control), next-sentence suggestions (low scaffolding), and next-paragraph suggestions (high scaffolding). Our findings reveal a U-shaped impact of scaffolding on writing quality and productivity (words/time). While low scaffolding did not significantly improve writing quality or productivity, high scaffolding led to significant improvements, especially benefiting non-regular writers and less tech-savvy users. No significant cognitive burden was observed while using the scaffolded writing tools, but a moderate decrease in text ownership and satisfaction was noted. Our results have broad implications for the design of AI-powered writing tools, including the need for personalized scaffolding mechanisms.

The rapid deployment of security robots across our society calls for further examination of their acceptance. This study explored human acceptance of security robots by theoretically extending the technology acceptance model to include the impact of autonomy and risk. To accomplish this, an online experiment involving 236 participants was conducted. Participants were randomly assigned to watch a video introducing a security robot operating at an autonomy level of low, moderate, or high, and presenting either a low or high risk to humans. This resulted in a 3 (autonomy) × 2 (risk) between-subjects design. The findings suggest that increased perceived usefulness, perceived ease of use, and trust enhance acceptance, while higher robot autonomy tends to decrease acceptance. Additionally, the physical risk associated with security robots moderates the relationship between autonomy and acceptance. Based on these results, this paper offer recommendations for future research on security robots.

Human–robot work collaboration can be complementary where both the robot and employee use their strengths to complete the task at hand. But who is ultimately responsible for the task? Employees may assume more or less responsibility when working with robots, which can have a direct impact on job outcomes. In this paper the authors use the job characteristics model as a framework to investigate changes in job characteristics and their impact on job responsibility and worker outcomes as a result of robot implementation in restaurants. The results identify changes in task significance, job autonomy, and feedback from the job as important predictors of changes in experienced responsibility toward work. The study also found a significant positive relationship between job responsibility and motivation to work.

Abstract Most studies of trusted digital repositories have focused on the internal factors delineated in the Open Archival Information System (OAIS) Reference Model—organizational structure, technical infrastructure, and policies, procedures, and processes. Typically, these factors are used during an audit and certification process to demonstrate a repository can be trusted. The factors influencing a repository's designated community of users to trust it remains largely unexplored. This article proposes and tests a model of trust in a data repository and the influence trust has on users' intention to continue using it. Based on analysis of 245 surveys from quantitative social scientists who published research based on the holdings of one data repository, findings show three factors are positively related to data reuser trust—integrity, identification, and structural assurance. In turn, trust and performance expectancy are positively related to data reusers' intentions to return to the repository for more data. As one of the first studies of its kind, it shows the conceptualization of trusted digital repositories needs to go beyond high‐level definitions and simple application of the OAIS standard. Trust needs to encompass the complex trust relationship between designated communities of users that the repositories are being built to serve.

The rapid growth in the use of security robots makes it critical to better understand their interactions with humans. The impacts of anthropomorphism and interaction scenarios were examined via a 3 x 2 between-subjects experiment. Sixty participants were randomly assigned to interact with one of three security robots (Knightscope, RAMSEE, or Pepper) in either an indoor hallway or an outdoor parking lot scenario in a virtual reality cave. There were significant differences only between Pepper and Knightscope with Pepper rated higher in anthropomorphism, ability, integrity, and desire to use than Knightscope but the interaction scenario has no effect.

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.

Robots are increasingly being deployed as security agents helping law enforcement in spaces such as streets, parks, or shopping malls. Unfortunately, the deployment of security robots is not without problems and controversies. For example, the New York Police Department canceled its contract with Boston Dynamics in response to backlash from their use of Digidog, an autonomous robotic dog, which sparked fears in the public. However, it is unclear to what extent affected communities have been involved in the design and deployment process of robots. This is problematic because, without input from community members in the processes of design and deployment, security robots are likely to not satisfy the concerns or safety needs of real communities. To gain deeper insight into people's perceptions of security robots - including both potential benefits and concerns - we conducted 17 semi-structured interviews addressing the following research questions: RQ1. What characteristics do people ascribe to security robots? RQ2. What expectations do people have about the function and role of security robots? RQ3. What are people's attitudes toward the use of security robots? Our study offers several contributions to the existing literature on security robots.

