The Wiley Blackwell handbook of the psychology of team working and collaborative processes 9781118903261, 1118903269, 9781118909997, 243-245-271-2

Table of contents :
Content: About the Editors 000 About the Contributors 000 Foreword 000 Series Preface 000 Supported Charity: Railway Children 000 Introduction 1 1 The Psychology of Teamwork and Collaborative Processes 3 Eduardo Salas, Ramon Rico, and Jonathan Passmore Part I Overview of Team Effectiveness 13 2 Factors that Influence Teamwork 15 Julie V. Dinh and Eduardo Salas 3 Team Performance in Knowledge Work 43 Daniel J. Slyngstad, Gia DeMichele, and Maritza R. Salazar 4 Transnational Team Effectiveness 73 Dana Verhoeven, Tiffany Cooper, Michelle Flynn, and Marissa L. Shuffler Part II Antecedents to Team Effectiveness 103 5 Team Design 105 John L. Cordery and Amy W. Tian 6 Team Composition 129 Mikhail A. Wolfson and John E. Mathieu 7 Team Diversity: A Review of the Literature 151 Bertolt Meyer 8 Change in Organizational Work Teams 177 Floor Rink, Aimee A. Kane, Naomi Ellemers, and Gerben van der Vegt 9 Status Effects on Teams 195 Kun Luan, Qiong Jing Hu, and Xiao Yun Xie 10 Cross Cultural Teams: What are They and What Makes Them Effective 219 Ningyu Tang and Yumei Wang Part III Team Effectiveness: Processes, Emerging States and Mediators 243 11 Teamwork Processes and Emergent States 245 Rebecca Grossman, Sarit Friedman, and Suman Kalra 12 Team Decision Making 271 Tom W. Reader 13 Teamwork under Stress 297 Aaron Dietz, James E. Driskell, Mary Jane Sierra, Sallie J. Weaver, Tripp Driskell, and Eduardo Salas 14 Conflict in Teams 317 Lindred L. Greer and Jennifer E. Dannals 15 Team Leadership 345 Daan van Knippenberg 16 Team Cognition: Team Mental Models and Situation Awareness 369 Susan Mohammed, Katherine Hamilton, Miriam Sanchez Manzanares, and Ramon Rico 17 Team Trust 393 Ana Cristina Costa, and Neil Anderson 18 Psychological Contracts in Teams 417 Carlos Maria Alcover, Ramon Rico, William H. Turnley, and Mark C. Bolino 19 Affect and Creativity in Work Teams 441 March L. To, Neal M. Ashkanasy, and Cynthia D. Fisher 20 Team Reflexivity and Innovation 459 Michaela C. Schippers, Michael A. West, and Amy C. Edmondson Part IV Team Effectiveness Tools and Outputs 479 21 Team Performance Measurement 481 Michael A. Rosen and Aaron S. Dietz 22 Developing and Managing Teams 503 Charles P. R. Scott, and Jessica L. Wildman 23 Team Performance in Extreme Environments 531 William B. Vessey and Lauren B. Landon 24 Team Development Interventions 555 Deborah DiazGranados, Marissa L. Shuffler, Jesse A. Wingate, and Eduardo Salas Part V The Future of Teams 587 25 The Future of Teams 589 Michael A. West Index 000

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The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes

Wiley Blackwell Handbooks in Organizational Psychology Series Editor: Jonathan Passmore The aim of the Wiley Blackwell Handbooks in Organizational Psychology is to create a set of uniquely in‐depth reviews of contemporary research, theory, and practice across critical sub‐domains of organizational psychology. Series titles will individually deliver the state‐of‐the‐art in their discipline by putting the most important contemporary work at the fingertips of academics, researchers, students, and practitioners. The series offers a complete reference for those seeking to develop a comprehensive understanding of the field. Published The Wiley‐Blackwell Handbook of the Psychology of Coaching and Mentoring Edited by Jonathan Passmore, David B. Peterson, and Teresa Freire The Wiley‐Blackwell Handbook of the Psychology of Leadership, Change and Organizational Development Edited by H. Skipton Leonard, Rachel Lewis, Arthur M. Freedman, and Jonathan Passmore The Wiley Handbook of Psychology of Training, Personal Development and E‐Learning Edited by Kurt Kraiger, Jonathan Passmore, Sigmar Malvezzi, and Nuno Rebelo dos Santos The Wiley‐Blackwell Handbook of the Psychology of Occupational Safety and Workplace Health Edited by Sharon Clarke, Tahira Probst, Frank Guldenmund, and Jonathan Passmore The Wiley Blackwell Handbook of the Psychology of Positivity and Strengths‐ Based Approaches at Work Edited by Lindsay G. Oades, Michael Steger, Antonella Delle Fave, and Jonathan Passmore The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore Forthcoming in 2017 The Wiley Blackwell Handbook of the Psychology of Recruitment, Selection and Retention Edited by Harold Goldstein, Elaine Pulakos, Jonathan Passmore, and Carla Semedo The Wiley Blackwell Handbook of the Psychology of the Internet at Work Edited by Guido Hertel, Richard Johnson, Dianna Stone, and Jonathan Passmore

The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes

Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore

This edition first published 2017 © 2017 John Wiley & Sons Ltd Registered Office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial Offices 350 Main Street, Malden, MA 02148–5020, USA 9600 Garsington Road, Oxford, OX4 2DQ, UK The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK For details of our global editorial offices, for customer services, and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com/wiley‐blackwell. The right of Eduardo Salas, Ramón Rico, and Jonathan Passmore to be identified as the authors of the editorial material in this work has been asserted in accordance with the UK Copyright, Designs and Patents Act 1988. 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, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. Limit of Liability/Disclaimer of Warranty: While the publisher and authors have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. It is sold on the understanding that the publisher is not engaged in rendering professional services and neither the publisher nor the author shall be liable for damages arising herefrom. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Library of Congress Cataloging‐in‐Publication Data Names: Salas, Eduardo, editor | Rico, Ramón, editor | Passmore, Jonathan, editor. Title: The Wiley Blackwell handbook of the psychology of team working and collaborative   processes / edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. Other titles: Handbook of the psychology of team working and collaborative processes Description: Hoboken : Wiley-Blackwell, 2017. | Series: Wiley-Blackwell handbooks   in organizational psychology | Includes bibliographical references and index. Identifiers: LCCN 2016039779 (print) | LCCN 2017000426 (ebook) |   ISBN 9781118903261 (hardback) | ISBN 9781118909973 (pdf) |   ISBN 9781118909997 (epub) Subjects: LCSH: Psychology, Industrial. | Teams in the workplace–Psychological aspects. |   BISAC: PSYCHOLOGY / Industrial & Organizational Psychology. Classification: LCC HF5548.8 .W5395 2017 (print) | LCC HF5548.8 (ebook) |   DDC 658.4/022019–dc23 LC record available at https://lccn.loc.gov/2016039779 A catalogue record for this book is available from the British Library. Cover image: © busypix/Gettyimages Cover design by Wiley Set in 9.5/11pt Galliard by SPi Global, Pondicherry, India 10 9 8 7 6 5 4 3 2 1

Contents

About the Editors About the Contributors Foreword  Series Preface Supported Charity: Railway Children

vii ix xvii xix xxi

Introduction 1

1

The Psychology of Teamwork and Collaborative Processes Eduardo Salas, Ramón Rico, and Jonathan Passmore

Part I    Overview of Team Effectiveness

2



3



4

Factors that Influence Teamwork Julie V. Dinh and Eduardo Salas Team Performance in Knowledge Work Daniel J. Slyngstad, Gia DeMichele, and Maritza R. Salazar Transnational Team Effectiveness Dana Verhoeven, Tiffany Cooper, Michelle Flynn, and Marissa L. Shuffler

Part II  Antecedents to Team Effectiveness

5



6



7



8



9

Team Design John L. Cordery and Amy W. Tian Team Composition Mikhail A. Wolfson and John E. Mathieu Team Diversity Bertolt Meyer Change in Organizational Work Teams Floor Rink, Aimée A. Kane, Naomi Ellemers, and Gerben van der Vegt Status Effects on Teams Kun Luan, Qiong‐Jing Hu, and Xiao‐Yun Xie

3

13 15 43 73

103 105 129 151 177 195

vi Contents

10 Cross‐Cultural Teams Ningyu Tang and Yumei Wang

219

Part III Team Effectiveness: Processes, Emerging States and Mediators 243

11 Teamwork Processes and Emergent States Rebecca Grossman, Sarit B. Friedman, and Suman Kalra 12 Team Decision Making Tom W. Reader 13 Teamwork under Stress Aaron S. Dietz, James E. Driskell, Mary Jane Sierra, Sallie J. Weaver, Tripp Driskell, and Eduardo Salas 14 Conflict in Teams Lindred L. Greer and Jennifer E. Dannals 15 Team Leadership Daan van Knippenberg 16 Team Cognition: Team Mental Models and Situation Awareness Susan Mohammed, Katherine Hamilton, Miriam  Sánchez‐Manzanares, and Ramón Rico 17 Team Trust Ana Cristina Costa, and Neil Anderson 18 Psychological Contracts in Teams Carlos‐María Alcover, Ramón Rico, William H. Turnley, and Mark C. Bolino 19 Affect and Creativity in Work Teams March L. To, Neal M. Ashkanasy, and Cynthia D. Fisher 20 Team Reflexivity and Innovation Michaéla C. Schippers, Michael A. West, and Amy C. Edmondson

Part IV  Team Effectiveness Tools and Outputs

21 Team Performance Measurement Michael A. Rosen and Aaron S. Dietz 22 Developing and Managing Teams Charles P. R. Scott and Jessica L. Wildman 23 Team Performance in Extreme Environments William B. Vessey and Lauren B. Landon 24 Team Development Interventions Deborah DiazGranados, Marissa L. Shuffler, Jesse A. Wingate, and Eduardo Salas

Part V    The Future of Teams

25 The Future of Teams Michael A. West

245 271 297 317 345 369 393 417 441 459

479 481 503 531 555

587 589

Index 597

About the Editors

Eduardo Salas, Ph.D.  Eduardo is a Professor and Allyn R. & Gladys M. Cline Chair in Psychology at Rice University. Previously, he was a Trustee Chair and Pegasus Professor of Psychology at the University of Central Florida, where he also held an appointment as Program Director for the Human Systems Integration Research Department at the Institute for Simulation and Training (IST). Before joining IST, he was a senior research psychologist and Head of the Training Technology Development Branch of the Naval Air Warfare Center Training Systems Division. During this period, Dr. Salas served as a principal investigator for numerous research and development programs that focused on teamwork, team training, simulation‐based training, decision‐making under stress, safety culture, and performance assessment. Dr. Salas has co‐authored over 450 journal articles and book chapters and has co‐edited 27 books. He is a Past President of the Society for Industrial/Organizational Psychology and the Human Factors and Ergonomics Society, a Fellow of the American Psychological Association, and a recipient of the Meritorious Civil Service Award from the Department of the Navy. He is also the recipient of the 2012 Society for Human Resource Management Losey Lifetime Achievement Award, and the 2012 Joseph E. McGrath Award for Lifetime Achievement. Ramón Rico, Ph.D.  Associate Professor at the University of Western Australia Business School. He is the outgoing editor of the European Journal of Work and Organizational Psychology, and incoming associate editor of the Organizational Psychology Review. His work has been published in the Academy of Management Review, Journal of Management, Journal of Applied Psychology, Journal of Business and Psychology, and European Journal of Work and Organizational Psychology. His current research interests include team adaptability and coordination, team cognition, team diversity, task design characteristics, multiteam systems, and team process and effectiveness. He is also a consultant to government agencies, corporations, and sport clubs (Bank of Spain, Unión Fenosa, Real Madrid CF). Jonathan Passmore, D.Occ.Psych.  Jonathan is Professor of Psychology at the University of Evora, Portugal, and managing director of Embrion, a psychology consulting company. Prior to this he worked for PricewaterhouseCoopers, IBM Business Consulting, and OPM. He is a chartered psychologist, holds five degrees, and has an

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About the Editors

international reputation for his work in coaching and leadership. He has written and edited over 25 books on the themes of leadership, personal development, and change, including editing the Association for Coaching series of coaching titles. He speaks widely at conferences across the world from the U.S. to Europe and Asia and has published over 100 journal papers and book chapters. He was awarded the Association for Coaching Global Coaching Award for his contribution to practice and research in 2010, the British Psychology Society Research Award for his research into safety coaching in 2012, and the Association for Business Psychology Chairman’s Award for Excellence in 2015. He sits on the editorial board of several journals including Coaching: An International Journal and the International Coaching Psychology Review. Jonathan lives with his wife and two small children in the U.K. In his spare time he likes to swim, walk, and run.

About the Contributors

Carlos‐María Alcover, Ph.D.  Carlos is a Professor of Social and Organizational Psychology at the Universidad Rey Juan Carlos, Madrid, Spain. He received his Ph.D. in social psychology from the Complutense University of Madrid. His research has focused on early retirement and psychological wellbeing, bridge employment, psychological contract and exchange relationships in organizations, and membership and temporal m­atters in work teams. Neil Anderson, Ph.D.  Neil is Professor of Human Resource Management and Research Director (HRM-OB Group) at Brunel Business School, Brunel University London, U.K. His major research interests are in the areas of applicant reactions, employee selection, creativity and innovation at work, and science–practice relations. Neal M. Ashkanasy, Ph.D.  Neal is Professor of Management at the University of Queensland, Australia with research interests in ethical behavior, leadership, culture, and emotions. He is a past editor‐in‐chief of the Journal of Organizational Behavior. Professor Ashkanasy is a Fellow of the Society for Industrial and Organizational Psychology. Mark C. Bolino, Ph.D.  Mark is a Professor of Management and the Michael F. Price Chair in International Business in the Price College of Business at the University of Oklahoma. He received his Ph.D. from the University of South Carolina. His research interests include organizational citizenship behavior, impression management, global careers, and psychological contracts. Tiffany Cooper  Tiffany is a Ph.D. student in industrial and organizational psychology at Clemson University. Her current research interests revolve around the automated assistance of team behaviors through the use of physiological measures. Specifically, she is interested in automating the assessment of workload within teams to create more effective mutual monitoring and backup behaviors in working units.

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About the Contributors

John L. Cordery, Ph.D.  John is the Provost and Senior Deputy Vice‐Chancellor at Curtin University. His primary research interests relate to work design and how it affects employee behavior and wellbeing, and the ways in which collaborative structures, such as teams and communities of practice, function in organizations. Ana Cristina Costa  Ana Cristina is Senior Lecturer Human Resource Management and Organizational Behavior at Brunel Business School, Brunel University, London, U.K. Her major research interests include trust in organizations, innovation and psychological w­ellbeing, applicant reactions, and employee turnover. Jennifer E. Dannals  Jennifer is a doctoral candidate at Stanford University’s Graduate School of Business. Her research is focused on how individuals use distributions of group behavior to infer the social norm. Other current research projects include: the role of uncertainty in influencing cooperative and competitive decisions; examining the market effects of diversity announcements from Google and other tech firms; and the effects of role differentiation on team coordination, perceptions of equality, and team performance in startup teams. Gia DeMichele  Gia is a doctoral candidate at Claremont Graduate University’s (CGU) Division of Behavioral and Organizational Sciences. She received an MA degree in positive organizational psychology from CGU. Her research is oriented around communication on complex teams in high‐risk environments. Deborah DiazGranados, Ph.D.  Deborah is an Assistant Professor at Virginia Commonwealth University, School of Medicine. She earned her Ph.D. in industrial/organizational psychology from the University of Central Florida. Her research is focused on understanding the influence of teamwork and leadership processes on individual and team‐level outcomes. Dr. DiazGranados has developed curriculum that focuses on developing team and leadership competencies and skills within the healthcare context. Dr. DiazGranados’ research also considers the influence of context on how teams function and leaders lead. Aaron S. Dietz, Ph.D.  Aaron is a human factors psychologist and faculty at the Johns Hopkins University School of Medicine with dual appointments in the Armstrong Institute for Patient Safety and Quality and Department of Anesthesiology and Critical Care Medicine. Julie V. Dinh  Julie is a doctoral student in industrial/organizational psychology at Rice University. After graduating with highest honors in psychology at the University of California, Berkeley, she conducted research in health and clinical behavioral sciences. She is a Graduate Research Fellow of the National Science Foundation. James E. Driskell, Ph.D.  James is the president and a senior scientist at Florida Maxima Corporation in Winter Park, Florida. He received his Ph.D. from the University of South Carolina in 1981. Tripp Driskell, Ph.D.  Tripp is a research scientist at Florida Maxima Corporation. He received his Ph.D. in applied experimental and human factors psychology from the University of Central Florida.



About the Contributors

xi

Amy C. Edmondson, Ph.D.  Amy is Novartis Professor of Leadership and Management at Harvard University. She studies the social psychology of learning in organizations; her book, Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy, explores teamwork as a dynamic flexible process. Edmondson received her Ph.D. in organizational behavior from Harvard. Naomi Ellemers, Ph.D.  Naomi studied at the University of California at Berkeley, and the University of Groningen, the Netherlands, where she obtained her Ph.D. in 1991. She holds a chair as Distinguished University Professor at Utrecht University, the Netherlands. She also is member of the Supervisory Board of the consultancy and accounting firm PWC in the Netherlands. Cynthia D. Fisher, Ph.D.  Cynthia is Professor of Management at Bond Business School, Bond University, Australia. She writes on moods and emotions at work and employee attitudes and work behavior. Professor Fisher is a Fellow of the Society for Industrial and Organizational Psychology. Michelle Flynn  Michelle graduated from Clemson University with a degree in p­sychology and a minor in business administration. Her main research interests include leadership and team development, cross‐cultural teams, and organizational culture. Sarit B. Friedman  Sarit is a doctoral student in Hofstra University’s Applied Organizational Psychology program. Her research interests include teams, group dynamics, and conflict management. She obtained her M.A. in social organizational p­sychology at Teachers College, Columbia University, where she was also certified in conflict resolution and mediation at the Morton Deutsch ICCCR. Lindred L. Greer, Ph.D.  Lindred is Assistant Professor of Organizational Behavior at Stanford University. Her work focuses on the impact of team composition on intragroup conflict and team performance. She has a particular interest in how teams, especially early stage startup teams, are composed in terms of power, status, and leadership structures, and when and why particular forms of team composition may fuel power struggles and conflicts. Her research appears in academic journals such as the Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, and Science. Rebecca Grossman, Ph.D. Rebecca is an Assistant Professor of Industrial/ Organizational Psychology at Hofstra University. Her research focuses on teams (team processes and emergent states, team diversity, measurement of team constructs), training (individual and team training, transfer of training, instructional features), and complex settings (multicultural, virtual, and/or distributed teams, extreme and/or high‐risk e­nvironments, multiteam systems). Katherine Hamilton, Ph.D.  Katherine is a lecturer in the College of Information Sciences and Technology at Penn State University. She received her Ph.D. in industrial/organizational psychology from Penn State in 2009. Her research focuses on how to improve team effectiveness, particularly as it relates to team cognition, team conflict, and team virtuality. Qiong‐Jing Hu  Qiong‐Jing Hu is a doctoral candidate at Guanghua School of Management, Peking University, China. His research interest is focused on organizational proactive behavior (e.g., voice), status and power, and ethical leadership.

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About the Contributors

Suman Kalra  Suman is a Ph.D. student in the Applied Organizational Psychology program at Hofstra University. Previously, she was a training consultant, specializing in experiential learning workshops and interventions on areas like team building, leadership, and interpersonal skills. Her research interests include role negotiation and power dynamics and their impact on decision making in complex team structures. Aimée A. Kane  Aimée A. Kane is an Associate Professor of Management in at the Palumbo‐Donahue School of Business at Duquesne University, Pittsburgh, PA. She holds a Ph.D. and a M.S. in organizational behavior and theory from the Tepper School of Business at Carnegie Mellon University, Pittsburgh. Her research has been published in leading academic journals such as the Academy of Management Annals, Organization Science, and Organizational Studies. She currently serves on the editorial board of Organization Science. Lauren B. Landon, Ph.D.  Lauren is a Team Risk research portfolio scientist in NASA Johnson Space Center’s Human Factors and Behavioral Performance Element through KBRwyle, specializing in the training of spaceflight teams. She received her Ph.D. in industrial and organizational psychology from the University of Oklahoma. Kun Luan  Kun Luan is a doctoral candidate at the School of Management of the Zhejiang University, China. Her research interests include team diversity and faultlines, team learning, status and power, and team coordination behaviors. John E. Mathieu, Ph.D.  John is a professor and the Friar Chair in Leadership and Teams at the University of Connecticut. His interests include models of team and multiteam effectiveness, and cross‐level models of organizational behavior. He has worked with several Fortune 500 companies, the armed services, and federal, state, and public organizations. Bertolt Meyer, Ph.D.  Bertolt is a professor of organizational and economic p­sychology at Technische Universität Chemnitz, Germany. He received his Ph.D. in organizational and social psychology from Humboldt University, Berlin. His work focuses on teamwork, especially on team diversity and faultlines. He is an associate editor of Small Group Research. Susan Mohammed, Ph.D.  Susan is a professor of industrial/organizational psychology at Penn State University, investigating team cognition, team composition/diversity, and the role of time in team research. Her articles have appeared in Academy of Management Journal, Journal of Applied Psychology, Personnel Psychology, and Organizational Behavior and Human Decision Processes, among others. Tom W. Reader, Ph.D.  Tom is an assistant professor in organizational psychology at the London School of Economics, U.K. He is an expert on organizational psychology and workplace safety in “high‐risk” industrial settings (e.g., aviation, medicine, energy). His research examines how organizational decision‐making is shaped by team dynamics and organizational culture. Floor Rink, Ph.D.  Floor is a full professor in organizational behavior at the Faculty of Economics and Business at the University of Groningen, the Netherlands. She is an expert on identity processes within and between groups, and uses this expertise to explain organizational issues such as diversity, mobility, and group innovation. She conducts field



About the Contributors

xiii

studies as well as experiments, and has published her work in psychological and management outlets. Her research is (partially) funded by scientific funding agencies, government agencies, and private organizations. Michael A. Rosen, Ph.D.  Michael is a human factors psychologist and associate professor at the Armstrong Institute for Patient Safety and Quality at the Johns Hopkins University School of Medicine with joint appointments in the School of Nursing and Bloomberg School of Public Health, Department of Health Policy and Management. Maritza R. Salazar, Ph.D.  Maritza is an assistant professor at the Paul Merage School of Business at the University of California at Irvine. She earned her Ph.D. degree in management from the Stern School of Business at New York University. Her research c­enters on the mechanisms that facilitate effective collaborative processes and outcomes in interdisciplinary teams. Miriam Sánchez‐Manzanares, Ph.D.  Miriam is Assistant Professor of Organizational Behavior in the Department of Business Administration at Carlos III University of Madrid, Spain. Currently she is Vice‐Dean of Business Administration at the same university. Her papers have appeared in the Academy of Management Review, Journal of Applied Psychology, Journal of Management, Journal of Business and Psychology, and European Journal of Work and Organizational Psychology, among others. Her current research interests include team diversity, team cognition, team adaptability and coordination, and virtual teams. Michaéla C. Schippers, Ph.D.  Michaéla is Professor of Behavior and Performance Management at the Rotterdam School of Management, Erasmus University, the Netherlands. Current research concentrates on team reflexivity, team diversity and team leadership, as well as goal setting, and academic performance. Other projects concern virtual teams, behavioral operations management, and social exclusion/inclusion. Charles P. R. Scott  Charles is a doctoral candidate in Florida Institute of Technology’s Industrial Organizational Psychology program and the research manager for the Institute for Cross‐Cultural Management. His specialties include team effectiveness, team leadership, and complex team dynamics and structures (global teams, diverse teams, virtual teams). Marissa L. Shuffler, Ph.D.  Marissa has over 10 years of experience conducting basic and applied research in the areas of teamwork, leadership, and organizational effectiveness. She is an assistant professor of industrial/organizational psychology at Clemson University. Her areas of expertise include team and leader training and development with an emphasis on high‐risk and complex environments. Her work to date includes an edited book, over 45 publications, and over 100 presentations. Mary Jane Sierra, Ph.D.  Mary Jane is an organizational psychologist for the Centers for Disease Control and Prevention. She earned her Ph.D. in industrial and organizational psychology from the University of Central Florida. Daniel J. Slyngstad  Daniel is a doctoral candidate at Claremont Graduate University’s (CGU) Division of Behavioral and Organizational Sciences. He received an M.A. degree in organizational behavior and evaluation from CGU. His research is oriented around

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About the Contributors

information processing and innovation for teams and organizations operating in highly complex, dynamic environments. Ningyu Tang, Ph.D.  Ningyu Tang is a professor at Antai College of Economics and Management, Shanghai Jiaotong University, China. Her current research focuses on cross‐cultural management, diversity management, inclusion management, and inclusive leadership. Amy W. Tian, Ph.D.  Amy Tian is currently a Senior Lecturer in HRM in the School of Management at Curtin Business School, Curtin University. Amy received her PhD in Management in 2011 from Cardiff Business School, Cardiff University, UK. Her current research focuses on three main areas: (1) the relationship between strategic HRM practices impact upon employees’ work-related attitudes and behavior, as well as organizational performance; (2) the relationship between strategic HRM and knowledge management processes at the individual and organizational level; and (3) strategic HRM and multiculturalism at work. A theme unifying these interests is the importance of strategic HRM in enabling and/or promoting positive employee work-related outcomes and organizational performance. March L. To  March is Assistant Professor of Management in the Business School of the Hong Kong Baptist University. His research interests include wellbeing, creativity, moods and emotions, and multilevel conceptualization of the work phenomena. His ­primary methodological interest is experience sampling methodology. William H. Turnley, Ph.D.  William is the Forrer Chair of Business Ethics and a p­rofessor of management at Kansas State University. He received his Ph.D. in organizational behavior from the University of South Carolina. His research interests include psychological contracts, organizational citizenship behavior, impression management, and business ethics. Gerben van der Vegt  Gerben is a professor of organizational behavior and academic director of the Research Institute SOM at the Faculty of Economics and Business at the University of Groningen, the Netherlands. His research focuses on the processes associated with the integration of knowledge and expertise in work teams and organizations, team learning, and interteam and multi‐organizational coordination and collaboration. His work has been published in journals like the Academy of Management Annals, Academy of Management Journal, and Journal of Applied Psychology. Daan van Knippenberg, Ph.D.  Daan is Professor of Organizational Behavior at the Rotterdam School of Management, Erasmus University, the Netherlands. His research interests include leadership, diversity, team performance, creativity, and social identity. Daan is editor‐in‐chief of Academy of Management Annals. Dana Verhoeven  Dana is a Ph.D. student in industrial and organizational psychology at Clemson University. She graduated from the University of Central Florida with a B.S. in psychology and her research interests include team process, shared leadership, team trust/ distrust, and team training. Specifically, she is interested in identifying individual and team profiles to create effective teams. William B. Vessey, Ph.D.  William is a research scientist for KBRwyle with the Team Risk research portfolio in NASA’s Human Factors and Behavioral Performance Element



About the Contributors

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at Johnson Space Center, focusing on team functioning in spaceflight. He received his Ph.D. in industrial and organizational psychology from the University of Oklahoma. Yumei Wang  Yumei is a doctoral candidate at Antai College of Economics and Management, Shanghai Jiaotong University, China. Her current research interests include cross‐cultural management and values in the workplace. Sallie J. Weaver, Ph.D.  Sallie is an industrial‐organizational psychologist and assistant professor in the Department of Anesthesiology and Critical Care Medicine at the Johns Hopkins School of Medicine. Her Ph.D. is from the University of Central Florida. Michael A. West, Ph.D.  Michael is Senior Fellow at the King’s Fund, London; Professor of Organizational Psychology at Lancaster University Management School, U.K; and Emeritus Professor at Aston University, Birmingham, U.K. The focus of his research over 30 years has been team and organizational innovation, culture and effectiveness, particularly in healthcare organizations. Jessica L. Wildman, Ph.D.  Jessica is Assistant Professor and Institute for Cross‐Cultural Management Research Director at the Florida Institute of Technology. She earned her Ph.D. in industrial/organizational psychology from the University of Central Florida in 2011. Her research interests include interpersonal trust dynamics, multicultural work performance, and global team processes. Jesse A. Wingate  Jesse earned a MEd (Higher Education Administration) from the University of Vermont, a B.S. (Psychology) from St. Lawrence University, and is currently a Ph.D. candidate in Counseling Psychology at Virginia Commonwealth University. He  formerly worked as a career advisor at Dartmouth College and the University of Richmond. Mikhail A. Wolfson  Mikhail is a doctoral student in the Department of Management at the University of Connecticut. He received his B.A. in psychology at the University of Massachusetts, Amherst in 2011. His primary areas of interest include team composition, informal learning, multilevel modeling, network analysis, and unobtrusive measures. Xiao‐Yun Xie, Ph.D.  Xiao‐Yun Xie is an professor of leadership and organization. Since completing his Ph.D., he joined the faculty of School of Management of Zhejiang University, China. Currently, his research focuses on faultlines, identification, and intragroup conflict in a teamwork setting.

Foreword

The World Bank reported in 2012 that capital investment in teams represented approximately 20% of the world’s economy, owing to the necessity for organizations to increase their efficiency and effectiveness. Little or no work in organizations is performed today by an individual who is working alone. Thus, the ability of an individual is important, but not sufficient, for enhancing a team’s effectiveness. This is because of the amount of communication, coordination, mutual support, and sharing of information required of the i­ndividuals that comprise a team. A team consists of two or more individuals who have the same goals to attain. These individuals perform interdependent tasks collaboratively to produce shared deliverables. Because of the intense global competition for increasing market share, increasing profits, and decreasing costs, organizations are forming cross‐functional teams to examine ways in which they can improve customer service, discover new revenue streams, and meet, if not exceed, shareholder demands. Consequently, a team’s performance is a more complex interactive undertaking than solely improving the skills of an individual. A team’s leader must foster in the team’s members a shared mental model of the goals and ways of attaining them, the role each member plays in goal pursuit, as well as the interdependent roles of the other team members. A team’s leader must foster widespread agreement within the team of what success looks like. A team will fail if its members perceive that they are being pulled in different directions by competing goals. All of the above is easy to espouse, not so easy to put into practice. Hence, the reason why this book was commissioned and forms part of a wider series of eight titles across the industrial organizational psychology domain. The team of editors for this title represents an outstanding group of academics, lead by Eduardo Salas. Ed is an eminent scientist practitioner who is as interested in practice as he is in developing evidence‐based frameworks for guiding practice in different contexts. He is one of only two people who have received both the awards for Distinguished Contributions to Psychology as a Science and as a Profession from the prestigious Society for Industrial‐Organizational Psychology. Under Ed’s leadership, the editorial team have selected “the” experts on all aspects of forming, developing, and maintaining high‐performing teams. Hence, this book will prove to be a stimulus for action in the workplace that is based on solid empirical research. In doing so, this book also points out areas for researchers to pursue to further enhance team effectiveness. Gary Latham Secretary of State Professor of Organizational Effectiveness Rotman School of Management University of Toronto

Series Preface

Welcome to this sixth book in the Wiley Blackwell Industrial and Organizational Psychology series. The focus of this series title is on teams and collaborative processes at work and builds on the previous five titles in the series on leadership and change, coaching and m­entoring, training and development, health and safety, and positive psychology. Collaboration has for centuries been a key feature of success for any enterprise. Since the industrial revolution and rise of large‐scale manufacturing of the nineteenth century, through to the development of global corporations of the twenty‐first century, team working and collaboration, both within and across the organization boundary, are essential. Teams are the only way NASA, Google, or IKEA can operate to deliver their products, services, or mission. Understanding how to recruit, develop, and manage teams is what this book is all about. This title is, however, just one of eight books in this series totaling over 200 chapters and two million words on industrial and organizational psychology, and we believe this is the largest single contribution to the field. We believe this series differs in four ways from other titles in the field. First, the focus for the title is aimed at the academic researcher and student, as opposed to the practitioner, although scholar‐practitioners may also find this an interesting read. The aim of this book is to offer comprehensive coverage of the main topics of inquiry within the domain and in each of these to offer a comprehensive critical literature review of the main topic areas. Each chapter is thus an attempt to gather together the key papers, book chapters, and ideas and to present these for the serious researcher, student, and academic as a starting point for research in the key topics of industrial and organizational psychology in a focused (10,000 word) chapter. The book thus aims to operate as a starting point for any in‐depth inquiry into the field. Second, while many books take a UK/European or a US/North American approach with contributors drawn predominantly from one continent or the other, in this series we have made strenuous efforts to create an international feel. For each title in the series, we have drawn contributors from across the globe, and encouraged them to take an international as opposed to a national or regional focus. Such an approach creates challenges. Challenges in terms of language and spelling, but also in the way ideas and concepts are applied in each country or region. We have encouraged our contributors to highlight

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Series Preface

such differences. We encourage you as the reader to reflect on these to better understand how and why these differences have emerged and what implications these have for your research and our deeper understanding of the psychological constructs that underpin these ideas. Third, the chapters avoid offering a single perspective, based on the ideas of a single contributor. Instead we have invited leading writers in the field to critically review the literature in their areas of expertise. The chapters thus offer a unique insight into the literature in each of these areas, with leading scholars sharing their interpretation of the literature in their area. Finally, as series editor I have invited contributors and editors to contribute their r­oyalties to a charity. Given the international feel for the title we selected an international charity – The Railway Children – a charity that supports runaway and abandoned children across the world. This means approximately 10% of the cover price has been donated to charity and in this way we collectively are making a small contribution to making the world a slightly better place. With any publication of this kind there are errors. As editors we apologize in advance for these. Jonathan Passmore Series Editor, Wiley Blackwell Handbooks in Organizational Psychology

Supported Charity Railway Children

Railway Children supports children alone and at risk on the streets of India, East Africa, and in the UK. Children migrate to the streets for many reasons, but once there they e­xperience physical and sexual abuse, exploitation, drugs, and even death. We focus on early intervention, getting to the street kids before the street gets to them, and where p­ossible we reunite them with their families and communities. In addressing the issue, we work through our three‐step change agenda to: •• Meet the immediate needs of children on the streets – we work with local organizations to provide shelter, education or vocational training, counseling and, if possible, r­eintegration to family life. •• Shift perception in the local context – we work with local stakeholders to ensure that street children are not viewed as commodities to be abused and exploited, but as c­hildren in need of care and protection. •• Hold governments to account – if we are to see a long‐term, sustainable change for the children with whom we work, we must influence key decision‐makers, ensuring that provisions for safeguarding children are made within their policies and budgets. Last year we reached over 27,000 children; 14,690 of these were in India where we reunited 2,820 with their families. In the UK, we launched our research, “Off the Radar,” which revealed the experiences of over 100 of the most detached children in the UK. Many of these children received no intervention either before leaving home or once they were on the streets. We have made recommendations that include emergency refuge for under 16 s and a wrap‐round of other services, such as Misper schemes, local helplines, outreach, and family liaison to allow children and young people to access interventions in a variety of ways. To find out more about our work, or to help us support more vulnerable children, please go to www.railwaychildren.org.uk or call 00 44 1270 757596

.

Introduction

1

The Psychology of Teamwork and Collaborative Processes Eduardo Salas, Ramón Rico, and Jonathan Passmore

Introduction Teams are an integral part of society. This handbook endeavors to tease apart the psychological aspects of teamwork and understand the applications and ramifications of teams, both within organizations and in society at large. In this short introductory chapter, we aim to briefly review the nature of teams and the developing research agenda, before highlighting the chapters contained in this book.

What Are Teams? While the layperson may understand a team simply as a group of people, the scientific literature has spent considerable effort exploring and defining distinguishing characteristics. What makes a given group of people a team – that is, what sets it apart from any other unit? At its core, a team can be operationalized as a set of two or more individuals that adaptively and dynamically interacts through specified roles as they work towards shared and valued goals (Dyer, 1984; Salas, Dickinson, Converse, & Tannenbaum, 1992). Researchers have also identified several other features that characterize the unique dynamics specific to teams, including: existing to perform organizationally relevant tasks; exhibiting task interdependencies (e.g., workflow, goals, knowledge, and outcomes); interacting socially (face‐to‐face or virtually); maintaining and managing boundaries; and being embedded within an organizational context that sets boundaries, constrains the team, and influences exchanges with other units in the broader entity (Arrow, McGrath, & Berdahl, 2000; Hackman, 1987; Kozlowski & Bell, 2003; Kozlowski, Gully, Nason, & Smith, 1999; Salas et al., 1992). Clearly, teams are more than just a collection of people; they are functional units, a complex and crucial

The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

4 Introduction c­omponent of broader human systems. While the science of teams has been expanding rapidly in academic spheres, so too has their import in practice. Since the late 20th century, the global economy has seen drastic changes economically, strategically, and technologically. In response, organizations have shifted, from focusing on hierarchically structured, individual work to structuring of collective efforts more e­ f ficiently (Lawler, Mohrman, & Ledford, 1995). Owing to increasing competition, c­onsolidation, and innovation, organizations must tap into, and make sense of, diverse skills, expertise, and experience. Accordingly, teams have emerged as a core building block of organizations (Kozlowski & Bell, 2012). Much can be accomplished when many minds are put together. The growing awareness of teamwork in the public consciousness, however, is not the only compelling reason for its study. Teams are historically and demonstrably essential to the functioning of organizations and societies. Failing to value and invest in teamwork can have catastrophic consequences, varying in scope from the relatively personal (e.g., a surgery) to the international (e.g., a military engagement). Oftentimes, such unfortunate turns of events can be prevented or contained if participants had been able to coordinate their efforts, adapt to the environment, and overcome stressors as a unit (Salas, Stagl, & Burke, 2004). The promotion of synchronicity in teams has therefore come to the forefront as a crucial way to affect change and influence outcomes. This handbook therefore begins by  breaking down the basic theoretical underpinnings of teams before understanding their  importance across differing contexts, and looking towards the future of research and practice.

The Developing Research Agenda for Teamwork Our hope in writing this book was to develop a rich, comprehensive resource on the psychology of teamwork for those in academia and industry alike. This handbook is intended to offer students breadth and depth of knowledge and researchers a sound and stimulating basis upon which to build their lines of inquiry, while also elucidating evidence‐based practices useful to scholar‐practitioners. In order to facilitate deeper understanding, it has been organized to take readers from a macro to a micro perspective on teams, beginning with broad strokes and narrowing down to more specific, detailed components.

Part I The first section of the handbook gives a bird’s‐eye view of the teamwork literature. The authors describe factors that influence team performance, in terms of overall effectiveness, contextual efficiency, and intrateam synchronicity. These chapters give a general summary of teams in terms of psychological dynamics and greater societal significance. Part I begins with a chapter by Julie V. Dinh and Eduardo Salas, which provides an overview of the processes underlying teamwork. While both taskwork (e.g., work‐related goals) and teamwork (the behavioral, attitudinal, and cognitive interactions that drive such performance) are critical for efficient team performance, it is paramount to understand and strongly implement the latter across contexts. In particular, nine critical considerations, based on earlier work (Salas, Shuffler, Thayer, Bedwell, & Lazzara, 2015), shape the nature of teamwork, including core processes and emergent states  –  conflict, coordination, communication, coaching, and cognition – and contextual factors – composition, culture, and context. The model examined in this chapter captures the team dynamics explored in further detail later in the handbook.



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In Chapter 3, Daniel J. Slyngstad, Gia DeMichele, and Maritza R. Salazar discuss team performance. The chapter provides a thorough review of conceptualizations of performance before proposing a three‐dimensional framework by which to judge team functional utility. They pay special attention to knowledge work, which, due to its inherent complexity, can pose unique challenges in performance measurement. The conclusion synthesizes strong theoretical and empirical research into an integrative framework for team effectiveness in knowledge work. Chapter 4 examines team effectiveness from a transnational perspective. Dana Verhoeven, Tiffany Cooper, Michelle Flynn, and Marissa L. Shuffler explore team performance through a theoretical lens of cultural diversity within a team. This approach is particularly useful given today’s rapid globalization and its ramifications for team performance across contexts (such as the development and functioning of geographically dispersed teams). The chapter follows the input–process–output (IPO) framework to discuss, in detail, the components of team effectiveness, before offering alternate, comprehensive models and future directions for research.

Part II Having set a comprehensive framework through which to understand teams, the second section of the handbook focuses on antecedents of team effectiveness. Each of the chapters addresses considerations regarding the formation and structure of teams, including design, composition, diversity, membership, and status. The first step in creating a team is understanding and responding to the constraints in which it will operate. Current socioeconomic trends have forced researchers and practitioners to rethink how they organize the work and design teams that compose organizations. Today, teams are frequently formed and disbanded rapidly, distributed across m­ultiple sites, and composed of members simultaneously working on myriad projects, with different bosses competing for their attention. “Further, these teams’ work increasingly demands substantial coordination and integration of specialized expertise within and outside of the team” (Cross, Ehrlich, Dawson, & Helferich, 2008, p. 75). As such, it is critical to understand how these new realities affect the way in which we design teams. Chapter 5 explores the fundamental design elements that express what it means to be a team. Authors John L. Cordery and Amy W. Tian review recent research and theory r­elating to team constitution, team structure, and external support as it informs the effective design of organizational teams. Beyond the design of teams, it is important to compose teams appropriately. In Chapter 6, Mikhail A. Wolfson and John E. Mathieu summarize research and advancements in the vast team composition literature. After describing conventional team composition approaches and their shortcomings, the authors propose the incorporation of network theory and methods as a potential solution. In particular, meta‐networks and multiplex ties may help model the complex nature of teams, exposing areas of need, revealing unique combinations of interpersonal ties, and, in combination, helping optimize individual knowledge, skills, and abilities. The authors thus contribute to the literature by clearly delineating a social network approach that can facilitate a better understanding of team composition. An important piece of team diversity is found within composition. Research on team diversity has produced many promising, but also many inconsistent, findings. In Chapter 7, Bertolt Meyer organizes the literature on this regard into different streams, differentiated by the ways in which they resolve the bi‐theoretic approaches to diversity: the information/ decision‐making paradigm, which predicts positive effects of team diversity, and the social

6 Introduction categorization paradigm, which predicts negative effects. Taken in summary, the conceptualizations suggest that practitioners who seek to reap the benefits of team diversity should increase team members’ diversity beliefs and avoid the formation of homogeneous subgroups. Finally, new multilevel/contextual and status‐based models of team diversity extend the theoretical foundations of diversity research beyond the bi‐theoretical approach. Membership change in organizations inevitably results in the introduction of newcomers, who typically represent a numerical minority in the teams that they join (Choi & Levine, 2004). Theories propose that newcomers, with their different background, are important sources of innovation that facilitate team performance and can thus enhance the long‐term survival changes of teams. Chapter 8’s review of over 50 years of research on this topic demonstrates that this potential is often not realized. Authors Floor Rink, Aimée A. Kane, Naomi Ellemers, and Gerben van der Vegt suggest that the three team receptivity components  –  team reflection, knowledge utilization, and newcomer acceptance  –  are interrelated and jointly influence sustained team performance. This framing sheds light on the variables that facilitate team receptivity to newcomers. Inherent to the discussion on membership within organizations is that of individual role within a larger structure. Explaining the role that status plays in teams and organizations had been a main concern for scholars from the fields of social psychology, sociology, and management. Chapter 9 begins with a review of the definition of status and, more importantly, differentiates it from other related concepts (e.g., power and influence). Kun Luan, Qiong‐Jing Hu and Xiao‐Yun Xie then review the status effects on individual behaviors, team processes, and outcomes, as well as interteam interactions based on different theoretical perspectives. Insightful directions for future exploration that contribute to develop team status study are offered. Section 2 closed with Chapter 10 by exploring the use of cross-cultural teams, a growing trend resulting from current globalization processes. Understanding and achieving cross-cultural team effectiveness are key to the success of many multinational companies. Accordingly, authors Ningyu Tang and Yumei Wang use the IPO framework to consider each of the components that have been studied, considering linkages between each of them. Inputs include cultural diversity, individual characteristics, team factors, and organizational factors, while processes involve action processes (e.g., coordination, learning), interpersonal processes (e.g., cooperation, communication, conflict), and psychological processes (e.g., psychological safety, negative affective state, team shared value). Outputs can be measured through performance (e.g., task performance, creativity performance) and affective reactions (e.g., wellbeing, satisfaction). The chapter then identifies several gaps and consequent directions for future research, in both theory and methods, using the input–process–output analysis, and concludes with the proposal of a more comprehensive multilevel cross‐cultural team effectiveness model.

Part III The third section of the handbook takes a finer‐grained look at dynamics within teams: core processes, emerging states, and mediators. Each of the authors describes, in detail, different psychological forces that both affect and stem from team interaction. Section 3 starts with Chapter 11 by discussing teamwork processes and emergent states. Authors R­ebecca Grossman, Sarit B. Friedman, and Suman Kalra use an adaptation of the traditional IPO model, the input–mediator–output–input (IMOI) framework, to frame the discussion, given the interconnected and cyclical nature of teamwork processes. In doing so, they are able to fully describe the affective, behavioral, and cognitive mechanisms that influence teamwork, including, respectively: cohesion, team confidence, and



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team trust; transition processes, action processes, and interpersonal processes; and team mental models, transactive memory systems, and team learning. Following the synthesis of knowledge on team processes and emergent students, future directions for research are proposed, with particular emphasis on the rapid globalization of labor and teamwork. A critical determinant of team processes is in decision making, which involves gathering, processing, and communicating information in support of arriving at task‐relevant decisions. In order to fully understand this complex process, Tom W. Reader pulls from the social and applied psychology literature, to identify determinants of effective decision making, integrating them into key findings and illustrating them with key examples from history and practice. The author reviews research on the group processes that influence behavior in teams, teamwork and decision making, and relevant and appropriate interventions. Chapter 12 concludes with a four‐point treatise on future areas of inquiry – that is, more interdisciplinary, systematic, naturalistic, and culturally competent research. Decision making, as well as many other team processes, can be impacted significantly by stress. Chapter  13 focuses on the ever‐changing and unpredictable real‐world environments that challenge teamwork. Aaron S. Dietz, James E. Driskell, Mary Jane Sierra, Sallie J. Weaver, Tripp Driskell, and Eduardo Salas first present an overview of stress within the team context before examining its influence on team performance. Of special note is their parsimonious model framework, used to describe stress, its effects on teamwork, and moderators thereof. The authors then delve into the literature to identify team research in extreme environments and discuss issues in measurement of stress at the team level. Altogether, the chapter demonstrates thorough understanding of antecedents of the broad spectrum of stressors and their consequent influence on team processes. Intragroup conflict is arguably one of the most important behavioral processes in teams. Research has consistently shown that while conflicts about personal tensions or underlying status concerns can harm team outcomes, debates over work‐related matters can improve team outcomes. In Chapter 14, Lindred L. Greer and Jennifer E. Dannals review recent research aiming to understand the moderators and antecedents of team conflicts, with the hopes of understanding the conditions that give rise to productive team conflicts and minimize destructive team conflicts. Two key themes emerge from the literature. Firstly, teams which have evolved norms encouraging the expression of open, cooperative, non‐emotional task debates are more likely to reap the benefits of conflict in teams. Secondly, status c­oncerns are an insidious challenge to teams, and often may explain why more destructive conflict forms arise in teams, such as process conflicts. As research in this area moves f­orward, and more attention is paid to the development of such conflicts from individual motivations to group‐level processes, the role of individual conflict behaviors, and the interrelation among the conflict types, we hope that researchers can reach consensus on the understanding and management of conflict in teams. One critically important avenue which can shape conflict, as well as other key dynamics, within teams is through leadership. Chapter 15 lays groundwork for the argument that team leadership research needs to refocus and prioritize the development of team‐specific leadership theory. Rather than applying generic leadership models to teams, author Daan van Knippenberg closely ties in integrative theories of team processes. This is no small feat, as the development of integrative process theory is likely the main challenge of the team research field as a whole. Due to the broad‐ranging importance of such integrative process theory and the key implications for team performance, no subfield of team research will yield a greater return on investment than team leadership research. Team cognition has been recognized as one of the most noteworthy developments in team research and accordingly, as Chapter 16 illustrates, this research has maintained an upward trajectory with no signs of waning. Within the team mental models and situation

8 Introduction awareness literatures, authors Susan Mohammed, Katherine Hamilton, Miriam Sánchez‐ Manzanares, and Ramón Rico identify several important future study needs that will continue to extend these research streams. However, across team mental models and situation awareness research, there are significant opportunities for intersection and integration that would not only enhance these respective literatures, but also advance the science of team cognition as a holistic entity. As such, the authors believe that some of the most exciting developments in the future will result from merging concepts from multiple team cognition literatures, identifying causal linkages between different forms of team knowledge, and testing how each differentially predicts various team processes and effectiveness indicators. Team cognition is facilitated by trust, another crucial concept in teamwork, among members. Chapter 17 reviews a large body of literature and empirical findings relevant to team‐level trust. Ana Cristina Costa and Neil Anderson show that there has been a concerted effort among researchers to uncover and quantify the substantial number of a­ntecedents held to be related to team‐level trust. In turn, relationships are shown with other, more distal outcomes such as team performance and team innovativeness. Developments in some areas of this body of knowledge have been considerable, now allowing a far more comprehensive and finer‐grained understanding of relationships between individual, team, and organizational‐level variables and team trust as a pertinent outcome. More specifically, the movement of research towards multilevel processes has been key in advancing our understanding of trust at work phenomena. Given the role that team trust plays in a host of outcomes at the individual, team, and organizational levels of analysis, this chapter sets out a holistic and timely narrative review of our understanding of team trust in w­orkplace settings that will stimulate further research. The psychological contract construct has enhanced the focus on the analysis of relations between employees and organizations in the last two decades. In Chapter 18, research into the psychological contract in work teams is reviewed. While this research is not particularly extensive, authors Carlos‐María Alcover, Ramón Rico, William H. Turnley, and Mark C. Bolino endeavor to paint a broad canvas, including the multiple agency context and multiple foci social exchange relationships in the development and fulfillment of the psychological contract, which also considered the links between leader–member exchange theory, peer justice, social support in teams, and psychological contracts. The results of this review highlight a field in which research is still at an early stage and where promising lines of inquiry exist to capture the specific features of new forms of individual–organization relations existing in contemporary work contexts. Given the rise of team‐based structures in organizations, it is imperative to understand how and when collective emotional states may impact critical team functions, such as creativity. In Chapter 19, March L. To, Neal M. Ashkanasy, and Cynthia D. Fisher seek to make a key conceptual extension from individual to group creativity, noting that this entails an extra degree of complexity. They begin with a review of research findings on affect and its effects at the level of individual creativity, and follow up by describing the research that has extended individual phenomena to the group level, including discussion of the dynamic nature of creativity in groups. Finally, the authors identify the inadequacies of the conceptual extension in current group research and offer recommendations for future research. The aspects of group affect addressed in this chapter will hopefully enable scholarly efforts to develop a more integrative theory concerning the (complicated) effects of affect on creativity in contemporary workplace settings. Part III ends with an important component of team cognition: reflexivity. In Chapter 20, Michaéla C. Schippers, Michael A. West, and Amy C. Edmondson emphasize that team reflexivity can help innovation and thus aid processes of teams that operate in a demanding, knowledge‐intensive context. A proposed model of antecedents and consequences of



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team reflexivity may help researchers and practitioners further explore and apply team reflexivity, which can be a powerful way of overcoming the group information‐processing problems inherent in team‐based knowledge work. Indeed, the human capacity to reflect is a valuable but often underutilized resource, enabling team productivity, innovation, and effectiveness. Unfortunately, because focused research is in its infancy, this chapter also serves as a call to study the conscious use of reflexivity in teams and other settings in which people are working to achieve shared goals. The arguments and model presented here will spur new research and new understanding of the mechanisms that underlie team reflexivity and its role in enhancing team innovation.

Part IV The fourth section explores methods of managing and assessing teams. The study of teamwork does not end once a team has formed and performed – just as essential to the team functioning are maintenance of psychological dynamics and measurement of output. Relatedly, extreme environments can critically influence the manner in which teams are organized and evaluated. In these and other cases, interventions may be required to assist in the optimal functioning of teams. The authors in this section discuss, in depth, the factors that can influence and capture teamwork during and after the performance period. In order to properly manage teams, organizations must be able to accurately and comprehensively evaluate indices of teamwork. Chapter 21 provides an overview of fundamental concepts in the measurement of team performance. Authors Michael A. Rosen and Aaron S. Dietz describe factors that may influence assessment, including purpose, content, location, frequency and timing, and method. Special attention is paid to emerging strategies that are unobtrusive, integrative, and comprehensive, particularly in light of the dynamic and often physically distributed nature of teams today. Excitingly, as research understanding and methodological tools develop, the measurement of team performance becomes more advanced and robust. Chapter  22 centers upon the development and management of team performance. Charles P. R. Scott and Jessica L. Wildman rigorously review the current literature to extract empirically sound findings on management of work teams. In order to do so methodically, they use the IMOI framework and dichotomous categorization (core processes and emergent states) to organize mediators of team development and management, beginning with outputs and working backwards towards antecedents. Their methodical review on the scientific literature and best practices makes sense of a confusing body of work, yielding interventional recommendations for practitioners and academic areas of interest for researchers. Naturally, team management is taxed when it occurs in extreme environments. Chapter 23 focuses on these teams that are socially isolated from other teams and individuals, physically confined for long periods of time, and exposed to significant danger due to prevailing environmental factors (Palinkas, 2003). Examples of such contexts include polar bases and expeditions, spaceflight, and offshore oil rigs, wherein effectual teamwork is both critical and challenged. Authors William B. Vessey and Lauren B. Landon explore how these circumstances can influence team composition, cohesion, conflict, leadership, and communication, offering deep insight to nuances of each factor as well as countermeasures to buffer against ill effects. They conclude with emerging areas in the team research literature, including team effects, multiteam systems, and selection, composition, and interventions. Relatedly, interventions are an incredibly useful tool for restructuring and directing teams. In Chapter 24, Deborah DiazGranados, Marissa L. Shuffler, Jesse A. Wingate, and Eduardo Salas offer a comprehensive explanation of the team lifecycle before focusing

10 Introduction specifically on team development. They describe processes and concepts key to the team intervention arenas of training, building, chartering, and coaching. Both the scientific literature and corporate practices indicate movement towards integration of interventional approaches, emphasis on both functional and dysfunctional aspects of teamwork, and incorporation of globalization and technology trends in development.

Part V The fifth section closes this title with Chapter 25 offering a perspective on the future of teamwork research. Michael A. West’s chapter presents a novel, holistic perspective on teamwork. He extracts central principles of humanity  –  interconnectedness, belonging, and compassion – and expands upon them within the context of teams and the modern world. With philosophical clarity and scientific precision, he describes the teamwork l­iterature, its projected directions, and the larger questions it can help answer.

Conclusion We have carefully curated this handbook to provide both breadth and depth in describing teamwork. Our authors demonstrate a broad range of subject matter expertise, expanding the field’s understanding of teamwork by proposing novel models of understanding, relevant implications for practice, and compelling areas for future research. In order to emphasize the globalizing nature of teamwork, we have compiled knowledge from internationally minded contributors from diverse backgrounds. Finally, we firmly believe in the interrelatedness of research and practice as a driver in the science of teamwork, and have thus taken a scholar‐practitioner approach throughout the handbook. We hope to provide our audience, at any stage in academia or industry, with thorough insight into the current state of teamwork practice.

References Arrow, H., McGrath, J. E., & Berdahl, J. L. (2000). Small groups as complex systems: Formation, coordination, development, and adaptation. Thousand Oaks, CA: Sage. Choi, H. S., & Levine, J. M. (2004). Minority influence in work teams: The impact of newcomers. Journal of Experimental Social Psychology, 40, 273–280. Cross, R., Ehrlich, K., Dawson, R., & Helferich, J. (2008). Managing collaboration: Improving team effectiveness through a network perspective. Californian Management Review, 58(4), 74–98. Dyer, J. L. (1984). Team research and team training: A state of the art review. In F. A. Muckler (Ed.), Human factors review (pp. 285–323). Santa Monica, CA: Human Factors Society. Hackman, J. R. (1987). The design of work teams. In J. W. Lorsch (Ed.), Handbook of organizational behavior (pp. 315–342). Englewood Cliffs, NJ: Prentice Hall. Kozlowski, S. W. J., & Bell, B. S. (2003). Work groups and teams in organizations. In W. C. Borman, D. R. Ilgen & R. J. Klimoski (Eds.), Handbook of psychology. Vol. 12: Industrial and organizational psychology (pp. 333–375). New York: Wiley‐Blackwell. Kozlowski, S. W. J., & Bell, B. S. (2012). Work groups and teams in organizations. In N. W. Schmitt & I. B. Weiner (Eds.), Industrial and organizational psychology Vol. 12: Industrial and organizational psychology (2nd ed., pp. 413–469). Hoboken, NJ: John Wiley & Sons, Inc. Kozlowski, S. W. J., Gully, S. M., Nason, E. R., & Smith, E. M. (1999). Developing adaptive teams: A theory of compilation and performance across levels and time. In D. R. Ilgen & E. D. Pulakos (Eds.), The changing nature of performance: Implications for staffing, motivation, and development (pp. 240–292). San Francisco, CA: Jossey‐Bass.



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Lawler, E. E., Mohrman, S. A., & Ledford, G. E. (1995). Creating high performance organizations: Survey of practices and results of employee involvement and TQM in Fortune 1000 companies (2nd ed.). San Francisco, CA: Jossey‐Bass. Palinkas, L. A. (2003). The psychology of isolated and confined environments: Understanding human behavior in Antarctica. American Psychologist, 58(5), 353–363. 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. Norwood, NJ: Ablex. Salas, E., Shuffler, M. L., Thayer, A. L., Bedwell, W. L., & Lazzara, E. H. (2015). Understanding and improving teamwork in organizations: A scientifically based practical guide. Human Resource Management, 54(4), 599–622. Salas, E., Stagl, K. C., & Burke, C. S. (2004). 25 years of team effectiveness in organizations: Research themes and emerging needs. International Review of Industrial and Organizational Psychology, 19, 47–91.

Part I

Overview of Team Effectiveness

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Factors that Influence Teamwork Julie V. Dinh and Eduardo Salas

Introduction Today, the word “team” often calls to mind the highly visible groups seen in the media, such as the Manchester United football team or the US Navy SEALs. In truth, however, teams are all around us, running critical day‐to‐day processes. As smaller, more specialized units of organizations, teams are involved in many facets of society, from military operations and healthcare systems to research groups and private companies. Together, individuals are able to accomplish work possible only through united efforts – that is, through teamwork, or the funneling of interdependent actions of individuals towards a common goal (Marks, Mathieu, & Zaccaro, 2001). By harnessing the strengths of many, teams have the potential to offer greater adaptability, productivity, and creativity than can be offered by any one individual (Gladstein, 1984; Hackman, 1987). Furthermore, they can provide more complex, innovative, and comprehensive solutions to organizational problems (Sundstrom, de Meuse, & Futrell, 1990). Increasingly, organizations are turning to team‐ based structures in order to contend with growing complexities of the environments in which their employees operate (Katzenbach & Smith, 1993). Given the benefits and increasing awareness of teams, it is worthwhile exploring the underlying factors that influence teamwork. This chapter aims to define and describe teamwork as a set of actions and processes that contribute towards group and organizational goals.

