Handbook of Virtual Work 1802200495, 9781802200492

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HANDBOOK OF VIRTUAL WORK

Handbook of Virtual Work Edited by

Lucy L. Gilson Dean, Peter T. Paul College of Business and Economics, University of New Hampshire, USA

Thomas O’Neill Professor, Department of Psychology, University of Calgary, Canada

M. Travis Maynard Associate Dean for Graduate Programs, College of Business, Colorado State University, USA

Cheltenham, UK • Northampton, MA, USA

© Lucy L. Gilson, Thomas O’Neill and M. Travis Maynard 2023

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA A catalogue record for this book is available from the British Library Library of Congress Control Number: 2023933756 This book is available electronically in the Business subject collection http://dx.doi.org/10.4337/9781802200508

ISBN 978 1 80220 049 2 (cased) ISBN 978 1 80220 050 8 (eBook)

EEP BoX

Contents

List of contributorsviii Acknowledgmentsxix Introductionxx PART I

TECHNOLOGY: THE FOUNDATION FOR VIRTUAL WORK

1

Bringing technological affordances into virtual work Jennifer L. Gibbs and Nitzan Navick

3

2

Role of communication technologies in virtual work Anu Sivunen, Jeffrey W. Treem and Ward van Zoonen

21

3

Virtual collaboration: human foundations augmented by intelligent technology Terri L. Griffith and Utpal Mangla

41

4

Using AI to enhance collective intelligence in virtual teams: augmenting cognition with technology to help teams adapt to complexity Anita Williams Woolley, Pranav Gupta and Ella Glikson

67

5

Principles on how to manage interactions between human workers and artificial intelligence/machine learning technologies Michael A. Zaggl and Ann Majchrzak

89

6

Refocusing human–AI interaction through a teamwork lens Christopher Flathmann, Beau G. Schelble and Nathan J. McNeese

PART II

109

THE PEOPLE MAKE THE VIRTUAL PLACE

7

When the time-space continuum shifts: telework and alterations in the work–family interface Timothy D. Golden and Valerie J. Morganson

130

8

Remoteness or virtuality? A refined framework of individual skills needed for remote and virtual work Erin E. Makarius and Barbara Z. Larson

146

9

Emotions and emotional management in virtual contexts Isabel D. Dimas, Teresa Rebelo, Marta P. Alves and Paulo R. Lourenço

164

10

Digital nomads: curiosity or trend? Robert C. Litchfield and Rachael A. Woldoff

186

v

vi  Handbook of virtual work PART III VIRTUALITY AND VIRTUAL TEAM INPUTS 11

Virtuality and the eyes of the beholder: beyond static relationships between teams and technology Patrícia Costa and Lisa Handke

12

Leadership and virtual work in a pandemic and post-pandemic world 216 Claudia C. Cogliser, William L. Gardner, Haimanti Ghosh and Azucena Grady

13

Faultlines in virtual teams Sherry M.B. Thatcher and Ramón Rico

235

14

The surge in digitalization: new challenges for team member collaboration Thomas Hardwig and Margarete Boos

257

199

PART IV VIRTUAL TEAM PROCESSES AND EMERGENT STATES 15

Virtual teams and team cognition Stephen M. Fiore, Rhyse Bendell and Jessica Williams

280

16

Understanding trust in virtual work teams Angie N. Benda, William S. Kramer, Mary E. Baak and Jennifer Feitosa

305

17

Bouncing back as a virtual team: essential elements of virtual team resilience Nohelia Argote, Chloe Darlington, Jennifer Feitosa and Eduardo Salas

325

18

Engendering creativity in temporary virtual project teams: the case of a product design firm Petros Chamakiotis and Niki Panteli

PART V

347

THE ORGANIZATION: CONTEXT, CULTURE, AND SYSTEMS THAT SUPPORT VIRTUAL WORK

19

Organizational context and climate for virtual work Emma Nordbäck and Niina Nurmi

363

20

Virtuality and inclusiveness in organizations Jakob Lauring, Charlotte Jonasson and Marta Jackowska

384

21

Embracing the digital workplace: a SMART work design approach to supporting virtual work Bin Wang and Sharon K. Parker

22

Global multinational organizations and virtual work Miriam Erez, Ella Glikson and Raveh Harush

23

Orchestrating dynamic value networks: interface-focused pathways to enhance coordination and learning Sanjay Gosain, Arvind Malhotra and Omar A. El Sawy

403 425

442

Contents  vii PART VI CONCLUSION 24

Virtual work – where do we go from here? Setting a research agenda Thomas O’Neill, M. Travis Maynard, Lucy L. Gilson, James M. Hughes and Nathaniel Easton

466

Index483

Contributors

EDITORS Lucy L. Gilson is the Dean of the Peter T. Paul College of Business and Economics at the University of New Hampshire. She received her Ph.D. from the Georgia Institute of Technology (Georgia Tech) and spent the first 22 years of her career on the faculty of the University of Connecticut. Her research examines teams in different organizational settings performing a diverse range of jobs to understand how creativity, empowerment, leadership, and virtual communication influence effectiveness. Dr. Gilson’s work has been published in the Academy of Management Journal, Journal of Applied Psychology, Journal of Management, Leadership Quarterly, Group & Organization Management, Journal of Organizational Behavior, as well as many other academic journals and edited books. In 2021, she edited a Special Virtual Teams issue of Organizational Dynamics. In 2019, the Web of Science named her one of the world’s most highly cited researchers. M. Travis Maynard is a Professor and the Associate Dean for Graduate Programs at Colorado State University. His research focuses on the effect of team members interacting via technology (virtual teams), and the impact that organizational climate has on team dynamics and performance. He is currently conducting research projects with the U.S. Army and NASA looking at team resilience and adaptation. Dr. Maynard’s work has been published in the Journal of Management, Academy of Management Journal, Journal of Applied Psychology, Journal of Organizational Behavior, Group & Organization Management, Small Group Research, Organizational Psychology Review, and Human Performance. Thomas O’Neill (Ph.D.) is a leading researcher in the areas of high-performance teamwork, virtual team and leader effectiveness, flexible remote and hybrid work, human–autonomy teaming, conflict and conflict management, personality, and assessment. Tom has published over 75 peer-reviewed journal articles in outlets such as Journal of Applied Psychology, Journal of Management, Academy of Management Learning and Education, Organizational Research Methods, Organizational Behavior and Human Decision Processes, Computers in Human Behavior, and Human Resource Management Review. He has worked extensively to translate the science into practice through consultations, workshops, public lectures, training, and other resources. In 2014 Tom created ITPmetrics.com, which contains free team diagnostics and feedback reports that support team development – over 500,000 assessments have been taken.

CONTRIBUTORS Marta P. Alves (Ph.D., Organizational Psychology, University of Coimbra, Portugal) is a Professor at the Department of Psychology and Education of University of Beira Interior viii

Contributors  ix (UBI), Portugal and a researcher at NECE-UBI (Research Centre in Business Sciences). Her current research interests, as well as her recent published work, are focused on applied social psychology and include team dynamics, social network analysis, health psychology, and scale validation studies. Nohelia Argote is a doctoral student in the Organizational Behavior Ph.D. program at Claremont Graduate University. She received her M.A. in Industrial Organizational Psychology with a specialization in Group Processes and Organizational Behavior from CUNY, Brooklyn College. Her research interests encompass teams, diversity, gender, and belonging in the workplace. She is currently working on projects focusing on incivility, team belonging, and virtual team resilience. Mary E. Baak is a consultant who helps companies engage in organizational transformation initiatives. Her career interests are centered around the future of work, leadership development, and organizational effectiveness. She holds a Master’s degree in Industrial and Organizational Psychology from the University of Nebraska at Omaha. Angie N. Benda is a doctoral student in Industrial-Organizational Psychology at the University of Nebraska Omaha. Her research is centered around teams, with a special interest in virtual teams. She is currently working on projects that are related to understanding trust and the trust repair process in virtual teams. Rhyse Bendell is a doctoral student in Human Factors and Cognitive Psychology at the University of Central Florida and work for the Team Performance Laboratory headed by Dr. Florian Jentsch. Rhyse’s research bridges engineering, modeling & simulation, human factors, user-centered design, and cognitive science to contribute to methods for optimizing individual and team performance. Margarete Boos (Ph.D., University of Bonn) is Professor of Economic and Social Psychology at the University of Göttingen, Germany. Her research focuses on group psychology, especially coordination and leadership, and on methods of interaction analysis. Her current projects investigate collaboration in distributed teams, as well as communication and performance in medical teams. Petros Chamakiotis (Ph.D.) is an Associate Professor of Management at ESCP Business School in Madrid, Spain, where he is also the Scientific Director of the M.Sc. in Digital Project Management & Consulting. He is part of the “Reinventing Work” ESCP Chair, funded by BivwAk at BNP Paribas in France, and he is affiliated with the Digital Futures at Work Research Centre in the UK. His published and forthcoming papers focus primarily on virtual teams, hybrid working, digital platforms, and online communities. He studies the implications of these technology-enabled configurations in a range of empirical contexts, including non-organizational ones, such as the Global South and the context of forced migration. Petros currently serves as an Associate Editor for the Information Systems Journal. Claudia C. Cogliser is the Benninger Family Professor of Management in the Rawls College of Business at Texas Tech University. She is also affiliated faculty with Women’s and Gender Studies at Texas Tech. Her research has leadership as its primary focus with interests in authentic leadership and leader–follower relationships.

x  Handbook of virtual work  

Patrícia Costa is an Assistant Professor at ISCTE – Lisbon University Institute, Portugal, where she is a faculty member of the Human Resources and Organizational Behavior department. Her research areas include team effectiveness, virtual teamwork, team work engagement, virtual communication, and well-being at work. She has been and is currently involved in internationally funded projects on work well-being, together with scholars from diverse countries. Her research has been published in journals such as Journal of Occupational and Organizational Psychology, European Journal of Work and Organizational Psychology, Journal of Personnel Psychology, and Frontiers in Psychology. Chloe Darlington earned her M.A. from Claremont Graduate University in 2021 with a dual concentration in positive organizational psychology and evaluation. Her research focuses on inclusion and creativity in the workplace and remote work’s impact on work–life balance. She works as a consultant for an organization where she aims to help close the academic-practice gap by applying empirically validated research in her projects. Isabel D. Dimas (Ph.D., Organizational Psychology, University of Coimbra) is a Professor of Organizational Behavior and Human Resources Management at Faculty of Economics of University of Coimbra. She is a research member at the Centre for Business and Economics Research, and her research is focused on team functioning and effectiveness, leadership of people and teams, conflicts and negotiation, and emotions in organizations. Her work has been published in international journals such as Small Group Research, Computers and Human Behavior, Journal of Work and Organizational Psychology, and Negotiation and Conflict Management Research. Nathaniel Easton is a doctoral student in organizational behavior at the University of Connecticut. His research interest center around team’s ability to function despite elements of disfunction causing a disruption. He is working on multiple different projects that focus on resilience, pandemic response, and virtual teams. Omar A. El Sawy is the Kenneth King Stonier Professor of Business Administration, Professor of Data Sciences and Operations at the University of Southern California’s Marshall School of Business. His research interests include digital business strategy in messy environments, business models for digital platforms, and real-time management. His work has been published in MIS Quarterly, Information Systems Research, Strategic Management Journal, Decision Sciences, Long Range Planning, and Production and Operations Management. Miriam Erez is Professor Emeritus in Organizational Psychology and Management, Faculty of Industrial Engineering & Management, Technion. She is the Vice Dean of the MBA program, Founder and Chair of the Knowledge Center for Innovation, Technion, and Deputy Dean of the Academy of Management Fellows Group. She appeared in the top 2 percent of researchers in business and management (2020). Erez is the recipient of the 2005 Israel Prize for Management Sciences and Business Administration and in 2002 the Distinguished Scientific Contributions to the International Advancement of Applied Psychology. Erez’s research areas are innovation and entrepreneurship, cross-cultural, global organizational behavior, and work motivation. She has authored two books, co-edited five books, and published over 100 journal papers and book chapters. She has advised over 100 graduate students.

Contributors  xi  

Jennifer Feitosa is an Associate Professor of the Psychological Science Department at Claremont McKenna College. She is a recipient of a Fulbright U.S. Scholar Award at the Universidad Carlos III de Madrid. Her main research interests include diversity, teamwork, and measurement. She answers questions, such as “How can we maximize the benefits of diversity in teams?” Her work has been published in the Journal of Organizational Behavior, American Psychologist, Journal of Business Research, Human Resources Management Review, and she has presented over 60 conference papers. Stephen M. Fiore is Director, Cognitive Sciences Laboratory, and Professor with the University of Central Florida’s Cognitive Sciences Program in the Department of Philosophy and Institute for Simulation & Training. Dr. Fiore is Past-President, and was a founding Board Member for the International Network for Science of Team Science (INSciTS), as well as a founding Board Member and Past-President of the Interdisciplinary Network for Group Research (INGRoup). He maintains a multidisciplinary research interest that incorporates aspects of the cognitive, social, organizational, and computational sciences in the investigation of learning and performance in individuals and teams. He has published in journals across a number of disciplines, including Topics in Cognitive Science, Human Performance, Psychological Science in the Public Interest, Small Group Research, Information Systems Management, Nature Ecology & Evolution, and Nature Human Behavior. Christopher Flathmann is an Assistant Research Professor and Associate Director of the Team Research Analytics in Computational Environments (TRACE) Research Group within the division of Human-Centered Computing in the School of Computing at Clemson University. Dr. Flathmann received a Ph.D. in Human-Centered Computing from Clemson University in 2023. His research interests broadly focus on creating human-centered AI, but his specialty is human-AI teamwork and how humans adapt to AI technology. His research has received multiple best paper awards/nominations from top peer-reviewed HCI and CSCW venues. William L. Gardner is the Paul Whitfield Horn Distinguished Professor, Jerry S. Rawls Chair in Leadership, and Director of the Institute for Leadership Research in Rawls College of Business at Texas Tech University. His research focuses on authentic and charismatic leadership, and cognitive, motivational, and social influence processes within organizations. Haimanti Ghosh is a doctoral student in the Area of Management at Texas Tech University’s Rawls College of Business in Lubbock, TX. Her research interests include emotions, leadership, and organizational behavior. Jennifer L. Gibbs is a Professor of Communication with an affiliated appointment in the Technology Management Program (TMP) at the University of California, Santa Barbara. She has been studying virtuality and virtual work for over 25 years. Her research focuses on collaboration and innovation in global virtual teams and other remote work arrangements, as well as the ways in which new technologies such as enterprise social media and artificial intelligence are transforming organizations. She recently published two books (Distracted: Staying Connected without Losing Focus (Praeger 2017) and Organizing Inclusion (Routledge 2020)), and has published her work in top-tier journals such as Administrative Science Quarterly, Organization Science, Human Relations, Management Communication Quarterly, Small Group Research, Journal of Computer-Mediated Communication, and Communication

xii  Handbook of virtual work Research. Professor Gibbs is currently Editor of Communication Research and is the immediate past chair of the CTO (formerly OCIS) division of the Academy of Management. Ella Glikson is an Assistant Professor at the Graduate School of Business Administration at Bar Ilan University. Her research focuses on technology- and AI-mediated communication and working in virtual teams. Her research has been published in such journals as Academy of Management Annals, Academy of Management Learning & Education, Journal of World Business, Journal of Service Research, New Media & Society, and Small Group Research. Timothy D. Golden is a Professor of Management at Rensselaer Polytechnic Institute. His research focuses on remote work, telework, telecommuting, and virtual interactions. He has conducted research in these areas for over 20 years, and his research has appeared in leading academic journals, such as the Journal of Applied Psychology, Journal of Management, Academy of Management Perspectives, Human Relations, and Leadership Quarterly. Dr. Golden has been interviewed and cited in hundreds of media outlets worldwide, and he has served as an expert consultant to a number of large and small companies seeking advice based on his research. Sanjay Gosain is currently the Director of Decision Science at Okta and was previously the senior manager of analytics at Salesforce. Prior to joining the industry, he was an assistant professor at the University of Maryland. His research has been published in MIS Quarterly, Journal of Management Information Systems, Information Systems Research, and Journal of Marketing. Azucena “Sheny” Grady is a doctoral student in the Area of Management at Texas Tech University’s Rawls College of Business in Lubbock, TX. Her research interests include sustainability, entrepreneurship, strategy, and technology. Terri L. Griffith holds the Keith Beedie Chair in Innovation and Entrepreneurship at Simon Fraser University’s Beedie School of Business. Her research crosses organizational design, technology and information systems, and individual and team performance – published in journals such as Organization Science, MIS Quarterly, Academy of Management Review, and Academy of Management Discoveries. Professor Griffith considers remote and hybrid work, with her most recent research taking on a “bottom-up” approach to automation, including artificial intelligence. She is the 2022 President of the International Society of Service Innovation Professionals. Pranav Gupta is an Assistant Professor at the Gies School of Business at the University of Illinois, Urbana Champaign. He investigates how humans work together adaptively and how technology can augment our capacity for dynamic coordination. His research projects focus on understanding the emergence of collective intelligence in self-organized teams using lab experiments, archival data, and agent-based simulation models with the goal of improving in real time the algorithmic coordination of collaborators’ distributed knowledge (collective memory), distributed attention (collective attention), and diverse goals (collective reasoning). Lisa Handke is a postdoctoral research associate/lecturer at the Division of Social, Organizational and Economic Psychology at Freie Universität Berlin, Germany. Her research focuses on virtual teamwork, telework, meetings, and work design and has been published in various outlets such as European Journal of Work and Organizational Psychology,

Contributors  xiii Organizational Psychology Review, and Small Group Research. She is and has been involved in research projects funded by different German granting agencies (e.g., German Research Foundation, German Foundation for Volunteering) to conduct research on intrateam communication in various virtual contexts, such as dispersed development teams or online volunteers. Thomas Hardwig (Ph.D., University Goettingen) is Senior researcher at the Cooperation Office Universities and Trade Unions at the Faculty of Social Sciences at the University of Göttingen, Germany. His research focuses on the management of spatially distributed teams and software-supported collaboration in organizations. His current project deals with digitalization and job strain in schools. Raveh Harush is an Assistant Professor at the Graduate School of Business Administration at Bar Ilan University. His research focuses on the joint effects of multiple identities on organizational behavior, including within diverse, multicultural, and virtual teams. His work has been published in academic journals such as Academy of Management Learning & Education, Group & Organization Management, and Journal of Cross-Cultural Psychology. James M. Hughes is a doctoral student in Industrial-Organizational Psychology at the University of Connecticut. His research interests include occupational health and well-being, optimizing virtual work for mental health, and organizational systems theories. He is working on multiple projects that aim to optimize employee health and productivity, including projects developing advanced wearable sensors, as well as work with the Connecticut Department of Correction. Prior to arriving at UConn, James did independent consulting work in market research, employee engagement, and people analytics for AGL & Associates. Marta Jackowska is currently employed in the private sector working with and in virtual teams. She has her Ph.D. degree from Aarhus University focusing on the dynamic organizing of virtual teams. In relation to method, Marta Jackowska is specifically focusing on epistemic network analysis as a way to reveal patterns in qualitative data. Her work has, among other things, been published in the Journal of International Management. Charlotte Jonasson is Associate Professor in the Department of Psychology and Behavioural Sciences, Aarhus University. Her research focuses on international management and virtual teams. She is currently conducting research on organizational failure and digitalization. Charlotte Jonasson’s work has been published in Organization Studies, British Journal of Management, and Human Resource Management Journal. William S. Kramer is an Assistant Professor in Industrial and Organizational (I/O) Psychology at the University of Nebraska Omaha. He completed his Ph.D. in I/O Psychology from Clemson University in 2018. His main research interests include the examination of teams in unique contexts, the impact of national culture on team processes, and the use of novel statistical approaches for theory development and analysis. He has worked on grants funded by several different organizations including NASA, ARI, ARL, NSF, and the DoD and has co-authored over 15 peer-reviewed publications and 70 conference presentations. Barbara Z. Larson is Executive Professor of Management at Northeastern University’s D’Amore-McKim School of Business. Her research focuses on the personal and interpersonal skills that individuals need to work effectively in virtual environments. Her research has been published in Strategic Management Journal, Academy of Management Perspectives,

xiv  Handbook of virtual work and Management Science, among other outlets. She worked for 15 years in international finance and operations leadership prior to her academic career and earned her DBA at Harvard Business School. Jakob Lauring is Professor in the Department of Management, Aarhus University. Jakob Lauring’s research interests are focused on international management with specific interest in global virtual teams. He has published more than 100 international articles in outlets such as Journal of World Business, British Journal of Management, Human Resource Management Journal, and International Business Review. Robert C. Litchfield is an Associate Professor in the Department of Business at Washington & Jefferson College. Rob’s research focuses on creativity and innovation, identity, and the future of work. His research has been published in journals including Academy of Management Review, Group and Organization Management, Journal of Product Innovation Management, Strategic Organization, Journal of Creative Behavior, and Motivation & Emotion. Rob’s book, Digital Nomads: In Search of Freedom, Community, and Meaningful Work in the New Economy (2021), is published by Oxford University Press. Paulo R. Lourenço (Ph.D., Organizational Psychology, University of Coimbra) is Associate Professor of Work, Organizational and Personnel Psychology at University of Coimbra (Portugal), and researcher at CeBER. His work has been published in scientific journals, such as Negotiation and Conflict Management Research, Small Group Research, and Journal of Business Ethics. His research interests include work teams, emotions, conflict, and leadership. Ann Majchrzak is emerita at Marshall School of Business, fellow of the Association for Information Systems, current senior editor at MIS Quarterly, and recently past senior editor at Organization Science. Her research focuses on widening the circle of stakeholders engaged in solving grand challenges and wicked organizational problems through the use of sociotechnical systems and large-scale collaborative processes. Erin E. Makarius is an Associate Professor of Management at the University of Akron in the College of Business. Her research focuses on managing boundaries including organizational, work–life, technological, and international boundaries, with emphasis on the role of relationships, reputation, and remote work. Her work has been widely published in journals such as Organization Science, Journal of Management, Academy of Management Perspectives, Journal of World Business, Organization Studies, and Harvard Business Review, among others. She worked in human resources prior to her academic career and earned her Ph.D. from The Ohio State University. Arvind Malhotra is the H. Allen Andrew Distinguished Professor of Entrepreneurship and Strategy at the UNC-Chapel Hill’s Kenan-Flagler Business School. His current research interests include future of work and organizations, crowdsourcing, and extra-organizational structures for innovation. His work has been published in MIS Quarterly, Information Systems Research, Journal of Management, Harvard Business Review, MIT Sloan Management Review, Human Resource Management Journal, and Long Range Planning. Utpal Mangla (MBA, PEng, CMC, ITCP, PMP, ITIL, CSM, FBCS) is a General Manager responsible for Telco Industry & EDGE Clouds in IBM. Prior to that, he was the VP, Senior Partner and Global Leader of Telco, Media, Entertainment Industry’s Centre of Competency.

Contributors  xv In addition, Utpal led the “Innovation Practice” focusing on AI, 5G EDGE, Hybrid Cloud and Blockchain technologies for clients worldwide. In his role as senior executive in business with P&L responsibility and thought leader in emerging technologies, Utpal’s mission is to fuel growth by building, scaling and implementing differentiated competitive market service solution offerings to meeting business imperatives of customers. Nathan J. McNeese is the CECAS Dean’s Professor, Assistant Professor, and Director of the Team Research Analytics in Computational Environments (TRACE) Research Group within the division of Human-Centered Computing in the School of Computing at Clemson University. Dr. McNeese received a Ph.D. in Information Sciences & Technology from The Pennsylvania State University in 2014. His research interests span across human–AI teaming, human-centered AI, and the development/design of human-centered collaborative tools and systems. He currently serves on multiple international/societal program and technical committees, in addition to multiple editorial boards including Human Factors. He is a previous member of multiple National Academies of Science initiatives, and previous member of the Army Research Lab HERD Technical Advisory Board. His research has received multiple best paper awards/nominations and has been published in top peer-reviewed HCI and HF venues over 100 times. In addition, he has acquired over $35 million in research funding from agencies such as NSF, ONR, AFOSR, and AHRQ. Valerie J. Morganson is an Associate Professor and Coordinator of the IndustrialOrganizational (I-O) Psychology M.A. program at the University of West Florida. Her research focuses on finding practical solutions to address work–family issues as well as gender issues in the workplace. Dr. Morganson teaches undergraduate and graduate courses related to I-O Psychology and research methods. She has published in numerous journals including Journal of Business and Psychology and Journal of Occupational Health Psychology. Nitzan Navick is a doctoral student in organizational communication at UC Santa Barbara. Her research in the area of remote work examines topics such as boundary management, work–life balance, technological affordances of information and communication technologies, and pays particular attention to the experiences of marginalized and underrepresented populations. Some of her recent research includes a mixed-methods examination of boundary management among underrepresented college students working and learning remotely during the COVID-19 pandemic, cybersexual harassment in remote work teams focusing on the experiences of victims and the affordances involved in perpetration, and concertive control in self-organizing online communities. Emma Nordbäck (D.Sc. (Tech.)) works as Assistant Professor of Management and Organisation at Hanken School of Economics in Finland. Her research focuses on virtual work arrangements ranging from globally distributed teams to remote work, with a primary focus on team-level processes and outcomes. Her work has appeared in journals such as Journal of Management Information Systems, Journal of Organizational Design, Journal of Computer-Mediated Communication, Journal of Applied Communication Research, and Organizational Dynamics. Niina Nurmi (Ph.D.) works as Assistant Professor of Organizational Design and Leadership in the Department of Industrial Engineering and Management at Aalto University, School of Science. Her research explores issues and effects of work design, leadership, and employee

xvi  Handbook of virtual work well-being in virtual work and global virtual teams. Her work appears in journals such as Journal of International Business Studies, Journal of Management, Journal of World Business, Organization Studies, and Stress & Health. Niki Panteli (Ph.D.) is a Professor of Digital Business at Royal Holloway University of London and an Adjunct Professor at the Norwegian University of Science and Technology. Her main research interests lie in the area of digital transformation, virtual teams and virtual collaborations, and online groups and communities. Within this field, she has studied issues of trust, conflict, identification, and collaborations in the online environment. She led and participated in several research projects and her work appeared in numerous top-ranked academic journals. She is currently the co-director for the Digital Organisation and Society (DOS) Research Centre at Royal Holloway University of London. Sharon K. Parker is an ARC Laureate Fellow, John Curtin Distinguished Professor at Curtin University, and Director of the Centre for Transformative Work Design. Her research interests include work design, work and technology, proactive behaviour, perspective taking, mental health, and job performance. She has published more than 150 internationally refereed articles, including publications in top tier journals such as the Journal of Applied Psychology, Academy of Management Journal, and the Annual Review of Psychology. Examples of current research projects include mental health amongst fly-in-fly-out workers and work redesign in the aged care sector. Teresa Rebelo (Ph.D., Organizational Psychology, University of Coimbra) is a Professor (tenure track) of Work, Organizational, and Personnel Psychology at University of Coimbra. She is a research member of CeBER (Centre for Business and Economics Research), and her research interests are focused on organizational culture, organizational and team learning, team dynamics, as well as on predictive validity of selection methods. Ramón Rico is a Professor of Management at Carlos III University in Madrid. His research focuses on team adaptation, adaptive leadership, team diversity, and multiteam systems effectiveness. He is currently conducting research projects with the U.S. Army and the Spanish Ministry of Science looking at team adaptation and adaptive leadership. Dr. Rico’s work has been published in the Academy of Management Annals, Academy of Management Review, Academy of Management Journal, Journal of Management, Journal of Organizational Behavior, Organizational Psychology Review, Group & Organization Management, Small Group Research, and European Journal of Work and Organizational Psychology. Eduardo Salas is the Allyn R. & Gladys M. Cline Chair Professor and Chair of the Department of Psychological Sciences at Rice University. He is a Past President of the Society for Industrial/Organizational Psychology (SIOP) and the Human Factors & Ergonomics Society (HFES). He is a Fellow of the American Psychological Association (APA), Association for Psychological Science, and HFES. He is also the recipient of the 2012 Society for Human Resource Management Losey Lifetime Achievement Award, the 2012 Joseph E. McGrath Award for Lifetime Achievement from the Interdisciplinary Network of Group Research for his work on teams and team training and the 2016 APA Award for Outstanding Lifetime Contributions to Psychology. Beau G. Schelble is a Ph.D. student at Clemson University studying Human-Centered Computing in the Team Research in Computational Environments (TRACE) Research Group

Contributors  xvii within the School of Computing. His research interests lie in enhancing team cognition within human–machine teams and designing human-centered AI for better human–AI interaction. Anu Sivunen is a Professor of Communication in the Department of Language and Communication Studies, University of Jyväskylä, Finland, where she also leads the CoCoDigi research group (Communication and Collaboration on Digital Platforms). Her research focuses on organizational communication technologies and social media, communication processes in virtual teams and in other distributed work settings, as well as organizational space. Her work has been published in journals including Academy of Management Annals, Human Relations, Journal of Computer-Mediated Communication, and Journal of Organizational Behavior. Sherry M.B. Thatcher is the Skinner Professor of Business in the Department of Management and Entrepreneurship at the University of Tennessee-Knoxville’s Haslam College of Business and she currently serves as the Editor-in-Chief of the Academy of Management Review. Her research interests focus on diversity, identity, and conflict, and she is one of the leading experts in the area of team faultlines. Her work appears in the top journals of our field, including the Academy of Management Review, Academy of Management Journal, Academy of Management Annals, Journal of Applied Psychology, Organization Science, Journal of Management, Journal of Management Studies, and Small Group Research. Jeffrey W. Treem is an Associate Professor of Communication Studies in the Moody College of Communication at The University of Texas at Austin. His work explores the relationship between communication practices and social perceptions of expertise, primarily in organizational contexts. Ward van Zoonen is an Associate Professor of Organizational Dynamics in the Digital Society at Erasmus University. He is also affiliated with the University of Jyväskylä as Junior Visiting Professor in the Department of Language and Communication Studies. His research explores the implications of digitalization in organizations for the ways in which employees communicate and collaborate. Bin Wang is an Associate Professor at Shanghai University. His research interests include work design, virtual work, and learning from failure. His work has been published in Academy of Management Annals, Journal of Organizational Behavior, Human Resource Management Journal, and Applied Psychology. Jessica Williams is a Ph.D. student at the University of Central Florida. Jessica is a Graduate Researcher at the Institute for Simulation and Training as a member of the Team Performance Laboratory under Dr. Florian Jentsch and currently works in collaboration with the Cognitive Sciences Laboratory under Dr. Stephen M. Fiore on the Artificial Social Intelligence for Successful Teams (ASIST) program. Anita Williams Woolley is a Professor of Organizational Behavior and the Associate Dean of Research at Carnegie Mellon University’s Tepper School of Business. Her research focuses on team collaboration and collective intelligence, and her current projects include studies funded by DARPA and the National Science Foundation focusing on how artificial intelligence can enhance the quality of collaboration in teams. Dr. Woolley’s research has been published in Science and PNAS as well as many top journals in management, applied psychology, and

xviii  Handbook of virtual work computer science, and she currently serves as a Senior Editor at Organization Science and a founding Associate Editor of Collective Intelligence. Rachael A. Woldoff is an urban sociologist and Professor of Sociology at the University of West Virginia. Her research focuses on community, neighborhoods, crime, race, housing, and urban change. She is currently conducting research on public housing residents’ experiences of displacement. Her books include High Stakes: Big Time Sports and Downtown Redevelopment (Ohio State University Press 2004), Priced Out: Stuyvesant Town and the Loss of Middle-Class Neighborhoods (NYU Press 2016), and White Flight/Black Flight: The Dynamics of Racial Change in an American Neighborhood (Cornell University Press 2011), which was awarded the Best Book Award by the Urban Affairs Association. Her most recent book is Digital Nomads: In Search of Freedom, Community, and Meaningful Work in the New Economy (Oxford University Press 2021). Michael A. Zaggl is an Associate Professor at the School of Business and Social Sciences at Aarhus University, Denmark. Before joining Aarhus University, he led a research group at the Technical University of Munich, TUM School of Management, where he also received a Habilitation degree. His research includes distributed and digital innovation, crowdsourcing, technologically enabled search, and data-driven decision-making. His research has been published in leading journals, including Academy of Management Discoveries, Journal of the Association for Information Systems, and Research Policy.

Acknowledgments

We did not know when we agreed to edit this book that the virtual work world would be where it is today. The three of us have been working in this space for two decades and for most of that time, virtual teams and hybrid work were interesting topics, but not the reality for a large percentage of the population. What a fun time to be editing a handbook on virtual work! This book, and our thinking on the topic of virtual work would not have been possible without the help of our many coauthors, graduate students, and colleagues – many of whom have agreed to contribute chapters to this handbook. Thank you all for sharpening our thinking, pushing us to do more, helping us continue to learn, and embracing the virtual work wave. Because, of course, all of the work for this handbook has taken place virtually. We start with a shout out to Ana Margarida Passos and Patricia Costa who introduced Lucy and Travis to Tom in Portugal! Who knew that a brief meeting at Catolica Lisbon would result in a multi-year partnership and friendship that has yielded scholarly articles, practitioner articles, a special issue of Organizational Dynamics, and now the Handbook of Virtual Work. Our shared passion for understanding teams and virtual work has fostered a great collaborative team and for that we say – Obrigado. With regard to thank you’s, we need to add a HUGE shout out to Ph.D. students and coauthors James Hughes and Nathaniel Easton. James and Nate are not even our own students, but they said yes to joining us on this journey and none of this would have been possible without their help, support, and attention to detail. We hope you had some fun along the way with this project and learned a lot that will take you far as you embark on your academic careers – Thank you. Last but by no means least, we want to thank our families. Without you supporting us as we coordinated virtual meetings across different time zones, we would not have been able to complete this book on time!

xix

Introduction

The three editors of this handbook have been interested in gaining a deeper understanding of team effectiveness for several decades. This led each of us (in different ways) to seek to understand how interacting virtually changed the dynamics of organizational teams. As a result, collectively, we have conducted reviews of the virtual team literature (e.g., Martins, Gilson & Maynard, 2004; Gilson, Maynard, Jones Young, Vartiainen & Hakonen, 2015) and have examined virtual teams empirically with a focus on topics such as virtual team leadership (e.g., Hambley, O’Neill & Kline, 2007) and how team virtuality shaped team processes and performance (e.g., Maynard, Mathieu, Rapp & Gilson, 2012). That said, each of us have an appreciation that individuals work within teams, and that teams are nested within organizations. Accordingly, some of us have focused on the individual effects of virtual work (e.g., O’Neill, Hambley & Bercovich, 2014; O’Neill, Hambley, Greidanus, MacDonnell & Kline, 2009) and we rounded out our team with two graduate students who had interest in team effectiveness and virtual workplaces. One of our first meetings as a full team centered around how we would pull together the various topics that are applicable to virtual work which is, in our mind, the appropriate topic to consider versus just focusing on telework, virtual teams, or organizational-level factors. Likewise, without the presence of technology, virtual work is not possible. As such and as discussed in the introduction to each section of the handbook, the book is structured around these topics/levels: technology, individual virtual work, virtual teams, and organizational-level factors to consider regarding virtual work. When our team started discussing this handbook, the COVID-19 pandemic was just starting to take shape and we anticipated that this would make virtual work even more salient in organizations. However, we could not have fully anticipated the impact that the pandemic would truly have. In fact, as we continued our work over the last year on this handbook, much of the world embraced virtual work on a scale we could never have imagined. Likewise, many argue that the world of work is irrevocably changed and will forever have some component of virtual work. In fact, earlier this week, CNN reported that remote work is likely here to stay. This shift is likely to have both positive and negative consequences. Specifically, there are many individuals that are pleased by the increased opportunities to work remotely (e.g., Maurer, 2021). In contrast, a lead article in the BBC news feed highlighted the difficulty of managing a hybrid workforce and CNBC recently commented on the number of CEOs that are starting to call their employees back to the office. Similarly, such a transition back to the office has both positive and negative consequences for employees, teams, and entire organizations (e.g., Sundaram, 2020). So, while we thought this handbook was timely before, we know it is now! However, while the pandemic has made the topic of virtual work more prominent, it is not a new phenomenon within organizations or a new topic within the academic literature. Specifically, companies have had employees working remotely (historically referred to as teleworkers) for decades. Likewise, researchers have been examining the effect of virtual work and computer mediated communication on employees’ well-being, team performance, and organizational outcomes for just as long. That said, the pandemic provided a global lived experience that has significantly broadened and deepened our understating of virtual work. xx

Introduction  xxi In fact, what has changed in the last couple of years is the pace of acceleration to virtual work. Prior to the pandemic, in most cases virtual work was traditionally embraced in a planned and systemic way, matching employees’ skills, knowledge, and abilities to technological capabilities and tasks needs. In contrast, in the face of a global pandemic, everything changed, and organizations were forced to have all but their essential workers transition to virtual work almost overnight. Accordingly, there was little to no time to plan, process, or develop guidelines for how the new reality, of how all communication being via technology, would work. And yet, somehow work got done, employees remained productive, motivated, and engaged. Likewise, teams met deadlines, onboarded new members, and organizations remained productive and able to conduct business as normal, well maybe business as new normal. In fact, given how well the transition to virtual work turned out for many organizations, they are now evaluating whether they want to go back to in-person work, remain virtual, or use some form of hybrid configuration. Likewise, individual employees are evaluating their current jobs and organizations in terms of how much flexibility they provide, and, in many cases, employees are leaving their current employer for one that is more flexible in terms of the extent to which they can manage their work virtually. As a result of what has been experienced over the last couple of years, what happens next will be very interesting for scholars of virtual work along with managers and their employees. Many employees have embraced working virtually. With no commute, they have more time and money to spend in other areas of their lives. At the same time, many organizations are struggling to come up with how best to transition employees back to the office in some aspect to make use of the office space that organizations have invested in. While there were certainly some struggles along the way, many managers have now learned how to work with their teams remotely, and technology advancements are occurring almost daily allowing for different forms of performance monitoring, collaboration, and team bonding. And yet, not everyone is happy. Many yearn for the camaraderie they experienced at the office, and we have seen significantly higher levels of burnout and stress. In part, these levels of burnout and stress have been attributed to the ability that these technological advancements possess to enable employees to work from anywhere at any time. As a result, many employees feel that their teammates, managers, and organizations expect them to be available and work from anywhere at any time placing a stress on work/life balance. Accordingly, within this handbook we build upon the foundation of research and practice that was in place regarding virtual work prior to the pandemic. We say build upon because many of the things believed to be true about virtual work were proven with the wholesale transition to virtual work that occurred globally. However, this is not to say that everything turned out as expected. In fact, we learned over the past couple of years that many of the things that the virtual work literature thought was “known” turned out to not be true or turned out to be different in some respect when put into practice. Accordingly, the authors of the various chapters included in this handbook have leveraged what was known about virtual work based on prior research, but also have highlighted phenomenon that may have behaved differently than expected over the prior years of extensive virtual work during the pandemic. And based upon this, the various authors shine the light on areas where the virtual work literature should continue to develop to better understand how virtual work actually happens in practice. Again, there are many ways to discuss virtual work. In this book we take a holistic approach based upon a basic assumption that without technology, virtual work cannot happen.

xxii  Handbook of virtual work Accordingly, we start our discussion with several chapters that discuss the importance and impact of technology on virtual work. Next, we recognize that without individuals there are no teams. As such, following the chapters on technology, we have several chapters that discuss individual and virtual team topics. Finally, we recognize that without the individuals and teams there are no organizations and therefore, this handbook concludes with a section on organizational-level virtual work topics. This interconnected, nested view allows us to take a holistic integrated approach to virtual work. In so doing, our hope is that we provide a structure for the reader of this handbook to better understand the various streams of work that are focused on virtual work so that the readers of this handbook can contribute to the evolution of virtual work research and practice over the coming decades as the topic of virtual work is only going to become more and more important.

REFERENCES Gilson, L. L., Maynard, M. T., Jones Young, N. C., Vartiainen, M., & Hakonen, M. (2015). Virtual teams research: 10 years, 10 themes, and 10 opportunities. Journal of Management, 41(5), 1313‒1337. Hambley, L. A., O’Neill, T. A., & Kline, T. J. (2007). Virtual team leadership: The effects of leadership style and communication medium on team interaction styles and outcomes. Organizational Behavior and Human Decision Processes, 103(1), 1‒20. Martins, L. L., Gilson, L. L., & Maynard, M. T. (2004). Virtual teams: What do we know and where do we go from here? Journal of Management, 30(6), 805‒835. Maurer, R. (2021). SHRM: Half of workers wish to remain remote permanently. https://​www​.shrm​.org/​ hr​-today/​news/​hr​-news/​pages/​shrm​-half​-workers​-wish​-remain​-remote​-permanently​.aspx Maynard, M. T., Mathieu, J. E., Rapp, T. L., & Gilson, L. L. (2012). Something(s) old and something(s) new: Modeling drivers of global virtual team effectiveness. Journal of Organizational Behavior, 33(3), 342‒365. O’Neill, T. A., Hambley, L. A., & Bercovich, A. (2014). Prediction of cyberslacking when employees are working away from the office. Computers in Human Behavior, 34, 291‒298. O’Neill, T. A., Hambley, L. A., Greidanus, N. S., MacDonnell, R., & Kline, T. J. (2009). Predicting teleworker success: An exploration of personality, motivational, situational, and job characteristics. New Technology, Work and Employment, 24(2), 144‒162. Sundaram, V. (2020). Five challenges organizations face with a return to the office (and the solutions). https://​www​.forbes​.com/​sites/​for​besbusines​sdevelopme​ntcouncil/​2020/​09/​24/​five​-challenges​ -organizations​-face​-with​-a​-return​-to​-the​-office​-and​-the​-solutions/​?sh​=​4c4bf9a6181a

PART I TECHNOLOGY: THE FOUNDATION FOR VIRTUAL WORK It goes without saying that without technology there would be no virtual work. Technology that enables virtual work is not new and technological disruptions ranging from the introduction of the telephone to high-speed internet connectivity have all changed the way individuals, teams and organizations work. Dating back to the 1940s, linked computers were used in scientific research and by the military for calculations, decryptions, and sharing of information. In science, the Williams–Kilburn tube was developed and tested in 1947 at Manchester University and is reported to be the first high speed electronic memory and the precursor to Tim Berners-Lee’s introduction of the World Wide Web (WWW) in 1989 that allowed for information sharing and research collaboration between universities around the world. The corporate world is said to have started using mainframe computers in the 1950s and 1960s to centralize data-processing centers and allow employees to share work documents (e.g., Beniger, 1986; Cascio & Montealegre, 2016). When it comes to virtual work, there are several well-documented organizational examples from the 1990s (e.g., Cisco, Sun Microsystems) of early adoption by companies in the technology sector. However, with the introduction of the commercial internet and email technology, the ability to have employees work from anywhere at any time grew exponentially. While initially, technology meant dial-up connections and the ability to store documents in a shared location, advancements have accelerated where today we are talking about the role of technology – specifically artificial intelligence (AI) – not simply as a technology, but as a team member. In this section, the chapters take a forward-looking view on how technology can now enhance virtual work, collaboration, and collective intelligence that will result in a more effective and efficient workforce. This section starts with a chapter by Gibbs and Navick proposing a technological affordances perspective that bridges past examination on the role of technology, but more importantly focuses on the possibilities of what and how technology can be integrated into virtual work moving forward. Technological affordances emphasizes the unique ways in which individuals and organizations can use technology. This lens is applied to both interpreting existing research on virtual work as well as providing a roadmap for future research on virtual work driven by a technology affordance frame. 1

2  Handbook of virtual work In the second chapter, Sivunen and colleagues provide an overview of the role of communication technologies in virtual work. They review three shifts in the use of communication technologies and discuss the implications of these shifts for virtual work. The shifts address the high perceived value of digital tools, the spatial and temporal inter-connection between digital tools and their use, and the adoption of open technologies that are more flexible and accessible. The implications of these shifts are considered with respect to visibility, constant connectivity, and growth of technologies and their usage in virtual work. Next, Griffith and Mangla examine virtual collaboration and how it can be augmented by intelligent technology. The authors take a past, present, and future perspective, and offer a three-dimensional theory of collaboration suitable for incorporating the roles of intelligent technology. They examine current reviews of human and intelligent technology in the workplace and summarize what emerged from these reviews. The authors also detail how intelligent technology has supported IBM’s virtual delivery process of business tools. They conclude with an innovative concept involving thinking in 5T, in other words, thinking about the applications of intelligent technology with respect to target, talent, technique, technology, and times. In a similar vein, Woolley et al.’s chapter addresses the use of AI to enhance collective intelligence in virtual teams. Here, the authors examine the emergence of collective intelligence using transactive memory, transactive attention, and transactive reasoning systems. They then consider how the use of AI in teams could contribute to the development of collective intelligence in teams through the framework of the three cognitive systems. Finally, the authors consider themes involving humans’ trust in AI, they note the different antecedents of cognitive and emotional forms of trust, and the implications for teaming with AI. Zaggl and Majchrzak’s chapter addresses the management of interactions between human workers and artificial intelligence as well as machine-learning technologies. They extend previous literature on human–machine interaction by invoking a dynamic “principal–agent” framework. Second, they develop design principles on how to manage interactions between humans and technology based on combinations of interdependence and learning crossed with information provision, evaluation, and monitoring. This represents an innovative and novel application of principal–agent theory (i.e., importing from human–human to a human–agent context). Finally, the chapter by Flathmann and colleagues rounds out the technology and virtual work section. They propose that the literature on human–automation interaction (HAI) and human–AI interaction should be refocused by interpreting it through a teamwork lens. They discuss examples of real-world human–AI interaction along with models of teamwork that help organize the various constructs considered in human–AI interaction. An agenda for future research on human–AI interaction in virtual contexts is advanced by merging human–human and human–autonomy teamwork lenses.

REFERENCES Beniger, J. R. (1986). The Control Revolution: Technological and Economic Origins of the Information Society. Harvard University Press. Cascio, W. F., & Montealegre, R. (2016). How technology is changing work and organizations. Annual Review of Organizational Psychology and Organizational Behavior, 3(1), 349‒375.

1. Bringing technological affordances into virtual work Jennifer L. Gibbs and Nitzan Navick

Scholars have been studying virtual work for over two decades, since the late 1990s, and even longer dating back to the research on computer-assisted collaboration in the 1970s and 1980s. Despite its growing prevalence over a number of decades, the Covid-19 pandemic truly brought virtual work to the forefront and normalized it, as safety concerns made it the only option for many knowledge workers. The resulting temporary and permanent work-from-home mandates have fundamentally transformed the workplace and changed the nature and meaning of virtual work. Although it has been defined in multiple ways, most definitions of virtual work center around work that is conducted across geographical and temporal boundaries, and which is conducted largely through information and communication technologies (ICTs) (Gibson & Gibbs, 2006; Kirkman & Mathieu, 2005; O’Leary & Cummings, 2007). A co-citation analysis of the literature found that virtual work research tends to be siloed into three main research domains: telecommuting, virtual teams, and computer-mediated work (Raghuram et al., 2019). While these areas are all forms of technologically mediated work, they refer to differing work arrangements: telecommuting or telework refers to individuals working outside of the office from home or another shared space (Gajendran & Harrison, 2007), whereas virtual teams are composed of members working in formal offices from different locations (Gilson et al., 2015). Computer-mediated work occurs more broadly in organizational settings and is not limited to teams (Makarius & Larson, 2017). When we talk about virtual work in this chapter, we are referring to all three domains. This chapter reviews prior research on the role and impacts of ICTs in virtual work. It then reviews the technological affordances perspective and argues for the need to incorporate this perspective into the virtual work scholarship. While much of the prior literature on virtual work has been dominated by implicit comparisons with face-to-face interactions that assume that technology will have detrimental effects on workplace interaction (Gibbs et al., 2008), an affordances perspective draws attention to not just the drawbacks of ICTs but the affordances or possibilities for action engendered by particular media (Treem & Leonardi, 2013). The chapter ends by highlighting contemporary trends facing virtual workers and proposing future research directions to invigorate the virtual work literature and better account for technological innovations and other changing workplace trends.

UNDERSTANDING THE ROLE OF TECHNOLOGY IN VIRTUAL WORK While technology use is heavily present in virtual work, the role of technology remains undertheorized in much of this scholarship. For instance, a review of 15 years of virtual teams research found that only about half of the studies reviewed empirically measured technology 3

4  Handbook of virtual work use (Gibbs et al., 2017). Of those studies that do measure technology use, there is a lack of consistent and comparable measures (Gilson et al., 2015). A common measure of technology use is the percentage of communication that takes place using computer-mediated communication (CMC) (e.g., Rapp et al., 2010); however, measures differ across studies both in terms of which technologies are studied and how their usage is measured. Technology is often studied under the umbrella term of “virtuality”, especially in the virtual teams literature. The term “virtual” has been used loosely to refer to phenomena as different as geographical dispersion, electronic dependence, cultural diversity, and dynamic structure (Gibson & Gibbs, 2006). Studies tend to either study the impact of a single technology in isolation, or lump diverse technologies together into vague concepts such as “electronic dependence” or “virtuality” (Gibbs et al., 2017). While there are benefits to studying such broader concepts, such terms do not account for the fact that diverse technologies (e.g., email, instant messaging, videoconferencing, social media) may be used very differently and have different consequences for virtual workers. In addition, many studies of virtual work take technology use for granted by studying virtual work arrangements, without explicitly measuring or assessing the types of technology or their usage patterns. According to a review of ten years of virtual teams scholarship (Gilson et al., 2015), most of this research finds that technology is either detrimental to, or has no effect on, virtual team performance. Gibbs et al. (2008) similarly found a comparable pattern in older scholarship on virtual teams, and termed this dominant view the “deficiency model”. A deficiency model assumes that virtuality in general, and technology use specifically, has negative effects on team performance. It is grounded in the assumption that the limited nonverbal cues in CMC that provide important sources of social information in face-to-face interactions automatically lead to negative consequences and challenges for virtual team interaction and performance, by making it more difficult to achieve understanding and form interpersonal relationships. For instance, Andres (2012) found that technology use hindered team communication by leading to lags in information exchange, more misunderstandings, and less coherent messages. This view is rooted in a conceptualization of CMC as inherently impersonal and task-oriented compared with face-to-face interactions (Walther & Burgoon, 1992). It is important to note that most studies of virtual teams focus on conventional tools such as email, chat, and discussion boards rather than new and emerging technologies (e.g., social networking tools, 3D virtual environments, cloud technologies, or ubiquitous computing platforms), such that research has not kept pace with practice (Gilson et al., 2015). Newer technologies are more interactive and immersive, and thus may be more conducive to interpersonal relationship formation. Despite the prevalence of the deficiency model in virtual teams research, other research on computer-mediated work has found that technology use has positive effects. For instance, Bryant and colleagues (2009) found that the use of certain communication technologies could decrease social loafing. CMC use has also been found to decrease status differences (Anderson et al., 2007) and increase participation equality among team members – although it makes it more difficult to reach a consensus (Hollingshead & McGrath, 1995), and that the use of multiple media can help to manage task complexity (Kock & Lynn, 2012). The more recent research on enterprise social media (ESM) documents benefits for knowledge sharing and relationship formation, including enhancing strategic self-presentation (DiMicco et al., 2009; Leonardi & Treem, 2012), providing network transparency that motivates participation (Brzozowski et al., 2009), and facilitating cross-boundary communication (Gibbs et al., 2015).

Bringing technological affordances into virtual work  5 Still other studies recognize that various technologies have nuanced positive and negative effects, depending on contextual factors that shape how they are used. For instance, an early study of a group decision support system (GDSS) proposed and found that it had both positive and negative effects on conflict management, depending on the nature of the GDSS and how the group applied it (Poole et al., 1991). Much of the computer-mediated work research at the organizational level draws on perspectives from sociomateriality or practice theory that recognize that new technologies are mutually imbricated with social contextual factors (Leonardi, 2011; Oborn et al., 2019; Orlikowski, 2007) and that their use and impacts depend on the social contexts in which they are embedded (Barley, 1986; Mazmanian, 2013). This line of research moves away from a focus on positive or negative effects of technology toward broader theorizing of the ways in which technology contributes to organizational transformation. Studies also take different approaches to the study of technology and make different implicit assumptions about the role of technology and its effects on work processes and outcomes. Gibbs et al. (2017) categorized these studies into three main types: “effects” studies that use student samples to test the effects of technology on team processes and outcomes, “process” studies that rely on organizational field studies to assess the role of technology in organizational processes, and “comparative” studies that use laboratory studies to compare the effects of face-to-face and CMC teams or compare effects of different technologies. While “process” studies are more likely to document benefits or nuanced impacts of technology, “effects” and “comparative” studies are more likely to document negative effects – perhaps because of their (implicit or explicit) comparison with face-to-face interactions. Differences in treatment of technology as a challenge or benefit are also evident across different domains of virtual work, with the telework and virtual teams literatures being more likely to document challenges, while the CAW domain is more likely to regard technology use as beneficial (Raghuram et al., 2019). In the next section, we propose the affordances perspective as a useful theoretical framework that can help to integrate the fragmented virtual work scholarship.

THE AFFORDANCES PERSPECTIVE Organizational scholars across disciplines such as communication, management, and information science are increasingly taking an affordance approach to explain the unique ways in which actors perceive and utilize technology to communicate across their organizations. The concept of affordances was originally defined by Gibson (1979), who refers to an affordance as an action possibility. Gibson argued that affordances exist in the natural environment as attributes of objects that require an interaction between actor and said object, that are independent of the actor’s perception, needs, or goals, but that are relative to the actor’s perception and capabilities. For example, an acacia tree may provide a place of refuge for a bird, but a food source for a giraffe. In this way, objects may afford different possibilities for different actors. Since Gibson’s original work in ecological psychology, scholars of human–computer interaction have adapted this framework to explain the user–technology relationship. Norman’s (1988) human-centered design perspective has proven useful to explain technological affordances. Similar to that of Gibson, this perspective implies that affordances are perceived by the actor, however, it differs in that it argues that affordances may also be shaped by the user (McGrenere & Ho, 2000). This perspective focuses on the technology’s design, how users perceive the features of the ICTs with which they engage, and treats affordances

6  Handbook of virtual work as inherent or “built-in” to the technology. In other words, this perspective implies that a particular role of a pre-existing affordance depends on whether and how the actor perceives the affordance, and thus how the actor applies it (Rice et al., 2017). In contrast, Hutchby (2001) was among the first to illustrate a way of analyzing technological shaping of sociality through examining affordances. Hutchby examines affordances through a social constructivist lens which illuminates how social processes are involved in all aspects of technology. He claims that scholars have become too fixated on the social shaping of technology, which undermines the exploration of the technological shaping of social action. His work argues for recognition of both the enabling and constraining materiality of artifacts and that our interpretations and uses of technology are constrained in analyzable ways by the ranges of affordances available through them. This position differs in that it goes beyond the affordances-as-inherent approach, such as that assumed by scholars such as Norman (1998), to contend that users may either perceive or not perceive affordances which are socially constructed and frame users’ actions. As we will demonstrate in the following sections, affordances can indeed either enable or constrain communication, can produce positive, negative, or paradoxical outcomes, and can have both intended and unintended consequences. An affordance approach to studying virtual work may help to explain why individuals using the same technology may engage in similar or disparate work practices and behaviors and uncover distinct possibilities for action in technology-reliant work settings (Treem & Leonardi, 2013). Additionally, studying virtual work through the lens of affordances can help scholars and practitioners alike to encourage the positive outcomes, mitigate the negative consequences, and help workers to reconcile the many contradictions associated with technology use for work. However, there is still much debate about the epistemological and methodological approaches to studying affordances, and so we will conclude with several recommendations for extending this type of work.

TECHNOLOGICAL AFFORDANCES IN THE WORKPLACE More recent theorizing on affordances indicates that media afford co-construction and sharing of intersubjective meaning (Suthers, 2006). The media affordances perspective has been heavily adopted in the literature on ESM to explain organizational knowledge sharing, self-presentation, and participation. For example, work done in the area of organizational media reveals that affordances pose significant implications for organizational communication processes. Treem and Leonardi (2013) argue that the dominant affordances of ESM such as organizational wikis, social networking applications, and blogs may undermine organizational socialization tactics, help to reduce uncertainty and increase information-seeking behaviors among organizational members, support interpersonal relationships among colleagues, increase knowledge sharing, reduce resource dependency, allow for more participation in discursive construction, and increase opportunities for surveillance. A wide variety of affordances have been identified in the literature. Some of these include visibility, persistence, editability, association, personalization, pervasiveness, searchability, evaluability, signaling, self-presentation, accessibility, social presence, privacy, and anonymity (DeVito et al., 2017; Fox & McEwan, 2017; Gibbs et al., 2021; Leonardi & Treem, 2020; Navick & Mazur, 2021; Rice et al., 2017). These various affordances have been found to both

Bringing technological affordances into virtual work  7 enable and constrain communication, produce both positive and negative outcomes, and have a variety of social, material, or procedural consequences. Out of all of the technological affordances that have been studied by this line of research, visibility has been given the greatest attention. Communication scholars have theorized visibility as the defining feature of contemporary CMC (Treem et al., 2020) and argued that visibility should be understood as the root affordance of CMC (Flyverbom et al., 2016). Visibility refers to the ability to make behaviors, knowledge, preferences, and network connections that are typically invisible, visible to others through ICTs (Treem & Leonardi, 2013; Leonardi & Treem, 2020). Some examples of features that afford visibility are status updates, personal profiles, commenting and opinion expression functions (e.g., like buttons), content publishing, or pushing content to subscribers/followers (DiMicco et al., 2009; Farzan et al., 2008; Holtzblatt & Tierney, 2011). Leonardi and Treem (2020) contend that digital connectivity arising from the increasing digitization, digitalization, and datafication of work is leading to greater behavioral visibility in organizations. For more on this shift, see Chapter 2 in this volume by Sivunen et al. Visibility has generally been discussed as a unidirectional affordance in which information goes from being invisible to visible, and to have generally positive outcomes. However, recent research on workplace cybersexual harassment (Navick & Mazur, 2021) exemplifies how the visibility afforded by various technology can be bidirectional and also produce negative outcomes. In their study, Navick and Mazur found that just as technology affords visibility, some technologies afford the potential for strategic invisibility through direct messaging, temporary image sharing, and other technological functions. This strategic enactment of invisibility makes incidents of cybersexual harassment among remote co-workers easier and more likely to be perpetrated as the harassment can be done out-of-sight of other organizational members. Similarly, several other studies have suggested that experienced remote workers are aware of the affordances of their technology and frequently utilize strategies and work-arounds in order to either enable or constrain communication with other organizational members (Gibbs et al., 2013; Leonardi et al., 2010). This research stream demonstrates not only user awareness of affordances, but users’ ability to manipulate virtual communication through them and the potential for both positive and negative outcomes that can arise. Paradoxes and Tensions While the technologies that enable virtual work have introduced various capabilities and affordances that allow for increased work flexibility, knowledge sharing, collaboration and other elements of productivity and convenience, they present a clear set of challenges and discrepancies as well. One way in which these discrepancies in virtual work experiences have been captured is in research examining tensions and paradoxes. Both paradoxes and tensions help scholars to explain how the use of communication technologies pulls virtual workers in different directions and represents constructs that stand in conflict with one another. In the context of virtual work, they are experienced with communication technology uses that are in opposition or self-contradictory. However, tensions and paradoxes provide flexible analytical constructs that simultaneously explain positive and negative consequences and their interrelationships. A number of paradoxes have been observed in virtual work research examining the paradox of far-but-close in perceived proximity (Wilson et al., 2008), the autonomy paradox

8  Handbook of virtual work (Mazmanian et al., 2013), the connectivity paradox (Leonardi et al., 2010), and the vitality paradox (Nordbäck et al., 2021). Affordances, in particular, have presented a unique set of tensions, paradoxes, and strategic responses. For example, Leonardi et al. (2010) investigated the connectivity paradox and found that teleworkers who cited flexibility and focus as desirable qualities of working away from the office felt too connected to the office despite having a remote work arrangement. Workers felt that rather than affording the desired flexibility, communication technologies which afford greater visibility led to far more last-minute communication from colleagues and supervisors about changes and tasks, leading workers to have no actual control over their schedules, as they were at the mercy of their visible availability. In another similar study on the management of tensions arising from the affordances of ESM, Gibbs et al. (2013) found that the excessive openness of ESM platforms created a dialectic of openness and closure for distributed knowledge workers. This created the need for them to manage tensions in their work of visibility vs. invisibility (e.g., being accessible to others while protecting their time), engagement vs. disengagement (e.g., engaging with incoming information streams without getting sucked in), and sharing vs. control (e.g., openly sharing knowledge while also maintaining confidentiality of proprietary information and protecting their own job security). As a result of these tensions, workers drew on contradictory affordances to both share knowledge and to strategically limit what others could see, to manage demands on their time and attention while maintaining face with colleagues. These findings illustrate some of the paradoxical ways in which virtual workers manage tensions arising from technology use. This is just one way in which the affordances view captures both positive and negative consequences of technology.

CHALLENGES AND DEBATES IN THE AFFORDANCES LITERATURE Despite the many benefits of studying virtual work from an affordances perspective, explicating and measuring affordances has been a challenge for many scholars working in this area. Primarily, the ways in which affordances have been conceptualized in the extant literature are highly inconsistent. Additionally, there have been a number of epistemological and methodological debates on whether or not affordances can be quantified, and if they can be, how they should be operationalized. Critics of the perspective have responded to both of these points in question in various ways. For example, in their review of the affordances literature, Evans et al. (2017) identify three inconsistencies regarding the use of the term “affordances”. First, there appears to be a lack of interdisciplinary exploration and a lack of engagement of other scholarship exploring the same affordances. Second, studies often identify lists of affordances without conceptually developing each one. Third, much research claims to adopt an affordance perspective in instances where the discussed affordance does not meet the commonly accepted definition. As a result of their findings, Evans et al. (2017) present a conceptual framework for understanding affordances using three criteria for evaluating assumptions about them: (1) confirm the proposed affordance is neither the object not a feature of the object, (2) confirm the proposed affordance is not an outcome, and (3) confirm the proposed affordance has variability. Additionally, most of the research examining affordances thus far has been predominantly interpretive and examines affordances as emergent constructs that can be captured using

Bringing technological affordances into virtual work  9 qualitative methods that capture a wide range of individual experiences and perceptions of technology users. Other researchers have attempted to quantify affordances, but have treated affordances as fixed or invariant. For example, many experimentalists operationalize affordances using dichotomous categorization such as “absent” vs. “present” and “low” vs. “high” (Schouten et al., 2007; Walther, 2009). Others have measured users’ motivations around affordances (Sundar & Limperos, 2013) and specific channels or types of channels and their affordances (Ledbetter, 2009). However, even among quantitative scholars, this approach has been critiqued, with some scholars arguing for a need to assess perceived affordances in a unified, psychometrically sound manner that goes beyond dichotomous categories, motivations, and technology itself (Fox & McEwan, 2017; Kuo et al., 2013; Rice et al., 2017). In response to these critiques, scholars such as Fox and McEwan (2017) and Rice et al. (2017) developed scales to assess perceptions of multiple social affordances across multiple communication channels and contexts. Fox and McEwan (2017) justify the need for their assessment tool in arguing that individual differences among technology users in terms of cognitive capacity, media literacy, or physical limitations could affect how a user may evaluate an affordance of their technology, and moreover, how users could fail to even recognize their presence at all. Rice et al. (2017) similarly argue that there is great value in operationalizing perceptions of organizational media affordances quantitatively in that doing so presents the possibility for identifying a consistent and broad set of affordances that transcends individual motivations and use of particular media. These epistemological and methodological debates raise questions as to how affordances should be conceptualized, how they should be operationalized, and whether affordances are inherent or perceived. However, what is clear is that across this great debate there is a consensus that affordances should continue to be studied. This also suggests that the affordances perspective offers methodological flexibility and utility for scholars from a variety of epistemological camps. The next section will outline several ways in which taking an affordances perspective can help to enrich and unite the scholarship on virtual work.

BENEFITS OF TAKING AN AFFORDANCES PERSPECTIVE FOR VIRTUAL WORK SCHOLARSHIP Bringing an affordances perspective into virtual work scholarship has the potential to enrich this area in several important ways. First, this approach allows scholars to examine the pros, cons, and more nuanced paradoxical outcomes and effects of technology use. Second, the affordances approach is highly versatile and can be examined across disciplines, paradigms, and through various methods thus helping to bridge and integrate the virtual work scholarship. Third, it provides the flexibility and adaptability required to respond to the ever-changing role of technology in the practice of virtual work and beyond. Theorizing the Role of Technology First, the affordances lens provides a fruitful framework for theorizing the role of technology and the user–technology relationship. Much of the virtual work scholarship treats technology as a backdrop and focuses on social and psychological processes and outcomes, without explicitly theorizing about the role of technology characteristics. Further, the virtual work research

10  Handbook of virtual work that exists often takes a “deficiency model” approach that assumes that CMC is inherently deficient in social cues and will negatively impact team processes and performance (Gibbs et al., 2008). This view is also known as the cues-filtered-out (CFO) perspective (Culnan & Markus, 1987). It includes a number of older CMC theories including Social Presence Theory (SPT) (Short et al., 1976) and Media Richness Theory (MRT) (Daft & Lengel, 1986). SPT argues that systems with greater social presence, in terms of bandwidth or number of nonverbal communication cues, increase the salience of others as well as the warmth and friendliness of social interactions. MRT focuses on the key construct of information richness and argues that users perform a rational process of matching up the medium with the task or message, and that there is an optimal match between the equivocality of the communication task and the richness of communication media used. These theories share the assumptions that (1) media use is determined by inherent characteristics of the medium (such as social presence or richness) and (2) that the lack of socioemotional cues in CMC make it inherently unsuitable for interpersonal communication (Walther & Parks, 2002). Other theoretical perspectives, such as the Social Influence model (Fulk, 1993; Fulk et al., 1990), focus more on social norms and pressures that drive media choice and the ways in which technologies are adopted within particular social and organizational contexts. While CFO perspectives have dominated the virtual teams and virtual work research, this perspective has been challenged by CMC scholars. For instance, Social Information Processing (SIP) Theory (Walther & Burgoon, 1992) rejects the notion that the lack of nonverbal cues makes CMC inherently impersonal or unsuitable for interpersonal relationships, arguing that communicators are motivated to form impressions and develop relationships even in the absence of individuating information online, and that they may place greater importance on the fewer cues available. Further, when given more time, CMC interactants are found to form satisfying interpersonal relationships (Walther, 1992; Walther & Parks, 2002), and they may even form hyperpersonal relationships that reach intimacy more quickly than in person (Walther, 1996). While the CMC research has moved beyond the view that CMC is inherently impersonal and task-oriented through development of SIP Theory (Walther & Burgoon, 1992) and the Hyperpersonal Perspective (Walther, 1996), these theoretical perspectives have not permeated the virtual work research and remain confined to the research on interpersonal communication. Treem and Leonardi (2013) were the first organizational scholars to theorize the affordances of social media (such as blogs, wikis, and social network sites) in organizations. They outline four key affordances: visibility, association, editability, and persistence, and theorize that they have the potential to transform organizational communication processes including socialization, information sharing, and self-presentation. Fulk and Yuan (2013) further theorize that the affordances of ESM can help to overcome organizational knowledge-sharing challenges including location of expertise, motivation to share knowledge, and building social capital with knowledge providers. Ellison et al. (2015) propose that enterprise social network sites (e.g., Yammer or Slack) are beneficial for relationship formation and knowledge sharing due to the fact that they provide greater identity information in the profile, which acts as a social lubricant to help initiate conversations with strangers across distributed organizations. An affordances lens thus shifts the paradigm from the assumption that face-to-face communication is the “gold standard” or optimal form of communication, and allows for nuanced consideration of both benefits and drawbacks of technology use, and the tensions that often arise among them. This helps to overcome technological determinism, or the view that tech-

Bringing technological affordances into virtual work  11 nology has deterministic outcomes on social behavior (Sturken & Thomas, 2004), a view that has been heavily critiqued as limiting our understanding of the ways in which digital technologies are implicated in virtual work. Bridging and Integrating the Virtual Work Scholarship Second, affordances can be studied across a range of contexts, levels of analysis, and methods. This includes studies of telework at the individual level, team-level interactions, and broader organizational contexts. As empirically demonstrated by a co-citation analysis of the virtual work literature, scholarship has been fragmented across contextual domains, being siloed into three main areas: telecommuting, virtual teams, and computer-assisted work (CAW) (Raghuram et al., 2019). This has implications for the level of analysis studied, as the telecommuting research (e.g., Bailey & Kurland, 2002; Gajendran & Harrison, 2007) tends to focus on the individual level, the virtual teams research (e.g., Gibson & Gibbs, 2006; Hinds & Mortensen, 2005) tends to focus on the team level, and the CAW domain (e.g., Walther, 1992; Daft & Lengel, 1986; DeSanctis & Poole, 1994) spans individual, group, and organizational levels. These disparate research clusters also differ in terms of the types and range of technologies studied, as well as whether they regard technological mediation as an asset or a hindrance (Raghuram et al., 2019). The virtual work literature is also fragmented in terms of conceptualizations of virtuality, methods, and team types. The term virtuality has long been contested, with various typologies being developed (Chudoba et al., 2005; Gibson & Gibbs, 2006; Kirkman & Mathieu, 2005). For instance, Kirkman and Mathieu defined team virtuality in terms of three dimensions: the extent of use of virtual tools to coordinate and perform team processes, the amount of value derived from them, and the synchronicity of team member interaction. Chudoba et al. conceptualized virtuality in terms of organizational discontinuities (changes in expected conditions) and identified three dimensions of virtuality: team distribution, workplace mobility, and variety of practices. Gibson and Gibbs defined virtuality in terms of four dimensions: geographical dispersion, electronic dependence, national diversity, and dynamic structure. Other researchers have eschewed the term “virtual” and studied similar phenomena under the label of distributed work or teams (Cramton et al., 2007; Hinds et al., 2002; O’Leary & Cummings, 2007) or computer-assisted groups (Hollingshead & McGrath, 1995). While some scholars regard geographical dispersion as the defining feature of virtuality (O’Leary & Cummings, 2007), others do not consider it a necessary condition (Kirkman & Mathieu, 2005). More recently, scholars have problematized the notion of virtuality by arguing that digital technologies have become so ubiquitous that virtual teams can no longer be distinguished as separate from non-virtual teams (Gibbs, 2017; Gilson et al., in press). The virtual teams literature is highly interdisciplinary, spanning disciplines as diverse as management, communication, information systems, computer science, engineering, and applied psychology (Gilson et al., 2015). The research has also been bifurcated methodologically into experimental laboratory studies using student samples and field studies using in situ organizational teams, without consideration of how team type and design impact research findings (Gibbs et al., 2017). Research is often compared without consideration of whether the team types and configurations are actually comparable, when the design and method used may lead to biases in research findings around topics such as leadership, cultural diversity, and technology use. For instance, Gibbs et al. found that research finding that virtual teams

12  Handbook of virtual work perform poorly compared with face-to-face teams was often based on short-term, zero history lab studies in which participants had limited time to learn a new technology, and that longitudinal studies of naturally occurring teams found that technology played a more positive role in team processes. Taking an affordances approach can help to bridge and integrate the virtual work scholarship across its traditionally siloed domains (Raghuram et al., 2019) and designs (Gibbs et al., 2017). Affordances can be studied at any level of analysis, and studies have examined them at the individual or interpersonal level (e.g., Erhardt & Gibbs, 2014), at the team level (e.g., Erhardt et al., 2016), and at the organizational level (Gibbs et al., 2013; Majchrzak et al., 2013; Treem & Leonardi, 2013). Indeed, the theoretical and empirical frameworks that have been developed to assess affordances are all applicable across a range of levels of analysis (Evans et al., 2017; Rice et al., 2017; Leonardi & Treem, 2020). The affordances approach can help resolve conflicting findings as well as make room for new forms of virtual work, such as the remote work arrangements of the Covid-19 pandemic imposed by employers’ work-from-home mandates. Remote work within contexts like Covid-19 is similar to telework in that it involves working from a home office, yet it differs in that it has been forced upon millions of workers without support in terms of space, infrastructure, equipment, or child care, rather than being a voluntary or privileged arrangement. Responding to the Ever-Changing Technological Landscape Finally, an affordances perspective is flexible and responsive to the ever-changing technological landscape in organizations. It can be hard for scholars and practitioners to keep up with the constant proliferation of new technological innovations in the workplace. Focusing on particular channels or platforms provides limited explanatory value and risks becoming irrelevant once these technologies become obsolete (Ellison & boyd, 2013). It also misses broader connections across various tools that may be used in similar ways toward similar ends. For instance, teams have moved from reliance on group-decision support systems (GDSS) (DeSanctis & Poole, 1994; Poole et al., 1991) to knowledge management systems to ESM (Slack, Google Hangouts, Yammer), to artificial intelligence tools such as chatbots and learning algorithms (Araujo, 2018; Bailey & Barley, 2020) and use each platform for similar goals of collaboration, decision-making, and knowledge sharing. Focusing on particular platforms makes it difficult to compare results across studies. By contrast, the affordances lens is enduring across specific tools and technologies. Its focus on the relationship between the goals and perceptions of users and the capabilities of the technology draws focus away from the tool or platform itself and toward its capabilities and how it allows users to attend to personal or organizational goals. Thus, it helps to provide theoretical explanations that take into account the role of technological features while also having broader relevance and being more enduring over time. In this way the affordances perspective is both responsive to particular contexts while also generalizing across contexts to provide broader understandings of the role of technology in virtual work.

Bringing technological affordances into virtual work  13

THE “NEW NORMAL” OF VIRTUAL WORK AND ITS IMPLICATIONS Current events such as the influx of an increasingly diverse workforce, increased accessibility of technological devices, and the rapid adoption of virtual work arrangements in response to the Covid-19 pandemic have all brought virtual work to the forefront. Because of this, scholars studying virtual work must account for these drastic changes in the virtual work landscape, study them more closely, and examine their social implications and consequences. We present three major issues in the virtual work literature and recommend further investigation of these and other major changes to the world of work through an affordance perspective. Diversity, Equity, Inclusion, and Social Justice (DEIJ) Issues The extant virtual work literature typically relies on fairly homogenous samples of Western, educated, industrialized, rich, and democratic (WEIRD) knowledge workers or students from well-resourced programs. This poses potential issues in practice, as such privileged samples have been critiqued for their lack of generalizability (Afifi & Cornejo, 2020; Brookshire, 2013). Unlike traditional knowledge workers, workers in lower income jobs (whether factory or service workers, gig workers, or manual laborers) are likely to face inequities in technological resources, training, and support as well as the access they have to virtual work arrangements (e.g., frontline workers). Underrepresented student populations who experience a variety of underrepresented intersectionalities and respective complications are also at a disadvantage (Navick, 2021). This became especially clear during the Covid-19 pandemic, in which forced remote, ICT-reliant work drastically exacerbated existing inequities for students from communities that have been disproportionately impacted by the pandemic (Van Dorn et al., 2020): non-traditional, first-generation, and marginalized student groups that experienced navigational challenges even prior to the pandemic (Yosso, 2005), and students from low-socioeconomic status (low-SES) who are at risk of struggling to sustain access to reliable technology (Gonzales et al., 2020). While some scholars have responded to the few organizational and virtual work studies that focus on DEIJ issues by critiquing the existing literature (Ayega & Muathe, 2018), and attempting to address the research gap by examining gender issues or socioeconomically diverse populations (Navick, 2021; Navick & Mazur, 2021), few studies have taken differences in gender, race, class, sexuality, ability, and more into account when studying affordances and virtual work. We therefore propose examining how affordances may either enable or constrain individuals from diverse backgrounds, mitigate or exacerbate inequalities among different populations of workers, or capturing the variation of affordances in perception, usage, and motivation for usage across a diverse population as excellent and much-needed research avenues for scholars interested in extending this line of work. Constant Connectivity and Well-Being Rapid developments in technology enable greater access to telework and higher flexibility in work hours, both which have implications for remote workers’ turnover intentions, work–life balance, and well-being (Kossek et al., 2006). When considering virtual workers’ well-being, the connectivity paradox is one that workers and organizations should pay particular attention

14  Handbook of virtual work to, especially now when virtual work is more commonplace than ever. Scholars have discovered that using communication technology for virtual work not only diminishes the perception of distance to others (Wilson et al., 2008), but simultaneously increases the expectations of constant connectivity for workers who are geographically distributed. This creates a paradox for workers who find the potential benefits of a telework arrangement negated by the communication technologies they use to facilitate it (Leonardi et al., 2010). Constant connectivity is defined as the expectations and opportunities to perpetually maintain contact with the office (Wajcman & Rose, 2011). This is often exemplified by expectations for immediate responses to asynchronous communication from others, afforded by technology. Technological affordances, such as visibility, lead to constant connectivity behaviors that are unhealthy or stressful for workers. We know that teleworkers in search of flexibility and focus often find themselves more connected to the office than ever, particularly because of increased communication from well-meaning colleagues who wish to be inclusive (Leonardi et al., 2010; Gibbs et al., 2013). However, this practice leads to constant connectivity behaviors, in which virtual workers are always engaging with incoming information that is not bound by time or space. When constant connectivity becomes the norm, either at the team level or more broadly at the organizational level, expectations of being continually available might become an enforced rule, either through team-level concertive control (Barker, 1993; Gibbs et al., 2021) or at the institutional level. In an environment in which virtual workers are constantly bombarded with information, expected to keep up with it all, and continuously struggle to find socially legitimate ways to protect their jobs, workers develop their own strategic responses to reconcile tensions such as engaging in dissimulation behaviors, disengaging, and selectively sharing knowledge. Studying strategic responses to issues effecting the well-being of virtual workers, such as the issue of constant connectivity, is important for both scholars and practitioners as it can help to identify best and worst practices, establish healthy organizational norms or help to raise awareness about unhealthy norms surrounding the action potential of technology, and thus improve the well-being of virtual workers. While scholarly and corporate discourse generally promotes connectivity and collaboration as unmitigated goods, they also demand an increasing amount of energy and emotional resources from virtual workers, and these “costs of connectivity” should be further examined in future research. The Effects of Covid-19 While some of the extant work surrounding technological affordances in the workplace has highlighted distributed work contexts, in particular (Ellison et al., 2015; Gibbs et al., 2013, 2015; Navick & Mazur, 2021), the affordances view has not been widely adopted by virtual work scholars. For instance, there is limited work on affordances in virtual teams, especially in the recent context of forced remote work due to the Covid-19 pandemic (Waizenegger et al., 2020). In parallel with the growing adoption of remote and virtual work arrangements, recent events such as the pandemic have complicated and challenged our views of technology, as virtual work has become the new normal. When face-to-face communication presents risks to one’s physical health, new technological affordances become apparent and applications such as Zoom and Microsoft Teams become some of the few options for workplace collaboration. Companies that have long resisted virtual work have realized how much work can actually be conducted virtually. Some have even extended their work-from-home mandates indefinitely (Hadden et al., 2020). While on one hand this has shifted our thinking away from a defi-

Bringing technological affordances into virtual work  15 ciency view of communication technology, on the other, new concerns about “Zoom fatigue” have arisen. While virtual work helps to preserve our physical health, it may be imposing new burdens on our mental health. This raises new questions about technological affordances and constraints in our current social context, and how our uncertain and changing circumstances are shaping the ways in which we incorporate technology into virtual work, as well as what the future of the workplace will look like. The new era of Covid-19 of “forced” remote work (Nordbäck et al., 2021) has ushered in new directions for research on virtual work. Some of these new research avenues include studying the adoption of virtual or hybrid team collaboration in the post-Covid-19 era and the potential changes in (social) team dynamics, usage and potential of digital collaboration platforms in the post-Covid-19 era and its impact on communication effectiveness, knowledge sharing and decision-making, and a comparison of efficiency and effectiveness of team collaboration between pre-Covid-19 and during Covid-19 through an affordances lens (Waizenegger et al., 2020).

FUTURE DIRECTIONS SUMMARY To address some of the challenges and gaps of studying affordances, we present four recommendations for applying an affordance approach to virtual work. First, it is critical that scholars studying affordances continue to apply and develop the affordances identified in previous literature (e.g., see DeVito et al., 2017; Fox & McEwan, 2017; Leonardi & Treem, 2020; Rice et al., 2017). Second, scholars should continue to analyze the paradoxes and tensions that arise in technology use due to competing pressures to connect and disconnect (Leonardi et al., 2010; Gibbs et al., 2013). Third, in order to ensure that researchers are actually studying affordances, it is vital to separate affordances from technology features, use, and usage outcomes. Lastly, we recommend applying an affordance perspective to a variety of current and relevant workplace topics such topics of DEIJ (Ayega & Muathe, 2018; Navick, 2021; Navick & Mazur, 2021), the implications of constant technology use and worker well-being (Kossek et al., 2006) and the impact of the Covid-19 pandemic (Waizenegger et al., 2020).

CONCLUSION In this chapter we have reviewed prior research on the role and impacts of technology in virtual work and have argued for the need to incorporate an affordances perspective into virtual work scholarship as an opportunity to enrich and unify this fragmented and ever evolving research domain. Given the critical role of technology in enabling virtual work, more nuanced and up-to-date theorizing about the role of technology is an important step to broadening our understanding of remote, distributed, and virtual work. We provide examples of technological affordances that have been identified in the literature thus far, examine various positive, negative and paradoxical outcomes and consequences of technological affordances, and discuss the ongoing epistemological and methodological debates on how to best conceptualize and operationalize them. Most importantly, we highlight the benefits of taking an affordance approach to virtual work research in terms of theorizing the role of technology, helping to bridge and integrate the virtual work scholarship across disciplines and paradigms, and keeping abreast of

16  Handbook of virtual work the ever-changing technological landscape. This will also help researchers extend scholarship to include new forms of virtual work in the contemporary environment, new technological innovations, and the changing significance of these technologies and work arrangements.

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2. Role of communication technologies in virtual work Anu Sivunen, Jeffrey W. Treem and Ward van Zoonen

Sophie works as a manager in a global company, where she leads a virtual team whose members are distributed across three continents and five time-zones. She typically starts her workdays at 7 am when she checks her emails on her mobile phone after waking up. She goes through the notifications of the various enterprise social media channels while drinking her morning coffee and replies to the Slack messages her team members have posted during their workday, while Sophie was still sleeping. As her kids get ready for school she scrolls through her LinkedIn and Twitter feeds and responds to updates from her network. On her way to work, she checks her calendar on her iPad. Using her company’s cloud storage services Sophie downloads the documents she needs for an upcoming meeting. Arriving at work, Sophie takes a Microsoft Teams call with some of the regional managers working from different countries. After the meeting Sophie spends some time crunching the numbers on her laptop to get a report ready for the quarterly reports. The report is due at the end of the work day (6 pm GMT, which means Sophie, located in New York only has until 2 pm). While still working on the report, Sophie ends up reading discussions from one of the company’s enterprise social media channels, and notices a document with relevant information to be included in the report. In the evening, after having a dinner with her family, Sophie receives an urgent text on her phone from the sales manager from Asia about a workshop that is starting at the same time in Shanghai. She gets the issue solved and turns her smart phone to silent mode for the night. This story illustrates the central role that communication technologies play in everyday work life. Advances in information and communication technology have shaped the possibilities for collaboration and coordination and have given rise to virtual work modes. Collaboration and coordination of work in a geographically and temporally distributed virtual team would not be similar or even possible at the present scale without advanced technological infrastructure and communication technologies and applications used for virtual work. Communication technologies is an umbrella term for various kinds of tools used in virtual work and scholars have defined them in several ways. Information and communication technologies (ICTs) refers to all technologies used to handle information and communication. As we mainly focus on the communicative aspects of these technologies in this chapter we label them communication technologies and define them as devices, applications and associated hardware and software that receive and distribute digital information among people and are connected through internal intranet or external internet and wireless networks (see the full definition of ICTs from Rice & Leonardi, 2014, p. 426). Thus, our definition excludes for example project management software (which we would label information technology) or crowdsourcing technologies, as our focus is mainly on internal organizational communication technologies. As the possibilities for communication and collaboration across distance and temporal boundaries have increased, so have the interests of scholars to study the role of technologies 21

22  Handbook of virtual work in virtual work contexts. Contemporary work in organizations, even among employees sitting beside each other or in the same building, is commonly conducted virtually in the sense that communication and interaction is mediated by the use of technologies. Oftentimes employees voluntarily choose to use communication technologies and communicate with each other outside office hours, not necessarily because of distance or time zone differences, but because it makes their work easier (Gilson et al., 2021). Because the use of communication technologies is so commonplace in organizations, we draw from literature beyond that which is strictly concerned with practices of “virtual work,” as separating virtual work research as its own stream of literature would ignore meaningful insights into how communication technologies support varied forms of working. Instead, we argue that what we know about communication technology use in various organizational contexts can also inform us about technology use in virtual (or contemporary) work. This chapter introduces three shifts in the use and study of communication technologies and shows how these shifts have implications for communication and collaboration in virtual work. These three shifts and the three implications for virtual work are presented in Figure 2.1.

Source: Authors’ own.

Figure 2.1

Three shifts in the use and study of communication technologies and their implications for virtual work

Role of communication technologies in virtual work  23

THREE SHIFTS IN THE STUDY AND USE OF COMMUNICATION TECHNOLOGIES AT WORK Research on technology use in organizational settings has tried to keep pace with technological advances and the varying ways in which users and technologies shape work practices. Specifically, we identify three major shifts in the study of virtual work and communication technology use that reflect the varying ways (1) communication technologies are valued, (2) communication technology use is bounded with space and time, and (3) communication technologies are used for closed and open collaborations. The Shift in the Value of Technology The ways in which technology dependence is seen and valued in virtual work research has been somewhat dualistic. The reliance on technologies has been seen either as a hindrance relative to face-to-face communication or as a benefit, enabling new possibilities for virtual workers (Raghuram et al., 2019). Early research on virtual work and computer-mediated teams was interested in the differences technology use created for communication processes. Although the results were not always unanimous, a number of studies identified hindrances associated with technology-mediation and the use of communication technologies by virtual workers. Studies compared computer-mediated groups to face-to-face groups, often in experimental settings (see e.g., a review by Scott, 1999) and found that computer-mediated groups took more time and were more task-related in their collaboration (Hiltz & Turoff, 1986) and performed poorer in negotiation and intellective tasks (Hollingshead et al., 1993) compared with face-to-face conditions. Other scholars have argued that because of the constraints of virtuality, virtual workers find it difficult to create strong bonds with their colleagues and supervisors (Golden, 2006), have various types of conflicts (Hinds & Bailey, 2003) and perceive losses in quality of performance and member satisfaction (Schweitzer & Duxbury, 2010). It was within the early studies of technology use that several seminal theories were created that still have a strong impact in the discussion of communication technology use and virtual work. Theories such as Media Richness Theory (Daft & Lengel, 1986), Social Presence Theory (Short et al., 1976), and Electronic Propinquity Theory (Korzenny, 1978) argue that the fewer the channels available within a medium, the more difficult it is to perform equivocal tasks, and the less attention is paid by the user to the other participants involved in communication exchange (Walther, 1995). These theories were grouped together and labeled as cues-filtered-out approach (Culnan & Markus, 1987), as these theories posit that technology-mediation reduces the social cues available in face-to-face communication. The arguments within the cues-filtered approach often have a deterministic stance towards technology use as technology mediation is seen to have a predetermined negative impact on virtual work due to the characteristics of technologies that limit expressions and modes of communication. This approach has also been labeled as an objective approach, focusing on the structural properties of virtuality (Handke et al., 2021). Along with the cues-filtered-out approaches a more adaptive perspective started to emerge in the early 1990s. Instead of deterministic thinking of technology as something that automatically filters social cues from communication, scholars started to identify and create theories that focused on users’ adaptation and learning in technology-mediated contexts. These theories, such as Social Information Processing theory (Walther, 1992), Social Identity Model

24  Handbook of virtual work of De-individuation Effects (Lea & Spears, 1992; Spears & Lea, 1994) and hyperpersonal communication perspective (Walther, 1996), presented a different view that emphasizes how users adapt to communication technologies, adapt the technology to their needs, as well as use the technologies advantageously if given enough time. Thus, this approach has sometimes been labeled as cues-left-in approach (Axtell et al., 2004), or the social construction theories (Handke et al., 2021). Theories in this approach see the dependence of technology in virtual work as a possibility or even as a benefit compared with face-to-face communication. Cues-left-in approaches focus on the availability of cues in mediated communication (Sproull & Kiesler, 1986), the ways in which computer-mediated communication conveys socio-emotional cues (Rice & Love, 1987) and how users adapt to these cues available. Theories in this approach suggest that the perceptions regarding communication technology use are more important than the objective, structural properties of the technology (Handke et al., 2021). It was also within this approach where the importance of time in mediated communication was first considered. For instance, Social Information Processing theory suggested that social cues are in fact available but may take longer to take effect in computer-mediated communication compared with face-to-face communication (Walther, 1992). Still, the focus in the cues-left-in approach is heavily on the technological features and the characteristics of the studied technologies, such as their textual or asynchronous mode and the differences these modalities make compared with face-to-face communication, even though they are studied from a subjective perspective. In recent years scholars have eschewed more deterministic views of communication technology use by virtual workers in favor of more relational views that focus on the possibilities for action that exist in a particular context as shaped by the abilities of an individual and the materiality of technology. In other words, technologies may afford workers with opportunities to take a variety of actions (i.e., make it easier to do things), or constrain certain actions (i.e., make it harder to do things), but workers retain the agency as to whether to engage in specific actions. Examining communication technologies from an affordance perspective highlights the ways in which technologies can support or constrain forms of communication that make virtual work possible. When technology mediation in virtual work is seen as an affordance, the focus is not on the particularities of different communication technologies and their features but on the outcomes users may perceive when interacting with those features (Treem & Leonardi, 2013). Affordances are possibilities (or constraints) of action which are constituted in the relationship between users and the materiality of technologies with which they come to contact (Treem & Leonardi, 2013; Evans et al., 2017). According to such relational view, affordances of communication technology can change across different contexts even though the technology’s materiality does not (Treem & Leonardi, 2013; see also Gibbs & Navick, Chapter 1 in this volume). In virtual work research, various scholars have built on the affordances laid out by Treem and Leonardi (2013) who first proposed visibility, persistence, editability and association as some of the key affordances of organizational communication technologies. Gibbs and co-authors (2013) highlighted the role of visibility in the collaboration of a virtual team and showed that the use of technologies in virtual work affords not only visibility but also invisibility to virtual team members. Majchrzak and co-authors (2013) illustrated how the use of social media affords online knowledge sharing to shift to communal knowledge conversations through various affordances, such as persistence and association. Van Zoonen and colleagues

Role of communication technologies in virtual work  25 (2021) showed how communication persistence played a role in both virtual and co-located teams’ collaboration as it mitigated the impact communication technology use had on supplemental work. Even though affordance perspective is not opposing the challenges related to communication technology use found in cues-filtered-out approaches, it shifts the thinking from technological limitations to possibilities and benefits of technology mediation in virtual work (Raghuram et al., 2019). This way affordance perspective – along with technological advancements – has paved the way for identifying the role of certain affordances, such as communication visibility (Treem et al., 2020), in virtual work and the importance of visibility in knowledge work of all types. Thus, the first shift in the role of communication technologies in virtual work is related to how communication technologies are perceived. When communication technologies are seen as a determining factor in virtual work and the emphasis is primarily on the features of technology, researchers (and practitioners) risk downplaying the agency of workers. Looking at communication technology through the lens of affordances enables virtual workers to perceive and use communication technologies in distinct and often unexpected ways, while also recognizing that the material features of technology remain common to each person who encounters them (Treem & Leonardi, 2013). This perspective also helps explain why despite the increasing flexibility of communication technologies to support varied behaviors and activities, workers often use technologies in similar, patterned ways based on shared goals and roles. We propose that adopting an affordance framework to the use of communication technologies enables virtual workers to be better aware of the multiple outcomes of communication technology use that can also change across contexts. In the last section of the chapter we will highlight these implications of the affordance framework for virtual work, specifically the implications of the “root affordance” (Flyverbom et al., 2016), visibility. The Spatiotemporal Shift in the Use of Technologies Research on virtual work and communication technology use has also shifted with regards to how technologies are used in space and time. As communication technologies have advanced, so has their use and the meaning of virtual work. Along with the technology mediation, temporal and spatial boundaries have been traditionally seen as core aspects of virtual work (Raghuram et al., 2019). Asynchronous communication through different technologies has been seen as an endogenous feature in virtual teamwork as global virtual workers need to collaborate across time zones. In contrast, in traditional team research it has often been taken for granted that communication between team members is mainly synchronous, face-to-face and co-located. The focus on asynchronous technologies (such as email) has shaped the way communication technologies have been studied in virtual work. Studies that focused on asynchronous technologies found that there are differences in virtual team members’ speed of access to information and misinterpretations occur easily as it can be harder to exchange contextual information through asynchronous and text-based communication technologies (Cramton, 2001). The use of asynchronous communication technologies in virtual work and the emphasis on them in virtual work research meant also that issues related to temporal coordination, such as smooth information flow, timely feedback and interruptions were often identified as prob-

26  Handbook of virtual work lematic (Montoya-Weiss et al., 2001) and the importance of synchronous online or occasional face-to-face meetings were highlighted for virtual workers (Hinds & Cramton, 2014). Similarly, communication technologies used for virtual work were more location-dependent in the early days of virtual work research. Studies focused on telepresence and video conferencing rooms that were used for virtual team meetings at the office in each team members’ location (Standaert et al., 2013). In order to participate in global virtual team meetings, members had to extend their work days (Saunders et al., 2004) and conduct after-hours work at the office to be able to use video conferencing tools and other communication technologies and databases that operated behind organizations’ firewalls. It was only after advancement in communication technologies, such as the widespread use of mobile technologies, cloud computing and virtual private networks as well as the development of new types of open organizational communication systems when virtual workers (or any knowledge workers) were technically able to work from anywhere and at any time. Scholars have suggested that managers of virtual workers should utilize the possibilities provided by these new technologies as after-hours communication is no longer limited to asynchronous, text-based communication but can be easily done also synchronously through mobile video applications (such as Zoom, Skype or FaceTime) (Boswell et al., 2016). This spatiotemporal shift in research that focused on technology use as bounded to a certain time of the day and place of work (e.g., communication technologies used at the office behind firewalls) to technologies that were used flexibly anytime and anywhere (e.g., use of smart phones to conduct work on the go) has had an impact on the way technology use in virtual work research is treated and studied. Similarly, to cues-filtered-out and cues-left-in theories that emphasize the features of communication technologies, some of the concepts presented in early studies of the spatiotemporal shift of communication technologies are no longer as relevant as technological infrastructure has developed. Early research on communication technology use outside of work hours and the workplace focused on teleworkers, but the ability (and requests) to be available beyond the temporal and spatial constraints of the workplace through technology have become more common to employees of all types (Boswell et al., 2016.) This has also blurred the definition and meaning of virtual work. The multivalent entanglement of communication technologies in contemporary workplaces and practices have transformed nearly all (knowledge) workers to become virtual workers (at least to some extent). It may no longer be only a challenge for (global) virtual workers to build trust, team identity and social connections through communication technologies, as this may apply equally well to most “traditional” knowledge workers. Ultimately, the challenges related to communication technology use and crossing temporal and spatial boundaries may be even more prevalent for “traditional knowledge workers” compared with highly virtual workers, as highly virtual workers are better prepared for and adjusted to these spatiotemporal discontinuities in their work (Sivunen et al., 2016). Thus, working outside office hours, away from colleagues is no longer an exclusive characteristic of virtual or telework, but occurs in many types of knowledge-intensive professions. In fact, during the coronavirus pandemic (COVID-19), virtual work (defined as work conducted by crossing temporal and spatial boundaries with the help of communication technologies), became the modus operandi in most organizations. The number of office workers who transitioned to working from home at unusual hours because of homeschooling their children or managing other family issues during the day mushroomed overnight in March 2020. The spatiotemporal shift that was started by the advancement of communication technologies became

Role of communication technologies in virtual work  27 even more pronounced with new organizational policies and job designs regarding hybrid and virtual work. For example, some companies, such as Microsoft and other corporations, made an announcement that they may allow employees to work permanently remotely (Page, 2020). Even though organizational policies can change quickly and new trends regarding virtual and remote work may come and go, the question remains: how should virtual work be defined in the age of advanced technologies that blur spatiotemporal boundaries – and what are the implications of these blurred spatiotemporal boundaries to (virtual) workers? Thus, the spatiotemporal shift in the use of communication technologies highlights where and when communication technologies in virtual work can (or should) be used. The advancement of technologies alongside flexible and remote work policies have created new possibilities (and constraints) for virtual working in terms of worktime and workplace. We argue that virtual workers should be aware of their spatiotemporal work practices and when and where they are connected to their work. In the final section of the chapter, we will highlight the implications of the flexible spatiotemporality in virtual work, such as constant connectivity, and what this means for virtual workers. The Shift from Closed Technologies to Open Technologies It is only within the past few decades that individual workers have had personal and widespread access to communication technologies. Previously, the tremendous costs associated with computing infrastructure, software, and storage meant that workers relied upon organizations to provide most of the technological resources that supported communication. Organizations routinely spend millions of dollars on the adoption and implementation of robust, organization-wide initiatives such as enterprise resource planning tools, knowledge management systems, or intranets. Given the tremendous investment in securing or developing these technologies, it is not a surprise that organizations would exercise a large amount of control over how these technologies are used. We use the term closed technologies to refer to the use of technologies that have static features, limited use, and restricted information access. Such technologies include, for example, email and phone calls. Alternatively, we view open technologies as those with evolving functionalities, broad potential use, and visible information access. Examples of such technologies are collaboration tools enabling file sharing and editing across users, and various enterprise social media platforms. The shift from closed to open technologies is characterized by three changes in organizational communication technologies: (a) the growth of communication technologies that operate as organizational public goods, (b) the widespread availability of inexpensive collaborative tools, and (c) the increased use of communication technologies that make information visible to organizational members. Growth of communication technologies as organizational public goods When work is collocated, the spatiotemporal needs for communication are more narrow, as interactions are more likely to take place – or at least be possible – through direct exchanges between parties. Individuals can meet in a conference room, walk over to someone’s desk, pass someone a file folder, or otherwise engage in discrete forms of in-person communication. These forms of communication can be viewed as largely closed in the sense that the communication is only accessible to the parties directly involved in the communication at the time, and in the place, that is originally expressed. It is not surprising then that early communication technologies in organizational settings – phones, email, databases, online repositories – largely

28  Handbook of virtual work facilitated work practices involving discrete participants and restricted access. Though these technologies allow individuals to communicate across times and distances, they were not originally intended to support changes in the modalities, configurations, or sites of work itself. However, over time individuals and organizations realized that the flexibility of communication technologies could support new, more fluid and disparate forms of organizing and as a result collocation, or even a main physical site was no longer required for many organizations to function effectively. In particular, the growth of the internet and its ability to support ubiquitous access to software, information, and other individuals meant workers had equal access to needed resources even when working virtually. For example, Waizenegger et al. (2020), suggested that technological affordances enable equal opportunities of communication regardless of physical proximity. However, in virtual work contexts these same affordances may make it easier for workers to hide information and engage in strategic disclosure behavior, making the virtual environment less inclusive (Gibbs et al., 2013). Still, one way to conceptualize the ways that communication technologies support work in general, and virtual work specifically, is to understand communication technologies as a form of public good that potentially benefits all organizational members (Fulk et al., 1996). As a public good, communication technologies are non-excludable, meaning they are available to all workers, and are non-rivalrous, which means the use of them by any individual does not diminish their accessibility or availability to other parties (Samuelson, 1954). The organization supplies the material and physical infrastructure to support the goods (i.e., the organization pays for the internet connections, software licensing, and data storage), and all workers – virtual or collocated – have the ability to take advantage of the benefits. Communication technologies within organizations can operate as two different types of public goods: connective or communal (Fulk et al., 1996). Connective public goods are those that support direct interaction among users, for example a phone or email system. The main limitation of connective public goods is that although they provide the potential for any user to connect with any other user, actual communication is dependent upon use of the system by both parties (i.e., the person needs to pick up the phone when you call, or respond to an email). Communal public goods provide a shared pool of information, data, or content that all organizational members can access, for example a file repository or discussion board. The central challenge presented with the use of communal public goods is that the value of any system is directly dependent upon the willingness of users to contribute information so that it is available for the benefit of others. The success of communication technologies as public goods depends on the organization generating a critical mass of users such that the good retains value to users over time (Markus, 1987). Connective goods require a critical mass of users to provide value (i.e., enough people need to use the same chat system so that they can reach the people they need information from), and communal goods require a critical mass of content to provide value. Increasingly organizational communication technologies offer features that are connective (i.e., chat, direct messaging), and communal (i.e., asynchronous posting, file sharing and storage), making them multifunctional public goods. The greater openness associated with communication technologies as public goods also means that the activity on these systems is more visible to other organizational members (Treem & Leonardi, 2013). Workers may be hesitant to use a communication technology until it is clear to them that there is some personal benefit (Kalman et al., 2002). A recent study adopting a public goods model to study the use of an online knowledge sharing platform demonstrated that collectors and contributors of this platform aimed to reduce individual and

Role of communication technologies in virtual work  29 collective costs while trying to obtain communal and connectivity benefits (Rice et al., 2019). When workers do communicate through public goods, that communication may be influenced by performative pressures associated with knowledge that interactions may be available to other coworkers. Additionally, workers, particularly those in more competitive environments, may be hesitant to share knowledge communally and potentially give up an individual advantage they might have over others (Leonardi & Treem, 2012). Other challenges are associated with the maintenance of organizational public goods, which over time can become disorganized and filled with redundant or outdated information. Over time, activity on organizational communication systems can become unwieldy and specific information may be difficult to locate. Even though the shift from closed to open communication technologies has created a more open communication environment in terms of accessibility and responsibility, the lack of distinct ownership related to the maintenance, management, and governance of these organizational public goods presents obstacles for organizations that seek to scale and sustain these technologies. Widespread availability of inexpensive collaborative tools The growth of cloud computing and the ubiquity of digital connectivity (i.e., near constant internet access) has led to the dominance of a software as a service (SAAS) model in which individual users increasingly interact directly with applications online as opposed to through a locally hosted network or computer operating systems. One consequence of this is that access to collaborative tools has increased substantially, facilitated both by lower costs associated with use and dramatic growth in applications available to individuals. Whereas the enterprise communication software space was formerly dominated by a couple global corporations (i.e., Microsoft, IBM), those companies now compete with a wider set of offerings from other large technology companies (i.e., Google, Salesforce) as smaller startups (i.e., Slack, Atlassian), and even social media organizations (i.e., Facebook). Your average worker today likely has access to multiple applications – on either organizational or personal devices – to chat, send messages, upload/download files, host meetings, call, access shared repositories, or search organizational directories. The challenge for contemporary workers is less about having access to communication technologies or the associated information they provide, and more about sorting through the different options available and the volume and breadth of possible information. The shift from closed to open communication technologies has introduced the growth of flexible, easily accessible communication technologies available to workers, and was described by McAfee (2006) as “Enterprise 2.0.” He described technologies representative of this shift as comprised of six components creating the acronym SLATES: search, links, authoring, tags, extensions, and signaling. Collectively and individually, these characteristics facilitated a more dynamic organizational communication environment, making it easier for workers to participate actively, increasing the potential visibility of activity, and creating greater connectivity among individuals and content. In particular, the ability of workers to author, or easily produce and distribute, communication throughout the organization represented a dramatic shift in the nature of communication for workers, and virtual workers specifically, because it decentralized communication opportunities. Moreover, adoption and implementation of these new communication technologies was often emergent, meaning that it was the result of an individual worker or team introducing a new tool as opposed to the organization formerly acquiring or purchasing the technology, or management prescribing use. Workers were, and

30  Handbook of virtual work are, able to play a more direct and central role in choosing among available communication technologies to support virtual work. The class of Enterprise 2.0 technologies that has received the most scholarly and organizational attention are enterprise social media, a label encompassing a wide array of Web-based platforms that allow workers to (1) communicate messages with specific coworkers or broadcast messages to everyone in the organization; (2) explicitly indicate or implicitly reveal particular coworkers as communication partners; (3) post, edit, and sort text and files linked to themselves or others; and (4) view the messages, connections, text, and files communicated, posted, edited and sorted by anyone else in the organization at any time of their choosing. (Leonardi et al., 2013, p. 2)

Increasingly, as the features of enterprise social media platforms expand and various applications become interoperable, these technologies can operate as a broader infrastructure for virtual work. For example, robust collaborative platforms such as Google Workspace or Microsoft 365 provide integrated access to social features such as worker profiles, discussion groups, file sharing (with commenting), productivity tools (word processing, data management), video conferencing, and chat. Increasingly organizations are utilizing platforms such as these as the central forms of organizational communication such that all workers, whether they be virtual or at a physical organizational site, are using the same technologies for daily tasks. This widespread accessibility of collaborative tools has created a much more open environment of technology use and broadened conceptions of what might be considered an organizational communication technology. For instance, individuals will commonly communicate with coworkers on publicly available social media platforms (i.e., Facebook, Twitter, LinkedIn), use personal messaging applications to coordinate work with team members (i.e., Google Chat, Skype, WeChat, iMessage), or share and store files on individually owned accounts (i.e., Google Drive, Dropbox) (e.g., van Zoonen et al., 2016a, 2021). Many organizations are now adopting or considering Bring Your Own Device (BYOD) policies that not only permit, but encourage individuals to use personal devices such as phones and computers to conduct work (Stephens, 2018). This new open environment of work communication presents challenges for individuals who must navigate the often permeable boundaries between work and non-work communication (van Zoonen et al., 2016b). Individuals who have both a mix of different types of connections on public social media face pressure to navigate the mix of different contexts and communicative demands (Costa, 2018). Additionally, when individuals use similar communication technologies for work and non-work tasks it can be difficult to resist pressures to continue to respond to work-related communication beyond traditional work hours (Mazmanian, 2013). The opportunity for workers to play a greater role in choosing work communication technologies, and the choice of workers to blend work and non-work communication on platforms, presents new demands for workers in terms of managing communication in ways that meet individual and organizational goals. Increased information visibility to organizational members There is another way in which communication technologies have ushered in more open communication among workers, and that is by decentralizing and democratizing opportunities for the production and distribution of communication within organizations. Traditionally, organizational communication efforts were restricted to either task-based, team-focused interactions, or communication provided by organizational leadership or corporate communication staff.

Role of communication technologies in virtual work  31 The increase in enterprise social media in organizations often means that any organizational member has the ability to broadcast communication in a manner that is potentially visible to the entire organization. This means that for example, communication originally sent to a discussion thread of three people on enterprise social media may become visible to a lot larger audience across organizational units and sites and the sender cannot be sure of the audience of her message in advance (Laitinen & Sivunen, 2021). These communication technologies are often implemented expressly to facilitate this type of openness operating as a way to surface the thoughts, concerns, and general activity of an organization (Brzozowski, 2009). Research on the use of communication technologies by workers demonstrates that they are well aware of the ways their communication may be visible to others in the organization (Treem, 2015). Moreover, workers know that much of the communication that takes place on open technologies such as enterprise social media is visible over time, available to third-parties who may not have been involved in the original communication, and accessible to others whom the communication was not intended to reach. The accessibility of communication technologies to third-parties increases the volume, breadth, and diversity of workers that may have access to different forms of organizational communication. Leonardi (2017) uses the metaphor of “leaky pipes” to describe the potential effect this change in communication has on the flow of information and knowledge within organizations. A potentially valuable consequence of the openness of technologies is that it can support better coordination of work among organizational members. For instance, when workers are able to view the communication of others it can improve knowledge sharing and reduce the need for individuals to duplicate previous efforts (Leonardi, 2014). Specifically, the visibility of information associated with open communication technologies can aid in workers’ development of metaknowledge regarding both who knows what in the organization as well as who knows who (Leonardi, 2015). Moreover, open communication technologies are commonly used to indicate the work status and availability of workers, allowing for greater coordination of communication and ideally resulting in fewer missed messages or difficulties in scheduling. Overall, the knowledge gained through the use of communication technologies that make information about available resources, knowledge, or time can be used to improve coordination among workers who would otherwise have little insight into organizational members’ expertise, relationships, or work tasks. Overall, the shift from closed to open communication technologies has changed the ways virtual workers can connect with and see the content shared by their colleagues as well as share content themselves that can be seen by wider organizational audiences than through closed technologies. Along with the growth of communication technologies as organizational goods as well as with the widespread availability of inexpensive collaborative tools, the shift from closed to open communication technologies has brought several implications for virtual workers. In the last section of the chapter, we will discuss these implications regarding the use of open technologies in virtual work.

32  Handbook of virtual work

IMPLICATIONS TO VIRTUAL WORKERS’ COMMUNICATION BY THE THREE SHIFTS IN THE USE AND STUDY OF COMMUNICATION TECHNOLOGIES Visibility in Virtual Work The first implication that the shift in the value of communication technology has brought to virtual workers is the opportunity to see the use of communication technologies through the lens of the various possibilities for action (i.e., affordances) they present. An affordance framework recognizes virtual workers as active agents who are able to use communication technologies in unique ways, beyond the features of technology common to each person who encounters them. We will next highlight the implications of one specific affordance for virtual work, visibility, which has also been labeled as the “root affordance” of communication technologies (Flyverbom et al., 2016). In the context of virtual work, the primary role of technology has traditionally been viewed as a means to retain connection and communication with the organization and its members. The goal has been not merely to enable individuals to conduct work, but also provide the ability for workers to maintain ongoing contact with organizational members, resources, and clients or customers. These concerns mean that the visibility of virtual workers, understood as how easily their actions are seen by others, how accessible they are, and the nature of their communication with other organizational members, are an ongoing negotiation. Increases in virtual work mean that greater attention needs to be paid to the ways in which organizations and individuals engage in processes of visibility management (Flyverbom, 2019). Understanding visibility management requires recognizing workers’ visibility as a multidimensional concept. Communication visibility can be defined as “the outcomes of activities through which actors strategically or inadvertently: (a) make their communication more or less available, salient, or noticeable to others, and (b) view, access, or become exposed to the communication of others, as they (c) interact with a particular sociomaterial context” (Treem et al., 2020, p. 46). Put differently, a complete understanding of how workers’ communication is visible requires knowledge on who is communicating, who is observing, and under what conditions is the communication taking place. These dimensions can be viewed as interdependent levels, in which increased possibilities in any one dimension may complement or impede the overall consequences for visibility. For instance, in an environment where a worker wants to communicate knowledge to others in order to be seen as an expert in a particular domain, they might make a number of efforts to communicate this to coworkers by mentioning it in meetings, listing it in an online profile on an intranet, or actively answering questions about the topic. However, if no coworkers are listening in the meetings, or looking for experts in this area, that communication might never be noticed, rendering that worker’s expertise functionally invisible. Or, if the organization has no intranet, or no online interest groups, or few meetings, it will be difficult for that worker to communicate their expertise to others in visible ways. Communication visibility operates as the confluence of workers’ willingness to make communication visible, and their ability to do so in given organizational conditions. The implications of communication visibility to virtual work are manifold. When workers in a virtual team, project group or organization are able to make their communication more available or noticeable to others through communication technologies, this can mitigate some of the common challenges identified in virtual work research, such as the problem of sharing

Role of communication technologies in virtual work  33 contextual knowledge across various sites (Cramton, 2001) or signaling one’s commitment from subsidiary locations (Cristea & Leonardi, 2019). On the contrary, virtual workers who may want to opt out from being available and visible to others (e.g., Gibbs et al., 2013) may find it more difficult as advanced organizational communication technologies provide more visibility and digital traces of workers’ behaviors (Leonardi & Treem, 2020). Virtual workers may also view, access or become exposed to the communication of other people, teams and projects from the other side of the globe. The ability to observe other’s communication in virtual work can create an onlooker effect (Sergeeva et al., 2017) enabling virtual workers to make inferences of their coworkers across spatial and temporal distances. As digital communication becomes the dominant form of organizational communication in many contexts, attempts to avoid communication visibility may be not only impossible, but also undesirable as invisibility may raise suspicions or limit work opportunities. The COVID-19 pandemic has further exacerbated our reliance on digital communication and will likely continue to do so for some time to come (Leonardi, 2020). The visible traces of communication and behavior left by (virtual) workers can be routinely and effortlessly captured and analyzed to generate fine grained insights into the work process (Muzio & Doh, 2021). As the work environment shifts to offering greater opportunities and challenges associated with communication visibility, workers are likely to develop competencies associated with visibility management. These communicative skills, knowledge, and abilities relate to a better understanding of how communication is materially processed and represented to others. Increasingly, the communication workers produce and share is evaluated through algorithms designed to assess effectiveness or optimize work practices. In turn, algorithms also play a role in determining the communication that workers see on platforms such as social media (both organizational and public-facing). One large unknown is whether or how generations of workers who have always had near ubiquitous access to digital communication technologies for personal use will approach the use of technologies in the workplace differently than those who previously used analog communication or had limited individual access to technologies. It is likely that workers will differ in meaningful ways regarding the skills, abilities, and competencies they are able to deploy in using digital technologies in work settings. Ongoing research is needed to investigate the different ways that workers seek to manage communication visibility within contexts of organizing and how strategies (purposeful or accidental) might contribute to differing organizational and individual outcomes. Constant Connectivity in Virtual Work The spatiotemporal shift in the use of communication technologies has also created implications for virtual work. Workers have to respond to new expectations on where and when they work and are available to their colleagues and supervisors. The spatial and temporal flexibility and autonomy that is often intertwined with the idea of virtual work (Raghuram et al., 2001) can also lead to constant connectivity and blurring of work–life boundaries (Mazmanian et al., 2013). As crossing of spatiotemporal boundaries enabled by communication technologies has become common in all workplaces, it is even more important to look at the consequences this shift in the use of technologies can have for workers of all kinds. Finally, connectivity around the clock can still be more intensified in global virtual work where the closest collaborators often reside in different time-zones and the expectations to be available all the time can be more extensive (Lirio, 2017; Nurmi & Hinds, 2020). Thus, the current work ecology, suffused

34  Handbook of virtual work with communication technologies, does not only create opportunities but also pressures for connectivity across spatiotemporal boundaries. Connectivity generally refers to the mechanisms, processes, systems, and relationships that link individuals and collectives (e.g., teams, organizations) by facilitating informational, social, and material exchanges (Kolb et al., 2012). The proliferation of communication technologies in organizational settings has led many scholars to suggest that the associated connectivity is an inherent feature of contemporary work (Wajcman & Rose, 2011; Büchler et al., 2020). Communication technologies typically support connectivity at work, but also extend this connectivity to after-hours (e.g., Boswell et al., 2016; Richardson & Benbunan-Fich, 2011). Although workers have some agency in moving between different states of connectivity, punctuating periods of high connectivity (hyperconnectivity) with periods of low connectivity (hypoconnectivity) (Kolb et al., 2012), studies have suggested that switching off completely may become increasing difficult, thereby creating a “always-on” environment (Cavazotte et al., 2014), leading to constant connectivity (Wajcman & Rose, 2011) or perpetual connectivity (Leonardi et al., 2010). Overall, even though connectivity is not inherently good or bad, research has repeatedly demonstrated how connectivity may be instrumental and detrimental to work performance, collaboration, and individual well-being. For instance, connectivity through communication technologies has been found to generate work interruptions, but also increase task accomplishment (Sonnentag et al., 2018). Ten Brummelhuis and co-authors (2021) provide a deeper understanding of the implication of connectivity by demonstrating that availability is positively related to work performance, through communication effectiveness, but interruptions may reduce work performance. In turn, Nurmi and Hinds (2020) demonstrate that various connectivity behaviors lead to higher job satisfaction, lower work–life conflict and turnover intention, through improvement of interpersonal relationships. Finally, some studies reported positive outcomes of connectivity (i.e., accessibility) for employee engagement (ter Hoeven et al., 2016), while others indicated that constant connectivity may prevent psychological detachment leading to detrimental impacts on employee well-being (Büchler et al., 2020). Regardless of the implications, global virtual work is likely to involve high levels of connectivity. This is in part due to the need, or pressure, to perform after-hours work across geographical locations and time zones (Lirio, 2017; Nurmi & Hinds, 2020) and in part because the technologies used to facilitate virtual work inherently allow employees to be always on. Consequentially, the use of communication technologies has been linked with increases in work–life conflict as the boundaries between work and life have become more porous (e.g., Boswell & Olson-Buchanan, 2007; van Zoonen et al., 2020; Wright et al., 2014). Research on the negative implications of constant connectivity often grounds these assumptions on theories of recovery, suggesting that extended periods of connectivity may prohibit the replenishment of resources and reversal of demands workers are exposed to during the workday. Use of Open Technologies in Virtual Work Finally, the shift from closed to open communication technologies also generates various implications for virtual workers. The use of open technologies can increase visibility and accessibility of communication to third parties, and has a variety of consequences for communication among virtual workers. On one hand, workers have greater opportunities to learn more about the activities and knowledge of coworkers. Research demonstrates that the use of

Role of communication technologies in virtual work  35 more open communication technologies such as enterprise social media increases multiple forms of meta-knowledge. Specifically, the use of enterprise social media has been demonstrated to improve message transparency, who knows what, and network translucence, or who knows who in the organization (Leonardi, 2015). On the other hand, the ability of third-parties to view communication can also serve as a form of surveillance and increase the anxiety workers face when communicating. As a result, workers may restrict communication to that which is the least threatening, or most beneficial. Specifically in virtual work where face-to-face contacts are limited, it may be that technology-mediated communication plays a more pronounced role and workers may be more careful with regards to messages they write and digital traces they leave behind. As mediated communication is the only way in virtual work to signal about one’s engagement (Cristea & Leonardi, 2019), mediated messages as well as other behavior, such as likes and clicks, may be more strategic than in face-to-face communication. For example, a study of liking and following behaviors on enterprise social media found that workers were most likely to follow and like material from managers and influential workers (Mark et al., 2014). These results call into question whether the openness associated with communication technologies empowers virtual workers, or whether it reinforces existing status differentials. There is no doubt that the shift to more open technologies – in terms of opportunities for use and participation, choice among features and platforms, and the ability to manage visibility – has enabled growth in virtual work. However, what is not clear is who benefits from this openness in communication, and under what conditions. Additionally, studies of how workers view the openness of communication via technologies, and how these technologies are used, raise questions about the extent to which the possibilities of openness are realized or resisted.

CONCLUSIONS Both the communication technologies that are used for virtual work and the ways in which communication technology use is studied in virtual work have changed during the last decades. This chapter has highlighted these changes by presenting three shifts in the use and study of communication technologies over time. The shift in the value of communication technology and technology mediation, the spatiotemporal shift in the use of communication technologies, and the shift from closed to open communication technologies result in various implications communication technology use has for virtual workers. Communication visibility, constant connectivity and openness of organizational communication technologies with unknown audiences are just a few examples of these implications that virtual workers are facing. One thing to consider is also that overreliance on communication technologies may come at the expense of social affordances – that is, the possibilities for actions that people may provide each other within an environment. Some employees may feel more alienated by the physical separation and face-to-face contacts in virtual work contexts despite the digital communication opportunities. Thus, it is important to pay attention to the potential downsides of excessive use of communication technologies, and whether there are differences in variables like the inclusiveness of our face-to-face and virtual connections. Finally, the proliferation of communication technologies in all types of knowledge work as well as various societal and economical changes and disruptions, such as the COVID-19 pandemic, have made the transition to virtual work modes even faster than it was perhaps

36  Handbook of virtual work previously predicted. Organizations in various industries and sectors across the globe became virtual overnight and this transition to technology-mediated remote work has shaped the ways virtual work is seen, valued and experienced by employees in different positions also in the future. Therefore, it is perhaps time to consider what actually differentiates virtual work from other types of knowledge work, and whether the concept of virtual work no longer has utility to the advancements in the use of technology as well as the crossing of spatiotemporal boundaries in many kinds of professions. In many respects, all contemporary work that is conducted digitally is virtual. Whatever the verdict, the study of the role and use of communication technologies in all kinds of (knowledge) work remains an important topic to be studied for years to come.

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3. Virtual collaboration: human foundations augmented by intelligent technology Terri L. Griffith and Utpal Mangla

INTRODUCTION Speaking about the maintenance of offshore wind farms, Sara Bernardini, Professor of Artificial Intelligence at Royal Holloway, University of London, notes: Space provides a good example of humans working with robots. The current Mars exploration programme uses a team of robots, from helicopters to rovers, that can withstand extreme conditions. Astronauts are deployed selectively, where human ingenuity is most needed and risk to life is lowest. Likewise, future offshore work will be about humans being in the control room, developing and managing robotics and learning the skills required to work in teams with them. (University of Bristol, 2021)

Social science is playing catch-up to engineering reality. The above example is drawn from an announcement of a system where a robotic boat deploys a robotic assessment drone, that, if needed, deploys a crawler repair robot, all while human collaborators are safely onshore. Intelligent technologies take on roles at every step and form of collaboration. However, our review suggests that we are in only the early stages of social science contributing to these collaborative activities. Whether collaborations of humans and intelligent technologies or collaborations of humans supported by intelligent technologies, we need to use the reality and possibility of intelligent technology to extend our thinking and approach to research. This is a time of great variation as scholars across disciplines work to understand what frameworks we can continue using and areas where we need to begin anew. We borrow the term intelligent technology from Bailey and Barley (2020). This allows us to draw a broad circle around artificial intelligence (AI) and the suite of associated technologies (e.g., machine learning, big data, robotics, smart sensors, the Internet of Things, and analytics). We also use Bailey and Barley’s (2020) stand as a backdrop on how we should approach research on intelligent technologies. They call for a unified approach that includes interdisciplinary studies covering the roles of power, ideology, design, use and institutional change along a technological trajectory. They say, “Our notion is that these studies would build off each other to cover the range of relevant actors, events, practices, and dynamics involved in a technological change over time” (p. 2). We cannot do a deep dive from power to institutional change in this one chapter, but we do leverage our interdisciplinary, academic–industry author team to highlight current practice and future possibilities with the nuance possible from the combination of academic breadth and real-world experience. The result is a broader perspective on virtual collaboration than that presented in much of the literature (cf., Wageman et al, 2012). We go beyond collaborations focused on knowledge development (Faraj et al., 2011) and decision making (Baltes et al., 2002) to include physical interactions (e.g., robot wrangling, Edge et al., 2020) and collaborations with less interdepend41

42  Handbook of virtual work ent goals (Wageman, 2001). We expand the feature-based perspective on technology understanding (Griffith, 1999) to include human and organizational dimensions and the dynamics possible with machine learning. We include examples from industry applications of intelligent technologies to virtual collaboration. These particular applications are developed through facilitated processes. We posit that the complexity noted by Bailey and Barley (2020) calls for this interdisciplinary facilitated design. Facilitated designs offer both top-down and bottom-up expertise in that the facilitators bring process and technical expertise while the users and developers bring a deep understanding of the context. We need both types of expertise for the most effective virtual collaboration. This chapter serves as a research toolkit for an audience of scholars from across social science, engineering, computer science, and affiliated fields. We describe it as a toolkit given the chapter spans foundational concepts, the rich background of prior social science-related research, scaffolds for theorizing, summaries of reviews and perspectives related to intelligent technology and virtual collaboration, and offers a snapshot of some of the earliest empirical work on more and less virtual collaboration in settings with intelligent technology. We designed the chapter to provide a starting point for the additional research we need in this fast-paced, rapidly changing environment.

BACKGROUND Collaboration Definitions of collaboration often include dimensions similar to those used to describe “real” teams (versus nominal groups): shared goals, interdependent work activities, and clear membership boundaries (e.g., Hackman, 1987). Speaking of distributed workgroups, Sheldon et al. (2006) note: “at its most basic level, [collaboration] entails members of a given group who are working together and jointly contributing their respective knowledge toward achieving a shared group goal” (p. 1385). Common goal and interdependence is also part of the definition underlying Bedwell et al.’s (2012) integrative multilevel perspective: “We define collaboration as an evolving process whereby two or more social entities actively and reciprocally engage in joint activities aimed at achieving at least one shared goal” (p. 130). However, team scholars call for a broadening of team research and this pushes us to expand our definition of collaboration. Consider this from a special issue editorial of the Journal of Organizational Behavior, “the very notion of a traditionally defined ‘team’ may become increasingly outmoded. Our domain in this special issue is collaboration [emphasis in the original], which we define as ‘team-like behavior over time and across projects’—a definition that includes but is not restricted to what has traditionally been studied as ‘teams’” (Wageman et al., 2012, p. 301). They conclude: Relax the definitional elements of what makes a real team and explore what is interesting in contemporary collaboration. Some of the habitual routines in teams research make it hard to focus attention on interesting and important new phenomena that do not happen to meet the traditional definition of teams. Yet, many of the constructs and approaches developed in teams research offer real insight— when attentively used—about these new kinds of collaboration. As we have argued, attentiveness to

Virtual collaboration and intelligent technology  43 boundaries, stability, and interdependence as constructs in our research can better be used to draw our attention to intriguing phenomena than to rule certain things “out” of our domain of interest. (p. 312)

We agree that we should relax the definitional constraints. We begin from what we see as the most basic definition of collaboration: “individuals who make joint efforts to create value” (de Vreede & Briggs, 2019, p. 76). We relax the team elements further to bring us into the age of intelligent technologies: Collaboration describes entities making joint efforts to create value. (See also DeCostanza et al., 2018 and Larson & DeChurch, 2020 for a discussion of traditionally social definitions of collaboration expanding to include technology.) Figure 3.1 illustrates our three-dimensional perspective on collaboration – joint efforts to create value: membership boundaries, goal interdependence, and task interdependence. For each of these dimensions we offer range markers but allow that these are more and less “lumpy” continua; this is a simplified view of the collaboration dimensions given it doesn’t address whether all possible points on the continua, or their vertices, are practical. And finally, we intend that this expression of collaboration leaves space to consider whether and how intelligent technologies are incorporated into collaborations with humans. The perspective is agnostic on the type of entities doing the work. Some of the collaborators may have intelligent technology as tools. Some of the collaborators may be intelligent technologies. As we note below, tool versus teammate when it comes to intelligent technologies is a rich research arena (e.g., Larson & DeChurch, 2020).

Source: Authors’ own.

Figure 3.1

Simplified dimensions of collaboration

The “zero” point for membership is identified where a single entity is engaged. Efforts among unidentified collaborators (e.g., anonymous crowd collaborations) are next up the range, fol-

44  Handbook of virtual work lowed by identified but temporary or fluid (Mortensen & Haas, 2018) efforts. The far endpoint describes identified, stable, work that continues over time. We use Raveendran et al.’s (2020) definition of goal interdependence: the extent to which there is a common goal – regardless of whether or not the entities work together. However, we weave in aspects of reward interdependence by using range markers of idiosyncratic (no reward interdependence), meritocratic (rewards based on individual contribution), mixed/ hybrid (combinations between meritocratic and egalitarian) and egalitarian (equally shared). While we leverage the meritocratic, hybrid/mixed, and egalitarian terms from Wageman and Gordon (2005), we leave open whether or not the entities hold shared values around the distribution – and what that might mean when collaborating with intelligent technologies. Task interdependence varies from no interdependence (one entity can complete the task) to high interdependence where all entities must contribute (Wageman, 1995). We also anticipate that future research will break out the forms of the contribution across pooled, sequential, and reciprocal (Thompson, 1967) forms of workflow (Raveendran et al., 2020). To summarize: we can design, manage, and study collaboration across the extent that entities are known and stable (membership boundaries), how goals are aligned (goal interdependence), and the form of the effort (task interdependence). Each dimension conflates others (e.g., Raveendran et al., 2020), but we offer that combinations of this simple set may serve many purposes and give us inroads to understanding and designing the mix of human and intelligent technology interaction. Focused examinations are likely to tease apart the various dimensions. Consider Zhao et al.’s (2020) detailed examination of human/robot collaboration across the traditional forms of workflow interdependence (pooled, sequential, and reciprocal, Thompson, 1967). However, we also see value in the higher-level examination of collaboration as we consider virtual collaboration in an era with increasing levels of intelligent technologies. Virtual Collaboration Zammuto et al. (2007) speak directly of virtual collaboration in their editorial, “Information Technology and the Changing Fabric of Organization.” As technology in organizations moves from the background to a position of critical opportunity, Zammuto and colleagues argue that we need to simultaneously unpack the black boxes of technology and organizations as we design for the future: When we open up the black box of the IT system that supports virtual collaboration and combine it with a fine-grained analysis of how people cognitively process and interact with others’ knowledge virtually, we can better explain what might happen in the cycles of synchronous and asynchronous virtual interactions over time in such virtual collaborations. (p. 754)

Virtualness, like collaboration, is tracked across a multidimensional space. We could integrate the virtualness dimensions with the collaboration figure above (if we had science fiction multidimensional graphics capabilities). Virtualness is bounded by level of technology support for the work, percentage of time collaborators are apart while collaborating, and degree of physical distance (e.g., Griffith et al., 2003). Additionally, dynamic structure, national diversity, and other dimensions speak to the virtualness of a collaboration (Gibson & Gibbs, 2006). As we bring this perspective into an era with intelligent technologies, we acknowledge that the interactions can include technological and human entities (e.g., Larson & DeChurch, 2020; Murray et al., 2021).

Virtual collaboration and intelligent technology  45 We offer Table 3.1 as a window into the work we find illustrative of research relevant to virtual collaboration, but connected to non-intelligent technology and not mentioned elsewhere in this chapter. Some specifically address virtual collaboration, while others speak to both more and less virtual environments. Our perspective is that, except in experimental settings designed to be face-to-face, almost all work is virtual to a degree (Gilson et al., 2021; Griffith et al., 2003). Table 3.1

Additional relevant research

Reference

Findings/Focus

DeSanctis & Poole (1994)

Adaptive structuration theory: dynamics of the interplay between technical and social processes of technology use. Presented in the context of group decision support systems.

Griffith et al. (1998)

Sociotechnical considerations of facilitation in group support system use.

Colquitt et al. (2002)

Computer-assisted communication improves decision-making performance of teams high in openness to experience. The effect is mediated by the “efficiency with which teams integrated verbal and computerized forms of communication” (p. 402).

Thomas & Bostrom (2010)

Five triggers for sociotechnical adaptation in computer-mediated collaborations: (1) external constraint, (2) internal constraint, (3) information and communication technology (ICT) inadequacy, (4) ICT knowledge, skills, and abilities inadequacy, and (5) trust and relationship inadequacies.

Nicolini et al. (2011)

Objects and artifacts play diverse roles in collaboration: “mundane” infrastructure support, facilitation of work across boundaries, triggering cross-disciplinary collaboration.

Wang & Haggerty (2011)

Key individual knowledge, skills, and abilities for “virtual competence.”

Howison & Crowston (2014)

Collaboration theory based on superposition in the context of open-source software development: “the process of depositing motivationally independent layers of work on top of each other over time” (p. 29).

Faraj et al. (2015)

Behavioral and structural antecedents of leadership in online communities.

Schmitz et al. (2016)

Adaptive structuration theory at the individual unit of analysis.

Srivastava & Chandra (2018)

Role of social presence and trust in the context of virtual world collaborations.

Griffith et al. (2019)

Role and evaluation of individual systems savvy – the capacity to see and adapt across the interdependencies of technological and social/organizational systems.

Parker et al. (2019)

Untrained people design simplified low variety work. Multi-study analysis of triggers to promote better (more enriched) work design strategies.

Leonardi & Treem (2020)

Develops the concept and consequences of behavioral visibility: “The sociomaterial performance of the behavior of people, collectives, technological devices, or nature in a format that can be observed by third parties through minimal effort [generally through digital sources] such that patterns, causes, or motives can be inferred” (p. 1605).

Yan et al. (2021)

Offers a multidimensional network [both human and technology nodes] perspective for transactive memory systems.

Source: Authors’ own.

Also, most interactions with intelligent technologies are virtual either by location or by design. Regarding location, at least some of the intelligence, data, or other interactive aspects are likely remotely accessed. Regarding design, Glikson and Woolley (2020), for example, split their review of trust and AI across physical robots, virtual agents (e.g., bots that appear on your screen), and embedded AI where the intelligence is invisible to the user (e.g., traffic apps). These design choices, for the moment made by humans, play a role in human perceptions of

46  Handbook of virtual work AI interactions (e.g., Wynne & Lyons, 2018). Tangibility, transparency, and immediacy, for example, affect trust in AI differentially (Glikson & Woolley, 2020). As our interactions with AI become more sophisticated it may be helpful to have a realistic understanding of where the intelligence is housed or whether the intelligence can be made more or less physical. This lens is similar to how an understanding of context (e.g., time of day experienced by the team members, the meaning of silence) is valuable in human virtual teams (e.g., Cramton, 2001). These foundations from the teams literature and consideration of more and less traditional technologies offer strong starting points for future research leveraging intelligent technologies. Additionally, it is likely that many of the underlying focal technologies are evolving (or already have) into intelligent versions. Consider the improvements of various email platforms as an example: we’ve gone from traditional email to email that is sorted based on machine learning and offers sentence suggestions as we type.

BUILDING A SCAFFOLD TO STUDY VIRTUAL COLLABORATIONS WITH INTELLIGENT TECHNOLOGIES Having provided some foundations for collaboration, we turn to the broader human, technical, and organizational system to offer a scaffold for future research. We ask for specificity around the features of the systems under study. This includes the inputs, mediators, outcomes, and dynamics in the systems, as well as a heightened focus on research team membership and acknowledgment of the research team’s background. Are our lenses those of engineers, social scientists, or combinations? Communities like Responsible Research in Business and Management (RRBM) and Highly Integrative Basic and Responsive (HiBAR) Research Alliance highlight the value of research that keeps downstream use top of mind. To the extent that our research teams include colleagues from industry, we are one step closer to providing work of value to practice. Where we are page-length constrained, multimodal approaches such as online supplements, online simulations, and code and data repositories may support this background material and/or provide additional links from research to practice. Feature Specificity We expand from Griffith’s (1999) technology features perspective to include human and organizational dimensions. Figure 3.2 is a high-level depiction of the kind of filtering and trade-offs that offer roadmaps for research across virtual collaboration and new technologies. Features are the building blocks of the different components of the system. Technology features include keyboards, sorting algorithms, speed, and technology customizations. Examples of human features include knowledge, skills, and abilities. Organizational features include team versus individual work, training opportunities, and workflows. We acknowledge the dynamics, research opportunities, and complexity offered by machine learning (the technology today may not be the technology we have tomorrow), human learning, and organizational change. As illustrated in Figure 3.2, designers start with a state-of-the-art of known and unknown features. Design filters from the state-of-the-art to the system as designed. Again, with known and unknown features. Users engage with sets of features and may develop their own features through use. The importance of designers’ thinking, users’ selections and designs, and the

Virtual collaboration and intelligent technology  47

Source: Authors’ own adapted from Griffith (1999).