Recently, chatbots have been deployed in health care in various ways such as providing educational information, and monitoring and triaging symptoms. However, they can be ineffective when they are designed without a careful consideration of the cultural context of the users, especially for marginalized groups. Chatbots designed without cultural understanding may result in loss of trust and disengagement of the user. In this paper, through an interview study, we attempt to understand how chatbots can be better designed for Black American communities within the context of COVID-19. Along with the interviews, we performed design activities with 18 Black Americans that allowed them to envision and design their own chatbot to address their needs and challenges during the pandemic. We report our findings on our participants’ needs for chatbots’ roles and features, and their challenges in using chatbots. We then present design implications for future chatbot design for the Black American population.

Robot co-workers, like human co-workers, make mistakes that undermine trust. Yet, trust is just as important in promoting human-robot collaboration as it is in promoting human-human collaboration. In addition, individuals can signif-icantly differ in their attitudes toward robots, which can also impact or hinder their trust in robots. To better understand how individual attitude can influence trust repair strategies, we propose a theoretical model that draws from the theory of cognitive dissonance. To empirically verify this model, we conducted a between-subjects experiment with 100 participants assigned to one of four repair strategies (apologies, denials, explanations, or promises) over three trust violations. Individual attitudes did moderate the efficacy of repair strategies and this effect differed over successive trust violations. Specifically, repair strategies were most effective relative to individual attitude during the second of the three trust violations, and promises were the trust repair strategy most impacted by an individual's attitude.

Trust is vital for effective human-robot teams. Trust is unstable, however, and it changes over time, with decreases in trust occurring when robots make mistakes. In such cases, certain strategies identified in the human-human literature can be deployed to repair trust, including apologies, denials, explanations, and promises. Whether these strategies work in the human-robot domain, however, remains largely unknown. This is primarily because of the fragmented and dispersed state of the current literature on trust repair in HRI. As a result, this paper brings together studies on trust repair in HRI and presents a more cohesive view of when apologies, denials, explanations, and promises have been seen to repair trust. In doing so, this paper also highlights possible gaps and proposes future work. This contributes to the literature in several ways but primarily provides a starting point for future research and recommendations for studies seeking to determine how trust can be repaired in HRI.

Despite the benefits of automated vehicles (AVs), there are still barriers to their widespread adoption. Expectations about AVs have been identified as one of the most important factors in understanding AV adoption. Therefore, by understanding the public's expectations of AVs, we can better understand whether or when AVs are likely to be adopted on a wide scale. Individual differences, including demographics and personality, have been identified as factors that impact technology expectations and adoption. However, it is not clear whether and how individual differences can influence expectations of AVs. To examine this, we conducted an online survey with 443 U.S. drivers who were recruited and divided into subpopulations by age, gender, ethnicity, census region, educational level, marital status, income, driving frequency, driving experience, and personality traits. Results revealed that drivers' expectations of AVs differ significantly by age, gender, ethnicity, education levels, marital status, drive frequency, drive experience, and personality. More specifically, higher expectations are more often generated by drivers who are younger, men, White non-Hispanic, more highly educated, never married, with a higher frequency of driving, with less driving experience, and who are high in extraversion, agreeableness, conscientiousness, and emotional stability. The results of this study provide a foundation for future research related to expectations and have important implications on future design and development of AVs.