Defining Teamwork In discussing teamwork, it is first important to define teams themselves. Teams are “a distinguishable set of two or more people who interact dynamically, interdependently, and adaptively towards a common and valued goal/objective/mission” (Salas, Dickinson, Converse, & Tannenbaum, 1992, p. 4). As mentioned above, teams can exist and perform The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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Overview of Team Effectiveness

in a number of contexts – from private industries to governmental research. Within each of these teams, it is key to organize members’ efforts internally and align them towards external goals. In order for teams to be effective, they must successfully engage in both taskwork and teamwork (Burke, Wilson, & Salas, 2003) – two distinctly different dimensions. Taskwork refers to the performance of specific tasks needed to achieve team goals. Tasks are those work‐related activities that individuals or teams engage in as an essential function of their organizational role (Wildman et al., 2012). Taskwork typically becomes the key focus as teams work towards their goals, but is majorly aided by teamwork. Teamwork involves the shared behaviors, attitudes, and cognitions that make team functioning and the achievement of their goals possible (Morgan, Salas, & Glickman, 1993). This adaptive, dynamic, and episodic process can make the difference between success and failure, regardless of team members’ task‐relevant expertise (Gregorich, Helmreich, & Wilhelm, 1990; Salas, Shuffler, Thayer, Bedwell, & Lazzara, 2015; Schmidt, Keeton, Slack, Leveton, & Shea, 2009; Smith, 1979). For example, a surgical team’s taskwork involves successfully c­ompleting the many stages of an operation, from perioperative patient preparation to postoperative recovery. In order to accomplish these goals, the members must engage in teamwork and effectively orchestrate their actions; the anesthesiologist must coordinate the administration of anesthesia, while the surgeon must communicate with the supporting staff as he or she operates. Both taskwork and teamwork are crucial to effective team performance, with each one b­olstering the other. This chapter, in particular, will focus on teamwork, specifically as it describes the more general conditions within a group necessary for success. Teamwork consists of three psychological facets: attitudes, behaviors, and cognitions (Cannon‐Bowers & Bowers, 2011; Cannon-Bowers & Salas, 2014; Cannon‐Bowers, ­Tannenbaum, Salas, & Volpe, 1995; Salas, Cooke, & Rosen, 2008). Team‐level attitudes are those internal states which affect interactions, such as mutual trust, cohesion, and collective efficacy. Team‐level attitudes have been associated with improved team outcomes, including satisfaction, organizational commitment, and performance (Costa, 2003). Team behaviors refer to the processes necessary to engaging in teamwork, including information exchange, support of team members during critical stressors, and monitoring progress in order to detect errors and problems. Clearly, behaviors are vital for successful outcomes, or performance, in a variety of domains (Mathieu, Maynard, Rapp, & Gilson, 2008). Finally, team cognition describes the structure and representation of knowledge among members, allowing teams to plan and execute actions efficiently. In a meta‐analysis of 65 studies, DeChurch and Mesmer‐Magnus (2010) found that cognition has consistently been linked to outcomes. Indeed, even when a team possesses extensive task‐related knowledge, they will fail if members cannot trust one another and successfully coordinate behavior and share knowledge (Mathieu et al., 2008). As such, it is critical to foster all three dimensions of teamwork – positive attitudes, behaviors, and cognitions – within teams.

Critical Considerations The dimensions of teamwork may be further organized into specific categories. A study by Salas and colleagues (2015) consolidated and distilled findings in the field into a heuristic of nine critical considerations, as shown in Table 2.1. Six of these involve core processes, or the conversion of inputs to outcomes through affective, behavioral, and cognitive mechanisms, and emergent states, or resultant properties of a team (Marks et al., 2001): (1) cooperation; (2) conflict; (3) coordination; (4) communication; (5) coaching; and (6) cognition. In addition to these core processes, Salas and colleagues (2015) identified three influencing



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Table 2.1  Critical considerations. Critical consideration

Core process and emergent factor

Cooperation Conflict Coordination Communication Coaching Cognition

Influencing condition

Composition

Context Culture

Definition The motivational drivers of teamwork. In essence, this is the attitudes, beliefs, and feelings of the team that drive behavioral action. The perceived incompatibilities in the interests, beliefs, or views held by one or more team members. The enactment of behavioral and cognitive mechanisms necessary to perform a task and transform team resources into outcomes. A reciprocal process of team members’ sending and receiving information that forms and reforms a team’s attitudes, behaviors, and cognitions. The enactment of leadership behaviors to establish goals and set direction that leads to the successful accomplishment of these goals. A shared understanding among team members that is developed as a result of team member interactions including knowledge of roles and responsibilities; team mission objectives and norms; and familiarity with teammate knowledge, skills, and abilities. The individual factors relevant to team performance; what constitutes a good team member; what is the best configuration of team member knowledge, skills, and attitudes (KSA); and what role diversity plays in team effectiveness. Situational characteristics or events that influence the occurrence and meaning of behavior, as well as the manner and degree to which various factors impact team outcomes. Assumptions about humans’ relationships with each other and their environment that are shared among an identifiable group of people (e.g., team, organization, nation) and manifest in individuals’ values, beliefs, norms for social behavior, and artifacts.

Source: Salas et al. (2015).

conditions in their nine critical considerations: (1) composition; (2) culture; and (3) context. These factors describe the contexts within which the aforementioned core processes and emergent states operate. The variance in these dimensions can both directly impact team outcomes and indirectly influence performance through the above‐mentioned processes and emergent states (Salas et al., 2015). Indeed, there are interdependent relationships between each of these nine critical considerations, which are illustrated in Figure 2.1.

Core processes and emergent states As stated previously, core processes and emergent states describe key dynamics that influence teamwork. Of these, certain core processes, such as cooperation and coordination, represent an overarching construct of more granular concepts (e.g., trust, cohesion, team orientation, situation monitoring). In particular, cooperation encompasses all affective components involved in teamwork, whereas coordination encapsulates behaviors such as backup behavior and mutual support. Cooperation  Cooperation, as a comprehensive consideration, captures the motivational drivers of teamwork  –  that is, the attitudes, beliefs, and feelings of the team that drive behavioral action (Salas et al., 2015). The literature covers a number of important indices

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Overview of Team Effectiveness

Influencing conditions

Core processes and emergent states

Context

Cooperation

Communication

Conflict

Coaching

Coordination

Cognition Composition

Culture

Figure 2.1  Critical considerations of teamwork (adapted from Salas et al., 2015); KSA = knowledge, skills, and abilities.

of team cooperation, each of which contributes to team cohesion and performance. Cohesion, a broader construct encompassing cooperation and other core processes, describes the degree to which team members desire to remain in the team and are committed to the team goal (Forsyth, 2009). Cooperation is essential to maintaining the deep involvement, positive responses, and strong communication indicative of high cohesion (Landy & Conte, 2009). Furthermore, cooperation can be broken down into constituent parts or subconstructs, each of which contributes to teamwork as a whole. While, as m­entioned earlier, cooperation is a complicated, broad consideration, the remainder of this section describes some of the dynamics involved therein. Collective efficacy describes the collective sense of competence or perceived empowerment to control the team’s performance or environment (Katz‐Navon & Erez, 2005; Mathieu, Gilson, & Ruddy, 2006; Zaccaro, Blair, Peterson, & Zazanis, 1995). Teams whose members demonstrate collective efficacy tend to exert more effort, take more strategic risks, have better performance, and be more satisfied (Knight, Durham, & Locke, 2001; Lester, Meglino, & Korsgaard, 2002). Collective efficacy, as an important component of cooperation in the field, can be fostered through the promotion of “early wins” (Tasa, Taggar, & Seijts, 2007). Newly formed teams who experience high levels of initial success can consequently develop a collective sense of accomplishment – momentum that will help boost later performance (Salas et al., 2015). By helping teams feel in control, agentic, and capable, organizations can improve cooperation and consequent performance. Another central component of cooperation is trust, the shared belief that all team members will contribute appropriately and as necessary and protect the team (Bandow, 2001; Salas, Sims, & Burke, 2005). Trust has been shown to influence the level of intrateam monitoring and moderate the relationships between team training proficiency and performance and task and relationship conflict (Langfred, 2004; Salas et al., 2015). Trust, as an extensively studied dimension, has been linked to citizenship behaviors, organizational commitment, job satisfaction, positive attitudes towards the organization, and greater levels of performance (Colquitt, Scott, & LePine, 2007; Costa, 2003; Dirks & Ferrin, 2002; Kanawattanachai & Yoo, 2002; Kirkman, Rosen, Tesluk, & Gibson, 2006;



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Langfred, 2007; Webber, 2008). Relatedly, psychological safety, or the shared feeling of safety within a team allowing for interpersonal risk taking, builds upon a foundation of team trust and has been linked to team effectiveness (Edmondson, 1999). The development of trust can be fostered through the practice of “checking in,” or discussion of prior and relevant experiences before task performance. For example, operating teams preparing for surgery may benefit from sharing previous experiences in similar surgical situations (Salas et al., 2015). This exercise has twofold benefits. First, it allows members of a team to ascertain each others’ abilities, an essential antecedent to trust (Mayer, Davis, & Schoorman, 1995). Second, the discussion may foster a sense of commonality and community, as members begin to realize they have experienced similar experiences in the past. Social identity theory and social categorization (Tajfel & Turner, 2004; Turner, Hogg, Oakes, Reicher, & Wetherell, 1987) suggest that individuals who perceive similarity with others may also associate them with predetermined assumptions, giving way to a sense of predictability and comfort. That is, individuals are likely to trust others who appear to be similar (Brewer, 1979; Brewer & Kramer, 1986; Kramer & Brewer, 1984). Therefore, these pre‐performance discussions may aid the facilitation of trust and related cooperative attitudes, influencing teamwork potential positively. Members who trust one another may also be more likely to face challenges with a group‐ level mindset. Team/collective orientation describes the general preference for, and belief in, the importance of teamwork (Eby & Dobbins, 1997; Jackson, Colquitt, Wesson,  & ­Zapata‐Phelan, 2006). A similar concept, shown to impact team performance, is team learning orientation, or the shared belief regarding the degree to which team goals are geared towards learning (Bunderson & Sutcliffe, 2003). Finally, goal commitment, or the determination to achieve team goals, has been suggested as a critical attitude for effective teamwork, though it has also been proposed as a subdimension of the aforementioned cohesion construct (Beal, Cohen, Burke, & McLendon, 2003). Regardless, fostering group‐centered orientations in team members can increase overall propensity to cooperate. Conflict  At the opposite end of the spectrum from cooperation is conflict, or the perceived incompatibility in interests, beliefs, or views held by one or more team members (Jehn, 1995). Conflict is an inevitability, given inherent differences among individuals  –  and indeed, one of the classic models of team development involves a “storming” stage, in which members are expected to work out differences in opinion and perspectives (Tuckman, 1965). Conflict, too, may range in magnitude, manifesting as something as simple as a brief discussion or escalating into heated argument based on irreconcilable differences (Jehn, 1995, 1997). Conflict becomes a particular issue for teams when it leads to errors and breakdowns in performance (Salas et al., 2008), and when it is magnified by the complexity of the team’s task (De Dreu & Weingart, 2003). Broadly, conflict is a result of perceived lack of resources or treatments due to the actions or inactions of another party. In a team setting, such conflict can be task‐based (that is, when there are differences in perspective regarding the execution of tasks) or r­elationship‐based (when interpersonal differences create annoyance or tension among members). The literature suggests the inclusion of a third dimension, process conflict, which involves the division and delegation of tasks and responsibilities among team m­embers (Behfar, Peterson, Mannix, & Trochim, 2008; Jehn, 1997). The literature is divided in its perspectives towards the ramifications of these different dimensions of conflict. Some argue that relationship conflict is the most detrimental to team performance, while task conflict can actually increase performance in specific situations (Bradley, Postlethwaite, Klotz, Hamdani, & Brown, 2012). That is, task conflict can create a forum for team members in which they can explore multiple, potentially conflicting

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Overview of Team Effectiveness

solutions to problems; after generating ideas and alternatives, the team can then select the most viable option. Such open discussion would lead to stronger, more thoroughly developed outcomes, particularly in troubleshooting tasks that require innovation and creativity. In contrast, however, De Dreu and Weingart (2003) found that both relationship‐ and task‐based conflict have strong negative correlations with team performance and member satisfaction. Furthermore, although conflict may have initial beneficial impact, over time this effect wears away and lowers group cohesion (Copeland & Wida, 1996; Klein & Christiansen, 1969). Still other studies suggest a more complicated relationship between these types of conflict. Shaw et  al. (2011) found a moderating effect of relationship conflict on task conflict and team performance: when relationship conflict was low, task conflict had a c­urvilinear relationship with team performance; when relationship conflict was high, there was a negative and linear relationship. That is, when team members had positive interpersonal relationships, task conflict was beneficial, but when those interpersonal relationships were strained, task conflict exacerbated and lowered team performance. Relatedly, researchers have found that external conditions can foster positive and beneficial task conflict. Bradley and colleagues (2012) demonstrated that a psychologically safe climate, in which team members feel comfortable openly sharing information without threat of repercussion, can reduce relationship conflict and promote a small, beneficial amount of task conflict. It is clearly important to continue these lines of research in order to better understand how different types of conflict may influence each other and impact overall team performance. Owing to the potentially negative and complicated ramifications of conflict, it is crucial for organizational leadership to consider the management and resolution thereof. Through the proactive development and reactive implementation of conflict management strategies, teams can help correct more serious consequences of conflict. Prior to performance, teams should clearly identify norms and guidelines surrounding conflict. Furthermore, once conflict arises, teams should address problems in a straightforward fashion, relying upon the previously identified conflict management strategies (Salas et al., 2015). Studies show that these strategies alleviate negative impacts of conflict, including on team cohesion (Tekleab, Quigley, & Tesluk, 2009). Proactive and direct management of conflict has been shown to create healthy, open, constructive environments conducive to team performance (Cameron, 2000; Campbell & Dunnette, 1968; Montoya‐Weiss, Massey, & Song, 2001). The ability to anticipate and resolve conflict in real‐world settings can make the difference between sweeping success and critical failure. Coordination  Coordination is the enactment of behavioral mechanisms necessary to perform a task and transform team resources into outcomes (Sims & Salas, 2007). Specifically, coordination involves “orchestrating the sequence and timing of interdependent actions” (Marks et  al., 2001, p. 363). Team‐level strategies are applied to align knowledge and actions towards a common goal (Arrow, McGrath, & Berdahl, 2000; Brannick, Prince, Prince, & Salas, 1995). A systematic meta‐analysis by Rousseau, Aubé, and Savoie (2006) integrated 29 frameworks that focused on teamwork behaviors, emphasizing coordination as a vital dimension. Coordination can be explicit (i.e., team members directly and intentionally plan and communicate in order to manage interdependencies) or implicit (i.e., members anticipate team needs and organically, dynamically adjust their behaviors without instruction) (Rico, Sánchez‐Manzanares, Gil, & Gibson, 2008). Numerous field and laboratory studies have found that both types of coordination drive team performance. Well‐coordinated teams are able to obtain information from other members when needed and move easily from



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one task to another (Swezey & Salas, 1992). Effective coordination can ensure positive outcomes, while breakdowns thereof may lead to increased errors and misunderstandings, consequently derailing performance (Sims & Salas, 2007). Teams who experience coordination loss typically expend energy in different directions or fail to synchronize their work on time‐critical tasks, which will naturally harm overall performance (Landy & Conte, 2009). Coordination can manifest itself in myriad ways, both within and between teams. Individuals within a team may occupy the same or complementary roles with varying degrees of interdependence (Guastello & Guastello, 1998), thereby influencing the organization of efforts. In a meta‐analysis of 92 studies, Stewart (2006) found that within‐team coordination corresponded with higher team performance. Coordination takes on even greater import in more complex organizational structures, such as multiteam systems, in which multiple teams must work together towards a common goal (Mathieu et al., 2008). Several studies have found that effective coordination at this level also facilitates coordination within the comprising teams (de Jong, de Ruyter, & Wetzels, 2005; K­irkman & Rosen, 1999; Mathieu & Schulze, 2006). Such nested coordination may be particularly important for dynamic, multilevel organizations, such as those in the medical and military sectors. In order to maximize coordination, it is important to define team member roles in a manner that is clear yet not overly rigid (Salas, Rosen, Burke, Goodwin, & Fiore, 2006). Doing so can maximize contributions of all team members, prevent redundancies in work, and guide expectations regarding individual roles and responsibilities. Clarifying routines and distribution of responsibilities leads to more effective teamwork (Gersick, 1988; G­ersick & Hackman, 1990; Weick & Roberts, 1993). Importantly, however, teams should remain relatively flexible such that, should unexpected issues arise, individual team m­embers may step up to fulfill necessary responsibilities (Salas et al., 2015). Towards this end, Rosen and colleagues (2011) developed an index of behavioral markers of team adaptability, highlighting the importance of coordination. Their suggestions include e­ffective communication of status and needs, as well as the use and observation of cues indicating synchronization of behaviors. In addition to role clarification and structuring as means of increasing coordination prior to task performance, teams may also use debriefing sessions to learn from experiences after the fact. Following performance episodes, teams may review positive and negative aspects as they relate to the efficiency of their coordination (Smith‐Jentsch, Cannon‐Bowers, Tannenbaum, & Salas, 2008). Debriefs allow teams the space to developmentally reflect on aspects of performance and have been empirically linked to positive outcomes (Ellis, Ganzach, Castle, & Sekely, 2010). Research has shown that properly constructed debriefs can increase team coordination and other performance outcomes by 20–25% (Tannenbaum & Cerasoli, 2013). The practice of reflection can help draw attention to and identify areas of improvement, especially in the planning and organization of efforts. Communication  It is no surprise that communication is a critical component of teamwork, as it is essential to all types of interpersonal and organizational relationships. Communication can be approached from two different perspectives. The first categorizes communication as a linear‐like transfer of information between sender and receiver (Deetz, 1994). However, this straightforward characterization does not fully capture the internal and external nuances that influence the sending, interpretation, and response of such communication, particularly in a team context (Salas et al., 2015). A fuller definition illustrates communication as a transactional process, in which communicators can send

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Overview of Team Effectiveness

and receive information simultaneously and influence these pathways (Barnlund, 2008). According to this model, details (e.g., the specific sender and the method of delivery) influence the interpretation and response of messages. In line with this thinking, we define communication in teams as a reciprocal process of team members’ transmission and receipt of information, which thereby reforms a team’s attitudes, behaviors, and cognitions (Craig, 1999). The importance of team communication is well documented in the literature and in practice. Industries such as aviation, military, and health care have noted effective team communication’s role in reducing errors (Helmreich, Merritt, & Wilhelm, 1999), self‐adjusting plans in light of teamwork breakdowns (Smith‐Jentsch, Zeisig, Acton, & McPherson, 1998) and acknowledging proper information (Weaver et al., 2010). Indeed, Elder and Dovey (2002) cite communication breakdown as a major source of preventable errors in healthcare delivery. Furthermore, in a 2009 meta‐analysis of 72 studies, Mesmer‐ Magnus and DeChurch (2009) found that information sharing positively and significantly predicts team performance. The research supports the intuitive belief that communication is critical to teamwork. Communication also inherently influences other aspects of teamwork, such as coordination and conflict (LePine, Piccolo, Jackson, Mathieu, & Saul, 2008; Rosen et al., 2011). Communication structures are the medium through which information flows, consequently influencing a team’s ability to work together and accomplish goals (Dyer, 1984). Teams that communicate effectively may use both explicit (wherein messages are transmitted and acknowledged overtly) and implicit forms (in which information is more passively conveyed) (Espinosa, Lerch, & Kraut, 2004). Indeed, it is important to note that even effective teams may exchange few words during performance episodes, instead relying on nonverbal cues and an ingrained understanding of one another’s roles and expertise in order to accomplish goals (Entin & Serfaty, 1999). Communication, in its various forms, can be used to convey important messages to members of a team. Existing literature in the field offers two methods of optimizing team communication. The first, which is dependent on context, centers on increasing accessibility of information. In face‐to‐face interactions, team members should be encouraged to share information uniquely held by each individual. In practice, however, members typically only share commonly held information (Mesmer‐Magnus & DeChurch, 2009). Therefore, it is critical to team success to encourage the disclosure of unique information, meaning expertise or task‐relevant information held by a single team member. Alternately, in virtual environments, information should be openly available (Mesmer‐Magnus & DeChurch, 2009; Mesmer‐Magnus, DeChurch, Jimenez‐Rodriguez, Wildman, & Shuffler, 2011). In these settings, teams typically exchange only task‐specific messages, consequently losing the rich contexts afforded by openness of information. In order to develop cooperative attitudes and increase efficiency, team members should be encouraged to share information openly rather than selectively (Mesmer‐Magnus et al., 2011). Second, closed‐loop communication procedures should be implemented in order to acknowledge the receipt of information and clarify any discrepancies in interpretation (McIntyre & Salas, 1995). Closed‐loop communication occurs when team members confirm that a message has been successfully relayed and received (Salas et al., 2005). Teams should establish closed‐loop communication protocols prior to performance e­pisodes in order to ensure that all team members send, receive, and process information in a shared, appropriate manner (McIntyre & Salas, 1995). In doing so, information exchange challenges can be minimized, promoting successful communication and team performance (Salas et  al., 2015). The clearer communication is, the more efficient t­eamwork is.



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Coaching  What happens when teams fail to coordinate, communicate, or overcome conflict efficiently? On their own, teams may not necessarily recognize when breakdowns occur or understand how to optimize expertise and resources (Hackman, 2011). Strong coaching, or leadership, both internally and externally, can provide the needed direction and support to help teams overcome this potential for process loss (Hackman & Wageman, 2005; Zaccaro, Rittman, & Marks, 2002). Coaching refers to the host of activities performed by both individuals and teams for the sake of team effectiveness (Hackman & Wageman, 2005). Here, however, we define it as the enactment of leadership behaviors to establish goals and set direction towards the successful accomplishment thereof (Fleishman et  al., 1992). This functional perspective promotes an understanding of specific coaching behaviors that must be enacted for team success (Salas et al., 2015). Leadership behaviors can initiate structure and contribute to overall team effectiveness (Burke, Stagl, Salas, Pierce, & Kendall, 2006). Most significantly, leadership behaviors facilitate the emergence of effective processes and states in teams, including cooperation (e.g., motivation and affect) and coordination (Hackman, 2011; Zaccaro et al., 2002). Research has provided recommendations regarding specific approaches to coaching (Coultas, Bedwell, Shawn, & Salas, 2011; Hackman & Wageman, 2005). For example, coaches must be attuned to needs of teams before, during, and after performance (K­ozlowski, Watola, Jensen, Kim, & Botero, 2009). Not only this, but team coaches must also attend to the overall needs of the team as well as the individual needs of members (Salas et al., 2015). Consequently, leadership strategies may not necessarily relate directly to task performance; rather, diagnosing and solving problems may be addressed through guidance of team members. That is, coaching in this sense involves “building teamwork, not doing the team’s work” (Hackman, 2002, p. 167). Salas and colleagues (2015) posit that perhaps the most critical responsibility of leaders in teams is diagnosing and addressing team problems as they arise. Coaching can recognize and correct vital team errors or problems and provide guidance in challenging situations. Baran and Scott (2010) elaborate on this in a field study of firefighters, citing the importance of coaching behaviors such as direction setting, role modeling, sensemaking, and framing in “near miss” situations. In a 2007 meta‐analysis, these behaviors have been positively correlated with perceived team effectiveness, productivity, and learning (Burke, Sims, Lazzara, & Salas, 2007), as well as follower job satisfaction, leadership effectiveness, satisfaction with leader, and group performance (Derue, Nahrgang, Wellman, & H­umphrey, 2011). In fact, a need for shared leadership among multiple individuals (both formally and informally) can often arise in order to appropriately facilitate teamwork (Morgeson, DeRue, & Karam, 2009). Although the assumption is often that coaching stems from a single individual (Conger & Pearce, 2002), given the import of strong leadership, many contexts can demand more complex and adaptable forms of guidance. Coaching can thus originate from one or several leaders, internal or external to the team, including those officially acknowledged as leaders and others who informally step up when the need arises (Morgeson et  al., 2009). Furthermore, the distribution of leadership responsibilities among m­embers often results in a number of positive benefits (Zaccaro & DeChurch, 2012). Shared leadership can facilitate effective teamwork, enhance team performance, and reduce workload on individuals (Balkundi & Harrison, 2006; Carson, Tesluk, & Marrone, 2007; Conger & Pearce, 2002; Mehra, Smith, Dixon, & Robertson, 2006), especially when the distribution of leadership is based on expertise. Through the recognition of performance and process gaps, coaching can dynamically guide and foster team development and performance throughout the team lifecycle

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Overview of Team Effectiveness

(Hackman & Wageman, 2005; Kozlowski et al., 2009). As such, it is critical to understand how to apply coaching and leadership in team design, development, and performance (Salas et al., 2015). Cognition  Within team research, there is a robust body of literature on shared team knowledge or cognition. Team cognition is a foundational component of effective team processes, as it allows teams to enter performance episodes with a mutual baseline understanding of how to engage in the task at hand (Salas et al., 2015). Specifically, cognition refers to the shared understanding among team members that develops as a result of team member interactions, including shared mental models and transactive memory systems (Klimoski & Mohammed, 1994). Cannon‐Bowers, Tannenbaum, Salas, and Volpe (1995) describe a shared mental model as a knowledge‐based team competency essential to team effectiveness. It allows people to describe, explain, and predict the behavior of others, providing team members with a baseline, common understanding of task requirements, and improving coordination processes and consequent performance (Marks et al., 2001; Smith‐Jentsch, Mathieu, & Kraiger, 2005). Transactive memory systems describe the collective, shared memory of a group in two senses: internal (what individuals know p­ersonally) and external (what individuals know can be retrieved from other sources, including from other team members) (Peltokorpi, 2008). Team cognition may also involve: knowledge of roles and responsibilities; team mission objectives and norms; the situation within which the team is operating; and familiarity with teammate knowledge, skills, and abilities (Wildman et al., 2012). The failure to develop team cognition can result in impaired performance and negative outcomes, including life‐threatening outcomes. Team research has extensively examined the effects of team cognition. In a 2010 meta‐ analysis, DeChurch and Mesmer‐Magnus found that team cognition serves as an important foundation for teamwork and is strongly related to emergent affective states and team processes and performance. Accordingly, a 15‐year review of the team cognition literature identified a number of empirically evaluated, team‐level outcomes, including team norms, coordination, communication, team performance, team viability, and strategy implementation (Mohammed, Ferzandi, & Hamilton, 2010). Furthermore, both theoretical and empirical research suggests relationships of team cognition with team adaptation (Burke et al., 2006; Resick et al., 2010) and implicit coordination (Rico et al., 2008). Without the pooled resources of team cognition, teamwork would be severely impaired, if near impossible. By establishing shared understanding of team objectives, roles, expertise, and situational variables, teams can preemptively avoid potential missteps and failures (Salas et al., 2015). As such, it is important to establish a clear shared understanding of team functioning – but one that is also amenable to appropriate changes during the team lifecycle. Guided team self‐correction is a debriefing strategy, developed around an expert model in which team members are given responsibility for systematically diagnosing and solving team problems (Smith‐Jentsch et al., 1998). This type of team training can help teams develop a more accurate sense of team knowledge, consequently improving team process and performance (Smith‐Jentsch et al., 2008). That is, teams that leverage self‐correction should also be able to effectively establish the shared cognition necessary to enhance teamwork. Another strategy which increases team cognition is cross‐training, which involves having team members learn the tasks of other team members. This type of training develops more accurate understanding of member roles and responsibilities by creating shared task models and developing knowledge regarding specific tasks (Blickensderfer, Cannon‐ Bowers, & Salas, 1997; Salas et  al., 2005). It has been shown to have potentially high impact on teams, with performance increases of 12–40% following implementation



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(Volpe, Cannon‐Bowers, Salas, & Spector, 1996). However, it should be noted that cross‐ training is only beneficial when its benefits outweigh the process loss in time and energy needed to learn the task – for example, when the tasks are not highly complex or specialized (Salas et al., 2015). Other types of training which have been shown to increase shared team knowledge include team interaction and computer‐based models (Marks, Z­accaro, & Mathieu, 2000; Smith‐Jentsch, Baker, Salas, & Cannon‐Bowers, 2001). Training can be a highly effective, proactive step towards establishing better cognition among members of a team. While there is an abundance of research on the outcomes and practical aspects of cognition, however, it is significantly more difficult to study the developmental, psychological underpinnings thereof. Owing to the deeply internal and inherently dynamic nature of human thought, it is substantially more challenging for researchers to measure and e­xamine team cognition. Regardless, research to date suggests that there are certain m­ember characteristics, including tenure, experience, and similarity, which play a role in the development of shared knowledge (Salas et  al., 2015). More research is needed in order to more fully understand the antecedents of cognition in teamwork. Altogether, these teamwork processes and emergent states are critical to team performance. Cooperation, conflict, coordination, communication, coaching, and cognition ensure that teams are motivated and capable of executing behaviors and processes crucial to success.

Influencing conditions Teams do not perform in a vacuum, however; in considering teamwork and performance, it is essential to take into account the surrounding environment. While the aforementioned critical considerations derive from dynamics internal to the team, external forces on  teams, such as composition, context, and culture, have significant impact as well. Influencing conditions are the factors that influence these core teamwork processes and emergent states, shaping the manner or degree to which teams engage in teamwork. Composition  By exploring how composition influences effectiveness, organizations can develop selection systems that aid managerial decisions when forming teams. Team composition refers to the attributes of team members, including skills, abilities, experiences, and personality characteristics (Guzzo & Dickson, 1996). This involves understanding several component parts, including: individual factors relevant to team performance; what constitutes a good team member; what the best configuration of team member knowledge, skills, abilities (KSAs), and other characteristics is; and the role that diversity (that is, differences among team members, including function/role, occupation/discipline, culture, race/ethnicity, and gender) plays in team effectiveness (Cannon‐Bowers & Bowers, 2011). Unsurprisingly, composition has been one of the most heavily researched areas in the teamwork literature, having been related to team effectiveness for over 50 years (Mann, 1959). Indeed, the study of composition entails a major area of interest in industrial/organizational psychology: the selection of individuals who can best contribute to the team. Many studies have indicated that cognitive ability and personality traits of individuals can predict team performance, thereby emphasizing the importance of selecting appropriate team members. A meta‐analysis by Bell (2007) found that all of the “Big Five” personality traits (extraversion, agreeableness, conscientiousness, openness to experience, and e­motional stability) relate to performance in field settings. For example, in a study of 652 employees comprising 51 work teams, Barrick, Stewart, Neubert, and Mount (1998) found that teams with members high in cognitive ability, conscientiousness, agreeableness,

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Overview of Team Effectiveness

extraversion, and emotional stability received higher supervisor ratings of team performance. In particular, variance in, and minimum level of, conscientiousness is associated with team performance – such that organizations may want to implement a baseline level of conscientiousness while selecting for variability thereof. Other studies have found similar results tying abilities and traits to improved performance (Morgeson, Reider, & Campion 2005; Neuman & Wright, 1999; Stewart 2006), implying that certain individual differences are beneficial to team effectiveness. On the other hand, other research has yielded conflicting or more complex outcomes. A study by Barry and Stewart (1997) did not find a relationship between conscientiousness and team performance – a particularly unexpected result, given the strong role of the former on an individual basis. Furthermore, they found that there was a curvilinear relationship between team members’ extraversion and team performance, suggesting that it is important to strike the right balance of personalities within a group. Indeed, there can be complicated – and disparate – relationships between personality and team performance. Further research has looked at finer‐grained distinctions in individual member traits, considering achievement orientation, dependability, assertiveness, and locus of control (Mathieu et al., 2008). The selection of individuals based on traits and as it relates to team functioning can be multilayered and complex, advancing the case for further research. While early studies focused primarily on the role of team member personality, the past decade has seen a resurgence of interest in disentangling individual from team contributions. For example, several studies have demonstrated that generic teamwork skills, above and beyond unique individual technical (that is, taskwork) skills and abilities, determine team success (Baker & Salas, 1992; Stevens & Campion, 1994). This work has led to the identification of several important teamwork KSAs, including provision and acceptance of feedback, adaptability, and problem solving (Salas, Rosen, Burke, & Goodwin, 2009). Stevens and Campion (1994) furthered this line of inquiry by developing and validating a measure of teamwork KSAs within individuals towards strengthening composition. Composition can be influenced, and in turn influence, a number of team‐level factors. For example, teams who have a strong team orientation, or propensity for working with others in group settings (Salas et al., 2005), are more likely to successfully achieve group goals (Driskell, Salas, & Hughes, 2010). As such, organizations can select members who rate high in team orientation in order to ensure that members are willing to work in a cooperative manner (Driskell et al., 2010). Team members who thrive in collective environments will also be more likely to be motivated to contribute to behavioral mechanisms (such as coordination and communication) and work towards team goals (Mohammed & Angell, 2004). These group orientations are more likely to lead to stronger team cognition, such as shared mental models, which are organized ways for team members to think about how the team will work. Inherently, the composition of a team will influence these factors related to the previously mentioned core processes and emergent states. In practice, organizations should approach composition holistically, understanding the characteristics of the individual and group in composition, in addition to the demands of the performance episode at hand. As mentioned previously, effective teamwork goes beyond assembling a team of experts with the needed taskwork knowledge (Salas et al., 2005); members must also be well versed in teamwork generic skills. As such, organizations may seek to measure and select team members based on both task‐specific knowledge and more generic teamwork‐related capabilities. Following the selection stage, organizations can foster healthy team composition through careful strategizing. By identifying trainable individual composition variables that affect team performance, organizations can guide training and development decisions (Stevens & Campion, 1994). For example, teams can undergo remedial training to develop and improve KSAs in which they are



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weak. While composition is commonly thought of in terms of initial selection, in fact, it can be strengthened through organizational practices. Diversity  Organizations should also consider member diversity, an important aspect of team composition. We may categorize diversity in terms of demographic diversity (e.g., observable attributes or demographic characteristics such as age, gender, and ethnicity) and psychological diversity (which involves underlying attributes, such as skills, abilities, personality characteristics, attitudes, beliefs, and values, and may include functional, occupational, and educational backgrounds; Landy & Conte, 2009; Van Knippenberg & Schippers, 2007). These distinctions in types of diversity have been determined and explored by prior research. In considering the influences of time and perceived versus actual diversity, one study found that the impact of demographic, surface‐level differences decreases over time. On the other hand, discrepancies in psychological, deep‐level factors strengthen over the team lifecycle (Harrison, Price, Gavin, & Florey, 2002). Accordingly, increasing variation in individual KSAs and addressing deep‐level differences may greatly improve team effectiveness (Harrison et al., 2002). There is a great body of research on diversity and its effects within teams. Tsui, Egan, and O’Reilly (1991) found that demographically different individuals are less psychologically and behaviorally committed to the organizations (e.g., more likely to be absent from work and leave the organization), potentially due to discomfort. Teams with diverse functional backgrounds and skills also face greater challenges in coordination and c­ommunication (Ilgen, 1999). On the other side of the coin, however, other research has also identified benefits of diversity. Watson, Kumar, and Michaelsen (1993) found that, while homogeneous groups initially performed more effectively, demographically diverse groups became more effective at identifying and creatively addressing complex problems over time. Similarly, psychological diversity has also been shown to relate to better idea generation and decision making (Horwitz & Horwitz, 2007; Magjuka & Baldwin, 1991)  –  that is, diverse individuals bring different perspectives and therefore generate more innovative, creative solutions. The outcomes of diversity can be related to the core process of conflict; naturally, differences among individuals may generate disagreement, which can ultimately be both a strength and weakness in terms of team performance. Overall, diversity within teams appears to be a double‐edged sword, with benefits and challenges unique to specific forms and types thereof (Jackson & Joshi, 2004; M­illiken & Martins, 1996). Context  While factors within and between team members are certainly impactful, external influences must also be considered. Teams operate within a wide variety of contexts that can influence their functioning, including determining what components of teamwork are more or less important. Here, we define context as situational characteristics or events that influence the occurrence and meaning of behavior, as well as the manner and degree to which various factors (e.g., team member characteristics, team behavioral processes) impact team outcomes (Johns, 2006). Research indicates that contextual variables can change the effectiveness of various types of processes (e.g., communication, type of information sharing) and emergent states (e.g., trust) in achieving team outcomes (Salas et al., 2015). According to Johns (2006), organizational context can be approached more generally, as a “big picture” involving occupation, location, time, and rationale, or as discrete phenomena, such as task and physical contexts. Specifically, the task context includes factors such as team or individual autonomy, uncertainty, accountability, and available resources. The physical context, on the other hand, involves observable features of the working

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Overview of Team Effectiveness

environment (e.g., temperature, lighting, and decor). These contextual influences relate to the availability of resources necessary for performance, thereby potentially enhancing team interactions and increasing team effectiveness (Salas et al., 2015). In other words, context is powerful because it can shape the very nature in which team members interact with one another. Organizational contexts can include the rewards system, training system, managerial support, and technology (Landy & Conte, 2009). For example, teams are more successful if rewards and objectives are team based, rather than oriented to individuals (Hackman, 1987). Organizational climate, the collective agreement regarding the perception of formal and informal organizational policies, practices, and procedures, is perhaps one of the most relevant variables for workplace teams (Reichers & Schneider, 1990; Salas et  al., 2015). These factors set the tone of the organization, communicating their values and subsequently shaping the way that teams function. For example, organizations that prioritize teamwork may convey that message to their employees through the establishment of team‐based rewards or by creating collaborative, open work spaces (Salas, Kosarzycki, Tannenbaum, & Carnegie, 2004). In contrast, emphasizing individual success could detract from motivation to cooperate, instead leading to various, detrimental forms of conflict such as competition and perceived unfairness (Mitchell & Silver, 1990; T­josvold & Yu, 2004; Van Mierlo & Kleingeld, 2010). Rather, by aligning policies and procedures, such as selection, reward, and performance management, appropriately, a teamwork s­upportive context can be established. Physical context  The physical context can include a great number of environmental aspects of the work setting, such as tools, work spaces, information display, shift work, work pace, machine controls, and safety culture. This can also be studied more specifically through engineering or human factors psychology, an academic subfield which examines the capacities and limitations of humans with respect to a particular environment (Landy & Conte, 2009). These environmental components will naturally influence the ways in which the team operates. For example, a team of astronauts working in space flight will inherently have drastically different physical constraints than a project team in a consulting corporation; the close‐proximity, high‐stress setting will then influence other core processes and emergent factors, such as the manner of communication and coordination. Physical contexts set the stage in which both teamwork and taskwork are performed. Task context  The task context, in that it describes the manner of work to be performed, naturally also influences teamwork. For example, a contextual factor with dramatic impact is external threat and stress. Extreme environments have a real, important impact on how teams think, feel, and behave (Salas et al., 2015). Fire and rescue squads, medical teams, flight crews, and military units are examples of teams placed in “extreme environments” (involving isolation, confinement, or high levels of threat and risk; Stuster, 1998), in which they are highly susceptible to danger, disaster, and potentially catastrophic errors. In high‐stakes situations with high levels of time pressure, individuals are also more likely to take risks (Van Mierlo & Kleingeld, 2010). However, owing to the intense bonding that results from these experiences, teams may actually develop greater cohesion (Kanas et al., 2001). In such settings, leadership and identification with the missions can mitigate stress and enhance mission culture, organizational citizenship behaviors, and motivation (Grömer, Frischauf, Soucek, & Sattler, 2007). Organizations can also implement trainings with emphases on standard protocols and development of decision‐making skills in order to minimize errors of judgment (Salas et  al., 2015). These complicated, high‐stakes



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contexts clearly differentiate team functioning from that of project teams. Teams who work in corporate, office settings will clearly experience much different task contexts than those in extreme environments. Stressors will differ in source, type, and magnitude (e.g., administrative and interpersonal challenges). Task contexts therefore significantly determine the constraints in which teamwork is to be performed. One factor which straddles the line between physical and task contexts is geographic distribution. Typically, teams that are physically separated are at a disadvantage, as they lack the face‐to‐face interaction that helps establish and maintain goals (Hertel, K­onradt, & Orlikowski, 2004) and develop personal relationships, cohesion, and trust (Gibson & Gibbs, 2006). These conditions result in challenges, including distribution, goal incongruence, identity, and coordination issues (Salas et al., 2015). In order to offset the lack of social cues (Kirkman & Mathieu, 2005), organizations can facilitate initial face‐to‐face meetings to encourage trust and effective behavior patterns (Cascio & Shurygailo, 2003; Monalisa et al., 2008). Leadership strategizing and coordinating can also improve interteam coordination and overall multiteam system performance (D­eChurch & Marks, 2006). One especially effective and increasingly popular means of addressing geographic distribution lies in the growing use of technology and virtual e­nvironments. Research by Goodman and colleagues found that technology directly affects team processes and performance, and should be included in models of team effectiveness (Goodman, 1986; Goodman, Ravlin, & Schminke, 1987). In particular, the past few years have seen rapid technological advances which have made collaboration across time and space possible. Virtual, distributed team and multiteam systems are now able to cross national or organizational boundaries, creating new types of contexts in which work can be performed. Virtual environments can also improve the sharing of unique knowledge, though openness of information sharing may still be hindered; this is particularly important given that open information sharing is more important in virtual settings (Mesmer‐ Magnus et al., 2011). While new technology is not a panacea and needs to be applied in ways that encourage teamwork, it is evident that it makes much stronger team collaborations possible. Culture  While context describes the environmental setting of teamwork, culture refers to the broader societal and interpersonal dynamics surrounding it. Although culture can be a complex, multifaceted idea, here it is defined as: the assumptions people hold about relationships with each other and the environment that are shared among an identifiable group of people (e.g., team, organization, nation; Gibson, Maznevski, & Kirkman, 2009). Culture is a driving force for member values, norms, and behaviors, which can originate from any level of group (including teams, an organization as a whole, a field or discipline, at the national level, or across other faultlines; Salas et al., 2015). In particular, the cultural values of the organization, team, and members within a team can have great impact on teamwork. Cultural values shape the way that individuals view themselves in relation to the team, thereby trickling down into teamwork attitudes (e.g., trust and collective efficacy), cognitions (e.g., shared mental models), and behaviors (e.g., information exchange and backup behavior; Shuffler, DiazGranados, & Salas, 2011), including communication and conflict management (Taras, Kirkman, & Steel, 2010). In fact, research suggests that culture can have greater predictive power than personality traits on outcomes such as commitment, citizenship behavior, identification, and team‐related attitudes (Taras et al., 2010). While there are a great number of cultural dimensions that have been explored, here we discuss examples of some of the most widely researched facets: individualism–collectivism,

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Overview of Team Effectiveness

power distance, and long‐term/short‐term orientation. Cross‐cultural teams will likely include individuals who differ across many of these dimensions. •• Individualism–collectivism refers to a spectrum of behavioral orientation that touches upon the deep structure of culture. Specifically, it refers to the degree to which individuals view themselves as unique individuals or as part of a collective (such as a team). It is particularly relevant in the teamwork literature as it has implications for whether and to what extent members will engage in teamwork processes (Bell, 2007). •• Power distance is the degree to which individuals value or acknowledge hierarchy and status, which has natural implications for intrateam interaction. It has been cited as a primary contributing factor in the accidents of Avianca Flight 052 in 1990, which led to the deaths of 73 people, and Korean Air Flight 801 in 1997, which led to 228 f­atalities, as well as a major cause in cases of medical error. In these instances, power distance can create a deeply ingrained culture of respect for hierarchy; that is, individuals are less likely to voice potential errors from concerns of inappropriateness or disrespect (Helmreich, 1994, 2000; Strauch, 2010). In high‐stakes contexts, such deference or failure to identify errors can result in grave consequences. Power distance can thus influence the structure of a team and consequent interactions between members and performance outcomes. •• Long‐term/short‐term orientation involves the manner in which individuals view time and focus their efforts and goals accordingly (Hofstede & Hofstede, 2001). Individuals with long‐term orientations will plan into the future (e.g., overarching company visions), while those with short‐term orientations may focus on more immediate output (e.g. quarterly earning reports). Differences may lead to team members perceiving scheduling, goal setting, and deadlines in clashing ways (Waller, Conte, Gibson, & Carpenter, 2001), which will also likely result in miscommunication and conflict when developing timelines and meeting deadlines. Given that economies and organizations are becoming increasingly globalized, research has begun to tease apart the effects of various cultural differences on team processes and performance. A 2010 meta‐analysis by Stahl, Maznevski, Voigt, and Jonsen found a number of advantages associated with culturally diverse teams, including higher levels of creativity and satisfaction. Conversely, heterogeneity in cultural values and norms can also be a source of conflict and process loss, particularly in that it can lead to a lack of social integration (i.e., cohesion, identity, and commitment), communication, and shared meaning (Salas et al., 2015). Cultural diversity inherently can entail a number of other barriers preventing effective team processes, such as language and miscommunication and norms regarding punctuality and work habits. As such, organizations with diverse individuals and groups should actively develop climates that emphasize overarching and uniting norms and values. They may also emphasize effective teamwork processes regardless of status. For example, airline industries have implemented crew resource management protocols that focus on effective coordination and communication among team members, which can strengthen rapport in spite of discrepant cultural backgrounds (Salas et al., 2015). In light of the preexisting cultural dynamics that individuals may bring with them into a team context, it is important that organizations create healthy working cultures. In a team, each person brings their own influences, norms, and beliefs into their interactions. However, this does not necessarily need to be detrimental if members are able to meld their cultural values into new, hybrid cultures that acknowledge similarities among team members (Earley & Mosakowski, 2000). A hybrid team culture is defined as an emergent set of norms, rules, expectations, and behaviors that individuals within a team create



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themselves after a period of interaction (Salas et al., 2015). The strength of the culture is determined by the degree to which members share these values; however, the establishment of any unifying team culture alone can be beneficial during team interaction. F­urthermore, highly heterogeneous groups that set norms for appreciating differences can contribute to the overall goal of the team and maximize team performance (Mannix & Neale, 2005). Fostering the development of a team identity and culture can build up other critical considerations (such as cooperation and coordination), helping overcome individual differences between team members.

Evaluation While it is clear that a number of variables may affect team processes, the question remains  –  how do we measure and assess teamwork? Team output can be measured in myriad ways, including performance, innovation, and member wellbeing (Brodbeck, 1996). As teams – and therefore team goals – become more important, managers have become interested in evaluating team performance more concretely (Hedge & Borman, 1995). With few exceptions, too, team‐based employees prefer appraisal on the group level (Waldman, 1997). Should organizations respond in kind, it can send the message that team performance is important to organizational success (Reilly & McGourty, 1998). However, in practice, team‐based evaluations and feedback can present unique c­hallenges. First, most organizations do not have formal means of providing team‐level assessments. Second, teams are often specialized in their roles and responsibilities, thereby complicating the process of implementing generalized appraisal systems. For example, who should evaluate the team performance – the manager, member, or clients (Scott & Einstein, 2001)? Discussions of 360‐degree feedback suggest that all of these sources can provide valuable information to the team (Hallam, 2001), but the central issue remains: organizations need to develop ways of delivering useful and relevant feedback to teams. Moreover, organizations also need to develop metrics to measure behavior and output. Naturally, certain teams will have more measurable indices, such as those involved in production (e.g., a team of factory workers can be assessed by their output); other teams, however, will have fewer clear indicators of performance (e.g., a team of school administrators may have more complicated or subjectively rated duties). In these cases, team objectives must be defined carefully, such that they can be used as standards for evaluation. Assessment may thus come from direct measures of team output, measures of the quality of the product, and 360‐degree assessments from the manager and internal and external customers (Reilly & McGourty, 1998). Furthermore, when assessments are c­onducted it is important to use both judgmental and objective measures when possible  –  particularly as results may differ depending on the particular evaluation measure (Gladstein, 1984). On a practical level, organizations should consider “next steps” post‐evaluation. As organizations move to team‐based work, it is important for managers and executives to create conditions that foster efficient collective action (Hackman, 1992; Landy & Conte, 2009). In order to move organizational cultures away from individual‐oriented thought and towards more group‐based perspectives, organizations can use a combination of team goals, feedback, and rewards (Pritchard, Harrell, DiazGranados, & Guzman, 2008). Effective teams, by seeking feedback about productivity and quality goals, can better develop strategies for achieving goals (Landy & Conte, 2009). Over time, team goal setting, appraisal, and feedback should become more common as organizations continue to emphasize teamwork (Hedge & Borman, 1995).

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Overview of Team Effectiveness

Future Research Teamwork has been growing in visibility, both academically and in practice. Over the past 15 years, a great number of reviews and meta‐analyses of the teamwork literature have been published, summarizing a vast body of knowledge (Salas et al., 2015). Organizations, too, are increasingly realizing the benefits and importance of teams in addressing complex problems. However, one of the widespread challenges of scientific research is translating basic science into practical application (Briner & Rousseau, 2011; Thayer, Wildman, & Salas, 2011). In the future, it is hoped that more research will apply empirical, science‐based findings into team work settings. Furthermore, while considerable light has been shed on the considerations in teamwork, there is still much to be learned about the interplay among these dimensions. The aforementioned core processes, emergent states, and contextual factors do not exist independently of one another; rather, they are part of an open system wherein a change in one factor may influence another (Katz & Kahn, 1978). Future research should explore and test the theories behind the full “map” of teamwork. Relatedly, as the body of work on teamwork increases, researchers can explore increasingly specific questions. This is particularly important considering the nuances of teams in various industries and lines of work  –  recalling the earlier example of extreme environments as opposed to laxer task contexts. In practice, organizations should consider tailoring procedures and interventions to the context in which the team operates. In other words, “one size does not fit all when it comes to teamwork” (Salas et al., 2015, p. 14). Finally, it is critical to explore the nature of teamwork in light of trends in the workplace. For example, advances in technology will continue to push forward new virtual contexts and increasingly complex organizations will implement multiteam systems. The influences of these trends on core processes such as conflict and cognition may demand specialized training and development programs (Shuffler et al., 2011). Relatedly, technology will also continue to develop in ways that can help create algorithms targeting “dream teams”; accordingly, it is important that researchers explore teamwork and taskwork KSAs, as well as team complementarity in various contexts and across task types (Salas et al., 2015).

Conclusion Given the shift to team‐based structures in today’s organizations, it is becoming more important to understand team processes. The existing, extensive body of literature has formed a strong foundation through which we can understand teamwork and the dynamics involved therein. However, the dynamic and complicated nature of organizations demands that research continue to explore teamwork  –  both as it changes in accordance with current trends and as it becomes more complex owing to the nature of its contexts. This should be well within the capabilities of the field, given the past successes of the science of teams. Research in teamwork is a worthwhile endeavor, given its salient and tangible benefits.

Acknowledgements This work was supported in part by contract NNX16AB08G with the National Aeronautics and Space Administration and NBPF03402 with the National Space Biomedical Research Institute to Rice University. The views expressed in this work are those of the authors



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and do not necessarily reflect the organizations with which they are affiliated or their s­ponsoring institutions or agencies.

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Stahl, G. K., Maznevski, M., Voigt, A., & Jonsen, K. (2010). Unraveling the effects of cultural d­ iversity in teams: A meta‐analysis of research on multicultural work groups. Journal of International Business Studies, 41(4), 690–709. http://doi.org/10.1057/jibs.2009.85 Stevens, M. J., & Campion, M. A. (1994). The knowledge, skill, and ability requirements for t­ eamwork: Implications for human resource management. Journal of Management, 20(2), 503–530. Stewart, G. L. (2006). A meta‐analytic review of relationships between team design features and team performance. Journal of Management, 32(1), 29–55. Strauch, B. (2010). Can cultural differences lead to accidents? Team cultural differences and their effects on sociotechnical system operations. Human Factors, 52(2), 246–263. Stuster, J. (1998). Human and team performance in extreme environments: Antarctica. Human Performance in Extreme Environments, 3(1), 117–120. Sundstrom, E., de Meuse, K. P., & Futrell, D. (1990). Work teams: Applications and effectiveness. American Psychologist, 45(2), 120–133. http://doi.org/10.1037/0003‐066X.45.2.120 Swezey, R. W., & Salas, E. (1992). Guidelines for use in team‐training development. In R. W. Swezey & E. Salas (Eds.). Teams: Their training and performance (pp. 219–245). Westport, CT: Ablex. Tajfel, H., & Turner, J. C. (2004). The social identity theory of intergroup behavior. In S. Worchel & W. G. Austin (Eds.). The psychology of intergroup relations (pp. 7–24). Chicago: Nelson‐Hall. Tannenbaum, S. I., & Cerasoli, C. P. (2013). Do team and individual debriefs enhance performance? A meta‐analysis. Human Factors, 55(1), 231–245. Taras, V., Kirkman, B. L., & Steel, P. (2010). Examining the impact of culture’s consequences: A  three‐decade, multilevel, meta‐analytic review of Hofstede’s cultural value dimensions. Journal of Applied Psychology, 95(3), 405–439. Tasa, K., Taggar, S., & Seijts, G. H. (2007). The development of collective efficacy in teams: A m­ultilevel and longitudinal perspective. Journal of Applied Psychology, 92(1), 17–27. Tekleab, A. G., Quigley, N. R., & Tesluk, P. E. (2009). A longitudinal study of team conflict, conflict management, cohesion, and team effectiveness. Group and Organization Management, 34(2), 170–205. Thayer, A. L., Wildman, J. L., & Salas, E. (2011). I‐O psychology: We have the evidence; we just don’t use it (or care to). Industrial and Organizational Psychology, 4(1), 32–35. Tjosvold, D., & Yu, Z. (2004). Goal interdependence and applying abilities for team in‐role and extra‐role performance in China. Group Dynamics: Theory, Research, and Practice, 8(2), 98–111. Tsui, A. S., Egan, T., & O’Reilly, C. (1991). Being different: Relational demography and organizational attachment. Administrative Science Quarterly, 37(4), 549–579. Tuckman, B. W. (1965). Developmental sequence in small groups. Psychological Bulletin, 63(6), 384–399. Turner, J. C., Hogg, M. A., Oakes, P. J., Reicher, S. D., & Wetherell, M. S. (1987). Rediscovering the social group: A self‐categorization theory. Oxford, UK: Blackwell. Van Knippenberg, D., & Schippers, M. C. (2007). Work group diversity. Annual Review of Psychology, 58, 515–541. Van Mierlo, H., & Kleingeld, A. (2010). Goals, strategies, and group performance: Some limits of goal setting in groups. Small Group Research. 41(5), 524–555. Volpe, C. E., Cannon‐Bowers, J. A., Salas, E., & Spector, P. E. (1996). The impact of cross‐training on team functioning: An empirical investigation. Human Factors, 38(1), 87–100. Waldman, D. A. (1997). Predictors of employee preferences for multirater and group‐based performance appraisal. Group and Organization Management, 22(2), 264–287. Waller, M. J., Conte, J. M., Gibson, C. B., & Carpenter, M. A. (2001). The effect of individual perceptions of deadlines on team performance. Academy of Management Review, 26(4), 586–600. Watson, W. E., Kumar, K., & Michaelsen, L. K. (1993). Cultural diversity’s impact on interaction process and performance: Comparing homogeneous and diverse task groups. Academy of Management Journal, 36(3), 590–602. Weaver, S. J., Rosen, M. A., DiazGranados, D., Lazzara, E. H., Lyons, R., Salas, E., … others. (2010). Does teamwork improve performance in the operating room? A multilevel evaluation. Joint Commission Journal on Quality and Patient Safety, 36(3), 133–142.



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3

Team Performance in Knowledge Work Daniel J. Slyngstad, Gia DeMichele, and Maritza R. Salazar

Introduction Research on work teams has been conducted for over a century (Sundstrom, McIntyre, Halfhill, & Richards, 2000), and the pace of research on teamwork has only intensified in recent decades (Mathieu, Maynard, Rapp, & Gilson, 2008), reflecting both the ever‐ increasing frequency with which teams are employed and their growing social impact (Wuchty, Jones, & Uzzi, 2007). Organizational reliance on teamwork in a markedly complex global work environment (e.g., Fiore, Salas, & Cannon‐Bowers, 2001; Hackman & Morris, 1975; Lewin, 1951) prompts its continued evolution, meriting that still more attention be directed to the team level of analysis. No construct has been more widely invoked than team performance, and it remains unsurprisingly integral to the study of teamwork (Bommer, Johnson, Rich, Podsakoff, & MacKenzie, 1995). As “collectives who exist to perform organizationally relevant tasks” (Kozlowski & Bell, 2003, p. 334), teams are explicitly imbued with specific organizational functions that must be fulfilled within a range of acceptable standards to justify the team’s continued existence. Although much has been learned concerning how to help teams perform at their optimum (Kozlowski & Ilgen, 2006), many central questions remain, particularly in the context of knowledge‐intensive teamwork. In this chapter we review past and present conceptualizations of team working. We then explore definitions, dimensions, and measurement before considering the gaps in the l­iterature within the future research section.