Figure 3.2

Conceptual model of technology, human, and organizational features and outcomes

broader set of dimensions are steps toward Bailey and Barley’s (2020) call for increasing the scope of our research agendas. The implication of a features-based approach to virtual collaboration is that specifics matter (and, no doubt, we could be more specific here). Our research can consider features that are known and unknown, selected or ignored, created by designers, users, or intelligent technologies across any stage of the process, and across the array of human, technological, and organizational dimensions. We do not have to consider all features at all levels, but we suggest that greater specificity will help us build more effective research pipelines. A features-based approach may also keep us more attuned to missing variable bias. Wolf and Blomberg’s (2019) field study of IT architects working with an intelligent system offers an example of the kind of specificity we hope to see more of. They offer (p. 6) a schematic diagram of the workflow, a description of how the architects interact with the system, and then an image of how the system responds to queries from the IT architect. Future research could go a step further and provide links to video playbacks or even demo sites where we can showcase the specific interactions we design and study. Framework Specificity We also suggest an input, mediator, output, input approach as a framework for assessing virtual collaborations. Ilgen et al. (2005) explain:

48  Handbook of virtual work …the I-P-O [input-process-outcome] framework is deficient for summarizing the recent research and constrains thinking about teams [and collaborations, in our view]. As an alternative model, we use the term IMOI (input-mediator-output-input). Substituting “M” for “P” reflects the broader range of variables that are important mediational influences with explanatory power for explaining variability in team performance and viability. Adding the extra “I” at the end of the model explicitly invokes the notion of cyclical causal feedback. Elimination of the hyphen between letters merely signifies that the causal linkages may not be linear or additive, but rather nonlinear or conditional. (2005, p. 520)

You and Robert (2017) and O’Neill et al. (2022), for example, offer illustrative IMOI perspectives for intelligent technology and teamwork. The IMOI approach offers another layer to our virtual collaboration analysis. (O’Neill et al., 2022 use the IMO framework to organize their human–autonomy teaming review.) Research Team Specificity Bailey and Barley (2020) highlight that scholars of work, technology, and organization need to build interdisciplinary research teams to address all aspects of the timeline. We can’t expect any one scholar to cover the full approach (see also, Glikson & Woolley, 2020; Piorkowski et al., 2021). We built our authorship team to take on virtual collaboration’s design, use, and future-facing aspects. Just as we ask scholars to be specific about the features in play in their research, we also ask for specificity around the technology trajectory timeline and expertise brought to bear. All of this specificity takes space and attention that may not be possible given our current publishing norms. We have seen greater specificity where research designs are preregistered (Nosek et al., 2018) via online portals. Some outlets separate publication of supplementary materials or have removed page restrictions (with costs and benefits, Pop & Salzberg, 2015). However we do it, we hope for research with greater specificity across the human, technical, and organizational dimensions of collaboration and virtualness.

INTELLIGENT TECHNOLOGIES AND VIRTUAL COLLABORATION In the two following sections, we provide examples of current research. First, we cover recent reviews and perspective pieces related to more and less virtual collaboration in settings with intelligent technologies. Second, we begin building a repository of current empirical research. Until our autonomous research agents catch up with our needs, we hope our human counterparts will add to this repository. Early Perspectives on Virtual Collaboration and Intelligent Technologies We say “early perspectives” here, but acknowledge that research considering intelligent technology started before the 1955 naming of artificial intelligence (McCarthy et al., 2006). The first instance of robotics in literature is ascribed to the 1868 novel, The Steam Man of the Prairies by Edward S. Ellis (Reid, 2017). We focus on recent conceptual presentations of intelligent technologies and work. We offer an interdisciplinary set of examples, including several conference papers given various norms across the fields. We start with examples of

Virtual collaboration and intelligent technology  49 broad considerations and follow with work more focused on virtual collaboration and intelligent technologies. The noted contributions provide insights into inputs, mediators, outcomes, and dynamics important to our future work. They are presented in rough order somewhat paralleling our examination of the literature. We find it heartening that we have this foundation to build on. These early perspectives each make connections to research based on human social systems. We primates have evolved our social systems for the last 52 million years (Shultz et al., 2011). Our work with intelligent technologies has far to go to match this history, even with machine learning in the mix. Form of technology is a major consideration in Larson and DeChurch’s (2020) review of leadership and technology. They consider four perspectives: technology as context, technology as sociomaterial, technology as creation medium, and technology as teammate. Building from DeCostanza et al. (2018), Larson and DeChurch use the term “technology” for cases where a human’s efforts are augmented. If instead, the technology offers a unique contribution, they categorize it as an agent. They also make distinctions regarding embodiment. They draw twelve leadership implications by crossing the four perspectives across the nuances of technology and agency. Glikson and Woolley (2020) review the empirical work on trust in artificial intelligence. They consider a range of AI embodiment, extending from physical representations (e.g., humanoid autonomous robots) to AIs embedded such that their existence is not signaled – consider the intelligent technology behind tools like Google Maps. In between are two-dimensional representations (Max Headroom comes to mind) and intelligent agents like chatbots. They find that the form of embodiment and level of intelligence affect the levels and types of trust humans develop. Seeber et al. (2020) use a survey approach to identify AI issues and future research questions in team collaboration. Sixty-five collaboration researchers participated in the process and offered 819 future research questions. Seeber and colleagues applied content analysis to suggest three design themes: machine artifact, collaboration, and institution. Tying back to our IMOI terminology, they also organize seven outcomes for collaboration (e.g., knowledge, trust) and ten mediators (e.g., pace, creativity). O’Neill et al.’s (2022) review offers history across the shifting frames for “human–autonomy teams (HATs)” (p. 1). In the 1980s, conceptualizations generally presented autonomous agents as tools subservient to humans, with collaborative work across humans and technology only in early stages. They organize their timely review using an IMO model (suggesting the IMOI in their discussion of future work). They conclude, “what we found was what appears to be relatively haphazard collections of independent and dependent variables considered in relatively narrow (rather than integrative) empirical studies” (p. 24). We agree with their assessment that this scattering of research makes sense at this stage of our understanding – and that reviews are a valuable opportunity to stop and take stock of what we can do next. Murray et al. (2021) provide a theoretical framework to understand our current and future relationships with intelligent technology, agency, and organizational routines. The framework covers human/technology conjoined agency with assisting technologies, conjoined agency with arresting technologies, conjoined agency with augmenting technologies, and conjoined agency with automating technologies – based on the locus of agency (human or technological) in protocol development and action selection. Position in the two-by-two framework signals the degree and predictability of change in routines as well as dynamics across responsiveness to feedback. For example, in cases with augmenting technologies (technologies with agency

50  Handbook of virtual work in protocol development) and human agency in action selection, they expect a moderate increase “in the degree of a routine’s change, compared to conjoined agency with assisting [non-agentic] technologies” (p. 560). This is based on the technology’s expected ability to focus on insights from the data and programmed objectives versus being bounded by preference for consistency, experience, or any non-goal based implications of switching protocols. Kellogg et al. (2020) offer a “6 Rs” analysis of how algorithmic controls appear in the workplace. Workers are directed by restricting and recommending, evaluated by recording and rating, and disciplined by replacing and rewarding. They connect worker experiences (none of them positive) to each of the Rs. They conclude with a final R: work resistance (to algorithmic control) and review the related research. Critical to the future of virtual work is the possibility of new occupations and actions: algorithmic curation, algorithmic brokerage, and algorithmic articulation. They highlight the potential of increased worker agency, a key part of our conclusions to follow. Lyons et al. (2021) also speak to the issues of human–autonomy teams (HATs). They review various interactions across humans, and then machines and humans with a focus on the conditions leading to HATs versus other kinds of interactions. Where tasks and processes might benefit in the social queuing ascribed to team interactions, they look to the predictors of developing HATs. They scope high autonomy, with agency (cf., Murray et al., 2021) differentiating automation from autonomy, perceived humanness, and human–machine task interdependence. They suggest, “human propensities for social cooperation are expected to facilitate human–machine performance where both machine autonomy and interdependence are high” (Lyons et al., 2021, p. 6). Benbya et al.’s (2021) special issue editorial, “Artificial Intelligence in Organizations Implications for Information Systems Research” (Journal of the Association for Information Systems), also helps describe the current research landscape. They write that the issue is the first joint (with MIS Quarterly Executive) special issue in information systems – certainly speaking to our request for broad perspectives on this topic. They provide an extensive set of research opportunities focused on AI, summarizing a set of 63 research questions in a table described as: “Toward a Research Agenda for IS Research [broadly construed] on AI in Organizations.” We say broadly construed as the questions go far beyond any one field. The Appendix is useful for those less versed in intelligent technology as it covers example forms and functions of artificial intelligence. We also find reviews and “perspectives pieces” that focus on specific technologies and dynamics. For example, Ötting et al. (2020) bound their meta-analysis of human–robot interaction (HRI) by remote and proximate interactions, but exclude social, emotional, moral, or cognitive aspects – what they call social HRI. They say, “In social HRI, human and robot interact as peers or companions with the goal to create relationships. As we wanted to investigate task-related interactions at work, we focused on proximal and remote interactions with successful task execution as the common goal, and we excluded social HRI from our scope” (p. 3). This framing highlights the need for specificity across the span of IMOI – in future research, we may want to examine the role of HRI relationships as mediators to task performance. They summarize that success builds from feedback, visibility of affordances, and adaptability and autonomy of the controller (algorithms and their software implementation). Human-likeness was not a predictor of success. Hancock et al. (2020) take on what we think is one of the most important aspects of virtual collaboration at this stage: AI-mediated communication. We share their dimensional founda-

Virtual collaboration and intelligent technology  51 tions as Table 3.2 with our additions of exemplar tools in use today. They highlight the benefits and the burdens of increased use of AI-mediated communication. Can we trust that what we read, see, or hear is coming from who or what we think it is? (We are using Google Docs and Grammarly as we write this – should each intelligent technology-suggested edit be noted? Do we have to share how far we’ve moved the slider on Zoom’s appearance enhancer? Our students have asked if using Grammarly is considered plagiarism.) Hancock and colleagues raise important questions for policy and interpersonal relationships. Table 3.2

Dimensions of AI-mediated communications

Dimension

Definition

Examples

Magnitude

The extent of the changes that AI enacts on

Correcting spelling errors vs. generating entirely new

messages

messages (e.g., Gmail)

The media in which AI operates (e.g., text,

Suggesting text replies (e.g., Gmail) vs. modifying

audio, video)

one’s appearance in video (e.g., Zoom)

The goal for which AI is optimizing the

To appear attractive, trustworthy, humorous,

messages

dominant, etc. (e.g., Grammarly)

The degree to which AI can operate on

Sender chooses between AI suggested messages vs.

messages without the sender’s supervision

AI engages in conversation with minimal input from

Media type Optimization goal Autonomy

the sender (e.g., intelligent agents functioning as chatbots) Role orientation

The role that the AI is operating on behalf of

Sender: offering messages to enhance reply

(e.g., sender vs. receiver)

efficiency vs. Receiver: assessing whether sender is potentially lying (e.g., Turnitin.com)

Source: Adapted from Hancock et al. (2020).

Diederich et al. (2022) review 262 studies of conversational agents. They assess each by human characteristics, agent design, and agent performance across perception and outcomes. “[A]gents assume different forms, which are distinguished by communication mode, embodiment, and the context in which they are used” (p. 98). Bittner et al. (2019) also focus on conversational agents in their development of a review-based taxonomy. Both pieces offer detailed maps for future research. Zheng and Jarvenpaa (2021) focus on technological anthropomorphism when engaging with intelligent technologies. They integrate psychology’s general three-factor anthropomorphism theory (which does not speak to technology) with the Need-Affordance-Features model from information systems (Karahanna et al., 2018). Zheng and Jarvenpaa propose that different affordances (such as personal control and sociality) may affect anthropomorphism. They discuss the positive and negative effects and resulting design implications. Finally, another important arena of research focuses on the variety of forms of telepresence. We have human telepresence where people use a robotic instantiation to be present in a remote location (e.g., Beane & Orlikowski, 2014). But telepresence also includes teleoperation, where humans serve as more and less robotic wranglers (e.g., Menzel & D’Aluisio, 2000). These are complex issues that no doubt will shift as the technologies, our understandings, and experiences develop. Jung and Hinds (2018) speak to the importance of the specification and study of robotics in the workplace, home, and other settings – branching out from laboratories and into rich social settings where we explicitly can address these complexities.

52  Handbook of virtual work These reviews and perspectives pieces offer excellent starting points across a range of goals, theory, and technology. We look forward to additional research bringing human, technical, and organizational dimensions together in our study of collaboration. Early Evidence on Virtual Collaboration and Intelligent Technologies There is an explosion of empirical work crossing virtual collaboration and intelligent technologies. As they must, these efforts take on narrower slices from the perspectives offered above. Table 3.3 offers illustrative empirical work, coded by description of the intelligent technology, description of virtualness, and our dimensions of collaboration. We sampled across engineering, computer science, and social science. We see this as a mosaic of findings rather than a systematic review. We remain at the broad, early stage of discovery – both in terms of understanding the findings below and how best to include work from different research philosophies.

APPLICATIONS OF AI IN VIRTUAL COLLABORATION Table 3.3 shows the range of consideration given to intelligent technology under the broad umbrella of research related to collaboration. In the section below, we offer a more focused example. Specifically, we look at how intelligent technology supports IBM’s virtual delivery process, especially during the COVID-19 office closures. Virtual delivery leverages intelligent technology-enabled collaboration platforms with agile practices and expertise in virtual leadership. The practice supports consultants and technology designers as they collaborate with customers building out digital transformations. Following the virtual delivery discussion, we offer Table 3.4 with IBM client experience examples from five industries of intelligent technology collaboration applications. This is a snapshot sharing current practices in one large organization. We hope these few examples, drawn from the personal experience of the second author, trigger possible research questions as we continue our consideration of collaboration. Virtual Delivery in Call Centers During the COVID pandemic, many clients shut down their physical delivery and support locations. For some, work continued in virtualized environments. Clients ready for virtual collaboration – human to human and then leveraging intelligent technology collaboration – were able to keep their business operations intact even in a rapidly changing environment. Call centers (also known as contact centers), where human agents respond to customer queries and requests, offer an almost standardized example given their ubiquity and importance. In North America, March 2020 marked an abrupt transition where physical call centers were closed due to the pandemic. At the same time, the volume of calls to the call center increased significantly since customers needed help as they, too, were affected by pandemic adjustments. Organizations with existing remote work infrastructures could get ahead of the curve. Process and technology techniques like remote agent desktop (routing calls to agent laptops no matter where the agent was), automated request ticket filtering tools, and remote workplace security and collaboration tools allowed some organizations to maintain call center operations

(H) person teams

for task support/

F2F: 2H, 1 Robot

generator

(5 or 6 H). Never met.

Remote teammates

(5 or 6 H)

Remote Teammates

Autonomous guess

(jargon.ai)

collaboration tool

Fleischmann et al. (2021) Emotion AI

Otter.ai, Slack)

and captioning (Skype,

Fleischmann et al. (2021) Automated transcripts

Gillet et al. (2021)

NA

NA

NA

Notes tensions to address in organizational

153 design sketches.

awareness and communication efficiency.

All chatbot designs stimulate emotion

response.

Identified, Temp

Identified, Temp

Identified, Continuing

Egalitarian

Egalitarian

Egalitarian

Reciprocal

Reciprocal

Reciprocal

more even participation during the game.

interactions among participants, leading to

Robot’s adaptive gaze behavior shaped

performance-oriented team members.

more pronounced for future-oriented and

communication effectiveness. Effect

Passive pervasive agent led to higher

Psychological safety plays a role.

speakers show higher levels of acceptance.

motivation are lower after use. Non-native

performance expectancy and hedonic

Effort and anxiety decrease with use, though

in AI versus trust in people” (p. 196).

control, learning versus evaluation, and trust

employee control versus management

relationships, privacy versus transparency,

NA

NA

Reciprocal

breakdowns, provide resources to assist user

Chatbots help to acknowledge potential

Results/Outcomes

policy making: “disruption versus help in

NA – Survey

Identified, Temp

Egalitarian

NA

Interdependence

Interdependence NA

Task

Goal

evaluate meeting data

Meeting recordings and

Cardon et al. (2021)

workshop)

F2F (design

soundproof booths

Identified, Temp

NA

Membership

algorithmic tools that

NA

distributed teams

emotion management for located separately in

Remote: 3 human

Chatbot team facilitator

Benke et al. (2021)

Benke et al. (2020)

NA – Individual

Scenarios of chatbot

Ashktorab et al. (2019)

interaction

Intelligent Technology Virtualness

Illustrative empirical work

Author(s) (Date)

Table 3.3

Virtual collaboration and intelligent technology  53

Synthetic pilot

Conversational agent

Amazon Echo

Low-level intelligent

McNeese et al. (2018)

Seeger et al. (2021)

Shaikh & Cruz (2019)

Staaby et al. (2021)

software robots [RPA]

Google Reply API

Hohenstein et al. (2021)

“Synthetic teams performed as well at

affiliative” (p. 4).

NA – Individual

assistant

F2F: 2H, intelligent

NA – Individual

NA

Identified, Temp

NA

NA

Egalitarian

NA

NA

Reciprocal

NA

more meaningful work.

Automation of routine work via RPA enables

interactions and creative performance.

in teams can decrease face-to-face verbal

Given time scarcity, intelligent assistants

perception of anthropomorphism.

type, and dispositional factors influence the

Anthropomorphic technology design, task

synthetic teams” (p. 262).

all other measures compared to control and

Experimenter teams performed better across

teams but processed targets less efficiently.

Pooled

being viewed as being more cooperative and

smart reply use results in communicators

smart reply use is viewed negatively, actual

less cooperative. “Even though perceived

responses lead to perceiving partners as

However, perceptions of algorithmic

emotionally positive language are supported.

and negative outcomes. Efficiency and

AI can shape communication with positive

Results/Outcomes

the mission level as control (all human)

Egalitarian

Pooled

Interdependence

Interdependence Idiosyn.

Task

Goal

chat

Identified, Temp

Unknown

Membership

1 synthetic pilot via

F2F: 2H,

reply engine

Remote, 2H, smart

Intelligent Technology Virtualness

Author(s) (Date)

54  Handbook of virtual work

Music Co-Creation

Google Hangouts Chat

Suh et al. (2021)

Mieczkowski et al.

of multiple agent members appeared to be the least effective. Concluded that multi-agent

by partitions or 1H and 2 simulated AI

Reciprocal

agent

Egalitarian

Remote: 3H separated Unknown

performance in multi-human HATs.

autonomous agents as teammates. Better

processes involved in the perception of

HATs are impaired due to social cognitive

Human autonomy teams (HATs) composed

sender’s social attractiveness” (p. 12).

though this bias may have undermined the

incorporate this bias into their own language,

a positivity bias, senders in our study did not

“Although AI language tends to have

roles” (p. 1).

Simulated autonomous

Reciprocal

altering users’ collaborative and creative

interpersonal stalling and friction, and 5)

a force for group progress, 4) mitigating

safety net in creative risk-taking, 3) providing

collaboration, 2) acting as a psychological

seeding a common ground at the start of

during creativity via: “1) implicitly

AI may influence human social dynamics

Results/Outcomes

Musick et al. (2021)

Idiosyn.

Reciprocal

Interdependence

Interdependence Egalitarian

Task

Goal

reply engine

Unknown

Identified, Temp

Membership

(2021)

Remote, 2H, smart

(2H + AI)

Remote Video Conf

Intelligent Technology Virtualness

Author(s) (Date)

Virtual collaboration and intelligent technology  55

Note: A more detailed version is available here: rebrand​.ly/​AI​-collab​-studies.

outputs into existing collaborative work

affect forward-facing services.

learning (based on historical data) might

practices. Concerns noted from how machine

in full integration of the system’s algorithmic

other IT architects

Architects were supportive of the potential of

with an agent and may see it as a teammate. the tool’s NLP capabilities. Challenges found

Unknown

collaboration with

Egalitarian

work. Human teammates accept working

computer agents in hybrid collaborative

Potential synergy across humans and

Results/Outcomes

for proposals

Identified, Continuing

Reciprocal

Interdependence

Interdependence Egalitarian

Task

Goal

& tool, with

F2F: Individual

Identified, Temp

Membership

processing for requests

Wolf & Blomberg (2019) Natural language

1 robot

Intelligent collaborative F2F: 5H,

Wiethof et al. (2021)

agent (writing)

Intelligent Technology Virtualness

Author(s) (Date)

56  Handbook of virtual work

Virtual collaboration and intelligent technology  57 effectively. However, the acceleration of planned digital transformations triggered by the abrupt transition (Soto-Acosta, 2020) challenged even the most forward-looking organizations. IBM leverages “co-create” environments where consultants join with client teams to identify opportunities, support prototyping, and jointly create solutions. Face-to-face sessions became virtual with the pandemic closures. Intelligent technologies offer support for these activities by improving the quality of foundational requirements documents and the prediction of defects. These capabilities enable faster development sprints and code deployments amongst the teams – even with strict security requirements. IBM’s experience with its clients indicates that the human component remains critical. Leaders must support motivation and engagement. Organizations that build a culture of collaboration communities, provide effective remote tools, and offer e-learning of new technologies in this “new normal” are more successful than those that do not. As the world transitions from the full-shut down of offices, we expect organizations to operate in a physical-remote hybrid environment for many years. While there were challenges, client organizations found tangible benefits and opportunities following their COVID pandemic responses. Businesses removed regional and geographical restrictions and optimized travel and business costs. Organizations tap into global resources through virtual collaboration and virtual delivery of services. Intelligent workflow platform technologies are now prevalent, including AI, blockchain, cloud, and Robotic Process Automation (RPA). Table 3.4 offers specific examples from five industries. In each application, the first step is to ensure that all employees have a remote desktop/laptop with remote collaboration tools, security resiliency, virtual desktops, analytics, automation tools, and software licenses for remote use of the tools. These are not science fiction examples. These are examples of effective real-world use of intelligent technologies in both customer-facing and back-office environments. Relatively small adjustments across organizational processes, human interaction, and technology tools allowed people to use intelligent technologies to improve collaboration processes and outcomes. We can leverage frameworks speaking to human and technology agency, such as that offered by Murray et al. (2021), to better build theory supporting these new uses of intelligent technology. As these examples illustrate, industry uses intelligent technologies in their virtual collaborations, and we must connect quickly to offer relevant guidance. Table 3.4

Examples of virtual delivery processes at IBM

Client

Process Before Use of

Current Role of Intelligent

Differential Value of Intelligent Technology

Industry

Intelligent Technology

Technology in Virtual

in the Virtual Collaboration

Collaboration Telecom

Call center agents recommend Implementing automated digital

Improved cross-sell and up-sell revenue by

customers additional features workflow processes to solve customer 10%.

 

to meet their mobility

needs reduced the dependency on call 15% increase in customer satisfaction (Net

requirements. Provide

center agents. Using AI and analytics Promoter Score).

cross-sell and up-sell

technologies, customers can upgrade

opportunities to businesses.  

and add new functionalities.  

 

58  Handbook of virtual work Client

Process Before Use of

Current Role of Intelligent

Differential Value of Intelligent Technology

Industry

Intelligent Technology

Technology in Virtual

in the Virtual Collaboration

Collaboration Healthcare

Teams operating in

Creation of an “agile” collaboration

Seamless delivery of three major deployments

a more globally localized

tool to ensure global teams continue

within a period of two months by leveraging

environment to deploy

to function effectively in an agile

a remote team operating in “follow the sun”

releases for their supply chain environment to maintain seamless enterprise systems.

model.

deployment. Buildout of a 24/7 virtual support and on-time virtual delivery system. Moving to contactless delivery using intelligent technology. Usage of “distributed” agile collaboration tools for various stages of the project lifecycle. Moving reporting to dynamic dashboards with online reporting.

 

 

 

Social

Healthcare services for

Ensuring continuity of healthcare

  Building intelligent operations has opened

Services

citizens. Case management

services using telehealth through

a whole new set of possibilities and options

system used for physical

remote delivery methods. Building

for citizens and government institutions.

appointments and delivery of automated intelligent processes services.

If patients require urgent help, they can

for linking case management

send notifications and triggers. Automated

systems enables timely reminders

workflow provides notification to nurses and

of appointments and continuity

support staff.

of operations through virtual and face-to-face collaboration tools.  

 

 

Digital

Avoid errors and delays in

Building a “virtual engineer”

  Creation of “virtual engineers” has reduced

Networking

creating new request tickets

robot to create automated tickets,

the errors in ticket creation by over 70%,

and incidents for technical

analyze ticket functions, determine

improved predictability, and enabled timely

support.

the incident category, and predict

resolution of issues during the service level

resolution.

agreement timelines. Key performance indicators for Level 1 effort (most skilled support staff) have improved by 50%, enabling top support engineers to be re-deployed for other business activities.

 

 

 

Utility

Improve efficiency and

Creation of a cloud-based platform

  Enabled quick deployment of automated

effectiveness of billing

for teams to collaborate and build

processes into production. Reduced

processes.

automated processes. The intelligent

operational costs by 30%

system is self-healing, and automated Improved customer satisfaction by answering notifications happen when a pattern in over 70% of queries through AI-enabled billing usage is identified.

virtual assistant

Collaboration tools enable rapid

Removed error-prone manual tracking of data

design thinking and JAM (innovation) in spreadsheets and local databases. sessions between business and

Mitigated inconsistency, errors, and manual

development teams.

effort in administrative and operational process.

Source: Authors’ own.

Virtual collaboration and intelligent technology  59

DISCUSSION We took a past, present, and future perspective on virtual collaboration in a world with intelligent technologies. Our goal is to bring together a mosaic of tools, scholarship, and examples to energize and support future research and practice. As noted above, organizations are moving quickly, and we are perhaps playing catchup both to our colleagues in industry and academic computer science and engineering fields. Past Drawing from the past, we offer a three-dimensional collaboration model, acknowledge the multidimensional nature of virtual work, document virtual collaboration research with traditional (non-intelligent) technologies as on-ramps to follow-on research focused on intelligent technology, and expand a features-based model of sensemaking. These foundations offer a basic toolkit to understand the explosion of research leveraging intelligent technologies and virtual collaboration. Present The empirical literature is varied and, appropriately, at early stages. We share experimental, ethnographic, and survey-based presentations. While we see some empirical work on intelligent technologies and virtual collaboration in our mainstream journals, the pipeline in many cases is slower than the development and use of the technologies in organizations. Conference papers across organization science, information systems, computer science, and engineering are useful resources. There is a growing body of perspectives and frameworks on how we will work with intelligent technologies. This chapter joins those efforts to give us a running start into the future. We look forward to research that helps us understand aspects of intelligent technology that are distinctive from our foundational work in traditional technologies. How intelligent does a technology need to be such that it changes our expected results? Will our better understanding of intelligent technologies change our views and relationships with traditional technologies? Future For the future, we highlight the strong statements of Bailey and Barley (2020), Glikson and Woolley (2020), and Piorkowski et al. (2021): This is complex interdisciplinary work that we must do with interdisciplinary scholars onboard. Additionally, design and implementation are both top-down and bottom-up activities. We will be working with narrow, custom applications of intelligent technology (rather than a general intelligence) for a long while. Effective custom technologies require a deep understanding of both the work and the technical opportunities to provide anticipated value. The best understanding and ideas for improvement are often situated at the point where the work is done. Work designed with the incumbents and offering organizational and technological choice offers the greatest opportunity to “the increasingly turbulent environments of modern organizational life” (Kolodny et al., 1996, p. 1483 – and noting this work’s prescience).

60  Handbook of virtual work Top-Down and Bottom-Up Design Digital transformation of work is complex and beyond any single set of expertise. Piorkowski et al. (2021) describe the value of education, both to avoid problems and create synergy. Their work focused on AI and business expert stakeholders. We would like to see the scope broadened to include individual users at all levels. We propose that there are benefits to facilitated design with both top-down and bottom-up efforts. Intelligent technologies (as well as steam power, electricity, and computing) are “general purpose” (Brynjolfsson et al., 2021) in that they are pervasive but require iterations of specific applications and complementary innovations to have the anticipated benefits. Our premise is that applications and complementary innovations are tied to the number and diversity of people engaged. Consider our use of personal computers and the internet today – the accessibility of tools to build and disseminate new creations ushered in the digital era. As all of us begin thinking of how intelligent technologies can help us in our work, we expect a long spring to follow past “AI winters” (Hendler, 2008). Sophisticated versions of top-down and bottom-up approaches have found success. Roscoe et al. (2019) describe Arizona State University’s Center for Human, Artificial Intelligence, and Robot Teaming, a research center coupled with engineering education that leverages psychology and engineering. While this is a top-down approach, the university is building a cadre of people who can work from the bottom-up. “To build a future in which multidisciplinary teams of engineers and psychologists work together seamlessly, it makes sense to consider both sides of the equation” (p. 404). Evidence of the value of multilevel, multi-entity design is already visible within the creation of intelligent technology models. Wilder et al. (2020) consider the benefits and burdens of both the human and machine learning systems. Rather than training to the “best” models of human decision-making and the “best” models of machine learning, they train such that the technology complements the human capabilities. They design for a system where different team members (humans, intelligent technologies) take on the portions of decision-making best suited to their skills. In both scientific discovery and medical diagnosis collaborations, the systems built with this skill-aware approach outperform the individual performance of either machines or humans. We see this finding as paralleling the outcomes of effective transactive memory in human collaborations (e.g., Ren et al., 2006). But we don’t have to wait for Arizona State’s undergraduates to enter the workforce or all intelligent technologies to be intertwined with our human capabilities. Merely opening the door to opportunities may have significant benefits if thinking about how to use intelligent technologies in our work becomes an everyday occurrence. Just as we are seeing an explosion of intelligent technology research, there is also an explosion in research and practice focused on job crafting (Parker et al., 2017; Zhang & Parker, 2019). Job crafting speaks to how employees independently initiate adjustments to their job’s task, cognitive, and relational components (Tims & Bakker, 2010; Wrzesniewski & Dutton, 2001). Broadening job crafting theory to include work, rather than just traditional jobs (e.g., Lazazzara et al., 2020), and placing a greater emphasis on technology tools (e.g., Bruning & Campion, 2018) presents a lens to see everyone as innovators in their work. Across fields, scholarship can offer insights into the application of intelligent technologies and virtual collaboration to this broader community. Perhaps by providing even basic insights, we can trigger bottom-up work crafting

Virtual collaboration and intelligent technology  61 such that this wave of new technology use can push toward the dynamics and benefits seen with personal computer use in the 1980s (e.g., Krueger, 1993). Thinking in 5T As we teach to prepare our students to innovate in their work, we leverage sociotechnical systems (Trist & Bamforth, 1951) with a conceptual and rhetorical twist: We offer the concept of “Thinking in 5T.” Figures 3.3a (a two-dimensional depiction) and 3.3b (photo of a SkwishTM – wooden dowels connected with elastic to form a malleable three-dimensional model) are images that help share the idea that our work is an interconnected mix of talent, technology, and technique – all aligned to a target and for the times at hand. This 5T rebranding of sociotechnical systems comes from observing the differential awareness of sociotechnical systems (low) and concepts like six sigma process improvement (high). To open the door to thoughtful, broad-based, multidisciplinary, top-down, and bottom-up experimentation, we need an easy-to-remember framework that protects against silver bullet thinking (Brooks, 1987). Anecdotal evidence suggests that Thinking in 5T is a sticky way to share the multidimensional, multi-entity perspective we need.

Source: Authors’ own.

Figures 3.3a and 3.3b

Thinking in 5T

CONCLUSION This is a complex time for the study of collaboration. We must open the black boxes of human and technological dynamics simultaneously (Zammuto et al., 2007) as intelligent technologies join our ranks as tools and teammates. We see that much of our human collaboration is virtual. Then, to the extent that the intelligence of intelligent technologies is rarely housed “face-to-face” with human collaborators, almost all collaboration with intelligent technologies is virtual. The complexity of this environment means we must be even more specific in illustrating the context of our research. Our research and its implications are more powerful where

62  Handbook of virtual work greater specificity enables individuals and teams to knowledgeably adjust their collaborations from the bottom up and receive top-down organizational support.

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66  Handbook of virtual work Wrzesniewski, A., & Dutton, J. E. (2001). Crafting a job: revisioning employees as active crafters of their work. Academy of Management Review, 26, 179–201. Wynne, K. T., & Lyons, J. B. (2018). An integrative model of autonomous agent teammate-likeness. Theoretical Issues in Ergonomics Science, 19(3), 353–374. Yan, B., Hollingshead, A. B., Alexander, K. S., Cruz, I., & Shaikh, S. J. (2021). Communication in transactive memory systems: a review and multidimensional network perspective. Small Group Research, 52(1), 3–32. You, S., & Robert, L. (2017). Teaming up with robots: an IMOI (inputs-mediators-outputs-inputs) framework of human–robot teamwork. International Journal of Robotic Engineering, 2(1), 1–7. Zammuto, R. F., Griffith, T. L., Majchrzak, A., Dougherty, D. J., & Faraj, S. (2007). Information technology and the changing fabric of organization. Organization Science, 18(5), 749–762. Zhang, F., & Parker, S. K. (2019). Reorienting job crafting research: A hierarchical structure of job crafting concepts and integrative review. Journal of Organizational Behavior, 40(2), 126–146. Zhao, F., Henrichs, C., & Mutlu, B. (2020). Task interdependence in human–robot teaming. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 1143–1149. Zheng, J. F., & Jarvenpaa, S. L. (2021). Thinking technology as human: affordances, technology features, and egocentric biases in technology anthropomorphism. Journal of the Association for Information Systems, 22(5), 1429‒1453.

4. Using AI to enhance collective intelligence in virtual teams: augmenting cognition with technology to help teams adapt to complexity Anita Williams Woolley, Pranav Gupta and Ella Glikson

With the rapid expansion of technology has come a surge in the size of the body of knowledge relevant to many problems. As Einstein (1949) predicted, among others (e.g., Jones, 2009), this has resulted in individuals developing narrower expertise and, consequently, a rise in the use of teams for developing new innovations and solving problems (Wuchty et al., 2007). Furthermore, the size of these knowledge production teams is likewise expanding (National Research Council, 2015), fueled by the increasing use of members who are physically distributed (Minton-Eversole, 2012). While teams have been traditionally defined as a stable group of people with a clear membership boundary that share responsibility for an outcome (Hackman, 1987), increasingly we see work carried out in more complex, multi-team systems (Marks et al., 2005), where members are part of multiple teams simultaneously (O’Leary et al., 2011) leading to many collective work contexts where team membership is both unstable and unbounded (Mayo et al., 2021; Wageman et al., 2012). And with the continued development of artificial intelligence, we see that more and more collectives include machine-based autonomous “teammates” (Lematta et al., 2019; Musick et al., 2021; O’Neill et al., 2020), further stretching the concept of team. Given the dynamism surrounding teamwork, many have called for conceptualizing teams in less static terms and more as complex adaptive systems (Arrow et al., 2000; Cronin et al., 2011; Hackman, 2012; Kozlowski & Klein, 2000; Mathieu et al., 2019). Relatedly, research in the teams and organizational literatures has drawn on concepts originating from work on intelligence (Csaszar & Steinberger, 2021; Knott, 2008; March, 2006; Mayo & Woolley, 2021; Riedl et al., 2021; Woolley et al., 2010). Intelligence is a useful concept for thinking about capability in systems that need to adapt to dynamic complexity, as for decades it has been studied in the context of individual, biological, and technological systems as an ability to adapt and achieve goals in a wide range of environments (Legg & Hutter, 2007). Thus conceptualizing team capability in terms of collective intelligence captures the need for the ongoing information processing and adaptation necessary for teams to operate effectively in the increasingly complex, dynamic, and information-rich environments they face. Three functions that underlie the emergence of intelligent behavior in any system are those related to memory, attention, and reasoning processes. These are core functions that characterize the systems of the human brain, but researchers have observed that they are essential in other intelligent systems as well. Building on this perspective, the Transactive Systems Model of Collective Intelligence (Gupta & Woolley, 2021) articulates how transactive memory (TMS), attention (TAS), and reasoning (TRS) systems emerge from individual-level cognitive processes and member interactions to shape the emergence of collective intelligence in teams.

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68  Handbook of virtual work These systems interact with and adapt to each other as they dynamically respond to changing environmental complexities. In this chapter, we briefly review the organizational and technological developments that have driven the increasing environmental complexity surrounding teamwork, including the rise in the use of virtual teams and remote work, and the concomitant growth in the percentage of workers who experience multiple-team membership (MTM) where they are part of multiple different work or project teams simultaneously (O’Leary et al., 2011). We will then describe the transactive systems model for collective intelligence (Gupta & Woolley, 2021) which articulates the emergence and adaptation of the collective cognition driving teams’ capability to operate in an increasingly complex, dynamic environment. Developing a deeper understanding of the basic functions underlying the emergence of collective intelligence suggests insights for how technology, and particularly artificial intelligence, can augment these systems. Therefore, we extend the transactive systems model with further consideration of complementary AI technologies, and the emerging literature on how humans collaborate and develop trust in AI. The quality of the human–machine relationship is what will enable or limit what synergistic capabilities will be possible. In integrating these literatures, we identify avenues for future research to identify strategies for developing higher levels of collective intelligence in teams. See Figure 4.1 for an overview.

THE GROWING COMPLEXITY OF TEAMWORK While cooperation and teamwork have been essential to human evolution since the dawn of humankind, technology has fueled a variety of trends that have increased the complexity and dynamism characterizing teamwork over the last several decades. While teamwork traditionally took place between physically co-located team members, continuous improvement in communication technology has enabled more and increasingly effective distributed collaboration. This has led to the rise in remote work and virtual teams, initially via “telecommuting” over modest geographic distances but now enabling organizations with employees located around the world that have no physical office space and who have never met in-person. A concomitant trend is the dramatic growth of information and the associated scientific and technological developments, leading to the need for deeper and narrower specialization in many fields and occupations (e.g., Donini-Lenhoff & Hedrick, 2000). Consequently, to incorporate all the knowledge needed, more problems require teams of experts for resolution (Haeussler & Sauermann, 2020; Wuchty et al., 2007), and each of those experts are needed to make contributions on larger numbers of teams, leading to the rise of multiple team membership (MTM; O’Leary et al., 2011). MTM and technology-enabled collaboration are mutually reinforcing, as technology enables information-sharing and also increases the demand for distributed collaboration which allows teams to access expertise from literally anywhere in the world, further increasing MTM. Here we give a brief overview of the developments in MTM and virtual teams as hallmarks of the increase in teamwork complexity, giving rise to a need for understanding how artificial intelligence can be used to enhance collective intelligence.

Enhancing collective intelligence in virtual teams  69

Source: Authors’ own.

Figure 4.1

The possible impact of artificial intelligence on collective intelligence

Virtual Teams and Remote Work Increasing de-centralization and globalization of work practices, together with growth in communication technologies, has led to a rapid acceleration in the adoption of virtual work since the 1990s, in which members are geographically dispersed and coordinate their work predominantly with electronic information and communication technologies (Hertel et al., 2005). While initially all varieties of “virtual teams” were undifferentiated by researchers, over time they have come to treat “virtuality” as a continuous dimension reflecting different levels or degrees (Kirkman & Mathieu, 2005; Wildman et al., 2012). Teams working and communicating in a shared physical space with little to no technological intervention are considered “low virtuality” while teams conducting work remotely and using technology for all coordination and communication are considered “high virtuality” (Kirkman & Mathieu, 2005). Fully virtual, geographically distributed teams have been a common practice for many years and have attracted a lot of research attention, highlighting the challenges which are unique to this mode of work, such as difficulties in coordination, collaboration and communication (Gilson et al., 2015; Marlow et al., 2017).

70  Handbook of virtual work While technology-driven changes drove the growth in the use of virtual teams, during the last three decades, research has identified ways in which technology has also improved computer-supported collaborative work (e.g., Ackermann & Eden, 2020). One focus has been to develop technological solutions to issues such as the domination of conversation by certain team members, or social loafing. Research has looked at ways to help make these issues more salient by providing “awareness” systems which display or provide feedback on relative member speaking times or work effort, which seem to help some teams self-manage more effectively (e.g., Bodemer & Dehler, 2011; Glikson et al., 2019; Gutwin & Greenberg, 2002). While awareness systems merely reflect group behavior back to members, advances in AI are starting to enable tools that can help improve the content of communication. Hancock et al. (2020, p. 90) described this AI-mediated communication between people in which “a computational agent operates on behalf of a communicator by modifying, augmenting, or generating messages to accomplish communication or interpersonal goals.” Such technology holds great promise, especially for culturally diverse virtual teams, as well-developed systems can enhance impression management, avoid communication failures, and aid in avoiding or resolving conflict. At the same time, it is possible that manipulation of communication content by correcting spelling and grammar, changing sentence wording or providing a machine translation might reduce the authenticity of team member interaction, or create a mismatch between verbal and non-verbal cues. For example, since some misspellings or punctuations are intended as non-verbal and emotional cues (Blunden & Brodsky, 2021; Sidi et al., 2021), “fixing” them changes the interpretation of the receiver and can diminish the effectiveness of the communication. Thus, while all technological advances need to be adopted with caution, there are also exciting opportunities for improving communication and resulting collective intelligence. With the advances in communication technology has come wider availability of videoconferencing, which many view as providing better opportunities for collaboration and knowledge sharing by providing near face-to-face experiences. However, research has demonstrated that the impact of richer communication channels is not always beneficial (Eisenberg et al., 2021; Glikson et al., 2019; Tomprou et al., 2021). For instance, recent research has shown that the use of rich media, such as videoconferencing, can reduce vocal synchrony among collaborators which leads to a decrease in collective intelligence (Tomprou et al., 2021). In another study, researchers found that when team members differed in their language skills, they did not benefit from using rich media such as videoconferencing (Eisenberg et al., 2021). Consequently, as our understanding of collaboration and related drivers of collective intelligence develops, we will be better able to predict how and when technological developments in communication and collaboration will be beneficial. The development of technology that has enabled and fueled remote, virtual teamwork has also enabled teams to draw on expertise from anyone, anywhere, resulting in a concomitant rise in multiple team membership (MTM), where individuals are members of multiple teams simultaneously (O’Leary et al., 2011). Multiple Team Membership As the landscape of work has shifted to knowledge-intensive work, workers have become increasingly specialized and organizations have adapted by adopting team-based work structures. To leverage their resources and transfer knowledge effectively, these organizations

Enhancing collective intelligence in virtual teams  71 often have employees working on multiple teams at the same time. Indeed, researchers have found that 65 to 95 percent of employees, across a wide range of functions and industries, are juggling multiple team memberships (MTM; O’Leary et al., 2011). MTM is a special case of a broader organizational structure known as multi-team systems (MTS) in which a network of teams are defined by process and output interdependencies (Margolis, 2020; O’Leary et al., 2012). While MTS generally involve teams that are interdependent in terms of their task work, and sometimes share team members, MTM is focused on the specific case in which teams work on largely independent projects but share members. Consequently, MTS settings tend to be different from those where MTM is more common; for example, MTS research highlights settings where tightly coupled teams work towards a large, shared goal like building an airplane (Mathieu et al., 2017), whereas teams involved in MTM are more common in cross-functional settings like healthcare, professional services, and software development (O’Leary et al., 2012). MTM reflects the increasing complexity of work coupled with the pressure for organizations to be as efficient as possible (Chen et al., 2019). While MTM improves overall organizational performance by leveraging the expertise of knowledge workers across multiple projects, it increases the complexity of the task environment by creating new interdependencies between otherwise independent projects. In addition, most of the extant research shows that members of multiple teams suffer productivity losses (Crawford et al., 2019) due to frequent task switching as well as attentional fragmentation (Yaghootkar & Gil, 2012) and attempting to juggle multiple communications simultaneously (Reinsch et al., 2008). There is some evidence that moderate levels of MTM can be beneficial (Bertolotti et al., 2015) particularly if teams use communication technology effectively, while others show that any multi-teaming lowered productivity (Crawford et al., 2019). An important element relates to attention allocation; when workers could allocate a larger percentage of time to a focal team, that team’s functioning and ability to develop shared cognition was enhanced and resulted in better performance (Cummings & Haas, 2012; Maynard et al., 2012). However, in most MTM contexts, the shift towards MTM afforded by distributed and asynchronous virtual work increases the complexity and dynamism of the environment surrounding teamwork. Furthermore, it demands a different perspective on how to design work, such as when it is not possible to optimize team processes for performance because members are working on many different teams and tasks simultaneously. We suggest that this shift in the context of teamwork demands that organizations shift from thinking about how to optimize team processes towards particular performance outcomes, and instead design teams and systems for collective intelligence.

COLLECTIVE INTELLIGENCE Intelligence, whether in the context of an individual, a collective, or a technological system, is broadly defined as the ability to achieve goals in a wide range of environments (Legg & Hutter, 2007). Extant work suggests that three core cognitive functions underlying intelligence in any system are memory, attention, and reasoning processes. These functions are essential to intelligent functioning whether the system is the human brain (Luria, 1973) or another biological, technological, or hybrid system (Malone & Bernstein, 2015). Thus, a foundation for the emergence of intelligence is the development of systems accomplishing these functions. In line

72  Handbook of virtual work with this, Gupta and Woolley (2021) theorized that three functionally distinct socio-cognitive systems emerge in collectives: (a) Transactive memory systems (TMS) for governing the coordination of members’ limited and distributed knowledge and skills; (b) Transactive attention systems (TAS) for governing the coordination of members’ limited attention; and (c) Transactive reasoning system (TRS) for governing the coordination of effective collective goals while fulfilling members’ diverse motivations. The emergence and mutual adaptation of these systems provide the foundation for the emergence of collective intelligence, enabling the collective to adapt in response to environmental complexity. Thus, the design problem of collective intelligence is to put together a set of heterogeneous, boundedly rational members who can coordinate their distributed cognitive resources to formulate a series of joint decisions and actions to accomplish goals across a wide variety of environments. Below we briefly describe the three transactive systems that handle the collective memory, attention, and reasoning functions that ultimately lead to the emergence of collective intelligence. Transactive Memory System Transactive memory system (TMS) is a collective understanding team members develop regarding each member’s skills, making all aware of who knows what and from whom to retrieve knowledge to achieve effective coordination (Ren & Argote, 2011). TMS is a dynamic system consisting of members’ understanding of each other’s knowledge and skills relevant to the task and the inter-member processes that facilitate allocation and retrieval of information to and from the most appropriate member. While the concept was initially developed in the context of couples in close relationships, it has since been extended to the group level (Ren & Argote, 2011) and demonstrated to be strongly related to collective intelligence (Kim et al., 2016). TMS involves three transactive processes that maximize the capacity for storing information in the team and utilizing it effectively: updating, allocation, and retrieval. Learning and updating “who knows what” occurs as members work together on interdependent tasks and observe others’ competencies. Allocation occurs as members direct new, incoming information to the member most likely to store it successfully. This is enhanced by updating, and also expands the capacity of the team to store knowledge. Specialization develops as a consequence of consistent allocation of new information within a particular domain to the same member (and information from other domains to other members). Retrieval is, in turn, enhanced by updating and allocation, as members direct inquiries in certain domains to the associated member, which minimizes the time required for retrieving knowledge. By reliably and successfully responding to others’ inquiries in a knowledge domain, each member establishes credibility with the others and the team forms shared beliefs about each member’s expertise. Consequently, specialization and credibility are often used as behavioral indicators of a well-developed TMS (Ren & Argote, 2011). Transactive Attention System In any information processing system, the available attention of members puts a hard limit on the capacity of the system to handle information. To manage this important resource, teams develop a transactive attention system (TAS), which is a dynamic system enabling members to manage their collective attentional capacity by facilitating their understanding of other

Enhancing collective intelligence in virtual teams  73 members’ focus and availability. TAS operates via the inter-member, transactive processes that facilitate allocation of individual attention and retrieval of joint attention when needed. TAS is theorized as a complement to TMS as groups distribute tasks and information, balancing between drawing on expertise and optimizing the efficiency of the use of attentional resources. The TAS involves three inter-member transactive processes that facilitate the efficient utilization of collective attention: updating, allocation, and retrieval. Updating understanding of members’ availability involves keeping track of each members’ workload and temporal demands as well as individual and collective priorities (informed by the TRS; see next section). Attention allocation is facilitated by an effective updating function, as this facilitates members’ decisions regarding when to shift their attention from one task to another or whom else might be available. As the number of members in the collective increases, the amount of availability and allocation signals to keep up with increases exponentially, and thus the difficulty and importance of a high-functioning TAS-drive allocation ability. Similarly, attention retrieval is also aided by an effective updating system and will facilitate work that is highly interdependent and requires quick hand-offs and information exchanges for task completion. Groups with strong collective attentional retrieval capability will exhibit organized patterns of synchronous attention, sometimes manifest as “burstiness,” where periods of independent work are punctuated by periods where members are highly responsive to each other’s requests. In recent work, burstiness has been shown to predict collective intelligence (CI) (Mayo & Woolley, 2021; Riedl & Woolley, 2017). Transactive Reasoning System While TMS and TAS work together to ensure efficient and effective utilization of the available member expertise and attention, they do not guarantee that the team is pursuing the right goals or that its members are highly motivated to go after these goals. A transactive reasoning system (TRS) facilitates the collective decision making that is needed to evaluate collective goals in the context of a constantly changing environment to ensure the pursuit of those with the greatest value, as well as alignment between individual and collective goals (Bacharach, 1999; Locke & Latham, 1990). Thus, we propose TRS as a dynamic system consisting of members’ knowledge of rewards available in the environment, along with their own and others’ goals and motivational needs, which drive the transactive processes whereby members maximize joint rewards via negotiation and alignment around these goals and priorities. As with TMS and TAS, TRS involves three inter-member transactive processes – updating, allocation and retrieval – that reduce experienced uncertainty by facilitating individual and collective reasoning. Updating occurs via inter-member communication and observation of the environment and one another. Research at the intersection of motivation and entrepreneurship suggests that highly motivated individuals are better at recognizing and creating opportunities from their environment (Carsrud et al., 2009). In addition, through experience and interaction with other members, individuals will observe what others are more responsive to or learn what they care about and draw inferences about their goals. Allocation relates to the collective’s ability to negotiate their preferences to allocate priorities and achieve goal alignment, with implications for the distribution and allocation of resources to goals. Retrieval relates to the process of garnering the commitment and effort of existing and prospective members (including their time, skills, social capital, etc.) in the service of collective goals. Recent work on

74  Handbook of virtual work transactive goal dynamics has articulated how individuals in interdependent relationships form a single unit wherein they adopt and hold other- and system-oriented goals. When members adopt a dense set of goals that are aligned with the system’s goals, then they will be willing to devote more resources towards their joint goals as well as provide goal support (Fitzsimons et al., 2016). This results in higher levels of goal persistence and commitment to the collective (Koestner et al., 2012; Moreland et al., 1993). This is supported by research on game theory demonstrating the power of “we-reasoning” mechanisms for persistence on collective goals, even in the face of competing personal goals (Bacharach, 1999). Thus, a well-developed TRS enables the provision of more resources and clearer goal priorities to TMS and TAS to utilize and execute upon efficiently. In addition to the bottom-up processes whereby TMS, TAS, and TRS emerge independently to handle collective memory, attention and reasoning functions, a critical element of intelligence in any system is the ability to adapt to changes in complexity. Adaptation requires that these systems engage in mutual regulation in response to environmental complexity. To achieve this, TMS, TAS, and TRS are mutually engaged in multiple feedback loops that trigger each other in response to such changes. For instance, a team that insists that only a particular expert tackles all tasks in their area of expertise (overemphasis on TMS), may quickly see that expert becoming a bottleneck. A balanced team (TMS and TAS) will show awareness of the bottleneck building up and find other members who are not experts but have adequate skills and availability to take some of the experts’ workload sooner rather than later. As we develop a deeper understanding of the emergence of these transactive cognitive systems, we also begin to see the role that technology can play in enhancing CI by enhancing the particular functions of each of these systems, particularly in the complex, dynamic environments and increasingly common remote work arrangements associated with MTM. Many of the typical cues that humans have evolved to depend upon for developing collective cognition are not present in these circumstances due to the lack of face-to-face interactions resulting from geographic distribution, technology-mediated communication and asynchronous work. Therefore, technology that could amplify or supplement the usual cues for developing shared memory, attention and reasoning could provide significant benefits. Many examples already exist for how technology is used by individuals to successfully augment individual cognition; in the next section we highlight these as well as describe very recent and developing technologies for augmenting collective cognition and, ultimately, collective intelligence.

USING ARTIFICIAL INTELLIGENCE TO ENHANCE CI Artificial intelligence (AI) offers a tremendous array of opportunities to enhance organizational performance in many areas. Unlike the automation made possible by earlier generations of technology, the AI of this fourth industrial revolution (Schwab, 2016) represents the possibility of sharing or even taking over control from humans in the context of human–machine collaboration. While ethics-related debates are ongoing with respect to the ways that AI agency should be limited (Danks, 2019; Falco et al., 2021), algorithmic management is already in place in organizations such as Uber and Airbnb, where workers’ compensation is fully managed by an algorithmic system without any human involvement (Cheng & Foley, 2019). Setting controversies aside for the moment, here we focus on the potential AI offers for enhancing collective intelligence by supplementing human cognition and coordination, par-

Enhancing collective intelligence in virtual teams  75 ticularly in the context of virtual work. After discussing both established and emerging trends in AI technology and their role in augmenting human cognition, we discuss the role of human trust in AI, which will be a critical piece in determining the degree to which AI will enable higher levels of collective intelligence. AI Enhancing CI in Virtual Teams by Augmenting Human Cognition As the use of AI is considered for a growing range of applications, a number of scholars have posed frameworks to conceptualize the relationship between AI-based technologies and human collaborators, typically differentiated by the level of capability and agency endowed in the AI (Malone, 2018; Murray et al., 2021; O’Neill et al., 2020). Consequently, depending on the level of control the AI has vis-à-vis the human, AI can play the role of an assistant, a coach, or a manager, which we will describe in more detail. At lower levels of agency, AI technologies function more as tools or assistants which can extend individual human capabilities while the human maintains full discretion and control (Malone, 2018; Murray et al., 2021). Many examples of such technologies are already widely used, and supplement all of the cognitive functions discussed, including memory, attention and reasoning. For example, humans have long used technology-based memory aids, from less sophisticated calendars to more capable digital assistants. There is even evidence that as users become accustomed to memory aids such as search engines and collection tools being available, they stop remembering different pieces of information and instead encode where we can go to find it again (Sparrow et al., 2011). Similarly, “to do” lists have also been used for a long time as tools to guide and focus attention, and more sophisticated versions proactively alert users to shift focus by, for example, connecting schedules and traffic information to prompt users that it is time to leave for an appointment to get there on time. We are also increasingly receiving algorithmic assistance for reasoning and decision making in a growing number of areas, whether it is to suggest products we might like or a movie we might enjoy based on our past choices on online platforms or prioritizing some search results over others when we are seeking information online to inform a decision or solve a problem. Increasingly these “assistants” operate in the background and without users explicitly considering the role they might be playing in guiding memory, attention, or reasoning processes. Thus, while users retain control over cognition and behavior as test AI tools are used, they can wield more influence in some cases than users realize. At an intermediate level of agency, AI technologies can function as coaches (Gupta & Woolley, 2021). Here, instead of (or in addition to) helping individuals make decisions or carry out taskwork, coach-level AI technology provides structure around the work (Murray et al., 2021) and enhances coordination among individuals, proactively offering insights or nudges that supplement or enhance transactive memory, attention and reasoning. For example: (1) For transactive memory, coach or peer-level AI tools help users find new information of relevance to them (Kittur et al., 2019) or identify who else might have expertise in an area relevant to their work. For example, in a virtual MTM context, Gupta and Woolley (2018) found that virtual MTM workers coordinating with a higher variety of different teammates were much more effective if they had access to information dashboards that helped them track information about other members’ developing areas of expertise, enabling them to allocate work more effectively.

76  Handbook of virtual work (2) Tools that foster transactive attention help coordinate the division of labor and “hand-offs” among team members at task transitions. One example is a platform that supports virtual “flash teams” (Retelny et al., 2014), or temporary, project-based virtual teams, where the platform breaks complex, multi-faceted projects into segments and coordinates task assignments and hand-offs among contributors. (3) Transactive reasoning is enhanced in virtual teams by tools that help detect or even shape the tendencies or preferences of collaborators to guide decision making. One subtle example is in the form of a “nudge,” based on the concept that has been popularized by behavioral economists as subtle changes to the choice environment which use people’s natural tendencies to lead them to choices that are more beneficial (Thaler & Sunstein, 2009). Nudging could be particularly helpful in the context of collective reasoning by either shaping preferences and behaviors or prompting collaborators to explicitly discuss or decide on issues they might not otherwise. One example is real-time feedback or awareness systems, which makes information salient to team members such as the relative level of contribution of each member to shared work, which discourages social loafing and increases accountability and trust (Glikson et al., 2019). Another example is an AI coach facilitator, who prompts members to consider and discuss different aspects of coordination in their joint work as it detects different issues with coordination (Gupta et al., 2019). At the highest levels of capability and agency, AI technology can serve as a manager or even in the role of fully designing the structure and the activity of carrying out virtual teamwork, perhaps even taking over control from humans (Murray et al., 2021). At this level, some tools can operate in the background, outside of awareness. When human users are aware of the involvement of AI, the nature of their reaction to this level of capability is an important determinant of the level of CI that can result. For example, there is variance in the degree to which users believe that AI can be a valid collaborator or “teammate,” with whom shared team cognition can be developed (Musick et al., 2021). It is also at this level of operation where workers who perceive coercion may pretend to comply but find ways to subtly undermine the algorithmic authority (Faraj & Pachidi, 2021; Pachidi et al., 2021). Even if humans are not rebelling against an authoritarian algorithm, there is considerable evidence that there are benefits to keeping “human in the loop” and structuring an AI–human partnership rather than giving the AI a more autonomous or authoritative role (De-Arteaga et al., 2020; Dietvorst et al., 2015). As AI is given a broader role in managing human collaboration, there are some interesting opportunities for structuring and facilitating work in ways to greatly enhance collective memory, attention and reasoning. (1) A core element of effective transactive memory in collectives is the development of humans’ shared understanding of members’ expertise. For this reason, extant work demonstrates that team composition, particularly as it impacts attributes such as cognitive diversity, is beneficial for the development of TMS (Aggarwal & Woolley, 2019; Todorova, 2020). However, teams are not always composed with sufficient diversity; groups and teams in many settings tend to be fairly homogenous unless effort is made to maintain diversity, as the tendency towards homophily leads members to self-select into groups that are similar to them and out of groups that are more diverse (McPherson et al., 2001). Ongoing research cutting across the organizational psychology and operations research domains has examined models for optimizing team composition, either

Enhancing collective intelligence in virtual teams  77 by drawing on optimal profiles (Donsbach et al., 2009; Omar et al., 2018) or combining heuristics with user preferences to balance the combination (Santhanam et al., 2011; Wax et al., 2017). In addition to getting sufficient expertise diversity in a team to foster robust TMS, it is essential that team members themselves have some understanding of who knows what to lay the groundwork for TMS to form and the team to integrate expertise (Liang et al., 1995; Woolley et al., 2008). (2) The management of attention appears to be another area where input from both an algorithm and humans produces better results. While the field of operations research has worked for decades developing algorithms for optimizing work scheduling and routing processes to maximize the efficiency of resource use (e.g., Trick et al., 2012) the complexities of human cognition make full automation of task assignment and scheduling ineffective in many situations. For example, a scheduling situation many struggle with is finding common times for meetings. While certainly an algorithm can easily schedule a meeting for a group of people if provided the necessary information, when people schedule their own meetings they factor in a whole variety of preferences and contingencies that made early versions of scheduling programs too rigid to be useful (Sen & Durfee, 1998). In response, researchers have developed more effective tools that can incorporate contingencies to identify situations that follow typical rules versus others where human judgment needs to be involved (Cranshaw et al., 2017). Similarly, with regard to distributed task work, as internet-based collaboration became more widespread, fueling the rise of crowdsourcing to accomplish isolated tasks (Afuah & Tucci, 2012), researchers explored how to leverage the ability of algorithms to break down complex tasks into smaller parts for distributed work and then recombine them into integrated products (Kittur et al., 2011). However, just as with memory processes, some observed it was hard for humans to contribute meaningfully without having a sense of the “big picture” (Hahn et al., 2016) which ultimately creates a bottleneck in collaborations where only a small number of people do most of the work. Thus, research continues to focus on ways to develop systems that can enhance the efficiency of attention allocation while allowing the level of engagement in decision making that is necessary for humans to contribute effectively. (3) Finally, with respect to collective reasoning, some areas of decision making have fully automated the combination of human inputs to produce decisions that are in some cases much more accurate than what groups decide via discussion and interaction (Surowiecki, 2004). More recent work has ventured into areas where human judgment is difficult to emulate but crucial to incorporate, such as medical decision making, where the majority of studies conclude that humans and algorithms together outperform either alone (Topol, 2019). In the publishing industry, Murray et al. (2021) describe how The New Yorker uses an unstructured machine learning program to select cover stories by examining an array of factors that it deems relevant including article quality, topic trendiness, and author reputation. The evaluation software then directly interfaces with its layout system to produce the cover, with the goal of minimizing editorial involvement. However, the algorithm could also select undesirable cover stories if it learns to value controversial authors with large online followings, language that generates online engagement by creating conflict, or superficial trends lacking the substance they typically aspire to deliver in their magazine.

78  Handbook of virtual work Looking further into the future and considering how AI could further enhance CI in virtual teams suggests the need for algorithms that can better emulate human judgment. Consequently, there is growing interest in endowing machines with the ability to understand human mental states, or a “Machine Theory of Mind” (Rabinowitz et al., 2018). There is also growing agreement that, in addition to understanding individual mental states to enhance individual capability, we also need to enable machines to understand how humans collaborate with each other and how machines can enhance collective intelligence (Riedl et al., 2020). The development of a “Machine Theory of Collective Intelligence” (Gupta & Woolley, 2021) requires better models for the development of collective cognitive activities including memory, attention, and reasoning which are the foundation of collective intelligence. Creating such a theory requires that we identify the key inputs necessary for a machine to interpret what it observes to adequately predict and influence the collective actions of a group of humans and, ideally, to enable higher levels of CI. An important foundation for a Machine Theory of CI would be the identification of observable collaborative process indicators to diagnose the state of a given collective. Recent research has identified three collaborative processes that are good diagnostic indicators of collective functioning and consistently associated with CI, including the level of collective effort, coordination of appropriate task strategy, and matching members to tasks and roles to achieve appropriate skill use (Hackman & Wageman, 2005; Riedl et al., 2021). In an appropriately instrumented environment, it can be possible for systems to gauge the quality of virtual teamwork collaboration and intervene or make other adjustments to facilitate improvement. For example, monitoring the level of effort and motivation by tracking member activity levels or emotional states (Van Kleef et al., 2012) can provide early indications of diminishing motivation levels and trigger a review of goal clarity and alignment. Indicators of coordination via activity patterns (Mayo & Woolley, 2021) can be diagnostic of the appropriateness of task strategy and prompt a review of systems for managing attention. Finally, the match between member expertise and time spent on tasks requiring that expertise, along with an increase in specialization among group members, can help diagnose appropriate skill use (Riedl et al., 2021). Deficits in this can prompt the initiation of corrective steps such as team training or the provision of additional information systems to help manage and direct information encoding, storage, and retrieval (Ren & Argote, 2011). Furthermore, with additional development of Diagnostic AI capabilities, such a system could take in many indicators such as those described and decide if, when, and how to intervene. With the continued development of AI, it is certainly possible to imagine systems that could completely “manage” and organize the interactions of virtual group members to routinely create high levels of CI. However, we also know humans seek autonomy and need to trust technological systems before complying with them. Consequently, the question of how humans develop trust in AI is of central importance. AI and Trust To effectively collaborate with others, whether humans or machines, collaborators need to develop trust. Trust is defined as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party” (Mayer et al., 1995, p. 712). Trust has been studied in a variety of team settings, including virtual teams and

Enhancing collective intelligence in virtual teams  79 multi-team systems, and is a key factor in the willingness of people to use different types of machines (Ferrario et al., 2020; Hoff & Bashir, 2015; Vogelpohl et al., 2020). Without trust, even highly capable systems may not be used, which will have a negative impact on collective intelligence (Ghazizadeh et al., 2012; Lee & See, 2004). On the other hand, inadequately high trust in under-developed machines may lead to over-reliance, leading to lower collective intelligence. Therefore, to facilitate high collective intelligence, there is a growing need to understand the factors that may facilitate and hinder human trust in the technology that is facilitating their work. Here we briefly review a few high level trends in work on trust in AI, differentiating between cognitive trust and emotional trust, which appear to have different antecedents in human–AI relationships just as they do in human–human relationships (i.e., Cook & Wall, 1980). For a more complete review, please refer to Glikson and Woolley (2020), Lockey et al. (2021), or Malle and Ullman (2021). Cognitive trust in AI Early research on automation and technology acceptance suggested that human trust in technology was based on the impression of the technology’s capability to carry out work. Thus, the more reliable the technology appeared to be, the more it was trusted. However, reliability is traditionally observed as a function of performance. When performance is readily observable, that is straightforward; however, many AI tasks are less easily observed or evaluated, such as a medical diagnosis or a hiring decision, where the counterfactuals are difficult or impossible to compare. Furthermore, such algorithms could still enhance collective intelligence even if the accuracy or reliability is not perfect, as long as its predictions are better than what the humans would do on their own. But how can we trust a technology whose reliability is unknown? The transparency of AI mechanisms can greatly enhance its perceived trustworthiness, and there are many efforts to develop “explainable AI” for this purpose (e.g., Ghahramani, 2015). However, most AI-enabled systems are black box algorithms, which means that no human or group of humans can reproduce the decision making process. Typically, black box algorithms do not follow well understood rules, but are “trained” with labeled data to recognize patterns or correlations in data, and as such can classify new data humans in a manner not detectable by humans (Durán & Jongsma, 2021). Under the conditions of relative or unknown reliability and low transparency, there are other inputs influencing the trust in AI, including emotions (Komiak & Benbasat, 2006). Emotional trust in AI How a technology is physically presented to users has a significant influence on their affective response, such as whether it is likable and trustworthy. Indeed, the external characteristics of artificial agents have a substantial influence on how we react emotionally (Hoff & Bashir, 2015). There are two particular aspects that consistently impact human emotions and trust, which include the form of embodiment and its level of anthropomorphism (i.e. human-likeness; Lee et al., 2006; Waytz et al., 2014). AI technology can be represented in different forms of embodiment, ranging from a fully embodied robot physically interacting with a user, to a virtual agent with a voice or appearing as an avatar with a name on a screen, to a fully embedded software application that is in essence invisible to users. Extant work suggests that the form in which humans interact with AI has implications for their emotional trust. For instance, Qiu and Benbasat (2009) found that

80  Handbook of virtual work the virtual embodiment of a recommendation agent significantly improved users’ enjoyment and trust (compared with an embedded agent), increasing perceptions of social presence. Thus, there is some evidence that we tend to place more emotional trust in AI when there is greater physical embodiment, giving more trust to virtual agents than embedded AI, and more trust to robots than virtual agents. However, more research in this area is needed, as very few empirical studies directly compare different types of embodiment (Glikson & Woolley, 2020). How human or animal-like a representation is also affects human emotions and trust. Presenting robots or virtual agents in a manner reminiscent of a living thing increases how much human users like it. For instance, Lee, Park, and Song (2005) found that a dog-like robot’s ability to improve its responsiveness had a significant effect on the robot’s likability and humans’ trust and increased users’ willingness to spend more time with it. Adding human-like features to a robot can also facilitate human trust (van Pinxteren et al., 2019). However, human-like representation can also evoke expectations of human-like capabilities, including communication skills, which in turn can cause disappointment and loss of trust when these expectations are not met (Ben Mimoun et al., 2012). The literature on how specific features of AI facilitate trust is growing rapidly (for a review see Glikson & Woolley, 2020; Malle & Ullman, 2021). Developing trust in AI in virtual teams For AI to enhance CI, the human team members need to develop the level of trust necessary to use or cooperate with the activities the AI facilitates. It stands to reason that the trust of team members in the AI technology would need to be fairly uniform, as even one uncooperative member could significantly undermine the ability of the team to realize intended benefits. Indeed, recent research on intra-team trust has pointed to the problems associated with a lack of consensus within a team related to trust, which is particularly common in culturally diverse teams (de Jong et al., 2020). If that variation in trust extends to an AI-based assistant, coach, or manager, it could lead to significant problems. Furthermore, extant research suggests that in some cases it might be quite difficult for trust consensus to form in relationship to AI technology depending on the technology’s embodiment. In research reviewed by Glikson and Woolley (2020), they identified a trend in the literature suggesting that, particularly with robots, users tend to approach new technology with low trust and suspicion, but that trust improved over time in the process of use. In thinking about how this might unfold in teams, this suggests that if team members set aside their doubts and try to work with the technology, over time there is a possibility it could gradually become more incorporated into the team. By contrast, when interacting with virtual or embedded AI technology, users exhibit initial excitement, curiosity and high trust, as well as very high expectations (Hoff & Bashir, 2015). In a team, this poses significant risks, as team members may place too much trust in the AI capabilities, and potentially may not even notice when its delivery is falling short. And if the team members do observe that the technology does not live up to their high expectations, the repair of trust required for them to use it again could be a very long and difficult process (Dzindolet et al., 2003; Manzey et al., 2012).

Enhancing collective intelligence in virtual teams  81

CONCLUSIONS: AI, CI AND THE FUTURE OF TEAMWORK Teamwork, and especially virtual teamwork, has become increasingly complex over the last several decades. Technological advances have both driven this growth in complexity but also provide potential opportunities for teamwork to thrive in the midst of it. We argue that these changes demand a shift in how researchers and organizations think about setting teams up for success. Rather than structuring teams to perform a particular task, the environmental complexity teams face requires they be designed for collective intelligence, which will enable them to accomplish goals across a wide range of environments. Here we described the transactive systems model of collective intelligence, which identifies the processes whereby individual memory, attention and reasoning gives rise to transactive memory, attention, and reasoning processes in teams. These systems engage in mutual regulation as they adapt to changes in a team’s task environment, supporting the emergence of collective intelligence. Technology has been both the source of complexity in teamwork as well as its potential solution. The growth in communications technology and internet collaboration has enabled the explosion of remote and virtual teamwork. The concomitant rise in specialization fueled by the technology-driven information revolution (Orton, 2009) has also led to more team-based work, including more virtual teams and multiple team membership. The complexity associated with managing virtual MTM is in large part a product of individual cognitive limitations. However, as we make more strides in AI and its ability to develop the social intelligence necessary to interact with humans, we start to see possibilities emerging for how technology might help manage the complexity it created. We described some examples of current and emerging AI technologies that provide opportunities to enhance memory, attention and reasoning in individuals and teams, and perhaps even increase collective intelligence. However, it is critical that we develop a better understanding of how to create the trust in AI that teams will need for this possibility to be realized. Human collaborators need to have realistic expectations of what the technology can and cannot do, and then be willing to cooperate with their AI-based peers, coaches or managers. If we succeed in developing the socioemotional relationships that will be necessary to allow true collaboration between AI and human teammates, then we can begin to imagine many exciting possibilities for teamwork moving forward. Imagine a system that could be easily embedded in the digital work environments of virtual teams, that could quickly and competently translate any language such that collaborators’ native language no longer matters. Or that could quickly find an expert in practically any area a team is lacking knowledge and facilitate the integration of the expert into the existing team’s workflow. Maybe there could be a digital project manager that proactively communicates and manages hand-offs and the flow of information, tracking members’ workload and availability and moving tasks forward as quickly as possible. And perhaps this same project manager continually tracks the motivation and effort of different team members and proactively intervenes to mitigate any misalignment of goals or developing conflict. As researchers, it is also important for us to think about the implications for the ways we conduct research on teams moving forward. If we could succeed in developing a Machine Theory of Collective Intelligence, it would articulate a host of variables that could serve as measures of team process, perhaps unobtrusively captured as byproducts of team collaboration in digital environments. These could become important companions to some of the self-report

82  Handbook of virtual work measures that many field studies often rely upon, as well as provide the potential for much larger samples and robust study designs. AI-based team members could even serve as confederates in laboratory experiments! Some of these notions make some researchers fear that there will come a day when we no longer have any “real” human relationships, and it is interesting to consider if that will be the case, or will our partnership with AI enable humans to spend more of our time utilizing our uniquely human, social capabilities? Perhaps if more of the burden of coordinating work could be off-loaded, and work got done more efficiently, then spending less time working might become a possibility. There are obviously a whole host of ethical, legal, regulatory, cultural and normative practices that need to be navigated to make such a system possible, not to mention a lot more research and development of digital and social technology. However, we see a variety of exciting possibilities for more teams to reach higher levels of collective intelligence by design, and for all of us to experience the benefits.

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88  Handbook of virtual work Waytz, A., Heafner, J., & Epley, N. (2014). The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. Journal of Experimental Social Psychology, 52, 113–117. https://​doi​.org/​ 10​.1016/​j​.jesp​.2014​.01​.005 Wildman, J. L., Thayer, A. L., Rosen, M. A., Salas, E., Mathieu, J. E., & Rayne, S. R. (2012). Task types and team-level attributes: Synthesis of team classification literature. Human Resource Development Review, 11(1), 97–129. Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330(6004), 686–688. Woolley, A. W., Gerbasi, M. E., Chabris, C. F., Kosslyn, S. M., & Hackman, J. R. (2008). Bringing in the experts: How team composition and work strategy jointly shape analytic effectiveness. Small Group Research, 39(3), 352–371. Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039. Yaghootkar, K., & Gil, N. (2012). The effects of schedule-driven project management in multi-project environments. International Journal of Project Management, 30(1), 127–140. https://​doi​.org/​10​ .1016/​j​.ijproman​.2011​.02​.005

5. Principles on how to manage interactions between human workers and artificial intelligence/machine learning technologies Michael A. Zaggl and Ann Majchrzak

In the digital workspace, artificial intelligence (AI) and machine learning (ML) technologies have entered the daily activities of a wide range of professions (Brynjolfsson & McAfee, 2014; Davenport & Harris, 2007; Lee & Shin, 2020; Ransbotham, 2017; Zerilli, Knott, Maclaurin, & Gavaghan, 2019). We define AI/ML technologies as technologies that support human decision-making, or make decisions on humans’ behalf through the classification and/or prediction of events and cases without being explicitly programmed (Mitchell, 1997; Zanzotto, 2019). These technologies have been described as assisting humans in their professional work by supporting workers’ operational activities and automating workflows, either improving outcome quality or reducing costs. However, such tools have also been found to have serious negative consequences for humans and the economy (Kane, Young, Majchrzak, & Ransbotham, 2021). In this chapter, we analyze the challenges of avoiding these negative consequences and provide a framework for conceptualizing how to avoid such consequences. AI- and ML-based technologies offer challenges for work in ways that differ from earlier technological innovations that have been used in work situations. Earlier technical innovations – relative to AI/ML technology – maintained a relatively clearer physical and cognitive separation between human workers and technology. Technologies reaching from the steam engine to robotics mostly supported or substituted physical labor. Interrelations between the two are often rather simple and standardized. For example, at industrial assembly lines, robots and humans work together, each having specific tasks. Such technologies work best under a high extent of task modularity, that is when certain tasks can be unambiguously assigned either to humans or the technology (Puranam, Raveendran, & Knudsen, 2012; von Hippel, 1990). Thus, activities of humans and machines can be relatively unambiguously distinguished from each other. By contrast, when it comes to AI/ML technology, the integration between human work and technology work is more complex and less apparent. In the digital workplace and the context of knowledge work, AI- and ML-based technologies have become ingrained in human workers’ operations, and task modularity between human and machine tasks is very low (Ransbotham et al., 2020). Thus, a clear-cut modularization of shared labor is not possible. The outcome of humans jointly working with AI/ML technology becomes a complex product of reciprocally interdependent tasks (Puranam & Raveendran, 2013; Thompson, 1967). Ex post, the contributions of humans and machines cannot be disentangled, and the contributions of humans and technology are aggregated and work outcomes become causally ambiguous. The task modularity is weaker (i.e., task interrelatedness becomes stronger) when the distinctions between human workers’ tasks and machine tasks become blurry. For example, AI-based newsfeeds select the content to which a journalist, who works on a story, is exposed. In this 89

90  Handbook of virtual work way, technology manages the worker’s attention based on their prior selection of news items. Another example is AI-based created art (Epstein, Levine, Rand, & Rahwan, 2020). The technology, when selecting content, has learned to emulate the human worker’s preferences and interests. Because this learning is imperfect, the outcome cannot be unambiguously ascribed to the human or the technology. Aggregated and causally ambiguous outcomes have undesired characteristics because they create issues for accountability, legal culpability, ethics, quality of work, inability to identify and resolve causes of problems, and the increasing likelihood of unstoppable and egregious (“normal”) accidents (Perrow, 1999). Our goal of this chapter is to shed light on the relationship between human workers and “machines” (i.e., AI/ML technology) by developing a framework that leads to design principles to manage the human–machine interactions and reduce causal ambiguity, which is the inability to trace back characteristics of an outcome to either the technology or the human worker. We first highlight two characteristics of AI/ML technology that drive causal ambiguity in the context of knowledge work and decision making. These two characteristics are (a) interdependencies among the technologies, and (b) learning by the technology. Both characteristics lead to complexity and causal ambiguity of the aggregated outcome of the human–machine interaction. (We recognize that sometimes the human’s role in the interaction is almost completely usurped.) Second, in order to specify the interactions between human workers and AI/ ML technology, we build on principal–agent theory (Eisenhardt, 1989; Jensen & Meckling, 1976; Ross, 1973). Principal–agent theory assigns the human worker with the role of the principal and AI- and ML-based technology the role of an agent. The theory allows us to classify three relational actions that both human workers and AI/ML technology must engage with when interacting with each other: (i) information provision, (ii) performance evaluation, and (iii) monitoring. Third, by connecting the relational actions with the two characteristics of AI/ ML technology causing causal ambiguity, we outline the challenges of human–machine interactions and derive six principles of how to manage them. The principles help human workers to reduce causal ambiguity by making the interactions and the information flow more explicit. Thus, the design principles contribute to maintaining human workers’ integrity. We provide two major contributions. First, we go beyond the literature that acknowledges the complex interrelations between humans and machines by characterizing these relationships as different forms of principal–agent interactions and describing their dynamics. Second, we develop design principles on how to manage the interactions between human workers and AI/ ML technology in order to minimize causal ambiguity and maintain human workers’ integrity.

HOW ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNOLOGY CREATE CAUSAL AMBIGUITY AI/ML technology is a general-purpose technology (Brynjolfsson & McAfee, 2014). As with many other such technologies, AI and ML are used in a large variety of applications. In the work context, AI/ML technology constitutes a primary source of automation. However, in contrast to “traditional” automation technology, which does not use such technology, AI/ML technology endogenously evolves without the need for explicit instructions (Zanzotto, 2019). This endows AI/ML technology with a degree of autonomy or, at least, the ability to develop such autonomy (O’Neill, McNeese, Barron, & Schelble, 2020; Parasuraman, Sheridan, & Wickens, 2000).

Interactions between human workers and AI/ML  91 Thus, ML/AI technology is potentially advantageous as it has a stronger potential to emulate human decision-making and behavior – augmenting human work (Barro & Davenport, 2019; Davenport & Kirby, 2015; Dellermann et al., 2019; Raisch & Krakowski, 2021; Stone et al., 2016). Autonomy (partly) relieves human workers from fulfilling decision tasks. However, it also creates the risk of reducing the human’s autonomy. Endowed with its own autonomy, AI/ ML technology may also substitute or augment workers’ activities (Autor, 2015; Stone et al., 2016; Tschang & Almirall, 2021). Table 5.1

Examples of substitution vs augmentation perspective and traditional automation technology vs AI/ML technology

 

Substitution perspective:

Augmentation perspective:

Technology replaces human workers

Technology assists/complements human workers

“Traditional” automation technology

A.

B.

Spinning Jenny

Sewing machines

Printing press

Visual programming tools

ATM AI/ML technology

C.

D.

Self-driving cars

Pattern recognition (image)

Online bots

Newsfeeds Dashboards

Source: Authors’ own.

In order to clarify the issue, Table 5.1 provides a simplified view by distinguishing between traditional automation technology, which does not involve AI/ML technology, and AI/ML technology; and between substitution and augmentation of human work by the technology. In Cell A (substitution through traditional automation technology), technology replaces whole job roles and careers. Examples are the “Spinning Jenny”, a mechanized spinning wheel, which changed labor and societal conditions from the late eighteenth century in Britain (Allen, 2009); Gutenberg’s printing press in the fifteenth century in Germany (Sasaki, 2017); and ATMs (Automated Teller Machines) replacing many jobs in banking in more recent years. In contrast to the substitution perspective, there are those traditional automation technologies that provide augmentation (i.e., they take an assisting role to humans so that technology facilitates human work rather than replaces it). The collaboration between machines and humans makes work processes more efficient or work outcomes more effective. Important is that the human is not entirely replaced and the machine does not work on its own. As shown in Cell B, technology without AI and ML can provide significant help to humans such as by making it easy to program through visual aids or sewing garments with a sewing machine. Sewing machines, for example, need to be operated by a human, who can work creatively with these machines (Nayak & Padhye, 2018). In total, these technologies can also threaten human workers by saving human labor but they do not have the capability to replace their job roles completely since humans contribute significantly. Instead, the technologies augment certain human skills by providing complementary skills. AI/ML technology can also be used to completely replace human workers (see Cell C in Table 5.1), in this way, AI/ML technology has often been characterized as a substitute for human knowledge work (Autor, 2015; Stone et al., 2016). Examples include self-driving cars

92  Handbook of virtual work and trucks replacing human drivers (Badue et al., 2021; Stone et al., 2016), and online bots (Ferrara, Varol, Davis, Menczer, & Flammini, 2016) replacing customer service associates (Hadi, 2019; Tuzovic & Paluch, 2018) and judicial or administrative public service jobs (Niler, 2019; Zerilli et al., 2019). Cell D shows the focus of our chapter: AI/ML technology that is designed to augment human work. In this cell, AI/ML technology augments specific human skills (Davenport & Kirby, 2015; Raisch & Krakowski, 2021). This causes human activities to become more closely intertwined with machine activities than in the other three cells. For example, a radiologist is guided by the prediction generated through pattern recognition and categorization of an image resulting from a magnetic resonance scan. In this way, the AI/ML technology supports the radiologists but does not replace their job (Meek, Lungren, & Gichoya, 2019); instead, the technology’s advantage over the human in identifying certain patterns complements the diagnosis. AI/ML technology takes the role of a highly specialized agent providing specific functionality to the radiologist. In this augmenting use of AI- and ML-based technology, the actions of the human and the machine are closely interrelated. When the task is completed (the diagnosis of a patient), it is impossible to trace back and separate the contributions of the human and the machine. The radiologist will submit her diagnosis and it is unclear to what extent it builds on the machine or human experience. Another example is gene synthesis, which also facilitates knowledge work by allowing teams of researchers to synthesize genes using automation technology instead of relying on natural specimens; thus, the technology allows the teams to augment their abilities in creating new genetic sequences (Zaggl & Pottbäcker, 2021). A newsfeed informing a journalist working on a news story is another example of augmentation-focused AI/ML technology. This technology directs the attention of the human worker. The news items suggested by the technology build on the journalist’s prior selections. However, the newsfeed technology cannot write the story on its own. AI/ML dashboards are similar to the example of the newsfeed. They also direct a manager’s attention through the selection of the information and its representation. Thus, when the story is finished and written up, it becomes murky what has been contributed by the journalist and what has been contributed by the technology. Also, dashboards based on ML technology learn from the human worker as it augments the human’s work. Some cases of human–machine interaction blur the borders between human workers and technology even further, such as the creation of art using AI/ML technology (Epstein et al., 2020). In the following, we elaborate on the augmentation perspective and outline synergies that are enabled when human workers and AI- and ML-based technology work together closely. Then, we outline two characteristics of this interaction. These two characteristics are the (main) reasons for causal ambiguity in human–machine collaborations, creating challenges for managing this interaction. Synergies between Humans and AI/ML Augmentation Technology In the augmentation perspective, per definition, AI/ML technology takes a complementary role to human skills and vice versa (Raisch & Krakowski, 2021). AI/ML technology provides several kinds of advantages overcoming shortcomings of human workers (Zerilli et al., 2019). First, information processing capabilities are much higher in computers (and increasing). Relative to the human worker, a computer’s advantage lies in processing large amounts of data very quickly in a shorter time than humans do. This makes AI/ML technology very successful

Interactions between human workers and AI/ML  93 in pattern recognition. Compared with humans, AI/ML technology can take more training data into account, which leads to higher quality outcomes (i.e., more accurate predictions and decisions). Second, error and efficiency are not dependent on the workload, in contrast to human decision-makers (Danziger, Levav, & Avnaim-Pesso, 2011; Endsley, 2016). Third, even though AI/ML technology can be biased depending on the training data used, it is less prone to subjective biases, such as anchoring (Kahneman, 1992; Tversky & Kahneman, 1974) or confirmation (Edwards & Rodriguez, 2019). Fourth, AI/ML technology, in contrast to humans, has no inherent motivation to behave selfishly or opportunistically. By contrast, humans also have advantages over AI/ML technology and computers in general. First, humans can abstract the data and technology outcomes to a higher level of understanding (Pohl, 2008). Second, human workers are able to take into account the specific and highly unique contextual conditions affecting decisions. This ability increases the ethical and localized quality of the decision-making outcome (Frize, Yang, Walker, & O’Connor, 2005). Third, humans are better than computers at estimating and forecasting the range and possibility of consequences of decisions. For example, the pilot of the 2015 Germanwings plane crash in Prads-Haute-Bléone (France) could set the autopilot to an altitude of merely 100 feet above sea level (Bureau d’Enquêtes et d’Analyses, 2015), and the technology was not able to anticipate the tragic consequence of this setting. Instead, the technology relies on a responsibly acting human worker who is aware of the impact of instructions. The different and complementary advantages of each actor in the human to AI/ML technology relationship can lead to synergies in which they interact with each other closely in performing a task. The human and the cockpit technology together fly the plane or the car. The human and the recommendation engine together help decide on the next movie to watch. The human and the AI-enabled machine decide together on the best material and orientation to create a cut. However, this close interaction, while often highly productive, may lead to causal ambiguity, which becomes particularly relevant when problems occur and the desired outcome is not achieved (e.g., an incorrect diagnosis is given). Next, we identify two characteristics of AI/ML technology that explain how human–machine interaction leads to causal ambiguity. Reasons for causal ambiguity when humans and AI- and ML-based technology interact Causal ambiguity in a human to AI/ML technology relationship – which we defined as the inability to trace back characteristics of an outcome to either the technology or the human worker – can be attributed to at least two characteristics of the technology: (a) the interdependencies among the specific tools of the AI/ML technology, and (b) the fact that the ability to learn is embedded into the technology, thereby making it dynamic. These two characteristics create a situation in which an external observer, and even the human worker, finds it to be unclear as to how to attribute contributions. Is the radiologist performing the diagnosis and the radiologist simply rubber-stamping the technology’s prediction or is the radiologist ignoring the technology’s prediction building the diagnosis merely on gut feeling? What political stance does the journalist bring to the table or is the political viewpoint advocated in their column a result of an unbalanced selection of sources? Who is responsible for the decision made? Interdependencies among the AI/ML technology components Interactions between the human worker and AI- and ML-based technology usually are repeated. Automation, almost per definition, is used for interactions that occur routinely and repeatedly over time. As time accumulates, the technology components used will change. If

94  Handbook of virtual work a part of a system has been automated, it often becomes viable to automate other parts too. For example, AI- and ML-based decision support mechanisms are often added to existing systems (Elgendy, Elragal, & Päivärinta, 2021; Lyytinen, Nickerson, & King, 2020). With the cumulation of joint task solving, more and more human decision-making characteristics are adopted by AI/ML technology. If multiple AI/ML technologies are used by the same worker or the same team, they create multiple machine–human relationships (Elgendy et al., 2021; Lyytinen et al., 2020). These relationships begin to create an increased number of interdependencies, which become difficult to track back to a specific source when something goes wrong. The accumulation of different technologies adds causal ambiguity, including complexity and confusion to the human–machine system. Humans have increasing difficulties in discerning information sources, especially when they work in teams in which humans indirectly interact with machines (Zaggl & Pottbäcker, 2021) – that is, humans interact with humans who have interacted with machines. The influence from the machine indirectly loops through the human who is unaware that a machine was involved. Learning by the technology The second aspect that is typical of the interrelations between humans and AI/ML technology is the technology’s ability to learn from the outcomes of the task as well as from the human. Learning is specific to AI/ML technology. Similar to interdependencies among AI/ML technology components, learning also requires repeated interactions. The technology has the ability to improve its decisions as it gains experience from feedback that occurs as a result of the interaction. Learning is essential to the definition of AI/ML technology (Mitchell, 1997; Zanzotto, 2019). In the example of the radiologist, learning by the technology occurs as new data is added to the learning data and as diagnoses and image recognition are approved or overwritten by the human. The ongoing interactions produce more learning data. In the example of the journalist using the newsfeed, the newsfeed system can learn as its recommendations are self-reinforced by using choices made by the journalist or other similar journalists. As with the interdependencies of technology, the learning by the technology also causes causal ambiguity, complexity, and confusion. Since the ML pattern matching will be adjusted in many ways to accommodate the new data, it becomes nearly impossible to track back to whether current decisions are the results of today’s ML pattern matching, yesterday’s, or the human. Consequently, every decision made becomes a reflection of a tight coupling between humans and technology. This tight coupling may in fact be efficient, but it becomes quite difficult to sort out what to change when problems arise. How to break up the tight coupling is what led us to conceptualize the relationship between humans and AI/ML technology as a principal–agent problem.

PRINCIPAL–AGENT RELATIONSHIPS Principal–agent theory is a basic framework describing interactive relationships between unequal partners (Eisenhardt, 1989; Ross, 1973). Typically applied to human-to-human interactions, principal–agent theory explains why people develop specializations and how these specializations can be utilized through delegation. In principal–agent theory, the principal is typically conceptualized as an actor with a problem and the agent as the actor with the necessary skills and knowledge to solve the problem (Harris & Raviv, 1978; Ross, 1973). Thus,

Interactions between human workers and AI/ML  95 agents are specialized experts for solving a specific class of problems. This specialization creates an information asymmetry to the agent’s advantage. The agent can evaluate the relevant information better than the principal can. A classic example is a lawyer who serves as an agent on behalf of a client (the principal). The lawyer knows more about the legal domain than the client does and thus can make better decisions. By applying principal–agent theory to interactions between humans and AI/ML technology, we suggest that the principal in the relationship is the human (who has a decision to be made or a problem to be solved), and the AI/ML technology is the agent (with a specialized set of capabilities to help address the decision or the problem). In this way, the principal and the agent have an asymmetric relationship in that the agent has more information about specific aspects of the problem and more control over specific parts of the situation than the human worker (Eisenhardt, 1989; Ross, 1973).1 Having more information requires endowing the AI/ML technology also with more autonomy. Otherwise, the special set of capabilities cannot be utilized. Autonomy of technology has been identified as a central construct in human–machine interactions (Endsley, 2016; Parasuraman et al., 2000). However, this constitutes a dangerous situation because it leaves the human vulnerable to the decisions of the AI/ML technology. It creates a black box situation. What is needed in order to reduce this problem is to examine the kinds of interactions between humans and technology, which in turn leads to ways to manage the interactions. Principal– agent theory provides a way to structure and examine these relationships. To manage the interactions between principal and agent, the literature has emphasized that the relationship is governed by three different relational actions. We build on these relational actions as a basis for managing human–machine relationships. The actions are: (i) information provision from the principal to the agent, (ii) performance evaluation of the technology, and (iii) monitoring of both the agent and the principal (Laffont & Martimort, 2002). See Figure 5.1 for a graphical display of these three relational actions. Information Provision Typical of any principal–agent relationship is that the principal needs to provide information to the agent. This information is essential for instructing the agent and fulfilling the task (Eisenhardt, 1989; Kumar & Faynzilberg, 2000). This includes in addition to the specification of the desired outcome, constraints in the procedure, critical background information, and work and information flow requirements, such as check-in points, validation checks, work-in-process decisions, alternative decision paths considered, sources of data used, and alternative data sources considered and used. In the example where the lawyer is the agent, the client would provide the lawyer with all necessary information about the case up-front and update this information if necessary. We refer to this aspect of the principal–agent relationship as information provision. When it comes to the collaboration between human workers and AI- and ML-based technology, any instruction from the human side to the technology is a form of information provision. The technology needs to accept inputs on desired outputs but it should also allow queries, in-process redirections, and maintain what-if scenarios. The human worker provides input in the form of a sample or a query to the machine. For example, the radiologist submits a scan of a patient to an ML-based software that tries to find patterns hinting at cancer tissue. The radiologist could also query an ML-based software with the demographic data of the patient

96  Handbook of virtual work

Source: Authors’ own.

Figure 5.1

Principal–agent framework: relational actions between human workers and AI/ML technology

(age, gender, etc.) to estimate the probability that a certain kind of cancer has developed. Also, a manager selecting the key performance indicators (KPIs) of their dashboard provides the technology with information on how to perform. The technology will produce the KPIs and arrange them based on the specifications. Performance Evaluation/Feedback In classic principal–agent relationships, the principal evaluates the agent’s outcome and performance as soon as the agent has delivered a result (e.g., a recommendation or a suggested decision). Often the compensation of the agent is partly coupled to the outcome quality (Harris & Raviv, 1978; Holmström, 1999). Such a performance-dependent compensation should incentivize the agent and increase its effort and avoid opportunism (Ittner, Larcker, & Pizzini, 2007; Prendergast, 1999). Performance evaluation and evaluation in general also provide feedback information that allows the agent to improve; put simply, to learn. The agent can receive a form of bonus that is paid if the project is completed in time and within budget. We refer to this aspect of the principal–agent relationship as (performance) evaluation. Applying this to human–machine relationships where AI/ML-based technology is the agent instead of a human actor, performance evaluation takes a different – although comparable – role. AI/ML technology receives feedback and input that is deliberately aimed at learning. For example, the radiologist using image recognition technology can improve the machine by providing it with new or additional training data. The need for compensation is absent.

Interactions between human workers and AI/ML  97 However, the analogy of a bonus is not too far-fetched in the context of human–machine interactions since, for example, reinforcement learning is built on the same idea of rewarding desired behavior. Monitoring In classic principal–agent relationships, the principal keeps track of the agent’s activities. However, the principal cannot perfectly oversee the agent’s activities because of the information advantage on the agent’s side (Spremann, 1987). In the traditional principal–agent relationship, observing helps to control the agent’s behavior. We refer to this aspect of the principal–agent relationship as monitoring. In relationships between humans and AI/ML technology, monitoring is necessary to avoid undesired behavior of the technology. Interestingly, the literature on automation has put the technology in charge of monitoring, mainly because technology has advantages over humans such as it does not suffer fatigue and has fewer limitations in data processing (Bainbridge, 1983; Zerilli et al., 2019). However, from the principal–agent relationship, it becomes clear that it is the technology that needs to be monitored. Monitoring is especially important given the fact that AI- and ML-based technology adapts. Online bots, for example, cannot be unleashed without continuously observing them, as shown by the famous example of the Twitter bot “Tay”, which was terminated because it learned to post obscene and racist tweets (Davis, 2016).

CONNECTING THE CHARACTERISTICS OF AI/ML TECHNOLOGY WITH THE RELATIONAL ACTIONS In this section, we juxtapose the two characteristics of AI/ML technology that drive causal ambiguity (i.e., interdependencies among the AI/ML technology components and learning by the technology) with the three relational actions (i.e., information provision, performance evaluation, and monitoring) from the principal–agent perspective. This allows us to derive several principles for the design of the AI/ML technology-to-human interactions in order to reduce causal ambiguity. Table 5.2 provides an overview. (Cell A) Information Provision × Technology Interdependencies When considering the challenge based on interdependencies among technology components as a driver of causal ambiguity together with information provision, we develop a perspective that highlights how causal ambiguity can be reduced by standardizing information exchange. The challenge lies in the issue of the “black box character” of how the information provided by the human worker diffuses and changes when processed by different AI/ML technology components. The interdependencies among the technology components create causal ambiguity because it is not possible to infer from the outcome to what extent it has been shaped by the input information or the components of the technology. Every information input the human workers enter into the technology is processed. If the technology consists of multiple components that have been assembled providing specialized functions and perhaps interacting with each other, the diffusion of the information becomes murky. It is unclear which element of the

98  Handbook of virtual work Table 5.2

Overview of the challenges and design principles resulting from the connection of the relational actions with the characteristics of AI/ML technology (a) Interdependencies of Technology (Multitude (b) Learning by Technology (Technology

(i) Information Provision

of different technologies that partly interact)

evolves and changes)

A. Challenge: Ambiguity about how query

D. Challenge: Information provision can drive

and selection information diffuses through the

unintended learning (e.g., newsfeeds)

multitude of interdependent systems

Design Principle 4: Notifying human workers

Design Principle 1: Establishing input

about how information provision is used by the

information templates and standards

technology

(ii) Performance Evaluation B. Challenge: Ambiguity about how evaluation

(iii) Monitoring

E. Challenge: Ambiguity about how evaluation

(feedback) information diffuses through the

information influences the model (as training

multitude of interdependent systems

data, for reinforcement learning); lack of

Design Principle 2: Standardizing evaluation

accountability

criteria at each interface between technology

Design Principle 5: Regular scrutinizing of

components C. Challenge: Ambiguity about what is being

learning data F. Challenge: Observing current states are

monitored at any given point in time given that

insufficient; change is usually ignored or

everything is a reflection of multiple technologies underestimated and interactions

Design Principle 6: Monitoring the status of

Design Principle 3: Establishing process actions

the learning technology itself to decide on the

to be monitored and expectations for action

need for rollbacks, or acceleration of learning

reviews and corrective action if monitoring falls below expected thresholds

Source: Authors’ own.

technology receives the information and in which form. The information might be transmitted in its original form, enriched, reduced, or altered. Thus, human workers face a black box to which they submit information without understanding how it is used to influence the outcome. In order to mitigate the problem of ambiguity about information diffusion and change, we propose Design Principle 1: Establishing input information templates that define and enforce standards for input information. These templates ensure the minimization and standardization of information inputs. Only essential information should be submitted to the system. The submitted information should be in a standardized format that is unchanged when information is passed through the different parts of the system. The minimization and standardization both help the human worker to develop an intuition of the relevance of the input data, thus the technology as a black box becomes a little less dark. Minimization and standardization also support encapsulation, which means that the information is not unnecessarily mingled with parts of the system but remains isolated from the rest of the system. In our example of the radiologist, Design Principle 1 means that only the x-ray image is submitted to the system as the essential information for the specific analysis. Other information such as the patient’s demographics (if these data are not explicitly processed by the technology) should not be submitted to the technology. This ensures the minimization of information input. Only if the ML prediction model uses both forms of data (image and demographics) together should they both be entered. Further, the image needs to conform to a standardized format, which ensures the principle of standardization. Different formats contain different metadata (e.g., timestamp of the image, camera device), so using different formats will inevi-

Interactions between human workers and AI/ML  99 tably violate the criterion of minimization. The chosen standard format should contain only the necessary metadata (minimization). Similarly, when the radiologist uses a query to estimate the baseline probability of whether a particular patient has developed a certain symptom, they should only submit the information that is relevant for the model. (Cell B) Performance Evaluation × Technology Interdependencies Ambiguity about the diffusion of information applies not only to the provision of information but in a similar way when feedback information is produced for the purpose of evaluation of the AI/ML technology. Technology interdependencies represent a similar danger when the technology is evaluated as it does for information provision. The information submitted for the purpose of evaluation contributes to the ambiguity of how information is used by the AI/ML model(s). The more information is submitted, the more difficult it will be for the human workers to understand the technology, its evolution, and the technology’s predictions/ recommendations. Interdependences of technology components make it unclear how the evaluation information diffuses. Some components can use the information for different purposes. Evaluation information is particularly critical when it is a (partial) outcome of the human–machine interaction. If so, self-reinforcing effects can be created by some technology components. Of course, feedback information is essential for AI technology to work, thus we aim to optimize the trade-off between richness of feedback information and parsimony and put forward Design Principle 2: Standardizing evaluation criteria at each interface between technology components. Only if interfaces between the technological components are properly defined can causal ambiguity be prevented. The information flow through these interfaces can be understood and managed. In the example of the radiologist, Design Principle 2 demands a definition of how evaluation information enters all the technological components of a medical ML system. The radiologist might enter into the system whether a previously predicted diagnosis turned out to be correct after proceeding with surgical intervention. The design principle ensures transparency of how this information is diffused to the component of the system. (Cell C) Monitoring × Technology Interdependencies The accumulation of technology represents a critical challenge for monitoring. Monitoring has the purpose of controlling the technology. It should avoid (or at least reduce) deviations between the actual and the desired behavior of the technology; it has a corrective function. The increasing complexity because of technological accumulation and interdependencies makes monitoring highly important but also difficult. Different systems need to be observed as individual components as well as their interactions and their holistic workings. From a skeptical perspective, tightly coupled systems without enough slack to correct mistakes inevitably lead to failure and accidents (Perrow, 1999). Thus, monitoring needs to be precise and immediate. Monitoring is particularly challenging when it comes to AI/ML technology because monitoring itself is a task that is preferably delegated to automation technology. The technology is not subject to human weakness such as fatigue. Human attention is highly limited, especially when compared with technology – thus humans are not the natural choice when it comes to monitoring tasks (Bainbridge, 1983). However, if technology is used for monitoring, the part of the technology expands further and more interdependencies are created. Thus, monitoring

100  Handbook of virtual work always needs to involve human activities, and – consistent with the principal–agent framework – we focus on the necessity of humans (the principals) monitoring AI/ML technology (the agent). Design Principle 3: Establishing multiple process actions to be monitored and expectations for action reviews and corrective action if monitoring falls below expected thresholds. Conforming to the principle means to specify objects of monitoring and defining thresholds and limits as well as the actions to be triggered when limits are exceeded. This simplifies monitoring and makes it possible to have humans in the monitoring loop. It also allows adding further technology that supports the monitoring activities by humans as long as this monitoring technology remains isolated from the rest of the system that is subject to the monitored activities. (Cell D) Information Provision × Learning by Technology The ability of AI/ML technology to learn from the interactions with the human workers represents a challenge for the provision of information to the technology. Information intended for the purpose of information provision provided by the human worker to the AI/ML technology can be used for learning. This misappropriates the function of information provision, and the human worker becomes confused about the nature of the human–machine collaboration. The worker does not understand in which ways the technology influences the collective decision-making and behavior. Without noticing human workers can create echo chambers through self-reinforcement effects, which occur, for example, in information feeds (Barberá, Jost, Nagler, Tucker, & Bonneau, 2015). This is often inevitable. In the example of the journalist using a newsfeed to keep her up-to-date with current events and sentiments, the mingling of information provided with evaluation is inevitable. Thus, it is impossible to maintain a clear separation between information provision and feedback information. Therefore, we advocate a basic awareness of this fact and propose Design Principle 4: Notifying human workers about how information provision is used by the technology. As soon as provided information is used for technology learning – basically overstepping the function of information provision – human workers need to be notified about this fact. As is the case in many usage-driven systems that build on reinforcement learning or recommendation systems, this overstepping is often inevitable. Thus, the principle should ensure transparency by notifying the human worker when entered information is used for purposes other than the necessary provision of information in order to execute the task at hand. The human worker should become aware of whether the input is used for instructing the machine or also for the purpose of learning. Newsfeeds, for example, disguise the fact that through the selection of certain news items (i.e., information provision), the technology not only executes the current task but also uses the same information input to learn about the worker’s individual preferences. Such a blending of the information that is needed for using the system and the information processed as a basis for technology learning could leave the human workers with the illusion that the information they are exposed to is much more objective than it actually is. Notifying the human worker creates awareness about the usage of information and also helps the human workers to see potential biases that could be produced even by small shortcomings in information quality or coverage. It cannot resolve the problem of creating an echo chamber for the human worker, but it could make it easier to break out of such a self-reinforcing cycle and make the human worker deliberately search for opposing sources. The journalist, in our

Interactions between human workers and AI/ML  101 example, who uses newsfeeds to inform their reporting, can actively search for evidence countering the opinion that the newsfeed presents when he is notified about the mingling between the selection of news items and the perpetuation of certain kinds of results. (Cell E) Performance Evaluation × Learning by Technology The ability of many AI/ML technologies to learn from the interactions with the human workers represents a challenge as it creates ambiguity about how evaluation information influences the models. Because programming as the main instructional mechanism is replaced by feeding in data (Zanzotto, 2019), much of humans’ deliberation and reflection that determine the technologies and their workings are removed. When it comes to performance evaluation information and technology learning, more sophisticated solutions are necessary than simply raising awareness through notification. Feedback information is supposed to fuel learning. It is essential in particular for the notion of machine learning. Thus, exploiting evaluation information as feedback to improve the technology is key for the effectiveness of human–machine systems. Therefore, information intake is not only a necessity (as it is with information provision) but desired. However, AI/ML technology should be prohibited to develop without the human worker’s progression that parallels and counterbalances the technological development; a process, which we refer to as collaborative development. Collaborative development between humans describes a co-evolution; as humans adapt their behavior based on the possibilities and constraints introduced by AI/ ML technology as well as they change and adapt the technology (Dafoe et al., 2021). Ideally, technology improves along with human skills and their understanding of the technology. This avoids technology learning without human workers’ awareness. Collaborative development involves active and deliberately acting humans that directly engage with the technology. To manage the information flow that stimulates learning and enables collaborative development between humans and machines, we suggest Design Principle 5: Regular scrutinizing of learning data. All feedback information should be assessed as it drives the learning by the system. In the example of the Twitter bot “Tay” (Davis, 2016), the assessment and the scrutinizing of the tweets the bot was exposed to and on which basis it learned would have helped to anticipate the bot’s negative development. Scrutinizing this learning data, which was clear in sexist and racist nature and probably purposefully created by internet trolls, would have made it easy to anticipate the bot’s future character. Consequently, a technology’s development can be corrected just by knowing the evaluation data. (Cell F) Monitoring × Learning by Technology The necessity to monitor AI/ML technology, in general, is well known (Parent-Rocheleau & Parker, 2021). We can specify this to the main issue with monitoring when it comes to learning technology; this issue is the change of the technology. On the one hand, change through learning is the key capability of AI/ML technology and its main advantage. As argued before, change does not occur through deliberate and reflected instructions, for example, through programming as is the case with traditional automation technology but through learning based on training data or reinforced learning mechanisms (Zanzotto, 2019). Therefore, AI/ML technology can adapt to conditions and develop strengths that do not require an a priori or top-down design. On the other hand, change is a serious and continuous threat because the technology

102  Handbook of virtual work can also develop undesired characteristics, as the Twitter bot “Tay” (Davis, 2016). Thus, regular scrutinizing of the information used for learning, as proposed by Design Principle 5, is not sufficient. Although this principle reduces the necessity of monitoring, it cannot satisfactorily replace monitoring for assessing the status of the technology. Both learning data and learning technology can interact in ways that prohibit knowing the state of technology when only knowing the learning data. Thus, a design principle for monitoring the learning technology is needed and we put forward Design Principle 6: Monitoring the status of the learning technology itself to decide on the need for rollbacks, or acceleration of learning. Regular interventions that assess and realign the technology in its role as the agent of the human worker are an essential part of the principle. In contrast to Design Principle 5, this principle addresses the state of technology (its current performance, behavior, etc.) and not the learning information that shaped this state. Put simply, Design Principle 5 looks at the learning stimuli whereas Design Principle 6 is about the state of the learning technology. Design Principle 6 requires implementing interventions that check how the collaborative development between human workers and AI- and ML-based technology has shaped the technology. Stress tests exposing the AI/ML technology to extreme learning stimuli in an isolated sandbox reveal weaknesses. The goal is to monitor – and where appropriate to realign and correct – the AI/ML technology based on its original purpose and design. Essential is that a relatively consistent expectation of the technology’s purpose and its performance are predefined. Such definitions ideally are already specified before the time of technology implementation. Design Principle 6 also helps to adjust the human worker’s expectations for the technology. The monitoring activity needs to occur frequently since the technology evolves dynamically and possibly in unexpected directions. The activity can result in a continuation of the human–machine system as it is, the correction of the technology, or – in the most extreme case – the timely termination of the technology. Reversely, increasing the sensitivity to learning stimuli and acceleration of learning are possible if the stress tests can corroborate reliability and robustness of the technology.

DISCUSSION AND CONCLUSION In this chapter, we aimed to shed more light on the interactions between human workers and AI/ML technologies. We distinguished AI/ML technologies from earlier forms of automation and found that AI/ML technology is often more closely interrelated with human workers than in the case of traditional automation technology. Task modularity – the separation between technology and human workers – becomes less clear in the case of automation with AI/ ML technology. The close interrelation creates the problem of complexity and especially causal ambiguity. We identified the characteristics of AI/ML technology underlying these issues: interrelatedness of AI/ML technology and learning by the technology. By building on principal–agent theory (Eisenhardt, 1989; Ross, 1973), we could clarify the roles of humans and technology as principal and agent and allocate responsibilities and autonomy to each of them. Moreover, this conceptualization provides a means for examining their relationship by looking at three relational actions: information provision, performance evaluation feedback, and monitoring. We suggest specific design principles for managing this relationship in which both the interaction process between human and machine as well as the outcomes of the

Interactions between human workers and AI/ML  103 human–machine collaboration are considered in a standardized manner. These design principles help to manage human–machine collaboration and reduce causal ambiguity. Limitations of Design Principles The design principles do not come without cost. They increase the demands on the human worker. For example, Design Principle 4 proposes the notification of the human workers when the information they provide to the system is not just used as instruction but instead further drives the learning by the AI/ML technology. The human worker needs to comprehend this notification, take it seriously, and process it by taking contextual information and ethical and moral viewpoints into account. If the human worker instead ignores the notification, the principle is futile. Thus, this design principle puts additional burden on the human worker. Designing efficient ways to effectively notify are critical. Therefore, this and all other principles need to be considered wisely and their effectiveness needs to be validated. The responsibility to implement the principles is with the organization in which the human– machine collaboration takes place. Much can be done by educating the workers. However, this is not sufficient and precise governance is needed. Although a few design principles could be (partly) automized themselves (e.g., Design Principle 3 and Design Principle 4), we want to hint at the fact that this would not only create further potential for causal ambiguity and demand for more design principles, it would also weaken the power of the principles to keep in check AI/ML technology (Kane et al., 2021). Before implementing the design principles, they need to be empirically tested and perhaps further specified to be applicable for certain AI/ML technologies in specific contexts. Our knowledge about human workers’ understanding of how AI/ML technology accumulates and learns needs to be improved. Although we know much about the criteria making human workers better collaborate with machines (Lyons, Wynne, Mahoney, & Roebke, 2019; Nass, Fogg, & Moon, 1996; Wynne & Lyons, 2018), we have only a rough insight into their understanding of the technologies working. Without a rudimentary understanding, it is impossible to estimate the technologies’ influence and overcome causal ambiguity. Future research needs to address this issue first. Research Implications The information and autonomy asymmetry between humans and AI/ML technology is inherent in the relationship and will continue to exist unless explicitly managed. The technology has information hidden from the human worker and driving the autonomy of the technology. Building on human-to-human principal–agent relationships, we have argued that this asymmetry should be managed since it can harm workers’ autonomy and gives the AI/ML technology too much power. Our design principles propose ways to achieve this. They rely on standardization of the information that is shared about the interaction, process, and outcomes, how the shared information will be evaluated, and how learning and progress will be monitored. The developed design principles help to tackle the issue of complexity and causal ambiguity. A result of the tight interaction between human workers and technology is that the outcomes become causal ambiguous. Causal ambiguity is not just a problem for accountability – to decide who is responsible in case of failure, but more importantly to ex ante allocate responsibilities in the system. Perrow (1999) describes the characteristics and tendency of

104  Handbook of virtual work complex human–machine systems to produce failures. Causal ambiguity is a central element in the sources of failure because it is highly difficult to anticipate the outcomes of causal ambiguous systems. The developed principles shed more light on human–machine collaboration and thus allow the untangling of the interrelationships between humans and AI/ML technology. The journalist, for example, can better judge to what extent her story has been influenced by the working of technology. Overall, the principles also contribute to prior research that has suggested emancipatory approaches, such as committees and audits, to keep in check AI- and ML-based technology (Kane et al., 2021). The principles provide detailed guidance for designing modern work environments involving human workers and AI/ML technology. Overall, the principles contribute to more effective, efficient, and secure interactions between humans and technology. Besides this more direct contribution in the form of the developed design principles, our framing has three additional implications for future research: (1) managing the information and autonomy asymmetry between human–AI/ML technology interactions, (2) managing the causal ambiguity for attribution of problems as outcomes of bi-directional human–AI/ ML technology interactions, and (3) (conceptual) work on the two characteristics of AI/ML technology–human relationship separately and together. While much research has discussed the design of AI/ML technology (Domingos, 2012; Lee & Shin, 2020), our analysis suggests the value of conducting research on the relationship between human workers and technology (Gillies et al., 2016; Wynne & Lyons, 2018). In our conceptualization, that relationship is rife with information and autonomy asymmetries. These asymmetries create a situation of likely failure of the relationship by giving too much power and control to the technology, leaving little for the human to do than monitor outputs. Moreover, when problems arise in performance, the causal ambiguity attributed to the tight coupling between the human and the AI/ML technology cannot be sorted out quickly or effectively (Amodei et al., 2016), leading to increased probability of disastrous “normal” accidents (Perrow, 1999). Thus, we suggest engaging in a review after events that create problems in performance, and then incorporate corrective actions into the human–machine relationship. Therefore, research is needed that focuses on this relationship, either using the relational actions we have identified, or others that help to define this relationship in a manner that moves design thinking and theorizing forward. The issue with tightly coupled AI/ML technology-to-human relationships is the causal ambiguity that surfaces when something goes wrong – and of course, something will always go wrong (Perrow, 1999). While much current research is focused on explainable AI (Doran, Schulz, & Besold, 2017), we suggest that causal ambiguity can never be eliminated since it is inherent in the learning of the technology and the interdependencies among the tools in the technology. The issue is not to discern how the AI/ML technology makes all its decisions and explains them, but instead makes the decisions that affect the relationship with the human. For example, does the human need to know which part of the dataset was held out for a training sample as long as the human is told it was a random sample of a gender-biased population? Similarly, does the technology need to know that the human ignored recommendations because they were offered when the human was attending a personal matter, or simply that the recommendation was neither important enough nor created a strong enough signal to capture the human’s attention? As we continue research on explainable AI, our conceptualization suggests that the focus of such research should start with a customer journey map of a realized process of that interaction, with two-way information sharing. Obviously, all contingencies

Interactions between human workers and AI/ML  105 cannot be assessed, but at least those more likely to lead to performance problems created by the relationship can become the focus of such research. Finally, we have suggested that, at a minimum, there are two characteristics of AI/ML technology that create the causal ambiguity inherent in the human–machine relationship: interdependencies and learning. We have analyzed each characteristic separately and jointly. Both provide a useful lens for future research. Future research should build on these characteristics and consider how other features specific to AI/ML technology affect the human–machine relationship. Categorizing AI/ML technologies and identifying a more comprehensive set of characteristics that are relevant for their interaction with human workers is the next logical step. Examples of such characteristics are prediction quality, the number and difference of variables considered, the transparency of information processing, and the traceability of prediction of the technology (e.g., deep neural networks vs single-layer perceptron network). We selected these two characteristics – interdependencies and learning – because they drive causal ambiguity, which then creates a problem of managing the ambiguity when using AI/ ML technologies.

NOTE 1. There is a non-overlapping characteristic when applying principal–agent theory to relationships between humans and technology. In human–human principal–agent relationships, the agent can behave opportunistically by using her information advantage for her personal gain. In the example of the client–lawyer relationship, the lawyer could recommend starting a lawsuit even though she knows that the chances of winning are too low. She can present the chances in a better light and convince the client to decide to go to trial. Her benefit would be that she could charge fees for her work in the trial. It is difficult to find such opportunistic behavior even in the most sophisticated AI/ML technologies as long as we can assume that the technology has not been deliberately manipulated. Thus, opportunistic behavior is a non-overlapping characteristic when applying the principal–agent framework to human–machine collaboration.

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6. Refocusing human–AI interaction through a teamwork lens Christopher Flathmann, Beau G. Schelble and Nathan J. McNeese

Alongside the rapid development and progression of AI algorithms, parallel efforts have been made to apply AI algorithms to human-facing systems. Over the last few decades, computational systems have been mostly relegated to automating basic and repetitive tasks alongside humans, thus creating human–automation interaction (Lee & See, 2004). However, the last few years have seen major strides made in artificial intelligence (AI) allowing for more dynamic and complex problems to be solved. What separates the technologies is that automation is often task-oriented and lacks the flexibility to handle changes in the task it was designed for (known as brittleness), but AI has the capability to handle tasks with dynamic features and may even have the potential to handle multiple tasks in a dynamic environment, which also makes it more capable of working alongside humans in these environments (Wynne & Lyons, 2018). For example, implementations of automation would include using a robotic system to repeatedly place a specific object in a delivery bin, but an AI system would be able to receive various items and sort them into different delivery bins based on various features such as color, shape, and even function. Thus, recent developments have sought to incorporate complex AI systems alongside humans to build more efficient workforces and collaboratively solve problems that would be too complex and dynamic to be automated (O’Neill et al., 2020; Woods et al., 1991). The result of this process is human–AI interaction, which refers to both the physical and digital interactions that humans have with technological systems that are infused and governed by AI algorithms (Amershi et al., 2019). Unfortunately, while the field of human–automation interaction served as a suitable starting point for human–AI interaction research, the rapid growth in the computational capabilities of AI has more distinctly separated the technology from its automation predecessor (Lyons et al., 2021; O’Neill et al., 2020). Subsequently, the field of human–AI interaction is outgrowing its foundation in human–automation interaction, and a new research perspective is required to continue the advancement of the domain. The increased variety and complexity that has separated AI from automation has in turn impacted the complexity and variety in human–AI interaction research, making the field unfocused (Yang et al., 2020). The unfocused nature of human–AI interaction that currently exists demonstrates that the needs and requirements of the field may not be fully sustained by adapting human–automation interaction principles. Consequently, to allow the field to build off existing literature and focus on existing principles, a different research paradigm should be prioritized as a foundation for human–AI interaction over human–automation interaction. This chapter argues that teaming is the most relevant paradigm through which the field of human–AI interaction can be focused due to three factors: the prevalence of teamwork in the modern workforce; the widespread use of AI systems by modern teams; and the fact that AI systems are becoming more like teammates and less like automation tools. 109

110  Handbook of virtual work Given that this is a handbook of virtual work, we wish to make the observation that human– AI interaction, by its very nature, is virtual. Because virtuality is by definition involving interaction through technology (Kirkman & Mathieu, 2005), and AI requires a technological medium through which human–AI interaction is possible, the current chapter on human–AI interaction offers a unique perspective on virtual work. Moreover, modern and future virtual work will naturally involve a degree of human–AI interaction, such as through team task recommender systems (discussed later). Thus, the unique perspective provided by this chapter, which views human–AI interaction from a teaming perspective, will only become more relevant as the integration of AI and in turn the degree of virtuality present in teams that use AI will only increase as the technology continues to advance. The following chapter is split into three overarching sections that outline the currently unfocused state of human–AI interaction and present teaming as a perspective that can be used by researchers to refocus the domain. Section 1 details the currently broad and unfocused state of human–AI interaction as well as the domain's current attempts to consolidate research efforts. Section 1 additionally provides five different examples of real-world human–AI interaction sub-domains and discusses their differences and impacts on modern teams. Section 2 details a solution to help refocus human–AI interaction, that is, viewing and conducting human–AI interaction research through a teamwork lens. More specifically, Section 2 outlines how the concept of teaming meets the requirements of human–AI interaction, provides unique research opportunities for human‒AI interaction, and discusses current research in teaming-oriented human‒AI interaction. Section 3 provides specific recommendations for research objectives and the teaming principles that will help complete the aforementioned objectives. Additionally, Section 3 takes the opportunity to provide recommendations for regulations and standardizations and discusses some open-ended questions regarding the current state of human‒AI interaction research.

HUMAN‒AI INTERACTION AND WHERE TO FIND IT While the initial operationalization of human‒AI interaction was a simple iteration of human‒automation interaction where AI was trained to handle basic tasks efficiently, recent research efforts have shifted the design of AI towards something known as human-centered AI. The research, development, and implementation of human-centered AI looks to shift AI from being a task-oriented technology to being a technology that holistically considers their entire environment, which includes the task, the humans they work with, and their impacts on society. This shift towards human-centered AI has mostly been in response to public perceptions towards AI that highlight the potential negatives of the technology if it is not designed with humans in mind. Consequently, this shift in the perception surrounding AI research also further separates it from human‒automation interaction as automation is often more heavily oriented towards task specific requirements, which makes the domain limited in its ability to handle the requirements of human-centered AI. Thus, this section highlights these requirements through two main contributions: (1) a detailing of the current state of human-centered AI and the public perceptions that led to a shift towards that perspective, and (2) a discussion of various applications of human-centered human‒AI interaction that demonstrate the variety and breadth of the field.

Refocusing human–AI interaction  111 Public Perceptions on AI and Responses to Those Perceptions Trending perceptions about AI Due to the broader integration of AI technology into society in recent years, many researchers have taken a step back to examine the current challenges facing the human factors comprising human‒AI systems. While research communities often see AI as a promising technology that is much more flexible and adaptable than its automation predecessor, societal perceptions often concentrate on the potential negatives of the technology. For instance, media outlets often emphasize the adverse outcomes of AI without balancing that coverage with the positive potential of the technology (Brennen, 2018). Moreover, these common perceptions boil over into other forms of media and representation that impact peoples’ perceptions of AI. Examples of this media include popular film and TV series such as The Terminator, Blade Runner, Ex Machina, and even Westworld. While these depictions of AI appear highly fictional, especially to researchers, the concerns they raise can be valid, and they can often inspire negative perceptions of AI technology in society (Cave et al., 2018). Fortunately, these concerns and negative perceptions have since inspired new research centered around addressing the deficiencies and challenges facing the roles and implications of AI systems in society. Responding to society’s concerns with human-centered AI Due to societal concerns, recent research efforts to improve human‒AI interaction and societal perceptions of AI technology have led to an emphasis on a concept known as human-centered AI. Human-centered AI shifts the focus towards making AI systems responsible and capable actors within society, which includes improving their compatibility with humans alongside its technological capabilities (Xu, 2019). For instance, bias of AI systems has become highly talked about recently due to its clear and identified impact, and researchers have begun not only outlining its effects on society but also proposing solutions to prevent biased AI systems from impacting society (Ntoutsi et al., 2020). Other popular topics of research include the design and implementation of explainable AI systems, which seeks to remove the common concern around questionable black-box algorithms (Abdul et al., 2018). Additionally, work is being done to examine the holistic experience humans have with the systems as opposed to their initial reactions, which is key to understanding how traditionally social concepts and perceptions may impact system acceptance and use in the long-term (Knijnenburg et al., 2012). Efforts to build more human-centered AI systems are not limited to the above examples; however, the variety of the above examples demonstrate the current breadth of human-centered human‒AI interaction research and having a consolidated research foundation is critical to ensuring that all of these concerns are addressed not only individually but holistically as well. Attempts at consolidating human-centered AI research Additionally, internal work is still being done to explicitly refocus and consolidate human‒AI interaction research to provide more convenient lists for researchers and practitioners to follow when designing AI systems. A recent landmark publication in human‒AI interaction consolidated various efforts to provide researchers with a holistic list of essential design considerations outlined by practitioners and researchers. These considerations were derived from modern AI systems and include, but are not limited to, adherence to social norms, mitigation of bias, clearly defined capabilities, and constant transparency (Amershi et al., 2019). Each rec-

112  Handbook of virtual work ommendation was derived from literature in specific sub-domains of human‒AI interaction, and each looks to research and optimize specific AI technologies for use by humans. Unfortunately, while providing researchers and practitioners with eighteen helpful guidelines, this work also demonstrates the almost unmanageable complexity of human‒AI interaction. Additionally, while some of these guidelines have a foundation that can be derived from human–automation research, many of them are unique to human‒AI interaction and may be hard to design for without a foundation derived from previous research. Thus, while the guidelines outlined by this work and others are critical human‒AI interaction, the operationalization of each of these guidelines for every system built is a daunting task that most likely will not be accomplished. Moreover, this task becomes more complicated when one accounts for the rapid pace that the field of AI is developing at, which necessitates rapid acceleration in the field of human‒AI interaction, meaning this list will need to change constantly. Seeing Human‒AI Interaction in the Wild Now that the current state of human-centered human‒AI interaction has been outlined, it is important to discuss various implementations of human‒AI interaction, and in doing so, the breadth of the field can be demonstrated while also highlighting common goals that are shared between different sub-domains. First, AI-enabled recommender systems will be discussed, which are applications that filter large amounts of data to provide users with a small selection that is more manageable. Second, AI-enabled management systems will be addressed, which refers to systems that utilize AI technology to coordinate and assign teams’ resources and personnel so humans can focus on other, less data centric tasks. Third, virtual agents will be highlighted, which represent a sub-domain in human‒AI interaction that often looks to use AI to make effective social interactions with humans through virtual characters. Fourth, AI-enabled robots, which utilize AI algorithms to physically interact with humans and other objects, will be reviewed. Finally, self-driving vehicles, which are automobiles that are controlled either partially or completely by computational AI algorithms, will be analyzed. These five sub-domains also represent different manifestations and roles of AI technology that differ widely between each other. AI-enabled recommender systems One of the most common utilizations of human‒AI interaction is in the sub-domain of recommender systems. Recommender systems often use AI and machine learning algorithms to filter vast arrays of options down to smaller, more manageable selections that humans can choose from (Resnick & Varian, 1997). These systems are often incorporated in contexts ranging from media content platforms (i.e., YouTube or Netflix) to e-commerce sites (i.e., Amazon or eBay). Such systems can be critical in helping teams make fast and accurate decisions by removing options that do not merit human consideration (Wu et al., 2020). Interactions with these systems are often more minor as humans generally interact with the data provided by the system rather than directly interacting with the systems itself. While more interactive recommender systems have been created, they often utilize other interaction techniques, such as virtual agents, to facilitate human interaction.

Refocusing human–AI interaction  113 AI-enabled management systems Virtual management systems often take more generalized roles and provide basic assistive duties, such as scheduling, information retrieval, or resource management for teams or individuals, and they have seen their role increase significantly in the past decade due to the rise in popularity of smart home assistants and scheduling management systems (Perez Garcia et al., 2018). Like recommender systems, virtual management systems are critical in modern society as they provide a way for teams and individuals to offload simplistic tasks that computers are more capable of handling efficiently. For instance, healthcare AI systems are frequently required to adapt on the fly to the dynamic availability of healthcare professionals and the wants and needs of patients (Yu et al., 2018). While management systems are required to be highly in-tune with the teams they service they often minimize the amount of direct interaction humans have with them. These considerations result in systems remarkably different from other AI systems. Their degree of autonomy needs to be more significant to minimize the need for human intervention, allowing humans to focus on other essential tasks that AI cannot handle, thus resulting in minimal but critical instances of human‒AI interaction. AI-enabled virtual agents Virtual agents often provide the greatest interaction for humans; however, the actual interaction and conversation are often seen as the most critical aspect of their design. Typical representations of virtual agents include video game characters or agents that handle humans’ interactions with AI-enabled management systems. However, the goals of these agents often center not around their technical abilities but around their ability to interact with humans positively. For instance, anthropomorphism, including voice selection, graphical representation, and natural language ability of virtual agents, become critical factors in determining the content and quality of interactions humans have with these assistants (Rafailidis & Manolopoulos, 2019). Interactions with these agents are often more prolonged and social, but these systems become more practical when implemented as an interaction modality for a more complex technology. AI-enabled robotic systems AI-enabled robotics refers to using AI models to complete necessary robotic functions, such as “learning, planning, reasoning, problem solving, knowledge representation, and computer vision” (Murphy, 2019). What separates robotic systems from other examples of human‒AI interaction is that the tasks they look to complete and their interactions with humans often occur in a physical space and require the manipulation of physical objects. Popular examples may range from home robotics, such as automated appliances (i.e., Roombas), to complex manufacturing systems, such as vehicle assembly systems. The usage of these systems is critical to modern teams as they can alleviate the physical stress and requirements of specific workloads while also fully enabling individuals to complete tasks not typically possible. Unfortunately, their emphasis on physical environments and physical safety can often make it more difficult to bridge research from other, more digital sub-domains within human‒AI interaction (Khandelwal et al., 2017). AI-enabled self-driving vehicles Finally, a rapidly growing instance of human‒AI interaction is self-driving vehicles, where AI systems can either partially or entirely operate a vehicle. This technology can help revolution-

114  Handbook of virtual work ize modern society by providing individuals incapable of driving a means of transportation while also providing a safer and more convenient environment for humans. Specifically for modern workforces, the advent of autonomous vehicles will allow individuals to utilize their cars as mobile offices that allow them to work during their commute, thus reducing overall downtime. Additionally, entire fleets for delivery and transportation have the potential to become autonomous. Interactions with these systems can differ greatly based on the level of autonomy the AI systems has, with low levels needing interactions extremely similar to normal vehicles and high levels of autonomy requiring extremely simplistic interactions often done through apps. Like general robotics systems, the physical safety of self-driving vehicles is often researched; however, larger-scale scheduling problems also become a concern due to the potential multitude of vehicles that can exist simultaneously (Badue et al., 2021). The five sub-domains outlined above in addition to the prior discussion on human‒AI interaction’s recent expansion create a picture of a research domain that is currently unmanageable for individual researchers to fully comprehend, especially when using a perspective solely derived from human–automation interaction. The above domains illustrate various roles and responsibilities created for AI systems, many of which are dynamic in nature and execution. Thus, a new approach to human‒AI interaction is needed that meets the needs of human‒AI interaction research, provides a common starting point for research to relate to, and helps bolster and ground the results of researchers in the community.

USING A TEAMING PERSPECTIVE TO REFOCUS HUMAN‒AI INTERACTION To help focus the field of human‒AI interaction, this chapter proposes the use of a teamwork perspective where AI is a team member as opposed to a tool for automation, which has multiple benefits to the field of human‒AI interaction. First, the field of teaming has decades of research with topics including the use of technology by teams, teammate perceptions and their impacts on teaming, and even the ability for humans to team with non-humans (Salas et al., 2005). Using this past literature will greatly benefit the field of human‒AI interaction as many new and popular research concepts can be bolstered through highly similar and more researched concepts within teaming. For instance, the popular research topic of awareness in human‒AI interaction would heavily benefit from a strong foundation in team cognition, which is a topic in teaming with decades of research demonstrating its measurement and benefit to teams. Second, teaming provides a host of unique research topics that have yet to be fully explored in human‒AI interaction but would provide a needed foundation for future, critical research. For instance, the concept of leadership in human‒AI interaction, which is currently understudied in human‒AI interaction would be effectively “jump started” using teaming literature, thus increasing the pace at which critical research can be conducted and verified (Flathmann, Schelble, & McNeese, 2021; Larson & DeChurch, 2020). Finally, the examples of human‒AI interaction highlighted above all have real-world applications within teaming, with manufacturing teams heavily utilizing robotics systems (Cherubini et al., 2016), healthcare teams becoming reliant on virtual management systems (Sezgin et al., 2020), and analytics teams utilizing recommender systems to increase productivity (Damiani et al., 2015), just to name a few examples. Thus, the teamwork lens also

Refocusing human–AI interaction  115 provides an explicit path towards integrating human‒AI interaction research into real-world systems and the teams that will inevitably use them. The above justifications for the use of a teamwork lens when conducting human‒AI interaction research (represented in Figure 6.1) will be further elaborated on below with specific examples on teaming concepts that could be highly relevant to human‒AI interaction.

Source: Authors’ own.