In conditionally automated driving, in-vehicle alert systems can provide drivers with information to assist their takeovers from automated driving. This study investigated how display modality and information influenced drivers’ acceptance of the in-vehicle alert systems under different event criticality situations. We conducted an online video study with a 3 (information type) × 3 (display modality) × 2 (event criticality) mixed design involving 60 participants. The results showed that considering drivers’ perceived usefulness and ease of use, presenting why only information was not sufficient for takeovers as compared to what will only information and why + what will information. Participants reported higher ease of use in the combination of speech and augmented reality condition when compared to the speech only condition. High event criticality led to drivers’ lower perceived usefulness and more negative opinions of the displays. The findings have implications for the design of in-vehicle alert systems during takeover transitions.

Human personality has been identified as a predictor of robot acceptance in the human–robot interaction (HRI) literature. Despite this, the HRI literature has provided mixed support for this assertion. To better understand the relationship between human personality and robot acceptance, this paper conducts a meta-analysis of 26 studies. Results found a positive relationship between human personality and robot acceptance. However, this relationship varied greatly by the specific personality trait along with the study sample’s age, gender diversity, task, and global region. This meta-analysis also identified gaps in the literature. Namely, additional studies are needed that investigate both the big five personality traits and other personality traits, examine a more diverse age range, and utilize samples from previously unexamined regions of the globe.

Collaborative work between humans and robots holds great potential but, such potential is diminished should humans fail to accept robots as collaborators. One solution is to design robots to have a similar personality to their human collaborators. Typically, this is done by matching the human’s and robot’s personality using one or more of the Big Five Personality (BFI) traits. The results of this matching, however, have been mixed. This makes it difficult to know whether personality similarity promotes robot acceptance. To address this shortcoming, we conducted a systematic quantitative meta- analysis of 13 studies. Overall, the results support the assertion that matching personalities between humans and robots promotes robot acceptance.

Trust is vital to promoting human and robot collaboration, but like human teammates, robots make mistakes that undermine trust. As a result, a human’s perception of his or her robot teammate’s trustworthiness can dramatically decrease [1], [2], [3], [4]. Trustworthiness consists of three distinct dimensions: ability (i.e. competency), benevolence (i.e. concern for the trustor) and integrity (i.e. honesty) [5], [6]. Taken together, decreases in trustworthiness decreases trust in the robot [7]. To address this, we conducted a 2 (high vs. low anthropomorphism) x 4 (trust repair strategies) between-subjects experiment. Preliminary results of the first 164 participants (between 19 and 24 per cell) highlight which repair strategies are effective relative to ability, integrity and benevolence and the robot’s anthropomorphism. Overall, this paper contributes to the HRI trust repair literature.

Extrapolation from low-fidelity design iterations is especially critical in HRI. An initial proposal for low-fidelity to higher fidelity extrapolation is developed using insights from cognitive multimedia learning theory to account for the effects of prototype medium and three types of cognitive demands. Inspired by Donald Norman and others, our proposal leverages tightly controlled and multi-authored scenarios through crowdsourcing to create additional potential evidence as a kind of experimental “stress test.” We motivate our proposal by investigating the intersection of emotion and human control, which is understudied outside of autonomous vehicles (AV) and HRI research. Evidence for positively moderated emotional effects in text-based AV scenarios as well as tentative evidence for our extrapolation proposal are identified.

Automated Vehicle (AV) buses hold great potential, yet it is not clear if Americans will choose to ride them. Trust and attitudes, often influenced by individual differences, are vital predictors of technology acceptance and AVs are no exception. To deepen our understanding of individual differences as they pertain to AV buses, this paper presents the results of a national survey of 401 participants located in the United States of America. Findings from this survey indicate that individual differences influenced trust, attitude, and intention to ride AV buses. Specifically, trust in AV buses differed by individual's age and bus riding frequency while attitudes toward AV buses differed by individual's age, ethnicity, and bus riding frequency. Finally, intention to ride an AV bus differed by age, gender, ethnicity, and bus riding frequency. Based on these results, we propose a research agenda that seeks to inform future research on acceptance of AV buses.