Contemporary Teamwork Contemporary teamwork is increasingly characterized by a shift towards the knowledge‐ based provision of services and innovative product development (Drucker, 1999; Gardner, Gino, & Staats, 2012; Lovelace, Shapiro, & Weingart, 2001), involving tasks oriented The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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around the flexible use of information rather than the creation or delivery of static, broadly applicable (i.e., non‐customizable) products or services (Von Nordenflycht, 2010). The extended education, extensive training, and experiential depth often required to perform knowledge work prompt the cultivation of intense specialization (Kozlowski & Ilgen, 2006), and thus knowledge‐intensive teams are frequently composed of individuals with disparate skill sets and competencies, seeking to integrate highly varied knowledge contri­ butions (Gardner et al., 2012). Work in today’s organizations thus faces a confluence: the ubiquity of teamwork and a qualitative shift in its type. The emergence of this form of collaboration presents undeniable obstacles to effective teamwork, as teams with members varying dramatically in specialization and expertise can have difficulty performing well (Journet, 1993) owing to the heightened burden imposed by individual and collective information processing (Emmanuelides, 1993; Olson, Walker, & Ruekert, 1995), as well as communication and team structuring difficulties that often co‐occur (Salazar, Lant, Fiore, & Salas, 2012). Yet, being able to leverage vastly different sources of expertise is rapidly becoming a prerequisite to solving complex problems (Ancona & Caldwell, 1992; Cronin & Weingart, 2007), and organizations that master the facilitation of high performance in knowledge‐intensive and expertise‐diverse teams seize an opportunity for significant advantage. Before this can occur, however, scholars and practitioners must have a clear understanding of the nature of performance across varying teamwork contexts. Team performance is a complex, multilevel construct that emerges from how a team’s members “think, do, and feel” (Day, Gronn, & Salas, 2004, p. 863) in a collaborative and adaptive fashion. Particularly in teams of knowledge workers, performance can be remark­ ably difficult to deliberately influence, or even to define and assess. Indeed, most discus­ sions of team effectiveness do not explicitly address the performance construct, but instead seek to elucidate teamwork processes hypothesized to mediate or moderate it (Mathieu et  al., 2008). Conventional models of effectiveness, still frequently and erroneously e­mployed to describe knowledge‐intensive teamwork today (Kozlowski & Bell, 2013), are often overly reliant on mechanical, sequential connections between inputs and team out­ puts (e.g., an input–process–output or IPO model; McGrath, 1984). While we do not suggest that traditional models have been rendered unilaterally irrelevant (they are still quite adequate in some teamwork contexts), they nonetheless reflect the comparatively less complex, more concrete coordination schemes of the work that they were created to describe (e.g., a sequentially interdependent manufacturing task), and do not adequately reflect a large portion of teamwork being conducted in today’s organizations (Davenport, Thomas, & Cantrell, 2002; Drucker, 1999). Although the inadequacies of IPO‐type frameworks are well documented (cf. Ilgen, Hollenbeck, Johnson, & Jundt, 2005), the nature of team performance itself has also changed, from the concrete to the cognitive. It follows, then, that organizations employing teams of knowledge workers must under­ stand what it means for teams to perform well, as well as how to facilitate high performance in innovation‐driven work contexts that are a product of the information age (Edmondson & Nembhard, 2009). To this end, we review past and present conceptualizations of team performance and team effectiveness, and advance that a knowledge‐intensive team’s performance can be described broadly and holistically as its functional utility. While this chapter acknowledges the wide range of team performance metrics and definitions that have been previously articulated (cf. Cohen & Bailey, 1997; Mathieu et al., 2008; Sund­ strom et al., 2000), our focus is necessarily restricted to the less tangible outcomes most relevant to the challenges presented by knowledge‐intensive teamwork. Three overarching dimensions are subsequently put forth to summarize metrics and measurement approaches available to capture a team’s retrospective and future functional utility to parties external to the team (both extra‐ and intra‐organizational), as well as to the team itself and to each



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of its individual members. Additionally, we briefly summarize a model of team effectiveness, the integrative capacity framework, which serves as the optimal complement to this c­onceptualization of performance, and present future directions for research.

Past and Present Conceptualizations of Performance: A Brief Overview A look backwards: Static IPO models Traditional discussions of team performance have focused largely and straightforwardly on broad issues of motivation and coordination (cf. Steiner, 1972), and the process gains or losses that ensue from synergy or misalignment, respectively, using the IPO framework (Guzzo & Shea, 1992; Nijstad, 2013). The team’s alignment with its organizational context, including reward systems, communication channels, feedback, and so on, is also included, resulting in a model of nested, interconnected, static mechanical systems, with the addition of the occasional feedback loop (cf. Cummings & Worley, 2009). Although we have admittedly oversimplified, this approach suggests adjustments at any level of analysis (e.g., individual, team, organization) that produce process gains, such as an individual member becoming more motivated and increasing his or her contribution to team activities (e.g., Hackman, 1983; Tziner & Eden, 1985), or a manager leveraging strategic changes (e.g., updating technology or increasing alignment between tasks and outcomes) that increase the collective efficiency of team collaboration (e.g., Saavedra, Earley, & Van Dyne, 1993; Wageman, 1995), will virtually necessarily lead to higher performance (Gist, Locke, & Taylor, 1987; Hackman, 1987). Team performance has thus conventionally been seen to result directly and almost exclusively from changes in team coordination processes (stemming from motivational shifts or team process adjustments) that are primarily observable behaviors (e.g., member A increased her contribution, enabling the team to produce more widgets). Teams that have as their primary performance objectives speed, efficiency, volume, or consistency (e.g., Batt & Appelbaum, 1995; Goodman & Leyden, 1991) are highly compatible with these static models. For instance, Kozlowski and Bell (2013), highlighting team typologies used in reviews of team effectiveness, cite production (e.g., automobile manufacturing) and teams providing static services (e.g., a flight attendant team) as notable examples of these types of teams. Defining and measuring the performance of teams whose processes are better captured by static models is less opaque, as the performance outcome is well defined, not easily disputed, highly quantifiable, and often provides self‐evident value to the organization (e.g., a finished car which can be sold). With regards to dimensions of teamwork, these (relatively) less complex teams are more likely to be characterized by a sequential or pooled interdependence among members, which places a much greater emphasis on individual contributions (McGrath, 1997; Van De Ven, Delbecq, & Koenig, 1976), to operate in environments that are less dynamic and characterized by external influence (Kozlowski, Gully, Nason, & Smith, 1999), and to have fixed roles and goals that do not fluctuate widely over time (Bell & Kozlowski, 2002). All of these features permit the existence of concrete and easily measured performance outcomes, and straightforward methods of increasing team performance by minimizing process losses and maximizing process gains (Nijstad, 2013). This suggests that a potential reason for the lack of explicit attention paid to team performance as a construct stemmed from a lack of necessity. Yet, the traditional view can be somewhat reductive in its assumption that team processes can be readily and simply observed,

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Overview of Team Effectiveness

mapped, and targeted for intervention without a consideration of the emergent team‐level states that reciprocally influence teamwork processes, temporal concerns (Marks, M­ athieu,  & Zaccaro, 2001), or group cognitive phenomena (cf. Hinsz, Tindale, & V­ollrath, 1997), each of which are of critical importance in contemporary knowledge‐ intensive teamwork.

The impact of the qualitative shift towards knowledge‐intensive teamwork The observation that the fundamental nature of a large segment of work (although c­ertainly not all) is shifting towards the individual and collective processing of information (Huckman, Staats, & Upton, 2009) likely does not surprise the reader. It does, however, carry implications for the challenges of facilitating high performance in teams of knowledge workers that will need to be overcome. One such challenge is simply inherent to the immense variety of its forms. Indeed, teams engaged in knowledge work can include professional research teams (e.g., medical research teams; Salazar et al., 2012), research and development teams in private firms seeking to develop innovative products (e.g., software development teams; Faraj & Sproull, 2000), or teams aiming to provide a service that is tailored to a particular client (e.g., management consulting teams; Gardner et  al., 2012), among many other examples. Such diversity, coupled with less tangible performance o­utcomes, makes it more difficult to understand general facets of performance and the principles of effectiveness in teams of knowledge workers. As an additional com­ plication, knowledge‐intensive teams are frequently expertise‐diverse, cross‐functional, or m­ultidisciplinary (O’Connor, Rice, Peters, & Veryzer, 2003). Teams of knowledge workers do share general features, however. Namely, the degree of reciprocal interdependence (cf. Thompson, 1967) required to perform the complex tasks typical of knowledge‐intensive teamwork is often much higher (Bailey, Leonardi, & Chong, 2010), and thus for many teams, especially those seeking to perform well in dynamic, fast‐paced environments, coordination demands are more strenuous (e.g., m­edical and military teams; Burke, Salas, Wilson‐Donnelly, & Priest, 2004; DeChurch & Mesmer‐Magnus, 2010a; 2010b; McGrath, 1997). Ilgen and colleagues (2005) attempt­ ed to summarize the increasingly complex nature of modern teamwork and the deficiencies of the static IPO model by introducing the revised input–mediator–output–input (IMOI, or IMO) model, which attempted to account for the reciprocal, cognitive, and affective nature of teamwork not captured in older, process‐based approaches. The IMO model permits recognition of the emergent, team‐level states that arise from teamwork, and the inclusion of the bidirectional, multilevel, and temporal contextual considerations inherent in a team’s functioning (Marks et al., 2001; Mathieu et al., 2008). Further, changes in the boundary conditions of teamwork elevate the importance of considering information processing at the team level (Kozlowski & Ilgen, 2006), and relatedly, how expertise is distributed across team members, the inherent compatibility of team members’ perspectives (Cronin & Weingart, 2007), and the methods by which teams cultivate and use a “shared” mental model of their tasks (Kozlowski & Bell, 2013). Understanding high performance in knowledge‐intensive teamwork thus additionally requires a detailed consideration of a team’s collective ability to integrate knowledge (Balakrishnan, Kiesler, Cummings, & Zadeh, 2011). Such integration is achieved through the emergent combination of diverse intellectual contributions across team members (Bunderson & Sutcliffe, 2002; Drach‐Zahavy & Somech, 2001), and is buttressed by the more concrete collaborative processes of the type previously summarized in IPO frameworks (Salazar et al., 2012).



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Successful integration across such different expertise‐based “thought worlds” (Dougherty, 1992) has been previously judged to be somewhat futile due to representational gaps (Cronin & Weingart, 2007), or “intellectual distance,” between team member interpreta­ tions of taskwork. Effective and useful integration of knowledge, though, does not require perfectly compatible and entirely shared mental models (cf. Klimoski & Mohammed, 1994), but instead an interdependent constellation of truly shared knowledge and isolated knowledge still accessible to the team (Cannon‐Bowers & Salas, 2001), such that it is at times sufficient for members to simply know who knows what (i.e., transactive memory; Moreland, 2000; Wegner, 1986) or to have an understanding of other members’ mental representations of taskwork and task contexts (Huber & Lewis, 2010) in order to structure taskwork appropriately and perform well. A more fundamental challenge for knowledge‐intensive teamwork, however, beyond the methods of improving performance, is in the definition and measurement of performance itself. Particularly for teams seeking to integrate the knowledge of their members, the conceptualization of performance is intimately tied to context (Mathieu et al., 2008). The operationalization (and facilitation) of a team’s performance may vary depending on both external and internal features, and thus decisions concerning how to evaluate team performance cannot simply be imported and applied to a team without modifications that take into account numerous contextual issues. External features can include general dimensions such as the type of task and its complexity (e.g., high‐technology product development), the type of team (e.g., project team), the organization that employs the team, and the level of analysis, among many others (Cohen & Bailey, 1997; Kozlowski et al., 1999; Salazar et al., 2012). The impact of these external influences on teamwork and team performance cannot be understated (Hackman, 1987), and the combination of external factors that influence teamwork determines, to a large extent, what it would mean for a team to perform well in a given context. Internal features such as team composition (Hollenbeck, DeRue, & Guzzo, 2004; Moreland & Levine, 1992), developmental stage (Kozlowski et al., 1999), or temporal phases of the teamwork process (Marks et al., 2001) also influence performance conceptualization (Guzzo & Dickson, 1996). Given the remarkable variety of teams engaged in knowledge work and the nuanced internal and external contexts in which they operate, the evaluation of the quality of a team’s performance and its facilitation has become obfuscated without straightforward guidelines for definition and measurement, and an outline of broader performance dimensions to consider (Ilgen, 1999; Mathieu et al., 2008).

A Flexible Definition: Performance as Functional Utility Team performance has been defined and measured in myriad ways (Table 3.1), ranging from specific, tangible, objective criteria (e.g., number of widgets produced) to outcomes requiring a greater degree of subjective evaluation such as the innovative contribution to a field or organizational repertoire (Sawyer, 2012) or the application of creative ideas to the development of useful products and services (Drucker, 1985), and can involve self‐ reports or data sources external to the team (Sundstrom et  al., 2000). Owing to the remarkable complexity and nuanced variety of knowledge‐intensive teamwork, an action­ able definition of team performance must permit the flexible derivation of evaluative performance standards that can be applied across teamwork contexts, and provide a means of navigating the nebulous task of assessing and facilitating team performance without attempting to employ a “one size fits all” solution. Thus, we advance that a work team’s performance outcome is that which makes a team useful, or its functional utility

“The consequences or results of performance behaviors”

Team performance outcomes

“The extent to which members exhibit the requisite competencies necessary to perform their jobs” Composite Blends performance behaviors, outcomes, and role‐based performance Knowledge “The extent to transformation which a team combines its distinct expertise and work into a unified whole”

“Actions relevant to achieving goals”

Team behaviors

Role‐based

Direct alignment between team performance and organizational outcomes

Definition

Organization level

Performance type

Table 3.1  Types of team performance.

Top management

Type of team (example)

Problem solving and overall effectiveness

Manufacturing Top management Surgical, etc.

Functions in a Interdisciplinary or An innovative idea knowledge‐ or solution to a transdisciplinary based world complex problem science requiring expertise from diverse knowledge fields

Mathieu et al. (2008)

Balakrishnan et al. (2011, p. 2) Salazar et al. (2012)

Type of team interdependence

Reciprocal Not as useful for “traditional” teams (e.g., manufacturing teams) performing the same repetitive task

Pooled, sequential, or reciprocal

Sequential or Team processes are reciprocal not traditional outcomes Requires consideration Sequential or reciprocal of multiple perspectives, intra‐ and extra‐ organization Not all roles are Sequential or clearly defined reciprocal

Only useful for teams Pooled or reciprocal with decision‐ making power

Drawbacks

Does not consider More accurately the degree to captures the which knowledge team experience was integrated

Successful completion of tasks assigned

Able to infer more direct link between outcome and organization performance Identifies and measures team processes Can be used to account for extra‐ organization perspective Can compare across teams

Benefits

Manufacturing

External ratings of performance or satisfaction

Learning behaviors

Financial gains

Performance example

Mathieu et al. (2008, p. 416) Chen (2005)

Manufacturing Mathieu et al. Surgical (2008, p. 416) Edmondson (1999) Construction/ Mathieu et al. maintenance (2008, p. 416) Tesluk & Mathieu (1999)

Barrick et al. (2007) Mathieu et al. (2008)

Authors



Team Performance in Knowledge Work

49

(Argote & McGrath, 1993; Goodman, 1986). As a note, this definition should not be misconstrued as oversimplification. Instead, it intentionally reveals the elevated impor­ tance of the task of context‐specific definition and measurement by scholars and practi­ tioners (Salas, Stagl, & Burke, 2004). An accurate assessment of a knowledge‐intensive team’s performance is best achieved when tailored to specific organizations or, if highly differentiated (cf. Lawrence & Lorsch, 1967), specific aspects of a single organization, specific teams, or specific projects. The evaluation of a performance outcome’s functional utility is holistic, and includes not only the direct past and future benefits to the organization and to the team, but also the interdependent, indirect, multilevel benefits derived from that outcome through stakeholder utilization and individual team member participation (i.e., the intrapersonal result of collaborating to produce it). Thus, a useful performance outcome is one that cre­ ates value for the organization within which the team is embedded (e.g., profit, reputa­ tion), but also necessarily includes the value to the outcome’s intended user (e.g., client, society, or customer if external to the organization), and often to the team itself (e.g., provides justification for the team’s continuing operation) and its individual members (e.g., strengthens a member’s capacity for future collaboration or results in the learning of a new skill), all of which are likely to be useful for the organization employing the team and constituent individuals who will be placed on future teams. The evaluation of a team’s performance must, therefore, inevitably be based on a composite of outcomes (Lester, Meglino, & Korsgaard, 2002; Van der Vegt & Bunderson, 2005) and be evaluated on a case‐by‐case basis. The approach proposed is reminiscent of the “balanced scorecard” (BSC; Kaplan & Norton, 1992) that has been widely applied to management accounting to monitor performance at the organizational level (Atkinson et  al., 1997; Kaplan & Norton, 2007), but is more inclusive of diverse measures and c­ompatible with the innovation‐driven climate of knowledge work (Voelpel, Leibold, E­ckhoff, & Davenport, 2006). Further, it is critical that aspects of the composite measure are chosen and evaluated by parties who, by virtue of their own relevant substantive exper­ tise, are qualified to make evaluative judgments (e.g., an interdisciplinary team leader with experience in multiple disciplines; Sawyer, 2012). A composite measurement of team performance additionally warrants the inclusion of a mixture of vantage points (i.e., self‐ versus other‐report, internal versus external to the organization) across a set of criteria (Salas, Stagl, & Burke, 2004) that span, as much as is desirable, the spectrum of concreteness of observation (i.e., subjective–objective contin­ uum; Shadish, Cook, & Campbell, 2001). Less concrete measures, or those that do not exclusively operationalize the utility of the outcome (e.g., customer or client satisfaction), but nevertheless may capture the value of the team’s efforts to the organization (e.g., reflecting the potential for repeat business), are also important to incorporate (Malina & Selto, 2001). A more direct, objective measure of a team’s functional utility has self‐evident importance. A prime example for knowledge‐intensive teamwork is the innovativeness of an idea, product, or service to a field of practice or to an organization’s response reper­ toire, as judged by subject matter experts (other practitioners in the field, a manager with expertise in the domain of interest, an informed consumer, etc.).

A note on measurement As has been outlined in the preceding sections, the nature of teamwork in general (e.g., knowledge work), and the context of a team in particular (e.g., innovative prod­ uct development), shape the conceptualization and operationalization of team performance. The proposal of performance metrics wholly customized to the contexts

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Overview of Team Effectiveness

of individual teams’ taskwork is conceptually appealing (Hauser & Katz, 1998) as well as logically consistent with the intangible realm of information processing (Bontis, Dragonetti, Jacobsen, & Roos, 1999), and a multifaceted, composite, context‐specific performance measure is a viable approach towards this end (Mathieu et al., 2008). For the sake of feasibility, however, a balance must still be sought to ensure that a performance metric need not require complete reinvention in every context, but rather incremental modifications. It is therefore necessary to specify relevant general dimen­ sions of a composite performance metric that can be flexibly applied to different team­ work contexts. Indeed, multidimensional performance metrics are already familiar concepts to scholars (Bourne, Mills, Wilcox, Neely, & Platts, 2000; Keegan, Eiler, & Jones, 1989), though they often merely allude to their importance. Further, when uti­ lized by practitioners, the rationale for the selection of different dimensions and proce­ dures for evaluation is often not systematically articulated or planned, rendering their flexible and effective application difficult for those operating “in the trenches” (Salas, Burke, Fowlkes, & Priest, 2004). Cohen and Bailey (1997) have noted that different types of teams prompt a tendency to rely on specific types of measurements. Although partially unavoidable, the common metrics employed to assess different types of teams frequently do not make use of a holistic, temporally nuanced definition of performance or adequately reflect the specific contexts in which teams are embedded, and can thus risk failing to capture the essence of performance for a particular team and hinder practical implementation (Salas, Cooke, & Rosen, 2008). Even when metrics are correctly chosen, evaluators must still possess the requisite exper­ tise to assess the quality according to selected performance outcomes (Ligon, Graham, Edwards, Osburn, & Hunter, 2012), an obstacle compounded by the highly specialized nature of knowledge work. Critically, reports from the project’s intended user (e.g., c­ustomers or stakeholders) were by far the least commonly used (Cohen & Bailey, 1997), despite the magnitude of their importance in determining the functional utility of a team providing a highly customized product or service. Indeed, for teams of knowledge workers seeking to create an innovative product, such as an R&D team, the degree to which the team performs well is to a large extent determined by the product’s utility to those outside the organization, such as a client or customer (Csikszentmihalyi, 2013; Drucker, 1985; Sawyer, 2012).

Higher‐Order Dimensions of Team Functional Utility We put forth a set of relevant dimensions of team performance to be used to judge an outcome’s functional utility, to be considered and applied as necessary, that permit an incorporation of recent conceptual developments in the study of work increasingly domi­ nated by the production and use of information by dynamic, adaptive teams of knowledge workers interacting with their external (intra‐ and inter‐organizational) environment (Ilgen et al., 2005; Marks et al., 2001). The list is not intended to be exhaustive, but is rather meant to establish a preliminary typology. The dimensions include: (1) the useful­ ness of a performance outcome to an external party, with two subdimensions pertaining to both a client or customer (i.e., inter‐organizational), and to the organization employing the team (i.e., intra‐organizational); (2) the extent to which the outcome ensures the continued functioning of the team, or its value to the team itself; and (3) the value derived by individual team members by virtue of their participation (i.e., functional utility stem­ ming from team member growth). The aim of such an effort is to provide practitioners with an abstract, conceptual starting point from which to systematically address the



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measurement of team performance across contexts and to put forth guidelines for tailoring the evaluation of relevant performance dimensions to specific teams, as well as to raise scholarly awareness of the deficit in explicit attention paid to understanding the team performance construct in knowledge‐intensive teamwork (Davenport et  al., 2002; Drucker, 1999; Ramirez & Nembhard, 2004). Additionally, although examples of specific measures are given for illustration, a com­ prehensive overview of the vastly numerous team performance measures available (cf. Co­ hen & Bailey, 1997; Sundstrom et  al., 2000) is beyond the scope of this chapter. It is worth noting, however, given that much of the emphasis in knowledge‐intensive team­ work is placed on the creation of a novel idea or product, or on the creative application of existing ideas to new contexts (Brown & Eisenhardt, 1995), that evaluating the performance of these teams often includes some assessment of their innovativeness (Ama­ bile, 1996; Gino, Argote, Miron‐Spektor, & Todorova, 2010), whether radical (cf. Duchesneau, Cohn, & Dutton, 1979) or incremental (cf. Munson & Pelz, 1979). For clarification, although organizational scholars occasionally attempt to differentiate between creativity and ­innovation, such that innovation additionally includes successful implementation, c­reativity researchers likewise acknowledge a utilization component (i.e., that novelty alone does not render a product or service creative or innovative; Csikszentmihalyi, 2013; Sawyer, 2012). Thus, we do not attempt to make a precise distinction between terms. The dimen­ sions proposed are grounded in the premise that utility and the provision of value are of singular importance when evaluating the performance of teams of knowledge workers.

Dimension 1: Usefulness to an external party A team’s external beneficiaries can be both extra‐ and intra‐organizational, as both are external to the team. As with most dichotomies, the functional utility provided to users inside and outside of the organization is interdependent. Similar measures can be used in both cases (cf. Cohen & Bailey, 1997; Mathieu et al., 2008; Sundstrom et al., 2000), albeit evaluated from different perspectives, and a positive evaluation of a knowledge‐ intensive team’s performance outcome by a party external to the organization often also implies its value to the organization itself. Indeed, although it will be shown that the extra‐organizational perspective is most often necessary when evaluating the quality of a performance outcome in knowledge‐intensive teamwork, the incorporation of a measure assessing utility for an extra‐organizational user often depends on the link between that measure and the provision of value for the organization itself (e.g., it implies potential for  repeat business, a bolstered reputation, increased profit, etc.). For the purpose of this  chapter, however, both aspects of Dimension 1 nevertheless warrant separate consideration. Dimension 1a: Value provided to extra‐organizational users  The utility of an outcome, assessed from the perspective of a party external to the organization that will make ulti­ mate use of the product or service, has long been known to be highly relevant when judg­ ing a team’s performance (Collins & Porras, 1994; Drucker, 1973; Slater, 1997). This is readily apparent when considering teams operating within firms whose consumers are large market segments (e.g., teams developing handheld technology products for mass distribution), as positive customer reception is a critical component of current and future success (McColl‐Kennedy & Schneider, 2000). Yet, this maxim has been somewhat underemphasized when formally and systematically measuring the performance of knowledge‐intensive teams seeking to develop highly customized products or services for individual customers or client organizations (Cohen & Bailey, 1997), perhaps due to the

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Overview of Team Effectiveness

intangible nature of many of these outcomes. Specifically, managers evaluating teams of their own knowledge workers, even if they possess substantive expertise in the requisite domain of practice (e.g., information technology infrastructure development), will likely lack the explicit or tacit institutional knowledge (cf. Lam, 2000; Scott, 2008) of the external client organizations for which those teams are providing services or developing products (i.e., they will be less capable of judging whether the outcome is useful for any external organization in particular). In teams of this nature (e.g., management consulting teams, information technology service teams, application development teams), the cred­ ible perspective of the client is thus indispensable in the determination of the team’s performance. Unfortunately, no single type of measure or construct will guarantee a valid assessment of the usefulness of a team’s performance outcome to an extra‐organizational user, and the appropriateness of the measure will vary by context. For instance, general customer satisfac­ tion measures (Janz, Colquitt, & Noe, 1997; Jehn, 1997; Wageman, 1995) may be used in some circumstances, but care must be taken to ensure that these assessments include aspects that capture the outcome’s utility specifically (e.g., whether it improves a feature of the client organization, or its innovativeness from the perspective of the client), and that they are evaluated by those who have relevant expertise, both institutional and substantive. Direct assessments of the functional utility of a team outcome by the client organization could include multifaceted measures of quality (Ancona & Caldwell, 1992; De Dreu & Weingart, 2003), the extent to which client needs have been fulfilled (Kirkman, Rosen, Tesluk, & Gibson, 2004; Lewis, 2004; Lynn, Skov, & Abel, 1999; Zellmer‐Bruhn & Gibson, 2006), or the wider implementation of a service team’s recommendations (e.g., a management consulting team’s suggestions) or dissemination and incremental modification of their products (Appelbaum & Steed, 2005; Gable, 1996; Katzenbach & Smith, 1993). Other groups of extra‐organizational beneficiaries, even less often considered, and who may not make direct use of a team’s performance outcome but nevertheless derive value from it, can include company shareholders and community stakeholders (e.g., patient populations who will benefit from the performance outcomes of medical research teams, or community members who will benefit from the development of technologies to reduce pollution; Shadish, Cook, & Leviton, 1991). A comprehensive performance assessment may also include the functional utility provided to these indirect users, as widespread use by these extra‐organizational groups can similarly imply the high quality of the team’s performance outcome. It must be noted that such a measure would be applicable (or weighted heavily) only in situations where the connection between the team’s efforts and the indirect use of a team outcome by these parties is observable or easily inferred, as teams are frequently not involved in its widespread implementation or in aiding indirect beneficiaries, and thus often cannot be held accountable if such p­henomena do not occur. Additionally, elements that serve as necessary minimums for acceptable performance (cf. Davidson, 2005), but do not explicitly assess utility for the client, may still be incor­ porated into a composite measurement, as they are likely to bring current or future value to the organization providing the product or service. Such measures, evaluated by an extra‐organizational party, can include the timeliness of delivery (i.e., congruence with an agreed upon timeline, not simply the objective speed of project completion) of a prod­ uct or service (Peters & Karren, 2009; Salas, Cooke, & Rosen, 2008), professionalism (Page, 2006), responsiveness to the external party’s wishes or concerns (Gefen & R­idings, 2002), or other desirable components of a team’s interaction with clients or customers. Alternatively, such metrics may instead serve as checks of team fidelity in representing



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the  organization rather than being explicitly incorporated into the composite team performance measure. Dimension 1b: Providing intra‐organizational value  While the provision of value to a relevant extra‐organizational party is certainly imperative to judging the performance of a team, it is rendered irrelevant if no value is delivered to the team’s own organization. The functional utility of a team’s outcomes for its own organization is thus perhaps the most obvious criterion for team performance. Many of the measures described in the preceding section are also appropriate for this purpose, and can be conceptualized and weighted in much the same way, provided they are evaluated by qualified internal raters. Indeed, many traditional measures of team performance are still applicable in knowledge‐intensive team­ work and often directly assess this dimension, which can include project quality, innova­ tion, timeliness, and budget adherence or revenue generation (Cohen & Bailey, 1997). These measures are frequently evaluated from both the team member and managerial perspective (Ancona, 1990; Ancona & Caldwell, 1992; Gladstein, 1984). The use of both team self‐evaluations and manager evaluations is of great importance when assessing the team’s value to the organization, as it simultaneously accounts for bias and inconsistencies inherent to self‐reports (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003) and permits the evaluation of the team’s performance outcome by those who may understand it best: the team members themselves. In some cases, as when examining top management teams or teams of influential decision makers in the organization (Jackson, 1992), team performance outcomes can be tied directly to organizational performance (Hambrick & Mason, 1984), and thus these performance measures are most often included in the considerations of a top management team’s performance (e.g., equity, returns, sales growth; Eisenhardt & Schoonhoven, 1990; Finkelstein & Hambrick, 1990; Michel & Hambrick, 1992), and could be straightforwardly considered as assessments of such a team’s functional utility. The connection between the performance of teams who do not make larger strategic decisions and the performance of the organization, however, is far more convoluted and indirect (Delarue, Van Hootegem, Proctor, & Burridge, 2008; Dyer & Reeves, 1995), and thus organizational performance should not be taken as an indicator of team success unless the path of influence from a team’s performance to organizational performance is readily apparent. The value derived by the organization employing the team can also be indirect. One additional type of metric in teamwork contexts that involve the use and generation of information, which can be highly related to a firm’s competitive stance, concerns the extent to which a team’s performance outcome fuels organizational learning (Roloff, Woolley, & Edmondson, 2011; Senge, 1990). For example, the production of an innova­ tive team outcome that subsequently undergoes institutionalization (cf. Hatch, 2013), and is then incrementally modified and provided to future clients, results in the broaden­ ing of an organization’s behavioral repertoire (Huber, 1991) and increases the firm’s gen­ eral capacity to provide useful products and services (Edmondson, 2002). A metric assess­ ing this type of contribution (or the potential for it) may be incorporated into existing measures of outcome quality or innovativeness (with dimensions explicitly assessing the possible addition to an organizational capacity or repertoire), or it may be assessed sepa­ rately. Given the indirect nature of such a benefit, the applicability of such a measure is contingent upon the removal of organizational impediments that might prevent a team’s innovativeness from impacting an organization‐level capacity (e.g., information silos, r­outine rigidity; Edmondson, Bohmer, & Pisano, 2001).

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Overview of Team Effectiveness

Dimension 2: Ensuring the continued functioning of the team Performance assessment is increasingly seen as an ongoing process, not just as the static evaluation of a single team outcome. Indeed, a one‐time, retrospective performance assessment (e.g., those that commonly assess Dimension 1) is often insufficient in judg­ ing future performance, as it fails to account for temporal features of the team context (Ilgen et al., 2005; Kozlowski & Bell, 2013), and will overlook difficulties that may be likely to arise in the future, even if a team has recently performed well. A potentially overlooked dimension of team functional utility, then, addresses facets of performance that occur in conjunction with or aid in the process of the production of single out­ comes, and serve to ensure that a team can continue to work together in the future and retains the capacity for continued value generation. Teams of knowledge workers are expensive investments, and can require extensive individual and team training and orga­ nizational resources to ensure effective collaboration (Mathieu, Tannenbaum, & Salas, 1992; Salas, DiazGranados et al., 2008). Thus, an evaluation of their capacity to con­ tinue to function well is highly w­arranted. A team’s compromised ability to collaborate, or simply its stagnated innovative capacity, will cause the effectiveness of the team to decline over time, and potentially adversely affect organizational functioning via wasted resources, declining revenues, or missed opportunities to provide excellent service to clients (Mathieu et al., 2008). Evaluation of a team’s potential to continue to perform well necessarily requires an assessment of actual or predicted performance from a longitudinal perspective, either epi­ sodic (Marks et  al., 2001) or developmental (Ancona & Chong, 1999). Applications of episodic temporal concerns to team effectiveness have proliferated relatively recently (Ma­ thieu et al., 2008). Marks and colleagues (2001), for example, summarize episodic temporal concerns via the delineation of action and transition phases of teamwork, in which teams are either engaged in taskwork or evaluating their own collaborative efforts and planning future taskwork, respectively. Developmental temporal issues, by contrast, concern long‐term stages in a team’s lifecycle (Kozlowski et al., 1999) and have been the subject of discussion for several decades, spawning numerous frameworks (cf. Tuckman, 1965). Importantly, different metrics will be useful in evaluating a team’s current and future performance depending on the team’s episodic and developmental stage. A team in an action phase seeking to create an innovative product, for instance, would benefit more from measures addressing coordination on taskwork (e.g., Hoegl & Gemuenden, 2001) than a team in a transition phase, which would be better served by assessing the extent to which they are constructively reflecting on past collaborative effectiveness (e.g., P­atterson et al., 2005) associated with a particular product development effort. Addi­ tionally, an older team (i.e., members with longer team tenure) would benefit from measures emphasizing the diversity of information shared in team project discussions (Salazar et al., 2012), while a team that has just formed would likely require measures assessing the forging of common knowledge, or a sense of cohesion (e.g., Marks et al., 2001). Independent of their temporal context these measures appear to conflict, but each can imply a team’s continued ability to function at different stages and phases of teamwork. A widely researched construct that addresses a team’s potential for continued function­ ing is team viability, which can include measures of team members’ collective satisfaction with collaboration, intentions and desires to continue working together, effective commu­ nication, problem‐solving capacity, and so on (Sundstrom, De Meuse, & Futrell, 1990), and assesses the possibility that a team may “burn itself up” (cf. Kozlowski & Bell, 2013) through conflict or internal division. Katz (1980, 1982) has suggested that team viability



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decreases with team age due to factors such as groupthink or increased familiarity (Cannon‐ Bowers, Tannenbaum, Salas, & Volpe, 1995; Hackman, 1992). Thus, related measures of a team’s potential for continued high performance may reflect the extent to which their collaboration reflects the sharing of new ideas (Cooke et al., 2003), constructive contro­ versy (Chen, Tjosvold, Zhao, Ning, & Fu, 2011), and an orientation towards collective intellectual development (Bunderson & Sutcliffe, 2003; Hirst, Van Knippenberg, & Zhou, 2009). As much as is possible, the measures listed for Dimension 2 that include a longitudinal component should be tailored to specific team projects to gain a more complete under­ standing of the team’s performance over the course of producing a specific outcome. In this way, assessments of single outcomes are not bounded by overly restrictive, skewed retrospective assessments. In addition, however, the measures listed may also be employed as regular progress reports that inform efforts taken to help formatively facilitate team effectiveness and increase performance in the future. In this way, they can be used to create a closer relationship between an organization’s strategic direction and a team’s execution of incremental goals via the regular provision of feedback (Marks et  al., 2001). Such information could be used to inform the assignment of team responsibilities, make changes to the team’s membership, or administer targeted training interventions, in addition to other modifications that may serve a team’s nuanced contextual needs (Bikson, Cohen, & Mankin, 1999).

Dimension 3: Functional utility from team member growth The omnipresent nature of teams in knowledge work implies neither that individuals remain on the same team for their entire organizational tenure, nor that individuals are members of just one team at a time. In fact, the opposite is true, and multiple team m­embership is commonplace (O’Leary, Mortensen, & Woolley, 2011; Zika‐Viktorsson, Sundstrom, & Engwall, 2006). Further, an inevitably larger responsibility has been imposed on individual employees who participate in knowledge‐intensive teamwork (e.g., the need to manage one’s time; Wageman, Gardner, & Mortensen, 2012), and recent developments have heightened the importance of individual growth and talent management in contemporary organizations (Oldham & Hackman, 2010). Participation in multiple project teams serves to fuel organizational adaptability and learning, as members of teams use their experiences and knowledge gleaned from the c­ollaborative production of specific performance outcomes in a single team and proceed to share and apply their knowledge in other team contexts (Roloff et al., 2011). Thus, individual growth is directly relevant to a performance outcome’s functional utility. A team that can deliver highly valuable growth opportunities for its members over the course of a project likely has an impact on both the team’s existing capacity for continued effective­ ness and on the organization’s general capacity to create innovative products and deliver competitive services. The growth of an individual team member can include increased proficiency in task­ work, the acquisition of new task‐relevant knowledge, skills, and abilities, improved interpersonal or collaborative skill, or enhanced self‐management capacity, all of which can be assessed and compiled to assist the process of team (or individual) task and proj­ ect assignment, or to inform the composition of future teams (Tannenbaum, Mathieu, Salas, & Cohen, 2012). Without belaboring the issue of context, it should be evident to the reader at this point that some acquired skills and competencies are more valuable than others in a given team or organization, and the utility of a team outcome for individual development is not tied solely to the amount of growth of individual members,

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Overview of Team Effectiveness

but rather to its type. As  mentioned, qualified raters would be required to establish which competencies are more valuable for specific teams and as contributions to the repertoire of the organization as a whole, and teams which provide access to highly sought after skills could serve as future assignments for promising employees who would benefit from further development. Certainly, teams that are better able to deliver these growth outcomes to their members have comparatively more utility to the organization and to their members than those that do not, but it must be emphasized that this dimension does not supersede performance metrics assessing task‐relevant performance (Dimension 1) or the potential for a team’s continued high functioning (Dimension 2). A team that cannot execute taskwork effec­ tively will likely not be worth retaining for the personal development that its collaborative environment provides (e.g., in circumstances where members learn much about what not to do) if it delivers low‐quality products or services to clients. Additionally, to accurately assess team performance outcomes, team member development should be tied to the o­utcome of the specific project being evaluated.

A note on selecting measures and weighting dimensions We have posited the three dimensions above as overarching categories of team performance metrics for knowledge‐intensive teamwork that permit holistic, multi‐temporal assessment of team performance with regards to project outcomes. However, the dimensions listed above are by no means independent from one another. Indeed, the same measure may satisfy multiple dimensions depending on the teamwork context, and a high rating on a measure in one dimension (e.g., implementation by the client or the acquisition of a skill by a team member) is likely to be associated with ratings on another (e.g., manager‐rated evaluations of team viability). It is probable that there will be a vast array of measures of  equivalent conceptual and methodological quality that can be used to evaluate the performance of a single team, but it is also likely that potential measures of team performance in each dimension may conflict with the team’s goals or the nature of their taskwork if not chosen properly (e.g., a measure of implementation for a team that has not been tasked with the provision of recommendations). It is important, then, that the practitioner or researcher chooses a collection of mea­ sures that are appropriate to the context, and that he or she conscientiously selects valid and reliable metrics (cf. Crano, Brewer, & Lac, 2015), using appropriate methodology (e.g., survey versus objective measures; Podsakoff et al., 2003), that take into account the team’s developmental stage and temporal phase (Marks et al., 2001), and use multiple measures from different vantage points to triangulate (cf. Shadish et al., 2001) the evalu­ ation of a team’s performance. It is equally important, however, to balance the pursuit of technical accuracy and methodological rigor with concerns of feasibility, such that the administration of the composite measure is not unnecessarily expensive for the organiza­ tion, overly burdensome or time consuming to parties who have many competing respon­ sibilities, or excessively complex for the analyst (Brannick & Prince, 1997; Braverman & Arnold, 2008). Each dimension may vary in importance across teamwork contexts, and it is also impor­ tant to weight evaluative dimensions according to their criticality and relevance (Davidson, 2005). Those evaluating temporary, specialized project teams participating in parallel structures seeking to make and implement recommendations for organizational change (cf. Cummings & Worley, 2009), for instance, would likely disproportionately weight performance metrics that assess the retrospective value of a performance outcome to the organization (e.g., recommendation quality, organizational implementation), and may not



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require metrics from either Dimension 2 or 3. Most teams, however, are not specialized enough to warrant altogether discarding one of the dimensions summarized, and will likely require a blend of all three. This requires those seeking to create a measure to evaluate team performance to use contextual features to determine which dimension should be prioritized (given that a complete assessment of each dimension may be infeasible or unnecessary), and further, which measures should be chosen. As has been reiterated several times in this chapter, and as is implied by the expression sometimes invoked in techno­ logical reviews to summarize the trends towards mass customization, “context is king” (e.g., Weisbeck, 2014), and contextual concerns are paramount in decisions concerning the measurement of team performance, especially in knowledge work. Further, the con­ ceptualization and innovative use of metrics in the evaluation of team performance have direct implications for how one can facilitate its improvement.

An Integrated Framework for Team Effectiveness in Knowledge Work By itself, the possession of an actionable definition of performance in a particular context does not ensure that a team actually performs well. Work teams are considered to be adaptive, dynamic systems (McGrath, Arrow, & Berdahl, 2000), and performance out­ comes emerge from a team’s internal sociocognitive processes and affective states that are, in turn, influenced by those outcomes in a reciprocal fashion (Fleishman & Zaccaro, 1992; Salazar et al., 2012). A sufficient understanding of team performance is thus incom­ plete without coverage of the mechanisms by which it is achieved, as performance is not easily divorced from the effectiveness framework within which it is embedded. For clarifi­ cation, team effectiveness frameworks (in this case, IMO models) are inherently broader than considerations of the team’s performance, and include additional constructs (processes and states) and interdependencies (e.g., Cohen & Bailey, 1997; Guzzo & Dickson, 1996; Mathieu et  al., 2008; Salas, Stagl, & Burke, 2004). Cohen and Bailey (1997), for example, deconstruct effectiveness into three general categories: performance (e.g., outputs, innovations), member attitudes (e.g., team commitment), and behavioral outcomes (e.g., safety behaviors). We draw heavily from Salazar and colleagues’ (2012) emerging conceptualization of team effectiveness, which is most applicable to settings in which team members with very different sets of expertise (e.g., interdisciplinary research teams, surgical teams) must g­enerate innovative outcomes through the integration of their knowledge. Salazar and colleagues (2012) propose that a team’s ability to produce innovative outcomes, and thus to perform well, depends largely on a team’s integrative capacity, or their potential to assemble and generate knowledge through sets of interdependent, reciprocal, and tempo­ rally dependent social and cognitive processes, mediated by emergent states. The integra­ tive capacity effectiveness framework is itself a modified IMO framework with additional considerations that connect several extant areas of research, including those explicitly discussing interdisciplinary collaboration (Fiore, 2008; Stokols, Misra, Moser, Hall, & Taylor, 2008), the integration of perspectives across diverse (functional) expertise more generally (Bunderson & Sutcliffe, 2002; Cronin & Weingart, 2007; Lovelace et al., 2001), and both phasic (Marks et  al., 2001) and developmental (Ancona & Chong, 1999; K­ozlowski et al., 1999) temporal influences on team performance. Although each category of processes presented can and has been the topic of numerous other publications, such detail is beyond the scope of this chapter.

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Overview of Team Effectiveness

Cognitively integrative behaviors and information processing Information processing and emergent, innovative combinations of knowledge across team members with varying specializations occupy a central role in knowledge‐intensive team­ work (Drach‐Zahavy & Somech, 2001), and research on the topic of collective cognition and its derivatives has greatly intensified in the past two decades (Cannon‐Bowers & Salas, 2001; DeChurch & Mesmer‐Magnus, 2010a, 2010b). Although, as will be shown, knowledge integration is supported by critically important socially integrative behaviors, innovative team performance in the context of knowledge work depends ultimately on the ability of a team to creatively combine different sources of information, and to generate either a radically innovative idea or product, or an original application of existing ideas to customized, context‐dependent products or services through the development of a shared, team‐level cognitive architecture. Cognitively integrative behaviors posited to facilitate performance are the following: knowledge consideration, knowledge accommodation/assimilation, and knowledge trans­ formation. Each of these can be regarded as processes in IMO effectiveness frameworks (cf. Ilgen et al., 2005), and the result of these reciprocally interacting cognitive behaviors leads to the development of innovative products and services through the emergent combination of the knowledge across each team member (Salazar et al., 2012). Knowledge consideration can be considered to be a prerequisite to integrating separate perspectives, and refers to the deliberate and careful consideration of other members’ suggestions and ideas. Such consideration notably requires, among other features, the willingness to share suggestions and information (Edmondson, 1999), openness to diverse perspectives that permits the elaboration of potentially foreign or intellectually distant ideas (Homan, Van Knippenberg, Van Kleef, & De Dreu, 2007), and a superordinate team identity in cases when the value of the contributions of another member are not immediately obvious (Kane, 2010). Once contributions have been made visible, elaborated upon, and actively considered by team members (Mohrman, Gibson, & Mohrman, 2001), members can begin to either assimilate information into their existing, compatible schemas with only slight alterations (cf. Piaget, 1952), or make accommodations whereby their perspectives are altered to account for new information about how to perform the team’s task (Gibson, 2001). Such collective additions and alterations of knowledge contributions create a varied tapestry of “shared” information that can be brought to bear on the team’s tasks, such that knowledge products, or knowledge transformations, occur emergently at the team level (Salazar et al., 2012). The results of knowledge transformation are performance outcomes consisting of either radical innovation or the creative customization of existing products or services to new contexts. Cannon‐Bowers and Salas (2001) highlight that there are several conceptualizations of what constitutes “shared knowledge” (e.g., identical information, overlapping knowledge‐ based competencies, compatible knowledge) and variation regarding which cognitions must actually be shared (i.e., task‐relevant knowledge, task‐related knowledge, knowledge of team members, and attitudes/beliefs) to promote high performance. There are virtually innumerable sets of potential distributions of shared and isolated knowledge and expertise concerning tasks, team members, and general attitudes. As should not be surprising at this point, the teamwork context shapes who needs to know what, who must know who knows it (i.e., transactive memory; cf. Wegner, 1986), and which beliefs (concerning the task and other team members) are conducive to high performance, such that the high quality performance outcomes can emerge from many different possible paths to knowledge integration. There are thus many ways to use team member knowledge and expertise “effectively” (Gardner et al., 2012).



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Concretely, for example, a team attempting to innovatively tailor a service (e.g., management consulting) to a particular client may be required to construct a team with certain intrinsically compatible sets of expertise or competencies (e.g., a statistician or applied methodologist, an industry specialist, someone with previous experience providing the service, etc.) relevant to the team task (Campbell, 2005). Although compatibility is a type of “shared” knowledge (cf. Cannon‐Bowers & Salas, 2001), granting team members the potentiality of mutual understanding, members will likely also need to possess some identical information about the client context (i.e., institutional information) that informs which suggestions are appropriate in the context of the task, the client, and the specialties of other team members. Further, team members will also be required to collectively pos­ sess a transactive memory system (Stasser, Stewart, & Wittenbaum, 1995; Wegner, 1986), permitting individuals with insufficient experience with certain subtasks to direct the attention of other, more suitably specialized team members to contextual challenges that they themselves cannot address (e.g., the member who has provided similar services in the past must be able to identify and refer issues of importance to the statistician or method­ ologist), ensuring that each skill set is effectively utilized. While, as has been shown, external contextual features such as the team’s performance goal (e.g., high technology product development) will determine team composition and expertise distributions (Ancona & Caldwell, 1992; Ancona & Chong, 1999) and the measurement of team performance to a large extent, context additionally plays a crucial role in the manner in which cognitively integrative behaviors are linked to the definition and measurement of performance outcomes. To evaluate the innovativeness of performance outcomes in a team of expertise‐diverse knowledge workers, we must understand how individual cognitive behaviors were combined to produce the team’s knowledge products. Specifically, we must be able to observe how the ideas from different team members r­epresenting different areas of expertise were presented, modified, and incorporated into the team performance outcome to understand its value to an organization or its clients, again emphasizing the role of the savvy evaluator (Csikszentmihalyi, 2013; Sawyer, 2012). The emergence of a team‐level knowledge product, its conceptualization, and evaluation, is influenced by external features of the teamwork context, and is thus inseparable from these cognitive processes.

Socially integrative behaviors and the underemphasized role of leadership The cognitively integrative behaviors mentioned are themselves dependent on social processes that enable their continued functioning (Salazar et al., 2012). Socially integra­ tive behaviors are loosely classified as those either promoting enhanced communication effectiveness, or permitting optimal structuring of taskwork (although these are not independent from one another). Crucially, feedback concerning performance outcomes originating from both inside and outside the team can influence a team’s willingness or motivation to engage in these socially integrative behaviors in the future, impacting future effectiveness. Behaviors that facilitate communication include perspective seeking (Bernstein & Davis, 1982; Davis, Conklin, Smith, & Luce, 1996; Grant & Berry, 2011), or the deliberate attempt by team members to understand the general attitudes, beliefs, approaches, and methods of other members (contrasted with knowledge consideration, which addresses the consideration of specific ideas), efforts to connect others in the team who may not know they share common ground on an issue (Obstfeld, 2005), and the act of suggesting ideas or offering expertise to other team members (Faraj & Sproull, 2000;

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Overview of Team Effectiveness

Liang, Fahr, & Fahr, 2012). Behaviors that facilitate optimal task structuring include attempts to promote coordination with other members on taskwork (Hoegl & Gemuenden, 2001; Marks et al., 2001) either explicitly or implicitly (Rico, Sánchez‐Manzanares, Gil, & Gibson, 2008), team self‐observation of past collaborative efforts for the purpose of pro­ moting awareness and self‐modification of work methods, termed reflexivity (Patterson et al., 2005), and leader visioning and goal setting behavior (Pearce & Sims, 2002; Wang & Howell, 2010). While each of the aforementioned socially integrative behaviors is relevant, leader vision­ ing and goal setting is the least well articulated in expertise‐diverse knowledge work (Klein, 1996; Litchfield, 2008; Winter & Berente, 2012), especially with respect to team performance (Burke et al., 2006). In teams of highly trained workers, visioning and super­ ordinate goal setting often emerges from a collective process of information sharing and mutual agreement, such that resulting goals capture the individual interests of each m­ember (Haslam, Wegge, & Postmes, 2009). In highly specialized knowledge work, however, communication difficulties resulting from the large intellectual distance between expertise‐diverse members can thwart the development of a collective understanding of a shared team direction (Eigenbrode, O’Rourke, & Wulfhorst, 2007). Thus, team leaders seeking to develop shared visions or goals cannot rely upon a self‐evident problem for knowledge‐intensive teams to solve, and must often deliberately facilitate a process of problem construction, framing the team’s goals in a way that reconciles or circumvents apparent incompatibilities between individual team member objectives, and cultivating an  environment in which teams can effectively integrate their knowledge and produce high quality knowledge products (Okuda, Runco, & Berger, 1991; Reiter‐Palmon, M­umford, & Threlfall, 1998). To illustrate the relationship between problem construction and performance, former head of research at General Motors, Charles Kettering, often e­mployed John Dewey’s adage, “a problem well put is half solved” (Carmichael, 2013).

The mediating role of emergent states The process by which individual social behaviors influence the intangible realm of knowledge integration at the team level occurs via mediating emergent states, or team‐ level “cognitive, motivational, and affective states of teams, as opposed to the nature of their member interaction” (Marks et al., 2001, p. 357). Emergent states are critical to a team’s performance cycle (Day et al., 2004), and serve as proximal outcomes that must be achieved in order to serve as inputs to knowledge integration and the creation of high quality performance outcomes. If critical emergent states do not develop, or develop in a dysfunctional manner, the team will struggle to successfully meet its performance goals, due to a faulty infrastructure supporting team‐level cognitive processes, and a reluctance to engage in important individual‐level behaviors that promote teamwork. One such state that has been widely documented is psychological safety, or “a shared belief … that the team is safe for interpersonal risk taking” (Edmondson, 1999, p. 350), such as offering unique information or a different perspective, drawing attention to a conceptual or procedural mistake, or asking for clarification and help (e.g., a “stupid question”). A team‐level perception of psychological safety will permit more opportunities for members to learn from one another without fear of negative social evaluation, and improve on or modify the contributions of other members, facilitating high performance in knowledge‐intensive teamwork (Sanner & Bunderson, 2015). Other emergent states relevant to social behaviors discussed above are a collective team identity that serves to solicit commitment to team goals from members (Klein, 2005), a group learning orienta­ tion or openness to diverse perspectives that serves to foster the consideration of a wide



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range of potentially useful ideas and knowledge contributions (Lovelace et al., 2001), and team trust (Sonnenwald, 2003), among others (cf. Salazar et al., 2012).

Future Research The intricacies and nuances inherent to team effectiveness and team performance in com­ plex, dynamic knowledge work contexts, along with the continuously shifting nature of work conducted in today’s teams, prompt the emergence of many questions for future research. One such area, already briefly mentioned, is the role of leadership, particularly in teams of individuals possessing a high degree of specialization (Clark, 2013). While the role of leader problem construction has been implicated as a core leadership function in these types of teams (Reiter‐Palmon et al., 1998), the act of successfully facilitating the creation of a truly integrative team aim, purpose, or work problem can be extraordinarily difficult (Wageman, 2013; Wageman, Nunes, Burruss, & Hackman, 2008). The emergence of high performance in highly specialized knowledge‐intensive teamwork, however, depends on it (Okuda et al., 1991), yet we know very little about the specific behaviors and techniques that leaders leverage to achieve this feat, or about the characteristics of the leaders themselves (DeChurch & Marks, 2006). Although it is likely that leaders who are successful in problem construction possess a greater “integrative capability,” due to a diverse set of professional experiences that permit them to better understand the goals of each constituent’s individual aims, we know neither which kinds of past professional experiences are most important, nor which communication or team structuring behaviors (cf. Salazar et al., 2012) render diverse team member interests complementary, or how leaders account for temporal concerns while engaging in problem construction in knowledge‐diverse teams (Clark, 2013). Relatedly, additional research could also be directed towards cultivating an understand­ ing of the role of virtual teamwork in teams with highly specialized members. This is particularly intriguing given the observed difficulty associated with the construction of a shared problem and the challenges involved in the creation of a shared mental model in teams collaborating virtually (Cohen & Alonso, 2013). Investigations concerning which leader problem construction strategies are most effective in virtual contexts, or how a holistic consideration of team performance and the feedback required to facilitate high performance is compatible with virtual collaboration in knowledge‐diverse teams, could provide great benefit in terms of adapting this more complex view of teamwork and team performance to the virtual teams. More broadly, scholarship would benefit from continuing to empirically investigate the effectiveness of team training in different teamwork contexts. In a meta‐analysis examining the link between team training and performance and other process outcomes, Salas, D­iazGranados, and colleagues (2008) discovered that 12–19% of variance in examined outcomes was accounted for by training interventions. While encouraging in the aggregate, much remains to be learned concerning which trainings are most effective for specific teams (e.g., medical teams have received additional attention; Guimond, Sole, & Salas, 2009). Future research could address how team training can best incorporate team composition, particularly as specialization increases and member intellectual distance widens, which skills should be trained (e.g., specific taskwork versus meta‐skills), how the set of relevant trainable skills varies across team contexts (e.g., when applied members who p­articipate in few teams versus those who participate in large numbers of teams in multi­ team systems; Martin & Bal, 2007), and which members should be trained and when (e.g., training interventions targeted at leaders, individual team members, or the team as a whole) for optimal performance gains.

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Finally, as models of team effectiveness and teamwork constructs become increasingly complex (e.g., shared mental models and modes of information processing, integrative capacity and other updated IMO frameworks), effort must be taken to develop and apply innovative measurement techniques that are capable of capturing the level of nuance p­roposed in contemporary theoretical models (Cohen & Alonso, 2013; Curtis, 2013; Mohammed & Dumville, 2001; Mohammed, Klimoski, & Rentsch, 2000). More consideration must be given to the misalignment between how these constructs are d­iscussed and how they are commonly measured, as it is frequently difficult to make h­olistic inferences with conventional cross‐sectional survey methodology. The need to dynamically examine collaborative processes and emergent states across time further complicates the issue of measurement. Network analysis is a promising avenue for such endeavors, as well as longitudinal designs that intentionally incorporate multiphasic models of teamwork (cf. Marks et al., 2001).

Conclusion Teams have never been more important, and knowledge work never more prevalent. These developments are somewhat obvious. More difficult to perceive is the manner in which these larger demographic trends shape what it means for a team to perform well, and how teams can be made more effective. Those participating in teamwork are being increasingly required to manipulate and produce knowledge while collaborating in c­ontexts of striking nuance, working with other individuals possessing diverse professional backgrounds that make communication and collaboration more challenging. Although team performance was perhaps never fully captured with a single, retrospective assessment, it is now of paramount importance that performance additionally be assessed with regards to its temporal and multilevel context. This is accomplished by equating team performance with its functional utility, a broad definition that permits regarding team performance as a continuous process having implications for future team and organizational performance, as well as a static outcome. We further proposed that a team’s performance should be judged by those who are best qualified to evaluate it, regardless of whether they reside within or outside the organization that employs the team under scrutiny. Three dimensions were put forth as a preliminary step towards the incorporation of recent developments into  this conceptualization of performance, (1) the value provided to extra‐ and intra‐ organizational parties, (2) the value delivered to or invested in the team itself, and (3) the value cultivated by individual members in an interconnected, multiteam organizational context. Lastly, we elaborated on a model of team effectiveness, the integrative capacity framework, which serves as the optimal complement to this conceptualization of performance, and the understanding of teamwork in the information age.

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4

Transnational Team Effectiveness Dana Verhoeven, Tiffany Cooper, Michelle Flynn, and Marissa L. Shuffler

Introduction The current increase in global and dynamic teams within organizations lays the foundation for this integrative chapter on models of team effectiveness (Hinds, Liu, & Lyon, 2011). This chapter aims to explore team effectiveness through a theoretical lens of cultural diversity within a team in order to enhance team effectiveness. Teams are “a distinguishable set of two or more people who interact, dynamically, interdependently, and adaptively toward a common and valued goal/objective/mission” (Salas, Dickinson, Converse, & Tannenbaum, 1992, p. 4). There have been multiple reviews conducted examining the different factors that impact team effectiveness (Hackman, 1987; Ilgen, Hollenbeck, Johnson, & Jundt, 2005; Kozlowski & Ilgen, 2006; Marks, Mathieu, & Zaccaro, 2001; Mathieu, Maynard, Rapp, & Gilson, 2008). However, this chapter is unique in that it will encompass an international perspective within the context of team effectiveness. As the number of transnational corporations continues to rise, organizations must continually rely on geographically dispersed teams to complete work (Hinds et al., 2011). This chapter provides an international perspective on team effectiveness and examines implications for teams of this nature within models of team effectiveness. Hackman and Wageman (2005) define team effectiveness in terms of objective and subjective components by examining three key areas of team effectiveness including outputs, social processes, and learning. They explain that the objective output in terms of the product, service, or final decision should meet or exceed standards of those who receive and use the output in order to be regarded as effective. In addition, team effectiveness can also be measured in terms of the social processes the team may use which can enhance the team’s ability to work interdependently in the future and can assist in detecting and correcting errors early on in the team’s performance cycle. Finally, team effectiveness can be assessed subjectively in terms of individual learning, which is greatly impacted by the group experience and satisfaction of individuals within the team. The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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Teams that manage to balance these components over time are regarded as effective teams (Hackman & Wageman, 2005). Overall, team effectiveness literature has focused on an input–process–output (IPO) framework for team effectiveness. This entails various individual, organizational, and task‐related factors that serve as inputs, or antecedents, for team processes. Team processes are the interactions that members have when trying complete tasks. The final aspect of this model includes the team output, which embraces both performance and affective reactions. However, more recent reviews acknowledge that this model does not wholly represent the nature of team effectiveness (Ilgen et al., 2005). Instead, researchers have focused on an input–mediator–output (IMO) framework that incorporates various, dynamic aspects of the “process” sect of team effectiveness. Specifically, Ilgen and colleagues (2005) pose that mediators more accurately represent the linking mechanism of this model, which encompasses both processes and emergent states. This chapter will follow suit with this classification in order to appropriately address the dynamic state of teams. With the growing utilization of dispersed teams, individuals now work around the world in order to complete interdependent team tasks. Further, this globalization of the workforce has caused teams to face new challenges within the workplace. This chapter will take on an international perspective to address the challenges of globalization. Specifically, this chapter will begin by examining the international context of teams, followed by the review of several prominent team effectiveness models. Then a thorough review of team inputs, mediators, and outputs will be discussed and a prominent team effectiveness framework for the future will be provided. The chapter will conclude with directions for future research to enhance the ever dynamic team effectiveness literature.