Figure 6.1

Graphical metaphor representing the use of teamwork as a focusing mechanism for human‒AI interaction

Teaming is a Standard for Human‒AI Interaction As mentioned above, when viewed as a standard for interaction, teaming can become holistically inclusive of the requirements and considerations outlined by human‒AI interaction research. Moreover, AI supported teamwork, a sub-domain of both human‒AI interaction and teamwork, necessitates a stronger adherence to human‒AI interaction design considerations as the impacts of agent design and their interactions with humans are observably apparent through teamwork. For example, trust, a critical component of human‒AI interaction (Glikson & Woolley, 2020), has been heavily researched within the field of teamwork (Costa, 2003). Fortunately, the existence of a robust research foundation around trust made it much easier to develop and validate the importance of trust in AI supported teams (McNeese et al., 2019). This finding is but one of many examples of teamwork research having a firm and historical foundation that new research is finding important to human‒AI interaction. Unfortunately, without a teaming perspective, human‒AI interaction would lack a historic and studied foundation that allows modern research to build on historical findings. While lacking this foundation would not prevent human‒AI interaction research from being conducted, it will ultimately hinder the pace at which this research can occur as the prior research foundation from which experiments and hypotheses can be derived has not yet been fully established. Utilizing a teaming lens provides that foundation and experimental history, allowing for

116  Handbook of virtual work a natural starting point for researchers and practitioners to hypothesize how AI’s design and development may impact humans through interaction. Additionally, it is essential to note that the utilization of teaming as a scoping mechanism does not require the evolution of AI platforms to a level that is comparable to humans. Using a teaming perspective allows the collaboration and coordination of multiple different entities, regardless of their ecology. Historically, teaming has not been a phenomenon exclusive to humans, as animals have been shown to possess the ability to form and operate within teams of varying complexity (Anderson & Franks, 2001). Moreover, teaming provides a means of removing the barriers between these ecological differences, with humans and animals having teamed up for centuries. Research has even shown that the model of human‒animal teams is a robust and comprehensive example for human‒robot interaction as language and interaction barriers must be overcome in similar ways (Phillips et al., 2016). Thus, the variety of sub-domains within human‒AI interaction can not only be accounted for by teamwork, but their unique strengths can be leveraged through teaming. As a final note, the ability for teaming to bridge ecological barriers also extends to its ability to bridge technological barriers. As mentioned above, the concept of teamwork is already familiar to recommender systems, virtual agents, virtual management systems, self-driving vehicles, and robotics research. Thus, bridging the gaps between these domains and allowing cross-collaboration can be more easily facilitated through the shared commonality of teamwork. The key to this collaboration is understanding the unique roles humans and AIs can take to create interactions that are more than the sum of their parts. When viewed and implemented through a teamwork lens, human‒AI interaction can ultimately create meaningful interaction regardless of the entities of the team, the team’s environment, or goals. Unique Affordances Provided by a Teamwork Perspective Tying human‒AI interaction concepts to more robust teaming concepts Beyond meeting the requirements for human‒AI interaction, adopting a teamwork perspective also provides unique affordances that will advance the field of human‒AI interaction. These affordances are supported by the extensive amount of literature focused on studying traditional human‒human teamwork. The affordances offered by the existing human‒human literature give human‒AI interaction researchers an advantage not offered to virtually any other field of research. Specifically, human‒AI interaction can benefit by leveraging the well-known tools, concepts, and strategies from human‒human teams to enhance things like cohesion between the human and the AI (Mou & Xu, 2017), levels of effective shared understanding between the two (Schelble et al., 2022), and even bias mitigation (Flathmann, Schelble, Zhang et al., 2021). Human‒AI interaction can uniquely benefit from using the foundation developed by human‒ human teaming for several reasons, specifically, that teams are widespread in our society, existing in a wide variety of different environments and situational contexts. This diversity in application and theory makes adapting the traditional human‒human teaming research to human‒AI interaction far more manageable than starting from scratch. Human‒human teaming is ubiquitous across the various contexts of the workforce, which means it exists in nearly every application and domain in which AI may one day play a role. This ubiquity gives researchers a significant head start in researching and improving human‒AI interaction in settings like healthcare and entertainment while also anticipating the various roles AI may one day play in our society. Using the healthcare setting as an example,

Refocusing human–AI interaction  117 human‒human teaming researchers have discovered several concepts, guidelines, and tools specific to improving teaming in healthcare (Weller et al., 2014). These improvements benefit not only the teams themselves but tangential outcomes like medical device design and training programs, all of which are tied to the teams’ overall goal of patient outcome. Developing valuable insights in human‒AI interaction can be advanced by adopting various concepts of team dynamics from human‒human teaming literature. By its very nature, human‒ AI interaction is research into the dynamic relationship between the human and the AI they are engaging with to complete a task or query. As such, the teamwork perspective has a great deal to offer in the way of valuable concepts, frameworks, and instruments. For example, several concepts within human‒AI interaction would benefit from being framed through teaming concepts like team cognition and awareness. Human‒human teaming has been developing the concept of team cognition for decades (Orasanu, 1992), which embodies the idea that individuals within a team have shared knowledge that is organized and distributed amongst themselves (Cannon-Bowers et al., 1993). This concept and even its measurement tools can easily be adapted and applied to essentially any human‒AI interaction; however, more importantly, it provides a validated and practical framework to improve the shared understanding and effectiveness of human‒AI interactions. Alternatively, awareness provides an example more deeply rooted in design principles but has long been of significant importance to both practice and theory. Awareness describes how individuals within a collaborative environment know what others are doing, why they are doing it, and how this relates to their activities (Dourish & Bellotti, 1992; Gross, 2013). Given how important explainable AI and transparency have become in the various guidelines to human‒AI interaction, the teaming concept of awareness represents decades of work that can be quickly and easily adapted to frame better human‒AI interactions. Transitioning difficult concepts from teaming Expanding human-centered AI design to meet the requirements of teaming necessitates unique design considerations for AI agents that need to be further developed and researched. Certain teaming concepts can enhance multiple aspects of human‒AI interaction like usability, trust, and efficiency. Unfortunately, these concepts may not be as easily transferred to human‒AI interaction contexts. Such concepts require a better understanding of the unique dynamics between humans and AI, especially in teaming. These gaps have begun to be addressed by the literature, but a great deal of work remains. This challenge is exacerbated by the breadth of the current work in human‒AI interaction as many sub-domains within the field are closely related to these two specific concepts like AI-managed agents, while others are far more distant, like recommender systems. It is worthwhile to conduct this future research and place human‒AI interaction into the lens of teaming so the disparate contexts within the field can begin to work from the same models and theories, reaping more benefits from one another’s work. For instance, leadership is a monumental construct within the teaming literature with several models, theories, and empirical work (e.g., Morgeson et al., 2010); however, these concepts do not directly port to human‒AI interaction because of the unique role AI takes in the relationship of human‒AI interaction. Thus, while leadership dynamics have clear ramifications for human‒AI interaction in systems where humans interact with an AI they perceive as subordinate or vice versa, additional steps must be taken before human‒human leadership principles can be applied to human‒AI interaction. As another example, influence describes the effect that one user’s experiences, perceptions, and actions have on other team members in

118  Handbook of virtual work any capacity, but generally on their perceptions, actions, and effectiveness (Gruenfeld et al., 2000). This concept deals directly with interpersonal dynamics in teaming and has significant implications for human‒AI interactions, but again, unfortunately, there is a dearth of literature analyzing influence between humans and AI. Though AI research is still in its relative infancy, that is no reason not to look towards the future and address these challenging concepts, advance the field, and ensure we are genuinely prepared to implement applied AI for human interaction. Current human‒AI interaction work viewed through a teaming lens The teaming community has begun to recognize the importance of integrating existing teaming concepts with AI teammates, producing valuable research for human‒AI interaction design (O’Neill et al., 2020). This research can be seen as a stepping stone to producing research more applicable to applied human‒AI interaction. For example, initial conceptual models have been developed for leadership in AI-supported teams (Flathmann, Schelble, & McNeese, 2021), human perceptions of the ideal AI teammate (Zhang et al., 2021), and human‒AI cooperation (Schelble et al., 2021). While team cognition is not as distinct from human‒AI interaction as leadership and awareness, it has also received attention to determine how the concept in humans is affected by AI (Schelble et al., 2022). Applying the findings from this research into design applications for human‒AI interaction is the next step in reframing human‒AI interaction research and requires heavy collaboration between practitioners and researchers. Shaping and Designing AI to Promote Teaming Once a strong connection between teaming and human‒AI research communities is established, efforts can be made to deploy teaming principles into real-world human‒AI systems rapidly. This connection is critical as reducing the lag time between the establishment of frontier research and considering that research in real-world system design is critical to improving user experience and overall perceptions of AI systems. This deployment will revolve around creating and implementing design recommendations for AI systems that specifically target teamwork functions in human‒AI interaction. Beneficially, the field of human‒AI interaction also places a heavy emphasis on design recommendations for real-world systems. This benefit ensures that the research community is not uprooted and forced to incorporate the practice of developing design recommendations, and instead, the existing recommendations only need to adopt a teaming mindset. Early design recommendations will need to focus on the actual individual behaviors of AI systems and how their design impacts and considers teaming principles. These recommendations will generally target the task-level functionality AI systems have with the teams that interact with them. For example, design recommendations may center around the inclusion of critical human factors, such as awareness or acceptance, and how AI systems should consider these factors alongside technical performance when deciding on what action to take. Currently, some preliminary work is being done in this area through a teaming perspective to examine the balancing of an AI’s technical capabilities and its compatibility with human collaborators (Bansal et al., 2019). However, this work does not represent the majority of human‒AI interaction work, and a greater number of researchers and studies are needed to create a wealth of design recommendations for AI practitioners.

Refocusing human–AI interaction  119 Second, design recommendations need to be researched and made for the mediums and modalities humans use as support technologies for human‒AI interaction. While AI systems are often seen as standalone agents with their own personal interaction mediums, future systems would benefit from tighter integration with other teaming processes and technologies. For instance, groupware used for team collaboration provides a highly opportunistic entry point for virtual agents to interact with humans. The interlinking of groupware and AI systems would allow for a more natural integration of AI systems into teaming processes as teams, especially virtual teams, would already be comfortable interacting through the digital medium. Additionally, reducing the number of and simplifying these interaction modalities would reduce the overall complexity and overhead of implementing human‒AI interaction into teaming environments, which would help overall acceptance and usage of the technology. Work has already begun in this area, with research efforts targeting the changes that need to be made to modern groupware systems to facilitate AI better by utilizing its unique strengths to improve the overall groupware experience (Flathmann et al., 2020). This work is an example of how a teaming perspective on human‒AI interaction improves the contributions of individual actors and has the potential to use AI as a separate supportive technology for human‒AI interaction. Finally, work needs to be done on the actual evaluation of AI systems’ impacts and acceptance in real-world teaming environments. While design recommendations are helpful for implementation, feedback from actual users is critical in the development of AI systems for human‒AI interaction. Thus, if a teaming perspective is taken into consideration, the final stage of integrating that perspective would be to elicit feedback from real-world teams on their experiences, preferences, and needs of human‒AI interaction. Fortunately, leveraging human‒human teaming literature also provides a substantially stronger foundation to work from, giving human‒AI interaction researchers valuable measurement tools that have already been developed and validated, requiring only minor adjustments for their specific use cases. This advantage can be exemplified by the premise of studying trust in human‒AI interactions. For instance, feedback regarding humans’ levels of trust with AI systems can be more easily measured using existing teaming measures, such as organizational trust (Mayer et al., 1995), team trust (Jarvenpaa et al., 1998), team member trustworthiness (Jarvenpaa et al., 1998), the propensity to trust (Jarvenpaa et al., 1998), and cognition-based trust (McAllister, 1995). This feedback from users and real-world measurements would then be directly looped back into the research, design, and production of AI systems to be used by teams. Using the three methods described above for using a teamwork lens to conduct research will result in human‒AI interaction transforming into human‒AI teaming research. The goal is for AI systems to become more than a simple tool and rather top function as a teammate by having a greater level of autonomy, a specific role, and a constant presence on their assigned team (O’Neill et al., 2020). Conducting human‒AI teaming research merits the ability for researchers to create active AI systems that holistically consider their individual task, the current state of their assigned team, and other environmental considerations. This approach ensures that interactions between humans and AI systems are not only human-centered but that the interactions themselves do not exist in a vacuum that is not applicable to other research environments or the real world. Conducting human‒AI teaming research allows researchers to ensure the requirements of human‒AI interaction are met while also going a step further to provide AI systems with the unique strengths inherited from being a teammate rather than a tool.

120  Handbook of virtual work

REDEFINING HUMAN‒AI INTERACTION AND ITS RESEARCH TRAJECTORY With an understanding of what human‒AI interaction has the potential to look like through a teaming lens, it is vital to understand how this mindset shift should be realized in the coming years. Unfortunately, the current state of human‒AI interaction is ultimately due to a lack of standardization in the community. While that standardization was initially forwent to encourage human‒AI interaction research to keep pace with computational AI research, the lack of standardization has ultimately led to an unfocused and fragmented research domain. While attempts have been made to consolidate those fragmented research efforts, those attempts have only further highlighted the unmanageable state of human‒AI interaction research that ultimately hinders its consideration in real-world AI systems. This chapter ultimately proposed the transition from human‒automation interaction to teaming as the best lens to view human‒AI interaction through as it provides a solid research foundation in previous literature and a means of directly linking human‒AI interaction in research environments to real-world environments. However, while the presentation of this alternative domain demonstrates its applicability and utility to human‒AI interaction, it is still necessary to explicitly outline the actions that need to be taken in the coming years to solidify the contributions of teaming to human‒AI interaction and vice versa. Thus, this section will outline the immediate goals for the next decade for human‒AI interaction research when taking a teaming perspective. Additionally, the section will recommend standardizations and regulations for the human‒ AI interaction community derived from teaming, which will be critical to maintaining a focused nature in the human‒AI interaction community. Finally, this section will close with a discussion of possible points of consideration and research questions that future human‒ AI interaction and human-centered research will be able to answer when looking through a teamwork lens. Ultimately, while the above contributions are crucial to demonstrating how human‒AI interaction can be better focused and consolidated through teaming, the remaining discussion details the immediate action items that need attention to advance human‒AI interaction in research and real-world contexts in concert. Specifying Important Research Objectives and Relevant Teaming Literature With human‒AI interaction being viewed through a teaming lens, the coming decade presents a tremendous opportunity for meaningful advancement in understanding the relationship between humans and AI. As AI becomes more ingrained in our society through several of the technologies discussed in the current chapter, like self-driving vehicles, robotics, and virtual agents, the opportunity and necessity to make these advancements are necessary. It is essential to address these challenges in human‒AI interaction to accomplish what human‒computer interaction has done for decades: develop better design guidelines and solve design problems before deploying emerging technologies. What is more is that these challenges must be met at an unprecedented pace given the rapid technological advancements being made in AI, which is made even more difficult given the time-consuming nature of human subjects research. Many researchers have begun to help make these strides through human‒AI teaming (O’Neill et al., 2020), tackling challenging topics such as awareness (Dubey et al., 2020), perceptions (Musick et al., 2021), trust (McNeese et al., 2021), coordination (Musick et al., 2021), and practical integration (Flathmann et al., 2019; Schelble et al., 2020). This collection of AI-supported

Refocusing human–AI interaction  121 teaming literature is an excellent example of how many different topics are being targeted, but each builds and contributes to the others through the common thread of teamwork (Table 6.1). Table 6.1

Examples of related literature to help frame multiple goals in different human‒AI interaction contexts

Research Objectives

Human‒AI Interaction Context

Relevant Teaming Literature

Design transparent AI systems that are

Self-Driving Vehicles, Recommender

Designing for Awareness in Collaborative

explainable by the end-user

Systems, Virtual Agents, AI-Enabled

Teaming Technology (Dourish & Bellotti,

Robotics, Management Systems

1992; Gross, 2013)

Determine the degree of

Virtual Agents, Management Systems,

Anthropomorphism in Human‒AI Teaming

anthropomorphism that is most

AI-Enabled Robotics

(Fraune, 2020; Ososky et al., 2013)

Develop evidence-based training

AI-Enabled Robotics, Management

Team Training Development Guidelines

programs and protocols to improve

Systems

(Burgess et al., 2014; Shuffler et al., 2018;

compatible with human interaction

Swezey & Salas, 1992)

safety, efficiency, and performance Combat and mitigate to the highest

Virtual Agents, Recommender Systems

Causal Mechanisms of Bias in Teaming (Ashworth & Heyndels, 2007; FitzGerald &

degree possible bias in AI systems and

Hurst, 2017)

their human users Produce AI systems and interfaces

Self-Driving Vehicles, AI-Enabled

Complacency in Human‒AI Teaming and

that reduce automation bias and user

Robotics, Management Systems

Healthcare Teaming (Grissinger, 2019;

complacency to improve effectiveness

Wright, 2015)

and safety

Source: Authors’ own.

The existing AI-supported teaming research has revealed several exciting findings relating directly to interaction and design, highlighting how using teaming metrics in human‒AI interaction research allows data from real-world subjects to be collected and interlink theory with application. For example, design plays an essential role in developing awareness of the AI for human users (Mercado et al., 2016). Specifically, these trust factors were improved by conveying the AI’s confidence level through an icon’s level of transparency (Mercado et al., 2016). A finding improving trust is vital as many other human‒AI teaming studies have revealed several indications that humans hold a particular bias against working with an AI teammate (Demir et al., 2018; Walliser et al., 2017), especially when the number of AI teammates outnumbers them (Musick et al., 2021). Finally, there are several unexplored areas in AI-supported teamwork that would massively benefit human‒AI interaction as a whole that require additional research. These topics include a need for more field studies, a focus on team cognition and influence, and achieving higher collective performance by AI-supported teams. Standardizing and Regulating Human‒AI Interaction to Ensure Human-Centeredness In addition to understanding the next research steps facing human‒AI interaction, it is also important to outline some needed standardization and regulation currently void in the domain. While the goal of this chapter is not to fully outline all needed regulations in the human‒AI interaction space, it is still worth demonstrating how teaming standardization and regulation can be used to directly inform the human‒AI field, thus improving the quality and pace of creating human‒AI regulations and standards. Additionally, there is currently a large void of government regulation that consistently leads to questions regarding liability when dealing

122  Handbook of virtual work with the consequences of AI systems. Some domains have begun including this regulation, such as management systems and newly created data privacy laws (Goldfarb & Tucker, 2011); however, sub-domains such as self-driving vehicles are harmed by a lack of needed regulation (Brodsky, 2016). Table 6.2 provides an example of how teaming research can help inform regulations or standardizations for each of the discussed sub-domains in human‒AI interaction. Table 6.2 is not meant to be the final word; however, we offer it as a starting point. Table 6.2

Example regulations or standards for human‒AI interaction and teaming research and practice provide solid foundations

Human‒AI Interaction

Standardization & Regulation

Example in Teaming

Regulation Regarding the Safety Standards and

Occupational Safety and Health Administration

Context Self-Driving Vehicles

Liability of Non-Human Operated Vehicles

(Wiseman, 1995)

Regulation of the Level of Interference in

American Bar Association Model Rules of

Recommendations from External Sources

Professional Conduct (American Bar Association,

Virtual Agents

Standardizations that Limit and Monitor the

Deception in Collaborative Virtual Teams

Communication of Disinformation to End-Users

(Twyman et al., 2020)

AI-Enabled Robotics

Standardization of Training Practices with Robotic Team Training Guidelines (Burgess et al., 2014;

Management Systems

Regulation on the Communication of Protected and Health Insurance Portability and Accountability

Recommender Systems

2020)

Systems Sensitive Data to Different Human Operators

Swezey & Salas, 1992) Act (Chan & Lee, 2020)

Source: Authors’ own.

Important Considerations and Closing Thoughts on Human‒AI Interaction We believe that it is vital to take a step back and evaluate the current trajectory of human‒ AI interaction and ask critical questions regarding how that trajectory and the goals of the domain need to change. This chapter ultimately provides this evaluation by posing questions regarding human‒AI interaction and providing light discussion from a teamwork perspective. Ultimately, it is up to the community to decide the answers to these and other questions; however, the goal of presenting them is to ensure teams, which are predominant in the environments being targeted by human‒AI interaction, are critically considered in the research domain when moving forward. If teaming is not exclusive to humans, does AI need to be made in humanity’s likeness? A large portion of computational AI research and human‒AI interaction research currently seeks to artificially replicate human performance levels and human intelligence. While this is a noble effort, it is important to consider if this is the correct approach to building AI systems, especially when interacting with humans through teaming. Earlier, this chapter discussed how teaming provides a means of viewing interaction and collaboration without requiring humans for that interaction. Ultimately, human‒animal teams do not seek to replace humans with animals but utilize the unique strengths of humans in concert with the unique strengths of animals to create effective teams. The same should be valid for human‒AI interaction, and this is clear when taking a teaming perspective. Teaming allows AI systems to exist as their own entity and not as an inferior form of humans, which inevitably leads to unrealistic expectations

Refocusing human–AI interaction  123 of the AI and an overall negative experience for the user. AI systems excel at data processing, scheduling, and other computation-heavy tasks, often difficult for humans. On the other hand, humans are social beings that work best in social roles and environments. Thus, chasing the dream of AI becoming a replicant of humans ignores the strengths of both parties. The research community will have to determine if humans should remain the finish line for AI advancement moving forward. Is it possible, and is it desirable for human‒AI interaction to maintain pace with AI research? A large portion of this chapter discusses how the pace of human‒AI research can suffer from fragmentation and cause it to lag behind computational AI research. While this chapter removes those hindrances by using a teamwork lens, it is important to ask if human‒ AI interaction research should maintain pace with computational AI research. Traditionally, safety regulations and concerns regarding human subjects research provide a hurdle that slows the research community and is also integral to its ethicality. Unfortunately, real-world systems are often tested on humans without the same care and attention to ensure industry-led research maintains pace with its own computational AI research efforts. Ultimately, maintaining this pace in this way can harm the research in the long term, especially in the way of the acceptance and trust humans have for systems tested this way. Thus, the research community must standardize AI technology and the process of human‒AI interaction research to ensure the safety and comfort of humans. Unfortunately, this decision may slow down the pace of research and cause it to lag behind computational AI research, but the potential harm caused by unsafe human‒AI research is grand. Thus, the community will need to answer this question and determine how the integration of AI systems should be researched while still ensuring that research is timely and safe. In conclusion, while great work has and is being done in the field of human‒AI interaction, its unfocused nature prevents the domain from reaching its full potential. Not only does this fragmentation slow down overall research but it also hinders the ability for researchers to connect their work to others in the domain, which is one of the most important goals in conducting research. The teaming perspective provides research in human‒AI interaction with the opportunity to build from a solid foundation created from decades of research and also provide a critical point of commonality between research work. Moving forward, utilizing a teaming perspective when conducting research will not only accomplish important research goals in human‒AI interaction, but it will also help AIs integration into society and also ensure the goals of human‒AI interaction are optimal for both the technology and the humans using it.

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PART II THE PEOPLE MAKE THE VIRTUAL PLACE While virtual work may be fundamentally reliant on technology, it pales in comparison to the vital importance of the human worker. Advances in work technology and structures are, after all, meant to be for the benefit of the person and persons doing the work. While all work environments have their challenges and their opportunities, the virtual individual represents a unique manifestation of the modern working world. Virtual workers are by no means a monolith – in fact, employees in the virtual space are as diverse in their needs, skills, motivations, and structures as those in any job, organization, or industry. From freelancers working while trekking the globe to an office worker interfacing with distant colleagues – recognizing the importance of the human element is of utmost concern. To that end, in this section we start with a chapter by Golden and Morganson that offers a substantive review of the implications of telework on managing work–life balance. Recognizing that telework is a unique occupational environment, Golden and Morganson take a boundary management perspective to demonstrate how the traditional challenges of maintaining work–life balance can be notably compounded in a virtual environment. The authors do note the advantages of the work environment as well, arguing that telework can be a powerful flexible work arrangement that can lead to significant work and life enrichment. These competing forces create a tenuous environment that can either facilitate or hinder work–life outcomes depending on the quality and structure of the arrangement. The authors conclude with listing evidence-based best practices for individuals to reduce conflict between their work and life domains and to maximize work–family synergies. The chapter by Makarius and Larson proposes a two-dimension framework of telework along the lines of remoteness and virtuality and investigates the framework’s implications for assessing the critical skills needed for an individual’s success along both dimensions. The authors reviewed and critically evaluated a significant history of operational definitions in the virtual work space, noting that modern working environments can and should be assessed on technology usage independently from geospatial dispersion of workers. By assessing both the unique and the interrelated challenges posed by remoteness and virtuality, the authors were able to determine critical areas, such as communication and cognitive maintenance skills, that warrant both theoretical and pragmatic attention.

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Part II: The people make the virtual place  129 Following this, Dimas and colleagues provide a thorough examination of the emotional component of working in virtual environments, blending together themes of highlighting the human and well-being components while recognizing the importance of maximizing performance. Leveraging insights from the emotions as social information (EASI) model, the job-demands-resources model, and emotional contagion theory, the authors argue that cognitive-emotional considerations can help explain and contextualize the double-edged sword of virtual work’s impact on well-being. While workers may feel empowered with the flexibility that virtual work can bring, there were also noticeable dangers of feeling negative emotions such as isolation, loneliness, worry, and guilt. The authors conclude with a series of practical recommendations for optimizing emotional well-being in virtual and remote work environments. We conclude this section with Litchfield and Woldoff’s review and assessment of digital nomads. Noting a rise in media attention but a lack of empirical research, Litchfield and Woldoff assessed the current state of understanding of digital nomads and offer a definition centered on location independence. That is, individuals who leverage technology to maximize geospatial independence from work. Reviewing the extant literature as well as drawing from their own multi-year ethnographic research on digital nomads in Bali and Indonesia, the authors argue that digital nomads represent a niche subsect of virtual workers worthy of additional attention. Digital nomads often represent remote work taken to its most extreme, and as such can highlight critical aspects of the remote work ecosystem, while illustrating the broader push and pull factors that bring people into this unique lifestyle. Further research into this niche population as well as other notable subsections of virtual workers may yield a path forward for understanding critical components of virtual work.

7. When the time-space continuum shifts: telework and alterations in the work–family interface Timothy D. Golden and Valerie J. Morganson

The popularity of telework has grown exponentially in recent times. With the Covid-19 pandemic that began in 2020, the surge in telework use has renewed emphasis on this work mode as a viable alternative to in-office work. Prior to the pandemic, telework was estimated to comprise approximately 16 percent of the total U.S. workforce with over 26 million persons who worked at home on an average day (Bureau of Labor Statistics, 2019). However, with the onset of the pandemic, telework has grown dramatically, and estimates indicate that as much as 63 percent of the U.S. workforce was teleworking as of April 2020 (Hickman & Saad, 2020) with most other regions around the world including Europe (Eurofound, 2020) and Asia (Erer, 2020) exhibiting similar records. This dramatic growth in teleworking appears here to stay as a permanent way that work is conducted. Spurred by the greater independence and personal time afforded by remote work during the pandemic, far greater numbers of employees are demanding telework on a permanent basis (Samuel, 2021). With the increased ability to juggle work and family demands (Allen et al., 2013; Golden et al., 2006), telework has become an increasingly accepted way of working that is being adopted by corporations as part of their overall workforce strategy. Moreover, following the unprecedented and dramatic quarantines of the pandemic, many corporations have found that telework is a viable work option that enables work to be accomplished without sacrificing quality. As a result, many corporations have made widely publicized announcements that they are adopting remote work practices on a permanent basis, and offering employees the option to work as a teleworker rather than resuming traditional in-office ways of working (e.g., McGregor, 2021; Patrick, 2021). It is against this backdrop that this chapter reviews the current state of telework knowledge, with the particular emphasis on research related to the work‒family interface. Given that work and family are the two dominant spheres in our lives that generally apply to the human condition (Greenhaus, & Beutell, 1985), we focus on work–family impacts as perhaps the most fundamental non-work outcome of telework. Given the centrality of work and family in our lives, understanding if and how telework might affect our ability to manage the boundaries between these two domains takes on increased importance, with wide-ranging consequences for our well-being and life contentment. Our chapter therefore takes the following approach. First, we discuss what precisely is meant by telework, and compare it to other similar terms and conceptualizations. We follow this by highlighting how the extent of telework is a key aspect of interpreting any telework research, and how this must be taken into consideration whenever we attempt to understand the implications of telework. Our chapter’s main emphasis on telework research as it pertains to the work–family interface follows shortly thereafter. In our review, our intent is not to 130

Telework and the work–family interface  131 offer an exhaustive review of all telework research, but rather to focus more precisely on the work‒family literature’s findings in terms of the specific implications for the teleworker’s ability to manage the interface between work and family. We hope that in so doing, we are able to provide insights into the nature of this delicate balance between work and non-work spheres of life and how these are influenced by the ability to work as a teleworker. We conclude with suggestions for future research, in the hopes that the next steps can be taken in this ever-more-important field of research.

TELEWORK TYPES AND DEFINITIONS Telework is a broad and encompassing term that is often used in varying ways and with a variety of intended definitions. This presents significant challenges for trying to understand what the implications are of telework for our work and our non-work lives. Moreover, there are a variety of terms often used interchangeably with telework, and this adds to the confusion. Additionally, the meaning and use of the term telework has evolved over time, further complicating any attempts to understand telework research in a comprehensive manner. We therefore briefly review some of the important aspects of this terminology in the hopes of adding some clarity to the use of the telework term and its implications. The term telework has often been used interchangeably with terms such as remote work, telecommuting, flexible work, work-from-home, distance work, virtual work, and flexplace, among other terms. These different terms, although often having overlapping meanings, are also often used in distinct ways. Unfortunately, at times the same terms are used in different ways across studies, and this has the misfortunate result of making it difficult to compare outcomes across different research studies. As part of this, telework has also been researched in different disciplines, ranging from management and psychology to information systems, communications, real estate, and others, and each discipline reflects a varying emphasis on understanding this work mode. The resulting varied use of terms and conceptualizations, however, provides a maze of concepts that challenges even the most determined of scholars. Based on a review of the literature, we rely upon the work of others to define telework as “a work practice that involves members of an organization substituting a portion of their typical work hours (ranging from a few hours per week to nearly full-time) to work away from a central workplace – typically principally from home – using technology to interact with others as needed to conduct work tasks” (Allen et al., 2015, p. 44). This definition is rooted in the original definition advanced by Jack Nilles (1994) and based on the work of a number of adopted conceptualizations of telework (e.g., Bailey & Kurland, 2002; Gajendran & Harrison, 2007; Golden & Veiga, 2005; Golden et al., 2006; Konradt et al., 2000). Common to these conceptualizations is the premise that telework involves working away from a centralized in-person office, as well as the use of technology to communicate and interact with others. Moreover, telework also involves time spent working away from others who are part of a larger organization, rather than working as an independent contractor or self-contained business unit. Recently, telework has been used synonymously with the terms remote work or hybrid work (e.g., McGregor, 2021; Patrick, 2021), and the pandemic and resulting exponential growth of telework during this time has blurred some of the earlier distinctions. While remote work used to solely connote work done exclusively away from the office on a full-time basis (Staples

132  Handbook of virtual work et al., 1999), it has now morphed into a more contemporary meaning used interchangeably with telework (e.g., Samuel, 2021). Similarly, the term hybrid work is now being used more colloquially as meaning telework done on a part-time basis (e.g., part of the work week in the office and part done at home), and this is akin to the concept covered in the next paragraph when we discuss the extent of an individual’s telework as a main structural feature important to understanding telework research.

THE EXTENT OF TELEWORK: A KEY FOR UNDERSTANDING RESEARCH FINDINGS Telework is not an all-or-nothing work mode, and this distinction is key for understanding the effects of telework on the whole host of outcomes that result from working in this manner. The extent of telework, or the degree to which an individual works away from the office as a teleworker, is fundamental to interpreting any effect that teleworking might have on work and non-work aspects of life. Since the extent of telework varies greatly (ranging from a few hours per week to nearly full-time), understanding the extent of telework practiced by any group or sample of participants has direct implications for being able to interpret the findings of any telework study. In this vein, understanding how much time is spent teleworking away from an organization’s office is key to accurately interpreting existing research. The extent of telework has generally been assessed as either the proportion of the work week spent working away from the office, or as the amount of time measured as the number of hours per week spent working from home. This distinction of assessing the extent of telework and measurement techniques for doing so has largely been solidified in existing research through an ongoing stream of articles (Golden, 2006a, 2006b, 2007, 2012; Golden & Eddleston, 2020; Golden & Gajendran, 2019; Golden & Raghuram, 2010; Golden & Veiga, 2005; Golden et al., 2006; Morganson et al., 2010; Virick et al., 2010). These and other articles have brought to the forefront the need to assess the extent of telework in any research investigating this work mode, so that we can accurately understand the lived realities of participants in each telework study. Understanding the effect of the extent of teleworking on outcomes of telework makes more accurate interpretations of research possible, since without this only broad comparisons are possible and results may misrepresent the experience of participants in these studies. Using the analogy advanced in earlier literature (Allen et al., 2015), telework may be much like the dosage for medications – whereby either too much or too little medication may have an unintended effect and might even be detrimental. In much the same way, understanding the implications of the extent of telework in research conducted about this work mode is essential if we are to accurately capture, interpret, and predict how telework affects work family conflict or any other outcome of teleworking. We therefore urge researchers to at a minimum report the extent of teleworking in their studies, so that more informed and accurate comparisons can be made across research findings. Society’s contemporary experience with teleworking during the pandemic beginning in 2020 has only accelerated the need to understand how the extent of telework might alter work and non-work outcomes of telework. As indicated by widespread and permanent shifts in telework as a result of the pandemic and the ensuing partial return to offices, “hybrid” telework or remote work appears to be the wave of the future (e.g., McGregor, 2021; Patrick, 2021).

Telework and the work–family interface  133 In this new label emerging for the extent of telework, referred to generally as “hybrid remote work”, employees work part of their time at home and part of their time in the organization’s central office (Mekouar, 2021). The adoption of this way of working on a permanent basis, both by large and small companies across many different industries and sectors around the globe, demands that research account for this aspect of telework. In this way, accounting for the extent of teleworking in research may turn out to be “the key” determinant of work and non-work outcomes experienced by teleworkers. Thus, given the massive scale on which this part-time teleworking is being adopted on a permanent basis, it seems imperative that research account for the extent of telework in all current and future research studies. In the following paragraphs regarding telework’s implications for the work‒family interface, we therefore attempt to account for the extent of telework whenever possible. While this distinction has been reported or accounted for in some of the existing articles, a significant proportion of existing research has overlooked this key aspect of telework. Our review therefore considers how the extent of telework might affect work‒family conflict wherever it has been reported in the articles, although this key aspect is only available in some of these studies.

TELEWORK AND THE WORK‒FAMILY INTERFACE Telework seems to have varied impacts on employees’ attempts to juggle their work and personal lives. Although telework can be a resource to manage role conflict, it can also blur boundaries between work and personal life domains – potentially with paradoxical effects (Gajendran & Harrison, 2007; Olson-Buchanan et al., 2016). In this section, we review research literature linking telework to multiple role involvement in negative and positive ways. Then, we discuss circumstances in which the impacts of telework on work‒life may be more complex. Consistent with research in this area (Kossek, Baltes et al., 2011), we will generally use the term work‒family. However, the family domain refers broadly to personal life or non-work roles, including some that may be non-familial. Boundary Management Perspective In a traditional, office-based work environment, work and personal life spheres are separated by time and location. However, in a telework setting, both spheres occupy the same space, potentially at the same time. Along these lines, work‒family theorists have described boundaries (Ashforth et al., 2000) and borders (Clark, 2000) that workers cross as they transition between their major life roles (see Allen et al., 2014 for a theoretical review of boundary management). Whereas boundary flexibility refers to the potential for temporal and spatial transitions between roles, permeability refers to the potential for a person to physically or psychologically navigate between roles (Allen et al., 2014). Blurring refers to both flexibility and permeability simultaneously (Allen et al., 2014). In a telework setting, the boundaries between work and family become more blurred than they would in a traditional work setting. Research provides some support for teleworking via a boundary management perspective. Disengaging from work can be particularly challenging if work demands are high such that they encroach on workers’ time and mental resources. More permeable boundaries are linked with higher levels of conflict (e.g., Cho et al., 2020; Leung & Zhang, 2017; Jostell & Hemlin, 2018; Kossek et al., 2006; Yang et al., 2019). The term telepressure refers to workers feeling

134  Handbook of virtual work compelled to focus on work (e.g., responding to emails) outside of work (Barber & Santuzzi, 2015; Kao et al., 2020). Telepressure is linked with conflict between work and family roles (Kao et al., 2020) and burnout (Barber & Santuzzi, 2015). Employees who work from home often find themselves overworked and working longer hours, and this infringes on other non-work activities (Eddleston & Mulki, 2017). Another set of terms to describe the boundaries/borders between work and family domains are: segmentation (i.e., rigidity and structure in separation between roles) and integration (i.e., flexibility and lack of distinction between roles). Individuals may vary not only in the extent to which their work allows them to integrate or segment, but also may vary in their own preference for segmentation or integration (Kreiner, 2006). Likewise, individuals may vary regarding which boundary management strategies they employ to separate their personal life spheres (Kossek et al., 2006). Using a repeated measures design, Allen et al. (2021) found that the preference to segment roles was positively associated with work‒life balance. Similar to the concept of telepressure, work groups may develop norms of communication technology use (e.g., emailing outside of business hours). These norms may misalign with individuals’ segmentation preferences and can exacerbate work‒family conflict (Yang et al., 2019). Maintaining boundaries and creating distinctions between work and life roles may be particularly important in a telework setting, as discussed further in the sub-section on individual differences below. Work‒Family Conflict Work‒family conflict refers to mutual incompatibility between work and personal life domains. Greenhaus and Beutell (1985) described three types of role conflict. Work‒family conflict can be strain-based (i.e., stress from one role is transmitted to the other), time-based (i.e., insufficient time to fulfill one role due to demands in the other role), and behavior-based (i.e., when behavior appropriate in one role inappropriately occurs in the other role). Teleworking reduces commute time, which allows for more time to be spent in the personal life domain (Klopotek, 2017). Likewise, working away from the office can provide an opportunity to distance oneself from some of the stressors of work such as office politics and coworker interruptions. Abendroth and Reimann (2018) examined work‒family conflict, separating out by types: strain and time-based conflict. They found that telework was positively related to both. Perhaps telework also impacts behavior-based conflict as well in that teleworkers are more able to be their authentic selves when away from the office (e.g., having personal things on their desk outside of camera range that they would not want to share at work). Work‒family conflict is bidirectional, meaning that work can intrude upon family (work-to-family conflict; WFC) and family can intrude upon the work domain (family-to-work conflict; FWC). Substantial research has conceptualized telework as a flexible work arrangement, allowing workers an opportunity to have control to reduce work‒family conflict (Allen et al., 2013; Byron, 2005; Gajendran & Harrison, 2007). Teleworking is associated with greater perceptions of autonomy (Gajendran & Harrison, 2007). Much of the research that has explored telework as a flexible work arrangement draws on resource theories (e.g., Edwards & Rothbard, 2000), assuming time and energy are finite resources. According to this line of theory, flexibility is a resource needed to adapt to conflicts between work and personal life roles. Telework has been linked to work‒family conflict, including both WFC and FWC. For example, Golden et al. (2006) found that the extent of teleworking was negatively related to

Telework and the work–family interface  135 WFC, indicating that telework helped ease the effect of work on family life. However, the extent of teleworking was also associated with increased FWC, suggesting that telework also has the potential to increase the amount of conflict experienced as a result of family demands interfering with work. These effects were found to be altered by contextual factors related to both the job and family, indicating that additional factors may play an important role in how telework impacts conflict between work and family. In another study comparing different types of work arrangements (Higgins et al., 2014), teleworkers were compared with in-office employees working 9 to 5 as well as employees working compressed work weeks and flextime arrangements. Results from this study found that teleworkers were found to have higher work‒ family conflict than employees working compressed work weeks or 9 to 5 schedules. Similar to Golden et al. (2006), teleworkers reported higher FWC. In a further refinement, Lapierre and Allen (2006) found that telework was linked with time-based, but not strain-based, FWC. These studies underline the importance of differentiating WFC and FWC when attempting to understand the implications of telework, as well as the complexities and subtleties when considering telework and conflict at the work‒family interface. At a more general level, meta-analytic evidence has found teleworking to be negatively related to work‒family conflict. For example, Allen et al.’s (2013) meta-analysis found a negative relationship between telework and WFC. Likewise, an earlier review by Gajendran and Harrison (2007) found telework to be negatively related to work‒family conflict, and this relationship was stronger for those who teleworked more extensively per week. The link between telework and FWC has been more elusive, however. Allen et al. (2013) found no significant relationship between telework and FWC, and the earlier review by Gajendran and Harrison did not separate out WFC from FWC. The difference in findings may therefore be due to Allen et al. (2013) taking into consideration both WFC and FWC. As Allen et al. (2013) explain, working away from the office may increase perceptions of responsibility. In this way, similar to the concept of telepressure, teleworkers may feel the need to do even more in both the work and non-work domain, and the pressure felt to contribute to these domains may vary depending upon the extent to which the individual teleworks per week. One of the reasons for mixed and weak findings linking telework to work‒family conflict is that most studies have relied upon a single time point of data collection using an existing group of teleworkers. However, a few recent studies have used more sophisticated research designs. Darouei and Pluut (2021) surveyed employees who worked at home and at the main office multiple times per day for a period of ten days. They found that on days working away from the office, workers reported less WFC compared with days when they worked in the office. The effect was partially due to reduced time pressure when working from home. Another study by Delanoeije and Verbruggen (2020) used a quasi-experimental design. They assigned workers to one of two conditions: non-teleworking and teleworking for two days per week. Although WFC was similar prior to the telework intervention, those in the telework group reported less WFC post-intervention. Additionally, those in the telework condition reported less stress and lower WFC on days they teleworked. Taken together, findings examining the link between telework and WFC lend support to the viewpoint that telework can serve as a flexible work arrangement. It should be noted a possible exception to the above findings regards telework conducted under mandatory conditions, such as during the pandemic beginning in 2020. Under these conditions of force lockdowns with all family members present in the household and the absence of external childcare arrangements, many working mothers and fathers may not find relief

136  Handbook of virtual work from work‒family conflict during telework (e.g., Anderson & Kelliher, 2020). In such situations, telework may not alter work‒family conflict in ways prior research has demonstrated. The degree to which telework is mandatory or voluntary may therefore influence findings regarding the link between telework and both WFC and FWC. Additionally, some research has also investigated telework from the perspective of “road warriors”, who spend much of their work weeks at distant client sites and deal with work‒ family conflicts from a distance (e.g., Ahuja et al., 2007). Morganson et al. (2010) found that workers based primarily at customer locations or satellite offices reported less work‒life balance support than workers at the main office or those based primarily at home. This research has contributed to the conversation on teleworker work‒family conflict by raising issues related to remote management of work and family domains and the stress inherent in juggling these while continually shifting work locations. Enrichment As a separate concept from conflict, work‒family theory recognizes that work and personal life domains can interact in positive, complementary, or enhancing ways (Marks, 1977; Sieber, 1974). Although there is a lack of research examining the link between telework and enrichment outcomes (Olson-Buchanan et al., 2016), preliminary findings suggest that telework and enrichment are likely to be related. McNall et al. (2009) linked flexible work arrangements to work‒family enrichment, and in turn to job satisfaction and turnover intentions; however, they did not have enough teleworkers in their sample to examine its impact on enrichment. Autonomy is positively linked with enrichment (e.g., Siu et al., 2010). Like conflict, enrichment can be bidirectional such that work can enrich one’s personal life and vice versa. Greenhaus and Powell (2006) describe how work and personal life can positively impact one another via two paths. First, the instrumental path refers to how resources from one domain can be directly transferred into the other domain. As an example of the instrumental path of enrichment, teleworkers may gain time management and boundary management skills that help them in their personal life domain. Likewise, workers may gain resources such as financial compensation for internet or use of office equipment for personal use that directly benefits them in their personal life domain. In a study of teleworkers during the pandemic, Hoffman (2021) reported that participants spent better quality time with pets and family members. Dog owners in particular reported socializing and getting physical activity as they took their dogs out for walks during the workday. Second, Greenhaus and Powell (2006) describe the affective path of enrichment as the indirect effect of one role on another via moods and emotions. As an example of family-to-work enrichment, telework provides a unique opportunity to share a window into one’s personal life domain (e.g., a mom seeing a child intrude into her coworker’s camera view during a virtual meeting feels a sense of solidarity). Research finds a link between mood and telework such that employees tend to report more positive mood on days when they telework (Anderson et al., 2014). To echo Olson-Buchanan et al. (2016), more research is needed to further explore the positive ways that telework can create synergies between work and personal life roles.

Telework and the work–family interface  137 Moderators The various conditions under which telework can occur may influence the effect that telework has on work and non-work outcomes. Additionally, some work characteristics may be more amenable to some individuals than to others. Accordingly, research and theory have examined factors that influence the relationship between telework and work‒family outcomes. In the meta-analyses noted earlier concerning the relationship between telework and work‒family conflict, researchers found evidence for the presence of moderators (Allen et al., 2013; Gajendran & Harrison, 2007). Demographics likely impact to whom telework is available, as well as serving as a moderator of the impact of telework on work‒family outcomes. As Zhang et al. (2020) discuss, telework may influence division of household work and societal gender disparities. In a large sample of German workers, they found that children play a major role in who teleworks; those without children are more likely to telework. Among those who had children and teleworked, partners were more likely to telework than single parents, and females were more likely to telework than males. With the inherent difference in family circumstances, there may not be a “one-size-fits all” answer to how much telework is best. Demographics and individual differences may temper the degree to which telework is useful as a family-friendly support. Shockley and Allen (2007) found that the relationship between telework and work‒family conflict was moderated by family responsibility. Telework had a more mitigating effect on both WFC and FWC for those with higher family demands. Solís (2017) examined worker responsibilities as a moderator of the relationship between telework (a yes/no variable) and conflict. They found that telework was linked with lower FWC only for those with low responsibilities. However, their operationalization of telework likely created range restriction. Troup and Rose (2012) studied the effect of formal and informal telework on time spent on childcare and satisfaction with the distribution of childcare tasks. Using an Australian sample, they found gender served as a moderator, such that women especially appreciated the flexibility of informal arrangements. The authors speculated that informal arrangements might better match the unpredictable nature of childcare. Golden et al. (2006) found that household size moderated the link between extent of teleworking and FWC. The relationship was positive among those with a larger household and negative for those with a smaller household. They discuss how contextual differences may interact in complex ways to temper the impacts of telework on work‒family outcomes. These studies suggest that as a family-friendly support, telework is not equally available or equally effective for everyone. These initial studies suggest that individual differences may have a useful place in future research, as we elaborate later. Allen et al. (2013) differentiated the concepts of flextime and telework (which they referred to as flexplace) under the umbrella of flexible work arrangements. They note that workers may not have much autonomy at home or at work. In either location, schedules may be rigid. In support of this idea, Golden et al. (2006) found that the negative relationship between extent of telework and WFC was stronger for those with high (vs. low) schedule flexibility. Flexibility and control seem to be important resources for reducing role conflict (Basile & Beauregard, 2016). In general, family-friendly policies are most apt to be used when they are viewed as supported by the organization (Allen, 2001). Moreover, the supervisor plays a key role in influencing support perceptions and policy usage (Allen, 2001; Kossek, Pichler et al., 2011).

138  Handbook of virtual work Specific to telework, Abendroth and Reimann (2018) found that a supportive work‒family culture buffered the relationship between telework (a yes/no variable) and WFC. They also noted that supervisors play a role in buffering the impact of telework on WFC. Telework may be implemented as a family-friendly support or to meet business needs. Although much of the research on telework and work‒family has viewed telework as a flexible work arrangement, some studies examine the impact of non-voluntary telework. Lapierre et al. (2015) focused their study on non-voluntary telework and its impact on work‒family conflict in a longitudinal study. In contrast to the meta-analyses reported earlier, they found a positive relationship between extent of telework and work‒family conflict. The relationship was exacerbated for employees who had less confidence about their ability to balance work and personal life roles. Studies have also examined work that is outside of traditional work hours and conducted after a full day in the office. Ojala et al. (2014) for example differentiated formal teleworking from a concept they refer to as homeworking, which they defined as working overtime hours after work from home. These authors suggest that teleworking should be differentiated from homeworking because homeworking can increase role conflict. Likewise, Dettmers et al. (2016) gathered data on a daily basis, sampling workers who were required to be available outside of working hours. Results provided support for the proposition that being required to be available outside of work hours was linked with poorer recovery from work. Along similar lines, using an experience sampling design that employed data collection over a period of ten working days, Cho et al. (2020) found that off-the-job communication demands were related to WFC. However, impacts were moderated such that individuals who reported greater boundary control reported suffering less WFC impact.

INDIVIDUAL STRATEGIES FOR MANAGING WORK‒FAMILY BOUNDARIES The findings we have reviewed so far raise the question of how to best make telework “work” to reduce conflict and maximize synergies between work and family. Fortunately, studies have begun to explore individual strategies for managing work‒family domains. Below we review several overlapping qualitative studies and some recent quantitative results about work‒family boundary management in a telework setting. In a qualitative study of teleworkers, Basile and Beauregard (2016) found that teleworkers recreate boundaries using various techniques. They described physical boundaries as the most common theme (e.g., having your own workspace), time-based strategies (e.g., imposing an end time or scheduling with family members), behavioral strategies (e.g., shutting down one’s laptop), and communication strategies (e.g., requiring family members to knock to enter the designated workspace). Fonner and Stache (2012) found similar results in their qualitative study of teleworkers. Their sample reported using temporal and spatial strategies to manage boundaries. They also discussed expectation-setting strategies. As a temporal strategy, their respondents noted the importance of having a routine, which could be focused around timing or based on completion of tasks. They elaborated on the ways that teleworkers communicated with family members and coworkers, describing how they use direct communication, electronically shared their availability, strategically used space and physical cues (e.g., staying the designated work-

Telework and the work–family interface  139 space), or selectively responded to communication (e.g., screening calls) to segment their roles. Regular work habits created implicit understanding of availability. In a more recent review of teleworker practices that facilitate boundary management between work and family, Golden (2021) identified “best practices” among teleworkers for effectively managing this interface between work and non-work domains. These best practices were structured by considering four categories of boundary management tactics: behavioral tactics, temporal tactics, physical tactics, and communicative tactics. Behavioral tactics incorporate ways of behaving and social practices which are part of the actions teleworkers enact in order to respond to the incongruence between work and family domains. Temporal tactics involve decision making on the part of the teleworker as to when and how much time should be devoted to either work or non-work activities. Physical tactics involve using or constructing tangible or physical boundaries and objects to demarcate the areas where work and family domains come into contact with each other. And fourth, communicative tactics pertain to setting expectations with others regarding the desired level of separation to be maintained between the teleworker’s work and non-work domains, and ensuring that these expectations and desires are expressed and maintained. In addition to identifying these main categories of best practices for teleworker boundary management techniques, Golden also discussed how individual variation in preferences for segmenting or integrating work and family during telework can lead to unique work practices that can optimize outcomes for the individual teleworker. In a study of teleworker habits during the pandemic, Allen et al. (2021) found evidence of similar boundary management strategies including creating physical and psychological boundaries, employing a routine, purposefully disconnecting from a role, and communicating boundaries with others. They also elaborated on a physical tactic, called “managing physical artifacts”, which refers to placing items that symbolize the work or home domain separate (e.g., keeping work technology in the office space, using separate laptops for work and life roles). Teleworkers in their sample also reported leveraging technology to create boundaries, such as setting alarms, using apps to signal the end of work time, and using separate work and home profiles. Quantitative studies on boundary management in a telework setting also seem to favor creation of boundaries. As noted earlier, more permeable boundaries are linked with higher levels of conflict (e.g., Cho et al., 2020; Kossek et al., 2006; Leung & Zhang, 2017; Jostell & Hemlin, 2018; Yang et al., 2019). Allen et al. (2021) found that teleworkers with a designated workspace reported higher work‒family balance than those who did not.

FUTURE RESEARCH DIRECTIONS Although telework literature has grown quickly in the last couple of decades, practice has far out-paced research. The global pandemic due to Covid-19 only underlined the importance for better understanding how working away from the office has important implications for work and personal life outcomes. Substantially more research is needed to help inform how individuals and organizations can employ telework in a way that meets the needs of the employer, employee, and their families. Here we discuss several directions for future research. From the outset, it is important to acknowledge that issues at the work‒family interface can take different forms, as elaborated earlier in the chapter. Yet, research has primarily taken

140  Handbook of virtual work a conflict perspective in studying how telework relates to work‒family issues. Focusing solely on removing conflict may have a limited impact, since conflict is inherent in maintaining multiple roles such as employee and caregiver. In contrast, it may be more reasonable to expect to achieve increased work‒family balance, which embodies the concepts of satisfaction and efficiency (see Casper et al., 2018 for a review and conceptual development of work‒family balance). In addition to examining work‒family balance, perhaps research focusing on enrichment can also inform how to better develop and maintain affective and instrumental connections between work and personal life roles. Indeed, telework has consistently been associated with job satisfaction (e.g., Gajandran & Harrison, 2007; Golden & Veiga, 2005; Virick et al., 2010) and telework is widely seen as a work perk (e.g., Subin, 2021). As discussed throughout this chapter, there are many important distinctions to defining and operationalizing telework. Perhaps chief among these is the need to understand how the extent of telework impacts work‒family outcomes (Golden, 2006a, 2006b, 2007, 2012; Golden & Eddleston, 2020; Golden & Gajendran, 2019; Golden & Raghuram, 2010; Golden & Veiga, 2005; Golden et al., 2006; Morganson et al., 2010; Virick et al., 2010). Just as the optimal dosage of medicine depends upon characteristics such as age and weight, maximizing the effect of teleworking on work‒family outcomes likely depends upon the extent of teleworking, as well as accounting for individual and organizational characteristics. Individual characteristics could be demographic such household size and gender (Golden et al., 2006; Zhang et al., 2020), but could also be psychological constructs. For example, Leslie et al. (2019) described a model of work‒family ideologies: individuals may differ to the degree that they view work and personal life domains as limited or fixed versus expandable, on segmentation or integration ideologies (similar to the concept of boundary management preferences defined earlier), and on how they prioritize work and personal life. The concepts in Leslie et al.’s framework seem relevant to telework and merit further research and development. For example, it might be that individuals with ideologies toward work prioritization and segmentation may struggle to achieve work‒family balance in a telework setting, particularly with dependents at home. Individual differences may likewise impact how and which coping strategies and boundary management styles individuals enact, and in turn, subsequent telework outcomes. In an earlier section, we summarized qualitative research on individual strategies for managing work‒family domains. However, quantitative research could test which strategies tend to work and when. An experimental research design where individuals are assigned either to a control group or a training group (e.g., to be trained on effective time management, boundary management, and communication techniques) could be useful in advancing telework effectiveness. Research on job crafting suggests that workers can be trained to change their approach to work in order to achieve professional growth and performance (van Wingerden et al., 2016). Furthermore, supervisor and organizational support are documented to be important negative antecedents of work‒family conflict (e.g., Kossek, Pichler et al., 2011). Work‒family literature supports that supervisors can be trained to engage in family supportive supervisor behaviors (Hammer et al., 2011; Kelly et al., 2014). Perhaps, then, employees can be trained to telework effectively and supervisors can be trained to work with employees to support teleworkers in finding a productive and healthy balance. Prior reviews of telework have called for research to move away from cross-sectional designs toward more complex ones (Allen et al., 2013, 2015; Gajandran & Harrison, 2007). Although we have cited some recent studies that have employed multiple time points of data collection, experimental and longitudinal designs are still needed. Both experimental and repeated meas-

Telework and the work–family interface  141 ures designs can better account for individual differences, which may be particularly important in understanding how telework impacts work‒family outcomes. Individuals may opt into telework as a way to alleviate conflict, causing a major confound in cross-sectional designs. Moreover, while some variables may draw individuals to telework (e.g., integration preference and a tendency to prioritize family), the strategies that teleworkers described as being effective (e.g., maintaining and voicing clear boundaries from family; Basile & Beauregard, 2016; Fonner & Stache, 2012) may ultimately cause them to be ineffective in their attempts to juggle their work and family lives. More research is needed to study these possibilities and the many other potential subtle distinctions inherent in telework.

SUMMARY Telework is a rapidly expanding work practice that has received renewed attention and interest in the wake of the global pandemic. Research and theory regarding telework suggests that it can impact individual attempts to juggle work and personal life roles in both positive and negative ways. However, research has primarily taken a boundary management and conflict approach. In general, telework may be viewed as a flexible work arrangement that can assist workers in reducing work‒family conflict. However, there are clearly tradeoffs. Some initial findings suggest strategies teleworkers can use to balance work and personal lives in a telework setting. However, substantially more research is needed to fully understand the complex ways that work and home lives interact for different individuals and how telework can be best utilized.

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8. Remoteness or virtuality? A refined framework of individual skills needed for remote and virtual work Erin E. Makarius and Barbara Z. Larson

At the height of the COVID-19 pandemic of 2020, nearly 70 percent of the entire US workforce was working remotely (Global Workplace Analytics/Owl Labs, 2020), a number that rises further if only considering jobs which can be performed remotely. As vaccines became widespread and workers began to return to office settings, surveys conducted in the first half of 2021 have suggested that most eligible US workers wish to continue working remotely at least part of each work week (Harvard Business School Online, 2020; Miller, 2021). This apparently fundamental transformation of the workforce – and particularly the knowledge-based workforce – suggests that a greater focus on the individual experience of both virtual work and remote work is needed in management scholarship. Our distinction between virtual and remote work is intentional, and in this chapter, we make the argument that greater differentiation between these contexts – including the challenges of each context, the influence of each context on workers, and the skills needed to succeed in each context – needs closer attention in future research. Consistent with prior research that distinguishes the individual experiences of electronic dependence and copresence (Bartel et al., 2012; Gibson & Gibbs, 2006; Gibson et al., 2011; Hinds & Bailey, 2003), we also suggest that an increased understanding of both virtuality and remoteness may be an important addition to the study of individual work outcomes. In this chapter, we first define and differentiate between the constructs of virtuality and remoteness. We indicate that the constructs of virtuality and remoteness have been implicitly conflated, or used interchangeably, in studies of virtual work, telecommuting, remote work, and related fields. Even our own prior work (Makarius & Larson, 2017; Larson & Makarius, 2018) suffers from this conflation, as we generally viewed remote work as simply a subset of virtual work, rather than a combination of the two conditions of “remoteness” and “virtuality”. We began to re-think this framework as we conducted research during the COVID pandemic, when we encountered workers who were highly experienced in virtual work, for example, with global teammates, but were experiencing a high level of remoteness for the first time. This experience suggested to us that remoteness needs further specification as a construct distinct from that of virtuality. We use definitions of virtuality and remoteness to describe the characteristics of and challenges with the contexts of virtual work and remote work. Next, we review research on the individual skills which have been associated with more effective virtual and remote work outcomes and draw out key themes inherent to each work context to further demonstrate the differences. Last, we focus on the theoretical and practical implications of distinguishing virtuality and remoteness and suggest an agenda for future research.

146

Remoteness or virtuality?  147

VIRTUALITY AND REMOTENESS As a starting point for our discussion of research at the individual level, we define how we are differentiating between the constructs of “virtuality” and “remoteness”. We indicate these differences as well as the challenges and effects of each factor in Table 8.1. Table 8.1

Distinguishing virtuality and remoteness

 

Virtuality

Both

Remoteness

Definition

Degree of use of technology

Not applicable

Degree of physical separation from

Common Challenges

tools to communicate with others

others in the organization (whether

(regardless of location)

on same team or not) *Reduced social presence and

*Technological Issues; (Gibson & *Decreased situational

respect (Bartel et al., 2012)

Gibbs, 2006)

awareness and related cognitive

*Information sharing (Cramton,

biases (Cramton, 2001; O’Leary *Increased integration of work and

2001)

et al., 2014)

non-work contexts (Kossek, 2016;

*Reduced social interaction

Raghuram & Wiesenfeld, 2004)

(Saunders et al., 2004; O’Leary Relevant Skills

Technological skills

& Cummings, 2007) Explaining situational context;

(Task-technology fit;

engaging in behaviors to develop management; voice; signaling)

communication norms; virtual

perceived proximity

Boundary management tactics

competence)

Interpersonal skills (developing

(physical, temporal, behavior,

Knowledge management

trust; structuring the

communicative)

(Coordinating information,

unstructured; establishing

directing/maintaining cognitive

cadence)

Visibility skills (impression

resources; sharing mental models)

Source: Authors’ own.

Virtuality We follow prior definitions of virtuality as the extent to which individuals use technology to interact with others (Kirkman & Mathieu, 2005; Makarius & Larson, 2017). The construct of virtuality has been described in various ways and is generally (though not always) treated as a continuous or multi-modal, rather than binary, variable (Gilson et al., 2015; Martins et al., 2004). Earlier research that tended to focus on geographic distance or the extent of geographic dispersion (e.g., Bell & Kozlowski, 2002) has largely given way to definitions that incorporate multiple dimensions of virtuality, such as technology use (e.g., Kirkman & Mathieu, 2005) and time-zone differences (Chudoba et al., 2005, O’Leary & Cummings, 2007). For instance, Gibson and Gibbs (2006) unpack four measurable elements of virtuality: geographic dispersion, electronic dependence, structural dynamism, and national diversity. They find differences in the effects of each element on innovation in teams, and argue that each of these elements should be considered as theoretically independent constructs. As another example, Purvanova, Charlier, Reeves and Greco (2021) recently define a bimodal (high/low) measure of virtuality using the extent of spatial dispersion and the level of in-person interactions. Yet Kirkman and Mathieu’s (2005) framework of team virtuality explicitly excludes spatial dispersion, arguing that teams can be highly virtual without geographic distribution:

148  Handbook of virtual work We contend that geographic and other forms of member dispersion are indeed likely to lead teams to adopt more virtual means of coordination but that member geographic dispersion is not a prerequisite for team virtuality. In other words, a team with co-located members does not automatically preclude members from interacting virtually or even prevent the team from being highly virtual. (p. 702)

We agree that the construct of virtuality should be considered distinct from that of spatial dispersion, and that the two factors are not necessarily correlated. We note that the common phenomenon of focus in most measures of virtuality is the use of technology, and as such, many of the antecedents and effects of virtuality are related to technologically mediated communication. Yet, these findings do not fully explain the challenges and outcomes that emerge from the absence of physical co-location, and the skills required to work effectively in a technologically mediated setting are not identical to those needed for effective work in a non-co-located setting. Bartel and colleagues (2012) indicate that “virtual work and physical isolation are not synonymous and may be unrelated” (p. 744), while Breuer and colleagues (2016) argue that use of electronic communication has emerged as the “minimal consensus in the literature” in definitions of virtuality (p. 1153, fn3). For these reasons, we suggest that physical separation should be considered as a distinct factor in the development of a framework of skills for remote and virtual work. Remoteness Building on the construct of physical isolation (Bartel et al., 2012), we define remoteness as the extent to which a worker is physically separated from their organization. Importantly, our baseline measure for remoteness is separation from any members of the organization, rather than specific members of the organization, such as supervisors or teammates. Past research has typically focused on dispersion of teammates (e.g., Gibson & Gibbs, 2006) or supervisor-employee dyads (Staples et al., 1999), but we argue that these measures do not fully capture the range of possible remote-work settings. This definition of remoteness bears similarities to the construct of telecommuting intensity, defined as “the extent or amount of scheduled time that employees spend doing tasks away from a central work location” (Gajendran & Harrison, 2007, p. 1529). Yet, remoteness allows for the wider range of work conditions which are becoming increasingly prevalent in the post-COVID workplace. Telecommuting intensity has also implicitly conflated conditions of remoteness and virtuality, with the underlying assumptions that telecommuters are (a) primarily working at home, and (b) using technology tools to communicate with co-workers in the office (e.g., Allen et al., 2015). With the post-pandemic rise of the hybrid workplace, there are a quickly proliferating range of work contexts which contain differing levels of remoteness and virtuality, suggesting that these two dimensions should be considered separately. As a practical example, an employee may work in an office full-time, but co-located with employees who are not members of their own work group, or even their business unit. This hypothetical employee will benefit from the in-person physical infrastructure provided by a company location, will experience at least some artifacts of organizational culture, and will have at least limited opportunities for social interaction with members of the same organization, none of which they would have if they were traditionally telecommuting. Similarly, even before the COVID-19 pandemic, some companies had begun to co-locate employees based on the locations of their homes, in co-working spaces that help employees connect with the organization, even if not with their direct work teams (Spreitzer et al., 2015).

Remoteness or virtuality?  149 Table 8.2 further contextualizes virtuality and remoteness, with examples of work settings which exhibit higher and lower levels of each factor. The “traditional” face-to-face office with co-located co-workers and supervisor may offer a context relatively low in both dimensions, though arguably, even co-located co-workers often engage in at least some virtual work (Makarius & Larson, 2017). This context is outside of the scope of most of our discussion, except as a point of comparison for contexts high in virtuality and/or remoteness. Some settings require skills to address more remoteness but less virtuality, such as the work of field technicians and truck drivers, in which there is much physical separation from the organization, but less technologically mediated communication. Other work settings require skills to address more virtuality and less remoteness, such as the context encountered by members of globally distributed teams. Finally, settings high in both remoteness and virtuality are the norm for high-frequency telecommuters, including those engaged in both work-from home and work-from anywhere conditions (Choudhury et al., 2021). The distinction between remoteness and virtuality is salient to consideration of individual skills needed for remote and virtual work, as we show that different skills are more or less important for each context. Table 8.2

Work contexts with varying levels of virtuality and remoteness

 

Low virtuality

High virtuality

Low remoteness

Traditional work (e.g., in-office, co-located)

Virtual work (e.g., global virtual teams)

High remoteness

Remote work (e.g., independent field workers,

Distributed work (telecommuting, work from

e.g., truck drivers)

anywhere, etc.)

Source: Authors’ own.

The ability to work with high virtuality (that is, the ability to utilize relatively high-quality, low-cost technology tools to communicate with co-workers) is what makes some effective remote work possible. In that sense, virtuality can be considered an enabler of remoteness in work, or a separate condition which is not synonymous with remoteness. To further differentiate the skills needed to contend with virtuality versus those needed for remoteness, below we review some of the primary challenges and consequences of each context and then discuss skills most relevant to address those challenges.

CHALLENGES OF VIRTUALITY AND REMOTENESS We begin the discussion of the challenges of virtuality and remoteness with two caveats. First, there are well-documented benefits of both virtuality and remoteness, including increased productivity (Bloom et al., 2015; Choudhury et al., 2021), increased creativity (Jia et al., 2009; Thompson, 2021), increased perception of job autonomy (Gajendran et al., 2015), and decreased work–family conflict (Golden et al., 2006), among others. However, these benefits can be offset, or even erased, by challenges that virtuality and remoteness pose to the individual worker. The purpose of individual virtual and remote-work skills is precisely to mitigate the impact of these challenges, so that workers (and their employers) can reap the benefits of the work context. The salience of these skills has only increased as virtual and remote work settings have become more prevalent since the inception of the COVID-19 pandemic. For this reason, we focus here on how best to mitigate the downsides, rather than highlighting the upsides.

150  Handbook of virtual work Second, because much of the extant research was conducted in contexts with high levels of both virtuality and remoteness, it is difficult to absolutely categorize the skills that address one factor versus the other. For purposes of this chapter, we suggest the following categorizations as a matter of emphasis, highlighting the skills that are most important for contexts of high virtuality versus those which are most important for contexts of high remoteness, and noting skills that are important in both settings. We do argue that further delineation of the skills most important for each contextual factor would help to advance our understanding of remote and virtual work in the future. Challenges and Skills Most Relevant to Virtuality Given that virtuality is defined by the use of technological tools for communication, replacing face-to-face communication, it is unsurprising that the central challenges of virtuality relate to technology use itself, along with technologically mediated transferring of information. The most relevant skills to address these challenges relate to technological skills such as the fit, virtual competence, and communication norms related to technology. In addition, higher complexity in coordination and information sharing due to electronic communications place a greater emphasis on skills related to knowledge management. Technology choice is a significant challenge for the virtual worker. Communications technology features vary by medium, and a given technological tool may be more or less suited to a given work task (O’Leary & Cummings, 2007; Zigurs & Buckland, 1998). Individual workers may become skilled at task-technology fit (Goodhue & Thompson, 1995; Malhotra & Majchrzak, 2014), or matching “available communication technologies to different types of interpersonal interactions” (Maruping & Agarwal, 2004, p. 976). For instance, task-media-member compatibility is a type of fit where synergy between tasks, technology, and competencies of individuals working with one another is established (Kirkman & Mathieu, 2005). Task-technology fit is based on media synchronicity theory, which indicates that individuals should determine the best technological medium for the situation (Dennis et al., 2008). Some technologies are better suited for convergence processes, which are useful in helping everyone have common understanding. Using technology with higher synchronicity is better for these processes, such as videoconferencing. Other technologies work well for conveyance, when the main goal is the transmission of information. For these processes, technologies lower in synchronicity are better, such as email or a webcast (Dennis et al., 2008). Another lens on technology choice can be found in the literature on technological affordances, or the range of potential action enabled by the user’s relationship with a given technology (Evans et al., 2017). This approach suggests that optimizing fit requires a more dynamic consideration of the interaction between a user’s work routines and available technologies (Leonardi, 2011). Individuals should consider the type of task, member preferences, and processes or reasons for communicating before determining which type of technology to use to communicate. For instance, the use of video – typically considered the gold standard for rich virtual communication – does increase facial expression synchrony (demonstrating the benefit of increased visual cues), but also decreases speaking-turn equality, resulting in reduced collective intelligence in a virtual dyad (Tomprou et al., 2021). Research has also indicated that richer communication media convey more authentic emotion and mid-range communication media such as

Remoteness or virtuality?  151 telephones reduced emotional leakage and were perceived as more authentic (Brodsky, 2021). These recent findings further point to the need to carefully determine the best technology to fit the task and interaction. Communication technology itself can also enable the shifting of communication norms within an organization (e.g., Klaas et al., 2012). Communication norms refer to preferences and typical communications that are fairly well known in an office environment. For instance, if someone has their office door closed, then a norm would be communicated to not walk in to ask a question. Yet communicating using technology makes those norms less visible and a plethora of technological tools makes it difficult to know what to use to communicate with whom and when (Moser & Axtell, 2013; Walther & Bunz, 2005). Virtual communication skills can be related to expressiveness (facial expressions and cues), coordination (timing of responses, topic initiation and closure), attentiveness (showing interest and empathy), and composure (confidence, certainty) that together are suggested to influence satisfaction and effectiveness when communicating virtually (Spitzberg, 2006). For example, although emoticons are being used more often in virtual interactions between co-workers, text-based smileys can be negatively related to competence and may not be related to warmth or likeability (Glikson et al., 2018). Individuals should explicitly establish communication norms with others they are working with virtually (Moser & Axtell, 2013) that could indicate what to use when, what response times are expected, and specific details for using each technology (e.g., number of people, types of subject lines, reply all or not). Moreover, communication technology provides challenges when working virtually, extending beyond those of limited bandwidth or faulty internet connections. An individual’s level of anxiety about, and experience with, technology use predicts their overall evaluation of self-efficacy with respect to remote work (Compeau & Higgins, 1995; Staples et al., 1999). Individuals high in virtual competence have virtual work self-efficacy and virtual media skills (Wang & Haggerty, 2011). Virtual work self-efficacy refers to an individual’s belief that they can use technology effectively and confidence in utilizing various technology tools (Compeau & Higgins, 1995). This includes self-efficacy in figuring out how to use technology to accomplish individual tasks as well as to collaborate with others. Virtual media skill is an individual’s ability to use different types of technological tools (Wang & Haggerty, 2011). Skill level is important as individuals may know communication norms or what technology to use when but may not have high ability in using those tools. Virtual competence can be developed through training on differential technological tools, practice, and feedback. In addition to technological issues, virtuality makes the act of information transfer more fallible, and the information itself more subject to misinterpretation (e.g., Cramton, 2001; Hollingshead, 1996). Reliance on technology for task-related communication can cause organizational knowledge to be better-documented and more accessible (Breuer et al., 2016). For example, email communication leaves a clear record of communications, and virtual file-sharing can result in a centralized repository of knowledge. Yet this documentation tends to exclude the tacit information informally gleaned in a face-to-face setting, which can be greatly reduced as virtuality increases (Raghuram, 1996). Bélanger and Allport (2008) also suggest that increased availability of explicit information in a virtual setting (e.g., via collaborative technology) may reduce behaviors that enable transfer of implicit information between virtual co-workers. Individual knowledge management skills that are significant in high-virtuality contexts include coordinating information (Cramton, 2001; Cramton & Hinds, 2004), directing and

152  Handbook of virtual work maintaining cognitive resources (Larson & Makarius, 2018; Makarius & Larson, 2017), and developing shared mental models (Maynard & Gilson, 2014). Individuals skilled in coordinating information engage in both information seeking and information sharing to ensure virtual co-workers are on the same page (Gilson et al., 2015; Kanawattanachai & Yoo, 2007). Information seeking focuses on obtaining task relevant information whereas information sharing relates not only to providing information, but also verifying that information is interpreted correctly (Cramton, 2001; Hinds & Mortensen, 2005). Directing and maintaining cognitive resources (Larson & Makarius, 2018; Makarius & Larson, 2017) are also important skills in contexts high in virtuality. Directing includes planning, which is thinking ahead and organizing relevant steps before engaging in a task (Jurado & Rosselli, 2007), and reasoning, which incorporates problem solving when encountering new or uncertain situations. This is relevant when working virtually as resolving complex issues over technological mediums has been demonstrated to be difficult (Straus & McGrath, 1994). Maintaining cognitive resources involves monitoring and updating knowledge (Miyake et al., 2000). This could involve integrating information from various sources, transferring knowledge across different domains, or organizing content that is held in one’s memory (Hinds & Mortensen, 2005; Reyt & Wiesenfeld, 2015). Shared mental models develop when individuals have a “common understanding regarding the requirements of the task and how their work will be coordinated” (Maynard & Gilson, 2014, p. 4). It is sometimes thought of as a shared cognition representing the overlap among mental representations of various aspects of the team and the task (i.e., team members being on the same page about how the team will operate and what is being done). This research suggests that using synchronous technology with high transmission velocity and having a centralized location for information can help develop shared mental models (Zigurs & Buckland, 1998). Yet, allowing messages to be edited (rehearsability) and reexamined (reprocessability) can benefit shared understanding as well, even if the communication process may take longer (Maynard & Gilson, 2014). Individuals working virtually can use skills such as documentation and utilizing a centralized resource (e.g., webpage, Slack page, Teams group) to share and access information (Reyes et al., 2021). Challenges and Skills Most Relevant to Remoteness There are challenges and skills which are more relevant to remoteness, or being physically separate or isolated from others in the organization. In contexts with high remoteness and low virtuality, challenges of reduced social presence and respect (Bartel et al., 2012; Golden & Raghuram, 2010) and increased integration of work and non-work activities (Kossek, 2016; Raghuram & Wiesenfeld, 2004) become even more prominent. Greater physical separation from others in the organization has been found to be related to isolation and lower quality connections (Bartel et al., 2012; Gajendran & Harrison, 2007), likely leading to lower organizational identification and commitment and a higher intent to leave (Bartel et al., 2012; Golden et al., 2008). As such, the skills that are more pertinent in this context relate to countering the challenges of remoteness such as lower perceived respect due to isolation and increased integration of work and non-work due to a lack of a traditional office setting. Visibility skills can help address lower social presence and perceived respect and boundary management skills allow individuals to deal with increased integration of work and non-work.

Remoteness or virtuality?  153 Visibility skills help counter the often-felt reduction of social presence respect in the remote work environment (Bartel et al., 2012). Remote workers often work longer and harder to go above and beyond in their tasks knowing that their work is not as visible as it would be if they were in a traditional office (Gajendran et al., 2015; Mulki et al., 2009). Yet there may be other ways to enhance visibility while working remotely. Visibility skills incorporate aspects of impression management (Barness et al., 2005), using voice behaviors (Koehne et al., 2012), and signaling reliability (Cristea & Leonardi, 2019). Impression management can include behaviors focused on supervisors or the job that are intended to improve perceptions of competence and likeability (Barness et al., 2005; Ferris et al., 1994). Remote workers may need to be more active in managing their impressions because no one is around to see how hard they are working (Bailey & Kurland, 2002). Remote work contexts enhance impression motivation without limiting impression opportunities (Barness et al., 2005). Yet the type of strategies used to manage impressions may be important. Job-focused behaviors, such as taking credit of positive events or trying to make events look better than they are (Wayne & Ferris, 1990), are more likely to negatively relate to performance evaluations whereas supervisor focused impression management, like praising or offering to help supervisors, positively relates to performance evaluations (Barness et al., 2005). Remote workers can enhance visibility by using voice behaviors to establish a remote social presence (Fonner & Roloff, 2012; Koehne et al., 2012). One way to do this is to overcommunicate and advertise availability. A caveat of this, however, is that although communication media can increase social presence, this also increases work interruptions, which can then reduce job satisfaction and ultimately, organizational identification (Fonner & Roloff, 2012). Another way is to speak up during virtual meetings to make presence known or to find a co-located colleague to provide support and speak up when remote workers cannot be present (Koehne et al., 2012). This amplification of voice can enhance the status for both the remote worker and the individual that helped amplify their voice (Bain et al., 2021). Visibility in the remote work environment can also be enhanced by signaling reliability and commitment in work relationships (Cristea & Leonardi, 2019). For work relationships to be cultivated remotely, individuals assess whether others are available for help, respond when needed, and have skills for work-related issues (Schinoff et al., 2020). This can be signaled in various ways, such as incorporating an “as promised” in text-based exchanges or responding to emails, even just to indicate that more time is needed to find an answer. Understanding relationship particulars can be important, such as the level of comfort with self-disclosure of personal information or whether you “click” without an in-person relationship (Schinoff et al., 2020). Individuals can enhance visibility by signaling psychological safety in interactions with others through asking questions, seeking perceptions, and encouraging co-workers to share ideas and feedback (Edmondson & Mortensen, 2021; Makarius et al., 2021). Boundary management skills are relevant in contexts with high remoteness because workers are often separated from the organization yet engaged in interactions that can be non-work related due to the greater permeability and integration of those spaces when working outside of an office environment (Ashforth et al., 2000; Kossek, 2016; Makarius et al., 2021; Yang et al., 2021). Technology that enhances worker availability – such as company-provided mobile phones – also blurs work–life boundaries and can result in reduced job satisfaction and organizational commitment, and increased turnover (e.g., Ferguson et al., 2016). In a related paradox, increased connectivity from mobile devices can leave employees feeling more job autonomy, even as they experience less actual autonomy (Mazmanian et al., 2013). These findings indi-

154  Handbook of virtual work cate that the boundaries between work and non-work are particularly blurred in highly remote contexts. Moreover, individuals differ in their preferences for integrating or segmenting work and non-work (Kreiner, 2006; Nippert-Eng, 1995; Rothbard et al., 2005) and environmental influences can foster or promote boundary work (Bourdeau et al., 2019; Edwards & Rothbard, 2005). Research on managing work and non-work boundaries offers several skills that may be valuable in contexts high in remoteness. For instance, Kreiner, Hollensbe, and Sheep (2009) suggest behavioral, temporal, and physical tactics that can be used to help manage boundaries between work and non-work. Behavioral tactics may include leveraging technology such as automated out-of-office messages to facilitate boundaries, prioritizing work and non-work demands, or allowing permeability, such as children interrupting at certain times of the day or pets welcome in meetings when working remotely. Temporal tactics relate to managing time. These might incorporate planning and setting expectations about availability (what hours of the day, days of the week, etc.), taking breaks when needed (during the day or for vacation or a getaway) and establishing work rhythms that work best, skills that have been demonstrated to be valuable when working remotely (Cramton & Hinds, 2004; Koehne et al., 2012; Schulze & Krumm, 2017; Wilson et al., 2013). These might be particularly important as research during the COVID-19 pandemic indicated that the digital workday is unending and that rarely do any teams have their members working the exact same hours (DeFilippis et al., 2020). Temporal tactics might also relate to task switching, an important remote work skill (Jurado & Rosselli, 2007; Reyt & Wiesenfeld, 2015). Last, physical tactics can include adapting physical boundaries and manipulating workspaces as well as managing physical artifacts, like having separate technological devices for work versus personal use (Kreiner et al., 2009; Kossek, 2016). Challenges and Skills Related to Both Virtuality and Remoteness While most of the phenomena we discuss here relate more closely to either virtuality or remoteness, there are two which are significantly influenced by both settings (though sometimes in different ways). These include decreased situational awareness and resulting perceptual and attributional biases and reduced social interaction. Both physical separation and technologically mediated communication – separately or together – tend to leave individuals with less awareness of situational variables influencing their counterparts. Situational awareness includes awareness of both an individual’s level of accessibility and availability, as well as awareness of task-related knowledge (Espinosa et al., 2007). Virtual and remote settings typically result in fewer visual cues and less information about the individual’s physical surroundings and personal circumstances (Malhotra & Majchrzak, 2014). Not only are situational variables less observable in virtual and remote settings, some research suggests they are also less likely to be communicated (e.g., Hiltz et al., 1986; Schinoff et al., 2020; Walther, 1996). The result of this deficit of contextual information is that attributional biases tend to be enhanced in virtual and remote settings, with individuals more likely to make dispositional attribution of unexpected or unwanted behavior from their counterparts, such as non-response or an unclear response to an email (Cramton, 2001). Negative dispositional attributions can lead to doubt about a co-worker’s integrity or intent, resulting in a damage to the work relationship via a reduction in trust (Jarvenpaa & Leidner, 1999).The decreased situational awareness in contexts that are high in virtuality and remoteness highlights the need for skills in countering construal tendencies and avoiding attribution

Remoteness or virtuality?  155 biases. Virtual and remote workers need to make a much more dedicated effort to give situational detail to their co-workers, to mitigate the likelihood of unfair dispositional attribution and stereotyping (Cramton et al., 2007). A related danger of virtual and remote settings is psychological distance, a “subjective experience that something is close or far away from the self” (Trope & Liberman, 2010, p. 440) can result from absence of situational information about virtual or remote others (Wilson et al., 2013). Greater psychological distance contributes to higher-level construals, or more abstract mental images, of distal others. High-level construals, because of their emphasis on categorization and homogeneity, result in a bias toward dispositional attributions and broad categorizations (Wilson et al., 2013). Workers can impact psychological distance by developing a sense of “perceived proximity” with co-workers, by communicating frequently, developing a shared identity with the co-worker, and signaling their own desirability as a co-worker by developing a reputation for reliability, availability, likeability, and support (O’Leary et al., 2014). We note here that this skill set requires developing a habit of directed effort to communicate situational variables which would normally emerge without comment (or sometimes even conscious observation) in a face-to-face setting. It seems likely that having a basic awareness of the perceptual and attributional biases inherent in remote and virtual work would support development of this habit. Both high-virtuality and high-remoteness settings face a reduced level of social interaction and a lack of spontaneous communication (O’Leary & Cummings, 2007). Virtuality and remoteness serve as barriers to forming friendships with colleagues (Schinoff et al., 2020). Friendships typically form through proximity, such as organizationally sanctioned events or informal interactions with others (Lin & Kwantes, 2015). For instance, the “watercooler effect” refers to the beneficial outcomes such as positive emotions, information and idea sharing, social bonding, and organizational citizenship behaviors that may result from office chit-chat and information conversations in the workplace (Methot et al., 2020). As such, interpersonal skills are more relevant in this context where individuals are more physically separate from the organization and using technology to communicate virtually with others (Makarius & Mukherjee, 2020; Schinoff et al., 2020). Important interpersonal skills include developing trust (Crisp & Jarvenpaa, 2013; Yakovleva et al., 2010), structuring social interactions (Moser & Axtell, 2013), and establishing cadence (Schinoff et al., 2020). Trust is particularly important for both virtuality and remoteness, as there is greater uncertainty and an increased risk of misunderstanding in these contexts (Breuer et al., 2016; Hinds & Bailey, 2003; Raghuram et al., 2001, 2019). Skills in developing trust can be built through predictable communication patterns, demonstrated competence, and reliability in responding to communications and completing tasks (Crisp & Jarvenpaa, 2013; Jarvenpaa et al., 1998; Yakovleva et al., 2010). Being proactive in communicating and maintaining a positive tone can also be useful for trust development (Jarvenpaa & Leidner, 1999). Retaining documentation of interactions can enhance trust as well, by reducing perceived risk (Breuer et al., 2016). If possible, initial interactions held face-to-face can promote trust and other collaborative behaviors when working virtually and remotely (Hill et al., 2009). The COVID-19 pandemic highlighted the need to structure the unstructured social interactions in contexts high in virtuality and remoteness (Larson et al., 2020). A lack of impromptu exchanges in the hallway mean that individuals need to be more proactive and intentional when engaging in social interactions (Schinoff et al., 2020). Individual workers that are remote and using virtual tools to communicate could structure social interactions through

156  Handbook of virtual work scheduling one-on-one video chats or inviting colleagues to attend a virtual networking event. Individual-level relationships are always important to nurture and develop but require more intentionality when workers do not have the opportunity for face-to-face interaction (Golden et al., 2008; Schinoff et al., 2020; Wilson et al., 2008). Serendipitous encounters that are informal and synchronous in nature, such as a virtual water cooler, can be structured to provide opportunities for interaction and connections with others (Bojinov et al., 2021; Lane et al., 2020). Developing relational cadence refers to patterns of interactions between virtual co-workers (Schinoff et al., 2020). It is related to understanding who a person is and how to interact with them. This goes beyond general interpersonal skills and refers to how individual colleagues work with one another and helps anticipate when and how interactions with virtual co-workers will go (Schinoff et al., 2019). Friendship-related cadence can be developed through sharing personal information such as weekend plans or a funny story, starting calls by asking how someone’s day or week is going, starting video calls ten minutes early to allow time for chit-chat ahead of formal interactions, and even holding office hours to allow virtual co-workers to stop in and chat without scheduling something in advance. We believe this is a developable skill as the process of establishing relational cadence is intentional and purposeful (Schinoff et al., 2020).

IMPLICATIONS FOR THEORY, PRACTICE, AND FUTURE RESEARCH The COVID-19 pandemic shifted the way people think about work, particularly regarding the setting individuals want to work in. As the dynamics of the work environment change, a greater distinction is needed between the characteristics of virtuality and remoteness to address the challenges of each setting and help individuals develop skills to be successful. In this chapter, we choose to treat remoteness and virtuality as two useful criteria for understanding (and choosing) relevant work skills. We maintain that as organizational models of work become increasingly varied (Vaduganathan et al., 2021), it becomes more important to address virtuality and remoteness as separate phenomena, with separate challenges, and requiring work skills that, while overlapping, are considerably different. We discuss the implications and future research that focuses on this distinction between virtuality and remoteness. We echo prior research (Bartel et al., 2012; Gibson et al., 2011) that virtuality and remoteness should be considered separately. One important implication of this is that researchers should more clearly define their research in terms of both virtuality and remoteness. Although much of the research on virtual teams and telecommuting has focused on high virtuality and high remoteness settings, a better understanding is needed of more organic virtual work that arises among colleagues (Larson & Makarius, 2018; Raghuram et al., 2019) as well as remote workers who use less virtual means of communicating (e.g., Schinoff et al., 2020). For instance, Makarius and Larson (2017) indicate that research on the cognitive processes associated with initiating organic virtual work would be valuable as well as the influence of organic virtual interactions on creativity and engagement. Refining and empirically validating a construct of remoteness is an important next step in this regard. A construct of remoteness could go beyond the actual physical separation that we focus on in this chapter and could potentially include cognitive, affective, behavioral components that examine the felt separation and related effects as well. Physical separation and

Remoteness or virtuality?  157 perceived proximity have been found to be distinct (Wilson et al., 2008), but more research could explore the mechanisms that mitigate the effects of remoteness and enhance perceived proximity. Moreover, theoretical development in strategic human capital related to virtuality and remoteness would allow organizations to find ways to develop individuals and build the skills of highly virtual and/or highly remote workers. Strategies for selecting, socializing, evaluating and training individuals who are working virtually and/or remotely are needed (Makarius & Larson, 2017). For instance, initial research in the area of socialization indicates virtual watercoolers for remote interns that allowed them to interact with senior managers related to full-time employment offers, higher performance, and more positive attitudes about their experience (Bojinov et al., 2021). Theoretical and empirical studies in the area of hybrid work are also needed. More research examining the nature of hybrid work and how to further define, conceptualize, and differentiate that setting as well as the skills that are most relevant would be beneficial. We expect that a core challenge of research on hybrid work will be the construction of measures that capture the degree of hybridity. In addition, effectively measuring hybridity may require separate measurement of remoteness and virtuality. For example, a hybrid work setting could entail as little as one remote day per week (20% remoteness) or as much as four days (80%). On the other hand, the degree to which virtuality varies on remote versus in-office days will depend on the nature of the work being done. For example, an individual who performs most work in a global virtual team could experience almost no variation in the degree of virtuality day-to-day, regardless of whether the work is being performed remotely or in-office. There is considerable speculation – but virtually no research to date – about the individual-level skills most important for effective hybrid work. Mortensen and Haas (2021) suggest that valuable skills in the hybrid environment include adaptability, organization, and coordination, yet these will need to be tested empirically. We anticipate that individual-level hybrid work skills might focus more on increased task switching, as employees adapt to shifting back and forth between an office environment high in virtuality and working from home with a greater level of remoteness. Similarly, skills in collaborating with others who fall in different quadrants of Table 8.2 (e.g., some in a traditional office; some highly virtual; others working remotely) will be valuable to understand going forward. Research on hybrid work should also address the implications of participating in hybrid team interactions on workplace diversity and inclusion. Research suggests that individuals who feel marginalized in the office may feel freer to speak up in a more remote setting (Dubrovsky et al., 1991), yet on the other hand, may suffer career consequences as a result of greater remoteness (e.g., Bloom et al., 2015). While we believe that the burden of addressing inclusion of hybrid workers remains with managers and leaders in organizations, individual workers from underrepresented backgrounds could possibly benefit from the identification of skills and behaviors which can help them leverage the advantages and mitigate the negative impacts of hybridity on their professional contributions and career trajectories. Moreover, all individuals could build skills in addressing inclusion by making sure all voices are heard in virtual meetings, and by reaching out to include those who are working remotely. Finally, we have observed anecdotally that one of the biggest challenges facing employers after the initial pandemic lockdown had subsided was maintenance of organizational culture among many individually located remote workers. Most extant research has implicitly treated telecommuting or other remote work as a condition of a minority of people in an organization.

158  Handbook of virtual work As it appears that remote work may become a much more substantial context for many individuals (and perhaps for a majority of employees in some companies), it will be important to study the dissemination of organization-level norms and values, via artifacts of organizational culture, to a highly individualized workforce. In practice, separating virtuality from remoteness can allow managers to focus their efforts and investment more precisely and effectively for talent acquisition and employee development, by targeting skills that are most important for the levels of virtuality and remoteness that exist in a given job setting. Applying this decision rule to Table 8.2 suggests several specific recommendations. First, training for workers who are located in an organizational site but working with distributed teammates (a high-virtuality, low-remoteness setting) should focus more on developing self-efficacy with respect to the use of technology, and a strong ability to choose technology which meets the needs of a given situation, as well as emphasizing the different skills and means of information transfer. On the other hand, field workers and others with high-remoteness, low-virtuality job settings, will benefit from a focus on maintaining visibility within the organization as well as managing work/non-work boundaries. Employees telecommuting and working from home, whether home is near or far from the office (high-virtuality, high-remoteness), will benefit from training that is balanced between both of these sets of skills. All three categories of employees should receive training in both the avoidance of common attributional biases as well as the development of successful relationships with virtual others. Virtuality and remoteness will continue to affect the way individuals work for the foreseeable future. By understanding the skills needed in each context, organizations and educators can strategically plan ways to train and develop individuals to be more successful when working virtually and/or remotely. Intentionally considering the best skills to develop in each context of work may help better prepare individuals for the future of work.

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9. Emotions and emotional management in virtual contexts Isabel D. Dimas, Teresa Rebelo, Marta P. Alves and Paulo R. Lourenço

With the growing presence of information and communication technologies in work settings (e.g., telework, virtual teams), working without or with little face-to-face interaction has become a common feature. The study of emotions in these work environments is fundamental to promote workers’ well-being and satisfactory professional relationships, with positive consequences on performance. The interest in studying emotions in the workplace is relatively recent, compared with other topics such as leadership, motivation, or job satisfaction. Indeed, what came to be called as the “affective revolution” in organizational psychology and organizational behavior, began in the 1990s (Ashkanasy & Dorris, 2017). Since then, we have seen an increase in studies focused on the role of emotions in work contexts, from their expression and management to their positive and negative outcomes. The definition of emotion is not consensual, and other constructs, such as affect, mood or temperament, appear to be conceptually associated, but are also considered as distinct by several authors in literature reviews (e.g., Barsade & Gibson, 2007; Kelly & Barsade, 2001) and in empirical studies (e.g., Bartel & Saavedra, 2000). According to Barsade and Gibson (2007), affect is a broader construct, which includes feeling states (moods and emotions) and feeling traits (dispositional affect). Gray and Watson (2001), based on the literature review on this topic, identify the main differences between emotion and mood. Emotions are mostly related to a specific object or event, and they usually involve intense affective states of short duration. Mood, in turn, is described as a diffuse and less intense affective state, but with a longer duration, which can change due to external events. The concept of temperament (or dispositional affect) is also distinct and is usually referred to as a stable individual trait that defines how we perceive others and the world and is considered a predisposition to react in a certain way in the face of certain events (Gray & Watson, 2001; Weiss & Cropanzano, 1996). In general, definitions and approaches to emotions are essentially based on individual and internal conceptions, with the cognitive and physiological dimensions prevailing over the interpersonal and social dimensions (Parkinson, 1996). Ashforth and Humphrey (1995) adopt an integrating definition of emotion as “a subjective affective state” (p. 99), which includes basic emotions, such as joy or anger, as well as social emotions, such as guilt, shame, or envy. Ashkanasy (2003), in turn, highlights that, beyond their internal manifestations, emotions also have external manifestations (such as facial expression and posture). Taking the different definitions of emotions together, in this chapter emotion is conceptualized as (a) a subjective affective response to a stimulus, which (b) includes internal and external manifestations, and (c) guides the individual’s further actions (Ashkanasy & Dorris, 2017; Frijda, 1986). Traditionally, the study of emotions has predominantly focused on the antecedents and consequences of emotions at the individual level (van Kleef, 2009). However, rather than just 164

Emotions and emotional management in virtual contexts  165 being felt, emotions are expressed in social contexts and influence others’ feelings, perceptions, and actions (van Kleef, 2016). Furthermore, van Kleef (2016) also states that the social effects of emotions and their functionality depend on the extent to which the agents are able to adjust (or regulate) their emotional expression in order to achieve their goals while facing the constraints and demands of the social context. Thus, understanding the role of emotions and emotion management in virtual contexts requires consideration of both the individual and interpersonal perspectives and also of emotion regulation. Accordingly, adopting both an intrapersonal and an interpersonal lens, the aim of this chapter is to contribute to clarifying how working virtually affects the emotions that the individuals feel and the way they express and manage them, which in turn will affect others’ emotions and behaviors. Organized in six sections, this chapter focuses on three essential processes regarding emotions that comprehend the individual and interindividual levels of analysis: feeling, expressing, and regulating (cf. Figure 9.1). In the first section, with a focus on feeling emotions, we explore the impact of working virtually on the emotions and well-being of individuals, as well as on their attitudes and results. We then turn, in the second section, to the interpersonal level of analysis, focusing on the influence of virtuality on the way emotions are expressed and interpreted by others. Expressing emotions can affect others’ behaviors through an inferential process but also through contagion, a fundamental process in both virtual and face-to-face interpersonal contexts, which is addressed in section three. Regulating emotions in virtual contexts, which involves both intrapersonal and interpersonal features, is our focus in the fourth section. Based on the analysis of the literature reviewed, we conclude the chapter by discussing some directions for further research (the fifth section) and by providing some implications for practice (in the sixth and final section).

Source: Authors’ own.

Figure 9.1

Conceptual model of the chapter

166  Handbook of virtual work

REMOTE WORK AND EMOTIONAL WELL-BEING: A DOUBLE-EDGED SWORD Weiss and Cropanzano’s affective events theory (1996) considers that people’s behavior and performance in the workplace depend on how employees are feeling in response to the work environment. Objects and events in the workplace are proximal causes of emotions experienced and manifested by people in that context in a particular moment, which, in turn, have an effect on work attitudes and behaviors. Based on this framework, e-remote work, telework or telecommuting can elicit emotions and affective states, which have a significant impact on workers’ well-being. In this section, we present empirical studies and literature reviews about the emotional well-being of people who use information and communication technologies to work in a location outside their usual work settings. Charalampous et al.’s (2019) systematic review about the effects of remote e-work on the well-being of knowledge workers suggests a positive association between the amount of teleworking and both job satisfaction and organizational commitment and a negative relationship with emotional exhaustion. Moreover, although research reviewed by the authors shows quite inconclusive results about the impact of working remotely on experienced emotions, most of the studies indicate that e-remote work is positively associated with the experience of positive emotions (e.g., joy, happiness) and negatively related to negative emotions (e.g., anxiety, guilt). These findings continue to be supported by subsequent studies. For example, Delanoeije and Verbruggen (2020) revealed that within a group of employees that could choose to work from home on at most two days a week, individuals showed lower stress and higher work commitment on teleworking days compared with non-teleworking days. Indeed, following Windeler et al. (2017), part-time telework can provide a recovery opportunity, attenuating the negative relationship between social interaction and emotional exhaustion, since employees intensify task focus and constrain social contacts when they are teleworking. According to Charalampous et al. (2019), a higher sense of autonomy, control, and flexibility with respect to work schedules and locations may contribute to explaining the positive association between remote work and emotional well-being. Contrastingly, professional isolation and perception of threats in career advancement are referred to by the author as explaining the association between teleworking and negative emotions. Research also indicates a curvilinear relationship (i.e., inverted U-shape), between the amount of remote work and job satisfaction (e.g., Golden, 2006; Virick et al., 2010), suggesting that working outside the office via technologies more than a certain number of hours per day or days per week can have pernicious effects on individuals’ affective states. The relatively contradictory results may be explained by moderated factors related to individual differences, job context, task characteristics, and work conditions, which may affect the relationship between remote work and employees’ emotional states. First, workers’ individual characteristics, such as personality and skills, play an important role in the association between remote e-working and emotional well-being. For example, employees with the worker type of high drive and low enjoyment present higher levels of satisfaction in high or low levels of teleworking (Virick et al., 2010); higher levels of openness (Luse et al., 2013) and agreeableness are positively related to favorable attitudes toward virtual work (Clark et al., 2012); employees with more emotional stability report higher levels of emotional well-being when working remotely (Perry et al., 2018); self-discipline was shown to prevent procrastination and feelings of loneliness in teleworkers (Wang et al., 2021); and

Emotions and emotional management in virtual contexts  167 the capacity of self-goal setting mediates the positive association between telework and job satisfaction (Müller & Niessen, 2019). These individual differences may suggest that worker discretion around work location and schedule might be seen as a potential protective factor of an individual’s emotional well-being, allowing workers to be flexible in balancing face-to-face interaction and remote e-work. Second, different results were found in distinct professional groups and work contexts. De Vries et al.’s (2019) study with a municipality’s public servants showed negative effects of teleworking, including greater professional isolation and less organizational commitment on the days the employees worked exclusively from home. Some years before, Mann and Holdsworth (2003) observed that, compared with office-workers, full-time journalists who worked from home reported more negative emotions (e.g., loneliness, irritability, worry and guilt). Finally, based on the recent systematic review developed by Charalampous et al. (2019), adequate technology equipment, organizational and social support, good relationships with leaders and colleagues, work–family balance, low task interdependence, defining clear goals, giving performance feedback, and providing participation can be considered as protective factors against the adverse impact of remote work on emotional well-being. At this moment, we can already find some studies about the effects of virtuality on emotions during the COVID-19 pandemic. In response to the pandemic, organizations all around the world have been encouraged to adopt remote work when and where possible. So, working from home using information and communication technologies was externally imposed and both organizations and employees may not be prepared to effectively implement the adjustments and changes in work daily routines in a short time. This abrupt and unexpected shift could entail additional risks with an important impact on workers’ emotional experiences, particularly during lockdown periods, because the demands of technology use and household and childcare obligations increased very significantly during this time frame. In fact, there is empirical evidence that daily job and home demands during telework, in the period of pandemic restrictions, were positively associated with emotional exhaustion, which in turn is negatively related to job performance (Hadi et al., 2021). Moreover, the same study highlights the importance of crafting one’s daily leisure to moderate the impact of both job and home demands on emotional exhaustion. Another study during the COVID-19 pandemic showed that technostress (i.e., stress facing technology demands and requirements) experienced by teachers was negatively associated with the perception of self-efficacy regarding the use of technology in educational activities and also motivation levels to continue to work online (Panisoara et al., 2020). Providing adequate technology conditions and equipment to their employees is shown to be positively associated with employees’ commitment (Ilozor et al., 2001). Furthermore, Wang et al.’s (2021) study with a heterogeneous sample of individuals who were working from home during the pandemic also highlights the importance of social support from colleagues as a predictor of the decrease in emotional exhaustion by reducing loneliness, procrastination and work-to-home interference. Also, support provided by organizations to telework tasks accomplishment was revealed to prevent workers with high interdependent tasks from becoming emotionally exhausted when confined to working from home during the pandemic (Chong et al., 2020). To conclude, the effects of virtuality on the emotional well-being of employees can be perceived as a double-edged sword, which can be analyzed through the lens of the job

168  Handbook of virtual work demands-resources model (Bakker & Demerouti, 2007), based on job design and job stress theories. Job characteristics have an impact on workers’ well-being and the literature suggests that virtual work comprises both job demands and resources. According to the model, job demands refer to physical, social, emotional, cognitive and organizational features of the work that involve continuous physical and psychological effort and skills, and associated costs. Job resources include work-related aspects (physical, psychological, social, or organizational) that are functional in achieving work goals, moderating the impact of job demands and their respective costs or promoting personal growth and learning. First, the experience of job strain during remote e-work would depend on the intensity and duration of work demands, as technology-related skills (Panisoara et al., 2020), work–home interference (Hadi et al., 2021), professional isolation and threats to career development opportunities (Charalampous et al., 2019). On the other hand, job resources associated with remote working conditions, namely work autonomy and flexibility (Charalampous et al., 2019) and support from organization (Chong et al., 2020) and colleagues (Wang et al., 2021) may have a motivational role in increasing work commitment and stimulating individual development. Therefore, the emotional well-being of teleworkers (or remote e-workers) would depend on the resources available in the work environment and/or fostered by colleagues, leaders and organizations and on the protective role that those resources may have in the negative emotional impact of job demands.

EMOTIONS AS SOCIAL INFORMATION: EXPRESSING AND INTERPRETING EMOTIONS IN VIRTUAL CONTEXTS In the previous section, the effects of working remotely through information and communication technologies on the individual emotions and results were addressed. However, although emotions are an intra-psychic phenomenon (Frijda, 1988), they can be verbally communicated and also observed by others through nonverbal and paraverbal cues. Emotions are signals that the agent sends to observers regarding the agent’s goals and intentions, about how the context is perceived and concerning the characteristics of interpersonal relationships maintained between those who interact (Hareli & Rafaeli, 2008). This information will influence the behaviors, feelings, and thoughts of those who observe, affecting future interactions. Therefore, in addition to their intrapersonal effects, emotions have a social function as they help individuals find adaptive ways to relate to one another (van Kleef, 2016). According to the emotions as social information (EASI) model (van Kleef, 2009), the two key mechanisms that drive the social effects of emotional expressions are inferential processes and affective reactions. Based on others’ emotional expressions, observers infer information about their feelings, needs, attitudes, and behavioral intentions. Such inferences may in turn influence the observer’s behavior. The agent’s emotions may also influence the observer’s behavior by generating affective reactions, either via emotional contagion processes (a fundamental process in interpersonal contexts that will be developed in the next section) or by impressions and interpersonal liking. A core idea of the EASI model is that emotions provide information (van Kleef, 2016). Expressing and interpreting emotions has been argued to be highly dependent on the availability of nonverbal cues (e.g., tone of voice, facial expressions) (Burgoon et al., 2002). Since communication tools (e.g., videoconferencing, email) vary regarding the presence of nonverbal

Emotions and emotional management in virtual contexts  169 cues, this brings us to the debate of whether the medium used to share emotional information will influence the interpersonal effects of emotional expression or not. Specifically, this refers to the extent to which interactions that take place through computer mediated tools, compared with face-to-face interactions, threaten the process of emotional expression and, as a result, condition the quality of present and future social interactions. Two different perspectives are important to mention at this point: one, the cues-filtered-out perspective (Culnan & Markus, 1987), which has its roots in theories such as social presence theory (Short et al., 1976) and media richness theory (Daft & Lengel, 1986), advocates that the absence of nonverbal cues reduces socioemotional warmth and hampers communication (Hassanein & Head, 2007); the other one, associated with social information processing theory (Walther, 1992, 2015), states that the absence of nonverbal cues will not necessarily lead to a reduction in the quality of social interaction, because communicators adapt to the communication capability of the medium. The arguments of each perspective will be briefly described below. Social presence theory (Short et al., 1976) defines social presence as the degree of salience of others across different communication media. According to Short and colleagues, social presence is composed of two dimensions: intimacy (i.e., degree of connectedness that is perceived by communicators) and immediacy (i.e., level of psychological distance between communicators). Since intimacy and immediacy are influenced by the presence of both verbal and nonverbal cues, social presence will depend on the ability of communication media to transmit those cues. Accordingly, social presence is a quality of the medium. Likewise, media richness theory (Daft & Lengel, 1986) also comprises the idea that information and communication technologies filter out important contextual and social cues, which are typically conveyed via nonverbal cues. This theory considers richness (i.e., the extent to which communication tools are able to clarify ambiguity and amplify understanding) as the key characteristic in terms of effectiveness of an information and communication tool. According to this perspective, lower levels of richness in communication media reduce the quality of communication and social interactions. An extension of media richness theory was later presented by Dennis and Valacich (1999) in their media synchronicity theory, which proposes five functionalities of media that yield communication capabilities: immediacy of feedback, symbol variety, parallelism, rehearsability, and reprocessability. From this perspective, the evaluation of the richness of a medium will depend on the characteristics of the situation, with a communication medium that is high in richness being the one that best fits the situation. Maruping and Agarwal (2004) applied this perspective to different types of interpersonal interactions in virtual teams. Concerning the process of expressing and managing emotions, the authors stated that the information and communication tool used for the best effectiveness should be high in feedback immediacy, symbol variety (i.e., the number and variety of cues that can be transmitted), and parallelism (i.e., the number of communication channels enabled). In contrast with the cues-filtered-out perspective, social information theory (Walther, 1992) states that experiencing intimacy is highly dependent on the interactants, rather than on the communication medium itself. In virtual contexts, individuals will adapt to the characteristics of the medium and adopt different strategies to convey socioemotional cues. For instance, in written communication, language can be used to express emotions and social information. However, in virtual contexts, compared with face-to-face environments, more messages and more time are needed to transmit socioemotional information, and the amount of time may be even greater when tools used to communicate are asynchronous (Walther, 2015).

170  Handbook of virtual work Even if social information theory considers that the absence of nonverbal cues in information and communication technologies does not necessarily lead to a reduction in the quality of social interaction, it does not deny the existence of differences between communication media (Oh et al., 2018). Thus, both perspectives converge on the idea that different communication media vary in the extent to which they convey socioemotional information through verbal and nonverbal cues. However, social information theory adds that individual communication strategies will also make a difference because interactants will adapt to the medium in order to achieve their communication goals (Walther, 2015). Considering what has been said above, we can conclude that higher levels of efficiency in terms of expressing emotional information are achieved when both verbal and nonverbal cues are present. However, with the course of time, interactants will adapt to the characteristics of the channel and will be able to effectively share their emotions and to infer the emotional states of others via, for instance, asynchronous text-based interactions. Indeed, as supported in previous studies (e.g., Walther et al., 2005) individuals are able to communicate affective messages through language. Text-based communication can even allow for the transmission of unintentional cues of emotion, as recently evidenced in a series of studies conducted by Blunden and Brodsky (2020). Beyond textual information, other forms of expressing emotions in virtual contexts have also been studied, such as repetitious punctuation marks, capitalization of letters, or emojis (and their emoticons ancestors). Regarding emojis, previous studies have tried to clarify whether their use in written communication could facilitate emotional sharing, compensating for the absence of contextual information that is present in face-to-face interactions. While some studies support the use of emojis to convey emotional information (e.g., Kralj Novak et al., 2015), there is also evidence that contradicts this positive influence (e.g., Robus et al., 2020), namely because there is no consensus regarding the meaning of many emojis. As a whole, however, studies suggest that emojis tend to improve emotion communication and interpretation, enhancing processing speed and understanding of verbal messages (Boutet et al., 2021). While further studies are necessary to have concluding evidence regarding the effectiveness of using emojis, namely across contexts, it seems that using emojis may improve the efficiency of emotional sharing and interpretation in virtual text-based interactions.

SPREADING AND INDUCTION OF EMOTIONS: EMOTIONAL CONTAGION IN VIRTUAL CONTEXTS While, in the previous section, we focused on how digital interactions influence the expression and inference of emotions, in this section, we will approach the affective influences (specifically, emotional contagion) of sharing emotions in virtual contexts. The individual’s behavior can be adjusted to be convergent, coordinated and synchronized over time with others’ behavior in the context of a social interaction. This can occur both in cognitive expression (Salancik & Pfeffer, 1978) and in emotional expression (Barsade et al., 2018). The most recognized construct in the literature regarding sharing of emotions is emotional contagion. Emotional contagion can be defined as “a process in which a person or group influences the emotions or behavior of another person or group through the conscious or unconscious induction of emotion states and behavioral attitudes” (Schoenewolf, 1990, p. 50). Although initially

Emotions and emotional management in virtual contexts  171 studied with the dyad as the unit of analysis (Totterdell et al., 1998) this process is nowadays recognized as a multilevel construct which can be found in dyads, groups, organizations, and societies (Barsade et al., 2018). Overall, the literature suggests that emotional contagion includes some main characteristics: (1) it involves moods and emotions (Barsade et al., 2018); (2) it can be automatic, subconscious, unintended and uncontrollable, as in the form of “primitive emotional contagion” (Hatfield et al., 1992), but also conscious, resulting from an emotional comparison process made by a person regarding the emotions of the others around her/him or produced by people involving a deliberate intention to influence the emotions of others, as in affective impression management (Kelly & Barsade, 2001); (3) it is a multilevel phenomenon that can be induced by one or more people (Barsade et al., 2018); (4) it can be produced by both agents and receivers of emotions (Howard & Gendel, 2001); and (5) it is a form of social influence where individuals have the power to change others’ emotions, influencing their feelings, attitudes and behaviors as a result (Barsade et al., 2018). Studies carried out with the aim of clarifying the process of emotional contagion, tried, overall, to answer the questions of (a) whether both types of emotions (i.e., positive and negative) cause emotional contagion and (b) the extent to which the emotional valence influences the speed and power of emotional contagion. Regarding the first question, several studies consistently revealed that both positive and negative emotions produce the phenomenon of emotional contagion (e.g., Bhullar, 2012; Fan et al., 2014). Regarding the second question, although the literature suggests that negative events are more powerful and produce quicker responses than neutral or positive events (Cacioppo et al., 1997), the studies are not conclusive, suggesting that this is a domain where more research is still required. Indeed, we can find studies suggesting that negative emotions lead to greater emotional contagion than positive emotions (e.g., Bartel & Saavedra, 2000), but also pointing to the opposite (e.g., Bhullar, 2012), or even studies that suggest that there is no difference in the power of the emotional valence in producing emotional contagion (e.g., Barsade, 2002). The literature devoted to demonstrating that emotional contagion occurs in the workplace shows that it takes place in different types of jobs and functions and at different levels of analysis (Tee, 2015). Overall, most studies in this area tend to suggest positive effects of positive emotional contagion and negative effects of negative emotional contagion. For example, positive emotional contagion improves group cooperation, decreases conflict, and increases perceived task performance (Barsade, 2002), as well as increasing teamwork engagement (Torrente et al., 2013) and team members’ feelings of pride and joy regarding their work (Smith et al., 2007). In the same way, leaders who spread a positive mood to their followers lead to more coordination within the group (Sy et al., 2005). In the same vein, the positive attitudes of employees (such as smiling) influence the appraisal of service quality made by customers (Barger & Grandey, 2006). In contrast, negative emotional contagion tends to diminish trust and cooperation, increases conflict, and decreases perceived task performance (Anderson & Thompson, 2004; Barsade, 2002) as well as contributing to collective burnout and emotional exhaustion (Bakker et al., 2001). Although most research on emotional contagion involves face-to-face interactions (Barsade et al., 2018), in the last two decades a line of research which suggests that the spread of emotions and emotional contagion also occur outside face-to-face interactions has emerged (e.g., Cheshin et al., 2011; Hancock et al., 2008). For this research line, technology constitutes a channel for emotional expression and serves to model emotions (Serrano-Puche, 2015).

172  Handbook of virtual work Accordingly, emotional contagion processes can also be found in contexts characterized by interactions via computer-mediated communication, such as email or other forms of purely text-based interaction (Barsade et al., 2018; Belkin, 2009). Given the frequent use of textual forms of communication in current organizations, this research line assumes great relevance and, as we will highlight below, also contributes to advancing the theoretical understanding of emotional contagion. The first steps regarding the analysis of emotional contagion in the electronic communication environment date from the beginning of the 21st century. The work of Thompson and Nadler (2002), in the context of negotiations, was probably the first to suggest the presence of emotional contagion between individuals who interact through computer-mediated communication (namely by email). The results found in this study revealed that individuals unconsciously reproduce the linguistic structure of each other’s messages as well as the emotional connotations of the received messages (such as their tone), and even the lag time of the responses. Later empirical studies not only reinforced the results of Thompson and Nadler’s (2002) work, showing that emotional contagion can occur without face-to-face interaction, but also produced new insights regarding the factors that lead to emotional contagion and concerning the effect of the emotional valence when computer-mediated communication is used. Taken together, the results of the studies conducted in this area highlight, firstly, that emotional contagion may occur in the absence of nonverbal cues and strengthened the assumption regarding the capacity of text-based communication to produce this phenomenon. Indeed, the literature showed that individuals exposed to texts with emotion-laden content (for both positive or negative emotional states) can be influenced via conscious expression of emotional content of the text (e.g., Hine et al., 2010). Furthermore, those individuals are able to perceive emotional cues based on textual indicators, accurately detecting specific emotions such as happiness or anger (e.g., Cheshin et al., 2011). Additionally, research has also supported the role of other factors in the emotional contagion process beyond the text content, such as the time lag used in the responses (e.g., Hancock et al., 2008), the behavior style of the partners in the electronic communication (e.g., more resolute, or more flexible) (e.g., Cheshin et al., 2011), the stage of the partners’ relationship or the expectations regarding the emotional tone of the partners (e.g., Belkin et al., 2006). It should be noted that while research tends to suggest that people are able to accurately detect discrete emotions and be “infected” by those emotions via text-only communications, as mentioned in the previous section some literature also suggests that the same cues can be interpreted differently, leading people to apprehend different emotions or even become confused. For example, the length of an email message can suggest positive or negative emotions, and the use of emojis/emoticons can produce imprecise interpretations (Cheshin et al., 2011; Walther & D’Addario, 2001). Furthermore, the research also suggests that although positive emotional contagion tends to be more common than negative emotional contagion, once disseminated, negative emotions had a stronger emotional influence on virtual partners compared with positive emotions (Belkin et al., 2006). The fact that it is easier to communicate positive than negative emotions can explain why positive emotional contagion tends to be more frequent than negative. A selective memory bias effect that leads people to pay more attention to negative than to positive information and the cultural norms, in which negative writing expressions of emotions

Emotions and emotional management in virtual contexts  173 are not common, may explain why negative emotions are more noticeable and contagious, and thus contribute to clarifying the stronger influence of negative emotions (Belkin et al., 2006). Finally, studies conducted in a research line referred by Barsade et al. (2018) as social network research, which mainly focuses on examining emotional contagion via social networks (e.g., Ferrara & Yang, 2015; Kramer et al., 2014) also showed, on one hand, that both positive and negative emotions can be transmitted and disseminated on these platforms, thus being “contagious”, and, on the other hand, that the emotional content communicated by an user is influenced by the emotional content of the posts of the others, leading the user to post emotional-laden contents with the emotions (positive or negative) that are over-represented in their network (Barsade et al., 2018; Mui et al., 2018).

EMOTIONAL MANAGEMENT: PUTTING VIRTUAL WORK SETTINGS INTO THE EQUATION Managing emotions in the virtual workplace is a topic that has deserved the attention of researchers and practitioners, given the evidence that emotions experienced in the virtual work context have an impact on the emotional well-being of workers and those who work with them (through the emotions expression and emotional contagion). This attention has been intensified by the consequences of the COVID-19 pandemic for work settings. In fact, consultancy sites, blogs, and specialized magazines have been publishing articles with a more practical orientation centered on techniques and hints to better manage emotions in remote work and virtual teamwork. For example, in 2020, Forbes Agency Council published a post of Valerie Chan about emotional intelligence in remote work environment, and MIT Sloan Management Review published an article by Liz Fosslien and Mollie Duffy on practical tips to manage stress and emotions in remote settings. Regarding research, several studies on this topic can be found, particularly focusing on emotional intelligence and emotional labor. Research on emotional intelligence relies on multiple conceptualizations and non-equivalent measurement approaches (see Ashkanasy & Daus, 2005, and Ashkanasy & Dorris, 2017, for the distinction between emotional intelligence research streams). The four-branch model of emotional intelligence (Salovey & Mayer, 1990; Mayer & Salovey, 1997) is a widely supported integrative model of emotional intelligence (Pitts et al., 2012). It comprises (a) perceiving and appraising others’ emotions, (b) use of emotions for promoting rational thinking and problem-solving, (c) understanding emotions, and (d) managing and regulating emotions. Emotional intelligence has been studied in the scope of research on the effect of individual differences on behaviors and attitudes at work (Rodrigues & Rebelo, 2020), and according to Ashkanasy and Dorris (2017), it has been the dominant variable studied in the context of emotions in work settings. Since its introduction in the literature, commonly credited to Salovey and Mayer (1990), as the ability to deal effectively with emotions and emotional knowledge, emotional intelligence has received great acceptance from business management practitioners as a promising predictor of job performance (Rodrigues & Rebelo, 2020). Goleman (1995) contributed greatly to its popularization. However, this popularity led to criticisms of the construct (Ashkanasy & Dorris, 2017), and early research caused intense debate regarding the theoretical and applied value of emotional intelligence for predicting human behavior in the workplace (Rodrigues & Rebelo, 2020). Nonetheless, meta-analyses now support emotional

174  Handbook of virtual work intelligence as a valid predictor of job performance (Joseph & Newman, 2010; O’Boyle et al., 2010), as well as its positive relationship with job satisfaction (Miao et al., 2017). Regarding research on emotional intelligence in virtual work settings, several studies on this topic can already be found, particularly focusing on the role of emotional intelligence in the effectiveness of virtual teams. For example, emotional intelligence in virtual teams seems to be a driver of team viability, with quality of communication being one mechanism through which this influence exists (Pitts et al., 2012). Beyond team viability, the leader’s emotional intelligence has a positive relationship with virtual teams’ performance and team members’ satisfaction, mediated by transformational leadership behavior (Mysirlaki & Paraskeva, 2020). Still, emotional intelligence appears to be a stronger positive predictor of collaboration among team members working in face-to-face teams compared with team members working in virtual teams (Cole et al., 2019). However, virtual teams can benefit from emotional management interventions due to the fact they could increase members’ competency, for example, in using resources such as paralinguistic cues, management of timing, and emojis/emoticons to transmit socioemotional information (Gamero et al., 2021). These authors concluded that providing virtual teams with a collective ability to manage expressions and displays of emotional cues, those interventions could improve interpersonal relationships and teamwork. Expanding the level of analysis to e-commerce network firms, Li et al. (2019) concluded that the emotional intelligence of their workers is strongly linked to the firm’s sales performance. These authors introduce the concept of “virtual network emotional intelligence”, defined as “the individual or team’s ability to manage emotions when interacting with others or to cope effectively with some condition in the virtual network” (p. 3). This conceptualization is similar to the definition advanced by Mio (2002, p. 1129) for team virtual emotional intelligence: “virtual teams are aware of emotions and able to regulate them and this awareness and regulation both inward and outward via the virtual channels in the virtual space”. The effort of contextualizing the construct of emotional intelligence into virtuality, based on previous emotional intelligence models, is meritorious. It could give rise to a refinement and an adaptation of this construct to the specific relational and communicational characteristics of this work environment. However, to the best of our knowledge, little research on this construct proposal has been produced. Research on emotional labor in virtual communication work contexts has also been carried out. Emotional labor, a term coined by Hochschild (1983), and defined as the awareness of the emotional expressions that a job requires, as well as the strategies the person uses to express those emotions, has received a great deal of attention, particularly regarding front-office job positions (Grandey & Gabriel, 2015). Emotional labor has been studied with a focus on two contrasting kinds of emotion regulation strategies: surface acting and deep acting. In surface acting, workers do not show what they are really feeling to those with whom they interact, and emotionally act according to what is expected and accepted. By contrast, in deep acting emotional consonance exists, that is, workers show emotions that are consistent with how they feel. In deep acting, workers seek to modify their felt emotions to align them with expected and accepted expression of emotions (Ashkanasy & Dorris, 2017). In short, surface acting involves modifying the expression of emotions, whereas deep acting involves modifying the emotions felt (Grandey, 2000). Empirical evidence points to the harmful effects of surface acting, which appear to be stronger than those of deep acting, being related to negative mood, emotional exhaustion, and lower levels of job satisfaction (Ashkanasy & Dorris, 2017), as well as to depersonalization

Emotions and emotional management in virtual contexts  175 (Matteson & Miller, 2012). Consistent with findings regarding face-to-face interactions, the results of a study carried out by Auger and Formentin (2021), based on a sample of higher education professors teaching online during the COVID-19 pandemic, indicated that professors experienced signs of emotional exhaustion caused by surface acting. That is to say, it was caused by the disconnect they felt between trying to support students when they themselves felt sad and anxious. Even before the pandemic, the rise of call centers and e-commerce led to the emergence of studies focused on emotional labor on online communication settings. In general, the consequences of emotional labor in call centers are like those in other service jobs, with surface acting being associated with individual and organizational negative outcomes, such as a decrease in employees’ well-being and turnover intentions (Jaarsveld & Poster, 2013). More recent research points in the same direction. For example, the study of Kim and Choo (2017) indicates that surface, but not deep, acting was positively associated with depressive symptoms in call center workers. In a similar vein, Ishii and Markman (2016) concluded that online customer service workers also engage in emotional labor, indicating that those workers who engage in surface acting are less satisfied with their job and more likely to experience burnout. In this study, the construct of emotional presence was included, defined as “the degree to which communicators in a mediated context perceive they are engaging in emotional communication with a real person” (p. 659). The findings revealed that workers perceive the highest emotional presence in phone conversations, compared with email and chat. The results also indicate that those who feel a higher degree of emotional presence in phone conversations tend to experience higher job satisfaction and less burnout. This result is in line with the media characteristics approach (e.g., social presence theory, media richness theory), reinforcing the use of richer channels (as it is the phone over the other analyzed channels). As it allows more nonverbal cues (e.g., tone of voice), it facilitates the management of more difficult issues with customers. Nevertheless, among other variables, such as gender, job autonomy or supervisor support (Grandey, 2000), customer behavior should be considered in online services too. For instance, Gabriel and Diefendorff (2015) provided evidence that customer behavior is contagious for online service workers’ behavior and causally influences changes in their emotions, emotion regulation, and vocal tone. To sum up, although further research is still necessary to have more evidence regarding the nuances of managing emotions in virtual work settings, the findings of the existing studies point to the same directions of the findings regarding face-to-face interactions, reinforcing the positive outcomes of emotional intelligence and the shortcomings of surface acting emotion regulation strategies.

CONCLUSION AND FUTURE DIRECTIONS Emotions are inherent in all work contexts, whether online or offline. They influence how people think, decide, and react, their levels of well-being and the results achieved, as well as the interactions that individuals establish with others. Despite the knowledge already generated on emotions in virtual work settings, as well as the recognized importance of the topic to the scientific community and to practitioners in a technology-based organizational world, there is still a lot to explore regarding emotions and emotional management. Therefore,

176  Handbook of virtual work increasing research in this field is crucial. Throughout this chapter, adopting both an intrapersonal and an interpersonal lens, we have tried to help clarify how individuals’ emotions and their management are influenced by working virtually, and how they influence the way people work together in digital environments. We structured our analysis around three main processes related to emotions: feeling emotions, expressing emotions (which drives interpretation and contagion processes), as well as regulating emotions. Based on this structure, below we will identify some gaps in the knowledge and provide an agenda for further research. Feeling Emotions Empirical evidence suggests that workers’ satisfaction and emotional well-being may be fostered if they have the chance to adjust and shift the location of their work according to individual and job needs and requirements (e.g., Delanoeije & Verbruggen, 2020; Virick et al., 2010; Windeler et al., 2017). Considering job demands-resources theory (Bakker & Demerouti, 2014), an extension of the job demands-resources model, workers’ proactive role in changing the work setting through job crafting is fundamental and should be addressed. In particular, based on this theoretical framework, employees who have the opportunity to actively change and adjust the design of their jobs, in terms of both physical and cognitive work demands and resources, are expected to show greater job satisfaction, well-being, and work engagement. Thus, further research is needed to clarify the role that person–job adjustment plays in the association between e-remote work characteristics or conditions and individual emotional well-being. Also, concerning the study of the impact of e-remote work on emotional well-being, further research should include longitudinal designs to analyze whether emotions change over time, since individuals may adjust to both demands and resources associated with teleworking (Mann & Holdsworth, 2003; Wang et al., 2021). Moreover, as suggested by Charalampous et al. (2019), future studies should focus on the well-being of those workers who are working full-time at the office when some colleagues are remote e-workers. For example, Golden (2007) suggested that the prevalence of teleworkers in a workplace was negatively associated with the satisfaction of co-workers who remained in the office. Since e-remote work would certainly prevail after the COVID-19 pandemic, more research is needed in the future to analyze the impact of hybrid work arrangements which will involve shifting between different work locations on both workers’ emotional well-being and performance. Concluding, since working remotely can be considered as a double-edged sword, future studies should focus on personal and organizational conditions (including the respective interaction) that could maximize the potentialities, minimize the threats and balance the demands and the resources of a hybrid work context at different levels of analysis. Expressing, Interpreting, and Spreading Emotions Regarding emotional expression, building on the EASI model (van Kleef, 2009, 2016), we reviewed different theories and research that tried to acknowledge the extent to which the digital medium used by interactants may condition the process of sharing and inferring emotions. Although there are different perspectives on this topic (Culnan & Markus, 1987; Walther, 1992), studies tend to suggest that emotions can be effectively shared and interpreted through either synchronous or asynchronous communication media (e.g., Blunden & Brodsky,

Emotions and emotional management in virtual contexts  177 2020; Kralj Novak et al., 2015). However, when the medium used conveys both verbal and nonverbal cues, the process of sharing emotions tends to be more efficient in terms of time and interactions required (Walther, 2015). How much time interactants will need to adapt to the different media in order to share and interpret emotions appropriately is, however, an open question. Moreover, whether previous face-to-face interactions will influence emotional sharing via virtual media and how hybrid interactions (i.e., interactions that include both face-to-face and virtual interactions) shape this process are also avenues for further research. A stream of research that is growing in the area of expressing emotions through digital media concerns the use of substitutes for face-to-face nonverbal cues like, for instance, the use of emojis in written communication (e.g., Kralj Novak et al., 2015; Riordan, 2017). A suggestion for further studies in this area is to better explore whether the use of emojis is effective across different contexts, as well as to clarify which emojis are interpreted consistently by different users (Boutet et al., 2021). The use of emojis to share internal states in written communication is an intentional strategy that interactants may implement, and, in this sense, can be interpreted as less spontaneous than, for instance, facial expressions of emotions. Previous studies reveal that, in addition to this intentional strategy that can be used by interactants, written communication may also convey unintentional cues (e.g., typos), which tend to be interpreted as more authentic emotional information than intentional strategies (Blunden & Brodsky, 2020). However, further studies are necessary to explore what kind of signals people base their inferences on and how this shapes different work interactions (e.g., negotiations). The dominant perspective in terms of sharing and interpreting emotions in virtual contexts suggests that the more the platform used parallels face-to-face interaction and/or the interactants implement strategies to compensate for the absence of nonverbal cues, the more effective this process tends to be. A growing body of research highlights, however, that, in some circumstances, media which are less rich in terms of social presence might be positive for expressing emotions, namely when people are more vulnerable (Oh et al., 2018). Accordingly, exploring the influence of the type of emotion expressed, the objectives of the interactant and the interaction context are also avenues for further studies. Regarding the emotional contagion process, overall, the research has already made an undeniable contribution to the understanding of this phenomenon in contexts of electronic communication. Indeed, as we illustrated with some studies concerning the topic, the research focused on computer-mediated communication showed that (a) this phenomenon occurs via electronic devices, namely through text-based messages characterized by the absence of nonverbal cues, (b) there is a set of factors which influence the process and the way of the receivers interpret the emotions spread by the agents (such as the time lag or the partners’ relationships), and (c) positive emotional contagion is more frequent, although less powerful, than negative emotional contagion. Despite the relevant advances in the topic, the researchers should continue investing in analysis of the phenomenon to contribute to a greater understanding of it and produce new insights into the practice (Tee, 2015). For example, studies analyzing which emotional valence (positive or negative) is more likely to spread virtually are still needed, as well as a more detailed examination of the spread of specific emotions and their effects (Barsade et al., 2018). Additionally, the literature on emotional contagion showed that the mood of leaders is transferred to their followers, influencing their performance (e.g., Volmer, 2012) and, also, that previous findings suggest that the emotional leader-follower interactions in virtual contexts may be different from those of face-to-face interactions (e.g., Purvanova & Bono,

178  Handbook of virtual work 2009). Thus, analyzing the emotional contagion in the context of leadership studies may be an avenue for further studies devoted to virtual contexts. Analyzing the bidirectional effect of emotional contagion (from leaders to followers and vice versa), as well as how this phenomenon influences leadership and the followers’ outcomes is an example of these studies (Tee, 2015). Furthermore, future research may investigate the influence of individual characteristics (such as the personality) of the receivers, since the literature in face-to-face contexts (e.g., Sonnentag & Volmer, 2009) suggests that such characteristics may influence the way the individuals are affected by the agents. Also, similarly to the research already conducted in face-to-face contexts, other contextual factors (e.g., the type of social media used or the social interdependence) that may influence the emotional contagion in an electronic communications context could also be studied (Barsade et al., 2018; Cheshin et al., 2011). Finally, it should be noted that most research in virtual contexts tends to analyze text-based interaction. This is justified, first because the research on emotional contagion in virtual contexts started by focusing on searching for evidence regarding the presence of the phenomenon in interactions without nonverbal cues and second, because text-based communication was until now the prevalent form of electronic communication found in organizational contexts. However, the use of a great diversity of electronic media to communicate (text-based but also based on audio and video) is increasingly a current reality in organizations. Therefore, studies analyzing other (and richer) forms of electronic communication are also required (Barsade et al., 2018). Regulating Emotions Regarding emotional management in virtual work settings, this body of research still has plenty of opportunities, including those provided by the huge increase in teleworking caused by the COVID-19 pandemic. Even post-pandemic, telework is here to stay, as is hybrid work, which requires both face-to-face and virtual interactions. Thus, research on effective ways in which workers can learn to regulate their emotions in this kind of work context is still required to enhance the understanding of how to preserve their well-being (Restubog et al., 2020). Emotional intelligence is considered a key competency for members of virtual organizations, as it plays an essential role in the process of interpersonal communication mediated by technology, but it has been treated with minor importance in the literature on virtual management (Broniewska, 2010). Thus, continued research into the ability to perceive and analyze others’ emotions and the capacity to understand and express emotions, as well as how to regulate and adjust emotions, considering the characteristics of virtual work settings (Li et al., 2019; Mio, 2002), is another avenue for further studies. As mentioned above, defining and operationalizing the construct of emotional intelligence into virtuality, based on previous emotional intelligence models, could give rise to a refinement and an adaptation of this construct to the specific relational and communicational characteristics of this work environment. The process of sharing emotions tends to be more efficient in terms of time and interactions required when the medium used conveys both verbal and nonverbal cues (Walther, 2015). This is a relevant finding for online services employees since they have little time and few interactions with customers, or possibly just one. Thus, it is worth continuing researching on the use of different communication channels (video calls, phone, chat, email) in online services. On the other hand, customer behavior influences changes in emotions and emotion regulation of online service workers. They tend to feel worse when customers are not treating

Emotions and emotional management in virtual contexts  179 them well, resulting in an increase of emotion regulation via surface and deep acting (Gabriel & Diefendorff, 2015). Consequently, the duration or the frequency of the interaction with customers may exhibit differences in employees’ regulation strategies and outcomes (Ishii & Markman, 2016). Thus, studying the interaction of the channel used and the customer behavior (pleasant vs unpleasant) in emotional regulation strategies of online service employees is another relevant avenue for research.

PRACTICAL IMPLICATIONS To conclude this chapter, some important implications for practice can be outlined, based on the literature and empirical findings presented above. To improve emotional well-being in virtual work settings, managers and organizations should be encouraged to: 1. Provide appropriate technological conditions, equipment and support to their employees in order to diminish the negative effect of technology demands on emotions and emotional well-being (e.g., Ilozor et al., 2001; Mann & Holdsworth, 2003). 2. Foster leader-member exchanges to reduce professional isolation (de Vries et al., 2019), for example, through clear and regular communication with workers about job responsibilities, goals, and performance feedback, and also by monitoring workers’ progress and supporting career development (e.g., Ilozor et al., 2001). 3. Support continuing training in the development of soft skills, such as self-leadership strategies (Müller & Niessen, 2019). 4. Create organizational cultures and climates that generate employees’ well-being (Ashkanasy & Dorris, 2017; Grandey et al., 2015). In the same vein, to increase emotional well-being, workers who work remotely and/or through information and communication technologies should: 5. Maintain good relationships with colleagues, creating routines for interacting and communicating openly and regularly, also through face-to-face meetings, to mitigate the effects of professional isolation (Charalampous et al., 2019). 6. Try to maintain social connectedness outside the workplace to prevent isolation (Anderson et al., 2015). 7. Negotiate the conditions for job crafting with their supervisors to weigh the extent of remote and office work, with the purpose of fostering a person-job fit (Tims et al., 2016). 8. Try to define clear boundaries between work and home/personal life in terms of time and space to promote a better balance between different daily demands (e.g., Golden, 2012) and make regular, short pauses in work, engaging in home leisure activities, such as crafting (Hadi et al., 2021) or mindfulness (Pattnaik & Jena, 2021). At the level of emotional expression, it is important to: 9. Spread positive emotions through electronic communication as a strategy to induce positive emotional contagion, thus increasing aspects such as cooperation, coordination, and performance (Barsade, 2002; Sy et al., 2005) as well as preventing or diminishing the emergence of negative emotions (and their negative consequences) related to virtual work.

180  Handbook of virtual work 10. Make use of communication media that convey both verbal and nonverbal cues to share emotional information (Maruping & Agarwal, 2004) whenever possible, and particularly when work interactions are at an early stage (Walther, 2015). Concerning emotional management in virtual work settings, knowledge and training interventions are key drivers. For example, it is important to: 11. Train online service employees in methods of emotional regulation and service recovery (Gabriel & Diefendorff, 2015). 12. Provide knowledge and training interventions to better prepare e-leaders and e-employees in how to interpret others’ emotions, and express and regulate their own emotions when they are remotely interacting or working in teams (Gamero et al., 2021; Mysirlaki & Paraskeva, 2020).