In conditionally automated driving, drivers engaged in non-driving related tasks (NDRTs) have difficulty taking over control of the vehicle when requested. This study aimed to examine the relationships between takeover performance and drivers’ cognitive load, takeover request (TOR) lead time, and traffic density. We conducted a driving simulation experiment with 80 participants, where they experienced 8 takeover events. For each takeover event, drivers’ subjective ratings of takeover readiness, objective measures of takeover timing and quality, and NDRT performance were collected. Results showed that drivers had lower takeover readiness and worse performance when they were in high cognitive load, short TOR lead time, and heavy oncoming traffic density conditions. Interestingly, if drivers had low cognitive load, they paid more attention to driving environments and responded more quickly to takeover requests in high oncoming traffic conditions. The results have implications for the design of in-vehicle alert systems to help improve takeover performance.

We conduct a study of hiring bias on a simulation platform where we ask Amazon MTurk participants to make hiring decisions for a mathematically intensive task. Our findings suggest hiring biases against Black workers and less attractive workers, and preferences towards Asian workers, female workers and more attractive workers. We also show that certain UI designs, including provision of candidates' information at the individual level and reducing the number of choices, can significantly reduce discrimination. However, provision of candidate's information at the subgroup level can increase discrimination. The results have practical implications for designing better online freelance marketplaces.

Robots are becoming an important way to deliver health care, and personality is vital to understanding their effectiveness. Despite this, there is a lack of a systematic overarching understanding of personality in health care human robot interaction (H-HRI). To address this, the authors conducted a review that identified 18 studies on personality in H-HRI. This paper presents the results of that systematic literature review. Insights are derived from this review regarding the methodologies, outcomes, and samples utilized. The authors of this review discuss findings across this literature while identifying several gaps worthy of attention. Overall, this paper is an important starting point in understanding personality in H-HRI.

Human-robot teams offer both benefits and new challenges. Human robot teams combine the advantages of automation such as high accuracy, speed, and repeat-ability with the flexibility, adaptability, and creative problem-solving commonly associated with humans. Several challenges, however, must first be addressed to effectively leverage such teams. One challenge is understanding effective human-robot team design (HRTD). HRTD is vital as the wrong team can lead to potentially negative outcomes. The theoretical model and methodology presented are the planned first steps towards the establishment of guidelines based on statistical models that can recommend an optimal human-robot team design based on a given set of criteria.

For safe navigation around pedestrians, automated vehicles (AVs) need to plan their motion by accurately predicting pedestrians' trajectories over long time horizons. Current approaches to AV motion planning around crosswalks predict only for short time horizons (1-2 s) and are based on data from pedestrian interactions with human-driven vehicles (HDVs). In this paper, we develop a hybrid systems model that uses pedestrians' gap acceptance behavior and constant velocity dynamics for long-term pedestrian trajectory prediction when interacting with AVs. Results demonstrate the applicability of the model for long-term (> 5 s) pedestrian trajectory prediction at crosswalks. Further, we compared measures of pedestrian crossing behaviors in the immersive virtual environment (when interacting with AVs) to that in the real world (results of published studies of pedestrians interacting with HDVs), and found similarities between the two. These similarities demonstrate the applicability of the hybrid model of AV interactions developed from an immersive virtual environment (IVE) for real-world scenarios for both AVs and HDVs.

Collaborative crowdsourcing is a well-established model of work, especially in the case of open source software development. The structure and operation of these virtual and loosely-knit teams differ from traditional organizations. As such, little is known about how their behavior may change in response to an increase in external attention. To understand these dynamics, we analyze millions of actions of thousands of contributors in over 1100 open source software projects that topped the GitHub Trending Projects page and thus experienced a large increase in attention, in comparison to a control group of projects identified through propensity score matching. In carrying out our research, we use the lens of organizational change, which considers the challenges teams face during rapid growth and how they adapt their work routines, organizational structure, and management style. We show that trending results in an explosive growth in the effective team size. However, most newcomers make only shallow and transient contributions. In response, the original team transitions towards administrative roles, responding to requests and reviewing work done by newcomers. Projects evolve towards a more distributed coordination model with newcomers becoming more central, albeit in limited ways. Additionally, teams become more modular with subgroups specializing in different aspects of the project. We discuss broader implications for collaborative crowdsourcing teams that face attention shocks.

