Human–Robot Similarity and Willingness to Work with a Robotic Co-worker

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 coworker. We discuss several implications for the theory of human–robot interaction and design of robots.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140719/1/HRI 2018_Similarity_0103.pd

Motivational Theory of Human Robot Teamwork

This paper presents a theory that allows us to better understand motivation in human‒robot teamwork. Teamwork with robots often involves both physical and mental activities. This implies that motivation might be particularly important to the success of human robot teams. Unfortunately, there is much we do not know with regards to the role of motivation in effective teamwork with robots. In this paper we propose the “Motivational Theory of Human‒Robot Teamwork” to better understand teamwork in human‒robot teams. In doing so, we leverage the research on robot personality.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145157/1/Motivation and Personality (2 cols) July 19 2018.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145157/4/Robert 2018.pdfDescription of Motivation and Personality (2 cols) July 19 2018.pdf : Preprint ArticleDescription of Robert 2018.pdf : Published Versio

The Role of Knowledge Control and Knowledge Asymmetry in Trusting and Collaborating with AI-Teammates

There is an extensive literature that facilitates our understanding of how new information technologies are adopted and accepted. However, there is little empirical work that studies how innovative technologies such as Artificial Intelligence (AI) agents can be team-players with humans in the workplace. Using Actor-Network Theory, this research-in-progress work proposes a new conceptual model that aims to aid our understanding of how human perceptions regarding the asymmetry they perceive between their knowledge and that of their AI teammates and their ability to retain control over the knowledge they share with AI teammates on their level of trust in AI teammates and their willingness to collaborate with them. A 2X2 scenario-based survey study will be conducted and structural equation modeling will be used to empirically validate this model. Potential contributions to theory and practice are discussed

Human-Robot Teaming Configurations: A Study of Interpersonal Communication Perceptions and Affective Learning in Higher Education

Technology encourages collaboration in creative ways in the classroom. Specifically, social robots may offer new opportunities for greater innovation in teaching. In this study, we combined the established literature on co-teaching teams with the developing field of machine actors used in education to investigate the impressions students had of different team configurations that included both a human and a robot. Participants saw one of three teams composed of a human and a social robot with different responsibilities present a short, prerecorded lecture (i.e., human as lead teacher-robot as teaching assistant, robot as lead teacher-human as teaching assistant, human and robot as co-teachers). Overall, students rated the human-led team as more appealing and having more credibility than the robot-led team. The data suggest that participants would be more likely to take a course led by a human instructor than a social robot. Previous studies have investigated machine actors in the classroom, but the current findings are unique in that they compare the individual roles and power structures of human-robot teams leading a course

L’implantation de la robotique collaborative et la gestion des ressources humaines dans le secteur manufacturier : soutenir le changement et l’adoption

Ce mémoire de maîtrise explore l’implantation de la robotique collaborative en entreprise sous l’angle des pratiques de gestion et des facteurs humains. La visée initiale de ce projet de recherche visait préalablement à circonscrire l’apport que peut prendre la gestion des ressources humaines (GRH) lors de ce type d’implantation technologique, qui implique une collaboration humain-machine plus accrue qu’auparavant. Initialement, l’objectif était donc d’identifier les pratiques de GRH à mettre en place lors de l’implantation de robots collaboratifs. Cela dit, comme ce projet de recherche présente une démarche exploratoire semi-inductive, l’objectif de recherche a évolué vers plusieurs objectifs. Cette ouverture sur de nouveaux objectifs est subséquente aux résultats obtenus lors de la revue systématique de la littérature et de la collecte de données afin de dresser un portrait plus juste, adapté à l’état des connaissances et au terrain. Les objectifs poursuivis sont les suivants : 1) identifier les pratiques de GRH et d’autres pratiques organisationnelles en matière de gestion du changement facilitant l’implantation et l’adoption des robots collaboratifs 2) identifier les facteurs associés à l’humain, au robot et à l’environnement qui influencent l’implantation des robots collaboratifs, l’adoption et la collaboration entre l’opérateur et le robot

Robots and COVID-19: Re-imagining Human–Robot Collaborative Work in Terms of Reducing Risks to Essential Workers

The COVID-19 pandemic has led to the widespread adoption of physical distancing to prevent the disease’s spread. Physical distancing, however, is not always feasible for essential workers. Robots are one proposed solution to help ensure that essential work is performed while reducing the risk of COVID-19 exposure among essential workers and their families. The COVID-19 pandemic has, however, highlighted the ability and inability of robots to directly replace human labor. At present, much of the discussion has focused on the need for technical developments in robotics. This perspective is short-sighted because it and fails to leverage the collaborative nature of work between humans and robots. In response, this article acts as a call to shift the conversation away from technical developments and toward a focus on human and robot work redesign.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/165332/1/Robonomics Editorial Jan 16 2021 (Posted).pdfDescription of Robonomics Editorial Jan 16 2021 (Posted).pdf : PreprintSEL

Human-Machine Interaction and Human Resource Management Perspective for Collaborative Robotics Implementation and Adoption

The shift towards human-robot collaboration (HRC) has the potential to increase productivity and sustainability, while reducing costs for the manufacturing industries. Indeed, it holds great potential for workplaces, allowing individuals to forsake repetitive or physically demanding jobs to focus on safer and more fulfilling ones. Still, integration of humans and machines in organizations presents great challenges to IS scholars due to the complexity of aligning digitalization and human resources. A knowledge gap does persist about organizational implications when it comes to implement collaborative robotics in the workplace and to support proper HRC. Thus, this paper aims to identify recommended human resources management (HRM) practices from previous research about human-robot interaction (HRI). As our results highlight that few studies attempted to fill the gap, a conceptual framework is proposed. It integrates HRM practices, technology adoption dimensions and main determinants of HRC, in the objective to support collaborative robotics implementation in organizations

Accepting the Familiar: The Effect of Perceived Similarity with AI Agents on Intention to Use and the Mediating Effect of IT Identity

With the rise and integration of AI technologies within organizations, our understanding of the impact of this technology on individuals remains limited. Although the IS use literature provides important guidance for organization to increase employees’ willingness to work with new technology, the utilitarian view of prior IS use research limits its application considering the new evolving social interaction between humans and AI agents. We contribute to the IS use literature by implementing a social view to understand the impact of AI agents on an individual’s perception and behavior. By focusing on the main design dimensions of AI agents, we propose a framework that utilizes social psychology theories to explain the impact of those design dimensions on individuals. Specifically, we build on Similarity Attraction Theory to propose an AI similarity-continuance model that aims to explain how similarity with AI agents influence individuals’ IT identity and intention to continue working with it. Through an online brainstorming experiment, we found that similarity with AI agents indeed has a positive impact on IT identity and on the intention to continue working with the AI agent