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Emotions and emotional management in virtual contexts  183 Hine, M. J., Murphy, S. A., & Ambwani, V. (2010). Emotional contagion in computer mediated communication. International Journal of Knowledge, Culture and Change Management, 10(6), 149‒163. https://​doi​.org/​10​.18848/​1447​-9524/​cgp/​v10i06/​49987 Hochschild, A. R. (1983). The Managed Heart: Commercialization of Human Feeling. University of California Press. Howard, D. J., & Gendel, C. (2001). Emotional contagion effects on product attitudes. Journal of Consumer Research, 28, 189‒201. https://​doi​.org/​10​.1086/​322897 Ilozor, D. B., Ilozor, B. D., & Carr, J. (2001). Management communication strategies determine job satisfaction in telecommuting. Journal of Management Development, 20(6), 495‒507. https://​doi​.org/​ 10​.1108/​02621710110399783 Ishii, K., & Markman, K. M. (2016). Online customer service and emotional labor: An exploratory study, Computers in Human Behavior, 62, 658‒665. https://​doi​.org/​10​.1016/​j​.chb​.2016​.04​.037 Jaarsveld, D., & Poster, W. R. (2013). Emotional labor over the phone. In A. A. Grandey, J. M. Diefendorff, & D. E. Rupp (Eds.), Emotional Labor in the 21st Century: Diverse Perspectives on Emotion Regulation at Work (pp. 153‒173). Routledge. Joseph, D. L., & Newman, D. A. (2010). Emotional intelligence: An integrative meta-analysis and cascading model. Journal of Applied Psychology, 95(1), 54‒78. https://​doi​.org/​10​.1037/​a0017286 Kelly, J. R., & Barsade, S. G. (2001). Mood and emotions in small groups and work teams. Organizational Behavior and Human Decision Processes, 86(1), 99‒130. https://​doi​.org/​10​.1006/​obhd​.2001​.2974 Kim, H.-J., & Choo, J. (2017). Emotional labor: Links to depression and work-related musculoskeletal disorders in call center workers. Workplace Health & Safety, 65(8), 346–354. https://​doi​.org/​10​.1177/​ 2165079916667512 Kralj Novak, P., Smailović, J., Sluban, B., & Mozetič, I. (2015). Sentiment of emojis. PloS One, 10(12), Article e0144296. https://​doi​.org/​10​.1371/​journal​.pone​.0144296 Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788‒8790. https://​doi​.org/​10​.1073/​pnas​.1320040111 Li, J., Feng, L., & Zhai, J. (2019). Multicriteria evaluation of the emotional intelligence of small retail e-commerce network firms. Mathematical Problems in Engineering, 2019, Article ID 9798174. https://​doi​.org/​10​.1155/​2019/​9798174 Luse, A., McElroy, J. C., Townsend, A. M., & DeMarie, S. (2013). Personality and cognitive style as predictors of preference for working in virtual teams. Computers in Human Behavior, 29(4), 1825‒1832. https://​doi​.org/​10​.1016/​j​.chb​.2013​.02​.007 Mann, S., & Holdsworth, L. (2003). The psychological impact of teleworking: stress, emotions and health. New Technology, Work and Employment, 18(3), 196‒211. https://​doi​.org/​10​.1111/​1468​-005X​ .00121 Maruping, L. M., & Agarwal, R. (2004). Managing team interpersonal processes through technology: A task-technology fit perspective. Journal of Applied Psychology, 89(6), 975. https://​doi​.org/​10​.1037/​ 0021​-9010​.89​.6​.975 Matteson, M. L., & Miller, S. S. (2012). Emotional labor in librarianship: A research agenda. Library & Information Science Research, 34(3), 176‒183. https://​doi​.org/​10​.1016/​j​.lisr​.2012​.02​.003 Mayer, J. D., & Salovey, P. (1997). What is emotional intelligence? In P. Salovey & D. Sluyter (Eds). Emotional Development and Emotional Intelligence: Implications for Educators (pp.  3‒31). Basic Books. Miao, C., Humphrey, R. H., & Qian, S. (2017). A meta-analysis of emotional intelligence effects on job satisfaction mediated by job resources, and a test of moderators. Personality and Individual Differences, 116, 281‒288. https://​doi​.org/​10​.1016/​j​.paid​.2017​.04​.031 Mio, R. R. (2002). On defining virtual emotional intelligence. Proceedings European Conference on Information Systems, Gdańsk, 1124–1133. Mui, P. H. C., Goudbeek, M. B., Roex, C., Spierts, W., & Swerts, M. G. J. (2018). Smile mimicry and emotional contagion in audio-visual computer-mediated communication. Frontiers in Psychology, 9(2077). https://​doi​.org/​10​.3389/​fpsyg​.2018​.02077 Müller, T., & Niessen, C. (2019). Self-leadership in the context of part-time teleworking. Journal of Organizational Behavior, 40, 883‒898. https://​doi​.org/​10​.1002/​job​.2371

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10. Digital nomads: curiosity or trend? Robert C. Litchfield and Rachael A. Woldoff

The term “digital nomad” has rapidly become a hot remote work buzzword. With almost no systematic research and significant media attention, digital nomads are simultaneously in danger of being mythologized as passport-stamping unicorns and of losing all distinction as they become synonymous with a generalized category of remote workers. For example, media coverage of digital nomads often focuses on wealthy travelers (e.g., Griffith, 2020) or social media influencers (e.g., Paddock, 2021; Pearson-Jones, 2019) on one hand while, on the other, referencing estimates suggesting there are in excess of 10 million digital nomads in the U.S. alone (e.g., Lufkin, 2021). Some reports suggest digital nomadism may simply be a label for all remote work that involves a relocation (Lufkin, 2021), while others claim companies need nomad-specific policies (Everson, King, & Ockels, 2021). In this chapter, we aim to differentiate digital nomads within the virtual work lexicon, explain why they constitute an important subject for research, and suggest research directions that reveal how this group of extreme remote workers relates to broader trends. Given the thin research base, this chapter is necessarily grounded more in our own research (Woldoff & Litchfield, 2021) and accounts given by journalists and non-scholarly writers than is typical in a phenomenon with a longer history. Yet with its newness, we hope that readers will take our review of digital nomads as a sign that scholars have much to learn both by studying digital nomads themselves and by contemplating the relationship between this group and other aspects of the future of work. Although digital nomadism predates the COVID-19 pandemic, the pandemic is an important backdrop for expanded interest in digital nomads. Despite its many tragic outcomes, the pandemic spurred emergency working conditions that have revealed at least two very significant facts about remote work. First, remote work, even long-term, fully remote work, is possible for a great many jobs without productivity losses. This is not really a new insight, but instead a rediscovery of years of telework research findings (for review, see Allen, Golden, & Shockley, 2015). Second, many people prefer remote work to traditional, office-based working. Although there is also some evidence of the attractiveness of remote work options (e.g., Thompson, Payne, & Taylor, 2015), leaders and observers have been surprised at the magnitude of employees’ preference to continue working away from the office in locations of their own choosing post-pandemic. The changes that the COVID-19 pandemic mandated constitute the worst possible version of remote work. Yet even in this challenging environment – trapped at home, isolated from colleagues, friends, and the novelty of strangers, and confined with family or other roommates – individuals have discovered the potential for new, more satisfying ways of working. Now, employees who have not been forced to return to the office are contemplating what the future of work might look like for them. Location independence – the autonomy that is achieved by treating one’s residential and work locations as variable and personally controllable – has developed from a daydream into a real option for more people. Though some media outlets have revealed a sense of schadenfreude in their negative depictions of location-independent 186

Digital nomads: curiosity or trend?  187 workers (Griffith, 2020), legions of workers remain intrigued by choices that have opened up for them. Below, we begin by defining digital nomads and situating them within the literature of remote and expatriate work. Next, we discuss the values and conditions that lead individuals to choose this lifestyle, revealing through these conditions how and why digital nomads may deserve research attention. After that, we turn to specific research directions that may allow scholars to use digital nomads as a lens through which to study the evolving ecosystem of remote work.

DIGITAL NOMADS DEFINED The term “digital nomad” appears to have formally entered the vocabulary of virtual work through a 1997 book written by Tsugio Makimoto, an executive at technology giant Sony, and his coauthor David Manners. They predicted that technology would soon grant so much mobility that individuals would be able to choose again whether to maintain their lives as “settlers” who live in a single location, or to adopt a new style of nomadism. Consistent with their definition, digital nomads are known today as individuals who work online and are independent of any particular work location. More specifically, digital nomads treat their residential and work locations as variable and personally controllable, which allows them to live what they often call “location-independent” lives. Although popular media accounts often depict digital nomads as traveling constantly, location independence does not necessarily imply constant movement. For instance, Reichenberger (2018) interviewed 22 digital nomads to arrive at a progressive definition of nomadism. She defined “Level 1” nomads as those who “utilize their location independence on a limited spatial level while continuously remaining in their home environment”; “Level 2 then includes occasional and intermittent travel with subsequent returns to the home environment termed ‘homebase’”; and “Level 3 is characterized by constant full-time travel with no permanent residence or homebase to return to, thus maximizing the location independence provided by their working conditions” (Reichenberger, 2018, p. 371). This definition shows digital nomadism need not be represented as an all-or-nothing phenomenon. Reichenberger’s (2018) work notes that a pre-condition for digital nomadism is the ability to work remotely from a location of one’s choosing, and that digital nomadism begins as individuals exercise this choice. A problem with the breadth of this definition, however, is that it risks conflating all remote workers with digital nomads. Taking seriously the “nomad” aspect of digital nomadism suggests some degree of movement away from one’s home. Yet this, too, is complicated. Whereas the popular image of digital nomads is one of constant travel akin to a perpetual vacation or business trip, many self-identified digital nomads travel only very slowly – changing locations after months rather than moving every few days or weeks – a trend that has itself been named as the “slowmad” movement (see, e.g., Kuppens, 2020). Following from the definition of location independence, one conclusion may be that digital nomads are individuals who have location independence (i.e., they are able to vary both their work and living locations as they choose, subject only to the constraint that they must have access to technology and high-speed internet) and actively choose to change their working and living locations in some way that separates them from others who maintain lives based in a single

188  Handbook of virtual work location. Hence, not all individuals who work from home, from cafés and coffee shops, or even from coworking spaces are digital nomads. Digital nomads first attracted attention as international travelers, and this is no coincidence. Perhaps the foundational text of the digital nomad movement, a book called The 4-Hour Workweek (Ferriss, 2007), popularizes international travel as a way to “join the new rich.” The idea is to engage in what Ferriss (2007) termed “geoarbitrage” by earning money in a strong currency (such as the U.S. dollar or euro) through remote work while living in a country with a weaker currency, allowing the individual to live a more luxurious lifestyle than they could afford in their home country. Putting aside the neo-colonial implications of this practice, the bare economic reality for many digital nomads is that the switch to remote work is also a switch away from conventional, full-time employee status into freelancing or entrepreneurship that brings with it a significant reduction in income, at least in the short term. Thompson (2018a) focused on the increased precarity in income brought on by the shift to nomadism, noting in particular that the ease of movement afforded by high skills and a passport from a wealthy nation are often keys to success in the lifestyle. Our research, which includes in-depth interviews with 70 digital nomads, two rounds of ethnographic fieldwork approximately one year apart in the digital nomad hub of Bali, Indonesia, and almost three years of online followup through social media, partially supports Thompson’s (2018a) findings. Yet moving away from the “digital nomad conferences” that were the primary source of Thompson’s (2018a, 2018b) contacts and to interactions with digital nomads in situ in the digital nomad hub of Bali, Indonesia leads to a broader perspective that includes individuals from a wider range of countries in the population of digital nomads (Woldoff & Litchfield, 2021). As a result of the primary association of digital nomadism with international travel, academic perspectives on cross-national migration and expatriate employment are perhaps the easiest to apply to digital nomadism. However, these perspectives are generally imperfect fits for at least two reasons. First, international migration research generally does not examine voluntary moves from wealthier societies to less-wealthy societies. Second, migration research that does consider such moves is usually focused on organizations rather than on individuals (Baruch, Altman, & Tung, 2016). Hannonen (2020) notes that digital nomadism can be seen as standing between tourism and migration. Building on Hannonen’s (2020) insight and incorporating work specifically into digital nomadism suggests that the phenomenon may stand at the nexus of tourism, migration, and business travel. Literature on remote work, in contrast, has primarily adopted the historical lens of telecommuting that views remote work as a form of commuting through technology with the assumption that the worker is based in a fixed “home” location (Allen et al., 2015). Thus, even as research has begun to appreciate the need to consider new forms of remote working (Baruch, Dickmann, Altman, & Bournois, 2013), little systematic academic research has considered this form of self-initiated remote work linked to travel. Despite the focus on international travel in defining digital nomads to date, the pandemic has made it clear that nomadism may also occur within the bounds of one’s home country. In the U.S., digital nomads have linked themselves to the “van life” phenomenon of individuals and families living from vans and recreational vehicles, with businesses cropping up to serve them (Brady, 2020; Chang, 2020). Although nomadism is perhaps easier to see in a large country such as the U.S., it could presumably occur anywhere. As a definitional concern, we see no reason to limit the term “digital nomad” to those who travel internationally. Merriam-Webster added the word in October 2021 to its dictionary, defining a digital nomad as “someone who

Digital nomads: curiosity or trend?  189 performs their occupation entirely over the internet while traveling,” adding “especially: such a person who has no permanent fixed home address.”

ANTECEDENTS TO DIGITAL NOMADISM Why do individuals become digital nomads? If international migration research, expatriate employee research, and research on telework do not adequately explain entry into the lifestyle, what does? Our research, conducted prior to the onset of the COVID-19 pandemic, identified antecedents to nomadism in terms of “push factors” driving individuals to leave their old lives and “pull factors” drawing them into the nomad lifestyle (for discussion of the concept of push and pull factors in migration research, see Lee, 1966). Consistent with other researchers’ findings, job burnout is a frequently cited part of digital nomads’ reasons for leaving (see also Reichenberger, 2018), and dissatisfaction with the lifestyle and the cost of living in top-tier cities was also mentioned by many of our informants. While some digital nomads undoubtedly are pushed into the lifestyle by failures in their careers (Thompson, 2018a), most informants for our research were successful (if unfulfilled) in their prior work. Echoing many sentiments in favor of remote work that would later be expressed by employees during the COVID-19 pandemic (e.g., PWC, 2021), we found that digital nomads generally experienced the in-person culture of work as oppressive. Factors “pulling” digital nomads into the lifestyle include both destination-specific interests and general inclinations toward travel (Reichenberger, 2018). Values, including freedom, play an important role in lifestyle entry and maintenance, as does the desire to be immersed in a community of likeminded others. Finally, having the knowledge and skills to be able to work online, or to be confident of pivoting to online work, is an important antecedent to digital nomadism. Table 10.1 summarizes these push and pull factors. Table 10.1

Forces pushing and pulling digital nomads into the lifestyle

Forces Pushing Digital Nomads into the Lifestyle

Forces Pulling Digital Nomads into the Lifestyle

Job burnout

Destination-specific interests

Dissatisfaction with top-tier cities

General orientation toward travel

Career failure

Values

Career success that is experienced as unrewarding

Desire for likeminded community

Dissatisfaction with traditional organizational cultures

Knowledge and skills to work online

Source: Authors’ own.

Freedom All published research appears to agree that a desire for “freedom” is perhaps the most important force pulling individuals into the digital nomad lifestyle (see Cook, 2020; Reichenberger 2018; Thompson, 2018a, 2018b; Woldoff & Litchfield, 2021), but researchers have differed on the meaning and implications of this freedom. Based on interviews with 22 digital nomads, Reichenberger (2018) developed a tripartite definition of freedom as a motivating force for nomads: (1) professional freedom, defined as “the motivation to select and structure work-related tasks in a self-imposed manner” (p. 371); (2) spatial freedom, “the motivation to

190  Handbook of virtual work live and work in a variety of places and inextricably connected with freedom to learn and experience” (p. 372); and (3) personal freedom, which was not defined except as a second-order result of professional and spatial freedom. Whereas Reichenberger (2018) suggests that freedom is an important motivator, Thompson (2018a, 2018b), who conducted 38 interviews with digital nomads whom she met through attending several digital nomad conferences, developed a contrasting view that the “freedom” espoused by writers about digital nomadism was largely illusory. Taking as her primary lens a critique of the lifestyle as an exercise in neo-liberalism that largely benefits employers rather than individuals, Thompson concludes that the freedom of digital nomads was largely “the freedom to work constantly, as their precarious and competitive salary is often decreasing and at the mercy of an algorithm” (2018a, p. 15). Cook (2020), who interviewed 16 digital nomads at least three times over a four-year period and conducted some participant observations in coworking spaces in Thailand, incorporated both the pros and cons of freedom to focus on the tension between freedom and discipline for individuals working outside of typical organizational structures. Cook identifies the tension between the freedom of movement and the discipline to stay in one place long enough to gain the benefits of familiarity and routines for productivity, the tension between a choice of technological devices for work and the discipline to use them in ways that prevent distractions, the tension between the freedom to work from anywhere and the discipline to choose a location such as a coworking space where one can work productively, and the tension between work and leisure as they manage deadlines across time zones while theoretically free to work when and where they please. Ultimately, Cook concludes that the discipline and adaptability needed to be successful as a digital nomad are not so different from what is required of employees in general nowadays. Thus, Cook, too, seems to come down on the side of freedom as mostly illusory. Without denying that freedom can be viewed as real or illusory, our research sidesteps the issue of its “objective” truth value by framing freedom as a value held by digital nomads. Drawing on digital nomads’ descriptions while keeping in mind their parallels to the value individualism in psychology (Oyserman, Coon, & Kemmelmeier, 2002) and articulations of choices in community formation in urban sociology (Fischer, 1982), we suggest that the value for freedom means: “defining oneself as an individual in explicit contrast to social structures and institutions, especially those that appear to offer security and stability in return for conformity to rules or other collective social obligations to families, communities, organizations, and societies” (Woldoff & Litchfield, 2021, p. 80). Other Values and Goals In addition to the desire for freedom, researchers have identified several other values and goals related to digital nomadism, though some research findings are conflicting. Thompson (2018b) argues that digital nomads can be characterized through their prioritization of leisure. In contrast, Reichenberger (2018) suggested that digital nomads aspire to experience equal fulfillment from work and leisure in the context of ongoing travel. Cook (2020) suggests that digital nomads are focused more on work than leisure, and that they evolve goals for self-discipline in order to help them sustain their lifestyle. Embracing all of these findings, our research found evidence that many nomads prioritized leisure or work–life balance, yet most remained highly work-identified and continued to focus much of their energy on work. Digital nomads in our

Digital nomads: curiosity or trend?  191 research also identified with four values (in addition to freedom): personal development (i.e., “striving to increase self-awareness and capabilities through structured activities”; Woldoff & Litchfield, 2021, p. 82), sharing (i.e., “sharing their knowledge, skills, and time with other nomads”, p. 85), positivity (i.e., “values for optimism and proactivity in themselves and others”, p. 87), and minimalism (i.e., subscribing to some part of the ideas of lifestyle minimalism, particularly with regard to aesthetics and ethics implying that simplifying one’s life and spending money on experiences rather than things is to be preferred). Desire for Likeminded Community Most research on digital nomads has focused on freedom and mobility, but our research found that the most important factor pulling digital nomads toward specific destinations was the desire to access an in-person community of others whom they perceived to be likeminded. Cook (2020) presented data suggesting that nomads do sometimes return to the same destinations, but focused primarily on the role of such repeat visits in self-discipline. Although familiarity with a destination certainly does aid in the development of practices conducive to disciplining oneself for productivity, digital nomads reported to us that they gravitated toward “nomad hubs” in order to find place-based community. Belinda, a twenty-two-year-old digital nomad entrepreneur originally from Germany, told us that her main motivation for choosing where to travel was “the community. I actually travel to places where I feel very good and where I think there are good people because when you travel it can be quite lonely sometimes and having the right people around you – who maybe also stay long term – it’s just very nice to have” (Woldoff & Litchfield, 2021, p. 75). Knowledge and Skills for Online Working Digital nomads vary in age, skills, and work experience (Cook, 2020; Reichenberger, 2018; Thompson, 2018a; 2018b; Woldoff & Litchfield, 2021). Nevertheless, successful digital nomads have or are able to quickly acquire knowledge and skills that they can convert into marketable value online. The popular lore of digital nomads often leaves the impression that most are online influencers on platforms such as Instagram (e.g., Chandran, 2021; Paddock, 2021), or that they are leveraging some form of financial privilege (Griffith, 2020). Academic research generally recognizes that nomads’ privilege comes more from their skills (Cook, 2020; Reichenberger, 2018; Woldoff & Litchfield, 2021) and, for international nomads, from the fact that most hail from countries of origin where their passports allow relatively unfettered travel (Thompson, 2018a; 2018b; Woldoff & Litchfield, 2021). Although the age range for digital nomadism is broad, spanning essentially the entire working life course, the largest cohort of digital nomads is Millennials in their 30s (Thompson, 2018a, 2018b; Woldoff & Litchfield, 2021). Our research suggests a fairly typical path for digital nomads where, after attending college and working for eight to ten years in a professional capacity, they became disillusioned with their lives and traditional career pathway before entering the digital nomad lifestyle as an attempt to reinvent their lives and careers. As a result of their years in the workforce in career-track jobs, most entered digital nomadism with significant professional skills that they could parlay into some initial freelance work to carry them forward into their new lives – often working on contract for the employer they left behind.

192  Handbook of virtual work Although digital nomads can work in any occupation where online, mobile working is possible, research has found that their employment is dominated by a relatively small number of categories. For instance, we found that digital nomads’ employment is highly concentrated in technology, marketing, ecommerce, and coaching (a category that includes health, business, artistic, romantic, and spiritual practitioners; see Woldoff & Litchfield, 2021, pp. 115‒116). Cook (2020) and Reichenberger (2018) also reported occupations for their samples that largely fit within these categories. Thompson (2018a, 2018b) did not list full occupational data for her sample, but we considered most anecdotes that reveal occupations within her report to be generally consistent with these categories. We also found that most digital nomads begin to view their work less in singular occupational terms and more as a “portfolio of projects” wherein they use their skills in different ways across occupational categories and even employment statuses (i.e., traditional remote employee, freelancer, entrepreneur) in order to balance earning a consistent income with doing work they find fulfilling (Woldoff & Litchfield, 2021).

RESEARCH DIRECTIONS Despite their media attention, digital nomads, when defined as individuals who actively vary their residential and work locations according to their own preferences, remain a relatively fringe group of workers. Accelerating remote work trends, the COVID-19 pandemic has dramatically raised interest in all forms of virtual work including digital nomadism. As perhaps the most extreme form of remote working, digital nomadism certainly deserves attention as a kind of boundary condition in the world of virtual work. However, we argue that digital nomads also deserve research attention because they represent early advocates and adopters of new working trends that are interesting increasing numbers of individuals and pressing many employers to adapt. Indeed, considered as exemplars of the marketing concept of “lead users” (von Hippel, 1986), extreme users who adapt products and services to meet their own needs and may ultimately initiate broader innovation, digital nomads have leveraged technical advances to break new ground in social aspects of remote working in ways that may ultimately have profound implications for the future of knowledge work. In this part of the chapter, we give some shape to those aspects of the future of work by considering digital nomads and the ecosystem that surrounds them. Digital Nomads as Individual Remote Workers Scholars studying the more precarious working arrangements in the gig economy, including remote work, often seem vexed by the apparently one-sided loss of economic security, income, and even social stability facing this segment of the workforce (Duffy, 2017; Gregg, 2011; Scholz, 2017; Scholz & Schneider, 2016; Thompson, 2018a, 2018b). Without denying the truth that knowledge workers, like so many others, are subject to increasing precarity (Kalleberg, 2009), we suggest that these arguments miss a fundamental point: many workers are willing to trade a lot for what they experience as freedom. A clear takeaway from our own research is that digital nomads feel stability is illusory. Backing digital nomads’ perceptions, research shows that employment stability is declining (Bidwell, 2013). Although activist-minded social theorists arguably prescribe greater organization of labor as the “solution” (Scholz, 2017; Scholz & Schneider, 2016; Thompson, 2018a, 2018b), digital nomads often do not see a lack

Digital nomads: curiosity or trend?  193 of stability as a problem and mostly do not prefer collective action. Though their attitudes may be frustrating to outsiders, we found that these highly proactive, entrepreneurial, individualistic people consistently rejected narratives that paint them as victims of economic or organizational circumstances. Digital nomads’ individualistic, proactive, and entrepreneurial attitudes suggest interesting future avenues of research. For instance, research on well-being at work might consider whether and how such attitudes could insulate individuals from the possible negative affective consequences of precarious work (Kalleberg & Vallas, 2018). Identity researchers might investigate connections between a digital nomad identity and resilience in precarious work (Caza & Milton, 2012), preferences for transient work (Litchfield, Hirst, & van Knippenberg, 2021), and even entrepreneurial identity (Navis & Glynn, 2011). Entrepreneurship research might further examine digital nomad identity in conjunction with decisions to start businesses. As many digital nomads identify as creative professionals, creativity researchers might also be interested in applying the identity lens of digital nomadism to research on creative identities (Tierney, 2015). Researchers might also consider digital nomad values in conjunction with or in contrast to openness and other traditional personality traits. The changing landscape of remote work following the COVID-19 pandemic also invites research into the breadth of nomad employment. Although published research found nomads’ employment to be concentrated in a limited sphere, a big part of this might be attributed to the fact that, prior to the pandemic, individuals needed to be very proactive and/or highly skilled to make the case to employers that they should be allowed to work remotely or to feel confident striking out on their own as freelancers and entrepreneurs. The pandemic opened remote work to nearly anyone whose work can be performed remotely and has greatly reduced the longstanding requirement to either quit one’s job or work out idiosyncratic deals with supervisors to gain remote work privileges (cf., Allen et al., 2015). Future research is needed to understand whether and/or how the occupational ranks of digital nomads may expand. Digital Nomad Communities Digital nomads congregate in hubs around the world such as Bali, Indonesia, Chiang Mai, Thailand, Medellin, Columbia, and Lisbon, Portugal. While our research has addressed questions about community in these hubs, many aspects remain unsettled. In particular, researchers might study these communities from angles that could inform our general knowledge of remote work. For instance, many digital nomads vary their work locations to suit their work tasks. When they need ideas or assistance, they may work in a café or a more social coworking space; when plowing through implementing a project, they may choose to work from home or another less social setting. As the world uses more remote and hybrid working, such issues of task–environment fit might benefit from more theory and research. In digital nomad communities, individuals choose not just where to work but around whom to work. Often, they work alongside others who do not share employers or professions. How might employers leverage these potential networks? What are the risks of such networks? Future research might study employer policies or other leadership interventions to manage such new working environments. For instance, research might consider how or whether managing digital nomads differs from managing more typical remote working arrangements. As they choose their communities, digital nomads also expose themselves to different learning and development opportunities. Researchers might study how the company one keeps in

194  Handbook of virtual work these day-to-day environments leads to different breadth and depth of professional knowledge acquisition. Digital nomads also use these discretionary environments to build contacts for employment. Researchers might examine how such arrangements affect contracts for freelance workers or job changes for more traditional employees. Broader Ecosystems for Remote Work Digital nomads, who tend to travel to places where tourist or other infrastructure already exists, highlight the importance of a broader ecosystem to support remote work. Given the restrictions that often accompany working across borders, the new push toward remote worker visas for knowledge workers is a key to supporting digital nomads (Humphries & Hoeller, 2021). Beyond simply opening borders, a variety of destinations have implemented incentives aiming to draw remote workers (Krueger, 2021). Research is needed to learn what kinds of incentives are most effective at drawing the critical mass of talent necessary to jumpstart a robust remote worker community. Entrepreneurship research has considered what kinds of ecosystem components are needed to drive new business formation and success (Kauffman Foundation, 2021). Our work on digital nomads suggests that some, but not all, of these components are in place in digital nomad communities. Social support and employment markets that may aid digital nomads in doing their work and reinventing their identities may be particularly critical, but research is needed to define, elaborate, and understand what ecosystem components fuel the more general success of virtual worker communities. Richard Florida’s (2002) influential theory on creative class cities has suggested that they may productively compete to lure employers and workers alike. Digital nomads we studied largely rejected the idea that creative class cities were likely to be fulfilling for creative workers in the long term, but they agreed that most of the amenities Florida proposed were desirable. Research is needed to understand what specific amenities might be most important to foster remote working communities and what amenities might best attract and retain a healthy population of virtual workers. Given that digital nomadism is associated with what we have termed “work tourism” – time-limited sojourns by individuals to remote-working communities for the purpose of building networks and virtual work skills while intending to return to a permanent home (Woldoff & Litchfield, 2021) – research is also needed to better understand intersections between virtual work and tourism.

CONCLUSION Digital nomads have rapidly attracted the popular imagination, but a careful articulation of the phenomenon reveals that the number of people who actively vary their work and residential locations according to their personal whims remains small, even if it is rapidly growing. Nevertheless, the study of remote work can benefit from increased consideration of digital nomads because they have used the tools of mobility to innovate both technical and social aspects of virtual work. Although the research literature on digital nomads is currently limited to just a few carefully conducted studies, we hope that this chapter will inspire researchers to think about how digital nomadism relates to a host of issues relevant to the future of work – whether virtual or in-person.

Digital nomads: curiosity or trend?  195

REFERENCES Allen, T. D., Golden, T. D., & Shockley, K. M. (2015). How effective is telecommuting? Assessing the status of our scientific findings. Psychological Science, 16, 40‒68. Baruch, Y., Altman, Y., & Tung, R. L. (2016). Career mobility in a global era: Advances in managing expatriation and repatriation. The Academy of Management Annals, 10(1), 841‒889. Baruch, Y., Dickmann, M., Altman, Y., & Bournois, F. (2013). Exploring international work: Types and dimensions of global careers. The International Journal of Human Resource Management, 24(12), 2369‒2393. Bidwell, M. J. (2013). What happened to long-term employment? The role of worker power and environmental turbulence in explaining declines in worker tenure. Organization Science, 24, 1061–1082. Brady, S. (2020). How these digital nomads are helping travelers embrace the van life movement. Lonelyplanet.com. Retrieved July 16, 2021. https://​www​.lonelyplanet​.com/​articles/​embracing​-van​ -life​-as​-a​-digital​-nomad Caza, B.B., & Milton, L. (2012). Resilience at work: Building capacity in the face of adversity. In K. Cameron, & G. Spreitzer (Eds.), The Oxford Handbook of Positive Organizational Scholarship (pp. 895‒908). New York: Oxford University Press. doi: 10.1093/oxfordhb/9780199734610.013.0068 Chandran, R. (2021). Wary tourist hotspots hit back at Instagram influencers. Christian Science Monitor. Retrieved July 26, 2021. https://​www​.csmonitor​.com/​World/​2021/​0422/​Wary​-tourist​-hot​-spots​-hit​ -back​-at​-Instagram​-influencers Chang, B. (2020). Kibbo is rethinking #VanLife to create a community of digital nomads paying up to $3,650 a month to roam at will. Business Insider. Retrieved July 15, 2021. https://​www​.businessinsider​ .com/​kibbo​-rethinking​-vanlife​-model​-create​-community​-of​-digital​-nomads​-2020​-8 Cook, D. (2020). The freedom trap: Digital nomads and the use of disciplining practices to manage work/ leisure boundaries. Information Technology & Tourism, 22, 355‒390. Duffy, Brooke Erin. (2017). (Not) Getting Paid to Do What You Love: Gender, Social Media, and Aspirational Work. New Haven: Yale University Press. Everson, M., King, S., & Ockels, C. (2021). Your company needs a digital nomad policy. Harvard Business Review. Retrieved November 16, 2021. https://​hbr​.org/​2021/​07/​your​-company​-needs​-a​ -digital​-nomad​-policy Ferriss, Tim. (2007). The 4-Hour Workweek: Escape 9‒5, Live Anywhere and Join the New Rich. New York: Harmony Press. Fischer, Claude S. (1982). To Dwell among Friends: Personal Networks in Town and City. Chicago: University of Chicago Press. Florida, Richard. (2002). The Rise of the Creative Class. New York: Basic Books. Gregg, Melissa. (2011). Work’s Intimacy. Malden, MA: Polity Press. Griffith, E. (2020). The digital nomads did not prepare for this. The New York Times. Retrieved November 9, 2020. https://​www​.nytimes​.com/​2020/​11/​08/​business/​digital​-nomads​-regret​.html Hannonen, O. (2020). In search of a digital nomad: Defining the phenomenon. Information Technology & Tourism, 22, 335‒353. Humphries, M., & Hoeller, S. (2021). Destinations welcoming remote workers. Insider.com. Retrieved July 29, 2021. https://​www​.insider​.com/​countries​-welcoming​-remote​-workers​-live​-and​-work​-2020 Kalleberg, Arne L. (2009). Precarious work, insecure workers: Employment relations in transition. American Sociological Review, 74, 1‒22. Kalleberg, A. L., & Vallas, S. P. (2018). Probing precarious work: Theory, research, and politics. In A. L. Kalleberg, & S. P. Vallas (Eds.), Precarious work, volume in Research in the Sociology of Work (31, pp. 1‒32). London: Emerald Publishing. Kauffman Foundation. (2021). Entrepreneurial ecosystem playbook. Retrieved July 29, 2021. https://​ www​.kauffman​.org/​ecosystem​-playbook​-draft​-3/​ecosystems/​ Krueger, Alyson. (2021). Want to move to our town? Here’s $10,000 and a free bike. The New York Times. Retrieved July 29, 2021. https://​www​.nytimes​.com/​2021/​04/​30/​realestate/​bentonville​ -arkansas​-moving​-incentive​.html Kuppens, Anne. (2020). Slowmading – The Benefits of Traveling Slow. Nine Coliving. https://​www​ .ninecoliving​.com/​blog/​slowmading Lee, Everett S. (1966). A theory of migration. Demography, 3(1), 47‒57.

196  Handbook of virtual work Litchfield, R. C., Hirst, G., & van Knippenberg, D. (2021). Professional network identification: Searching for stability in transient knowledge work. Academy of Management Review, 46, 320‒340. Lufkin, B. (2021). Is the great digital nomad workforce actually coming? BBC.com. Retrieved November 11, 2021. https://​www​.bbc​.com/​worklife/​article/​20210615​-is​-the​-great​-digital​-nomad​ -workforce​-actually​-coming Merriam-Webster. (2021). Digital nomad. In Merriam-Webster.com dictionary. https://​www​.merriam​ -webster​.com/​dictionary/​digital​%20nomad Navis, C., & Glynn, M. A. (2011). Legitimate distinctiveness and entrepreneurial identity: Influence on investor judgments of new venture plausibility. Academy of Management Review, 36, 479‒499. Oyserman, D., Coon, H. M., & Kemmelmeier, M. (2002). Rethinking individualism and collectivism: Evaluation of theoretical assumptions and meta-analyses. Psychological Bulletin, 128(1), 3–72.  https://​doi​.org/​10​.1037/​0033​-2909​.128​.1​.3 Paddock, R. C. (2021). American woman deported from Bali after calling it “queer friendly.” The New York Times. Retrieved January 21, 2021. https://​www​.nytimes​.com/​2021/​01/​20/​world/​asia/​kristen​ -gray​-bali​-deported​.html Pearson-Jones, B. (2019). Digital nomad travels the world managing Instagram accounts from his phone. Daily-Mail.com. Retrieved November 11, 2021. https://​www​.dailymail​.co​.uk/​femail/​article​ -7626629/​Digital​-nomad​-25​-travels​-world​-managing​-Instagram​-pages​-PHONE​.html PWC. (2021). It’s time to reimagine where and how work will get done. Retrieved August 12, 2021. https://​www​.pwc​.com/​us/​en/​library/​covid​-19/​us​-remote​-work​-survey​.html Reichenberger, I. (2018). Digital nomads – a quest for holistic freedom in work and leisure. Annals of Leisure Research, 21, 364‒380. Scholz, Trebor. (2017). Uberworked and Underpaid: How Workers are Disrupting the Digital Economy. Malden, MA: Polity Press. Scholz, Trebor, & Schneider, Nathan (Eds). (2016). Ours to Hack and to Own: The Rise of Platform Cooperativism, A New Vision for the Future of Work and a Fairer Internet. New York: OR Books. Tierney, P. (2015). An identity perspective on creative action in organizations. In C. E. Shalley, M. A. Hitt, & J. Zhou (Eds.), Oxford Handbook of Creativity, Innovation, and Entrepreneurship (pp. 79–92). New York: Oxford University Press. Thompson, B. Y. (2018a). Digital nomads: Employment in the online gig economy. Glocalism: Journal of Culture, Politics, and Innovation, 1‒26. doi: 10.12893/gjcpi.2018.1.11 Thompson, B.Y. (2018b). The digital nomad lifestyle: (remote) work/leisure balance, privilege, and constructed community. International Journal of the Sociology of Leisure, 2(1–2), 27–42. Thompson, Rebecca J., Payne, Stephanie C., & Taylor, Aaron B. (2015). Applicant attraction to flexible work arrangements: Separating the influence of flextime and flexplace. Journal of Occupational & Organizational Psychology, 88, 726‒749. Tsugio, Makimoto & Manners, David. (1987). Digital Nomad. New York: Wiley and Sons. von Hippel, E. (1986). Lead users: A source of novel product concept. Management Science,  32, 791–806. doi: 10.1287/mnsc.32.7.791 Woldoff, R. A., & Litchfield, R. C. (2021). Digital Nomads: In Search of Freedom, Community, and Meaningful Work in the New Economy. New York: Oxford University Press.

PART III VIRTUALITY AND VIRTUAL TEAM INPUTS Research has shown that virtual teams can be successful and productive when attention is given to what have long been considered as necessary inputs for team success and viability. Meaning that, when we think of virtual teams as teams, the drivers of successes are not unique. What is unique, however, is the role that technology can play in the functioning of a team. Accordingly, the definition of what makes a team virtual has also fluctuated over the years – though there is a fundamental assumption that there needs to be some level of interdependence among members, working on a shared task, with a reliance on technology to either communicate or complete the task. Early work on virtual team definitions heavily discussed and emphasized geographical distribution and reliance on technology. Over time, the notion of virtuality as a continuum has taken hold. With the rise in virtual work and team membership, the boundaries of what makes a team have in many instances blurred with individuals working on multiple teams simultaneously and “dropping” in and out of teams as their expertise is needed. Therefore, we start our discussion at the team level of analysis, in an area where the extant literature has struggled – the definition of virtuality. The concept and definition of virtuality continues to evolve, and the first chapter in this section dives into understanding the dynamic relationship between technologies and team members. Costa and Handke aim to help the reader move away from qualifiers such as “how much” teams are using technology to define virtuality and move towards an understanding of the dynamic relationship between technology and how individuals use them. Furthermore, they argue that teams can actively shape what role technology plays in their teamwork. Conversely, technology can dictate how and why teams are formed. This chapter uses comprehensible examples to illustrate the need for an integrated perspective on teams and technology. It has long been argued and shown that leadership is critical for team success. The value of leadership was clearly apparent when the COVID-19 pandemic forced many teams to pivot to virtual work very quickly, often for the first time. This shift placed a premium on leadership and forced many leaders to adapt, evolve, and consider new leadership styles. The chapter by Cogliser and colleagues outlines a number of challenges beyond COVID-19 (i.e., great resignation, globalization, economic issues, etc.) that are forcing teams to work virtually and leaders to lead remotely. The authors further assess multiple different forms of leadership and examine how well these forms of leadership support virtual work given the challenges that

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198  Handbook of virtual work today’s teams, team members, and organizations are facing. The chapter concludes by offering practical implications for both formal and informal leadership in virtual settings. In the next chapter, Thatcher and Rico explore the state of faultline research and the implications that faultlines can have on virtual teams. Noting the lack of research focusing on virtual team faultlines, the authors focus on identifying future research avenues that involve the overlap between faultlines and virtual teams. Prior team faultline research has provided evidence that faultlines typically have a negative impact on group processes and outcomes, so this chapter concludes with guidance and best practices for properly managing the threats faultlines pose to virtual teams. As virtual work becomes increasingly common in different workplaces, the impact on team collaboration cannot be overlooked. In their chapter, Hardwig and Boos walk through the implications of increased virtuality on team collaboration. With previous empirical evidence as a backdrop, this chapter explores effective ways to evolve to meet the needs of an increasingly digital workplace without comprising team collaboration, leading to a model that breaks down the implications of antecedents, processes, and consequences of virtual work on team collaboration. The importance of shared mental models and trust in order to maintain team collaboration is highlighted, followed by recommendations for work design.

11. Virtuality and the eyes of the beholder: beyond static relationships between teams and technology Patrícia Costa and Lisa Handke

Collaborating with others using any sort of digital technology is probably something we all now do on a regular basis. Whether you send an occasional email to a co-worker or are working on a project with someone you have never met in person, being a member of a virtual team is now a given in most occupations. The experiences we have when working virtually, however, are not all the same. If you think back to the multiple virtual teams you have worked with, you can easily conclude that being virtual says only a little about the way you felt and worked. For instance, in some teams you may have felt that the meetings were unproductive, no one provided useful feedback, and team members had a great deal of difficulty understanding what others meant. Other meetings were unproductive because you, and everyone else, kept chatting about weekend activities that were unrelated to work – you left this feeling slightly guilty, yet happy to work with such a good group of co-workers. After other meetings, you left feeling that the team was able to successfully achieve their goals and create meaningful connections between people. Furthermore, you may have been in teams where collaborative software was used only as a file repository or in teams where a software program was always open on your laptop, as members used it regularly to exchange information, update projects, and share throughout the day. Finally, in some of your experiences you may have felt that being virtual was a hassle, creating unnecessary obstacles to effectiveness whereas in others, you could almost feel the presence of your co-workers even if they were many miles away. These examples provide a brief illustration of the vast array of how individuals and teams deal with and experience “teamworking virtually.” More importantly, feeling close or distant to your teammates and evaluating your interaction as effective or ineffective does not necessarily reflect physical distance or a given percentage of technology-mediated interaction. That one team whose meetings were highly ineffective because people kept steering the conversation to social topics could have been the one that has never met face-to-face, and the team using collaborative software could have been one where members were all sitting together in a shared open space. In this chapter, we address how individuals and technology influence one another. By shifting the focus away from objective properties of virtual teamwork, such as the type of communication media used, we highlight both the social and psychological dimensions of working in a virtual team. We propose a more dynamic and everchanging view of the relationship between teams and technology that enriches our perspective on how the two co-evolve. The goal of this chapter is, therefore, to explore different constructs that reflect perceived (vs. structural) properties of virtual teams, and explore their consequences for team functioning. In the first part of this chapter, we clarify how the relationship between technology and work has been treated in the academic literature over time. We highlight a shift towards considering 199

200  Handbook of virtual work the reciprocal influences between people and technology, and towards acknowledging that teams experience technology in distinctly different and unique ways. In the next sections, we explore how this perspective can be used to address the way technology is embedded in team relationships (technologized team relationship), the co-construction of meaning around technology usage (information and communication technology shared mental models) and finally, how team members experience (team perceived virtuality) and think about (virtuality beliefs) their degree of virtuality.

TECHNOLOGY, PEOPLE, AND TEAMS The word technology originates from the Greek words techno (skill or art) and logia (science, doctrine, or theory). Technology is therefore, the systematic treatment of an art, i.e., the use of techniques, methods, processes, or skills to develop any kind or art or craft. Examples of technology can therefore include primitive hunting tools made of shaped stone, an agricultural plow, Shakespeare’s writing pen, or a doctor’s surgical scalpel. In any case, humans have always relied on some sort of technological tool to perform their work. Consequently, technology and work have always shared a close bond. The predominant technological infrastructure innovations over the last few decades have been digitally based electronic tools, systems, devices, and resources that generate, store, or process data (Day et al., 2010; Steinmueller, 2000). Indeed, from the introduction of the mainframe computer in the 1960s, each new technological development has significantly shaped and changed how people trade goods and services, exchange knowledge, and has enabled faster and cheaper collaboration and coordination. This digital era (Cascio & Montealegre, 2016) is based on information and communication technology (ICT) which, from the advent of the World Wide Web, created large communication networks between geographies and individuals. Nowadays, the notion of ubiquitous computing reflects the fact that digital technology permeates everything, as it is embedded in most activities including both work tasks and relationships. Indeed, from the moment we wake up (and start the day by checking the news on our phone), prepare breakfast (following a healthy recipe our friend sent us via instant messaging), go to work (organized through a rideshare app), and go to sleep at the end of the day (with our smartwatch on to check our heart rate and sleep quality), technology is always, and often literally, in our pockets. Yet, how technology is addressed in the management literature is not unequivocal, which contributes to a fuzzy representation of how it influences work in general, and teamwork in particular.

TECHNOLOGY AS A CONTEXT From not being at all accounted for (treated as an “absent presence,” Orlikowski & Scott, 2008), technology has a long history of being considered a “context” where work happens, with fixed characteristics that constrain teamwork in a fixed way (Larson & DeChurch, 2020). For instance, “cues-filtered-out” theories such as media richness theory (Lengel & Daft, 1984) or social presence theory (Short et al., 1976) assume that technologies shape communication through the nature and frequency of cues they can transmit. Specifically, these theories assume that technology filters out important cues through restrictions that occur naturally as a func-

Virtuality and the eyes of the beholder  201 tion of its design and capabilities (e.g., the capacity to transmit sound and visual cues or the speed with which information is sent). For instance, while face-to-face interaction transmits non-verbal (e.g., body language), paraverbal (i.e., voice), and verbal (i.e., content) information in real-time, some forms of technology-mediated interactions (e.g., email) transmit text-only messages at an asynchronous pace. The capacity to transport these cues is expected to influence how well certain tasks can be achieved through a technology. Specifically, tasks that are more complex or require a higher degree of member interdependence are thought to require a higher variety and/or frequency of cues, meaning that some technologies will be better suited than others. This perspective can be prescriptive, as it suggests that the information a given medium can transmit is always the same (i.e., an email could never convey paraverbal information), and depends only on its structural characteristics (i.e., emails cannot transmit sounds or images). In sum, these theories assume that what technologies can achieve and thus, which tasks and situations they are suited for, is fixed. These approaches treat the material (e.g., technology) and the social (e.g., individuals’ actions) aspects of organizational life as separate and distinct entities. Technology is considered as something exogenous, which behaves in a stable and predictable way – for example, when an email is sent, it always reaches the receiver within a couple of seconds; it’s never “lost by the postman.” Individuals, on the other hand, engage with technology in order to pursue a certain objective. We tend to attribute how individuals use technology either just to human agency (e.g., I use emails in a certain way because I know how to) or to stable features of the technology (e.g., I use emails in a certain way because that’s what the software requires me to do). However, these interactions may be dynamic and change over time and across organizations (for example, individuals may vary in the way they use email, such as by using more formal or informal language). Technology features are also not fixed – for example when using email, a user might schedule the time it is sent or add “hidden” recipients using the “bcc” function. Technology features should therefore not be considered as physical properties which individuals use in a fixed way, without being able to adjust, change, or adapt.

TECHNOLOGY AS A SOCIAL CONSTRUCTION In addition to being considered a contextual variable, technology is often conceptualized as a “the constitutive entanglement of the social and the material in everyday organizational life” (Orlikowski, 2007, p. 1438). This approach started to gain relevance in the early twenty-first century, with the proliferation of online communities (Larson & DeChurch, 2020). In these communities, both individuals and technology act together to build a network of relations (Soga et al., 2020), meaning that individuals develop their work and personal relationships through technology, which enables them to share content within the affordances (cf. Chapter 1) and constraints of virtual space. This perspective asserts that rather than having stable intrinsic properties that evoke similar experiences among its users, the features of a technology can serve different functions or purposes for different individuals and teams. For example, let us consider a collaborative software that allows members of a team to send direct messages to one another. The direct message feature may be used by group members to lobby their ideas to individual co-workers privately, prior to public discussion. This usage can reflect a political move such as an intention to influence co-workers to agree with a given perspective. Conversely, another group may use this feature differently, by creating group chats to allow

202  Handbook of virtual work individuals who are working on interdependent tasks to share information, strictly with the intent of collaborating towards a collective goal. In the case of technology as social construction, the function of technology is less related to its specific built-in features (e.g., direct messaging and group chat), but rather to how individuals use or appropriate those features. These specific uses, in turn, have consequences for teamwork. Research examining this perspective has already shown that, for example, electronic monitoring systems work differently depending on the attitudes individuals hold about those systems (Alge & Hansen, 2014). Regardless of the features that monitoring systems allow for, how they are implemented and perceived varies across individuals and teams. What is more, the adoption of technology triggers the need to adjust social processes that are unrelated to the task. For example, Miles and Hollenbeck (2014) stress that switching to or using teleconference software imposes both a socialization and learning task on those involved in that communication. Hence, when individuals realize that a given technology can be used for something, they create a function for that technology, which is simultaneously dependent on (e.g., the technology needs to allow for including a picture, and constrains how many MB the file can have), but yet distinct from (e.g., the type of picture is chosen by individuals) its built-in features. Technology and Virtual Teams The above perspective influences our perception of teams that interact via technology, regardless of the degree to which they do so (i.e., team virtuality). Most definitions of virtual teams consider the team’s reliance on technology as a necessity that results from members working together from different locations. Other conceptualizations of virtuality argue that it is the processes emerging from the team’s use of technology that make a team virtual (see Foster et al., 2015 for a review of team virtuality definitions). More specifically, the latter approach maintains that while teams working from different locations have to use technology to interact, this does not mean that co-located teams do not. Team members who work at the same site, or even in the same office may also rely predominately on technology to communicate. For example, collocated teams might use email for exchange information because it allows them to go back to messages at a later point in time and it gives them more time to think about what they want to say before responding, or maybe because they simply cannot be bothered to engage in long discussions with talkative colleagues. Even though this approach acknowledges that all teams can be virtual to some extent, notwithstanding their degree of co-location, the effect that technology use has on team interactions has a tradition of being considered as fixed (e.g., Lengel & Daft, 1984; Short et al., 1976; Walther & Parks, 2002; Ishii et al., 2019). This theorizing reflects what is referred to as a cues-filtered-out perspective (represented by media richness and social presence theory), as it equates the physical properties of a technology (e.g., whether it can transmit sound) with its affordance, and – as an extension – with the way it is used and perceived. However, this would also imply that teams with comparable technology usage (i.e., similar reliance on the same type of technology) should have comparable experiences when interacting with technology. While this may hold true in a range of laboratory experiments which confirm the tenets of media richness theory by showing lower levels of communication, performance, and satisfaction for virtual versus face-to-face teams (for meta-analytic evidence, see e.g., Baltes et al., 2002; de Guinea et al., 2012), their generalizability to “real” teams is somewhat restricted. For instance,

Virtuality and the eyes of the beholder  203 in most of these studies, participants were students who did not know each other and worked together only for a very short time span, usually using only one technology (e.g., text-based instant messaging) that was prescribed to them. They had no prior history of working together, nor did they have time to build common representations of technology usage. In contrast, in field studies – where team members worked together for a longer period of time and had more autonomy concerning the way they structure their work (which could include the choice and use of technology) – the negative effects of technology usage appear much lower or to even disappear completely (for a review of these studies, see also Gibbs et al., 2017; Handke et al., 2018; Handke, Klonek et al., 2020). Findings of this nature are explained through social and temporal influences on technology (e.g., adaptive structuration theory, DeSanctis & Poole, 1994; channel expansion theory, Carlson & Zmud, 1999; social information processing theory, Walther, 1992). These theories assume that as individuals gain experience in working together, they build up important knowledge about each other, the task, and the technology they are using to accomplish this task. This knowledge helps them improve both the way they send (i.e., encode) as well as receive (i.e., decode) information. For instance, to avoid misunderstandings, irony in text-based messages is often made more explicit through the addition of emoticons. Likewise, knowing that other team members currently have a lot on their plates can mean that one does not get offended if these do not immediately reply or their response is very brief. Accordingly, team members learn to compensate for the more physical properties that are lacking in certain technologies (e.g., by using emoticons to compensate for a lack of non-verbal information or filling in the gaps with contextual knowledge acquired through prior interactions), thereby appropriating technology in a way that meets their needs. Similarly, even teams who rely heavily on technology can exhibit high levels of relational communication (e.g., messages disclosing personal information) once they have built up a level of team understanding between members (e.g., Kock, 2005; Walther, 1992, 1996; Walther & Burgoon, 1992). In sum, technology permeates most aspects of our lives and our interactions with others. However, it is not just the technology that shapes our interactions, but also our interactions that shape how we define, use, and experience technology. Based on this assumption of an intricate process of mutual reciprocal influence between individuals, teams, and technology, the following sections are devoted to explaining four concepts that enable a more profound understanding of the entanglement between team members and technology.

TECHNOLOGIZED TEAM RELATIONSHIP Introduced by Soga and colleagues (2020), the concept of technologized team relationship refers to “the team relationship that is formed and/or sustained through the intermediation of web 2.0 technologies” (p. 1). This perspective suggests that technology itself – even if it is used only as a medium to transport information – has an agentic role. Having an agentic role means that technology has the ability to elicit a response from the human users with consequences for the relationship between team members. This response either happens because the technology itself is designed to provoke a reaction, such as pop-up notifications that prompt individuals to check messages, or because technology fosters an involuntarily response, that is, that was not intended or even considered when designing and programming the technology. For example, individuals may feel angry at their co-workers for the disruption caused by

204  Handbook of virtual work pop-up notifications while they are trying to concentrate on their work – which was probably not intended by the app’s developers! Technology shaping human reactions and relationships is not new. The above-mentioned “cues-filtered-out theories” (e.g., media richness theory, Lengel & Daft, 1984; social presence theory, Short et al., 1976), suggest that technologies influence individuals through the way they are designed, meaning that many features built into a medium (e.g., pop-up notifications) are expected to have a specific impact on users. For instance, using a leaner medium (i.e., text) is expected to have a negative impact on coordination under time pressure: for example, we all would expect negative results if a team of firefighters were to communicate via email when fighting a fire! In this traditional sense, technologies constrain human interactions in ways that can be foreseen or predicted when considering the design of a particular technology. This means that team members know that when using emails, their communication is going to be slower than if they were communicating face-to-face (e.g., because typing takes longer than speaking, or reading takes longer than listening). The technology is thus simply a medium that transports information, and the way that information is transmitted and received depends on identifiable properties in that technology’s design. Unintended Consequences Considering the agentic role of technology in its interaction with people can also lead to consequences that were not foreseen when developing the technology because there is a reciprocal influence between humans and technology: humans shape technology and technology usage, and technology shapes social relationships over time, going beyond more a simple cause-effect logic. Consider, for example, the instant messaging groups you belong to. These groups are intended to facilitate communication between individuals, to foster social connections, and to help in information sharing. However, how you use each of the groups is probably different. In some of them – say, a group of co-workers that is working on an interesting project – one would expect active participation. In this group, you are on top of all interactions, checking messages when you receive them, sharing gifs, ideas, participating in discussions…. Because of your active role, you will also have some ability to steer decision-making in this group – it’s you who decides to have a Secret Santa celebration and who shares a Doodle link to set the date. In contrast, in other groups, maybe a parents’ group from your kids’ class, responsible for organizing the end of the year party, you have muted the notifications and only check them once a week because you are not interested in seeing all the school gossip on a daily basis. In this group, you might fail to see all the messages because you do not engage in all exchanges and as result, you might be unaware that you needed to take a specific item to school on Monday, or your miss an invite for a playdate organized by that group – these are all unintended consequences; yet, they exist because of the different ways individuals engage with the same technology. Consequences for Teams In practice, technology features can lead to distinctly different outcomes than the ones predicted using theories on media use. Instead of always increasing communication and coordination, how individuals use technology can lead to social isolation or the formation of subgroups that leave out certain group members even though the technology was designed to enhance

Virtuality and the eyes of the beholder  205 participation in communication. Thus, increased participation (afforded by the technology) is not a given. Leaders, for instance, may use social platforms only to lobby for their preferred decisions (Weick et al., 2005). This, in turn, can result in excluding individuals who do not use these technologies, regardless of their reasons (which can be as simple as a lack of familiarity with the technology or technical constraints like not having the correct web camera). Similarly, technology can be used to withhold (instead of share) information from all or some team members, which is facilitated by some technology features such as direct messaging. Again, the possibilities offered by technology interact with individuals’ motivations to shape the team relationship, together with their hierarchical position and technological know-how. Hence, the intended consequences of technology for the interaction between individuals are often replaced by unintended ones, reflecting the complex nature of the interaction between humans and technology. What is more, individuals can dynamically engage in renegotiating their relational power, (i.e., how much they can influence others’ behavior and attitudes), and how they react to technological affordances. For example, some project management platforms automatically send emails to all members involved in specific tasks whenever someone posts a comment on the platform’s task subsection. If one team member wishes to delete his or her email address from the list and the platform does not support this, they can ask other members to stop sending “irrelevant” messages, or react in a passive-aggressive manner by reply “ok” to all messages seen as irrelevant, or contact the developers and ask that the functionality of deleting one’s email be added. Continuous iterations between individuals and technology, over time, generate opportunities for teams and team members to explore and adapt how they use a technology. This can lead to consistent shared understandings of both teamwork and technology usage, which will be addressed in the next section.

ICT SHARED MENTAL MODELS Mental models are cognitive representations of knowledge, formed by individuals about how they interact with the environment (Resick et al., 2010). For example, we all hold a mental model of a “chair,” including its prototypical characteristics (i.e., has four legs, a place to sit, and a backrest) and its function in our lives (i.e., place to sit). When applied to teams, Shared Mental Models (SMM) refer to the organized representation and understanding of the knowledge team members share about relevant task and team aspects (Stout et al., 1999; Klimoski & Mohammed, 1994). This cognitive model shared by team members allows them to perform more effectively, anticipating the needs of other members and adjusting their behavior to what is needed at a given moment (DeChurch & Mesmer-Magnus, 2010). As an example, if team members all share the understanding that writing a client report requires several attempts and iterations, the collective response to a first draft is likely to be constructive criticism and feedback, which in turn is what is expected from the original writer. Similarly, if team members share the acknowledgment that they should not work during the weekend, they will feel OK with ignoring an email sent on a Saturday, and the sender of that email (if they belong to the team) will interpret the absence of an answer accordingly. Overall, SMM have consistently been associated with high performance and team satisfaction (Cannon-Bowers et al., 1993; Santos & Passos, 2013).

206  Handbook of virtual work The literature on SMM distinguishes subtypes of mental models teams may share: teamwork (e.g., interaction patterns), taskwork (e.g., priorities), temporal (e.g., working speed), and equipment (e.g., how to handle a given machine). Recently, Müller and Antoni (2020, 2021) proposed a new form of equipment SMM, to address the exponential development and use of ICT: ICT-SMM. ICT-SMM reflects the “shared knowledge structures about the task-specific ICT use among team members” (2020, p. 188). More specifically, ICT-SMM are about knowing how and when to use ICTs, how to adapt to the constraints of given media, about knowledge on ICT functionalities (Schulze & Krumm, 2017), and about the etiquette norms for using ICT (Bernstein, 2014; Landry & Lewiss, 2021). Shared Mental Models About Technology Thinking about the virtual teams you have worked with, you may be able to identify those where there was a shared understanding by all team members, and others where SMM about ICT were nonexistent. You may even realize that part of the miscommunication or lack of coordination in that teams was due to an insufficient “common language” about the functionalities and usage of different communication media. For example, if all team members know that email is the preferred medium to communicate, they will expect others to email them with requests instead of calling on the phone. Yet, if this is unclear, someone may call a team member with an urgent request; the receiver, however, because they do not expect to be called, may look at the missed call and decide to call back at the end of the workday. Similarly, to overcome the constraints imposed by document sharing (such as sending large documents by email or having too many edited versions of the same report), some teams develop an understanding that when a given task requires inputs from multiple sources, they will use a SharePoint. Consequently, every time team members need to contribute individually, they know to log into the group’s SharePoint. Another example is the use of “reply to all” function; some may share an understanding that everyone should be in the loop, therefore select this option, whereas others agree to limit the amount of incoming emails by including only those directly involved in the task on the recipient list. Taken together, it is not surprising that ICT-SMM are related to better performance and coordination, as well as to less workload frustration (Müller & Antoni, 2019). Consequences for Teams Two things seem to be important in translating the existence of similar ICT-SMM into team relevant outcomes such as performance or commitment (Müller & Antoni, 2021). One is the aforementioned task-oriented communication, by which members are explicit about when and how to use ICT when collaborating. The other is the consistent use of ICT in the team, reflecting an accumulation of similar experiences with the selected media. Teams that talk about ICT use and explicitly plan how and when to use technology, will develop more similar ICT-SMMs over time. Therefore, members’ experiences and the group discussion around ICT usage shape the content and strength of ICT-SMM. For example, a team may have a distinct SMM regarding email communication generated over time based on team norms for conveying emotional cues in written messages using caps for excitement, but not for anger. What is critical here is that teams need to clarify how they intend to use technology beforehand or when onboarding new members. In addition, discussion needs to

Virtuality and the eyes of the beholder  207 be held on what media will be used under different situations and set some communication ground rules. Every time there is a misunderstanding there is also an opportunity to update a team’s ICT-SMM. As stated previously, virtual teams benefit from developing a shared understanding of how and when they use technology to communicate and coordinate. If it is true that their experiences with ICT, as argued above, influence how they use technology, it is also relevant to consider how technology usage, in turn, shapes their experience of virtual teamwork. Indeed, some teams may perceive the same technology as more useful for their work that others, and yet again, some teams will use the same technology for different purposes (e.g., using emails to unidirectionally disseminate information to the entire team or using them for back-and-forth discussions with individual team members). Due to these different perceptions and behaviors, teams’ experiences with technology will differ. Moreover, over time, team members can change their opinion with regards to the usefulness of a certain technology and may even be surprised by how technology has changed (positively or negatively) the way they interact with one another. As a result, the extent to which teams rely on technology may not be the central determinant of their actual experience of working together virtually. Accordingly, as we describe in the following section, an important question to ask may not be “are teams virtual?” but rather “do teams perceive themselves to be virtual?”

TEAM PERCEIVED VIRTUALITY As discussed at the beginning of this chapter, defining team virtuality from a structural perspective (e.g., which technologies and how often team members use them) does not sufficiently describe how team members will perceive their interactions when working virtually. When we think of our own teamwork experiences, working virtually does not always feel the same. Sometimes working virtually can be excruciatingly cumbersome, with team members struggling to reach a shared understanding of their goals and tasks. In other instances, working virtually can be fun, with the exchange of hilarious gifs, memes and videos making virtual teamwork more fun than face-to-face meetings. Accordingly, the fact that we work together virtually (i.e., through technology) may not be the actual problem and conversely, face-to-face interaction is not necessarily the solution – it is the experiences we make and how we feel when working together virtually. That is, team members will not necessarily consider their reliance on technology problematic unless they experience disruptions in their interactions. Moreover, these disruptions do not necessarily arise from technology use and can occur in face-to-face team interaction, such as when members do not keep each other in the loop about current taskwork (see Watson-Manheim and colleagues’ discontinuity approach, Watson-Manheim et al., 2002, 2012). When disruptions occur during team interactions, members will try to make sense of these experiences. For instance, a team of software developers may notice that their collaboration is suffering as a result of a number of misunderstandings and failures to reply to requests in a timely fashion. To understand these issues, team members will think about what actually happened and what in their work environment could explain why this happened. In this example, the team might realize that the misunderstanding happened because a team member was not clear on what the other was working on. It is through their experiences and subsequent explanations that teams find themselves in a state that Handke and colleagues (2021) describe as team perceived virtuality (TPV).

208  Handbook of virtual work TPV is defined as a cognitive-affective emergent state that considers team virtuality as something that arises from team experiences, rather than directly from fixed, structural properties (such as the technology itself). That is, TPV can arise when team members use technology, but does not necessarily have to. TPV can be low in face-to-face teams who experience their collaboration as gratifying, but may just as well be very high for those that do not. TPV is characterized by two dimensions, namely collectively experienced distance and collectively experienced information deficits. TPV Dimensions Collectively experienced distance is an affective dimension that is closely related to concepts such as perceived proximity (Wilson et al., 2008) or electronic propinquity (Korzenny, 1978) and thus, reflects the degree to which team members feel distant from each other, meaning that their relationships are cold, impersonal, and unaffectionate. Inversely, teams where members feel close to one another are characterized by interpersonal liking, warmth, and even friendship and members feeling less distant from one another. Feelings of distance can be, but do not have to be, related to physical distance. It is just as possible to be physically close, yet feel distant from one another and the other way around. For example, we may feel very close to team members we have not been in the same room with for months at a time because we keep each other in the loop about our problems, send each other funny videos, and regularly get together for virtual coffee breaks. Conversely, at the office, we may feel completely disconnected from those sitting right next to us because we really have nothing to say to each other. Collectively experienced information deficits, in turn, constitute the cognitive dimension underlying TPV. This dimension describes the extent to which team members perceive their information exchange as being flawed through inefficiency, incompleteness, and untimeliness. These perceptions are closely related to the concept of information richness and thereby describe experiences characterized by the inability to adequately convey meaning and reach a joint understanding. Once again, these perceptions can arise as a function of the physical properties of the technology team members use (e.g., responses via email take longer than on the phone), but they do not have to. For instance, some virtual teams may find that the time they have to wait for the others to react to their communication makes it very difficult to coordinate their actions, thereby experiencing a high degree of information deficits. Conversely, others may find that they are much better at giving each other detailed feedback via email than in person, thereby experiencing a low degree of information deficits when using technology. Collectively experienced distance and information deficits can be considered as two related, yet distinct dimensions. This means that while teams can find themselves in a state characterized by similarly high or low levels of information deficits and distance, it is also possible for team members to have high values on one and low values on the other. For example, the virtual meetings characterized by the inability to reach a shared understanding regarding goals and tasks could have been held by the very same team that enjoys sending each other funny pictures and letting each other know what they did on the weekend. This would be a state where team members experience high levels of information deficits, yet low levels of distance. Conversely, teams may also find themselves in a state where they are extremely effective at exchanging information and know what they have to do to reach their collective goal, yet feel extremely disconnected from each other emotionally. This would be an example of high

Virtuality and the eyes of the beholder  209 level distance and low information deficits. Realizing that teams can find themselves in states differing on these two dimensions should help us understand not only why some virtual teams perform better than others (or even better than face-to-face teams), but also why sometimes working virtually can feel more satisfying than in other cases. That is, sometimes working virtually is satisfying, but not necessarily effective, whereas in other cases, it can be the exact opposite. Consequences for Teams The theory on TPV holds that the lower the perceived virtuality (i.e., low distance and low information deficits), the better for team outcomes. Therefore, actions that promote feelings of closeness and feelings of communication effectiveness should all be beneficial for virtual teams. For example, sharing personal information or promoting channels for informal communication can both increase the feeling of proximity. Instead of having a collaborative software where all the information exchanged is work-related, creating a specific “room” for sharing personal information (e.g., photos of a pet, a recipe, …) can be helpful in increasing levels of familiarity between members which has been shown to be beneficial for information sharing and elaboration (Maynard et al., 2019). Differentiating between what virtuality means in terms of teams’ actual experiences, which are characterized both by distinct emotions (i.e., feeling distant from one another) as well as cognitions (i.e., thoughts reflecting deficits when exchanging information within the team) can help us understand the actual nature of being a virtual team. However, whether virtual teams consider themselves as productive, happy, and viable may depend not only on the experiences themselves, but also on teams’ general attitude towards working virtually. Specifically, teams and their members may differ in their virtuality beliefs, which we will describe in the following section.

VIRTUALITY BELIEFS When thinking about a current virtual team(s) it is likely that you will compare it to a team (or teams) you have worked with previously. Let us assume that you perceive your current virtual team as terrible – it is inefficient and frustrating and you would miss none of these people if you never talked to them again. You are not surprised that it is inefficient – you have worked virtually with other people in the past and while you liked some of them more personally, you always found it extremely difficult to coordinate at a distance. You often think that everything would just be so much quicker and easier if you could just all sit down together, at the same table, to talk about your ideas, and agree on a strategy to move forward. Accordingly, your belief that virtual teamwork is a good thing is probably not very strong or – as we will call it here – your virtuality beliefs are low. The term “virtuality beliefs” is derived from the concept of diversity beliefs, which describes the value of diversity versus homogeneity that individuals hold for their work group, that is, whether they consider diversity to be beneficial to group functioning (e.g., van Knippenberg & Haslam, 2003; Homan et al., 2007). Research here suggests that in order to harness the benefits of diversity, individuals need to value it. Accordingly, studies have demonstrated that when team members hold pro-diversity beliefs and worked on more diverse

210  Handbook of virtual work teams they demonstrated higher levels of group identification and performance (Homan et al., 2007). Applied to the context of virtual teamwork, virtuality beliefs describe the beliefs that individual team members hold toward team virtuality (and specifically technology use), that is, whether they consider team virtuality as being beneficial or detrimental to team functioning. Similar to the effects of diversity beliefs, we propose that more positive virtuality beliefs will improve virtual teams’ functioning. Forming Virtuality Beliefs Similar to what has been found for diversity beliefs, virtuality beliefs are likewise based on stereotypes, expectations, and prior experience that define whether an individual believes that working virtually is as good, or even better, than working face-to-face. For instance, as team members get to know each other better, they may be less worried about making jokes or disclosing personal information on their team’s Slack channel. Through these exchanges, they will likely experience Slack as a “rich” channel (see channel expansion theory, Carlson & Zmud, 1999) and will consequently develop ICT-SMM that represent Slack as a technology suited for socio-emotional communication. Accordingly, over time, team members will hold the expectation that working virtually does not have to diminish feelings of closeness and may in fact allow for more flexibility when it comes to working and sharing a laugh or two. As a result, a team may value virtual teamwork just as much – or even more – than working face-to-face. In contrast, team members whose ICT-SMM is that technology-mediated communication is cold and impersonal, or who are generally hesitant to use technology at work and particularly in interaction with others, are more likely to hold the belief that virtuality is detrimental to team functioning (for factors that influence the attitude and motivation to work virtually, see also Schulze & Krumm, 2017). Consequences for Teams Even though positive virtuality beliefs are likely to be part of a self-perpetuating cycle (with positive beliefs leading to positive experiences leading to positive beliefs, see also Fulk et al., 1990), they can also be promoted or enhanced by a culture that values virtual teamwork and the use of technology. That is, the stereotypes and expectations we have towards virtual teamwork will also be shaped by how others see it, by whether it is encouraged by the organization, and how it fits into our lives. For instance, if all our colleagues prefer working at the office, our partner constantly says how much they hate virtual meetings, and implicit norms suggest that if we work remotely, we need to at least check emails from dusk until dawn, it will be hard to develop positive virtuality beliefs. Conversely, realizing that email is a tool that helps us go back to earlier information exchanges which might otherwise be forgotten, a mechanism to draw on experts from around the globe, and knowing that one’s organization is open to flexible work practices, will make it much easier to build and maintain positive virtuality beliefs. Hence, fostering an organizational culture that values virtuality can help develop more positive mindsets about virtuality. Here, the role of the leader in championing virtuality is important. Leaders can set the tone, highlight the advantages of working virtually, and role model behaviors they want others to follow such as, especially, hold frequent meetings to discuss how working virtually is affecting the team and its individuals.

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FUTURE RESEARCH Conceptualizing virtual work as something that changes and evolves as a function of team members’ collective experiences with one another and the technology they use to communicate with has several implications for future research. First, it tells us that we need to reach beyond easily quantifiable, yet purely structural perspectives to describe and measure team virtuality. Therefore, rather than asking teams to indicate the physical distance between members, or how many technology-enabled meetings they have per week, month, or year, we need to find ways to capture how these structural properties translate into actual team experiences. This calls for the use of measures that capture perceptions related to virtual teamwork, such as those used in ICT-SMM work (e.g., Müller and Antoni, 2020) or conceptually described in work on TPV (e.g., Handke et al., 2021). Furthermore, the development of these measures calls for a reflection on the aggregation process, dependent on the theoretical definition of the construct. For example, using collective experiences of distance and information deficits, a team’s overall score on TPV will be computed on the basis of individual perceptions. If we expect that the contribution of each team member is equally important to the shared perception, this would call for a composition model (e.g., with TPV reflecting the team-level mean of individuals’ TPV perceptions). However, certain members of the team (e.g., the team leader or more active team members), could impact the shared perceptions more strongly than others, which in turn would imply that a compilational, rather than compositional model of TPV, would be a more accurate depiction. We thus encourage future research to consider how these perceptions are formed at the team level. Second, the role of technology in shaping team interactions is probably more complex than acknowledged in the extant virtual teamwork literature. Team members can appropriate technologies in many different ways depending on their personal preferences and the team’s communication norms. The same technology (with the same physical properties) can thus have an entirely different function and thereby completely different effects depending on the team in question. What is more, its use is likely to change over the course of a team’s lifecycle as team members become more aware of its functions and how these match their specific needs and preferences. Accordingly, we would encourage research to examine the role of team members’ technology appropriation, which may be achieved through the collection and analysis of behavioral data, such as by automatically tracking which programs and functions are used when, how often, and ideally also for which purpose (which may be achieved through coding recordings or transcripts of team interactions, see also Chudoba, 1999; Fuller & Dennis, 2009). Third, the agentic role that technology plays in intra-team relationships (as described in the section on technologized team relationships) could still use additional conceptual clarity. Here we acknowledge the growing body of research on human–autonomy teaming (HAT; for a review, see O’Neill et al., 2020). As we gradually allow technologies to evolve from mere tools that depend on our actions and decisions to agents that have the capability to steer team interactions, they will simultaneously change from intermediaries to actual team members. For instance, the technologies we previously used to transmit information between team members could develop into agents that execute certain actions based on the information gathered through intra-team communication (e.g., redirecting tasks from members with higher levels of workload to those with lower levels). As a result, technologies would not simply shape relationships between (human) team members, but may themselves be acknowledged as team

212  Handbook of virtual work members that actively contribute toward the shared team experience (and consequently also influence perceptions of team virtuality). Finally, multi-team membership is increasingly common, that is, having individuals that belong to more than one team simultaneously. Considering that each team may have different (and even opposing) perceptions on technology and its usage, and different levels of effectiveness in doing so, makes it important, albeit complicated to explore how the experiences in one team may influence experiences in another team(s), and how individuals are able to create a meaningful synthesis between these different perspectives.

CONCLUSION The exponential evolution of digital, information, and communication technology has transformed the way we work, and how we collaborate with others. The concept of virtual teams is becoming an oxymoron, since all teams now communicate, to some degree, via technology. Hence, the nature of teams themselves now includes their interaction with and through technology. As a result, teams who deal effectively with technology have an advantage over those who are less competent in communicating virtually, such as through developing a shared understanding, or developing positive beliefs about technology. How we think about virtual work continues to change, in parallel with its increasing prevalence in our (work) lives. The more individuals and teams interact with and through technology to accomplish their shared goals and tasks, the more likely they are to learn how to leverage and adapt technology to achieve the teams’ outcomes. Plus, the development of technology itself allows for higher quality interactions and for more functionalities. Whereas historically one would send a fax, or exchange one email (and sometimes maybe even attach a file!), now you chat on Slack throughout the working day with many co-workers while all working simultaneously in real-time on the same document in a SharePoint. Similarly, in the early days of digital communication it was often a struggle to let others know that you were excited about an idea – today we have more than 100 emojis at our disposal – including, for reasons that are beyond our knowledge, crystal balls, showers, and more! The more familiar we are with technology-based interaction, and the more technology allows us to exchange richer information, the less relevant older definitions of virtuality (e.g., the percentage of time spent online) become. New concepts that focus on the social construction of meaning and on shared experiences hold meaningful keys to understand teams’ effectiveness in this digital age.

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12. Leadership and virtual work in a pandemic and post-pandemic world Claudia C. Cogliser, William L. Gardner, Haimanti Ghosh and Azucena Grady

In March 2020 after a few months of adapting to the coronavirus pandemic, the world of work changed as nations moved radically into lockdown with the majority of organizations shifting non-essential workers immediately to remote work. Most of these employees were then faced with virtual teamwork for the first time. Just months into the pandemic, it was clear that an increasing number of organizations believed the shift to virtual work would remain long term: 127 firm leaders reported that 82 percent intended to allow remote work once employees returned to the office, and 47 percent would allow employees to work remotely full time going forward (Gartner, 2020). Despite the enduring impact of COVID-19, there are other crises of global importance that have the potential to increase the amount of virtual work organizations face. Russia’s invasion of Ukraine on February 24, 2022, and its ensuing refugee crisis highlights a much more widespread refugee challenge involving tens of millions of people across the globe. The climate crisis and the resulting human displacement is a disruptor of historic proportions even as the world must transition to a low carbon economy and the changes to organizational processes that will result. Income disparities and poverty within the United States and across the world that were once in decline, are again on the rise from the fallout of the pandemic (Sumner et al., 2020). Rising inflation in 2022, the threat of a recession, and historically high gasoline costs are also likely to affect changing employee and organization perspectives on virtual work. In terms of what it means to be a manager today, the “success of remote work has reimagined how corporate work gets done, as well as where the work takes place,” and 83 percent of employers consider the quick shift to remote work to be successful (PwC, 2021, p. 1). This is not to say that a face-to-face office environment will quickly be an artifact of pre-pandemic times, but the changing role of the workplace is now a given, despite some prominent CEOs indicating otherwise. As a notable example, the electric vehicle news site Elektrek published a May 31, 2022 leaked email from Tesla’s leader Elon Musk stating that “[a]nyone who wishes to do remote work must be in the office for a minimum (and I mean *minimum*) of 40 hours per week or depart Tesla” (Lambert, 2022). This policy is at odds with leadership from the other company connected with Musk at the time of the writing of this article. On March 3, 2022, Twitter’s CEO Parag Agrawal mandated a much more flexible approach, stating that: Wherever you feel most productive and creative is where you will work and that includes work from home full-time forever. Office every day? That works too. Some days in office, some days from home? Of course. That’s actually how most of you feel. (Agrawal, 2022)

The purpose of our chapter is threefold. First, we provide a COVID-informed assessment on virtual work and the various forms of leadership required to support it. Second, we review 216

Leadership and virtual work  217 a myriad of challenges of virtual work as we navigate sustainable employment post-COVID, including emerging economic uncertainties, the great resignation of 2022, rising inflation and threat of recession, and other grand challenges (e.g., economic disparities; work‒life balance; sustainability; health and well-being; diversity, equity, and inclusion) for which we believe leaders are central in addressing. Finally, we conclude with practical implications for managers operating in a virtual team context. We offer our insights through the lens of virtual work operating on a virtuality spectrum rather than a dichotomy such that we consider virtual work ranging from fully virtual to hybrid forms that involve both face-to-face and virtual participation.

INTRODUCTION The Twitter leadership perspective on employee autonomy regarding virtual work is consistent with the way many employees are viewing their emergence into a post-pandemic workplace. Microsoft’s Work Trend Index survey found that hybrid work (a compromise of sorts with a combination of face-to-face and virtual work) was up 7 percent from 2021 to 38 percent, and 53 percent of people were likely to consider transitioning to hybrid work in the next year (Microsoft, 2022). It is not just employees expressing a new preference for remote work; many firms and their leaders are embracing it as well (Apple, Citigroup, IBM Corp, Google, Meta, Microsoft, Salesforce). In 2021 one report found that 44 percent of executives believed that employees should be in the office just two to three days per week to maintain the organizational culture and productivity, while only 21 percent recommended face-to-face office participation of five days each week (PwC, 2021). McKinsey & Company surveyed 100 C-suite executives in January 2021, and found that 90 percent of these firms will be combining remote and on-site work moving forward (Alexander et al., 2021) and it is clear that many employees will have options about where, when, and how they will be working. Contrasting these points of view, the Microsoft Work Trend Index revealed that half of managers surveyed reported that their company already requires, or plans to require, full-time in-person work in the year ahead. This percentage is even higher for leaders in the manufacturing (55%), retail (54%), and consumer goods (53%) industries. (Microsoft, 2022)

These conflicting perspectives reflect the conundrum leaders are facing as they look toward the future of work. Organizational scholar Anthony Klotz coined the term the “Great Resignation” in his May 2021 Bloomberg interview when predicting extraordinary turnover in the United States post-pandemic economy (Cohen, 2021). The numbers are multiplied, he says, by the many pandemic-related epiphanies—about family time, remote work, commuting, passion projects, life and death, and what it all means—that can make people turn their back on the 9-to-5 office grind. (Cohen, 2021, p. 1)

Unfortunately, the resignation numbers are staying steady even into mid-2022. The Bureau of Labor Statistics reported in its June 1, 2022 Job Openings and Labor Turnover Summary that the number of people who quit in April 2022 were 4.4 million, with 12-month total separations of 71.6 million1 (U.S. Bureau of Labor Statistics, 2022).

218  Handbook of virtual work The strongest arguments against continuing with or increasing virtual work is that job performance suffers, often because of a challenge to collaboration, networking, and socialization. Again, surveys provide equivocal information. PwC found that employers reported increased levels of productivity in January 2021 compared with June 2020. A September 2021 survey of 2,050 full-time workers in the United States by Owl Labs in collaboration with Global Workplace Analytics found that 83 percent of employees were at the same productivity level or higher working remotely as compared with the office, and 76 percent said that continuing to work from home would make them happier (State of Remote Work 2021). Our perspective is that the changing nature of remote work has profound implications for both leaders and followers in virtual teams or those performing work away from the office even for a proportion of their total work (Larson & DeChurch, 2020; Newman & Ford, 2021; Purvanova & Kenda, 2018). Indeed, leaders set the stage for a high level of job performance regardless of the level of virtuality in their follower’s work. In addressing these challenges, leaders have an essential role to play in developing people, structure, and process solutions to tackle these challenges in the short term (Bick et al., 2021). Leaders need to stay current on advances in information and communication technology (ICT) to determine the best ICT across performance tasks of the team. While ICT facilitates highly interdependent teamwork, its effectiveness depends on how well the team’s leader (or leaders) “communicate, collaborate, and coordinate teamwork using the available technologies and if they are able to surmount the social challenges that virtual teams entail” (Kozlowski et al., 2021, p. 1). Specific challenges related to followers in virtual work, regardless of the level of virtuality, involve “feelings of isolation; barriers to building rapport and community; lack of impromptu interactions, decreased team cohesion; pressure to balance personal and professional life; demoralization due to less daily direction and misunderstanding; and amplification in the lack of clarity” (Bick et al., 2021, p. 5). Despite an uncertain future without an end in sight for the coronavirus pandemic, the last two decades’ scholarship on managing virtual work in teams provides good news for the current state of affairs. This body of work indicates that hierarchical leadership is less strongly related to team performance in virtual versus face-to-face teams (Hoch & Kozlowski, 2014). Accordingly, we consider the merits in virtual settings of alternatives to hierarchical leadership, such as shared (Hoch & Kozlowski, 2014), functional and visionary (Eseryel et al., 2020), complexity (Uhl-Bien, 2021a, 2021b), authentic (Gardner et al., 2021), and what Purvanova and Kenda (2018) describe as “paradoxical virtual leadership” – the notion that dealing with the paradoxical nature of virtual work requires a synergistic approach to leadership that enlists divergent forces to balance paradoxical tensions and address the brave new world of virtual work.

VIRTUAL WORK CHALLENGES AND OPPORTUNITIES While the COVID-19 pandemic stimulated a dramatic uptick in virtual work, it also revealed unique ways in which challenges found in society as a whole may become manifest in a virtual context. Within the framework of this larger conversation, it is important that we recognize the conceptual approach of virtuality and move away from the false dichotomy of fully virtual versus fully collocated teams. Following Kirkman and Mathieu (2005), we consider team virtuality along three dimensions:

Leadership and virtual work  219 (a) the extent to which team members use virtual tools to coordinate and execute team processes (including communication media such as e-mail and videoconferencing and work tools such as group decision support systems [GDSS]), (b) the amount of informational value provided by such tools, and (c) the synchronicity of team member virtual interaction. (p. 702)

We also underscore that virtual teams are teams first (Gilson et al., 2021), and that long-established models of team processes and leadership in teams may in many cases still hold, while in other contexts, concepts specific to virtual work provide better insights. Drawing from Wheelan (2013), we believe that high performing virtual teams are similar to traditional teams in that they need a shared understanding of common goals, role clarity on tasks that are assigned in a purposeful way for each team member, a strong communication plan and communication flow among members, team member engagement, strong team structures appropriate for the work, and clear strategies for decision making in the team. Strong team cohesion and cooperation is key. Both formal and informal leadership play a role in determining team performance. For this chapter, we consider the two features of informal leadership identified by Guo et al. (2022, p. 900) who noted that an informal leader (a) does not have formal authority over those who are their influence targets in the group or team, and (b) demonstrates influence over those despite this lack of formal authority. The Influx of Larger Societal Forces within a Virtual Context With the dramatic increases in virtual work that followed the onset of COVID-19 worldwide, larger societal trends became magnified and contextualized. These societal trends include: (a) globalization processes that had for decades stimulated the move to virtual work pre-pandemic were compounded as non-virtual work was removed as an option for workers of global organizations (Stratone et al., 2022); (b) technological challenges of virtual work were compounded with the rapid influx of inexperienced virtual workers; (c) work–life boundary challenges (Leroy et al., 2021); (d) growing economic disparities that made the option for remote work inaccessible for many with fewer economic resources (Denkenberger et al., 2015); (e) the move towards a gig economy where freelancers sell their services to the market and contributed to virtual work without having a “home” organization (Petriglieri et al., 2019); (f) generational and cultural differences that create communication and team process challenges (Harlow, 2021; Varagur, 2021; Zhou et al., 2022), and (g) changing attitudes of employees as they face mass resignations and a return to face-to-face work that require different leadership behaviors than even seen before in virtual team scholarship (Feitosa & Salas, 2021; Reisinger & Fetterer, 2021). Globalization and the mandate for virtual work Globalization has served as one of the primary forces contributing to the increasing rise of virtual work. As businesses, non-profits, and other organizations expanded their reach to span global markets and stakeholder groups, the travel expenses, real estate costs, and other costs associated with face-to-face interactions have become prohibitive, necessitating the move toward virtual work (Kilcullen et al., 2021). The ability to effectively coordinate highly interdependent and specialized remote employees who are often geographically dispersed can lead to increased productivity and innovation, increased profits, and reduced costs to the environment. In short, newly remote employees as well as those well-versed in virtual work faced a myriad of challenges.

220  Handbook of virtual work Technology challenges Employees were on a steep learning curve to familiarize themselves immediately with ICT such as knowledge repositories (e.g., SharePoint, Dropbox), instant messaging (e.g., WhatsApp, Telegram, Microsoft Teams, Slack), project management tools (e.g., Monday, Smartsheet, Wrike), online discussion boards (e.g., Slack, Microsoft Teams, Trello), videoconferencing platforms (e.g., Zoom, Microsoft Teams) and, of course, the old standard – email. The leadership imperative involved in training a sizable portion of the workforce to utilize virtual media to communicate and coordinate with colleagues from different time zones, while learning and establishing norms and expectations for virtual work was considerable (Rudolph et al., 2021). Chong, Huang, and Chang (2020, p. 1410) referred to task-related disruptions and inhibitions resulting from the pandemic as C19 task setbacks that required employees to “appraise unforeseen problems, unlearn their existing automatic tasks scripts promptly, learn new ways of operations, and adapt to updated rules and advisories, all of which are complex cognitive activities that deplete resources rapidly” (p. 1410). The implications of the rapid shift to remote or telework and its accompanying need for competency in ICT has been shown to have a direct effect on intrinsic motivation where people strive to meet their needs for competence, relatedness, and autonomy as articulated in self-determination theory (Deci & Ryan, 2000). Trougakos, Chawla, and McCarthy (2020) found that anxiety was negatively related to psychological need fulfillment, mediated by emotion suppression. In short, the three domains of self-determination theory (competence – in this case regarding capabilities toward ICT and other task requirements in the rapid shift to remote work – along with relatedness and autonomy) had a positive relationship with work outcomes (goal progress, family engagement, and reduced somatic complaints). It is not just bringing employees up to speed to new ICT that leaders were responsible for; another requirement for leaders was to determine when and how these ICT would be deployed. As Newman and Ford (2021) offer, buying and distributing technology does not guarantee ICT effectiveness in virtual teams. Leaders need to determine how ICT can foster clear communication to compensate for the decrement in media richness (e.g., reduced nonverbal cues) and spontaneous communication that developed in face-to-face office work. But adding more technology and time with that technology is clearly not the answer. One of the new expressions now common since 2020 is the concept of “Zoom fatigue,” defined as feelings of exhaustion arising from video meetings (Nesher Shoshan & Wehrt, 2021). A number of themes emerged in qualitative studies related to Zoom fatigue: experienced loss, comparisons with the times before remote work, technical problems, and “Zoom as an island” (a positive aspect of videoconferencing). To counter the negative experiences of video conference exhaustion are two strategies that the leader can control: (1) improving the management of video conferencing (e.g., by the leader handling the calls in a moderating and coordinating fashion), and (2) ensuring the efficacy of ICT (making sure employees have the appropriate ICT at the outset of remote work) (Nesher Shoshan & Wehrt, 2021). The selection and timing of ICT use should directly be related to the specific tasks the team must perform. We draw from the four perspectives of technology outlined by Larson and DeChurch (2020) and their implications for leadership as a guide. The first of these – technology as team context – views ICT as separate from the team (some teams use more ICT while others use less). This perspective considers that leaders must compensate for the challenges virtual teams face from ICT use in terms of constraints to team processes such as establishing norms about communication and coordination (Malhotra et al., 2007), fostering trust through

Leadership and virtual work  221 communicating expectations (Marlow et al., 2017), and cultivating psychological safety and other psychological experiences at work despite the lack of face-to-face encounters (Gibson & Gibbs, 2006; Gibson et al., 2011). A second technology perspective is that of technology as sociomaterial team practices (Larson & DeChurch, 2020) that come about when teamwork and technology are mutually dependent for coordinating and communicating. The ICT in this case gives rise to work practices. In this perspective, leaders and followers work jointly to determine the ways that ICT is employed to meet team needs but it is ultimately the team leader that can “shape technology practices in order to foster the development of functional affective and cognitive states and enactment of team processes” (Larson & DeChurch, 2020, p. 7). Larson and DeChurch (2020) offer a third perspective that focuses on the way that ICT shapes who forms teams and how they do it – technology as creation medium. In project management teams, for example, leadership is often represented by a hierarchical formal leader along with peer or shared leadership that tends to be more emergent during the project’s life. In short, team leaders can use ICT, specifically team formation technologies, to foster the development of team processes. The final technology perspective outlined by Larson and DeChurch (2020) is one that we believe has less impact for virtual team leadership post-pandemic but is one that is of concern to today’s workers. This is labeled technology as teammate such that ICT itself fulfills a role as an agent in the team, and not just a tool. This concept refers to two forms of human–agent teams: human–robot teams and human–AI (artificial intelligence) teams. Despite fears that technological progress will displace workers (Davidow & Malone, 2014), humans working alongside automation can be highly productive. Today’s human–agent practices involve “agents taking on more advanced executive functions, like choosing the team, providing feedback on team processes, or intervening to stimulate controversy over team decisions” (Larson & DeChurch, 2020, p. 11). Work–life boundary challenges While many non-essential workers enjoyed the benefits of working from home such as reduced commutes and flexible working hours, they also experienced new challenges such as distractions (e.g., barking dogs, childcare, easy access to entertainment and snacks) that have faced virtual team members for decades. The effect of interruptions is not trivial, by any means, but the effects are likely to depend on the type of interruption that occurs. Leroy et al. (2021, p. 1450) identify five types of interruptions at work: (a) intrusion (“disrupt one’s workflow and demand that one switches attention even though they would rather continue what they were doing”), (b) distraction (“interruptions that do not really demand that one switches attention”), (c) breaks (“create a pause in one’s workflow, allowing for restorative activities”), (d) multitasking (“interruptions can lead to multitasking, by which one tries to work on several tasks or attend to several demands simultaneously, maybe switching back and forth between them in short iterations”), and (e) surprises (“when something directly related to the task/ activity at hand affects its rate of progress in unexpected ways”). Leroy et al. (2021) found that nonwork interruptions (compared with work interruptions) were the highest for distractions, intrusions, and multitasking. From a gender perspective, women reported more interruptions than men, and this occurred for both work and nonwork interruptions. Interestingly, this difference occurred pre-pandemic, but increased since the pandemic. Another interesting finding was that COVID-19 had increased interruptions, even

222  Handbook of virtual work for those who worked from home before the pandemic. These interruptions had consequential and negative relationships with work outcomes such as increased work to family conflict and lower task performance (Leroy et al., 2021). Economic disparities and barriers to virtual work A disturbing 50-year trend among many developed and developing nations has been an expansion in the level of income disparity across socio-economic groups (Kniffin et al., 2021). COVID-19 brought these disparities into focus in a variety of ways. For one, the most at risk members of the population were those from lower economic strata and underrepresented groups, as they were disproportionately impacted with high infection and mortality rates. Additionally, they were overrepresented among “essential” workers, and hence they were exposed to the virus more often and extensively and the health risks involved. Another more subtle implication of income inequality and remote work that quickly became salient during the pandemic is that the home environments of more versus less affluent workers were more conducive to working from home (larger or private workspaces, better technology, etc.). Indeed, the simple practice of requiring Zoom participants to turn their cameras on, made differences in economic status visible and thereby raised concerns about invasions of privacy. The “gig economy” and freelance virtual contributors Technological advances that facilitate virtual work have contributed to the rise of the “gig economy” where individual workers sell their services directly to the marketplace (Petriglieri et al., 2019). While the freedom that comes to freelance workers can be exhilarating, the precarious and ambiguous nature of such work creates emotional tension and anxiety. At the height of COVID, governments around the globe shut down for varying lengths of time to minimize community spread, resulting in loss of employment for many workers. For laid off workers in possession of in-demand skills and expertise, a move towards freelancing was the only viable avenue towards employment. However, harnessing the commitment and focus of such independent workers by those who secure their employment constitutes a different type of challenge for organizations that have heretofore relied on face-to-face full-time and permanent employees. The manifestation of generational and cultural differences in a virtual work context The pandemic-accelerated movement toward global virtual teams has contributed to heightened levels of team diversity as members from different generations and cultures come together in pursuit of shared outcomes (Harlow, 2021; Kniffin et al., 2021). Decades of research on the challenges and benefits of team diversity document that both process gains and losses are common (Horwitz & Horwitz, 2007). Cultural differences in terms of language, values, customs, and norms often serve to impede group processes, at least until the members come to know and trust one another, at which time the diversity of knowledge and perspective can actually enhance group processes and outcomes. Similarly, generational differences in work values and practices may foster misunderstanding and distrust, as stereotypical notions of generational differences – whether myth or fact-based – give rise to dysfunctional interactions and conflict (Lyons & Kuron, 2014). At the same time, virtual contexts have been shown to contribute to “swift trust” as team members, despite their differences, come to quickly trust one another because they have no choice if they wish to meet impending deadlines for team tasks (Crisp & Jarvenpaa, 2013). Thus, cultural and generational diversity amongst virtual

Leadership and virtual work  223 workers creates both leadership challenges and opportunities for harnessing employee knowledge, skills, expertise, cooperation, and commitment to achieve collective work outcomes. Attitudes of employees have changed as they face a return to any level of face-to-face work The “Great Resignation” arose from the decisions of millions of workers to quit their jobs as they began to see signs of recovery from the pandemic (Klotz, 2021). Although the reasons for these resignations varied, for many the decision arose from deep reflections on their mortality and quality of work life as they reevaluated what they want from work. Many workers who engaged in virtual work for the first time discovered hidden benefits such as recapturing time previously spent on commuting, educating their children within the safety of their home, acquiring skills through online classes, and thinking about their futures and what was most important to them moving forward. To entice such workers to return to the office, leaders must recognize that attitudes towards work have changed. Employees desire greater flexibility and autonomy when it comes to job arrangements, coupled with more personalized, engaging, rewarding, and meaningful work (Geisler, 2021). While the quest for meaning at work is a decades-old trend, it has accelerated and spread through its own contagion process to encompass an ever-growing segment of the workforce post-pandemic who simply will not return to face-to-face work without positive inducements in the form of intrinsic rewards and gratifying interactions. The question is – are today’s leaders up to the challenge of providing such flexible, engaging, and meaningful work in our post-pandemic world?

LEADERSHIP IN A VIRTUAL CONTEXT To us, the answer to the preceding rhetorical question is “maybe.” There are encouraging signs that suggest the answer may be “yes” as many organizations have altered their approach towards leadership to provide workers with the flexibility and meaning they crave. However, as our opening discussion of Elon Musk’s mandate for workers to return to the workplace or find employment elsewhere suggests, other leaders seek to return to pre-pandemic work arrangements. While there will undoubtedly be contexts where this approach is successful, there are many where such efforts to turn back the clock will be abysmal failures. In particular, as organizations confront heightened levels of environmental complexity and uncertainty, they are increasingly required to be flexible and nimble as they respond to changes in the economy, technology, societal expectations, and governmental policies. In the face of rapid change, traditional organizational hierarchies and hierarchical leadership perform poorly (Uhl-Bien & Arena, 2017). Evidence of the limitations of hierarchical leadership is provided by Hoch and Kozlowski (2014) who found that the relationship between hierarchical leadership and team performance declined as team interactions became increasingly virtual. Hence, while a variety of forces such as changes in worker expectations regarding participation and input into decision making, and a quest for heightened levels of work engagement and involvement have encouraged organizations to move away from hierarchical forms of leadership, trends toward virtual work have certainly contributed to this shift. Below we consider a variety of alternative leadership styles that appear to be more conducive to success in virtual settings.

224  Handbook of virtual work Shared Leadership Shared leadership is “a dynamic, interactive influence process among individuals where the objective is to lead one another to the achievement of collective outcomes” (Pearce & Wassenaar, 2014, p. 9). In contrast to hierarchical leadership, Hoch and Kozlowski (2014) found that shared team leadership was significantly related to team performance regardless of the degree to which the team operated virtually. However, in a subsequent theoretical article, Hoch and Dulebohn (2017) proposed that team personality composition serves as a predictor of the emergence of shared leadership, and that team virtuality moderates this relationship. Specifically, they predicted that teams that are characterized by a high proportion of members who are high in conscientiousness, agreeableness, and/or emotional stability will be more likely to embrace shared leadership, which will in turn contribute to virtual team performance. The reasoning underlying these predictions is based on expectations and prior research that suggests: (1) conscientious team members will be more likely to engage in task-directed virtual team leadership behaviors to secure task accomplishment; (2) agreeable team members will be adept at establishing relational linkages, trust, and shared knowledge with other team members; and (3) more- versus less-emotionally stable members will be inclined to demonstrate the interpersonal facilitation, problem-solving behaviors, positive attitudes, and stress-resistance that contributes to shared leadership. Additionally, they posited curvilinear relationships between team composition extraversion and openness to experience with shared leadership, such that moderate levels of team extraversion and openness are positively associated with shared leadership, whereas low and high levels are negatively related to shared leadership. Here, Hoch and Dulebohn (2017) reasoned that while team members low in extraversion would be reluctant to pursue the types of cooperative relationships required for shared leadership, those who are high in extraversion would prefer not to share the leadership spotlight. Hoch and Dulebohn (2017) also anticipated that these relationships would be moderated by the degree of team virtuality, such that the posited relationships would be stronger as the degree of virtuality increased. The core assumption behind this expectation is that compared with co-located teams where rich information cues may create strong situations where personality influences will be less impactful, virtual team settings constitute comparatively weak situations where team personality composition will be more impactful. Together, the extant conceptual and empirical literature suggests that shared leadership is likely to be favored and more effective in virtual work settings than traditional hierarchical forms of leadership. Additionally, surface (e.g., generational and cultural differences) and deep level diversity (e.g., team member personality) is likely to influence the receptiveness of virtual teams to shared leadership, as well as the effectiveness of such leadership. Functional versus Visionary Leadership A second perspective on leadership within virtual contexts is provided by Eseryel and colleagues (2020). They advance a theory of leadership in self-managing virtual teams that highlights two complementary types of leadership that are well suited to virtual work: functional and visionary leadership. Within this context, leadership is described “as a process that results in the creation, reinforcement, and evolution of shared mental models and shared norms that influence team member behavior toward the successful accomplishment of shared goals”

Leadership and virtual work  225 (Eseryel et al., 2020, p. 424). Functional leadership operates within and serves to reinforce existing mental models of team operations and relationships as well as shared norms to impact team contributions. Note that functional leadership includes the specific task-oriented (e.g., planning and scheduling work, initiating structure, coordinating member activities, problem solving, securing resources, providing feedback) and relationship-oriented (e.g., gatekeeping, conflict resolution, expressing gratitude, fostering trust and confidence, providing recognition) behaviors that separate leaders from non-leaders in teams (Morgeson et al., 2010). In contrast, visionary leadership is leadership that instigates changes in the shared mental models and norms of the team. Eseryel and associates (2020) posit that both types of leadership are required in virtual self-managing teams as they achieve a paradoxical combination of shared and distributed functional leadership that is complemented by centralized visionary leadership. Finally, they assert that functional leadership in the form of substantive team member contributions serves as a prerequisite for visionary leadership as established mental models and norms are required before changes and refinements to such models and norms can be introduced. We view Eseryel and colleagues’ (2020) model of functional and visionary leadership in self-managing virtual teams as consistent with the shared leadership perspective previously discussed. In essence, it explains how shared leadership comes to fruition by highlighting the role of how shared mental models and shared norms play within virtual teams, as well as the importance of functional and visionary leadership to their maintenance and refinement. As such, we see these two basic and complementary forms of leadership as being especially conducive to dynamic and fluid virtual work. Substitutes for Leadership In a now classic article, Kerr and Jermier (1978) introduced the concept of substitutes for leadership by positing that specific aspects of the task (e.g., routine and unambiguous, intrinsically satisfying), subordinate (e.g., ability, professional orientation), and organization (e.g., cohesive work group, self-managing work teams) can enhance, neutralize or substitute for leader behaviors. Bell and Kozlowski (2002) proposed that within virtual team contexts, structural factors may serve as substitutes for direct leadership influence. Empirical support for this assertion is provided by the previously discussed Hoch and Kozlowski (2014) study, which demonstrated that the positive relationship between structural supports (e.g., reward systems, communication, and information management) became stronger as the degree of virtuality increased. The implication of these findings is that where virtual work is increasingly pervasive, structural supports such as fair and reliable reward systems, and transparent communication and information management can serve to offset potential process losses arising from a lack of task direction in the absence of hierarchical leadership. Complexity Leadership With growing environmental complexity and uncertainty, adaptive leadership that is responsive to emergent forces such as a global pandemic becomes increasingly important for organizational survival (Uhl-Bien, 2021b). One leadership perspective that provides guidance on how such adaptability can be achieved is complexity leadership theory (Uhl-Bien, 2021a, 2021b; Uhl-Bien & Arena, 2017). This theory is grounded in complexity theory which focuses

226  Handbook of virtual work on interacting units that are dynamic and adaptive, and the complex pattern of structures and behaviors that emerge which are typically unique and difficult to predict (Marion & Uhl-Bien, 2001). Here, complexity refers to “rich interconnectivity” (Uhl-Bien & Marion, 2009). The notion of complex adaptive systems, which “are neural-like networks of interacting, independent agents who are bonded in a collective dynamic by common need” (Uhl-Bien & Marion, 2009), is used to explain how emergent processes can help organizations adapt to turbulent environments. Such emergent processes constitute an alternative explanation for how organizations learn, innovate, and adapt. Complexity leadership theory identifies three types of leadership processes that facilitate organizational adaptivity: operational leadership, entrepreneurial leadership, and enabling leadership (Uhl-Bien & Arena, 2017). When faced with complexity, leaders need to embrace the operational system of the organization to promote efficiency and produce results. However, such operational leadership recognizes that innovation and adaptability are necessary to achieve requisite work outcomes and organizational survival. Therefore, they work to protect the entrepreneurial endeavors of the organization from pressures for order that naturally arise from the ambiguity and perceived risks that accompany complexity. “A key role of operational leaders in the complexity leadership framework is converting emergent ideas into organizational systems and structures that produce innovation and ongoing results” (Uhl-Bien & Arena, 2017, p. 15). They do so by sponsoring innovative ideas, aligning resources to support them, and executing the new approach to promote organizational fitness and performance. Entrepreneurial leadership involves the creation and enactment of novel ideas, innovative solutions, and new products and services (Uhl-Bien & Arena, 2017). It occurs within local contexts, where local refers to the settings and network of relationships where work gets done. Entrepreneurial leadership is motivated by complexity forces that challenge organizations to find new work processes and products such as Zoom’s new Whiteboard tool that allows participants to use a virtual whiteboard during Zoom meetings. They do so by promoting and nurturing creative and collective processes whereby organizational members bounce ideas off one another and iteratively develop them until an innovative process or product emerges. Enabling leadership fosters the development of emergent solutions by promoting interactions among people who need to collaborate, increasing their interdependence on one another, supporting constructive conflict, and securing access to requisite information and resources with the ultimate goal of implementing innovative ideas (Uhl-Bien & Marion, 2009). It also involves a commitment to ensure that entrepreneurial and operational processes are connected and aligned. Uhl-Bien and Arena (2017, p. 16) report that a “key implication of our research is that understanding, developing and rewarding enabling leadership practice is critical for organizational success and survival in today’s complex world.” Clearly, as the pandemic dramatically illustrates, the time has come for organizations utilizing virtual work to embrace complexity leadership as an essential approach for not only surviving dynamic and emergent events, such as a pandemic, but for thriving as adaptive entities that can navigate and prosper in the age of complexity. Paradoxical Virtual Leadership Another leadership perspective that has important implications for the post-pandemic virtual workplace is Purvanova and Kenda’s (2018) theory of paradoxical virtual leadership. The purpose of this theory is to advance our understanding of virtual leadership by synthesizing

Leadership and virtual work  227 insights about leadership and virtuality. To do so, they use paradox theory to demonstrate that virtuality is an inherently paradoxical phenomenon and, as such, the primary function of virtual leadership is to deal with paradox. Purvanova and Kenda advance a model of paradoxical virtual leadership that identifies three distinctive leadership styles: synergistic, selective, and stagnant. Synergistic leaders view virtuality through a both-and cognitive lens; they achieve synergistic solutions through the integration of divergent forces, adopting multiple, and often opposing, actions to leverage the power of paradox and synergize the paradoxical tensions of virtuality. In contrast, selective leaders apply an either-or cognitive lens on virtuality, as they strive to either address the challenges of virtuality or embrace its opportunities. As a result, they are unable to balance paradoxical tensions. Finally, an avoidant cognitive lens is used by stagnant leaders, who fail to recognize the paradoxes of virtuality, and consequently are unable to effectively lead virtual teams. To advance their model, Purvanova and Kenda (2018) build upon the extant virtual teams literature to identify several paradoxes of virtual work. These paradoxes reflect tensions that create both challenges and opportunities for leaders and followers. Example paradoxes of virtuality arising from a technology dependence paradox include the: (1) “touch” tension (technology-mediated interactions are more impersonal due to a lack of nonverbal cues, but also less susceptible to bias toward underrepresented groups); (2) data tension (technology creates “big data” which can produce information overload, but enhanced processing power and tools for improved decision making); and (3) task tension (technology-enabled, round the clock work is exhausting, but also more dynamic and intrinsically motivating). Sources of tension resulting from a geographic dispersion paradox include the: (1) dispersion tension (geographic dispersion creates feelings of isolation, but also promotes increased flexibility); (2) time tension (geographic dispersion makes it challenging to coordinate work across multiple time zones, but also makes it possible to “work around the clock”); and (3) culture tension (geographic dispersion invokes language and cultural barriers, but also promotes cultural diversity). A final source of tension involves a human capital paradox whereby virtual team members tend to be fixated with task accomplishment at the expense of the development of social capital, but nonetheless infuse high levels of knowledge capital into the team. The key takeaway from Purvanova and Kenda’s (2018) theory is that they assert that only the both-and that characterized synergistic leadership provides the recognition of the paradoxical elements of virtual work required to unlock its hidden potential. Authentic Leadership Authentic leadership is not a style of leadership per se. Instead, it has been described as the “root construct” underlying positive forms of leadership. That is, because authentic leadership is grounded in a knowledge of the self and a commitment to core values, it is posited to enhance positive approaches to leadership (Avolio & Gardner, 2005). Whether such approaches involve transformational, ethical, servant, socialized charismatic, or some combination, they are posited to be more impactful when they are authentic as opposed to “pseudo” manifestations of such leadership that are not based on core values and/or fail to draw upon the leader’s strengths. The core dimensions of authentic leadership include: (1) self-awareness (an understanding of how one derives meaning from the world and how this meaning-making process influences one’s view of the self and the world one lives in); (2) balanced processing (the assessment of ego-relevant information – whether favorable or unfavorable – in a rela-

228  Handbook of virtual work tively objective fashion); (3) relational transparency (the disclosure of information to others in an open and transparent manner); and (4) an internalized moral perspective (an adherence to core ethical standards, values, and principles as an act of personal volition) (Gardner & Carlson, 2015). Consistent with the perspective of authentic leadership as a “root construct,” we propose that any of the approaches to virtual leadership described above will be more effective if they are grounded in a commitment by the leader to authenticity. Such a commitment has been shown to foster interpersonal trust – the importance of which is heightened in virtual settings as described above. While the requirements for and impediments to achieving trust in a virtual context have been central to virtual team scholarship both conceptually and empirically (Cheng et al., 2016; Jarvenpaa et al., 1998; Jarvenpaa & Leidner, 1999), trust challenges were exacerbated during the pandemic as persons who were experiencing remote work for the first time joined the cadre of virtual workers. In these new virtual and hybrid teams, employees had to learn to work with and trust their colleagues, many of whom come from different cultures and speak different languages, when interacting through ICT. Hence, we consider leader authenticity within virtual work settings to be essential to restoring the trust of workers. At the same time, we recognize that the complexity and paradoxical tensions inherent to virtual work make the attainment of authentic leadership – and followership – challenging (Gardner et al., 2021).

PRACTICAL IMPLICATIONS FOR FORMAL AND INFORMAL VIRTUAL LEADERS Establish and Monitor Trust The fundamental importance of trust to effective team processes has long been recognized within the general and the virtual teams literature (Costa et al., 2018). Hence, it is not surprising that establishing and monitoring trust emerged as the #1 recommendation for leaders and teams operating virtual settings during the pandemic, and that this focus still remains in the face of hybrid forms of organizing two and a half years later (Feitosa & Salas, 2021). While previous research on virtual teams highlighted how to establish trust, Feitosa and Salas (2021) point out that many intact work teams operating during the pandemic had already built trust. They suggest that a challenge to trust in virtual teams comes from maintaining and monitoring trust while doing this from a distance, often asynchronously, and through ICT (Feitosa & Salas, 2021). Other challenges to trust in today’s virtual context come from shifting balances in job demands from new ICT and resources such as social support (Dinh et al., 2021), which can impact how leaders go about building cognitive and affective trust (Schaubroeck et al., 2011). To build and maintain cognitive trust (trust that is based on cognitions related to performance including competence, responsibility, reliability and dependability McAllister, 1995), Dinh et al. (2021) offer four recommendations. First, they suggest that leaders outline shared goals, benefits, and risks through regular check-ins with followers, establish values for the team that move beyond conventional performance metrics, and address shared stakes and risk for team members from their personal investment. Boundaries and norms should be made explicit while also catering to follower flexibility. The onboarding process for new team members has both

Leadership and virtual work  229 formal and informal mechanisms for learning team culture with job expectations set collaboratively. Third, follower engagement is monitored through emotional intelligence (monitoring one’s own along with others’ emotions), balancing work demands (ensuring that meetings and tasks are truly necessary for task performance and an avoidance of Zoom fatigue), and assessing engagement in “adaptable, alternating, and non-intrusive ways” (p. 4). Last, they suggest that the leader’s role in establishing role clarity among the team is an important task for maintaining cognitive trust. Role assignments should be clearly defined, expectations communicated, and assigned to the individual based on their personal circumstances. Affective trust, defined by McAllister (1995) as emotional connections between individuals who exhibit genuine care and concern between both parties in the relationship. Dinh and colleagues (2021) outline four practices for maintaining affective trust in virtual teams. First, we again see psychological safety highlighted as an imperative, and in this case it is the mechanism by which leaders can develop an inclusive community. The second strategy for enhancing affective trust lies in fostering a sense of commitment to organizational relationships through an emphasis on both the organization’s and the leader’s dedication to the team members’ personal and professional endeavors while also emphasizing shared interests and values. It is the leader’s role to provide access to resources such as ICT and appropriate training along with services that lead to employee work–life balance and well-being such as affordable childcare, family leave policies, caregiver support policies, and the like. Team compassion behavior was seen as particularly critical in managing the resource demands of remote work caused by COVID-19 (Wee & Fehr, 2021). In particular, these work–life balance strategies have potential to lessen the impact of frequent interruptions and multitasking of women that was found during the pandemic (Haas, 2022; Leroy et al., 2021). Two other leadership strategies suggested by Dinh et al. (2021) for strengthening affective trust are to connect meaningfully outside of work and to encourage collaboration in and across their virtual teams when a team member needs assistance at a mission-critical point in time. First, team leadership can provide support for work–life integration, which is continually affected by environmental strains (the pandemic, inflation, etc.). This requires the leader being aware of the nonwork aspects of their members’ lives. Specific strategies can include virtual social events (happy hours, coffee breaks) as well as other opportunities at work for team members to share nonwork information. It is incumbent on the leader to recognize that painful or emotional information may be revealed and it is up to the leader to be supportive in these emotional conversations. The leader must be aware of emotionally dismissive language (“everyone feels like that” or “you shouldn’t feel bad – you have a great job”), and validate, understand, acknowledge, and offer specific support (Wilson, 2022). Finally, it is vital that team members understand when and how to help each other in terms of building affective trust (Dinh et al., 2021). Both formal and informal leaders can set the tone by communicating empathy and encouraging cross-training and backup behavior. They keep employees informed on various tasks across the team so team members know when to step in when needed. The cross-training or “system of redundancies” (p. 4) also has a “second in command” made explicit with team members, so the stepping in is not an ad hoc process. In summary, both formal and informal leaders need to customize trust-building strategies to each individual and situation, in part to avoid overwhelming followers, recognizing that the nature of trust development and maintenance changed in 2020 and continues to evolve today (Dinh et al., 2021).

230  Handbook of virtual work Embrace Alternatives to Hierarchical Leadership Our discussion of shared, functional, visionary, complexity, and authentic leadership emphasized the role that these approaches can play in promoting team member autonomy, engagement, intrinsic motivation, and trust. As our discussion of the “Great Resignation” revealed, these are not simply niceties in the minds of many post-pandemic virtual workers – they are requirements of the job (Klotz, 2021). Indeed, they may serve as the key to reconciling some of the paradoxical tensions of virtual work. For example, to address distracting interruptions and associated negative work outcomes may require an analysis of work processes to “facilitate management of nonwork responsibilities that may concurrently exist along work responsibilities” (Leroy et al., 2021, p. 1459). The key to changing these processes is the leader’s granting of autonomy to employees – both for work structuring as well as work–life boundary management. By its very nature, shared leadership empowers team members to cooperate in the pursuit of common goals, rather than relying on the direction of a hierarchical leader (Pearce & Wassenaar, 2014). Functional leadership in virtual contexts helps promote the reinforcement of shared mental models and team norms, while visionary leadership helps them update these as necessary. Paradoxical virtual leadership helps teams to manage the inherent paradoxical task, geographic dispersion and human capital tensions of virtual work to arrive at synergistic solutions to team challenges (Purvanova & Kenda, 2018). Complexity leadership promotes adaptive responses and innovation – keys to organizational survival and thriving in our dynamic world (Uhl-Bien, 2021a). Structural supports in the form of fair reward systems and transparent communications and information management can enhance team effectiveness, especially in virtual settings (Hoch & Kozlowski, 2014). Finally, we assert that when these approaches are coupled with leader authenticity, their positive effects on team trust and interpersonal processes and work outcomes can be magnified (Gardner et al., 2021). Given these potential benefits, efforts to foster these forms of formal and informal leadership through innovative leadership development processes (Day et al., 2021) should be pursued.

NOTE 1. Total separations include quits (voluntary turnover), layoffs and discharges, and other separations. In April 2022, the percentage of quits to total separations was 73.3%.

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Leadership and virtual work  231 Bell, B. S., & Kozlowski, S. J. (2002). A typology of virtual teams: Implications for effective leadership. Group & Organization Management, 27(1), 14‒49. https://​doi​.org/​10​.1177/​1059601102027001003 Bick, R., Hazan, E., Khan, H., Lacrois, S., Sarrazin, H., & Welchman. (2021). The future of work: Reskilling and remote working to recover in the “next normal”. https://​www​.mckinsey​.com/​ business​-functions/​mckinsey​-digital/​our​-insights/​the​-future​-of​-work​-reskilling​-and​-remote​-working​ -to​-recover​-in​-the​-next​-normal Cheng, X., Fu, S., & Druckenmiller, D. (2016). Trust development in globally distributed collaboration: A case of U.S. and Chinese mixed teams. Journal of Management Information Systems, 33, 978‒1007. https://​doi​.org/​org/​10​.1080/​07421222​.2016​.1267521 Chong, S., Huang, Y., & Chang, C. D. (2020). Supporting interdependent telework employees: A moderated-mediation model linking daily COVID-19 task setbacks to next-day work withdrawal. Journal of Applied Psychology, 105(12), 1408‒1422. https://​doi​.org/​10​.1037/​apl0000843 Cohen, A. (2021). How to quit your job in the great post-pandemic resignation boom. Bloomberg Businessweek. Retrieved June 10, 2022, from https://​www​.bloomberg​.com/​news/​articles/​2021​-05​-10/​ quit​-your​-job​-how​-to​-resign​-after​-covid​-pandemic​?sref​=​85rT08Vo Costa, A. C., Fulmer, C. A., & Anderson, N. R. (2018). Trust in work teams: An integrative review, multilevel model, and future directions. Journal of Organizational Behavior, 39(2), 169‒184. https://​ doi​.org/​10​.1002/​job​.2213 Crisp, C. B., & Jarvenpaa, S. L. (2013). Swift trust in global virtual teams: Trusting beliefs and normative actions. Journal of Personnel Psychology, 12(1), 45‒56. https://​doi​.org/​10​.1027/​1866​-5888/​a000075 Davidow, W. H., & Malone, M. S. (2014). What happens to society when robots replace workers? Harvard Business Review Digital Articles, 2‒5. https://​search​.ebscohost​.com/​login​.aspx​?direct​=​true​ &​db​=​bth​&​AN​=​118648056​&​site​=​ehost​-live Day, D. V., Riggio, R. E., Tan, S. J., & Conger, J. A. (2021). Advancing the science of 21st-century leadership development: Theory, research, and practice. The Leadership Quarterly, 32(5), 101557. https://​doi​.org/​https://​doi​.org/​10​.1016/​j​.leaqua​.2021​.101557 Deci, E. L., & Ryan, R. M. (2000). “What” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227‒268. Denkenberger, D., Way, J., & Pearce, J. M. (2015). Educational pathways to remote employment in isolated communities. Journal of Human Security (Librello Publishing House), 11(1), 34‒44. https://​ doi​.org/​10​.12924/​johs2015​.11010034 Dinh, J. V., Reyes, D. L., Kayga, L., Lindgren, C., Feitosa, J., & Salas, E. (2021). Developing team trust. Organizational Dynamics, 50(1). https://​doi​.org/​10​.1016/​j​.orgdyn​.2021​.100846 Eseryel, U. Y., Crowston, K., & Heckman, R. (2020). Functional and visionary leadership in self-managing virtual teams. Group & Organization Management, 46(2), 424‒460. https://​doi​.org/​10​ .1177/​1059601120955034 Feitosa, J., & Salas, E. (2021). Today’s virtual teams: Adapting lessons learned to the pandemic context. Organiozational Dynamics, 50, 1‒4. https://​doi​.org/​10​.1016/​j​.orgdyn​.2020​.100777 Gardner, W. L., & Carlson, J. D. (2015). Authentic leadership. In J. D. Wright (Ed.), International Encyclopedia of Social & Behavioral Sciences (2nd ed., Vol. 2, pp. 245‒250). Elsevier. Gardner, W. L., Gullifor, D. P., Carlson, J. D., Cogliser, C. C., & Williams, M. L. (2021). Barriers to authentic leader–follower relationships: Implications for human resource management. In J. H. Dulebohn, B. Murray, & D. L. Stone (Eds.), Leadership: Leaders, Followers, and Context (pp. 181‒218). Information Age Publishing. http://​www​.infoagepub​.com/​series/​Research​-in​-Human​ -Resource​-Management Gartner, I. (2020, July 14, 2020). Gartner survey reveals 82% of company leaders plan to allow employees to work remotely some of the time https://​www​.gartner​.com/​en/​newsroom/​press​-releases/​2020​ -07​-14​-gartner​-survey​-reveals​-82​-percent​-of​-company​-leaders​-plan​-to​-allow​-employees​-to​-work​ -remotely​-some​-of​-the​-time Geisler, J. (2021). The great resignation: Realty or myth? HFM Magazine (October). https://​www​.hfma​ .org/​topics/​hfm/​2021/​october/​the​-great​-resignation​-​-reality​-or​-myth​-​.html Gibson, C. B., & Gibbs, J. L. (2006). Unpacking the concept of virtuality: The effects of geographic dispersion, electronic dependence, dynamic structure, and national diversity on team innovation. Administrative Science Quarterly, 51(3), 451‒495.

232  Handbook of virtual work Gibson, C. B., Gibbs, J. L., Stanko, T. L., Tesluk, P., & Cohen, S. G. (2011). Including the “I” in virtuality and modern job design: Extending the job characteristics model to include the moderating effect of individual experiences of electronic dependence and copresence. Organization Science, 22(6), 1481‒1499. https://​doi​.org/​10​.1287/​orsc​.1100​.0586 Gilson, L. L., Costa, P., O’Neill, T. A., & Maynard, M. T. (2021). Putting the “TEAM” back into virtual teams. Organizational Dynamics, 50(1). https://​doi​.org/​10​.1016/​j​.orgdyn​.2021​.100847 Guo, Z., Heidl, R. A., Hollenbeck, J. R., Yu, A., & Howe, M. (2022). When discretionary boundary spanning relationships cease becoming discretionary: The impact of closed ties on informal leadership perceptions. Journal of Applied Psychology, 107(6), 898‒916. https://​doi​.org/​10​.1037/​apl0000911 Haas, M. (2022). Women face a double disadvantage in the hybrid workplace. Harvard Business Review Digital Articles, 1‒6. https://​search​.ebscohost​.com/​login​.aspx​?direct​=t​rue​&​db​=b​ th​&A ​ N​=​156046242​ &​site​=​ehost​-live Harlow, J. (2021). Gen Z is facing work–life struggles. TD: Talent Development, 75(6), 14‒15. https://​ search​.ebscohost​.com/​login​.aspx​?direct​=​true​&​db​=​bft​&​AN​=​150603967​&​site​=​ehost​-live Hoch, J. E., & Dulebohn, J. H. (2017). Team personality composition, emergent leadership and shared leadership in virtual teams: A theoretical framework. Human Resource Management Review, 27(4), 678‒693. https://​doi​.org/​10​.1016/​j​.hrmr​.2016​.12​.012 Hoch, J. E., & Kozlowski, S. W. J. (2014). Leading virtual teams: Hierarchical leadership, structural supports, and shared team leadership. Journal of Applied Psychology, 99(3), 390‒403. https://​doi​.org/​ 10​.1037/​a0030264 Horwitz, S. K., & Horwitz, I. B. (2007). The effects of team diversity on team outcomes: A meta-analytic review of team demography. Journal of Management, 33(6), 987‒1015. https://​doi​.org/​10​.1177/​ 0149206307308587 Jarvenpaa, S. L., Knoll, K., & Leidner, D. E. (1998). Is anybody out there? Trust in global virtual teams. Journal of Management Information Systems, 14(4), 29‒64. Jarvenpaa, S. L., & Leidner, D. E. (1999). Communication and trust in global virtual teams. Organizational Science, 10(6), 791‒815. Kerr, S., & Jermier, J. M. (1978). Substitutes for leadership: Their meaning and measurement. Organizational Behavior & Human Performance, 22, 375‒403. Kilcullen, M., Feitosa, J., & Salas, E. (2021). Insights from the virtual team science: Rapid deployment during COVID-19. Human Factors, 18720821991678. https://​doi​.org/​10​.1177/​0018720821991678 Kirkman, B. L., & Mathieu, J. E. (2005). The dimensions and antecedents of team virtuality. Journal of Management, 31(5), 700‒718. Klotz, A. (2021). The COVID vaccine means a return to work. And a wave of resignations. NBC News, May 30. Kniffin, K. M., Narayanan, J., Anseel, F., Antonakis, J., Ashford, S. P., Bakker, A. B., Bamberger, P., Bapuji, H., Bhave, D. P., Choi, V. K., Creary, S. J., Demerouti, E., Flynn, F. J., Gelfand, M. J., Greer, L. L., Johns, G., Kesebir, S., Klein, P. G., Lee, S. Y., … Vugt, M. v. (2021). COVID-19 and the workplace: Implications, issues, and insights for future research and action. American Psychologist, 76(1), 63‒77. https://​doi​.org/​10​.1037/​amp0000716 Kozlowski, S. W. J., Chao, G. T., & Van Fossen, J. (2021). Leading virtual teams. Organizational Dynamics, 50(1). https://​doi​.org/​10​.1016/​j​.orgdyn​.2021​.100842 Lambert, F. (2022). Elon Musk asks all Tesla employees to come back to the office or quit. Electrek. https://​electrek​.co/​2022/​06/​01/​elon​-musk​-tesla​-employees​-come​-back​-office​-or​-quit/​ Larson, L., & DeChurch, L. A. (2020). Leading teams in the digital age: Four perspectives on technology and what they mean for leading teams. The Leadership Quarterly, 31(1), 101377. https://​doi​.org/​ https://​doi​.org/​10​.1016/​j​.leaqua​.2019​.101377 Leroy, S., Schmidt, A. M., & Madjar, N. (2021). Working from home during COVID-19: A study of the interruption landscape. Journal of Applied Psychology, 106(10), 1448‒1465. https://​doi​.org/​10​.1037/​ apl0000972 Lyons, S., & Kuron, L. (2014). Generational differences in the workplace: A review of the evidence and directions for future research. Journal of Organizational Behavior, 35(S1), S139‒S157. https://​doi​ .org/​10​.1002/​job​.1913 Malhotra, A., Majchrzak, A., & Rosen, B. (2007). Leading virtual teams. Academy of Management Perspectives, 21(1), 60‒70.

Leadership and virtual work  233 Marion, R., & Uhl-Bien, M. (2001). Leadership in complex organizations. The Leadership Quarterly, 12(4), 389‒418. https://​doi​.org/​10​.1016/​S1048​-9843(01)00092​-3 Marlow, S. L., Lacerenza, C. N., & Salas, E. (2017). Communication in virtual teams: A conceptual framework and research agenda. Human Resource Management Review, 27(4), 575‒589. https://​doi​ .org/​10​.1016/​j​.hrmr​.2016​.12​.005 McAllister, D. J. (1995). Affect- and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of Management Journal, 38(1), 24‒59. Microsoft. (2022). 2022 Work Trend Index: Annual Report. https://​ms​-worklab​.azureedge​.net/​files/​ reports/​2022/​pdf/​2022​_Work​_Trend​_Index​_Annual​_Report​.pdf Morgeson, F. P., DeRue, D. S., & Karam, E. P. (2010). Leadership in teams: A functional approach to understanding leadership structures and processes. Journal of Management, 36(1), 5‒39. https://​doi​ .org/​10​.1177/​0149206309347376 Nesher Shoshan, H., & Wehrt, W. (2021). Understanding “Zoom fatigue”: A mixed-method approach. Applied Psychology: An International Review, 1. https://​doi​.org/​10​.1111/​apps​.12360 Newman, S. A., & Ford, R. C. (2021). Five steps to leading your team in the virtual COVID-19 workplace. Organizational Dynamics, 50(1). https://​doi​.org/​10​.1016/​j​.orgdyn​.2020​.100802 Pearce, C. L., & Wassenaar, C. L. (2014). Leadership is like fine wine: It is meant to be shared, globally. Organizational Dynamics, 43, 9‒16. Petriglieri, G., Ashford, S. J., & Wrzesniewski, A. (2019). Agony and ecstasy in the gig economy: Cultivating holding environments for precarious and personalized work identities. Administrative Science Quarterly, 64(1), 124‒170. https://​doi​.org/​10​.1177/​0001839218759646 Purvanova, R. K., & Kenda, R. (2018). Paradoxical virtual leadership: Reconsidering virtuality through a paradox lens. Group & Organization Management, 43(5), 752‒786. https://​doi​.org/​10​.1177/​ 1059601118794102 PwC. (2021). It’s time to reimagine where and how work will get done: PwC’s US Remote Work Survey. https://​www​.pwc​.com/​us/​en/​library/​covid​-19/​us​-remote​-work​-survey​.html Reisinger, H., & Fetterer, D. (2021). Forget flexibility. Your employees want autonomy. Harvard Business Review Digital Articles, 1‒9. https://​hbr​.org/​2021/​10/​forget​-flexibility​-your​-employees​ -want​-autonomy Rudolph, C. W., Allan, B., Clark, M., Hertel, G., Hirschi, A., Kunze, F., Shockley, K., Shoss, M., Sonnentag, S., & Zacher, H. (2021). Pandemics: Implications for research and practice in industrial and organizational psychology. Industrial and Organizational Psychology: Perspectives on Science and Practice, 14(1‒2), 1‒35. https://​doi​.org/​10​.1017/​iop​.2020​.48 Schaubroeck, J., Lam, S. S. K., & Peng, A. C. (2011). Cognition-based and affect-based trust as mediators of leader behavior influences on team performance. Journal of Applied Psychology, 96(4), 863‒871. https://​doi​.org/​10​.1037/​a0022625 State of Remote Work 2021. (2021). https://​glob​alworkplac​eanalytics​.com/​whitepapers Stratone, M.-E., Vătămănescu, E.-M., Treapăt, L.-M., Rusu, M., & Vidu, C.-M. (2022). Contrasting traditional and virtual teams within the context of COVID-19 pandemic: From team culture towards objectives achievement. Sustainability (2071‒1050), 14(8), N.PAG-N.PAG. https://​doi​.org/​10​.3390/​ su14084558 Sumner, A., Hoy, C., & Ortiz-Juarez, E. (2020). Estimates of the impact of COVID-19 on global poverty. WIDER Working Paper Series wp-2020-43, Helsinki, Finland: World Institute for Development Economic Research (UNU-WIDER). Trougakos, J. P., Chawla, N., & McCarthy, J. M. (2020). Working in a pandemic: Exploring the impact of COVID-19 health anxiety on work, family, and health outcomes. Journal of Applied Psychology, 105(11), 1234‒1245. https://​doi​.org/​10​.1037/​apl0000739 Uhl-Bien, M. (2021a). Complexity leadership and followership: Changed leadership in a changed world. Journal of Change Management, 21(2), 144‒162. https://​doi​.org/​10​.1080/​14697017​.2021​.1917490 Uhl-Bien, M. (2021b). Complexity and COVID-19: Leadership and followership in a complex world. Journal of Management Studies, 58(5), 1400‒1404. https://​doi​.org/​10​.1111/​joms​.12696 Uhl-Bien, M., & Arena, M. (2017). Complexity leadership: Enabling people and organizations for adaptability. Organizational Dynamics, 46, 9‒20.