Organizations are rapidly deploying artificial intelligence (AI) systems to manage their workers. However, AI has been found at times to be unfair to workers. Unfairness toward workers has been associated with decreased worker effort and increased worker turnover. To avoid such problems, AI systems must be designed to support fairness and redress instances of unfairness. Despite the attention related to AI unfairness, there has not been a theoretical and systematic approach to developing a design agenda. This paper addresses the issue in three ways. First, we introduce the organizational justice theory, three different fairness types (distributive, procedural, interactional), and the frameworks for redressing instances of unfairness (retributive justice, restorative justice). Second, we review the design literature that specifically focuses on issues of AI fairness in organizations. Third, we propose a design agenda for AI fairness in organizations that applies each of the fairness types to organizational scenarios. Then, the paper concludes with implications for future research.

It is our pleasure to welcome you to this PACMHCI GROUP issue. For over 25 years, the GROUP research community has supported the development of robust scholarship at the intersection of Computer Supported Cooperative Work, Human Computer Interaction, Computer Supported Collaborative Learning and Socio-Technical Studies. This volume is the latest product of our efforts to validate and integrate the strong work happening within this broadly-conceived community. We hope that the range of papers presented here reflects our intent to be international, interdisciplinary, and inclusive, both in our organization of the review process as well as within the set of accepted papers. This PACMHCI journal volume in the GROUP series features studies of collaboration in multiple settings, including social media, online communities and game development. It also showcases architectures and frameworks for collaboration, and sociotechnical studies in domains ranging from sports to fake news. As Editors, we are particularly pleased to continue the legacy of presenting and disseminating the new and exciting work being developed in our community. We come together every two years to creatively display and build off of ideas from a wide range of disciplinary and topical areas including computer science, design, engineering, information science, management science, sociology, work and labor studies, and values-in-design, among others.

It is our pleasure to welcome you to the first PACMHCI GROUP issue. For over 25 years, the GROUP research community has supported the development of robust scholarship at the intersection of Computer Supported Cooperative Work, Human Computer Interaction, Computer Supported Collaborative Learning and Socio-Technical Studies. This volume is the latest product of our efforts to validate and integrate the strong work happening within this broadly-conceived community. We hope that the range of papers presented here reflects our intent to be international, interdisciplinary, and inclusive, both in our organization of the review process as well as within the set of accepted papers. This first PACM journal volume in the GROUP series features studies of collaboration in multiple settings, including social networks, editing systems, and mixed reality. It also showcases sociotechnical studies in domains ranging from outdoor activities to scientific projects.

This all-day workshop aims to promote convergence among its participants on research related to working with intelligent machines. We define intelligent machines as both material (e.g., robots) and immaterial (e.g., algorithms) computing technologies that can be characterized by autonomy, the ability to learn, and the ability to interact with other systems and with humans. The workshop has three goals: identifying specific research problems around work and intelligent machines, developing a common language base that can facilitate interdisciplinary collaboration among researchers, and identifying information and cyber-infrastructure needs to support convergent research. Workshop activities will facilitate interdisciplinary dialogue and strive to generate high-impact research ideas to advance each of these goals.

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.

One particular problem CSCW and HCI scholars have sought to address through the design of collaborative systems is the issues associated with diversity and creativity. Diversity can promote creativity by exposing individuals to different perspectives and at the same time make it difficult for teams to leverage their differences to be more creative. This paper asserts that through the promotion of cooperation, collectivism will help ideation team members overcome the challenges associated with diversity and promote creativity. To examine this assertion, we conducted an experimental study involving 107 individuals in 33 idea-generation teams. Collectivism was promoted through priming. The results confirm our assertion: collectivism created conditions that facilitated creativity when teams were high in perceived diversity. Collectivism also facilitated more satisfaction among teammates by offsetting negative perceptions of diversity. These results offer new insights on collectivism, perceived diversity and creativity.