International Perspective and Cultural Constructs As a result of globalization, transnational teams are increasingly becoming a reality in the workforce (Earley & Mosakowski, 2000). Transnational teams include members that come from different backgrounds and therefore differ in cultural values (Staples & Zhao, 2006). As we delve into this multicultural future it is important to examine international cultural perspectives in our models of team effectiveness. Empirical studies have linked aspects of cultural diversity in relation to team outcomes, to assess increases or decreases in performance. Both positive and negative aspects of team diversity have been included in team effectiveness research (Staples & Zhao, 2006). S­pecifically, increased creativity, innovation, and flexibility have been progressive outcomes of team diversity (Jehn, Northcraft, & Neale, 1999; Lau & Murnighan, 1998; McLeod, Lobel, & Cox, 1996). In contrast, increased cultural diversity causes communication difficulties, misunderstandings, decreased cohesion and increased conflict, and ultimately decreases performance (Hambrick, Davison, Snell, & Snow, 1998; Lau & Murnighan, 1998; Williams & O’Reilly, 1998). The increasing use of work teams in organizations has led transnational companies to structure their work through the use of multicultural teams (Groves & Feyerherm, 2011). With this expansion, recent literature has begun to acknowledge the need for an international perspective in the context of team effectiveness. The cultural context of teams stretches the team effectiveness model to reflect the expansion of multinational work teams (Gibson, Zellmer‐Bruhn, & Schwab, 2003). Having the international perspective within this chapter is an essential step in identifying how these variations may impact overall team effectiveness. Specifically, recognizing the importance of cultural diversity as an



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input within a team will facilitate team effectiveness and the design of contexts that foster successful outcomes for teams (Nouri et al., 2013).

Input–Process–Output vs. Input–Mediator–Output Research has continually cited the prominence of the IPO dynamic in teams research (Hackman, 1987; Marks et al., 2001). However, this formation lacks the depth and scope of the input–mediator–output models. The latter focuses on the dynamic nature of teams and emphasizes that mediators are a better resonation for this model because they consist of processes, emergent states, and a combination of both processes and emergent states. Specifically, the simple IPO models only allow for behavioral processes as the mediator between team inputs and outputs. Ilgen and colleagues (2005) discuss three specific reasons the IPO model is not sufficient for the dynamic nature of today’s teams. First, they explain that the IPO framework is not all inclusive of the potential factors that link inputs to outputs. Specifically, the mediating link between inputs and outputs is not always a process. In contrast, the input–mediator– output framework goes beyond behavioral processes to also include emergent states and mediators that involve a combination of behavioral processes and emergent states (Ilgen et al., 2005). Emergent states include both cognitive and affective states. Second, the IPO framework does not acknowledge the cyclical dynamic relationship that is present in work teams. The IPO model implies a linear path and disregards the importance of feedback loops (Ilgen et al., 2005). The IMO model explains that performance outcomes can serve as an input for future processes and emergent states. Finally, the IPO model implies direct relationships in which an input leads to a process leading to an output. However, interactions exist between inputs and processes, various processes, and emergent states, as detailed in many IMO frameworks. Evaluating teams in terms of the IMO model allows us to approach teams in a dynamic and multilevel way in which we assess the characteristics, processes, and properties that are attributed to groups (Cronin, Weingart, & Todorova, 2011). Further, the IMO approach to team effectiveness creates a theoretical foundation that explores how individual components impact group‐level elements and how group‐level components can also impact individual‐level components (Cronin et al., 2011). Prior to the theoretical development of the IMO taxonomy, Hackman (1987) developed an IPO model of team effectiveness that is familiar among teams researchers and still in use frequently today (Figure  4.1). He discusses how organization and group‐ based inputs lead to various group processes, with group synergy serving as a moderator of that relationship (Hackman, 1987). In turn, team processes lead to the level of group effectiveness, with material resources the team has at their disposal as a moderator of the relationship. This early IPO model set the foundation for team effectiveness models, in which researchers began to approach team studies in a holistic manner, prompting consideration to be given to the various team inputs, mediators (e.g., processes), and outputs. Later developments in team effectiveness research considered an IMO framework, although this term had yet to be coined. Subsequent models included many variations in terms of the variables included as their respective inputs, mediators/processes, and outputs. Additionally, these IMO models theoretically incorporate various types of moderators (e.g., group synergy and material resources) between the relationships of the inputs, mediators, and outputs. Further, the models often consider the temporal and dynamic nature of teams in their frameworks (Salas, Stagl, Burke, & Goodwin, 2007).

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Material resources 1. Sufficiency of material resources required to accomplish the task well and on time Organizational context 1. Reward system 2. Education system 3. Information system

Process criteria of effectiveness 1. Level of effort brought to bear on group task 2. Amount of knowledge and skill applied to task work 3. Appropriateness of task performance strategies used by the group

Group design 1. Structure of task 2. Composition 3. Performance process norms

Group effectiveness 1. Task output acceptable to those who receive or review it 2. Capability of members to work together in the future is maintained or strengthened 3. Members needs are more satisfied than frustrated by the group experience

Group synergy 1. Reduce process loss 2. Create synergistic process gains

Figure 4.1  Normative model of team effectiveness.

For example, Tannenbaum, Beard, and Salas (1992) developed an input, throughput, output model (Figure 4.2). This inclusive model details the importance of context when studying a team. This model outlines the significance of the team context in terms of both organizational and situational characteristics being incorporated throughout a team’s performance cycle. The inputs of this framework are task, work, individual, and team characteristics. These are followed by the team throughput, which includes team processes along with the effect of team interventions on said processes. Finally, the team outputs considered in this model include team performance, team changes, and individual changes. Another important aspect of this model is the inclusion of a feedback loop where the o­utputs loop back to new inputs as feedback. A few years later, Dickson and McIntyre (1997) developed a model of teamwork that also seems to follow an IMO framework (Figure 4.3). This model denotes the importance of communication within a team, noting that communication is vital during all aspects of teamwork. This framework begins with team orientation and team leadership, which may be considered as inputs. These then affect how a team monitors performance, which leads to the feedback and backup behaviors a team member provides. When team feedback, monitoring, and backup behaviors are all occurring in unison, a conductive environment for team coordination is created. This coordination leads to team learning, which loops back to impacting the team orientation and team leadership. One thing to consider with this model is its thorough description of overall team process. Therefore, this model may be more suited for teams actively working together, as its emphasis on team processes may not capture the full picture that is team effectiveness. Specifically, this model fails to consider some of the key inputs and outcomes of effectiveness within teams, owing to the emphasis it places on team processes. Another framework that denotes the importance of specificity in team effectiveness models is Burke, Stagl, Salas, Pierce, and Kendall’s (2006) model that explores team



77

Transnational Team Effectiveness Organizational and situational characteristics Reward systems Resource scarcity

Management control Levels of stress

Organizational climate Environmental uncertainty

THROUGHPUT

INPUT Task characteristics • Organization • Type • Complexity

Work structure

Team characteristics

• Task KSAs • General abilities • Motivation • Attitudes • Personality • Mental models

• Power distribution • Member homogeneity • Resources • Climate • Cohesiveness

OUTPUT Team changes

Team processes

• Work assignment • Team norms • Communication structure

Individual characteristics

Intergroup relations competition

• Coordination • Communication • Conflict resolution • Decision making • Problem solving • Boundary spanning

Team interventions • Individual training • Team training • Teambuilding

• New norms • New roles • New communication patterns

• New processes Team performance • Quality • Quantity • Satisfaction/viability Individual changes • Task KSAs • Attitudes • Motivation • Mental models

Feedback

Figure 4.2  Team effectiveness framework (Tannenbaum, Beard, & Salas, 1992) ); KSA = knowledge, skills, and abilities.

Communication

Communication

Team orientation

Communication

Feedback Monitoring

Coordination Backup behavior

Team leadership

Learning loop

Figure 4.3  Model of teamwork (Dickson & McIntyre, 1997).

adaptation (Figure 4.4). In contrast to Dickson and McIntyre’s (1997) model, this model acknowledges and considers multiple team inputs, such as knowledge, attitudes, traits/ abilities, and job design characteristics. The authors discuss how these inputs influence the emergent state of team adaption, which develops over time as team members utilize their resources to change present behaviors, cognitions, or attitudes to meet demands.

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Overview of Team Effectiveness Adaptive team performance

Adaptive cycle Individual characteristics

Cue

Situation assessment: Phase 1 Cue recognition Meaning ascription

Knowledge Task expertise Team expertise Mental models Attitudes Team orientation

Shared mental modelsPhase 1

Team situation awarenessPhase 1

Emergent state

Plan formulation: Phase 2 Emergent state

Psychological safety

Shared mental modelsPhase 2

Traits & abilities Openness to expenence Cognitive ability

Emergent states

Team adaptation Plan execution: Phase 4

Team situation awarenessPhase 2

Mutual monitoring Coordination Communication Back-up behavior Leadership

Emergent states

Team innovation Team modification

Shared mental modelsPhase 3

Job design characteristics Self-management Team learning: Phase 4

Team situation awarenessPhase 3

Psychological safetyPhase 3

Feedback

Figure 4.4  Input–throughput–output model of team adaptation (Burke et al., 2006).

Throughout the duration of the adaptation cycle, feedback allows teams to revise their shared cognitions and adaptive inputs, creating a recursive approach to team adaptation. Notably, this model varies from the previously reviewed models because is it not oriented around task or team performance, but instead focuses on the ways in which adaptation may emerge within a team. Although this model omits the importance of team outcomes within the IMO framework, it advances our conceptualizations of the dynamism of team processes while considering team adaption in a temporal manner. Considering the temporal aspects of teams has increased in importance when studying the effectiveness of teams. As signified in the previously discussed models, the issue of t­emporality often arises within the cyclical nature of the IMO models (e.g., input to p­rocess to output back to input again). Various frameworks have emerged that highlight the importance of time in terms of team effectiveness. Marks and colleagues (2001) created one of the key temporal models that has advanced our theoretical understanding of team processes and temporality (Figure 4.5). This model uses an IPO framework and emphasizes the recurrent nature of task completion cycles. In their model teams go through an input, process, and output in different transition and action phases for varying lengths of time at different rates of occurrence. Specifically, teams may go through different phases at different frequencies for varying tasks. For example, one team may rapidly go from a transition phase to an action phase and back to a transition



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Task 1

I P1…N O

I P1…N O

I P1…N O

I P1…N O

I P1…N O

Transition

Action

Transition

Action

Transition

I P1…N O Task 2

Task 4

Action

Transition

I P1…N O Task 3

I P1…N O

Transition

I P1…N O

I P1…N O

Action

Transition

I P1…N O

I P1…N O

I P1…N O

Transition

Action

Transition

Time I = input, O = output P1…N = process 1 to process 2

Figure 4.5  The rhythm of team task accomplishment (Marks et al., 2001); I = output, P1…N = process 1 to process 2.

phase continuously, while another team could have one short transition phase, then one long action phase, followed by one transition phase for their task. Another interesting temporal model that incorporates theories from previous models is the Morgan, Salas, and Glickman (1993) model (Figure 4.6). This framework draws upon multiple models to characterize the lifecycle of a team. They use the team development stages characterized by Tuckman (1965) with some slight expansions from incorporating other theories. The phases they include are forming, storming, norming, performing I, reforming, performing II, conforming, and deforming. This model also draws upon the Gersick (1988) model where “forming” occurs when the team starts. Following this stage, the team will go through phase 1 (e.g., storming, norming, and performing) to the r­eforming stage. This stage is analogous to the midpoint where a team changes its strategy in Gersick’s model, which leads to the second phase and task completion. Additionally, Morgan and colleagues’ (1993) model pays specific attention to the team and task development over time. They do this by discussing where team and task development are separate within the stages until they convene and team members are able to complete the task. Finally, within this model all functions are based on the environmental demands and constraints in the context of the society and organization in which a team is located. Although numerous models of team effectiveness have emerged, many of these frameworks fit into an aspect of the IMO framework. Therefore, we purpose that researchers

FIRST MEETING

PRE-FORMING

FORMING

TRANSITION

PHASE I

(Beginning of the cycle)

STORMING

NORMING

(Met point of the cycle)

PERFORMING

REFORMING

PHASE-II

COMPLETION (End of the cycle)

PERFORMING-II CONFORMING

DE FORMING Fleview of accomplishments

Development of task assignments TASKWORK SKILLS

Recycle Orientation to task

TEAMWORK SKILLS Testing of dependence

Emotional response to task demands

Intragroup conflict

Open exchange of relevant interpretations

Development of group cohesion

Withdrawal from task Emergence of solutions

Development of role relatedness

Adjustment of framework

Refinement of roles

Drive-to completion

Completion and delivery of task

Fulfillment of roles

Adjustment to environmental demands

Investigation of group

Exiting from group

Remembering group ENVIRONMENIAL DEMANDS AND CONFUCIANISM [Social and organizational context]

Figure 4.6  A general model of team evolution and maturation (Morgan et al., 1993).



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consider the dynamic nature of teams by taking an individual‐level, group‐level, and systems‐level approach to team effectiveness in regards to culturally diverse teams. The following sections will explore the IMO model in more depth by explaining various inputs, mediators, and outputs that create effective international teams and explore common variables present within team effective models.

Inputs to Team Effectiveness The input portion of the team effectiveness model has frequently been documented by multiple levels of input including individual input, team input, organizational input, and task input. Individual inputs are characteristic of the individuals in a team, team inputs are applicable to the overall team, organizational inputs relate to differences in the overall organization, and task inputs relate to facets of the task. Although mentioned separately, individual, team, and organizational inputs all relate to each other in a hierarchical order. Specifically, individuals are nested within a team and a team is in an organization. Therefore, any input at one given level could partially affect the other levels, as shown by the connecting arrows in Figure 4.7.

Individual‐level inputs Culture  The primary individual input to be considered is the international culture of an organization. The country or location and overall cultural input play a complex role in team effectiveness. In certain cultures, norms can vary greatly; work days might be arranged differently, teams may view their arrangements and desires for interdependence and performance differently, and they may have vastly different preferences for the process and types of interpersonal interactions (Gelfand, Erez, & Aycan, 2007; Taras, Kirkman, & Steel, 2010). There are certain classifications that can characterize some of these differences that are more generalizable across these different cultures and are important to consider. ONE well‐known model is Hofstede’s taxonomy of cultural dimensions. Hofstede is widely known for advancing the culture literature, specifically because he studied culture as a multidimensional construct. He created a four‐dimensional model to classify differences Inputs

Mediators

Outcomes

Process

Performance

Emergent states

Other team outputs

Task Organization Team Individual

Figure 4.7  Adapted input–mediator–outcome model (Mathieu et al., 2008).

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in national culture, which were identified as individualism–collectivism, power distance, masculinity–femininity, and uncertainty avoidance (Hofstede, 1980). A fifth dimension of long‐term vs. short‐term orientation was later added. Though this model is frequently used and referenced, it is important to note that there have been critiques regarding its  lack of inclusivity and for not viewing international cultures from other directions (Taras et al., 2010). The most commonly studied dimension under Hofstede’s model has been individualism vs. collectivism. Hofstede (1980) defines this continuum as “the degree to which people in a country prefer to act as individuals rather than members of groups” (p. 6). Next under Hofstede’s categorization is power distance, which refers to the distribution of power in terms of inequalities among members. In a high power distance culture, inequalities among members are accepted and expected whereas low power distance cultures promote equality. Masculinity–femininity characterizes cultures based on assertiveness, success, competition, or quality of life, personal relationships, and care for others (Hofstede, 1980). Masculine cultures are driven by success and are assertive and competitive in n­ature, while feminine cultures are classified as friendlier. Uncertainty avoidance is the last of the four cultural dimensions that Hofstede originally proposed. This construct refers to how a culture experiences ambiguous or unknown situations. Cultures that are high on this aspect do not fear novel situations and enjoy challenging tasks (Owen & Sweeney, 2002). Lastly, long‐term vs. short‐term orientation evaluates how a culture maintains ties to its past while dealing with future challenges. Cultures with a long‐term orientation are focused on futuristic goals and are willing to delay immediate success for long‐term gains. Cultures with short‐term orientation focus on immediate needs with less regard to a future outlook (Taras et al., 2010). Essentially Hofstede’s cultural dimensions identify specific elements of a culture’s foundation. In his studies, these cultural values are defined as “the collective programming of the mind which distinguishes the members of one human group from another” (Hofstede, 1980). The four original dimensions that he proposed are independent of one another, although subsequent research has studied their overlaps as well. To fully understand how geographically dispersed and culturally diverse teams perform, it is important to consider the varying cultural determinants and values that may be underlying the team. Therefore, this chapter emphasizes the importance of this team input and the relevance of transnational teams throughout the following sections. Personality  Personality has been found to link to both the mediators and outputs of team effectiveness overall. According to a meta‐analysis by Bell in 2007, on average all aspects of the Big Five in personality were directly related to performance. Individual personality traits such as conscientiousness and extraversion have also been related to processes such as backup behaviors (Porter et  al., 2003). Additionally, research conducted in Spain found that personality factors, such as agreeableness, lead to higher job satisfaction (Acuña, Gómez, & Juristo, 2009). Different combinations of personality factors also partially determine the overall relationship between team mediators and outcomes (Bradley, Klotz, Postlethwaite, & Brown, 2013). In relation to other countries, the five‐factor measure of personality has been found to be a generalizable measure of personality to different cultures (Harris Bond & Wing‐Chun Ng, 2004). For example, a study in China found the five‐factor personality trait of agreeableness to be linked to constructive controversy (Wang, Chen, Tjosvold, & Shi, 2010). However, there are other factors that need to be considered beyond the Big Five, one of which is locus of control. Locus of control varies from internal to external. Internal locus of control explains that individuals perceive that they control their lives and the events that happen



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in their lives. On the other hand, individuals with an external locus of control view their lives as being at the mercy of their environment. A meta‐analysis by Judge and Bono (2001) found that internal locus of control had a positive relationship with both satisfaction and performance. Similarly, research conducted in Austria found that a higher internal locus of control was found to lead to higher levels of performance, especially when the team’s locus of control composition was homogeneous (Saud Kahn, Breitenecker, & Schwarz, 2014). Competency/experience  Another input for team effectiveness is how competent individuals are within a team. If an individual is competent he or she is better able to perform. This is of course important for the actual tasks involved in a job, but equally important are a person’s knowledge, skills, and abilities. These can lead to processes such as coordination (Salas, Stagl, & Burke, 2004). In China, Wang (2003) found that team competency was key for team leadership and change. Another input is individual experience. Higher levels of experience have been found to lead to lower outputs of errors in teams (Huckman, Staats, & Upton, 2009). Additionally, in Germany, Van der Vegt, Bunderson, and Oosterhof (2006) found that differences in level of expertise lead to differences in helping behaviors and team commitment. Orientation  Another composition factor is team orientation. There are two types of o­rientation that are commonly studied: team orientation and goal orientation. Team orientation is the individual propensity for accomplishing work as part of a team (Driskell & Salas, 1992), and goal orientation is a factor of team composition that Porter (2005) specified as what drives the individual’s goal‐related actions. Goal orientation was found to have an effect on processes such as backup behaviors and performance overall. Koopman et al. (1999) found that things such as performance orientation differ significantly across Europe. These differences in performance orientations could have an effect on what inputs would be more relevant to various cultures. Demographics  Another way teams can differ is demographically. In the past this has been found to relate to team effectiveness in multiple directions depending on the demographic aspect (Mathieu et al., 2008). Additionally in considering the international aspect, groups that are more diverse culturally have temporal issues with process where there are more issues early on (Watson, Kumar, & Michaelsen, 1993). Faultlines  One aspect of team input that is dependent on these demographic differences is faultlines or ideas or generalizations sometimes based in demographic classifications that a team sees and uses to divide the team into categories (Lau & Murnighan, 1998). Previous research has found that faultlines have a negative relationship with processes and effectiveness overall, but that these relationships can vary in strength (Bezrukova, Jehn, Zanutto, & Thatcher, 2009; Rico, Molleman, Sánchez‐ Manzanares, & Van der Vegt, 2007). A study by Lau and Murnighan (2005) looking at members of teams in China and Canada found that teams of cross‐sex and cross‐e­thnicity with weak faultlines had a negative relationship with group learning, psychological safety, group satisfaction, and expected performance. Further, team differences in status, position, or expertise can influence the team’s processes and effectiveness. Perceived expertise in a team member was found to lead to increased commitment from a member of a team who perceived him‐ or herself to be at a lower level of expertise (Van der Vegt et al., 2006).

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Team‐level inputs Team context  Another team‐level input is the context in which the team functions. Specifically, virtuality is a key aspect of teams today that has significantly impacted the way teams function. For example, there are now differences between the types of ways a team interacts (completely virtual, face‐to‐face, or a mix of the two). It has been found in the literature that teams that are only virtual can lead to decreased functioning in team processes and performance/perceptions of performance in comparison to face‐to‐face teams (Warkentin, Sayeed, & Hightower, 1997). Additionally, the degree of technology used significantly affects performance (Driskell, Radtke, & Salas, 2003). Having mixed modal teams with both virtual and face‐to‐face aspects can allow for greater team processes to occur (Pinsonneault & Caya, 2005). In China, Guo, D’Ambra, Turner, and Zhang (2009) also found that when teams interacted in a face‐to‐face environment over virtual environments they had higher process levels (i.e., cohesion) and higher outcomes, such as satisfaction. Team training  Team training has been found to improve team ratings of performance (Salas, Cooke, & Rosen, 2008). Team‐level training, instead of individual training, should be used to improve the way the team interacts to complete a task or function. In China, this kind of intervention was found to be beneficial (Guo et  al., 2009). However, the method of team training delivery is an important factor to consider. There are many delivery options for training from the context (e.g., a face‐to‐face lecture or computer simulation) to the specific methods of training (e.g., cross‐training, adaptation training, or correction training; Salas, Nichols, & Driskell, 2007; Sitzmann, Kraiger, Stewart, & Wisher, 2006). The types of technology used to administer training can have an influence over the effectiveness of team training in multiple ways. Specifically, research generally surmises that virtual training, such as simulation games, results in higher self‐efficacy among other improved outcomes (Sitzmann, 2011). Internationally, work conducted in Australia by Nunnink, Welsh, Abbey, and Buschel (2009) found that after teams in the intensive care unit of a hospital received simulation training over video training, they had higher levels of self‐efficacy and scored better on a written test. Team leadership  Leadership is a team‐level input that can definitely have an effect on the overall performance and the processes/emergent states of team functioning. The context of the leader can be seen in multiple ways (e.g., external leaders or shared leadership). Previous research established that the actions of an external leader have a very large impact on overall team processes, such as adaptability, and performance (Burke et  al., 2006). An  example of  external team leadership input is leader style (e.g., transformational leadership). Transformational leadership is a common leader style characterized by those who facilitate subordinates’ growth and align their goals with those of the leader themselves, the group/ team, and the overall organization (Bass & Riggio, 2006). Studies in China have found that transformational leadership is positively related to both mediators/processes and performance (Sun, Xu, & Shang, 2014; Zhang, Cao, & Tjosvold, 2011). Other than external leaders, there can also be situations where a team shares the role of leadership throughout all members of the team by delegating all leadership responsibilities within the team. This shared leadership input is positively related to processes and team performance overall (Yoo & Alavi, 2004). Team structure  Structure is another input of team effectiveness that directly influences productivity (Day, Gronn, & Salas, 2004). Specifically, the reasons and ways a team is arranged play a part in productivity and what processes are more frequent and necessary (Bunderson & Boumgarden, 2010; Stewart & Barrick, 2000). Team structure is classified



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as either functional or divisional teams. Functional teams have only unique information while divisional teams are more able to access the same information. Cross‐functional teams aim to bridge the all‐or‐nothing approach with unique knowledge sharing. A study in Australia, Canada, Denmark, Finland, the United Kingdom, and the United States found that this cross‐functional team structure is positively related to processes and outcomes (Hauptman & Hirji, 1999). Another structural input that has an effect on the overall team process and effectiveness is the team size. According to LePine, Piccolo, Jackson, Mathieu, and Saul (2008), certain larger teams have higher team process and higher performance than smaller teams.

Organization‐level inputs Human resource systems  One major category of organizational inputs is human resource systems. There are numerous types of systems (e.g., control and commitment systems) that might have an effect on team outcomes or processes within an organization. Specifically, the type of human resource system in place can impact the relationship with effectiveness either positively or negatively. Arthur (1994) found that commitment systems were associated with greater production and lower error rates. In one example from England, Guest (2002) found that using an ideal set of high performance human resource management practices was linked to performance and higher satisfaction. Climate  The next organizational‐level input is the climate. Safety climate is an example of organization climate where safe procedures and member safety take a priority and has been found to increase overall organizational levels of performance (Jalali & Khorshid, 2015). Additionally, in India a climate of openness, confrontation, trust, authenticity, proactivity, autonomy, collaboration, and experimentation was examined and found to positively relate to innovation, interpersonal relationships, job satisfaction, and commitment (Sharma & Sharma, 2010).

Task inputs Task interdependence  Interdependence is often studied through how much the team has to interact with each other to get the job done (Aubé & Rousseau, 2005). Interdependence can be influenced by some of the individual aspects mentioned earlier, such as varying skill level. Additionally, task interdependence involves the degree to which an individual’s task completion depends on others. This can often be thought of in four different types: pooled, sequential, reciprocal, and interactive (Bell & Kozlowski, 2002). Pooled is where all individuals complete tasks separately. Sequential is when the task process goes through team members in one direction, such as on an assembly line. Reciprocal is when a task process flows through one person to another, similarly to sequential; however, the process can also return back through team members to complete the task. The last and most complex is interactive. This involves a task in which all members constantly communicate, collaborate, and work through problems together simultaneously (Riopelle et al., 2003). In Germany, Hertel, Konradt, and Orlikowski (2004) found that task interdependence was higher in teams that were also more efficient. This coincides with LePine and colleagues’ (2008) finding that a higher level of task interdependence leads to improved team processes and increased performance. Further, Hauptman and Hirji (1999) found that teams in Australia, Canada Denmark, Finland, the United Kingdom, and the United States with highly interdependent tasks had increased effective team processes when coordination and integration mechanisms (e.g., leader consensus seeking, job rotation) were high.

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Task difficulty  Task difficulty is essentially the task component complexity which involves “the number of distinct acts that need to be executed in the performance of the task and the number of distinct information cues that must be processed in the performance of those acts. As the number of acts increases the knowledge and skill requirements for a task also increase” (Wood, 1986, p. 66). Task difficulty has been shown to have effects on performance and effectiveness. Multiple studies in the past have found that increased task difficulty can increase performance up to a point; however, when the difficulty exceeds cognitive abilities or knowledge, skills, and abilities, performance tends to go down again (Chae, Seo, & Lee, 2015). This is especially true in culturally diverse teams, as the relationship between diversity and conflict increased when there was a complex task (Stahl, Maznevski, Voigt, & Jonsen, 2010). However, in a study of Chinese and American teams, it was found that teams’ task difficulty did not make a difference in the individual’s affective perceptions about the other team members’ abilities (Diamant, Fussell, & Lo, 2008). Task environment  In teams, the task environment and types of tasks can range on a continuum from more static to more dynamic (Riopelle et al., 2003). Repetitive or static tasks involve the team working on similar tasks most of the time; on the other hand, a team can have tasks that are relatively dynamic or more uncertain where the types of tasks and necessary knowledge and skills the team needs to complete the tasks can substantially d­iffer (Wood, 1986). Additionally, Marks and colleagues (2001) state that tasks in a team have temporal shifts between action and transition phases. This partially contributes differently to the level of how dynamic or static a task is, depending on frequencies of transitions or level of action vs. transition. Internationally, Gibson (1999) analyzed students from the United States and Hong Kong and found that when task uncertainty is high, team m­embers tended to work separately, whereas teams tended to work together more when there was low task uncertainty.

Mediators/Processes of Team Effectiveness Understanding the processes and mediators that individuals utilize in team‐based work is vital to interpreting how inputs and outputs are related. Specifically, these aspects of the team effectiveness model will aim to explore why some teams are more or less effective than others within the context of international and culturally diverse teams. To address this issue, the following sections will cover various processes and emergent states that intervene between the inputs and outputs of teams. As seen in Figure 4.1, the roles of processes and mediators are dynamic and nonlinear.

Processes Processes are actions that unfold over time that explain the relationship between an input and an output (Tannenbaum, Mathieu, Salas, & Cohen, 2012; Marks et al., 2001). Marks and colleagues (2001) have conceptualized team processes as three distinct phases when trying to complete goals. These include the transition phase, action phase, and i­nterpersonal phase. Transition phase processes  During the transition phase team processes are preliminary in nature and oriented around evaluation or planning to meet team goals (Marks et  al., 2001). Specifically, the taxonomy set forth by Marks and colleagues (2001) states that the



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processes of mission analysis, goal specification, and strategy formation and planning t­ypically occur during transition phases. Mission analysis involves interpreting and evaluating the team’s mission and identifying key tasks involved, environmental conditions, and  team resources. Goal specification occurs when team members clearly identify and prioritize their goals geared toward mission completion. Finally, strategy formulation is the development of secondary courses for mission execution. Action phase processes  Processes that occur during the action phase focus on activities that move the team toward goal completion (Marks et al., 2001). This phase takes place when the team actively works to complete tasks and consists of monitoring progress toward goals, systems monitoring, team monitoring and backup responses, and coordination activities. Monitoring progress toward goals involves interpreting system information to assess and communicate team progress toward mission completion. Systems monitoring occurs when team members track the team’s current resources and environmental conditions in relation to goal attainment. Team monitoring and backup behavior entails helping other team members complete their tasks by providing feedback and physically performing another teammates’ tasks. Coordination is arranging the timing and order of interdependent actions. Team adaptation is another notable process that occurs during the action phases of a team. Adaptation involves the process of differentiation, integration, and application of knowledge allowing teams to execute various processes (Burke et al., 2006). Learning is an essential aspect of adaptation, but typically learning occurs prior to team adaptation. Specifically, Burke and colleagues (2006) proposed that there is an adaptive cycle that occurs involving other processes including situation assessment, plan formulation, plan execution, and team learning. In turn, these processes lead to various emergent states such as shared mental models, team situation awareness, and psychological safety; these emergent states then lead to team adaptation (Burke et al., 2006). Team innovation and team modification are also aspects of team adaptation. Research conducted using an international information technology firm emphasized the importance of adaptation in both verbal and virtual means of communication within cross‐cultural teams to reach team effectiveness goals (Anawati & Craig, 2006). Interpersonal phase processes  Interpersonal processes aim to monitor team relationships and occur throughout the duration of both the action and transition phases (Marks et al., 2001). These processes are distinct from emergent states because they occur over time while emergent states may emerge at any point during the team’s duration. Interpersonal processes include conflict management, motivation and confidence building, and affect management. Conflict management has both preemptive and reactive aspects such that preemptive conflict management aims to create conditions that prevent, control, or guide team conflict prior to it occurring, while reactive conflict management is the actual process of working through task and interpersonal disagreements. Motivation and confidence building involve creating and maintaining a sense of collective confidence, motivation, and task‐based cohesion. Finally, affect management entails monitoring team member emotions during mission achievement.

Emergent states Emergent states are dynamic properties that vary due to the context of the team, processes, and outcomes (Marks et al., 2001). Emergent states are described as member cognitions, values, motivations, and attitudes. Specifically, emergent states refer to the cognitive, motivational, and affective states of teams.

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Cognitive emergent states  Team cognitions emerge from individual cognitions and team processes. While the individual level can be considered as knowledge, team processes utilize this individualized knowledge to produce team cognition (Salas, Nichols et al., 2007). When considering the cognitive emergent states we explore shared mental models, s­trategic consensus, team learning, and team transactive memory systems. Shared mental models are team members’ shared understandings or representations of knowledge within their team (Mathieu, Heffner, Goodwin, Cannon‐Bowers, & Salas, 2005). Members can have multiple mental models at any given time. The most commonly studied shared mental models are task and team shared mental models. Task shared mental models are teammates’ shared perception of their taskwork; task shared mental models are critical in teams with unpredictable tasks (Mathieu, Heffner, Goodwin, Salas, & Cannon‐ Bowers, 2000). Team shared mental models emphasize the importance of understanding members within a team and how each member will interact with others, which is crucial when working in a cross‐cultural team (Mathieu et al., 2000). Strategic consensus consists of managers at the top, middle, and lower levels of an organization having shared strategic priorities (Kellermanns, Walter, Lechner, & Floyd, 2005). This emergent state is differentiated from shared mental models because strategic consensus focuses on priority agreement while shared mental models entail having knowledge shared by team members (Mathieu et al., 2008). Team learning is the change in knowledge due to experiences (Ellis et  al., 2003). Research on team learning has found that it mediates the relationship between psychological safety and team performance (Edmondson, 1999). In addition, teams composed of individuals with high cognitive ability and even workload distribution enhanced team learning while learning decreased in teams composed of individuals high in agreeableness (Ellis et al., 2003). In a global pharmaceutical company, team learning was positively related to both task performance and the quality of interpersonal relationships (Zellmer‐Bruhn & Gibson, 2006). Transactive memory systems involve both processes and emergent states and are defined as a “collection of knowledge possessed by each team member and a collective awareness of who knows what” (Mathieu et al., 2008, p. 431). Transactive memory systems are positively related to various outcomes including goal performance and external and internal group evaluations (Austin, 2003). A study conducted across four different countries – Australia, Hungary, Thailand, and the United States  –  utilized MBA graduate students to examine the relationship between transactive memory systems and performance (Yoo & Kanawattanachai, 2001). They revealed that early communication within the team was significantly related to the development of transactive memory systems. However, the relationship between volume of communication and performance outcomes diminished once transactive memory systems were established (Yoo & Kanawattanachai, 2001). Motivational emergent states  Team motivational states enable a team to achieve goals by enhancing the team’s desire and enthusiasm for completing work. The motivational emergent states we will explore include team confidence and team empowerment. Team confidence is an emergent state that encompasses both team efficacy and potency. Team efficacy is the shared belief in the group’s collective ability to execute goal‐oriented actions to complete tasks (Kozlowski & Ilgen, 2006). Potency refers to the team’s collective belief that they will be successful (Guzzo, Yost, Campbell, & Shea, 1993). M­ultiple meta‐ analyses have found that team efficacy and potency at the team level are both positively related to team performance (De Dreu & Weingart, 2003; Gully, Incalcaterra, Joshi, & Beaubien, 2002). In addition, potency has been shown to predict group performance above and beyond group ability, indicating the importance of having confidence within a



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team (Hecht, Allen, Klammer, & Kelly, 2002). Research conducted in Taiwan confirms and extends these results by linking legal citizenship and economic citizenship to team e­f ficacy, which in turn enhanced performance (Lin, Baruch, & Shih, 2012). Team empowerment consists of both structural and psychological aspects (Mathieu, G­ilson, & Ruddy, 2006). Structural empowerment examines the impact that authority delegation and responsibility can have on performance. Psychological empowerment is individual team members’ collective belief that they are responsible for their team’s actions and have the authority to control their work environment (Mathieu et al., 2006). When looking at emergent states, we will consider psychological empowerment, not structural empowerment, for theory building. A recent meta‐analysis signified that psychological empowerment is positively related to task and contextual performance (Seibert, Wang, & Courtright, 2011). In addition, empowerment has played a mediating role in Danish elderly care research. Specifically, Nielsen, Yarker, Randall, and Munir (2009) found that team empowerment positively and significantly mediated the relationship between transformational leadership and both job satisfaction and wellbeing for caretakers. Affective emergent states  Team affect examines the feelings (i.e., moods and emotions) within and among team members. Although individual affect is shaped by one’s trait level of positive or negative affect, affect is also dramatically impacted by other people (Collins, Lawrence, Troth, & Jordan, 2013). We approach affect as a state within this context and examine cohesion, trust, and climate. Cohesion explains the level of commitment a team has to their overall task and each other (Goodman, Ravlin, & Schminke, 1987). Overall, cohesion is more positively related to  efficiency than effectiveness (Beal, Cohen, Burke, & McLendon, 2003). A review c­onducted in Australia points out the importance of other functions such as “accurate performance feedback, success in adversity, good communication and conformity to norms” to enhance cohesion (Mickan & Rodger, 2000). The emergent state of trust is the willingness of an individual to believe that another person’s actions will be beneficial or non‐detrimental to their own, or the team’s, best interest without monitoring or regulating the other party (Mayer, Davis, & Schoorman, 1995). Trust within a team is critical to its success. Specifically, in Germany the relationship between transformational leadership and team effectiveness (e.g., satisfaction and performance) was mediated by both trust in the team and trust in the supervisor (Braun, Peus, Weisweiler, & Frey, 2013). Climate is unique in that it can serve as both an input and a mediator within team effectiveness research. As a mediator, climate refers to the shared perceptions of both informal and formal policies, practices, and procedures of an organization (Schneider, White, & Paul, 1998). In turn, these perceptions impact how the team handles various situations and their overall team effectiveness. Specifically, an organization’s safety climate, service climate, and justice climate all mediate the relationship between team inputs and team effectiveness. An organization’s safety climate involves employee perceptions of the organization’s attitudes toward safety and their overall work environment. Safety climate is influenced by an organization’s policies, practices, and procedures and related to various performance outcomes. Researchers at the Israel Institute of Technology found that providing employers with feedback focused on enhancing the organization’s safety and goal‐oriented communication improved the organization’s safety climate (Zohar & Polachek, 2014). Further, meta‐analytic results indicate that safety climate is positively related to both employee safety compliance and participation (Clarke, 2006).

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Service climate encompasses employees’ shared perception of the organization’s expected behaviors in regards to customer wellbeing and customer service (Schneider et  al., 1998). Service climate has been positively linked to numerous organizational o­utcomes. A study conducted in a Guangdong hospitality industry found that employee customer orientation significantly and positively related to customer satisfaction, e­mphasizing the importance of service climate within an organization (He, Li, & Keung Lai, 2011). Justice climate refers to the team’s perception of how they are treated as a whole within their organization (Greenberg, 1990). Additionally, organizational justice examines fairness within a workplace. Procedural justice focuses on the perceived fairness of the process implemented to achieve goals and involves both process control and bias avoidance in resource allocation. In Spain, a study of hotel employees examined the relationship between justice climate and burnout at the unit level. Results indicated a positive relationship b­etween individual justice perceptions and burnout; this relationship was moderated by organizational justice climate such that the stronger unit level of justice climate lowered burnout within the unit despite low perceptions of individual justice (Moliner, Martínez‐Tur, Peiró, Ramos, & Cropanzano, 2005).

Outputs of Team Effectiveness Finally, while a seemingly simple concept, team outcomes may be one of the least well defined of all the team constructs (Salas, Nichols et al., 2007). Much of this is due to the fact that team outcomes have been characterized over the years in a variety of ways as a multidimensional construct (Cohen, 1994). In their review of team effectiveness models, Salas and colleagues (2007) note the existence of 138 such models, many with their own unique conceptualization as to what teamwork outcomes really include. Drawing from this wide range of models, Salas and colleagues (2007) propose that our definition of o­utcomes for teams can largely be a value judgment that is driven by individual, team, and environmental contextual factors. Overall, team outcomes provide a means for capturing how well – or how poorly – teams are interacting behaviorally, affectively, and cognitively, and can therefore be critical for determining when and how to intervene as needed (Hackman & Wageman, 2005). There are multiple ways to measure this as team outcomes capture much more than simply performance (Mathieu et al., 2008). We will review s­everal of these components of outcomes below and discuss the international implications for team outputs.

Performance Performance is one of the most widely used outcome measurements in teams research. There are multiple levels of performance including role performance, team performance, and organizational performance. Organizational performance is dependent on what h­appens in the organization (Steers, 1975). For example, profitability depends on team processes such as cohesion and cooperation (Barrick, Bradley, Kristof‐Brown, & Colbert, 2007). This has also been found to be a measure of importance internationally (Chin & Pun, 2002). Beal and colleagues (2003) differentiated between team’s performance behaviors and actual outcomes of performance. When a team is behaviorally performing they are exhibiting actions linked to optimal teamwork or task completion (e.g., cognitive task performance, feedback seeking, error discussion, and error discussion together with experimentation).



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This is similar to role performance, an individual measure that is based on the actual actions and not necessarily their outcome. Essentially, whether or not a team member exhibits the actions necessary to competently function in their role is an important o­ utcome, especially in high stakes environments (Vigoda, 2000). An international example of this is when Matveev and Nelson (2004) measured American and Russian communication competence and found that the related behaviors explained 20% of the team’s performance. The other measure of performance or outcome is the direct outcome of a team’s actions (Salas, Sims, & Burke, 2005). This is the type of outcome typically associated with the word performance. This can be measured through supervisory ratings, output of product, if the task was completed, and/or how efficiently or accurately the task was completed (Hackman, 1990). In Australia, they looked at how multiple studies have examined both of these types of performance and found that both inputs and mediators were a large part of the type of performance brought about in their exploratory study (Griffin, Neal, & Parker, 2007).

Satisfaction Another measure of team‐level outcome is satisfaction. Satisfaction is an affective state of fulfillment and can be considered in multiple ways (Rozell & Scroggins, 2010). There can be job satisfaction, team satisfaction, organization satisfaction, satisfaction with opportunities, and so on. This type of outcome is measured internationally in team effectiveness literature (Mickan, 2005). For example, a study was done in Canada and India with 24 virtual teams and team trust and role clarity resulted in higher levels of satisfaction (Edwards & Sridhar, 2005).

Viability Viability is another outcome that has been frequently measured in the past. Viability is a team’s desire to work as a team again or continue working as a team (Mathieu, et  al., 2008). It has been found that team factors such as a high team density or interpersonal closeness typically have higher levels of viability (Balkundi & Harrison, 2006). Additionally, in an international context the idea of viability is a measure associated with team effectiveness outcomes (Tjosvold, Poon, & Yu, 2005).

International perspectives of team outcomes Overall team outcomes are in a large part dependent on the inputs and mediators of processes and emergent states, and therefore when looking at outputs it is important to focus on the whole team effectiveness model (Ilgen et al., 2005). It is crucial to understand where everything fits in a model and to take culture into account, as discussed e­ arlier, although sometimes relationships can be similar across cultures; international difference inputs can affect levels of various team mediators (Stahl et  al., 2010). Additionally, even though many of the same outcome measures such as performance, performance behaviors, viability, and satisfaction are measured and accepted as team effectiveness outcomes, this doesn’t mean those outcomes will be at the same levels (Chin & Pun, 2002; Mickan, 2005; Tjosvold et al., 2005). Therefore, when outcomes are being examined internationally and multiculturally, it is important to take the unique effects of inputs and mediators into account (Salas et al., 2008).

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Comprehensive Models of Team Effectiveness for the Future As outlined above, there are numerous factors that influence team effectiveness. In order to create a more practical model, Salas and colleagues set out to offer a taxonomy inclusive of the most influential components that impact team performance that are depicted in the majority of the aforementioned teamwork taxonomies (Salas et al., 2005). Their goal was to boil down the substantial literature of team effectiveness to identify the five key contributors to teamwork. The “Big Five” of teamwork was developed to describe the “essence of teamwork” by highlighting key processes the authors argue are the center of interdependent interactions within teams (Salas et al., 2005) (Figure 4.8). This model outlines that the five core teamwork processes are (1) team leadership, (2) team orientation, (3) mutual performance monitoring, (4) backup behavior, and (5) adaptability. Additional coordinating mechanisms including shared mental models, closed‐loop communication, and mutual trust also influence the “Big Five.” Together these eight components interact dynamically to form teamwork. However, this theoretical framework of teamwork omits several key components

THE CORE

Team leadership

P9

P1 Mutual performance monitoring

P1

P2

Team orientation P10

Backup behavior

P3 Share mental models

P6 P8

P4

P3 P5

Adaptability

P7 Team effectiveness

Closed-loop communication

Figure 4.8  The “Big Five” teamwork model (Salas et al., 2005); P = process.

Mutual trust



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Individual characteristics

Shared cognition

Roles and requirements

Expectations

of other team effectiveness models, such as the inputs and outputs of the team. Therefore, a final theoretical model is posed that aims to boil down all of these team effectiveness models into one comprehensive, yet simplified, model of team effectiveness (Salas, Stagl et al., 2007). After a critical literary review, Salas, Stagl and colleagues (2007) created a comprehensive multilevel model of team effectiveness that aimed to incorporate aspects of the previous models and variables (Figure 4.9). This model follows an IPO framework, but is unique in that it creates a dynamic, temporally based approach to team effectiveness. Overall, the model aims to uncover key processes and variables that facilitate effective teams.

Team leadership/ coaching

Adaptive

Template Communication

Team characteristics

Individual performance outcomes

Template Interpersonal

Task characteristics

Template Coordination

Template Work structure

Team performance outcomes

Other

Templates processes shared vis mwork ion Tea

Organizational environment

Figure 4.9  The conditions and processes of team performance (Salas, Stagl, Burke, & Goodwin, 2007).

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At a high level, this model includes team inputs that facilitate teamwork and team performance. These input categories are individual characteristics, team characteristics, task characteristics, and the work structure. Overall, these inputs influence the throughputs of the team. The throughputs, or mediators, of this model include team adaptation, communication, interpersonal, coordination, and other. Together, these teamwork processes create a shared vision within the team. Further, the processes are influenced by team leadership and coaching, in addition to the organization environment. This model also outlines the moderating role of individual‐level cognition (i.e., expectations about roles and requirements) on the relationship between team inputs and throughputs. That is, team members actively interpret team inputs through an ongoing process to develop expectations in terms of their obligations. Members who possess accurate expectations are more likely to engage in the proper team processes at the appropriate times. These team processes lead to shared cognition, which increases as teamwork occurs and, in turn, influences future teamwork behaviors. Finally, team performance consists of both individual performance and team performance outcomes. These outcomes create system feedback that will influence future inputs in subsequent performance episodes (Salas, Stagl et al., 2007). In addition, team leadership influences, and will be influenced by, both expectations and shared cognition of the team. The organizational environment also impacts the team processes, the expectations of the team, and the shared cognition of the team.

Future Research We suggest that future research continue to use the more comprehensive IMO framework, rather than the IPO framework. Further, researchers should strive to capture the entire IMO model including an input, mediator, output, and feedback loops. Research is often conducted on only two of these constructs (e.g., only evaluating an input and an emergent state), possibly missing part of the present dynamic relationships. In addition, we urge teams researchers to pay due consideration to social networks, measuring international and intra‐national effectiveness dynamics over time, and multiteam systems in future team effectiveness endeavors. Social network analysis examines relational factors within an organization (Brass, 1984). A strength of social network analysis is that members within a team rate all other members of the team on a particular construct, creating a richer representation of team properties without solely relying on self‐report data or supervisory ratings. Previous studies have found that within‐team centrality has been positively linked to team innovation and performance (Tsai, 2001). However, the use of social network analysis within global c­ulturally diverse structured teams has yet to be examined. Specifically, how might various processes or emergent states such as trust and potency vary within culturally diverse teams? This is just one example of the numerous characteristics that would benefit from being examined using network centrality that should be considered in the future. Looking at culture internationally and intranationally, the literature is insufficient concerning the relationship between cultural diversity and team effectiveness. While research has emphasized culture at an individual level of analysis, this cannot be aggregated to generalize culture at other levels (e.g., national level). Rather, a multilevel approach is necessary to integrate micro‐level and macro‐level findings (Fischer, Ferreira, Assmar, Redford, & Harb, 2005). This approach is a critical step towards developing t­heoretical models of team effectiveness with cultural contexts (Gibson et al., 2003).



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Additionally, examining the dynamics of international teams and their changing nature longitudinally is an area of research where we are just beginning to scratch the surface (Cronin et al., 2011). Some preliminary studies of international teams with heterogeneous cultural values have found that in the early stages of team formation they exhibit more shock and conflict due to cultural discrepancies, which lead to initial ineffective teamwork (Zellmer‐Bruhn & Gibson, 2013). However, it has been shown that as a team works together longer, they can create universal team perceptions that disseminate the effects of their initial value‐based cultural differences (Maloney & Zellmer‐Bruhn, 2006). This can be done through the bridging of faultlines, developing swift norms, having an overall global acceptance culture in an organization, and ensuring that individuals understand the varying cultures within their work team (Maloney & Zellmer‐Bruhn, 2006). This, however, does not nearly begin to capture a full characterization of international teams’ effectiveness. A greater focus on interaction processes over time and the various ways culture can have an effect on said processes and outcomes temporally needs to occur (Zellmer‐Bruhn & Gibson, 2013). Multiteam systems (MTS) are “two or more teams that interface directly and interdependently in response to environmental contingencies toward the accomplishment of collective goals. MTS boundaries are defined by virtue of the fact that all teams within the system, while pursuing different proximal goals, share at least one common distal goal; and in so doing exhibit input, process, and outcome interdependence with at least one other team in the system” (Mathieu et al., 2001, p. 290). Although multiple researchers have emphasized the importance of studying multiteam systems, the literature in this domain is lacking. Future research should aim to identify the various processes and emergent states that may be present within these systems at the team and system level, keeping in mind that the processes and emergent states may be different at different levels. In addition, a special focus on international teams and their specific processes and emergent states should be considered as the number of globalized teams and organizations c­ontinues to rise.

Conclusion As business globalization continues to rise, it is crucial to understand the role culture plays within these teams in order to reduce potential process losses and performance detriments. This chapter provides researchers and practitioners with a review of the numerous team effectiveness models that should be considered when working with international teams. Understanding these dynamic relationships can pave the way for organizations to develop sufficient means for monitoring the effects that team dispersion and cultural diversity may have within a team. Specifically, this chapter can help managers to create team appraisals that assess the team’s effectiveness through an international lens. That is, managers can assess the various cultural determinants relevant to the individuals within their teams, and further assess the various inputs and processes/emergent states present within the teams and how they relate to overall team effectiveness. Through these assessments, organizations can provide their teams feedback to facilitate team effectiveness. While this chapter aims to provide an overarching framework for considering international teams, it is worth noting that there is no “one size fits all” approach to creating team effectiveness. The proposed model by Salas, Stagl and colleagues (2007) aims to facilitate and encourage practitioners and researchers to address the need to consider teams in a dynamic, multilevel, and temporal manner. This recognition is the first step in creating an effective team as it encompasses the complex nature of teams and teams research.

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Overall, this chapter provides a review of the most prevalent team effectiveness models using an international perspective, revealing the globalized relevance of team effectiveness literature and the IMO framework. After reviewing numerous team effectiveness models, we provide researchers and practitioners recommendations to consider when utilizing international teams in the future.

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Part II

Antecedents to Team Effectiveness

5

Team Design John L. Cordery and Amy W. Tian

Introduction Rapid advances in information technology, increasing competition, globalization, and demographic changes are forcing researchers and practitioners to rethink how they organize the work that people perform and, in the process, to fundamentally reconsider the way in which teams are designed and deployed in organizational settings (Cordery & Parker, 2008; Crawford & LePine, 2013; Edmondson, 2012; Mathieu, Maynard, Rapp,  & Gilson, 2008; Oldham & Hackman, 2010; Tannenbaum, Mathieu, Salas, & Cohen, 2012; Wageman, Gardner, & Mortensen, 2012a). Thus, it is frequently observed that teams are becoming much more fluid and dynamic, both in their composition and temporal span (Mathieu, Tannenbaum, Donsbach, & Alliger, 2014), more likely to involve g­eographically dispersed members who interact virtually via digital technology rather than meeting face to face (Gibson, Huang, Kirkman, & Shapiro, 2014), and allocated a relatively high degree of empowerment or self‐leadership responsibility (Cordery, Morrison, Wright, & Wall, 2010). As Cross, Ehrlich, Dawson, and Helferich (2008) comment, (t)eams today are frequently formed and disbanded rapidly, distributed across multiple sites, and composed of members simultaneously working on myriad projects, with different bosses competing for their attention. Further, these teams’ work increasingly demands substantial coordination and integration of specialized expertise within and outside of the team (p. 75).

How is this affecting the way in which we design teams? In this chapter, we explore the fundamental design elements that express what it means to be a team. In doing so, we review recent research and theory relating to team constitution, team structure, and external support as it informs the effective design of organizational teams. We also provide a short review of the needs for future research. The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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What is Team Design? A common definition of teams is as: collectives who exist to perform organizationally relevant tasks, share one or more common goals, interact socially, exhibit task interdependencies, maintain and manage boundaries, and are embedded in an organizational context that sets boundaries, constrains the team, and influences exchanges with other units in the broader entity (Kozlowski & Bell, 2003, p. 334).

In line with this definition, and earlier identification of the key constructive elements of team design (e.g. Cohen & Bailey, 1997; Stewart, 2006; Wageman, 2001), Morgeson and Humphrey (2008, p. 46) describe team design as “the specification of team membership; definition and structure of a team’s tasks, goals, and members’ roles; and the creation of organizational support for the team and a link to the broader organizational context.” In this chapter, discussion of the process and core elements of team design is organized in terms of three basic features: the constitution of a team, the internal structure of its activities, tasks, goals and roles, and the provision of external support.

Constituting the Team Perhaps the most important team design decisions relate to the team’s formation  –  its constitution. Such decisions fundamentally distinguish a team from other social groupings that may exist in work settings, for example networks, work groups, departments, and communities. According to Wageman, Hackman, and Lehman (2005), three basic e­lements are involved in constituting a ‘real’ team: First, (teams) have clear boundaries that reliably distinguish members from nonmembers. Second, team members are interdependent for some common purpose, producing a potentially assessable outcome for which members bear collective responsibility. Finally, real teams have at least moderate stability of membership, which gives members time and opportunity to learn how to work together well (p. 377, italics added).

Boundaries The boundaries of a team can be conceptualized as “the lines within, and the lines around a team ‐ the internal and external “edges” of the team” (Espinosa, Cummings, Wilson, & Pearce, 2003, p. 158). Sundstrom, de Meuse, and Futrell (1990), have described external team boundaries as “features that (a) differentiate a work unit from others (Cherns, 1976); (b) pose real or symbolic barriers to access or transfer of information, goods, or people (Katz & Kahn, 1978); or (c) serve as points of external exchange with other teams, customers, peers, competitors, or other entities (Friedlander, 1987)” (p. 121). According to Yan and Louis (1999) the function of such boundaries is to buffer and protect the ‘productive core’ of the team from unwanted environmental disturbance and incursions. Much of the research into external boundaries at the team level has focused on their clarity and permeability, and how this influences member identity and team effectiveness. Hackman (2002, p. 45) suggests that teams require certainty as to where their boundaries lie, because difficulties arise “when a team’s boundaries are so unclear that its membership is uncertain, or when they are so permeable that there is a never‐ending flow of people in



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and out of the group.” Such groups have been described as “underbounded systems” (Alderfer, 1980) and, by virtue of being exposed to unbuffered environmental turbulence and being unable to sustain a true sense of collective identity and purpose, are seen as lacking the capacity to develop and sustain effective performance strategies. However, there is also the risk that team boundaries can become over‐specified and inflexible – “overbounded systems” (Alderfer, 1980; Hackman, 2002), wherein the team boundaries are clear but impervious, and the team is consequentially unable to adapt effectively or respond to changes in its external environment. As Faraj and Yan (2009) state: The essential problem for a team is to create boundaries that are porous enough to allow resources and information in but resistant enough to avoid uncertainty about who is on the team and whether these members are accountable for its collective outcomes (p. 604).

The challenge of designing and maintaining definite team boundaries is magnified by virtue of the fact that teams also commonly have multiple boundaries, both internal and external‐facing, and people are often members of multiple teams (Zaccaro, Marks, & DeChurch, 2012). How does one decide where to draw the external boundaries of a team? While this eventually becomes a question of “who” is on the team (Mathieu et al., 2014), it is primarily a decision about “what” (activities, tasks and roles) will be incorporated within the team’s boundaries. One criterion that can be employed here centers on task interdependencies that are intrinsic to the work process. Thus, teams may be formed to encompass a natural unit of work, where the boundaries of a team encompass the production of a whole p­roduct or complete service (Hackman & Oldham, 1980; Sundstrom et al., 1990). In sociotechnical s­ystems approaches to designing work systems (Baxter & Sommerville, 2011; Davis, Challenger, Jayewardene, & Clegg, 2014; Mumford, 2006), which frequently involve team‐based work, decisions about team boundaries are often made in order to contain major process interdependencies, so that necessary sharing of information and knowledge is not impeded (Cherns, 1976; Fox, 1995; Pasmore, 1995). In some cases, such as when the work process is characterized by relatively simple interdependencies (see later section on task interdependence), this may result in a broad span of activities, tasks and roles encompassed by the team, while in others, where the interdependencies are more complex, a relatively small span may result. Deciding where precisely to draw those team task boundaries in order to contain significant task interdependencies is frequently difficult, particularly since teams themselves are, more often than not, interdependent with other entities (e.g. other teams, customers) within and outside the organization’s boundaries (D­eChurch & Zaccaro, 2010). Another criterion that can be derived from sociotechnical systems principles is that, wherever possible, team boundaries should be designed so that categories of problem that have the potential to substantially disrupt performance (termed “key v­ariances”) are not exported across organizational boundaries (Cherns, 1976). Of course, external team boundaries, once established, require active maintenance (F­araj & Yan, 2009), which requires boundary spanning behavior on behalf of team m­embers to ensure that effective links are maintained between the team to and critical elements of its external environment (Marrone, 2010).

Shared Purpose Hackman (2002) distinguishes between what are termed ‘co‐acting groups’ and teams. The former are members of the same supervisory or organizational unit, but there is no shared group outcome for which they are collectively held accountable and hence they

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cannot be described as a team. Outcome interdependence (Campion, Medsker, & Higgs, 1993; Mathieu et al., 2008) is viewed as a necessary prerequisite for collective accountability (Wageman et al., 2005) and a beneficial contributor to team effectiveness (Hülsheger, Hülsheger, Anderson, & Salgado, 2009; van der Vegt, Emans, & Van der Vliert, 2001). De Dreu (2007, p. 628) notes that “(u)nder cooperative outcome interdependence, team members assume they swim or sink together and that they benefit from each other’s performance.” He found that the more members depended on each other for a collective outcome, the more information sharing went on, the more people learned from each other, and the more effective the team was overall. Beersma, Homan, Van Kleef, and De Dreu (2013) also found that teams with high outcome interdependence (in the form of shared rewards) were more engaged with their work. However, they also found that the impact of outcome interdependence on team coordination and team performance was dependent on the overall regulatory focus of the team with ‘prevention‐focused’ teams demonstrating better coordination and performance when outcome interdependence existed.