234  Handbook of virtual work Uhl-Bien, M., & Marion, R. (2009). Complexity leadership in bureaucratic forms of organizing: A meso model. The Leadership Quarterly, 20(4), 631‒650. https://​doi​.org/​https://​doi​.org/​10​.1016/​j​.leaqua​ .2009​.04​.007 U.S. Bureau of Labor Statistics. (2022, June 1). Job openings and labor turnover summary. https://​www​ .bls​.gov/​news​.release/​jolts​.nr0​.htm Varagur, K. (2021, 08/23/). To Gen Zers working from home, the office is a remote concept. Wall Street Journal – Online Edition. https://​search​.ebscohost​.com/​login​.aspx​?direct​=​true​&​db​=​bft​&​AN​=​ 152561357​&​site​=​ehost​-live Wee, E. X. M., & Fehr, R. (2021). Compassion during difficult times: Team compassion behavior, suffering, supervisory dependence, and employee voice during COVID-19. Journal of Applied Psychology, 106(12), 1805‒1820. https://​doi​.org/​10​.1037/​apl0001001 Wheelan, S. A. (2013). Creating effective teams: A guide for members and leaders (4th ed.). Sage Publications. Wilson, S. N. (2022). How supportive leaders approach emotional conversations. Harvard Business Review Digital Articles, 1‒7. https://​search​.ebscohost​.com/​login​.aspx​?direct​=t​rue​&​db​=​bth​&​AN​=​ 155748470​&​site​=​ehost​-live Zhou, Z. E., Pindek, S., & Ray, E. J. (2022). Browsing away from rude emails: Effects of daily active and passive email incivility on employee cyberloafing. Journal of Occupational Health Psychology. https://​doi​.org/​10​.1037/​ocp0000325

13. Faultlines in virtual teams Sherry M.B. Thatcher and Ramón Rico

FAULTLINES IN VIRTUAL TEAMS The rise of the internet and the development of several communication technologies at the end of the twentieth century ushered in a flurry of activity by businesses eager to capitalize on the potential of these technologies (Cascio, 2000; Gibson & Cohen, 2003). One use of these technologies was the rise of the virtual team (VT), whereby a collection of team members operated across different physical locations and time zones using communication technologies (Rico & Cohen, 2005). Consequently, numerous team researchers became interested in how the experience and effectiveness of VTs was likely to differ from the experience and effectiveness of traditional, collocated teams resulting in a plethora of studies comparing these two types of teams (Gilson et al., 2015). During this same period, there was an increasing interest in the role of diversity on organizational teams (Liu et al., 2019). Although the results of this research overall reflected that diversity matters, theoretical and methodological challenges existed as researchers tended to focus on the diversity of a singular attribute. Lau and Murnighan (1998) in their seminal piece, introduced the concept of faultlines, defined as “hypothetical dividing lines that may split a group into subgroups based on one or more attributes” (p. 328). With the introduction of a faultline measurement tool (Thatcher et al., 2003), research on faultlines burgeoned. Despite the overlap in the rise of these two emerging research streams that affect teams, there have only been a handful of studies investigating the impact or role of faultlines on VT interactions and effectiveness. The dearth of studies is surprising because VTs are often composed in ways that simultaneously highlight different team members’ attributes (e.g., expertise, ethnicity, and physical location – Gilson et al., 2015). In other words, faultlines are often inherent in the composition of VTs. Considering the impact that team faultlines exert on teams (Antino et al., 2019), and that VTs are a particularly relevant context for faultlines, it is important that we understand the role of faultlines in the functioning and effectiveness of VTs. After a brief introduction of the VTs and faultlines research, we describe the current state of the research that exists at the intersection of faultlines and VTs. We then extrapolate knowledge from the team faultlines literature to suggest a myriad of topics for researchers interested in both faultlines and VTs. Our final section provides guidance to leaders on how best to manage faultlines in VTs. Virtual Teams Early research on VTs focused on teams where members in different physical locations interacted via electronic communication systems (Rico & Cohen, 2005). In other words, the distinctions between traditional, collocated teams and VTs were around physical proximity and the use of electronic systems to communicate (see also influential reviews by Martins et al., 2004; Hertel et al., 2005). However, with the exponential rise and use of communication 235

236  Handbook of virtual work technology tools, many teams began to interact using technology even when they were not physically distant. Thus, over time researchers have broadened their conceptualization of VTs around the virtuality dimension (i.e., “a) the extent to which teams use virtual tools to coordinate and execute team processes…; b) the amount of informational value provided by such tools, and c) the synchronicity of team member virtual interaction” – Kirkman & Mathieu, 2005, p. 702). As a team task dimension, team virtuality offers a more parsimonious way to understand how every team uses technology in pursuing their team goals. This conceptualization allows scholars and practitioners to consider team virtuality as a continuum and a common defining characteristic of all teams. Research on VTs has been steadily increasing over the last two and a half decades as changing work environments have made team virtuality and its impact on team processes and outcomes crucial to understand. In fact, the COVID-19 pandemic has put virtual working and VTs front and center in the minds of business leaders and their employees. Although many existing teams have switched to virtual working, we focus our attention on VTs that are created to take advantage of employee skills through communication technology, regardless of where the employee is physically located. Although other chapters in this book provide a more thorough review of the existing literature on VTs, it is useful to provide a brief review of the state of the literature that provides context to our discussion around faultlines in VTs. Numerous early studies investigated the effects of demographic characteristics and KSAOs as key inputs of VT effectiveness showing that differences in technical experience is a good predictor of VT performance (e.g., Kayworth & Leidner, 2000), and that nationality differences negatively influence VT creativity directly as well as when interacting with differences in technical experience and age (Martins & Shalley, 2011). Later studies focused on the effects of gender and personality composition, multiple team membership, congruent combinations of task and technology, the extent of virtuality, and leadership styles on VT effectiveness. As a whole, the study of such topics reveals that individual openness to experience and extraversion facilitates working in VTs, technology that supports task demands ensures VT performance, and high virtuality levels impair VT functioning (Gilson et al., 2015; Rico & Cohen, 2005). Further, virtuality has different interactive effects on the relationship between leadership styles and VT outcomes, such that it strengthens the positive relationship between inspirational leadership and commitment, and it weakens the negative relationship between hierarchical leadership and team task outcomes (Hoch & Kozlowski, 2014). A number of group processes and emergent states have been studied as mediators of the antecedents–VT effectiveness relationship, including communication, coordination, conflict, team identity, potency, efficacy, commitment, cohesion, psychological safety, and empowerment (e.g., Au & Marks, 2012, Cummings et al., 2009; Kanawattanachai & Yoo, 2007; Kirkman et al., 2004; Lin et al., 2010; Maynard et al., 2012; Ortega et al., 2010). However, the bulk of this research has focused on trust creation and development, due to the critical nature of trust for knowledge sharing among VT members (e.g., Henttonen & Blomqvist, 2005; Coppola et al., 2004; Rico et al., 2009). Finally, evidence suggests that there are many challenges to working in VTs, but when team processes are positive, VTs are more satisfied, viable (members want to continue working together in the future) and committed to their host organization (e.g., Horwitz et al., 2006; Ortega et al., 2010). As described in the recent review by Gilson and her colleagues (2015), the way in which VTs are staffed tends to result in substantial team member diversity, especially with respect

Faultlines in virtual teams  237 to location, expertise, nationality, ethnicity, and cultural background. Given the substantial role that diversity plays in VTs, it is important that we not only understand how each type of diversity impacts VTs processes and outcomes but also how multi-attribute diversity (i.e., faultlines) influences the functioning and effectiveness of VTs. For example, initial VTs in the information technology industry were made up of Indian software coders and American software development specialists (e.g., Metiu, 2006). These teams often found themselves with two subgroups that differed with respect to physical location, expertise, nationality, ethnicity, cultural background, and lingua franca fluency. Thus, we must consider the diversity configuration of a VT and, to that end, we examine the role that faultlines play in VTs. Team Faultlines Initial faultlines research focused on the extent to which the alignment of several demographic attributes formed potential subgroups (dormant faultline strength – Lau & Murnighan, 1998), impairing team performance by altering key team processes, such as team conflict and communication (Lau & Murnighan, 2005; Thatcher et al., 2003). A prototypical example of a VT with a faultline is a programming team composed of four people, where two English males are senior-level programmers based in Perth, Australia, and where two Indian females are junior-level programmers based in Mumbai, India. This VT example reflects the alignment of several attributes (e.g., gender, nationality, ethnicity, team role, physical proximity, and temporal proximity) and would be construed as a strong faultline differentiating two homogenous subgroups. Following evidence that faultlines tend to negatively impact group processes and outcomes, researchers then focused on the situated nature of faultlines by uncovering moderators on the faultlines-team process–outcome relationship, such as diversity beliefs, team autonomy, and team identification (Bezrukova et al., 2009; Homan et al., 2007; Rico et al., 2007). Reviews of research on the consequences of faultlines (Liu et al., 2019; Meyer et al, 2014; Thatcher & Patel, 2012, 2014) confirm that demographic faultlines have a fairly consistent detrimental main effect on team processes and outcomes. The bulk of team faultlines research has focused on dormant (i.e., hypothetical) faultlines, where subgrouping is assumed to exist based on a calculation of demographic attribute alignment (Jehn & Bezrukova, 2010). However, the effects of faultlines on team processes and outcomes are more intense when team members perceive subgroups based on one or more attributes; that is, when faultlines are activated (Jehn & Bezrukova, 2010). For that reason, both original theorizing (Lau & Murnighan, 1998) and faultlines research reviews (e.g., Meyer et al., 2014; Liu et al., 2019) encourage more research on activated faultlines. Activated faultlines tend to intensify the effects of dormant faultlines (e.g., Jehn & Bezrukova, 2010; Pearsall et al., 2008) and represent an informal sensemaking structure that guides the social reality of team members (Antino et al., 2019). As reflected above, both dormant and activated faultlines can impact diverse teams (Thatcher, 2013). Given that virtual teams likely have members that differ on a number of attributes, it is important to understand how faultlines influence VT functioning and effectiveness. Thus, we now discuss what we know about faultlines in VTs.

238  Handbook of virtual work

WHAT WE KNOW FROM EXTANT RESEARCH ON FAULTLINES IN VIRTUAL TEAMS Surprisingly, as alluded to in the introduction of this chapter, we found only eight studies that explicitly focus on the effects of dormant or activated faultlines in VTs (Chiu & Staples, 2013; Cramton & Hinds, 2005; Graham & Daniel, 2021; Hinds et al., 2014; Peñarroja et al., 2020; Polzer et al., 2006; Straube et al., 2018; Gibbs et al., 2017); the main features and findings of these studies are summarized in Table 13.1. Additional research on global teams that contains evidence directly relevant to understanding faultlines in VTs is integrated in this discussion. As described earlier, a faultline in a VT may result from the alignment of several attributes (e.g., gender, nationality, ethnicity, team role, physical proximity, and temporal proximity) resulting in homogeneous subgroups. Because of the high attribute similarity within the subgroups and the high attribute difference across the subgroups, we would predict that this type of VT would experience a multitude of negative effects found in prior research on faultline-based teams. In fact, research findings on faultlines in VTs parallel the general pattern of results regarding the study of faultlines in collocated teams; faultlines harm VT outcomes (e.g., decision quality and creativity) through the alteration of key team processes (increased intrateam conflict, decreased trust, and impaired elaboration of task-relevant information) because of the social categorization processes and intergroup biases that faultlines promote (Gibbs et al., 2017; Graham & Daniel, 2021; Polzer et al., 2006; Straube et al., 2018). Despite the consistency in the pattern of results, there is much to learn from an in-depth review of these studies. There are three important takeaways around the attributes that make up a faultline in VTs. In particular, early studies examining faultlines (or subgroups) in VTs find that, in addition to demographic differences, team members use geographic location as part of the categorization process (Cramton & Hinds, 2005; Polzer et al., 2006) leading to activated faultlines in VTs. In fact, the distribution of team members over multiple locations (i.e., team configurational dispersion – O’Leary & Cummings, 2007) can function as an activated faultline trigger. Thus, an important first finding is that geographic dispersion, despite not being a demographic attribute, is a salient categorization triggering activated faultlines in VTs. A second, and related finding, is that the negative effects of geographic location faultlines are heightened when the geographic subgroups are aligned with nationality (Polzer et al., 2006) and gender, age, tenure, and ethnicity (Li & Hambrick, 2005). Further, one study found that the effects of geographic location faultlines were strengthened when there were two subgroups of equal size (Polzer et al., 2006). Given that VTs often cross national and professional boundaries, the confluence of differing values, social systems (i.e., labor and educational systems), and functional boundaries, it is not surprising that additional differences that align with geographic location faultlines result in stronger negative effects on VT functioning and effectiveness (van der Kamp et al., 2015). A third finding in studies investigating faultlines in VTs is the importance of status and power differences as a relevant attribute that aligns with geographic location. Although Metiu (2006) did not explicitly investigate faultlines, her field study of a virtual software development team with members in India and on the west coast of the United States found that the alignment of geographic location and status perceptions negatively influenced collaboration and members’ interpretations of the relationships across the two subgroups. Hinds and colleagues (2014) conducted an ethnographic study of 96 globally distributed members of software development teams. Although they found evidence of faultlines based on lingua

Faultlines in virtual teams  239 Table 13.1

Summary of studies focusing on the effects of faultlines in VTs

Citation

Type of Study

VTs Studied

Cramton &

Theoretical

Lab/Field VTs

Type of Faultlines Main Findings Reported Considered Dormant/Activated Authors present a set of ten propositions that

Hinds, 2005

analyze the conditions under which international distributed teams encounter subgroup differences and can counteract the tendency toward ethnocentrism. Teams with an attitude of mutual positive distinctiveness, will be able to learn from subgroup differences, making the most of cross-national relationships and their management.

Polzer et al.,

Correlational Study Simulated VTs

2006

Activated

(Students)

Geographical dispersion of team members is a variable around which faultlines form and get activated in VTs. The effects of faultlines were stronger when they emerged from two subgroups that were of equal size and where each subgroup was homogeneous around collocation and nationality. Such faultlines raised conflict and diminished trust.

Chiu &

Experimental Study Lab VTs (Students) Activated

Staples, 2013

Teams were composed of members creating a faultline based on gender and collocation. Faultlines induced conflict and impaired decision process quality; these effects were reduced through task elaboration. Further, faultlines were reduced when team members disclosed personal information to out-group members who were attracted to that disclosure.

Hinds et al,

Qualitative Study

Field VTs

Activated

2014

Faultlines in software development VTs were activated by power struggles. Language was revealed as a potential variable around which faultlines are created, which also reinforced the emotional processes triggered by faultlines.

Gibbs et al.,

Literature Review

Lab/Field VTs

Dormant/Activated Proposition that faultlines in student VTs are likely

2017

to consistently produce negative consequences, whereas subgroups caused by faultlines in organizational VTs are likely to produce both negative and positive consequences.

Straube et al., 2018

Correlational

Simulated VTs (Students)

Dormant

Gender- and age-based faultlines were found to inhibit the capacity of teams varying in their virtuality to compensate for low communication intensity by using richer communication channels, and vice versa, impairing self-reported VT performance.

240  Handbook of virtual work Citation

Type of Study

VTs Studied

Type of Faultlines Main Findings Reported Considered

Peñarroja et al., Experimental Study Lab VTs (Students) Activated

An online affect management intervention

2020

decreased the effect of faultlines, based on home university and an adventure profile, on relationship conflict.

Graham & Daniel, 2021

Literature Review

Lab/Field VTs

Dormant/Activated Proposed that transformational leadership would reduce the negative relationship between faultlines in VTs and quality of completed VT project, VT effectiveness, and commitment to VT short-term projects.

Note: Some of these papers do not explicitly use the term faultlines but their descriptions of subgroups and subgroup interactions are consistent with those caused by a faultline.

franca fluency, location, and nationality in all teams, only teams that contained power contests experienced the negative effects of faultlines. As reflected in the review of VTs conducted by Gibbs and colleagues (2017), researchers using students as samples for studies on VTs may not be capturing the status and power dynamics that infuse VTs in real-world organizational teams. Researchers studying faultlines in VTs have used the VT context to explore different processes and outcomes. Lab research investigating faultlines in VTs found the well-known negative relationships between conflict and performance (Gibbs et al., 2017). Further, VTs with faultlines experienced a reduction of trust and reduced decision quality (Chiu & Staples, 2013; Polzer et al., 2006). Research conducted on real VTs broadened the scope of these findings by showing that faultlines alter power dynamics, impair team coordination and harm shared identity building (Chrobot-Mason et al., 2009; Hinds et al., 2014; Metiu, 2006; Sidhu & Volberda, 2011). Other studies have focused on strategies to manage the faultline effects on team processes and outcomes in VTs. A study by Chiu and Staples (2013) found that when VT members favorably perceive other team members’ self-disclosure behaviors (i.e., sharing personal information to others), there is a reduction in activated faultlines. Peñarroja and his colleagues (2020) found that affect management reduces the level of relationship conflict when there are diversity faultlines in VTs due to an increase in team resilience. Extant studies have revealed that reducing media richness can weaken the salience of demographic characteristics and the associated identity threats jeopardizing team communication and engagement processes (Driskell et al., 2003). To that end, Straube and colleagues (2018) found that VTs with weak activated faultlines were able to strategically use media richness to improve their performance perceptions; however, strong activated faultlines impaired VTs’ capacity to modify their communication behaviors which reduced their perceived performance (Straube et al., 2018). Despite the dearth of papers investigating VTs with faultlines, current research on faultlines and our knowledge of the challenges associated with VTs provides a number of opportunities for integrating these two literatures. Given the renewed interest in VTs because of the COVID-19 pandemic and because VTs are often susceptible to faultlines due to their inception process, this is a timely endeavor. We now provide a series of ideas to advance research on faultlines in VTs.

Faultlines in virtual teams  241

WHAT CURRENT RESEARCH ON TEAM FAULTLINES SUGGESTS FOR VIRTUAL TEAMS Below we discuss research on three general topics in the faultlines literature that have implications for VTs with faultlines: Explanatory mechanisms through which faultlines impact VTs effectiveness; team structural variables which likely moderate the impact of faultlines on VT processes and outcomes; and a longitudinal view of faultlines in VTs. Although there are many other areas of research interest that integrate the faultlines and VTs literatures, we focus on those we believe will have the strongest impact on these literatures, as depicted in Figure 13.1.

Figure 13.1

A longitudinal framework summarizing the mediators and moderators of the relationship between faultlines and effectiveness in VTs

Explanatory Mechanisms by which Faultlines Impact VTs Effectiveness Researchers investigating faultlines, including those studying VTs, have mainly focused on the group processes of intrateam conflict (mainly relationship conflict), trust, and the elaboration of task-relevant information as being the mediators of the faultlines–outcomes relationships (Liu et al., 2019; Thatcher & Patel, 2012, 2014). However, recent research on team faultlines has considered other mechanisms that are likely to be especially relevant in VTs, including activation, polarization, status conflict, and incivility. Activation As described earlier, activated faultlines have stronger effects on team outcomes than dormant faultlines (Thatcher & Patel, 2012). Most research on activated faultlines has evidenced the situated and context-dependence nature of the activation process (Meister et al., 2020). The VT environment not only represents a context where there is likely to be several demographic

242  Handbook of virtual work differences but where geographic dispersion also represents a dormant faultline. Further, the nature of the team interaction, through communication technologies, provides many opportunities for faultlines to become activated. In fact, we posit that faultline activation is more likely in VTs than in collocated teams, even with the same demographic profile. Some evidence points to this; although Polzer and his colleagues (2006) did not explicitly study activation, they did find that location differences became salient as teams engaged in their task. They also found that VTs configured into fewer equally sized subgroups experienced the most negative effects from faultlines, particularly when such subgroups also included homogeneous members and tasks were distributed according to geographical location (Polzer et al., 2006). O’Leary and Mortensen’s (2010) study on the effects of subgroup size and configural dispersion in geographically dispersed teams also provides insight into this topic. They find that teams that have geographically isolated dispersion (every team member is alone at their site) have more team identification, less conflict, and fewer coordination problems than those where a subgroup was present. However, when teams have subgroups containing two or more individuals at a particular geographic location and imbalance in the size of subgroups across geographic locations, they have less team identification, more conflict, and more coordination problems. Together, these results suggest that geographic collocation represents a salient feature of the team that likely promotes faultline activation. However, there is still much we do not know about how specific spatial combinations and differing subgroup sizes, combined with other team member attributes, influence faultline activation and outcomes in VTs. Another area that is ripe for research is the extent to which specific attributes become more or less salient when teams are virtual. We know that task-related individual attributes (e.g., functional background and professional KSAs) are relevant for VT performance (Maynard et al., 2018) and are relevant social categories leading to faultline formation (Antino et al., 2019; Meyer et al., 2011); thus, it is likely that these attributes will be more salient when they exist in VTs. Research has found that the lack of informational cues makes any information particularly relevant for categorization (Todorov & Uleman, 2004). Hence, locational differences that are the hallmarks of VTs may magnify the salience of existing demographic faultlines, in addition to being one of the faultline attributes. Thus, a clear area of inquiry is around which attributes become more or less salient, and hence activate a faultline, when working in a VT. Also, Meister and her colleagues recently argued that faultline activation should be considered as an ongoing process and not as a state (Meister et al., 2020). Given that VTs have many available communication tools, the speed at which faultline activation takes place may be influenced by the communication tool used by the VT. A VT’s level of virtuality may also influence the speed of faultline activation; VTs operating with high virtuality may not experience faultline activation for quite some time because of a low level of social presence (Rourke et al., 2001). However, the opposite may also be argued; the spontaneous trait inference paradigm reflects that people infer and categorize others with just a few descriptive words (Todorov & Uleman, 2004). Polarization Another process in the study of activated faultlines to be analyzed in VTs is subgroup polarization (i.e., the split of the team into subgroups holding extreme opposing opinions – Mäs et al., 2013). Polarization differs from activation in that polarization is about the magnitude of differences between subgroups. Faultlines theory explains how subgroups converge on ideas and beliefs, through a mix of conformity processes within subgroups and competition pro-

Faultlines in virtual teams  243 cesses between them (Bezrukova et al., 2009, 2010; Lau & Murnighan, 1998; Thatcher et al., 2003). As subgroup members holding similar opinions communicate arguments that expose each other to new arguments supporting their current opinion, they develop more extreme and polarizing views (Myers, 1978; Feliciani et al., 2021). Thus, polarization contributes to create diametric opposition between subgroups (Lau & Murnighan, 1998). Such opposition reinforces the tendency for liking and favoring the ideas proposed by members of their own subgroup, while disregarding the ideas coming from other subgroup members (Nishii & Goncalo, 2008). This polarization process creates a self-reinforcing spiral of commitment around the validity of the proposed ideas, a biased evaluation of the ideas, and a stronger defense of the ideas (Moscovici & Zavalloni, 1969; Nishii & Goncalo, 2008). As a result, subgroup positions result in a widened faultline causing an escalation in conflict, a reduction in information elaboration, and a decrease in team performance (Bezrukova et al., 2007, 2009; Gibson & Vermeulen, 2003). We expect polarization to be more acute for VTs resembling those described by Polzer et al. (2006) where subgroups of collocated members interact across different geographies. Collocation among subgroup members will reinforce subgroup interactions as different subgroup locations will minimize team-level interactions. For those who are collocated, there will be more opportunities to share informal communication and develop a common identity (Thatcher & Meister, 2021), increasing subgroup agreement and idea convergence. Over time, we posit that the interaction limitations that high virtuality imposes on VTs will cause subgroups to progressively develop different information, become more biased toward the ideas of their own subgroup, leading to a polarization that reinforces subgroup separation. Status conflict Our review of faultlines research in VTs revealed the relevance of status differences between subgroups; unfortunately, there is not much research conducted on this topic (Gibbs et al., 2017). Early conceptual development around faultlines pointed to the relevance of status differences for the emergence and maintenance of subgroups in teams (Lau & Murnighan, 1998; Carton & Cummings, 2012). Despite pleas for empirical work on faultlines and status-related constructs (i.e., status faultlines, status conflict), this request has been neglected until recently. Antino and his colleagues (2019) found that activated faultlines threatened team justice climate which created high levels of status conflict, ultimately impairing team performance. Status conflict (i.e., the attempt to defend or elevate one’s own [or subgroup’s] relative status – Bendersky & Hays, 2012) can occur in teams with activated faultlines because these members experience status differences and it is instinctual to pursue a strategy that preserves one’s positive social identity. When subgroups are salient and members perceive threats around job resources and status, subgroups will engage in status conflict in an effort to alter the status relations (Chattopadhyay et al., 2008). Consequently, faultline teams experiencing status conflict deviate their efforts from the task, and focus instead on actions that maintain or enhance their status position, impairing team performance (Antonio et al., 2019). Other research on status conflicts suggests additional negative impacts on team members, such as disconnecting from members of other subgroups, resulting in biased information seeking and judgments (Vescio et al., 2003), and increased attention to subgroup duties and goals that impair overall team effectiveness (Worchel et al., 1998). Given that many VTs are staffed such that subgroups are fairly obvious (i.e., based on location, time zone, language, ethnicity), it is important to consider incorporating status

244  Handbook of virtual work differences and status conflict in research attempting to understand how faultlines influence VT functioning and performance. When adding virtuality as a dimension of a faultline-based team, in addition to grappling with the usual subgroup issues, team members must also rely on communication technologies that are inferior in providing informational value and synchronicity when compared with face-to-face interactions (Kirkman & Mathieu, 2005). Further, the reduced social presence in which VTs operate increases the experience of psychological distance and disconnection between members from each subgroup (Feliciani et al., 2021), exacerbating status perception differences. Thus, we expect that VTs with faultlines will experience status conflict which will ultimately damage team outcomes. Further, clearer status differences between subgroups (e.g., home office personnel vs satellite office personnel; lingua franca fluency differences) will likely exacerbate the extent to which faultline-based VTs experience status conflicts. Incivility A fourth explanatory mechanism recently investigated in faultlines research is that of intrateam incivility (i.e., low-intensity deviant behaviors like derogation, discrimination, withholding of information, or avoidance of others with intent to harm the target – Porath & Pearson, 2012) which has been shown to mediate the activated faultlines–team effectiveness relationship (Antino et al., 2018). As activated faultlines threaten the larger team identity, subgroup members tend to induce intrateam incivility to enhance the positive identity of their own subgroup (Dovidio & Gaertner, 2010). However, using intrateam incivility to enhance a positive subgroup identity not only erodes overall team identification (Tavares et al., 2016), but increases stress, depression, and negative physical symptoms (Bowling & Beehr, 2006). Not surprisingly, intrateam incivility has been associated with a broad set of withdrawal behaviors in organizational settings (e.g., Schilpzand et al., 2016). Given that VTs operate in an environment with reduced levels of communication, members in teams with activated faultlines are also likely to experience the types of identity threat that lead to intrateam incivility. VTs operating with high levels of virtuality and a reduced social presence have more opportunities to avoid interactions (e.g., not attend meetings), withhold information (e.g., avoid email requests), and make derogatory comments (Baumeister et al., 2001; Zivnuska et al., 2020). To make things worse, incivility tends to escalate and self-perpetuate in virtual settings (Stieglitz & Dang-Xuan, 2013; Yin et al., 2020). Further, negative information travels more quickly online than in person (Stieglitz & Dang-Xuan, 2013), creating a stronger influence on those receiving it (Baumeister et al., 2001; Yin et al., 2020). Thus, we suspect that in VTs with faultlines, we are likely to see high levels of intrateam incivility as members attempt to counteract identity threats associated with faultlines using the destructive potential of computer-mediated communication. Further, we encourage research on several possible moderators that could reduce the impact of faultlines on intrateam incivility in VTs including role expectancies, behavioral norms, subgroup size, and subgroup status (Motro et al., 2021; Paulin & Griffin, 2016). Possible Team Structural Moderators In addition to research on the mechanisms underlying the faultlines-process/outcomes relationships in VTs, there are a number of structural moderators that we encourage researchers to

Faultlines in virtual teams  245 investigate. The three moderators which we believe are especially pertinent for faultline-based VTs are: team structure clarity, task role assignment and goal structuring, and leadership. Team structure clarity Recent research shows that the negative impact of team faultlines is reduced when teams have a clear structure (Antino et al., 2019). Team structure clarity is defined as the degree to which team activity is coordinated and prioritized through understandable procedures, which organize the team through an elaborated vertical and horizontal division of tasks (Bunderson & Boumgarden, 2010). The main indicators of team structure proposed by Bunderson & Boumgarden (2010) are: hierarchy (i.e., formal responsibility positions clarity), formalization (i.e., roles, procedures, and priorities clarity), and specialization (i.e., team members capabilities clarity). Managers can provide clear team structures that set common expectations and schemas, helping team members understand the value of potential differences emanating from team subgroups (Fiol et al., 2001). Having been found to be important in teams where virtuality is not a concern, we believe the moderator of team structure clarity is even more critical in teams operating in virtual environments. Virtual team members may have difficulty assessing where, how, and to whom information needs to be reported; hence, hierarchical clarity in VTs helps create more efficient task-related interactions that stimulate shared task representations (Rafaeli et al., 2009). Formalization clarity is also valuable for VTs, as it provides predictability around member interactions and their sequencing, such that common representations about the task and proper usage of communication tools can be achieved in the team (Müller & Antoni, 2020). Finally, specialization clarity contributes to a functioning transactive memory system, which helps VT members perform effectively by decreasing the amount of communication needed to perform their tasks (O’Leary & Mortensen, 2010; Yoo & Kanawattanachai, 2001). Taken together, team structure clarity in VTs with faultlines should focus team member attention on shared cognitive representations, counteracting subgrouping effects and benefitting overall team performance (Schmidtke & Cummings, 2017). Moreover, team structure clarity should help VT members reduce identity threats associated with having a faultline (Antino et al., 2019). Task role assignment and goal structuring Research on team faultlines has shown that task role assignments and goal structure interventions are valuable for managing the negative effects of faultlines on processes and outcomes (Rico et al., 2012). Further, research on VTs suggests that management practices related to task and goal interdependencies increase VTs effectiveness by improving team motivation (Hertel et al., 2004). Task role assignment strategies articulate team roles such that they increase the perception of overlapping attributes between members of different subgroups, weakening previous subgroup salience and associated intergroup biases (Bettencourt et al., 2007). Goal structure interventions, such as the creation of a team goal, aim to integrate subgroups into an overarching group by making subgroup distinctions less salient (Bezrukova et al., 2009; Dovidio & Gaertner, 2010). Combining both strategies in strong faultline teams increases the elaboration of task-relevant information, reduces conflict, and increases team performance (Rico et al., 2012). These results can be extrapolated to VTs, particularly if we consider VT configurations around collocated subgroups across different geographical locations. In this regard, van der

246  Handbook of virtual work Kamp et al.’s (2015) research found that introducing a superordinate team identity weakens relationship conflict in a global software development team composed of members from a Dutch bank and an Indian software development firm. Rico and his colleagues (2012) found that the benefits of a superordinate team identity (goal structuring) intervention was more pronounced when combined with a cross-cutting role intervention across subgroups. Because VTs generally engage in fewer informal interactions relative to collocated teams (Hoch & Dulebohn, 2017; Warkentin et al., 1997), there may be more ambiguity around team work and team goals. We believe that by assigning one or more members of each subgroup to an integrative role that ensures a coordinated functioning of the whole VT or structuring a goal that motivates every VT member, teams can harness the negative effects of faultlines. Leadership Leaders influence social identity processes in teams (Hogg & van Knippenberg, 2003); thus, they are in a privileged position to affect the extent to which faultlines influence teams. However, there is limited evidence regarding the role of leadership on faultline-based teams, which has motivated calls to promote research addressing this important gap (e.g., Liu et al., 2019; Thatcher & Patel, 2012). To date, only a few studies have investigated the role of different types of leadership in managing dormant faultlines (e.g., Gratton et al., 2007; Kunze & Bruch, 2010), and just one study has investigated the role of leadership in activated faultline teams (Antino et al., 2018). We know even less about leaders in faultline-based VTs, but there is value in studying this relationship. Malhotra and his colleagues (2007) suggest that VT leaders could prevent faultlines by ensuring team diversity is leveraged through the implementation of a transactive memory system that clarifies who knows what. To do this, leaders should assign team members to common tasks in an effort to facilitate mutual learning, increase appreciation of team member differences, and promote decategorization, which reduces stereotyping (Homan et al., 2007). However, once tasks are fulfilled, team members should be reassigned to new tasks to avoid subgrouping (Malhotra et al., 2007). Maynard and colleagues (2018) emphasize that leaders who increase team awareness of team KSAs promote the elaboration of task relevant information that maximizes team effectiveness and viability. Research has also pointed to the importance of understanding the position occupied by leaders in the faultline, such as when the leader belongs to one of the subgroups (Meyer et al., 2015). Ocker and colleagues (2011) see leaders as a VT faultline strengthener, particularly when leaders belong to a larger or dominant subgroup. A recent review proposes a moderating role of transformational leaders (i.e., emphasizing idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration – Bass & Avolio, 1993) on the relationship between VTs faultlines and team performance (Graham & Daniel, 2021). These studies confirm the value in doing more research on the role of leaders in faultline-based VTs. In sum, the three moderators discussed in this section are those that are likely to have a discernible impact on whether, or how, faultlines impact team processes and outcomes, and deserve further study. A Longitudinal View of Faultlines in VTs Like other team-based research, we know little about the effects of time on VTs with faultlines. In fact, the majority of faultlines research (when not considering VTs) assumes that faultlines

Faultlines in virtual teams  247 are stable and fixed which impairs our knowledge of the phenomenon (Meister et al., 2020). Given that VTs often change membership (Malhotra et al., 2007) a longitudinal understanding of faultlines is especially important to investigate. Overall Meister et al. (2020) argue that in order to really understand the strength of an existing faultline, and the extent to which it is activated, we need to consider a team’s shared history. Furthermore, we must consider the degree to which there is faultline entrenchment, the consensus of team members regarding the existence and composition of clear subgroups in the team. According to their view, the faultline entrenchment concept is distinct from the faultline activation process; this distinction is especially relevant for VTs. Even within the faultlines literature, the faultlines activation process or what triggers a dormant faultline to become perceived, is not well-established. In their qualitative field study, Chrobot-Mason et al. (2009) identified five main faultline triggers: differential treatment, different values, assimilation, insult/humiliating actions, and simple contact. Within virtual teams, Polzer et al. (2006) find that team configurational dispersion is a faultline trigger. Meister et al. (2020) complement these views by arguing that at different points in the teams’ history, specific diversity attributes may become salient and ignite the faultline activation process. VTs offer a great opportunity to explore the faultline activation process. The ability to track VT communications provides a non-obtrusive window into communication and social interaction patterns (e.g., Jehn & Bezrukova, 2010); further, this approach enables researchers to track any changes in communication and interaction patterns. In addition, content analysis of communication logs may reveal how and when faultlines activation occurs around relevant social or task categories (e.g., Meyer et al., 2011). When considering faultlines over time, it is not only important to consider faultline activation, but it is also critical to understand the faultline entrenchment process. In this regard, Meister et al. (2020) explain that the degree of faultline entrenchment at a particular time depends on the team’s accumulated faultline experience. Thus, if a faultline’s entrenchment trajectory is low, then a new strong triggered faultline may not increase subgroup division; but if such a trajectory is high, faultline-based subgroups will be resistant to changes even when new non-aligned faultlines are triggered. As such, the longer an activated faultline exists, the worse it becomes for team processes and outcomes (Thatcher & Meister, 2021). Again, VTs represent a perfect environment to test these theoretical developments due to their high variability around their duration and the ongoing recombination of their members (Malhotra et al., 2007). Researchers could observe how a strong faultline may be triggered during initial interactions with a recently formed VT with members who have no previous working experience. Researchers could then observe the faultline entrenchment trajectory to see whether recategorization or decategorization strategies strengthen or weaken the trajectory (Rico et al., 2012). We have pointed researchers to a number of interesting questions at the intersection of faultlines and VT literatures. In this final section, we provide actionable strategies for practitioners who wish to lead successful VT teams, especially if those teams have faultlines.

MANAGING FAULTLINES IN VIRTUAL TEAMS The very nature of VTs leads to the propensity for VTs to experience dormant faultlines where team members are aligned into subgroups; the geographic and potential language dispersions coupled with constraints associated with communication technologies only magnifies these

248  Handbook of virtual work faultlines providing a challenging environment for managers. We build on existing research around counteracting subgroups and faultline deactivation (Rico et al., 2012; Thatcher & Meister, 2021; van der Kamp et al., 2015) to provide VT faultlines management strategies around four main themes: team structuring, building trust and communication norms, diversity training and identity play activities, and appropriate leadership. From Team Staffing to Team Structuring An initial way to minimize VT problems associated with faultlines is to structure teams with minimal opportunities for dormant faultlines. Crafting a team where the member attributes do not align along clear demographic subgroups would be an ideal departing point. Evidence from computational simulation studies shows that demographic crisscrossing (e.g., ensuring that not all males are engineers, and not all females are marketers) is useful to preventing faultline strength (Mäs et al., 2013). Although many VTs have natural faultlines (e.g., lingua franca, nationality, geographic location), managers can intentionally configure teams where there are multiple crisscrossed subgroups rather than two homogenous subgroups (Polzer et al., 2006). Because faultlines are also formed around informational attributes such as education level or functional area (Bezrukova et al., 2009), deep-level attributes such as personality types and values (Meister et al., 2020), and team members’ physical and/or geographical location (Polzer et al., 2006), team staffing should be complemented with team structure interventions (Rico et al., 2012). Team structure interventions deactivate team faultlines through a combination of proactive task role assignments (e.g., crosscutting roles of members from different subgroups to do a common task) and goal structuring (e.g., emphasizing a common overarching team goal) strategies. Complementing these structural interventions, Thatcher and Meister (2021) propose two decategorization strategies to counteract perceived subgroup division: (1) shake membership up by moving people either physically or cognitively (i.e., through job rotations, project allocations, geographic assignment); and (2), engage bridge builders who reduce the separation between subgroups by acting as “cultural brokers”. There are situations where faultlines in VTs may be desirable (Gibson & Vermeulen, 2003). For example, faultlines may be valuable for organizations that are considering expanding operations to another cultural environment. A team may contain one subgroup of existing employees from headquarters and one subgroup of new employees who have the cultural and geographic background to help with the expansion. A subgroup of new employees is preferable to a single employee in this situation as their collective understanding and experience of the new environment is valuable to the team. The pre-existing condition in this situation that makes faultlines valuable is the need for valuable information that only the subgroup of new employees can provide. Another possible context where faultline-based VTs may be valuable is when two or more companies based in different locations are merging. An effective merger occurs when the best employees, procedures, and organizational cultures are retained; this is more likely to happen when both top management teams (TMTs) from the original companies have faultlines. Although a company merger is always a difficult process, recent research reports that the presence of teams without diversity in both TMTs of the merging company creates perceptions of strain and uncertainty risking an effective merger (Grotto & Andreassi, 2020). In other words, the presence of faultlines on both sides of the merger is likely to help the merged company in

Faultlines in virtual teams  249 the end; rather than creating a new faultline between members of the merged companies, the diverse views already existing in each TMT will allow for more appreciation of the perspectives of the acquired company in a different location. From Cohesion to Trust: Bridging Faultlines through Communication Technology VTs have an ample panoply of communication media which could be strategically used to cope with their needs by reducing virtuality levels (Kirkman & Mathieu, 2005), addressing task requirements (Rico, Cohen & Gil, 2006), and compensating for different communication frequencies (Straube et al., 2018). For example, VTs could use richer and more synchronous media (e.g., Zoom, Microsoft Teams) when extensive planning or assessment is required and leaner and asynchronous media (e.g., email, Slack) when task completion is required. When VTs do not use such strategic matching, extensive reliance on richer and synchronous communication tools tends to increase social presence and amplifies the salience of demographic attributes, which may reinforce existing faultlines (Polzer et al., 2006). Thus, strategic use of communication tools reduces the negative impact of faultlines because it aligns communication media and task requirements with a focus on team performance (Rico & Cohen, 2005) resulting in positive feedback effects on team integration, commitment, and identity (e.g., Casey-Campbell & Martens, 2009). The strategic use of communication tools in faultline-based VTs can also help develop trust, something that tends to be missing in faultline-based teams that experience deleterious effects on performance (Thatcher & Patel, 2012; Lau & Murnighan, 2005; Polzer et al., 2006; Mach & Baruch, 2015). Extant research in virtual project teams evidences that trust develops early on through task-oriented communications, and is then maintained by creating a predictable pattern of communications (Rico et al., 2009). Thus, it is important for managers of faultline-based VTs to help teams develop communication norms by structuring early communications around task-based concerns and encouraging a predictable pattern clarifying when member responses will be received and in what form the communication should take. Ensuring trust early in the team development and maintaining it through project completion will tie the team together and counteract the divisive forces that faultlines exert on VTs. From Diversity Appreciation to Identity Play When team members believe that diversity is valuable to team functioning (i.e., strong diversity beliefs) then salient subgroups are less likely to result in detrimental group processes and negative performance (Homan et al., 2007). A clear managerial application of these findings is that promoting pro-diversity beliefs can be a beneficial way of reducing the negative effects of team faultlines on team performance. Because VTs tend to operate in knowledge-intensive settings and extant research has revealed that diversity is more valuable for knowledge-intensive, non-routine tasks rather than for simpler, routine tasks (van Knippenberg & Schippers, 2007), pro-diversity belief promotion is likely to have important payoffs for VTs, especially when introduced at team formation. One way to promote pro-diversity beliefs is through diversity training programs. Such training should complement company-wide diversity training programs and be focused on developing attitudes and opinions concerning diversity and not any specific stereotyped group (Homan et al., 2007). One potential diversity training approach for faultline-based VTs is using identity

250  Handbook of virtual work play (i.e., experiencing how people react to a different appearance of oneself – Kafai et al., 2010). Virtual settings are especially suited for training using this kind of role play, and the recent gamble by major internet companies towards a “metaverse” make this kind of role play even more feasible in the coming years. Facilitators could assign team members an avatar with a background profile that differs from their real-world self (Taylor, 2006) and encourage them to engage in identity play as if they had the characteristics associated with that avatar. Extant research in team faultlines revealed that priming perceptions about personal characteristics through a few descriptive sentences (Todorov & Uleman, 2004) is enough to make personal characteristics salient and conform to subgroups around them (Rico et al., 2007). Through identity play, facilitators can help a VT confront individual team member assumptions and reflect about the values supporting such assumptions (Thatcher & Meister, 2021). In sum, both diversity appreciation and identity play strategies could be integrated in diversity training programs in VTs. The Leader Role Despite the limited attention that research on team faultlines has devoted to the role of leadership, the developments summarized in this chapter lead us to suggest certain strategies to counteract the negative role of faultlines in VTs. Leaders have a relevant role in shaping the appropriate behaviors, attitudes, and knowledge to help the team overcome negative effects associated with faultlines. For example, they should promote pro-diversity beliefs and help VT members see the value that the teams’ diversity and faultlines may have for the team. In this regard, Homan et al. (2007) emphasized the importance of leading by example in communicating leader endorsement of the value of diversity and exemplifying how team performance may benefit from diversity. Further, Maynard et al. (2018) pointed to the leader role in helping VT members increase professional familiarity between members to enhance the elaboration of task-relevant information and the emergence of ability-based trust (i.e., based on their knowledge, skills, and abilities). Both processes should reduce the negative effects associated with faultlines (Chiu & Staples, 2013). Leaders must also be aware of their potential inclusion on a particular side of the faultline. When leaders, by the nature of their demographic attributes or location, are thought to be part of a specific subgroup, team functioning and outcomes can suffer (Meyer et al., 2015). Leader differential treatment of members not only legitimates subgroups by activating a faultline (Antino et al., 2018), it can be used by subgroups to leverage access to meaningful resources and opportunities (e.g., promotion opportunities – Martin et al., 2018) and threaten the interests of other subgroups. Equal and high levels of interaction exchanges between leaders and members increases team members’ identification with the overall team and improves social integration (Guan et al., 2013; Rico et al., 2007). Because location is such a salient attribute in VTs, the leader must be especially attentive to team members that are not collocated with the leader in an effort to prevent a faultline from becoming activated and/or to diminish any potential negative consequences of a faultline on VT functioning and outcomes. Based on the available evidence across the intersection of the faultlines and VT literature, we have provided a set of tools that managers can use to lead and manage VTs that have faultlines.

Faultlines in virtual teams  251

CONCLUSION As technologies continue to evolve and allow us to interact in different ways with people around the globe, the use of VTs will only increase. The confluence of employees in these VTs who have different demographic profiles, task-based skills, language fluency, values, geographic residency, and interests ensures that teams will experience both dormant and activated faultlines. There are many potential research questions around the integration of VTs and faultlines that present opportunities for researchers. Managers of VTs have the opportunity to lead their teams in such a way as to restrict dormant faultlines from becoming activated, as well as to minimize the potential damage that faultline-based teams can cause. Further, we challenge managers to see VTs with faultlines as an opportunity to build bridges across the many chasms that exist in faultline-based VTs.

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256  Handbook of virtual work Zivnuska, S. L., Carlson, D. S., Carlson, J. R., Harris, K. J., Harris, R. B., & Valle, M. (2020). Information and communication technology incivility aggression in the workplace: Implications for work and family. Information Processing & Management, 57, 102‒122.

14. The surge in digitalization: new challenges for team member collaboration Thomas Hardwig and Margarete Boos

The COVID-19 pandemic has led to a surge in digitalization in many sectors of the economy. According to Gartner’s (2021b) Global Labor Market survey, the number of employees in research and development working remotely for two or more days per week has doubled during this time. Many employees are working remotely for the first time (Bonin et al., 2020). Therefore, the question of how virtual teamwork and remote collaboration can be designed has been placed at the top of the agenda for companies (Brenner et al., 2021, p. 141). How should work be organized in the future, after the pandemic? Interestingly, the consistent results of various surveys suggest that the majority of employees do not want to return to the office permanently. In fact, a great number wish to continue working from home for at least two days a week (Bonin et al., 2020; Fana et al., 2020; Gartner, 2021b; RW³ Culturewizard, 2020). This means that, even if the prevalence of remote work does not continue, a return to pre-crisis levels of in person work is unlikely. Consequently, there is talk of a “new normal” (Carroll & Conboy, 2020) and a “human centric” model hybrid work (Gartner, 2021a). The surge in digitalization is also likely to raise new questions for research on virtual teamwork. For decades, the prevalence of virtual teams has been increasing (Gilson et al., 2015; Hoch & Kozlowski, 2014). In a study conducted before the COVID-19 pandemic, 89 percent of clients of a global consulting firm reported being on at least one virtual team (RW³ Culturewizard, 2018). In today’s global economy it has become inevitable, even for small companies, to organize collaboration across temporal and spatial distances as well as across company boundaries in network-like structures (Child, 2015), facilitated by the spread of digital technology. There are barely any teams left today that do not make use of media-based forms of communication or do not work on shared resources in a virtual space. Modern collaboration technologies differ fundamentally from earlier groupware solutions in that they integrate the organizational logic of the social network and create a virtual collaboration space (McAfee, 2009; Weissmann & Hardwig, 2020), or “information space”. The information space, as a leap in productive forces, fundamentally differs from the information systems of the past in that a new sphere of social action emerged. Whereas traditional computer systems offered or demanded interaction between only humans and machines, the information space opens up a novel stage for interaction between humans. (Boes et al., 2017, p. 165)

It is not just the possibilities of technology that raise questions. The surge in digitalization and its “unprecedented impact on the workplace and organisational practices” (Carroll & Conboy, 2020, p. 1) must also be processed by employees in order to enable a sustainable work organization and team culture. Hybrid workplaces create a situation in which work design must cope with a novel mix of work requirements, with permanent office-based employees working in hybrid teams with colleagues working from home or other mobile locations. 257

258  Handbook of virtual work In areas of skilled knowledge work, companies face pressure to accommodate the preferences of their employees. A study by Global Workplace Analytics (2021) estimates the annual cost savings from two to three days of home office per week at $11,000 per employee. However, it also reports problems with social integration (especially for newly recruited employees) and company cohesion. Similarly, a Gartner survey (Gartner, 2021b, p. 4) reports that 49 percent of respondents do not feel productive when working from home. Taken together, it becomes apparent that the surge in digitalization is associated with considerable challenges not only for companies, but also for employees (Fana et al., 2020; Wang et al., 2020). These observations also raise questions as to whether research can shed light on some of the prerequisites, conditions, and possible limits of virtual team collaboration. How does the increased virtuality affect collaboration in areas of knowledge work, and how can it be mastered in distributed teams? We conjecture that recommendations concerned with earlier groupware-based virtual collaboration are no longer sufficient, but that more advanced concepts are required to enable hybrid teamwork or to offset the corrosive effects of increased virtuality on team cohesion and performance. Current measures of the design of virtual teamwork have focused on the qualifications of team members and team leaders (Blackburn et al., 2003; Krumm et al., 2016; Schulze & Krumm, 2017), the provision of technology (Riopelle et al., 2003), supportive processes for the establishment of teams, and teambuilding (Boos et al., 2017). We propose that a holistic socio-technical concept of work design that is specifically oriented towards coping with virtuality is needed. Socio-technical work design approaches are being increasingly used to cope with the digitalization of work (Mohr & van Amelsvoort, 2016; Pasmore et al., 2019; Winby & Mohrman, 2018). Against this background, we want to clarify what guidance the rich literature on team member collaboration can provide for a future of digital collaboration. Our chapter aims to propose a model that describes the antecedents, processes, and consequences of team member collaboration within virtual work. Since team cohesion and team productivity are particularly challenged by increasing virtuality, we will concentrate on two central factors – team mental models and trust – affecting team collaboration and the outcomes of teamwork. Finally, we evaluate the state of the literature on collaboration within virtual teams to derive suggestions for improving the work design of virtual teams. This chapter is structured as follows: First, we will clarify the concept of virtuality. We no longer distinguish between classic (face-to-face) and virtual teams, but instead assume that all teams are virtual to some degree. We conceptualize virtuality as a multidimensional continuum and describe these dimensions. Second, teams in modern organizations are increasingly expected to function as social units, assembled on a short-term basis and subject to increased levels of instability and fluidity (Bushe & Chu, 2011; Tannenbaum et al., 2012). In particular, due to the dynamic nature of virtual work (Chiu et al., 2017), the use of digital media (Chatterjee et al., 2017), and the high degree of self-organization in agile work concepts (Neumer & Nicklich, 2021), team boundaries are becoming more blurred, with overlapping membership in different teams becoming more frequent. The qualities of a “real team” (Wageman et al., 2005) – interdependence, boundedness, stability, authority – which are considered critical for team effectiveness, are now being eroded (Neumer & Nicklich, 2021, p. 45). In addition, the trend toward “projectification” (Midler, 1995) leads to a prevalence of project-based or temporary forms of work and teams. The extent to which a team is a permanent organizational and social unit, determines the degree to which a task establishes a temporary (or permanent) social context. Since it is well

The surge in digitalization  259 established that highly interdependent tasks are particularly challenged by virtuality (Bell & Kozlowski, 2002), we consider the nature of a team’s task as a key factor in analyzing the impact of virtuality on team collaboration. Third, we will present a model of the antecedents, processes, and consequences of team collaboration within virtual work. We base our literature overview on the “collaborative performance framework” by Bedwell et al. (2012) which systematizes the most important factors that influence team collaboration. Fourth, we explain our concept of work design which we also use for the literature review. Building on this framework, we describe how the constructs of shared mental models and trust moderate the relationship between virtual collaboration and performance. Finally, we evaluate existing recommendations for work design, summarize their results, and make suggestions for further research and practical applications.

VIRTUALITY What makes virtual teams virtual is geographical dispersion and the use of technologically mediated communications. The members of virtual teams are not collocated; their primary work sites are different from one another. […] Virtual teams also rely on electronically mediated communication to stay in touch and get their work done. (Cohen & Gibson, 2003, p. 4)

The absence of face-to-face interactions makes a team virtual (Bell & Kozlowski, 2002). The use of communication tools is the consequence of geographical distribution. Virtuality should be understood as a continuum, raising the question of whether “virtualness” might be a characteristic of every team (Martins et al., 2004, p. 807). Schweitzer and Duxbury (2010) identify six dimensions of virtuality used in the literature: geographic dispersion, the dependence on communication technology, boundary-spanning, asynchrony of communication, temporality, and cultural diversity. In addition to these, team lifespan with its effect on team dynamics may be another crucial dimension. From the six dimensions identified, only two can be clearly conceptualized as differences in virtuality: spatial distribution and asynchrony. Both prevent face-to-face interaction (Schweitzer & Duxbury, 2010). The use of technology, for example, is a practical consequence and not in itself a defining feature. Its significance depends on the extent to which face-to-face contacts are possible in parallel. Team taxonomies (Foster et al., 2015; O’Leary & Cummings, 2007; Schweitzer & Duxbury, 2010) are mainly aimed at enabling a clear team classification to support empirical research (Hollenbeck et al., 2012; Wildman et al., 2012). Foster et al. (2015) added virtuality as a dimension to Hollenbeck et al.’s (2012) model, that distinguished skill differentiation, authority differentiation, and temporal stability. Physical distribution is mentioned as a distinctive characteristic of a virtual team by Wildman et al. (2012). Hosseini et al. (2015) conceptualize virtuality as the degree of deviation from face-to-face communication that leads to a change in the quality of collaboration due to lower intimacy. It should be noted that objective proximity should not be confused with subjectively perceived proximity (Wilson et al., 2008). Modern collaboration platforms might facilitate high perceived proximity (Wilson et al., 2008): frequent, deep and multi-lateral communication, identification with the team due to joint achievements, involvement in social networks, and creation of structural assurance due to transparent roles and systems. Therefore, a greater intensity of media-based communication can be a solution instead of a problem (Tietz & Mönch, 2015).

260  Handbook of virtual work Additionally, hybrid working leads to a complex interplay of face-to-face and media-based interactions. Perceived proximity therefore depends very much on whether and to what extent team members can still meet and maintain casual, informal encounters. Understanding collaboration from a virtuality perspective is critical in answering questions around the proper design of hybrid work and virtual collaboration. For example: Is it important for team collaboration to have members work jointly in an office? Should an uneven distribution of virtual collaboration (some team members work in the office, others at home) be allowed? What role can collaboration platforms play in promoting team cohesion? These are questions to which current research can only provide few answers due to its traditional focus on the extent of media-mediated communication.

COLLABORATION In the existing literature, we observe some confusion regarding the use of the term “collaboration” and its related concepts (Bedwell et al., 2012; Camarinha-Matos & Afsarmanesh, 2008). Camarinha-Matos and Afsarmanesh (2008, p. 312) proposed a classification of joint activities ranging from simple communication to full collaboration with increasing levels of integration, risk taking, and commitment. While communication is merely the sharing of information, groups who align their activities to achieve complementary goals advance to coordination. If individuals also share resources to achieve complementary as well as mutually compatible goals, they reach cooperation. The most complex and demanding collective requirement – collaboration – “involves mutual engagement of participants to solve a problem together” (Camarinha-Matos & Afsarmanesh, 2008, p. 311). In contrast to coordination or cooperation, a collaboration task cannot be performed by division of labor. Thus, collaboration denotes the most complex team and project process, with specific requirements and benefits for achieving results that cannot be reached by one worker alone or by an aggregate of individual workers. Collaboration is rare and of high value because it enables innovation and the solution of complex problems. However, collaboration does not occur in all joint endeavors, not even among highly specialized knowledge workers. Workers are in different situations during the day, sometimes communicating or coordinating, sometimes collaborating, and very often working alone. Which type of work prevails depends on the demands of the job and the team situation. Collaboration is the most beneficial of joint endeavors, the most demanding, and the one that usually only takes place over short episodes during a workday (Hardwig et al., 2020). Teams with highly interdependent tasks (Bell & Kozlowski, 2002) experience a comparatively high proportion of collaborative situations – but even they do not collaborate all the time.

COLLABORATIVE PERFORMANCE FRAMEWORK In fluid team constellations, temporary and dynamic team situations, and with multiple team memberships, we can no longer conceptualize a team as a bounded social unit and must focus on the process of collaboration in which team members are actively involved. Team members may come together for a single task and never meet again or they may come together repeatedly to accomplish multiple tasks. The more fluid and dynamic team constellations are, the

The surge in digitalization  261 more relevant is the question of how team working conditions can be designed such that team effectiveness is enabled by these conditions. A model that focuses on collaboration as a process was proposed by Bedwell et al. (2012). It is based on the input-mediator-output-input framework (IMOI) (Ilgen et al., 2005). The authors claim that their model is applicable to the collaboration of different social units (individuals, teams, and organizations). In this chapter, we focus on the team level, that is, within-team collaboration. In the following section, we present our version of the collaboration model (Figure 14.1), based on Bedwell et al. (2012) with further development based on our research (Hardwig, 2021). The collaboration process and its contextual factors are located in the center of the model. The process depends on input factors and has an outcome.

Source: Based on Bedwell et al. (2012).

Figure 14.1

Team collaboration framework

As input, Bedwell et al. (2012) name only the knowledge, skills, and abilities of those involved in the process. This conceptualization of team input factors is rooted in their human resource management perspective. In extending the framework to address virtual team effectiveness (Caya et al., 2013), we have made two additions to the model: First, we propose to consider the task as an input variable, since the task establishes the temporary team context. As we have elaborated above, the collective requirements and the structure of the task determine collaboration processes (Bell & Kozlowski, 2002). Thus, the task is the most determining element of teamwork. Second, we add the collaboration space as an input variable, the level of which depends on the degree to which asynchronous collaboration tools are used instead of face-to-face interaction. The outcome of a collaboration process may be a product, idea or even just a common understanding of an issue. In addition to such concrete outcomes, participants also gain experi-

262  Handbook of virtual work ence and might enhance their expertise. This means that collaboration affects the social system that carried out the process of collaboration. Due to individual as well as collective learning, the prerequisites and conditions for the next cycle of teamwork are changed, for example by the team developing group norms of reliable cooperation, forming shared mental models, or developing a way of dealing with information that is conducive to collaboration. Because of this iterative nature, mediators of the collaboration process are of particular interest. The collaborative process consists of behaviors and “emergent states.” The model includes six key behaviors: (1) task execution, (2) adaptation, (3) extra-role behavior, (4) information processing, (5) leadership, and (6) sense-making (Bedwell et al., 2012, p. 138). These behaviors can be differently appropriate to successfully cope with the task at hand. Behaviors evolve through the experience gained during an episode of collaboration. Team members may improve or aggravate the alignment of their individual behavior with the team – their behavior can be more or less purposeful and effective, etc. As a result, the capabilities of a team develop. Emergent states are “properties of the team that are typically dynamic in nature and vary as a function of team context, inputs, processes, and outcomes” (Marks et al., 2001, p. 357). Emergent states include cognitions, motivations, as well as affect and values that emerge from the process. Bedwell et al. (2012) added trust, shared mental models, and social identification. Emergent states involve both the development of individual emotions and knowledge of team members as well as collective emotions and knowledge that guides collaboration. We adjust the contextual factors, making them more apt for the discussion of work design measures. First, we add working conditions (degree of autonomy, skill use, job feedback, social and relational aspects). Working conditions influence task performance to such an extent that the characteristics of the task cannot fully explain the results of the collaboration process. As an example: Tasks can be performed under more or less enabling organizational conditions. Second, we look at the organizational framework, including the structure of communication and leadership as well as the organizational culture. As a third factor, we include the temporal conditions of collaboration (team lifespan, duration of collaboration episodes, frequency of interaction, continuity of membership) (Bedwell et al., 2012; Marks et al., 2001). The model illustrates that the preconditions for the process can be very different and that earlier learning experiences determine the abilities to work together successfully. Previous experiences shape individual perceptions and collective behaviors. They also determine the extent to which team members are confident to engage in intensive collaboration. The conditions for success are thus likely to be much more favorable in well-established teams or project constellations than in spontaneous ad hoc project teams. However, it is not only the learning and development processes that are decisive for successful collaboration, but also the basic contextual conditions, which can be influenced through goal-directed work design. Bedwell et al. (2012, p. 139) recommend different activities of planning, providing a suitable framework as well as leading, selecting, and staffing the teams. However, they largely limit themselves to measures of selection and development of personnel. From our perspective and experience of working with collaboration platforms, this is too narrow (Hardwig & Weissmann, 2021; Weissmann & Hardwig, 2020). The influencing factors captured in Bedwell et al.’s (2012) model point to a need for team work design that extends beyond human resource management.

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WORK DESIGN Early research examining working experiences during the pandemic documented several negative consequences of increased virtuality on team collaboration. Workers reported deteriorated quality of social relations, perceived limitations in the quality of interactions, and in the exchange of ideas and information. They also criticized the restrictions on their autonomy due to increased level of control (Fana et al., 2020). Other challenges included increased work– home interference, ineffective communication, procrastination, and loneliness (Wang et al., 2020). The consequences mentioned here are by no means exhaustive or surprising and largely correspond to the findings from other literature on the consequences of increased virtualization of teamwork (Handke et al., 2020). This means, that there are several challenges to be overcome if remote work is to become the “new normal.” A focus on work design should help ensure that neither employee motivation and well-being nor team effectiveness and innovation capacity are impaired. Work should also allow for opportunities for personal development and be conducive to employees’ health. In this respect, the new post-COVID work design must be human-centered in order to create sustainable conditions for remote collaboration. Since modern organizations involve higher flexibility in terms of content, time, and spatial arrangements, as well as greater autonomy for workers and teams, it makes sense to use a definition of work design as “the study, creation, and modification of the composition, content, structure, and environment within which jobs and roles are enacted” (Morgeson & Humphrey, 2008, p. 47). Job design and team design are treated in an integrated way (Morgeson & Humphrey, 2008). “These broader definitions recognize that work design can be in part crafted by the incumbent, and they allow for work design to be considered at the team level” (Parker et al., 2017, p. 404). The model for work design by Parker and Grote (2020) classifies the goals and intervention strategies of work design. In contrast to the model of collaboration (Figure 14.1), which describes analytical relationships of input and output factors and mediators of teamwork, work design focuses on the possibilities for intervention by companies to design work in a human-centered way. The Parker and Grote (2020) model responds to many of the new challenges of digitalized work. The model is also applicable to the design of virtual team collaboration. We have made some minor changes to the original model in order to adapt it to the subject of this chapter (Figure 14.2). These changes assign lower importance to technology design and greater relevance to team development. The model is based on the assumption that the purpose of work design is the creation of positive outcomes for the workers as well as the organizations that employ them. In addition to the classic motivational factors (job satisfaction, engagement, commitment) as the basis for economic success, it includes promotion of good health, learning, and skill maintenance as important objectives of work design. Therefore, the goals of work design (Figure 14.2) and the outcomes of the model of collaboration (Figure 14.1) converge. Work design can be conceived as the means to achieve desirable outcomes of remote collaboration according to human-centered criteria. In research on work design, basic dimensions (autonomy and control, skill use and variety, importance of job feedback, social relations, and job demands) have been established as conducive to human-centered work design (Parker et al., 2017). The importance of autonomy, job feedback, job demands (task interdependence, task complexity, time pressure), and job

264  Handbook of virtual work

Source: Based on Parker and Grote (2020).

Figure 14.2

Work design as key to achieve benefits of virtual teamwork

resources (technical resources, social support) for team effectiveness and team cohesion in virtual collaboration was recently confirmed in a meta-analysis of 48 studies (Handke et al., 2020). It is fair to assume that complex knowledge-intensive activities are determined by an interaction of these dimensions, which drastically increases the demands on such teams in the case of high virtuality. Parker and Grote (2020) propose four areas of intervention in work design that – in our view – are also relevant for distributed collaboration. Only two of these – strategies A and D – are usually addressed in the literature on distributed teams. Since the absence of strategies B and C in previous research does not mean that they are not relevant for distributed collaboration, we present all four of them. Intervention strategy A, the proactive design of work, is applied when virtual teams are formed or changed. This strategy focuses on the development of a team with its design being adjusted throughout its life cycle (Boos et al., 2017; Hertel et al., 2005). Here, the prerequisites for effective communication, a good task-media fit as well as successful team development can be established through work design. According to Parker and Grote (2020), this intervention must integrate aspects of the strategies listed below. Intervention strategy B focuses on the selection, procurement, and design of the technology used by a team. For the success of distributed teams, the quality of the available information and communication technologies is central. It determines, for example, the degree to which contextual information is communicated and the opportunity for more distant team members to participate in team processes. However, in the context of work design, this requires a number of technical and organizational decisions to be made during procurement and implementation for the platform to optimally support the team in performing their tasks (Weissmann & Hardwig, 2020). For this purpose, intervention at the level of technology must be linked to strategy A.

The surge in digitalization  265 Intervention strategy C is concerned with the policy level of organizational, occupational, national or global regulations of work. With respect to macro forces, it is no coincidence that technology most often benefits employers over employees, given the relative power of employers in social and economic systems. This situation means that higher-level policies and regulations are needed to help ensure safe, healthy, and meaningful work designs, such as policies around technology, precarious work, monitoring, and the like. (Parker & Grote, 2020, p. 26)

Intervention strategy D concentrates on the competencies, skills, and personal development of the employees and how these can be achieved. There is a great wealth of literature on this topic. Ample research contributes to the understanding of the digital competences of both team members (Krumm et al., 2016; Schulze & Krumm, 2017) and leaders (Gross, 2018; Hill & Bartol, 2016; Prystupa-Rzadca & Latusek-Jurczak, 2014). Remote leadership, for instance, requires a heightened awareness of the team goal, specific coordination skills, the implementation of appropriate monitoring and evaluation tools and an enhanced sensitivity for disruptions on the socio-emotional level (Hertel et al., 2005). Given the lack of knowledge about socio-technical system design in company practice (Baxter & Sommerville, 2011), we find the extension of the qualification strategy D proposed by Parker and Grote (2020) to be very valuable: “To help reorient the discussion towards adapting technology to better suit humans we need to better educate and train key stakeholders about work design. This includes employees and managers, as well as those involved in new system procurement, design, and implementation” (Parker & Grote, 2020, p. 27).

THE IMPORTANCE OF TRUST IN VIRTUAL COLLABORATION The relevance of trust as a predictor for team performance has increased as the nature of work has become more interdependent and far more risk taking with increasingly more flexible and unpredictable work arrangements. Without doubt, trust is key to the success of modern-day work environments where teamwork, decentralized structures, requirements for flexibility, innovation, and high levels of cooperation all feature as vital elements for success. (Costa & Anderson, 2017, p. 393)

Trust reduces the complexity of collaboration when team members rely on voluntary contributions from collaboration partners over whom they have no control. The positive expectation that cooperation partners will behave benevolently (i.e., will contribute) enables more effective cooperation. We assume that team trust facilitates specific risk-taking behaviors such as reducing defensive control, open discussion of conflicts and mistakes, mutual feedback, and sharing of confidential information, which in turn should lead to more efficient coordination of team members’ resources (time, effort, knowledge, etc.) to the team task. Moreover, team trust should enhance cooperation and social exchange. (Breuer et al., 2016, p. 1152)

However, team members who are trusting of others expose themselves to the risk of this expectation not being met. This is because the trustor expects the trustee to be guided not only by his or her own interests, but by common goals (Morrison-Smith & Ruiz, 2020).

266  Handbook of virtual work Team trust is an emergent psychological state. Costa and Anderson (2017) proposed that team trust should be considered from a multilevel perspective as individual, team, and organizational levels interact. The social and structural preconditions for team trust as well as the outcomes can be located at all three levels. Strategies of top management, human resource practices, and aspects of corporate culture are explicitly mentioned here because they can have a beneficial or detrimental effect on the emergence of trust in teams. The relationship between team trust and team performance is well established empirically, and confirmed by several meta-analyses (Breuer et al., 2016; Jong et al., 2016; Paul et al., 2021). The empirical results support the “fundamental assumption in the trust literature that trust matters most when parties are dependent on each other” (Jong et al., 2016, p. 1143). Specifically, this means that team performance benefits from trust when task interdependence, authority differentiation, and skill differentiation are high. How virtuality affects the relationship between trust and team performance remains unclear. On the one hand, Breuer et al. (2016) conclude that trust has a positive effect on team-related attitudes, information processing in teams and team performance when virtuality is high. In contrast, the meta-analysis by Jong et al. (2016) did not show that team virtuality or temporal stability of a team have a relevant impact on trust and team performance respectively. This is surprising as one would expect that “teams that interact virtually are considerably less likely to develop trust” (Morrison-Smith & Ruiz, 2020, p. 7). This contradiction may be resolved by the fact that Jong et al. (2016) treated virtuality as a unitary construct and defined it as degree of electronic communication. Treating virtuality as a multidimensional construct, Paul et al. (2021) have shown how varying dimensions of virtuality moderate the relationship between intrateam trust and team effectiveness. Following Schweitzer and Duxbury (2010), they divided virtuality into three variables: “team time worked virtually,” “number of different locations,” and “distance from team members.” Two dimensions – distance virtuality and member virtuality – moderate the relationship between trust and team effectiveness. An effect of time worked virtually was not confirmed. The authors explain that as follows: “teams that choose to work much of their time virtually may have already established a trusting relationship with each other, or likewise, teams that choose to work f2f may do so because they start with low levels of trust that may not be overcome by the time the project is complete” (Paul et al., 2021, p. 194). In sum, effects of using digital media for team communication can be very ambivalent. On the one hand, the findings of media richness theory (McGrath & Hollingshead, 1994) still apply: all things being equal, communication using digital tools is less rich than face-to-face communication. On the other hand, longer durations of virtual communication do not mean that team collaboration gets worse. Instead, communication tools can compensate for the shortcomings of virtual communication, especially since modern collaboration platforms open up a much broader variety of communication channels and social practices (Anders, 2016; Hardwig et al., 2019). Paul et al. (2021, p. 194) concur with this explanation: The Schweitzer and Duxbury instrument used to measure team time worked virtually, but does not include virtual meetings. Virtual meetings may provide many of the same benefits as f2f meetings. Improvements in video conferencing tools and network bandwidth have substantially improved the quality of virtual meetings. […] More research on the differences between f2f meetings and virtual meetings may shed light on their impact on trust development and team effectiveness.

The surge in digitalization  267 Along these lines, Breuer et al. (2016) have previously examined documentation of interactions (defined as the recording and storing of interactions between team members as written text, audio, or video) as an independent moderator of the relationship between team trust and team effectiveness. They found that, on the one hand, documentation is an independent construct distinct from virtuality and, on the other hand, it has a measurable impact on team performance. Documentation of team interactions weakened the relationship between trust and team performance “consistent with our assumption that documentation reduces the perceived risk in teams due to reprocessability of interactions […] and related facilitation of control and peer monitoring during teamwork” (Breuer et al., 2016, p. 1158).

THE IMPORTANCE OF TEAM MENTAL MODELS IN VIRTUAL COLLABORATION While trust is an emotional and relational emergent state that is crucial for the performance of a team “findings show team cognition positively predicts team task-related processes, motivational states, and performance” (DeChurch & Mesmer-Magnus, 2010, p. 48). The cognitive underpinnings of team performance have only recently come into greater focus with the concepts of team mental models (TMM) and shared mental models (SMM) gaining acceptance. Both terms are more or less interchangeable (Kneisel, 2020). We use TMM in this text to avoid confusion with cognitive models at the individual level. Team mental models are used to describe the perception of a work situation shared by team members: “Being on the same page” is the metaphor used repeatedly in the literature to describe this collective cognitive state (Maynard & Gilson, 2014; Mohammed et al., 2010; Müller & Antoni, 2020). TMM are “organized mental representations of the key elements within a team’s relevant environment that are shared across team members” (Mohammed et al., 2010, p. 877). In TMM, individual mental models overlap or converge at the team level (Maynard & Gilson, 2014). Shared knowledge being acquired through shared experience is often tacit knowledge (Polanyi, 2009). While explicit knowledge can be easily shared with others, tacit knowledge is difficult to make explicit and can often only be shared through common practice (Nonaka & Takeuchi, 1995). For teams, different contents of TMM can become relevant depending on the situation. Empirical research initially focused on two forms of TMM: knowledge shared by the team regarding their tasks (task mental model) and knowledge regarding how they need to interact in order to accomplish the tasks (team mental model) (Maynard & Gilson, 2014; Mohammed et al., 2010, p. 880). Each had unique effects on subsequent team processes (Mathieu et al., 2000). In recent years, more types have been studied (Grossman et al., 2017; Konradt et al., 2015). A reflection on the importance of TMM in virtual collaboration is challenged by the fact that the subject is still emergent (Florea & Stoica, 2019; Maynard & Gilson, 2014). “To date, there has been almost no empirical research on team or shared mental models in the virtual environment” (Schmidtke & Cummings, 2017, p. 668). As the degree of virtuality of collaboration increases, the dynamics and quality of communication also changes. The possibilities for exchanging and using information and for integrating it into a common knowledge base are diminished by the use of communication technologies (Curşeu et al., 2008). This makes it more challenging for teams to develop a viable TMM

268  Handbook of virtual work (Caya et al., 2013). Therefore, the complexity of TMM increases as virtuality increases (Schmidtke & Cummings, 2017). It is not yet clear how the use of information technology influences the development of TMM, and yet its fit to the team’s task plays a central role for virtual team effectiveness (Maynard & Gilson, 2014). Müller and Antoni (2020) proposed to add another subtype of the TMM typology, information and communication technology (ICT) member models: “We define ICT SMM as shared knowledge structures about the task specific ICT use among team members. ICT SMM consider the question ‘Do we have the same understanding of which ICT we use for a specific purpose?’” (Müller & Antoni, 2020, p. 188). These conceptual considerations respond to the fact that virtuality can significantly change the characteristics of TMM. Initially, the focus of analysis was mainly on similarity (Mohammed et al., 2010), “that is, the degree to which team members all share the same representation of the situation” (Schmidtke & Cummings, 2017, p. 664). Mohammed et al. (2010) found, a high similarity in how TMM influences team processes and impacts team performance. However, other results are still ambiguous (Schmidtke & Cummings, 2017). Besides similarity, accuracy is an important factor, describing the usefulness of TMM. Accuracy refers to the degree to which the mental model correctly characterizes the situation or concepts it represents. This quality of TMM is a prerequisite for the team to process the tasks correctly and effectively (Kneisel, 2020). As the accuracy of the TMM increases, team performance improves (Schmidtke & Cummings, 2017). A third characteristic of TMM, its complexity, seems to play a greater role with increasing virtuality. Complexity is characterized by the amount of unique information, or number of components, represented in the mental model. Increased levels of complexity of a group’s mental models will be associated with decreasing levels of accuracy and similarity of that group’s mental models. Increasing complexity of the mental model therefore limits the performance of a team (Schmidtke & Cummings, 2017). There is consensus in research that TMM influence team processes and are related to team performance (Caya et al., 2013; DeChurch & Mesmer-Magnus, 2010; Mathieu et al., 2000; Maynard & Gilson, 2014). “TMMs fulfill multiple functions, such as allowing team members to interpret information in a similar manner (description), share expectations concerning future events (prediction), and develop similar causal accounts for a situation (explanation)” (Mohammed et al., 2010, p. 879). The interaction of task-related and team-related mental models is important for team effectiveness (Grossman et al., 2017). However, there is no clear distinction between the two aspects as roles and tasks tend to be intertwined (Schmidtke & Cummings, 2017). The stronger the task interdependence, the more important task and team related mental models become (Maynard & Gilson, 2014). To what extent these results on the importance of TMM can be transferred to virtual collaboration is not yet fully understood, due to a lack of sound empirical evidence. Nonetheless, Müller and Antoni (2020) showed that ICT team mental models have significant influence on team coordination and performance. If no rules for use are specified (“high flexibility of ICT use”), IT-related TMMs play a greater role in coordination (Müller & Antoni, 2020). Somewhat in contrast to this, the expectation that team reflection would be less pronounced in virtual teams than in face-to-face teams as team members receive less feedback on task performance and team processes has not been confirmed empirically (Konradt et al., 2015). In this respect, there is still work to be done.

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WORK DESIGN TO STRENGTHEN TRUST AND TEAM MENTAL MODELS How can work design intensify trust relationships and improve TMM? The first strategy we discuss we label ‘strategy A’ (B, C, and D are all depicted in Figure 14.2) and relates to team composition and design of work roles. When establishing a new team, consideration must be given to whether the advantages of distributed cooperation outweigh its disadvantages for building trust and TMM. Relevant for the dynamics in a team is not only the spatio-temporal distribution of its members, but also the configuration of the whole team. The number of locations to be integrated in the team, how many team members work in isolation, and the differences in team sizes at different locations have been found to have an impact on team dynamics (O’Leary & Cummings, 2007). In particular, an uneven distribution of team members seems to promote sub-group formation and thus negative group dynamics (O’Leary & Mortensen, 2010). In particular, an uneven distribution of team members seems to promote sub-group formation and thus negative group dynamics (O’Leary & Mortensen, 2010). HR professionals and managers should not over-distribute teams if trust building is important, “namely when team members work in a highly interdependent manner, with other members who possess unique skills and have different levels of authority in the team” (Jong et al., 2016, p. 1144). With high task interdependence, TMM are also of greater importance (Maynard & Gilson, 2014) and the complexity of mental models increases with virtuality (Schmidtke & Cummings, 2017). Therefore, we can apply the recommendations related to fluid teams proposed by Bushe and Chu (2011) to reduce task interdependency by simplifying tasks, team constellations, and role concepts as far as possible to reduce the need for shared mental models to virtual teams. “If roles in teams are highly formalized, then such simulation type planning and training can increase the ability for team members to anticipate the coordination needs of other roles (what information and action is needed by others, when?) without relying on mutual adjustment” (Bushe & Chu, 2011, p. 185). If a high degree of geographical distribution is unavoidable for collaboration, this challenge must be overcome by systematic design (Ford et al., 2017). Paul et al. (2021) recommend that managers seek to form teams in which the members have existing trust relationships. The duration of the team constellation is also important for the emergence of TMM. Morrison-Smith and Ruiz (2020, p. 7) cite a number of sources, which suggest promoting social exchanges early on in the life of a team or project or creating opportunities for casual, non-work-related interactions between collaborators to enhance trust. In general, trust should be an important objective in the externally supported team building process (Boos et al., 2017). In the process, the cognitive and affective basis of trust should be fostered (Jong et al., 2016, p. 1144). “[…] when building virtual teams characterized by high distance […], managers should place more emphasis on early trust building” (Paul et al., 2021, p. 195). In addition, systematic reflection and feedback can also promote the development of TMM and lead to better coordination and team outcomes (Caya et al., 2013; Geister et al., 2006; Kneisel, 2020; Konradt et al., 2015). Kneisel (2020, p. 162) emphasizes the importance of high quality TMM, but points out that “the building up of correct task conceptions is thus not necessarily bound to specific intellectual requirements of the team members or time-consuming training interventions but can be achieved through regular reflection breaks.” Furthermore, it is important that team members are aware of having a common understanding of their task-specific ICT use as this leads to successful team coordination and perfor-

270  Handbook of virtual work mance (Müller & Antoni, 2020). The development of a team’s shared knowledge depends on learning processes in which the team members also take individual risks. Therefore, the importance of creating psychological safety for the development of knowledge must be emphasized (Cauwelier et al., 2019), linking back to the topic of trust. Trust can be developed through teambuilding activities, where communication rules and team values are discussed and agreed upon, as well as through the technology used to ensure that all team members are integrated. “Our study shows that trust is increased by regular team process feedback, specifically the exchange of perceptions about their mutual collaboration. This communication might have helped the team members get acquainted with one another and build up a relationship” (Geister et al., 2006, p. 484). Following Breuer et al. (2016), documenting team interactions seems to be a viable complement to trust-building activities. Intervention strategy B (Figure 14.2) focuses on the selection, procurement, and design of the technology used by the teams. Due to the lack of empirical literature on the role of IT in the emergence of TMM in virtual teams, we have little specific guidance on this topic. However, the comments on designing technology to promote trust are likely to be applicable to TMM as well. Specifically, a general recommendation here is that communication technologies have to optimally match the requirements of the task and the collaboration process in the team. In a situation of high geographical distribution, electronic media can be used to reduce the need for trust by transparently displaying and documenting interactions and making them reprocessable (Breuer et al., 2016). Morrison-Smith and Ruiz (2020, p. 24) describe four design features that are relevant for building trust: “assist the creation of common ground and work standards; facilitate communication; provide mechanisms for work transparency; and design lightweight, familiar technology.” Collaboration platforms can improve communication and collaboration and also foster social presence between members, team cohesion, and team members’ commitment to the team and company (Hardwig et al., 2019). For trust-building, the feeling of being part of the team and of being heard and acknowledged by the team is relevant, and technology can support this. Trust can also be created by sharing more information about the collaboration partners on collaboration platforms, which provide information about their competence, personal work focus, and interests (Morrison-Smith & Ruiz, 2020). In recent years, the importance of visual media has become the focus of attention due to the availability of appropriate internet bandwidth. Practical experience suggests that the mutual visibility of team members has a positive effect on team cohesion and the development of trust. Technology, creating the sense of a shared workspace through open video connections seems particularly effective (Morrison-Smith & Ruiz, 2020; Tietz & Mönch, 2015). With a view toward the building trust in teams, intervention strategy C (Figure 14.2) primarily addresses the policy level of company-wide regulations of work. In the existing team literature, the organizational level is rarely considered. Yet, the organizational level is relevant in determining what support services Human Resource Management (HRM) provides to teams, for example, for process design and team development (Bedwell et al., 2012; Boos et al., 2017). In addition, the available infrastructure for virtual collaboration must be deployed company-wide. It should be uniform, easy to use and, above all, reliable. Reliability promotes structural trust in the company’s own systems for communication and collaboration (Morrison-Smith & Ruiz, 2020). The principles and regulations governing the use of collaboration platforms must also be coordinated throughout the company and – depending on the national system – negotiated with employee representatives or trade unions (Weissmann & Hardwig, 2020). For a successful utilization of the tool’s potential for more intensive collab-

The surge in digitalization  271 oration within the company, a trust-based corporate culture is important. It is “necessary to create a safe space for platform usage in which the employees are protected and can have confidence that they can use the platform productively for their work while also being protected in terms of privacy rights and data security.” Management “can set a frame in agreements for such a trust-based collaborative culture, while also building trust by establishing clear principles of usage for work with collaboration platforms. This concerns performance and behaviour control in particular which can be ruled out in those agreements” (Weissmann & Hardwig, 2020, p. 23). Such arrangements represent an example of possible positive influences of the organizational level on trust in teams (Costa & Anderson, 2017). Intervention strategy D (Figure 14.2) concentrates on the competencies, skills, and personal development of the employees and how these can be achieved. It is also possible to influence trust-building in teams by means of individual training or training of managers. Thus, special training can increase the ability to form accurate mental models (Morrison-Smith & Ruiz, 2020). With more virtuality, a higher training effort becomes necessary. Schmidtke and Cummings (2017) have identified five types of training that might improve teamwork: (1) coordination and adaption training that helps teams to adapt to changes; (2) cross training to improve mental model accuracy; (3) guided team self-correction to keep the team focused, constructive and productive; (4) technology and communication training to learn how to use communication tools and how to share and interpret information with these; (5) interpersonal skills training to emphasize the ability to interact with team members from different cultures. The goal should be to promote both the accuracy of the TMM and its similarity (Mohammed et al., 2010). In addition, employees can be prepared for the requirements and specific regulations of collaboration in their own company through targeted onboarding processes (Ford et al., 2017).

CONCLUSION The current surge in digitalization has led to a significant increase in virtual collaboration. With unprecedented naturalness, team members today flexibly choose the location from which they make their contributions to the team task. They not only use an array of technological possibilities to communicate, but also share a virtual space in which they work on shared documents, software code, and concepts. Many teams find themselves in hybrid work situations in which parts of the team are involved in face-to-face collaboration while others participate virtually. In this respect, the current surge in digitalization represents a renewed challenge for modern teams. Their ability to function is already strained by short-term team life cycles, multi-team membership, changes in team composition, and fluid team structures. In light of this, questions arise as to what research can contribute to addressing these challenges. After reviewing the state of research on team member collaboration, we find that the surge of digitalization primarily threatens the performance of interdependent, hierarchically differentiated teams that rely on specialized skills – as is typical in skilled knowledge work. Empirical research suggests a correlation between the degree of virtuality and the performance of a team and other outcome variables. This correlation is moderated by factors of trust and TMM. The stronger the virtuality, the more difficult it is to build trust and develop TMM, which in turn can impede team performance.

272  Handbook of virtual work Our impression is that research on virtual team member collaboration has now reached a state of maturity. This state can be summarized in a comparatively complex IMOI model of collaboration based on a proposal by Bedwell et al. (2012). The model might be transferable to collaboration between other social entities such as team–team collaboration or inter-organizational collaboration. The collaboration model fits in with the work design model proposed by Parker and Grote (2020) for coping with the digitalization of work. The work design model focuses on different levels of action (individual, team, technology, higher-level systems) and distinguishes four intervention strategies. Applying these strategies, virtual teamwork can be designed in a broad and multi-facetted way. Team composition, organizational embedding of technology, and regulatory integration of teams into the organization are only a few examples of measures relevant for the successful design of virtual and hybrid teamwork. These factors all support our initial claim that a holistic socio-technical design approach, pointing beyond classic HRM measures and including technology and organizational design, is well suited for the new work “normal.”

IMPLICATIONS FOR FURTHER RESEARCH Nevertheless, in reviewing the extant literature a number of shortcomings and new questions were also uncovered. For example, the degree of virtuality increases due to the diversity of locations and hybrid work situations, however, collaboration platforms also provide a social location for virtual collaboration that can generate social proximity and thus counteract virtuality. This might suggest that some of the previous findings with regard to increased use of electronic media with increased virtuality are no longer valid today. The construct of virtuality must be conceptualized in a more differentiated way the interplays with face-to-face contacts and media-supported communication must be taken into account. For example, if intense, rich exchanges occur between team members during brief episodes of collaboration, this could be of greater importance for the formation of TMM than if these exchanges take place during moments of coordination. There is evidence that video team meetings (Paul et al., 2021), the use of collaboration platforms (Anders, 2016; Hardwig et al., 2019), and also the documentation of team activities in electronic media (Breuer et al., 2016) counteract the negative effects of virtuality. To sum up, first, we face the conceptual challenge of understanding the current degree of virtualization in a differentiated way. Second, we need further research under real working conditions that can shed light on current processes of digitalization and their impact on team performance.

IMPLICATIONS FOR PRACTICE For practical purposes, we advise managers to carefully deliberate on the implementation of virtual teams. In particular, interdependent, hierarchically differentiated teams relying on specialized knowledge need targeted socio-technically sound work design measures in order to be able to work successfully. Otherwise, there is a risk that teams fail or that team members will have to spend too much energy to ensure team performance and compensate for weak work design. This is likely to be associated with dissatisfaction, negative health consequences, or

The surge in digitalization  273 increased employee turnover. Research on team member collaboration provides very concrete indications of how the surge in virtual work can be managed through human-centered work design. Companies can be guided by the principles of socio-technical work design and the four intervention strategies proposed by Parker and Grote (2020). Ensuring the performance of teams and the interoperability of technical (company-wide networks and IT landscape) and social systems (e.g., overarching role concepts) also requires organization-wide concepts of technology design as well as the establishment of organizational regulations, support frameworks, and infrastructures. In companies, it should not only be managers and experts in industrial engineering who perform work design. For virtual teams, it is important that team members are also involved in this endeavor. To this end, the competencies of all participants for socio-technical work design must be developed (Parker & Grote, 2020). Creating human-centered work design concepts for hybrid work should maximize individual freedom to choose when and where to work without overburdening teams or exposing them to health hazards. It is an exciting challenge. Research on team member collaboration can already contribute a great deal to the human-centered socio-technical design of this specific form of work. It will be able to do so even better if further research is carried out to refine this concept.

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PART IV VIRTUAL TEAM PROCESSES AND EMERGENT STATES It is important to remember that teams are greater than just a collection of individuals. Virtual teams are more than just individuals working across distances or using technology – the experience of interacting with others in these environments leads to the generation of unique phenomena, challenges, and opportunities. As a result, the secret sauce or “black box” when it comes to team success always lies in “how” teams get things done and work together – team processes and emergent states. This section intends to capture how several of these phenomena emerge in virtual settings, their impacts on virtual teams, and how they can be leveraged and manipulated for optimizing team outcomes. This section opens with a chapter that focuses on team cognition, which serves as the foundation for both attitudes and behaviors in teams. Fiore, Bendell, and Williams dive into the role of team cognition as a virtual team process. This exploration begins with uniting cognitive and organizational sciences through the integration of theoretical concepts that are presented in the form of a human–machine hybrid. Here, the authors specifically argue that in a virtual context, the role of enabling technologies must be properly accounted for. The chapter closes with guidelines and recommendations for augmenting virtual team cognition by viewing teams as an extended cognitive system that encompasses both members and their enabling technologies. The next chapter, from Benda, Kramer, Baak, and Feitosa turns the focus on trust development. While trust is a foundational element for all teams hoping to work cohesively, virtual teams are even more dependent on trust than a team operating in a traditional office environment. With this context in mind, the authors examine trust as both an emergent and a shared state. The authors provide a new conceptual framework which recognizes trust consequences across the transition, action, and interpersonal phases. Additionally, team trust is explored at a multi-level perspective highlighting the impact individual and organizational level constructs have on team trust. They conclude by calling attention to the need to repair trust in virtual teams while expanding trust measurement capabilities. While trust and shared cognition will help, it is important to acknowledge that virtual teams, like all teams, will face adversity and setbacks and that recovering from them is critical to team success. In their chapter, Argote, Darlington, Feitosa, and Salas dive into virtual team resilience. Similar to the faultlines research discussed in the chapter by Thatcher and Rico in Part III, the authors here point to need for additional empirical team resilience research in 278

Part IV: Virtual team processes and emergent states  279 the virtual domain. The authors propose a multidimensional model for virtual team resilience to help facilitate resilience in future virtual teams. This model incorporates crucial phases including anticipating, monitoring, responding, and learning and their impact on psychological safety and team efficacy. In conclusion, this chapter includes practical tips to building, managing, and maintaining team resilience within virtual teams. This section and the team section of the handbook close with a case study of a virtual project team. Teams have different life spans, and this chapter explores virtual project teams, which are temporary and tasked with innovation. Chamakiotis and Panteli explore the impact virtual project teams have on creativity by using a product design firm as a case study. The results depict how creativity evolves throughout the virtual project team lifecycle and highlights threats to creativity including, in this case, a complete lack of face-to-face communication. In conclusion, Chamakiotis and Panteli discuss the theoretical implications and practical applications from this study.

15. Virtual teams and team cognition Stephen M. Fiore, Rhyse Bendell and Jessica Williams

Modern day teams are becoming increasingly virtualized due, at least in part, to improvements in virtual collaboration technologies that can assist and enable team processes in multiple ways. These advancements in information and communication technologies have facilitated growth in remote working (also referred to as telework, distributed work, and flexible work arrangements; Wang et al., 2021), which can be attributed to factors including distributed expertise. The ability to adapt to virtual work arrangements is essential as work performed online is increasingly critical to team success (Marlow et al., 2017). In 2020, the start of the COVID-19 pandemic required approximately 80 percent of organizations to implement remote work policies and strategies to maintain personnel safety, including actions such as shifting teams to working remotely, staggering schedules, and limiting in-person work presence (Meluso et al., 2020). Though necessary for public health and safety, these changes disrupted team member norms and routines, and prior teams research has found that they can lead to reduced team performance (Meluso et al., 2020). Given the impact of the pandemic, over half of organizations surveyed indicated that the remote work policies implemented due to COVID-19 are likely to remain permanent (Meluso et al., 2020). The use of virtual teams is only expected to continue growing in prevalence (Dulebohn & Hoch, 2017; Meluso et al., 2020). Given these evolving dynamics, it is important to investigate how virtual work affects team cognition. From this, we can understand how to improve team effectiveness, as well as how to best design systems to support virtual team collaboration (Cordes, 2016). A large portion of teams already work together using virtual tools, such as email, and cloud-based drives, as well as online team collaboration platforms. Further, this nearing-ubiquity of virtual collaboration tool use among modern teams transcends domains and leads one to question what characterizes a virtual team. One of the primary characteristics of virtual teams is the utilization of tools to facilitate communication regardless of geographic location or time differences within the team (Marlow et al., 2017). Prior definitions have often identified virtual teams based on factors such as geographic dispersion, limited face-to-face contact, asynchrony, and the use of technology to facilitate team communication and collaboration (e.g., Dulebohn & Hoch, 2017; Krawczyk-Bryłka, 2017; Lacerenza et al., 2015). This has led to the traditional categorical conceptualization of teams as either face-to-face or virtual, which may not be appropriate for modern teams (Marlow et al., 2017) and it is unrealistic to consider only categorical separations of those “pure” team types (Mihhailova, 2006). Rather, the virtuality of teams should exist on a continuum, where less-virtual teams cooperate almost entirely face-to-face and highly virtual teams cooperate exclusively online through technologies, including greater utilization of more sophisticated collaboration tools (Hacker et al., 2019; Krawczyk-Bryłka, 2017). This allows space in the definition of virtual teams for hybrid teams whose operations and processes are influenced by the degree of virtuality of the team, and whose members are partially co-located and may cooperate both face-to-face and through communication technology. In fact, almost every team has some elements that allow it to be considered partially 280

Virtual team cognition  281 virtual (Kirkman & Mathieu, 2005; Krawczyk-Bryłka, 2017). Thus, the virtuality of teams should be understood to exist on a spectrum where a team may operate fully in-person, fully remote, or use a hybrid model. Research on virtual teams often focuses on traditional team factors, such as communication, to examine similarities and differences between co-located teams (Aritz et al., 2018). This includes studying attitudinal factors, like trust or cohesion, to understand how distribution could inhibit their development (Dulebohn & Hoch, 2017). Others have looked at behavioral factors, like shared leadership, to examine how this might attenuate challenges associated with virtual interactions (Han et al., 2018). In that same vein, some research has considered enabling technologies that can improve remote collaboration and support team processes, such as facilitating the sharing of distributed knowledge and integration by team members in order for virtual teams to collaborate effectively (Alsharo et al., 2017). In a review of this literature, Gilson et al. (2015) describe a number of factors that have been studied to identify particular themes emerging in the literature on virtual teams. They discuss traditional areas, such as leadership and interpersonal processes, as well as technology and variations in the form of virtuality. Notably absent from their review is any discussion of team cognition. In this chapter, we redress that gap by reviewing research examining team cognitive processes in the context of virtual collaboration. In considering how to best support virtual team processes, team cognition is advantageous because cognition is the foundation of attitudes and behaviors in teams. More broadly, team cognition is foundational to organizational dynamics and is one of the strongest predictors of team effectiveness (Mesmer-Magnus et al., 2011; Niler et al., 2021). Towards this end, we unite the cognitive and organizational sciences via integration of theoretical concepts to offer insights on virtual team cognition, conceptualized as a form of human–machine hybrid. First, we provide a definition of team cognition encompassing knowledge, functions, and process as they occur in teams. We then link theory from the organizational sciences about teamwork and taskwork to describe how this increases our understanding of technology connecting virtual teams. From that, we discuss concepts on cognition coming out of the cognitive sciences to describe how technology can support virtual team cognition. Finally, we offer guidelines and recommendations for how virtual team cognition can be augmented by viewing such teams as an extended cognitive system encompassing members and their enabling technologies.

TEAM COGNITION FRAMEWORK: KNOWLEDGE, FUNCTIONS, AND PROCESS Team cognition represents a subset of research coming out of the social and organizational sciences. It encompasses the cognitive processes emerging during complex and dynamic interactions between individuals, the team, and their technology (Salas & Fiore, 2004). In expert teams, that cognition includes overlapping as well as complementary knowledge, is well organized, and easily accessible and applied. This fosters coordination by allowing members to anticipate needs and actions and provide knowledge when needed. As such, cognition arising during collaborative work is foundational to the execution of coordination in teams. For the purposes of supporting virtual team cognition in future organizations, we must move beyond traditional views of cognition as only involving humans. The challenge for the 21st-century organization is the effective integration of a workforce with enabling, and varia-

282  Handbook of virtual work bly intelligent technologies. Organizations need to understand how to build human–machine hybrids interacting across levels of cognitive and collaborative entities found in multiple forms in the modern organization. To support this, we additionally draw from cognitive science and cognitive systems engineering theories (Hollnagel et al., 1986; Flach, 2008; Woods & Roth, 1986). These were developed as a theoretical lens through which to interpret complex cognition and more fully describe cognition in natural settings (Hutton et al., 2003; Klein et al., 2003; Flach, 2008). In support of advancing team cognition theory, we leverage these ideas to develop “a framework for studying and understanding cognitive processes as they directly affect performance of natural tasks [with representative macrocognitive functions described as] decision-making, situation awareness, planning, problem detection, option generation, mental simulation, attention management, uncertainty management, [and] expertise” (Klein et al., 2000, p. 173). Holding to this broader schema for examining cognition, Hollnagel (2002) regarded five features for what constitutes these more complex forms of cognition. First, across natural and artificial cognitive systems, the process and product of cognition will be distributed. Second, cognition is not self-contained and finite, but a continuance of activity. Third, cognition is contextually embedded within a social environment. Fourth, cognitive activity is not stagnant, but dynamic. Last, artifacts aid in nearly every cognitive action. Importantly, these latter notions fit well with our arguments concerning virtual team cognition as cognition extended across people and machines (e.g., Fiore et al., 2010; Fiore & Wiltshire, 2016; Klein et al., 2004). In sum, we integrate these perspectives for the purposes of guiding organizations in their understanding and support of virtual team cognition (VTC). This chapter advances thinking about organizational effectiveness by integrating the study of team cognitive knowledge, team cognitive functions, with the team cognitive process of coordination. These form a critical unit for studying how humans and machines interact in virtual teams. For the purposes of this chapter, we define team cognition as the phenomenon arising from the dynamic interdependency of inter-individual and intra-individual factors as members interact with their environment, their technology, and each other. Much like individual cognition, team cognition, occurring at the level of the team, extends beyond more than just the team’s knowledge. It also includes team cognitive functions such as collaborative problem solving, decision making, and planning. Thus, we argue that team cognition consists of three major components – team cognitive knowledge, functions, and processes (see Table 15.1). Too often, research conflates these but, for supporting virtual team cognition, we argue that clearly differentiating between them is critical to promote organizational effectiveness. Team cognitive knowledge can be viewed as the internalized knowledge structures or knowledge stock of a team. This includes concepts such as shared mental models and transactive memory systems. Team cognitive functions are the complex activities such as problem solving, decision making, and planning, that draw on this knowledge. The separation of team cognitive knowledge is necessary to understand how teams use and build this knowledge when interacting over time and space. Differentiating team cognitive functions helps us understand the context for the work, as well as consider the supporting technologies that can augment the capability of the team. In conjunction, these concepts help us conceptualize the differing kinds of technologies needed to coordinate virtual team activities. We next elaborate on our conceptualization of team cognition to ground our exploration of how virtuality may attenuate complex forms of teamwork.

Virtual team cognition  283 Table 15.1

Team cognitive knowledge, functions, and processes Team Cognition Framework

Team Cognitive Process

Team Cognitive

Shared Mental Models – Organized knowledge structures distributed across

Team Coordination

Knowledge

a team and that are based upon integration of team and task knowledge with

– the ways in which

comprehension and assessment of current situation.

a team sequences and

Transactive Memory Systems – Combination of the knowledge possessed by each

times their actions

individual and a collective awareness of who knows what with team members’

to achieve effective

meta-knowledge, consensus/agreement, and accuracy as necessary components.

performance.

Team Cognitive

Collaborative Problem Solving – Coordination of actions and knowledge among

Functions

individuals and teams as they build, generate, and transform their individual and collective knowledge to solve novel and complex problems. Team Decision Making – Process of reaching a decision undertaken by interdependent individuals to achieve a common goal and that includes ability to gather and integrate information, use sound judgment, identify alternatives, select the best solution, and evaluate the consequences. Team Planning – Development of alternative courses of action. This involves decision making about how team members will go about achieving goals, discussion of expectations, relay of task-related information, prioritization, role assignment, and communication of plans to members.

Source: Authors’ own.

Team Cognitive Knowledge Teams who have shared mental models, described as a shared knowledge of their interdependencies, the problem to be solved, and the terminology used across their representative disciplines, are better able to coordinate activities (DeChurch & Mesmer-Magnus, 2010; Gentner & Stevens, 1983; Wilson & Rutherford, 1989). Shared mental models have been found to affect critical mission processes and outcomes such as team planning, team decision making, and problem solving (e.g., Burke et al., 2006; DeChurch & Mesmer-Magnus, 2010; Fiore & Schooler, 2004; Rico et al., 2008; Salas et al., 2005). Another key knowledge component are transactive memory systems, “defined as the shared division of cognitive labor with respect to encoding, storing, and retrieving knowledge from different but complementary areas of expertise” (Huber & Lewis, 2010, p. 8). This enables team members to attend more closely to information that pertains to their area of expertise, thus allowing members to become more specialized and aware of who knows what as relevant for given task needs (Austin, 2003; Hollingshead, 2001; Lewis, 2003; Moreland & Myaskovsky, 2000; Wegner, 1987), and has been related to coordination and successful team performance in a variety of settings (Kozlowski & Ilgen, 2006; Liang et al., 1995; Marques-Quinteiro et al., 2013; Zhang et al., 2007). Team Cognitive Functions The modern organization is inherently complex and often characterized by novel and ill-structured problems, often with no known solution, which require collaborative problem solving across multiple distributed teams of diverse disciplinary expertise (Fiore & Schooler, 2004; Orasanu, 2005). Many of these problems are characterized as complex given that the

284  Handbook of virtual work tasks in which these arise are dynamic, varying as a function of temporal demand; additionally, many of the variables involved do not display a one-to-one relationship (Klein, 2006; Quesada et al., 2005; Watts-Perotti & Woods, 2007). Relatedly, team decision making is defined as “the process of reaching a decision undertaken by interdependent individuals to achieve a common goal” (Orasanu & Salas, 1993, p. 328; Klein, 2008). It is distinguished from individual decision making by the fact that collective decision-making processes are able to draw upon a richer repertoire of strategies that support adaptive cognition (Entin & Serfaty, 1999) that helps gather information, make a judgment about it, and select an appropriate response or course of action (e.g., Cannon-Bowers et al., 1995; Mosier & Fischer, 2010). Finally, team planning is a team cognitive process conducted before or during a given interaction. It typically includes setting goals, clarification of team member roles, prioritization of tasks, assessment of what types of information all team members require access to and those only required by specific team members, and also, how team members intend to back each other up in the event of errors (Stout et al., 1999; Marks et al., 2001). Team Coordination as Team Cognitive Process One of the long-standing issues in team cognition research is the relationship between team cognition and coordination. Team research studies coordination as it encompasses the ways in which a team sequences and times their actions to form a coherent functional unit and achieve effective performance (Marks et al., 2001). Research has established that team cognitive knowledge is correlated with team coordination and team effectiveness. This critical aspect of teamwork, that is, the synchronization and coupling of team interaction, is driven by shared and complementary knowledge across the team, as well as team member awareness of this distribution. Thus, the focus of team cognition in complex organizations is on the coordination of knowledge held by members engaged in collaboration. Critical to maintaining virtual team effectiveness is understanding that coordination spans the boundaries of teammates, teams, and technological systems. In this context, knowledge coordination describes the awareness and use of team member expertise. That is, in situations where team member knowledge is distributed, for a given team to effectively execute its tasks, it needs to know where to find critical information and knowledge as well as how and when to apply that knowledge in service of team cognitive functions and processes. To be effective, virtual teams must manage this team cognitive process; that is, they need to maintain understanding and awareness of what knowledge is being coordinated in a given context, both within and between individuals, and how it remains coordinated over time. In turn, this supports team cognitive functions. A given team might focus on problem solving, for example, when dealing with an event like diagnosing a public relations threat. A team might be more reflective when engaged in planning, for example, to deal with changes in product manufacturing. Or teams might interact in support of decision making, for example, when identifying the best method for delivering supplies. In each of the above team cognitive functions, team cognitive knowledge will need to be coordinated. Knowledge coordination can manifest itself in many ways, but, generally, it requires utilization of shared mental models and/or transactive memory systems held by the team. For example, it can be used to draw on role knowledge of a team member, or it can be used to leverage complementary expertise on the team. Knowledge coordination can be as “low tech” as team communication to determine who knows what, or it can be enabled by

Virtual team cognition  285 technology where members, for example, access expert systems to identify relevant personnel. We bring these distinctions to the forefront because understanding the role of coordination in team cognition allows us to more clearly characterize the methods and technology needed to support cognitive and coordinative demands essential for effective performance.

TEAM COGNITION IN VIRTUAL TEAMS In the following sections we summarize representative portions of the research on virtual teams and organize them around the above framework of team cognition. First, we review the literature discussing team cognitive knowledge, and how our understanding of shared mental models and transactive memory systems varies based upon the context of interaction (i.e., virtual or not). Then we discuss team cognitive functions, and how these may vary dependent on the context (e.g., virtual team problem solving). Finally, we review the literature addressing team cognitive processes when members are distributed versus co-located. In this way, we expand the virtual team research space by connecting it more specifically to a framework for examining team cognition. Our summary highlights studies that pay particular attention to team cognitive knowledge, processes, and functions. We note that, traditionally, in the organizational sciences, researchers will talk about some of the above factors as inputs, moderators/mediators, or emergent states. Although we do not disagree with these characterizations, they are more appropriate when considering specific models of collaborations. In this chapter, we are providing a conceptual framework, founded on the science of cognition, in order to help the field better think through the implications of the various facets of team cognition. From this, research will be better able to specify the degree to which team cognitive knowledge, functions, or processes act as moderators or mediators, or are emergent states, in the context of team effectiveness. Virtual Team Cognitive Knowledge Knowledge-sharing potential is one of the primary factors that differentiates marginally virtual teams from highly virtual teams; however, it is unclear which form of virtuality provides overall greater potential for effective sharing. Marginally virtual teams (i.e., those with some members co-located), tend to benefit from the rich communication modes afforded by complex social signals such as conversational gestures, facial expressions, bodily postures, and so on, but often suffer from reduced ability to leverage information communication technologies that facilitate visualization or manipulation of knowledge artifacts. A salient example of the marginally virtual format that highlights those features is the standard, face-to-face conference room meeting, which tends to place the responsibility for knowledge presentation on a small subset of individuals. Face-to-face interactions promote effective knowledge sharing by supporting attentional capture and focusing resources, but often limits the work that can be accomplished on a given knowledge set by reducing the ability of collocated team members to operate on information. For example, communication is primarily verbal, with little use of representational aids other than, possibly, a shared view of a presentation or agenda. Said another way, highly virtual teams may experience notable drawbacks from the lean nature of the communication modes afforded by current technologies, but experience distinct benefits from the requirement that team members engage in synchronous and asynchronous activity through

286  Handbook of virtual work tools that are often explicitly designed to afford knowledge management, visualization, manipulation, and sharing. The following section explores a subset of experimental investigations that attempt to characterize the knowledge-sharing differences between marginally and highly virtual teams. Particular attention is given here to the development of shared mental models and transactive memory systems in highly virtual teams, and the potential for such teams to support knowledge sharing. Coordinative artifacts can be effective tools for the facilitation of knowledge sharing in both marginally and highly virtual teams, but are more likely to be determinants of knowledge-sharing effectiveness in highly virtual scenarios. Some insight into the impact of shared artifacts on virtual team cognition was provided in a study reported by Saikayasit and Sharples (2009), showing that virtual teams establish more robust shared mental models through the use of a virtual whiteboard. Their study compared two groups of eight dyads on the performance of a complex decision-making task, and manipulated knowledge-sharing opportunities by requiring teams to operate in a synchronous, distributed manner while providing one group with access to shared-representation facilities. The nature of the task, a house-hunting scenario that required the integration of multiple types of information from a variety of provided sources, was conducive to the use of the virtual whiteboard tool, but, interestingly, teams in both groups exhibited similar performance outcomes. The notable differences between the groups arose from the structure and types of communications between team members as well as the formation of shared mental models. Teams with access to the shared representation tool developed their team mental models through the exchange of a greater number of evaluative, strategic, and structuring communications than did teams that were limited to audio channels. Thus, we have evidence of tools supporting processes relevant to team knowledge development. Others have similarly examined how visualization tools used by highly virtual teams can influence shared mental models. Siemon, Redlich, Lattemann, and Robra-Bissantz (2017) presented four-person teams with a complex problem related to the management of radioactive waste. Unlike the task in Saikayasit and Sharples (2009), this did not have correct or readily scoped answers and, therefore, required additional creativity and teamwork interactions. The primary manipulation – provided visualization technologies – had notable impacts on the development of shared mental models such that teams benefited from the ability to share knowledge via the supportive technology, and demonstrated increased comprehension of the problem space, as well as satisfaction with team performance. Interestingly, the usage of the visualization tool altered the nature of communications on teams such that text-based exchanges were primarily targeted at the use of the tool rather than any task-related content. Here we see that the tool helped virtual teams better understand their problem space and increased shared knowledge of their problem. A limitation reducing the generalizability of these results is that teams in the experimental condition had access to only a single supportive technology that was to be used in a clearly defined manner. By design, team members engaged in their interactions with well-developed shared mental models regarding the purpose for, and appropriate use of, the virtual whiteboard tool and did not need to dedicate resources to the coordination of tool use. More recent research has examined what happens when there is a wider range of available technologies with overlapping capabilities. Müller and Antoni (2020) showed how it is critical for highly virtual teams to quickly establish a team mental model of the appropriate or agreed-upon use of a diverse array of tools. Their study examined 31 teams across two

Virtual team cognition  287 different IT companies through the administration of questionnaires focused on information communication technology mental models as well as team coordination and performance. Although their results showed that individuals’ perceptions of shared mental models did not correlate at the level of teams, both individual- and team-level perceptions did have a strong influence on perceived effectiveness of team coordination and performance. Additionally, they noted that highly virtual teams rarely explicitly discuss or agree upon the appropriate use of information communication technologies thereby reducing the potential to establish or develop those necessary shared mental models. We find then that an additionally important component is shared mental models about the use of technology in support of teamwork. Here we note that team charters, which are generally beneficial for teamwork, can help even more with virtual teams (Asencio et al., 2012). Although shared mental models regarding team communication technologies are critical, they are not likely to make up for the detrimental effects of inappropriately matched technologies and task demands in highly virtual teams. In this context, research has demonstrated the importance of appropriately matching the capabilities of virtual team’s communication technologies to their tasking. Aiken, Gu, and Wang (2013) demonstrated this via a survey of business students regarding how technologies were used for their group projects. Conducted over six weeks, their study showed that perceived technology fit had a significant relationship with teams’ satisfaction as well as a mediating effect on perceived knowledge-sharing effectiveness. Others have similarly examined how technologies interact with variations in virtuality A study conducted by McNeese, Pfaff, Santoro, and McNeese (2008) compared teams across three levels of virtuality and supporting technologies. Employing a team problem-solving task based on a hypothetical search and rescue mission, their study compared team processes, interactions, and outcomes across face-to-face, auditory teleconferencing, and communication set in an online role-playing virtual world. Each condition was experienced by approximately ten teams of three individuals whose knowledge-sharing interactions were recorded in addition to responses to post-task survey measures covering teamwork and task outcomes. Perhaps unsurprisingly, face-to-face interactions were perceived to be the most effective and supportive of teaming. But an important take-away from this study was that perceived success and perceptions of the teaming support was lowest for the audio teleconferencing. The Second Life teaming scenario received mixed feedback from participants who both noted that the technology was not a good fit for facilitating their task, but that its provision of a virtual space promoted the perception of team cohesion and thereby success. Additionally, teams in the online virtual world condition produced more accurate responses to some elements of the search and rescue task that may have been a result of the limitation to text-based chat. That is to say that text-based chat supported by virtual “collocation” was perceived as inappropriate for the task, but was not wholly non-viable for team success. Recent studies have also explored the technology affordances that best support the development of transactive memory systems in highly virtual teams. Ge and Lang (2020), for example, conducted a survey of 158 individuals actively participating in virtual teams. They found that the richness of the communication media utilized on their teams positively impacted their perceptions of effective transactive memory system development. That is, lean communication media such as text-based or audio-conferencing were less effective for supporting transactive memory than were rich modalities such as synchronous video conferencing. Additionally, their study found that team identity was positively linked to the development of transactive

288  Handbook of virtual work memory systems. This mirrors other research addressing more attitudinal factors and how these are attenuated due to virtual interactions. This includes affecting how experience of team cohesion changes team perceptions (Carlson et al., 2017), as well as team trust (Xue et al., 2012), and how these alter information-sharing potentials. Overall, then, this can moderate development of transactive memory systems (TMS), and, generally, we see that, again, low information richness affects development of TMS. Virtual Team Cognitive Functions A critical goal of successful knowledge sharing in teams is to support cognitive functions such as collaborative problem solving, decision making, and planning. Accordingly, researchers and practitioners alike want to better understand the impacts, positive and negative, of highly virtual teaming as compared with more traditional modes of team interaction. Note that, although some of the prior reviewed studies did have tasks that constitute our definition of team cognitive functions, their emphasis was more on team cognitive knowledge. The following studies foregrounded the team cognitive functions to understand what facets of interaction influenced outcomes. Early work in this area studied engineering students tasked with solving business-related problem sets either in face-to-face settings or through a virtual, asynchronous conferencing system (Jonassen & Kwon, 2001). Notably, teams in the computer conferencing groups tended to demonstrate a different sequence of activities in order to generate their solutions to the problem. As they made use of the pervasive affordance of the communication technology to readdress problems and reconsider solutions in two distinct phases, they were unlike face-to-face groups that attempted to complete the entire problem-solving phase in a single period. Interestingly, for this longer duration task, which could be addressed asynchronously, teams in the highly virtual condition perceived their experience as higher quality and more satisfying despite the limitations of the technology mediated communication. A final interesting finding was that teams in the computer-mediated condition tended to engage in communications that were primarily task focused and related. As an important aside, this is a potentially problematic shift if teams need to bolster interpersonal relationships to improve attitudinal factors in their team (e.g., trust). Another early study of team cognitive functions provided important insights about teams with longer life cycles (Orvis et al., 2002). When teams engaged in relatively more interactions, 70 hours over six months, they were found to dedicate a large portion of their communications to social exchanges. Their study was conducted in the context of a course conducted by the United States Army Armor School in Fort Knox, Kentucky. It included 41 students as well as some instructors and advisor who met for 12-hour sessions over the course of seven weekends. A key difference between this study and Jonassen and Kwon (2001), is that, although all interactions were conducted online and through text-based channels, teams conducted their activities synchronously and in sessions of much longer duration. Outcomes of behavioral and communication analysis showed that team problem solving in this scenario did not differ notably from a typical face-to-face course in that the patterns of interaction did not deviate significantly nor was social interaction totally eliminated. From this, we note that long duration interactions tend to overcome limitations produced by virtuality. Additional research studying problem solving mediated by technology (Andres, 2013), was also in somewhat of a disagreement with the study of the technology limited virtual

Virtual team cognition  289 teams conducted by Jonassen and Kwon (2001). This study found that technology-mediated collaboration, as seen in highly virtual teams, introduces problems for collaborative problem solving. This was shown to be due to information exchange delays, as well as inhibited cognitive processing and reduced clarification and learning opportunities at the team level. These findings came out of a study of Management Information Systems undergraduates, separated into face-to-face or videoconferencing teams. They were assigned a problem-solving task requiring them to improve the information system of a hypothetical university. Teams generated design documentation, system prototypes, and pseudocode to address the problem as well as completed survey ratings at the midpoint and end of their task, which were used to evaluate their teaming process and effectiveness. Analyses indicated that highly virtual teams suffered as a result of reduced effectiveness of information exchange patterns, as well as grounding behaviors, and, perhaps most importantly, inability to collaborate their knowledge sharing and decision-making efforts. More recent research has further examined the issue of time from the standpoint of short-duration teams and time-pressure constraints (Paul et al., 2018). This experiment showed that team climate and group atmosphere can still be critical without the opportunity to develop over the course of months. They examined 24 three-person teams of undergraduates who employed a synchronous communication technology to execute problem solving of a task in which they were to collaboratively design a data model for a database. Teams experienced one of four conditions determined by a two-by-two framework imposing time constraints versus no time constraints and promising of reward versus no reward. Neither of those manipulations had an effect on teams’ problem solving nor on shared understanding or perceived conflict; however, perception of group atmosphere had a positive impact on perceptions of shared understanding and a negative effect on perceived conflict. Separately, Paul et al. (2018) examined the role of cultural diversity with the assumption that more diverse teams would struggle to establish shared understanding, but rather found the opposite as more diverse teams were associated with greater perceptions of shared understanding. Again we see that, although there may not be process or outcome differences, perception differences can occur. In addition to developing differently over time, highly virtual teams are expected to exhibit distinct temporal dynamics in their decision-making sequencing. This issue was explored in Goel and Rehm’s (2017). study of decision making in highly virtual teams through application of the punctuated equilibrium model. In this study, 343 students were formed into 84 teams that used an online role-playing game environment as a virtual exchange medium to approach a complex decision-making task over the course of five months. These highly virtual teams aligned with the expectations of punctuated equilibrium theory for the first portion of their decision making in that they showed evidence of a relatively long first phase, with fairly few structure or stance changes. They then deviated from the midpoint transition expectations of the equilibrium theory by demonstrating multiple short phases in which notable shifts occurred in team stance rather than the fewer transitions of larger magnitude predicted by the model. This outcome is particularly interesting because it suggests that, while highly virtual teams may be similarly influenced by task pressures and the realization of the need for a transition towards more effective teaming, they do not seem to be able to navigate that transition in the smaller number of long duration attempts seen in face-to-face teams. Knowledge sharing in virtual teams is necessarily impacted by the technology-related factors discussed thus far, but may also be heavily influenced by the difficulties of establishing interpersonal relations, trust, cohesion, and other team traits necessary for effective perfor-

290  Handbook of virtual work mance. Individual experience with the technologies leveraged by a team can, for instance, play a significant role in determining how teams develop. This was directly explored in a study of the relationship between team development and individual experience as well as openness in the context of knowledge sharing (Carlson et al., 2017). This involved an experiment with 152 two- or three-person teams of undergraduate and graduate students who were tasked with preparing solutions to ten survival-type scenarios through interactions on a web-based interface that provided text-based communication and voting capabilities. Although their study did not employ an experimental manipulation, they did collect a range of survey responses to capture perceived team cohesion, openness, experience with communication technologies, as well as perceived effectiveness on the decision-making task. No main effect of prior experience was found; however, their analyses did reveal a moderation effect such that teams with more experience using communication technologies perceived themselves as more open and effective at the task. Additionally, outcomes suggested that perceptions of team cohesion play an important part in the sense of effectiveness experienced by highly virtual teams. Virtual Team Coordination Team coordination is foundational to teamwork, in general, but particularly challenging in virtual interaction. From the team literature, Marks, Mathieu, and Zaccaro (2001) defined team coordination as “orchestrating the sequence and timing of interdependent actions” (p. 363). It is critical to team cognition as it encompasses the ways in which a team sequences and times their behaviors to achieve effective performance and thus form a coherent functional unit (e.g., Elias & Fiore, 2012; Fiore et al., 2003). In virtual team research, it is similarly conceived with team coordination defined as the ability for teams to work together to achieve goals with greater efficiency, cooperation, and less confusion and misunderstandings (Aritz et al., 2018). Recent research on virtual team coordination has made effective use of online gaming platforms (Yang et al., 2020). In a study of team-based eSports game (i.e., a competitive online video game with tournaments, skill-based divisions, and large-scale competitions), role coordination in virtual teams was examined as it related to team performance and member retention, along with identification of the factors leading to better role coordination. They found that role coordination was a factor in win probability and in team member retention. Most notably, they found that coordination mediated the relationship between team familiarity, which has typically been shown to have a direct positive impact on performance, and performance (Yang et al., 2020). Thus, team familiarity increased coordination to increase wins in this context. Others similarly found online gaming to be valuable for research on virtual team coordination in high-pressure and time-sensitive contexts (Freeman & Wohn, 2019). Their approach was to interview eSports players to gain a sense of coordination activities and platform functionalities that help to facilitate coordination. They found that in both amateur and professional players, a mix of offline and online activities with team members improved team coordination. Further, effective teams used a mix of strategies for member selection and team maintenance both online and offline. To promote adequate member selection, member retention, and team cohesion, effective virtual teams attempt to alleviate potential interpersonal conflicts, ability differences, or incompatible traits by bringing in the new player to temporarily play with the team in a “trial” process (i.e., an interview and audition for the role where both interpersonal interaction and performance are used as evaluation criteria). This not only allows the team to

Virtual team cognition  291 try and ensure their new member will have a positive impact and be reliable, but the new player is also given the opportunity to assess the team themselves to determine whether they would be a good fit given their experience with the trial (Freeman & Wohn, 2019). As evidence of the effectiveness of their approach, coordination increased because of tryouts improving team member selection. Research has also more broadly sought to understand how the use of varying collaboration technologies impact virtual team coordination (Aritz et al., 2018). This study included 75 teams of 304 undergraduate students who were instructed to pick the collaboration tools they would like to use, with Google Docs (90.8%) and email (82.8%) as the most commonly picked collaboration platforms (Aritz et al., 2018). They identified teams that were well-coordinated, somewhat-coordinated, and poorly coordinated to compare the perceptions of challenges faced by the virtual team according to the degree of coordination ability. They found that well-coordinated teams were more likely to experience benefits from collaborative technology platforms, like Google Docs, and also were better able to assess the benefits and constraints of other platforms, such as email. They also found that virtual team members found rich and social channels to be more effective, and richer communication channels were found to improve team coordination (Aritz et al., 2018). Here we have another indication of familiarity with, or facility in, particular forms of technology. Higher coordination depends on facility with, and ability to discern the value of, different technologies. Similarly, technologies providing more information rich communications were also related to better coordination.

VIRTUAL TECHNOLOGY COMPETENCIES Research has indicated that, in some cases, team members’ general competence with virtual technologies can positively impact teaming, and that specific experience with a given communication or collaboration technology can facilitate team cognition functions as well as coordination. Recent research has found evidence for the importance of technological competence on virtual teams (Carlson et al., 2017). Though not statistically significant, their results showed that the presence of a technologically experienced team member seemed to positively impact overall team effectiveness. Note that this study highlights two problems with determining the value of technological experience: defining experience and measuring the impact of experience. One blocker to identifying a set of competencies that may be trained or acquired in service of virtual teaming is that, with respect to specific technologies, existing reports tend to rely primarily on duration of experience to quantify competence (cf., McNeese et al., 2008; Müller & Antoni, 2020). Evaluating competence as a function of length or frequency of engagement with a technology poses a problem for this domain because this does not allow for evaluation of particular functions, capacities, and affordances which may be supported by a given tool or learned by an individual. Additionally, there are a sufficient number of technologies available that few empirical studies have been conducted on any given tool or application context, especially with attention to team processes or performance. On this issue, Schulze and Krumm (2017) provided an informative review of Knowledge-Skills-Abilities-Other Characteristics (KSAOs) of virtual team members that, notably, indicated a largely unexplored role of technology-specific KSAOs in virtual teamwork as compared with facets such as communication or conflict management KSAOs. That

292  Handbook of virtual work is to say, although available literature has examined individual competencies that affect virtual teaming ability, competence with particular technologies has not been a focus for empirical research. Admittedly, there are several barriers to that sort of research considering the rapid turnover of technologies supporting virtual teams as well as the lack of reference data that one would require in order to determine that a given negative or positive outcome was a result of individual or team competencies as opposed to features of the technology under investigation. Currently, the virtual teams literature supports the conclusion that it is beneficial to teams for members to be exposed to the team’s chosen communication technology for as long a duration and as frequently as possible, and that familiarization with communication modalities supported by other technologies (e.g., instant messaging through a different interface) may transfer in some cases (Carlson et al., 2017; McNeese et al., 2008; Müller & Antoni, 2020). It is relevant to note here that there are many factors that may interact with technological competency, and warrant attention when considering the value of training which targets technological versus interpersonal competence. For example, the findings of Carlson et al. (2017) that suggest an impact of technological competence, more strongly align with research showing that factors, such as an individual’s openness to new experiences or willingness to adopt novel technologies may, in fact, be more critical to teaming success and perceptions of team cohesion (Kauffmann & Carmi, 2018; Müller & Antoni, 2020). The importance of perceived cohesion explored by Carlson et al. (2017) echoes previous findings such as those of Xue, Liang, Hauser, and O’Hara (2012) who emphasized the importance of team climate for successful teaming. As a component of team climate, cohesion was found to be positively related to team attitude as well as positive knowledge-sharing intention. Additionally, Xue et al. (2012) demonstrated that similar effects may be found for factors such as team trust and innovativeness. Together these findings suggest that it may be critical for highly virtual teams to find avenues to support the development of interpersonal relations and thereby promote the development of a positive team climate. Such interpersonal relationships may be particularly critical for establishing trust in virtual teams. Previous research has shown that the lack of physical presence and interaction increase the difficulty that humans experience in developing high levels of trust which can have negative impacts on knowledge-sharing potential. These outcomes are highlighted by Kauffmann and Carmi (2018) who surveyed 259 teams from around the world regarding their perceptions of affective trust, cognitive trust, and team effectiveness among other team performance related variables. Critically, their study showed that cognitive trust in particular mediated the relationship between communication and knowledge sharing thus indicating that teams that do not develop trust in task-oriented capabilities and intentions may suffer by sharing knowledge ineffectively. Accordingly, competencies required for performance of virtual teams may be more meaningfully related to the communication, self-management, intercultural, and conflict management KSAOs reviewed by Schulze and Krumm (2017) than to technological experience or competence.

SUMMARY In this section we reviewed a subset of the empirical literature on virtual teams with a focus on team cognitive knowledge, functions, and process. We see that virtuality can attenuate team cognitive knowledge depending on factors such as the information richness of the technology

Virtual team cognition  293 as well as familiarity of team members with each other or the technology. We also see how team cognitive functions can be altered in that virtuality influences important components of the interaction (e.g., communication), while also supporting, through the requirement of technology, how the teams uses information in support of their tasks (e.g., representational aids). Our goal was to provide a particular team cognitive lens through which to interpret the rich and growing research space on virtual teams. What is clear is that research needs to not simply consider whether and how virtuality affects team cognition. Rather, research is needed to understand how the contextual variations of virtuality, and the technology mediating the interactions, can produce a superior form of team cognition. We see this as an important challenge for the future of work in that organizations need to understand how to integrate personnel more effectively with technology. In doing so, we can create human–machine hybrids leveraging what humans do well and complementing that with technological augmentation based upon what machines do well. Towards that end, we turn now to a discussion of how technologies can be designed to support team cognitive knowledge, functions, and process, thereby enabling improved effectiveness in virtual teams.

TECHNOLOGY FOR VIRTUAL TEAM COGNITION In the final section, we link the above findings to notional technological scaffolds that can be adapted to support team cognitive knowledge, functions, and processes. To achieve this, we draw from traditional organizational theory and the idea of team and task competencies, and link this with cognitive systems thinking. Through this we are better able to examine how technology can amplify VTC. To this end, we adapt theories of teamwork competencies (e.g., Cannon-Bowers et al., 1995; Mathieu et al., 2000) to consider how a heterogeneous mix of technologies can more precisely support virtual team cognitive knowledge, functions, and processes. This approach is particularly useful for addressing future VTC needs in that it helps to better understand the relation between task characteristics and team competencies and the differentiation between generic and specific competencies. First, regardless of the task context or the organizational setting, all team members need what are referred to as team-generic competencies (e.g., communication skills). Second, some competencies are considered to be team-specific, as they are argued to apply in particular team situations. In this instance competencies are more directly related to individual teams and include knowledge of roles within the team and the abilities held by team members. Task-generic competencies are those necessary across task situations (e.g., exchanging information and planning), whereas task-specific competencies could include understanding objectives or using appropriate methods. We suggest that this classification scheme provides a powerful means for understanding what kinds of virtual team technology are needed for differing organizational needs. Traditional team research has examined competencies to understand functions such as planning, decision making, and problem solving. We adapt this to illustrate how technologies can support these varied competencies. For example, a given technology could support planning by managing information to align team interdependencies, such as real-time data targeting role-specific information (e.g., Nemeth et al., 2004; Ewenstein & Whyte, 2009). Another component could integrate graphical representations and/or simulations to support team cognitive functions (problem solving, decision making). For example, simulations could help team

294  Handbook of virtual work members identify critical parameters and represent such data that it helps elicit appropriate team member participation (Balakrishnan et al., 2008; Lu et al., 2010; Roschelle & Teasley, 1995). The confluence of such technology-enabled competencies maximizes support of the presentation and manipulation of mediating, coordinating, and attention focusing team and task artifacts necessary for effective virtual team cognition. Further, considering the distributed nature of the cognition, that is, interactions between the members, knowledge bases, and supporting technologies, this facilitates the development of team cognitive knowledge and, thus, supports coordination. In sum, this can help organizations better position themselves to integrate teamwork with enabling technologies.

GUIDELINES FOR INTEGRATING TEAMWORK/TASKWORK THEORY FOR VTC This synthesis of the teamwork and taskwork concepts, combined with the notion of generic and specific team and task competencies, provides an important foundation for organizations to understand how to support virtual team cognition. In this section, we break out the generic team and task factors to illustrate the differing requirements for performance supporting context driven, team-contingent, task-contingent, and transportable competencies for VTC. By more precisely describing how technology can support VTC, we provide guidance on how to develop a collaborative synergy where cognition and coordination are amplified. This can be used to produce a more detailed understanding of VTC as it relates to the needs of specific teams as well as those that are more generic for all teams. We use this integration to provide a set of guidelines for VTC management that takes into account the role of enabling technologies. As shown in Table 15.2, these are devised to unite perspectives and guide organizations through a more detailed consideration of the types of technology amplifying virtual team cognition. Table 15.2

Team and task competencies framework for VTC technology guidelines Relation to the Task

  Relation to the

Specific

Team Generic

Specific

Generic

Guideline 1.

Guideline 2.

Context Driven

Team Contingent

Guideline 3.

Guideline 4.

Task Contingent

Transportable

Source: Authors’ own.

Our first guideline is for the cell on “Context Driven” needs. To ensure the effectiveness of context-driven technologies, they should be designed to be specific to both the team and the task. In our team cognition framework, these would pertain to team cognitive knowledge, functions, or process. These should support, for example, data coming in from social media on a product launch (task element related to decision making), and/or visualizations helping to monitor team progress towards objectives (team element related to problem solving). The point is that it needs to be a technology specifically designed to support taskwork and teamwork tailored to a particular context.

Virtual team cognition  295 Our second guideline pertains to “Team Contingent” needs. To ensure the effectiveness of team contingent technologies, they should be designed to be specific to a team, but generic to a task. That is, these would transcend team cognitive knowledge, function, and process. These should support, for example, understanding processes related to characteristics of the team such as structure, roles, and interdependencies. The point is that technology requirements are only focusing on teamwork needs, separated from any task such teams can pursue. Our third guideline is for the cell on “Task Contingent” needs. To ensure the effectiveness of task contingent technologies, they should be designed to be specific to the task but generic as to the team. Again, in relation to our team cognition framework, these could pertain to any of team cognitive knowledge, functions, or process. These should support, for example, data pertaining to factors such as task sequencing and development of mental models relevant to problem/decision requirements. The point is that technology requirements are independent of team features and emphasizing task needs supportive of team cognition. Our final guideline pertains to “Transportable” needs. To ensure the effectiveness of transportable technologies, they should be designed to be generic to a team and to a task and support any form of team/task. Even more than the team contingent cell, the technologies would transcend team cognitive knowledge, function, and process. These should support, for example, processes that help manage discussions (e.g., conflict resolution) or support effective communication (e.g., centralized messaging). The point is that it is a more general form of technological aid that could have a broader impact on team process and performance without the need to be tailored to any particular context. In sum, managing virtual team effectiveness requires integration of the physical and virtual worlds by gathering and maintaining real-time data and information, connecting the specialized knowledge of team members separated by time and space in service of task accomplishment. Central to VTC effectiveness is an ability to push and pull information via integrated technologies that are part of a larger organization-wide system connecting employees with not only work artifacts but also distributed knowledge resident in people (e.g., team members) and places (e.g., databases). In convergence, these help virtual teams store and manipulate information in service of building knowledge while managing member interactions during task accomplishment. By drawing from established theory in team research, this framework provides greater specificity to the team and task functions technologies can support for VTC. Further, the teamwork/taskwork framework and the associated generic/specific competencies, help to conceptualize how various technologies can be seen as a component of a distributed cognitive system – that is, a virtual team as a human–technology hybrid supporting team cognitive knowledge, functions, and process.

OFFLOADING AND SCAFFOLDING FOR VIRTUAL TEAM COGNITION Whereas the prior section considered what technologies might support, we next discuss how they could support virtual team cognition. We integrate the teamwork and taskwork dimensions with theory from the cognitive sciences to provide explanatory mechanisms that can help organizations think about their technology needs. Further, we consider how these support knowledge coordination in support of team cognitive functions. Specifically, this section is devised to help organizations understand how offloading and scaffolding help coordinate

296  Handbook of virtual work knowledge in virtual team cognition. We suggest that these are forms of coordinative processes that support team cognitive functions (i.e., planning, decision making, and problem solving), and impact the use of team cognitive knowledge (i.e., shared mental models and transactive memory systems). Offloading is generally the act of using the environment as a semi-permanent archive for information, broadly construed, that can be readily available and accessed when needed. But it is also used to mitigate encoding and working memory demands. Research in individual and team cognition sometimes refers to this as a problem of “workload” in that information is overloading processing capability. By using the environment (e.g., some technology) as a repository for information, offloading serves the purpose of a short-term and long-term memory aid, freeing up cognitive resources that can then be allocated towards other team processes. In this sense, it replaces what was previously an internal form of cognitive processing such as holding an item in working memory or retrieving something from long-term memory. Offloading helps expand conceptions of team cognition by specifying which parts of a virtual team (human or machine) support this cognitive function. Considering this in the context of team cognition, we argue that offloading is particularly helpful for team cognitive knowledge. Depending on the complexity of the environment, or the familiarity of the members, both shared mental models, and transactive memory systems can benefit from offloading. As described earlier, many of the technologies supporting virtual teams did, indeed, help with SMM and TMS. Scaffolding takes the form of technologies that directly support processes by helping to mediate the interaction between individual and team-level cognitive activity. Scaffolding, in this sense, supports social and cognitive interaction broadly, as well as the analysis, discussion, debate of items relevant to the team’s task, and the development of the team’s shared understanding. Considering this in the context of team cognition, scaffolding supports team cognitive functions. Each function of planning, decision-making, and problem solving is facilitated by any form of scaffolding that allows team members to manipulate cognitive artifacts external to themselves. Specifically, technological scaffolds externalize and share knowledge, allowing for representation and discussion of information and ideas, provide storage and access to information, allowing for more informed comparisons and evaluations. Through this, scaffolding acts as a means for social-cognitive interaction that facilitates conversation, communication, and collaboration. In short, given the distributed nature of virtual teams, and how varied forms of technology connect them, offloading and scaffolding are essential for effective knowledge coordination when teams work across time and space. Further, these allow for deeper analysis of knowledge coordination because they help us understand which technologies, in conjunction with team members, reduce the complexity of memory-intensive tasks and/or the manipulation and use of information in support of virtual team cognition. As described earlier, many of the technologies supporting virtual teams did, indeed, help with SMM and TMS. Similarly, they enabled improved team cognitive functions. We add, though, that these likely supported enabling processes such as knowledge coordination. As such, offloading and scaffolding are critical concepts to consider for the support of virtual team cognition and we next provide notional recommendations on how organizations can leverage these to improve virtual team effectiveness.

Virtual team cognition  297

RECOMMENDATIONS FOR INTEGRATING OFFLOADING AND SCAFFOLDING TO SUPPORT VTC When organizations attend to VTC needs for offloading and scaffolding they can better conceptualize how technologies can be chosen to enable differing kinds of team process and/ or performance outcomes. Further, the adoption and adaptation of these constructs from the organizational and cognitive sciences provide greater precision in consideration of the kinds of technological platforms for supporting virtual teams. In Table 15.3 we provide a set of recommendations for offloading and scaffolding technologies to support VTC. To ground these concepts and applications, the following example is provided to illustrate the various ways offloading and scaffolding can support teamwork and taskwork. In this example, a company is about to release a new product and needs to consider manufacturing timelines, distribution constraints, as well as marketing and finance considerations for their customers. A virtual team, made up of different units within the company, is assembled to discuss and plan for the product’s launch. This is a multidisciplinary team with complementary forms of expertise, and with members located around the world. As such, they have both synchronous and asynchronous work activity requiring coordination and integration of knowledge. They need to take into account member roles and responsibilities while considering the interdependencies across tasks and roles. This includes, for example, coordinating the complementary, and sometimes competing, needs of manufacturing and distribution with marketing and finance. More generally, the coordination of knowledge in this example is used to support various team cognitive functions. Although our focus is on team cognition, we include recommendations for taskwork as well. In this way, virtual team effectiveness can be improved by considering both task and team offloading and scaffolding. Table 15.3

Recommendations for technologies supporting VTC

Support VTC taskwork through offloading task elements into technologies. These indirectly support team cognition by freeing up resources normally dedicated to a task, allowing them to be used for other needs. Recommendation 1.1. VTC technologies

Taskwork offloading could be done to support long-term memories by providing easy

should support long-term memory

access to an operating manual for the company’s proprietary distribution algorithm.

requirements of the task and act as

Rather than trying to recall details about model parameters, and risking errors because

a memory aid that helps team members

of faulty memory, team members can draw from resources because of offloading and

meet objectives.

more accurately fill in the gaps for a given need.    