Sequential group decision-making processes, such as online event scheduling, can be subject to social influence if the decisions involve individuals? subjective preferences and values. Indeed, prior work has shown that scheduling polls that allow respondents to see others' answers are more likely to succeed than polls that hide other responses, suggesting the impact of social influence and coordination. In this paper, we investigate whether this difference is due to information cascade effects in which later respondents adopt the decisions of earlier respondents. Analyzing more than 1.3 million Doodle polls, we found evidence that cascading effects take place during event scheduling, and in particular, that early respondents have a larger influence on the outcome of a poll than people who come late. Drawing on simulations of an event scheduling model, we compare possible interventions to mitigate this bias and show that we can optimize the success of polls by hiding the responses of a small percentage of low availability respondents.

One advantage of crowds over traditional teams is that crowds enable the assembling of a large number of individuals to address problems. The literature is unclear, however, about when crowd size leads to better outcomes. To better understand the effects of crowd size we conducted a study on the retention and performance of 4,317 articles in the WikiProject Film community. Results indicate that crowd composition, specifically diversity and experience, is vital to understanding when size leads to better retention and performance. Crowd size was positively related to retention and performance when crowds were high in diversity and experience. Retention was important to determining when crowd size led to better performance. Crowd size was positively related to performance when retention was low. Our results suggest that crowds benefit from their size when they are diverse, experienced, and have low retention rates.

Collaborative crowdsourcing is an increasingly common way of accomplishing work in our economy. Yet, we know very little about how the behavior of these crowds changes over time and how these dynamics impact their performance. In this paper, we take a group development approach that considers how the behavior of crowds change over time in anticipation and as a result of their evaluation and recognition. Towards this goal, this paper studies the collaborative behavior of groups comprised of editors of articles that have been recognized for their outstanding quality and given the Good Articles (GA) status and those that eventually become Featured Articles (FA) on Wikipedia. The results show that the collaborative behavior of GA groups radically changes just prior to their nomination. In particular, the GA groups experience increases in the level of activity, centralization of workload, and level of GA experience and decreases in conflict (i.e., reverts) among editors. After being promoted to GA, they converge back to their typical behavior and composition. This indicates that crowd behavior prior to their evaluation period is dramatically different than behavior before or after. In addition, the collaborative behaviors of crowds during their promotion to GA are predictive of whether they are eventually promoted to FA. Our findings shed new light on the importance of time in understanding the relationship between crowd performance and collaborative measures such as centralization, conflict and experience.

Geographic dispersion has been proposed as one means to promote cooperation and coordination in teams high in perceived diversity. However, research has found mixed support for this assertion. This study proposes that the inclusion of perceived distance helps to explain these mixed results. To test this assertion, we examined 121 teams-62 collocated and 59 geographically dispersed. Results demonstrate that perceived distance explains when geographic dispersion benefits teams high in perceived diversity. Results also indicate that the type of perceived diversity matters (surface-level vs. deep-level diversity). This study contributes to our understanding of distance and diversity in teams.

This study was conducted to determine whether monitoring moderated the impact of trust on the project performance of 57 virtual teams. Two sources of monitoring were examined: internal monitoring done by team members and external monitoring done by someone outside of the team. Two types of trust were also examined: affective-based trust, or trust based on emotion; and cognitive trust, or trust based on competency. Results indicate that when internal monitoring was high, affective trust was associated with increases in performance. However, affective trust was associated with decreases in performance when external monitoring was high. Both types of monitoring reduced the strong positive relationship between cognitive trust and the performance of virtual teams. Results of this study provide new insights about monitoring and trust in virtual teams and inform both theory and design.