Stability On the face of it, stability in membership would appear to be necessary in order for a team to be able to develop the collective emergent states and processes that have been found to drive effective team performance (Marks, Mathieu, & Zaccaro, 2001; Mathieu et  al., 2008). Indeed, in a longitudinal study of self‐managing teams, van der Vegt, Bunderson, and Kuipers (2010) found that turnover impacted negatively on team effectiveness. However, Harrison and Humphrey (2010, p. 334) found that research into team stability “has produced equivocal results that suggest anywhere from negative, to positive, to curvilinear relationships between constant membership and effectiveness.” It is clear that contemporary teams are becoming increasingly characterized by fluctuating membership (Wageman et al., 2012a). For example, describing the characteristics of teams that are externally oriented and highly adaptive to changes in their operating e­nvironment (called ‘X‐teams’), Ancona, Bresman, and Caldwell (2009, p. 218) identify flexible membership as a key team design feature: “Whereas traditional teams often define themselves, and protect their group identity, by maintaining a stable membership, X‐teams change membership easily. New members are added and subtracted as the work changes.” It has been suggested that a reason that some teams may thrive under conditions of high member turnover is because the potentially disruptive effects of membership change in teams may be mitigated by having role stability, whereby newcomers come into an existing well‐established and clearly defined role (Choi & Thompson, 2005; Higgins, Weiner, & Young, 2012), and also through careful matching of newcomers to performance‐critical roles (Humphrey, Morgeson, & Mannor, 2009). A further challenge to membership stability as an essential team design criterion is provided by the rise in multiple team membership, whereby members divide their time between a number of different teams, such that membership is temporally unstable (Bertolotti, Mattarelli, Vignoli & Macrì, 2015; O’Leary, Mortensen, & Woolley, 2011). For example, O’Leary et al. (2011) have proposed that multiple team membership (specified in terms of the number and variety of team memberships held by individuals) is potentially harmful to both individual and team learning, and team performance. They argue that an increasing number of team memberships will reduce opportunities for integration between members, thereby negatively impacting on team learning. They further suggest that the relationship between number of multiple memberships and team productivity is likely to be inverse U‐shaped, with it initially improving team norms, but then introducing performance delays.



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The variety of multiple team memberships is seen as reducing team productivity, because of increasing information diversity and associated coordination costs, and having an inverse curvilinear relationship to team learning because the positive effects of increased information exchange are eventually countermanded by reduced analogical learning.

Composition Up to now, we have been considering the constitution of the team from the perspective of determining “what” characterizes the team, and not “who” makes up the team. Team composition is frequently depicted as a key part of team design (Morgeson & Humphrey, 2008; Stewart, 2006). Decisions about team composition have to do with determining what the attributes of team members should be in order to maximize the team’s likelihood of success (Millhiser, Coen, & Solow, 2011). Recently, Mathieu et al. (2014) have presented a taxonomy of team composition effectiveness models that compares individual‐based models with more holistic team‐level approaches. Highlighting the complexity of decision making in this area, they point out that: when a member joins a team, she brings knowledge, skills, and abilities that yield a certain fit with the position and role(s) that she occupies. She also brings a set of team‐oriented knowledge, skills, and abilities, and her features contribute to a multitude of diversity and other team profiles (p. 150).

These knowledge, skills, abilities and other characteristics, in turn, may substantially alter existing team profile and structure, triggering a range of consequential behavioral effects (e.g. creating or mitigating fault lines, strengthening or weakening team performances, improving or worsening interpersonal relationships). The area of team composition is one that is receiving increasing attention from researchers interested in providing guidance on how to ensure that teams have the right type and mix of knowledge, skills, abilities, background and experience, and is the subject of Chapter 6 in this handbook.

Structuring the Team Given the extent to which structure has been studied and found to be a critical element of organizational design, the structuring of teams as organizational subunits has been relatively under‐researched (Mathieu et al., 2008). A number of different ways of conceptualizing team structure appear in the literature, creating some confusion as to what structure actually means when applied at the team level (Bunderson & Boumgarden, 2010). For example, Morgeson and Hofmann (1999, p. 252) state “the structure of any given collective (e.g., a work team) can be viewed as a series of ongoings, events, and event cycles between the component parts (e.g., individuals).” By contrast, Stewart and Barrick (2000, p.135) define team structure as “team relationships that determine the allocation of tasks, responsibilities and authority.” Others describe team structure in terms of work design characteristics such as autonomy, team composition, and conduct norms e.g. (Hackman, 2002; Wageman, et al., 2005). In recent times, however, there has been a move to align definitions of team structure with those that have traditionally been applied at the organizational level (Bresman & Zellmer‐Bruhn, 2013; Bunderson & Boumgarden, 2010). From this perspective, team

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structure refers to features of team design that serve to shape and constrain the activities of team members. In the sections that follow, we examine two key aspects of team s­tructuring: activity structure and authority structure.

Activity Structure The design of teams is frequently described in terms of the structuring of the activities that take place within a team. Bunderson and Boumgarden (2010) identify three principal types of activity structuring: specialization, hierarchy, and formalization: Specialization refers to the horizontal division of labor (e.g. into different roles and role responsibilities), hierarchy refers to the vertical division of labor (e.g. into supervisors and subordinates), and formalization refers to the explicit articulation of procedures, priorities, and regulations that govern relations between and among these differentiated roles (p. 610).

Bunderson and Boumgarden (2010) argue, based on the literature on organizational structure (Walton, 2005), that specialization, hierarchy and formalization are co‐varying indicators of an underlying bureaucratic team structure construct. They examined the relationship between the overall extent of team activity structuring (assessed as an aggregate measure of the amount of specialization, hierarchy, and formalization) and team learning in self‐managed production teams performing relatively repetitive tasks. They concluded that increasing levels of activity structure helped create conditions that were conducive to information sharing and learning in such teams. Interestingly, they found no evidence of a curvilinear (inverse‐U) relationship between team structure and learning, which supports the view that bureaucratic controls are not necessarily dysfunctional (Adler, 2012; Adler & Borys, 1996). The context in which this study takes place is also important. The authors deliberately chose teams who were performing a relatively stable set of tasks, and suggest that the influence of structure may well be different (possibly less beneficial) in instances where the team task is characterized by higher levels of unpredictability (e.g. see Cordery, et al., 2010). In a recent study, Bresman and Zellmer‐Bruhn (2013) examined the effects of both organizational and team activity structure (also conceptualized in terms of specialization, hierarchy, and formalization) on learning in teams. Again looking at self‐managed teams, this time research and development teams, they also found that team structure had a positive influence on various forms of team learning. Despite these recent studies that suggest that increasing structural constraints over activities in teams have overwhelmingly beneficial effects, some caution is required. Different aspects of activity structure may, in some circumstances, produce non‐beneficial outcomes. For example, a study by Jones, Jones, and Kelly (2013) found that specialization of knowledge in teams can have both positive and negative consequences for members. In particular, they found that members of cross‐functional teams who possessed unique knowledge were more likely to report being left “out of the loop” in team decision making compared with members who possessed shared knowledge, leading to lower levels of psychological need fulfillment. Hirst, Van Knippenberg, Chen, and Sacramento (2011) looked at the impact of formalization on creativity in teams. They found that the impact of formalization depended on goal orientations within the team. A prove orientation was negatively associated with creativity for teams with high formalization and positively associated with creativity when formalization was low. In contrast, when formalization was low, an avoid orientation was negatively associated with creativity, whereas the relationship was not significant for teams high on formalization.



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To the extent that activity structuring (especially horizontal and vertical specialization) results in subgroup formation within teams (e.g. subgroups of people performing similar activities), this may also exert both positive and negative effects on team outcomes. For example, Cummings and Cross (2003) found that hierarchical and core–periphery structures in teams were negatively associated with team performance. Carton and Cummings (2012) suggest that the relationship between subgroup formation and team learning is likely to be inverse U‐shaped, with negative consequences at both low and high levels of subgroup formation. However, Humphrey et al. (2009) have argued that structuring a teams activities around a core–periphery distinction can be beneficial. Drawing on the sociotechnical systems literature, they define the strategic core as the: role or roles on a team that (a) encounter more of the problems that need to be overcome in the team, (b) have a greater exposure to the tasks that the team is performing, and (c) are more central to the workflow of the team (p. 50).

In a study of baseball teams, they found that teams performed better when experience and skill (and financial incentives) were concentrated in core role holders. A number of studies have examined the relationship between formalization and team effectiveness. For example, Bourgault, Drouin, and Hamel (2008) found that formalization was strongly related to the quality of decision‐making processes within g­eographically distributed project teams, and that this relationship became stronger the more team members were geographically dispersed. However, Hempel, Zhang, and Han (2012) found that formalization associated with jobs and roles within the team was negatively associated with feelings of team empowerment. Mathieu and Rapp (2009) looked at the impact of team charters, formalized statements of how activities within a team should be conducted, on team performance trajectories. They found that having a high‐quality team charter was particularly important for performance early on in the life of teams, and that charters helped enhance the beneficial effects of team performance strategies.

Task interdependence Task interdependence is a much studied feature of activity structure in teams (Mathieu et al., 2008), and often arise as a consequence of decisions regarding the specialization and differentiation of activities and tasks within teams, where the separation of activity e­lements generates explicit coordination requirements, or through the application of formalized routines that specify particular task and work flows. According to Puranam, Raveendran, and Knudsen (2012, p. 421) “two tasks are interdependent if the value generated from performing each is different when the other task is performed versus when it is not.” Within a team, task interdependence frequently refers to “the extent to which a job is c­ontingent on others’ work and other jobs are dependent on the work of the focal job” (Humphrey, Nahrgang, & Morgeson, 2007, p. 1336). Four forms of task interdependence, of increasing levels of complexity, are commonly associated with teams. Simply summarized: Under pooled interdependence, each member makes a contribution to group output without the need for direct interaction among work group members … Under sequential inter­ dependence, one group member must act before another can act … Under reciprocal interdependence, Person A’s output becomes Person B’s input and vice versa … under team interdependence, group members jointly diagnose, problem solve, and collaborate to complete a task (Saavedra, Earley, & Van Dyne, 1993, pp. 62–63).

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Thus, according to Wageman (1995); (m)anufacturing work may be designed so that individuals with distinct skills execute their part of the task‐one input into the final product‐independent of other workers. Alternatively, group members may be cross‐trained and work simultaneously and, at times, interchangeably, on completing the whole. And, finally, one might create a hybrid form in which members sometimes work alone at independent tasks and sometimes work together as a team (p. 147).

While such interdependencies are determined to some extent by the nature of technology and other team inputs (e.g. skills, resources), they are also shaped by choices made by team members and their managers. For example, Wageman and Gordon (2005) found that teams whose members possessed egalitarian values were more likely to develop highly interdependent ways of working, whilst those with meritocratic values were likely to evolve low levels of task interdependence. Task interdependence is consistently linked to team effectiveness (Campion et al., 1993; Campion, Papper, & Medsker, 1996; Gully, Joshi, Incalcaterra, & Beaubien, 2002; van Der Vegt et al., 2001). Specifically, it is held that task interdependence increases the extent to which team members must interact, cooperate and communicate with each other in the course of their work (Bailey, Leonardi, & Chong, 2010; Heath & Staudenmayer, 2000; Rico, Sánchez‐Manzanares, Gil, & Gibson, 2008; Tesluk, Mathieu, Zaccaro, & Marks, 1997). However, the effects of task interdependence on team performance have been found to interact heavily with other aspects of team design and context. For example, Stewart and Barrick (2000) found that the influence of task interdependence on performance depended on the nature of tasks. For conceptual tasks, task interdependence exhibited a positive and linear relationship, whereas for manual tasks, a U‐shaped relationship was found. Performance at low and high levels of interdependence was significantly worse than at moderate levels of task interdependence. Somech, Desivilya, and Lidogoster (2009) found that, at high levels of team identity, task interdependence tended to produce cooperative responses to conflict, whereas where team identity was weak, task interdependence was more likely to be associated with competitive responses. Finally, Maynard, Maynard, M­athieu, Rapp, and Gilson (2012) looked at drivers of global virtual team effectiveness. They found that task interdependence interacted with participation of members in the team, such that, at low levels of interdependence, participation was negatively related to effectiveness. Finally, activity structuring can result in teams being organized along functional or divisional lines (Hollenbeck et al., 2002). Functionally structured teams are those that contain people who perform similar tasks and roles, while divisionally structured teams are those which contain people who serve a particular market or geographic region, or who generate a particular product or service. Hollenbeck et  al. (2002) suggest that functional teams tend to be characterized by more specialized roles and higher levels of interdependence, whereas divisional teams typically contain roles that encompass a broader range of tasks and responsibilities and lower levels of interdependence. Dimotakis, Davison, and Hollenbeck (2012) provide evidence to suggest that functionally structured teams do better on tasks that have a prevention focus (i.e. maintaining the status quo) and divisionally structured teams do better on promotion‐focused tasks (i.e. being proactive).

Authority Structure The structuring of activities within a team can be differentiated from the allocation of decision‐making authority (although the two are not entirely independent: the creation of hierarchy or vertical specialization frequently implies some degree of centralization).



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Two main considerations arise here. First, how much autonomy, and what kind, should be provided to the team as a whole? Second, what should be the distribution of authority within the team? In early taxonomies, teams were frequently differentiated in terms of the degree of autonomy they were assigned. For example, teams have been described as falling along the following autonomy continuum: manager‐led, self‐managing, self‐leading, self‐designing, and self‐governing (Hackman, 1986; Manz, 1992; Stewart, Courtright, & Manz, 2011). Terms such as (semi‐) autonomous, empowered and self‐regulating teams have also been used to describe collective entities with enhanced decision‐making authority. Generally speaking, the types of decision that teams may have authority over include those to do with the regulation of the work itself (e.g. scheduling, resourcing, methods), independence (e.g. making strategic decisions about what work is to be done and why), and self‐governance (e.g. determining membership of the team, deciding who becomes the leader) (Cordery, 1996; Manz, 1992; Susman, 1976). Studies of the impact of team autonomy on team effectiveness have generally concluded that increased team autonomy is a positive thing, both in terms of team performance and the satisfaction and wellbeing of its members (Cordery, Mueller, & Smith, 1991; Stewart, 2006; Wall, Kemp, Jackson, & Clegg, 1986). In sociotechnical systems approaches, the conferral of autonomy to groups is linked to the notion of controlling key variances as close as possible, both temporally and locationally, to their point of origin, such that they are not transferred across organizational boundaries (Cherns, 1976). However, Stewart (2006)’s meta‐ analysis also concludes that considerable variability exists in the strength of the autonomy‐team performance relationship across different working environments and types of work, suggesting that there are significant contextual influences at play. Supporting this view, Cordery et al. (2010) studied the impact of an increase in team autonomy on the performance of teams of process operators in the wastewater treatment industry. They found that increasing autonomy did improve performance, but that the extent to which performance increased depended on the degree of uncertainty associated with the team task. In other words, the return on team autonomy appears to be much better in uncertain contexts. Hyatt and Ruddy (1997) found that the relationship between team autonomy and job motivation was moderated by interdependence, such that the positive relationship became less strong as task interdependence in the team increased. The idea that team autonomy can have differential effects is also highlighted by Rico, Molleman, Sánchez‐Manzanares, and van der Vegt (2007), who examined diversity faultlines in teams and their impact on team decision‐making quality and social integration and concluded that team task autonomy enhanced the benefits of team diversity (e.g. diverse expertise facilitating the development of innovative, high quality problem solving) where teams had weak faultlines, but that this same team task attribute enhanced negative aspects of team functioning when faultlines were strong (e.g. encouraged the development of subgroups, leading to poorer quality decisions). Finally, Haas (2010) has found that team autonomy and use of external knowledge interact positively to influence team performance. The impact of team autonomy was more positive when the knowledge was scarce, task uncertainty was high, the knowledge sources lay outside the organization, and when task pressures were high. Can team autonomy end up being too much of a good thing (Pierce & Aguinis, 2013)? There do indeed appear to be some potential downsides with high levels of team autonomy. High levels of team autonomy have been associated with negative consequences for both individual members and the organization as a whole (Barker, 1993; Levy, 2001). For example, Langfred (2007) argues that self‐managing (autonomous) teams are particularly susceptible to conflict, which in turn drives teams to alter their activity and authority structures in the direction of lower individual autonomy and reduced interdependence.

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What about the allocation of authority within the team? Two elements appear relevant here. The first is the extent of centralization of decision making, and the second is the distribution of autonomy between individuals and teams. Centralization at the team level has been defined (Hirst et al., 2011, p. 626) as “the extent to which within‐team decision authority lies solely with a team’s leader (decision making is centralized) or is shared between leader and members (decision making is decentralized and participative).” Sparrowe, Liden, Wayne, and Kraimer (2001) argue that decentralized networks are more effective because members share control over joint outcomes and ordinarily cannot receive maximum benefits unless they cooperate in their production. Hollenbeck, Ellis, Humphrey, Garza, and Ilgen (2011) use structural contingency theory (SCT; Burns & Stalker, 1961) to suggest that centralized structures in teams may be more efficient, because a central decision maker may have a more holistic perspective on the task environment, may be well placed to ensure the dissemination of knowledge throughout the team, & may also provide a reliability check that reduces decision‐making errors. However, they also suggest that these potential benefits of centralization of decision‐making authority may be countermanded by a tendency for centralized decision making to be less adaptable, slower, and less likely to foster team learning. Crawford and LePine (2013) expand on these potential disadvantages: It is also clear, however, that centralized taskwork structures create peripheral member dependence, which reduces both these members’ possibilities for action and their willingness to perform at optimum levels (Shaw, 1964). In a centralized group, peripheral members readily perceive that the central person is autonomous and controls the group with respect to task focused contributions and interactions. This reduces peripheral members’ satisfaction and  motivation by inhibiting the gratification of culturally supported needs for autonomy, recognition, and achievement” (p. 38).

Crawford and LePine (2013) also suggest that centralization will increase the risks that the decision maker will suffer information overload and, consequently, this may distort the information that they pass on. However, these contradictory indications for centralized decision making in teams can be reconciled to some extent by distinguishing between centralization in team taskwork and teamwork. Taskwork “involves members’ interactions with tasks, tools, machines, and systems to carry out the team’s work” (Crawford & LePine, 2013, p. 34), whereas teamwork refers to “member interactions to direct, coordinate, and monitor taskwork in order to achieve collective goals.” It is suggested that that centralization of taskwork will lead to higher team effectiveness, but only when paired with decentralized teamwork. They also propose that the centralization of highly interdependent (multiplex) taskwork and teamwork networks will be negatively associated with team effectiveness. Hirst et al. (2011) found that the impact of centralization on team member creativity depended to a great extent on the learning orientation of team members. For example, the relationship between learning orientation and creativity was only positive when there was low centralization, which also enhanced the negative relationship between an “avoid” learning orientation and creativity. Finally, Langfred (2000) highlighted the need to c­onsider individual and group autonomy separately when designing teams, because of the potential of each to have different effects on team cohesiveness. Langfred (2005) examined both individual and team autonomy and their impact on team performance. He found that individual and team autonomy interact in their effect on team performance, such that optimum team performance resulted when either individual autonomy was high and team autonomy was low, or when team autonomy was high and individual autonomy low.



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Creating a Supportive Context “If a well‐designed work team is a seedling, then the organizational context is the soil in which it is planted, the milieu that provides the nutrient needed for it to grow and bear fruit” (Hackman, 2002, p.133). While team constitution and structure are of vital importance to assure a successful team, there is also a need for team design to be aligned with the internal and external context in which the teams will operate (Cohen & Bailey, 1997; Hackman, 2002; Kirkman, Lowe, & Gibson, 2006; Kozlowski & Bell, 2003; Mathieu et al., 2008; Sundstrom et al., 1990). Two broad categories of contextual influence on team design have been identified by (Zellmer‐Bruhn & Gibson, 2006): those that operate at the team level (micro context), and those that operate at the level of the organization and its environment (macro‐context). Mathieu et al. summarized the key characteristics of the micro‐macro contextual distinction as follows: “micro‐contexts really describe team‐level inputs, whereas macro‐contexts refer to sources of influence that stem from a higher level of analysis” (2008, p. 454). The team‐level ‘micro‐­context’ involves aspects of the team context that “are often tailored to specific team needs, are likely to vary among teams within a subsidiary or firm” (Zellmer‐Bruhn & Gibson, 2006, p. 502).

Examples include leader behavior, technology availability and localized human resource management practices (e.g. recruitment, rewards). The macro‐context includes features of the organization and the broader environment that provide a similar context for all teams within an organization (Gibson & Dibble, 2013; Zellmer‐Bruhn & Gibson, 2006). E­xamples include (at the organizational level) organizational structure, culture, resources, strategy, and human resource management policies and practices, as well as (external to the organization), societal culture, industry characteristics and market competition.

Team Micro‐Context A number of different micro‐contextual influences on effective team design have been identified over the years. In this section, we briefly describe research relating to team l­eadership it supports the constitution and structure of teams.

Team leadership Perhaps the most frequently studied aspect of the team context relates to the ways in which external team leaders need to act in order to support and sustain teams (Burke, DiazGranados, & Salas, 2011; Yammarino et al., 2012). For example, Burke et al. (2006) identify links between commonly‐studied leader behaviors (e.g. initiating structure and consideration, transformational and transactional leader behavior, boundary‐spanning) and the elements of effective team design identified by Hackman (2002). Their meta‐analysis concluded that task‐focused leader behaviors, which included transactional leadership behavior, initiating structure and boundary spanning, contributed positively to the effectiveness and productivity of teams. Person‐focused leadership, including transformational leader behavior, consideration, empowerment, reward and recognition, was equally positively associated with effectiveness and productivity outcomes, and also with team learning. This and many other studies (e.g. Chen, Kirkman, Kanfer, Allen, & Rosen, 2007; DeRue, Nahrgang, Wellman, & Humphrey, 2011; Liu & Batt, 2010; van Ginkel & van Knippenberg, 2012) provide support for the general notion that the behavior of external team leaders is a critical factor in the capacity of team designs to deliver benefits to o­rganizations and their members.

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A more focused approach on the leadership requirements of teams has also emerged, initially through research examining the ways in which team leaders act to empower teams, both structurally and psychologically (Kirkman & Rosen, 1999; Mathieu, Gilson, & Ruddy, 2006; Maynard, Gilson, & Mathieu, 2012; Seibert, Wang, & Courtright, 2011; Wageman & Fisher, 2014). Researchers have identified five specific aspects of leader behavior that facilitate team empowerment (Arnold, Arad, Rhoades, & Drasgow, 2000; Srivastava, Bartol, & Locke, 2006). They are participative decision making, coaching, i­nforming, and showing concern for/interacting with the team. Hackman and Wageman (2004) take a more holistic and functional perspective on team leadership, proposing that: When there is room for the leader to maneuver, then his or her response to three questions strongly shape performance outcomes: (1) what kind of team to create; (2) how to structure the team; and (3) how and when to actively coach the team as it proceeds with its work (p. 52).

Elaborating on this, Morgeson, DeRue, and Karam (2009) identified 15 leadership key functions seen as necessary in order to support effective team‐based work at both transition and action phases in the performance cycle (Marks et  al., 2001). These include leader behaviors designed to (transition phase) compose the team, define its mission, establish expectations and goals, structure and plan its activities, train and develop the team, assist in sense‐making, and provide performance feedback. Other necessary leader functions (action phase) include monitoring, managing boundaries, challenging the team, helping the team perform, solving problems, providing resources, encouraging team self‐management, and supporting a positive social climate. Specific functional aspects of team leaders’ support for teams that have been studied, include those focusing on selecting the right mix of people to be on the team (Millhiser et al., 2011; Morgeson, Reider, & Campion, 2005), and coaching (Hackman & Wageman, 2005). Development of team members by means of leader coaching is seen as being particularly important for team effectiveness, given that its influence potentially spans both the transition and action phases of the team performance cycle. Generally, coaching by the leader has been found to be beneficial for team effectiveness, though not always so and so contextual factors appear to be important (Mathieu et al., 2008). For example, DeRue, Barnes, and Morgeson (2010) examined the relative impact of coaching leadership versus more directive leadership on team member effort and performance. They found that the effectiveness of leader coaching depended on whether or not the leader was perceived to be charismatic and whether employees had high levels of self‐efficacy. In the latter case, a directive style of leadership was more effective than coaching when team members’ self‐ efficacy was high. Hackman and Wageman (2005) have also argued that the importance of leader coaching may fluctuate according to the team’s stage of development. They proposed a temporal framework which identifies “motivational” coaching as important at the beginning, “consultative” coaching at the midpoint, and “educational” coaching at the end of a team’s performance cycle. They further argue that any beneficial impact of coaching is crucially dependent of how well the team has been constituted and structured. Research into the role played by external leaders must be considered alongside another body of research and theorizing that focuses on absorbing and distributing leadership functions within the team itself (Carson, Tesluk, & Marrone, 2007; Day, Gronn, & Salas, 2004; Yammarino et al., 2012). Shared leadership is defined as “an emergent and dynamic team phenomenon whereby leadership roles and influence are distributed among team members (D’Innocenzo, Mathieu, & Kukenberger, 2014, p.5). The recent meta‐analysis by D’Innocenzo et al. (2014) concluded that the relationship between shared leadership



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and team performance was overwhelmingly positive, suggesting that the emergence of leadership within teams is able to effectively substitute for some elements of external l­eadership (and potentially to compensate for the lack of supportive external leadership). It should be noted that the potential for shared leadership to develop in teams is likely to  be constrained and by bureaucratic features of team structure, such as hierarchy (vertical specialization) and centralization.

Organization Macro‐Context Mathieu et al. (2008, p.454) describe organizational contextual variables as “sources of influence that are external to the team, yet emanate from the larger organizational system within which they are nested.” Although the organizational context can be described in many different ways, we focus here on how team working is affected by human resource systems, organizational strategy and structure, and organizational climate and culture.

Human resource management systems It has long been recognized that particular “bundles” of human resource practices at the level of the organization have the potential to produce positive outcomes for organizations and those who work within them (e.g., Gong, Law, Chang, & Xin, 2009; Jackson, Schukler, & Jiang, 2014; Jiang, Lepak, Hu, & Baer, 2012). A collective or team‐based approach to work organization is frequently identified as an important element of human resource systems that seek to enhance employee motivation, involvement, commitment and performance (Armstrong et al., 2010; Gittell, Seidner, & Wimbush, 2010; Jiang et al., 2012; Pfeffer, 1998). Richter, Dawson, and West (2011) carried out a meta‐analysis of studies linking team working to organizational effectiveness. They found that teams were more likely to have an impact on a broad range of effectiveness indicators when accompanied by human resource management practices that supported team work. Particular configurations of human resource systems may either facilitate or hinder the use of teams as a work design strategy. For example, Youndt and Snell (2004) and Lepak and Snell (2002) suggested that organizations that emphasized a ‘collaborative’ human resource system which breaks down both vertical (i.e., hierarchical) and horizontal (i.e., cross‐functional) barriers are likely to create a supportive and trusting organizational environment, whereby less traditionally structured team designs (i.e., cross‐functional team, self‐managed work team, virtual team) can be implemented. In contrast, organizations that employ a “productivity‐ based” human resource system, which is focused on “buying” rather than “developing” human capital, are less likely to invest in employees development and encourage their commitment to the organization’s long‐term success. Consequently, these employees are less committed to the organization and more likely to leave. Team design in these organizations tends to be more structured, in order to facilitate more rapid replacement (Lepak & Snell, 2002). Team designs appear more likely to emerge in organizations emphasizing a “commitment‐based” human resource system (Chadwick, Super, & Kwon, 2014), or where those systems are seeking to promote flexibility and adaptability (Chang, Gong, Way, & Jia, 2013).

Organizational strategy and structure According to Zellmer‐Bruhn and Gibson (2006, p. 503), “strategy strongly affects team context, given that corporate strategy is tightly intertwined with structure and management.” Organizations that adopt a strategy focusing on innovation are more likely to employ a

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decentralized and less hierarchical organizational structure, in which more team autonomy and knowledge sharing can be facilitated (Hempel et  al., 2012; Kaplan, 2011). Zellmer‐ Bruhn and Gibson (2006) found that multinational organizations whose strategies emphasize local responsiveness were associated with higher levels of learning in teams, whereas strategies centered on global integration tended to result in lower levels of team learning. An organization’s strategy influences the ways in which teams are structured through its effect on organizational configurations and associated management practices. In that sense, team structure “serves as a bridge between organization‐level strategy decisions and staffing decisions” (Hollenbeck et al., 2002, p. 600). Bresman and Zellmer‐Bruhn (2013) examined the relationship between structure and the organizational and team levels, as it relates to team learning. They found that bureaucratic structural elements at the organizational level tended to have a negative impact on team learning to the extent that they constrained task autonomy. However, they also found that increased structuring at the organizational level actually facilitated team learning when there was an absence of such structure at the team level.

Organizational climate and culture Organizational culture and climate (Schneider, Ehrhart, & Macey, 2013) also appear to be relevant features of the context that determine whether team‐based work is likely to emerge and be successful. Organizational climate encompasses “shared perceptions regarding formal and informal organizational policies, practices, and procedures among organization members” perceptions that are likely to make particular aspects of work organization (such as working collaboratively in teams) more salient than others (Morgeson, Dierdorff, & Hmurovic, 2010, p. 355), and also affect the extent to which team members perceive that a supportive climate exists at the level of the team itself (Bashshur, Hernández, & González‐Romá, 2011; Somech & Drach‐Zahavy, 2013; Tesluk, Vance, & Mathieu, 1999; Wright, Barker, Cordery, & Maue, 2003). For example, Mathieu, Maynard, Taylor, Gilson, and Ruddy (2007) found that an ‘openness’ organizational climate was positively associated with more effective team processes. Within the much‐studied competing values framework of organizational culture, team‐based working may be seen as one of the artefacts of a ‘clan’ culture (Hartnell, Ou, & Kinicki, 2011).

Environmental Context Mathieu et  al. (2008, p. 454) define environmental contextual variables as “sources of influence that emanate from outside of the organization yet influence team functioning.” While it has long been recognized that the external environment has a profound influence on teams (Ancona, Bresman, & Kaeufer, 2002; Choi, 2002), empirical research in this area remains sparse (Gibson & Dibble, 2013). Among the studies that have incorporated environmental factors, the focus has tended to be on sociocultural influences, market dynamics, and rate of technological change (e.g., Keck & Tushman, 1993; Kirkman & Shapiro, 1997, 2001; Qian, Cao, & Takeuchi, 2013). In this chapter, we focus on two key environmental elements: cultural context and environmental dynamism.

Cultural context Culture may be defined as “the collective programming of the mind which distinguishes the members of one human group from another” (Hofstede, 1980, p. 25). Although c­ultures exist at many different levels below the national level (i.e., organizational, departmental and



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occupational cultures), it is argued that national cultural values are a highly salient source of individuals’ identity and hence exert a fairly constant influence on their behavior in o­rganizational settings (Gibson & Dibble, 2013). A number of studies suggest that national culture is likely to be an important influence on the successful design, deployment and functioning of teams (Kirkman et  al., 2006; Taras, Kirkman, & Steel, 2010). For example, collectivistic cultural orientations have been associated with more cooperation and positive attitudes towards teams, whereas individualistic cultural orientation are linked to the potential for increased conflict within and b­etween teams (Bell, 2007). Different levels of acceptance of teams across collectivistic and individualistic cultural value systems “may help to explain why team efforts often fail in highly individualistic countries such as the US” (Kirkman et al., 2006, p. 308). Gibson’s (2003) study of 71 US and Indonesian nursing teams demonstrated that collectivistic national cultural values influence nursing team’s performance in terms of quality of s­ervice, such that Indonesian teams exhibited significantly higher quality of service as compared with US teams. Researchers have also theorized that specific cultural values may foster employee o­pposition to particular features of team designs (Gibson & Gibbs, 2006; Kirkman & Shapiro, 1997, 2001). For example, it has been suggested that teams in high‐power distance cultures, which are characterized by hierarchical structures and clear lines of authority, are less likely to feel comfortable working in highly autonomous or loosely structured teams (Kirkman et al., 2006; Kirkman & Shapiro, 1997, 2001). Cheng, Chua, Morris, and Lee (2012) argue that employees from: cultures with low uncertainty avoidance tend to be more comfortable in the absence of clear structures and a formal leader … Such individuals are better able to meet the demands of  interdependence, coordination, and trust among culturally different team members in self‐managing teams (p. 393).

They also suggest that employees from high relationship orientation cultures may also have a culture linked preference for working in teams.

Environmental dynamism Environmental dynamism refers to the extent to which a firm faces an environment that is predictable and stable or changing and uncertain. Prior research into organizations’ dynamic capabilities has emphasized the importance of technological and market turbulence (Barreto, 2010). Technological turbulence refers to the rate of technological change, and market turbulence refers to the degree of instability and uncertainty within a firm’s markets (Helfat et al., 2007; Jaworski & Kohli, 1993). Dynamism in the environment is likely to influence the way organizations structure their teams. For example, organizations operating in a highly turbulent technological and market environment (e.g., information technology, biomedical technology, telecommunication, and data services) are likely to have to modify their products, services and processes continually in order to satisfactorily cater to customers’ changing preferences and high uncertainly in the market. These constant changes require teams to have greater knowledge management, information processing capabilities in order to effectively deal with complexity and uncertainly. This, facilitated by increasingly sophisticated digital technologies, has led to the evolution of new team‐like forms and structures. Tannenbaum et al. (2012) note that “the term team has also come to be used to refer to many different forms of collectives  –  some of which are arguably not teams in the traditional sense” (p. 21). These include virtual teams, (Malhotra, Majchrzak, & Rosen, 2007), global virtual teams

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(Gibson et  al., 2014), multi‐team systems (Zaccaro, Marks, & DeChurch, 2012), and organizational communities of practice (Kirkman, Mathieu, Cordery, Rosen, & Kukenberger, 2011). For example, organizational communities of practice are hybrid collective forms that share the defining characteristics of both communities of practice and teams, and which enable people to collaborate on knowledge management across wide geographical and temporal boundaries (Kirkman, Cordery, Mathieu, Rosen, & Kukenberger, 2013).

Future Research Looking to the future, we see a need for more focused research into how teams are designed, particularly given the increasing range of team‐like forms that are emerging in response to environmental change and turbulence (Wageman, Gardner, & Mortensen, 2012b). Four particular avenues for research appear particularly promising. First, it is clear that there is a trend for teams to become much less bounded and more permeable (Tannenbaum et  al., 2012). The increasing frequency of multiple team m­embership and the development of large team‐like knowledge management collectives such as organizational communities of practice, which frequently have very flexible and permeable boundaries, raises something of an existential question as to the limits to “u­nderboundedness” in teams. Just how permeable can a team boundary become before the team ceases to be capable of generating meaningful collective identities, goals, and performance? Researchers have begun to explore the ways in which these more open and  fluid collective forms impact on individual, team and organizational effectiveness (e.g. Cordery et al., 2015; Mortensen, 2014), but much more research is needed. Second, recent research into activity structuring portrays bureaucratic structure in teams as having a linear positive relationship with team effectiveness. Logically, however, there is likely to be a point at which such structuring ceases to be enabling, and instead becomes coercive and constraining (Adler, 2012; Adler & Borys, 1996; Barker, 1993), and engenders dysfunctional team states and processes, like faultlines. More research is needed into the boundary conditions that determine activity structuring’s effect on teams. Furthermore, while activity structuring may help teams as a whole perform better, what are the effects on individuals within those teams? What is good for collective performance may not enable individuals within those teams to learn and thrive, and may in fact generate less meaningful jobs on an individual level. Research into potential cross‐level effects of team structuring on individual empowerment, development and psychological wellbeing is needed to address such issues. Third, the increasing delayering of organizations (Hassard, Morris, & McCann, 2012) is leading to more and more decision‐making authority being pushed down to the team level. On one hand, this increases the opportunities for teams to make full use of their human and social capital in pursuit of higher performance levels, but on the other it increases the risk that teams will become isolated from their environment and develop work habits that are not effective (Haas, 2010), and also increase the job demands experienced by individual team members. Future research could investigate the nature of these risks, which are likely to include potential threats to the wellbeing of individual team members, in greater detail, and determine ways in which they can be effectively mitigated. Finally, as Mathieu et  al. (2008) have pointed out, contemporary organizations are hugely more complex than those that existed several decades ago, when teams first began  to become popular in organizational settings. For example, researchers have begun to unpack the ways in which organizational structure and team structure interact



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(Bresman & Zellmer‐Bruhn, 2013), but there is still much to learn about how the increasing dynamism and relational complexity evident in contemporary organizational forms (Bechky, 2006; Shipilov, Gulati, Kilduff, Li, & Tsai, 2014; Worley & Lawler, 2010) i­nfluences team‐level structures, and vice versa (e.g. Harvey, Peterson, & Anand, 2014).

Conclusion At the outset, we highlighted some of the challenges that face both managers and researchers in designing teams, in the face of unprecedented technological, geopolitical, social and economic change. Looking back over the research we have reviewed, it is clear that research to date offers considerable guidance to managers and team members as to what key elements of team design to focus on. In their constitution, teams require clear but permeable boundaries, some stability (if not in personnel, then in role composition), and a shared purpose and accountability at the team level. Teams also benefit from an activity structure that results from some specialization of tasks, decentralized decision making and a degree of formalized routines. Authority structure is also important, with teams benefiting from increased levels of autonomy for the team as a whole, and the decentralization of decision making within the team. Finally, it is important to locate teams within a supportive leadership environment, characterized by functional leadership activities such as coaching, and to align the design of teams with features of the organization and its operating environment. In conclusion, much has already been learned regarding what it takes to design a team but, in these times of rapid social, technological and economic change, much remains to be discovered in the changing teams landscape. As Wageman et al., (2012b) suggest, this may require us to relax our definitions of what constitutes a team and to open up more fully to the exploration of the changing ecology of teams.

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6

Team Composition Mikhail A. Wolfson and John E. Mathieu

Introduction Teams are commonly accepted as the basic building blocks of organizations (Ilgen, Hollenbeck, Johnson, & Jundt, 2005; Kozlowski & Ilgen, 2006; Mathieu, Maynard, Rapp, & Gilson, 2008). In fact, longitudinal surveys of Fortune 1000 firms have shown a consistent increase in the use of team‐based structures, from less than 20% in the 1980s to roughly 50% in the 1990s to over 80% in the 2000s (Garvey, 2002). Kozlowski and Bell (2003) defined teams as collectives: who exist to perform organizationally relevant tasks, share one or more common goals, interact socially, exhibit task interdependences, maintain and manage boundaries, and are embedded in an organizational context that sets boundaries, constrains the team, and influences exchanges with other units in the broader entity (p. 334).

Kozlowski and Bell emphasize several dimensions: the presence of shared goals, a degree of interdependence and a context. Given these attributes of a team, a team is only as good as the members that comprise it. In this chapter, we summarize research and advance­ ments in team composition. After this, we discuss the benefit of incorporating network theory and methods, in particular meta‐networks and multiplex ties, as a means of o­vercoming shortcomings of conventional team composition approaches, and model the complex nature of teams.

Teams and Team Types Even though when we hear the word “team” we inherently picture a group of people working together, the definition of the term “team” has become one that is rather diffuse. In addition to being an integral and increasingly popular way to accomplish work, The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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teams can come in many forms. For example, Sundstrom, De Meuse, and Futrell (1990) proposed a nine‐dimension model consisting of (1) the team’s industry, (2) the level of organizational hierarchy of the team, (3) the education level of team members, (4) the scope of team activities, (5) the degree of member autonomy, (6) the routinization of activities, (7) the amount of time members work together, (8) the degree of skill differentiation, and (9) the difficulty of evaluating team performance. Sundstrom et al. (1990) also generated four more general team types using their nine dimensional model. In their narrative review of the literature of teams, Cohen and Bailey (1997) proposed an alternative classification framework distilling four types: 1) project teams; 2) traditional work teams; 3) parallel teams; and 4) management teams. While some overlap can be seen among the different types specified by the Sundstrom et al. (1990) and Cohen and Bailey (1997) frameworks, they are difficult to synthesize and use meaningfully in practice or research. This remains the case for other typologies that have been offered (e.g., De Dreu & Weingart, 2003; Devine, Clayton, Philips, Dunford and Melner, 1999). Indeed, Hollenbeck, Beersma, and Schouten (2012) explored the various team types described in the management literature since the late 1980s and identified 42 different types of teams. Although the teams studied in the various articles were distinguishable in their type from one another, the more salient issue was how one would compare teams across all of these different team types. In order to manage the plethora of team types they identified, Hollenbeck et  al. (2012) proposed a three‐dimensional scaling model for team description. In their model, Hollenbeck and his colleagues build on traditional either/or categorical systems in order to create a scaled model where teams would have scores along three distinct team dimensions. First, skill differentiation explores the degree of members’ specialized knowledge or functional capacities that affect the difficulty of member substitution. Second, authority differentiation explores the degree to which one member, subgroup, or the collective as a whole has decision‐making responsibility. Third, temporal stability explores the degree of past and subsequently expected experience individuals within the team have working with one another. Interestingly, the skill differentiation dimension refers to the diversity of knowledge, skills, and abilities required to perform the various team positions and/or for the team to be successful overall. The author­ ity differentiation refers to workflow processes and the zone of influence or discretion afforded to different team positions or members. The temporal stability refers to the volatility in the system, and the extent to which individuals can develop work routines and familiar work patterns versus having to focus on performing their individual roles and accommodating new members. Importantly, the shift from considering team types to considering the different performance requirements and pressures on teams helps to develop a more refined and nuanced framework for aligning team composition. In the following section, we discuss research on team composition that explores advances in how teams are combined, and the value of considering individuals’ competencies in not  just task‐oriented, but also team‐oriented knowledge, skills, abilities and other c­haracteristics (KSAOs).

Team Composition Team composition is the configuration of member attributes and characteristics within a team (Levine & Moreland, 1990), and is thought to have powerful influences on team processes and outcomes (Kozlowski & Bell, 2003). According to Bell (2007) “Team composition is of interest to both researchers and practitioners as a means of



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increasing team performance because of the potential ease in manipulating team c­omposition through selection and placement” (p. 595). Given the interdependent nature of teams, team members must engage in a number of team processes or “inter­ dependent acts that convert inputs to outcomes through cognitive, verbal, and behavioral activities directed toward organizing taskwork to achieve collective goals” (Marks, Mathieu & Zaccaro, 2001; p. 357). The extent to which individuals within a team have greater task specific, and generic teamwork oriented capabilities, should lead to greater effectiveness in team functioning and performance (Mathieu, Tannenbaum, Donsbach, & Alliger, 2014). Researchers have consistently linked various aspects of team composition to team performance. In a relatively recent meta‐analysis, Bell (2007) linked the various person­ ality dimensions within teams to team performance. Conscientious team members engage in behaviors associated with goal completion and problem solving (Stewart, Fulmer, & Barrick, 2005), as well as backing up team members (Porter et al., 2003). Agreeable indi­ viduals tend to be more effective at interpersonal facilitation (Hurtz & Donovan, 2000) and seek to maintain social harmony while reducing within‐group competition (Graziano, Hair, & Finch, 1997). Extraverted individuals tend to display greater levels of attraction toward their teams (Kristof‐Brown, Barrick, & Stevens, 2005), and are more likely to seek help from other team members when needed (Porter et al., 2003). Emotionally sta­ ble individuals contribute positively to teamwork, which enhances team performance (Mount, Barrick, & Stewart, 1998), and create a relaxed atmosphere that promotes cooperation (Reilly, Lynn, & Aronson, 2002). And finally, open individuals can be more adaptable and make the necessary changes to continue in dynamic team environments (LePine, 2003). In addition to personality traits, other individual characteristics have consistently been linked to increased team performance. General mental ability can lead to the development of beneficial team processes related to coordination, such as shared mental models (Edwards, Day, Arthur, & Bell, 2006), and has been consistently linked to team performance (e.g. Devine & Phillips, 2001; Stewart, 2006). Emotional intelligence which has its roots in older concepts such as social intelligence (Thorndike, 1920) highlights the importance of skills needed to understand and experience emotions in a more adaptive manner (Salovey & Mayer, 1990). Emotional intelligence can influence how teams respond to stimuli that elicit emotion (Druskat & Kayes, 1990), and positive emotional reactions can lead to positive processes such as helping behavior (George, 1990). In addition to stable individual characteristics, member tenure and familiarity may have salient effects as teams pursue multiple goals over time (Marks et al., 2001). Teams have a history and a future (Brannick & Prince, 1997) which ultimately influence current behavior (Hackman, 1992; McGrath, 1990, 1991).

Team replacement scenario In order to make our discussion a bit more concrete, let us offer an example. Consider the following situation. You have a cross‐functional product development team that is about to lose one of its members, the members and replacements characteristics can be seen in Table 6.1. Donna, from operations, has good product knowledge, is an average team builder, and has great availability to take on new work. In your current team, two of the members have excellent availability, while two of them do not. Each of the mem­ bers are from different departments; operations, marketing, information technology (IT), and accounting. Two of the existing members have average to below average p­roduct knowledge, while two have great product knowledge, and two members have

132

Antecedents to Team Effectiveness Table 6.1  Team member and replacement characteristics. Availability (1–5)

Product Knowledge (1–5)

Interpersonal Skills (1–5)

Current members Alfred Operations Beth Accounting Cynthia IT Donna Operations Elliot Marketing

1 5 4 5 2

5 2 3 1 4

2 2 3 5 5

Potential replacements Sam Operations Jack Marketing Marie Operations Thomas IT

2 5 4 2

5 2 5 5

3 5 1 5

Name

Department

Values represent amount of given characteristic, with 1 indicating lowest, and 5 indicating highest.

excellent interpersonal skills while two do not. In terms of the potential replacements there are the following choices: •• Sam from operations has limited availability, excellent product knowledge, and average interpersonal skills. •• Jack from marketing is completely available, has moderate product knowledge, and excellent interpersonal skills. •• Mary from operations, has great availability, excellent product knowledge, but has terrible interpersonal skills and bad relationships with the current members. •• Thomas from IT has limited availability, excellent product knowledge, and excellent interpersonal skills. Choosing the replacement for Donna becomes an increasingly arduous task as we consider which aspects of the member characteristics are most important. Furthermore, the task becomes even more complicated if we consider multiple members leaving, and the possi­ bility of positions, roles, and members’ affective ties. Research and practice suggest that having the optimal mix of individuals in a team should lead to greater team functioning and outcomes. In particular, the mix of individuals’ KSAOs contributes significantly to their team’s effort, coordination, and ultimately performance (Bell, 2007; Ilgen, 1999). In their practitioner oriented chapter on optimizing team composition, Mathieu, Tannenbaum, Donsbach and Alliger (2013) interviewed a number of team composition subject matter experts and offered six different types of team composition decisions that would dictate which approach and considerations should be taken into account when staffing teams. One theme among these decisions is whether one is replacing one or more members of an existing team, or constituting a new team. A second theme involves how many or what percentage of the team needs to be staffed. The simplest type of decision was the one illustrated in our earlier example – determin­ ing a replacement for a single leaving member. These types of composition decision are likely to be easier, as they only concern one member, but may also offer greater variety in solutions then a first glance may reveal. While it may be tempting to attempt a “replacement in kind” by finding an individual that is most similar to the outgoing member, doing so may not always be necessary or optimal. An analysis of the KSAOs within the team may



Team Composition

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reveal that there are only a few key considerations that need to be replaced and/or that remaining members may have sufficient competencies to meet team task demands. The second type of decision, multiple member replacement, may pose a greater challenge, but may also offer a greater opportunity for a more innovative replacement strategy. Whereas there is increased difficulty in the number of factors to be considered, and the number of characteristics to be replaced, there is increased flexibility in a combination style approach rather than a “replacement in kind” or replacement in two kinds. We can begin to consider the mix of competencies among multiple new members or how, perhaps, tasks might be redistributed. For example, we could consider replacing the outgoing compe­ tencies with a combination of individuals. If we are losing two members, one of whom has great product knowledge, limited availability, and good interpersonal skills, while the other has great product knowledge, great availability and also good interpersonal skills, we can identify which competencies are key to replace. In this case, both individuals have great product knowledge and interpersonal skills, so perhaps we do not necessarily need both of the replacements to exhibit these characteristics. Furthermore, when replacing multiple members, we can redistribute role allocation, based on the distribution of KSAOs within the current and incoming members. The final type of decision, new member distribution, accounts for all of the difficulties of multiple member replacement, while including the added complexity of considering the value of heterogeneous versus homogeneous distribution of human capital. Mathieu et al. (2013) provide the example of allocating new state police officer academy graduates to geographic regions. In this scenario, strategic decisions have to be made concerning whether each region needs the same quality of officers, or whether some regions have a higher demand. Additionally, special units such as a special weapons and tactics team may require the most capable graduates, so that would affect allocation of members, and would naturally lead to a heterogeneous distribution of human capital. Notably, the Mathieu et al. (2014) framework does not differentiate task‐ and team‐ focused competencies. However, in our earlier example concerning replacing Donna, an emphasis on team‐focused KSAOs may skew the favor away from Marie, and toward Jack or Thomas, whereas focusing exclusively on competencies such as product knowledge would skew the favor away from Jack and toward Sam, Marie or Thomas. The literature on team composition indicates that team processes and effectiveness are affected by aspects of the group’s composition such as members’ skills, job and organizational experience, as well as the group heterogeneity as a whole (Mathieu et al., 2008). In exploring team composition, Kozlowski and Klein (2000) described two types of aggregation processes by which KSAOs combine to form a higher‐level variable such as team composition. In compositional processes, simple combination rules such as averages of team member competencies represent team human capital, and all of the lower‐level entities’ contributions are presumed to be equally important. In contrast, “in compilation models, the higher‐level phenomenon is a complex combination of diverse lower‐level contributions” (Kozlowski & Klein, 2000, p. 17). Thus, we draw the distinction between the two as compilation models taking on more complex integrations of individuals’ KSAOs in a way that allows for weighting for individuals’ abilities and contributions. In their review of the literature, Mathieu et al. (2014) categorized team composition frameworks along two dimensions: 1) model, and 2) focus. Within individual‐based models, one stream of research emphasizes an individual focus and explores individuals’ KSAOs such as cognitive ability or conscientiousness as related to job or position require­ ments. The other embraces a team focus, and explores the contributions of team members’ generic KSAOs such as cooperativeness or team orientation and team effectiveness. In both cases, however, the focus is upon individuals’ KSAOs aligning with requirements.

134

Antecedents to Team Effectiveness

Within the team‐based models one research stream embraces an individual focus and explores the relative contribution of individual members’ KSAOs such as the weakest member, or the cooperativeness of the most central member. Meanwhile, the other research stream embraces a team focus, and explores the distribution of KSAOs in the over­ all mix of individuals within a team, such as the average experience, or functional diversity. In both of these cases, however, attention shifts to the distributions, variances, extreme scores, or unique scores in member distributions. In other words, salience is placed on the collective and relative scores among members in a team and how they relate to team functioning.

Individual‐based models with individual focus Individual‐based models “focus either on individuals and job requirements, or on mem­ bers’ generic team‐related KSAOs” (Mathieu et al., 2014, p. 132). This approach derives from traditional personnel psychology and human resource frameworks, and hinges on the existence of discernible positions in a team, against which individuals’ KSAOs fit can be measured. Mathieu et  al. (2014) refer to these first set of models as the traditional p­ersonnel–position fit models, where the approach is to optimize the fit between individ­ uals’ KSAO profiles and the positions they occupy. In effect, this is an “all‐star” model where the assumption is that having the best individuals in different positions will enable a team to be effective (Heslin, 1965). Research has found support for traditional personnel‐position fit models. Cooke et al. (2003) indexed members’ specific position‐based knowledge, and found it to relate p­ositively to overall team effectiveness. In another empirical study, Offerman, Bailey, V­asilopoulos, Seal, and Sass (2004) found that individuals’ performance effectiveness ignificantly correlates with team effectiveness. Additionally, Harris, McMahan, and s­ Wright (2012) found that average individual position talent relates positively with team performance, especially when members have overlapping team tenure. While undoubtedly team members need to be capable at their positions, it is important to consider the value of team‐generic KSAOs, such as interpersonal skills from our example about replacing Donna.

Individual‐based models with team focus The other individual‐based model, referred to as the personnel model with teamwork c­onsiderations (Mathieu et  al., 2014), explores the contributions of individuals’ team‐ generic KSAOs as related to team effectiveness. Cannon‐Bowers, Tannenbaum, Salas, and Volpe (1995) discuss team‐generic competencies as being universally valuable across team situations. In a similar vein, Baker, Salas, and Cannon‐Bowers (1998) proposed that i­ndividuals’ competencies in terms of providing feedback, cooperating, commu­ nication, promoting team morale, adaptability, accepting feedback and coordinating their efforts with others, are generic team‐focused KSAOs that would be beneficial across teams. There has been considerable support for the personnel model with teamwork consider­ ations. Morgeson, Reider, and Campion (2005) found team members’ teamwork knowledge provided incremental predictive validity over traditional personality and a situation judgment test in predicting individual contextual performance in team settings. Similarly, Hirschfeld, Jordan, Feild, Giles and Armenakis (2006) found that team m­embers’ teamwork knowledge significantly predicted team effectiveness, above that of task profi­ ciencies. Additionally, Mumford, Van Iddekinge, Morgeson, and Campion (2008) showed



Team Composition

135

that members’ team role knowledge provided incremental validity over that of traditional personnel predictors of individuals’ teamwork behaviors. Finally, Mathieu, Tannenbaum, Kukenberger, Donsbach and Alliger (2015) developed measures in individuals’ role p­ropensities in teams and demonstrated their relationships with performance in teams.

Team‐based models with individual focus Team‐based models adopt a holistic view of individuals’ KSAOs that allow for the consideration of more complex combinations or team profiles of KSAOs. These approaches, which derive from social psychology and organization behavior and consider the team as a whole, rather than a collection of individuals in positions. In exploring team‐based models with an individual focus, Mathieu et al. (2014) referred to these models as relative contributions models, where the contributions of certain members are valued more than others. The logic here derives from Steiner’s (1972) work, in which he described disjunctive tasks, where the team performance can be carried by a single talented individual (e.g., decision making), and conjunctive tasks (e.g., personal computer component assembly) where the team performance is limited by the slowest member at any given stage in the process. At issue is that certain individuals (e.g., the strongest or weakest, respectively in the exam­ ples above) have a greater influence on overall team performance than do others, and therefore should be weighted more highly in any composition formula. Empirical evidence has also supported relative contributions models. In their meta‐analysis, Devine and Philips, (2001) found that both the highest and the lowest member cognitive ability c­orrelated positively with team performance. Configurations of members’ characteristics may also relate to team outcomes. For example, Belbin (1981, 1983) advanced a theory of nine distinct team role types: 1) idea generator; 2) resource investigator; 3) coordinator; 4) shaper; 5) monitor evaluator; 6) team worker; 7) implementer; 8) completer/finisher; and 9) specialist. Belbin’s team role model has been associated with both team behaviors and performance (for a recent review see Aritzeta, Swailes and Senior, 2007). Belbin, and others, have argued that successful teams that have all nine roles present, although individuals may display mul­ tiple roles, will be more balanced, which will result in higher levels of success. In other words, he advocates for a diverse team where members fulfill all nine role requirements. Meanwhile, Pearsall and Ellis (2006) advanced a position‐based theory. They found that the assertiveness of the most “critical team member” related positively to team performance and member satisfaction. In a similar vein, Humphrey, Morgeson, and Mannor (2009) provided empirical support suggesting that the experience of m­embers of “core” roles contributes more strongly to team outcomes than those of members in “peripheral” roles.

Team‐based models with team focus In contrast to relative contributions models, team profile models adopt a collective perspec­ tive by focusing the attention at the team level, and can be said to index composition in terms of descriptive statistics of members’ KSAOs. While relative contribution models explore the relative weighting of KSAOs for certain positions, the primary distinction among these models is that the KSAOs are considered collectively rather than exploring individuals’ fit with a specific position. Barry and Stewart (1997) found that the promotion of members with high extraversion regardless of their role, related in an inverted‐U fashion with cohesion. Kearney, Gerbert, and Voelpel (2009) found that team members’ average need for cognition offset the

136

Antecedents to Team Effectiveness

n­egative effects of team age and educational diversity on team processes and outcomes. In a similar vein, Bell, Villado, Lukasik, Belau, and Briggs (2011), in a meta‐analysis, argued that diversity effects should be examined in terms of specifics and contexts, and were able to show that functional and educational background diversities had positive effects on team performance, particularly so in the case of creativity and innovation.

Shortcomings of conventional team composition approaches Adopting selected score models whereby the most competent member or the weakest link has undue influence on the groups functioning, or aggregation based models that embrace an average of some competency in the team, have successfully predicted variance in team processes and outcomes (Mathieu et  al., 2014). But they are lacking in other respects. For  example, these models either ignore or oversimplify the distinct contributions of i­ndividuals within a network of interactions and communications. Humphrey and his colleagues explore team composition models that identify individuals whose particular contributions may be more salient to team success than others. Humphrey et al. (2009) first explored the idea of core and peripheral members of a team in the context of Major League baseball, proposing that the relationship between various job‐related skills and performance was stronger when the skills were concentrated in core members as opposed to non‐core members. In other words, their approach explicitly rejects the notion that all  members’ contributions should be weighted equally, and places a premium on the performance of a subset of members. Humphrey et al. (2009) embraced the complexity of team composition by proposing an unbalanced weighting of contributions, but continued to focus on a specific set of skills across all of the given roles. Mathieu et al. (2014) advanced an example of a surgical team and demonstrated that collective performance was attributable to: 1) the relative KSAOs that members’ possessed per position; 2) the presence of collective positive team orienta­ tion across all members; 3) the relative importance of some positions (e.g., surgeon) v­ersus others; and 4) the interdependencies among different member pairings. They suggested that adopting a social network approach would allow researchers to explore not only the weighting mechanisms of individual’s contributions toward team performance but also embrace the complexity of a variety of KSAOs being particularly salient at different p­ositions in an organization’s or team’s network.