Recommendation 1.2. VTC technologies

Taskwork offloading could support working memory by maintaining incoming data

should support working memory intensive

on new findings relevant to their product launch. The technology could visualize

task elements to free resources and help

manufacturing timeline data via graphs so that members do not have to maintain

team members meet task needs.

anything with their own memory. This frees up working memory resources to help understand and interpret the data.    

298  Handbook of virtual work Support VTC teamwork through offloading team elements into technologies. These more directly support team cognitive knowledge, that is, offload knowledge typically resident in shared mental models and transactive memory systems. Recommendation 2.1. VTC technologies

Teamwork offloading can be done in the context of TMS. Diagrammatic

should support long-term memory

representation of role expertise acts as an externalized element of TMS by detailing

requirements of the team and act as

who knows what; that is, who to go to for support of team tasks. This, then, can

a memory aid that reminds members

also support SMM. Here, teamwork offloading can support long-term memories by

of team factors needed to meet team

providing graphical representations of workflows between team members. Instead of

objectives.

members having to recall interdependencies on the team (e.g., connections between distribution with marketing), and potentially making errors that disrupt coordination, team members can draw from offloaded team knowledge to more appropriately consider dependencies when discussing work requirements.    

Recommendation 2.2. VTC technologies

Technology can also be developed as a form of “real-time” TMS support. Here,

should support working memory intensive

teamwork offloading could support working memory by showing members present

team elements to free resources and help

at a given meeting, their names, and their roles. The technology could provide short

support communication needed to guide

descriptions in text form under their images in a videoconferencing system (e.g.,

team interactions.

“Pat’s assistant, who is providing expertise in supply chain management”). With this, members do not have to take up working memory resources to remember who a person is and what is their job on a team. Similarly, such systems could be linked to elements of SMM. Specifically, team, task, and technology issues, all components of SMM, can be represented (e.g., reminders about team technologies to ensure shared understanding of a problem).    

Support VTC taskwork through scaffolding task elements with technologies. These indirectly support team cognition by freeing up resources normally dedicated to a task, allowing for manipulation and modification of these elements by the team. Recommendation 3.1. VTC technologies

Taskwork scaffolding can provide dynamically updated visualizations to guide

should scaffold task requirements by

information interpretation. For example, team members could overlay demographic

enabling externalization of knowledge

profiles to product distribution data via shared visual maps. This allows members

to help team members as they interact to

to externalize representations of customer data and do so using a common visual

work towards goals.

referent. From this, team member resources are freed up for additional task focus.    

Recommendation 3.2. VTC technologies

Taskwork scaffolding can provide visualizations that illustrate relationships between

should scaffold task requirements by

data and evidence. For example, team members could add alternative interpretations

enabling manipulation of knowledge to

of market share data via visual graphing technology. This allows members to

help team members collaborate to meet

externally manipulate complex information without taking up internal cognitive

objectives.

resources.    

Virtual team cognition  299 Support VTC teamwork through scaffolding team elements with technologies. These more directly support team cognitive functions, that is, supporting the complex cognition engaged during planning and decision making. Recommendation 4.1. VTC technologies

Scaffolding can dynamically support team cognitive functions. More than simply

should scaffold teamwork by enabling

accessing information, as in offloading, teamwork scaffolding can be done by

externalization of team-related factors that providing a means for team members to jointly construct representations (e.g., with support interaction processes.

shared white boards) while communicating. For example, conceptual mapping tools could be used to elicit who knows what and link relevant role knowledge across the team to help address product manufacturing and financing conflicts. From this, technological scaffolds could also be implemented to allow for prioritization of issues.    

Recommendation 4.2. VTC technologies

In support of team cognitive functions, teamwork scaffolding can also be done by

should scaffold teamwork by enabling

helping team members visualize arguments about options. By creating a concrete

visualization of collaboration processes

representation of arguments, and illustrating member pros and cons on relevant points,

supportive of team deliberations.

teams have more coherence to their discussion. For example, if there is discussion around marketing and product distribution strategies, shared white boards, or more formal tools that can illustrate propositional arguments, allow members to connect points and also refute them. Additionally, technological scaffolds could allow members to vote on differing options. 

Source: Authors’ own.

We provided this set of recommendations to guide organizations in theoretically grounded ways to support virtual team cognition. In the above requirements, the examples are meant to illustrate the power of offloading and scaffolding cognition. Through the consistent use of these, team cognitive knowledge is augmented by increasing awareness of various elements of the team (e.g., roles, responsibilities). Further, team cognitive functions are enhanced. That is, in these examples, because cognition is externalized via, for example, visualization on a shared workspace, resources are freed up for interpretation and deeper deliberations. Said another way, workload is decreased, thus, increasing cognitive resources through the technology acting as an extended cognitive system. Enabling these capabilities becomes increasingly important as, for example, the number of task or team issues increases. At some point, this will surpass the ability of working memory to mentally manage and manipulate issues. Overall, then, team cognition is amplified in virtual interactions through the appropriate design and implementation of technology for offloading and scaffolding. Finally, by framing these requirements within the context of teamwork and taskwork, organizations are better able to consider how technology can be used to amplify virtual team cognition. In this way, management of virtual teams can make significant advances by understanding and explaining the ways in which technologies can be construed as part of a larger organizational system that is, essentially, a hybrid human–technology team.

CONCLUSIONS In the modern knowledge-intensive economy, teams rely heavily on each other, their environment, and technologies to meet organizational objectives. This is especially the case for virtual teams. In this chapter we briefly reviewed representative research on virtual teams, organized

300  Handbook of virtual work around a notional framework for conceptualizing team cognition. This included studies emphasizing shared mental models or transactive memory systems, that is, what we label as team cognitive knowledge and how the degree of virtuality influences these. We additionally examined team cognitive functions, defined as team problem solving, decision making, and planning, to consider how these may be altered by the context. We then elaborated on how our framework can benefit organizations by guiding the use of technologies to support team cognitive processes in virtual teams. Effective offloading and scaffolding of teamwork and taskwork is foundational to virtual team cognition. This supports coordinating the appropriate use of team cognitive knowledge. And these enable more effective team cognitive functions by amplifying how teams plan, make decisions, and solve problems. In short, when technologies for virtual teams are designed well, offloading and scaffolding allows for ease of storage and manipulation, thus, extending team cognition beyond the limits of human biology. By recognizing that a virtual team’s interaction with, and through, technology creates an extended cognitive system, organizations can better understand the interplay between, not only team members, but also their technology. Further, team members are better able to manage how cognition emerges from interaction with each other and through a task and technological factors. With this, they are better able to use these technologies in their virtual team to appropriately offload or scaffold cognitive processes and improve virtual team cognition. In sum, this provides organizations with a vision for the future of virtual team cognition as a form of hybrid human–machine teaming. In this way, organizations are more likely to create new forms of teaming that fully leverage what intelligent technology does well, and what humans do well, to produce a collaborative synergy where cognition and coordination are amplified.

ACKNOWLEDGMENTS This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under contracts W911NF-20-1-0008 and HR00112020041, both awarded to the first author. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA or the University of Central Florida.

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16. Understanding trust in virtual work teams Angie N. Benda, William S. Kramer, Mary E. Baak and Jennifer Feitosa

Trust is considered a foundation to many of our daily interactions. This is no different for virtual team dynamics. As these teams rely on virtual tools to function, trust can establish basic needs such as relying upon and relating to teammates. Without trust, teams can struggle to plan and perform tasks cohesively (e.g., De Jong et al., 2016; Salas et al., 2005). This is an important concern within the virtual teams literature as trust seems to be even more influential to team performance (Breuer et al., 2016). Accordingly, a recent Forbes article highlights that high levels of trust are related to more engagement, productivity, satisfaction, and less stress and burnout in employees; creating the ideal profile of high-performing and lasting organizations (Smith, 2020). Put simply, enhancing trust in any team is crucial, but particularly those characterized by high levels of virtuality (Feitosa & Salas, 2020). The current shift from traditional office settings to remote work environments drives the need to review and understand how companies can establish, maintain, and enhance trust among virtual teams throughout their tenure. Importantly, building trust in virtual teams requires a different strategy than traditional team settings. For one, we know the importance of developing trust in virtual teams, as early as possible (Crisp & Jarvenpaa, 2013). Moreover, theoretical and empirical evidence is starting to accumulate regarding the potential for virtuality to diminish some of the initial detriments to trust from cultural diversity (De Jong et al., 2021; Feitosa et al., 2018). How can we then develop and maintain trust in virtual teams? This may shift the focus to non-verbal communication, communication frequency, and encouragement of conversations more social in nature (Ferrell & Kline, 2018; Smith, 2020). This review argues that understanding how trust is built and sustained in virtual teams is important for leaders, virtual team members, and researchers. This book chapter examines team trust as an emergent and shared state that has cognitive and affective components (Feitosa et al., 2020) by defining, integrating literatures, and paving the way for future research on team trust in the virtual context. Specifically, we draw from Marks et al.’s (2001) episodic and cyclical framework to explore behaviors that enhance and sustain trust in virtual teams. In addition, to provide an update of the nomological network of team trust in virtual teams, we emphasize inputs and team processes related to this emergent state. Lastly, we provide future directions for virtual teams research, suggesting more attention is turned to measuring and repairing trust.

CONCEPTUALIZATION AND OPERATIONALIZATION OF TRUST Trust has been conceptualized in many ways, with varying targets and scope. Accordingly, we start our review by defining trust across levels, dimensions, and types.

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306  Handbook of virtual work Across Levels Due to the complex nature of trust that emerges from different levels of analysis (e.g., dyadic trust, team trust, trust with one’s organization; Cummings & Bromiley, 1996), its definition varies throughout research. However, the most widely recognized definition at the dyadic level is the “willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that party” (Mayer et al., 1995, p. 712). This definition emphasizes an individual’s propensity to be vulnerable and posits that vulnerability will lead to proximal risk-taking behaviors (Breuer et al., 2020; Edmondson, 2008; Germain, 2011). Beyond the dyadic level, trust is also widely studied at the organizational level. Organizational trust encompasses how the employee and organization interact among the traits of benevolence, integrity, and ability, such that if an organization does not act with integrity an employee will lose trust in the organization (Schoorman et al., 2007). The premise of organizational trust is that the employer will be faithful in the execution of organizational goals, which ultimately will be beneficial for employees (Gilbert & Tang, 1998). Finally, trust can be measured and studied at the team level. Virtual teams are quick, dynamic groups that are constantly evolving to fulfill organizational needs (Jarvenpaa & Leidner, 1999). As such, understanding the unique influence that the virtual context imposes on teams requires an additional layer of analysis that captures this dynamism. For this reason, we adopt Feitosa and colleagues’ (2020) definition of team trust as “an emergent and dynamic shared state at the team-level whereby team members believe in one another’s competence and are willing to be vulnerable beyond task-related issues” (p. 480). This definition still conforms to the earlier conceptualizations of trust by emphasizing the importance of both cognitive and affective dimensions, but adds a layer of complexity acknowledging a dynamic and changing environment (Mayer et al., 1995; McAllister, 1995). Trust in virtual teams helps to improve communication, coordination, and morale as team members learn to be open and rely on each other, leading to increased effectiveness (Dinh et al., 2021). Across Dimensions There are five mainstream trust theories: calculative-based (McAllister, 1995), rational-based (Hardin, 1991), knowledge-based (Mayer et al., 1995), interpersonal-based (McAllister, 1995), and institutional-based trust (Shapiro, 1987). Each of these five main lines of trust research map onto two overarching buckets of trust, those being cognitive and affective trust (Greenberg et al., 2007; Mayer et al., 1995). Cognitive-based trust is defined as trust that stems from an individual’s perception of their colleagues’ ability, integrity, and knowledge, whereas affective-based trust is focused on the interpersonal and emotional relationships between individuals (McAllister, 1995). Cognitive-based trust theories place attention on the task, whereas affective-based trust emphasizes relationships between individuals (Feitosa et al., 2020). In comparison to the antecedents in traditional dyadic relationships, ability, and integrity fall under the cognitive-based trust umbrella (Feitosa et al., 2020; Mayer et al., 1995). By contrast, benevolence fits within the affective-based trust (Mayer et al., 1995).

Understanding trust in virtual work teams  307 Across Types The unique nature of virtual teams limits the ability for trust to develop in a traditional, dyadic face-to-face manner. However, virtual teams rely on different mechanisms including initial trust and swift trust in building overall team trust. Initial trust can be defined as placing trust in teammates based on first impressions (McKnight et al., 1998), whereas swift trust is rooted in the fact that temporary teams do not have as much time to focus on developing relationships as traditional teams (Meyerson et al., 1996). Regardless of team type being ad hoc, global virtual, or transitionary from face-to-face to virtual, teammates do not always have prior experience of working together, which can affect trust development (Zijlstra et al., 2012). Interestingly, however, research shows that previous interactions with teammates are not a prerequisite to trust in newly formed virtual teams (Jarvenpaa & Leidner, 1999). Furthermore, initial trust is cognitively based due to the initial, task-oriented nature of the team (Kanawattanachai & Yoo, 2002). Swift trust, much like initial trust, is based on cognitive information available about the team members, such as their skills and knowledge. Taken together, teams that fail to develop swift trust from initial trust are typically low-performing teams. Finally, due to the limited time for interaction in ad hoc teams, swift trust not only relies on team member action but also action from team leaders (e.g., Turesky et al., 2020). Thus, enabling swift trust in virtual teams relies on coordinated action from all members to sustain trust throughout a team’s tenure. Measurement Traditionally, the most common approach to trust measurement in teams is via identification of a measure of an individual’s perceptions of trust, either overarching trust or one dimension (e.g., cognitive), and taking a compositional approach to aggregation across teammates (Feitosa et al., 2020). Inherent to this approach are several assumptions including: (1) the team has similar enough perspectives to ensure aggregation via examination of intraclass correlation coefficients (ICCs) and mean within group agreement (rWG), (2) the team has enough time working on their task to develop a shared perspective of trust, and (3) the referent of the trust items aligns with the target conceptualization. Additionally, researchers need to consider whether there is something important about examining trust at one specific time point in a team’s life cycle or if multiple points should be examined longitudinally to examine the trajectory of trust over time. As mentioned above, the different levels, dimensions, and types of trust contribute to the complexity of properly measuring this construct. While full coverage of how trust is measured across levels is beyond the scope of this chapter, it is nonetheless important to consider these few ways for examining trust in teams.

PROPOSED FRAMEWORK An imperative consideration for teams, regardless of modality, is the consideration of such temporal elements. How teamwork is carried out varies greatly across a team’s tenure (Humphrey & Aime, 2014). For instance, it is common for a newly formed team to hold a strategic session whereby plans are laid out for achieving goals (Janicik & Bartel, 2003). However, toward the end of a team’s tenure, there is a need to focus on task completion and, in many cases, detailed planning becomes less important (Mohammed et al., 2015). While

308  Handbook of virtual work most of the literature that examines these temporal shifts in a team’s focus and priorities are centered on face-to-face work teams, the virtual teams’ literature approaches the tasking faced by virtual teams as specialized and influenced by temporal dynamics (e.g., ad hoc problem-solving teams; Montoya-Weiss et al., 2001). As such, it is important to acknowledge that trust in virtual teams will likely have a unique nomological network. While multiple temporal frameworks of teamwork have emerged over the years, Marks and colleagues’ (2001) episodic framework offers a foundation to understanding virtual team trust. According to this model, teamwork is divided into three overarching structures: transition and action phases, and interpersonal processes. The transition phase includes activities that prepare the team for engaging in future action to achieve the team’s goals (e.g., strategy planning). The action phase includes behaviors where teammates are actively working on accomplishing tasks (e.g., monitoring task environment). Finally, interpersonal processes are focused on social functioning of teammates during the entirety of the team’s tenure (e.g., conflict and affect management). We adopt this framework because extensive research has been conducted examining teamwork behaviors across the three phases. Additionally, the underlying interpersonal component of this framework helps to tie actions between members to team trust. Figure 16.1 summarizes how virtual team trust influences and is influenced by key constructs that we have drawn upon from the literature, with the caveat that the model is linear for parsimonious reasons, we expect virtual team trust to have more cyclical and dynamic relationships.

Source: Authors’ own.

Figure 16.1

Team trust framework

Understanding trust in virtual work teams  309

MULTI-LEVEL INPUTS Early research on trust in virtual teams addressed how trust develops at one unit of analysis, however, the past ten years of research has focused on how trust develops over multiple levels and its impact on each level (Dirks & De Jong, 2022). For instance, one of the benefits of virtual teams is that skilled, diverse individuals can come together and collaborate. However, those same individuals bring their own unique experiences such as prior teamwork into the team. Additionally, the organization’s individuals and teams also work under impact trust. Individual-Level Team members each bring varying levels of comfort and trust with technology. One commonly used tool for communication is email, which most individuals are familiar with (Reeves & Furst, 2004). However, with technology advances, the implementation of newer communication tools including teleconferencing, videoconferencing, and instant messaging through platforms like Microsoft Teams and Basecamp enhance the exchange of rich communication, mimicking face-to-face communication, and become less familiar (Kirkman & Mathieu, 2005). For example, videoconferencing, allows for team members to build cognitive trust in real time, enhancing and updating shared mental models (Klimoski & Mohammed, 1994). For team members who are not familiar with these technologies, getting help from other team members can advance affective and cognitive trust, and social connectedness by teaching the individual how to use the tool and utilizing others’ competence to promote knowledge sharing (Alsharo et al., 2017; Germain & McGuire, 2014). Notably, even though teams might have experience communicating via virtual tools, another unique challenge is when individuals lack trust in the tool itself. The degree to which individuals perceive the virtual tool as useful and easy to use are paramount in shaping their attitudes and behaviors toward the virtual environment (Glikson & Wooley, 2020). When individuals encounter repeated errors, their trust in the tool decreases. This decrease may have larger effects leading to not wanting to use the technology in the future, potentially limiting the collaboration and teamwork efforts that can be utilized. At more of an organizational level, an individual’s trust in the tool to perform the designated task is accompanied by their trust in the security of the platform, such that their information is protected and secured (Rudolph et al., 2021). Another individual-level factor of importance is prior experience working in virtual teams. Variance of prior experience between individuals means that teammates might have different initial levels of both affective and cognitive trust which, in turn, will grow at different rates (Coovert & Miller, 2017). Individual differences in a team member’s initial levels of trust can also be attributed to their propensity to trust others. Propensity to trust is a trait that details an individual’s generalized expectation to trust another (Mayer et al., 1995). An individual’s propensity to trust has been noted to significantly predict trust beliefs, intentions, and actions, above and beyond the Big Five measures of personality (Alarcon et al., 2018). This indicates individuals’ propensity to trust plays an important role in predicting other trust processes and can impact how swift trust is developed. Together, individual differences should be considered from a bottom-up perspective as factors such as trust in tools, comfortability with virtual tools, prior experience, and propensity to trust lead to how individuals will act in a team. Although the individual characteristics presented here are only a few of the individual-level differences

310  Handbook of virtual work that affect one’s likelihood to trust, additional factors such as culture and work ethic can impact trust (see: Cheng & Macaulay, 2014 and Feitosa et al., 2018). Thus, multiple individuals with various backgrounds and traits will impact overall team trust. Team-Level From a team composition standpoint, individual differences inherently influence a team’s functioning. Costa and colleagues (2018) mention team composition as one of the key team-level antecedents to trust, based on the many traits that may create an “us versus them” climate. In other words, every team is composed of individuals who are different from one another. When team member attributes, such as gender or national origin, misalign, there is the potential that the team becomes divided among the differing groups. Also called faultlines, these divisions result from perceived differences in team member characteristics (Lau & Murnighan, 1998). Although demographic differences may not initially affect teamwork processes, faultlines may become activated in response to many factors, such as conflict or world/organizational events. Whether faultlines exist among surface (e.g., race, age) or deep-level (e.g., beliefs, attitudes) diversity characteristics, when they become activated, these subdivisions are an obvious obstacle to maintaining team cohesion and trust (Paul et al., 2016). They create divisions, creating an in- and out-group, lowering affective trust, making it harder for teams to engage in coordinated cooperative behavior (Chiu & Staples, 2013). As teams become more virtual (i.e., have fewer synchronous interactions, rich in informational value), they face higher degrees of complexity and uncertainty (Cohen & Gibson, 2003). Tolerance for ambiguity is a measure of how a team processes and responds to uncertain and ambiguous situations (Furnham & Ribchester, 1995). Teams high in tolerance for ambiguity enjoy novelty and remain calm in ambiguous environments. By contrast, teams who are less tolerant of ambiguity may withdraw from the group when few social cues are present (Kramer et al., 2017). As virtual interactions are a conduit for teamwork processes, understanding how team members prefer to communicate is a cornerstone to developing and maintaining trust beyond the initial trust phase (Jarvenpaa & Leidner, 1999). In turn, team composition variables, particularly those related to uncertainty, should be considered as trust emerges and changes. Organizational-Level Virtual teams are situated within organizations and thus an organization’s culture affects virtual teams from the top-down. For example, if an organization’s culture promotes open communication, it should follow through when working in virtual teams, leading to increased trust. However, a culture of poor communication can hinder trust and satisfaction (Zeffane et al., 2011). At the organizational level, trust is impacted by leadership and how leaders help to initiate and maintain trust. Like organizational culture, effective leadership needs to be prioritized at the organizational level to translate to the team level. Organizational leaders can initiate trust within the company by recognizing their employees’ talent and skills by celebrating accomplishments (Greenberg et al., 2007). Not only can organizations recognize and reward their employees, modeling open communication from the organizational level helps promote trust throughout team and individual levels via shared information and resources. Leaders who openly share accurate top-down

Understanding trust in virtual work teams  311 information to employees predicted organizational trust (Singh & Srivastava, 2016). To promote strong communication in teams, organizational leaders can help in setting communication norms (Morrison-Smith & Ruiz, 2020). Leaders can also aid in team coordination by creating a team charter in which team members define rules and roles. A team charter serves as a living document whereby all members of the team contribute and agree upon actions and behaviors the team must follow (Furst et al., 2004; Kilcullen et al., 2021; Mathieu & Rapp, 2009). Leaders play an integral part in helping to promote not only trust within a team, but organizational trust. Details of additional actions leaders can take can be found in Table 16.1. Table 16.1

Leadership behaviors

Teamwork Phase

Leadership Actions

Transition

Introduce each team member and their background and skills Define roles for each team member Set communication norms Establish shared mental models

Action

Check-in with team members during one-on-ones Update communication norms Promote social conversation via chats

Interpersonal

Initiate online social events Reward task completion Celebrate team accomplishments

Source: Authors’ own.

A strong organizational culture that transcends to a virtual environment incorporates the use of organizational symbols. For example, during meetings organizational symbols can be used for the background or by even sending wall décor to employees (Newman & Ford, 2021). Organizational values and symbols can hold strong meaning for teams and incorporating them into the virtual environment can advance trust. The shared meaning that comes from an organizational symbol can help to achieve group cohesion and likeness, increasing affective trust (Ford et al., 2017). The verbalization and reminder of values during meetings acts as a referent of teams being on the same page and all working towards the same goals, which builds consensus among the team, enhancing both team and organizational trust. As organizations continue to navigate the increasingly virtual work environment, the organizational culture must be taken into consideration when forming teams. Specifically, when individuals perceive a high degree of organizational support, they are more likely to see their efforts as fruitful and enjoy working with their teammates (Sheng et al., 2010), in turn preserving interpersonal relationships and trust.

EMERGENT STATES The development and maintenance of trust does not happen in a silo. Other emergent states will be relevant, including shared team mental models (SMMs) and conflict. We discuss below how these two emergent states may influence team trust.

312  Handbook of virtual work Shared Mental Models and Conflict When high levels of trust exist, aligned SMMs and low levels of conflict are likely to emerge. SMMs represent organized knowledge held by individual team members that, when collectively examined, lead to a common knowledge foundation, allowing teammates to interact and coordinate more effectively (Cannon-Bowers et al., 1993; Mathieu et al., 2000). Due to the increased periods of independent work in virtual teams, SMMs are critical in ensuring teammates are working towards the same goals. When trust is established early and SMMs are formed, the benefits including role clarity and commonality in accomplishing goals will increase, having a positive impact on team outcomes (Schmidtke & Cummings, 2017). In virtual teams, members have less opportunity to share information, yet when they do, unique information is transmitted aiding in SMM development (Mesmer-Magnus & DeChurch, 2009, 2011). Minimal communication about the task creates low levels of trust and inhibits the development and refinement of mental models (Rosen et al., 2006). Relatedly, underdeveloped SMMs can cause task conflict due to the lack of understanding of the processes necessary to complete tasks. This can potentially lead to teammates completing the same tasks, wasting time, and impacting overall team progress. SMMs and information sharing are just as important to the action phase as they are for the transition phase. If initial trust does not evolve with the changing dynamics of the task during the action phase, there will be a lack of information transmitted, negatively impacting SMMs, increasing conflict, and weakening team performance (Breu & Hemingway, 2004; Davidavičiene et al., 2020; Grossman & Feitosa, 2018; Pinjani & Palvia, 2013). However, if affective trust improves over the virtual team’s life cycle, SMMs can lead to other positive communication processes, such as providing feedback and backup (Schmidtke & Cummings, 2017). However, it is important to note that task conflict can arise from misaligned SMMs if feedback is taken as a personal attack and can become a relationship conflict, harming overall team trust (Bierly et al., 2009). In this phase, having feedback and monitoring performance helps to promote continued task completion and performance. Feedback provides members of the team with an update and allows for corrections to be made. Further, teams with interpersonal trust that share information freely also generate new ideas that can lead to creative solutions (Pinjani & Palvia, 2013). Consequently, trust will be reinforced when proper levels of SMMs and conflict exist.

TRANSITION PHASE As the transition phase involves planning for how a task will be accomplished, it is often the first teamwork phase that virtual teams engage in. During this interaction, effective teamwork behaviors will include the formation of a team mission, goals, and an underlying strategy to accomplish their task (Marks et al., 2001). When initially meeting as a virtual team, team members have some level of initial trust and work towards establishing swift trust through planning and strategizing. Thus, each team member should be aware of any expectations and norms set by the group or organization as it sets the tone for future engagement (Feldman, 1984). If a virtual team has little prior experience working together, setting norms and expectations on how to communicate, frequency of communication, rules for collaboration and information sharing allow the team to begin with a strong foundation (Kilcullen et al., 2021).

Understanding trust in virtual work teams  313 Accordingly, research showed that an effective first meeting was the main determinant of high-performing virtual teams (Reeves & Furst, 2004). By identifying the group mission and purpose, these teams developed shared perspectives and goals to hold themselves accountable when independently engaged in taskwork. Hence, virtual teams should meet as soon as possible to analyze the group’s mission and specify goals to establish a strategy for task completion. One longitudinal study on virtual teams identified that teams who rushed or skipped the introductory phase of getting to know each other and establishing rules hindered the long-term development of trust because teams engaged in more conflict (Wilson et al., 2006). Due to their reliance on independent work, teams working virtually depend on consistent behaviors of teammates when determining levels of trust. Because virtual teams operate with fewer social cues, behaviors must be predictable so teammates can anticipate the productivity and needs of others, thereby allowing opportunities to engage in other beneficial teamwork behaviors such as adjusting goals or helping teammates that might be struggling (Rapp et al., 2014). Thus, planning expectations to meet goals is critical to initiating swift trust and creating expectations that promote trust over the team’s life cycle (Cascio & Shurygailo, 2003; Liu et al., 2008). If these steps are rushed or incomplete, the team will have weaker cognitive trust, leading to more conflict, as teammates lack an understanding of each other and their specific roles (Wilson et al., 2006).

ACTION PHASE Swift trust is not always enough to sustain a virtual team, and trust should be maintained throughout all subsequent teamwork phases, including during the action phase. After solidifying structure in the transition phase, additional team responsibilities emerge in the action phase and include monitoring task progress, coordination, systems monitoring, and backup behaviors which are initiated at the inception of the team (Bell & Kozlowski, 2002; Jarvenpaa et al., 2004; Marks et al., 2001). Much like the transition phase, there is an emphasis placed on cognitive trust with understanding the knowledge, skills, and roles of members of the team (Blackburn et al., 2003). As teams grow more confident and trusting in their task-based communication, a shift to task-knowledge coordination or the ability to organize information pertinent to completing the task becomes beneficial for team performance. Accordingly, in the action phase, communicating task-relevant information and emphasis on knowledge sharing is critical, as task-based communication lessens in frequency (Kanawattanachai & Yoo, 2007). Developing higher levels of cognitive-based trust through knowledge sharing and task-knowledge coordination can have a positive impact on team performance. After teams have developed and adapted mental models to the changing task environment, feedback can help further manage the task. A common teamwork behavior in the literature is to act as a coach and provide feedback, not only for task-related questions, but also for solving problems of miscommunication, misinterpretation, and conflict resolution (Blackburn et al., 2003; Greenberg et al., 2007). Relatedly, seeking feedback was associated with further clarification and shared knowledge while demonstrating interpersonal respect, decreasing the chances for interpersonal conflict and affective trust breakdown (Ayoko et al., 2012). Since virtual teams are dispersed across time and distance, having meetings to discuss the status of a project and how to review it is more challenging and may not be able to be conducted as

314  Handbook of virtual work often. Therefore, incorporating feedback into written communication can help address conflict and enhance trust.

INTERPERSONAL PROCESSES The interpersonal phase includes teams being able to manage conflict, interpersonal relations, and provide encouragement. The foundation for interpersonal processes starts with communication since interpersonal process span both action and transition phases. Communication allows for individual team members’ knowledge to be pooled and used to effectively solve complex problems. Information sharing also builds the foundation for creating common knowledge amongst the team, keeping everyone informed of task progress. Higher levels of trust result in more frequent knowledge sharing, whereas a lack of affective trust results in far less information being shared amongst virtual teammates (Breu & Hemingway, 2004; Davidavičiene et al., 2020). Interpersonal communication serves to define roles and set boundaries. Such boundaries help alleviate task uncertainty, a common challenge in virtual contexts (Ford et al., 2017; Jarvenpaa & Leidner, 1999). As examples of these norms, asking team members their preference for future correspondence early in the meetings (Reeves & Furst, 2004) and requesting team members to communicate frequently (Walther & Bunz, 2005) can lead to more trust and positive outcomes, such as less relationship-based conflict for virtual teams aiding in conflict management. These communication rules allow teams to have guidelines to fall back on during difficult or uncertain tasks (Morrison-Smith & Ruiz, 2020). In the action phase it is important to address and re-evaluate communication as the task changes (Walther & Bunz, 2005). For example, if a team member is leaving during the middle of the task, it will be important to communicate the work they have done so the person filling the role will be up to date. Communicating throughout the task cycle promotes confidence between team members and for the task (Crisp & Jarvenpaa, 2013). Communication serves to be a moderator, such that when communication is high, it will help teams to plan and coordinate team activity reinforcing cognitive and affective trust. Trusting environments and relationships help establish a sense of well-being and, in turn, knowledge sharing (Chumg et al., 2015; Walsh, 2019). Thus, monitoring a team’s psychological safety will help to ensure affective and cognitive trust are present over time. Psychological safety is defined by Edmondson (1999) as “a shared belief held by members of a team that the team is safe for interpersonal risk-taking” (p. 350). As such, it serves as an antecedent for virtual teams feeling safe to communicate feedback (Ortega et al., 2010). Psychological safety can be initiated through informal conversation and maintained by encouraging open communication (Lechner & Mortlock, 2021). Additionally, encouraging social conversations and team building via social chatrooms, allows the team to learn about others, facilitating interpersonal relationships and affective trust (Greenberg et al., 2007; Jarvenpaa & Leidner, 1999; Powell et al., 2004). Forming relationships helps to diminish psychological distance and provides mechanisms for creating inclusive environments and team cohesiveness (Powell et al., 2004). When teams have high levels of psychological safety, they will learn from each other, increasing team effectiveness (Ortega et al., 2010). Thus, developing affective trust through psychologically safe and inclusive environments helps to promote cognitive trust via team learning and cohe-

Understanding trust in virtual work teams  315 sion. Trust has been noted to share a reciprocal relationship with team cohesion, such that when trust increases team cohesion also increases (Paul et al., 2016). The relationship between trust and cohesion implies that teams low on either will struggle to coordinate activity and monitor task progress, as team members are not working together, or do not trust each other. In any team, conflict is inevitable. In virtual teams, conflict is reconciled through technology, thereby increasing the complexity of resolving the issues that arise (Griffith et al., 2003). In traditional teams, one can meet with a co-worker face-to-face and discuss the conflict, whether it be a disagreement about how to complete the task or a transgression. However, in a virtual team, meeting via technology requires more effort and coordination. As conflict creates affective responses (Ayoko et al., 2012), it naturally has a stronger impact on affective trust. Managing not only conflict but the underlying affective trust foundation is critical for team success. Building on communication strategies from earlier, having open, civil, and frequent communication in times of crisis helps to maintain trust by providing transparent commentary (Aubert & Kelsey, 2003; Jarvenpaa & Leidner, 1999). Thus, communication is truly the key to helping prevent, diagnose, and remedy conflict and minimize trust-related breakdowns. Psychological safety and trust act as tools to manage conflict, such that if virtual teams are psychologically safe and possess trust, recognizing conflict will be easier (Griffith et al., 2003). Poor conflict management is negatively associated with team performance and team satisfaction in virtual teams, over time eroding trust (Bierly et al., 2009). One way to overcome conflict is to assume initial trust or swift trust as this helps team members to notice conflict at earlier stages. Navigating and managing conflict is becoming increasingly necessary as virtual teams grow in complexity, with team members who work in different time zones. Addressing both task and interpersonal conflicts in a timely and transparent matter helps to ensure trust does not deteriorate (Turesky et al., 2020). In teams with high levels of trust, a collaborative approach, in which teammates resolve conflict for mutual benefit is better than a competitive approach in which the team is not focused on mutual benefits, but individual gains, such that a collaborative approach resulted in predicting team satisfaction, whereas competitive approaches shared no association in predicting team satisfaction (Liu et al., 2008). Thus, when more task conflict is present, teams will have lower levels of productivity in their planning and coordination of the task as well as lower levels of trust. When working in virtual teams, managing emotions is critical to reducing relationship conflict, since interpersonal communication can take on many different forms from text-based to audible. Emotions are not static and can be indirectly transferred between people, also known as emotion contagion (Barsade, 2002). Specifically, in virtual teams, both “happy” and “angry” emotions can be spread through text-based communication (Cheshin et al., 2011). Thus, when virtual teams communicate by email, emotions can be transferred across team members. Emotions should be affectively managed via communication channels, especially training the team to evaluate and respond to non-verbal cues improves positive affect management and increases engagement (González-Anta et al., 2021). When negative emotion is expressed and recognized, it can serve as an identifier for problems between team members or in the task. Sharing negative emotions serves not only as a solution to task conflict but also builds trust, by validating the concerns of the team (Ayoko et al., 2012). Further, due to the lack of informational value in email communication, the display of emotions can be misperceived. Factors related to both the sender and the receiver, such as the tenure of relationship and age, respectively influence the emotion sent and perceived in the message (Byron, 2008).

316  Handbook of virtual work For example, one study found email recipients perceived the messenger to be angry when there were typos in the email (Blunden & Brodsky, 2021). Moreover, if team members sending messages are unaware of the amount of emotion (or lack thereof) in their emails it can stimulate conflict (Blunden & Brodsky, 2021). Motivation is also important to interpersonal processes, and identified as a necessary component for virtual team effectiveness (Dube & Marnewick, 2016). Motivation and trust go hand-in-hand, such that having both affective and cognitive trust in virtual team members leads to higher levels of motivation (Zaccaro & Bader, 2003). Compared with working independently on a task, when individuals worked together with team members who are perceived to have increased knowledge, motivation to complete the work increased (Hertel et al., 2005). This signifies that motivation can be influenced by working with team members to increase motivation and interpersonal relationships via affective trust. Although virtual environments can challenge motivation, psychologically safe and cohesive environments allow motivation to persist and encourage productivity (Hertel et al., 2005; Lilian, 2014). Motivation can also be celebrating team members and their successes towards task completion. Rewarding and recognizing work is a tool that can be useful for building trust (Greenberg et al., 2007; Turesky et al., 2020). Rewarding cooperative teamwork helps to reinforce trust, affect, and motivation. Information sharing between team members mediated the relationship between cooperative rewards and trust (Ferrin & Dirks, 2003). Thus, providing incentives that focus on cooperation amongst the team will lead to increased information sharing, promoting trust. Not only that, cooperative incentives help to bring the team together, enhancing social presence, which creates pathways for further team motivation and loyalty to build (Haines, 2021). Overall, when motivation is high it helps to reduce conflict, in turn promoting communication, coordination, and feedback.

PUTTING IT ALL TOGETHER As teams progress through teamwork phases, interpersonal exchanges are simultaneously occurring. Creating successful virtual team environments relies on a strong start by setting teamwork and communication norms. While rules are being established, building interpersonal relationships that mirror face-to-face relationships helps to amplify affective trust. Since virtual teams are dynamic, monitoring the initial norms and rules that are established helps to refine SMMs and promote information sharing. Further, as conflict arises, referring to the SMM can prevent task conflict from escalating to relationship conflict. Monitoring of the team environment can also be facilitated through check-ins with team members, updating the team on task progress, and adding to the SMM. Although the activities occupied by transition and action processes are different, the underlying component of interpersonal relationship building helps to enhance each behavior in both phases. Thus, each phase is reliant on the other to create a virtual working environment where initial trust and swift trust are built upon, helping to create a productive and satisfied virtual team.

Understanding trust in virtual work teams  317

FUTURE DIRECTIONS OF RESEARCH Despite the abundance of existing research on how trust impacts virtual teams that we have presented, there are still many areas where research is lacking. Indeed, the sudden shift to virtual work that was experienced in 2020 has opened research to a plethora of new ideas for how employees work in and interact with virtual environments (e.g., Zoom fatigue; Fosslien & Duffy, 2020). The early groundwork in understanding how constructs such as communication affect the team (Jarvenpaa & Leidner, 1999) has shifted into understanding how specific tools longitudinally impact communication and trust, such as the importance of sharing information about yourself through asynchronous platforms as a way to promote a psychologically safe communication environment (Glikson & Erez, 2020). As such, in this section, we will highlight emerging trends and areas that are rife with opportunities for future research. One of the biggest challenges to any distributed team is building a shared culture and maintaining social contact (Gibbs et al., 2021). However, as more organizations adopt and accept the idea of working from home, there is an increase in the use of hybrid teams. These are teams where some members are collocated on-site and others are distributed or at home (Cheng et al., 2016). This structure begs the question: how can we maintain comparable levels of trust across the team or promote swift trust at the onset when those collocated are more likely to interact? Restated, how do we ensure that the existing, dormant faultline of location stays inactivated? While there is research that points to the fact that location is an easily activated faultline that can breed distrust (e.g., Earley & Mosakowski, 2000; Polzer et al., 2006), much of this research was conducted before virtual tools began incorporating functions allowing users to interact with one another that mimics face-to-face teamwork (e.g., shared whiteboards, screen sharing, collaborative document editing). Moreover, there is initial research showing that the need to work virtually has improved our ability to collaborate and manage conflict in this context (Klonek et al., 2020). As such, it will be important to learn whether location stands as an easily activated faultline and, if it is activated, the best approaches for deactivation and improving the trust in a struggling hybrid team.

REPAIRING TRUST IN VIRTUAL TEAMS Future research on trust is also shifting to identifying longitudinal impacts, such as how trust is repaired in virtual teams (Dirks & De Jong, 2022). Existing research shows that inherent to teams, any violation of trust is not just dyadic, it must be repaired at the team level (Kim et al., 2013). In virtual teams, the complexity is taken one step further as the team or its leader must determine how to best repair trust despite having decreased social presence. If a virtual team falls into a situation where subgroup perceptions are formed and rallied behind, group polarization is likely to occur and serve as a countervailing force to the maintenance of trust (Lee, 2007). There is some theoretical support suggesting a range of actions that a team use to repair trust, from useful methods (e.g., apologies and forgiving) to those which allow the problem to continue to fester (e.g., excuses and remaining silent; Lewicki & Brinsfield, 2017). Additionally, it has been proposed that the best method for repairing trust depends on several issues including the remaining team tenure (Lewicki & Brinsfield, 2017), whether relationship- or task-oriented trust has been broken (Lewicki & Bunker, 1996), and the existing dyadic relationships and

318  Handbook of virtual work roles within the team (Brodt & Neville, 2013). For short-term teams, trust repairment might be in the form of an apology or compensating action; whereas for long-term teams, reframing perceptions of trust or structural changes in the form of the policy can rebuild trust (Lewicki & Brinsfield, 2017). Interestingly, however, very little empirical research examining these ideas exists in neither virtual teams nor teams research and there are calls for more research across contexts (e.g., Grossman & Feitosa, 2018).

TRUST MEASUREMENT Numerous complexities emerge when attempting to understand how something as individualized as perceived trust is aggregated between three or more individuals. A standard practice in trust research is to survey everyone on the team and aggregate scores across which runs the risk of imparting bias and negatively impacting validity (Podsakoff et al., 2003). This approach assumes that team trust is a compositional variable that looks the same at both the individual and team levels. However, this is only the case for teams where there is an alignment of perceptions. For example, imagine a hybrid team where half of the team is collocated and perceives high levels of trust due to increased social presence, yet the other half who is dispersed reports low levels of team trust. Taking a compositional approach to aggregation, this team would be said to have an average level of team trust and we would lose the more interesting picture that there is a clear divide in perceptions based on location. Consequently, for examining trust in virtual teams, we must consider a more complex, compilational approach to measurement that considers the way trust is dispersed amongst teammates. One such approach for team trust research suggested by De Jong and Dirks (2012) is the examination of trust asymmetry – the degree to which perceptions of trust are shared across all possible dyads in a team. This is a more structural and network-driven approach that seeks to find differences in perceptions across teammates. Another, simpler approach to examining trust in a compilational fashion is via trust consensus. This determines the degree to which perceptions of teammates’ trust in the team are different from one another via the standard deviation of responses as the team-level variable (e.g., Mach & Baruch, 2015). Both approaches would serve as a natural fit for virtual and hybrid teams alike, as they would capture a more nuanced understanding of the discrepant perspectives that often occur in dispersed teams due to decreased communication and social presence. Finally, it is important to remember that virtual teams are often conceptualized as project teams and, as such, there is an inherent understanding that these teams are put together for a specific purpose, many beginning in a very ad hoc nature (Crisp & Jarvenpaa, 2013). In this sense, and following the dynamic nature of our proposed framework, it is very important for researchers to consider the longitudinal examination of trust development over time, as the initial levels are likely to ebb and flow as team members collaborate and get to know one another. While some research has begun examining topics like trust asymmetry (e.g., De Jong & Dirks, 2012), change in dimensionality of trust over time (e.g., Webber, 2008), or timing of trust measurement (e.g., Lewicki et al., 2006), it has not been in a virtual environment with limited social cues. As such, future research should take a dynamic approach to examining trust and consider not only how cognitive, affective, or swift trust change over time in virtual teams, but also how the trajectory of the development of these constructs differ from face-to-face or hybrid teams.

Understanding trust in virtual work teams  319

CONCLUSION Researchers have shifted their gaze to how trust in teams varies across multiple contexts. Indeed, over the past ten years, trust has been a central topic researched for virtual teams (Gilson et al., 2015). The evolution of the relationship between these constructs has led researchers from a simple dyadic view to a more complex, multi-level perspective. As this chapter explains, the importance of trust in virtual teams spans the entire life cycle and should be a constant consideration for teammates and leaders alike. In integrating what trust in virtual teams encompasses and mapping our constructs onto the Marks et al. (2001) temporal framework, we have provided a comprehensive, longitudinal understanding of trust in virtual teams. We hope that this chapter sparks additional research that will lead to an expansion of our framework and the examination of the understudied topics of trust in hybrid teams, repairing broken trust in a virtual setting and a more nuanced understanding of trust measurement.

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17. Bouncing back as a virtual team: essential elements of virtual team resilience Nohelia Argote, Chloe Darlington, Jennifer Feitosa and Eduardo Salas

The COVID-19 pandemic affected most organizations around the world. In March 2020, many companies across the globe transformed their office work into remote work. Many co-located teams were forced to become entirely virtual, including teams of the largest technology company in the world, Apple. However, even though most organizations were affected economically due to the pandemic, including Apple’s competitors. Apple’s Q4 revenues exceeded all expectations. Apple’s CEO Tim Cook recognized team resilience as the main contributing factor to their success even in the face of setbacks caused by the pandemic. During the Q4 conference call, Cook said: I want to offer one more comment on resilience because I think if I had to describe our performance this quarter in a single word, it’s resilient. When you pull back the lens to the entire fiscal year, it’s a testament to the team’s work and the resilience of the business in the era of COVID-19. (Apple Inc., Earnings Conference Call, October 29, 2020)

The question is, why did Apple succeed while other organizations failed? The answer lies in teams’ resilience – the ability to overcome and bounce back from a challenge (Alliger et al., 2015; Kirkman & Stoverink, 2021; Stoverink et al., 2020; Tannenbaum & Salas, 2021). It is inevitable that teams will face challenges, perhaps increasingly so in a virtual workplace. To avoid virtual teamwork pitfalls, teams must become resilient. With key characteristics such as anticipating, monitoring, responding, and learning (Glowinski et al., 2016) – it is clear that resilient teams can help organizations become more agile and responsive to societal needs as those quickly evolve. Although team resilience is arguably still in a stage of infancy compared with other team constructs, the urgent need to foster virtual teams that can properly understand their current resources, coordinate with each other when put in different situations, and constantly learn from each other calls for a deeper dive. Because of limited empirical literature in this domain, we draw from works deemed central to the conceptualization of team resilience (e.g., Alliger et al., 2015; Stoverink et al., 2020). We couch our discussion within the temporal components and supplement current findings with the integration of theory. Therefore, the purpose of this chapter is to describe and distinguish virtual team resilience, honing in on the key components that can make teams more resilient, and imparting actionable steps to enhance virtual team resilience in practice, providing behavioral markers for each key element. More broadly, we aim to critically examine and lay a foundation for understanding the nomological network of team resilience in virtual teams.

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WHAT MAKES A TEAM? During the past several decades, organizations have increasingly become more complex, leading to destined cases of adversity (King et al., 2016). Complexity and resulting challenges come in the form of global competitors, technological advances, and changing governmental regulations (to name just a few factors that feed the turbulent nature of conducting business today). We argue that teams have emerged as an organizational design poised to address challenges posed by these factors. Extensive research has shown that teamwork elevates performance (e.g., LePine et al., 2008; Salas et al., 2008b). Teams are also well suited to accomplish complex tasks because they “allow [team] members to share the workload, monitor the work behaviors of other members, and develop and contribute expertise on subtasks” (Mathieu et al., 2000, p. 273). Thus, companies have started relying on teamwork, and employees, in turn, believe teams are a critical component to their organizations’ success (Tannenbaum & Salas, 2021). However, not all individuals who work together, whether virtually or in person, are considered teams. A team is defined as two or more individuals who interact adaptively, interdependently, and share a common goal (Salas et al., 1992). A highly effective team exhibits consistent performance, team resilience, and continuing vitality (Tannenbaum & Salas, 2021). We focus on team resilience, the collective ability that teams possess and utilize when times get tough, and bouncing back is critical to the teams’ continued, collective success. Team Resilience Defined As teams form and develop, they can run into challenges that strain team relations and, importantly, performance. Team resilience is the ability to tolerate and overcome challenges in such a way that allows for the return of a relative level of performance or, even better, for performance that exceeds previous expectations (Alliger et al., 2015; Kirkman & Stoverink, 2021; Stoverink et al., 2020; Tannenbaum & Salas, 2021). It is also important to note that team resilience is a dynamic, multilevel construct that arises in the resources of team members, which later develops as a team-level construct (Gucciardi et al., 2018). Teams’ resilience arises through team members’ interactions activated by setbacks and challenges that the team experiences (Gucciardi et al., 2018). Teams must therefore possess an ability to choose their course of action efficiently. The initial response plan may be the wrong choice, but a resilient team will readily adapt and reconsider the most viable approach to bouncing back. Common challenges that require team resilience include time pressure, lack of resources, and crises. That is not to say that team resilience does not exist without a challenge, only that the ability is easily observed when teams are put under pressure (Alliger et al., 2015; Stoverink et al., 2020). Once a challenge does arise, several pathways may be taken to bounce back. Teams faced with an issue determine the need to adapt or persist – or use a combination of both approaches (Kirkman & Stoverink, 2021; Stoverink et al., 2020). There are times when changing a planned course of action (i.e., adapting) is not the most advisable path to bouncing back (Christian et al., 2017). Teams must therefore possess an ability to choose their course of action efficiently. The initial response plan may be the wrong choice, but a resilient team will readily adapt and reconsider the most viable approach to bouncing back. While highly resilient teams are not necessarily developed linearly, a multidimensional model can help parse out key characteristics that can support team resilience. Specifically, we draw from the four critical features identified in Glowinski et al. (2016), which include:

Virtual team resilience  327 anticipating (i.e., going beyond the current situations to predict other scenarios), monitoring (i.e., paying close attention to any variability in the context), responding (i.e., taking action or not as the situation changes), and learning (i.e., taking previous performance, errors, and experience into account). From a teams’ perspective, the phases convey how collective behavior is harnessed to bounce back from issues. For instance, a team that consolidates learning from past experiences is better equipped to anticipate, look for, and respond to future challenges, developing team resilience. A team that lacks the ability to optimally respond to any given situation can fracture team resilience. Consider a team that ignores warning signals because team members do not monitor team activities or discuss lessons learned from past mistakes. A more resilient team will be highly sensitive to their environment, using the phases as a system designed to build foolproof, coordinated capacity for when adversity strikes. This is not a dichotomy in which teams either do or do not engage in these behaviors. Rather, influential team resilience authors argue resilience operates along a continuum (Alliger et al., 2015; Stoverink et al., 2020). A highly resilient team can meet a challenge and address it efficiently, sometimes surpassing expectations. On the opposite end of the continuum are brittle teams who may overcome one challenge, but fail to meet additional challenges. This is important since team resilience can generate a virtuous or vicious cycle that builds either increasingly resilient or fragile teams (Alliger et al., 2015; Degbey & Einola, 2020). However, just because a team overcomes challenges several times in succession does not mean that they have necessarily built the capacity to always respond effectively (Alliger et al., 2015). They may falter, demonstrating a sign of fragility. The resiliency continuum serves as a reminder that team resilience should be considered a lifelong goal with countless learning takeaways. Teams that take effortful actions to continually build resilience will thus be well equipped to remain strong despite adversity time and time again. Distinguishing Team Resilience from Individual and Organizational Having presented resilience at the team level, it is good to address resilience at the organizational and individual levels of analysis. Examining structural and process characteristics will illuminate the distinct nature of team resilience (Stoverink et al., 2020). You cannot simply boil down organizational resilience or build up individual resilience to create a resilient team (Alliger et al., 2015), providing the need to critically understand team resilience on its own level. Table 17.1 describes individual, team, and organizational resilience. Consider the structural characteristic of interdependence. At the organizational level, a moderate amount of interdependence may be required (Stoverink et al., 2020), such as when a CEO and board of directors consider the decision to lay off a percentage of their workforce in the face of an economic challenge. At the individual level, essentially no interdependence is required (Stoverink et al., 2020); a person does not have to consult with anyone when contemplating how to reach a newly instituted, difficult sales goal. A team, by its nature, possesses interdependence not required at the individual or organizational level (Stoverink et al., 2020). Thus, a team relies on the collective work of its members to accomplish a task (Marks et al., 2001). By taking into account the voices and skills of each member, a team can then strategically consider the need to adapt or persist in the face of a challenge. Differing perspectives, rooted in the process of collaborative decision-making, offers another distinction between the levels of resilience. A collaborative decision-making process would be difficult at the organizational level, where leaders tend to make decisions for the

328  Handbook of virtual work Table 17.1

Resilience at the individual, team, organizational levels

Level

Characteristics

Case Example

Individual Resilience

Can rely on a single viewpoint when responding to

An employee who, upon the risk of termination

a challenge

by her employer for low volume sales,

 

successfully adapts her sales approach

No interdependence required   Draws from psychological and social support resources Team Resilience

Teams need one another to respond to a challenge

A team that, despite having its budget cut,

effectively

delivers an innovative new product to the market

 

in record-breaking time

A high level of interdependence is required   Draws from collaborative resources Organizational

Leaders tend to guide the process of responding to

An organization that, while being required to

Resilience

a challenge

lay off 50% of the company, receives global

 

recognition for maintaining a happy workforce

A moderate amount of interdependence may be required   Draws from formal leadership resources

company (Kirkman & Stoverink, 2021; Stoverink et al., 2020). Attempting to include everyone’s viewpoints when faced with a challenge impacting an entire organization could become too daunting, making leaders’ opinions the most expeditious approach. While collaboration may surface in the form of leaders seeking multiple, diverse opinions from employees of the company, the leaders ultimately can take their employees’ opinions into account and make an “executive decision” that is most directly linked to helping the company bounce back from an organizational challenge. At the individual level, intense collaboration is not needed, as employees can rely on their judgment call (Stoverink et al., 2020). When teams are presented with a setback, decisions must be made regarding who does what and how. This division of work involves communication, collaboration, and coordination to create a deeply shared response to the challenge at hand (Kirkman & Stoverink, 2021; Stoverink et al., 2020). Teams could, in fact, fail to overcome a challenge if they do not prioritize and believe in their ability to work together. Demonstrating resilience requires a definitive response to a challenge and signals the end of the decision-making process. We argue that teams are more likely to consider multiple avenues when bouncing back from a challenge. An organization may not have a pulse on what all employees are doing, and lack of such knowledge could impact the number of choices available before deciding how to address a pressing issue. Similarly, an individual who acts on their own does not have someone to confer with and may also possess a limited range of response options to a challenge. Teams, on the other hand, may have competing ideas that naturally offer an exploration into the number and ways to address an issue. Of course, teams could have too many ideas, suggesting a tension that needs to be resolved before reaching a decision. The point to drive home is that teams are poised to explore multiple, competing choices before arriving at a shared decision.

Virtual team resilience  329 Finally, we must discuss the idea that you can create a highly resilient team by assembling a group of independently resilient employees. While perhaps enticing to believe, this notion has been dispelled among resilience researchers (Alliger et al., 2015; Kirkman & Stoverink, 2021; Stoverink et al., 2020). The very nature of resilient individuals, who tend to rely on their single viewpoint when addressing setbacks, could lead employees to look out for themselves, not for their team (Kirkman & Stoverink, 2021). Resilience at the team level, as you can see, requires employees who are committed to working closely together, taking shared action steps to resolve issues presented to them. Understanding how team resilience differs from organizational and individual resilience has served as the foundation that precedes the steps one can take to build resilient teams. It leads us to the question of how team resilience functions when team members are not making shared decisions within the walls of an office but are instead working entirely virtually. Virtuality Before honing virtual team resilience, it is important to properly define what virtuality is. Working on a co-located team does not mean that team members refrain from utilizing virtual tools or are prevented from interacting virtually. For example, a team having a meeting in the same conference room may also send task updates through email. Kirkman and Mathieu (2005) define team virtuality in three dimensions: the extent to which team members use virtual tools to facilitate team processes, the value that team members receive while using virtual tools, and the synchronicity of virtual interactions. In other words, teams can have different degrees of virtuality. For that reason, we define virtual team resilience as the ability to bounce back from challenges that affect virtual team processes and outcomes in a positive, sustained manner, focusing on teams that use virtual tools and resources as an essential part of their team functioning. This virtuality context is the foundational feature differentiating team resilience from virtual team resilience. Technology-driven communication and geographic dispersion are two critical contextual factors impacting a virtual team’s ability to readily respond and adapt to issues, as they are salient, complex characteristics that influence virtual team processes (Mak & Kozlowski, 2019). While all teams, virtual or in-person, can find commonalities in their ability to develop and utilize team resilience, it is imperative that virtual teams approach resilience with this contextual lens in mind. From a communication standpoint, technology fuels virtual team processes. Virtual teams rely on technological communication to coordinate activities, monitor progress, and lend a helping hand when needed (Kirkman & Stoverink, 2021). Building relationships virtually as a team can be challenging compared with traditional face-to-face team interactions (e.g., team meeting in the office conference room; Liao, 2017), and teams arguably need to develop a sense of team efficacy when coordinating activities to the benefit of producing consistent results (Gully et al., 2002; Lindsley et al., 1995). As teams work towards collective goals, conflicts may arise and must be resolved over online communication (Ayoko et al., 2012), with the recognition that a lack of face-to-face interactions may result in misinterpretations of messages (Rico & Cohen, 2005). Research is mixed on outcomes from virtual communication, such as the effectiveness of virtual brainstorming (e.g., DeRosa et al., 2007; Allen et al., 2015). Virtual teams who demonstrate team psychological safety when offering ideas may positively influence this relationship (Bradley et al., 2012). While it is true that all teams need to possess

330  Handbook of virtual work communication skills, virtual teams must uniquely navigate technology-driven communication to drive forward virtual team processes. What Do Virtual Resilient Teams Look Like? We stated previously that resilient virtual teams can tolerate and overcome stressors in such a way that allows for positive, consistent performance, or performance that exceeds previous expectations. Resilient virtual teams have this ability whether or not obstacles are presented (Alliger et al., 2015). However, when teams encounter challenges, we will differentiate between virtual teams that are resilient and virtual teams that are not. Resilient virtual teams can preserve and improve a virtual team’s functioning while experiencing a challenge. At the end of a challenge, resilient virtual teams will become even stronger than before (Carmeli et al., 2013; Dimas et al., 2018), which is important for positive team performance. There are two main types of challenges all teams face, including virtual teams: chronic and acute. Chronic challenges are long-lasting, and they can be intense to mild, depending on the situation a team is facing. Acute challenges are, in general, more intense compared with chronic challenges, and they tend to be short-lived (Alliger et al., 2015). An example of a chronic challenge is, for instance, a virtual team where team members have ambiguous roles. Imagine you are part of a virtual team, where many of your team members are across the world, making it challenging at times for you to communicate with them (e.g., due to time zone differences). From the beginning of your employment, your supervisor does not let you know what your specific role in the team entails. You try your best to complete as many team tasks as you can. However, it turns out that you end up working on something another team member has already finished. Unfortunately, this issue becomes ongoing for you and your team. On the other hand, an example of an acute challenge would be a sudden lack of resources. For instance, imagine you are working from home, and someone hacks into your company’s database. Your entire team is barred from logging into their work accounts and loses access to all company files. We propose that only resilient teams will overcome chronic or acute challenges such as the ones mentioned. It is also important to highlight that although virtual teams face chronic and acute challenges like co-located teams, they tend to be affected by specific aspects such as physical factors (e.g., geographic distance and temporal and perceived distance, which is time-based and cognitive) (Morrison-Smith & Ruiz, 2020). A resilient virtual team effectively addresses and resolves all challenges presented to them, including geographical challenges, in a way that the entire team preserves vitality and resources (Alliger et al., 2015). Part of the life cycle of a team includes a continuous necessity to face setbacks, such as teammates changing their membership, environment, and technology (Argote & McGrath, 1993). These changes, in addition to physical factors previously mentioned, significantly affect virtual teams. Additionally, virtual teams need to possess a more remarkable ability to improvise, as virtual team members are in many cases less familiar with their teammates compared with co-located teams. This is because virtual team members do not get the opportunity to see their teammates face to face (Kirkman & Stoverink, 2021), and all communication is mainly virtual (e.g., email, Zoom).

Virtual team resilience  331 Resilient Global Virtual Teams Over the last decades, with continuous implementations of new technology and an increasingly globalized world, many teams have moved from working in offices to virtual environments. With all of these changes, teams are becoming globalized and virtual. Jarvenpaa et al. (1998) describe global virtual teams as teams composed of team members who are geographically located in different places of the world, are culturally diverse, and utilize primarily virtual methods of communication. Accordingly, culture and virtuality intersect in these teams (Kramer et al., 2017). Additionally, organizations implemented global virtual teams (GVTs) to tackle the complexities and expansions of their companies; with that, different challenges emerged. For instance, global businesses now have different needs in terms of their customers. Customers in this new age may speak different languages, and they may be part of different cultures. Since GVTs are in many cases composed of individuals with multicultural backgrounds who interact virtually, they encounter different setbacks compared with co-located teams (Lacerenza et al., 2015; Zander et al., 2012), where individuals may have homogenous cultural backgrounds. As mentioned, prior virtuality context is the foundational feature differentiating team resilience from virtual team resilience. In the case of GVTs, technology-driven communication and geographic dispersion are the two primary factors that play a crucial role in virtual teams’ ability to bounce back from setbacks (Mak & Kozlowski, 2019). This chapter had previously addressed technology-driven communication as a primary factor differentiating team resilience from virtual team resilience. However, it is essential to highlight how geographic dispersion plays a role in virtual teams’ ability to adapt and respond to issues, which plays a crucial part in GVTs processes. O’Leary and Cummings (2007) described three different features of geographical dispersion: spatial, temporal, and configural. Spatial dispersion refers to the actual physical location of team members and how separated they are from each other – for instance, a team composed of team members in South America, Europe, and Australia. Additionally, spatial dispersion has the potential to affect team trust. Ridings et al. (2002) suggest that since team members who work virtually do not see each other face-to-face, the lack of visual cues impacts the development of mutual trust among team members. However, there is also research that suggests that even though trust levels are low in the initial stages of a virtual team, trust will increase over time and be comparable to the levels of co-located teams’ trust, even with the lack of daily face-to-face interactions (Wilson et al., 2006). Trust is crucial in all teams, particularly in virtual teams. Therefore, there should be a shift from focusing on establishing trust to maintaining and monitoring it (Feitosa & Salas, 2020) since virtual team trust is developed over time. Another essential aspect of geographical dispersion is temporal dispersion, which refers to the extent to which team members work in different time zones. Temporal dispersion may come with more barriers for GVTs than co-located teams, especially regarding communication. Lacerenza et al. (2015) listed communication as one of the main challenges GVTs must overcome. For instance, different time zones. Picture yourself as part of a team where some of your teammates are in Hong Kong, and some are located in California. Even with open schedules, coordinating a virtual meeting is difficult. California’s time is 15 hours behind Hong Kong’s; therefore, it is impossible to schedule a meeting during regular 9‒5 work hours. In the same manner, making team decisions regarding a time-sensitive project will also be dif-

332  Handbook of virtual work ficult. It is also important to highlight that not all GVTs will have issues with communication. Marlow et al. (2017) described that virtual teams with high-quality communication, especially interpersonal communication, will develop trust by overcoming setbacks, which will help virtual teams achieve their goals, for example, positive high team performance. The last geographical dispersion feature is configural dispersion, which refers to how team members are distributed across different locations. According to O’Leary and Cummings (2007), configural dispersion is divided into three distinct characteristics: site configuration, which is the number of locations where team members work (i.e., different countries, states, cities). Another aspect of configural dispersion is isolation configuration, which describes how isolated team members are from other team members. For example, a team member who works in London and the rest of the team works in California. Lastly, balance configuration is the balance between subgroups of members across multiple locations; for example, a team that has two team members in one country and ten in another. It is important to highlight that while all teams, virtual or co-located, can find commonalities in their ability to overcome the setbacks previously highlighted, it is vital that GVTs, and virtual teams in general, approach resilience with the geographical dispersion and technology-driven communication lenses in mind. It is also important to highlight that virtuality should not be viewed as a negative aspect. On the other hand, virtuality has to be approached in a different manner, while taking primary consideration of geographical dispersion and technology-driven communication. Importance of Virtual Team Resilience The increasingly virtual workplace poses lasting ramifications for teams. As discussed, most teams already possess a level of virtuality, with teamwork conducted using virtual communication channels for efficiency’s sake or due to the globally diverse makeup of today’s organizations (Kirkman & Stoverink, 2021). Teams who possess the ability to work effectively in a virtual setting, addressing challenges and bouncing back from difficulties directly impact organizational outcomes in a virtuous cycle that builds upon resilience levels (Degbey & Einola, 2020). As the opening case example of this chapter shows, virtual team resilience provides that key pathway towards thriving in today’s highly dynamic workplace. Virtual teams that possess resilience serve as a crucial component of highly effective teams (Tannenbaum & Salas, 2021). These highly effective teams go on to sustain or surpass their expected performance levels, an essential indicator of a team’s success within an organization (Alliger et al., 2015). Yet, how do you ensure that virtual teams do not break apart at the onset of a challenge? Failure to do so can result in a loss in team functioning, and performance is likely to bear the cost (Alliger et al., 2015). Building resilience among virtual teams is thus likely to remain a chief concern for academics and practitioners alike as virtual teams are not likely to disappear from the workplace for the foreseeable future.

WHAT ELEMENTS DO VIRTUAL TEAMS NEED TO BE RESILIENT? A growing body of research addressed team resilience and its importance (e.g., Meneghel et al., 2016; Sharma & Sharma, 2016). Drawing from this literature, we present a model of four essential elements that support team behaviors, comprising a dual path of virtual team

Virtual team resilience  333 resilience as they emerge with the critical phases (see Figure 17.1). These elements in our model were chosen because (1) there is a significant amount of empirical and theoretical literature suggesting that each element individually will help build team resilience; (2) they are essential elements that help accomplish team effectiveness; and (3) they provide an integrated way to combine elements and phases from recent models while pushing science forward (i.e., Glowinski et al., 2016; Hartwig et al., 2020; Stoverink et al., 2020). Specifically, Stoverink et al. (2020) and Kirkman and Stoverink (2021) suggest constructs similar to team efficacy, shared mental model, and team psychological safety are related to virtual team resilience. Hartwig et al. (2020) highlight the importance of additional team states, such as team trust, and the cyclical aspect related to team outcomes, including performance. Glowinski et al. (2016) then provide the four critical phases of anticipating, monitoring, responding, and learning. To incorporate the cyclical nature, and to incorporate the difference between types of resilience, we start with monitoring with the rationale that anticipation cannot happen before these three other phases are in place, but they will then continue to influence each other. The presented model suggests that team trust, team performance, and psychological safety will be most influential for the monitoring and responding phases, while shared mental models, team adaptation, and team efficacy are important for learning and anticipating. When teams possess and utilize these elements, they effectively develop virtual team resilience. Couching our discussion within the four critical phases, we expand our theoretical understanding of virtual team resilience as it develops over time, considering implications between the integration of where a team stands in its changes in different performance cycles and how this influences the teams’ progress in building their virtual team resilience.

Note: Critical phases are on top shown with gray boxes; the key elements are shown with the traced boxes (i.e., trust, mental models, psychological safety, and team efficacy), supporting the performance and adaptive outcomes. Chronic and acute resilience emphasize different types of trust.

Figure 17.1

Virtual team resilience dual-path model

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MONITORING AND RESPONDING At the very basic components, teams have to monitor the situation and respond to them. First, monitoring involves being vigilant and observing the situation, but more importantly, it includes identifying patterns in the context. Second, responding includes what the team decides to do. It is through efficient and effective team performance that the team can show that the appropriate response to the situation happened. Below, we explore the two main elements that can support these phases. Virtual Mutual Trust Trust is a critical element in virtual team resilience (Varajao, 2021). It can be defined as “an emergent and dynamic shared state at the team-level whereby team members believe in one another’s competence and are willing to be vulnerable beyond task-related issues” (Feitosa et al., 2020, p. 480). It is important to note the emergence and dynamic nature of this state as these components will become challenging with the diminished monitoring that can occur in virtual teams. Traditionally, teams develop mutual trust when each individual on the team meets the expectations regarding their commitments, shows honesty while interacting, and does not exploit others’ willingness to help (Cummings & Bromiley, 1996). If there is a lack of team trust, team members will spend most of their time worrying about each other’s inputs instead of working together to accomplish the team’s goals (Salas et al., 2005). There are different perspectives on whether trust matters more for virtual teams than co-located teams regarding team effectiveness (e.g., Breuer et al., 2016). For instance, a high interdependent virtual team will need more trust than a low interdependent virtual team. Research shows that trust is essential for virtual teams since virtual communication and collaboration may result in team uncertainty and an increased perception of risk (Duarte & Snyder, 2011). However, it is important to address that all teams, regardless of their virtuality level, will need to develop and maintain trust to be effective. After establishing trust, organizations should also focus on maintaining and monitoring it (Feitosa & Salas, 2020). Mutual trust is indispensable in virtual teams since when team members work on their individual roles, they have to rely on their teammates to contribute and meet their tasks (Salas et al., 2005). Furthermore, mutual trust is also crucial to cultivate resilient teams since it is a large part of many teamwork mechanisms. One of the main reasons for this is that mutual trust will play an essential role in how team members perceive other individuals’ behaviors (Salas et al., 2005; Simons & Peterson, 2000). If mutual trust has not been established when teams encounter challenges, team members will tend to take unclear actions, such as missing deadlines due to external factors, not directly related to individual performance, as intentional. Consequently, the monitoring phase allows for different types of challenges to emerge, eliciting different types of trust. When chronic challenges emerge, they call for monitoring in a way that is rational, precise, and can deal with the long-lasting effects of such challenges (Dimas et al., 2018). In the chronic resilience path, high levels of cognitive trust will lead to team performance. Cognitive trust is based on reliability and dependability; this kind of trust lets team members work on their tasks without having to think about whether their team members are also working on theirs (Mayer & Gavin, 2005). This kind of trust allows team

Virtual team resilience  335 members to cooperate effectively and help each other in the presence of setbacks (Hartwig et al., 2020), which is essential to build resilience. On the other hand, some short-lived challenges may also emerge. Acute challenges are not less important due to their limited duration as they are often more intense. However, the type of trust that emerges is affective, relating it to emotions and the vulnerability of the team. In the acute resilience path, high levels of affective trust and emotional management will lead to team performance. When teams need to demonstrate their virtual resilience, they are prepared to respond as a close-knit group that trusts it can weather any storm. Furthermore, when virtual team members have high levels of affective trust, they will not only work on their tasks, but they will assist with their teammates’ tasks in the face of challenges and setbacks (Robert, 2016). Team members who cooperate in the face of challenges will display increased performance (Kanawattanachai & Yoo, 2007) compared with teams who do not demonstrate this behavior. Virtual Psychological Safety Cultivating psychological safety in a virtual environment presents a unique challenge to teams. At the team level, psychological safety refers to the belief that a team can confidently take risks (Edmondson, 1999). Rather than shy away from voicing their opinions, team members feel encouraged to speak up and contribute their ideas for the benefit of the group (Edmondson, 1999). However, the act of voicing an opinion comes with personal risk – team members must feel safe to speak up to spur team adaptability, innovation, and learning (Tannenbaum & Salas, 2021). They must also feel safe when admitting errors, as, at face value, a team member may appear incompetent to the detriment of their self-image (Edmondson, 1999). To distinguish it from the notion of trust, consider psychological safety as a team member’s belief that their team has faith in them, instead of the team member having faith in their team (Edmondson, 1999). While similar, psychological safety allows teams to push back on one another’s opinions (Bradley et al., 2012) and can allow teams to work through challenges without hesitating to speak up. Virtual teams may struggle with building relationships that spur psychological safety. Team members who take the time and demonstrate a genuine interest in setting up meetings with one another can develop relationships that enable team members to feel psychologically safe (Lechner & Mortlock, 2021). Teams who cultivate psychological safety should find that team members not only share more ideas; they have more time to spend on problem-solving and ideation. This is because less time needs to be spent monitoring the teams’ interpersonal communications, allowing teams to explore new and, at times, divergent perspectives (Bradley et al., 2012). By extension, teams who create a psychologically safe climate can see a positive increase in team learning and performance (Edmondson, 1999). Knowing they can confidently be themselves among their team, virtual team members can then constructively work together to overcome obstacles and return (or increase) their collective performance. To get to this place though, virtual teams need to invest crucial initial time in getting to know each team member. In feeling safe to offer ideas, a virtual team can prove they can withstand and bounce back from external pressures, responding accordingly.

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LEARNING AND ANTICIPATING At the core of resilience, teams anticipate situations beyond what they are experiencing. Before they can anticipate, they need to learn from the patterns they monitored and the way they responded to the situations. It is through efficient and effective team adaptation that the team can show that the appropriate team-level learning and anticipation happened. Below, we explore the other two main elements that can support these phases. Virtual Shared Mental Models Shared mental models – the feeling that each team member is “on the same page” (Tannenbaum & Salas, 2021) – can be essential to the success of a virtual team that cannot see what work is being conducted due to a lack of shared physical space and time. Research shows that teams who share similar perceptions of a situation are better suited to perform tasks in rapidly changing environments (Endsley, 1995), which we know teams face today (King et al., 2016). Salas and Fiore (2004) list three key components teams need to be aware of to acquire shared mental models: overlapping knowledge held by team members, understanding of the roles and skills each team member possesses, and a shared understanding of a specific problem when it presents itself. Team members are then able to draw on their knowledge to decide what actions to take that will align with the coordinated actions of the team (Mathieu et al., 2000). Virtual team members should have a firm grasp on the tasks their team members are undertaking and how work will be coordinated so that they will not be delayed by the virtuality of the team. Shared mental models develop as team members get to know one another and work towards a mutual understanding of each team members’ role and how they should collectively approach team tasks given a current situation (i.e., developing “if–then” expectations when working towards a goal) (Stoverink et al., 2020; Tannenbaum & Salas, 2021). Virtual teams need to be highly coordinated in their communication to ensure that when adversity strikes, shared mental models remind team members of their roles and likely behavior to overcome the adverse situation together (Stoverink et al., 2020). This spontaneous, coordinated reaction to adversity is due to foundational shared mental models formed through a track record of working together (Tannenbaum & Salas, 2021). Ensuring a team is united in their understanding of each other’s roles, capabilities, and anticipated responses will come in handy when unexpected challenges call for a team response. Getting team members on the same page can significantly help a team that is faced with a challenge. Especially if teams are dispersed throughout time zones, it is helpful to know each team member’s skills and team members who can serve as backups should the principal skilled team member be unavailable. Therefore, building shared mental models will go hand in hand with building virtual team resilience when knowing whom to turn to is just as important as learning who on the team one may share personal commonalities with. Virtual Team Efficacy Virtual team efficacy significantly contributes to virtual team resilience. Team efficacy allows virtual teams to feel that they are in control and directly influence their environment. Team efficacy can be thought of as a collective team belief that high-quality work will be consistently carried out (Gully et al., 2002; Lindsley et al., 1995). Even if team members are in the

Virtual team resilience  337 presence of adversity, they will have an attitude of confidence to meet their goals. To better understand team efficacy, it is important to highlight the difference between team efficacy and team potency. According to Kozlowski (2018), “efficacy represents a shared, task-specific expectation that the team can accomplish its goals, whereas potency is a more generalized sense of competence” (p. 208). Collective efficacy is categorized as shared team beliefs based on the team’s observations and interpretations (Lindsley et al., 1995, Zaccaro et al., 1995). It can change over time, and it can vary depending on the situation teams face (Gibson, 1999). This belief is based on what team members think about their team. According to Tannenbaum and Salas (2021), there are some instances when teams, including virtual teams, may have a false sense of collective efficacy. For example, when a virtual team is composed mainly of individuals with narcissistic personalities. In this case, the collective efficacy of these teams will likely be falsely high. Collective efficacy influences a team’s ability to feel that they are in control and directly influence their environment to achieve their goals. Virtual teams with high collective efficacy will have a higher probability of successfully achieving team goals. However, that does not mean that teams high in collective efficacy have better abilities than other teams (Tannenbaum & Salas, 2021). This simply means that these teams will perform better than teams with low collective efficacy and the same aptitudes. We consider virtual team efficacy critical to team maintenance (Delice et al., 2019; Feitosa et al., 2017), for teams who lose a belief in their collective efforts may lack the resilience they need to respond to challenges. In maintaining virtual team efficacy, including maintaining online communication, even long-established teams who never see each other face-to-face can ensure that they can bounce back from challenges.

PERFORMING AND ADAPTING Previous research suggests that team resilience is positively related to team performance (Alliger et al., 2015; Kirkman & Stoverink, 2021; Stoverink et al., 2020; Tannenbaum & Salas, 2021). According to Carmeli et al. (2013), teams with high levels of resilience will be more flexible and adaptable compared with less resilient teams. Teams who are resilient in the face of setbacks will be less likely to be affected by challenges; thus, teams who demonstrate this behavior will maintain or improve their performance. Furthermore, adaptability is also essential to developing virtual team resilience (Kirkman & Stoverink, 2021). There are many circumstances where virtual teams need to adapt to changing circumstances effectively. Therefore, it is important to highlight the factors that play a role in virtual team adaptability. According to Priest et al. (2002), stressors negatively affect team performance, pushing teams to adapt. Therefore, this is also a significant element of team resilience. Stress is defined as an environmental factor or event that negatively affects team performance; it is characterized by how team members evaluate the issue, address it, and respond (Hancock & Warm, 1989). Stress is the primary catalyst for teams to adapt, and depending on how well they adapt; it will determine how resilient they are. In other words, team adaptability is essential for teams going through a challenge and needing to bounce back (Salas et al., 2005) by changing an existing course of action. Unexpected issues that give rise to panic and stress highlight the importance of working through challenges as a team. Teams can take their knowledge one step further by contemplating how to adapt based on challenges. They can work through questions and processes regard-

338  Handbook of virtual work ing not only what to do when a team member is not present, but also how to handle challenges that force adaptation and the acquiring of completely new skills. As they do so, they will both streamline team processes and prepare for future challenges that will test their resilience. Distinguishing Virtual Team Resilience from Virtual Team Adaptability It is essential to highlight that virtual team adaptability is only an element of virtual team resilience, and it is not the same construct. Stoverink et al. (2020) suggest that virtual team adaptability is only a contributing factor that builds virtual team resilience. Therefore, both terms should not be used interchangeably. A resilient virtual team will be persistent when they need to utilize the same approach rather than adapt to change when accomplishing its goals (Stoverink et al., 2020). Being persistent is what differentiates virtual resilient teams from only adaptable virtual teams. Virtual teams often experience more interruptions than co-located teams; for instance, interruptions with technology (i.e., internet issues). Warkentin et al. (1997) suggest that persistence is beneficial for virtual teamwork. On the other hand, a virtual resilient team with high adaptability will be flexible and modify the course of action in the face of changing circumstances (Kilcullen et al., 2021; Tannenbaum & Salas, 2021). The COVID-19 pandemic is an excellent example of how most organizations’ dynamics were altered. Many teams had to adjust from being partially virtual or never virtual to being entirely virtual. In this situation, team adaptability played an important role.

ACTIONABLE STEPS TO BUILD VIRTUAL TEAM RESILIENCE Traditionally, team members in a co-located space can see when their colleague is free to bounce a few ideas around or call an impromptu team meeting in the office conference room to discuss a new project. Virtual teams cannot learn and grow together in this same manner (Feitosa & Salas, 2020). Team training can facilitate the process gains associated with virtually resilient teams, boosting team effectiveness (Alliger et al., 2015). The following tips, broken down by element, offer actionable steps virtual teams can take to actively build their resilience. Imbued throughout these actionable steps is the notion that teams should be proactive in developing virtual resilience. Proactive virtual team resilience development can be conducted in non-crises states, such that virtual team resilience is improved amidst everyday situations (Degbey & Einola, 2020). Seemingly minor challenges can threaten a team’s effectiveness and ability to bounce back (Alliger et al., 2015), highlighting the importance of addressing and building virtual team resilience before a major crisis occurs. A continual process of assessing and reflecting upon commonplace events can gradually build virtual team resilience so that when challenges do arise, teams are prepared to act (Degbey & Einola, 2020) and either return to relatively normal performance or surpass performance expectations. The cyclical nature of the anticipating, monitoring, responding, and learning phases (Glowinski et al., 2016) complements the notion that resilience can be built, as virtual team resilience is not manufactured in a sealed vacuum of time. Rather, teams evolve by using each phase as part of an entire system designed to develop virtual team resilience. Along the way, they learn how the phases relate to the ability to optimize collective performance (Glowinski et al., 2016). They begin to manage tightly coordinated activities while remaining flexible in searching for

Virtual team resilience  339 Table 17.2

Tips to build a resilient virtual team

Virtual Team Resilience

Tips to Build Virtual Team Resilience

Element Virtual team trust

● Maintain transparency whenever possible ● Focus on the end goals ● Find balance in monitoring teams, so team members don’t feel micromanaged ● Identify whether it is a chronic or acute challenge situation

Virtual team psychological

● Express gratitude for team members’ efforts

safety

● Encourage dissenting opinions, thanking team members for speaking up against the majority ● Establish team norms ● When mistakes happen, focus on what can be learned

Virtual shared mental models ● Institute and train for the use of online communication channels ● Create a team member knowledge base listing key information (e.g., role title, main tasks overseen, time zone, preferred communication channel) ● Facilitate relationship building among team members ● Provide updates on changes to team members’ roles, should they occur Virtual team efficacy

● Conduct setback simulations and review achievements and failures ● Review and when milestones happen, celebrate team accomplishments (e.g., through virtual weekly meetings virtual shoutouts) ● Direct the team’s attention towards what the team can control (e.g., task milestones, consistent communication of updates)

potential hiccups, acquiring useful information, and enacting a shared response to unknown challenges (Glowinski et al., 2016). Table 17.2 summarizes these actionable steps. Advice to Increase Virtual Team Trust Trust can play a critical role in virtual teams that need to rely on one another when addressing setbacks. Because trust is impacted by team members’ ability to see what work is conducted (Ford et al., 2017), transparency is important. Whenever possible, team members should actively communicate what they are working on and why. They should be open about mistakes and weaknesses to build upon existing trust levels among the team (Bandura et al., 1999). Further, trust should be monitored, with team leaders paying attention to how team members communicate and assess whether shared tasks are truly being completed by multiple team members (Feitosa & Salas, 2020). It is also important to address that organizations and team leaders should balance monitoring team members so team leaders can intervene if needed but not in a way that workers feel like they are being micromanaged. A good approach to finding a balance is using goal setting and debriefing, which will keep team members engaged (Salas et al., 2008a). With this, team leaders should not only believe in the importance of trust – they should be prepared to identify the breakdown of trust. For instance, if one team member completes most of a normally shared task, was this agreed upon (e.g., did the other team members have a more pressing task to work on), or did the team member simply not trust anyone else to complete the task? Recognizing and remedying these types of trust violations can help virtual teams from eroding established team trust (Feitosa & Salas, 2020), and can even encourage virtual team leaders to orchestrate trust-building exercises.

340  Handbook of virtual work Advice to Increase Virtual Team Psychological Safety How do you create a sense of psychological safety among your team members? Support from peers and leadership behavior are two key drivers (Frazier et al., 2017). For a virtual team, clear communication, grateful remarks, and facial expressions over video teleconferencing can promote a sense of psychological safety among the virtual team (Tannenbaum & Salas, 2021). A recognition for each team member’s virtual working context (e.g., knowing one team member deals with faulty internet, while another team member has just taken in their parent to care for them at home) and finding shared commonalities unique to working virtually, can go far in fostering a psychologically safe environment (Feitosa & Salas, 2020). The actionable steps that team leaders and members can take to promote psychological safety in virtual teams are accepting virtual team challenges, connecting as human beings, and discussing how the team wants to work together (Lechner & Mortlock, 2021). Regarding accepting virtual team challenges, it is important not to try to mimic a co-located team. Instead, it is crucial to understand the limitations virtuality has and work on them as a team. Virtual teams should also reframe problems as opportunities. Second, it is also essential to get to know all team members as humans and not just people behind computers to build positive relationships. For this, virtual team leaders can schedule virtual team-building events, regular virtual huddles, and informal meetings. Lastly, it is also important to discuss the team’s norms. Team leaders can train their team members to abide by and promote an agreed set of team norms to boost team morale (Feitosa & Salas, 2020). In co-located teams, team norms form organically since all team members are in the same location and can observe their team members’ behaviors. In virtual teams, this can be promoted by scheduling goal virtual setting sessions and setting group rules. Advice to Increase Virtual Team Shared Mental Models Virtual teams can be trained to use online communication channels to increase the flow of clear, concise information (Feitosa & Salas, 2020), putting everyone “on the same page” (Tannenbaum & Salas, 2021). Virtual team members also need critical information, as they may not readily have access to team members working at the same time (e.g., a team member working from home in Europe and a team member working from home in Canada). Using a virtual knowledge base containing information such as “who to turn to when this occurs” can provide handy access to information needed if and when a challenge arrives. Checklists and guides can include troubleshooting tips, recommended role assignments for certain challenges, and documentation of standard operating procedures (Alliger et al., 2015). Team leaders themselves must step up to promote the development of virtual team shared mental models. Given the reduced richness of information in online communication tools, team leaders cannot rely on organic connections between team members to develop (Liao, 2017) and should thus guide team members as they get to know one another and their respective roles. In this way, shared mental models are readily available in the face of adversity. Advice to Increase Virtual Team Efficacy Virtual teams need to develop the confidence to overcome setbacks when they occur. They can be tested through virtual simulations where the team undergoes a challenge and later debriefs

Virtual team resilience  341 to discuss what went well and what could be changed in the future (Alliger et al., 2015). This recursive process can help virtual teams see that they have what it takes to demonstrate resilience when the time comes. A small but important step that can increase virtual teams’ efficacy is the celebration of team milestones. Recognition and a review of what led the team to reach the milestone can facilitate the transformation of an effective team (Sheard & Kakabadse, 2004), further encouraging a virtual team’s sense of efficacy. For their part, team leaders can direct team members’ attention towards aspects of work that the team can directly control (Tannenbaum & Salas, 2021). Put together, these tips help to ensure that teams feel ready and capable of tackling challenges.

MANAGING VIRTUAL RESILIENT TEAMS Virtual teams who struggle to address and overcome challenges can learn how to build their resilience effectively through team development interventions designed to improve performance after critical events. Team debriefing is an intervention that emphasizes both individual- and team-level learning and reflection (Shuffler et al., 2018) and focuses on specific events to help teams plan for the future. Rather than relying on formal feedback whereby a team leader provides information to each team member, team debriefing engages teams together; they reflect on the outcome of a work endeavor and the steps involved in reaching that outcome (Shuffler et al., 2018). We recommend using team debriefing to build virtual team resilience, given its active engagement of the entire team as they reflect on challenges (challenges can be real or simulated for the intervention). Teams should consider both tasks and external factors involved in bouncing back from a challenge and the involvement of each virtual team resilience element. The latter would inform the need for other team interventions (e.g., a team-building intervention promoting psychological safety) (Shuffler et al., 2011, 2018) to improve virtual team resilience at the elemental level. Using Behavioral Markers to Build Virtual Resilient Teams Behavioral markers complement team-building interventions by diagnosing the presence and progress of each virtual team resilience element. They are observable behaviors that contribute to the consistent or superior performance (Klampfer et al., 2001) virtual resilient teams actively seek. These are observable, trainable skills that can be used to monitor or develop team resilience. Behavioral markers such as the examples provided in Table 17.3 serve as an assessment tool for teams aiming to build their virtual team resilience. Table 17.3

Behavioral markers to evaluate virtual team resilience elements

Virtual Team Resilience

Team Behavioral Markers

Element Virtual team trust

● Team members attend team-wide virtual meetings where updates and setbacks are openly discussed ● Team members manage task handovers without close supervision and while acknowledging virtual factors (e.g., time zone differences)

Virtual team psychological

● Team members use online communication channels to ask for help when needed

safety

● Team members routinely voice opinions on teleconferencing calls

342  Handbook of virtual work Virtual Team Resilience

Team Behavioral Markers

Element Virtual shared mental models

● Team members utilize online platforms to stay up to date on team tasks ● Team members conduct ongoing virtual meetings to learn about their colleagues’ roles

Virtual team efficacy

● Team members call attention to their colleagues’ accomplishments (i.e., online chat messages offering congratulations) ● Team members express confidence in meeting goals on teleconferencing calls

Maintaining Virtual Team Resilience As mentioned throughout the chapter, building resilience in virtual teams is essential. However, it is also crucial to actively work to maintain and enhance a virtual team’s level of resiliency. We recommend utilizing behavioral markers seen in Table 17.3 to determine if teams are maintaining their resilience. These observable and trainable skills can be used to assess if more team-building interventions and training are required to enhance behavioral markers. Maintaining virtual resilience is undoubtedly essential for all virtual teams in order to be effective. However, maintaining team resilience is even more critical for virtual teams conducting high-risk work (i.e., NASA teams). Therefore, virtual teams must proactively work to maintain and improve their resilience.

CONCLUDING REMARKS Developing resilience in virtual teams is key to building a highly effective team (Tannenbaum & Salas, 2021). Throughout the chapter, we described virtual team resilience, focusing on the importance of building resilient teams virtually who do not balk at the sight of a challenge while demonstrating the linkage between virtual team resilience and highly effective teams that sustain positive performance over time. We hope that the advice provided in this chapter will help organizations, leaders, and team members overcome the main challenges virtual teams face daily (i.e., communication, trust). The presented advice, broken down by element and phases, offers actionable steps virtual teams can take to build their resilience actively. Imbued throughout these actionable steps is the notion that teams should be proactive in developing virtual resilience. Additionally, the elements we have listed as critical to developing virtual team resilience can be used as an initial assessment for teams to determine whether they will withstand unexpected challenges. Teams who develop virtual team resilience and use it when a challenge arises will likely find that they can not only weather the storm (i.e., by pressing onwards or altering strategy) but can grow and thrive.

ACKNOWLEDGMENT The research described in this chapter was sponsored by the U.S. Army Research Institute for the Behavioral and Social Sciences, Department of the Army (Cooperative Agreement No. W911NF-19-2-0173 to Rice University). The views expressed in this presentation are those of the authors and do not reflect the official policy or position of the Department of the Army, DOD, or the U.S. Government.

Virtual team resilience  343

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Virtual team resilience  345 Mathieu, J. E., Heffner, T., Goodwin, G. F., Salas, E., & Cannon-Bowers, J. (2000). The influence of shared mental models on team process and performance. Journal of Applied Psychology, 85(2), 273–283. Mayer, R. C., & Gavin, M. B. (2005). Trust in management and performance: Who minds the shop while the employees watch the boss? Academy of Management Journal, 48(5), 874‒888. Meneghel, I., Salanova, M., & Martínez, I. M. (2016). Feeling good makes us stronger: How team resilience mediates the effect of positive emotions on team performance. Journal of Happiness Studies, 17(1), 239‒255. Morrison-Smith, S., & Ruiz, J. (2020). Challenges and barriers in virtual teams: A literature review. Applied Sciences, 2, 1‒33. O’Leary, M. B., & Cummings, J. N. (2007). The spatial, temporal, and configurational characteristics of geographic dispersion in teams. MIS Quarterly, 433‒452. Priest, H. A., Burke, C. S., Munim, D., & Salas, E. (2002). Understanding team adaptability: Initial theoretical and practical considerations. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 46(3), 561–565. Rico, R., & Cohen, S. G. (2005). Effects of task interdependence and type of communication on performance in virtual teams. Journal of Managerial Psychology, 20(3/4), 261–274. Ridings, C. M., Gefen, D., & Arinze, B. (2002). Some antecedents and effects of trust in virtual communities. The Journal of Strategic Information Systems, 11(3), 271–295. Robert Jr, L. P. (2016, February 27‒March 2). Monitoring and trust in virtual teams. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. Salas, E., Dickinson, T. L., Converse, S. A., & Tannenbaum, S. I. (1992). Toward an understanding of team performance and training. In R. W. Swezey, & E. Salas (Eds.), Teams: Their training and performance (pp. 3–29). ABLEX. Salas, E., Klein, C., King, H., Salisbury, M., Augenstein, J. S., Birnbach, D. J., … & Upshaw, C. (2008a). Debriefing medical teams: 12 evidence-based best practices and tips. The Joint Commission Journal on Quality and Patient Safety, 34(9), 518‒527. Salas, E., Sims, D. E., & Burke, C. S. (2005). Is there a “big five” in teamwork? Small Group Research, 36(5), 555‒599. Salas, E., & Fiore, S. M. (2004). Team cognition: understanding the factors that drive process and performance. American Psychological Association. Salas, E., DiazGranados, D., Klein, C., Burke, C. S., Stagl, K. C., Goodwin, G. F., & Halpin, S. M. (2008b). Does team training improve team performance? A meta-analysis. Human Factors, 50(6), 903–933. Sharma, S., & Sharma, S. K. (2016). Team resilience: scale development and validation. Vision, 20(1), 37‒53. Sheard, A. G., & Kakabadse, A. P. (2004). A process perspective on leadership and team development. Journal of Management Development, 23(1), 7–106. Shuffler, M. L., DiazGranados, D., & Salas, E. (2011). There’s a science for that: team development interventions in organizations. Current Directions in Psychological Science, 20(6), 365–372. Shuffler, M. L., Diazgranados, D., Maynard, M. T., & Salas, E. (2018). Developing, sustaining, and maximizing team effectiveness: An integrative, dynamic perspective of team development interventions. The Academy of Management Annals, 12(2), 688–724. https://​doi​.org/​10​.5465/​annals​.2016​.0045 Simons, T. L., & Peterson, R. S. (2000). Task conflict and relationship conflict in top management teams: The pivotal role of intragroup trust. Journal of Applied Psychology, 85, 102‒111. Stoverink, A. C., Kirkman, B. L., Mistry, S., & Rosen, B. (2020). Bouncing back together: Toward a theoretical model of work team resilience. Academy of Management Review, 45(2), 395–422. Tannenbaum, S., & Salas, E. (2021). Teams that work: The seven drivers of team effectiveness. Oxford University Press. Varajao, J., Fernandes, G., Amaral, A., & Gonçalves, A. M. (2021). Team resilience model: An empirical examination of information systems projects. Reliability Engineering & System Safety, 206, 107303. Warkentin, M. E., Sayeed, L., & Hightower, R. (1997). Virtual teams versus face-to-face teams: An exploratory study of a web-based conference system. Decision Sciences, 28, 975–996.

346  Handbook of virtual work Wilson, J. M., Straus, S. G., & McEvily, B. (2006). All in due time: The development of trust in computer-mediated and face-to-face teams. Organizational Behavior and Human Decision Processes, 99(1), 16‒33. Zaccaro, S. J., Blair, V., Peterson, C., & Zazanis, M. (1995). Collective efficacy. In J.E. Maddux (Ed.), Self-efficacy, adaptation, and adjustment (pp. 305‒328). Springer. Zander, L., Mockaitis, A. I., & Butler, C. L. (2012). Leading global teams. Journal of World Business, 47(4), 592–603.

18. Engendering creativity in temporary virtual project teams: the case of a product design firm Petros Chamakiotis and Niki Panteli

In a dynamic competitive environment where firms compete at a global scale, project teams are becoming increasingly virtual, comprising geographically dispersed members whose interactions are largely, if not exclusively, technology-mediated (e.g., Yeow, 2014). Scholars suggest that the unique characteristics of virtual project teams (VPTs; e.g., geographical dispersion) make it difficult for these teams to produce creative outputs (e.g., de Leede, Kraan, den Hengst, & van Hooff, 2008). Our position is that the coexistence of dispersion and reliance on Information and Communication Technologies (ICTs) is what makes VPTs different from traditional ones. Being creative may be challenging in cross-boundary and global VPTs (Schiller, Mennecke, Nah, & Luse, 2014), in which factors such as the team’s heterogeneity and distant subgroups might make it more difficult for VPT members to effectively communicate their ideas with one another (Chamakiotis, Dekoninck, & Panteli, 2013; Lee, Saunders, Panteli, & Wang, 2021; Martins & Shalley, 2011). Challenges also exist for teams that used to work face-to-face (F2F) in physically collocated environments and had to transition into VPTs unexpectedly, as it has been the case with the recent Covid-19 pandemic for example (e.g., Chamakiotis, Panteli, & Davison, 2021; Waizenegger, McKenna, Cai, & Bendz, 2020). At the same time, despite these challenges, VPTs have been seen as fertile environments for creativity (e.g., Chamakiotis et al., 2013; Chamakiotis & Panteli, 2017; Shachaf, 2008). Yet, little is known about creativity in VPTs in general (Gilson, Maynard, Jones Young, Vartiainen, & Hakonen, 2015) and the generation of creative ideas across the different temporal stages of a VPT lifecycle in particular. In this chapter, we focus on teams that used to work in a F2F environment and which had to quickly transition to a VPT. In particular, we take the case of new product design to study creativity during the lifecycle of a given VPT project. Creativity stages are generally highly unstructured (Frishammar, Floren, & Wincent, 2011), and a question arises about how these are shaped in the context of temporary VPTs. In VPTs, creativity has been seen as a process of four stages: idea generation, development, finalization/ closure, and evaluation (Nemiro, 2002). Contrary to a dominant (implicit) view that considers creativity and innovation two separate processes (e.g., Shalley & Gilson, 2004), with the former being about generating ideas, and the latter about implementing them and commercializing them, Nemiro (2002) argues that in the VPT environment the two processes are largely intertwined and can be understood by identifying the four aforementioned stages. In a subsequent study, Chamakiotis and Panteli (2017) found that – as the creative process evolves – creativity takes different forms, varying from idea generation, to generating solutions to problems. In this area, researchers have also examined the factors influencing (positively or negatively) creativity in the VPT context, as well as the role of leadership in promoting creativity in VPTs (Chamakiotis et al., 2013; Chamakiotis & Panteli, 2017; Ocker, 2005). We extend literature on this topic by examining the emergence of creativity across the lifecycle of a temporary VPT. We examine temporary, time-bounded VPTs, as opposed to 347

348  Handbook of virtual work on-going and/or permanent teams, which organizations increasingly see as a means for transcending organizational boundaries and capitalizing on global expertise and cross-cultural collaboration (Lee et al., 2021). Some scholars have studied aspects of creativity in VPTs, with Nemiro (2002) arguing that VPT members choose F2F media in the first and last stages of the creative process, and complete the second and third stages virtually using different types of ICTs. Similar studies on ICT-mediated work have highlighted the benefits of virtual work for creativity. For instance, Dennis, Minas, and Williams (2019) present the case of electronic brainstorming and argue that ICTs provide unparalleled opportunities for large-scale, multi-player creativity (e.g., in crowdsourcing environments) which cannot occur in traditional, F2F work environments. Example features include the anonymity aspect of some types of virtual work, which allows virtual coworkers to be critical (and thus constructive) of others’ creative ideas. However, these benefits may not be relevant in organizational VPTs in which teammates know one another. Additionally, there are no studies to our knowledge that have examined how the temporary VPT character may contribute to this. Consequently, the driving aim of the study has been to examine the factors that engender creativity within the lifecycle of temporary and newly transitioned VPTs. Recognizing that not all temporary VPTs are the same (Gibbs, Sivunen, & Boyraz, 2017), we adopt a case study approach (Cavaye, 1996; Yin, 2008) and focus on a group of engineers who typically work in a physically collocated, F2F team environment but had to reorganize themselves and form a VPT on the spot for the purposes of a specific project that could not be completed in the office and whereby all team members had to work from different geographical locations and collaborate virtually. The VPT we studied was a one-off project that lasted two full working weeks. We collected a rich qualitative dataset comprising individual interviews with all team members, observations (some captured on video), as well as other data (e.g., documentation, VPT communication excerpts). In what follows, we review relevant literature, present our case organization and research findings, and discuss our contributions, limitations, and future research directions.

CREATIVITY IN THE TEMPORARY VPT CONTEXT VPTs have been around for a long time, exceeding two decades (Lipnack & Stamps, 2000) and have recently regained popularity due to the Covid-19 pandemic and the lockdowns around the world that forced individuals from across industries to transition into different forms of virtual work (Chamakiotis et al., 2021). The traditional literature on VPTs views VPTs as project teams characterized by different types of member dispersion (i.e., geographical, temporal, organizational) and work on a project using selected ICTs (e.g., Lipnack & Stamps, 2000). VPTs differ from F2F teams due to their unique characteristics (e.g., inter-organizational dimension, geographical dispersion) and the discontinuities they create for VPT members and managers (Chamakiotis, 2020; Watson-Manheim, Chudoba, & Crowston, 2012). Further, virtual work raises additional boundaries and, in order for these to be overcome and for cohesion to develop, opportunities to meet F2F are paramount (Breu & Hemingway, 2004). Zander, Zettinig, and Mäkelä (2013) highlight the importance of creating a social context in this respect, so that VPT members can develop interpersonal relationships and trust. Notwithstanding these challenges, VPTs, and global VPTs in particular, provide opportunities for increased creativity and innovation due to the increased diversity of their members which

Creativity in temporary virtual project teams  349 has been found to lead to more ideas (Chamakiotis et al., 2013; Shachaf, 2008). Despite those findings, the ICT-mediated environment and other VPT characteristics may render this a challenging process (de Leede et al., 2008). Although some factors, such as heterogeneity/ diversity (Chamakiotis et al., 2013) and leadership (Chamakiotis & Panteli, 2017; Shalley & Gilson, 2004) have been recognized as factors influencing creativity in VPTs, the role of the temporary aspect of VPTs for creativity has not been examined. Teams – irrespective of whether they are collocated or virtual – may differ in terms of their level of continuity; some are permanent, while others are temporary. Project-specific teams are by their nature temporary. Chae, Seo, and Lee (2015) found that there were significant differences between the effects of task complexity on individual creativity and knowledge interactions, depending on whether teams were temporary or permanent. In particular, these authors argue that the above factors need to be considered in tandem if aiming for high degrees of individual creativity within teams. Although their study did not focus on VPTs specifically, our view is that task complexity, knowledge interactions, and the degree of a team’s temporality should be considered by VPT researchers and practitioners. In our study, we focus on the latter element and study creativity within a temporary VPT. The temporary, time-bounded nature of a VPT has been seen as an enabler to creativity as it helps the team to focus on its core purpose and improve its performance (Gevers, van Eerde, & Rutte, 2001); and as a “constraint” that can reduce team members’ intrinsic motivation (Amabile, Conti, Coon, Lazenby, & Herron, 1996), or, paradoxically, have a positive contribution to a VPT’s innovation activities (Chamakiotis, Boukis, Panteli, & Papadopoulos, 2020). VPTs have been seen as ideal environments for creativity and innovation. In a study of how individual, team, and technology-related factors influence creativity in VPTs, further to corroborating difficulties engendered by heterogeneity/diversity, for example due to national diversity (also found by Martins & Shalley, 2011), Chamakiotis et al. (2013) explain why VPTs are ideal for creativity. First, heterogeneity/diversity is likely to bring new talent and subsequently contributes to increased breadth and scope of idea generation. Second, the limited time for synchronous communication online may be seen as an opportunity for more readily generating and sharing ideas “together” with dispersed teammates. And third, the asynchronous character of ICTs affords the opportunity to be creative irrespective of dispersed teammates’ availability; that is, due to time-zone differences, ideas may be shared using asynchronous means. Therefore, diversity/heterogeneity aspects as well as the synchronous and asynchronous technological capabilities characterizing VPTs may offer a range of opportunities for creativity. Despite this emerging literature, the fact that an increasing number of new product development projects are conducted virtually (e.g., Yeow, 2014), and, more recently, the Covid-19 pandemic which has led to an unprecedented transition into new forms of (virtual) working (e.g., Waizenegger et al., 2020), the study of creativity in VPTs remains limited. Within this literature, scholars have studied: the stages of the creative process (Nemiro, 2002); factors (enablers/inhibitors) influencing creativity (Chamakiotis et al., 2013; Ocker, 2005); demographic differences (Martins & Shalley, 2011); creative performance (Kratzer, Leenders, & Van Engelen, 2006); creativity techniques (Torres-Coronas & Gasco-Hernandez, 2005); the relationship between leadership and creativity (Chamakiotis & Panteli, 2017; Wang, Hsieh, Menefee, & Pestonjee, 2011); and idea generation (Kerr & Murthy, 2004).

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RESEARCH SITE AND APPROACH We adopted the exploratory case study approach (Yin, 2008) with the use of qualitative methods. Case studies are suitable when aiming to gain an in-depth understanding of the phenomenon of interest within a single setting (Cavaye, 1996). We studied a small, UK-based firm, Alpha (a pseudonym), which was formally established in 2007. It has two directors and is split between two shareholders. Per its owner, manager and study participant, Geoff, Alpha is “about design and design thinking” and there is no specialization in terms of a specific category of products or industry: there is no common thread in terms of productive categories. It’s design thinking, it’s being able to take a problem and find a solution for it, or start from just an analysis of what they do, look for problems and opportunities […] well the way we work is either we solve problems or we look for opportunities. So sometimes you’re given a problem to start with and you have to find things that they can do that better, but the thread behind all of it is actually using design thinking to come up with new ways of doing things, systems or products. So, there is no common thread in terms of the categories. (Geoff, interview)

Creativity is therefore an inherent facet of Alpha’s philosophy, since it entails coming up with new ways, new systems, and new products. At the time of data collection, projects at Alpha were typically team-based and collocated with all team members involved in any given project working from the office. Therefore, use of ICTs for communication purposes among team members was minimal. Before our focus on the VPT which we present below, we sought to understand how the same team organized their work in the office and what techniques they used in their effort to design creative models. For example, it was noted that, for their brainstorming sessions, they typically used flipcharts which they hung on the wall, as well as colored markers and other similar materials. We studied Alpha’s very first VPT project which lasted for two weeks (Days 1 to 10). The participants were based in three different locations in the UK, with the exception of Geoff who also spent time overseas for work; therefore, during the virtual project the participants did not meet F2F. The design task for this project was to design a specialized glass-window for historic houses. The purpose was to: improve their thermal performance, eliminate condensation, and retain the period feel of the building. (email correspondence)

METHODS AND ANALYTICAL APPROACH The methodological approach adopted in this study was qualitative and involved interview data, non-participant observations (in a logbook and recorded), video data, written communication extracts, design outputs, and other materials produced by the team. Specifically, we (a) interviewed the three participants twice (before the VPT project, to understand the working culture at Alpha, and after the VPT project, to understand what was different in the VPT project); (b) recorded all 76 online sessions of the participants working together (2,365 minutes or approximately 40 hours); and (c) reviewed 21 pages of Skype IM communications and two logbooks (81 pages) of timestamped design work. Analysis was inductive and

Creativity in temporary virtual project teams  351 took into account the different types of data collected. We first counted all design ideas that emerged during the different stages of the VPT lifecycle in order to understand when creativity occurred. This resulted in Figure 18.1 which we present later. We then used the interview data to understand the VPT members’ perceptions of creativity throughout the VPT lifecycle, for example, what helped them be creative and how they shared their ideas using ICTs. This process followed a thematic analysis approach (Braun & Clarke, 2006) and was conducted using NVivo software. Most other data were used to familiarize ourselves with the context. The different types of collected data thus complement each other and are used for completion, in line with the interpretivist tradition (Symon, Cassell, & Johnson, 2018).

THE ‘GLASS-DESIGN’ VPT PROJECT AT ALPHA VPT Dispersion and Organization The VPT participants worked from three different locations as had been previously agreed. Patrick worked partially from a shared office and partially from home, Steve worked from home, and Geoff worked from his office, but also remotely while travelling overseas. Geoff was mainly on the go during this project, due to other professional commitments, and therefore, his involvement was more limited than usual. Therefore, most of the work was done by Steve and Patrick. During our interview with Geoff, he would often respond using “they” when we asked how the VPT was progressing, suggesting that he was not always an active participant. Adding to this dynamic was the fact that Steve and Patrick were junior employees with limited work experience compared with Geoff. Therefore, much of this project was performed by a dyad instead of a team of three. Consequently, the temporary and short character of the VPT, in combination with Geoff’s external commitments and the different levels of seniority within the team, was found to have an effect on team dynamics. ICT use Within the VPT The two participants communicated via Skype to set targets, discuss their progress, exchange ideas, and establish steps forward. They also used Skype instant messaging (IM) for minor coordination issues, such as agreeing what time they would meet next. The Skype IM and Group Calls data show that the team’s communications were both task-related and of a social nature too, replicating how they typically worked F2F. For example, it was observed throughout the VPT project that the three participants updated each other on personal issues, such as what they would do next. The frequency of use of ICTs with high degrees of synchronicity (i.e., IM and voice-over-IP) was high, while completely asynchronous communication (i.e., email) was minimal. The main collaboration ICT they used throughout the project was Prezi – a software program mainly used for presentations. Prezi allowed them to monitor their progress regularly and was shared among all three. Typically, they all met virtually as a team of three at the end of each day to check each other’s progress and make decisions regarding next steps. Steve and Patrick, however, usually used synchronous ICTs to communicate up to three times a day. Most meetings were voice-only (i.e., Skype Group Calls) and no video was used. During each virtual meeting, the participants watched an updated version of their Prezi file as a basis for communication.

352  Handbook of virtual work The VPT Lifecycle Early stages: launch and organization At the kick-off on Day 1, Geoff sent Patrick and Steve an email outlining relevant information about the product the team would have to work on, and he followed it up with a Skype Group Call. Technical difficulties prevented them from having Steve on the same Skype Group Call. The two members then exchanged negative experiences about working virtually in the past, indicating that the VPT environment was not seen as ideal. Once all three connected virtually, Geoff assumed a leadership position as the most senior member and highlighted three issues: (a) a simple combination of communication tools they would use to collaborate; (b) each participant’s availability and preference in terms of the location they would work from; and (c) the aims of the project. The aim of the project would be to generate some ideas about a potential solution, without jumping straight to it. He suggested, if we can spend this week exploring and investigating and then next week detailing, this will give us some time to come to some sort of useful conclusion. (Geoff, Day 1 meeting circa 10:30am, VPT observation extract)

Further to setting the aims of the project, discussing availability, and exploring patterns of communication for the duration of the project, during the first meeting, Geoff gave five directions that guided Patrick and Steve’s forthcoming work: (a) possible solutions currently available on the market; (b) cost issues; (c) time issues; (d) different materials (e.g., polycarbonate and also more flexible materials); and (d) system – looking at a business solution level. The remainder of Day 1 was focused on market research, which gave rise to some ideas. Specifically, the junior participants researched what was available on the market to improve insulation of traditional sash windows – often found in old buildings in the certain geographical areas of England. The junior participants performed individual brainstorming in addition to Internet research and gave each other quick updates via Skype IM. The two agreed on four criteria their solution should satisfy: (a) the solution should be a system, not an individual product; (b) it should be easily measurable; (c) it should be easily fitted into the sash window; and (d) it should be easily removed and stored. These criteria were used to guide their market research and brainstorming at that early stage of the design process. Once individual research was accomplished, they discussed their findings and identified the pros and cons of each idea. At the end of Day 1, the team conducted a Skype meeting and built on each other’s ideas using Prezi to guide one another through their individual progress. During the meeting, they discussed ideas about how they could measure, cut, fit, and remove and store the prospective end solutions, generating thereby a large number of ideas. The meeting closed by outlining expectations for the next day and agreeing on another team meeting at the end of the following day. Performing stage: idea generation, sharing, and computer-aided design (CAD) On Day 2 the participants built on the key ideas generated on the first day and produced the highest number of ideas (n=64). Their work from that point forward was based on the directions agreed on the first day. Geoff emphasized to the junior participants the importance of using the pen and paper technique, a creativity technique used within the F2F environment, however because they were geographically dispersed, the value of this technique might not be realized. The junior participants took a deeper look into the materials used in the products

Creativity in temporary virtual project teams  353 they identified during their Internet search and borrowed some ideas that could be potentially applied to their concepts. Day 2 also involved decision making, during the VPT meeting; it was then when Geoff made some decisions as to which ideas would make it further and which would not. These decisions were mainly based on criteria such as complexity and cost. During the rest of the week (Days 3 and 4) the two members engaged in high levels of individual work (independent research) that helped them come up with new concepts around a number of areas, including aesthetic ideas (e.g., Eden project), types of windows (e.g., pull-down blind), materials used (e.g., aluminum, glass), properties of different materials (e.g., heating, insulation), and ideas about functional issues (e.g., magnetic brackets). Most of Steve’s work focused on CAD and sketching up concepts, whereas Patrick was more focused on developing the concepts further. For example, Patrick made noteworthy progress working on existing concepts the VPT came up with in more detail, though the VPT felt there were still geometry and other issues. Geoff was also involved at unexpected moments, for example when at the end of the first week he gave Patrick a phone call instigating him to look into a particular artifact and get ideas from it. Overall, the work done (mostly individually) during Days 1–5 was discussed extensively at the end of the week when all ideas were explored further at the team level. Contrary to previous projects undertaken in the office, this project’s aim was to generate ideas to address the design problem presented to the junior participants and develop a selection of them further. Therefore, no final products were ultimately prototyped, and the emphasis was placed on creativity and engendering the right ideas, rather than actual prototype development. Analysis identified 215 design ideas throughout the VPT lifecycle. Though the key ideas around which the VPT worked emerged during the first week, the participants continued to be creative during the second week through virtual discussions on technical and functional issues. Creativity in the first week (Days 1‒5) took the form of idea generation in terms of concept generation, that is, what the envisaged design output(s) could look like, whereas the second week (Days 6‒10) was more about ideas the team had to generate to deal with technical and functional issues related to their designs. For example, certain materials that were suggested for certain parts of the design (e.g., about the frame) were very expensive to use. Other ideas concerned details about providing a service, including not only the window per se, but also the process that was necessary in order to replace an existing window. Overall, creativity occurred most days during the VPT lifecycle, and it peaked on Days 2 and 5, as also shown diagrammatically in Figure 18.1 which is based on our visual analysis of the generated ideas. The project was concluded on Day 10 when the project deliverables were sent to the client. During this concluding stage, no ideas were identified (Day 10). Assessing the Creativity of the VPT Project Though this was the first ever VPT project that Alpha experienced, Geoff was happy with the creativity in this project: I was quite pleased; we did actually come up with lots of different and diverse solutions. (Geoff, Interview)

Despite this overall assessment, some concerns were shared about working virtually:

354  Handbook of virtual work

Source: Authors’ own.

Figure 18.1

Creativity in the VPT lifecycle

all the time when you’re designing something and you’re sitting down designing what you are doing is you are starting from here, and to get to there, there is a series of small improvements and changes, it’s like climbing a set of stairs from one place to another and you have lots of steps and each of these steps represents a change or an improvement or something, and with this project it literally felt like there was much less refinement. It’s a crude process, somebody works on something in isolation, we have a meeting, they make some changes, they work on it in isolation, again we have a discussion, they make some changes, and you can do that maybe if there is one conversation a day the maximum you can have in two weeks is ten steps, you would probably have less conversations […] if there’s a three-day project looking at the framing system we literally went through three steps. (Geoff, Interview)

As it follows from the above quote, the VPT lifecycle was perceived as a much slower and an incremental process, compared with that in the F2F environment. This contributed to negative emotions and a sense of frustration due to the reduced pace of interaction and idea sharing, and its adverse effect on creativity. Geoff, in particular, also based on his past virtual experiences, was adamant that virtuality slows down the creative process. As he argued, [Working as part of a VPT] is much slower […] it really comes down to speed, we have a natural pace we work out in the conceptual stage it’s very intense, so lots of ideas, and it feels very frustrating just the pace is very slow probably five times slower and that feels very frustrating. (Geoff, Interview)

Despite the number of ideas generated during the VPT lifecycle (Figure 18.1) at the team level, there were misunderstandings that slowed down the team’s progress. These misunderstandings were not brought to light early on, but when the designs started to take shape:

Creativity in temporary virtual project teams  355 a blind idea with two rollers, and then there was another completely separate solid glazing thing, so the blind idea was a roller like a flexible film. And because [Patrick] mentioned a blind thing that triggered ideas, I then went away and drew a concept of a blind, I think he just described it, I don't think he had a sketch for it, I think he had an idea that we were talking, because I didn’t see an image […] so I thought I would draw up as well, so what he described was different from what I had, then we put the frame in as well to secure it, and basically recombined a lot of ideas. So, it had the roller idea but just at the top, but it didn’t have [Patrick]’s roller which we realized was a bit over the top and it wasn’t necessary. I think [Patrick] was talking about magnetic strips, so we had that on there as well, so we chatted the first day or two and just combined loads of the ideas, and I quite liked that idea, it was a good idea. (Steve, Interview)

Steve’s quote highlights the importance of photographic material for communicating ideas within a design team and reinforces Geoff’s arguments from the F2F component of this study that sharing design outputs on a common surface that is visible to all is crucial in design. Despite software availability, the participants experienced reduced visibility of each other’s ideas. Geoff furthered this view by arguing that, in the VPT environment, ideas cannot always be prototyped; or that the value of prototyping them in a virtual environment is not the same for all participants: The other thing I guess a problem with working remotely is the [in]ability to prototype, to review the prototype, and make decisions based on that. You just can’t do it. In the [F2F] project, we built in plastic tube a model of the ladder and we all sat around and looked at it and the changes we made as a result of having built and tested the prototype and spent time in the workshop. With this you can’t do it, one person can build a prototype but it’s much more difficult to share how it works with the others. One of us could build it and test but the other two wouldn’t be able to have the experience of being users. (Geoff, Interview)

Factors Influencing Creativity in Temporary VPTs Further analysis of the Alpha experiences with the glass-design VPT project has shown that there are different enablers and constraints that may engender or inhibit creativity in the VPT context. It is worth noting that Alpha has traditionally entailed collocated product design teams and the VPT studied was, at the time of the study, the exemption rather than the rule for this organization and its employees. As shown in Table 18.1, we distinguish between (a) factors influencing creativity in a F2F environment, which are non-transferable to the VPT context; and, most importantly; (b) factors influencing creativity in the F2F environment, which are transferable to the temporary VPT context; and (c) factors which are unique to the VPT context, which help elucidate the relationship between creativity and virtuality. Those engendering creativity are marked with (+) and those inhibiting it with (-).

DISCUSSION AND CONCLUSION The aim of the study presented here has been to examine the emergence of creativity within temporary, newly transitioned VPTs. In doing so, we studied the case of Alpha, a small product design firm in the UK, and focused on a team’s first attempt to complete a design project in a VPT environment. Creativity is at the heart of this organization which has typically operated

356  Handbook of virtual work Table 18.1

Team-related Factors

Factors non-transferable to

Factors transferable to the VPT

the VPT context

context

 

(+) Personal attributes: being

(-) Unfamiliarity with selected ICTs:

technologically savvy and educating

causing interruptions to the creative

others on use of relevant software

process

(+) ‘Chemical reaction’

  (+) Good team size, dynamics and

  (+) Geographical dispersion: (a) increases

between team members,

respect to each other’s ideas

participants’ individual sense of ownership

triggering generation of ideas

(-) High degrees of homogeneity in

and responsibility; (b) offering flexibility

(+) Physical proximity

team composition inhibiting creativity

and space for understanding in the early

offering immediate feedback

(+) Democratic and strength-based

stages of the VPT lifecycle; and (c)

and building on each other’s

leadership: giving room for all ideas to

geographical isolated members have less

ideas

be heard and utilizing members’ skills

distractions and can be more productive in

(+) Physical proximity

(+) Accepted central leader for

terms of ideas

offering an opportunity to

decision making

(-) Geographical dispersion: (a) not

Factors

related

Individual-

 

Factors engendering creativity in temporary VPTs

prototype ideas, giving rise to

allowing for collaborative brainstorming

more ideas

or for ideas’ prototyping; (b) inhibiting use

(+) Physical proximity

of pen and paper; (c) raising boundaries;

increasing communication

(d) reducing (subconscious) visibility and

speed, engagement levels,

speed; and (e) not allowing members to

enthusiasm and alertness

bounce off ideas, get instant feedback, and

(+) Inherent visibility of

build on each other’s ideas

the physical environment

(-) Feelings of isolation inhibiting

increasing opportunities for

creativity when members work in isolation throughout the VPT lifecycle

creativity

Technology-related Factors

 

(+) Use of CAD helping participants

(-) Cost- and ICT-proficiency-related

depict and share their ideas with the

factors influencing ICT selection

rest of the team

(-) Artificial character of synchronicity:

(+) Individual use of Internet helping

quality, accessibility and delays

participants identify and build on

(-) Reduced visibility causing uncertainty

existing ideas

and impressions of unavailability (-) Asynchronous CMC: slowness interrupting collaborative creative process (-) Synchronous CMC: slowness, time limitations and reduced productivity

(+) Organizational practice:

(+) Organizational approach: freedom

shared wall space for (a) better and openness improving (a) the

Organization-related Factors

Factors unique to the VPT context

visualization of ideas; (b)

understanding of the design task; and

ideas sharing; and (c) ideas

(b) the number of ideas

tracking

(+) Organizational practice: design

(+) Organizational focus on

process of controlled and divergent

the task: unstructured physical

character

work environment enhancing

(+) Organizational focus on the task:

creativity

lack of paperwork and peripheral

Source: Authors’ own.

activities (-) Unexpected absences causing changes in the direction of the project

 

Creativity in temporary virtual project teams  357 in a collocated environment. Due to various circumstances and the other commitments of its members, Alpha had to work on a new project virtually. Findings showed that, overall, the team performance in terms of the design deliverable and the degree of creativity was satisfactory to all members involved. ICT-related factors (e.g., availability of online design tools), strong team dynamics and the organization’s approach towards change and innovation had a positive influence on the emergence of creativity in the new virtual workspace. Although the organization was not equipped with advanced communication technologies that would allow team members to visualize their design ideas, and it did not have the know-how or level of experience of working virtually that large global organizations may have, the VPT we studied performed well and creatively. Our study makes several contributions, first, by improving understanding of where creativity is positioned within the VPT lifecycle. What we have shown is that creativity is evident throughout the VPT lifecycle, and not just at the beginning of the creative and innovation processes (Chamakiotis et al., 2020). This is because it is not only about generation of conceptual ideas, typically required at the beginning of the so-called “design process” (Howard, Culley, & Dekoninck, 2008), but also about other aspects of a product, such as its features and practicalities. Contrary to previous studies on VPT creativity, whose members combined virtual and F2F working (Chamakiotis et al., 2013; Nemiro, 2002), our study involved a purely virtual VPT (Griffith, Sawyer, & Neale, 2003) with geographically isolated members and no locational subgroups (O’Leary & Mortensen, 2010). We thus add to the VPT literature by showing that purely ICT-mediated and geographically isolated members can still perform creative tasks well in comparison with VPT members who have the opportunity to meet F2F or work F2F within locational subgroups. It could be argued that earlier research (e.g., Nemiro, 2002) considered F2F communication important for creativity in VPTs, typically early on in, or at the end of, the VPT lifecycle because of the lack of sophisticated ICTs in the early 2000s. However, despite the availability of more sophisticated ICTs that we see nowadays, F2F is still essential. This means that although synchronous (e.g., Zoom) and asynchronous ICTs (e.g., Google Drive) offer advanced technical capabilities, making online collaboration more reliable, effective and widespread (e.g., Gilson, Costa, O’Neill, & Maynard, 2021), these ICTs alone may not be enough for a successful VPT project. On the one hand, excessive use of some of these ICTs may lead to fatigue and burnout (Bennett, Campion, Keeler, & Keener, 2021), while recent literature, on the other hand, has shown that F2F interaction is important as it increases relationship satisfaction (Pollmann, Norman, & Crockett, 2021). Our participants had preexisting working relationships and an established social context that enabled them to work well in a VPT environment with no F2F contact. These findings were also confirmed during the recent pandemic when organizations moved online and reorganized their work in (emergent) VPT environments with little or no preparation and perhaps not always with the required infrastructure. These newly transitioned VPTs due to the Covid-19 lockdown were largely successful because of preexisting working relationships of their members and the social context that had been developed prior to the transition (Chamakiotis et al., 2021). We also identify factors engendering creativity in the professional VPT context. Although we are not the first to look at factors influencing creativity in the VPT context (Chamakiotis et al., 2013; Ocker, 2005), our study adds to existing research by categorizing those factors by individual, team, technology and organizational levels, and by differentiating between (a) factors influencing creativity in F2F environments, which are transferable to the VPT context; and (b) factors which are not transferable to the VPT context. Having looked at how the same

358  Handbook of virtual work team worked F2F before the VPT project has enabled us to isolate factors that are unique to the VPT environment. Thus, these factors promote an enriched understanding of the relationship between creativity and the unique characteristics of virtuality in the professional VPT context, which the extant literature has so far neglected. Additionally, non-transferrable factors engendering creativity, such as issues of better visibility of others’ ideas or the “chemical reaction” reported in Table 18.1 could still be feasible via the use of more advanced ICTs that were not available at Alpha. A noticeable strength of this study is that our case involved professional designers working for a real organization. Given that research on VPT creativity has been largely conducted in educational settings involving student participants (e.g., Nemiro, 2002; Ocker, 2005), our study helps to expand knowledge in the field by examining VPT creativity in a professional context. Although our participants were largely homogeneous and locally dispersed, our findings are significant and transferable to other contexts where organizations are forced to switch to a virtual way of working unexpectedly. Practitioners working in VPTs should develop practices that will allow them to turn the inhibitors to creativity (see factors labeled with (-) in Table 18.1) into factors engendering creativity. For example, although geographical dispersion may lead to feelings of isolation that may negatively impact employees’ creative performance, VPT practitioners should capitalize on the positive aspects of geographical dispersion, such as the sense of ownership and responsibility it may afford and the protection from distractions. In closing, we recognize that our study is not without limitations. Inevitably, the team that was examined in this study had certain unique characteristics which may be different to the characteristics of other VPTs found in the literature, for example, global, dispersed, culturally diverse. At Alpha, the participants were highly homogeneous (e.g., in terms of language and education), the team was not global but national, and the participants were geographically isolated from one another with no locational subgroups. However, with local VPTs being the norm in the (post) Covid-19 context, we believe our findings may be useful to individuals managing or working in VPTs whose work requires creativity in a range of industries. Different types of VPTs may behave differently, and our study focused on a single case study. Therefore, future research should seek to unpack these possible different behaviors in different types of VPTs. Finally, we are seeing new forms of virtual work take precedence; these involve working in what is said to be a “hybrid” environment, involving working partly from home and partly in the office, thus mixing professional with domestic and other activities. These new team configurations may introduce unprecedented benefits and also inhibitors for creativity that future researchers should seek to examine.

ACKNOWLEDGMENTS We would like to thank our case organization, Alpha, our research participants, and Dr. Elies Dekoninck (University of Bath) for introducing us to Alpha. The data presented in the chapter were collected during an earlier Engineering and Physical Sciences Research Council (EPSRC) research grant in the UK.

Creativity in temporary virtual project teams  359

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PART V THE ORGANIZATION: CONTEXT, CULTURE, AND SYSTEMS THAT SUPPORT VIRTUAL WORK In this final section of the Handbook, we introduce a set of chapters that examine organizational-level considerations for virtual work to work. These chapters are diverse and examine the organizational level topic from multiple different perspectives which is interesting because when we think of the ability to work virtually, what does an “organization” really mean? To start, Nordbäck and Nurmi provide a nice bridge from the team-level of analysis as they discuss the reality that virtual work is often performed away from an organization’s central office and as a result, the location(s) where work is happening needs to be more fully considered. In particular, these authors highlight that the old adage “context matters” still needs to be addressed when work is virtual. Namely, from a contextual standpoint, they highlight the importance of places and spaces, workplace design, and organizational configurations. In addition to context, they also posit that from an organizational perspective, people management is critical. Here they discuss the importance of organizational policies, social norms, and organizational climate. This chapter concludes with a section highlighting where future research should go in terms of understanding these components as well as gaining a deeper understanding of where individuals are actually conducting their work and how these local contexts shape virtual work. Next, Lauring, Jonasson, and Jackowska provide a chapter that dives into the topic of inclusiveness within virtual work – a topic that has not been extensively considered to date, but they suggest is very important within virtual contexts. In this chapter, the authors introduce and advocate for even more consideration of virtual diversity management. Lauring et al. lay a foundation for work that has considered inclusiveness in non-virtual contexts and highlight the positive outcomes that are likely to accrue from diversity in organizations by leveraging both the information-processing and social identity/social categorization theories. A discussion on the various types of diversity (e.g., gender, age, ethnicity/culture, language, disability) that are apt to be pertinent within virtual work settings and how the central ingredients of virtual work (i.e., ICT use and distance) is presented along with how they can have both posi361

362  Handbook of virtual work tive as well as negative effects on diversity management. Given these conflicting influences of diversity on virtual work, the authors contend that it is incumbent upon the organization and leadership to determine whether the scale is tipped toward the negative or positive impacts of diversity. The chapter closes with a discussion of the role of top management and line managers and what actions they can/should take to garner the positive effects of diversity within virtual contexts. Moving to work design and its role in virtual work, Wang and Parker start their chapter with a review of the literature on telework, virtual teams, and computer-mediated communication and weave into this review work that has considered these topics during the COVID-19 pandemic. The primary focus here is on two areas – the social challenges of virtual work as well as the task challenges inherent in this form of working. The authors outline a SMART work design approach to address both the social and task-related challenges. The SMART approach includes the topics of: Stimulating, Mastery, Agency, Relational, and Tolerable demands and includes specific work design factors that organizations can take in order to offset the social and task challenges that can be experienced within virtual work. The prevalence of virtual work in multinational organizations (MNOs) is the focus of the chapter by Erez, Glikson and Harush. These authors emphasize that knowledge sharing is essential within MNOs, but that cultural diversity present within MNOs, while essential to have diverse knowledge to share, can also hinder the ability of MNOs to have such diverse knowledge be shared. To overcome these challenges, they emphasize that characteristics of the media used to communicate and share that knowledge is especially impactful. They leverage the Media Synchronicity Theory and its dimensions (i.e., Parellelism, Rehearsability, Reprocessability) as well as Media Richness Theory to walk through the impact of working virtually within MNOs and its impact on knowledge sharing. A novel part of this chapter is how the authors demonstrate the effectiveness of lean communication channels for knowledge sharing in MNOs by using the discussion of GitLab. The authors highlight the need for more work advocating for the use of lean communication media as well as more consideration of the interplay of cultural diversity and communication media on knowledge sharing in MNOs. Finally, Gosain, Malhotra, and El Sawy provide a chapter that examines the importance of organizations working across their own organizational boundaries to create value. Information communication technology (ICT) is enabling cross-organizational collaborations to happen at a rapid pace. The authors provide numerous examples that demonstrate how these collaborations are enhanced through the use of technology with a particular emphasis on Application Programming Interfaces (APIs) and Blockchain. The underlying roadmap for this chapter is to explore how organizations can attain desired outcomes (i.e., value network flexibility and collective knowledge creation potential) through the careful orchestration of these cross-organizational collaborations. The authors suggest that this is made possible through two distinct pathways – Interface Structuring Pathway and Interface Minding Pathway. Propositions are introduced that posit specific mechanisms that are essential within each of these pathways and close their chapter by highlighting areas where future work is needed to better examine these cross-organizational partnership behaviors.

19. Organizational context and climate for virtual work Emma Nordbäck and Niina Nurmi

As companies are beginning to grasp the scope of the Covid-19 pandemic world, organizations are increasingly rethinking their organizational practices and norms to support more virtual and hybrid working. Not only are organizations relying ever more on geographically distributed teams to perform their tasks, but increasingly offering their employees workplace flexibility, making co-located knowledge work progressively more virtual. In this chapter, we adopt a broad definition of virtual work as “work [that is performed] away from a central office using technology” (Raghuram & Wiesenfeld, 2004, p. 259). This means virtual workers may be working in a variety of locations, including their home, client sites, coffee shops, public transport, and while traveling. Many organizations offer their employees virtual work options of this nature, who at a growing rate desire the flexibility to work away from the office. Recently, a McKinsey report on the future of work revealed that an astonishing 80 percent of workers want to do so remotely for at least part of the time (Craven, Liu, Mysore, & Wilson, 2020). Leading tech companies have paved the way for different directions in the future of virtual work. For instance, Facebook CEO Mark Zuckerberg has spoken about remote work being “the future”, and offers all his employees the opportunity to work from home forever, as long as they get their manager’s approval and their work is properly suited. Apple CEO Tim Cook announced in June 2021 that from September 2021, their employees would be required to work from the office three days a week, making their knowledge work increasingly hybrid (The Verge, 2021), that is, a mix of office-based and virtual work. Only a few days later, this initiative received strong pushback from Apple employees who wanted to see more flexibility, and the hybrid work initiative got postponed. Virtual and hybrid work promise greater access to talent, lower costs, a better employee experience, and increased productivity. While these potential benefits are substantial, mixing virtual and office-based working might be a challenge for many organizations. In fact, there are estimates stating that the majority of virtual collaborations fall short of expectations, and are considered unsuccessful (Ferrazzi, 2014). Research documents challenges in managing contextual issues, such as coordination problems (e.g., Espinosa, Slaughter, Kraut, & Herbsleb, 2007), and regarding innovative collaboration (e.g., Nurmi & Koroma, 2020) across geographical distance. Other research focused on the individual workers reports that virtual work may impose stressful job demands, such as communication overload, social isolation, and constant connectivity (Nurmi, 2010; Nurmi & Hinds, 2020). Hybrid work, which combines office work with remote work, is anticipated to be on the rise, and could reap the benefits of both co-located and virtual work, if managed well, but organizations are struggling to navigate the hybrid workplace. In order to succeed in virtual work, organizations need to be mindful of facilitating organizational-level practices and a climate that is conducive to virtual working. In this chapter, we build on a long-standing body of research on virtual work, and, specifically, 363

364  Handbook of virtual work synthesize the literature around the concepts of context and climate in virtual work, in order to answer the following question: How do organizational contexts and climates affect virtual work? The scope of this chapter is to provide a representative, rather than exhaustive, review of the prior literature. Our aim is to capture the state of the art in current trends in the research, and inspire new research directions. The review is structured along two key organizational level factors: (1) contextual influences on virtual work, which include aspects such as place, space, and configuration of workers across places and spaces, and (2) people management practices in virtual work, which include aspects such as policies, norms and climate. We conclude by providing directions for future research in the new era of virtual and hybrid work.