Subgroup formation, the emergence of smaller groups within teams, has been found to be detrimental to teamwork in virtual teams. Recently, however, an alternative view of the effects of subgroup formation proposes that the formation of subgroups is not always bad. When subgroups are based on identity characteristics like race and gender, they are like6ly to have negative effects, but when they are not, subgroups can have positive effects on teamwork. This paper empirically examines this proposition. Results of our study generally support the proposed assertion. When subgroups are not based on race or gender, they are positively associated with perceptions of social integration and open communication. However, when they are based on race and gender they are negatively associated with perceptions of social integration and open communication. The implications of this study demonstrate that subgroups may in many cases be beneficial rather than detrimental to virtual teams.

Crowds are increasingly being adopted to solve complex problems. Size and diversity are two key characteristics of crowds; however their relationship to performance is often paradoxical. To better understand the effects of crowd size and diversity on crowd performance we conducted a study on the quality of 4,317 articles in the WikiProject Film community. The results of our study suggest that crowd size leads to better performance when crowds are more diverse. However, there is a break-even point -- smaller, less diverse crowds can outperform more diverse crowds of similar size. Our results offer new insights into the effects of size and diversity on the performance of crowds.

This work investigates whether enrolling in a Massive Open Online Course (MOOC) with friends or colleagues can improve a learner's performance and social interaction during the course. Our results suggest that signing up for a MOOC with peers correlates positively with the rate of course completion, level of achievement, and discussion forum usage. Further analysis seems to suggest that a learner's interaction with their friends compliments a MOOC by acting as a form of self-blended learning.

Many managers and mentors for project teams desire more efficient and more effective ways of monitoring and predicting the quality of social relationships and the performance of teams under their purview. A previous study found that one form of linguistic mimicry, linguistic style matching, and some lexical features indicated team performance and mutual attraction in short-term, laboratory tasks. In this paper, we evaluate whether these measures also work as indicators for performance, shared understanding, and team trust in longer-duration project teams, using only limited, unobtrusively obtained communication traces. In our four-month evaluation using student project team emails, we found no support for LSM or most of the previously identified measures as practical indicators in our field setting. We did find some support for using future-oriented words to indicate team performance over time.

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.

Although organizations are using more virtual teams to accomplish work, they are finding it difficult to use traditional forms of leadership to manage these teams. Many organizations are encouraging a shared leadership approach over the traditional individual leader. Yet, there have been only a few empirical studies directly examining the effectiveness of such an approach and none have taken into account the team diversity. To address this gap, this paper reports the results of an empirical examination of the impacts of shared leadership in virtual teams. Results confirm the proposed research model. The impacts of shared leadership are multilevel and vary by race and gender. In addition, while shared leadership promotes team satisfaction despite prior assumptions, it actually reduces rather than increases team performance.

Consumers are demanding world-wide cellular access to the Internet. This requires a global standard and effective means of accessing the Internet from wireless devices. The IMT-2000 project attempted to fix the first problem by harmonizing all wireless interfaces into one global standard. However, this attempt failed and a new family of standards was issued. In response, the Operator Harmonization Group (OHG) is currently harmonizing two of the most popular CDMA standards: WCDMA and cdma2000. One major issue holding back complete harmonization is mobility management. WCDMA is used in Global System for Mobile Communications (GSM) systems where General Packet Radio Service/enhanced data for GSM evolution (GPRS/EDGE) protocols are currently being developed to act as the mobility manager. cdma2000 is designed for IS-95 and currently uses Mobile IP as its mobility manager. If one global standard is to be achieved, the same mobility management system has to be used by both WCDMA and cdma2000. The second problem deals with providing reliable access over unreliable wireless links. In this paper, a conceptual model integrating GSM and TCP/IP using Mobile IP as the mobility manager is presented. This model presents a method to implementing mobile IP over a GSM cellular network. The conceptual model also provides a solution to deploying TCP over wireless links. This model can serve as a useful starting place to harmonizing one global 3G standard.