Networks: A Bridge across Typologies, Roles, Positions, and Team Types Research on team types has come a long way in trying to establish a common lens through which researchers can compare and study teams. Hollenbeck et  al.’s (2012) three‐ dimensional scheme for depicting key features of teams provides a useful unifying approach. For example, along their lines, our example long‐term project team would likely have moderate levels authority differentiation, temporal stability and skill differentiation. Understanding the team task demands provides guidance as to the relative salience of dif­ ferent quadrants of Mathieu et al.’s (2014) team composition framework. More specifi­ cally, the nature of task demands highlights whether one’s focus should be primarily on optimizing the performance of individuals in roles versus the average and distributions of team members’ collective KSAOs, or some other approach. Although Mathieu et  al.’s framework suggests that different quadrants may be at a premium for different types of teams,



Team Composition

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any given application is likely to benefit from a blending or combination of considerations. Consequently, we recommend that such composition decisions be driven by both a systematic analysis of the team task demands (see Lee, Koopman, Hollenbeck, Wang & Lanaj, 2015) in combination with subject matter experts’ insights as to the value of unique blends of members’ talents. Consider the earlier example of replacing Donna in the product development team. The  information provided allows us to adopt many different models to choose a replacement. If we seek a “replacement in kind” where we try to best replicate Donna’s abilities, we could adopt either a traditional personnel‐position fit approach by focusing on product knowledge or a personnel model with teamwork considerations approach by focusing on interpersonal skills. If we continue to focus on the importance of Donna’s specific role, then perhaps there is a specific type of KSAO that is particularly salient to her position, so that one should take precedent, and a relative contributions model can be utilized. Or, per­ haps we can consider the aspects more holistically, and adopt a team profile model to explore the distribution of the different KSAOs across all existing members. But what if we were to consider bridging the gaps across these dimensional divides rather than choos­ ing one of the quadrant solutions? Utilizing social network analysis (SNA) would allow us to explore all of the different KSAOs in a holistic manner, while simultaneously accounting for the complex interdependent nature of the team. We visually depict the various networks that are present in our example in Figure 6.1. In the top network, we simply have the members of the current product development team, and in networks a through f, we show individuals’ availability, product knowledge, interpersonal skill, interdependence, familiarity and affect networks, respectively. Net­ works a, b, and c represent agent‐by‐attribute networks, and can be considered as align­ ing with the individual focus models of team composition effectiveness from Mathieu et al. (2014), whereas networks d, e, and f represent agent‐by‐agent networks, and can be considered team focused models, with network f showing the affective ties between individuals in the team, rather than their ties to the interpersonal skill attribute overall (network c). The added level of complexity in network f over network c allows us to explore more complex interpersonal relationships in our team, which may have mean­ ingful implications during member replacement. When we consider the models individ­ ually, we do not encounter a great deal of complexity, but when we consider all of the factors simultaneously, as seen in the meta‐network in Figure 6.2, or in networks with multiplex ties (Crawford & LePine, 2013), the reality is very c­omplex in Figure  6.3. Table 6.2 depicts a matrix of interdependent ties for our example network; Table 6.3 depicts the matrix of affective ties, and Table 6.4 depicts the matrix of familiariy ties, with greater values indicating stronger ties. So far, we have considered the members in the current team, and their respective KSAOs, but we have not yet featured the potential replacements. In Figure 6.4 we depict an agent‐by‐attribute meta‐network including both the original members of the product team, as well as the potential replacements with their links to the various attributes from network c in Figure  6.1. By employing this meta‐network approach, we can begin to explore the multiple roles individuals may occupy in a team along many different dimen­ sions, both individual‐ and team‐focused, as well as from a team model approach (Mathieu et al., 2014). While we have chosen to depict interpersonal skills as a characteristic of the individuals, one could also decide to explore the interpersonal ties between each of the members, current and replacements alike. In this case, the level of complexity increases dramatically, and the use of social network analysis becomes vital and allows us to explore not just the individuals and their KSAOs, but also the distribution of members’ KSAOs (i.e., node attributes) within the team’s network.

138

Antecedents to Team Effectiveness Individuals Alfred

Elliot

Agent by knowledge networks

Beth

Donna

a. Agent x availability

Agent by agent networks

Cynthia

d. Interdependence ties Alfred

Availability

Alfred

Elliot

Beth

Donna

Elliot

Cynthia

Beth

Cynthia

Donna

e. Familiarity ties

b. Agent x product knowledge

Alfred

Alfred

Product knowledge

Elliot

Elliot

Beth

Donna

Beth

Donna

Cynthia

c. Agent x interpersonal skills

Cynthia

f. Affective ties Alfred

Alfred

Elliot

Beth

Elliot

Beth

Interpersonal skills Donna

Figure 6.1  Network complexity.

Cynthia

Donna

Cynthia



139

Team Composition Table 6.2  Interdependent ties matrix. Alfred Beth Cynthia Donna Elliot Alfred Beth Cynthia Donna Elliot Sam Jack Marie Thomas

0 0 5 2 4 3 5 1 5

5 0 5 5 3 4 5 1 5

5 4 0 2 1 2 5 1 5

5 3 4 0 1 3 5 1 5

5 4 2 2 0 5 5 1 5

Sam Jack 3 4 2 3 5 0 5 1 5

Marie Thomas

5 5 5 5 5 5 0 1 5

1 1 1 1 1 1 1 0 3

5 5 5 5 5 5 5 3 0

Values represent strength of ties, with 0 indicating absence of a tie, and 5 indicating strongest tie.

Table 6.3  Affective ties.

Alfred Beth Cynthia Donna Elliot

Alfred

Beth

Cynthia

Donna

Elliot

0 4 1 5 3

4 0 3 2 4

1 3 0 4 5

5 2 4 0 4

3 4 5 4 0

Values represent strength of ties, with 0 indicating absence of a tie, and 5 indicating strongest tie.

Table 6.4  Familiarity ties.

Alfred Beth Cynthia Donna Elliot

Alfred

Beth

Cynthia

Donna

Elliot

0 5 4 1 1

5 0 4 5 2

4 4 0 2 2

1 5 2 0 5

1 2 2 5 0

Values represent strength of ties, with 0 indicating absence of a tie, and 5 indicating strongest tie.

Network Measures as Related to Team Composition In the following section, we discuss network measures as they relate to team composition. One of the core concepts of network analysis is centrality, which explores how focal a given entity (i.e., member or role) or attribute is in a network. In our replacement net­ work, we can consider how central an individual is in the network, or alternatively, we can consider the centrality of an attribute node such as availability, product knowledge, or interpersonal skills. Both considerations play prominently in the suitability of different replacement strategies, even in this simple example. We begin by discussing the following terms which are summarized in Table 6.5: density (an overall network measure), degree centrality, betweenness centrality, eigenvector centrality, and closeness centrality. Our first measure, density, is an overall network measure that captures the number of ties within a network relative to the number of possible ties. There is an inherent tradeoff with

Alfred Product knowledge

Availability

Elliot

Beth

Interpersonal skills Donna

Cynthia

Figure 6.2  Agent by knowledge meta‐network. Affective ties Familiarity ties

Alfred

Alfred

Interdependence ties

Elliot

Beth

Donna

Elliot

Beth

Cynthia

Cynthia

Figure 6.3  Agent by agent network with multiplex ties. Members’ ties Replacements’ ties

Marie

Sam Alfred

Alfred Product knowledge

Availability

Elliot

Jack

Beth

Interpersonal skills

Donna

Marie

Sam

Thomas

Product knowledge

Availability

Elliot

Jack

Beth

Interpersonal skills

Cynthia

Figure 6.4  Agent by knowledge meta‐network with replacements.

Thomas

Cynthia



141

Team Composition

Table 6.5  Network concepts and team composition. Measure

Definition

In‐team context

Density

Number of ties, relative to the number of possible ties.

Degree centrality Betweenness centrality

Node with the most connections. Node with the most best paths.

Eigenvector centrality

Node connected best overall. Node connected to other highly connected nodes. Node that is closest to all other nodes.

How well connected individuals within the team are to one another. Can lead to higher interpersonal affect, but can lower efficiency as a result of redundancy. Individual through whom a lot of work/ communication goes. Someone who can take on a gatekeeper or brokering role, or is able to bridge connections between groups. The leader, or leader of leaders. Someone who can influence important others.

Closeness centrality

Measure how long it takes for information to disseminate.

increasing network density in terms of integration versus differentiation. Higher density is representative of more interconnected individuals, easier dissemination of information, and increased redundancy in the event that an individual is removed. In  effect, higher network density yields greater network integration. However, higher levels of network density suggest a greater number of redundancies and limit members’ specialization. N­aturally a balance needs to be struck between specialization and integration in order to capitalize on the benefits of teams (Lewis & Herndon, 2011). In the case of our replacement example, with our meta‐network consisting of many networks (department, availability, product knowledge, and affective ties), there may be tradeoffs associated with increased network density as well. For instance, in the case of the department network, having every­ one cross‐trained or being a member of every department would facilitate integration of their efforts, but may simultaneously limit specialization, be costly, and ultimately be unnecessary. Alternatively, in the case of our affective ties network, having greater overall network density is likely to be beneficial, as it could lead to greater affective commitment and cooperation. Numerous network indices capture the features of individual nodes, rather than the overall characteristics of the network. Degree centrality, captures the extent to which a node has more connections with other nodes. More colloquially, this identifies the node that is the hub or represents the crossroads of the team. In contrast, if we consider the degree centrality of an individual’s attribute we can consider the presence of redundant, or absence of crucial KSAOs in our team. If we are considering the various agent‐by‐attribute networks, we can realize that in replacing Donna, we do not necessarily need an individual with high product knowledge, or from the operations department, and can instead focus on individuals with high availability or interpersonal skills. By realizing that there is a high level of degree centrality for the product knowledge node, and a lower degree centrality in the availability and interpersonal skill nodes, we can isolate which KSAOs are particularly important in a replacement. In exploring the final three centrality measures, we refer to Figure 6.5. Consider that our product development team has to work closely with another related product team. From Figure 6.5, we can see that Alfred and Frank have strong interdependence ties with one another, and that Donna has weak interdependence ties with Gail, Holly, and Juliette. The remaining members from the two product teams do not have any interdependence

142

Antecedents to Team Effectiveness

Alfred

Frank

Juliette

Gail

Ivan

Holly

Elliot

Beth

Donna

Cynthia

Figure 6.5  Interdependence ties network.

ties or, for the sake of this example, do not interact. The next centrality measure, betweenness centrality, captures the extent to which a node has the greatest number of best paths, or connects groups. If we examine the network in Figure 6.5, we can see that Alfred and Donna are the only individuals from our original product development team who can bridge connections to the other team. However, of the two of them, Alfred has to always go through Frank, which means that any communication to individuals from Frank’s team, are always an additional step away. In contrast, Donna, is connected to Gail, Holly and Juliette, and can interact with them directly, and can reach Frank and Ivan through any of her three connections. As a result, we can see that Donna, has the highest betweenness centrality. Unlike betweenness centrality, eigenvector centrality is concerned with nodes that are most connected to other highly connected nodes, rather than all nodes. In other words, it reflects the connectedness of the key contributors in the team. In this case, we can see that Frank and Alfred have extremely strong interdependence ties with their respective net­ works as well as with each other. In contrast, Donna has moderate interdependence ties within her own team and weak ties to the other team. Furthermore, the individuals that Donna is connected to have extremely weak ties to the other members in the network, with the exception of Frank. As eigenvector centrality can help identify individuals who can mobilize others, we can see that Alfred and Frank are in key positions to disseminate information and coordinate action. Finally, closeness centrality measures the sum of distances to all other nodes. Shorter con­ nection chains are associated with faster and more accurate communications than longer chains. Consider the case in which we need to disseminate immediate information across our product development teams, who would be the ideal individual to handle the task? We know from our network that we would likely be choosing between Donna and Alfred, as they are the two individuals who bridge the gap between our two product development teams. From the discussion of our previous measures, we also know that Alfred has stronger ties as a result of his strong interdependence tie to Frank. However, closeness centrality, unlike eigenvector centrality, focuses on the speed of dissemination of information. In our network, we know that Donna can access every member other than



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Frank or Ivan with one link and the remaining two with two links. Even though Alfred has stronger ties with the individuals with influence, in the event of disseminating time sensitive information, we would be inclined to seek Donna’s help. These are just some of the more basic social network indices associated with team com­ position. As we consider the role of replacing a team member or compiling a team, adopt­ ing an SNA approach could inform our staffing decisions. For example, if we consider our multiplex network where we can realize that, as we seek a replacement for Donna, we need to focus on specific interpersonal or affective relationships, as she was in a potential boundary spanner role within the larger organizational network. Furthermore, we might realize that focusing on a replacement in kind, by attempting to find an individual from the same department with identical product knowledge, may not be particularly salient, while replacing interpersonal skills and availability would be key. One of the greatest benefits of a SNA approach to team composition is that it allows us to consider pattern of interpersonal interdependence, affective, or familiarity within our teams and organizations. Rather than getting one omnibus measure of these inherently dyadic and interpersonal aspects, we can understand which individuals are positioned to embrace certain roles and facilitate communication and work flows. In the case of Donna, we see that her position in the larger organizational network, as represented in Figure 6.5, places her in a key boundary spanning position. If Donna is removed from our network, we are faced with only one means through which the two teams can interact. In replacing Donna, we would like to ensure that the replacement would have strong interper­ sonal skills, and availability, as they would likely step into a key boundary spanner role, and  ensure redundancy in the event that Alfred is unavailable to engage in cross‐team communication.

Networks and Team Composition Moving Forward What does all of the member churn, team composition and incorporating an SNA approach tell us moving forward in terms of team processes and outcomes? When we have individ­ uals leaving a group, or are consider recombining members into different groups, adopting an SNA approach allows us to consider the composition more holistically and affords greater flexibility in the composition process. But SNA can be utilized in many other aspects relating to team processes and outcomes. Consider the phenomena of field‐based learning (FBL), which is defined as “the non‐curricular development of knowledge, skills, and wisdom that is predominantly self‐ directed, intentional, and field‐based” (Cerasoli et al., 2014, p. 6). FBL is a more specific version of informal learning that occurs in the workplace that is intentional, volitional, and targets organizationally valued outcomes. Individuals engage in FBL through seeking feedback from others, experimenting, and observing their coworkers. In order for individuals to engage in FBL, they need to be in an environment that is conducive to learning, but also be surrounded by the correct individuals from whom they can learn. As FBL inher­ ently requires learning from or observing your peers, a lack of churn in team members may lead to a stagnation in knowledge. As some individuals leave and are replaced, new knowledge would likely become available to the remaining team members through o­bservation and seeking feedback. We can adopt an SNA approach to understanding the conditions under which FBL would occur. We can observe a meta‐network of individuals’ knowledge attributes that may be particularly salient for other members. For example, if the individuals who are leaving the team are replaced with individuals with identical knowledge and experience

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(i.e., the “clone” approach) it may facilitate a more seamless transition, but the team does not benefit from the infusion of new knowledge or perspectives. In contrast, if the replace­ ments have a novel set of knowledge and experiences, they can provide access to unique resources for the remaining members but may have more difficulty assimilating. In addition to knowledge, it is important to consider the various agent‐by‐agent ties that exist in the team and how they would change as a result of member churn. If the outgoing member was the “social glue” (high degree centrality in affect ties) that held the team together, then replacing that individual with someone with a great knowledge base may not be enough, given their unique role in the team’s network. It is also likely that it will be difficult for a newcomer to occupy the social glue role – necessitating that other members step‐up and assume that role. Moreover, if the individual who has the unique knowledge is isolated or has low closeness centrality in interdependence or familiarity ties then, it would be difficult for the other team members to reach out to her to seek feedback, or observe her work. While the previous examples highlight the potential value of member replacement for FBL, they do not account for the other dimension of FBL; experimentation. In order for individuals to feel comfortable enough to engage in experimentation, they would need to be in a climate that is accepting of mistakes and encourages individuals to step out of their comfort zones (Katz‐Navon, Naveh, and Stern, 2009). In this case utilizing a meta‐net­ work with multiplex ties would allow us to isolate key positions in the network where an influx of new knowledge could have the most positive effect. In an ideal scenario for FBL, we would like to see the novel knowledge to be present in an accessible area of the net­ work. More specifically, if we consider the same agent‐by‐attribute and agent‐by‐agent networks from our earlier examples, we would want the individual to have high degree or closeness centrality in a large number of the networks. In terms of the agent‐by‐agent networks, having the individual who is high in degree and closeness centrality in the affective ties network makes him or her more accessible for other people. As the affective ties would be stronger with this person, other members would feel safer stepping out of their comfort zone and be more likely to seek feedback and experiment. Having the individual with novel knowledge occupying a position that maximizes the familiarity ties may also be beneficial, for the same reasons as affective ties. However, at an extreme level, there may be a drop off, with overly familiar individuals having less to learn from one another. Additionally, with interdependent ties, there may a curvilinear relationship, as generally, higher interdependence may lead to great ease for observation but, at extremes may be indicative of limited availability which would hinder the ability to provide feedback. In addition to the agent‐by‐agent networks, the centrality or strength of ties an individual has to given attributes will be extremely important. If we were to have different types of knowledge rather than just product knowledge, the distribution of tie strength and degree centrality of a novel knowledge node may be a key component for FBL to occur. If an individual has a strong tie to a knowledge node with low degree centrality this would be indicative of unique knowledge. In addition to knowledge, the strength of the individual’s tie to availability may play a key role in their ability to provide feedback. Finally, a strong tie to the interpersonal skills attribute would likely be beneficial for indi­ viduals to provide feedback in a meaningful way, and to be comfortable with observation, and contributing to an environment that is safe for experimentation. However, each of these components of the meta‐network and the various agent‐by‐ agent ties, have more value in combination that alone. As we seek to determine where in the network we need an influx of knowledge, or how to replace a key member, it will be



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the interaction of many of these measures that will allow us to understand the complex effect of replacing an individual has on the interactions and presence of knowledge within a network.

Future Research We have focused on how embracing a social network approach inform team composition decisions in both member replacement and initial team formation. Given that social net­ works can inform team composition and functioning, there are fruitful areas of research to consider. Future studies could systematically explore the cost of turnover in teams and its impact on important organizational outcomes. For example, Hausknecht & Holwerda (2013) suggested five characteristics that influence productive capacity and collective performance: (1) leaver proficiencies, (2) time dispersion, (3) positional distribution (4) remaining member proficiencies, and (5) newcomer proficiencies. In exploring these characteristics, researchers can implement SNA in order to explore the network positions of individuals using meta‐networks and multiplex ties to better understand the nuanced effects that outgoing and incoming members would have on the functioning of the current team. Adopting an SNA approach would simultaneously allow researchers to explore agent‐by‐attribute and agent‐by‐agent networks, which consequently allows for the explo­ ration of the effect of loss of knowledge in specific key positions rather than just focusing on net loss. Future research could employ SNA to identify which key positions in the network of a team or an organization could benefit from the infusion of any of the key roles. In addition to member turnover, there are many ways to consider team composition. Mathieu et al. (2015) suggest that members’ experience and orientations can combine to yield predis­ positions to occupy six different team roles. These six roles include organizers, doers, challengers, innovators, team builders, and connectors. One of the benefits of their approach to team composition is that a single individual can potentially take on multiple team roles, so an individual could simultaneously be an organizer and a doer. For example, an individual who has strong affective ties with other members could be particularly effec­ tive at embodying the challenger (or “devil’s advocate”) role. Given the nature of the challenger role, having strong affective ties would allow for the challenger’s use of dialectical inquiry to be seen as beneficial to the group and lead to better ideas rather than as a combative act. Future research could also adopt an SNA approach to explore how centrality attributes can differentially predict individuals’ propensity to engage in beneficial team processes. Marks et al. (2001) suggest that team monitoring and backup behavior is a key action process that ensures effective team functioning. For individuals to be able to engage in effective backup behavior, they would need to be aware of what their fellow team members are engaged in, and have the capability to assist or replace them. In these instances, having some level of redundancy in competencies and interdependence may be essential. If indi­ viduals have high‐degree centrality in the interdependence network, and have similar strength ties to the relevant competency attribute nodes, then they may be in a position to more readily engage in backup behaviors. Finally, future research could employ SNA in predicting emergent leadership within a team. By identifying factors that facilitate emergent leadership in conjunction with network position in terms of key agent‐by‐agent ties, researchers can pinpoint not only the likelihood of emergent leadership but also the location in the network where it would be most effective.

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For example, individuals with high betweenness or eigenvector centrality would be more efficient in mobilizing individuals toward action, or swaying key members’ opinions. Although we have suggested only a few potential future research directions here, imple­ menting a social networks approach may be particularly beneficial in any situations where an omnibus measure is not sufficient in explaining underlying processes within a team.

Conclusion In this chapter, we have provided a summary of theory and research on teams and team composition. We contribute to the literature by proposing a social network approach to team composition as the logic and methodology would allow researchers to explore the complex nature of teams. Utilizing social networks can allow researchers to answer impor­ tant questions about team composition, process, and outcomes. Exploring the combination of agent‐by‐attribute meta‐networks can highlight glaring areas of need or areas of ineffi­ cient excess in terms of member KSAOs. In contrast, agent‐by‐agent networks can allow researchers to identify individuals who are primed take on key roles such as boundary spanners. Furthermore, the presence of multiplex ties in agent‐by‐agent networks can allow researchers to realize unique combinations of interpersonal ties that are particularly salient to teamwork. For example, combinations of interdependent and affective ties, could place emphasis on key dyadic relationships for collaborative work. In addition to exploring the aforementioned agent‐by‐agent and agent‐by‐attribute networks in isolation, combining the two types of networks into larger more complex meta‐networks can capitalize on the value of individuals’ KSAOs at specific locations within a network. As we seek to improve team processes such as affect management, indi­ viduals who are available, with strong interpersonal skills, and with high affective tie degree centrality would be prime candidates to ensure that a team continues to function effectively.

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7

Team Diversity Bertolt Meyer

Introduction Teams are the building blocks of modern organizations (Mathieu, Tannenbaum, Donsbach, & Alliger, 2014) and have become the most common form of organizational collaboration (Salas, Cooke, & Rosen, 2008). Their relevance in organizations is mirrored in the fact that the field of organizational behavior is increasingly concerned with researching groups and teams (Humphrey & Aime, 2014). Given that the success of organizations depends largely on the performance of teams, the processes occurring in teams that ultimately affect the outcomes of teamwork have been subject to decades of research in multiple fields, including work and organizational psychology, management, organizational behavior, social psychology, and communication, to name only a few. In many of these fields, teamwork is investigated through the lens of the input–process–output (IPO) model, which is considered the most dominant conceptual framework in team research (Ilgen, Hollenbeck, Johnson, & Jundt, 2005; LePine, Piccolo, Jackson, Mathieu, & Saul, 2008; Salas et al., 2008). Within the IPO model and within its further development, the input–mediator–output–input (IMOI) model (Ilgen et al., 2005), teamwork is conceptualized as an interaction of input, process, and outcome variables. One of the relevant input variables is team member diversity, which is assumed to have a substantial impact on team processes, “interdependent acts that convert inputs to outcomes through cognitive, verbal, and behavioral activities directed toward organizing taskwork to achieve collective goals” (Marks, Mathieu, & Zaccaro, 2001, p. 357). Processes specify how inputs are transformed into outcomes, which can be divided into performance (e.g., quality, quantity, safety) and team members’ affective reactions (e.g., commitment; LePine et  al., 2008). In sum, research on team processes has treated team diversity as a central input to team processes and, therefore, team performance. More recent research has also started to investigate team members’ health as another outcome of team processes (Wegge, Roth, Neubach, Schmidt, & Kanfer, 2008). The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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Thus, research on team diversity has a long history (Pfeffer, 1983) and has been carried out on different organizational levels. On the individual level, researchers addressing relational demography have investigated possible consequences of team diversity on individual team members. For example, they investigated the impact of differences b­etween individual team members and their team on individual‐level outcomes (Williams & O’Reilly, 1998). Team‐level research on team diversity investigates the impact diversity on team‐level process and outcomes, such as team conflict, team social integration, and team performance (e.g., Harrison, Price, Gavin, & Florey, 2002; van Knippenberg, De Dreu, & Homan, 2004; van Knippenberg & Schippers, 2007). Finally, research on the organizational level investigates the relationship between organizational diversity and organizational outcomes, usually performance (e.g., Dwyer, Richard, & Chadwick, 2003). In this review, I focus on the individual and the team level, as psychological processes mediating between interindividual differences and outcomes are usually conceptualized on these levels. The research pertaining to organization‐level diversity has been reviewed elsewhere (Shore et al., 2009). Finally, recent models on the effects of team diversity place an emphasis on contextual effects on multiple levels and, therefore, conceptualize the effects of team diversity from a multilevel perspective, where individual‐, team‐, and organization‐level effects interact and shape the outcomes of team diversity (Guillaume et al., 2014; Joshi & Roh, 2009). Before I review the models and empiric findings pertaining to the effects of team diversity, I would like to draw attention to the different ways in which researchers have justified the study of the consequences of team diversity in the past. In general, research on the consequences of team diversity can be grouped in two distinct discourses: The business perspective on diversity and the equality perspective on diversity (for a review, see van Dijk, van Engen, & Paauwe, 2012). In a nutshell, the business perspective assumes that, ultimately, diversity has the potential to increase team performance and, therefore, advocates supporting diversity “as a means to achieve, ultimately, organizational profit” (van Dijk et al., 2012, p. 73). However, at least implicitly, this value‐in‐diversity hypothesis allows the deduction that if diversity had no or negative consequences for profit, it would be appropriate to not advocate it. This argument stands in strong contrast to the proponents of the equality perspective on diversity (e.g., Zanoni, Janssens, Benschop, & Nkomo, 2010), which advocates that organizations have a moral obligation to ensure equal participation of the diverse members of society in the labor market, and that the absence of organizational diversity results in the exclusion of members of underrepresented or minority groups from employment opportunities. It is important to note that the studies on team diversity that make up the mainstream organizational behavior literature – and the ones reviewed in this chapter – are exclusively based on the value‐in‐diversity h­ypothesis. This is not to endorse this view on team diversity – it is merely a reflection of the m­ainstream in organizational behavior research.

Consequences of Team Diversity Globalization, urbanization, migration, demographic changes, and more employment opportunities for women are only some of the reasons for the increasing heterogeneity of industrialized societies and their labor markets (Abu‐Laban & Gabriel, 2002). Given that increasingly globalized organizations recruit from these heterogeneous labor markets, and given that teams are the building blocks of organizations, teams are becoming increasingly diverse. Therefore, the growth of the body of research on team diversity is a response to a concrete societal development.



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The remainder of this chapter reviews the literature on team diversity by following the predominant theoretical developments in a chronological order. Starting from the bi‐ theoretical approach of the social categorization and information/decision making approach of the 1990s, I proceed with proposing that diversity research can be categorized into different partially overlapping research streams. One stream, which appears to be particularly dominant in US institutions, attributes different values to different attributes, types, or dimensions of diversity, and contrasts, for example, the effects of psychological or deep‐level diversity with demographic or surface‐level diversity. Another stream of research, which seems to be more rooted in a European research tradition, advocates that any type of diversity can have both positive and negative consequences, depending on contextual and moderating variables. Finally, there is also a stream of research which investigates the consequences of diversity perceptions, while yet another research stream focuses on faultlines and subgroups. Here, researchers investigate the distribution of s­everal diversity attributes in teams. I review these four areas of research on team diversity before elaborating on more recent developments that include multilevel conceptualizations of team diversity and status perspectives. The chapter closes with the elaboration of some evident gaps in research on team diversity and the resulting potential future research opportunities. Over time, different researchers have offered different definitions for team diversity. It has been defined as “any attribute people use to tell themselves that another person is different” (Williams & O’Reilly, 1998, p. 81) and as “the distribution of differences among the members of a unit with respect to a common attribute” (Harrison & Klein, 2007, p. 1200). These definitions differ with regard to the importance placed on the subjective perceptions of team diversity by team members, an issue that I address below. While the first definition at least implicitly includes perceptions (“tell themselves”) within the definition of the construct, the second definition conceptualizes diversity as objective differences without any references to the team members’ perceptions. In this chapter, I therefore employ a definition that strikes a middle ground, i.e., which acknow­ledges that differences might be perceived: “Diversity refers to differences between individuals on any attribute that may lead to the perception that another person is different from self” (van Knippenberg et al., 2004, p. 1008, emphasis added).

The Bi‐theoretical Approach to Team Diversity In their review, Williams and O’Reilly (1998) summarized more than 40 years of empiric research on team diversity. They concluded that it is primarily based on two underlying theories that make different predictions regarding the effects of diversity: The social categorization/similarity attraction perspective and the information/decision making perspective. The social categorization perspective combines self‐categorization and social identity theories. Self‐categorization theory (Oakes, Turner, & Haslam, 1991; Turner, Hogg, Oakes, Reicher, & Wetherell, 1987) proposes that individuals categorize themselves as belonging to a specific social group (the in‐group) in contrast to others, who are categorized as belonging to the out‐group, if differences between perceiver and target become salient. The salience of differences with regard to a given attribute (e.g., gender, personality) depends on the importance of the attribute to the perceiver (the normative fit), on the inclination of the perceiver to access the cognitive representation of the attribute (accessibility), and on the attribute’s comparative fit, i.e., the extent to which

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the attribute is meaningful in order to distinguish between individuals in the given situation (Meyer, Shemla, & Schermuly, 2011; Oakes et al., 1991; Turner et al., 1987; van Knippenberg et al., 2004). It is one of the basic tenants of social identity theory that perceiving another individual as belonging to the out‐group leads to an intergroup bias, i.e., a more f­avorable evaluation of in‐group members and a less favorable evaluation of out‐group members (Tajfel & Turner, 1986). Therefore, if a team member p­erceives another team member as belonging to the out‐group, the resulting intergroup bias has potential detrimental consequences for workgroup functioning, such as  increased conflict, less cohesion, less trust, and less knowledge exchange (van K­nippenberg & Schippers, 2007). The similarity–attraction paradigm (Byrne, 1971) is often employed to make the same arguments as the social categorization approach in diversity research: It postulates that experiencing similarity with others, in terms of values and beliefs, is a pleasurable experience (Byrne, 1971) and usually leads to increased exchange with those who are perceived as similar (Phillips, Northcraft, & Neale, 2006). Thus, the similarity–attraction paradigm also postulates that exchanges between similar individuals are more fruitful than between dissimilar individuals. In contrast, the information/decision‐making approach (Williams & O’Reilly, 1998) proposes that within‐team differences pertaining to task‐relevant knowledge and perspectives can benefit team performance: A heterogeneous knowledge base increases the likelihood that one group member finds a solution to a problem (Gruenfeld, Mannix, Williams, & Neale, 1996; Gruenfeld, Martorana, & Fan, 2000; Wittenbaum & Stasser, 1996). In this way, heterogeneity can prevent premature consensus and can increase group‐level information processing (van Knippenberg & Schippers, 2007). The bi‐theoretical approach underlying team diversity has left researchers with the challenge of reconciling the contradictory predictions of the two theories. I propose that this has been done in four different ways over the years. The first stream of research has attributed the different values of diversity to different attributes, types, or dimensions of diversity (e.g., Bell, Villado, Lukasik, Belau, & Briggs, 2011; Horwitz & H­orwitz, 2007; Webber & Donahue, 2001; Williams & O’Reilly, 1998). The second stream of research has argued that perceptions of diversity are primarily associated with social categorization processes (see Shemla, Meyer, Greer, & Jehn, 2016, for a review). The third stream of research is based on the premise that any type of diversity has the potential to induce both social categorization and information/decision making processes (van Knippenberg et al., 2004; van Knippenberg & Schippers, 2007), depending on further moderating or contextual factors (Joshi & Roh, 2009). Researchers f­ollowing this tradition have investigated numerous potential moderators of the diversity–performance relationship, including the beliefs, attitudes, and personalities of team members (e.g., Homan et al., 2008; Homan, van Knippenberg, van Kleef, & De Dreu, 2007; Meyer & Schermuly, 2012; van Dick, van Knippenberg, Hagele, Guillaume, & Brodbeck, 2008; van Knippenberg, van Ginkel,  & Homan, 2013), and leadership (Homan & Greer, 2013; Kearney & Gebert, 2009). A fourth stream of research attributes the negative consequences of diversity, i.e., social categorization processes, to the formation of subgroups based on multiple diversity a­ttributes of team members, e.g., research on faultlines and subgroups (i.e., research on faultlines, e.g., Lau & M­urnighan, 1998, 2005; Meyer & Glenz, 2013; Thatcher, Jehn, & Zanutto, 2003; Thatcher  & Patel, 2012). In the following section, I review these four different ways of dealing with the bi‐theoretical approach to diversity. The chapter ends with a reflection on the limitations of the current state of research and outlines research endeavors that may have strong future potential.



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Attributing Different Effects to Different Types of Diversity Williams and O’Reilly’s (1998) first systematic review of the bi‐theoretical approach suggested that one way of dealing with its contradicting predictions is to associate different processes (and underlying theories) with different types or dimensions of diversity. Specifically, Williams and O’Reilly (1998) associated tenure diversity with the similarity– attraction paradigm, functional and background diversity with the information/decision‐ making paradigm (e.g., “functional background may serve as a proxy for the information, knowledge, skills, and expertise that individuals bring to a group”, Williams & O’Reilly, 1998, p. 101), age diversity with both paradigms, and gender diversity primarily with the similarity/attraction paradigm. Other researchers followed in proposing positive effects for some attributes such as functional diversity (e.g., Bunderson, 2003; Bunderson & Sutcliffe, 2002; Drach‐Zahavy & Somech, 2001) and proposing negative effects for other attributes such as gender diversity (e.g., Randel, 2002). However, meta‐analytical support for this research approach has been mixed. A first meta analysis (Webber & Donahue, 2001) which was explicitly framed as testing “the proposition that different types of diversity will differentially impact work group cohesion and performance” (Webber & Donahue, 2001, p. 141) found no effects for highly job‐related and less job‐related diversity attributes. Using a similar distinction between task‐related diversity (for which positive effects were expected) and bio‐demographic diversity (for which negative effects were expected), Horwitz and Horwitz (2007) found positive effects for task‐related diversity on performance, and no effects for demographic diversity. Neither dimension exhibited a significant impact on the social integration of work teams. Therefore, others concluded that the approach of attributing different processes to different dimensions of diversity is overly simplistic (e.g., van Knippenberg et  al., 2004; van Knippenberg & Schippers, 2007).

Different values of variety, separation, and disparity Partly motivated by the lack of consistent findings pertaining to different effects for different types of diversity, Harrison and Klein (2007) argued that diversity scholars have investigated very different things under the headline of diversity. They suggested to replace the broad term diversity with a set of three more specific terms that represent different types, i.e., conceptualizations and operationalizations, of team diversity: Separation, variety, and disparity. They argued that variety means that different levels of a given (usually nominal) variable, such as nationality, are assumed to contribute different inputs to a problem, and that variety is therefore assumed to have positive effects. Thus, if scholars conceptualize diversity on the basis of the information/decision‐making perspective, they should do so by applying the concept of variety (see Figure 7.1). However, scholars approaching diversity from a social categorization approach with its emphasis on in‐groups and out‐groups need to conceptualize diversity in terms of separation, where a (usually numeric) attribute such as age or tenure can be conceptualized in such a way that it splits the group into subgroups (see Figure 7.1). Finally, Harrison and Klein (2007) also proposed that a diversity attribute signaling status (such as power or income), which can be understood in terms of indicating a vertical hierarchy, should be conceptualized as disparity, where maximum levels occur if one team member has a high value on a status‐relevant attribute while all other team members score lower on this attribute (see Figure  7.1). Importantly, these three types of diversity are not inherently linked to a specific diversity attribute such as age, educational background, or tenure. Instead, Harrison and Klein argued that a given diversity attribute such as age can be conceptualized as either variety, separation, or disparity,

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Moderate

Maximum

Separation

Type of diversity

Variety

Disparity

Figure 7.1  Types and amounts of three meanings of within‐unit diversity according to Harrison and Klein (2007, p. 1202). © Academy of Management and the authors; reproduced with permission.

and that the research context must guide a researcher’s decision regarding the conceptualization and operationalization of diversity as one of the three types. This view marks a departure from the belief that a specific diversity attribute or dimension is more or less likely to elicit positive or negative effects by shifting the consequences of diversity from a specific attribute to a specific type. By claiming that any attribute can be conceptualized as any of the three types, Harrison and Klein (2007) introduced the view that all diversity attributes can lead to both positive and negative consequences into the research stream that differentiates between different dimensions of diversity. The research that has since adopted the distinction between variety, separation, and d­isparity has been synthesized in a first meta‐analysis (Bell et al., 2011) with mixed results: Bell and colleagues reported small positive effects for functional and educational background variety diversity, but found no effects for organizational tenure diversity, noting that it was often not conceptualized as variety. They also found small negative effects for race and gender variety diversity, but no effect for age variety diversity. Therefore, the assumption that conceptualizations of diversity as variety entrain positive outcomes has not been supported by the diversity literature so far.

Diversity Perceptions The studies reviewed thus far all have in common that they investigated the effects of team members’ objective diversity attributes on outcomes such as performance and cohesion. However, some researchers have also investigated the antecedents and consequences of team members’ diversity perceptions. Perceived diversity refers to “the degree to which [unit] members are aware of one another’s differences, as reflected in their internal mental representations of the unit’s composition” (Shemla, Meyer, Greer, & Jehn, 2016, p. S91).



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Earlier studies employed perceived diversity simply as an operationalization of objective diversity (Campion, Medsker, & Higgs, 1993; Campion, Papper, & Medsker, 1996; Fields & Blum, 1997). Still later studies that were originally positioned in the tradition of distinguishing between different dimensions of diversity (see above) employed the perceptions of specific diversity dimensions as mediating variables, following the notion that “if unnoticed by members, differences on a particular characteristic are unlikely to influence team behavior” (Zellmer‐Bruhn, Maloney, Bhappu, & Salvador, 2008, p. 42). For example, two studies (Harrison, Price, & Bell, 1998; Harrison et al., 2002) investigated the effects of deep‐level diversity, defined as “differences among [group] members’ attitudes, beliefs, and values” (Harrison et al., 1998, p. 98), and of surface‐level diversity, “differences in overt, biological characteristics that typically reflected in physical features” (Harrison et al., 1998, p. 97), on cohesion and team performance. In both studies, the authors assumed and found that if perceived by the team members, diversity on either dimension exhibits negative consequences. In other words, these two studies attributed the potential negative effect of team diversity to their perception. Similarly, Zellmer‐Bruhn et al. (2008) distinguished between perceived work style and perceived social category similarity. They proposed that decreased levels of perceived similarity on both dimensions are associated with heightened perceptions of the team being split into subgroups. These views can be integrated into the above‐ mentioned bi‐theoretical view on team diversity as they attribute negative effects of diversity that are predicted by the social categorization perspective to the perception of differences: If differences are perceived, i.e., if they become salient and are used for social categorizations, they have negative consequences. On the other hand, Zellmer‐Bruhn et al. (2008) used the information/decision‐making perspective on team diversity to argue that an increased exchange of information is associated with an increase of similarity perceptions (see also Liao, Chuang, & Joshi, 2008, for similar arguments). In sum, these researchers dealt with the contradictory predictions of the social categorization paradigm and the information/decision‐making paradigm by proposing that perceptions of diversity are associated with social categorization processes, while the benefits of diversity that are postulated by the information/decision‐making paradigm can only be expected in the absence dissimilarity perceptions. On a side note, Harrison and colleagues (1998; 2002) and Zellmer‐Bruhn and colleagues (2008) focused on perceptions because they wanted to include temporal dynamics in their models of team diversity. For example, in both studies, Harrison and colleagues argued that the passing of time increases the negative impact of perceived deep‐level diversity and decreases the negative impact of perceived surface‐level diversity, while Zellmer‐ Bruhn and colleagues argued that perceptions of work style similarity are subject to change over time, depending on the amount of information that team members share with each other, which was in turn associated with informational diversity. However, results on the effect of diversity perceptions are far from unanimous. For example, an attempt to replicate some of Harrison et al.’s (2002) findings was unsuccessful (Acar, 2010). Contrary to the original findings, Acar found that the negative effect of perceived surface‐level diversity on emotional conflict was visible both at the beginning and at the end of the team’s life cycle. Furthermore, a recent review on the effects of perceived diversity in teams (Shemla et al., 2016) showed that if diversity perceptions are elicited by asking team members to what extent their team as a whole is heterogeneous (instead of asking about perceived self‐to‐team differences or perceived subgroup splits), they can have both positive and negative outcomes. Whether such perceptions of team diversity are associated with positive or negative outcomes depends on a number of contingency factors that have been investigated across different studies,

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including team members’ attitudes towards diversity (Hentschel, Shemla, Wegge, & Kearney, 2013; van Dick et  al., 2008) and members’ goals and perspectives (Jehn, Northcraft, & Neale, 1999). In sum, attributing the potential negative consequences of team diversity to its perception, and attributing its potential positive consequences to the absence of diversity perceptions (or to the perceptions of similarity) also seems to be too simplistic to account for the complex mechanisms underlying the effects of team diversity. The limited success of this approach may also be due to the fact that the models proposing the psychological processes involving perceived diversity are somewhat deterministic as they do not include moderator or contingency variables. Four exemplary models that were postulated in research on diversity perceptions (Harrison et  al., 1998; Harrison et  al., 2002; Liao et  al., 2008; Zellmer‐Bruhn et al., 2008) propose that certain types of objective diversity (or member characteristics as in the study by Liao et al., 2008) lead to certain types of perceived diversity, and that these perceptions lead to negative consequences such as reduced cohesion and team social integration (Harrison et al., 1998; Harrison et al., 2002), more negative overall job attitudes (Liao et al., 2008), or increased subgroup formation (Zellmer‐Bruhn et  al., 2008). This argumentation does not leave room for potential positive effects of diversity: If diversity is present, it is likely to be perceived, and once perceived, nothing good can come out of it. The two ways of dealing with the contradictory predictions of the bi‐theoretic approach to team diversity that have been outlined so far have primarily been researched by scholars who conducted their research at universities and business schools in the US. However, the third approach to which I turn in the following section has been advocated by European researchers, particularly by researchers at Dutch institutions.

The Contingency Approach to Team Diversity: The Categorization–Elaboration Model In 2004, van Knippenberg and colleagues described research on team diversity as a field with several shortcomings. According to the authors, diversity research at the time developed an own tradition for each of the two underlying approaches of the bi‐theoretical approach, which associated the positive and negative effects of diversity to specific d­imensions. This research led to mixed results, and neither of the two developing distinct traditions recognized that team diversity was associated with both positive and negative effects (van Knippenberg et al., 2004). To overcome these issues, van Knippenberg and colleagues proposed the categorization–elaboration model (CEM) of team diversity, which combines the social categorization perspective and the information/decision‐making perspective into one theoretical model. The core proposition of the model is that “[a]ll dimensions of diversity may elicit social categorization processes as well as elaboration processes” (van Knippenberg et al., 2004, p. 1018). Specifically, the CEM proposes that the two processes interact: The possible benefit of team diversity lies in its potential to bring different knowledge and different perspectives to the team. If the task is structured in such a way that it can profit from such a multitude of perspectives, if team members possess sufficient task abilities, and if team members are motivated to work together, the team’s diversity is likely to engender more elaboration of task‐relevant information. Elaboration “is defined as the exchange of information and perspectives, individual‐level processing of the information and perspectives, the process of feeding back the results of this individual‐level processing into the group, and discussion and integration of its implications” (van Knippenberg et al., 2004, p. 1011).



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If a diverse team engages in elaboration and the aforementioned criteria are met, increased levels of creativity, innovation, and performance ensue. Thus, the CEM conceptualizes the elaboration of task‐relevant information as the core mediating process that can bring about the potential benefits of team diversity. According to the CEM, the potential negative effects of team diversity ensue if social categorizations and subsequent negative affective evaluative reactions attenuate the positive relationship between team diversity and elaboration. Specifically, in line with self‐ categorization theory (Turner et al., 1987), van Knippenberg et al. (2004) propose that comparative fit, normative fit, and the accessibility of social categories interact in determining the salience of (different) social categories within the team. However, whereas the self‐categorization theory proposes that salient categorizations automatically lead to intergroup bias (i.e., to a less favorable evaluations of out‐group members), the CEM proposes that the negative consequences of social categorizations only ensue if they are associated with a threat to team members’ social identity. In other words, the CEM proposes that merely perceiving someone else as different is not sufficient for negative consequences for the team. Negative evaluations among team members are only proposed to ensue if p­erceived differences are simultaneously perceived as a threat. If such negative evaluations occur, they are followed by increased levels of relationship conflict and decreased levels of cohesion, identification and commitment. These reactions to social categorizations under identity threat are subsequently thought to diminish the potential positive relationship between team diversity and elaboration. The model is depicted in Figure 7.2. In sum, the CEM proposes that social categorization processes moderate information/decision m­aking processes, and that any form of diversity can evoke both. Several studies have investigated various propositions of the CEM. For example, Homan and colleagues (2007) examined the relationship between informational diversity and team performance. They composed the teams in such a way that several diversity attributes split the teams into two homogeneous subgroups. These so‐called faultlines (Lau & M­urnighan, 1998, see below) make social categorization processes more likely. In line with the CEM’s predictions, they proposed and found that in the absence of perceiving diversity as a threat – i.e., in the absence of identity threat – informational heterogeneity increased team performance on an intellective task, and that this effect was mediated by the elaboration of task‐relevant information. To prevent their participants from experiencing identity threat in the face of team diversity, Homan and colleagues manipulated team members’ diversity beliefs, i.e. “beliefs about the value of diversity to work group functioning” (van Knippenberg, Haslam, & Platow, 2007, p. 209). The conceptualization and operationalization of identity threat as diversity belief has also been employed by other studies that explicitly build on the CEM: A study extending the findings by Homan et al. (2007) showed that pro‐diversity beliefs can attenuate the detrimental effects of faultlines and subgroups, but only if team members exhibit high levels of task motivation as well (Meyer & Schermuly, 2012). Van Dick et  al. (2008) showed that perceived diversity  – which they positioned in the role of social categorizations – is only associated with negative consequences, such as decreased identification, if team members hold pro‐similarity beliefs. (see also Hentschel et  al., 2013). Similar effects have also been observed when team members’ openness to experience was e­mployed instead of diversity beliefs (Homan et  al., 2008) and for team members’ attitudes towards diversity (Nakui, Paulus, & van Oudenhoven‐van der Zee, 2011). One important benefit of the processes that the CEM proposes lies in their potential malleability: Organizations can minimize the risk associated with diversity and capitalize on its benefits by increasing task abilities, employee motivation, and by trying to prevent the perception of differences as threatening, e.g., by instilling pro‐diversity beliefs among

Normative fit of categorization

Identity threat

Social categorization -Subgroups

Affective/evaluative reactions -Conflict -Cohesion -Identification -Commitment

Diversity -Comparative fit -Task-relevant information and perspectives

Elaboration of taskrelevant information and perspectives

Performance -Creativity -Innovation and decision quality

Task informational and decision requirements -Motivation -Ability

Figure 7.2  The categorization–elaboration model of work group diversity and group performance (adapted from van Knippenberg et al., 2004, p. 1010).



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their employees. Other studies that extend the CEM’s propositions also suggested that specific forms of leadership – transformational leadership and considerate leadership – have the potential to bring out the favorable effects of team diversity (Homan & Greer, 2013; Kearney & Gebert, 2009). This malleability of the diversity–outcomes relationship stands in contrast to the somewhat deterministic mediation models of team diversity that Harrison et al. (1998; 2002) had postulated previously. Despite the apparent advances that were spurred by the CEM, research on the effects of team diversity has since been far from conclusive. This is illustrated by the fact that two meta‐analyses found results that are difficult to reconcile with some of the CEM’s predictions. First, the finding that race and sex diversity have small negative relationships with team performance, whereas age diversity is unrelated to team performance (Bell et  al., 2011) contradicts the CEM’s assumption that no specific type or dimension of diversity is associated with a specific (i.e., inherently positive or negative) effect. The propositions of the CEM are also difficult to reconcile with the meta‐analytic finding that surface‐level diversity negatively affects outcomes related to effectiveness (Guillaume, Brodbeck, & Riketta, 2012). Furthermore, one can criticize the CEM as being somewhat tautological. Whatever the result of a study investigating the diversity‐performance relationship may be, the CEM can explain it: If the relationship is positive, information‐/decision‐making processes prevailed, if it is negative, social categorization processes were in place, and, if no relationship is found, both effects cancelled each other out. Thus, the mixed support for the CEM and the promise that certain contingency factors make certain effects of diversity more likely have spurred a continuing stream of studies investigating contingency factors, i.e., moderators, of the diversity‐outcomes relationship. Another meta‐analysis (Joshi & Roh, 2009) underscored the importance of this quest for contingency factors, as it showed that contextual effects on multiple levels of analysis have a strong influence on the diversity‐outcome relationship. I thus continue this chapter with a review of a substream of the diversity literature that has tried to overcome some of the conceptual limitations that were mentioned above by investigating the alignment of m­ultiple diversity attributes as a moderating factor of the diversity‐outcome relationship: Research on faultlines and subgroups.

Faultlines and Subgroups The research reviewed so far primarily investigated the impact of one type or dimension of diversity such as surface‐level diversity or age diversity on outcomes. Even if more than one type of diversity was investigated simultaneously, such as surface‐ and deep‐level diversity (Harrison et al., 2002) or social category and work style similarity (Zellmer‐Bruhn et al., 2008), the extent of their overlap was not taken into account. However, researchers investigating the consequences of faultlines and subgroups argue that the distribution of multiple diversity attributes within a given unit  –  a team or a department – has a significant impact on the effect of the unit’s diversity with regard to those attributes. These researchers refer to a hypothetical dividing line, which is created if multiple attributes align in such a way that they create relatively homogeneous subgroups, as a faultline (e.g., Bezrukova, Jehn, Zanutto, & Thatcher, 2009; Lau & Murnighan, 1998; Meyer, Glenz, Antino, Rico, & González‐Romá, 2014; Thatcher & Patel, 2012). Faultlines are thus conceptually similar to comparative fit (Meyer et al., 2011), the extent to which differences between people are perceived as correlated with a division into social categories (Turner et  al., 1987). Turner and colleagues suggested that comparative fit depends on the so‐called meta‐contrast ratio, which refers to the ratio between perceived

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differences between categories and perceived differences within a category. In other words, comparative fit is high if there are more differences between categories than within categories. This ratio of within‐category variance over between‐category variance is what most measures of diversity faultlines quantify (Meyer & Glenz, 2013). Therefore, faultlines and the resulting subgroups can be seen as a proxy for comparative fit (Meyer et al., 2011), which is why self‐categorization theory is also most frequently employed for predicting negative effects of faultline splits in teams (Thatcher & Patel, 2012). This conceptualization of faultlines aligns well with the categorization‐elaboration model (van Knippenberg et  al., 2004), which conceptualizes the formation of subgroups based on salient social categorizations as the core risk of diversity. Therefore, faultline theory (Lau & Murnighan, 1998) proposes that strong faultlines make social categorizations more likely and therefore predicts that faultlines lead to negative consequences for the team – at least if they form across attributes that are relevant for team members’ social identities (Carton & C­ummings, 2012). Importantly, faultlines and team diversity are related: In the absence of diversity with regard to multiple attributes, i.e., in homogeneous teams, there can be no faultlines. Also, if all team members are different from each other, i.e., in the case of maximum diversity, there can be no faultlines either. Therefore, faultlines are most likely to occur under medium levels of diversity. Faultlines have been shown to exert an effect above and beyond the effects of diversity with regard to the attributes comprising the faultline (Lau & Murnighan, 2005). In sum, the degree of alignment among multiple diversity attributes is one conditioning factor that makes it more likely for diversity to have negative effects on team processes. If organizations want to reap the potential benefits of diversity while avoiding its risks, one way of doing so is by preventing the formation of faultlines. However, even if they cannot be prevented, other measures such as crossing faultlines by enforcing c­ollaboration among team members from different subgroups (Rico, Sánchez‐M­anzanares, Antino, & Lau, 2012) or the instilment of pro‐diversity beliefs (Homan et al., 2007) can prevent negative effects. Organizational climate also influences the extent to which f­aultlines are detrimental (Bezrukova, Thatcher, Jehn, & Spell, 2012). While diversity research with regard to single attributes has produced somewhat inconsistent findings, as the meta‐analyses reviewed above indicate, two meta‐analyses have uncovered a small negative main effect of demographic faultlines (i.e., faultlines across demographic attributes such as gender, age, and ethnicity) on team‐level outcomes (Thatcher & Patel, 2012). Therefore, in more than 40 years of research on team diversity, faultlines appear to be the first construct for which meta‐analyses consistently find an effect that can be reconciled with the theoretic underpinnings of the construct. Despite these promising findings, research on faultlines and subgroups faces its own set of challenges. On a methodological level, a multitude of algorithms for detecting and quantifying faultline strength has it made difficult to compare findings across studies (Meyer & Glenz, 2013), although a recent review attempts to give explicit guidelines for choosing a faultline measure (Meyer et al., 2014). Furthermore, the combination of several diversity attributes into one measure of faultline strength leads to the question of how the different attributes contributing to a faultline should be weighted. If, for example, a faultline measure was used to quantify the extent of a split into hypothetical subgroups based on gender, age, and educational background, how does the gender difference between two subgroups relate to their age difference? Researchers have suggested to scale nominal attributes, such as sex, and numeric attributes, such as age, in such a way that a difference of one standard deviation of a numeric measure is equated with one categorical difference in the case of a nominal measure (Zanutto, Bezrukova, & Jehn, 2011). Although this approach mirrors common practice in moderation analysis (Aiken & West, 1991),



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it appears as somewhat arbitrary, because at least up till now, no psychological process has been proposed in support of such scaling approaches. On a theoretical level, the arguments speaking for the effect of faultlines hinge on p­erceptions: Hypothetical dividing lines are assumed to increase the likelihood that the splitting into subgroups is perceived, and these perceptions, if combined with identity threat, are assumed to engender the detrimental consequences of faultlines. Accordingly, faultline research has distinguished between objective, hypothetical faultlines – so‐called dormant faultlines, and actually perceived, i.e., active faultlines (Chrobot‐Mason, Ruderman, Weber, & Ernst, 2009; Jehn & Bezrukova, 2010). However, dormant faultlines do not necessarily lead to active faultlines and perceptions of subgroups or differences (Meyer et al., 2011). Furthermore, “researchers find that the presence of dormant faultlines has consequences even when faultlines are not activated” (Thatcher & Patel, 2012, p. 982). In other words, faultlines can have an effect on team processes without actually being perceived by team members. However, this finding is somewhat contradictory to the proposition that strong faultlines increase the salience of differences. Other theories, besides self‐categorization theory, which have been proposed for explaining the negative effects of faultlines hinge on their perception, as well (see Thatcher & Patel, 2012, for a review): Distance theory (Brewer, Manzi, & Shaw, 1993) proposes that faultlines make members of different subgroups experience more psychological distance from each other, which is again a process that relies on subjective experiences. Optimal distinctiveness threat theory (Brewer, 1991) assumes that team members seek an optimal balance of inclusion and distinctiveness within and between social groups. For meeting this desire for distinctiveness, the members of subgroups try to distance themselves from other s­ubgroups. This in turn detains collaboration between subgroups. These theories show that a psychological process theory that can explain the impact of faultlines in the absence of their salience is currently missing from diversity research. A first step into that direction has recently been taken by Meyer, Schermuly, and Kauffeld (2015), who proposed that faultlines can impact the social relations among team members (see also Reagans, Zuckerman, & McEvily, 2004, for a similar argument for single‐attribute diversity measures), which can in turn affect team members’ propensity to loaf. Initial findings by Meyer et al. (2015) support this theory: members of teams with strong faultlines exhibited more social loafing behavior – at least under certain boundary conditions. Other researchers have also reported an association between faultlines and perceived loafing (Ellis, Mai, & Christian, 2013). However, neither Meyer and colleagues nor Ellis and colleagues investigated the impact of faultlines on team members’ social relations as a mediating process. Therefore, the theory that faultlines impact team members’ motivational processes via their impact on teamwork ties (Crawford & LePine, 2013) without necessarily becoming salient requires further development and empiric evidence.

Subgroup theory So far, the reviewed diversity faultlines research rests on the – sometimes implicit – assumption that faultlines constitute separation (see Figure 7.1) with regard to multiple attributes and are therefore inherently detrimental. In other words, one can think of faultlines as a distributional moderator of the effects of multi‐attribute diversity: If diversity with regard to multiple attributes is present, it is more likely have detrimental effects if it is distributed in such a way that the attributes align to create homogeneous subgroups, as subgroups increase the likelihood of social categorization processes. However, Carton and Cummings (2012) argued that this is only the case if the attributes in question are important for the  team members’ social identities, i.e. if the attributes in question can inherently be

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conceptualized as diversity attributes signaling separation. In this case, Carton and Cummings (2012) speak of identity‐based subgroups. They further argue that diversity attributes signaling status (i.e., disparity diversity, see Figure 7.1) and diversity attributes signaling different knowledge and experience (i.e., variety diversity, see Figure 7.1), can also align to create status‐based subgroups and knowledge‐based subgroups. The formation of these different subgroups is, according to Carton and Cumming’s (2012) subgroup theory, associated with different team‐level outcomes that are governed by the presence of further moderators. They propose several moderators that are specific to the type of subgroup, and one moderator that affects the outcomes of all three types of subgroups, namely the number of subgroups. Specifically, Carton and Cummings (2012) argue that the negative impact of identity‐based subgroups is attenuated by an increasing number of subgroups, because they attenuate the comparison processes occurring between members of different subgroups. Further, they propose that the negative impact of resource‐based subgroups, which are assumed to trigger perceptions of unfairness, is reduced if the number of subgroups increases: An increasing number of resource‐based subgroups is proposed to decrease unfairness perceptions. Finally, subgroup theory proposes that the positive effects that are brought about by information‐based subgroups are diluted by an increasing number of informational subgroups, as an increasing number of subgroups makes it more difficult to synthesize the different perspectives that are held by the different members of different subgroups. Subgroup theory therefore proposes that the formation of subgroups along strong faultlines can yield positive team‐level outcomes if the subgroups are based on different informational backgrounds, i.e. on attributes constituting variety diversity. This proposition is in line with the argument that subgroups can yield more creativity in teams (Nishii & Goncalo, 2008) and with the finding that information‐based subgroups can have a positive influence on team‐level performance (Bezrukova et  al., 2009; Carton & Cummings, 2013). Subgroup theory also makes more detailed predictions regarding the subgroup structure of the team, as it takes the number and sizes of subgroups into account. Despite these advances, subgroup theory also has its issues. First, testing the predictions of subgroup theory requires sophisticated algorithms for detecting the subgroup structure of a team (see Meyer & Glenz, 2013; Meyer et al., 2014). Furthermore, empiric support for the predictions of subgroup theory has also been mixed, as a recent study found no effects of the number and relative sizes of subgroups (Meyer, Shemla, Li, & Wegge, 2015). Finally, on a conceptual level, its core proposition that faultlines and subgroups can yield both positive and negative consequences brings diversity research to a point where it already was before: As I argued above, early diversity research attributed different effects of diversity to different types of diversity. These views were later reconciled by the CEM that proposed that diversity can yield both positive and negative consequences, depending on certain boundary conditions. A large number of studies on faultlines (those reviewed by Thatcher and Patel, 2012, and by Meyer et  al., 2014) can be interpreted as conceptualizing faultlines as one of these boundary conditions: Diversity is more likely to have a negative effect if faultlines are present. What therefore looked like a step towards more consistency in diversity research now looks less promising, because the effects of faultlines are now again assumed to depend on the type of diversity and on moderating factors. In other words, diversity was assumed to yield positive and negative consequences and faultlines were assumed to make the negative effects more likely. Now, diversity research is at a point where faultlines are also proposed to have positive and negative effects, depending on the type of diversity and further moderators. This development is symptomatic for the entire field of diversity research, where the absence of clear findings is met with an ever‐increasing number of moderators and conditioning processes.



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Therefore, I conclude that one of the core challenges diversity research is to come up with more and new overarching theories instead of adding moderator after moderator to the existing ones. I highlight some of such developments in the subsequent section on theoretical developments that can potentially address these issues.

Multilevel and Status‐based Approaches to Team Diversity Conceptual and empirical work in the area of diversity research not building on the bi‐ theoretical approach is characterized by two general developments for which I provide examples below: Multilevel models of team diversity and models of team diversity that incorporate status processes in their argumentation. I outline these developments in the following section.