CONTEXTUAL INFLUENCES ON VIRTUAL WORK Virtual work occurs within the context of a larger organizational structure and national setting(s), where several factors influence how people work (Hinds, Liu & Lyon, 2011; Maloney, Bresman, Zellmer-Bruhn, & Beaver, 2016). For example, organizations that are geographically dispersed are likely to rely more on globally distributed virtual teams to conduct their core work activities. In such teams, members are embedded in different local contexts that may reflect an extensive number of factors, including organizational structures, policies and national cultures (Hinds et al., 2011). Despite decades of research on virtual work, relatively little attention has been given to articulating how contextual factors within and outside of the organization may impact virtual work. In the following sections, we highlight several of the contextual elements within and outside of the organization, and discuss their implications for research and practice concerning virtual work. We pay particular attention to the places and spaces where work is performed, organizational configurations for virtual teams, as well as people management and organizational climate. Places and Spaces Research on virtual work, including virtual teams, has commonly considered aspects of “virtuality”, such as degree of geographic dispersion and technology usage, when trying to understand the nature of virtual work (Gilson, Maynard, Young, Vartiainen, & Hakonen, 2015). Less attention has been paid to the fact that virtual work also involves work done from different places as well as material spaces (Rennecker, 2002). In this chapter, we will emphasize the situated nature of virtual work, and discuss the importance of considering both the physical and virtual features of different places and spaces where individuals and teams work. Virtual work is characterized by geographic dispersion and dependence on electronic communication technology (Gibson & Gibbs, 2006), but it is inevitably also performed from different places and material spaces. The virtual and material features co-exist and intertwine (Robey, Schwaig, & Jin, 2003), both enabling and restricting work through differing temporal and spatial configurations (Giddens, 1990; Schultze & Boland, 2000). Research has commonly distinguished between place and space. Place refers to “the experience of being in a bounded locality with unique qualities in which traditions are important determinants of behavior” (Schultze & Boland, 2000, p.189), where “individual or group actions, experiences, intentions, and meanings are drawn together spatially” (Seamon, 2014). Space represents

Organizational context and climate for virtual work  365 material properties and refers to “a time–space configuration experienced as being boundless, universal and infinite” (Schultze & Boland, 2000, p.189), thus enabling a sense of freedom to move around and interact beyond a locally bounded place. To exemplify the distinction between space and place, we may for instance think about a laptop including a wireless internet connection as one particular space. Organizations may transcend locally bounded places by the use of laptops as spaces, giving their workers the autonomy to work from anywhere, anytime. Depending on where they sit down with their laptop to work, they become embedded in places that may take different shapes, and workers have agency to engage in reshaping these places and the interactions within them (Richardson & McKenna, 2014). A place includes the laptop, which is situated in some physical location (e.g., an open-office space at the organization’s office, or perhaps the worker’s home). The location includes other people with some kind of social relationship, who are, in turn, embedded in a country with a particular national culture, and so forth. Thus, vastly different places are created, drawing on different configurations of spaces and people. For the individual worker, the place becomes a location that is made meaningful (Cresswell, 2004, p. 7), and a part of their identity (Grey & O’Toole, 2020). Individuals, furthermore, tend to ascribe different types of meaning and behaviors to different places. A place may as well be virtual (Casey, 1993, 1997), made possible through the electronic mode of information (Poster, 1990). A virtual place “is shaped by a dialectic interleaving of local and global, of presence and absence, and of being somewhere and being nowhere” (Schultze & Boland, 2000, p.189). In other words, it is interwoven with material and locally situated features of work. So, a person working in a virtual place is always enabled and constrained by both a virtual and physical place. You cannot, for instance, work virtually without being physically located somewhere. In order to illustrate the “virtual place”, we will elaborate on the case of an academic conference as a place where academics meet likeminded people, present their work, share ideas, enact certain norms, and maintain a certain set of relationships. In the case of a co-located conference, academics would travel to a certain city, stay at a certain conference hotel, walk into different-sized conference rooms to sit in chairs and listen and comment on presentations, exchange ideas and network with other scholars in the hallways, and so on. In doing so, academics who have traveled to the conference would “escape” their local home context, to fully immerse themselves in the context of the conference. In the case of an academic conference as a virtual place, such as online conferences conducted solely over technology, a completely different meaning is ascribed to place. That is, a virtual place is created which now contains both a virtual and physical place for attendees. The virtual place connects academics from various local places, who, using a video-conferencing tool as a space (e.g., Zoom), are able to come together in a mutual virtual place. There may be a vast difference in how this virtual place is experienced, though, depending on from what kinds of local place and context the academics attend. Take, for instance, a global conference, where academics attend from all over the world. A synchronous session will be impossible to schedule without temporal constraints. One person may have to wake up in the middle of the night to give a presentation, while another skips a family dinner to call in. While skipping a family dinner could happen in any case, whether the conference takes place co-located at a convention center or virtually from the participant’s home, the feelings that the place–time configurations create are vastly different. While attending a co-located conference distant from your home may feel like freedom for a parent with small children, who is relieved from all the day-to-day chores, attending an online conference from home, where the family

366  Handbook of virtual work “expects” their presence at the dinner table, may feel completely different. Now, the separation between personal and professional life is much more blurred, and the parent may experience guilt for not spending time with their family. The parent may, on the other hand, also enjoy the blurred location, finding it easier to combine family with work. Viewed from an equality perspective, online conferences may be seen as more inclusive, as they open their doors to people who lack funding to travel, yet less equitable as they privilege people with stable internet connections, and people without hearing and vision access barriers (Bolander & Fine, 2021). While the academic online conference is just one example of a virtual place, it helps illustrate why local place and contextual factors are so important to recognize when studying virtual work. It is never conducted from one single place but combines multiple places and spaces, and, thus, is never experienced similarly by all workers. While organizations may have powerful influences on the context in which virtual work is conducted, there are also contextual factors beyond their control. Recognizing what they can and cannot influence through organizational design is important for an organization utilizing virtual work, and organizational space is one such factor where it may be possible. Organizational Workplace Design for Virtual and Hybrid Work Virtual work is increasingly performed in a diverse set of spaces combining employees’ private homes, office buildings, and public spaces. While the most common form of workplace flexibility involves working from home, other flexible workplace designs are also increasing in popularity. A decade or so ago, we began witnessing new workspace design practices such as “activity-based workplaces”, “hot-desking”, “hubs”, and “co-working spaces”. Besides organizations’ economic objective of reducing costs by saving space, these have evolved to maximize productivity and well-being at work, and the opportunity for creativity by enhancing interactions between co-located peers (McElroy & Morrow, 2010; Myers, Gailliard, & Putnam, 2012; Toivonen & Friederici, 2015). In activity-based workplaces, designated desks and cubicles are replaced by hot-desking and shared floor sections. Such workspace designs are particularly attractive to organizations, as they increasingly consider different options for a more virtual and hybrid workplace, where staff combine office-based and virtual work; for instance, working part of the week from home and part from an organizational space. In hybrid work, staff conduct relationships both virtually and in close proximity (Halford, 2005), aiming to get the perks of both work environments. The interplay of the virtual and physical work environment has received limited attention in previous research on virtual work. The bulk of prior research has focused on aspects of the virtual environment, such as the influence of technology or degree of virtuality (Gilson et al., 2015). Based on extensive research on the physical work environment, we know, however, that it matters for worker attitudes and behaviors, influencing their well-being and performance (e.g., Ashkanasy, Ayoko, & Jehn, 2014). For instance, the removal of assigned desks may threaten workers’ control over the form and frequency of social interactions (Elsbach & Pratt, 2007). Moreover, the introduction of open offices has been linked to withdrawal from face-to-face interaction with physically proximate peers, and instead increased dependence on electronic communication (Bernstein & Turban, 2018). Along the same lines, Bosch-Sijtsema, Ruohomäki, and Vartiainen (2010) have demonstrated how clean desk policies damage the collective identity of the organization, through a decrease in informal conversation, collaboration, team cohesion, and team belonging. Office design may thus have important implications

Organizational context and climate for virtual work  367 for what kinds of community it creates, who collaborates with whom, and via which modes of communication (virtual or face-to-face). As organizations consider an increase in virtual and hybrid work, a large share of designated places is likely to be replaced by more activity-based and hot-desking practices. In doing so, organizations need to be conscious of the design of organizational space, as it may have powerful impacts on both individual work and collaboration. In addition, if organizations shrink the available office space, whose responsibility is it that the remote work environment is conducive to virtual work? Organizations may need to think about providing remote workers with a conducive space for work at home (e.g., height adjustable desks, ergonomic chairs, large computer screens that supports good ergonomics). There are, however, aspects of the home as a working environment that may be beyond the control of the organization (including family members or other people who share the living space, and the size of the dwelling). The physical home work environment provides an interesting avenue for future research, which has to date received almost no attention in the research on virtual work (Sander, Rafferty, & Jordan, 2021). An interesting question to explore could be how does the luxury of a personal workstation in a spacious home, compared with having to share a small apartment with other residents working side-by-side, influence workers’ ability to perform and connect with distant co-workers? The physical and virtual work environment combined both connects and disconnects people. What happens when workers are tied to both co-located and virtual worker relationships? Which employees stay connected, which are more prone to be left out of the loop? Which workgroups thrive, and which become dysfunctional? When organizations host both co-located and virtual work, relational dynamics becomes more complicated. In line with what would be expected from social identity theory (Tajfel & Turner, 1986), Collins, Hislop and Cartwright (2016) showed that teleworkers in a UK public sector organization commonly created stronger relationships with other teleworkers, while office-based workers favored the social support of co-located peers. This bred greater social disconnection between teleworkers and office-based workers. Other studies have shown that organizations hosting both styles of work may unintendedly create marginalizing effects (Koroma, Hyrkkänen, & Vartiainen, 2014). While social relationships can be developed virtually (Lee, Mazmanian, & Perlow, 2020; Schinoff, Ashforth, & Corley, 2020), prior studies have shown that, given the choice, employees commonly prefer face-to-face over virtual interactions (Golden, Veiga, & Dino, 2008; Vayre & Pignault, 2014). Employee preferences for virtual and face-to-face interactions may have changed because of the vast experience of remote work during the Covid-19 pandemic. Going forward, organizations need to pay more attention to the facilitation of an inclusive work environment for all employees, independent of days per week they spend at the office. There is a risk that place will be used as a source of power, leading to favorable outcomes (including career advancements) for people working in closer physical proximity to those in positions of power. This prospective scenario is yet to be researched, however, in order for it to act as evidence-based guidelines for organizations. Relational dynamics within hybrid work inevitably carries interesting implications for power dynamics. In an early study of hybrid work in 2005, Halford highlighted several tensions in relation to power and social relationships, including tensions around effort and reward, obligations and rights, as well as control and autonomy. A quote by a manager in Halford’s study is representative of the common perception around managerial control: “I mean I would make a rule, I would say ‘if you’re working at home you really must be communicable with. If you’re not answering your email, you’re not replicating your email, and you’re not picking

368  Handbook of virtual work up your voicemail, then there’s something wrong’” (Christine, Managers’ Focus Group, in Halford, 2005, p. 29). This perception on the part of management may undermine some of the common underlying reasons for remote work: the ability to have more flexibility and time to work, without constant interruptions (Leonardi, Treem, & Jackson, 2010). Research focusing on the employee perspective has, in line with this, shown that knowledge workers with workplace flexibility commonly experience decreased autonomy, due to perceptions of having to stay constantly connected over distance (Mazmanian, Orlikowski, & Yates, 2013; Leonardi et al., 2010). Future research should continue to explore how the hybrid work environment, combining virtual with physical space, alters power dynamics in organizations. Organizational Configuration Organizational configuration involves decisions on how work tasks are divided into smaller components, either by functional specialization or product/market focus, and how formal communication is organized (Burton, Obel & Håkonsson, 2020). Virtual work opens up new possibilities to decide where, when and how the organization’s members work. Spatial and temporal dispersion O’Leary and Cummings (2007) present an operationalization of virtual teams that includes a configurational dimension, in addition to spatial and a temporal dispersion. The spatial configuration dimension takes into account how members are locationally placed in relation to each other and the team leader. The configuration of a six-member virtual team having to work from home during the Covid-19 pandemic would, for instance, include a balanced configuration of 1-1-1-1-1-1, where each member works and communicates over technology from their home. In normal circumstances, however, the same virtual team might have a rather different configuration, with more than one member located at the same office site. A six-member virtual team could have one concentrated core of members in one place, and one isolated member in another (5-1), or be distributed across multiple sites with similar-sized subgroups in each location (e.g., 2-2-2). All of these configurations create vastly different organizational contexts, which have different implications. Organizations need to be aware of these as they organize work virtually and make decisions on sizing and structuring resources. It is quite common for organizations to create virtual teams comprising geographically co-located subgroups (i.e., two or more members per site). This is typical for multinational corporations that have a headquarters (HQ) and subsidiaries or satellite offices. Prior research has found multiple negative implications of such partially distributed teams, comprising different subgroup configurations. For instance, virtual teams with geographically dispersed subgroups have been characterized by weaker identification with the team, less effective transactive memory, more conflict, and more coordination problems (O’Leary & Mortensen, 2010). The same study by O’Leary and Mortensen furthermore showed that when there is an imbalance in subgroup size, a competitive, coalition-type mentality may be invoked, which exacerbates these negative effects, particularly within minority groups. The negative impacts of geographically dispersed subgroups, including conflict and decreased trust, might be exacerbated, if subgroups line up as homogeneous in nationality in each location (Polzer, Crisp, Jarvenpaa, & Kim, 2006). Similarly, virtual teams with two geographically distributed subgroups that differ in status (which might be the case in virtual teams configured across headquarters and subsidiaries, or across location(s) with English native speakers and loca-

Organizational context and climate for virtual work  369 tion(s) with non-native speakers), may weaken team identification and impair team functioning (Gibbs, Boyraz, Sivunen, & Nordbäck, 2020). These negative effects may, however, be minimized by discursively constructing each subgroup as equal in status, including a focus on “we” rather than “us” versus “them”, and being cautious in labeling subgroups based on their geographical location or affiliation (Gibbs et al., 2020). Organizations can learn from this team-level study when considering company-level communication practices in relation to virtual work, to ensure the integration of resources and feelings of inclusion. Multilingual organizations Multinational corporations commonly establish cross-country virtual work groups with imposed language asymmetry. This encompasses disparity between different members’ lingua-franca proficiency levels, ranging from high fluency to rudimentary skills (Harrison & Klein, 2007). There is an alarming amount of empirical evidence indicating that language asymmetry may increase language-performance anxiety and stress among non-native speakers (e.g., Neeley, 2013; Neeley, Hinds & Cramton, 2012; Tenzer & Pudelko, 2015; Tenzer, Pudelko, & Harzing, 2014). According to recent empirical evidence, language-performance anxiety tends to be particularly acute in units where language asymmetries are high (Tenzer et al., 2014). Tenzer and Pudelko (2015) established in their qualitative investigation among 15 multinational teams, for example, that the level of language anxiety among non-native team members correlated not so much with the person’s absolute language proficiency but more strongly with his or her relative proficiency compared with colleagues. Therein, the establishment of language asymmetry across sites is another aspect in relation to organizational configuration that has implications for virtual collaboration. Although workers may cope with language asymmetry through a psychologically safe language climate, this may in turn lead to a lower level of innovative organizational performance, due to a simplified lingua franca (Nurmi & Koroma, 2020). Designing organizational configuration for virtual work Depending on how the workforce is configured across sites, each location and worker is imbued with a very different situational context. Organizations need to be aware of how they configure and discursively construct virtual work, in order to avoid the negative impacts of potential co-located subgroupings or isolated members. Organizations should, for instance, avoid configuring virtual teams based solely on geographical location, which may result in an imbalance in expertise or functional background. When the expertise configuration is imbalanced, with the most experienced or knowledgeable workers residing in one location, and another (or several) location(s) more or entirely dependent on that site’s expertise, the subgroups are unlikely to be perceived equal. A study on leadership dynamics in globally distributed teams (Nordbäck, 2018), demonstrated the potentially toxic dynamics that may arise in global virtual teams with an imbalanced expertise configuration. While prior research has found primarily negative impacts stemming from partially distributed work groups, with geographically dispersed subgroups (e.g., Jehn & Bezrukova, 2010; Shemla, Meyer, Greer, & Jehn, 2014), studies have begun to explore the positive effects of such configurations (Gibbs et al., 2020; Mathieu, Maynard, Rapp, & Gilson, 2008). When subgroups are kept equal in status, they may serve as a powerful tool to speed up work and integrate expertise in organizations. Virtual teams may break up into subgroups or “cohorts” at various phases of the project, enabling closer collaboration in smaller groups and maximiz-

370  Handbook of virtual work ing the effective use of resources, then come together for gateway reviews or checkpoints to provide feedback and integrate their work (Mathieu et al., 2008). In the context of knowledge work (e.g., software development), organizations may leverage globally distributed subgroups to maximize the benefits of time zone differences through “follow the sun” practices, defined as “a round-the-clock work rotation method aimed at reducing project duration, in which the knowledge product is owned and advanced by a production site and is then handed-off at the end of each work day to the next production site several time-zones west” (Carmel, Espinosa, & Dubinsky, 2010, p. 21) While the goal is to optimize resources and speed production to accelerate time-to-market, problems may arise from misunderstandings and coordination costs. If one subgroup starting its shift is presented with errors or does not understand something in relation to the handover, the project may be delayed by at least a day or two, should the teams have no time overlap to communicate synchronously. While follow the sun practices may be attractive for organizations to explore from a time-to-market perspective, there may also be tradeoffs, as it certainly strengthens the locational subgroup divide, which in turn may cause attitudinal challenges such as weakened identification or increased conflict. Organizations may increase learning in teams, particularly by designing them with moderately strong subgroups, that is, with a moderate overlap across multiple demographic characteristics among a subset of team members (Gibson & Vermeulen, 2003). When there are some similarities within subgroups, information and insights are more likely to surface, and differences across subgroups, in turn, ensure diversity of thought. Organizational contextual factors may, furthermore, bolster these positive effects. In particular, knowledge management systems, performance management by an external leader, and team empowerment, strengthen the positive effect of moderately strong subgroups, which have an intrinsic motivation to engage in learning behaviors. Future research could explore additional organizational context factors that may further strengthen the positive effects of subgroups. Also, while Gibson and Vermeulen did not specifically study geographically distributed work, future research should explore subgroup strength in a virtual (and hybrid) setting, as well as the role of different sources for subgroups. For instance, Gibbs and colleagues (2020) suggested one potential way to break down strong locational divides is for organizations to experiment with cross-location pairs, to encourage cross-location bounding.

PEOPLE MANAGEMENT PRACTICES While many companies have successfully adopted virtual work practices in managing their workforce, others are struggling to make the transition, remaining stuck with traditional management practices that seem increasingly out of place. Managers have traditionally been reluctant to support remote working, as there are fewer opportunities for direct oversight and control over their personnel (e.g., Halford, 2005). Many employees and managers may also be hesitant to engage in virtual work due to its possible downsides, such as social isolation and lower likelihood of promotion (Griffith, Sawyer, & Neale, 2003; Staples, Hulland, & Higgins, 1999; Venkatesh & Johnson, 2002). At the same time, however, virtual workers (who are commonly knowledge workers), desire a considerable amount of autonomy from their leaders (Davenport, 2005), for instance in deciding where and when to work (Griffith, Nordbäck, Sawyer, & Rice, 2018). Today, companies have a hard time finding talent who does not desire or even require workplace flexibility. This leads us to consider how organizations

Organizational context and climate for virtual work  371 could maximize the positive effects of autonomy, and minimize the potential negative effects of virtual work. We now highlight the role of organizational level policies, norms, and climate in supporting successful adoption of virtual work. Organizational-Level Policies and Norms Organizational policies are foundational to shaping employee experiences, work-related behaviors (Nordbäck, Myers, & McPhee, 2017), and perceptions of work/non-work boundaries (Breaugh & Frye, 2008; Ciulla, 2000). In the context of virtual work, formal organizational policies have been linked to faster project completion, lower feelings of isolation, greater trustworthiness, and better alignment with organizational goals (Cooper & Kurland, 2002; Verburg, Bosch-Sijtsema, & Vartiainen, 2013). Organizational policies Formal virtual work policies typically concern flexible working arrangements, for example, work location flexibility (i.e., autonomy to determine where the work is done; Golden & Veiga, 2005), work scheduling flexibility (e.g., Fujimoto, Ferdous, Sekiguchi & Sugianto, 2016), and discretion to determine how to schedule work weeks, unpaid personal leave, or sick leave to care for ill children (Eaton, 2003). Numerous studies on flexible working arrangements show effects on positive employee outcomes, such as increased job satisfaction (Tausig & Fenwick, 2001; Kirby, 2006), increased work engagement (Griffith et al., 2018), and improved performance (Gajendran & Harrison, 2007). Research also shows that flexible working arrangements improve employer‒employee relationships, and mutually benefit workers and their organization. For example, when employers offer scheduling flexibility, workers often have more trust in the organization, organizational commitment, and job satisfaction (Scholarios & Marks, 2004). Also, when workers perceive their organization is willing to offer flexibility, commitment tends to rise and turnover intentions fall (Kirchmeyer, 1995). Further, their organizational commitment improves when employers demonstrate support for employees’ non-work life (e.g., Grover & Crooker, 1995). While flexible working arrangements are meant to increase employee autonomy over managing time‒space constraints, several challenges may arise from collapsing these boundaries. In the context of global virtual work, the challenges of blurring boundaries are accumulated by time-zone and cultural differences. Employees may need to work after-hours to be able to collaborate with their distant colleagues (e.g., Nurmi & Hinds, 2020; Ruppel, Gong & Tworoger, 2013). Cultural differences about when and how colleagues can be reached in work-related matters may further increase connectivity expectations. Thus, workers’ boundary management in global virtual work is an important area that has implications for employees’ work–life balance and personal relationships. Only a few (though increasing) studies have analyzed the tensions and paradoxes resulting from spiraling expectations of accessibility and responsiveness in global virtual work (Gibbs, Rozaidi & Eisenberg, 2013; Leonardi et al., 2010). Work/home boundary theory (Clark, 2000) suggests that crossing the boundaries of the work and non-work domains complicates individuals’ ability to disconnect from one domain and be available and engaged in another (Mazmanian et al., 2013).

372  Handbook of virtual work Social norms Although flexible working arrangements are designed to benefit both employer and employee, their adoption depends on an organization’s informal policies and social norms concerning how these formal policies should be applied, for example, whether workers feel safe to use the flexible work arrangements without being penalized (Eaton, 2003). Social norms are the unwritten rules and standards of behavior that are considered acceptable in a group or an organization (Cialdini & Trost, 1998). Studies show that social norms tend to affect employees’ application of policies related to flexible working arrangements. For example, Nordbäck et al. (2017) studied the power of co-worker discourses vs. organizational level policies in influencing whether and how employees utilized workplace flexibility. They contrasted two organizations with vastly different levels of workplace flexibility and found that in the more flexible organization, employees applied the policy to serve both individual and organizational needs by using their autonomy regarding scheduling and location. In the organization with a more rigid work arrangements policy, employees engaged in behaviors that merely reinforced the rigid policies by maintaining an “in office” culture. This reinforced a culture of cynicism, maintaining a common perception that distant workers were slacking off rather than working productively (Nordbäck et al., 2017). Social norms may also affect employees’ connectivity behaviors. Ambitious employees are more likely to stay connected to work after-hours (Boswell & Olson-Buchanan, 2007), and their example creates expectations for constant connectivity as the new ideal image for global professionals. Although some professionals may happily comply with these evolving expectations, the demands placed on virtual workers can lead to unanticipated and unevenly distributed personnel risks. In globally distributed organizations, high interdependence across different sites may furthermore require employees to communicate beyond their personal working hours, in order to find a mutually agreed time for a synchronous meeting. However, differing national legislation and social rules may make it hard to establish effective company-wide policies around technology use and boundary management. Barley, Meyerson, and Grodal (2011), for instance, revealed strong cultural expectations around appropriate response lags to emails, with some workers expecting responses within hours, and others within a day. Again, this finding highlights the notion of virtual work being situated, which becomes relevant to consider in future research and practice on organizational-level policies around virtual work. Nurmi and Hinds (2020) studied how global virtual workers enacted constant connectivity demands in fourteen multinational corporations, where human resource managers explained that constant connectivity and after-hours work was informally expected of global virtual workers. The results of the study showed that workers in different social roles (e.g., men vs. women) enacted the connectivity demands differently (women less than men), and the effects of compliance were positive for men but negative for women. Using the lens of social role theory (Wood & Eagly, 2012), Nurmi and Hinds (2020) explained that as women’s role identities are associated with caregiving responsibilities, and an emphasis on their role in the home continues in many societies, women may place more value than their male counterparts on protecting their personal time at home. As a result, female professionals may resist the implicit policies related to constant connectivity. Although legislation regulates organizations’ working time policies, thereby prohibiting excessive working hours (such as the Working Time Directive (2003/88/EC) that restricts working hours within the EU to a maximum of 48 hours per seven-day week, including

Organizational context and climate for virtual work  373 a minimum of 11 hours rest within every 24-hour period), constant connectivity has become an implicit expectation in virtual work. Technology-mediated communication commonly follows workers home at night, which causes stress (Barley et al., 2011), and work–family conflict (Butts, Becker, & Boswell, 2015). In order to regulate constant connectivity, organizations have begun to experiment with various formal policies around technology use and connectivity expectations, to support effective boundary management (Colbert, Yee, & George, 2016). Some companies (e.g., Boston Consulting Group) have experimented with giving employees one smartphone-free night per week (Perlow, 2012), and others (e.g., Van Meter) with bans on sending emails over the weekend, as well as before 7am and after 5pm on weekdays. Other companies are considering policies around video-conferencing calls in an attempt to minimize so-called “Zoom fatigue”. Even Zoom (the company) itself has encouraged its employees to take some meetings while on a walk, and to shorten scheduled meetings to minimize meeting fatigue (CNBC, 2021). By supporting more mindful use of technology, companies may help provide their employees with more time for focused thinking, better opportunities for recovery, and for effective collaboration (Colbert et al., 2016). Such norms and policies may, however, be difficult to implement, especially in global virtual organizations where employees collaborate across different time zones. Organizational climate Organizational climate represents the social context of an organization. It is defined as “relatively enduring quality of the internal environment of an organization that a) is experienced by its members, b) influences their behavior, and c) can be described in terms of the values of a particular set of characteristics (or attitudes) of the organization” (Tagiuri & Litwin, 1968, p. 27). The psychological climate of an organization is constructed from employees’ aggregated perceptions of the policies, practices and behaviors, which, in virtual organizations, develop through virtual interactions and exchanges (Glisson & James, 2002; Jones & James, 1979). For example, offering technology training, providing the appropriate technology, encouraging engagement in virtual work, facilitating career development, and ensuring that supervisors and co-workers are supportive of virtual workers, feed into employees’ perceptions of how conducive the climate is to virtual work (Adamovic, Gahan, Olsen, Gulyas, Shallcross, & Mendoza, 2021). Effects of organizational climate Organizational climate influences (both positively and negatively) the adoption of virtual work practices and employee outcomes. In particular, an organizational climate that satisfies employees’ need for advancement, self-fulfillment, and job realization is expected to foster work engagement (Bakker & Demerouti, 2007). Prior research on organizational climate has mainly been conducted in collocated organizations, and there is little empirical exploration of how climate develops and affects employees in virtual organizations. More specifically, although the literature on virtual work has studied the impact of team climate (e.g., Gibson & Gibbs, 2006), more research is needed on how organizational level climate supports virtual work. Adamovic et al. (2021) recently introduced the concept of virtual work climate, and defined it as “a domain-specific dimension of an organization’s overall climate, capturing the individual’s perceptions related to the organizational support of virtual work practices” (p. 4). Their empirical findings suggest that perceptions of an effective virtual work climate

374  Handbook of virtual work motivate employees who have particularly low self-efficacy in virtual work to adopt virtual work practices. Employees who perceive managerial support for virtual work are essentially interpreting the characteristics of the environment in a way that ultimately increases their participation in virtual work. For example, perceptions of offered virtual work arrangements (Feldman & Gainey, 1997; Neirotti, Paolucci, & Raguseo, 2013), perceived consequences for the virtual worker’s career development (Nurmi, 2018), and how supportive and encouraging your supervisor is for working virtually (Adamovic et al., 2021), foster the employee’s willingness to adopt virtual work practices. Relatively little research has concentrated on social support among virtual workers and their office-based co-workers. This is an important area of study, as virtual and hybrid work have the potential to negatively impact co-workers’ interpersonal relationships, and lead to a harmful “us and them” cultural divide (Golden, 2006, 2007). Empirical evidence also shows that virtual workers’ perceptions of the absence of typical organizational support (e.g., training and development), and other forms of social support available at the office (Gao & Sai, 2020; Redman, Snape, & Ashurst, 2009), decrease employee well-being and performance. An organizational climate that supports virtual work in turn includes psychological safety and peer support. In a psychologically safe organizational climate, people feel free to share knowledge and ideas, admit mistakes and ask for help, without fearing that others will embarrass, reject, punish, or think less of them for it (Edmondson, 1999, 2012). Psychological safety has been found to mitigate the negative impact of national diversity and virtuality on innovative performance, for example, by making it easier to leverage the benefits through more open conversation and more respectful, engaged interaction (e.g., Bradley, Postlethwaite, Klotz, Hamdani & Brown, 2012, Gibson & Gibbs, 2006). However, psychological safety can hamper innovative performance in multilingual virtual organizations, through simplified language use that produces less diverse ideas (Nurmi & Koroma, 2020).

FUTURE RESEARCH DIRECTIONS Based on our literature review, we suggest three directions for the future research: (1) Focus on a more situated understanding of virtual work, (2) fluctuating organizational configurations, and (3) people management practices for hybrid and virtual work. Focus on a More Situated Understanding of Virtual Work The local context that embeds organizational workers, and the configuration of workers across locations in relation to each other, have profound impacts on both individual workers and teams. Organizations have agency to create different spatial and temporal arrangements of work through decisions regarding office locations, the configuration of employee resources across sites, and physical office design. Depending on the combination of these, multiple local contexts come together to form global virtual work contexts and local (physical) work contexts. Figure 19.1 depicts an example of a global virtual team in a multinational corporation, with team members in three different locations (team configuration of 3-2-2). As can be seen in Figure 19.1, global virtual teams (GVTs) encompass multiple contexts, in that their members are embedded in local contexts, whilst simultaneously the GVT is embed-

Organizational context and climate for virtual work  375

Source: Nordbäck (2018), p. 16.

Figure 19.1

Local and global context in global virtual teams

ding members in a global virtual context. Team members’ local contexts include, for instance, a physical location, an organizational hierarchy, potentially with a local administrative supervisor, and the national culture, such as values and beliefs that influence each team member’s behaviors (Hinds et al., 2011; Maloney et al., 2016). The local context from where team members operate impacts their behaviors (Hinds et al., 2011). For instance, Thatcher and Patel provided an example of how external (local) context might have an impact in globally distributed teams: “Contextual situations that are understood in one location (e.g. lengthy business lunches) may be misunderstood by group members in other locations (e.g. attributions of laziness because of a lack of understanding about traditional work structures)” (Thatcher & Patel, 2012, p. 997). At the same time, team members are brought together as part of a single GVT, where they collaborate across different contexts, which might converge or diverge, or lead to adaptation towards mutual practices (Cramton & Hinds, 2014; Hinds et al., 2011). While studies acknowledging virtuality (intra-team variable, same for all members) have revealed much about how communication over increasing distance and reliance on technology impact team processes and outcomes, less is known about the role of team members’ local contexts, which oftentimes differs from one member to another (Gilson et al., 2015; Maloney et al., 2016). In other words, how workers are nested into their local contexts may have a substantial impact on their work behaviors, yet research has only begun to acknowledge this (Cramton & Hinds, 2014; Maloney et al., 2016). Thus, studying the multi-contextual environment surrounding virtual workers is a promising avenue for future research. Among other factors, the physical work environment of the home also provides an interesting avenue for future research (Sander et al., 2021). For instance, little attention has to date been paid to how the material design of work-from-home workspaces influences employee well-being and performance.

376  Handbook of virtual work In addition, how does the social environment (including other family members or residents) of the home (and the surrounding neighborhood) impact remote workers’ well-being and performance, as well as their connection with their virtual or hybrid team and organization? Are there other practices associated with the home (e.g., in relation to privacy preferences, cleanliness, and boundary-management practices) that facilitate or hinder co-worker relationships and team cohesion, as well as the performance and creativity of virtual workers and teams? Moreover, the role of the physical office environment in virtual work, including virtual collaboration, constitutes an interesting research avenue. It is likely that workplace design conducive to collaboration with proximate colleagues is less appropriate for working with distant colleagues. The tensions between different spaces and places for work requires more research. Fluctuating Organizational Configurations Covid-19 has shown that configurational aspects of resources may fluctuate over time. Our ongoing diary study of 69 personnel obliged to work remotely during the pandemic has uncovered shifting team dynamics, which can be attributed to the removal of co-located subgroups. Nearly all of the respondents previously located at minority sites reported they now felt much more included in their team (during office lockdown), and could finally make their voice heard. Those previously located at majority sites, commonly developed stronger relationships with staff at other sites during remote working, while ties with colleagues normally sitting close by at the office were jeopardized (unless task dependencies required close collaboration). Thus, organizations may change relational dynamics and work processes by altering time–space configurations. In hybrid work, configurations are indeed likely to be flexible, and fluctuate over time. As numerous workers return to offices to work in a more hybrid fashion, it would be interesting to study which work behaviors and team dynamics are here to stay, which revert to normal, and which adapt into something new. More generally, how long do effects on work processes and outcomes promoted by organizational configuration last (through organizational memory), or how easily are organizations able to alter work processes and outcomes by shifting organizational configurations? It is widely predicted that patterns of work as we know it will see offices used in a different way and hybrid working grow (Gratton, 2021; The Economist, 2020). The future workplace (virtual, office-based or hybrid) will produce new practices, along with management and organizational challenges around well-being and innovative performance (Halford, 2005; Kniffin et al., 2021). While there is an abundance of research on teleworking, these prior studies have primarily investigated situations where remote work is much more limited (e.g., to one day per week) than is likely for hybrid work. Given the ongoing work–life changes and forthcoming aftermath of Covid-19, research could seize this opportunity to really delve into the dynamics of hybrid working and its numerous possible organizational configurations. People Management Practices for Hybrid and Virtual Work People management, including organizational policies, practices and climate, is likely to have a big impact on how workers navigate the new era of hybrid and virtual work. But we know little about the management practices and climate that support hybrid and virtual work. Another major challenge is how to ensure secure and healthy working conditions when

Organizational context and climate for virtual work  377 employees work outside the office. Although the external contextual factors, such as those related to the employee’s home environment and technology infrastructure, are not under the direct control of leaders and organizations, it is important to better understand how these factors may influence the hybrid worker’s performance and well-being. In a hybrid organization, it is important to select people who can manage the constantly changing physical and social work environment, and collaboration methods. Not everyone thrives in a virtual or hybrid workplace, and neither is every industry well-suited to workplace flexibility. The person–environment (P-E) fit theory posits that individuals are attracted to and selected by organizations whose values, culture and climate are in line with their own important beliefs, values and desires (Kristof-Brown & Guay, 2011). Therefore, those employees who perceive a high P-E fit with hybrid work practices, policies and climate, are more likely to flourish and experience high levels of satisfaction, engagement and overall well-being (Kristof-Brown, Zimmerman, & Johnson, 2005). Prior research has identified characteristics of individuals who are likely to experience a high P-E fit in the virtual workplace. For example, those who lack experience in the use of technology (Olson & Olson, 2012), have poor self-management skills (Nurmi, 2011), and low boundary control (Cho, Lee, & Kim, 2019), are more likely to experience stress or a poorer work–life balance in virtual work. In contrast, employees with high digital fluency, that is, proficiency and comfort in achieving desired outcomes using technology (Colbert et al., 2016), and a preference for integration in managing the work/non-work boundary (Derks, Bakker, Peters, & van Wingerden, 2016), are more likely to experience a high work–life balance and P-E fit in virtual work. Recent frameworks that describe individual characteristics (personality, knowledge skills and abilities, experience, preferences, self-efficacy, IQ) relevant to functioning effectively in virtual work environments, may help managers select the best-fit employees for virtual work (e.g., Makarius & Larson, 2017). On the other hand, future research may also explore to what extent the demand to fit in is on the shoulders of organizations rather than on employees – who increasingly are in a position of power in relation to demanding workplace flexibility (The Verge, 2021). Furthermore, organizations employing virtual work practices need to manage their organizational culture. Research has suggested (Kniffin et al., 2021) that organizations (especially now, during the aftermath of Covid-19) need to find the right balance between tight and loose cultures, defined as tight–loose ambidexterity (Gelfand, 2019). As Kniffin and colleagues point out “Accordingly, as many workplaces tighten in response to their shaky economic standing, successful organizations will benefit from having flexible tightness—rules that bind employees together to prevent social isolation and loneliness, accompanied by the right dose of looseness, which affords employees latitude and autonomy where possible” (2021, p. 71). In line with this, Gibson (2020) recently called for organizational scholarship to move from a focus on “social distancing” in virtual work to “care in connecting”, in order to support well-being and the positive side of social connectivity in virtual work. Organizations may experiment with various organizational-level initiatives, including mandatory breaks away from the computer, shared fitness breaks, and out-of-office norms. At the same time, these initiatives should not strip away the individual’s autonomy to structure their workdays. Future research should continue to explore the boundaries between organizational-level, team-level, and individual-level control, in aspects related to employee flexibility and associated workplace behaviors and outcomes.

378  Handbook of virtual work

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Organizational context and climate for virtual work  383 The Verge. (2021, June 4). Apple employees push back against returning to the office in internal letter. Retrieved August 1 2021: https://​www​.theverge​.com/​2021/​6/​4/​22491629/​apple​-employees​-push​ -back​-return​-office​-internal​-letter​-tim​-cook Toivonen, T., & Friederici, N. (2015). Time to define what a “hub” really is. Stanford Social Innovation Review, April 7. https://​ssir​.org/​articles/​entry/​time​_to​_define​_what​_a​_hub​_really​_is Vayre, E., & Pignault, A. (2014). A systemic approach to interpersonal relationships and activities among French teleworkers. New Technology, Work and Employment, 29(2), 177–192. https://​doi​.org/​ 10​.1111/​ntwe​.12032 Venkatesh V., & Johnson, P. (2002). Telecommuting technology implementations: A within- and between-subjects longitudinal field study. Personnel Psychology, 55(3), 661–687. Verburg, R. M., Bosch-Sijtsema, P., & Vartiainen, M. (2013). Getting it done: Critical success factors for project managers in virtual work settings. International Journal of Project Management, 31(1), 68–79. Wood, W., & Eagly, A. H. (2012). Biosocial construction of sex differences and similarities in behavior. In J. M. Olson, & M. P. Zanna (Eds.), Advances in Experimental Social Psychology (Vol. 46, pp. 55‒123). Cambridge, MA: Academic Press.

20. Virtuality and inclusiveness in organizations Jakob Lauring, Charlotte Jonasson and Marta Jackowska

In a recent report1 on diversity in the labor market it is mentioned that the percentage of employed women is increasing worldwide. At the same time, it is stated that almost half of employees that were born around the millennium are racial or ethnic minorities. It is therefore not surprising that inclusiveness has become a central theme in research dealing with organizational performance in terms of talent management and innovation as well as fairness in recruitment and promotion (e.g., Dwertmann et al., 2016; Roberson, 2019). So far, this has mainly been explored in relation to the physical workplace (e.g., Leroy et al., 2022; Roberson & Perry, 2022; Tasheva & Hillman, 2019). However, the unpredictable and dynamic nature of the labor market as well as the increasing technological advances have prompted organizations to adapt through flexibility in their form and staffing (Larson & DeChurch, 2020; Mortensen & Haas, 2018). To respond to those changes, organizations have become more globalized, seeking more diverse human resources, and relying on virtual work. In addition, since the 2019 pandemic, the daily reality of many organizations has changed as millions of workers were forced to operate virtually (cf. Carnevale & Hatak, 2020; Kniffin et al., 2021; Yawson, 2020). This also includes individuals that for some reason are marginalized in the labor market. Hence, problems and opportunities connected to diversity and inclusion remain part of the daily work procedures even when the organization uses remote employees. The increasing diversity in the labor market combined with the growing application of technology for work communication have made it apparent that inclusiveness also needs to be examined in a virtual context. And although both organizational inclusiveness and virtuality have been relatively well explored, less has been done to combine the two concepts (see however Klitmøller & Lauring, 2016; Lauring et al., forthcoming; Lauring & Jonasson, 2018; Taras et al., 2019). Accordingly, there is a need for understanding the practices of inclusiveness in organizations where employees conduct a substantial part of their work interaction over a distance. It can even be argued that organizational inclusiveness and diversity management are more important in virtual organizations due to the need for a positive social atmosphere (Coppola et al., 2004) and a sense of connectedness despite dispersion (Boros et al., 2010). While most organizations have experienced increased diversity in recent years, the virtual context is likely to boost this even further. This is mainly because there are fewer geographical constraints for employment in organizations that work virtually thereby increasing cultural, racial, and religious diversity. Moreover, working virtually also offers more flexibility, which could be a preference for individuals who are normally not as closely connected to the labor market such as parents to small children, people with disabilities, or older employees close to the retiring age. There is therefore a good chance that organizations with a large degree of virtual work will have a greater need to include different types of minorities among their workers. Not all organizations, however, have much experience with managing virtually (Kniffin et al., 2021). Before the Covid-19 pandemic, less than 5 percent of workers in the USA and Europe worked primarily from home (Eurofound, 2017; www​.census​.gov/​programs​ 384

Virtuality and inclusiveness in organizations  385 -surveys/​acs). Remote working was therefore seen as an option mainly for a select group of higher-income earners (Desilver, 2020). This also meant that most organizations had little experience with organizing their general workforce virtually (Wang et al., 2020b) and even less with managing diversity in that way. This chapter addresses the challenge for organizations of being inclusive while managing employees who work virtually – a practice that could be labeled as virtual diversity management. We focus specifically on the inclusion of what has often been described as underrepresented group members – those individuals who already face challenges of inclusion in the physical workplace. These are individuals who are low in number or of lower organizational status. This category can, for example, be based on demographics (e.g., gender and age), functionality (e.g., disability), and origin (e.g., ethnicity/culture/religion and language). In this way, diversity can be described as consisting of surface-level attributes such as the readily detectable differences connected to gender, age, physical disability, ethnicity, and language as well as deep-level characteristics such as variation in knowledge, (cultural) values, perspectives, and (religious) beliefs (Harrison et al., 2002). Virtual work, on the other hand, should be understood as flexible work arrangements whereby employees collaborate in locations, at a distance from their central offices or production facilities. During this process, the employees have little physical contact with colleagues but can communicate with them using technological appliances, the so-called Information and Communications Technology (ICT) (Asatiani & Penttinen, 2019; Golden, 2007). The current chapter will first discuss the nature of the inclusion of diversities in an organizational context. Next, insights from this will be related to barriers and opportunities concerning ICT usage and distance as properties of virtual work. Finally, we will discuss practices that organizations can use to enhance their virtual diversity management.

INCLUSIVENESS IN ORGANIZATIONS The reality of virtual work is that it affects different people differently (Taras et al., 2019). For some individuals, the flexibility of working from home can be a blessing while the same situation could lead to anxiety and stress for others (Kniffin et al., 2021; Raghuram & Wiesenfeld, 2004). This indicates that when organizations use a remote workforce, many employees with many different preferences somehow need to be comprised in the work setting. This issue could be even more challenging when having to include individuals with different backgrounds, skills, and expertise as their routines are likely to vary even more, compared with a homogeneous workgroup. While often challenging, the current focus on inclusion in organizations can be seen as a response to a more diversified employee pool where talent management and innovation are essential for maintaining competitive advantages (Hoogendoorn et al., 2017; Muzio & Tomlinson, 2012). In addition to the globalization of firms, other socio-economic changes, such as more females in the workforce, have become noticeable for many organizations worldwide (Maturo et al., 2019). Finally, a number of countries are experiencing an aging population that is reflected in an increased age diversity in the workplace (Nagarajan et al., 2019; Wegge & Meyer, 2020), and individuals with disabilities are now more often being introduced to the labor market (Van Laer et al., 2020; Yu et al., 2019). Hence, if organizations aim to utilize the available workforce fully, they, to an increasing degree, need to be able to integrate and retain

386  Handbook of virtual work individuals with international backgrounds, people with disabilities as well as females and older employees. In addition to this, inclusiveness concerning sexual orientation is sometimes treated as part of the diversity management effort. Unfortunately, no research that we know of has assessed sexual orientation in connection to virtuality and therefore we will not deal with this theme any further in the current chapter. However, given the lack of research attention to the topic, it deserves more attention in the future. Understanding Central Elements of Inclusiveness Feeling that one belongs somewhere and is included in a social context are basic human needs (Baumeister & Leary, 1995; Stum, 2001). At a general level, inclusiveness can be said to be the involvement of all group members but to the extent that individual dissimilarities are not muted in the process (Schutz, 1958). Inclusion, in other words, requires that all individuals feel able to contribute meaningfully to shared goals regardless of the group affiliation and to be able to do so without conforming to dominant norms and values (Ferdman, 2014). In this way, managing inclusiveness entails the establishment of participation and dialogue to encourage shared decision-making as well as an appreciation of both high- and low-status employees’ contributions to the functioning of an organization (Nembhard & Edmondson, 2006). In recent years, inclusiveness research has mainly delved into the integration and involvement of individuals from marginalized groups. This is because such groups have been perceived as especially challenged (Mor Barak & Cherin, 1998; Randel et al., 2016). As such, organizational inclusion research generally focuses on removing hindrances to the participation of minority-group employees and on the integration of diversity in organizations (Mitchell et al., 2015; Nishii & Mayer, 2009). In relation to diversity, Shore et al. (2011) describe inclusiveness as the degree to which heterogeneous employees with unique characteristics can be considered an asset to the organization. Supplementing this valuing of diversity, Nishii (2013) has argued that the actual involvement of minorities in tasks such as decision-making should be seen as central to organizational inclusiveness. A combined perspective with a focus on both the task-related usefulness of the minority employees and a focus on inducing diverse members with feelings of being part of the organization was finally taken up by Leroy et al. (2022). They describe inclusiveness in terms of “harvesting the benefits of diversity on work tasks” and “cultivating value-in-diversity beliefs” thereby including a distinction between (1) task-related and (2) socio-emotional outcomes of inclusive management. A different perspective was first taken by Mor Barak and Cherin (1998) who saw inclusion at the group level as related to fairness and at the individual level as related to comfort in working for the organization. The focus on the unique individual was later advanced, describing the individual’s characteristics in relation to the workplace environment (Mor Barak, 2000). Roberson (2006a) primarily places emphasis on the group, where fair treatment, access, and influence are considered important dimensions of the group-related aspect of inclusiveness in organizations (see also Roberson, 2006b, 2019). Finally, Roberson and Perry (2022) described inclusive leadership as containing shared identity, reduced status differences, and participation and involvement indicating an orientation towards (1) the group or (2) the unique individual. Based on the above, we divide the elements of inclusiveness on the one hand into those concerning being an equal group member and those relating to being valued as an individual. On the other hand, we include dimensions related to outcomes in form of socio-emotional

Virtuality and inclusiveness in organizations  387 consequences (subjective feelings) and task-related consequences (functional actions). This provides us with a matrix including four cells, namely (1) belongingness, (2) uniqueness, (3) accessibleness, and (4) usefulness (cf. Shore et al., 2011). Those elements are described in greater detail in Table 20.1. These labels could be relevant concerning physical as well as virtual workplaces where employee diversity can exist. Table 20.1

Elements of inclusiveness

 

Equal group membership

Valued as individual

Socio-emotional

Belongingness

Uniqueness

consequences

Feeling welcomed

Feeling appreciated and valued

Identifying with the workgroup

Feeling accepted despite differences

Accessibleness

Usefulness

Involvement in decision-making

Experiencing usefulness of own unique resources

Availability of needed information

Experiencing others requesting unique resources

Task-related consequences

Equal access to resources (e.g., network)

Source: Authors’ own.

The socio-emotional dimensions belongingness and uniqueness are based on the work of Shore et al. (2011). Accessibleness and usefulness have been added by the authors of the chapter to encompass also task-related consequences of inclusiveness.

THEORETICAL CONSIDERATIONS ON ORGANIZATIONAL DIVERSITY The topic of diversity in organizations generally refers to the compositional dissimilarities among employees and is focused on “creating a working culture that seeks, respects, values and harnesses difference” (Schneider, 2001, p. 27). The notion of diversity management has mainly taken an organization perspective on the task-related consequences of inclusion, emphasizing the benefits to organizational performance. There are several ways in which diversity management has been theoretically conceptualized. In our discussion, we focus on the two primary ones as described below. The main theory to account for the positive implications of diversity is the information-processing theory (De Dreu & Weingart, 2003). Primarily related to the usefulness of the unique person (see Table 20.1), the theory predicts that dissimilar individuals complement each other with different perspectives and a variety of information (Dwertmann et al., 2016). As persons with different backgrounds and minority status have been seen as a proxy for informational variety, organizations that contain demographic heterogeneity have been assumed to gain knowledge assets, enabling more comprehensive analyses, and informed and creative solutions, due to the integration of different perspectives (Leroy et al., 2022; Mitchell et al., 2015; Stahl et al., 2010). Therefore, the information-processing effects of diversity are related to deep-level diversity characteristics such as differences in knowledge, values, beliefs, and perspectives (Harrison et al., 2002). The second stream of research adopts social identity and social categorization theories to address the access to group membership (see Table 20.1). These theories describe the influence of self-perception and social comparisons and provide insight into barriers in interper-

388  Handbook of virtual work sonal interactions across similarity-based groups (Byrne et al., 1966; Tajfel, 1982). As such, these theories propose that individuals differentiate strongly between their own in-group and relevant out-groups. This emphasizes similarities among individuals sharing group memberships and likewise differences among those belonging to other groups (Roberson et al., 2017). The consequence is that individuals categorizing themselves or others into social units will be less likely to interact with outsiders. Moreover, they will tend to have a positively biased view of those inside the group and a negatively biased perception of out-group members (Chakraborty, 2017; West et al., 2012). Social categorization mainly takes place when readily detectable diversity, surface-level attributes – such as those connected to gender, age, ethnicity, language, or disability – lead members to be perceived as a distinct, separate group (Harrison et al., 2002). The informal-processing perspective and the social categorization theory in combination indicate that values can be gained from the unique individual but that this can be reduced if the person comes from a group that is often discriminated (van Knippenberg et al., 2004). In what follows, we discuss the relationship between minorities defined by gender, age, ethnicity/ culture, language, and disability, and the opportunities and barriers in the virtual context. Inclusion in a Virtual Context The contemporary changes in work settings and the increasing labor market diversity present several possibilities and challenges for organizations. In the following, we outline the implications of virtuality for several groups. Gender has been argued to be important when managing inclusiveness because the effect of the difference in gender on the group member interaction is particularly strong compared with other attributes (Graves & Powell, 1996). This strong effect of gender can be explained by identity-based social and structural mechanisms, in particular social categorization (Mehra et al., 1998). However, there is also research suggesting that there are observable differences between the genders beyond those that are at the surface level. Kark and Eagly (2010) suggest that women, as a result of their upbringing and biology, are more socially minded and caring towards others. Similarly, it has been argued that women are often raised to put in a greater social effort and therefore be more aware and caring regarding the needs of others (Carlson, 1972). Hence, as the needs of others are often more difficult to assess remotely, gender could have positive implications for the organizational inclusiveness in a virtual context. As an example of this, Lind (1999) found that better conflict resolution skills made women more satisfied with working virtually. As with gender, individuals of similar age prefer to interact with each other due to categorization dynamics (Kunze et al., 2011). This effect can be based on visible surface-level differences such as appearance. However, research suggests that there are also significant value differences among employees across age cohorts, which could be the object of discrimination. Furthermore, the age cohort where some may initiate parenthood has been related to a negative bias in recruitment and promotion (Correll et al., 2007). Being a parent to small children can require a greater need for flexibility in the work organization which may be well combined with remote work (Ter Hoeven & Van Zoonen, 2015). Older employees could also have special characteristics in relation to working remotely. While older organization members appear to be more cautious and conservative (Bantel & Jackson, 1989), younger employees tend to take greater risks (Kooij et al., 2008). Age may also affect learning abilities. Even

Virtuality and inclusiveness in organizations  389 though experience can compensate for a lack of some cognitive skills (Kooij et al., 2008), age is generally associated with decline in novel problem-solving efficiency. More specifically, working memory, abstract reasoning, attention, and processing of novel information decrease as people age (Salthouse, 2019). This could indicate that older individuals in an organization could have more difficulties in adjusting to new technologies that are often required in virtual work. This reluctance or inability to deal with a technically mediated workday should therefore be taken into consideration when inclusive practices are designed virtually. As in the case of visible demographic differences, such as gender and age, ethnocultural differences can also often lead to social categorization. This can, for example, be related to deep-level differences in behaviors and attitudes based on dissimilarities in values, norms, religious beliefs, customs, and traditions (House et al., 2002; Lauring et al., 2018) as well as surface-level traits such as race and ethnic clothing. Those differences, for example, make it more difficult to trust those who are ethnically, racially, or culturally different from oneself (Hjort, 2014). Different ethnicities or cultural groups may also have different preferences for ways of working and being managed (Han & Beyerlein, 2016). The different norms and values present in ethnoculturally diverse groups could make it more challenging to integrate members from such minorities using virtual means because there is less emphasis on social elements (Mortensen & Hinds, 2001). Fortunately, there are some indications that employees in virtual organizations do not have the same degree of discriminatory attitudes towards those who are different compared with the situation in collocated organizations (Han & Beyerlein, 2016). Kirkman and colleagues (2002) demonstrate that working virtually can reduce team process losses associated with stereotyping, politics, and power struggles compared with how it is in a physical workplace life. This is similar to the findings by Mortensen and Hinds (2001), who argue that cultural dissimilarity could be less likely to create conflict in a virtual context due to a diminished focus on social-oriented interaction. Hence, there are both positive and negative consequences of virtuality connected to ethnocultural diversity. With a multitude of nationalities also comes the existence of many different linguistic groups. As with other described diversity characteristics, language may also have important identity implications affecting intergroup behavior (Lauring, 2008). As such, language can be a powerful force creating a sense of exclusion from key information processes, cooperation, and decision-making for those with insufficient language skills in the dominating language (Lauring & Nygaard, forthcoming). This could be the common corporate language or the parent company language (Tenzer & Pudelko, 2016). Even when using a common corporate language, communication will often be affected by an unfamiliar vocabulary, unusual accents, a slow speech rhythm, or frequent grammatical mistakes (Wells, 2013; Yashima et al., 2004). It is, as such, more difficult to get a message across when having to use a language that one does not speak fluently (Du-Babcock, 2006). This effect of language diversity, however, may not be similar in a virtual organization compared with the physical workplace. For example, Klitmøller and Lauring (2013) showed the written language to be more effective compared with verbal discussions in virtual settings. This was the opposite of the situation in face-to-face interaction. In a different study, Klitmøller and Lauring (2016) explored the effect of two types of virtuality on perceptions of inclusive language use. They found a positive association between workplace mobility and perceptions of employees’ openness to language diversity as well as between distributed work and perceptions of consistent common corporate language at the management level. Accordingly, they concluded that different types of virtuality have different effects on different types of inclusiveness. Lauring and Jonasson (2018) also studied

390  Handbook of virtual work inclusive language use in a virtual organization and found that inspirational motivation leadership could compensate for lack of linguistic inclusiveness while this was not the case with other types of leadership. As such, the effects of virtuality on language dissimilarities depends to a large extent on the types of communication (written/verbal) and the media used. Finally, people with disabilities have generally been found to be marginalized in an organizational context (Beatty et al., 2019). Although the group accounts for 15 percent of the world population, they are still significantly excluded from the labor market.2 Research shows that people with disabilities are constantly being underrepresented in employment and experience unequal opportunities (Beatty et al., 2019). In addition, depending on the form of impairment, these workers may experience different types of obstacles (Boman et al., 2015). As an example, the employment rate of people with a physical disability is higher compared with those with a psychological disability. However, regardless of the type of disability, virtual work is thought to alleviate some of the struggles that people with disabilities face. Especially in terms of physical disability, virtuality has the potential for diminishing the challenges of accessibility and mobility (Van Laer et al., 2020; Yu et al., 2019). With more organizations accepting remote work, virtuality may empower workers with disabilities to find employment more easily. Table 20.2

Potential positive and negative task-related consequences of different types of diversity in a virtual context

 

Potential consequences in a virtual context (positive and negative)

Gender

+ A focus on social relations can be useful to create cohesiveness virtually + Gendered conflict solutions can be supported virtually - Virtuality can be a double-edged sword in relation to family concerns

Age

+ A need for flexibility can be supported virtually - A decline in problem-solving capabilities can lead to difficulties in relation to the complexity of virtual work - Reluctance to deal with novelty can be a barrier to adopt new technology

Ethnocultural

+ Differences in behavior and values will have a less direct effect - An inherent mistrust between cultures can be difficult to remedy virtually

Language

+ Technology can provide assistance for written language communication - Verbal language usage can pose challenges for non-native speakers using imperfect media

Disability

+ The disabling effect of a physical organizational setting is reduced + Greater accessibility - The type of disability reflects the chances of employability

Source: Authors’ own.

To summarize, diversity in terms of gender, age, ethnicity/culture, language, and disability has a general connection to social categorization dynamics as well as specific consequences of a primarily task-related nature (see Table 20.2). However, the existing literature has found that virtuality sometimes changes the way such groups are perceived and function in organizations. Yet, more elaboration is needed of the different consequences of virtuality on diversity management – especially in relation to different dimensions of virtuality. We will now discuss the nature of virtual work in regard to its central characteristics of using ICT for communication and taking place at a distance and the consequences this could have on inclusion and diversity management.

Virtuality and inclusiveness in organizations  391

ICT AND DISTANCE RELATED CONSEQUENCES FOR VIRTUAL DIVERSITY MANAGEMENT Virtual work entails the possibility to use ICT to work from other places than one’s physical office, for example, at home, in a subsidiary business unit, or a shared office space (Raghuram et al., 2019). In line with this, one could argue that the defining characteristics of virtual work are (1) dependence on technology usage in relation to communication and collaboration and (2) dispersion due to physical and social distance (Gibson & Gibbs, 2006; O’Leary & Cummings, 2007). Both ICT and distance can have consequences that are negative as well as positive for organizational inclusiveness. Concerning ICT, it has been argued that one of the defining features of virtual work is the strong incorporation of various communication technologies such as email, telephone, or video conferencing (Gilson et al., 2015). ICTs have significantly advanced in recent years, including the adoption of team chat, blogs, wikis, and more recently video calling, audio processing, computer vision, and natural language processing among many others. While ICT can create new possibilities for locating and utilizing information and thus contribute to better decision-making, the use of ICTs also creates changes such as new interactions or network ties, new groups, and different types of task coordination (Leonardi, 2018; Wang et al., 2020a). Therefore, it can be argued that ICT usage represents both barriers and opportunities connected to inclusion in an organizational context. In relation to positive aspects of ICT, it has been mentioned that social and demographic factors matter less in virtual organizations due to lean communication. For example, studies show that ICT increases the task focus and reduces the disturbing group-related cues in collaboration (Klitmøller & Lauring, 2013). In like manner, Velez-Calle et al. (2020) demonstrated in their study on millennials in virtual teams that technology could diminish the manifestation of discrimination in the workplace. Hence, it can be argued that the use of ICT in interaction tones down differences and thus counters some discriminating tendencies. On the other hand, ICT also causes important barriers to connections between people. For example, employees who use ICT to interact and collaborate are less likely to be exposed to organizational information including symbols and metaphors that could create a sense of group identity. In consequence, individuals may to a greater extent objectify each other, often causing some degree of psychological detachment (e.g., Fussell, 1995; Konradt et al., 2003; Lea & Spears, 1991). Several researchers have related ICT to negative consequences in relation to group attitudes that may also affect inclusiveness (e.g., Castaño et al., 2013). It has among other things been argued that lean media usage can orient individuals to experience a weaker sense of collective (e.g., Cramton, 2001; Mortensen & Hinds, 2002). Thereby, ICT can move the focus from social interaction and individuals’ special needs and could thus counter the effort for the inclusion of minorities. Distance as the physical space between individuals could also have both positive and negative consequences (O’Leary & Cummings, 2007). In regard to the positive consequences of distance, it has been argued that distant employees are a central source of professional advice in cross-border interactions, which in turn could enhance their inclusion as unique individuals (Farh et al., 2021). Moreover, the greater the distance between the employee and the organization, the more likely it is that the person will perceive organizational practices in a more neutral and goal-oriented perspective while people emerged more fully in the organization’s social life will be biased by their own involvement (Liberman & Trope, 2008; Wilson et al.,

392  Handbook of virtual work 2013). As such, distant individuals will base predictions and evaluations on more general trends and a few superordinate goals (see Fujita et al., 2006; Wilson et al., 2013). In relation to dissimilar colleagues, it may be assumed that the physically distant individual will react more mildly to minorities’ mistakes to assimilate to the common norm as it is often of little functional importance (Trope & Liberman, 2010). Accordingly, it can be argued that distance neutralizes some of the harmful aspects of interpersonal dissimilarities. Many studies, however, also suggest that distance can have harmful consequences on relational aspects of inclusiveness. In this regard, Farh et al. (2021) argue that distant employees usually suffer a “geographic disadvantage” in building professional ties. This could be related to the findings by Armstrong and Cole (2002) that showed how connections between groups can be reduced by physical distance so that remote workers experienced difficulties relating to their colleagues due to the lack of shared understanding. Cramton (2001) and later Jackowska and Lauring (2021) also demonstrated that high distance may lead to more negative bias towards colleagues. The inclusion of personnel with special needs could be impaired because employee distance reduces the amount of information available and the development of good interpersonal relations (Hinds & Mortensen, 2005; Kiesler & Cummings, 2002). Distance can thus make inclusion and integration of minorities difficult in practice due to lack of information. The positive and negative effects of ICT and distance are depicted in Table 20.3. Table 20.3

Positive and negative consequences of ICT and distance in relation to organizational inclusiveness

 

Positive consequences

Negative consequences

ICT

An increased task focus and a reduction of disturbing

An increased anonymization and a weaker social

group related cues in collaboration

affiliation with colleagues reducing the feeling of joint identity

Distance

A more abstract, objective, and less emotional view on

An increased feeling of psychological detachment

the meaning of dissimilarities

from group members and reduced effort of including dissimilar colleagues

Source: Authors’ own.

As described above, ICT and distance have been portrayed to have both positive and negative aspects that could affect inclusiveness in organizations. The positive or negative aspects, however, may be activated differently depending on whether the diverse group experiences virtuality as an obstacle to their work or not (Watson-Manheim et al., 2002). As such, obstacles to work with diverse colleagues virtually can stay dormant if the group or organization learns how to overcome them (Watson-Manheim et al., 2012). This means that diversity, ICT, and distance in themselves are not necessarily a problem for virtual diversity management, but that contextual factors, such as the type of diversity, employee attitudes, leadership styles, and policies that shape individual perceptions can influence this (Bülow et al., 2019; Taras et al., 2019). This indicates that the way virtuality is handled by the management is central to whether or not the outcome is positive or negative for organizational inclusiveness, which we will focus on in the following.

Virtuality and inclusiveness in organizations  393

INCLUSIVE MANAGEMENT PRACTICES IN VIRTUAL ORGANIZATIONS Management and leadership practices have been argued to be vital in fostering organizational inclusion (Leroy et al., 2022; Roberson & Perry, 2022). Yet, insights into which specific initiatives and behaviors actually foster inclusion virtually are lacking. Still, the current research gives some indications for management, and below we discuss the potential role of top managers and line managers in ensuring the inclusion of minorities in virtual organizations. For the top management, there are two prominent components of virtual diversity management that need to be in place: (1) accountability structures, and (2) encouraging formal communication. Both of them can address intentional and unintentional forms of discrimination that occur at a workplace. Accountability structures are a central element in implementing policies and strategies that support inclusion elements of equal group membership, particularly in relation to the inclusion of gender, disability and ethnocultural/linguistic groups in the virtual organization. This is because organizations need to ensure that their line managers are feeling responsible for inclusiveness goals and that they can be held accountable if those goals are not followed. Such formal structures must signal that the organization is truly committed to its inclusiveness goals, and that they are central to the organization (Kalev et al., 2006). Generally, organizations can adopt the equal opportunities approach, stressing the importance of formal and informal activities focused on the examination and monitoring of processes at different levels to ensure that they are free from discrimination. In a virtual organization, a situation that can harm feelings of belongingness is, for example, the planning of meetings across time zones or without consideration to differences in work–life balances. In a situation where virtual workgroup members having different time preferences for work and meetings are never taken into consideration, this can reduce their feeling of being equal members of the organization. Accordingly, there should be formal guidelines concerning the planning of meetings across time zones. Other policies could formulate binding rules about online and English language access to important information so that specific nationalities are included. Finally, mandatory availability of specific hardware and software could ensure accessibility for physically disabled personnel. In addition to accountability structures, the organization needs to provide formal diversity communication in the form of statements to underrepresented as well as dominant group members about the dedication to inclusion and the appreciation of the unique contribution of minority individuals. This also relates particularly to considerations of using ICT for organizational inclusiveness in virtual settings. Such messages could include descriptions of the organization’s virtual diversity management policies and philosophies. Accounts like this could also be useful in relation to the recruitment of minority job seekers (cf. Avery & McKay, 2006). Here it should be mentioned that formal diversity communication needs to be honest about the actual situation. This is because artificial inflation of an organization’s inclusiveness engagement is likely to erode the trust in top and line managers and increase turnover and among talented minority employees (Onyeador et al., 2021; Wilton et al., 2020). For formal diversity communication to be effective in the virtual organization, policies and statements should be readily available and clearly visible on the organization’s webpages in a language that all its members can understand. Apart from top managers, line managers can also do much to improve virtual diversity management. For line managers, central elements to ensure inclusiveness virtually is to facilitate

394  Handbook of virtual work (1) a high level of trust towards the leader among minority members, (2) inclusive attitudes among employees in general, and (3) a shared understanding of the context and work objectives across dissimilar groups. Trust in leaders can be developed in minority group members if they are treated with equality/fairness and if the line manager is taking objective and transparent decisions (Brahm & Kunze, 2012; Breuer et al., 2020; Jarvenpaa & Leidner, 1999). As decision-making processes can appear less transparent to virtual employees, it is important that line managers openly explain the reasons for their decisions in virtual meetings or in written statements. In addition to the openness, it is also important that timely explicit conversations are held with representatives from minority groups in order to access their perspectives as it can be more difficult to assess their opinions and attitudes virtually (cf. Henttonen & Blomqvist, 2005). This is especially prominent when dealing with groups that are less comfortable with digital means of communication such as older employees. This can also be a problem in ethnoculturally diverse organizations including employees originating from regions with a tradition for high context communication such as Asians (cf. Jonasson & Lauring, 2012). They will often be reluctant to speak openly against a decision – especially not in a virtual meeting that may be recorded. Here it can be added that a virtual line manager also needs to listen especially carefully to individuals with lower proficiency levels in the corporate language as they will often avoid communicating verbally over the phone or in video meetings (Lauring & Klitmøller, 2015). Formalizing work processes could be a necessary way forward to create feelings of trust, objectivity, and transparency from minority group members (Kniffin et al., 2021). In this regard, Maznevski and Chudoba (2000) argue that the line manager should ensure that interaction follows rhythmic patterns when working virtually because debate and communication do not occur naturally in this context. Another task for line managers is the development of inclusive attitudes among organizational members, which is especially important in regard to both group-related and individuals’ socio-emotional as well as task-related inclusion. Inclusive attitudes refer to group members’ positive attention to internal dissimilarities and thus put the emphasis on pro-diversity beliefs (McKay & Avery, 2015; Shemla et al., 2016; van Knippenberg & van Ginkel, 2022). Such feelings have been argued to reduce negative stereotyping and social categorizations (Jehn & Bezrukova, 2010) and to increase information elaboration from unique individuals (Leroy et al., 2022; Shore & Chung, 2022). Edmondson et al. (2004) proposed that line managers who are available and accessible to employees both physically and psychologically may help to create an environment of approachability that reduces barriers to input. This shows an openness to others’ perspectives and may encourage employees to share more freely their unique ideas and knowledge and as a consequence feel useful. The development of inclusive attitudes also makes it worth considering the distance characteristic of virtuality. According to Amir’s (1969) contact hypothesis, a way to develop inclusive attitudes is for the line manager to facilitate frequent interactions with dissimilar others. This is because surface-level categorization and negative biases tend to decrease as a result of contact with actual persons (Mousa, 2020; Paluck et al., 2019). The frequent interaction needed for this to take place, however, is difficult to achieve virtually. Here it has been argued that the line managers should organize the work to cross between different types of diverse group members and between different worksites to increase accessibility, task integration and subsequently develop a feeling of shared identity and belongingness (Ahuja & Galvin, 2003).

Virtuality and inclusiveness in organizations  395 Creating a shared understanding in the diverse workgroups is the last of the tasks for the virtual line manager we will discuss in this chapter. This primarily relates to elements of inclusiveness and its task-related consequences of access and usefulness of diverse groups and individuals. Thus, developing a shared understanding can be an upfront investment to the collaboration in the organization by assisting the organization members in adjusting to the virtual reality and to the practices involved in inclusiveness and thus reduce future conflicts and communication demands (Meluso et al., 2020). In a heterogeneous context, a shared understanding can be the result of the employees’ trust in the leader and their positive attitudes towards dissimilar colleagues. A consequence of trust and interaction can be a shared understanding of values and objectives across the organization. If this can be achieved by the leader, the group can collectively come to understand its options, how employees define what is important, and how this affects the organization. Shared understanding, however, can be difficult to develop over a virtual meeting with the assistance of ICT. It is, therefore, crucial to firstly ensure the access, adoption, and application of ICT among employees. First, employees should be provided access to the physical and digital means of executing work, such as the laptop and associated software. Next, to successfully adopt the tools, employees should be offered digital skills training to increase their digital literacy and ensure continuous access to digital skill development. That can be achieved through employing professional staff or providing training material to overcome the barriers of working online. Lastly, to further utilize the available ICT tools, leaders can create shared digital spaces for informal communication and development of online communities of practice. That, in turn, will facilitate the application of the tools through encouraging and fostering participation. Although managing-by-walking-around is not feasible when people are working remotely, the rapidly expanded usage of videoconferencing has allowed for virtual sightlines (Nell et al., 2020). This approach is, however, a rather limited tool for leader information and not suitable for reaching a joint understanding of the organization's objectives. In this regard, Dionne and colleagues (2004) have argued that dialogue-based leadership can support virtual organization work outcomes by creating a shared understanding and thus facilitate a sense of direction. Purvanova and Bono (2009) similarly argue that this type of leadership strengthens the use of communication to motivate the organization members to reach a common goal. This is very important in virtual organizations where interaction is less natural due to the dependence on ICT and thus reduces the workgroup’s own possibility jointly to develop a clear sense of purpose (Hambley et al., 2007; Harvey et al., 2004; Hoyt & Blascovich, 2003). Accordingly, dialogue-based and goal-directed leadership may be particularly useful in virtual organizations given the difficulty of developing a collective sense of direction at a distance and through ICT.

CONCLUSION The global turn in organizations and the fierce competition for human resources have disrupted the existing labor market, prompting organizations increasingly to engage in diversity management and inclusive practices (Roberson & Perry, 2022; van Knippenberg & van Ginkel, 2022). Given the virtual character of modern organizational work, we discussed in this chapter the implications of virtuality on the inclusion of a diverse workforce, particularly those often disadvantaged by gender, age, ethnocultural differences, language, and disability. The chapter set out by listing central elements of organizational inclusion, namely belongingness,

396  Handbook of virtual work uniqueness, accessibleness, and usefulness. Those elements can be connected with the theorizing in diversity management predicting both the usefulness of the unique individual but also the difficulties in utilizing the potential due to barriers for group involvement based on social categorization. In other words, benefits can be obtained from the inclusion of talented minority group members, since discriminatory practices may function as barriers for this to truly unfold. While very little empirical research has integrated the two fields, this chapter aimed to connect the notions from diversity management to the consequences of managing virtually. Here we discussed the consequences of virtuality of each of five examined diversity traits, namely gender, age, ethnocultural/linguistic differences, and disability, and list the advantages and disadvantages. From this account, we dug deeper into the virtual context and elaborated, in relation to inclusiveness, on the consequences of ICT and distance separately. Taking departure in the insights developed in the previous sections we finally proceeded to describe and discuss potential managerial practices for exercising virtual diversity management.

NOTES 1.

https://​ www​ .mckinsey​ .com/​ featured​ -insights/​ diversity​ -and​ -inclusion/​ diversity​ -wins​ -how​ -inclusion​-matters. 2. https://​www​.worldbank​.org/​en/​topic/​disability.

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402  Handbook of virtual work Yawson, R. (2020). Strategic flexibility analysis of HRD research and practice post COVID-19 pandemic. Human Resource Development International, 23(4), 406‒417. Yu, H., Goggin, G., Fisher, K., & Li, B. (2019). Introduction: Disability participation in the digital economy. Information, Communication & Technology, 22(4), 467‒473.

21. Embracing the digital workplace: a SMART work design approach to supporting virtual work Bin Wang and Sharon K. Parker

If we can telework or shift our hours in a disaster, why can’t we always? (Yost, 2012)

Although advanced information and communication technologies (ICTs) have long enabled people to work and collaborate remotely with fewer time and space constraints (Wang et al., 2020), only a small proportion of people before Covid-19 worked virtually on a regular basis (e.g., teleworking and virtual team). The current large-scale working from home “experiment” during the outbreak of Covid-19 suggests that working virtually can be as productive as working in the office. For example, according to the recent research conducted in the UK (CIPD, 2021), 33 percent of employers reported that virtual work has increased productivity. Increasingly, an expectation is emerging amongst both employees and employers that at least some degree of virtual work will be the “new normal” for many workers after the pandemic. However, there can be limitations and challenges of virtual work, including poor communication, social isolation, work–home interference, work intensification, and procrastination, bringing negative impacts on employee work performance and well-being (Wang, et al., 2021b). If people continue working virtually without managers and other stakeholders recognizing and addressing these challenges, the effectiveness of virtual work will be limited. Therefore, it is theoretically and practically important to systematically consider how to optimize the benefits of virtual work and minimize its potential risks. We propose that a re-orientation of research approach is needed to achieve such a systematic analysis of virtual work. Prior to the pandemic, scholars predominantly focused on the question “how effective is virtual work?” (e.g., Allen et al., 2015; Gajendran & Harrison, 2007; Raghuram et al., 2019). In other words, researchers devoted considerable attention to identifying the individual and team outcomes of working virtually, as well as the set of boundary conditions that mitigate these relationships (Shin, 2004). Overall, this mainstream research identified optimistic conclusions about virtual work arrangements (Allen et al., 2015). However, the outbreak of Covid-19 has required many employees to change their ways of working. Millions of workers have made the shift to working remotely, irrespective of their tasks, preferences, and abilities. Well-documented benefits of virtual work have been questioned, while various challenges have emerged and/or become more pronounced in the virtual work context (e.g., Chong et al., 2020; Kniffin et al., 2021; Wang et al., 2021b). Accordingly, we believe that the time is ripe for scholars and practitioners to shift the locus of understanding from evaluating the effectiveness of virtual work to asking “how can we better design virtual work to boost individual work effectiveness and well-being?” (e.g., Klonek & Parker, 2021; Wang et al., 2021b; Xie et al., 2019). In this chapter, we introduce a work design perspective to the topic, which expands theory on virtual work, and generates useful guidance for managers and organizations to support their 403

404  Handbook of virtual work virtual workers. To lay the foundation for this perspective, we review the literature to identify major challenges that employees struggle with in virtual work settings. We then discuss how some of these challenges can be addressed through a sociotechnically oriented work design approach involving both altering the social subsystem (i.e., changing work characteristics) and the technical subsystem (i.e., technology re-design). We conclude with important directions for future research.

KEY CHALLENGES OF VIRTUAL WORK “Virtuality” refers to “the extent to which individuals use technology to interact with others, share ideas and information, and execute work” (Makarius & Larson, 2017: 160). Virtual work arrangements change how people perform their work tasks and interact with others. To identify the challenges arising from such change, we briefly review three streams of virtual work research: telework, virtual team, and computer-mediated communication (CMC) (Raghuram et al., 2019). Although we build on influential reviews on virtual work (e.g., Allen et al., 2015; Makarius & Larson, 2017; Raghuram et al., 2019; Wang et al., 2020), we particularly focus on research conducted during the outbreak of Covid-19. That is, because virtual work was primarily a “privilege” and was not widely adopted before the pandemic, previous research findings might have limited implications for future virtual work practices. For example, most studies prior to the pandemic identified the benefits of virtual work (e.g., Bloom et al., 2015), but these findings have been criticized for selection bias, focusing mostly on workers who possess sufficient resources, appropriate sorts of tasks, and/or have the required abilities to handle challenges accompanied with virtual work arrangements (Lapierre et al., 2016; Wang, et al., 2021b). Such a focus might underestimate pitfalls in virtual work settings when it is engaged in more broadly. The unprecedented scale of working from home work practices during the pandemic provides a unique context to investigate the challenges that individuals may struggle with in the digital/virtual workplace (e.g., Chong et al., 2020; Darouei & Pluut, 2021; Wang, et al., 2021a). Consistent with Wang et al.’s (2020) findings, we show that many virtual work challenges emerged in the processes of utilizing ICTs to communicate or collaborate with others (i.e., social aspects) and to execute work (i.e., task aspects). In the remainder of this section, we discuss how virtual work can lead to important challenges, first, with regard to social aspects of work and second with regard to technical aspects (summarized in Figure 21.1). Challenges in Social Aspects ICTs help to mediate interpersonal communication and collaboration in virtual work settings. Building upon computer-mediated-communication theories, the virtual work literature has revealed a series of social challenges, including poor communication, social isolation, and difficulties in the socialization process. Poor communication Research has been demonstrated that computer-mediated communication in virtual work practices, in conjunction with geographic dispersion, often reduces communication quality and communication effectiveness, There are well-documented negative consequences of such

Embracing the digital workplace  405

Source: Authors’ own.

Figure 21.1

A work design approach to supporting virtual work

poor communication in virtual work, such as conflicts (Hinds & Bailey, 2003), and decreased work effectiveness and well-being (Day et al., 2012). First of all, compared with face-to-face interaction, computer-mediated communication delivers somewhat limited social information. For instance, it can be more difficult to understand facial expressions and body language with digital communication technologies. Thus, computer-mediated communication can lead to relatively lower intimacy in social interactions, and raise the chance of miscommunication or misunderstanding (Nesher Shoshan & Wehrt, 2021). Besides, high-quality communication at work also requires shared experience and cognition among individuals, which can provide useful additional information and make communication processes smoother. However, as Greer and Payne (2014) argued, virtual work arrangements build a new physical boundary between employees because they work in different places and different time zones, resulting in reduced frequency and amount of communication (especially informal communication) at work (Blanchard, 2021; Jarvenpaa & Välikangas, 2020). Yang et al. (2021), based on the data collected from 61,182 US Microsoft employees over the first six months of 2020, supported this argument. These authors found that the remote work during the pandemic made the collaboration network of workers more siloed and resulted in a decrease in synchronous communication and an increase in asynchronous communication. In other words, the firm-wide virtual work practices diminished the communication process at work (e.g., sharing information across departments). Finally, as identified in Wang, et al.’s (2021b) qualitative study conducted during the outbreak of Covid-19, technological issues (e.g., poor internet connections) also can hinder the communication process among virtual workers. Social isolation Limited social cues in computer-mediated communication and rare face-to-face interactions can cause virtual workers to feel socially isolated. We find surprising differences between research findings prior to the pandemic and those observed during the pandemic.

406  Handbook of virtual work Pre-pandemic research findings of the relationship between virtual work on social isolation are inconsistent. Some empirical evidence supports the challenge of social isolation among virtual workers (e.g., Golden et al., 2008; Morganson et al., 2010). Taking Golden et al.’s study as an example, they found that isolation was negatively associated with job performance for employees who frequently worked away from the office. Another stream of research found that virtuality may not necessarily lead to isolation. Gajendran and Harrison’s (2007) meta-analytical study revealed a positive association between telework intensity (i.e., time spent on working away from the office) and employee–supervisor relationships, and a non-significant association between telework intensity and coworker relationship quality. According to Cooper and Kurland’s (2002) qualitative study, people can adjust their social behaviors to cope with isolation in virtual work settings, and virtual work arrangements therefore do not impair their social needs. It is also possible that individuals who prefer working alone might be those who choose to work away from the office, thus experiencing less isolation in virtual work settings. However, during the Covid-19 pandemic, there was consistent evidence of social isolation and its deleterious effects in virtual work (e.g., Blanchard, 2021; Galanti et al., 2021; Tavares et al., 2021; Toscano & Zappalà, 2020; Wang, et al., 2021b). This phenomenon could be explained by the inflexibility of virtual work policies at this time. The involuntary nature of virtual work during the pandemic did not provide people sufficient autonomy to decide when, where, and how to work remotely, which means they could not adjust their virtual work intensity or frequency, return to the office, and/or engage in high-quality face-to-face interactions whenever they felt isolated (Cooper & Kurland, 2002). In addition, due to social gathering restrictions during the pandemic, people were less able to fulfill their social needs through their personal social networks, which likely exacerbated the feeling of isolation when working from home. Difficulties in socialization The frequent absence of face-to-face interactions and geographic dispersion have been suggested to undermine the process of socialization of newcomers into the workplace (Asatiani et al., 2021). In a highly virtual work environment, organization members are more likely to have diverse cultural backgrounds, resulting in an insufficient and weak base of shared value among workers (Watson-Manheim et al., 2002). Moreover, individuals usually focus on work-related topics in computer-mediated communication, spending limited time on informal interactions that are essential for socialization. Therefore, it will cost newcomers in the virtual work context more resources to seek information, build relationships at work, and adjust their behaviors, cognitions, and skills necessary to fulfill their roles in the organization. Employers likely need to invest more resources to support the relatively longer organizational socialization process and to establish the preferred organizational culture (Nyberg et al., 2021). Difficulties in socialization might be more pronounced in remote work during the Covid-19 pandemic. Xiao et al. (2021) found that, compared with pre-pandemic levels, employees’ communication with coworkers was decreased. In other words, newcomers may have less opportunities to build connections with colleagues.

Embracing the digital workplace  407 Challenges in Task Aspects We also identified a set of challenges that virtual workers struggle with to accomplish their work tasks, including technostress, self-regulation failure, and work–home interference. Technostress Technostress, developed by management information system scholars, indicates the “stress experienced by end-users in organizations as a result of their use of ICTs” (Ragu-Nathan et al., 2008: 417). Considerable research has revealed various negative impacts of technostress on employees such as psychological strains and reduced work performance (e.g., Ayyagari et al., 2011; Wang et al., 2020). In the virtual work context, people frequently interact with ICTs to communicate with others and execute tasks, increasing the chance of experiencing technostress. Research has shown that intensive usage of ICTs at work can lead to stress in at least three ways (Wang et al., 2020). First, employees may suffer from information overload. ICTs indeed can help users process information-related tasks with less cognitive resources; in the meantime, the usage of ICTs brings large amounts of data/information, which in turn, can make users exhausted (e.g., Yu et al., 2018). Second, employees may experience high levels of learning requirements because of the fast pace of technology change and increased ICT complexity (Suh & Lee, 2017). Third, virtual workers can encounter various ICT-related hassles and interruptions, including technological incompatibility, information security threats, and ICT malfunctions (Wang et al., 2020). The abrupt shift to working from home during the pandemic has made employees more vulnerable to technostress. That is because there was little or no time for organizations to prepare sufficient IT infrastructure, safety procedures, and instructions for employees (Urbaniec et al., 2022). Self-regulation failure Nyberg et al. (2021) raised concerns about the lack of monitoring and motivation in virtual work. Because of the absence of supervisors and colleagues, some employees fail to resist temptations and do not appropriately regulate their behaviors and cognitive resources to accomplish work-related goals. In other words, they experience self-regulation failure. This can be accentuated because ICTs not only serve work-related goals, but also enable employees to handle personal issues (e.g., connecting with friends, online shopping, and playing online games). Less disciplined virtual workers may procrastinate engagement in primary tasks by cyberslacking (O’Neill et al., 2014). In addition to using ICTs in a counterproductive manner, virtual workers may also be easily distracted by interruptions from the personal domain and fail to concentrate on work tasks (Wang, et al., 2021a). As one participant in Wang, et al.’s qualitative study stated, “without the kind of pressure in the workplace, I was a little slack. I did more private things [during working time]” (Wang, et al., 2021b: 26). Wang, et al.’s (2021b) survey-based study further revealed that the challenge of self-regulation failure (e.g., procrastination) can greatly hurt virtual work effectiveness. Similarly, Troll et al.’s (2021) mixed-methods research on working from home during the Covid-19 crisis found that effective virtual work requires employees demonstrate self-control and appropriate self-control strategies.

408  Handbook of virtual work Work–home interference Before the pandemic, virtual work arrangements, especially teleworking, were considered as a desirable employee benefit because working from home can enable individuals to better balance work and private life. Gajendran and Harrison’s (2007) meta-analysis of 46 studies revealed that teleworking is associated with lower work-to-family conflict, and this beneficial relationship is greater for employees who engage in teleworking more frequently and for those with more teleworking experience. Similarly, Allen et al.’s (2013) meta-analysis of 58 studies also found a negative association between teleworking and work-to-family conflict. However, Allen et al. (2015) criticized that it is not appropriate to conclude that virtual work arrangements can reduce work-to-family conflict because of methodological limitations in the existing literature (e.g., absence of controlled field experiments). It is possible, for instance, that employees low on family responsibilities (e.g., living without children) are more likely to work virtually, not vice versa. Recent studies, probing deeper into the virtual work context, suggest that the desirable effects of virtual work on reducing work-to-home interference are questionable, at least during the pandemic. For instance, in two qualitative studies conducted during Covid-19, participants cited work-to-home interference as a major challenge encountered in virtual work (Tavares et al., 2021; Wang, et al., 2021b). One potential explanation for increased work-to-home interference is the involuntary nature of working from home during the outbreak of Covid-19, that is, people were forced to work remotely irrespective of their preferences and abilities. Another explanation is that many children were not able to be cared for outside of the home, and home schooling was commonly required. Leaving aside the uniqueness of the pandemic context, Delanoeije et al.’s (2019) daily diary study found that working outside of standard work hours plays a crucial role in predicting work-to-home interference. ICTs usage blurs the boundary between work and non-work domains, which can keep users constantly connected (Fonner & Roloff, 2012; Leonardi et al., 2010). Virtual workers can be reached by advanced ICTs outside regular working hours and they are expected to work longer hours (Kelliher & Anderson, 2010). Therefore, virtual workers will have fewer resources to participate in their home activities. Considerable research has revealed deleterious impacts of employing ICTs to perform work tasks after hours on work-to-home conflict (Boswell & Olson-Buchanan, 2007; Butts et al., 2015; Derks et al., 2015; Ferguson et al., 2016; Golden, 2012). In terms of home-to-work interference, meta-analytical studies before the pandemic reported a negative or a nonsignificant impact of virtual work arrangements on home-to-work interference (Allen et al., 2013; Gajendran & Harrison, 2007). In other words, virtual work arrangements sometimes help to reduce home-to-work interference (that is, family responsibilities interfere less with working roles), but sometimes they do not. However, studies during the outbreak of Covid-19 raised concern about interruptions from family domains; likely in part because of the uniqueness of the Covid-19 context in which employees had to work with children and didn’t necessarily possess a dedicated workspace at home (e.g., Allen et al., 2021; Wang, et al., 2021b).

Embracing the digital workplace  409

A SMART WORK DESIGN APPROACH TO SUPPORTING VIRTUAL WORK The challenges described above are not an inevitable outcome of virtual work practices. Taking the challenge of poor communication as an example, Fonner and Roloff (2010) found that telework intensity was not significantly related to information quality; scholars even reported a desirable effect of computer-mediated communication on communication effectiveness (Chidambaram & Jones, 1993). These findings are consistent with the well-established fit perspective in the computer-mediated communication literature (Dennis et al., 2008; Maruping & Agarwal, 2004), that is, workplace communication effectiveness depends on the fit between the communication technology and the work tasks being performed. We therefore propose that several of the above challenges can be addressed, at least in part, through a work design approach. The topic of work design or job design, developed over a century ago, focuses on “the content and organization of one’s work tasks, activities, relationships, and responsibilities” (Parker, 2014: 662). Often work design research has proceeded by investigating the impact and re-design of key work characteristics, or attributes of work such as job autonomy, that have psychological significance for workers. Work design researchers also recognize that the content and organization of work is strongly influenced by technology, and that this technology can be adjusted to create better work and meet human needs (Cherns, 1987; Parker & Grote, 2022; Trist & Bamforth, 1951). In particular, the socio-technical systems (STS) approach to work design, developed in the 1950s, remains powerful guiding research in the current digital workplace (e.g., Baxter & Sommerville, 2011; Bélanger et al., 2013). From the STS perspective, virtual work can be viewed as a system comprising interdependent social and technical subsystems (Cherns, 1987; Emery & Trist, 1965; Trist & Bamforth, 1951). The social subsystem encompasses “all that is human that members of an organization bring with them to work”, while the technical subsystem consists of “the tools, techniques, procedures, skills, knowledge, and devices used by members of the social system to accomplish the tasks of the organization” (Pasmore et al., 1982: 1184). STS theory states that system effectiveness largely relies on the joint optimization of social and technical subsystems, which implies people can achieve greater system effectiveness by adjusting both the social and technical subsystems simultaneously. Nevertheless, despite the plea for joint optimization, research and practice has tended to focus on one subsystem more than the other. For instance, early researchers guided by STS principles advocated for autonomous work groups to improve the social subsystem (e.g., increasing job autonomy, job identity, and social contact at work) in order to deal with the demands induced by the technical subsystem (Pasmore et al., 1982). On the other hand, another stream of research largely applies STS principles to technology design, with an emphasis on the technical subsystem (e.g., Baxter & Sommerville, 2011; Clegg, 2000). Management and organization scholars usually adopt the former perspective, taking the technology as a given and, therefore, narrowly viewing the social subsystem as the target for change. In contrast, scholars from the management information system and engineering research traditions tend to focus on the adjustment of the technical subsystem (Orlikowski & Barley, 2001). Based on STS principles, we argue that successful virtual work requires an integrated approach in which both social and technical subsystems are jointly considered and optimized.

410  Handbook of virtual work Table 21.1

How can managers and organizations support virtual work?

Virtual work

Target

challenges

subsystem

Tips for supporting virtual work

Challenges in social

Social

Achieving SMART work socially

aspects

subsystem

Stimulating: enhancing task/skill variety and complexity.

Poor communication

Mastery: (1) providing timely feedback; (2) enhancing role clarity; (3) technical

Social isolation

support and professional development.

Difficulties in

Agency: (1) providing scheduling-autonomy; (2) using electronic monitoring artfully;

socialization

(3) improving the flexibility of virtual work policies.

 

Relational: (1) providing social support; (2) creating opportunities for informal social

 

interactions and collaboration; (3) increasing task interdependence at the early stages

 

of virtual work. Tolerable demands: keeping workload manageable.

  Challenges in task

Technical

Achieving SMART work technically

aspects

subsystem

Stimulating: technical features/functions that meet one’s needs to challenge (e.g.,

Technostress

learn new knowledge, abilities, and skills).

Self-regulation failure

Mastery: technical features/functions that provide timely performance feedback.

Home–work

Agency: technical features/functions that support autonomy (e.g., allow users to

interference

custom the technology). Relational: technical features/functions that (1) facilitate social interactions (e.g., offer chatting channels); (2) facilitate one’s desire to influence others as well as one’s desire to be influenced by others (e.g., online knowledge community). Tolerable demands: technical features/functions that allow users to provide feedback (e.g., feedback hub).

Source: Authors’ own.

In what follows, we describe the SMART work design model (Parker & Knight, 2020) that can be drawn on to improve both the social and technical subsystem within a virtual work system (summarized in Table 21.1). SMART Work Design Model and Improvements to the Social Subsystem Virtual work arrangements, as a new way of working, have changed the nature of work (Demerouti et al., 2014). As Wang et al. (2020) identified, adoption of ICTs for accomplishing work and interacting with others exert mixed effects on work characteristics including job demands, job autonomy, and relational aspects of work, which in turn influences individual work effectiveness and well-being. For instance, evidence suggests that, overall, when ICT is implemented, information overload, interruptions, learning requirements, and scheduling autonomy tend to increase, whereas social support, decision-making autonomy, and emotional labor often seem to decline (Sonnentag et al., 2012; Wang et al., 2020). To boost virtual work effectiveness, it is necessary to make a conscious effort to re-design virtual work by considering and, if necessary, changing the social subsystem. The vast bulk of research has demonstrated that “good” work design in which work has work characteristics such as autonomy, task variety, and social support, and has low to moderate levels of job demands (e.g., workload), promotes individual motivation, well-being, and performance (e.g., Humphrey et al., 2007; Parker, 2014; Parker et al., 2021). One way to understand what constitutes “good” work design is the SMART model developed by Parker

Embracing the digital workplace  411 and Knight (2020). This model identifies five higher-order categories of work characteristics. The first four letters (S for Stimulating, M for Mastery, A for Agency, R for Relational) encompass a series of desirable job resources that are important in and of themselves and that can also mitigate the detrimental impacts of virtual work challenges. The last letter, T, stands for a moderate level of job demands. Tolerable demands can be achieved by reducing work demands, and/or by increasing job resources. Stimulating Stimulating jobs involve high levels of task variety, skill variety, skill use, and job complexity. In other words, individuals with highly stimulating jobs can complete tasks using a wide range of skills and abilities, engage in varied tasks, and solve challenging problems at work. Although challenging jobs can, in the extreme, lead to strain, they also improve employee performance via increased work motivation (Lepine et al., 2005). On the contrary, employees tend to be bored with less stimulating or routine tasks, and have relatively lower levels of morale and performance. In the virtual context, embracing complex jobs is conducive to facilitating organizational socialization processes. As Cooper-Thomas and Anderson argued (2006), learning is a key factor in organizational socialization. Newcomers learn from their colleagues, supervisors, mentors, and organizational literature to reduce the uncertainty at work and better understand their roles in the organization (Cooper-Thomas & Anderson, 2006). Complex tasks often require a set of interpersonal collaborations that facilitate interactions between newcomers and other organizational members. Through this kind of teamwork, newcomers master organizational knowledge and build workplace relationships. Moreover, when a job is highly stimulating, virtual workers likely show less self-regulation failure. Previous studies have shown workplace boredom is the main cause of counterproductive ICT use behaviors (e.g., cyberslacking) and procrastination (Metin et al., 2016; Pindek et al., 2018). Work design research suggests that boredom in the workplace is often derived from underload. Namely, people who have few challenging tasks to do will shift their attention to non-work-related issues (Metin et al., 2016). Therefore, managers should allow virtual workers to engage in a variety of tasks, utilize different skills at work, and work on challenging tasks. As a result, employees will likely better concentrate on their primary work tasks. Mastery Mastery emphasizes designing work so that workers know what they are expected to do (e.g., via enhancing role clarity) and know how well they are doing (e.g., by providing job feedback). In the virtual context, poor or ineffective communication in computer-mediated modes can result in role ambiguity, namely, there is uncertainty over the expectations of one’s role at work. The asynchronous nature of most interpersonal communication in virtual work can also reduce the effectiveness of performance feedback. Thus, managers need to clearly define virtual workers’ roles, manage performance effectively, and provide feedback regularly to keep employees in the loop (Kirkman et al., 2002). Mastery also plays a significant role in the organizational socialization process. Given reduced opportunities for informal social interactions in the virtual work context, it is challenging for newcomers to acquire enough information to fulfill their work roles (Fang et al., 2011). Managers need to clearly explicate their expectations and provide regular feedback, whether about outcomes or processes, to enhance virtual organizational socialization success (Cooper-Thomas & Anderson, 2006).

412  Handbook of virtual work Providing feedback and role clarity also helps virtual workers to self-regulate. Goal setting plays a crucial role in self-regulation (Locke & Latham, 2002). Setting specific yet challenging goals can motivate people to increase their effort to make goal progress, while timely feedback provides information about how people are doing, which helps individuals adjust strategies and the level of effort to achieve the goal (Locke & Latham, 2002). Finally, mastery can be supported amongst virtual workers by ensuring they have the necessary resources. Virtual workers during the pandemic struggled with a lack of ICT infrastructure (Bezzina et al., 2021). Employees need the appropriate tools to work away from the office and should have access to organizational resources/documents. As Day et al. (2012) found, personal technical assistance and ICT resource support alleviated the negative impacts of technostress (i.e., learning expectations and ICT hassles in their study) on individual well-being. We also recommend providing professional training in technology use to ensure workers have the knowledge, skills, and abilities required for virtual work (Tavares et al., 2021). Agency A well-designed job should support human agency by providing job autonomy; a key work characteristic. Jobs with higher levels of autonomy allow employees to take control of their work schedule, choose the most suitable work methods, make work decisions independently, and influence wider decision-making. The work design literature has shown that autonomy is positively associated with desirable individual outcomes such as enhanced job performance, job satisfaction, and well-being (Humphrey et al., 2007). According to lessons learned from the current pandemic, virtual work policies should themselves have flexibility (Wang, et al., 2021b). For example, some employees may prefer online interactions, while others prefer face-to-face communication, so managers should aim to give employees as much autonomy as feasible to choose where and how they work. Taking the challenge of social isolation as an example, when employees can decide the ways of working virtually, they can adopt strategies to fulfill their social needs, such as adjusting virtual work intensity, working at a coworking space, or participating in projects that require interpersonal collaboration. Such strategies, of course, need to be balanced alongside organizational requirements such that there will be clear and reasonable boundaries around the autonomy (e.g., employees might be expected to be physically present at particular times, or for particular tasks). Agency also plays a crucial role in mitigating demands from different domains. Virtual work arrangements blur the work–home boundary, which means that employees are expected to deal with work or family demands at the same time. Managers commonly believe that virtual workers have more autonomy in comparison to their counterparts in the office, and therefore, don’t provide additional autonomy. For instance, managers sometimes monitor virtual workers in the same way they monitor office staff. As Lautsch et al. (2009) identified, virtual workers supervised with a close monitoring approach reported higher work–family conflict, which is also supported by Parker et al.’s (2020) study conducted during the pandemic. Thus, managers should trust and empower their subordinates, focusing on managing by outputs (e.g., achieving goals) rather than inputs (e.g., physical presence). Golden et al.’s (2006) study provides empirical evidence for the importance of autonomy in virtual work practices. They found that the negative relationship between telework intensity and work–family conflict was moderated by scheduling autonomy, such that work–family conflict decreased at a faster rate

Embracing the digital workplace  413 for employees with higher levels of scheduling autonomy. In other words, when autonomy is available, virtual work arrangements can have a more positive effect on work–life balance. Relational Relational aspects are crucial for successful virtual work. First, social support can help virtual workers fulfill their psychological needs. Bentley et al. (2016) found that, in the remote work context, social support is negatively related to social isolation, which in turn, reduced psychological strain and increased job satisfaction. Based on data collected from virtual workers who worked from home during the pandemic, Wang, et al. (2021b) reported that social support can lead to greater life satisfaction via reduced loneliness. To facilitate social support, managers can encourage non-work-related communications through enterprise social media (e.g., Slack), organize informal online and offline social activities, and encourage employees to ask for, and provide, help. Task interdependence, or the extent to which one’s tasks are connected with other tasks (Grant & Parker, 2009), is an important relational element in virtual work. Previous studies have revealed detrimental impacts of task interdependence in virtual work settings, such as more work exhaustion (Windeler et al., 2017) and a higher level of experienced workload (Suh & Lee, 2017). A recent daily diary study conducted during the pandemic also revealed that the impact of daily Covid-19 task setbacks on end-of-day exhaustion was stronger for employees with higher levels of task interdependence (Chong et al., 2020). This might be because highly interdependent jobs usually require synchronous interpersonal communication (Golden & Gajendran, 2019), creating an extra level of demand. However, if we take virtual work arrangements as a given, task interdependence can also facilitate interpersonal communication and coordination (Klonek & Parker, 2021). Enhancing task interdependence is particularly necessary at the beginning of virtual work (e.g., for newcomers). The channel expansion theory (Carlson & Zmud, 1999) suggests that people can proactively adapt to the virtual environment and can develop their abilities and skills to clearly and correctly send and interpret information with richer experiences. In other words, negative consequences caused by computer-mediated communication will be diminished over time. Thus, managers should facilitate interpersonal communication and coordination at the early stages of virtual work and support newcomers, thereby promoting the process of socialization (Hertel et al., 2005). Tolerable demands As identified in the last section, there are various types of demands in virtual work, including work–family conflict, ICT-related demands, workload, and so on. Sometimes, for example, virtual workers are expected to work under time pressure, work longer hours, or be “always online” even outside regular working hours, which explains why some studies show that virtual work arrangements may increase work intensification (e.g., Chesley, 2014; Kelliher & Anderson, 2008, 2010). Work intensification has a series of detrimental effects, including impairing worker mental health and well-being. For example, higher levels of workload will lead to work–home interference (Wang, et al., 2021b) and exacerbate the negative influence of work–home interference on employees (Golden, 2012). Therefore, managers must thoroughly consider the intensity of work. Klonek and Parker (2021) offered some recommendations to effectively manage workload. For instance, managers can use an evidence-based approach

414  Handbook of virtual work to set work-related goals, make work–life balance part of the organizational culture, and hire additional support teams (e.g., outsourcing). Under certain circumstances, it is not realistic to reduce workload; instead managers and organizations must offer job resources to buffer the deleterious effects of overload on employees. The first four elements of the SMART work design model (i.e., Stimulating, Mastery, Agency, and Relational) emphasize job resources that can help individuals cope with demands. SMART Work Design Model and Improvements to the Technical Subsystem In the current digital workplace, most work elements such as routines and roles nowadays are embedded in technology or the technical subsystem. According to STS theories, the technical subsystem influences employee working experiences through either supporting or limiting particular behaviors. As Cooper and Foster (1971: 469) articulated, “any environment can be analyzed in terms of those features which make particular behaviors possible (supports) and those which preclude or limit particular behaviors (constraints).” Considerable attention has been devoted to eliminating deleterious constraints or to solving problems raised by constraints. For example, machines in mass production limit operators from utilizing their skills. Managers usually re-design the social subsystem to alleviate the negative impact of this constraint, such as by increasing job rotation. However, we can also achieve the SMART work and make desirable employee outcomes possible through the design of appropriate technology, which has received limited attention in previous literature. For instance, users may perceive more autonomy when the technology allows customization. We therefore see re-designing technology, especially the technology affordance, as an avenue for improving job quality and work system effectiveness. The term affordance refers to “the actionable properties between an object and an actor” (Zhang, 2008: 145). In the context of technology, affordance can be simply understood as the possibilities for performing certain actions based on particular technical features (i.e., what a user can potentially do through using the technology). Motivational affordances specifically indicate technical features of a given technology that determine whether and how it can support one’s motivational needs (e.g., needs for autonomy, relatedness, and competence, Zhang, 2008). A technology with high motivational affordances should allow users to take actions to meet their psychological needs (Karahanna et al., 2018; Peters et al., 2018; Zhang, 2008). For example, enterprise social media with chatting channels will facilitate online interactions, which in turn, helps to meet individuals’ social needs. The core tenet of the motivational affordances technology design approach is consistent with the SMART work design model, because those two approaches both aim to improve virtual working experiences. Notably, altering the social subsystem or re-designing work in a SMART manner tends to be “top-down” in so far as it requires the input of managers and other organizational stakeholders. Affordances, on the other hand, offer the potential for a series of self-initiated actions. That is, technical affordances can make job crafting behaviors possible, and employees can utilize these affordances to achieve a better person–job fit in a “bottom-up” manner (Wrzesniewski & Dutton, 2001). For example, virtual workers often collaborate with colleagues via enterprise social networking platforms (e.g., Microsoft Teams). If this platform affords informal social interactions (e.g., channels are provided for non-work-related online chatting), individuals will have more opportunities to interact with colleagues and to be socially connected with them.

Embracing the digital workplace  415 We argue that, in the virtual context, managers and organizations should not take the technology as a given; it’s equally important to improve the technical subsystem to support virtual workers (Parker & Grote, 2022). We believe the SMART work design model will also be effective when designing technology in the virtual work context. Specifically, incorporating Zhang’s (2008) ideals on motivational affordances, the current chapter recommends adding necessary technical features/functions that afford employees to work in a SMART way. Stimulating Zhang (2008) recommended introducing gamification in technology design to encourage employee motivation. That is, applying game elements or principles to design workplace technologies, such as earning points, managing a challenge, levels, badges, leaderboards, and so on, might enhance workers’ stimulating experiences. As Grünewald et al. (2019) argued, the game-like design “has the power to transform the activity of learning a new skill or onboarding new employees into an exciting challenge” (p. 557). For example, a complex training program could be divided into several challenges; people who hit a milestone will earn points and badges. Gamification is also conducive for the interpersonal process, thereby overcoming difficulties in social interactions among virtual workers. Taking group/team games as an example, these games require the corporation of team members, which would be a great opportunity for newcomers to acquire social information and establish networks (Colbert et al., 2016). Mastery In terms of coping with the challenges of virtual work in task aspects, technology should afford users to get immediate performance feedback, helping employees better master their jobs. Except for supervisors and colleagues, technology is the main source of feedback. Advanced ICTs used in virtual work have great abilities to fetch user data. Managers usually utilize ICTs to monitor their subordinates because of lower trust in virtual work settings. However, users might be able to adjust their strategies and improve if they can receive timely information about their work performance. Participants in Wang, et al.’s (2021b) study stated that electronic monitoring is acceptable and necessary when managers and organizations use it appropriately. Previous research has revealed that using electronic monitoring to control and punish employees is deleterious, but providing constructive feedback with the help of electronic monitoring contributes to employee growth and skill development (Ravid et al., 2020). Accordingly, we recommend organizations analyze the massive amounts of data generated during virtual work, but importantly, ensuring that virtual workers have access to relevant work-related data and real-time performance feedback. This technical feature also affords managers to create optimal challenges for employees (i.e., making work stimulating). By doing so, individuals will feel their jobs are stimulating but will not be exhausted by overload. Agency Managers need to pay attention to human autonomy in human–computer interaction and support human agency. Most existing research has focused on autonomous technology, while overlooking human agency. In fact, working with a standardized system often means limited employee decision-making and work method autonomy (Eriksson-Zetterquist et al., 2009). Scholars have recently called for more attention to support human autonomy (i.e., designing greater opportunities for workers to control and influence the technology) (Parent-Rocheleau

416  Handbook of virtual work & Parker, 2022; Parker & Grote, 2022). It is a common practice to afford personalization (e.g., application toolbar customization). Another advanced approach, discussed by Peters et al. (2018), is to build technical features into the system that can help users achieve goals more fluently by reducing obstacles or strengthening their capabilities. For example, assistive functions (e.g., time management applications) can give users better control over their work. Relational Given that most challenges in virtual work are caused by the absence of face-to-face interactions, we encourage managers to add more technical features/functions that facilitate social interactions, thereby improving the “R” element of virtual work. Song et al.’s (2019) qualitative study found that work-oriented technology and socialization-oriented technology exert different effects on users. Specifically, participants perceived more instrumental value of work-oriented technology, while perceiving more expressive value of socialization-oriented technology; socialization-oriented technology (e.g., WeChat in their study) plays a more important role in facilitating social exchange and social support in comparison to work-oriented technology. We believe that virtual workers will benefit from functions that afford informal online social interactions. Managers are encouraged to add (non-work-related) chatting channels into the enterprise social media, which can strengthen the emotional bond between virtual workers and other organizational members. According to Huang et al.’s (2015) study, non-work-related online posts had a positive spillover effect on work-related online posts, which, in turn, increased employee performance. In other words, facilitating informal online social interactions can not only mitigate challenges in social aspects (e.g., social isolation), but also contribute to greater performance. Another approach to promote social interactions is to build an online knowledge community (or corporate Wiki). People usually have a need to influence others (Zhang, 2008). As prosocial behaviors in the online knowledge community are transparent to a large-scale audience, individuals tend to be motivated to contribute or to show organizational citizen behavior (e.g., sharing knowledge and providing support) (Leonardi & Vaast, 2017). As a member of the knowledge community, on the other hand, employees will have more opportunities to learn organizational knowledge, seek and receive help. Tolerable demands Tolerable ICT-related demands do not mean technologies automatically execute primary tasks, substituting for humans in the virtual work practices. In fact, manageable and challenge technostressors as a motivating factor can enhance employees’ skills, tasks, and work–life activities, while too less of challenge technostressors will make work less stimulating (Tarafdar et al., 2019). The idea of Tolerable demands underscores the importance of reducing threat technostressors, including techno-insecurity, techno-overload, techno-invasion, techno-uncertainty, techno-complexity, and so on. Based on the SMART work design approach, improving the first four elements of the SMART model (i.e., Stimulating, Mastery, Agency, and Relational) in technology design can provide necessary resources for individuals to cope with such threat technostressors, thereby helping to keep the demands tolerable. Taking the relational aspect as an example, Bennett et al.’s (2021) research on videoconference meetings during the Covid-19 pandemic found that videoconference fatigue could be reduced if meeting attendees had higher feelings of group belongingness. That means increasing relational elements in computer-mediated communications is conducive to buffer demands in

Embracing the digital workplace  417 virtual work. Besides, technical features that support human agency are also useful. Managers and engineers need to add supportive functions to the technical subsystem which allow users to provide their feedback (e.g., Feedback hub), such that technologies will be upgraded improved user experiences.

CONCLUSION The unprecedented large-scale virtual work experiment has challenged our understanding of work. Practitioners and scholars need to embrace the digital way of working in the coronavirus-forged world by considering how to support virtual work and improve virtual work experiences. Based on a work design perspective, the current chapter frames virtual work as a system comprised of social and technical subsystems. As summarized in Figure 21.1 and Table 21.1, we introduced the SMART work design model to achieve a joint optimization of social and technical subsystems. Overall, we identified that previous research has been disconnected. Organizational scholars focus on employees’ experiences in a given technological context, while studies inspired by management informational system traditions are more interested in how to optimize the technical system. However, effective virtual work practices require interdisciplinary integration of accumulating knowledge. We hope this chapter can facilitate theoretical conversation across disciplines and spark greater interest in virtual work re-design. To move research on virtual work, we offer three promising directions for future research. First, we encourage the investigation of how impacts of work designs vary with the degree of virtuality. As Gibson et al. (2011) identified when examining the classic job characteristic model in the virtual team context, the effects of job characteristics vary with the level of virtuality. Their results showed that the relationship between task significance and perceived meaningfulness was significant only when electronic dependence (i.e., degree of reliance on electronically mediated communication) was higher; the relationship between job autonomy and perceived responsibility was greater for employees with lower electronic dependence; the relationship between feedback and the knowledge of results was greater when electronic dependence was at lower levels. In other words, virtuality enhanced the effect of task significance on meaningfulness, whereas it weakened the influence of job autonomy on responsibility as well as the influence of feedback on the knowledge of results. Based on this evidence, we advocate that work design theories need to be developed or modified in the highly digitalized context. Second, how individuals cope with virtual work challenges with motivational affordances is another promising research area. Only a few studies have addressed how employees cope with these changes in virtual work (e.g., Cooper & Kurland, 2002). In fact, employees are not passive recipients of changes in work design, but instead can proactively craft their jobs (Wrzesniewski & Dutton, 2001). According to Zhang and Parker’s (2019) hierarchical model of job crafting, individuals engage in job crafting by exerting efforts to seek positive aspects of work (i.e., approach crafting) or to avoid and/or escape from negative aspects of work (i.e., avoidance crafting). Individuals could play an active role in job crafting through managing to discover positive aspects of work (i.e., approach crafting) or avoid and/or escape from negative aspects of work (i.e., avoidance crafting). A recent meta-analytic review (Rudolph et al., 2017) has revealed the positive effects of job crafting on performance and well-being,

418  Handbook of virtual work which suggests that job crafting could be an effective strategy to help individuals thrive in ICT-enabled work. In fact, one early study (Cooper & Kurland, 2002) conducted in the remote working context found that employees crafted the relational aspects of work to achieve a person–job fit. Based upon Wrzesniewski and Dutton’s job crafting framework, current advanced technologies used in virtual work provide fruitful opportunities that are crucial for crafting. Therefore, future research can explore how individuals react to virtual work challenges with motivational affordances. Finally, we encourage scholars and practitioners to understand the digital workplace by combining both the virtual and physical offices. The digitization of the workplace doesn’t mean the death of the physical office; the physical office still has social value (e.g., enabling social contact and collaborating on a project). The future of work might likely be for many a “hybrid,” a location-flexible working arrangement. Thus, it’s important to explore how workers in the virtual office and counterparts in the physical office interact with each other. For example, virtual work practices may influence onsite workers’ psychological experiences. Previous studies including the current chapter have dominantly focused on virtual workers’ perceptions of social isolation. According to Rockmann and Pratt’s (2015) qualitative study, onsite workers reported similar levels of isolation, because the relational nature of onsite work had been changed by virtual work practices. We recommend to break the boundaries between the virtual office and the physical office, thereby obtaining a more comprehensive understanding of work in the current digital era.

ACKNOWLEDGMENTS This study was supported by Australian Research Council Australian Laureate Fellowship (Grant Number: FL160100033), National Natural Science Foundation of China (Grant Number: 72102140), and Shanghai Pujiang Program (21PJC057).

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22. Global multinational organizations and virtual work Miriam Erez, Ella Glikson and Raveh Harush

MULTINATIONAL ORGANIZATIONS, VIRTUAL WORK, AND KNOWLEDGE SHARING Today, over 80,000 multinational organizations (MNOs) operate globally. Their origins and headquarters are primarily in developed countries, while some are based in developing countries (Allès et al., 2018). Globalization and communication technology have enabled the emergence of these multinational organizations that operate in multiple countries, subject to numerous legal systems, cultures, and markets (Walgenbach et al., 2017). MNOs, international mergers and acquisitions, joint ventures, and alliances are becoming the rule rather than the exception, infusing cultural diversity into the workplace and promoting cultural pluralism. Cultural pluralism endorses and acknowledges cultural differences in values and behavioral norms and addresses cultural diversity as a valuable resource (Schachner, 2017). The fast growth and spread of communication technologies have created a virtual global world. Globalization has penetrated all life domains – business, education, art and sciences, environmental issues, health, and well-being. The virtual world consists of “virtual environments in which people experience others as being there with them” and can interact with each other (Schroeder, 2008, p. 2). Technological progress pushes MNOs into new business areas, such as the digital economy (Schwab, 2017), utilizing digital platforms to exploit technology-mediated communication that enables business transactions, alliances, and cooperation across national borders. Digital communication platforms, such as Zoom, Microsoft Teams, Slack, Google Meet, and so on, differ in their characteristics and capabilities while serving various organizational purposes. As a result, these platforms have become a critical success factor in ensuring MNOs’ effective communication, knowledge transfer, and intra-organizational cooperation (Bartlett & Ghoshal, 1992, 1998; Vora & Kostova, 2007). Once an organization crosses geopolitical boundaries, establishing new subsidiaries or acquiring and merging with local organizations, it faces the cultural diversity inherent in the host countries (Schotter et al., 2017). Thus, while technology facilitates organizations’ expansion across the globe, it also forces organizations to embrace the cultural diversity of their workforce. Cultural diversity, however, is a double-edged sword. On the one hand, cultural diversity can lead to misunderstanding and mistrust. However, diversity also increases the pool of knowledge, facilitating the creation of new knowledge necessary for MNOs’ sustainability (Stahl & Tung, 2014). To successfully compete in the complex global environment, MNOs need to dynamically change, innovate and adapt to the complex global environment in which they operate (Chatman et al., 2019). Indeed, the most competitive organizations, such as Amazon, Google, Alibaba, and Rakuten, are the ones that dynamically shape the business environment and make others follow. Unlike local organizations, MNOs face the tension 425

426  Handbook of virtual work between local (“tribalism”) and global (“universalism”) cultures (Naisbitt, 1994). Thus, global operations reflect the paradox of integrating and differentiating simultaneously. Bartlett and Ghoshal (1992) identified four types of MNOs based on the relative dominance of integration versus differentiation: global, multinational, international, and transnational. According to their model, global organizations emphasize global integration but not local responsiveness. In contrast, intensive local responsiveness and weak global integration reflect the multinational organization type. Furthermore, the international organization is low in global integration and local responsiveness. In contrast, transnational organizations have high levels of global integration and local responsiveness. Thus, transnational organizations provide the landscape for interactions among people from multiple cultures, operating with shared global organizational values and at the same time, maintaining their local, national identities (Gelfand et al., 2017). Knowledge is one of the most critical resources for the MNO’s sustained competitive advantage. Some researchers even conceptualized MNOs as social communities specializing in the creation and internal transfer of knowledge (Kawai & Chung, 2019; Kogut & Zander, 1993). Moreover, the centrality of knowledge transfer and knowledge sharing in MNOs highlights the importance played by virtuality in understanding how knowledge is transferred and shared among subsidiaries and headquarters (Duvivier et al., 2019; Michailova & Minbaeva, 2012; Vahtera et al., 2017). Variations in the integration–responsiveness balance reflect the power relations within MNOs and the importance attributed to the available knowledge in different organizational units (see Table 22.1). In high integration–low local responsiveness MNOs, more emphasis is placed on top-down communication and knowledge transfer from headquarters (HQ) to subsidiaries. Organizational rules, policies, and cultural values are being communicated top-down, with less attention to the unique resources available at the foreign subsidiaries. In contrast, the unique knowledge embedded in the local subsidiaries is highly valued in low integration–high responsiveness MNOs, where bottom-up knowledge and information come up from the subsidiaries to HQ. Nevertheless, when the integration level is low, there are no shared standards and policies and no unified organizational culture. The type of high local responsiveness and low integration makes it more difficult to share knowledge. Table 22.1

The integration and responsiveness balance impacts on knowledge flow between HQ and subsidiaries

 

Low Local Responsiveness

High Local Responsiveness

Low Integration

International organization: A few shared

Multinational organization: A few shared

rules and norms; low value assigned to local

rules and norms; high value assigned to

knowledge; difficulties in knowledge sharing

local knowledge; primarily bottom-up

Global organization: High emphasis on

Transnational organization: Shared

creating shared norms and rules; low attention

norms and rules and cultural pluralism;

to local knowledge; mostly top-down

high value assigned to local knowledge;

communication aimed at transferring

top-down and bottom-up knowledge

procedural knowledge to the subsidiaries

sharing, allowing the creation of new

knowledge sharing High Integration

knowledge

Source: Authors’ own.

Global multinational organizations and virtual work  427 The high integration–high responsiveness configuration supports two-way communication – top-down and bottom-up knowledge transfer. Yet, geographic distances and cultural differences between HQ and subsidiaries, and among different subsidiaries, can impede the knowledge-sharing process. Hence, in this context, communication media and interpersonal relationships become crucial in mediating the knowledge-sharing processes and MNOs’ sustainable competitive advantage. Following Bartlett and Ghoshal (1992), the four types of organizations represent two dimensions of low–high integration and low–high local responsiveness, as shown in Table 22.1.

KNOWLEDGE SHARING AND COMMUNICATION MEDIA Knowledge and Knowledge Sharing Knowledge, a source of MNOs’ competitive advantage, is a complex concept for which researchers have provided various definitions and typologies (Duvivier et al., 2019). In contrast to “information” which consists of facts and data, “knowledge” is the result of an internal process undertaken by an individual or a group. This internal process reflects the implicit, tacit knowledge embedded within people’s minds (Nonaka, 1994). Information and explicit knowledge that can be coded, stored, and retrieved for organizational use, even without direct interpersonal interaction such as HR written policies, serve formally organized activities (Szulanski, 2000). Thus, the digitalized organizational knowledge system serves as a conduit for communicating explicit knowledge. In contrast, the tacit knowledge gained via work experience is deeply influenced by individuals’ and groups’ cultural, organizational, and personal experiences (Duvivier et al., 2019). Tacit knowledge cannot be fully transferred or shared without interpersonal communication that enables the shared understanding within a shared knowledge context (Choi & Johanson, 2012). While some researchers use the terms “knowledge transfer” and “knowledge sharing” interchangeably, others emphasize the differences between these forms of knowledge management (Duvivier et al., 2019; Michailova & Minbaeva, 2012). Knowledge transfer suggests a relatively passive role for the target of the transferred knowledge. In contrast, knowledge sharing assumes that the knowledge receiver needs to be active to properly understand and assimilate the new information with the existing knowledge (Tahvanainen et al., 2005). In a culturally diverse context, the active knowledge-sharing process requires a receiver to dis-embed the knowledge from the sender’s context, translate, interpret, and integrate it into the receiver’s cultural framework (Becker-Ritterspach, 2006). Furthermore, researchers suggest that only proper understanding and recombining new knowledge enables learning and new knowledge formation (Argote & Hora, 2017). Thus, tacit knowledge should be shared rather than transferred because sharing requires mutual engagement and direct interpersonal interactions among senders and receivers who access diverse knowledge sources (Harzing et al., 2016). The need for high involvement by both sides in the knowledge-sharing process underscores the criticality of engaging and elevating the importance of interpersonal relationships, mutual liking, and trust (Noorderhaven & Harzing, 2009). Knowledge, defined as a person’s interpretation of information based on values, beliefs, and experiences, depends on the cultural context. Hence, multicultural backgrounds and experiences create a diverse knowledge base (Bender & Fish, 2000). While MNOs have access to

428  Handbook of virtual work more diverse knowledge than local organizations, their success in utilizing the diversity of knowledge depends on the effectiveness of the knowledge-sharing process. Cultural and language gaps could hinder the smooth flow of communication and knowledge sharing, leading to misunderstanding and miscommunication. Thus, cultural diversity is a double-edged sword, potentially bringing access to rich and diverse tacit knowledge yet causing friction related to the difficulty of knowledge sharing (Minbaeva et al., 2021). We suggest that the effectiveness of knowledge sharing depends on the characteristics of the communication media that serve this process. Recognizing the relative advantage of different communication media for interpersonal communication and knowledge transfer has become even more crucial during and post Covid-19, given the intensive use of technology-mediated communication in most organizations across the globe driven by the pandemic. Knowledge sharing and virtual communication media Despite working remotely and being geographically distant from their workplace, today people can synchronously or asynchronously interact while embedded in different social, physical, and cultural contexts (Cramton, 2001). Virtuality is no longer treated as a categorical concept that addresses whether or not a technology serves to mediate communication. Instead, it has become a continuous construct that combines different aspects of technology reliance such as frequency and synchronicity of communication and characteristics of the communicated content (Kirkman & Mathieu, 2005). Currently, all organizations are dependent to a certain extent on technology for communication and knowledge sharing, while the degree of reliance on communication technology varies across organizations. The relationship between virtuality and knowledge sharing is highly complex and may have different impacts on socio-emotional and performance outcomes at the individual, group, and organizational levels (Argote & Hora, 2017; Hajro et al., 2017; Reiche et al., 2019). The complexity of this relationship is evident in the extensive research that addressed this topic over the last thirty years in the areas of international business, remote work, virtual teams, and technology-mediated communication (Kirkman et al., 2013; Marlow et al., 2018; Maynard et al., 2019). Only recently, however, have researchers started to address the aspects of interpersonal relationships and familiarity as essential factors in the process of knowledge sharing (Eisenberg & Mattarelli, 2017; Fang & Chang, 2014; Glikson & Erez, 2020; Hinds & Cramton, 2013; Maynard & Gilson, 2021). Level of virtuality and knowledge sharing in MNOs Dependence on technology-mediated communication, that is, virtual work, is one of the main characteristics of MNOs. MNOs are often defined as goal-oriented enterprises composed of multiple members who reside in geographically dispersed locations and use technology to communicate and coordinate the fulfillment of a defined objective or task (Workman, 2005). Virtual work is necessary for MNOs to operate across geographical locations. Virtuality, however, is also considered a source of many challenges such as potential misunderstanding, conflicts, and mistrust (Gibson & Gibbs, 2006; Von Glinow et al., 2004). Whether communication processes enable an entirely virtual organization to overcome such challenges is the question we plan to answer via the case study of GitLab, a wholly virtual MNO. During the pre-pandemic years, MNOs made a great effort to ensure interpersonal face-to-face communication between HQ representatives and subsidiaries (Hinds & Cramton, 2013). This effort is evident in the significant investments by MNOs in different types of inter-

Global multinational organizations and virtual work  429 national assignments, such as expatriation (moving from the HQ to a subsidiary), in-patriation, and repatriation (moving from a subsidiary to an HQ), as well as short-term traveling (Collings et al., 2010; Harzing et al., 2016; Tahvanainen et al., 2005). Despite the association of international assignments with significant organizational, financial, and personal costs (Baruch et al., 2016), their benefits for knowledge transfer across different MNO units were irreplaceable (Han & Beyerlein, 2016; Jang, 2017; Taras et al., 2019). Specifically, co-location and direct interactions of expatriates with host-country employees were essential for facilitating a shared frame of reference necessary for the HQ–subsidiaries’ knowledge sharing (Gonzalez & Chakraborty, 2014). In addition, face-to-face meetings enabled the host unit’s absorptive capacity by elaborating on the transferred knowledge, explaining its value for commercial ends (Harzing et al., 2016; Jang, 2017). Face-to-face communication is considered the most fertile communication channel, being the richest in the number of contextual cues transferred at a given time (Daft & Lengel, 1986; Dennis et al., 2008; Kirkman & Mathieu, 2005), including in the context of cross-cultural knowledge sharing. Under this assumption, videoconferences and phone calls are richer and thus more suitable for bridging cultural differences than lean text-based communication. However, most studies that demonstrated the positive effect of rich media on team processes and performance, also included unmediated, face-to-face interactions (see Kirkman et al., 2013). In contrast to “the richer the media – the better” approach, scholars who focused on the linguistic aspects of knowledge sharing within MNOs found that non-native English speakers struggle with communicating via the rich media much more than via the lean media. For example, researchers found that the use of lean media, compared with richer media in European MNOs, reduced social categorization of non-native English language speakers and reduced stress and discomfort. (Klitmøller et al., 2015; Klitmøller & Lauring, 2013). Other researchers also found the relevance of English language proficiency for media choices and impact (Eisenberg et al., 2021; Shachaf, 2005; Tenzer & Pudelko, 2016, 2017). While early work considered face-to-face and rich communication media an effective way for sharing tacit knowledge across cultures, we suggest this claim is not always valid. Instead, we suggest that organizations providing global employees with a shared context, even if this context is virtual, allow for effective knowledge sharing and knowledge creation via lean media. To support our claim, we present the example of an entirely virtual organization – GitLab. GitLab is a web-based DevOps platform that enables professionals to perform all the tasks in a project, from project planning and source code management to monitoring and security.1 As a multinational software company, GitLab works “all remote” at the scale of more than 1000 employees located in more than 60 countries. GitLab has no physical office or HQ (except for postal and legal purposes). Employees at all hierarchical levels, including C-level managers, can work from any place they choose. Any step of a GitLab employee (e.g., hiring, onboarding, and firing) is performed remotely, except for a yearly company-wide gathering (Choudhury et al., 2020). One of the critical factors facilitating GitLab’s operations is reliance on asynchronous, lean communication. Importantly, the thinner the communication media, the less contextual information it transmits. For example, phone calls can share more contextual cues than text messages, which lack non-verbal cues such as tone of voice, a pause between words, and more (Neeley, 2021). The non-verbal cues provide a context in which the knowledge

430  Handbook of virtual work can be understood, interpreted, and integrated. Therefore, there is a common belief that the richer the interaction media, the more contextual cues are present, and hence, the better the knowledge-sharing process will be (Marlow et al., 2018; Tenzer & Pudelko, 2016). Nevertheless, in GitLab, the emphasis is on asynchronous lean communication that allows employees to work anytime they want from any possible place. So how does GitLab resolve the problem of sharing tacit knowledge? First, it provides a shared virtual organizational context. Second, it works toward shared meaning by using one platform through which all the communication is conducted, plus documenting all possible knowledge at one source that includes all company policies (Choudhury et al., 2020). Hence, GitLab designed its platform in explicit terms that allow all employees to use the same platform consisting of a single coded knowledge base accessible from anywhere in the world with no coding–decoding gap. In other words, GitLab implements one common technology-based language and culture for all its globally distributed employees to overcome language and cultural differences and miscommunication. It creates a common organizational work context for all employees across the organizational hierarchy and locations, reducing the conceptual gaps driven by different cultural and linguistic contexts. Finally, by eliminating any physical presence, GitLab avoids the difficulties related to knowledge sharing between HQ and subsidiaries or across various subsidiaries. GitLab’s virtual matrix global structure encourages employees to create ingroups based on work tasks and requirements rather than geographical co-location. As such, GitLab represents a global organization, emphasizing high integration, and its local responsiveness becomes less relevant. Furthermore, a public and open handbook shared by anyone within and outside the company enables integration. The handbook exhaustively documents its formal organizational structure and processes, and is developed, maintained, and edited as if it were a code repository (Choudhury et al., 2020). In addition, the shared platform enables the effective use of lean media for daily work. Benefits of lean media for knowledge sharing To better understand the possible benefits of lean media and how it may work in geographically dispersed organizations, we turn to the Media Synchronicity Theory (MST) that emphasizes three types of media capabilities: parallelism, rehearsability, and reprocessability (Dennis et al., 2008). Parallelism stands for the number of simultaneous transmissions that can take place concurrently. For instance, video interactions (such as via Zoom) are relatively low in parallelism, as only one conversation at a time can take place, and participants can speak only in a sequential manner. In contrast, platforms for instant messaging, such as Slack, used by GitLab, allow asynchronous parallel communication channels. Moreover, parallelism facilitates a simultaneous spread of knowledge to the entire organization and the public. For instance, GitLab’s use of its handbook allows everyone access to the same information simultaneously. Thus, high parallelism improves work productivity by transferring the same message only once to different recipients. Furthermore, multiple groups constantly update this handbook to reflect the most up-to-date organizational knowledge, essential in creating and sustaining GitLab’s corporate culture and thus facilitating employees’ identification with the organization. Rehearsability is the extent to which the media enables the sender to rehearse or fine-tune a message during encoding before sending. Media that support rehearsability allow the sender to carefully craft a statement before its transmission to ensure that the intended meaning

Global multinational organizations and virtual work  431 is expressed precisely, thus improving a recipient’s subsequent decoding and information processing. Rehearsability is less important for individuals with shared experiences or shared mental models as they can communicate using standard protocols or known symbols on a familiar subject. Rehearsability benefits culturally distant team members who differ in language proficiency (Neeley, 2021). It enables the sender to consider the context and possible interpretations of the message and encode it for a potential recipient, enabling more accurate decoding and understanding. Thus, rehearsability helps overcome language deficiency in newly established collaborations, facilitating perceived psychological proximity (Eisenberg et al., 2021). GitLab embraces rehearsability by intentionally choosing to rely on asynchronous lean communication. It encourages independence and transparency, allowing all employees to communicate whenever they want. The asynchrony provides the time needed to process the information from culturally and lingually different others and work on phrasing one’s queries or answers. Finally, reprocessability enables reexamining a message while decoding the communicated message or after the event. Reprocessability allows one to decode and revisit previous statements for further clarification. It enables the creation of a shared record as a knowledge base for shared understanding and assisting in quickly extracting important information. In addition, reprocessability enables building organizational memory, which retains the organizational knowledge that would otherwise get lost due to the high mobility of the workforce. Yet, the efficiency of reprocessability depends on advanced digital technology for coding, storing, and data mining. GitLab implements reprocessability by recording and sharing text-based interactions on the shared instant messaging platform and maintaining its online handbook of all organizational protocols and processes. Summarizing the benefits of lean media for knowledge sharing in organizations in general and particularly in MNOs, we highlight that by minimizing the number of contextual cues during an interpersonal interaction, lean media do not necessarily limit the transfer of tacit knowledge. Instead, they provide a common ground that serves as an inclusive organizational context unifying culturally diverse and globally dispersed employees and facilitating the creation of a global organizational culture. The additional contextual aspects accompanying knowledge sharing, such as motivation, could be acquired via extensive and meaningful social interactions, facilitating interpersonal familiarity and trust. At GitLab, the daily work occurs via lean asynchronous media. GitLab does, however, invest great effort in organizing special synchronous events such as its annual face-to-face meetings. Lean media and social knowledge Social and factual knowledge in organizations is intertwined. Nevertheless, each serves a different function and requires distinct media capabilities. Face-to-face social interactions and co-location enable social knowledge sharing, where employees benefit from getting to know each other and colleagues’ respective work contexts, intensifying effective knowledge sharing (Noorderhaven & Harzing, 2009). Furthermore, co-location assists in adjusting knowledge translation and interpretation for more successful sharing across cultures (Hinds & Cramton, 2013). Extant research has supported the importance of face-to-face interactions for sharing tacit knowledge across cultural and lingual boundaries (Coman et al., 2019; Harzing et al., 2016; Hinds & Cramton, 2013). There are, however, inconsistent findings concerning the effect of

432  Handbook of virtual work lean communication on interpersonal relationship building and implicit knowledge sharing. Most research has shown that lean communication serves explicit knowledge transfer but not implicit, tacit knowledge sharing (Duvivier et al., 2019). For instance, in a study by Yang et al. (2021) conducted in Microsoft company during 2020–2021, when Covid-19 blocked face-to-face communication in MNOs and even within local subsidiaries, researchers found that the collaboration network of information workers became more static and siloed, with fewer connections across networks. They also found a reduction in scheduled meetings and in-person communication. These changes resulted in less knowledge transfer and impaired complex information transfer, cross-fertilization, and creative idea generation. Furthermore, remote work attenuated the likelihood of forming and maintaining a shared organizational culture, a sense of belongingness, and cross-understanding (Yang et al., 2021). On the other hand, recent research suggests that lean communication helps overcome surface-level ethnic origin, age, and gender biases (Guillaume et al., 2012; Van Knippenberg & Mell, 2016). Furthermore, lean communication helps attenuate language barriers and create psychological proximity among culturally diverse employees in geographically remote locations (Choudhury et al., 2020; Eisenberg et al., 2021; Shachaf, 2005). It is well known that people can build close friendships and positive relationships even when miles apart (Maynard & Gilson, 2021). People may feel closer to a geographically distant person than a co-located coworker. Wilson et al. (2008) explained this paradox of far but close by suggesting that it is not the actual geographical distance that is essential but the subjective perception of proximity (Wilson et al., 2008). Indeed, perceived proximity facilitates trust and performance in global virtual teams (Eisenberg et al., 2021). Nevertheless, we note that relationships built via lean media do not always occur based on social knowledge and interpersonal familiarity. People tend to fill in the lack of social knowledge from their own life experience or pre-existing cognitive schemas, such as stereotypes, creating an inaccurate version of another person (Antheunis et al., 2019). Without face-to-face interaction, there is a limited chance that this inaccurate social knowledge will be updated or changed (Johri, 2012). Such situations might hinder the development of interpersonal relationships and tacit knowledge sharing. Cultural diversity adds another layer to geographical distance, making the development of social ties between people from different cultures even more vital for successful knowledge sharing (Gibson & Gibbs, 2006; Von Glinow et al., 2004). It is important to note that creating interpersonal relationships among culturally diverse collaborators via lean media vs. face-to-face communication is possible but takes more time and requires an active exchange of personal information (Glikson & Erez, 2020). Allocating time and virtual space for exchanging personal information and establishing familiarity and relationships is vital for facilitating mutual trust and higher productivity of virtual multicultural collaboration (Narayan et al., 2021). Therefore, we suggest that MNOs encourage their employees to get to know each other and build interpersonal relationships via communication technology rather than simply using it for task-related communication.