Multilevel and contextual conceptualizations of team diversity Most of the theoretic models predicting the effects of diversity and/or of faultlines on team performance that have been mentioned so far (e.g., Harrison & Klein, 2007; Harrison et al., 1998; Harrison et al., 2002; van Knippenberg et al., 2004; Zellmer‐Bruhn et al., 2008) have one thing in common: They are genuine team‐level models, i.e., they make predictions about how a team‐level property, team diversity or faultline strength, affects team‐level outcomes such as performance. However, team performance is a property that emerges from the individual properties of the team members (Kozlowski, 2012; Kozlowski & Chao, 2012) – from team members’ cognitive states (Ilgen et al., 2005) and from their conduct and interactions (Bonito & Sanders, 2011). Furthermore, relational demography research has shown that team members’ individual responses to diversity within their team can differ. For example, men and women react differently to gender diversity within their team (Chattopadhyay, George, & Shulman, 2008). Additionally, the performance of individual team members can differ in response to their team’s faultline structure, depending on which subgroup they belong to (Meyer, Shemla, et al., 2015). Therefore, the team‐level performance of a diverse team can be understood as an emergent property of individual team members’ behaviors and performances, which can exhibit substantial variance within a given team in response to the team’s diversity (Meyer, Schermuly, et al., 2015; Meyer, Shemla, et al., 2015). As these authors have pointed out, team‐level models of diversity do not leave room for within‐team differences which might cancel each other out if aggregated to the team level. Therefore, multilevel models of team diversity, which include the possibility that individual team members’ reaction to their team’s diversity might differ, appear as an important step towards a theory of diversity in work teams that is able to accommodate the multilevel complexities of today’s organizations (Kozlowski, 2012; Kozlowski & Chao, 2012). One step into this direction was taken by Joshi and Roh (2009), who proposed that the effects of team‐level diversity on team‐level outcomes are moderated by organization‐level and industry‐level contextual factors (see above). In other words, in their multi‐level conceptualization of team diversity, they investigated moderators above the team level. This view proposes that diversity does not carry an inherent meaning that can be derived from the bi‐theoretical approach, but that team diversity only acquires meaning in the wider context of the organization and/or communities in which the team is embedded. A theoretical model on the effects of team diversity that takes a multilevel perspective, including the individual level, team level, organizational level, and societal level, was offered by Guillaume and c­olleagues (2014); see Figure 7.3.

Legislation, socioeconomic situation, and culture Societal factors P6

Top management’s diversity beliefs

Organizational diversity management policies and procedures Organizational factors P5

Transactional and transformational leadership Work group climate for inclusion

Work group composition X

Individual attributes

Employee dissimilarity

P4a

P4c

P4b

Acceptance of performance standards

Identity concerns

P1

Inclusion in decision making

Equitable employment practices

Integration of differences

Work group identification

P2

Work group factors

Self-efficacy

Work motivation intrinsic and extrinsic

P3

Innovation, effectiveness, and wellbeing

Individual reactions towards diversity

Figure  7.3  Multilevel conceptual model of the effects of team diversity in organizations (Guillaume et  al., 2014, p. 786). © European Journal of Work and Organizational Psychology and the authors; reproduced with permission.



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The model proposes that an individual team members’ response to the diversity of their team is an interaction between the team’s diversity and the individual attributes of the individual. The outcome of this interaction affects the individual team members’ team identification and motivation, which in turn affect the individual’s level of effectiveness, innovativeness, and wellbeing. Implicitly, team performance subsequently emerges from these individual‐level responses. Importantly, the individual‐level mediating processes are in turn assumed to be moderated by individual‐level cognitive and affective states such as identity concerns and self‐efficacy, which are again assumed to be influenced by team‐level properties such as inclusion and fairness. These team‐level properties are affected by organization‐level attributes, which are again affected by the societal environment. Therefore, the model spans four levels of analysis (individual, team, organization, and society). This includes the possibility that different members of the same team react to the diversity within their team in different ways. While this approach is definitely promising, empiric tests of the model or some of its part are still missing. While the model by Guillaume et  al. (2014) is still waiting for empirical support, other contextual models of team diversity were successfully tested empirically: Leslie (2014) predicted and found that the presence of homogeneous ethnic subgroups in work groups negatively affects work group cohesion and performance, and that this effect is exacerbated if the larger community of the team members is also characterized by homogeneous ethnic subgroups. Similarly, Richard, Stewart, McKay, & Sackett (2015) introduced the concept of racial diversity congruence, which they operationalize as the degree to which store‐unit racial diversity and community racial diversity match. These authors predicted and fond that racial diversity congruence increases sales performance, and that the level of congruent diversity in stores and communities moderates this r­elationship. These theories are however of a smaller scope than the model proposed by Guillaume and colleagues (2014): First, they are only concerned with one specific type of diversity, namely racial diversity. Second, they only incorporate the team level and the community level without incorporating the individual level. However, it is noteworthy that both papers make reference to another psychological mechanism for explaining the effects of team diversity, namely status. I elaborate on this conceptual novelty in the f­ollowing section.

Status‐based theories of team diversity One implicit assumption of the information/decision making approach to diversity, which posits that people from different social categories bring different views to the team that are beneficial for team performance (see above), is that all of these views are of equal value to the team (van Dijk & van Engen, 2013). This means that potentially, for any given task, the views and experiences of, for example, women are as valuable for the elaboration of task‐relevant information as the experiences of men, if gender is conceived as variety (Harrison & Klein, 2007). However, there is ample evidence that individuals employ others’ demographic characteristics as cues for determining social status, i.e., “the amount of respect, influence, and prominence” group members enjoy in the eyes of others (Anderson, John, Keltner, & Kring, 2001, p. 117). For example, ethnicity and gender are important cues that people employ to infer status (Berger, Cohen, & Zelditch, 1972). In the context of work teams, the team task can signal the stereotypic value of team members’ social category for the given task: Chatman and colleagues (Chatman, Boisnier, Spataro, Anderson, & Berdahl, 2008) found that in teams that were working on a stereotypically masculine task (math problems), the male team members were more often deferred to and

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therefore exhibited the highest individual‐level performance. If the team was working on stereotypically female tasks (reading comprehension), this pattern reversed. These results show that team members employed stereotypic attributions of competence based on their team members’ sex, and that these attributions guided social interaction behavior and performance. Based on such findings, van Dijk and van Engen (2013) argued that status processes need to be taken into account in models of team diversity, because many social categories such as age, tenure, sex, and ethnicity are used to infer status, and status hierarchies have a profound impact on team functioning (e.g., Anderson & Kilduff, 2009; Bendersky & Hays, 2012; Groysberg, Polzer, & Elfenbein, 2011). The study by Leslie (2014) is the first to empirically test a model of team diversity that is based on status process, albeit a very specific one, as it only pertains to ethnic diversity. Leslie argued that, in the US, each ethnic category (e.g., Caucasian, African American, Asian) has a different societal status. She argued that ethnic subgroups are especially detrimental if the status difference between ethnic subgroups is large. Importantly, her model hinges on the proposition that, at least in the case of ethnicity, the social category cannot be separated from the status it conveys. Therefore, she employed the term ethnic status subgroup to refer to a subgroup of team members with a specific ethnicity that conveys a specific status. Her findings supported the model and showed that work group cohesion and performance was especially low if the work group was characterized by two ethnic status subgroups with a large status difference, especially if these subgroups mirrored the social structure of the larger social community. Thus, taking status processes into account into diversity research appears to be a novel and promising approach. However, to date, a specific theoretic model linking status processes to any dimension of diversity (and not just to ethnic diversity) is still missing. Therefore, the development of such a (multilevel) model of status processes and team diversity is one potential fruitful future research endeavor. Given that I structured this brief review of research on team diversity and faultlines in a temporal way, the section on multilevel models and status conceptualizations ends the part of the review that is set in the present. Continuing from here, I present some points that I believe have been missing from the diversity literature so far which might posit promising future research endeavors. I subsequently end the chapter with concluding remarks.

Future Research This section focuses on gaps in our current knowledge and the future research agenda for the coming decade in the issue of team diversity.

Team dynamics In general, recent theories on team processes highlight the temporal dynamics of teamwork (Humphrey & Aime, 2014; Mathieu et al., 2014). This is not a new development, as can be illustrated by classical models of team processes, such as the IMOI model (Ilgen et al., 2005) or the temporal framework of team processes (Marks et al., 2001). However, despite that fact that many theories on teamwork make references to the passing of time, empiric studies investigating team processes usually adopt a very static view on teams, at least in their analyses (Roe, Gockel, & Meyer, 2012). This is especially true for studies on team diversity. Although some studies employed a longitudinal design to test the impact of team diversity at several measurement points (e.g., Harrison et  al., 2002; Zellmer‐ Bruhn et al., 2008), no study covered in this review investigated the effects of changes of



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team diversity. Instead, all of the studies that have been covered so far treat team diversity as a stable trait of a team. Demographic diversity can change as the composition of the team changes. Given that team member entry and exit affect team processes (Ucbasaran, Lockett, Wright, & Westhead, 2003), it is likely that the change of diversity that team member entry and exit brings along can have a significant impact on team processes. However, no theory can currently account for the consequences of changes of team diversity or the faultline structure of a team. Therefore, as team research in general focuses stronger on dynamics and temporal processes, the development of such theory and according empiric studies appear to be a fruitful research endeavor.

Health and wellbeing Another limitation of diversity research lies in its somewhat narrow focus on outcomes related to performance. Therefore, one potential for future research on team diversity lies in broadening its scope to other outcomes besides variables associated with team effectiveness. Work and the social relations at work have a profound impact on individuals’ wellbeing and health (e.g., Schaufeli, Bakker, & Van Rhenen, 2009; Smart Richman & Leary, 2009), especially given that diversity affects social relations at the work place (Reagans et al., 2004) and experiences of conflict (Jehn et al., 1999). Indeed, a few studies have investigated the link between diversity and health: A study among workers with minority ethnic backgrounds found low levels of psychological health among participants with either very low or a high proportion of co‐ethnic colleagues in their work unit (Enchautegui‐ de‐Jesús, Hughes, Johnston, & Oh, 2006). Hoppe, Fujishiro, and Heaney (2014) found that the ethnic composition of work units and workers ethnicity interacted in affecting warehouse workers’ back pain and proposed helping and support behaviors as mediating processes. One study investigated in which way the gender composition of teams of tax workers in Germany affects subjective health (Wegge et al., 2008). The results showed that team size and gender diversity interacted in such a way that members of larger teams with high levels of gender diversity experienced less subjective wellbeing. While these studies demonstrate the fruitfulness of investigating health and wellbeing as outcomes of diversity, they hardly paint a complete picture. Further, they lack investigations of the psychological mechanisms that bring about the reported effects, which is why I believe that further conceptual and empiric studies on the diversity-wellbeing relationship are an important future area for research on team diversity.

Conclusion Concluding, one has to recognize that research on team diversity is a very diverse field that has produced many promising, but also many inconsistent findings. The review has shown that the diversity of the field can be organized into different streams which can be differentiated by the ways in which authors have tried to resolve the bi‐theoretic approach to diversity. One stream has linked specific dimensions of diversity to specific theories (e.g., by proposing that demographic diversity or separation diversity lead to social categorization processes, while variety diversity or informational diversity are assumed to cause information/ decision‐making processes). Another stream has dealt with the bi‐theoretic approach by linking diversity’s negative consequences to its perceptions (see Shemla et al., 2014, for a review), while yet another stream has dealt with it by proposing that social categorization processes are more likely if team diversity is distributed in such a way that it creates (hypothetical) dividing lines that split the team into relatively homogeneous subgroups

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(i.e., research on faultlines). Another stream of research has, resting on the categorization–elaboration model (van Knippenberg et  al., 2004), conceptualized the two core processes as interacting, and has investigated a slew of moderating factors that determine whether the positive or the negative outcomes of diversity prevail. Within this stream, findings show that if team members see value in diversity, the likelihood of diversity leading to positive outcomes increases (e.g., Homan et al., 2008; Homan et al., 2007; Nakui et al., 2011; van Dick et al., 2008). Therefore, the advice for practitioners who seek to reap the benefits of team diversity is to increase team members’ diversity beliefs and to avoid the formation of homogeneous subgroups. Finally, multilevel/contextual and status‐based models of team diversity extend the theoretical foundations of diversity research beyond the bi‐theoretical approach. Despite the issues that research on team diversity faces, this review has also shown that this burgeoning field of research is likely to continue being one of the most vibrant areas in organizational behavior.

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Richard, O. C., Stewart, M. M., McKay, P. F., & Sackett, T. W. (2015). The impact of store‐ unit–community racial diversity congruence on store‐unit sales performance. Journal of Management, Online before print. doi: 10.1177/0149206315579511 Rico, R., Sánchez‐Manzanares, M., Antino, M., & Lau, D. (2012). Bridging team faultlines by combining task role assignment and goal structure strategies. Journal of Applied Psychology, 97, 407–420. doi: 10.1037/a0025231 Roe, R., Gockel, C., & Meyer, B. (2012). Time and change in teams: Where we are and where we  are moving. European Journal of Work and Organizational Psychology, 21, 629–656. doi: 10.1080/1359432X.2012.729821 Salas, E., Cooke, N. J., & Rosen, M. A. (2008). On teams, teamwork, and team performance: Discov­ eries and developments. Human Factors, 50, 540–547. doi: 10.1518/001872008X288457 Schaufeli, W. B., Bakker, A. B., & Van Rhenen, W. (2009). How changes in job demands and resources predict burnout, work engagement, and sickness absenteeism. Journal of Organizational Behavior, 30, 893–917. Shemla, M., Meyer, B., Greer, L. L., & Jehn, K. A. (2016). A review of perceived diversity in teams: Does how members perceive their team’s composition impact team processes and o­utcomes? Journal of Organizational Behavior, 37(Supplement S1), S89–S106. doi: 10.1002/ job.1957 Shore, L. M., Chung‐Herrera, B. G., Dean, M. A., Ehrhart, K. H., Jung, D. I., Randel, A. E., & Singh, G. (2009). Diversity in organizations: Where are we now and where are we going? Human Resource Management Review, 19, 117–133. doi: 10.1016/j.hrmr.2008.10.004 Smart Richman, L., & Leary, M. R. (2009). Reactions to discrimination, stigmatization, ostracism, and other forms of interpersonal rejection: a multimotive model. Psychological review, 116, 365–383. doi: 10.1037/a0015250 Tajfel, H., & Turner, J. (1986). The social identity theory of intergroup behavior. In S. Worchel & W. G. Austin (Eds.), The social psychology of intergroup relations (pp. 7–24). Chicago: Nelson-Hall. Thatcher, S. M. B., Jehn, K. A., & Zanutto, E. (2003). Cracks in diversity research: The effects of diversity faultlines on conflict and performance. Group Decision and Negotiation, 12, 217–241. doi: 10.1023/A:1023325406946 Thatcher, S. M. B., & Patel, P. C. (2012). Group faultlines: A review, integration, and guide to future research. Journal of Management, 38, 969–1009. doi: 10.1177/0149206311426187 Turner, J. C., Hogg, M. A., Oakes, P. J., Reicher, S. D., & Wetherell, M. S. (1987). Rediscovering the social group: A self‐categorization theory. Oxford: Blackwell. Ucbasaran, D., Lockett, A., Wright, M., & Westhead, P. (2003). Entrepreneurial founder teams: Factors associated with member entry and exit. Entrepreneurship Theory and Practice, 28(2), 107–128. van Dick, R., van Knippenberg, D., Hagele, S., Guillaume, Y. R. F., & Brodbeck, F. C. (2008). Group diversity and group identification: The moderating role of diversity beliefs. Human Relations, 61, 1463–1492. doi: 10.1177/0018726708095711 van Dijk, H., & van Engen, M. L. (2013). A status perspective on the consequences of work group diversity. Journal of Occupational and Organizational Psychology, 86, 223–241. doi: 10.1111/ joop.12014 van Dijk, H., van Engen, M. L., & Paauwe, J. (2012). Reframing the business case for diversity: A values and virtues perspective. Journal of Business Ethics, 111, 73–84. van Knippenberg, D., De Dreu, C. K. W., & Homan, A. C. (2004). Work group diversity and group performance: An integrative model and research agenda. Journal of Applied Psychology, 89, 1008–1022. doi: 10.1037/0021‐9010.89.6.1008 van Knippenberg, D., Haslam, S. A., & Platow, M. J. (2007). Unity through diversity: Value‐in‐ diversity beliefs, work group diversity, and group identification. Group Dynamics: Theory, Research, and Practice, 11, 207–222. doi: 10.1037/1089–2699.11.3.207 van Knippenberg, D., & Schippers, M. C. (2007). Work group diversity. Annual Review of Psychology, 58, 515–541. doi: 10.1146/annurev.psych.58.110405.085546



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van Knippenberg, D., van Ginkel, W. P., & Homan, A. C. (2013). Diversity mindsets and the performance of diverse teams. Organizational Behavior and Human Decision Processes, 121, 183 – 193. doi: 10.1016/j.obhdp.2013.03.003 Webber, S. S., & Donahue, L. M. (2001). Impact of highly and less job‐related diversity on work group cohesion and performance: A meta‐analysis. Journal of Management, 27, 141–162. doi: 10.1016/S0149‐2063(00)00093‐3 Wegge, J., Roth, C., Neubach, B., Schmidt, K., & Kanfer, R. (2008). Age and gender diversity as determinants of performance and health in a public organization: The role of task complexity and group size. Journal of Applied Psychology, 93, 1301–1313. doi: 10.1037/a0012680 Williams, K. Y., & O’Reilly, C. A. (1998). Demography and diversity in organizations: A review of 40 years of research. Research in Organizational Behavior, 20, 77–140. Wittenbaum, G. M., & Stasser, G. (1996). Management of information in small groups. In J. L. Nye & A. M. Bower (Eds.), What’s social about social cognition (pp. 3–28). Thousand Oaks, CA: Sage. Zanoni, P., Janssens, M., Benschop, Y., & Nkomo, S. (2010). Unpacking diversity, grasping inequality: Rethinking difference through critical perspectives. Organization, 17, 1–21. doi: 10.1177/1350508409350344 Zanutto, E. L., Bezrukova, K., & Jehn, K. A. (2011). Revisiting faultline conceptualization: Measuring faultline strength and distance. Quality and Quantity, 3, 701–714. doi: 10.1007/ s11135‐009‐9299‐7 Zellmer–Bruhn, M. E., Maloney, M. M., Bhappu, A. D., & Salvador, R. (2008). When and how do differences matter? An exploration of perceived similarity in teams. Organizational Behavior and Human Decision Processes, 107, 41–59. doi: 10.1016/j.obhdp.2008.01.004

8

Change in Organizational Work Teams Floor Rink, Aimée A. Kane, Naomi Ellemers, and Gerben van der Vegt

Introduction Organizations often actively recruit new members or rotate employees between different work teams with the hope that their “fresh blood” will enhance work team performance. As the number of highly educated and skilled newcomers increases (e.g., OECD, 2014), their presence may indeed create a unique opportunity for teams to adapt their existing work practices and to improve their performance. The seminal work of Katz (1982), as well as more recent insights in the antecedents of team innovation (e.g., Anderson, De Dreu, & Nijstad, 2004), suggest that teams become less critical towards their own output when they do not regularly change in composition. Within stable teams, the ideas of the members tend to converge, which limits their interest in and ability to develop and implement new ideas or work processes (De Dreu & West, 2001). This suboptimal use of innovative potential arising from a lack of newcomers is, generally speaking, harmful for the quality of teamwork (Guimerà, Uzzi, Spiro, & Nunes Armaral, 2005). There is growing evidence, however, that the arrival of a newcomer does not automat­ ically spur positive changes in a team (e.g., Baer, Leenders, Oldham, & Vadera, 2010). Teams tend to have a strong preference for familiarity as, on balance, acquaintance and closeness creates trust and commitment (Ellemers, De Gilder, & Haslam, 2004; Liang, Moreland, & Argote, 1995; Van der Vegt, Bunderson, & Kuipers, 2010) and facilitates the coordination of team activities (Bunderson, 2003; Espinosa, Slaughter, Kraut, & Herbsleb, 2007; Littlepage, Robison, & Reddington, 1997). Moreover, teams often believe it is functional to repeat behavioral patterns that worked well in the past (Ziller, 1965). Accordingly, the classic socialization model of Moreland and Levine (1982) sug­ gests that the initial disruption that teams experience owing to newcomer entry often makes them reluctant to fully accept this person, as well as his or her task contributions (see Moreland & Levine, 2006).

The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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In this chapter, we examine the ways in which work teams respond to newcomers, here conceptualized as team receptivity. We distinguish among three theoretically exhaustive components of team receptivity to newcomers. The first component, which we refer to as team ref lection, entails the team’s tendency to reflect upon existing work processes, alter routines, and generate new ideas owing to the mere presence of a newcomer. The second component corresponds to the team’s inclination to utilize and adopt the newcomers’ unique knowledge, skills, and aptitudes, here labeled as team knowledge utilization. Com­ pared with these two characteristics of the team’s task behavior, the third component is more psychological in nature and encompasses the team’s willingness to accept the newcomer as a full team member. As is shown below, this tripartite conceptualization of team receptivity can be derived from the literature and provides a logical conceptual framework for organizing the divergent findings that exist on team receptivity and for clarifying the conditions under which teams are most likely to be completely receptive to newcomers (in all three ways). By examining the empirical research on these three components of team receptivity to newcomers, we provide an overview of the conditions under which newcomers can be an important source of external contacts and unique knowledge through which teams can increase their chances of long‐term survival (March, 1991) and in doing so we draw on our earlier work (Rink, Kane, Ellemers, & van der Vegt, 2013). Our review describes the kind of team responses to newcomers that are likely to spur innovation and permanent change. On the basis of this examination, we subsequently propose a future research program that helps solve prior disparate findings on a team receptivity to n­ewcomers.

Team Receptivity Our literature review is based on empirical articles published between 1960 and 2015 in the fields of management/organizational behavior and psychology. In each of these a­r ticles, team members have collaborated with sufficient intensity to establish a sense of team identity, which is important as it suggests that a default response to a newcomer is to see them as an outsider who has yet to earn the team’s trust (Delton & Cimino, 2010).

Team reflection The role that newcomers can have in team reflection was first explored by Ziller and col­ leagues in their 1962 paper (Ziller, Behringer, & Goodchilds, 1962). They established that teams experiencing membership change (so‐called “open” teams) generated more original ideas than stable or “closed” teams because the mere presence of a newcomer alone caused these “open” teams to reflect upon their existing work processes and task assignment. Subsequent research largely confirmed the classic finding that membership change involving newcomers enhances team reflection. Arrow and McGrath (1993), for example, found that teams confronted with a newcomer  –  either through experimental induction, or through spontaneous membership changes – reflect more on internal processes compared with relatively stable teams. Similar results have been obtained for discussion duration in teams that contained new members (Gorman & Cooke, 2011), for idea generation in teams who welcomed replacement members (Choi & Thompson, 2005) or previously departed members (Gruenfeld, Martorana, & Fan, 2000), and for innovative outcomes of teams with newcomers to the film industry (Perretti & Negro, 2007). Along related lines,



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­ emeth and Ormiston (2007) also found that open teams were more creative than closed N teams. However, in this study, closed teams thought of themselves as being equally creative as the open teams, indicating that teams may not recognize the team reflection advantage of newcomers. Not all membership change studies yielded straightforward effects for newcomer entry on team reflection, however. Membership change involving newcomers led to transactive memory system (TMS) inefficiencies in one study (Lewis, Belliveau, Herndon, & Keller, 2007), and impeded team learning behavior and task flexibility in a team field study (Van der Vegt et al., 2010). These findings suggest that the influence of newcomers on team reflection may be contingent on certain team characteristics, environmental characteristics, or on specific newcomer traits. We discuss such boundary conditions below. There are indeed several team characteristics that seem to mitigate the influence of new­ comers on team reflection. For example, a supplemental study conducted by Lewis et al. (2007) showed that newcomer entry did go hand in hand with efficient team cognitive processing when teams were clearly instructed to reflect upon the collective knowledge in their TMS prior to task execution. Moreover, Bunderson, Van der Vegt & Sparrowe (2014) showed that the arrival of a new member only provides the impetus for team reflec­ tion when teams assign a high status task role to the newcomer. Another study showed that newcomers enhanced team reflection when the teams were just newly formed (Hirst, 2010). Thus, what seems to matter for team reflection is not so much newcomer entry in itself, but rather whether the team’s longevity supports or impedes adaptation to member­ ship changes. Similarly, Baer et al. (2010) demonstrated that member rotations enhanced team reflection (i.e., in terms of enhancing idea generation) when there was little inter­ group competition in the direct work environment. This finding supports the team inno­ vation literature we referred to earlier, suggesting that newcomers may be necessary for teams to unleash their innovative potential under circumstances that are otherwise relatively secure and safe (Anderson et al., 2004). When, however, intergroup competition rises, newcomer presence is no longer related to team reflection. External threat can p­rovide a different reason for teams to think of ways in which they can become (or at least seem) more successful and competitive  –  independently of the presence vs. absence of newcomers. Finally, one study examined whether characteristics of the newcomer could influence team reflection, in this case, the newcomer’s social distinctiveness (Phillips, Liljenquist, & Neale, 2009). It was argued that a newcomer’s social distinctiveness entails more than just ‘being new’ in a team. Rather, it also encompasses whether or not the newcomer is differ­ ent from the old‐timers in terms of important social demographic categories (e.g., race, gender, or functional background). A comparison between team responses to newcomers who were similar in this respect and newcomers who were socially distinct revealed that team reflection was higher when confronted with a socially distinct newcomer. It was argued that teams unwittingly expect socially similar newcomers to agree with collective task perspectives, causing the team to stick to existing ideas despite the introduction of a newcomer. As these expectations regarding task agreement were lower for socially distinct newcomers, teams became more self‐reflective. In conclusion, most of the studies on team reflection support the stimulating prop­ erties of membership change. Although some boundary conditions have been established, there is substantial evidence that the mere arrival of newcomers can have profound effects on the work behaviors of old‐timers, with newcomers increasing the number of unique ideas that they develop, and enhancing team creativity (Choi & Thompson, 2005; N­emeth & Ormistron, 2007; Ziller et al., 1962) and innovative outcomes (Perretti & Negro, 2007).

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Team knowledge utilization Ziller and colleagues were the first to examine actual team adoption of unique newcomer knowledge (Ziller, Behringer, & Goodchilds, 1960; Ziller, Behringer, & Jansen, 1961). In one study, they only obtained suggestive evidence that open teams utilized the new­ comer’s task estimates to a greater extent than did closed teams, but these differences were not statistically significant (Ziller et al., 1960). Moreover, when, in a second study, an old‐timer and a newcomer possessed the same set of valuable task answers, both open and closed teams perceived greater influence on the team from the old‐timer than from the newcomer (Ziller et al., 1961). In another study, Ziller and Behringer (1960) did find behavioral evidence of team utilization of newcomer knowledge when teams believed that they had a history of task failure. Compared with the successful teams, many more of the poorly performing teams adopted the task estimates that the newcomer provided. The more recent body of literature also suggests that teams are not automatically willing to adopt unique newcomer input. For instance, a series of experiments revealed that teams were significantly less willing to agree with newcomers than with old‐timers, even when both expressed the same task criticisms (Hornsey, Grice, Jetten, Paulsen, & Callan, 2007). And, although Gruenfeld et al. (2000) found newcomer effects on team reflection, they were unable to establish such effects for team utilization of specific newcomer ideas. Two field studies show a similar pattern of results. In the first study, even the leaders of understaffed teams, who were in need of new members, indicated that their newcomers had relatively little influence (Cini, Moreland, & Levine, 1993). In the second, longitudinal study, teams were only willing to utilize newcomer knowledge after the newcomers were socialized into team, and had gained 18 months of work experience (Molleman & Van der Vegt, 2007). These seemingly contradictory effects of newcomer entry on team reflection versus team knowledge utilization have motivated a number of researchers, including ourselves, to examine whether there are moderating factors that determine a team’s willingness to utilize newcomer input. This research confirmed that teams were indeed more willing to adopt newcomer knowledge when their past performance had been poor (Choi & Levine, 2004), or when they had lower performance expectations (Hansen & Levine, 2009; see Ziller & Behringer, 1960). These studies further found that it matters whether teams had been working with an assigned or self‐chosen task strategy (Choi & Levine, 2004). In particular, teams adopt newcomer knowledge more often when their own work strategy had been assigned to them as they are less committed to their own approach in these s­ituations. Moreover, in one experiment (Rink & Ellemers, 2009b), the utilization of newcomer knowledge was greater in teams with a collective promotion focus that consider complex and unpredictable situations a challenge (see Higgins, 1997) than in teams with a collective prevention focus that aim to prevent failure and focus on formal task responsi­ bilities. And finally, another series of studies demonstrated that team knowledge utilization depended on the extent to which old‐timers themselves were secured of their own group membership (Rink & Ellemers, 2015). Old‐timers were more likely to see newcomers as a threat, and reluctant to adopt their unique knowledge, when they believed that they could lose their group membership. When, however, they were secured of their own group p­osition, old‐timers were more willing to utilize newcomer knowledge. Other studies demonstrated that newcomer characteristics also affect team utilization of newcomer knowledge. Hansen and Levine (2009), for instance, showed that teams are, on average, more likely to adopt a new task strategy from a newcomer when this person behaves more assertively. Most other work has examined whether the social distinctiveness of a newcomer is related to team knowledge utilization. Hornsey et al. (2007) demonstrated



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that one way in which socially distinct newcomers can enhance team agreement with their criticism of team practices, is to distance themselves from the teams to which they previously belonged. Rink and Ellemers (2009b) found that the utilization of knowledge stem­ ming from socially distinct newcomers depended on how long such newcomers would stay in the team. When a socially distinct newcomer only joins a team temporarily, teams seem able to adopt a task focus, and become relatively receptive to their unique knowledge. When, however, it is clear that a socially distinct newcomer has joined permanently, teams are more relationship focused and appear to need some shared sense of social similarity before adopting newcomer knowledge. Two other studies demonstrated that a joint membership in an overarching social cate­ gory can provide a basis of shared social similarity that is sufficiently strong to promote knowledge utilization (Kane, Argote, & Levine, 2005). Subsequently, Kane (2010) further established that this was the case in particular when the newcomer’s superior knowledge was low in demonstrability. Put differently, when it is not self‐evident for the team how the knowledge of the newcomer can contribute to the task at hand, teams are more likely to utilize a newcomer’s valuable knowledge when the newcomer seems socially similar to the old‐timers because he or she shares a superordinate identity with the team. Taken together, the literature on team knowledge utilization suggests that it is difficult to motivate teams to effectively use the unique knowledge and expertise of newcomers. Nevertheless, there is a greater inclination to utilize a newcomer’s knowledge when the team is not performing up to standard, or when the team believes that the newcomer truly wants to become part of the team. Such team beliefs can develop as a result of an overarching identity that connects a team with the newcomer, or as a result of newcomer (assertive) behavior that signals his or her interests in the team.

Newcomer acceptance Quite some studies also examined the interpersonally acceptance of newcomers as full mem­ bers in a team. This research demonstrates, unfortunately, that full team membership is not granted to newcomers immediately. As a consequence, newcomers frequently experience assimilation pressure, which deters them from sharing their unique perspectives. In their study of team reflection, for example, Ziller et al. (1962) already found that teams were not necessarily positive about the newcomer’s arrival even though they spurred idea generation. In another study (Ziller et al., 1961), open teams found it more pleasant to work with a newcomer than did closed teams, yet both teams preferred working with old‐timers. Three decades later, Arrow and McGrath (1993) confirmed that membership change tends to negatively influences team cohesiveness, particularly when these membership changes take place on a regular basis. Most of the recent studies also demonstrate that, on average, newcomer entry lowers perceptions of team comfort, team friendliness and team social integration (e.g., Nemeth & Ormiston, 2007; Van der Vegt et al., 2010). A recent series of experiments by Pinto, Marques, Levine and Abrams (2010) nicely illustrates the challenge that newcomers face in gaining team acceptance. Their level of acceptance was far lower than that of full members and equal to the acceptance of margin­ alized team members, regardless of how much they had conformed to team norms. Only full team members were able to enhance their position in and acceptance from the team by conforming to team norms. In fact, all behaviors (including norm deviations) of the full team members were monitored more closely, illustrating that team social dynamics are not as centered on the actions of newcomers or marginal members as they are on full team members.

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One of the other studies, which employed a unique task, a social card game in which teams formed hands to earn money (Arrow & Crosson, 2003), further demonstrates the reluctance of teams to socially include newcomers. Here, teams only included newcomers (at some cost to the team’s earning) when it was clearly unfair not to do so (i.e., when the potential newcomer who was not part of any other team had no other source of monetary pay‐off). This welcoming attitude and willingness to incur a cost to onboarding the new member disappeared, however, as soon as the newcomer could earn a nominal amount alone. Together, these findings converge with earlier work done by Moreland (1985) showing that merely being categorized as a newcomer (vs. old‐timer) dampens team expectations about a person, not only in terms of task performance, also in terms of liking and social competencies. One way to create newcomer acceptance in teams is to use extensive social­ ization practices (Kammeyer‐Mueller & Wansberg, 2003). As was previously discussed in the general introduction, however, the downside of using these practices is that new­ comers are placed in the role of knowledge recipients rather than in the role of potential knowledge providers with unique, valuable ideas. Yet, there are also several team characteristics and newcomer characteristics and/or behaviors that mitigate some of the interpersonal tensions that teams experience towards newcomers. These are discussed below. Consistent with the effect of team performance history on knowledge utilization, Ziller and Behringer (1960) found that poorly performing teams rated newcomers as more pleasant than did their more successful counterparts. For instance, in the study of Hauns­ child, Moreland, and Murrell (1994), successful work teams displayed a strong out‐group bias toward newcomers with whom they were to be merged. By contrast, less successful teams did not display these biases, arguably because the old‐timers were no longer strongly identified with the collective. In a similar vein, teams that are not optimistic about their future performance (Hansen & Levine, 2009) or teams that cannot attribute their performance to team efforts (Schwieren & Glunk, 2008) also display higher levels of n­ewcomer acceptance. Other studies have suggested that several newcomer characteristics can overrule the influence of team performance on newcomer acceptance. When newcomers clearly possess required task competencies, their acceptance is also less dependent on prior team performance. Old‐timers tend to develop positive expectations about the newcomer’s contributions on the basis of their experience or competencies, which makes them more willing to engage in social exchanges (Chen & Klimoski, 2003), independently of prior team success (Fromkin, Klimoski, & Flanagan, 1972). Finally, the social distinctiveness of a newcomer also predicts the extent to which a new­ comer is accepted as a full team member. One study reported greater team acceptance of a socially distinctive newcomer than a socially similar newcomer, but explained this by arguing for a “generosity error” as old‐timers did not like it when it was one of them was socially distinct. Accordingly, teams were thought to over‐adjust to newcomers in this experiment (Craig, 1996). Other studies mostly found that newcomers were more likely to be accepted when they were socially similar to, rather than socially distinct from the old‐timers (e.g., Joadar, Kostova, & Ravlin, 2007; Ziller et al., 1960). For instance, all of the recent experiments on socially distinct newcomers find that their presence lowers the quality of team interac­ tions and reduces overall levels of team identification (Phillips et al., 2009; Rink & Ellemers, 2009a; see also Schwieren & Glunk, 2008). This evidence suggests that the positive influence that such newcomers can have on team reflection and team knowledge utilization may be short‐lived, and calls for caution when introducing socially distinct newcomers as a way to boost overall team functioning.



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Joardar et al. (2007) propose that the acceptance of a socially distinct newcomer can be positively influenced by the traits and behaviors of these newcomers. They distinguished between relationship‐based and task‐based acceptance of newcomers, and found positive associations between the relationship‐based acceptance and, for example, newcomers’ cultural intelligence. In subsequent work, Joardar and Matthews (2010) found that task‐ based newcomer acceptance was positively correlated with newcomer conscientiousness, openness to experience, and extraversion. In other studies, teams are more willing to accept newcomers who display a proactive, open and/or agreeable personality, regardless of their social distinctiveness (e.g., Kammeyer‐Mueller & Wanberg, 2003). As for the behavior of newcomers, Hansen and Levine (2009) found that newcomers who were assertive not only wielded greater team knowledge adoption, they also gained more acceptance – at least when it was relatively unclear how the team would perform (Hansen & Levine, 2009). Similar findings were obtained when the newcomer showed involvement in team processes (Myers & McPhee, 2006) and actively requested information about the collective and its activities (Burke, Kraut, & Joyce, 2010). In conclusion, research highlights the reluctance of teams to immediately accept new­ comers as full team members. As is the case with team knowledge utilization, it seems that newcomers are accepted more easily when teams have a history of failure, or when new­ comers clearly signal that they are at least in some ways socially similar to or willing to adapt to the old‐timers. Although newcomers can again signal their similarity by empha­ sizing shared categorical features, research results suggest that even stronger effects may be obtained when newcomers display socially desirable behaviors. The danger of this strategy, however, is that when newcomers are too much focused on getting accepted and start to assimilate to the team, their mere presence may no longer elicit team reflection, and they are unlikely to share their unique knowledge and perspectives – a key predecessor to knowledge utilization.

Multiple team receptivity components In the next section, we provide a more detailed comparison of the three team receptivity components, by discussing the studies that have examined more than one component of team receptivity. Again, the early experiments of Ziller and colleagues stand out here as, of all the research that examined team receptivity to newcomers, this work is part of the limited number of studies that has examined more than one receptivity component at the same time. Starting with team reflection and team knowledge utilization, this combination has only been examined by Gruenfeld et al. (2000). They, however, only obtained results for team reflec­ tion, supporting our contention that the arrival of newcomers motivates teams to reflect on their existing work practices, but does not automatically stimulate teams to integrate their knowledge into the team decision‐making processes. A few more studies examined team reflection in combination with newcomer acceptance. Ziller et  al. (1962), for example, provided the first evidence suggesting that these two team receptivity components may be higher in open teams than in closed teams, particu­ larly when the teams received the newcomers through replacements rather than additions. Arrow and McGrath (1993) also found that newcomers can positively influence team reflection as well as team cohesion, but only when teams do not experience membership change too frequently. Recently, Van der Vegt et al. (2010) demonstrated in a field setting that high levels of team membership change decreased social integration processes as well as team learning behavior and task flexibility. Note, however, that studies by Nemeth and Ormiston (2007) as well as Phillips et al. (2009) yielded different effects for these team

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receptivity components: their results revealed that the introduction of newcomers had a positive influence on team reflection, but a negative influence on newcomer acceptance. As discussed previously, this negative influence on team social dynamics was even more pronounced for socially distinct newcomers. The remaining research that has included more than one team receptivity component examined newcomer acceptance in combination with team knowledge utilization. In one study, Ziller et al. (1960) found that these two team receptivity components were p­ositively associated in the case of socially distinct newcomers, but not in the case of newcomers who were socially similar to the team. In another study, Ziller & Behringer (1960) found that knowledge utilization and newcomer acceptance coincided after teams had just received negative performance feedback, a finding later largely replicated by Hansen and Levine (2009) under the condition that the newcomer behaved assertively. Yet again, other studies were unable to show alignment between newcomer acceptance and knowledge utilization. In their second study on open versus closed teams, Ziller et al. (1961) also examined both components, but only found evidence of newcomer acceptance. No corresponding objective evidence was found for knowledge utilization. Likewise, Cini et al. (1993) found that understaffed teams were willing to accept newcomers, but did not make an effort to utilize their knowledge. Finally, in the case of temporary newcomers, a reversal effect was obtained, such that teams were willing to utilize the knowledge of t­emporary newcomers, but felt that their presence was so disruptive as to lower their o­verall attachment to and identification with the team (Rink & Ellemers, 2009b). What is clear from research to date is that different components of team receptivity do not necessarily align, and that optimizing one aspect of the team’s receptivity to new­ comers (e.g., team reflection) while neglecting other concerns (e.g., newcomer acceptance or team cohesiveness) may well have deleterious effects on overall team performance.

From team receptivity to team performance Several studies have examined the effects of newcomer entry and membership change on team performance. Some studies assessed external perceptions of team performance, such as customer ratings of team service quality (Hausknecht, Trevor, & Howard, 2009) or leader ratings of team performance (Chandler, Honig, & Wiklund, 2005; Chen, 2005; Van der Vegt et al., 2010), but the majority of these studies measured team performance as the quality of objective team task outcomes, such as team problem solving (Hirst, 2010; Huckman, Staats, & Upton, 2009; Lewis et al., 2007; Naylor & Briggs, 1965; O’Connor, Gruenfeld, & Mcgrath, 1993; Summers, Humphrey, & Ferris, 2012; Trow, 1960), team productivity (Mathiyalakan, 2002), commercial performance (i.e., movie box‐office sales; Ferrani, Cattani, & Baden‐Fuller, 2009), citations of the team’s scientific output (Guimera et al., 2005), and the execution speed of computer simulation tasks (Zoethout, Jager, & Molleman, 2010). The findings of these studies are mixed. Some demonstrate a direct positive effect of newcomer entry on team performance (e.g., Guimera et al., 2005; Ferriani et al., 2009, Zoethout et al., 2010). Other studies, however, report a negative direct relationship bet­ ween newcomer entry and team performance (e.g., Huckman et al., 2009; Mathiyalakan, 2002). All remaining studies examined whether and how newcomer effects on team performance were moderated by team characteristics or newcomer behaviors. The classic study by Trow (1960), for example, indicates that the frequency of membership change negatively impacts on the relationship between newcomer entry and team performance. Teams frequently experiencing membership change benefit less from newcomers than  teams that are less frequently disrupted by replacements or additional members.



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Relatedly, Hausknecht et  al. (2009) found that the greater the number of newcomers entering a team, the more pronounced the detrimental effects of newcomer entry on team customer service quality. Several studies have revealed conditions under which newcomer disruptions may enhance overall team performance. Chandler et al. (2005), for instance, found that new­ comer entry was associated with performance gains for teams in environments with a high degree of technological and product change. Additionally, Naylor and Briggs (1965) established that team performance could benefit from newcomers in the team when these newcomers introduced relevant experiences (see also Fromkin et  al., 1972 for a similar effect of competence on acceptance). This effect was replicated by Summers et al. (2012), but at the same time, they also found that teams perform better, at least initially, when they place a newcomer in a strategically unimportant team role. Altogether the above studies suggest that the relationship between newcomer entry and team performance depends on the extent of disruption caused by onboarding newcomers and the degree to which the newcomers introduce relevant skills, competencies, or expe­ riences that may actually help teams to innovate. Unfortunately though, these studies did not specifically examine whether any of the team receptivity components were related to eventual team performance. As a result, research to date raises more questions than that it yields conclusions regarding the relationships among newcomer entry, team performance, and team receptivity.

The importance of alignment Our central proposition is that the team’s performance is most likely to benefit from n­ewcomers with unique knowledge and skills when the behavioral components of recep­ tivity (team reflection, knowledge utilization, or both), go hand in hand with the third, psychological component of team receptivity: newcomer acceptance. We build our reasoning on prior studies on team knowledge utilization, suggesting that a certain level of newcomer acceptance can help to make teams more receptive to new­ comer influence. Given that teams were found to utilize newcomer knowledge more often when newcomers shared an important social feature with the old‐timers (i.e., superordi­ nate identity; Kane et  al., 2005; Kane, 2010) or when newcomers publicly denounced their attachment to a previous identity (Hornsey et al., 2007), one can assume that teams rely, in part, on social considerations in deliberating how to respond to a newcomer. More­ over, other studies that treated newcomer acceptance as a predictor of other newcomer behaviors yielded suggestive evidence in this direction as well. Chen (2005), for example, found that newcomer performance contributed to team performance, but this was only the case when the team held positive expectations of the newcomer. The fact that this link is strongest when teams have a positive impression of the newcomer is in line with the notion that newcomer acceptance is important for teams to effectively utilize newcomers’ task contributions. Third, as most of the positive effects on behavioral indicators of the team’s responsive­ ness to the newcomer (team reflection and knowledge utilization) were obtained in short‐ lived experiments, it remains unclear whether these responses will sustain over time, past the initial disruptive phases (Arrow, McGrath, & Berdahl, 2000; Gruenfeld et al., 2000). Recall that the organizational learning literature suggests that the unique knowledge brought by newcomers is particularly important for the long‐term adaptation of teams to their external environment (March, 1991). Based on the finding that teams are more relationship focused when a newcomer’s position is permanent rather than temporary (Rink & Ellemers, 2009b), we argue that for such long‐term receptivity and concomitant

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performance benefits, it may be more important that the team interpersonally accepts a newcomer who contributes unique opinions and can be an agent of change. In the introduction to our article, we pointed out that teams tend to have a strong preference for familiarity and similarity, one reason being that these enhance mutual trust among team members (Liang et  al., 1995). This notion highlights another theoretical reason why newcomer acceptance may be an important prerequisite for team reflection, knowledge utilization, and ultimately enhanced team performance. Given that changes affecting the achievement of team goals and other deviations from team norms are relatively threatening to a team (Levine, Choi, & Moreland, 2003), Hornsey et al. (2007) argued that teams should be highly attuned to the motives of critics, or the instigators of change. Their research demonstrates that teams are better able to deal with criticism when they trust that the one providing or causing it is really devoted to the team. In the case of criticism voiced by an old‐timer, concerns about motives and intent are likely to be less relevant as the individual has already demonstrated his or her loyalty to the team. How­ ever, it is arguably more difficult for teams to assess the intentions of an unfamiliar new­ comer who often is socially distinct from at least some of the team members (see also the intergroup sensitivity effect; Hornsey & Imani, 2004). As long as it is unclear to what extent the newcomer wants to be included in the team, the team will be more apprehen­ sive of any novel ideas or criticism communicated by the newcomer (see also Ellemers & Jetten, 2013). To conclude, there is sufficient empirical evidence and theoretical ground to suggest that an alignment of the behavioral team receptivity components with the psychological acceptance of a newcomer, indicating complete team receptivity, is needed in order to obtain lasting team performance improvements as a result of a newcomer entry.

Future Research An important challenge for future research is to uncover conditions under which teams may enjoy the benefits of newcomer entry, without suffering the disadvantages. The research we reviewed on team receptivity to newcomers clearly demonstrates that team responses to newcomers are not determined by the newcomer alone. Team receptivity is also determined by characteristics of the team itself (e.g., Ziller & Behringer, 1960; Choi & Levine, 2004). We therefore illustrate our reasoning by identifying factors at the team and newcomer level of analysis that is likely to promote complete receptivity to new­ comers and enhance team performance. To further increase the practical applicability of our a­nalysis, in selecting these factors we add as an additional constraint that they are also a­menable to managerial intervention.

Status hierarchies in teams One important team‐level characteristic that we expect to influence the alignment of the three team receptivity components is the shape of a team’s status hierarchy. A large body of research demonstrates that status inequalities are inevitable in teams. Not all team posi­ tions are ranked equally in terms of the prestige, influence, and respect attached to them, meaning that there are team members with higher social status in the team than others (Anderson & Brown, 2010). Such hierarchical differentiations within teams are often related to differences in formal power accrued to individual from the roles they hold (Galinsky, Magee & Gruenfeld, 2003). However, even when such roles are less clear, status hierarchies tend to arise because team members hold implicit assumptions about the



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social status of others (Berger, Rosentholz & Zelditch, 1980; Berger, Ridgeway, Fisek & Norman, 1998). It is well known that the possession of certain attributes, such as demo­ graphic features or task competencies, can serve as a diffuse cue on the basis of which people grant others influence or respect (Berger et al., 1980). Although status differences constitute an integral part of team life, practice tells us that there are great variations in the magnitude of intra‐team status differences, with some teams having a steeper status distribution among members than others (Harrison & Klein, 2007). There is good reason to expect the gradient or slope of a team’s hierarchy to affect the three team receptivity components, and thus, team performance under membership change. In teams with steeper hierarchies, there are generally clear guidelines for the task behaviors that members need to demonstrate based on their relative ranks (see also Torrance, 1955). Accordingly, high‐status members generally can exert a great deal of influence on group decisions, whereas lower status members experience conformity pressure and yield to their input (Anderson & Brown, 2010; Van der Vegt, Bunderson, & Oosterhof, 2006). Such deference can be functional, as it can help teams to perform well under time pressure, or when confronted with simple task requirements (Bunderson, Van der Vegt, Cantimur & Rink, 2015; Cantimur, Rink  & Van der Vegt, 2015). However, steep status hierarchies also make low status members often feel that it is very difficult to stand out or to change anything about their personal position. Even when they feel c­ommitted to the team and realize they may personally benefit from its successes, they do not feel personally responsible for the team’s outcomes (Ellemers et al., 2004). They are therefore generally not highly motivated to self‐reflect on their task contributions, nor will they go out of their way to make an effort to adopt novel task strategies or to engage in new relationships with other team members, even if this may facilitate the achievement of  team goals (Bunderson et  al., 2014; Doosje, Ellemers, & Spears, 1995. In such c­ircumstances, it is less likely that the voices of newcomers will be heard, and that their contributions to the team task will be valued. By contrast, when teams have relatively flat status hierarchies, such that there are fewer differences in status among members, each individual is expected to participate in team decision making and there is more room for debate about competing perspectives (Brooks, 1994; Edmondson, 2002). There is suggestive evidence that this is particularly important when teams feel threatened (Jehn & Bendersky, 2003), as is often the case with newcomer entry and membership change. Thus, when a newcomer arrives, a flat status hierarchy may stimulate old‐timers to reflect on their collective performance. The above reasoning also relates to some of the mixed effects obtained for team recep­ tivity to newcomers. For example, while some studies have found that open teams are relatively receptive to newcomers, our literature review suggests that there may be boundaries to this effect. Flexibility in team composition is attractive from a management point of view, but it can also be a source of stress and instability when changes in team composition occur too often. As the status literature suggests that teams with steep status hierarchies are particularly resistant to change (Bunderson et al., 2014), their members may lack the motivation to deal with the disruption that a continuous flux of new mem­ bers brings along. But teams that endorse egalitarian hierarchies may be more successful in handling frequent membership changes. Within these teams, newcomers are less threatening to the status positions of the existing members, which are more or less equal to begin with. Moreover, these existing members all bear equal responsibility for w­elcoming new persons into the team and are used to dissent in group discussions. We therefore expect that open teams may only be able to handle their regular member­ ship changes well (i.e., better than closed teams) when there are little status differences among the members. That is, with an egalitarian status hierarchy, open teams are more

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likely to become completely receptive to newcomers and thus more likely to benefit from them in terms of sustained performance improvements. Notably, we elaborate on the effect of status hierarchies in teams primarily to illustrate the type of processes and relevant concerns that are likely to play a role at this level. This is by no means the only team‐level variable of relevance in this respect. Other team‐level variables that elicit similar concerns (e.g., an ‘open’ vs. ‘closed’ communication culture) may be equally consequential for the team’s ability to benefit from the inclusion of n­ewcomers.

Newcomer’s use of identity strategies Finally, we propose that newcomers themselves can become active agents who calibrate their behavior toward the team to increase team receptivity. Although research has increas­ ingly recognized that newcomers can proactively shape the socialization process, prior work has focused on what newcomers can do to improve their own role performance and assimilation (Harrison, Sluss & Ashforth, 2011; Morrison, 2002). Little empirical attention has been directed toward understanding how newcomers can proactively shape team‐level outcomes (see Hansen & Levine, 2009). Yet, we argue that newcomers can be aware of the fact that they are a potential source of influence in the team. Specifically, we suggest that the alignment of team receptivity components and hence impact of the new­ comer on the team’s performance can be facilitated when newcomers use an identity strategy that underscores their willingness to be included in and belong to the team. As indicated above, in order for teams to be open to the potentially disturbing forces of criticism, they need to feel assured that the person providing unique input acts with the team’s interests in mind. This is why critical comments from out‐group members and newcomers tend to be judged more negatively than similar comments from in‐group old‐ timers – the former have not yet proven their loyalty to, and willingness to investment in the team (Hornsey et  al., 2007; Hornsey & Imani, 2004). As a possible solution for n­ewcomers to increase their chances of getting accepted by their new team, Hornsey and colleagues have suggested that they might publicly denounce their previous team m­embership (Hornsey et al., 2007). However, this may not always be viable in work set­ tings, if only because such explicit statements may raise doubts about the performance of the previous team or the reasons for leaving that team, undermining perceptions of a newcomer’s expertise or overall loyalty. In practice, a frequently employed identity strategy among peripheral members, such as newcomers, is to emphasize their own uniqueness and/or individual task contributions that merit acceptance by the team (Jetten, Branscombe, Spears, & McKimmie, 2003; Phil­ lips et al., 2009). As long as newcomers are in a marginal position they may find it difficult to feel attached to the team or to enact the team’s identity. Instead, they tend to attach greater importance to their personal identity (Ashforth & Saks, 1996; Rink & Ellemers, 2011). This differentiating strategy might  –  unintentionally  –  emphasize their social d­istinctiveness, which will do little to increase newcomer acceptance. Moreover, team reflection and knowledge utilization are also likely to be hindered to the extent that a d­ifferentiating strategy causes the team to question the newcomer’s concern for team goals. In other words, although this strategy may clarify the newcomer’s potential worth to the team, it may at the same time undermine the team’s willingness to accept and include this knowledge. In line with the analysis we made above, this precludes full team receptivity and thus is not the optimal way to enhance team performance. We propose that newcomers are most likely to induce full team receptivity when they use an integrating identity strategy that clearly communicates the willingness to invest in



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the team and to employ their unique knowledge to the benefit of collective team goals. One relatively straightforward and practical way of signaling the importance of the team for the self and to communicate the perceived inclusion of the individual in the team is by using collective language (we, our, us) when indicating the ways in which unique individual knowledge may benefit the achievement of team goals (see also, Burke, et  al., 2010). Thus, we expect the use of collective language as a strategy newcomers can use to optimize the likelihood that the team will acknowledge them as a team member and utilize their unique knowledge. In this way, a newcomer can communicate distinct task contributions and relevant expertise while expressing his or her interest in the team and its goals, and without calling into question prior achievements or loyalties. When a newcomer conveys the intention to act as part of the team in this way, this should contribute to acceptance of the newcomer and his or her ideas. Some of our recent experimental results are consistent with the reasoning that new­ comer communications that integrate the self with the team can facilitate team receptivity to their ideas. Interestingly, this was particularly the case when the newcomer was socially distinct from the team. Teams were more likely to accept newcomers who compensated for their social distinctiveness by employing collective level language (indicating an integrating identity strategy) rather than individual language (indicating one’s personal identity), which subsequently increased the team’s consideration of their unique knowledge (Kane & Rink, 2015). These effects were less pronounced for socially similar newcomers. By demonstrating that newcomers who do not share a salient social category with the team are being evaluated on the basis of their initial behaviors, these findings support some of the earlier work on newcomer social distinctiveness. For the socially similar new­ comers who already experience a sense of commonality with the team, it is less important to immediately signal their team concern. As such, the integrative communication style seems a very concrete and practical strategy that the many socially distinct newcomers can  employ to increase complete team receptivity. After all, even when socially distinct newcomers do not possess task‐relevant knowledge, this strategy may help them to increase the team’s willingness to reflect upon existing strategies. In the end, such alignment of team receptivity components is likely to yield performance benefits in teams. Again, our reasoning and initial evidence illustrate that the way the newcomer commu­ nicates with the team is another factor that can either facilitate or undermine full team receptivity. Yet there may of course be different ways for newcomers to convey their willingness to be included in the team that deserve future research attention. Our consideration of team‐level and newcomer‐level moderators illustrates how condi­ tions at each of these levels of analysis may play a role in achieving complete team recep­ tivity. Additionally, these facilitators of team receptivity merit further consideration as they are relatively amenable to organizational interventions, compared to other potential facilitators (e.g., the team’s performance history). The team’s ability to acknowledge and profit from newcomer contributions, and the added value of introducing newcomers may be limited if organizations are unwilling or unable to do this.

Conclusion Membership change in organizations inevitably results in the introduction of newcomers, who typically represent a numerical minority in the teams that they join (Choi & Levine, 2004). Theories propose that newcomers, with their different background, are important sources of innovation that facilitate team performance and can thus enhance the long‐term survival changes of teams. But our review of over 50 years of research on this topic

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demonstrates that this potential is often not realized. Our review suggests that the three team receptivity components  –  team reflection, knowledge utilization, and newcomer acceptance  –  depend on each other and jointly influence sustained team performance. In this way, we hope to provide the foundation needed for developing a deeper under­ standing of when teams will be completely receptive to newcomers.

Acknowledgments The research ideas presented are part of a larger research program (472‐04‐044) granted to the first and third author by the Netherlands Organisation for Scientific Research.

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Hirst, G. (2010). Effects of membership change on open discussion and team performance: The moderating role of team tenure. European Journal of Work and Organizational Psychology, 18, 231–249. Hornsey, M. J., Grice, T., Jetten, J., Paulsen, N., & Callan, V. (2007). Group‐directed criticisms and  recommendations for change: why newcomers arouse more resistance than old‐timers. Personality and Social Psychology Bulletin, 33, 1036–1048. Hornsey, M. J. & Imani, A. (2004). Criticizing groups from the inside and the outside: An identity perspective on the intergroup sensitivity effect. Personality and Social Psychology Bulletin, 30, 365–383. Huckman, R. S., Staats, B. R., & Upton, D. M. (2009). Team familiarity, role experience, and performance: Evidence from Indian software services. Management Science, 55, 85–100. Jehn, K. A. & Bendersky, C. (2003). Intragroup conflict in organizations: A contingency perspective on the conflict–outcome relationship. Research in Organizational Behavior, 25, 187–242. Jetten, J., Branscombe, N. R., Spears, R., & McKimmie, B. M. (2003). Predicting the paths of peripherals: The interaction of identification and future possibilities. Personality and Social Psychology Bulletin, 29, 130–140. Joardar, A. & Matthews, L. (2010). An empirical investigation of group acceptance using the Big Five personality domains. Organization Management Journal, 7, 194–207. Joardar, A., Kostova, T., & Ravlin, E. C. (2007). An experimental study of the acceptance of a foreign newcomer into a workgroup. Journal of International Management, 13, 513–537. Kammeyer‐Mueller, J. D. & Wanberg, C. R. (2003). Unwrapping the organizational entry process: Disentangling multiple antecedents and their pathways to adjustment. Journal of Applied Psychology, 88, 779–794. Kane, A. A. (2010). Unlocking knowledge transfer potential: Knowledge demonstrability and s­uperordinate social identity. Organization Science, 21, 643–660. Kane, A. A., Argote, L., & Levine, J. M. (2005). Knowledge transfer between groups via personnel rotation: Effects of social identity and knowledge quality. Organizational Behavior And Human Decision Processes, 96, 56–71. Kane, A. A., & Rink, F. (2015). Newcomers as active agents: Team receptivity to integrating versus differentiating identity strategies. Group Dynamics: Theory, Research and Practice: 19, 91–105. Katz, R. (1982). The effects of group longevity on project communication and performance. Administrative Science Quarterly, 27, 81–104. Levine, J. M., Choi, H‐S., & Moreland, R. L. (2003). Newcomer innovation in work teams. In P. Paulus & B. Nijstad (Eds.), Group creativity: Innovation through collaboration (pp. 202–225). New York: Oxford University Press. Lewis, K., Belliveau, M., Herndon, B., & Keller, J. (2007). Group cognition, membership change, and performance: Investigating the benefits and detriments of collective knowledge Organizational Behavior and Human Decision Processes, 103, 159–178. Liang, D. W., Moreland, R., & Argote, L. (1995). Group versus individual training and group‐ performance: The mediating role of transactive memory. Personality and Social Psychology Bulletin, 21, 384–393. Littlepage, G., Robison, W., & Reddington, K. (1997). Effects of task experience and group experi­ ence on group performance, member ability, and recognition of expertise. Organizational Behavior and Human Decision Processes, 69, 133–147. March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2, 71–87. Mathiyalakan, S. (2002). A methodology for controlled empirical investigation of membership c­ontinuity and change in GDSS groups. Decision Support Systems, 32, 279–295. Molleman, E. & Van der Vegt, G. S. (2007). The performance evaluation of novices: The importance of competence in specific work activity clusters. Journal of Occupational and Organizational Psychology, 80, 459–478. Moreland, R. L. (1985). Social categorization and the assimilation of “new” group members. Journal of Personality and Social Psychology, 48, 1173–1190. Moreland, R. L., & Levine, J. L. (2006). Socialization in organizations and work groups. In J. M. Levine & R. L. Moreland (Eds.), Small Groups (pp. 469–499). New York: Psychology Press.