Global multinational organizations and virtual work  433

THE JOINT IMPACT OF CULTURE AND COMMUNICATION MEDIA ON MNOS’ KNOWLEDGE SHARING Cultural Diversity Following a visit at Call Centers in India, where Indian employees served customers from all over the world, Thomas Friedman coined the term: “The Word is Flat” (Friedman, 2005). The flat world reflects the accelerating process of globalization, defined as a growing economic interdependence among countries, as reflected in the increased cross-border flow of four types of entities: goods, services, capital, and know-how (Gupta & Govindarajan, 2001). The concept also implies lowering the cultural, political, and economic fences among countries (Petricevic & Teece, 2019). Nonetheless, when some regulations are removed, making global integration more feasible, other factors balance this process by emphasizing local distinctiveness (Prud’homme, 2019). MNO subsidiaries are embedded in diverse cultures that convey different meaning systems of values, norms, and practices (Erez & Earley, 1993; Gelfand et al., 2008). Culture shapes the individual self, as individuals internalize cultural values and norms through socialization (Erez & Earley, 1993; Leung & Morris, 2014; Markus & Kitayama, 1991). Cultural values and behavioral norms emerge at different levels of analysis (Erez & Gati, 2004). Most research has focused on cultures at the national (Hofstede, 2011; House et al., 2004) and organizational levels (Chatman & O’Reilly, 2016), looking at similarities and differences across nations and organizations. Culture, however, also emerges at the global, macro-level. A globally shared meaning system enables individuals, groups, and organizations to operate in the global work context (Erez, 2010). Hence, the world is flat and rocky at the same time. The “flatness” represents the free flow of products, technology, capital, and knowledge, whereas the “rockiness” represents geopolitical and cultural boundaries (Miron-Spektor & Erez, 2017). The “flat– rocky globe” paradox has shaped the tension between MNOs’ global integration and local responsiveness and challenges knowledge sharing between HQ and subsidiaries and among subsidiaries. Subsidiaries differ in their cultures but also their operations and functions. Cultures and their operations and functions are often interrelated. For example, national cultures differ in their openness to accepting diverse knowledge and creating new knowledge (Gelfand et al., 2011). In addition, cultural values such as low collectivism, low power distance, and low uncertainty avoidance positively correlate with innovation (Erez & Nouri, 2010; Gelfand et al., 2011; Taras et al., 2011). Therefore, cultures characterized by these values attract R&D centers. Israel is one example of the cultural value profile that has drawn almost 400 multinational corporations to set up their R&D centers there (Yeshua-Katz & Efrat-Treister, 2020). National cultures and their nested subsidiaries also differ in their level of tightness– looseness, reflecting the tolerance for deviation from the norm (Gelfand, 2018; Gelfand et al., 2011). Subsidiaries in tight cultures are likely to follow the rules, whereas loose cultures nourish innovation and entrepreneurship. Differences across cultures in the spread of Covid-19 exemplify the flat–rocky paradox. The Covid-19 pandemic spread all around the globe, testifying the flatness of the world. However, the spread and intensity of cases of Covid-19 and deaths varied across cultures. Gelfand et al. (2021) demonstrated that, unlike tight cultures, more cases of Covid-19 and higher death rates occurred in loose cultures where people do not

434  Handbook of virtual work strictly obey the rules of wearing masks, social distancing, and getting vaccinated (Gelfand et al., 2021). The distinction between tight and loose cultures adds to the challenge of cross-cultural communication. For example, employees in high power distance cultures expect their boss to instruct them what to do whereas employees in low power distance cultures wish to work autonomously. Hence, MNO managers face the challenge of adapting their leadership behavior to the cultural variations across subsidiaries. Virtual Communication Factors Affecting Socio-Cultural Identities in MNOs and Knowledge Sharing Social identity theory suggests that MNOs integrate their culturally diverse and geographically dispersed workforce toward one identified organization with its unique brand name and organizational culture, differentiating it from other MNOs. However, the lack of a shared physical context and shared experience, might hinder the emergence of shared mental models even in a culturally homogeneous organization, making it difficult to establish a shared organizational identity (Wiesenfeld et al., 2001). In addition, hybrid remote work, where some employees work in a shared physical space and others work remotely, creates sub-groups and social categorization of “us” vs. “them”, hindering shared knowledge and social exchange (Cheshin et al., 2013; Fiol & O’Connor, 2005). Social categorization processes can be even more disruptive in the presence of cultural fault lines, leading to conflicts and disagreements (Harush et al., 2018; Stahl et al., 2010). A potential way to ease the social categorization process in culturally diverse MNOs lies in developing shared social identities of the MNOs’ members. Employees who identify strongly with their MNO develop a dual, local, and global identity. A local identity reflects the sense of belongingness to the local, home-country community. In parallel, a global identity emerges as a person develops a sense of belongingness to the global work community (Erez et al., 2013; Harush et al., 2018). Global identity enables adaptation to global norms, facilitates cross-understanding when communicating with peers and subordinates from other cultures, and reduce relational conflicts (Glikson & Erez, 2013; Harush et al., 2018; Lisak et al., 2016; Lisak & Harush, 2021). Accordingly, global leaders with high global–local identities are more effective than leaders with low levels of global identity (Lisak & Harush, 2021). The former can more easily instill a sense of shared global identity while at the same time respecting the local cultural diversity. It has been shown that virtual, culturally diverse teams elect team leaders who score high on the dual, global–local identity (Lisak & Erez, 2015). Thus, MNO leaders with high global identities strengthen followers’ sense of belongingness to the global teams, facilitating knowledge sharing, trust-building (Crisp & Jarvenpaa, 2013; Culley & Madhavan, 2013; Daim et al., 2012; Polzer et al., 2006), and interpersonal familiarity (Glikson & Erez, 2020; Maynard et al., 2019; Maynard & Gilson, 2021) as well as reducing social ostracism (Fiset & Bhave, 2021; Hinds et al., 2013). Global identity emerges when individuals get exposed to culturally diverse and global work environments with sufficient levels of interpersonal trust (Erez et al., 2013). In addition, cultural intelligence – the ability to function effectively in intercultural contexts – enables adaptation to the global virtual work context and overcoming misunderstandings in virtual communication (Ang et al., 2015). As global identity, cultural intelligence develops under work contexts that provide opportunities to experience working in culturally diverse global team environments, with sufficient levels of interpersonal trust (Erez et al., 2013). Thus, to

Global multinational organizations and virtual work  435 enhance MNO employees’ global identity and other global characteristics, managers should facilitate trust-building by designing the communication environment to foster positive interpersonal relationships and identify and control sources of mistrust that hinder the acquisition of a global identity. For example, encouraging the exchange of personal information during the initial phase of team building could facilitate the emergence of a psychologically safe communication climate (Glikson & Erez, 2020). Organizations can also use lean media to reduce categorization and foster higher level of psychological proximity, even when the team members differ in their cultural and lingual backgrounds (Eisenberg et al., 2021). In addition, we suggest that providing specific and clear tasks and communication instructions minimizes misunderstanding and miscoordination (Erez et al., 2013). Research has also shown that organizations–employees’ reciprocal communication strengthen employees’ sense of support and positive emotions, in particular during changes, such as the pandemic (Sun et al., 2021). Thus, a significant challenge in facilitating knowledge sharing is establishing trust and positive interpersonal relationships among culturally diverse MNO employees who work virtually. This challenge requires special organizational attention to nurture the organizational climate that enables the development of MNOs’ corporate identity and the emergence of employees’ essential global characteristics of global identity and cultural intelligence. These global characteristics enhance positive interpersonal relationships, trust, collaboration, and knowledge sharing.

SUMMARY AND DIRECTIONS FOR FUTURE RESEARCH The present chapter conveys the complexity and paradoxes embedded in managing global organizations concerning virtuality and knowledge sharing. While in the past, face-to-face communication was considered by MNOs as the only appropriate way to share tacit knowledge across cultures, we demonstrated a different approach via the case of GitLab (Choudhury et al., 2020). Building upon Media Synchronicity Theory (Dennis et al., 2008), we addressed the benefit of lean media for cross-cultural collaboration in virtual multicultural MNOs. As virtual communication in MNOs becomes more prevalent due to Covid-19 restrictions, it becomes even more important to emphasize the essential factors for virtual multicultural work to be productive. Some of these factors are structural and could be challenging to achieve, such as reducing the separation and power differences between HQ and subsidiaries and minimizing the association between hierarchical relationships with a physical location in a specific country. Others are functional factors, such as how organizational rules and procedures are integrated, shared, and managed. In addition, we emphasize the importance of addressing the social aspects of virtual work, providing employees with time and opportunities to establish personal relationships, and encouraging them to do so. Facilitating the strength of a global identity and global mindset among leaders and employees might further assist in establishing trust and positive relationships, which are essential for knowledge sharing via any communication media or organizational structure. As technology keeps advancing, rich media such as virtual reality (VR) will enable a sense of shared virtual space, providing a different way to establish personal relationships and further improve interpersonal familiarity (Shumaker & Lackey, 2015). When VR technology matures for corporate use, it could become a valuable tool for creating and supporting interpersonal relationships. Nevertheless, the quantity and quality of the shared context will still

436  Handbook of virtual work be constrained unless a significant amount of working space moves to VR or further on to the metaverse. We call for future research to further examine the effectiveness of communication media for different types of knowledge sharing within different kinds of MNOs, addressing structural, functional, and relational organizational aspects. There is a growing need to learn more about the benefits and limitations of lean and rich media regarding cross-cultural communication and the cultural differences related to knowledge sharing and the use of communication media. Future research should focus on individual characteristics that might facilitate knowledge sharing, learning, leadership, and trust in MNOs, such as identifying with the virtual, global organization. Finally, future research should further explore the benefits of using new technological tools, such as VR, to strengthen the sense of a shared physical work context and new visualization methods for effective knowledge transfer and knowledge sharing.

NOTE 1.

https://​about​.gitlab​.com/​.

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Global multinational organizations and virtual work  439 Hinds, P. J., Neeley, T. B., & Cramton, C. D. (2013). Language as a lightning rod: Power contests, emotion regulation, and subgroup dynamics in global teams. Journal of International Business Studies, 45(5), 536–561. https://​doi​.org/​https://​doi​.org/​10​.1057/​jibs​.2013​.62 Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture, 2(1). https://​doi​.org/​10​.9707/​2307​-0919​.1014 House, R. J., Hanges, P. J., Javidan, M., Dorfman, P. W., & Gupta, V. (2004). Culture, leadership, and organizations. The GLOBE Study of 62 Societies, 848. http://​books​.google​.de/​books​?id​ =​_KsXEJQg9pkC​&​dq​=​intitle:​Culture+​Leadership+​I+​Organizations​&​hl​=​&​cd​=​1​&​source​=​gbs​_api Jang, S. (2017). Cultural brokerage and creative performance in multicultural teams. Organization Science, 28(6), 993–1009. https://​doi​.org/​10​.1287/​orsc​.2017​.1162 Johri, A. (2012). From a distance: Impression formation and impression accuracy among geographically distributed coworkers. Computers in Human Behavior, 28(6), 1997–2006. https://​doi​.org/​10​.1016/​j​ .chb​.2012​.06​.038 Kawai, N., & Chung, C. (2019). Expatriate utilization, subsidiary knowledge creation and performance: The moderating role of subsidiary strategic context. Journal of World Business, 54(1), 24–36. https://​ doi​.org/​10​.1016/​J​.JWB​.2018​.09​.003 Kirkman, B. L., Cordery, J. L., Mathieu, J., Rosen, B., & Kukenberger, M. (2013). Global organizational communities of practice: The effects of nationality diversity, psychological safety, and media richness on community performance. Human Relations, 66(3), 333–362. https://​doi​.org/​10​.1177/​ 0018726712464076 Kirkman, B. L., & Mathieu, J. E. (2005). The dimensions and antecedents of team virtuality. Journal of Management, 31(5), 700–718. https://​doi​.org/​10​.1177/​0149206305279113 Klitmøller, A., & Lauring, J. (2013). When global virtual teams share knowledge: Media richness, cultural difference and language commonality. Journal of World Business, 48(3), 398–406. https://​doi​ .org/​https://​doi​.org/​10​.1016/​j​.jwb​.2012​.07​.023 Klitmøller, A., Schneider, S. C., & Jonsen, K. (2015). Speaking of global virtual teams: Language differences, social categorization and media choice. Personnel Review, 44(2), 270–285. Kogut, B., & Zander, U. (1993). Knowledge of the firm and the evolutionary theory of the multinational corporation. Journal of International Business Studies, 24(4), 625–645. https://​doi​.org/​10​.1057/​ PALGRAVE​.JIBS​.8490248 Leung, K., & Morris, M. W. (2014). Values, schemas, and norms in the culture–behavior nexus: A situated dynamics framework. Journal of International Business Studies, 46(9), 1–23. https://​doi​.org/​10​ .1057/​jibs​.2014​.66 Lisak, A., & Erez, M. (2015). Leadership emergence in multicultural teams: The power of global characteristics. Journal of World Business, 50(1), 3–14. https://​doi​.org/​10​.1016/​J​.JWB​.2014​.01​.002 Lisak, A., Erez, M., Sui, Y., & Lee, C. (2016). The positive role of global leaders in enhancing multicultural team innovation. Journal of International Business Studies, 47(6), 655–673. Lisak, A., & Harush, R. (2021). Correction: Global and local identities on the balance scale: Predicting transformational leadership and effectiveness in multicultural teams. PLOS ONE, 16(9), e0258025. https://​doi​.org/​10​.1371/​JOURNAL​.PONE​.0258025 Markus, H. R., & Kitayama, S. (1991). Culture and the self: Implications for cognition, emotion, and motivation. Psychological Review, 98(2), 224–253. https://​doi​.org/​10​.1037/​0033​-295X​.98​.2​.224 Marlow, S. L., Lacerenza, C. N., Paoletti, J., Burke, C. S., & Salas, E. (2018). Does team communication represent a one-size-fits-all approach?: A meta-analysis of team communication and performance. Organizational Behavior and Human Decision Processes, 144. https://​doi​.org/​10​.1016/​j​.obhdp​.2017​ .08​.001 Maynard, M. T., & Gilson, L. L. (2021). Getting to know you: The importance of familiarity in virtual teams. Organizational Dynamics, 50(1), 100844. https://​doi​.org/​10​.1016/​J​.ORGDYN​.2021​.100844 Maynard, M. T., Mathieu, J. E., Gilson, L. L., R. Sanchez, D., & Dean, M. D. (2019). Do I really know you and does it matter? Unpacking the relationship between familiarity and information elaboration in global virtual teams. Group and Organization Management, 44(1). https://​doi​.org/​10​.1177/​ 1059601118785842 Michailova, S., & Minbaeva, D. B. (2012). Organizational values and knowledge sharing in multinational corporations: The Danisco case. International Business Review, 21(1), 59–70. https://​doi​.org/​ 10​.1016/​J​.IBUSREV​.2010​.11​.006

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23. Orchestrating dynamic value networks: interface-focused pathways to enhance coordination and learning Sanjay Gosain, Arvind Malhotra and Omar A. El Sawy

Recent years have seen an increasing emphasis on value co-creation using multi-firm networks as information and communication technologies facilitate connectivity across firms (Nenonen et al. 2019; Setia et al. 2015). Value co-creation requires firms to rely on specialized partners for complementary activities and recognize these partnerships as a source of relational rents and competitive advantage (Deken et al. 2018; Dyer and Singh 1998; Dyer et al. 2018). Thus, the locus of value creation is “no longer within the boundaries of a single firm, but occurs instead at the nexus of relationships between a variety of parties that contribute to the production function” (Schilling and Steensma 2001: 1149). Disruptive environmental shifts create a greater need for flexibility and the ability to continually reconfigure firm competencies (Bakker 2016; Eisenhardt et al. 2010; Rindova et al. 2012). In conjunction, the need for being responsive to change and sudden shifts in value creation requires firms to realize relational rents1 through synergistic combinations of resources and knowledge with current partners and external stakeholders (Altman et al. 2021), and yet be mindful of environmental change and discontinuities that are “competence destroying” (Cozzolino and Rothaermal 2018; McKinley 2020). We propose that the mode of inter-firm organizing enabled by newer information technologies deployed within and between firms will allow firms to be flexible and learn from partners to sense and respond to environmental shifts. There is evidence that traditional tiered and structurally inflexible value chains are now being reconfigured to form complex value networks.2 These networks – also labeled “ecosystems” – generate economic value for all firms by enhancing their strategic flexibility and knowledge through complex inter-organizational relationships and open information exchanges with a variety of partners who contribute specialized competencies (Fjeldstad and Snow 2018; Kullak et al. 2021). Value creation in these networks is based on the anticipation of customer needs and engaging with appropriate partners to provide those needs (Agarwal and Selen 2009; Malhotra et al. 2007; Rai and Tang 2010). Exploiting the knowledge of a network requires a focal firm to be equipped with capabilities that enable it to manage external relationships and therefore to leverage variety (typical of markets) while using authority to implement change (typical of hierarchies) (Kogut 2000). However, developing the capability to coordinate and share knowledge with changing partners is not trivial. Firms face a tradeoff between reach and richness in terms of their external partnerships (Fjeldstad and Haanœs 2001). Therefore, firms can either maintain short-term transactional contracts with a large number of suppliers, or long-term relational contracts with a few suppliers (Madhok 2002: 544). However, the emergence of Application Programming Interfaces (APIs) – defined as protocols and standards that allow applications across enterprises to exchange data and 442

Orchestrating dynamic value networks  443 functionality and create value based on information easily and securely3 – allowing firms to achieve both reach (efficient coordination with a greater range of partners) as well as richness (collaborative creation of new knowledge-based value). In essence, APIs allow the creation of inter-organizational platforms for value creation (Hein et al. 2019; Wulf and Blohm 2020). An example of the use of APIs to create value creation platforms is AccuWeather. AccuWeather uses APIs to share weather-related data with partners engaged in the development of connected cars, smart homes, apps, and so on,4 which then allows the partners to use the data to create value for their products and offerings. Further, highly developed IT infrastructure capabilities based on emerging technologies like Blockchain are enabling firms to exchange information and create new competencies with other firms in the value network (Lumineau et al. 2021; Malhotra et al. 2021). Emerging technologies like APIs and Blockchain will allow value networks and ecosystems to create value for customers by mobilizing knowledge resources through loosely coupled relationships with numerous existing partners and also looking out for emerging potential partners (Malhotra et al. 2021). The leveraging of inter-firm value linkages is recognized as a distinctive organizational capability conferring competitive benefits (Hoang 2001; Pagani 2013), but there is limited understanding of how this capability may be achieved. We outline interface-focused organizing mechanisms (pathways) to support value creation in conjunction with changing network partners. These organizing mechanisms serve not only to align transactions but also to serve as vehicles to manage skills and knowledge (Madhok 2002). We, therefore, focus on how dynamic5 value networks may be supported by IT to meet short-term and longer-term goals related to both dynamic coordination and knowledge creation for innovation (Agarwal and Selen 2009; de Reuver and Bouwman 2012). The realization of these outcomes ultimately enables enterprises to achieve efficiency and adaptability simultaneously (Weigelt and Sarkar 2012). We aim to make three distinctive contributions. The first is to outline a prescriptive model of organizing and managing inter-firm partnerships, labeled orchestration, that manages the interplay between short-term and long-term performance goals. Second, we detail two pathways to achieve the desired outcomes. The first pathway – the interface structuring pathway – relies on the management of inter-enterprise processes and related information exchange that reduce coordination needs. The second pathway – the interface minding pathway – relies on augmenting information processing to make enterprises mindful and efficient processors of information exchanged in the value network. Third, we provide implications for the design of information systems to support orchestration. In the next section, we discuss the orchestration approach to organizing and point to its dialectical nature. Finally, we discuss the implications of the ideas we put forth.

ORCHESTRATION APPROACH TO ORGANIZING As firms move away from vertical integration towards exploiting the competencies of other firms, they can engage their partners in arms’ length contracting, relational contracting (quasi-integration), or loosely integrated configurations (orchestration). Table 23.1 illustrates the characteristics of the three organizing approaches. Firms can also deploy dedicated IT platforms to connect their processes with business partners – in what has been labeled as electronic

444  Handbook of virtual work Table 23.1

Approaches to coordination of competencies

 

Arm’s Length Contracting

Coupling between value

Minimal – prices as coordinating Tight coupling

chain enterprises

mechanisms

Inter-enterprise business

Separable technological and

Quasi Integration

Orchestration Loose coupling

Dedicated inter-enterprise linkages Manage specificity

processes and information functional systems with low

– high specificity

sharing

levels of interdependence

Highly structured interface

Value chain structure

Many partners with similar

Few partners with deep

A large number of partners

products

relationships

with frequent restructuring;

Network structure embeddedness

rapid relationship building and

Semi-structures

processes

termination Partial embeddedness Performance focus

Efficiency in the execution of

Transaction efficiency

Value network flexibility and

routine tasks

Deepen existing relationships

collective knowledge creation potential

Note: Dyer and Singh (1998) characterize the arm’s length and quasi-integration (relational) approaches. Source: Authors’ own.

quasi-integration or virtual integration (Mallapragada et al. 2015; Nazir and Pinsonneault 2021). The focus of quasi-integration is on guarding against problems of opportunism and maladaptation related to physical assets (Masten et al. 1989; Subramani and Venkatraman 2003). In contrast to formal contractual mechanisms, the orchestration approach is focused on effective information processing, such that coordination and learning goals may be achieved without excessive dependencies on other firms. Achieving integration among interdependent organizations is necessary to be able to adapt (Malhotra et al. 2007) but there are downsides to overdoing it. Brown et al. (2002) contend that most companies cling to a managerial preference for controlling their activities tightly even if they contract out value-creating activities. However, managing processes loosely is expected to lead to strategic flexibility – as firms can make operational changes quickly and respond to environmental changes (Sawhney 2003). Also, the ability to connect easily to diverse partners with unique knowledge bases leads to a better understanding of customer needs and allows the blending of knowledge required for designing offerings to meet those needs. Enterprises have to open their boundaries to new ideas from outside to prevent rigidity, increase inventive serendipity, and benchmark their inventive capabilities against competitors and other external agencies while guarding against the rigidity of close ties with a limited set of partners. Distinctive network-based competence is derived from an organizations’ ability to interact with other organizations to share information and create new knowledge (Lavie 2006; Lipparini et al. 2014). In the IT industry, for instance, established hardware manufacturers, wholesale distributors and retailers frequently forge opportunistic collaborations to craft product introduction programs for innovative product lines introduced by new software companies. While the coordination of competence in dynamic value networks has been shown to lead to superior performance and financial results (Hinterhuber 2002), past research has not addressed the issue of how this coordination with changing partners can be accomplished. We

Orchestrating dynamic value networks  445 suggest orchestration as a mode of organizing to achieve coordination with and learning from evolving and emerging partnerships. Orchestration refers to loosely coupled patterns of exchanges among enterprises to integrate value creation activities (Nambisan and Sawhney 2011; Perks et al. 2017 ). The orchestration approach is directed towards the process interfaces among enterprises. In line with this, we propose ways of creating flexible yet efficient linkages, enabling orchestration goals to be realized. This requires a view of orchestration as a dialectic elaborated upon next. Orchestration as Dialectic Reflecting on the tensions between different forces that organizations face, Poole and van de Ven (1989) remark on the need for organizational theories that take into account stability and change simultaneously instead of considering them either/or. In response, researchers have suggested ways of creating “ambidextrous” organizations, which manage both stability and change (e.g., O’Reilly and Tushman 2004). We propose that the orchestration of inter-firm partnering may be a way to achieve ambidexterity. Orchestration is a state of tension. It requires a careful balancing act between the adaptation to the current business environment and adaptability for the future. Weick (1979) points to the expected tradeoff between adaptation and adaptability. Orchestration differs from traditional organizational network models in its fluidity over time, with engagement and disengagement of network partners – from which the capability to respond to change is derived. Several firms come together forming value-network connections within a potential network as an opportunity arises, then disband to move on to a different arrangement as opportunities shift in the competencies that are needed. While this kind of dynamic partnering may be more prominent in knowledge-intensive or creative contexts today, it is likely that increasing competitiveness will create the need for high levels of sensitivity to customer needs requiring firms to partner and co-produce customized offerings. In Prato (Italy), for instance, textile merchants have been operating in a large number of small groups that cohere into a purposeful, productive, and very adaptive system (Haeckel 1999). In the U.S., Cisco has disaggregated its value creation activity but still is loosely engaged – through information sharing processes – with the manufacturers, subcontractors, resource planners, and other companies on which it depends for value creation (Häcki and Lighton 2001). While others have introduced other network forms (i.e., cellular, holonic, platform, digital platforms, etc.), these focus mainly on the organization rather than the organizing (Ciborra 1996; Mathews 1996; Miles et al. 1997; Rai et al. 2019). We significantly build upon these prior conceptualizations by carefully articulating the adaptability goals and focusing on the role of IT in helping explain differences in organizational abilities to engage in the orchestration mode of value creation.

INTERFACE DIRECTED FRAMEWORK FOR ORCHESTRATION Orchestration necessitates developing capabilities that make the process of creating and altering inter-firm partnerships into simpler routines that can be repeatedly invoked. The firm hones these capabilities by not pursuing ties as one-shot opportunities, but by being mindful of its embeddedness in the network of current ties and proactively moving to reshape this

446  Handbook of virtual work network in light of changing business environment and competency requirements. We build a theoretical framework for orchestration by drawing upon insights from coordination and sensemaking theories that both deal with information processing as a central concern. We combine the coordination and sensemaking perspectives to build an integrative framework that enables orchestration to be realized. Coordination theory (van de Ven et al. 1976) offers two main mechanisms to achieve coordination among inter-dependent actors.6 The first mechanism involves coordination by plan that uncouples these dependencies through upfront structuring. The second mechanism is coordination by feedback that relies upon mutual adjustment between interacting actors. The two mechanisms reflect an approach that relies on upfront structuring and a learning-based approach relying on dynamic adjustment to achieve coordination. Typical applications of coordination theory consider dependencies, such as resource constraints or processing constraints that need to be resolved for coordinated processes. We suggest that stabilized cognitive structures pose an important dependency constraint as well. Enterprises embedded in a given network of partners will, over time, become deeply entrenched in a similar manner of thinking and processing information, such that they may not be able to think “outside the box.” It may therefore be necessary to amplify deviations from the existing frame of interpretations so that organizations can pay heed to anomalies that require new frames. Stabilized cognitive structures, therefore, need to be altered so that people can coordinate their new actions and invoke the modified structures to justify their commitments (Weick 1993). We assert that dynamic coordination of actions through orchestration not only leads to value network flexibility through effective coordination but also a greater collective knowledge creation potential. The latter learning-related outcome occurs because actions are bound to the actors’ cognitive commitments and coordination of actions also results in an alignment of macro cognitive structures over time (Weick 1993). Synthesis of insights from coordination theory and sensemaking literature suggests two pathways to achieving orchestration goals. The first pathway – interface structuring – relies on reducing interdependencies while preserving collaborative information sharing and process-based coupling among organizations. The second pathway – interface minding – relies on focusing organizational cognition on events that trigger reconfiguring of the value network. The key business objectives that we focus on are coordination-related (value network flexibility) and learning-related (collaborative knowledge creation potential). The following sections present these outcomes and then develop the two pathways allowing these to be achieved. Interface Focused Coordination Goal: Value Network Flexibility Value Network Flexibility refers to a focal enterprise’s ability to coordinate value creation processes efficiently with current partners and with changing sets of potential partners, in response to changes in market conditions. An enterprise may choose to forge highly specific and efficient process linkages and information exchange mechanisms with select partners or it may allow for change and develop reconfigurable interfaces that respond to changing business environments. As an example, consider an inter-enterprise interface for a purchase process. This interface would consist of many “handoffs” with a supplier. A reconfigurable interface would accommodate changes in terms and conditions, changes in product identification (SKU numbers), and changes in shipment procedures for changing suppliers.

Orchestrating dynamic value networks  447

Source: Authors’ own.

Figure 23.1

Theoretical framework

The need for flexibility arises due to a limited time of offerings in changing environments. Also, enterprises may be confronted by disruptions to their networks and need to quickly partner with a different set of companies. For example, the events of September 11, 2001, led to a large number of manufacturers struggling to cope with production changes at their partners. Ford Motors, for example, had to close five plants in North America due to parts shortages (Mello 2003). Similarly, in response to Covid-19, several enterprises had to scramble to find new partners to work with to produce necessary medical equipment (Malhotra et al. 2021). Several scholars have examined the notion of flexibility at the enterprise level. Sanchez (1995) defines flexibility as a firm’s ability to respond to various demands from dynamic competitive environments. Teece et al. (1997, p. 521) refer to high-flexibility firms as those with a capability to “scan the environment, to evaluate markets and competitors, and to quickly accomplish reconfiguration and transformation ahead of competition.” Sanchez (1995) suggests that strategic flexibility depends jointly on the inherent flexibility of the resources available to the firm and on the firm’s flexibility in applying those resources to alternative courses of action. In applying these perspectives to inter-organizational settings, we propose that rapidly reconfiguring inter-organizational linkages is a basis for attaining flexibility, so that firms may draw upon new partners to derive relational value and respond to changing environments.

448  Handbook of virtual work Interface Focuses Learning Goal: Collective Knowledge Creation Potential Collective Knowledge Creation Potential refers to the enterprise’s ability to explore and exploit the knowledge bases of changing value network partners. In knowledge-intensive industries, when the knowledge base of an industry is both complex and expanding and the sources of expertise are widely dispersed, the locus of innovation is to be found in networks of learning, rather than in individual firms (Powell et al. 1996; Sytch and Tatarynowicz 2014). Pooling of knowledge assets has been cited as a common reason for the pervasiveness of inter-organizational collaboration (Badaracco 1991; Deken et al. 2018), and learning through inter-firm networks has an important impact on firm performance in dynamic environments (Müller et al. 2021). In recent years, the resource-based view has been extended in a growing body of literature that sees collaborative practices as a viable method of knowledge creation and transfer (Alexy et al. 2018) Research also suggests that productivity advantages of some value networks may be due to network-level knowledge-sharing processes (De Silva et al. 2018; Dyer and Nobeoka 2000; Fait et al. 2019; Potter and Wilhelm 2020). Stable patterns of interaction among organizations result in the accumulation of experience related to the partner and their specific area of activity, and this, in turn, results in the creation of valuable shared knowledge. Enhanced value of knowledge sharing can lead to finding novel ideas across previously unconnected entities. Thus, organizations that can more effectively reshape their network linkages will be able to enhance their knowledge creation potential. Next, we elaborate on the two orchestration pathways – interface structuring and interface minding – that lead to the two outcomes associated with successful orchestration outcomes.

INTERFACE STRUCTURING PATHWAY FOR ORCHESTRATION Our first set of propositions is based on managing process and information exchange interfaces among enterprises in value networks. Under this pathway, the primary theoretical basis for the realization of orchestration outcomes is a structuring of inter-organizational linkages yielding loose coupling between interacting entities. Loose coupling between systems implies the existence of elements that are linked (“coupled”) to preserve some degree of determinacy. At the same time, these elements are subject to spontaneous change requiring some degree of independence to respond to the change – to not impact or be impacted by the linked entity (“looseness”). The elements of the orchestration model are identified on the basis that they advance both the looseness as well as the coupling. Coupling among organizations is enabled through IT-based integration mechanisms that connect processes and enable information sharing. Looseness is attained by imposing structure on the processes and information exchanges. Structuring essentially implies inscribing rules that regulate processes and information sharing routines into IT-based systems. This can then essentially limit the range of actions that organizational actors can engage in as they interact with partners. To prevent such structural inertia, we propose that enterprises in the value network focus on limited structuring and direct their energy towards two key imperatives. Limited structure is needed to provide an overarching framework to achieve coordination and support sensemaking in the face of change. Limited structure helps individuals make sense, while not constraining adaptability, while excessive structuring can hinder improvisa-

Orchestrating dynamic value networks  449 tional processes and social learning. By directing structuring efforts towards organizational interfaces, interdependencies may be minimized and still allow organizations to mutually adjust within the parameters of the overall specification. Next, we outline propositions for the inter-enterprise process and information exchange based elements that enable loose coupling and lead to orchestration outcomes. For each of the elements, we discuss how both looseness and coupling may be simultaneously advanced through two interacting dimensions. Modularly Structured Collaborative Processes The first mechanism to enable orchestration is to structure inter-enterprise processes in a manner that balances the needs for coupling and looseness. This may be achieved through strategic initiatives along the lines of the Collaborative Planning, Forecasting & Replenishment (CPFR) initiatives undertaken in several industries (McCarthy and Golicic 2002). Partners engaged in CPFR aimed to reduce overall process inefficiencies such as the need to maintain a buffer inventory. This is accomplished by collaborating on joint plans related to what is going to be sold, how it will be merchandized and promoted, in what marketplace, and during what time frame (Sherman 1998). Each partner then can focus on their specialized sub-processes with an agreed-upon set of interfaces. An example of such organizing is provided by DuPont’s response to a growing market for products with health claims. DuPont collaborated with new partners like Marks & Spencer to create a channel for such products that did not exist before (Hinterhuber 2002). Marks & Spencer performed the retail distribution while another partner, Unilever, focused on food processing, and DuPont focused on coordinating their demarcated sub-processes. To orchestrate value networks similarly, enterprises in the network need to adopt delineated process interfaces with clear rules of engagement while setting up collaborative structures such that the firms work towards overall partnership goals. Collaborative arrangements advance coupling Collaborative arrangements refer to institutionalized mechanisms for pooling resources controlled by multiple enterprises for their collective benefit. Collaboration can extend to areas such as joint marketing programs, strategic planning, pricing, sharing and development of technical skills, demand development, and new market creation. Collaborative arrangements have been shown to improve both coordination and learning (e.g., Bodin 2017; Gulati et al. 2012) by enhancing the absorptive capacity of participating enterprises. Collaborative processes based on social interaction and coactivity (Nahapiet and Ghoshal 1998) result in the creation of shared intellectual capital. Formally designed integrative mechanisms have been shown to facilitate knowledge sharing and innovation in various contexts (Jiang and Chen 2018; Sheremata 2000). Joint decision-making is one such organizing mechanism in which members from partner companies collaboratively make decisions (face-to-face or through technology) related to factors that influence the inter-linked processes and their outputs (Zhang and Cao 2018). Such collaborative processes result in increased knowledge for the collective, shared sensemaking, and distributed understanding that resides in the collectivity rather than its components. Partners, by working together, can tacitly understand and interpret issues related to inter-organizational processes. Socialization may also help to overcome conflicts among organizations arising from incentive incompatibilities and private benefits due to the formation of norms based on trust and reciprocity (Gerwin 2004). Thus,

450  Handbook of virtual work collaborative arrangements will enhance both coordination and learning-related goals in value networks. We propose that: Proposition 1a Collaborative arrangements among enterprises will enable high levels of coupling of activities, leading to transactional efficiency and collective knowledge creation in existing relationships. Modularity advances looseness The structuring of collaborative processes is accomplished by neatly separating the functionality of constituent parts. The modularity of inter-enterprise processes can be defined as breaking up of complex processes into sub-processes (activities) that are performed by different enterprises independently (such that sub-processes occur through overlapping phases or better still fully simultaneously) (von Hippel 1988; Sanchez and Mahoney 1996). Modular structures allow diverse enterprises to come together to pool their resources and capabilities to respond quickly and flexibly to environmental flux. Clark and Fujimoto (1991) suggested that modularity could be achieved by breaking complex tasks into sequential phases by function or by product segment. A better approach to modularity is designing sub-tasks that occur through overlapping phases or better still, fully simultaneously. An example of the modular process design is the new product introduction process implemented by an Original Equipment Manufacturer (OEM) in the IT industry. The process was broken down into components to be performed by specialized partners. The OEM took on the role of managing the product design activities. The wholesale distributor took on the charge of product assembly and direct delivery to end customers. The retailer enterprise took on the charge for service activities and after-sales technical support. Simultaneously, members from the OEM, distributor, and retailer enterprises jointly planned a marketing program for the new product that was then spearheaded by the distributor. In such a context, each entity from the value network is expected to perform a specific type of activity that is functionally distinct and has clear touch-points where other players are expected to receive or deliver information. Modular process architectures reduce the complexity of the context such that firms can increase their component-level learning (Sanchez 1997). They are also better able to identify gaps in their knowledge and understand how these can be filled by information from external sources. At the same time, firms develop a better understanding of their interdependencies linked to partner processes. Modular processes with clearly specified interfaces may provide a form of embedded coordination. This also frees up the enterprises from micro-managing the coordination process and provides them the latitude to focus on rich, higher value-adding information exchange (Koka and Prescott 2002). This reduction in cognitive load related to information processing needed for coordination increases the absorptive capacity of enterprises. Modular process structuring essentially reduces the level of enterprise interdependencies to a well-defined and focused interface, facilitating both coordination and learning in conjunction with changing partners. Thus: Proposition 1b Modular structuring of inter-enterprise processes will enable high levels of looseness of activities of different enterprises, leading to transaction efficiency and collective knowledge creation under changing value network configurations.

Orchestrating dynamic value networks  451 Synthesizing loose coupling Modularly structured inter-enterprise processes reduce coordination requirements and enable specialized local learning. Collaborative arrangements enable the specialized sub-processes to be integrated. In tandem, these lead to the attainment of coordination and learning goals without being excessively tied to current partners. Therefore we propose that: Proposition 1c Structured collaborative processes (collaborative arrangements and modular processes) enable organizations embedded in a value network to achieve higher levels of value network flexibility and collective knowledge creation potential. Structured Information Sharing The second mechanism to achieve loose coupling is through the structuring of information exchange among value network partners. This may be achieved through the specification of standardized templates that allow information to be interpreted by partners. For instance, partners in a value network may agree on the data elements to be contained in a purchase order. New standard specifications were created for different vertical markets, which make use of the extensible Markup Language (XML) – a flexible specification that allows documents to be self-describing (Drickhamer 2003). IT applications for inter-enterprise information exchange could take advantage of a new breed of interaction capabilities derived from flexible markup formats and ubiquitous and low-cost connectivity (Bischoff 2000). In recent days, the advent of APIs and Blockchain technology has emerged to share information between ever-changing partners in response to the dynamic marketplace. These initiatives are expected to yield network externality benefits leading to ease of partnering across enterprises and in dealing with change in the context of existing partnerships (Malhotra et al. 2021). Real-time information sharing advances coupling Real-time information sharing refers to the communication of information about relevant events to partners without a significant time lag from their actual occurrence. As an example, IT-enabled reengineering efforts in supply chains are pushing continuous replenishment programs (Parsa et al. 2017), with a key feature being the provision of real-time inventory positions by retailers to product manufacturers (Raghunathan and Yeh 2001). Sharing of this information results in reduced uncertainty for manufacturers, especially for relatively new products whose demand may be difficult to forecast, resulting in significant coordination benefits. Competing in the hypercompetitive environment requires divergent thinking to create fresh and insightful perspectives of a market environment replete with discontinuities (Foster and Kaplan 2001). These new perspectives are often derived by amalgamating information obtained from diverse sources. This requires a process whereby extensive communication is established with external constituencies and real-time information is exchanged (Eisenhardt and Martin 2000). Real-time information enables managers to rapidly sense market shifts earlier and respond faster to them (Eisenhardt and Schoonhoven 1996; Mendelson 2000). An example of a company leveraging real-time information sharing is Tibbett and Britten Group (T&B), a leading supplier of logistics services to retailers in the UK such as Marks & Spencer (M&S). T&B has built a centralized data repository for the M&S supply chain that can be accessed by all partners to get information on logistics processes. The system builds

452  Handbook of virtual work a profile of each partner and allows hundreds of participating companies in the supply chain to identify problems and propose solutions. Applications allow real-time control of processes. Real-time information sharing thus enables both coordination and learning-related goals to be realized with current partners: Proposition 2a Real-time information sharing among enterprises will enable high levels of coupling of activities of different enterprises, leading to transactional efficiency and collective knowledge creation in existing relationships. Information exchange templates advance looseness Information exchange templates refer to specifications that enable information exchanges to be coded and provide a grammar for information to be expressed. For value networks, these refer to common, interoperable, and executable representations of services, business transactions, global and local business processes, and service-level agreements (Petrie and Sahai 2004). Interdependencies create information needs from partners that require resolutions to reduce ambiguity and uncertainty (Tushman and Nadler 1978). For instance, transaction-related information shared among partners, such as invoices or purchase orders may be difficult to interpret without extensive understanding of the contextual factors specific to the relationship. Information exchange templates allow for the transmission of coordination information with little ambiguity. Templates also function as generalized cognitive frameworks, imposing an orientation on the action and granting actions legitimacy and meaning in some particular domain (Edwards 2000). Generative templates refer to the inscription of broad patterns of information exchange in inter-enterprise systems. These are then extended into specifics for each partner. Generative templates will allow limited degrees of freedom for enterprises to respond to change and yet prevent exchanges from becoming entirely idiosyncratic to a relationship. In practice, such generative templates could consist of structural specifications of information exchange and process sequences, but leave the specific data elements and activities to be selected from an admissible set of contingent possibilities. For instance, the high-level specifications might lay out partly specified documents to be exchanged to complete a purchase process with a supplier. The templates guiding and partly constraining information exchange will create “electronic brokerage” effects allowing for fluid partnering and mutual adaptation (Malhotra et al. 2007). Thus, we expect information exchange templates to permit coordination and knowledge sharing with changing network partners: Proposition 2b Information exchange templates will enable high levels of looseness of activities of different enterprises, leading to transaction efficiency and collective knowledge creation under changing value network configurations. Synthesizing loose coupling Real-time information exchange makes enterprises more responsive and better able to coordinate with their value network partners. It also enables enterprises to engage each other and enhance their collective stock of knowledge. At the same time, generative templates for information exchange ensure that the information exchange is not specific to a given partner so that enterprises can retain the flexibility to coordinate and learn with changing partners. Therefore, we propose that:

Orchestrating dynamic value networks  453 Proposition 2c Structured information sharing (real-time information sharing and information exchange templates) enables organizations embedded in a value network to achieve higher levels of value network flexibility and collective knowledge creation potential.

INTERFACE MINDING PATHWAY FOR ORCHESTRATION Our second set of propositions is based on advancing organizational capabilities to adapt to change by enabling mindfulness of partnering-related events. Research in turbulent industries indicates the need for enterprises to develop an understanding of “links in time” (Brown and Eisenhardt 1997: 29) – developing an acute sense of the need to change and coordinating different business activities to make rhythmic transitions. We propose that organizational development of capabilities that enable reflection on existing practices and generate mobilization and collective action to change these practices will enable adaptation in response to change. Mindfulness refers to the organization’s cognitive engagement with change in its specific context. We particularly focus on one aspect of mindfulness – the alertness to change in terms of the enterprise’s interdependencies upon value network partners and the changing competencies of current and potential partners. We propose that organizations should specifically attend to their interdependencies concerning value network partners, and also be mindful of changing competencies of different organizations that are current or potential partners. This will prepare them to be sensitive to developing relationship-specific processes that hinder adaptation. It will also alert them to path-dependencies that may constrain their future opportunities. Further, awareness of changing competencies of value network partners will enable organizations to obtain a sense of how they need to reconfigure value networks to capitalize on emerging markets and partner-dependent capabilities. Information systems enable enterprises to heighten their awareness of inter-enterprise dependencies. Awareness of such dependencies facilitates effective strategic change (Tillquist et al. 2002). Next, we detail the nature of these systems. Organizational Memory for Inter-Enterprise Activities Organizational memory is how knowledge from the past can be brought to bear upon the present activities (Stein and Zwass 1995). While some researchers take the point of view that this repository is stored in the minds of individuals in the organization (Walsh and Ungson 1991; Starbuck 1992), others propound that the repository needs to be independent of the vagaries of individuals for it to be effective (Levitt and March 1988). Such memory systems are less sensitive to individual nuances, more resistant to depreciation, and are easier to use (Argote 1999). Organizational memory reflects the ability of an organization to evoke remembrance of past experiences that are relevant to understanding and dealing with a change situation. Organizations enrich their sensemaking by combining it with interpretations from the past (Walsh and Ungson 1991) and can proactively capture and “memorize” interactions across touch-points to differentiate among their partners. The importance of organizational memory stems from the fact that intelligence is fundamentally a memory-based process and learning involves the dynamic modification of memory. Knowledge is also self-referencing, in that

454  Handbook of virtual work future knowledge requires an understanding of the concepts and accumulated learning from the past. Organizational research has demonstrated that past experiences determine the locus of an organization’s search for new knowledge and help the firm develop new routines (Rosenkopf and Nerkar 2001). Stored insights about an organization’s value network activities in the past can be brought to bear on present decisions related to the structuring of new value networks to respond to changing conditions and the emergence of new opportunities. Achieved memory has been specifically linked to the emergence of shared meaning and improved supply chain performance (Hult, Ketchen, and Slater 2004). Organizational memory of past change is particularly needed in the context of changing business environments since the value networks in which expertise is created and shared are frequently dismantled as organizations move on to collaborating with new partners. Quick response to partners is predicated on the sensing of broader patterns from memory that guide individual sensemaking. The presence of a memory of past episodes with value network partners would contain episodes related to previous partnering efforts, consequences of the efforts, and related lessons learned. Such systems, therefore, aid in linking up effectively with current partners for offering changes and in making better partnering decisions, and adjusting processes and content for new partners. Brown and Eisenhardt (1997) show that organizations that are successful in continuously changing and developing innovative products develop “links in time” – a sense of what transitions must be accomplished over time. These organizations develop a sense of where to go next while still being focused on current projects. We propose that organizational memory of past activities relevant to value network organizing will help in creating these links across time: Proposition 3 Organizational memory of past inter-enterprise activities (realized through information systems) enables organizations embedded in a value network to achieve higher levels of value network flexibility and collective knowledge creation potential. Distributed Sensemaking and Interpretation Support Organizational cognition, the basis for inter-organizational knowledge sharing and creation, is distributed in nature. This requires information systems that can represent multiple views – from global to minute, allow individual ownership of interpretations, and are emergent – that is, they allow dynamic configuration of interpretation (Boland et al. 1994; Jarvenpaa and Ives 1994). Technology support for distributed sensemaking allows entities using systems to “make interpretations of their situation and exchange them with other with whom they have interdependencies so that each may act with an understanding of their own situation and that of others” (Boland et al. 1994: 457). Boeing Co.’s commercial aircraft division, for example, has deployed a web-based solution that allows 11,000 users to query the bill of materials – an extensive repository on airplane components. Users can generate analytical insights related to specific parts and assemblies concerning their customer inspection data and supplier usage. Such information systems support helps in piecing together information obtained from diverse sources to derive a more holistic interpretation. Typical components of such systems are data mining software that supports analytical exploration activities, and customized “dashboards” that push event-linked

Orchestrating dynamic value networks  455 data to specific organizational roles. These systems can help uncover patterns in data and enable insights to be generated by supporting the processing of large quantities of raw data. The nature of such systems also reduces the knowledge specificity of information – a situation where the knowledge required to interpret or to obtain information is restricted to certain individuals or an organization (Sampler 1988). Also, as new partners are added to create new value in the value network, the information received from these partners can be interpreted more easily since multiple perspectives can be brought to the analyses and be integrated. Thus, the use of these systems can enhance the value-network flexibility by allowing for the addition of new partners and interpretation of information received from them without increased cognitive load. Weick (1990) suggests that new technologies and complex organizational systems pose sensemaking problems for managers and operators as they deal with incomprehensible failures because of how much of the technology is concealed from view and limits the mental models that people can work with. In response to such situations, Weick (1990) suggests the need for systems that allow collectives to form mental maps of events that are not evidently visible. Such systems should also allow for collective problem-solving and integration of perspectives. Distributed sensemaking systems provide the basis for fulfilling these sensemaking needs. Commitments to their deeply held mental models may constrain the meanings that people in an organization ascribe to events and objects. Distributed systems can allow such unstated commitments to collide as different people try to construct explanations for divergent interpretations and explanations. This is particularly valuable when enterprises are confronted with changing environments, where information from different partners may reflect their different mental models. The use of such systems allows multiple perspectives within an enterprise to be brought to bear on interpreting complex and real-time evolving information related to dynamic events such as value-network restructuring and effervescent market demands. Information systems support for distributed sensemaking and interpretation reduces the knowledge specificity of information related to changes in value network and market conditions and increases the potential for multiple perspectives being applied to interpret such information thereby enabling enterprises embedded in a value network to coordinate and share knowledge with changing partners. Therefore, we propose that: Proposition 4 Information systems support for distributed sensemaking and interpretation enables organizations embedded in a value network to achieve higher levels of value network flexibility and collective knowledge creation potential.

LINK TO PERFORMANCE Enhanced value network flexibility and collective knowledge creation potential will lead to the focal organization embedded in a value network being efficient in the management of current business demands while being adaptive to changes in the environment. The capacity to simultaneously exhibit efficiency and adaptability (ambidexterity) is shown to be a characteristic of most successful organizations and linked to long-term competitiveness (Gibson and Birkinshaw 2004; Junni et al. 2013) through increased innovation capacity (Andriopoulous and Lewis 2009). Hence, we propose that:

456  Handbook of virtual work Proposition 5 Higher levels of value network flexibility and collective knowledge creation potential will lead to superior business performance for the firm embedded in a value network.

DISCUSSION In this chapter, we have articulated the interface structuring and interface minding pathways to orchestration in dynamic value networks. We have also elaborated on how value network flexibility and collaboration knowledge creation potential can be achieved through those pathways and has generated propositions around them. These articulations and propositions provide a foundation for future research on how to enable enterprises to meet coordination and learning-related objectives in the context of their value networks. The two main pathways we have put forth are coherent actions that emphasize: (1) fitting two disparate enterprises together using structuring mechanisms to link up and manage interdependencies, and (2) quick sensing and adaptation to change derived from mindfulness of the firm’s embeddedness in a value network. The proposed antecedents allow for both static efficiency considerations and concerns about dynamically dealing with partnership changes to be addressed. In essence, orchestration outcomes are related to managing the interplay between short-term and long-term coordination and learning goals in a changing environment. The two pathways are elements of a duality, rather than distinct. The second pathway is based on individuals learning a collective repertoire of cognitions, normative frameworks, and behavioral patterns that makes them mindful of change. This will, in turn, result in the enactment of structure through interactions and practices that do not “lock-in” the organization to a given network of partners. We suggest the need to view organizing from a dynamic perspective and to be mindful of the “lock-in.” For example, a recent organizational response to competitive pressures has been to disaggregate value creation activities on a global scale to take advantage of differentiated competencies and cost structures. The typical outsourcing mindset, however, has been traditionally based on static one-shot analysis and may have adverse longer-term repercussions in situations where dynamic capabilities are warranted (Kern et al. 2002; Pavlou and El Sawy 2011). A firm’s skill at orchestration may also impact its network identity. Enterprises are part of ambiguous, complex, and fluid value networks and come to develop distinct network identities as they go through integration activities (Nazir and Pinsonneault 2012). Network identities capture the perceived attractiveness (or repulsiveness) of an enterprise as an exchange partner based on the enterprises’ unique set of connected relations with other enterprises, links to their activities, and ties with their resources. Enterprises that become successful at the orchestration mode of organizing would develop strong network identities and would likely be well-positioned to appropriate value in enterprise networks as well as becoming keystone players in digital platform ecosystems. Companies such as Cisco, Lenovo, and Dell, which excel in marshaling networks of suppliers, service providers, producers, infrastructure companies, and customers, are exemplars of the strategies we have proposed. In their use of the interface structuring pathway, these companies leverage emerging technologies to standardize technical hookups and speed up the organizing of data interchanges for existing and new partners. They also architect business process “chunks” that can be deployed to implement inter-enterprise processes from standardized parts. The leading companies have also created process guidelines and customized templates

Orchestrating dynamic value networks  457 for information sharing that allow new players to be quickly added to value networks that have been greatly facilitated by API capabilities and software. Progressive companies also continually evaluate their value propositions for their customers, and the competencies of their partners concerning it, standing ready to make changes to their value creation processes. Research has also documented firms structuring their exchanges with new partners by using contractual clauses that are largely non-enforceable but meant to serve as a “blueprint” to plan the collaboration and enable coordination. The orchestration model of organizing is not without its limitations. First, orchestration requires increased information processing to maintain a dynamic equilibrium, which may be dissipative in terms of organizational energy. Second, the focus of this framework is on the coordination of competencies in competitive contexts where value is derived from creating reconfiguration options. Organizations in relatively stable environments, or contexts where resource dependencies create binding inter-organizational commitments, will not see much value in orchestration. Third, the management of interdependencies may break down in conditions when interdependencies are complex in nature making it difficult to plan for contingencies. In such contexts, a priori specification and design of structured interfaces or generative templates may be unviable (McAllister 1995). Such conditions require the use of intensive and rich forms of communication based on shared cognitive structures and mechanisms that are contextual and organic, such as deep relations-based social capital (Kostova and Roth 2003). However, in conditions of rapid change, where organizations need to quickly adapt without the possibility of intense interactions, orchestration is a more effective organizing model. It is also important to highlight some of the assumptions that underlie the proposed theoretical model outlined by us. A critical assumption on which our propositions rest is that organizations engage in continuous incremental adaptation in response to change. An alternate perspective could be that organizations respond to change in a punctuated manner, where the change needed to move from one partnering relationship to another is accomplished in a sharp burst of reconfiguration, followed by a lengthy period of stability. A second assumption we make is that a partner’s activities cannot be directly controlled by an enterprise, thus direct supervision is not a reasonable strategy. This assumption may not hold in cases where the enterprise (such as Walmart) has highly dependent suppliers in an asymmetric power relationship. Third, we recognize that the propositions may not be as applicable for value networks that are focused on knowledge-intensive activities such as research and development, which have high levels of pooled task interdependence. Furthermore, while we have examined interface-focused enablers of orchestration, an alternative mechanism to achieve loose coupling could be based on assigning individuals to formal roles that sense information from partners, interpret it, and communicate it to the rest of the enterprise. The pivotal nature of boundary roles has been recognized in the coordination of inter-organizational processes, particularly in times of relation-building and change (Ganesan 1994). Boundary roles are expected to contribute towards looseness by also buffering the organizations from environmental uncertainties and disturbances to enhance the possibility of rational action within the organization (Scott 1992: 194). It is also worth pointing out that enterprises are experiencing an era of new opportunities afforded by developments in information and communication technologies. However, there has been a significant variance in the adoption and assimilation of these technologies. It is therefore an opportune time to empirically test if these variances help to explain the differences in value creation potential for different enterprises. Future research should explore how

458  Handbook of virtual work additional factors such as environmental uncertainty, technological change, and business complexity would affect the use of the two coordination pathways. Interactions among the two pathways and complementarities among mechanisms within each pathway may also be explored. An important issue to be investigated is whether structuring for managing interdependencies in the initial stages of a relationship helps to mind for change later in the relationship.

CONTRIBUTIONS AND IMPLICATIONS Past studies which have sought to understand the role of IT in enabling inter-enterprise value linkages have tended to focus on “electronic integration” (e.g., Zaheer and Venkatraman 1994; Nazir and Pinsonneault 2012). This neglects the possibility of “lock-in” to a given set of partners, resulting in the inability to respond to technological discontinuities (Narula 2002). Further, studies have predominantly been limited to examining coordination information exchange in relatively stable contexts and have not considered the opportunities from joint value creation and knowledge sharing. Organization theory has long suggested that only firms that employ organizational features that both push the firm towards exploration and pull it towards stability tend to have high performance (Rivkin and Siggelkow 2003). We adopt an inter-enterprise perspective to extend these ideas that seek value through the balancing of efficiency and adaptability needs. While there has been considerable research on understanding the drivers of dynamic value creation at the organizational level (e.g. Teece et al. 1997; Eisenhardt and Martin 2000; King and Tucci 2002; Teece et al. 2016), there has not been as much examination of capabilities and processes needed to drive value creation in inter-organizational arrangements (Duysters and Heimeriks 2002). We propose that it is important to consider the role of the networks that the firm is embedded in to enable firms to regenerate competitive advantage under the condition of rapid change. The notion of semi-structures and their effect on improvisation and change has begun to be recognized in research (Brown and Eisenhardt 1997). Change occurs because the semi-structures are rigid enough to organize the process of change and coordinate it, but are not so rigid that they cannot occur. We have delineated specific types of structures that will help firms flexibly position themselves vis-à-vis their network of potential partners. The structure should best be imposed to create modularity such that inter-organizational interfaces are very well specified, so that implicit coordination across enterprises is achieved. Enterprises may then proceed from this external specification and design their internal processes. This ensures that that change proceeds “outside-in”, triggered by changes at the interfaces of enterprises in the value network, resulting in conditions where enterprises are only partially embedded in their value networks. Finally, our work outlines important implications for the design of two classes of IT infrastructure. First, inter-organizational systems should be designed such that an enterprise’s processes and information flows encapsulate complex processing and only expose the functional interfaces that are needed to coordinate with other enterprises. The framework we propose points to the importance of structuring information flows but only to an extent that it does not rule out potential information requirements changes in light of a dynamic business environment. In recent times, APIs have emerged as a key cornerstone for application development. This enables loose coupling between application components that expose their functionality and calls upon external business functions through well-specified interfaces. We also

Orchestrating dynamic value networks  459 suggest the importance of nurturing the sensemaking frame shifts required for orchestration and for paying heed to the events, such as pandemics, that warrant drawing on the competence of new partners. This requires investment in building awareness of how competence can be procured from partners and blended with organizational expertise (Malhotra et al. 2021). It also requires a foundational level of knowledge to be able to seek out and evaluate partner skills, and the absorptive capacity to assimilate them (Cohen and Levinthal 1990; Flor et al. 2018; Fredrich et al. 2019; Malhotra et al. 2005). The role of information systems in capturing and disseminating knowledge and supporting its interpretation is important. These systems need to be designed to flexibly represent knowledge and to allow employees to surface their assumptions about partners and envision the impact of changes in the value network.

NOTES 1. Dyer and Singh (1998: 662) define relational rent as “a supernormal profit jointly generated in an exchange relationship that cannot be generated by either firm in isolation and can only be created through the joint idiosyncratic contributions of the specific alliance partners.” 2. A value network refers to patterns of routines and processes spanning firm boundaries that result in a base of shared reciprocity norms, language, and cognitive structures facilitating exchanges with low transaction costs between firms using a mediating technology that facilitates exchange relationships among organizations. 3. https://​www​.ibm​.com/​cloud/​learn/​api. 4. https://​medium​.com/​apis​-and​-digital​-transformation/​creating​-value​-from​-data​-three​-ways​-apis​-are​ -key​-6e8ac48aa93c. 5. Dynamic value networks refer to shifting patterns of exchanges among firms in response to environmental changes or changes in relative firm competencies. 6. Gulati et al. (2005) trace the source of ideas, viewing the essence of organizational adaptation as the generation of integrated responses to changed circumstances, to Chester Barnard with March & Simon following later in the same tradition.

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PART VI CONCLUSION

24. Virtual work – where do we go from here? Setting a research agenda Thomas O’Neill, M. Travis Maynard, Lucy L. Gilson, James M. Hughes and Nathaniel Easton

That virtual work is here to stay is now pretty much accepted by everyone regardless of age, gender, race/ethnicity, geographical location, job, organization, and industry. What has also changed in this domain is that historically, much of the research on virtual work was ahead of, or at least on par with what was happening in the field. The rapid shift to virtual work caused by the COVID-19 pandemic changed this. Now, unfortunately, the literature on virtual work is in a game of catch up trying to fully understand what was learned during the pandemic. On a positive note, many of the findings published in the extant academic literature held up when the world embarked on a massive “experiment” of a scale and magnitude that has never before been seen. At the same time however, this rapid shift to virtual work has also started to highlight some areas that are less well understood. While each of the author teams within this Handbook have detailed areas where work has progressed within the virtual work literature, they have also highlighted ideas that should be explored by future researchers. However, we felt it important to highlight some additional research directions that were not completely discussed within these individual chapters. Accordingly, in this chapter, we close the Handbook of Virtual Work by focusing on a number of research opportunities that we argue will be important to address over the coming years.

MENTAL HEALTH, WELL-BEING, AND TOTAL WORK HEALTH Practitioners and academics are becoming more aware of the importance of mental health and well-being of employees not only in terms of those individuals but also for the organization as a whole (e.g., Goetzel et al., 2002). Given this increased awareness and the fact that virtual work is only going to increase in prevalence going forward, considering the impact of such working styles on the topics of mental health and well-being becomes paramount and as such, we encourage researchers to give these topics the attention that they deserve as the virtual work literature continues to develop. As noted by Gilson et al. (2015), the topic of well-being had not been extensively considered over the decade of their literature review. These authors highlighted the fact that while telecommunicating research has discussed the positive and negative effects of working virtually, the topic has not been extensively considered at the team-level of analysis. As such, they called on research to give more attention to the topic of well-being in virtual team research. They laid out competing arguments to emphasize why the topic is deserving of research attention. Namely, they highlighted work that suggests that the use of computer-mediated communication (CMC) channels can lower levels of positive affect among members (e.g., Johnson et al., 466

Virtual work – where do we go from here?  467 2009). They also noted that Nurmi (2011) provided a study that noted that the dynamics within VTs can increase psychological strain and overload. While there have been some who have heeded the call for more research on well-being in virtual contexts, we still see a great deal of potential here, especially given that the topics of mental health and well-being are considered broadly. In fact, the options for the foci of such research within virtual work domains is relatively endless. For instance, there was work conducted during the COVID-19 pandemic that found that the use of virtual reality (VR) can assist with individuals’ mental and physical well-being (e.g., Siani & Marley, 2021). As such, it could be worthwhile for research to consider the type of technology used by virtual workers and what impact such technology decisions can have on well-being. In addition to the need for team-level considerations of virtuality on mental health and well-being, there are fruitful directions for future research at other levels of analysis. For instance, there may need to be examinations of whether certain individuals are more/less prone to experiencing the possible negative effects of working virtually. Similarly, are some individuals more apt to experience positive effects of working virtually? The popular press has discussed the different experiences felt by introverts and extroverts when working virtually and while there is limited research on the topic (e.g., O’Neill et al., 2009), we wonder how more recent experiences with work from home and hybrid work may look different than earlier, pre-pandemic studies? An extension of these research questions is whether these relationships differ across cultures. We are starting to see virtual work research expanding beyond the traditional U.S. and China-centric samples to include broader settings such as Africa and Egypt (e.g., Gabr et al., 2021; Imhanrenialena et al., 2021). This is a great trend of late within the virtual work literature and one that we hope continues in years to come to better reflect the locations where virtual workers reside. This point can take on even more depth however, given the prevalence of digital nomads and the reality that such individuals may be from one country but residing and working from another (e.g., Thompson, 2019; Woldoff & Litchfield 2021). Relatedly, there is evidence to suggest that leaders play an important role in their employees’ mental health (e.g., Stuber et al., 2021). As such, individual leaders may need to adjust their approaches when managing workers who engage with their tasks and teams virtually. For instance, it will be important to examine the relationship that different leadership styles (i.e., participative, transactional, transformational, etc.) have with employee mental health and well-being when working virtually. Likewise, it will be interesting to examine what factors moderate these main effect relationships. Additionally, we think that there will be continued interest in technology as a teammate (e.g., O’Neill et al., in press) research questions and we contend that examining the role that such practices have on human members’ stress, mental health, and overall well-being may be a fruitful direction for research. For instance, does having a non-human member add stress or might the effect be the opposite in that a computer teammate might be the ultimate in reliability and make no demands outside of the task. That said, given both the reliance on technology within virtual work and the fact that managers may not have complete control over who should work virtually or have control over when and how virtual work gets done, there is a need to examine the role of interventions more closely. Specifically, are there interventions at the team, organizational, or leader level that can ameliorate some of the negative consequence that the use of technology and working virtually can have on well-being and mental health. For instance, research can continue to explore the

468  Handbook of virtual work role that techniques such as mediation, sleep, physical activity, and mindfulness (e.g., Desai et al., 2021) have on virtual worker well-being. Over the last year, terms such as “Zoom fatigue” and “Zoom burnout” have become part of the everyday lexicon and yet, there is still much we do not know about the effects of working virtually on employee mental health and well-being. Are feelings of burnout short or long term? Do the negative and positive aspects of working virtually dynamically change over time as workers either successfully or unsuccessfully adapt to their new working conditions? Do intervention strategies (i.e., camera off for certain meetings or utilizing mindfulness techniques) work and if so, do the effects persist over the long term, or are they short term solutions? While research questions abound in this area, what makes this a particularly rich area of inquiry is that this is not simply an individual level question, but rather the effects can and should be considered at multiple levels of analysis. Recent attempts by organizations such as the National Institute of Occupational Safety and Health (NIOSH) have attempted to push beyond the limitations of just mental health, well-being, or physical health, but rather advocate for an integrative, holistic approach of “Total Worker Health” (A National Agenda to Advance Total Worker Health® Research, Practice, Policy, and Capacity, 2016). Using this perspective, it is possible to examine virtual work as a unique occupational environment that may have significant mental and physical health hazards that have yet to be thoroughly identified. Significant questions remain for employees, organizations, and policymakers regarding best practices for holistic employee health in virtual work. Who is responsible for maintaining the physical environment and safety of these workers? How do organizations leverage technologies in ways to positively influence worker health and well-being, such as with smart watches and fitness programs, while also protecting employee privacy and autonomy? Researchers in multiple domains, such as management, psychology, and industrial hygiene, have significant opportunities to help establish important norms and boundaries to protect virtual employees while maximizing productivity.

GENDER In an interesting discussion of their personal experiences working virtually during the recent COVID-19 pandemic, Gao and Sai (2020) speak to the isolation and loneliness that they experienced as single women living alone and working virtually as a result of the pandemic. Their work speaks to the reality that the well-being effects and other dynamics of working virtually may be experienced differently by individuals of different living situations including different genders. Early research within the virtual work literature examined gender effects and several pointed to the positive effects that virtual work could have for women, who were evidenced to be more satisfied than men with their virtual groups (e.g., Lind, 1999). In part this is because technology was seen to level the playing field. For example, everyone has the same size square or box around them on the screen or the order in which individuals are seen on the screen is based on who entered the call rather than seniority. Not to mention that when interacting virtually, work can be conducted more easily in a flexible manner thereby allowing mothers to pick up children after school and finish working after they have gone to bed. This early virtual research is contrasted with more recent work within the academic and popular press that consistently spoke to the strain that working women faced during the pandemic (e.g.,

Virtual work – where do we go from here?  469 Key, 2020). Specifically, it was noted that women had to work through a new modality while having to bear an unequal amount of childcare and household responsibilities. As such, the experience of the pandemic and the different effects of transitioning to virtual work between genders raises the question of whether we have a complete understanding regarding the role of gender within the domain of virtual work. As such, there are numerous avenues for research to consider gender within virtual work contexts. For instance, de Pillis and colleagues (2015) provided evidence that women were less likely to social loaf or exhibit “deadbeat” behavior as compared with male individuals within virtual teams. However, such findings beg the question of why this is the case and whether it applies in all contexts as Ruthotto and colleagues (2020) did not find a difference in participation within a virtual classroom based on gender. As such, it will be important to assess whether context plays a role in engagement differences by gender. And if such differences are noted within a particular context, it will likely be even more important to assess what the implications of this type of behavior are and how can teams and leaders create norms whereby all members contribute equally to a given task when working virtually. Accordingly, intervention studies that examine how such gender effects can be diminished would be interesting. While work to date has tended to primarily examine binary gender effects, we see fruitful and important directions of future work to consider more nuanced gender effects. Namely, as noted by McCarthy and colleagues (2022), there is a lack of understanding of the experience that non-binary individuals have in organizations. While there are numerous studies that suggest that gender differences may be less salient within virtual context (e.g., Kiss et al., 2015), it will be important to examine this supposition given that new (and richer) technologies that are more prevalent today as compared with early virtual work. As such, there is a great deal unknown about the experience that non-binary persons have working virtually. Does working virtually help these individuals or does working in such situations have negative consequences for such individuals when compared with more traditional, face-to-face work environments? Gender is not the only identity variable of interest, either – race, sexuality, age, class, and the interactive effects of identity, known as intersectionality, have all gained increasing attention (Kvasny et al., 2009; Rosette et al., 2018). While such topics have not been examined extensively within the organizational-focused research, there is work within other domains such as social work and language and culture that could be leveraged (e.g., Botta et al., 2021; Sultana, 2018). Exploration of intersectionality issues within the workplace can be of interest to multiple fields outside of management, and represents a notable domain in which team science, combining experts from different scientific backgrounds, can push the literature forward in a powerful and meaningful way.

KNOWLEDGE SKILLS ABILITIES AND OTHER (KSAOS) From a human resource management perspective, it is vital to understand the KSAOs needed for success in hybrid and remote work. Varty, O’Neill, and Hambley (2017) found that leaders of distributed teams needed to exhibit Relationship, Productivity, Flexibility, and Cross-Cultural competencies. At first glance, this tells us little beyond what the decades of research already conducted on leadership effectiveness already tell us (Bell & Kozlowski, 2002). The challenge is that when some or all of a team’s work is conducted virtually, leadership competencies need

470  Handbook of virtual work to be conveyed via technology and across physical distance (Kozlowski et al., in press). For example, for a leader to build relationships through a filter of electronic communications, the specific behaviors that demonstrate these competencies may be different. Relationship building through technology might involve using emoticons, an active presence on chat networks, proactively checking in, and finding a way to show care and compassion without the same non-verbal richness afforded by face-to-face interactions. Alternatively, Hoch and Kozlowski (2014) demonstrated that leaders of virtual teams created stronger teams when they adopted a decentralized leadership structure, whereas “less virtual” teams benefited more from a hierarchical structure. Although these are examples in leadership, the same issues apply to individual contributors/team members. For example, introversion and need for autonomy appears to be more important in hybrid work than in strictly office work, likely because it is those individuals who benefit most from the flexible work environment offered through hybrid work (O’Neill et al., 2009) and are taxed less by the need to interact with others in an office environment. Thus, it is starting to become clear that both the same, additional, and different expressions of existing KSAOs are likely needed for success in future work involving a degree of virtuality. What remains unclear is whether prior KSAO research findings will hold in the New World of Work (NWoW). What we mean by NWoW are the new collections of norms around where and when employees perform their duties – many leading organizations such as McKinsey (2022), Harvard Business Publishing (HBR, 2022), and SHRM (2022) use the conception of the NWoW to help prepare for anticipated changes to the future workplace. Given this anticipated transition, there is notable previous research to keep in mind when projecting what findings will still be relevant for the future workforce. For example, George, Gibson, and Barbour (in press) found support for Hoch and Kozlowski’s (2014) earlier finding that shared or distributed leadership appears more effective in virtual teams. Interview research conducted by Henke, Jones, and O’Neill (2022) sought to investigate what was learned from remote work during the pandemic. One theme was remote work skills and behaviors, and across leaders and individual contributors, these involved technology literacy, managing one’s own schedule, being intentional about interactions, creating a dedicated workspace, checking in with colleagues, and engaging in informal team activities. Interestingly, the most frequently mentioned issues for leaders were being flexible and adapting to employees’ personal needs. Elsewhere, Jones, O’Neill, Gibson, and McLarnon’s (2022) systematic review found that self-management, interpersonal, leadership, and technology-related competencies were key. It will be interesting to see whether and how these results evolve as organizations return to the new normal. Handke et al. (2021) argued that reducing subjective virtuality, rather than objective virtuality, is most important in teamwork. Subjective virtuality involved the perception of psychological distance between interdependent work colleagues, and when this is reduced teams are likely to be more functional. Finally, Gibson, Dunlop, Majchrzak, and Chia (2022) further extended the concept of medium-message matching to an individual competency involving repertoires of technology and the ability to use the right digital technology at the right time for the right message (see also Gibson & Grushina, in press). This moves us far beyond earlier approaches that looked at the experience or proficiency levels of individuals with different communication technologies as a determinant of effectiveness, which is appropriate given that through the pandemic nearly all office workers became much more proficient with and used to using electronic technology to communicate, coordinate, and collaborate.

Virtual work – where do we go from here?  471 So, given this and other research on the competencies needed for work that involves a degree of virtuality, what are the key directions for future research? First, research has yet to examine KSAOs in NWoW. Due to macro-economic conditions and the fact that by all indications productivity and performance improved during the pandemic (see above), on average, employees are demanding increased flexibility with respect to where and when work is accomplished (Brenan, 2020; IBM, 2020). Burning research questions involve how managers should assess productivity when productivity can no longer be monitored by in person attendance. Yes, the science of industrial and organizational psychology provides various approaches to work analysis that may shed light on what performance is for a particular role, but many of these methods are time intensive to carry out (e.g., task analysis; critical incident technique; Schippmann, 2013). Therefore, investigation of new technologies that expediently define the results or outcomes of a particular individual’s job, and their resulting appropriation and implementation by managers, needs to be assessed. Again, however, a focus on results and not hourly work presents a major paradigm shift for organizational compensation systems, time tracking, unionized environments, and perceptions of fairness. Relatedly, the role of electronic performance monitoring (EPM) and its pros and cons in a distributed work arrangement in the NWoW should be considered. We know employees generally resist being monitored (Aiello & Kolb, 1995), that new legislation in some countries such as Canada restricts or requires explicit policies about EPM, and that employees find work arounds such as “mouse jigglers,” but are there methods of EPM that suit both the role and context of the organization that are functional for all parties (or should this practice be abandoned altogether; see also Bhave, 2014)? With respect to competencies and future skills, this remains an evolving question. Researchers must connect and partner with industry to work on updating general competency frameworks (Campbell et al., 1993) to validate whether the competencies themselves are still valid, as well as the specific behaviors that employees need to enact to demonstrate these competencies in a world of work that is quite different from when they were first published. From a performance management point of view, how do leaders manage poor performers in the NWoW? Generally, there is a movement toward increased monitoring and the phrase “performance management,” which involves working with and documenting incidents for those who seem to be performing at a sub-par level. Managers wonder if they should limit work away from the central offices for those who they believe may not be performing well. Would a practice like this undermine or contribute to a broader team’s morale and the overall philosophy of hybrid work? Does it create a self-fulfilling prophecy? Is being physically in an office but working with others who are all remote any different than working from home or at a coffee shop and if so, how? Does it even make sense if managers are not themselves in the office on a typical day to visually supervise these employees, and is that a good use of a manager’s time anyway? Finally, in the NWoW, what training approaches make the most sense and have the most impact? If employees spend more and more time in front of screens, would they be willing to participate in computer-driven training initiatives? Does training need to evolve to become more blended, less didactic, and more on-demand? What are the most pressing KSAOs for leaders and individual contributors to learn? What accountabilities should be in place to demonstrate learning, attendance, and commitment to the training and learning objectives? It seems clear to us that a preponderance of research questions warrants consideration as organ-

472  Handbook of virtual work izational researchers continue to stay relevant and impactful in an experience of work that is qualitatively different than the one lived during and prior to the pandemic.

MULTIPLE TEAM MEMBERSHIP FACTORS Another factor that may be an additional stressor to individuals working virtually is that for many, the reality is not that they are no longer a member of a single team. Instead, the reality for most individuals who work virtually is that they are a member of multiple teams. In fact, some estimate that 65‒95 percent of knowledge workers are members of multiple teams at the same time (e.g., Mortensen et al., 2007; Zika-Viktorsson et al., 2006). In such situations, individual members must balance the competing demands of each team, and this may accentuate the stressors that such individuals face. Additionally, how much individuals dedicate to a given team may shape the resulting effectiveness of their teams. While discussed within a compelling theory piece over a decade ago (e.g., O’Leary et al., 2011), multiple team membership has not received sufficient attention overall or more specifically within the virtual work literature where such multiple team membership may be more pronounced because it is easier to ask people to drop into an online team meeting rather than coordinating logistics for in-person meetings. For example, as acknowledged by O’Leary and colleagues, we can envision multiple team membership having their effects at multiple levels of analysis. For instance, the task switching that is needed by individuals who are on multiple teams may increase the stress and strain experienced by such individuals and as a result, may reduce those individuals’ desire to stay within such teams and remain within organizations that require multiple team membership. In contrast, it may be that organizations that leverage multiple team membership allow more growth opportunities for employees and thus retention in such organizations may be higher. This begs the question of how many teams create the tipping point where such theorized individual-level challenges and/or benefits begin. This seems like an important empirical question for researchers to consider because it may be that there is an inverted-U relationship at play here whereby positive things that may accrue to individuals who are on multiple teams (e.g., more connections, individual learning, enhanced opportunities, personal growth, etc.) up to a point and beyond that point, negative factors (e.g., strain, stress, overload, feelings of being overwhelmed, as well as distractions and lower individual performance) may start to emerge. As such, we suggest that examining when such a tipping point occurs is important to consider. Likewise, we can envision work that attempts to dig into the individual-level factors that may cause the tipping point to be pushed out. For instance, individuals who have certain personality factors may not experience the downward effects of multiple team membership as soon as other individuals. Such a relationship would be in line with the work of Benoliel and Somech (2014) who found that participative leadership had different relationships with individual-level in-role performance depending on personality variables such as extraversion, agreeableness, conscientiousness, and neuroticism. As such, there may be value in examining various personality factors in shaping the effect of multiple team membership. Likewise, does individual experience with a given task or within a particular organization or team shape the influence of multiple team membership? For example, individuals with certain skill sets might be asked to contribute their skill, but once that work is done, they are no longer required to

Virtual work – where do we go from here?  473 interact or participate in the team’s work. How does this affect both the individual contributing the skill and the remaining team members – what are the effects on cohesion and shared mental models? Beyond the individual-level effects of multiple team membership, there has been limited work examining team-level effects. For instance, Maynard and colleagues (2012) provided evidence that the percentage of time allocated to the team (their proxy for multiple team membership) had a positive relationship with preparation activities, which in turn, had a positive impact on team effectiveness within their study of 60 global virtual supply chain teams. As alluded to, these authors leveraged a proxy variable by asking members to indicate the extent of time that members allocated to the team and then aggregated this score to the team-level of analysis. While this is a perfectly reasonable way to measure multiple team membership, it does assume that allocation is static, and members give the same amount of time to the team over time. This is likely an unreasonable assumption in most situations and therefore we would call on researchers to examine the dynamic nature of multiple team membership as doing so may provide some insights that currently are not apparent.

ORGANIZATIONAL POLICIES AND CULTURE Future virtual work research is by no means limited to the individual or team level of analysis. For instance, the return to office decisions that organizations are currently making and their consequences are timely and relevant topics for researchers to consider. Moreover, a culture that is friendly toward hybrid and remote work could be as instrumental to success as an unfavorable culture could doom the adoption, maintenance, and effectiveness of hybrid and remote work programs. Many organizations revisited their policies with respect to hybrid and remote work during the pandemic to make plans for the future of work in their respective organizations (Adnams, 2021). Typically, these consisted of cross-functional teams charged with recommending policies for issues such as where and when employees can work, stipends for work from home, IT, home office design, ergonomically friendly home office arrangements, parking and transportation considerations, training and development, compensation-related complexities, legal matters, and so on. Hybrid work was and continues to be a major challenge because the locations in which employees perform work duties can affect multiple facets of a business’s operations, and the effects vary depending on the nature of the business and its position in terms of distributed work maturity. Some of the more interesting policy decisions for organizational behavior and related researchers involve determining work modes, determining who belongs to what work mode, and whether and how to provide stipulations about the minimum number of days required to be in office (as well as during what times; Gratton, 2021). For example, Apple came under fire due to their decision to require employees to be in office on Mondays, Tuesdays, and Thursdays. Part of the challenge was that these decisions were made with no meaningful consultation – otherwise Apple executives might have discovered that employees actually tend to like being in the office on Wednesdays. Another problem was that the blanket policy ignored the activities employees perform and where those activities could be best performed (Berger, 2022) – in other words, the business need for employees to be in the office. Apple ended up losing a director of machine learning as well as many other key roles in the organization due

474  Handbook of virtual work to their rigid approach to hybrid work (Moss, 2022). On the flip side are Github and Dropbox CEOs. The co-founder and CEO had this to say about remote work (Sijbrandij, 2020): I proactively built an all-remote company after discovering early on that you don’t need everyone in the same building to achieve results. Our executives, managers, and individual contributors all work remote. We don’t even have an HQ. This avoids the complexities of having to cater to onsite and offsite employees. It also fosters a shared commitment to our unique way of operating and iterating to improve over time.

Dropbox’s Virtual First policy states that employees need to be in the office one day per week (Somers, 2021). The CEO, Drew Houston, pointed out that: “There’s so many great things about the remote environment … You have a lot of flexibility, you don't have to commute. But there’s no substitute for the in-person experience and technology can only go so far to simulate that. Going remote-only was not an option for us.” The examples above illustrate the vastly different positions executives take on hybrid and remote work. What would be valuable in future research is generating knowledge with respect to the criteria that are most important for informing decisions about organizing the locations of work. What organizational designs, industries, job and task characteristics, interactions, and other factors (e.g., temporal) provide the most crucial information with respect to determining where work is done? One could imagine ultimately a roadmap or decision tree that would ask decision makers the right questions in the right sequence to help them configure their organization for success. This of course would require a systematic program of research aimed to uncover these issues. However, with work becoming increasingly doable through electronic technology, we believe embarking on this research to be critical. Determining work modes and who belongs to what work mode is another interesting and sometimes thorny policy-related topic. A work mode is an individual’s assignment to a certain number of days in and out of the office within a timeframe. It could vary along a continuum, from all in office to all remote, for example. A middle ground could be 2.5 days in the office. While many employees and organizations desire a hybrid work mode, given the promise of flexibility, the reality is that this work mode is the most complex and chaotic. When (a) my work mode can be changing week by week as well as (b) the work modes of all with whom I interact, how do I find the right person at the right time for a work- or social-related connection? Managing team and inter-team coordination in a hybrid world of work, occurring at large scale, is a pressing research matter for virtual work researchers. So, while a policy is needed with respect to acceptable work modes, a policy or decision support system for assigning folks to work modes is also important. Decision-making authority also must be identified. In other words, on what basis is it determined if someone, or a role, is assigned to a particular work mode and who makes the final decision about that? If it is left to manager discretion, inconsistencies and biases across the organization will surely creep in (Gratton, 2021). Yet, managers and their staff understand their work tasks and requirements for success better than anyone else. Given all the policy creation over the past couple of years on this matter, it would be helpful if researchers could gauge the effectiveness of various approaches taken in those policies. Evidence regarding what works, and what are the pitfalls, would be enormously helpful. A final critical point in this section that we wish to identify briefly as an area for future research is the culture of the organization and top-management support. Our hunch is that some organizations have cultures and executives that are more supportive of hybrid and

Virtual work – where do we go from here?  475 remote work, thereby leading employees to be more willing to use these work modes, and paving the way for effective resource allocation, training, and reward structures that work (Dumitru, 2021). The flip side of it is when executives say they allow hybrid work but create a culture and environment that rewards those who spend long hours in the office with greater accessibility to bosses, more learning, development, and growth opportunities, and greater opportunity for promotions. Therefore, one might ask, what is a hybrid and remote friendly work culture? What do executives do to support and enable hybrid and remote work? What is the business case for hybrid and remote work and how does one conduct such business cases in their own organizations? These and other organizational culture and top-management team issues are ripe for future research at the organizational level (cf. Neeley, 2020).

THE FUTURE OF THE OFFICE Traditionally, in many organizations employees commuted to a central office for many reasons. Access to peers, supervisors, direct reports. Meetings were held by default in person. People worked roughly the same 9‒5, Monday to Friday schedule. Performance was measured by attendance and those who worked long hours in the office were often applauded as high performers, committed to their work and the organization’s success. Access to paper documents, company computers, software, the company intranet, collaboration spaces, and other technologies and physical tools was not accessible outside of headquarters (van Meel, 2011). The global pandemic changed all that. When virtually all corporate office workers were sent home without notice or a timeline for returning, organizations rapidly evolved to support remote work. Information technologies that permitted internet connections to electronic files, communication and collaboration software such as Zoom and Microsoft Teams, and home office furniture and equipment stipends or loans from the office were suddenly provided and permitted. Necessity drove this – while the global pandemic eliminated the central office, business needed to continue. Having spent two years working remotely, the workforce proved that business could continue and in fact do very well. Industry reports and organizational surveys indicated an improvement in employee productivity, on aggregate (e.g., DeFilippis et al., 2020; Global Workplace Analytics, 2020). People could work more flexibly, and more informally (e.g., standard office dress no longer applied for many; pets, children, roommates, or spouses might appear on someone’s video screen during a meeting). Meetings could run back-to-back because there was no longer a need to build in time to move from one meeting room, or building, to another. With commutes out of the equation and lunch breaks no longer universally held at noon, meetings could be held at a broader range of times. Did this come at a cost? Certainly. Both in terms of the infrastructure to set up remote workspaces, equipment, and information technology, as well as psychological and physical well-being costs related to work–life spillover, blurred boundaries, and less physical movement (see section on well-being). However, those infrastructure start-up costs have already been paid, and the maintenance is now routine. Well-being costs are ongoing, although this is a multi-faceted issue personalized to the individual, where some are thriving in remote work and others not (Microsoft, 2021).

476  Handbook of virtual work Despite the costs of remote work, many employees have adapted and wish to retain at least a degree of remote work in the future (Brenan, 2020; IBM, 2020). In theory, hybrid work could offer the best of both worlds – the social and collaborative benefits of the office + the flexibility and convenience of work from home. Now the question many executives and cross-functional organizational committees are faced with is: what is the purpose of a centralized office at all (cf. Cappelli, 2021)? Why should employees who can perform their jobs remotely be required to come to the office at all? Should employees be mandated to come to the office on certain days of the week or be present at the office a minimum number of days per week? What would a hybrid work policy look like, and what’s the best policy for an organization that will encourage retention, commitment, and engagement while supporting productivity, collaboration, flexibility, and deep focus work? What new equipment needs are required for success, efficiency, and inclusion (e.g., laptops, touchdown booking systems)? Who should get an assigned workspace and for what purpose? How should shared offices be handled with those individuals only working in the office on select days? How should meetings be held when in person, hybrid, and purely virtual meetings are all possible? We are currently at a juncture where organizations are looking for leading science to support these difficult decisions, and yet research is lagging practice in this respect (Mitchell & Brewer, in press). A future research agenda for the purpose of the office is needed. We view questions such as the above as vital for researchers to address. A common belief, especially among executives, is that the office leads to more spontaneous encounters that lead to new connections and innovation, and therefore some degree of in-person attendance at the office is needed (Wanichko, 2021). But is there any truth to this lay wisdom at all? Research using data from Microsoft did suggest that teams became more insular during the pandemic, working with each other more intensively but with those outside the team at lower intensities (Yang et al., 2022). Moreover, could there be contingencies, such as work design, an individual’s work activities and accountabilities, leadership style, organizational culture, and industry norms? From an organization of work perspective, groups of low-interdependence individuals performing routine work and managed independently by a manager with little true need to collaborate might not benefit from the office as much as highly interdependent teams in a dynamic work environment (Golden & Gajendran, 2019). This raises the possibility that different units in the organizations may have different policies, but then that raises fairness concerns and challenges – especially in a unionized environment. In healthcare, for example, there is a technostructure comprising corporate functions surrounding those deploying clinical functions. Perhaps the former can work remotely more than the latter due to different work demands, but that produces differential work opportunities across groups. Interestingly, even in healthcare, clinicians realized during the pandemic that many of their duties were possible through remote, or at least through virtual, means. Doctors could conduct follow-up appointments with patients when a physical exam wasn’t needed over Zoom or phone calls. These options were not typically given serious consideration prior to the pandemic. What might be a starting point for hypothesizing about the best uses of the office, therefore? Media synchronicity theory may provide one line of theorizing (Dennis et al., 2008). Media synchronicity theory essentially distinguishes between synchronous and asynchronous communication media. Synchronous communications are best reserved for convergence needs – such as when a group of individuals need to rapidly exchange perspectives, understand those perspectives and related assumptions, and generate agreement on issues (or a “shared

Virtual work – where do we go from here?  477 mental model” representing a current state of affairs). The office would likely be a good place for some synchronous communication because the in-person aspect of it is less cognitively demanding and more engaging due to being more natural (read: “Zoom fatigue”). This feature also lends itself to longer strategic or collaboration meetings, such as quarterly meetings, as well as organizational events such as celebrations, team building, and social gatherings. Asynchronous communication is better for conveyance needs; specifically, sharing information that can be understood at face value (Dennis et al., 2008). Email, instant messages, and document sharing are good examples of asynchronous communication (although occasionally exchanges on these media do happen in “real time”). According to media synchronicity theory, conveyance activities should happen around convergence activities. An example of this might be sending preparatory materials prior to a synchronous, in person meeting, with the objective of spending synchronous time on convergence activities. Inefficiencies become significant if employees are commuting to the office to attend a meeting that is mostly or all conveyance-based, since information in these meetings is normally shared in a one-way fashion with little need for interaction or involvement. The extent to which the office is therefore used for vital convergence activities through synchronous communication (e.g., in person meetings) might be a strong predictor of employee attitudes toward time spent in the office, productivity, and well-being. Future research should investigate this within the office and working from home contexts. An important issue here involves meeting management – which involves both the conduct of the in-meeting activities as well as those that surround the meeting (Kreamer et al., 2021; White, 2014). When should a meeting be run in a hybrid, in person, or virtual mode? Who makes this decision? With respect to hybrid meetings, how can they be conducted so that every participant is on a level playing field and with an eye toward including those who are remote as much as those who are co-located? Hybrid meetings are notoriously difficult to manage efficiently, to engage remote attendees, and to run seamlessly through the technology. If some team members are on site, but others are not, perhaps the meeting is best conducted virtually with those on-site individuals remaining in their personal workspaces rather than joining the meeting from a common room (rendering it hybrid). However, it will be important to structure such meetings in a way where the on-site workers do not resent those that are not in the office as it resulted in virtual meetings even though some are co-located. Longer meetings involving complicated decision making, long-term planning, and other forms of collaboration would certainly be more energizing and less draining than if done virtually (with hybrid being the least favored). What might be critical is establishing a focused team charter that deals with the elements of hybrid work specifically. However, business unit or organization-level policies may be needed for those that engage in extensive cross-teaming interactions, such as project management and multi-team coordination. Here we are speculating, and we encourage researchers to examine the existing meeting management literature and expand that literature to address these and other questions for hybrid work. What could be other uses of the office? Research opportunities include investigating the organizational culture, social, and reduced isolation benefits of the office (Cappelli, 2021). From a DEI perspective, a balance of office and work from home could help address the benefits of each for members of historically marginalized and equity-deserving groups, neurodiverse individuals, and individuals with disabilities. For example, marginalized groups reported fewer micro-aggressions during work from home, and Black employees reported better outcomes due to less need for “code switching” (Miller, 2021). On the flip side, do

478  Handbook of virtual work marginalized groups run the risk of being marginalized further by being overlooked for growth, development, and promotion opportunities when working remotely more often? Do those who choose to come into the office more often appear to be more committed, dedicated, and ambitious, and therefore access more leadership opportunities? We see the benefits of the office and benefits of work from home being potentially offset by the potential downsides of each. Future research should systematically look at the validity of the benefits and detriments of hybrid work for a range of variables, contexts, and peoples, and ways to design a hybrid work program that fits the context. Frameworks and guidance are needed for supporting this.

CONCLUSION It has certainly been an interesting couple of years in many respects but in terms of virtual work, the COVID-19 pandemic forced individual employees, teams, and organizations to pivot overnight to a greater emphasis on virtual work. As such, it resulted in a large-scale experiment of many of the ideas and recommendations developed from several decades of research investigating how best to structure virtual work. Fortunately, we had those research insights to help navigate the challenges introduced by this large-scale transition to virtual work. As with any experiment, some ideas proved out while some turned out to not be accurate hypotheses. Accordingly, we have lots of new insights about virtual work and the chapters in this Handbook have provided insights at various levels: technology, individual, team, and organization. Each chapter also highlighted areas where future work will be needed to continue to build our collective knowledge around virtual work. In this final chapter, we have highlighted a few select additional research areas that seem promising to investigate in the coming years. It is obvious that there is a sea of unanswered questions, dealing with the technological, psychological, sociological, and ecological implications of the ever-increasing integration of work, technological, and geospatial distance. It is likewise inevitable that these questions will evolve as our work does, and that despite the Sisyphean task of researching these dynamic changes, there will always be an opportunity to learn more ways to improve the quality of our work and the lives of those doing the work. Ultimately, the manifestations of virtual work will be as varied and unique as the work and the workers performing it. The global application and acceptance of virtual work, essentially overnight, could lead practitioners and researchers alike to consider the virtual space an unexplored area of research. This book should put to rest that notion but more importantly help practitioners and researchers effectively navigate an ever-evolving workplace. By spanning the impacts on individuals, teams, organizations, and our interactions with technology, this book has covered the diversity and breadth of the virtual work field and highlighted its importance in modern and future work. Hopefully this book has provided a significant roadmap for workers, industry leaders, and researchers to continue to advance the science and practice of working virtually. Fredrick Taylor forever changed the physical workplace with his scientific management theory. Answering the countless calls for more insight throughout the virtual work landscape gives researchers an opportunity to help lay the groundwork for a new normal. Continued effective and informative research in this space will go a long way in protecting and promoting a fair, inclusive, work environment.

Virtual work – where do we go from here?  479

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Index

5T concept 2, 61 “6 Rs” analysis 50 Abendroth, A. 134, 138 Adamovic, M. 373–4 adaptability 337–8, 443, 445 affect 164 affective events theory 166 affective path of enrichment 136 affective reactions 168 affective revolution 164 affective trust 229, 306, 312, 314–16, 335 affordances 1–20, 24–5, 28, 32–3 anthropomorphism 51 teamwork perspective 116–18 transactive memory systems 287 virtual teams 205 Agarwal, R. 169 age diversity 385, 388–9, 390 agency 49–50, 412–13, 415–16 agentic role, technology 203–4, 211 AI see artificial intelligence AI-mediated communication 50–51, 70 Aiken, M. 287 algorithms 33, 74, 77–8, 79, 111 Allen, T. D. 135, 137, 139, 408 allocation process, transactive systems 72, 73 ambidexterity 377, 445, 455 Amir, Y. 394 Anderson, N. 266, 411 Andres, H. P. 4 anthropomorphism 51, 79–80, 113 anticipation 327, 333, 336–7 Antino, M. 243 Antoni, C. H. 206, 268, 286–7 Application Programming Interfaces (APIs) 442–3, 451 Arena, M. 226 Argote, N. 278 Armstrong, D. J. 392 artificial intelligence (AI) 1, 41, 48 collective intelligence and 2, 67–88 design choices 45–6 embedded 49 embodiment 49, 79–80 human interaction 89–108, 109–27 public perceptions 111–14 research opportunities 50 teamwork perspective 109–27

trust in 45–6, 78–80, 115, 119, 121 virtual collaboration applications 52–8 Ashforth, B. E. 164 Ashkanasy, N. M. 164, 173 asynchronous communication 25–6, 259, 476–7 emotions and 176 lean media 429–31 team faultlines 249 virtual project teams 349, 351 work design 411 attention allocation 71, 73 attention systems 67, 72–3, 74, 76, 77 attributional biases 154–5 Auger, G. A. 175 augmentation AI/ML technologies 91–2 cognition/technology 67–88 intelligent technology 49–50 virtual collaboration 41–66 authentic leadership 227–8, 230 automation 50, 91–4, 109–10, 112, 114, 120 autonomy 91, 95, 113 asymmetries 104 automation distinction 50 enrichment link 136 leadership role 230 self-determination theory 220 self-driving vehicles 114 work design and 412, 415–16 awareness concept 117 awareness systems 70 Bailey, D. E. 41–2, 47, 48, 59 Barley, S. R. 41–2, 47, 48, 59, 372 Barsade, S.G. 164, 173 Bartel, C. A. 148 Bartlett, C. A. 426–7 Basile, K. A. 138 Beauregard, T. A. 138 Bedwell, W. L. 42–3, 259, 261–2, 272 behavioral strategies 138–9, 154 Bell, B. S. 225 Benbasat, I. 79–80 Benbya, H. 50 Benda, A. N. 278 Bennett, A. A. 416 Benoliel, P. 472 Bentley, T. A. 413 Bernardini, Sara 41

483

484  Handbook of virtual work Berners-Lee, Tim 1 Beutell, N. J. 134 bias 111, 121, 154–5, 172–3, 404 Bittner, E. 51 black box algorithms 79, 111 black box problem 95, 98 Blockchain 443, 451 Blomberg, J. 47 Blunden, H. 170 Bono, J. E. 395 Boos, M. 198 Bosch-Sijtsema, P. M. 366 bots 97, 101, 102 bottom-up design 60–61, 414 Boumgarden, P. 245 boundary management 133–4, 138–9, 153–4, 371–3 blurring of boundaries 133 boundary roles 457 Breuer, C. 148, 266–7, 270 Bring Your Own Device (BYOD) policies 30 Brodsky, A. 170 Brown, J. S. 444 Brown, S. L. 454 Bryant, S. M. 4 Bunderson, J. S. 245 burnout 134, 171, 175, 189, 357, 468 Bushe, G. R. 269 BYOD policies see Bring Your Own Device policies CAD see computer-aided design call centers 52, 57, 175 Carlson, J. R. 292 Carmi, G. 292 causal ambiguity 90–94, 97, 103–105 CAW see computer-assisted work CFO perspective see cues-filtered-out perspective Chae, S. 349 Chamakiotis, P. 279, 347, 349 channel expansion theory 413 Charalampous, M. 166–7, 176 Cherin, D. 386 Chiu, Y. T. 240 Chong, S. 220 Choo, J. 175 Chu, A. 269 Chudoba, K. M. 11 CI see collective intelligence clean desk policies 366 closed technologies 27–31 CMC see computer-mediated communication co-located subgroups 243, 368 co-location and knowledge sharing 431 Cogliser, C. C. 197

cognition 280–304 dimension, TPV 208–9 emotional considerations 129 functions 71, 74–5, 281–5, 288–90, 296, 299 knowledge 281–8, 296, 299 resources, directing/maintaining 152 structures 446 technology augmentation 67–88 trust 79, 228–9, 292, 306, 309, 314–16, 334–5 cohesion–trust relationship 315 Cole, P. 392 collaboration arrangements 449–51 approach, benefits of 315 decision-making 327–8 definitions 42–4 development 101–2 dimensions of 43 intelligent technology 41–66 performance framework 259, 260–263 platforms 30, 52, 270 problem solving 283 tools 27, 29–30, 201–2, 291 use of term 260 collective efficacy 337 collective intelligence (CI) 2, 67–88 Collective Knowledge Creation Potential 448 collectively experienced distance 208–9 collectively experienced information deficits 208–9 Collins, A. M. 367 communal public goods 28–9 communication challenges 404–5 media 427–35 norms 151 strategies 138–9 technologies 2, 21–40, 150–151 affordance perspective 24–5, 28, 32–3 collective intelligence 70 faultlines and 249 shared mental models 287 visibility 32–3 community networks 193–4 competencies general frameworks for 471 technology 291–2, 293–4 competitiveness 315, 445 compilational approach 318 complex adaptive systems 226 complexity, characteristics of 268 complexity leadership 225–6, 230 complexity theory 225–6 composition models 211, 318

Index  485 computational AI research 123 computer-aided design (CAD) 352–3 computer-assisted work (CAW) 5 computer-mediated communication (CMC) 4–5 deficiency models 10 emotional contagion 172 fit perspective 409 quality reduction 404–5 visibility 7 well-being and 466–7 computer-mediated work 3, 5 configurational dimensions 368–9, 376 conflict management 133–4 and trust 312, 315 connectivity 13–14, 33–4, 372–3 connectivity paradox 8, 13–14 constant connectivity 13–14, 33–4, 372–3 contact hypothesis 394 context-driven technologies 294 convergence needs 476–7 conveyance needs 477 Cook, D. 190–192 Cook, Tim 325 Cooper, C. D. 406, 414 Cooper-Thomas, H. D. 411 coordination artifacts 286 cooperative incentives 316 processes 296 theory 446 Costa, P. L. 197, 266, 310 coupling 448–53 Covid-19 pandemic 3, 14–15, 403, 404, 478 communication visibility 33 digitalization 257 emotional management 167, 173, 175, 178 face-to-face team transitions 347–8, 349 flexibility 447 gender effects 468–9 IBM virtual delivery process 52 ICT infrastructure 412 inclusiveness and 384 knowledge sharing 428, 432, 433–4 leadership 197–8, 216–34 the “new normal” 13–15, 257, 403, 470 office work impact 475–6 organizational configuration 376 remote work 12, 15, 146, 186, 188–9, 192–3, 280, 405–6, 473 self-regulation failure 407 social interaction skills 155 social isolation 406 spatiotemporal shifts 26 task interdependence 413

team faultlines 236 team resilience 325, 338 telework 130, 135–6, 139 temporal work tactics 154 videoconferencing 416 work–home interference 408 Cramton, C. D. 392 creative class cities 194 creativity 193, 279, 347–60 Cropanzano, R. 166 cues-filtered-out (CFO) perspective 10, 23, 26, 169, 200–202, 204 cues-left-in approach 24, 26 cultural diversity 25–7, 222–3, 331, 371, 389–90, 406, 427–8, 432–4 cultural intelligence 434 cultural pluralism 425 Cummings, A. 271, 331, 368 custom technologies 59 Darouei, M. 135 Day, A. 412 De Jong, B. A. 318 de Pillis, E. 469 de Vries, H. 167 DeChurch, L. 49, 220–221 decision making agency and 204 AI/ML technologies 89–90, 94–5 authority for 474 collective reasoning 77 resilience and 327–8 team cognition 283, 284, 289 DeCostanza, A. H. 49 deep acting 174 deficiency models 4, 10 DEIJ issues see Diversity, Equity, Inclusion, and Social Justice issues Delanoeije, J. 135, 166, 408 Dennis, A. R. 169, 348 design choices 45–6 design principles 98–102, 103 design recommendations 118–19, 121 deterministic approaches 10–11, 23–4 Dettmers, J. 138 Diederich, S. 51 Diefendorff, J. M. 175 digital communication platforms 425 digital communication visibility 33 digital nomads 129, 186–96, 467 antecedents to 189–92 definitions 187–9 freedom factor 189–90 digital skills development 395 digital workplace design 403–24

486  Handbook of virtual work digitalization 257–77 Dimas, I. D. 129 Dinh, J. V. 229 Dionne, S. D. 395 direct messaging 201–2 Dirks, K. T. 318 disabilities 385, 390 discriminatory practices 388, 391 dispositional attributions 154–5 distance work 131, 208–9, 391–2 distributed systems 454–5 distributed teams 264, 369–70 distributed workgroups 42 Diversity, Equity, Inclusion, and Social Justice (DEIJ) issues 13 benefits of 249–50, 386, 425 beliefs 209–10, 249 communication 393 configuration 235 equal opportunities 393 equality 366, 386 ethnicity 384, 389, 390 formal diversity communication 393 management 361–2, 384–5, 387–90, 391–2 racial minorities 384 document sharing 206 documentation and trust 267 Dorris, A. D. 173 Dulebohn, J. H. 224 Dutton, J. E. 418 Duxbury, L. 259, 266 dynamic value networks 442–64 e-remote work 166–7, 176 see also telework Eagly, A. H. 388 EASI model see emotions as social information model Edmondson, A. 314, 394 efficacy 151, 329, 336–7, 340–341 Einstein, Albert 67 Eisenhardt, K. M. 454 electronic monitoring 415 electronic performance monitoring (EPM) 471 Electronic Propinquity Theory 23 electronic quasi-integration 443–4 Ellis, Edward S. 48 Ellison, N. B. 10 email messages 172, 204 email platforms 46 embodiment, AI 49, 79–80 emergent states 262, 311–12 emojis 170, 172, 177, 212 emotional contagion 129, 170–173, 177–8, 315 emotional expression 165, 168–70, 176–80

emotional intelligence 173–4, 178, 229 emotional labor 174 emotional management 164–85, 315–16 emotional presence 175 emotional regulation 165, 178–9 emotional trust 79–80 emotions 164–85 definitions 164 enrichment and 136 feeling emotions 165, 176 interpreting 168–70, 176–8 mood distinction 164 spreading 176–8 team perceived virtuality 209 emotions as social information (EASI) model 129, 168–70, 176 enrichment 136 Enterprise 2.0 technologies 29–30 enterprise social media (ESM) 4, 6, 8, 10, 27, 30–31, 35 entrepreneurial leadership 226 entrepreneurship research 193–4 episodic framework, trust 308 EPM see electronic performance monitoring Erez, M. 362 Eseryel, U. Y. 224, 225 ESM see enterprise social media ethnocultural differences 389, 394 evaluation principal–agent relationships 96–7 technology interdependencies 99 Evans, S. K. 8 expatriation 429 expertise configuration 369 extensible Markup Language (XML) 451 face-to-face (F2F) work categorization problems 280 returning to 223 as rich media 429 transitioning to virtual 347–60 virtual teams mimicking 317 family domain, definition 133 family-to-work conflict (FWC) 134–7 Farh, C. I. 392 faultlines activated 237, 240, 241–4, 246–7 -based virtual teams 235–56 definition 235 demographic 236–7 dormant 237, 241–2, 247–8 entrenchment concept 247 geographic location 238, 242 management 247–50 research 198

Index  487 trust and 310 feature specificity, intelligent technology 46–7 feedback asynchronous communication 411 electronic monitoring 415 learning by technology 101 principal–agent relationships 96–7 teaming perspective 119 technology interdependencies 99 trust and 312, 313–14 Feitosa, J. 306 Ferriss, T. 188 Fiore, S. M. 278, 336 Flathmann, C. 2 “flat–rocky globe” paradox 433 flexibility boundary management 133 flexplace 131, 137 flextime 137 value networks 446–7 work 131, 134, 136, 366, 371–2, 385 Florida, R. 194 follow the sun practices 370 Fonner, K. L. 138, 409 Ford, R. C. 220 formalization clarity 245 Formentin, M. J. 175 Foster, M. 414 Fox, J. 9 freelance work 222 Friedman, Thomas 433 friendship-related cadence 156 friendships, barriers to 155 Fulk, J. 10 FWC see family-to-work conflict Gabriel, A. S. 175 Gajendran, R. S. 135, 406, 408 gamification 415 Gao, G. 468 GDSS see group decision support system Ge, H. 287 Gelfand, M. J. 433–4 gender 384, 385, 388, 390, 468–9 constant connectivity and 372 interruptions at work 221–2 telework and 137 generational differences 222–3 generative templates 452 “geoarbitrage” 188 “geographic disadvantage” 392 geographical dispersion 331–2, 368–9 George, C. 470 Ghoshal, S. 426–7 Gibbs, J. L. 1, 11–12, 24, 147, 240

Gibson, C. B. 11, 147, 370, 377, 417, 470 Gibson, D. E. 164 Gibson, J. J. 5 gig economy 192, 222 Gilson, L. L. 236, 281, 466–7 Glikson, E. 45, 49, 59, 79, 80 global identity 434–5 global organizations 425–41 global virtual teams (GVTs) 331–2, 371–2, 374–5 globalization 219, 425, 433 Glowinski, D. 326–7 goal interdependence 44, 74 goal setting 412 goal structuring 245–6 Goel, L. 289 Golden, T. D. 128, 134–5, 137, 139, 176, 406 Gordon, F. M. 44 Gosain, S. 362 Gray, E. K. 164 “Great Resignation” 217, 223, 230 Greenhaus, J. H. 134, 136 Griffith, E. 2, 46 Grote, G. 263–5, 272–3 group decision support system (GDSS) 5 group identity 391 groupware 119 Grünewald, H. 415 Guo, Z. 219 Gupta, P. 72, 75 GVTs see global virtual teams Haas, M. 157 HAI see human–automation interaction Halford, S. 367–8 Hancock, J. T. 50–51, 70 Handke, L. 197, 207, 470 Hannonen, O. 188 Hardwig, T. 198 Harrison, D. A. 135, 406, 408 Hartwig, A. 333 HATs see human–autonomy teams healthcare 476 Henke, J. 470 hierarchical clarity 245 Hinds, P. J. 34, 51, 238, 372, 389 Hoch, J. E. 223–4, 225, 470 Hochschild, A. R. 174 Hoffman, C. L. 136 Holdsworth, L. 167 Hollenbeck, J. R. 202, 259 Hollnagel, E. 282 Homan, A. C. 250 home-to-work interference 408 homeworking 138, 367, 376 Hosseini, M. R. 259

488  Handbook of virtual work Houston, Drew 474 HRI see human–robot interaction HRM see Human Resource Management Huang, Y. 416 human–agent teams 221 human–AI interaction 89–108, 109–27 human–automation interaction (HAI) 2, 109–10, 112, 114, 120 human–autonomy teams (HATs) 49, 50, 211 human capital paradox 227 human-centered AI 110, 111–12 human-centered design 5–6, 263–4, 273 human centric model 257 human–human teaming 116–17 human-machine interaction 89–108 Human Resource Management (HRM) 270 human–robot interaction (HRI) 50 human–technology teams 295, 299 Humphrey, R. H. 164 Hutchby, I. 6 hybrid measurement 157 meetings 477 remote work 133 teams 280, 317–18, 358 work 131–2, 476 digitalization 258 emotional management 178 human centric model 257 leadership 217 organizational configuration 376 people management practices 376–7 perceived proximity 260 research studies 157, 470, 473, 478 social categorization 434 workplace design 366–8 hyperpersonal communication perspective 24 I-P-O framework see input-process-outcome framework IBM virtual delivery process 52, 57–8 ICT-SMM models 200, 205–7, 210, 211, 268 ICTs see information and communication technologies idea generation 352–3 identity research 193 Ilgen, D. R. 47–8 IMOI framework see input-mediator-output-input framework impression management 153 incivility 244 inclusive attitudes 394 inclusiveness 361, 366, 384–402 income disparities 222 individual personality factors 472

individual resilience 327–9 individualism 190 inferential processes 168 influence 117–18 information asymmetries 104 information and communication technologies (ICTs) 3, 21, 347–51, 395 Covid-19 pandemic 412 diversity management 391–2 flexible work and 385 leadership and 218, 220–221, 228–9 mastery of 411–12, 415 shared mental models 200, 205–7, 210–211, 268 social subsystem and 410 technostress and 407 value creation 362 work–home interference 408 information deficits 208–9 information exchange templates 452 information–knowledge contrast 427 information overload 407 information processing 387, 446 information provision, AI/ML 92–3, 95–6, 97–101 information seeking skills 152 information sharing 152, 314, 451–3 “information space” 257 information systems support 454–5 information transfer 150, 151 information visibility 30–31 innovation–creativity distinction 347 input-mediator-output-input (IMOI) framework 48, 49–50, 261 input-process-outcome (I-P-O) framework 48 instant messaging groups 204 integration–responsiveness balance 426–7 intelligence conceptualization of 67 definitions 71 intelligent technology early perspectives 48–52 empirical work 52–8 research approach 41, 45 virtual collaboration 2, 41–66 inter-firm organization 442–64 interdependencies AI/ML technologies 90, 93–4, 105 interface focus 457 team resilience 327 technology components 97–100 interface-focused pathways 442–64 interface minding 443, 446, 448, 453–5 interface structuring 443, 446 international organizations 426

Index  489 international travel 188 internet ubiquity 28 interpersonal processes and trust 308, 314–16 interpersonal relationships 435 interpersonal skills 155 interpretation support 454–5 interruptions at work 221–2, 230, 338, 408 intersectionality 469 intimacy 169 intra-team communication 211 intra-team incivility 244 Ishii, K. 175 isolation configuration 332 Jackowska, M. 392 Jarvenpaa, S. L. 51, 331 Jermier, J. M. 225 job autonomy 412 job crafting 60, 140, 176, 414, 417–18 job demands 411, 413–14, 416–17 job-demands-resources model 129, 167–8, 176 job design see work design job resources 129, 167–8, 176, 411 Jonassen, D. H. 288–9 Jonasson, C. 389–90 Jones, S. K. 470 Jong, B. A. 266 Jung, M. 51 Kark, R. 388 Kauffmann, D. 292 Kellogg, K. C. 50 Kenda, R. 218, 226–7 Kerr, S. 225 key performance indicators (KPIs) 96 Kim, H.-J. 175 Kirkman, B. L. 11, 147–8, 218–19, 329, 333, 389 Klitmøller, A. 389–90 Klonek, F. 413–14 Klotz, Anthony 217 Kneisel, E. 269 Kniffin, K. M. 377 Knight, C. 411 knowledge definition 427 memory and 453–4 knowledge communities 416 knowledge coordination 284–5, 295–6 knowledge creation 448 knowledge management 151–2, 370 knowledge sharing communication media and 427–35 digital nomads 191 enterprise social media and 10 multinational organizations 362, 425–7

resilience and 336 team cognition 285–90 team mental models 267 trust and 314 value networks 448 Knowledge-Skills-Abilities-Other Characteristics (KSAOs) 291–2, 469–72 knowledge transfer 427 Kozlowski, S. J. 223–4, 225, 337, 470 KPIs see key performance indicators Kreiner, G. E. 154 Krumm, S. 291–2 KSAOs see Knowledge-Skills-Abilities-Other Characteristics Kurland, N. B. 406 Kwon, D. 288–9 Lang, C. 287 language asymmetry 369 language diversity 389 language proficiency 429 Lapierre, L. M. 135, 138 laptops as place 365 Larson, L. 49, 128, 156, 220–221 Lau, D. C. 235 Lauring, J. 361, 389–90, 392 Lautsch, B. A. 412 “lead users” 192 leadership 223–8 Covid-19 pandemic 197–8, 216–34 dialogue-based 395 distributed functional 225 emotional contagion 177–8 faultline-based teams 246, 250 formal 219, 228–30 functional 224–5, 230 global–local identity 434 goal-directed 395 hierarchical 218, 223, 230 human–AI interaction 114, 117 inclusiveness 386, 390, 393, 395 informal 219, 228–30 KSAOs and 469–70 mental health approaches 467 multiple teams 472 participative 472 remote 265 stagnant 227 substitutes for 225 trust in 228–9, 310–311, 339, 394 VT outcomes 236, 340 work design 265 “leaky pipes” 31 lean communication 362 lean media 429–32, 435

490  Handbook of virtual work learning interface-focused pathways 442–64 resilience and 327, 333, 336–7 socialization and 411 learning by technology 89–108 Lee, J. D. 80 Leonardi, P. M. 6–8, 10, 24, 31 Leroy, S. 221, 386 Leslie, L. M. 140 Li, J. 174 Lind, M. R. 388 line managers 393–4 Litchfield, R. C. 129 local–global context 375, 426 local identity, MNOs 434 location intelligent technology and 45 place as 365 location faultlines 238, 242 location independence 129, 186–7 “lock-in”, organizations 456, 458 Lockey, S. 79 loose coupling 445, 448–9, 451, 452–3 loose cultures 433–4 looseness 377, 444, 450, 452, 457 Lyons, J. B. 50 McAfee, A. 29 McAllister, D. J. 229 McCarthy, K. 469 McEwan, B. 9 machine learning (ML) 60, 77, 89–108 Machine Theory of CI 78 McNall, L. A. 136 McNeese, N. J. 287 Majchrzak, A. 2, 24 Makarius, E. E. 128, 156 Makimoto, T. 187 Malhotra, A. 246 Malle, B. F. 79 management AI/ML technologies 89–108 inclusiveness 392, 393–5 meetings 477 monitoring approach 412–13 of people 364, 370–374, 376–7 role ambiguity and 411 social subsystem perspective 409–10 trust and 271 virtual team resilience 341–2 management systems, AI-enabled 112, 113 “managing physical artifacts” 139 Mangla, U. 2 Mann, S. 167 Manners, D. 187

marginalized groups 386, 390, 477–8 market research 352 Markman, K. M. 175 Marks, M. A. 290, 308, 319 Marlow, S.L. 332 Maruping, L. M. 169 mastery 411–12, 415 Mathieu, J. E. 11, 147–8, 218–19, 329 Mayer, J. D. 173 Maynard, M. T. 250, 473 Mazur, A. P. 7 media affordances 6 media characteristics approach 175 media richness theory (MRT) 10, 23, 169, 177, 200, 202, 240, 266, 362 see also rich media media synchronicity theory (MST) 150, 362, 430, 476–7 meetings 475, 477 Meister, A. 242, 247–8 memory aids 75 memory systems 453–4 see also transactive memory systems mental health 466–8 mental models 267–71, 283, 286–8, 296, 312, 455 see also shared mental models mental states 78 mergers 248–9 Metiu, A. 238 migration 188 Miles, J. E. 202 mindfulness 453 minimalism 191 minimization, information provision 98–9 minority groups 384, 393–4 ML see machine learning MNOs see multinational organizations moderators, telework outcomes 137–8 modular structure 449–51 Mohammed, S. 268 monitoring learning by technology 101–2 management approach 412–13 mastery of technology 415 principal–agent relationships 97 resilience and 327, 333–5 technology interdependencies 99 monitoring systems 202 mood 136, 164 Mor Barak, M. 386 Morganson, V. J. 128, 136 Morrison-Smith, S. 269–70 Mortensen, M. 157, 368, 389 motivation–trust relationship 316 motivational affordances 414

Index  491 MRT see media richness theory MST see media synchronicity theory MTM see multiple-team membership MTS see multi-team systems Müller, R. 206, 268, 286–7 multi-attribute diversity 237 multi-team systems (MTS) 71 multilingual organizations 369 multinational organizations (MNOs) 362, 368–9, 425–41 multiple-team membership (MTM) 68, 70–71, 75, 212, 472–3 Murnighan, J. K. 235 Murray, A. 49, 57, 77 Musk, Elon 216, 223 Nadler, J. 172 Navick, M. 1, 7 Nemiro, J. E. 347 network-based competence 444 network identity 456 New World of Work (NWoW) 470–471 Newman, S. A. 220 Nilles, Jack 131 Nishii, L. H. 386 Nordbäck, E. 361, 372 Norman, D. A. 5–6 Nurmi, N. 34, 361, 372, 467 NWoW see New World of Work Nyberg, A. J. 407 Ocker, R. J. 246 OEM see Original Equipment Manufacturer office work future of 475–8 organizational policies 473 telework disconnection 367 offloading 295–9 Ojala, S. 138 O’Leary, M. B. 331, 368, 472 O’Neill, T. 48, 49 online communities 201, 416 online gaming 290–291 online working skills 191–2 onlooker effect 33 open offices 366 open technologies 27–31, 34–5 operational leaders 226 opportunistic behavior 105 orchestration approach 442–64 organization-wide design concepts 273 organizational climate 363–83 organizational configuration 368–70, 376 organizational contexts communication technologies 22

for virtual work 363–83 organizational culture 148, 210, 217, 310–311, 377, 473–5 organizational memory 453–4 organizational policies 371, 473–5 organizational public goods 27–9 organizational socialization 411 organizational trust 306, 310–311 organizing mechanisms 443–5 Original Equipment Manufacturer (OEM) 450 Ötting, S. K. 50 P-E fit theory see person–environment fit theory Panteli, N. 279, 347 paradoxes 7–8, 13–14 paradoxical virtual leadership 218, 225, 226–7, 230 parallelism 430 Parker, S. K. 263–5, 272–3, 362, 410–411, 412, 413–14, 417 part-time teleworking 132–3, 166 Paul, R. 266, 269, 289 pen and paper technique 352 Peñarroja, V. 240 people management 364, 370–374, 376–7 perceived proximity 7, 155, 157, 208, 259–60, 432 perceived virtuality 207–9, 211 perceptual biases 154–5 performance team resilience 326, 337–8 value networks link 455–6 performance evaluation 96–7, 99, 101, 411, 415 performance management 471 permeability, boundaries 133 Perrow, C. 103–4 Perry, J. L. 386 persistence 338 person–environment (P-E) fit theory 377 person–job adjustment 176 person–job fit 414 personality factors 166, 224, 472 Piorkowski, D. 59–60 place–space distinction 364–5 Pluut, H. 135 polarization 242–3 Polzer, J. T. 242–3 Poole, M. S. 445 Powell, G. N. 136 power distance cultures 434 power dynamics 367 Pratt, M. G. 418 precarious work 192 Priest, H. A. 337 principal–agent relationships 94–7

492  Handbook of virtual work principal–agent theory 2, 90, 94–5, 100, 105 product design 347–60, 450 productivity levels 217–18 productivity losses 71 professional freedom 189 project management platforms 205 project teams 249, 279, 318, 347–60 “projectification” 258 propensity to trust 309 prototyping 353, 355 proximity see perceived proximity psychological distance 155 psychological safety 229, 314–15, 329, 335, 340, 374, 435 public good 27–9 Pudelko, M. 369 punctuated equilibrium model 289 Purvanova, R. K. 147, 218, 226–7, 395 Qiu, L. 79–80 quasi-integration 444 R&D centers 433 Raveendran, M. 44 real-time information sharing 451–2 real-world systems 114–15, 118, 120, 123 reasoning systems 67, 72, 73–4, 76, 77 recommender systems 112 regulation of human–AI interaction 121–2 rehearsability 430–431 Rehm, S. 289 Reichenberger, I. 187, 189–90, 192 Reimann, M. 134, 138 relational actions 96, 97–102 relational cadence 156 relational dynamics 367 relational improvements 413, 416 relational power 205 relational rents 442, 459 relationship-oriented behaviors 225 reliability of AI 79 remote work 69–70, 363 call centers 52 Covid-19 pandemic 12, 15, 146, 186, 188–9, 192–3, 280, 405–6, 473 ecosystems for 194 emotional management 166–8, 176 hybrid work comparison 376 skills for 146–63 telework relationship 131–2 travel and 186–96 visibility skills 153 remoteness 128, 146–63 repatriation 429 reprocessability 431

resilience 278, 325–46 behavioral markers 341–2 resource-based view 448 resource theories 134 responding, resilience and 327, 333–5 retrieval process 72, 73 Rice, R. E. 9 rich media 429–30 see also media richness theory richness 443 Rico, R. 198, 246 Ridings, C. M. 331 “road warriors” 136 Roberson, Q. 386 Robert, L. 48 robotics AI-enabled 112, 113 Rockmann, K. W. 418 role ambiguity 411 Roloff, M. E. 409 Roscoe, R. D. 60 Rose, J. 137 Ruiz, J. 269–70 Ruthotto, I. 469 SAAS model see software as a service model Sai, L. 468 Saikayasit, R. 286 Salas, E. 336, 337 Salovey, P. 173 Sanchez, R. 447 scaffolding 295–9 Schmidtke, J. M. 271 Schulze, J. 291–2 Schweitzer, L. 259, 266 Seeber, I. 49 selection bias 404 selective leaders 227 selective memory bias 172–3 self-determination theory 220 self-driving vehicles 112, 113–14, 120, 122 self-regulation failure 407, 411 sensemaking theory 446, 454–5 sexual orientation 386, 469 shared cognition 278 shared leadership 224, 225, 230 shared mental models (SMM) 152, 200, 205–207, 210–211, 225, 267 ICT SMM 200, 205–7, 210, 211, 268 resilience and 336, 340 team cognition 283, 286, 296 trust levels 312, 316 shared understanding 395 sharing vs. control tensions 8 Sharples, S. 286

Index  493 Sheldon, O. J. 42 Shockley, K. M. 137 Shore, L. M. 386 Short, J. 169 Siemon, D. 286 signaling reliability 153 SIP Theory see Social Information Processing Theory situated nature, virtual work 364, 374–5 situational awareness 154–5 Sivunen, A. 2 skills online working 191–2 remote/virtual work 146–63 well-being association 166 SLATES technology components 29 SMART work design 362, 403–24 SMM see shared mental models social categorization theory 387–9, 434 social construction theories 6, 24, 201–3 social identity theory 23–4, 367, 387–8, 434 Social Information Processing (SIP) Theory 10, 23, 24 social information theory 169–70 social isolation 405–6 social knowledge 431–2 social media 10, 24, 30 social networks 173 social norms 372–3 social presence theory (SPT) 10, 23, 169, 200 social role theory 372 social subsystem 404, 409–14 social support 374, 413 socialization-oriented technology 416 socialization processes 406, 411, 449 societal trends, Covid-19 pandemic 219 socio-cultural identities 434–5 socio-emotional inclusiveness 386–7 socio-material team practices 221 socio-technical systems (STS) 61, 409 socio-technical work design 258, 265, 273, 409 software as a service (SAAS) model 29 Soga, L. 203 Solís, M. 137 Somech, A. 472 Song, Q. 416 space–place distinction 364–5 spatial dispersion 147–8, 331, 368–9 spatial distribution 259 spatial freedom 189–90 spatial strategies 138 spatiotemporal shift, technology 25–7, 33 specialization clarity 245 specificity intelligent technology 46–8

virtual collaboration 47–8 SPT see social presence theory stability of work 192–3 Stache, L. C. 138 standardization 98–9, 120, 121–2 Staples, D. S. 240 status conflict 243–4 stimulating jobs 411, 415 Stoverink, A. C. 333, 338 strain-based work–family conflict 134–5 Straube, J. 240 stress 167, 337, 407, 412, 416 structural supports 225, 230 structuring 448–9 STS see socio-technical systems subgroup configuration 368–70 subgroup polarization 242–3 subjective virtuality 470 substitution perspective 91–2 surface acting 174–5 synchronous communication 25–6, 176, 249, 349, 351, 476–7 synergistic leaders 227 tacit knowledge 427, 430, 431–2 Tannenbaum, S. 337 TAS see transactive attention systems task aspects, work design 404, 407–8 task-based communication 313–14 task competencies 293–4 task contingent technologies 295 task interdependence 44, 268, 269, 413 task modularity 89, 102 task-oriented behavior 225 task-oriented communication 206 task performance 262 task-related inclusiveness 386–7, 390 task-related individual attributes 242 task role assignment 245–6 task-technology fit 150 taskwork theory 294–5 Taylor, Fredrick 478 team concept of 67 definition 326 team adaptability 337–8 team cognition 114, 117–18, 278, 280–304 team composition 310 team contingent technologies 295 team coordination 284–5, 290–291 team diversity 222 team dynamics 117 team faultlines 198, 235–56 team functioning 210, 249, 329, 369 team maintenance 337

494  Handbook of virtual work team member collaboration 257–77 team mental models (TMM) 267–8, 269–71, 286–8 team norms 340 team perceived virtuality (TPV) 207–9, 211 team potency 337 team processes technology challenges 220–221 technology fueling 329 team resilience 278, 325–46 team staffing 248–9 team structural moderators 244–6 team structure clarity 245 team structuring 248–9 team trust 305–24 team viability 174 team virtuality conceptualizations of 236 definitions 11, 329 dimensions of 218–19 personality composition 224 see also virtual team... teaming perspective 114–19, 122–3 teamwork collective intelligence 67 complexity of 68–71 human–AI interaction 109–27 training for 271 teamwork competencies 293–4 teamwork theory 294–5 technical subsystem 404, 409–10, 414–17 technological affordances 1–20, 28, 205, 287, 414 technological determinism 10–11, 23–4 technologized team relationship 200, 203–5 technology 1–20, 23–5, 49 autonomy of 95 cognitive systems and 74 as context 200–201 as creation medium 221 feature-based perspective 42 leadership challenges 220–221 social construction of 201–3 as sociomaterial team practice 221 as team context 220–221 teams relationship with 197, 199–215 virtual team cognition 293–4 technology choice 150–151 technology competencies 291–2, 293–4 technology interdependencies 97–100 technology-mediated problem solving 288–9 technology as teammate 221, 467 technostress 167, 407, 412, 416 Teece, D. J. 447 telecommuting 3, 68, 131, 149 telecommuting intensity 148

teleoperation 51 telepresence 51 telepressure 133–4 telework 3, 131–3 definitions 131–2 emotions and 176, 178 office work disconnection 367 time-space continuum 130–145 two-dimension framework 128 work–family interface 130–145 work–home interference 408 see also e-remote work telework intensity 406, 409, 412–13 temperament 164 temporal constraints 365 temporal dispersion 331–2, 368–9 temporal dynamics 308 temporal strategies 138–9, 154 temporary teams 258, 347–60 ten Brummelhuis, L. L. 34 tensions 7–8 digital nomads 190 leadership 227 orchestration 445 Tenzer, H. 369 text-based communication 170, 172, 178 Thatcher, S. 198, 248 “Thinking in 5T” concept 2, 61 Thompson, L. 172, 188, 190, 192 tight coupling 94, 104 tight–loose ambidexterity 377 tight–loose cultures 433–4 time-based strategies 138–9, 154 time-based work–family conflict 134–5 TMS see transactive memory systems TMTs see top management teams tolerable demands 413–14, 416–17 tolerance for ambiguity 310 top management teams (TMTs) 248–9, 393 top-down design 60–61, 414 Total Work Health 466–8 tourism 188, 194 TPV see team perceived virtuality training NWoW approach 471 for teamwork 271 transactive attention systems (TAS) 67, 72–3, 74, 76 transactive memory systems (TMS) 67, 72–7, 246, 283, 287–8, 296 transactive reasoning systems (TRS) 67, 72, 73–4, 76 Transactive Systems Model of Collective Intelligence 67–8 transformational leadership 246

Index  495 transnational organizations 426 transparency 79, 339 transportable technologies 295 travel 186–96 Treem, J. W. 6–7, 10, 24 Troll, E. S. 407 Troup, C. 137 TRS see transactive reasoning systems trust 155, 305–7, 395 AI 45–6, 78–80, 115, 119, 121 asymmetry 318 consensus 318 definitions 78, 306 faultline-based teams 249 individual-level 309–10 initial 307, 312 interpersonal relationships and 435 leadership 228–9, 310–311, 339, 394 measurement of 307, 318 multi-level inputs 309–11 mutual 334–5 operationalization 305–7 swift 307, 312–13 team resilience 331, 334–5, 339 virtual collaboration 265–7 virtual teams 228, 278, 292, 305–24 work design for 269–71 ubiquitous technologies 4, 11, 28, 116, 200 Uhl-Bien, M. 226 Ullman, D. 79 updating processes 72, 73 Valacich, J. S. 169 value co-creation 442 Value Network Flexibility 446–7 value networks 442–64 van de Ven, A. H. 445 van der Kamp, M. 245–6 van Kleef, G. A. 165 “van life” phenomenon 188 Van Zoonen, W. 24–5 Velez-Calle, A. 391 Verbruggen, M. 135, 166 Vermeulen, F. 370 video use 150 videoconferencing 70, 220, 309, 373, 395, 416 virtual collaboration 2, 41–66 AI applications in 52–8 early perspectives 48–52 hybrid work and 260 team mental models 267–8 trust in 265–7 virtual interactions 105, 288, 299, 310, 373 “virtual network emotional intelligence” 174

virtual place 365 virtual project teams (VPTs) 249, 279, 318, 347–60 virtual reality (VR) 435–6, 467 virtual team cognition (VTC) 280–304 virtual team effectiveness 236, 241–7, 281, 284, 329, 336–7, 340–341 virtual team resilience 278, 325–46 virtual teams (VTs) 3 adaptability 338 collective intelligence 67–88 definitions 197, 202, 280 emotional management 174 faultline-based 235–56 leadership 221 remote work 69–70 trust 228, 278, 292, 305–24 work design 264 virtual work challenges 404–8 characteristics 391 contextual influences 364–70 definitions 3, 26, 363 individual skills for 146–63 intervention strategies 468 the “new normal” 13–15, 257, 403, 470 research agenda 466–82 virtual work climate 373–4 virtual work self-efficacy 151 virtuality challenges of 149–56 of communication technologies 22 conceptualizations of 202, 258 as a continuum 259 definitions 11, 110, 146, 147, 197, 329–30, 404 dimensions of 69, 236, 258–9 emotions and 165 knowledge sharing relationship 428–30 as multidimensional construct 266 remoteness distinction 146–9 skills framework 146–63 subjective perception of 470 telework and 128 use of term 4 variables 266 virtuality beliefs 209–10 virtualness, dimensions of 44 visibility 7–8, 24–5, 30–31, 32–3 visibility management 32–3 visibility skills 153 visionary leadership 224–5 visualization tools 286 voice behaviors 153 VPTs see virtual project teams

496  Handbook of virtual work VR see virtual reality VTC see virtual team cognition VTs see virtual teams Wageman, R. 44 Waizenegger, L. 28 Wang, B. 167, 362, 404, 407, 410, 413, 415 Warkentin, M. E. 338 Watson, D. 164 Weick, K. E. 445, 455 Weiss, H. M. 166 well-being 466–8 cognitive-emotional considerations 129 constant connectivity and 13–14 during Covid-19 475 digital nomads 193 in remote work 166–8 trust and 314 WFC see work–family conflict Wheelan, S. A. 219 Wilder, B. 60 Wildman, J. L. 259 Williams–Kilburn tube 1 Wilson, J. M. 432 Windeler, J. B. 166 Woldoff, R. A. 129 Wolf, C. 47 women see gender Woolley, A. 2, 45, 49, 59, 72, 75, 79, 80 work design 269–71, 366–8, 376 human-centered 263–4, 273 SMART approach 403–24 socio-technical concept 258, 265, 273 work–family balance 140 work–family conflict (WFC) 26, 134–6, 138, 408, 412–13 behavior-based 134 work–family interface, telework 130–145

work-from-anywhere 149 work-from-home 3, 363 boundary management 134 marginalized groups 477–8 telecommuting and 149 telework relationship 131 work design 375 work/home boundary theory 371 work–home interference 408, 412, 413 work intensification 413 work–life balance 128, 229, 377, 413–14 work–life boundary challenges 221–2 work–life conflict 34 work modes 474, 475 “work tourism” 194 workflow platform technologies 57 workplace design 366–8, 376 World Wide Web (WWW) 1 Wrzesniewski, A. 418 WWW see World Wide Web Xiao, Y. 406 XML see extensible Markup Language Xue, Y. 292 Yang, L. 432 You, S. 48 Yuan, Y. C. 10 Zaggl, M. A. 2 Zammuto, R. F. 44 Zander, L. 348 Zhang, S. 137, 415, 417 Zheng, J. F. 51 “Zoom fatigue” 15, 220, 229, 373, 468 Zuckerberg, Mark 363