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Morrison, E. (2002). Newcomers’ relationships: The role of social network ties during socialization. Academy of Management Journal, 45, 1149–1160. Myers, K. K. & McPhee, R. D. (2006). Influences on member assimilation in workgroups in high‐reliability organizations: A multilevel analysis. Human Communication Research, 32, 440–468. Naylor, J. C. & Briggs, G. E. (1965). Team‐training effectiveness under various conditions. Journal of Applied Psychology, 49, 223–229. Nemeth, C. J. & Ormiston, M. (2007). Creative idea generation: Harmony versus stimulation. European Journal of Social Psychology, 37, 524–535. O’Connor, K. M., Gruenfeld, D. H., & Mcgrath, J. E. (1993). The experience and effects of conflict in continuing work groups. Small Group Research, 24, 362–382. OECD (2014). Education at a Glance 2014: OECD Indicators, Paris: OECD Publishing. http://dx.doi. org/10.1787/eag-2014-en Perretti, F. & Negro, G. (2007). Mixing genres and matching people: A study in innovation and team composition in Hollywood. Journal of Organizational Behavior, 28, 563–586. Phillips, K. W., Liljenquist, K. A., & Neale, M. A. (2009). Is the pain worth the gain? The advantages and liabilities of agreeing with socially distinct newcomers. Personality and Social Psychology Bulletin, 35, 336–350. Pinto, I. R., Marques, J. M., Levine, J. M., & Abrams, D. (2010). Membership status and subjective group dynamics: Who triggers the black sheep effect? Journal of Personality and Social Psychology, 99, 107–119. Rink, F. & Ellemers, N. (2009a). The effects of newcomers on team innovation. Gedrag & Organisatie, 22, 294–306. Rink, F. A. & Ellemers, N. (2009b). Temporary versus permanent group membership: How the future prospects of newcomers affect newcomer acceptance and newcomer influence. Personality and Social Psychology Bulletin, 35, 764–775. Rink, F. & Ellemers, N. (2011). From current state to desired future: How compositional changes affect dissent and innovation in work groups. In J. Jetten & M. J. Hornsey (Eds.), Rebels in groups: Dissent, deviance, difference and defiance (pp. 54–72). Oxford: Blackwell. Rink, F. & Ellemers, N. (2015). The pernicious effects of unstable work group membership: How work group changes undermine unique task contributions and newcomer acceptance. Group Processes & Intergroup Relations, 18(1): 6–23. Rink, F., Kane, A., Ellemers, N., & van der Vegt, G. S. (2013). Team receptivity to newcomers: Evidence and future research themes. Academy of Management Annals, 7, 1–47. Schwieren, C. & Glunk, U. (2008). Mechanisms underlying nationality‐based discrimination in teams: A quasi‐experiment testing predictions from social psychology and microeconomics. Small Group Research, 39, 643–672. Summers, J. K., Humphrey, S. E., & Ferris, G. R. (2012). Team member change, flux in coordination, and performance: Effects of strategic core roles, information transfer, and cognitive ability Academy of Management Journal, 55(2), 314–338. Torrance, E. P. (1955). Some consequences of power differences on decision making in permanent and temporary three‐man groups. In A. P. Hare, E. F. Borgatta, & R. F. Bales (Eds.), Small groups: Studies in social interaction (pp. 488–489). New York: A. A. Knopf. Trow, D. B. (1960). Membership succession and team performance. Human Relations, 13, 259–269. Van der Vegt, G. S., Bunderson, J. S., & Kuipers, B. (2010). Why turnover matters in self‐managing work teams: Learning, social integration, and task flexibility. Journal of Management, 36, 1168–1191. Van der Vegt, G. S., Bunderson, J. S., & Oosterhof, A. (2006). Expertness diversity and interpersonal helping in teams: Why those who need the most help end up getting the least. Academy of Management Journal, 49, 877–893. Ziller, R. C. (1965). Toward a theory of open and closed groups. Psychological Bulletin, 64, 164–182. Ziller, R. C. & Behringer, R. D. (1960). Assimilation of the knowledgeable newcomer under conditions of group success and failure. Journal of Abnormal and Social Psychology, 60, 288–291.

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9

Status Effects on Teams Kun Luan, Qiong‐Jing Hu, and Xiao‐Yun Xie

Introduction Status is a primary social goal pursued by an individual or a group (Barrick, Stewart, & Piotrowski, 2002; Pettit, Yong, & Spataro, 2010). Social scientists have long made great efforts to understand the role of social status (Maslow, 1943; Piazza & Castellucci, 2014; Vroom, 1964). In addition to its importance in social life, status is one of the most fundamental attributes of an organization. Status as it relates to the amount of respect employees gaining from their colleagues can be formed out of employees’ past performance, personal attributes, expertise and formal positions (e.g., Magee & Galinsky, 2008) and emerge in nearly all types of groups and organizations. Extensive studies have shown that status can exert an influence on individuals’ motivation and behavior, the organization and  operation of work teams and between‐group interactions (e.g., Bunderson, Van der Vegt, & Sparrowe, 2014; Iacoviello & Lorenzi‐Cioldi, 2015; Pettit et al., 2010; Phillips, Rothbard, & Dumas, 2009; Scheepers, 2009). Its prevalence and potential effects on i­ndividuals and teams make it valuable to summarize the role of status in organizations. Moreover, the effects of status in the organizational setting deserve more attention against the background of the knowledge economy. Traditional organizations are hierarchically structured, and status is usually accompanied by formal and fixed power hierarchies (Weber, 1922). Those who occupy higher and powerful positions in these hierarchies are presumed to have status. However, to better respond to dynamic external environments, organizational hierarchies are becoming flatter and teams are becoming increasingly diverse in terms of their specializations (Nembhard & Edmondson, 2006). Flat structures separate status from the formal hierarchies, and the emergence of multidisciplinary teams has highlighted the importance of informal status to the team process, owing to its high relevance to employees’ expertise and competence (Berger & Conner, 1969;

The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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Berger & Zelditch, 1985). Therefore, it is necessary to understand status relations at both the intra‐ and interteam levels and, more importantly, how status effects occur at the individual, team, and interteam levels. This review is organized into three sections. In the first section, we give a detailed definition of status and differentiate it from social power. In the second section, we summarize status effects at the individual, team, and interteam levels. We cover the primary topics that have drawn most of the attention from researchers and are rooted in similar theoretical frameworks. In the final section, we provide some valuable directions for exploring status effects in the future.

Definition of Status Status is one of the most fundamental bases of social hierarchy. It is defined as the respect or admiration that an individual or a group enjoys in the eyes of others (e.g., Magee & Galinsky, 2008; Ridgeway & Walker, 1995). It can be either an intragroup or an intergroup description of one’s relative position as conferred by others (Magee & Galinsky, 2008). As a person’s status is determined by the extent of the respect or admiration others have for him or her, status is primarily subjective, which differentiates it most from other important bases of social hierarchy (e.g., power). Although some studies have defined status as a multidimensional construct (e.g., Anderson, John, Keltner, & Kring, 2001; Groysberg, Polzer, & Elfenbein, 2011), we agree with Magee and Galinsky’s (2008) opinion that attention (or prominence) and influence (or dominance) should be separated from status. According to the authors, “attention and the related process of person perception are more basic phenomena than status” (Magee & Galinsky, 2008, p. 360). Individuals can receive attention from others for several reasons besides status. And influence is, to some extent, a downstream effect of status. For example, although an individual may occupy a high‐status position in a social hierarchy, he or she can decide whether to use this favorable position to influence the low status of others. Therefore, despite the close relationship between status and attention or influence, they are conceptually distinct. In work teams, status usually emerges from the expectations others have about one’s relevance or contributions to team performance. According to expectation states theory (Berger & Conner, 1969; Berger, Fisek, & Conner, 1974), such expectations arise out  of the task‐related interactions of team members and are essentially properties of relations rather than actors as persons. Status characteristics theory (Berger, Fisek, & Norman, 1977) also holds that the conferral of status can be elicited from the possession of specific status characteristics (e.g., skills, previous performance, cognitive ability) and diffuse status characteristics (e.g., race, age, sex, or personality traits). A status characteristic is a characteristic of an actor with two or more states that are differentially evaluated in terms of honor, esteem, and desirability, each of which is associated with distinct moral and performance expectations (Berger, Rosenholtz, & Zelditch, 1980). Specific status characteristics influence people’s expectations about their competence in specific situations, and diffuse status characteristics activate both general and specific expectations about an individual’s future performance (Berger & Zelditch, 1985). Individuals who own status characteristics valued by others receive more opportunities to contribute to group tasks and more positive evaluations of their contributions than those who lack such characteristics. In addition, given the popularity of diverse workforces in teams and organizations, status differentiation and hierarchies are becoming more prevalent in daily working life.



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Differences between status and power As the two most important bases of social hierarchy, status and power are often mixed in organizations and work teams. For example, team leaders have the power to allocate resources and other team members usually show those leaders respect. Individuals who own power can use it to benefit the interests of others and hence enhance their status (Flynn, 2003). Meanwhile, individuals who are admired by others may have the privilege of obtaining superior resources and gain power as a result. Therefore, status and power are highly interrelated in some cases. However, status and power are naturally distinct constructs. Status is derived from the relative level of respect and admiration conferred upon one individual by others (e.g., Magee & Galinsky, 2008). Accordingly, although high‐status individuals can exert influence on low‐status individuals, others’ perceptions and evaluations determine the status position of the focal individual. In addition, by showing generosity (Flynn, 2003; Flynn, Reagans, Amanatullah, & Ames, 2006) or elevating peer‐rated competence (e.g., Anderson, Brion, Moore, & Kennedy, 2012), individuals can change their status. Meanwhile, different from status, the literature reveals that power can sometimes be delegated directly by supervisors and not necessarily conferred by followers (e.g., Hays & Bednersky, 2015). Furthermore, power refers to asymmetric control over socially valued resources (Magee & Galinsky, 2008). In a dyadic relationship, the low‐power actor i­nevitably relies more on the high‐power actor (e.g., Van der Vegt, de Jong, Bunderson, & Molleman, 2010). Owing to asymmetric dependence, low‐power actors are usually controlled by high‐power actors, which makes it difficult for low‐power individuals to obtain power. In sum, power h­ierarchies are more difficult to be reversed or changed than status hierarchies. Empirical findings have also revealed that although status and power often co‐vary, they can indeed have quite different influences on individuals’ psychological states and behavior. First, low‐status individuals are found to be more likely to struggle for high‐status positions than low‐power ones (Hays & Bendersky, 2015). Second, researchers propose that high‐status individuals are more likely to attend to others’ perspectives and act in a respectable way than high‐power ones because the former need to care about the impressions they made on others (Galinsky, Magee, Inesi, & Gruenfeld, 2006). In Blader and Chen’s (2012) five‐experiment study, they found that status was associated with greater fairness toward others and that power was associated with less fairness toward others. Furthermore, they found that power weakened the positive effect of status on justice behavior and that status promoted justice behavior only when the individual had a low rather than high level of power. Other evidence also shows that individuals who hold power are less i­nfluenced by normative pressure, focus more on themselves, and have a greater sense of independence but status has no such effect (Dubois, Rucker, & Galinsky, 2015; Fast, Halevy, & Galinsky, 2012; Galinsky, Gruenfeld, & Magee, 2003; Galinsky, Magee, G­ruenfeld, Whitson, & Liljenquist, 2008; Guinote, 2007; Keltner, Gruenfeld, & Anderson, 2003). Last but not least, in addition to influencing actors’ behavior, counterpart actors may react in different ways to individuals who hold status versus those who hold power. In two laboratory experiments, Fragale, Overbeck, and Neale (2011) found that perceivers expected positive interactions with high‐status individuals (warm) but negative interactions with high‐power, low‐status individuals (cold and dominant).

Status Effects on Teams Our review of the definition of status and how it is differentiated from power makes it clear that status hierarchy is a prevalent and unique determinant in both individual social life and the organizational setting. Owing to its prevalence, it is worthwhile exploring their

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effects on different outcomes. In this section, we review all of the important findings on status effects at the individual, team, and interteam levels based on different theoretical perspectives.

Status effects on individual behavior Studies have thoroughly explored the various effects status has on individuals according to a wide range of criteria at the individual level (e.g., Bothner, Kim, & Smith, 2012; Ellemers, Sleebos, Stam, & de Gilder, 2013; Howell, Harrison, Burris, & Detert, 2015; Lount Jr. & Pettit, 2012; Smith, Menon, & Thompson, 2012; Spataro, Pettit, Sauer, & Lount Jr., 2014; Wilkins, Wellman, Babbitt, Toosi, & Schad, 2015). We do not comprehensively review all of the various outcomes. Instead, we focus on the two most common dependent variables examined in status research, as they may be relatively more important to the c­ontext of work teams. We mainly draw on the key ideas rooted in status characteristics and expectation states theories to organize the previous findings about the effects of status on individual performance and opinion expression. Individual performance  Performance may be one of the most important outcomes as regards status effects at the individual level. It is closely related to the organizational setting and has garnered much attention from researchers. As status largely derives from past performance and expertise, high‐status individuals are expected to be more competent and contribute more to the whole team. Therefore, it is generally agreed that high‐status individuals are usually assigned more tangible (e.g., rewards) and intangible (e.g., information, opportunities) resources than low‐status individuals for achieving tasks and that low‐status individuals are left with inferior resources and opportunities. Accordingly, status serves as an asset for individuals who hold a high level of status to enhance their subsequent performance (Merton, 1968; Stuart & Ding, 2006). Despite the benefits that status offers to individual performance, the other perspective proposes that it may erode high‐status individuals’ capacity to perform due to complacency and lack of focus (Burt, 2010; Walker & Smith, 2002; Weber, 1978). Bothner et al. (2012) combined these two competing predictions into their research. Using panel data related to the PGA tour and NASCAR’s Winston Cup series, they found an inverted U‐ shaped relationship between status and individual performance. That is, individual performance improved with status to a very high level, after which individual performance waned as status increased. Other studies have focused on how high‐status individuals (e.g., high performers) influence low‐status individuals, as the latter are usually vulnerable and sensitive to the former (Bunderson & Reagans, 2011; Flynn & Amanatullah, 2012). Low‐status individuals consider high‐status individuals as team stars. They learn from them and are motivated to enhance their own status (Burke, Fournier, & Prasad, 2007; Lockwood & Kunda, 1997). However, high‐status individuals may be less cooperative and reluctant to share knowledge with low‐status individuals due to status‐related concerns (Groysberg et al., 2011; Overbeck, Correll, & Park, 2005). In this case, low‐status employees may feel pressured when interacting with high‐status employees and their performance may suffer as a result. Flynn and Amanatullah (2012) explained this c­omplex relationship. They found that individuals’ performance improved more in the presence of strong rather than weak performers. However, when individuals were involved in direct competition with strong performers, their own performance declined. This implies that when people are undertaking independent tasks, the presence of high‐status individuals



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may motivate low‐status individuals to perform better. However, when high‐ and low‐ status individuals interact, the latter may become strained rather than inspired and their motivation to work hard may diminish. Opinion expression  Status also influences how individuals express their opinions in a team. As team members’ status is based on their potential contribution to achieving team goals, high‐status individuals are more prominent; command more attention from peers and leaders; and have more influence over team resources, interactions, and decisions than low‐status ones (Anderson et al., 2001; Bales, Strodtbeck, Mills, & Roseborough, 1951; Berger, Cohen, & Zelditch Jr., 1972; Milanovich, Driskell, Stout, & Salas, 1998). Hence, high‐status individuals may feel more capable of influencing the status quo and be more willing to express their opinions or ideas than low‐status individuals. In addition to this heightened sense of capability, high‐status individuals may feel less constrained by others and therefore perceive safer in speaking up than low‐status individuals (Bienefeld & Grote, 2014; Janssen & Gao, 2013). In contrast, low‐status individuals are usually inhibited by the difference in status because their values are largely evaluated by high‐status individuals (Anderson & Brown, 2010). As low‐status individuals face the risk of being devalued and marginalized, they may suppress their true feelings and opinions out of a fear of negative social repercussions or personal consequences (e.g., Detert & Edmondson, 2011; Islam & Zyphur, 2005; Morrison & Milliken, 2000; Nembhard & Edmondson, 2006). Furthermore, status characteristics theory provides evidence that status differences can result in the neglect of contributions from low‐status individuals. Under such circumstances, low‐status individuals are less likely to proactively share their perspectives and insights (Bunderson & R­eagans, 2011) than high‐status individuals. In line with this perspective, Janssen and Gao (2013) found that perceived self‐status stimulated employees’ voice behavior (defined as the expression of challenging but constructive concerns, opinions, or suggestions about work‐related issues) in teams. In a sample of 1,490 aircrew members of a European airline, Bienefeld and Grote (2014) found that the crewmembers’ individual‐ level perceptions of psychological safety mediated the relationship between status and employee voice. This indicates that high‐status individuals experience a higher level of psychological safety than low‐status individuals and accordingly express their own ideas or suggestions more freely.

Status effects on team processes and outcomes Status effects at the individual level relate to how one’s status position influences his or her performance. In analyzing status effects at the team level, we aim to discuss the relationship between status hierarchy and team processes and outcomes. Magee and Galinsky (2008) defined status hierarchy as “a rank ordering of individuals or groups according to the amount of respect accorded by others” (p. 359). Many scholars have adopted a functionalist perspective on the effects of status hierarchies (e.g., Anderson, Srivastava, Beer, Sparato, & Chatman, 2006; Blau, 1964; Magee & Galinsky, 2008) and have converged on the idea that status hierarchies can enhance group effectiveness by providing several social functions (Anderson & Brown, 2010; Halevy, Chou, & Galinsky, 2011). In contrast, several studies have uncovered the negative c­onsequences of status hierarchies (Becker & Blaloff, 1969; Berdahl & Anderson, 2005; Ivancevich & Donnelly, 1975). To obtain a comprehensive understanding of these two research streams, we discuss the effect of status hierarchies from both the functionalist and bounded functionalist perspectives as follows.

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A functionalist account of  status hierarchies  Status hierarchies help groups solve two major problems. First, they establish a clear social order and facilitate group coordination (Magee & Galinsky, 2008). Team members can confer different status to different teammates based on their expectations about the focal actor’s performance and contribution to the team. Therefore, a well‐established status hierarchy helps employees to expect certain behavior from others, which provides the team with a high‐quality base of coordination. In addition, in a well‐established status hierarchy, high‐ranking individuals are granted control over decisions and allowed to direct the actions of others and low‐ranking individuals are expected to defer to high‐status teammates and follow their instructions (Berger et al., 1980; Keltner et al., 2003). In other words, teams not only access a clear order for coordination, but also gain a line of directions to follow (Halevy et al., 2011), which is helpful for improving team efficiency. When a status hierarchy is absent, work teams may suffer from status struggles or chaos (e.g., Bendersky & Hays, 2012), resulting in process losses and eventually jeopardizing team performance. In line with the functionalist perspective, Anderson and Brown (2010) argued that teams could resolve such struggles through a structured status hierarchy. A clear status hierarchy can facilitate the effective division of resources and influence. Using a sample of professional basketball teams, Halevy, Chou, Galinsky, and Murnighan (2012) found that hierarchical differentiation in pay and participation facilitated intragroup coordination and cooperation, and therefore improved team performance. In their study of self‐managed teams, Bunderson and Boumgarden (2010) found that a well‐formed team structure could promote team learning by encouraging information sharing, decreasing conflict frequency, and fostering a climate of psychological safety. Furthermore, Bresman and Zellmer‐Bruhn (2013) revealed that a high‐level team structure enhanced both internal and external learning by promoting team’s psychological safety. These empirical studies strongly support the notion that a more structured status hierarchy would significantly benefit the team process. Second, status hierarchies serve a motivational function, providing incentives for team members to behave pro‐socially and endeavor toward accomplishing shared goals (M­agee & Galinsky, 2008). In work teams, individuals who are perceived to contribute more to the collective goals are granted higher status, and those who are perceived to make fewer contributions or seen as undermining a team’s success are granted lower status. By rewarding team‐oriented behavior, a status hierarchy encourages team members to pay attention to collective interests and demonstrate their value to the team. At the same time, as a high level of status is accompanied by a high level of autonomy, positive self‐evaluation, and favorable resources, low‐status members are inclined to pursue high levels of status within their teams. Therefore, to move up in a status hierarchy, low‐status members are m­otivated to work toward shared goals and contribute to team performance. Research related to “competitive altruism” has shown that status motives encourage individuals to behave more generously (Hardy & Van Vugt, 2006; Barclay & Willer, 2007). In the context of electronic communication networks, Wasko and Faraj (2005) found that individuals who were motivated to gain personal reputations were more likely to contribute their knowledge. Furthermore, status can serve as compensation for high‐ level performers in a team. By assigning higher levels of status to individuals who contribute more, teams are able to keep these contributors committed to the collective and hence decrease their turnover intention (Huo, Molina, Binning, & Funge, 2010; Willer, Rogalin, Conlon, & Wojnowicz, 2013). A bounded functionalist account of status hierarchies  Although status hierarchies have potential benefits from the functionalist perspective, researchers have criticized their d­eleterious effects on team functioning (e.g., Bunderson & Reagan, 2011; Pfeffer & Langton, 1993;



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Smith & Tannenbaum, 1963). As Anderson and Willer (2014) stated, “the empirical record paints a much more complicated picture than the functionalist ideal would s­uggest” (p. 54). They proposed that the bounded functionalist perspective, like Simon’s (1957) notion of bounded rationality, provided a more accurate way to scrutinize the effects of status hierarchies. The bounded functionalist perspective admits that team members g­enerally intend to form a functional hierarchy that serves their goals effectively. However, it recognizes that people are usually limited by their ability to do so. In their review, Anderson and Willer (2014) pointed out two obstacles to functional perspective about status hierarchies. Although status is expected to emerge based on personal competence or contribution, teams often fail to discern individual merit a­ccurately. When those who contribute less to teams are allocated more status, the status hierarchy may in turn harm the team effectiveness. In a longitudinal study involving 55 multidisciplinary research teams, Joshi and Knight (2015) showed that whereas deference based on task contributions enhanced the research productivity of teams, deference based on social affinity exerted a negative effect on team performance. The second obstacle lies in the human desire for status. Although the desire to attain and maintain a high level of status in teams can lead people to focus on collective goals, their status enhancement attempts may have negative consequences. The desire for status tends to incur competition between high‐ and low‐status individuals, distracting them from task‐related issues. When competitive trials become severe, low‐status individuals focus more on their self‐interests rather than their teams’ collective goals (Hays & Bendersky, 2015), which can cause intragroup conflict and finally a decrease in team performance. Moreover, some individuals may undermine others in pursuit of status (e.g., Kyl‐Heku & Buss, 1996). Several studies have explored the dysfunctions of team hierarchies (e.g., Bunderson & Reagan, 2011; Christie & Barling, 2010; Greer & van Kleef, 2010; Van der Vegt et al., 2010). For example, Bunderson and Reagan (2011) posited that social hierarchies (status or power differential) could complicate a rational system model of collective learning by disrupting three critical learning‐related processes. Social hierarchies can negatively influence the focus of team members (especially low‐status members) on shared goals, hamper risk taking and experimentation, and block knowledge sharing. Tzabbar and Vestal (2015) investigated 7,162 scientific teams in the biotechnology sector between 1973 and 1999. They found that status asymmetry weakened the initial positive effect of geographic dispersion on the novelty of a team’s innovation and strengthened the negative effect at high levels of dispersion. Finally, Flynn et al. (2006) found that the desire for status led individuals to avoid seeking help from others. Furthermore, because perceptions of status are subjective and highly relevant to task demands, team members may not reach a consensus about a status hierarchy. An unstable status hierarchy may alter the behavior of high‐status team members, whose status and benefits are threatened by low‐status members. In such a case, high‐status members become vigilant and critical of the behavior of low‐status members. In addition, inconsistent perceptions of a status hierarchy generate disagreement over status. Bendersky and Hays (2012) developed the fourth type of intragroup conflict, status conflict, which they defined as a dispute or disagreement about team members’ relative status position within a working group. Such conflicts are thought to be ubiquitous and emerge due to the contested nature of status. When a team experiences a high level of status conflict, its members may disagree about who should occupy high‐status positions and who can dominate the decision‐making process. Such disagreements can induce severe competitive behavior and  thereby hindering information sharing and other cooperative behavior. Bendersky and  Hays (2012) found that status conflict exerted a significantly negative effect on team performance even after they controlled for task, relationship, and process conflicts.

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In Gardner’s (2010) working paper, she also revealed that teams that engaged in greater status disagreements experienced poor team coordination, increased relationship conflicts, and task conflicts.

Status effects on interteam interactions In addition to influencing individual behavior and intra‐team processes, status effects can be extended to the intergroup level. Group status refers to the relative position of groups on valued dimensions of comparison such as educational achievement and occupation. Studies in this stream has mainly focused on two outcomes, i.e., intergroup discrimination and helping behavior, and are theoretically grounded in social identity theory (Tajfel & Turner, 1979, 1986). Intergroup discrimination  According to social identity theory, an individual’s self‐ c­oncept partly derives from his or her knowledge of the membership in a social group together with the emotional significance attached to that membership (Tajfel, 1974). Put simply, people define themselves based on the groups to which they belong. Obtaining a positive self‐image is a primary goal people pursue in their social lives, and they consider the status of the group in which they hold membership as an important concern. If the status position of the group is high compared with that of other groups, then the group members gain a sense of superiority and their self‐image is enhanced to a large extent. However, if the status level of the group is lower than that of other groups, then the group members may feel threatened, as a positively valued social identity is derived primarily through favorable intergroup comparison. Individuals usually act favorably to in‐group members while exhibiting bias toward out‐group members (Hewstone, Rubin, & Willis, 2002). Such intergroup discrimination can be induced merely by categorizing people into different groups (e.g., Billig & Tajfel, 1973; Brewer, 1979; Tajfel, 1978). Through intergroup bias, in‐group members gain a positive self‐image and satisfy their need for positive self‐esteem. However, classic minimal group studies have usually involved groups that are implicitly equal in status, a rarity in real life. Indeed, group status has a powerful effect on intergroup strategies (Tajfel & Turner, 1979). Studies have shown that whereas members of high‐status groups express stronger in‐ group favoritism and perceive out‐group homogeneity, members of low‐status groups express less in‐group favoritism and perceive either in‐group homogeneity or an equivalent variability between the in‐group and out‐group (e.g., Boldry & Kashy, 1999; Commins & Lockwood, 1979; Guimond, Dif, & Aupy, 2002; Lorenzi‐Cioldi, 1998). Furthermore, members of high‐status groups perceive in‐group members as more human than out‐ group members while members of low‐status groups assign no privileged human status to the in‐group (Capozza, Andrighetto, Bernardo, & Falvo, 2012). Members in high‐status groups understandably obtain a positive self‐image in the process of intergroup comparison and thereby distinguish themselves from out‐group members to maintain a favorable distinctiveness (Sachdev & Bourhis, 1987, 1991). In contrast, an individual may desire his or her group to be more like another in terms of status when the individual’s own group cannot satisfy his or her need for a positively valued social identity (Sachdev & Bourhis, 1987; Tajfel, 1974). To further comprehend the effect of group status on intergroup behavior, three sociostructural contingencies should be considered, including status stability, status legitimacy, and the permeability of group boundaries. Status stability refers to the extent to which an



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alternative status position for a group as a whole is likely to be realized. Status legitimacy refers to the extent to which high‐ and low‐status groups accept the status structure as legitimate. The permeability of group boundaries refers to the extent to which group members can leave one group and join another (Tajfel & Turner, 1979, 1986). Researchers (e.g., Ellemers, van Knippenberg, de Vries, & Wilke, 1988; Ellemers, van Knippenberg, & Wilke, 1990; Mullen, Brown, & Smith, 1992; Turner & Brown, 1978; Vaughan, 1978; Verkuyten & Reijerse, 2008) have thoroughly explored the contingency effect of sociostructural variables. In a meta‐analytic study, Bettencourt, Dorr, Charlton, and Hume (2001) examined the moderating effect of status stability, legitimacy, and permeability on in‐group bias among high‐ and low‐status groups. Their results showed that when the status structure was stable, high‐status groups evaluated their in‐groups more favorably than did low‐status groups on both status‐irrelevant and status‐relevant dimensions of comparison. However, when the status structure was perceived as unstable, the high‐ and low‐status groups evaluated their in‐groups similarly on status‐irrelevant dimensions. In terms of the effect of status legitimacy, the high‐status groups evaluated their in‐groups more favorably than did low‐status groups on status‐irrelevant dimensions only when the differences in status were perceived as legitimate. The high‐status groups evaluated their out‐groups less favorably than did the low‐status groups on status‐relevant dimensions. This effect was greater when the differences in status were perceived as legitimate rather than illegitimate. As for the effect of status permeability, the high‐status groups identified more with their in‐groups than did low status groups on status‐relevant dimensions, and the effect was larger when the boundaries were impermeable rather than permeable. Finally, these three variables interacted to influence the effect of group status on in‐group bias. However, this was true only for status‐irrelevant dimensions. When status was unstable and perceived as illegitimate, the low‐ and high‐status groups were equally biased when the group boundaries were impermeable rather than permeable. Helping behavior  Although studies related to intergroup discrimination have focused more on intergroup competition or conflict, which is derived from the process of social categorization, identification, and comparison, research related to intergroup helping behavior has more often discussed how groups positioned in different social hierarchies demonstrate helping behavior to, seek help from, and react to helping behavior exhibited by other counterparts. In social life, intergroup helping behavior may occur for various reasons. One group may help another group out of a purely pro‐social motive or for the purpose of cooperation and future reciprocation. One group may seek help from other groups because it lacks the ability or resources to overcome the difficulties it encounters. Here, when we illuminate the status effects on intergroup helping, the driving factors of such behavior differ from the aforementioned traditional motives. As status differences are usually exhibited between groups, intergroup helping behavior may serve to maintain or enhance group status (e.g., Cunningham & Platow, 2007; Halabi, Dovidio, & Nadler, 2014). Such reasoning is in line with the arguments of social identity theory. Before analyzing the effect of group status, we examine the attributes of helping behavior to clarify how it may help groups to achieve their status motives. In the literal sense, helping behavior is a pro‐social behavior that one person exhibits to resolve the problems facing another. However, it may signal that the one who offers help is more competent than the one who receives help in specific situations (Nadler & Chernyak‐Hai, 2014). Therefore, proffering help may help one to gain status, whereas seeking or receiving help may jeopardize or decrease one’s relative status compared with the counterpart.

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Furthermore, helping behavior can be divided into two types: dependency and autonomy oriented. Dependency‐oriented help consists of providing a full solution to the problem at hand, which conveys the message that the recipient of the help is unable to solve the problem on his or her own and may require assistance with similar problems in the future (Nadler, 1997, 1998). Autonomy‐oriented help is partial and temporary and encourages the recipient’s independence by providing him with the tools to solve similar problems himself in the future (Nadler, 1997, 1998). As dependency‐oriented help underscores recipients’ inferiority, it is more effective for maintaining positive in‐group differentiation. Considering the nature of helping behavior, people may engage in altruism in a very instrumental way (Hardy & Van Vugt, 2006; Hopkins et  al., 2007; Van Vugt & Van Lange, 2006). Via experiments involving a minimal group and two real groups, Nadler, Harpaz‐Gorodeisky, and Ben‐David (2009) found that when intergroup relations were less secure, members of high‐status groups protected the identities of their in‐groups by providing dependency‐oriented help, but not autonomy‐oriented help, to the low‐status out‐groups. This result indicates that high‐status groups may use helping behavior to maintain their superiority, especially when out‐groups threaten their positive social i­dentity. Meanwhile, low‐status groups may use helping behavior as a strategy to demonstrate their group knowledge and boost their reputation. In two laboratory studies, Van Leeuwen and Tauber (2011) found that participants’ knowledge was positively related to out‐group helping in response to requests only among members of low‐status groups, and that such an effect disappeared when the help did not reflect in‐group knowledge. In addition, low‐ status groups may try to decrease their dependence on high‐status groups when they perceive the status structure as changeable. Nadler and Halabi (2006) conducted a series of studies to examine how low‐status groups responded to the help proffered by high‐status groups. In their first experiment, they found that when intergroup status relations were unstable, low‐status groups exhibited negative reactions to the help provided by high‐ status groups. Their two subsequent experiments, which used real groups, replicated this result and revealed that high‐level identifiers were less receptive to the help provided by the high‐status groups. Their final experiment found that low‐status groups sought the least amount of help from high‐status groups when the status relations were perceived as unstable and the help provided was dependency oriented. In this section, we review the important and far‐reaching findings related to status effects at different levels. Table 9.1 summarizes the major empirical studies to which we refer in this chapter.

Future Research In the previous section, we review the research related to status effects from three different levels. These studies have basically covered the major issues to which researchers have paid the most attention. In the following section, we discuss some emerging topics that are opening new avenues in this research area. We also point out several important issues which have received scant attention in the past but are in need of further development.

Status and power consistency/inconsistency Although status and power co‐vary, the two may deviate from each other in certain cases. For example, some high‐power individuals are not admired by other employees, and some respected people do not occupy a formal position of power. Although such circumstances are common in daily life, to date there have been few empirical attempts to reveal the

Method

University students

Frontline employees of manufacturing industry Professional soldiers MBA students

Experiment

Survey

Survey

Survey

Spataro, Pettit, Sauer, & Lount (2014)

Janssen & Gao (2013)

Ellemers, Sleebos, Stam, & de Gilder (2013) Bendersky & Shah (2012) Multiple samples

Multiple samples

Survey and experiment

Survey and Experiment

Multiple samples

Survey

Smith, Menon, & Thompson (2012)

Actors

Survey

Jensen, & Kim (2015) Bienefeld & Grote (2014) Marr & Thau (2014)

Credit unions

Social

Social

Organizational

Organizational

Social

Organizational

Organizational

Social

Organizational

Social and organizational

Multiple samples

Survey

Social

Empirical setting

Inhabitants

Sample

Howell, Harrison, Burris, & Detert (2015)

Status effects on individual behaviors Dubois, Rucker, & Experiment Galinsky (2015) Hays & Bendersky Experiment (2015) and survey

Authors

Table 9.1  Empirical literature on the status effects.

(Continued )

Perceived respect from other team members was positively related to individual perceptions of inclusion in the team and the perceived value of the self for the team. Those who gain status perform worse than those who maintain high‐status positions for the whole quarter and they perform no better than those in stable low‐status positions throughout. Under conditions of job threat, people with low status activated smaller and tighter subsections of their networks, whereas people with high status activated larger and less constrained subsections of their networks.

Individuals’ sense of power mediated the effect of social class on unethical behavior, but feeling of status did not. Status hierarchies are seen as more mutable than are power hierarchies and thus motive low status individuals to engage in more competitive behavior. Supervisors were more likely to credit those reporting the same amount of voice if the employees had higher ascribed or assigned status and were more likely to recognize voice from employees who had higher achieved status. Divorce rates of male Oscar winners and nominees increased following the Oscars but not of female Oscar winner and nominees. Crewmembers’ individual level perceptions of psychological safety mediated the relationship between status and speaking up. High‐status individuals experienced more “self‐threat” and thus had more difficulty performing well after status loss than do low status individuals who experienced a comparable loss of status. High‐status group members, to a greater degree than low‐ or middle‐ status group members, preferred to continue working with as well as accept more influence from peers who exhibited collaborative behavior more than peers who exhibited competitive behavior. Self‐perceived status mediated the positive relationship between supervisory responsiveness and voice behavior.

Key findings

University students Multiple samples

Experiment

Experiment

Survey

Health care professionals Multiple samples

University students

Experiment

Survey

Multiple samples

Experiment

Tzabbar & Vestal (2015)

Survey

Scientific teams

Status effects on team processes and outcomes Joshi & Knight Survey Scientific teams (2015)

Nembhard & Edmonson (2006) Wilkins, Wellman, Babbitt, Toosi, & Schad (2015)

Blader & Chen (2011) Fragale, Overbeck, & Neale (2011)

Multiple samples

Survey

Bothner, Kim, & Smith (2012) Fast, Halevy, & Galinsky (2012) Blader & Chen (2012)

University students

Experiment

Lount Jr & Pettit (2012)

Multiple samples

Sample

Survey and Experiment

Method

Flynn & Amanatullah (2012)

Authors

Table 9.1 (Continued)

Organizational

Organizational

Social

Organizational

Social

Social

Social

Social

Organizational

Social

Organizational and social

Empirical setting

Deference based on task contributions was positively related to team performance, while deference based on social affinity was positively related to team performance. Status asymmetry weakened the initial positive effect of geographic dispersion on the novelty of a team’s innovation while strengthened the negative effect at high levels of dispersion.

High status was associated with relatively greater fairness, while high power was associated with relatively less fairness. Status and power interacted, such that the positive effect of status on justice emerged when power was low and not when power was high. Outcome favorability had a stronger relationship with higher status parties’ reactions when procedure fairness was high rather than low. High status individuals, regardless of power level, were perceived positively – dominant and warm – whereas high power‐low status individuals were judged most negatively – dominant and cold. In cross‐disciplinary teams, high status individuals experienced greater psychological safety than low status individuals. High‐status group members endorsed zero‐sum beliefs about discrimination more than low‐status group members. In addition, zero‐sum belief endorsement moderated perceptions of changing bias.

Performance improved more in the presence of a high‐performing co‐actor than in the presence of a weak‐performing cofactor. However, performance declined when they were asked to compete directly with a strong performer. Possession of high status led individuals to trust others more and the belief that others have positive intentions toward us served as the underlying mechanism. Performance improved with status until a very high level of status was reached, after which performance waned. Power without status fostered demeaning behaviors toward others.

Key findings

MBA students

Research units

Professional basket teams Investment bank research analysts Self‐management production teams Surgical teams

Mixed design

Survey

Survey

Survey

Survey

Survey

Survey

Mixed design

Experiment

George, Chattopadhyay, & Zhang (2012)

Halevy, Chou, Galinsky, & Murnighan (2012) Groysberg, Polzer, & Elfenbein (2011)

Bunderson & Boumgarden (2010)

Chattopadhyay, Finn, & Ashkanasy (2010)

Christie & Barling (2010)

Gardner (2010)

Jetten, Hornsey, & Adarves‐Yorno (2006)

Professional‐service project teams Multiple samples

Professional basket teams

Pharmaceutical licensing units

Survey

Bresman & Zellmer‐Bruhn (2013) Bendersky & Hays (2012)

Social

Organizational

Organizational

Organizational

Organizational

Organizational

Organizational

Organizational

Social

Organizational

(Continued )

Group benefited – up to a point – from having high‐status members, controlling for individual performance. With higher proportions of individual stars, the marginal benefit decreased before the slope of the curvilinear pattern became negative. Greater team structure – i.e., higher levels of specialization, formalization, and hierarchy – promoted learning by encouraging information sharing, reducing conflict frequency, and fostering a climate of psychological safety. High‐status team members working with higher proportions of low‐status team members reported lower levels of negative emotions and negative behaviors, whereas low‐status team members reported higher levels of these outcomes when working with higher proportions of high‐status members. Status equity negatively affected individual performance and physical health for low status individuals when unproductive behavior was high. Teams with greater status disagreement had lower team coordination and experienced more relationship conflict and task conflict. Low‐status members are more likely to portray themselves as conformist than high‐status members.

Status conflict exerted a significant negative main effect, moderated the effects of task conflict on group performance, and hurt performance by undermining information sharing more than other types of conflict did. When perceived upward mobility was high, the proportion of temporary workers in the group was negatively related to employee attitudes and behaviors, and the relationship was positive when perceived mobility was low. Hierarchical differentiation in pay and participation facilitated intragroup coordination and cooperation, and thus enhanced team performance.

More team‐level structure was associated with more internal learning as well as more external learning.

Method

Quasi‐ experiment Survey

University students

Experiment

Guimond, Dif, & Aupy (2002) Boldry & Kashy (1999)

High school students

Experiment

High school students University students

Multiple samples

University students

Experiment

Experiment

University students

University students

Sample

Experiment

Nadler & Halabi (2006)

Nadler, Harpaz‐ Gorodeisky, & Ben‐David (2009) Cunningham & Platow (2007)

Capozza, Andrighetto, Bernardo, & Falvo (2012) Van Leeuwen & Tauber (2011)

Status effects on interteam interactions Halabi, Dovidio, & Experiment Nadler (2014)

Authors

Table 9.1 (Continued)

Social

Social

Social

Social

Social

Social

Social

Social

Empirical setting

Low‐status group members, more than high‐status group members, used outgroup helping as a strategic tool to demonstrate their group’s knowledge and boost its reputation. High‐status group members who perceived status relations as unstable would protect their in‐group identity by providing dependency‐oriented help to the low status out‐group. High‐status group members displayed more support for empowerment help to in‐group members than to low‐status out‐group members when the status relations were unstable rather than stable. Low‐status group members who were high identifiers would be unwilling to receive help from the high‐status groups when status relations were perceived as unstable and help was dependency‐oriented. High‐status group members displayed more in‐group bias than low‐ status group members. Low‐status groups consistently displayed out‐group favoritism while high‐status groups displayed ingroup bias only on ratings of leadership ability. High‐status groups perceived in‐group as more variable than the out‐group, whereas low‐status groups had no such bias.

High‐status group members were more willing to seek autonomy‐ oriented help than dependency‐oriented help from low‐status groups when status relations were unstable. High‐status group members perceived the in‐group as more human than the out‐group, while low‐status group members did not assign a privileged human status to the in‐group.

Key findings



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inconsistency between status and power (Fragale et al., 2011; Halevy et al., 2011). In one exception, Fast et  al. (2012) attempted to examine the interactive effect of power and status. Via a laboratory study, they revealed that individuals in high‐power/low‐status roles demonstrated more demeaning behavior toward their partners than did individuals in other conditions. Although their exploration was insightful, they focused only on a specific combination of status and power. More effort is required to determine which d­istinct types of behavior are demonstrated by individuals in high‐power/high‐status, low‐power/high‐status, and low‐power/low‐status roles. Another interesting issue relevant to the status/power inconsistency is informal leader emergence and its influence on team outcomes. Different from assigned formal leaders, informal leaders do not occupy high‐power positions but usually have high levels of status, owing to their expected team contributions (e.g., Tagger, Hackett, & Saha, 1999; Wolff, Percosolido, & Druskat, 2002). When a team has both a formal leader and an informal leader simultaneously, it may experience the effects of a status/power inconsistency. As such, the issues of how informal high‐status leaders interplay with formal high‐power leaders and how such interplay influences team cooperation and effectiveness require further exploration.

Exploring status effects based on a dyadic relation and interactive perspectives Previous studies usually focus on examining how status influences a single person’s psychological states and behavior (Lovaglia, 1995), but such an egocentric approach might provide us with biased results of status effects because it largely underestimates the relational nature of status. When we study status at a dyadic level, factors such as the c­haracteristics of counterparts, behavioral motives, and stability of the status structure may further complicate status effects. Take the practice of mentoring as an example. In general, high‐status individuals are supposed to be more capable and willing to share knowledge, as they are less constrained by “evaluation apprehension” (Bordia, Irmer, & Abusah, 2006). However, in the mentoring relationship, high‐status mentors may hide knowledge from their low‐status protégés when they feel the latter are threatening their status. Therefore, future status research should embed status in a dyadic relation or teamwork context. In addition to the dyadic status relationship, the configuration of high‐ and low‐status members in teams merits more attention (e.g., Chattopadhyay et al., 2010; George et al., 2012). In a survey involving surgery teams, Chattopadhyay et al. (2010) found that high‐ status team members working with higher proportions of low‐status team members reported fewer instances in which colleagues accused them of incompetence or breached norms of professional conduct, resulting in lower levels of negative emotions and behavior. However, low‐status team members reported higher levels of these outcomes while working with high proportions of high‐status m­embers. Groysberg et al. (2011) examined Wall Street sell‐side equity research analysts and found that groups significantly benefited to a certain extent from having high‐status m­embers, even after controlling for individual performance. However, with an increase in proportion of individual stars, the marginal benefit decreased before the slope of the c­urvilinear pattern became negative. These studies implied that in addition to dyadic interaction, the distribution/dispersion of high‐ and low‐status members in working teams also had a significant effect on team processes and outcomes.

210

Antecedents to Team Effectiveness

Status dynamics Until now, relatively little attention has been paid to the dynamism of status hierarchies and the effects of status shifts. However, as argued previously, status is primarily subjective and mutable, and can be changed over time (Bendersky & Shah, 2012; Hays & Bendersky, 2015). Therefore, it is important to explore how individuals respond to status gains/ losses, and the effect of a sudden status gain/loss. Jensen and Kim (2015) conducted an interesting study in this area. Using the setting of the Academy Awards (the Oscars), they found that the divorce rate of male Oscar winners and nominees increased after the Oscars telecast. They explained that sudden movements between status positions could be culturally and socially disruptive (status disruption), which accounted for the negative effect of winning an Oscar. Furthermore, a failure to move to a higher‐status position resulted in dissatisfaction and frustration, owing to counterfactual comparison (status deprivation), which accounted for the negative effect of not winning an Oscar. In addition to status gain, status loss is a dynamism that requires more exploration. Marr and Thau (2014) were the first to address this point by examining how status loss influenced high‐ versus low‐status individuals. Across a field study and two experiments, they found that high‐status individuals experienced more self‐threat than low‐status individuals and thus performed worse after status loss. However, having the opportunity to affirm helped high‐status individuals to self‐restore their high performance levels. The preceding two studies conducted pioneering work on status shifts at the individual level. However, the status dynamics inside teams require further unwrapping. Aime, Humphrey, Derue, and Paul (2014) found that heterarchical structures could enhance team creativity. Research related to informal leaders and shared leadership has also provided some evidence that status dynamics within the team may significantly influence team effectiveness (Carson, Tesluk, & Marrone, 2007; Pearce, Conger, & Locke, 2008; Wang, Waldman, & Zhang, 2014; Zhang, Waldman, & Wang, 2012). According to these f­indings, we expect that future studies may gain a comprehensive understanding of status effects if they examine the effects of status hierarchies based on a dynamic view and explore how within‐team status shifts influence team processes and outcomes.

Potential conflicts between global and local status In the current cross‐organization and department boom, a distinction between global and local status demands our attention. An individual’s global status in a larger collective such as an industry or a field may differ from his or her local status in a smaller group such as an organization or a department (Magee & Galinsky, 2008). For example, an expert in a field may not achieve a high‐status ranking in a multifunctional team, as other disciplines may play a more pivotal role in accomplishing specific tasks. Under such circumstance, global status conflicts with local status. We expect that some negative consequence may result when an expert is assigned a low status level in the local context. Managing the m­isalignment between global and local status poses a challenge for both researchers and practitioners.

Cross‐cultural comparison of status effects Cross‐cultural comparison of status effects may offer a promising but underexplored direction (Anderson & Brown, 2010). As far as we know, Blader and Chen (2011) were the first to touch on this issue in an empirical study in which they used samples from both the U.S. and China to examine how high‐status individuals responded to low‐status



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individuals. Although they did not theoretically propose a cross‐cultural difference in their hypotheses and they did not find any difference in the results, we believe their attempt was worthwhile. In fact, cultural background can be a substantial moderating factor of status effects. Compared with individualistic Westerners, individuals from collective Eastern cultures are more likely to withdraw from status self‐enhancement trials because they face additional social costs for violating group status hierarchies (Anderson et al., 2006). Whether status can function to motivate individual workers requires further exploration. In addition, as several studies have separated status from power to discuss status effects, some researchers have proposed that culture may influence the relative salience of status and power (Blader & Chen, 2012; Hays & Bendersky, 2015). For example, status in interdependent cultures is more likely to be salient than power because people in interdependent cultures focus more on interpersonal relationships than those in independent cultures. We propose that relative salience of status and power in diverse cultural backgrounds moderate status effects especially when the status deviates from power, and merit further attention. Finally, the effects of status hierarchies are thought to be more beneficial and functional in interdependent cultures (Halevy et al., 2011). People who come from environments that emphasize independence or uniqueness consider it difficult to conform (Jetten et al., 2006). Status hierarchies improve team performance because they can provide teams with a social order. Once a team member resists following such an order, the benefits of the status hierarchy decrease. As no evidence has shown that status effects at the team level are influenced by different cultural backgrounds, we advocate that such cross‐cultural comparison is necessary to obtain a comprehensive understanding of status effects.

Conclusion Considering the prevalence of status in social life and organizations, scholars from the fields of social psychology, sociology and management have done a lot work to explain the role status plays. In this article, we first review the definition of status, and more importantly, differentiate it from other related concepts, such as, power or influence. And then we summarize the status effects on individual behaviors, team processes and outcomes, as well as interteam interactions based on different theoretical perspectives. After reviewing those important previous studies, we provide several insightful directions for future e­xploration. We hope this article will provide researchers with some useful insights and contribute to the field of status study.

Acknowledgement This study was supported by National Natural Science Foundation of China (NSFC 71372056 and 71072115).

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10

Cross‐Cultural Teams Ningyu Tang and Yumei Wang

Introduction With the accelerating pace of globalization, cross‐cultural teams have become the basic units of many organizations (Leung & Wang, 2015). As Wildman and Griffith (2015) have commented, “it is undoubtedly the age of global team‐based work” (p. 1). However, these teams are facing with complexities such as cultural diversity and geographical dispersion, which makes achieving global team effectiveness a challenge (Lisak & Erez, 2015; Wildman & Griffith, 2015). Although there have been several reviews and one meta‐analysis involving cross‐cultural team studies (Gelfand, Erez, & Aycan, 2007; Stahl, Maznevski, Voigt, & Jonsen, 2010; Stahl, Mäkelä, Zander, & Maznevski, 2010; Tsui, Nifadkar, & Ou, 2007; Zhou & Shi, 2011), our knowledge of the cross‐cultural teams remains in the relationship between culture or cultural diversity and teams, and less is known about the way in which cultural differences affect intercultural encounters and what makes cross‐cultural teams effective (Gelfand et al., 2007). This chapter reviews research on cross‐cultural teams and cross‐cultural team effectiveness from 1996 to 2015, emphasizing research published in the past 10 years, which have not been included in the studies aforementioned. Considering team effectiveness, the popular input–process–output (IPO) framework (Hackman, 1987; McGrath, 1984) is used as a key reference. This study contributes to the study of cross‐cultural teamwork in three ways: 1  It supplements the literatures of cross‐cultural teams from 2005 to 2015, based on Gelfand et al. (2007), and outlines what is known, and what remains unknown within a cross‐cultural IPO framework. 2  It answers a call in the cross‐cultural team research for a procedural understanding of culture diversity and team performance. As Stahl, Mäkelä et al. (2010) have suggested, the consequences of cultural diversity might be dependent on whether internal processes developed into virtuous or vicious circles. The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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3  It looks ahead and outlines an agenda for a future cross‐cultural team effectiveness study. We propose a multilevel model, integrating some key variables that were not covered in extant studies, such as organizational input factors, transition processes, team coordination, and behavioral outputs.

Cross‐Cultural Teams A cross‐cultural team can be described as “a specific type of team in which has much as members must come from two or more different national or cultural backgrounds” (Earley & Gibson, 2002, p.3). According to previous studies, people use different terms to label cross‐cultural teams, such as “cross‐cultural” teams (Krishna, Sahay, & Walsham, 2004; Nouri et  al., 2013), “transnational” teams (Earley & Mosakowski, 2000; Haas, 2006; Lagerström & Andersson, 2003), “multicultural” teams (Brett, Behfar, & Kern, 2006; Matveev & Milter, 2004), “multinational” teams (Salk & Brannen, 2000; Tenzer, Pudelko, & Harzing, 2013; Tröster & van Knippenberg, 2012), “international” teams (Earley, 1999) and “global” teams (Hinds, Neeley, & Cramton, 2013; Maznevski & DiStefano, 2000). Generally, the terms “multicultural” and “multinational” are used most often, and there is no great difference between these terms. There are some specific taxonomies of cross‐cultural teams. For example, considering task differences, there are cross‐cultural management teams (Salk & Brannen, 2000; Tröster & van Knippenberg, 2012), multicultural construction project teams (Ochieng & Price, 2010) and transnational project teams (Haas, 2006). Looking at team management style, there are self‐managing multicultural teams (with neither formal leadership nor shared cultural norms, less hierarchy and more self‐direction; Cheng, Chua, Morris, & Lee, 2012) and formal multicultural teams (Tenzer et al., 2013). In terms of geographical distribution, there are true cross‐cultural teams (Tröster & van Knippenberg, 2012) and global virtual teams (Gibson & Gibbs, 2006; Hardin, Fuller, & Davison, 2007; Kotlarsky, van Fenema, & Willcocks, 2008; Maznevski, & Chudoba, 2000; Montoya‐Weiss, Massey,  & Song, 2001). Finally, considering cultural composition, cross‐cultural teams could be divided into three types: the token group (only one or two members are from a different cultural background), the bi‐cultural group (team members are mainly from two different cultural backgrounds) and the multicultural group (team members are from more than two different cultural backgrounds; Chen, 2005). According to Chen’s categorization, most of the cross‐cultural teams in previous studies are multicultural groups (Adair, H­ideg, & Spence, 2013; Brodbeck, Guillaume, & Lee, 2011; Cheng et al., 2012; G­uillaume, Van Knippenberg, & Brodbeck, 2014; Tenzer et al., 2013), while some are bi‐cultural groups (Hinds, et al., 2013; Nouri et al., 2013; Pieterse, Van Knippenberg, & Van Direndonck, 2013; Salk & Brannen, 2000). The history of cross‐cultural team research is not long (Gelfand et al., 2007). According to Earley and Gibson (2002, pp.16–17), there are three theoretical domains related to the general topic of cross‐cultural teams. The first is multinational structures and technology, which approach teaming from a more macro perspective, with a focus on organizational context, technology, political systems, and economic systems, such as the use of virtual teams, which has been enabled by internet technology (Townsend, DeMarie, & Hendrickson, 1998). The second domain is the composition and functioning of top management teams, which highlights the strategic implications of multinational teaming and focuses on firm‐level outcomes of interaction in such teams. The third is cognition, exchange and conflict, which addresses questions pertaining to the interpersonal and social aspects of cross‐cultural teams, such as group efficacy (Earley, 1999; Gibson, 1999), communication



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(Gibson, 1997; Larkey, 1996), and conflict (Doucet & Jehn, 1997; Lin & Germain, 1998). Generally, the more recent studies of cross‐cultural teams focus on these three domains, especially the third domain.

The IPO Framework and Cross‐Cultural Team Effectiveness The IPO framework (Hackman, 1987; McGrath, 1984) expresses the ways in which inputs lead to processes that in turn lead to outputs, which has had a powerful influence on empirical team effectiveness research (Mathieu, Maynard, Rapp, & Gilson, 2008). Inputs describe antecedent factors that facilitate or restrict member interactions. Processes describe how team inputs are transformed into outputs. Outputs are the results of team activities, which may include quality and quantity of performance and members’ affective reactions. Although the IPO model has been modified and extended as an “input–mediator– outcome–input” model (IMOI; Ilgen, Hollenbeck, Johnson, & Jundt, 2005), the IMOI model is a theoretical framework that has not been widely referenced or empirical examined. We therefore believe that an IPO model could be more useful for cross‐cultural team effectiveness studies. Based on a comprehensive and worldwide study of successful cross‐cultural teams, Snow, Snell, Davison, and Hambrick (1996) proposed the first IPO model of cross‐cultural team effectiveness, called “transnational team drivers, and design and management levers  – team process measures – key business results” (p. 3). They found that task ­complexity and importance, and multicultural dynamics were the two factors that would affect the c­omposition, operation, and performance of cross‐cultural teams. They also considered organizational design and management levers as team inputs. Team processes included a safe and trusting environment, camaraderie, flexibility, dependability, shared responsibility and commitment. Key business results included time to peak sales, regulatory approvals, study protocol, statistical analysis plan, data management plan, and clinical development. Therefore, it is reasonable to use an IPO framework to organize the literature and analyze cross‐cultural team effectiveness. We summarize the main literature in Table 10.1. The IPO framework of research in cross‐cultural team effectiveness is shown in Figure 10.1. The remainder of this section is organized as follows. Moving from right to left across Figure  10.1, we describe the outputs, processes, and inputs that have been studied concerning cross‐cultural team effectiveness, and the linkages among them in the framework, such as process–output, input–process, input–output, and IPO.

Team Outputs Hackman (1987) suggested three criteria for defining effective work groups. First, the outcomes of group efforts must meet or exceed the standards for quantity and quality set by organization. Second, the group experience must satisfy the personal needs of their members. Third, the social processes, which allowed the groups to function, must maintain or enhance the capability of group members to work together. In this sense, what constituted “effectiveness” has become far more complex in recent years (Mathieu et al., 2008). There are various output indicators in cross‐cultural team research, such as team performance (Cheng et al., 2012; Groves & Feyerherm, 2011; Nouri et al., 2013; Pieterse et  al., 2013), team effectiveness (Kirkman, Tesluk, & Rosen, 2001), team creativity (Gibson & Gibbs, 2006; Nouri et  al., 2013; Tadmor, Satterstrom, Jang, & Polzer, 2012), high‐quality solutions (Maznevski & DiStefano, 2000), decision quality

Table 10.1  Literature review with an input–process–output framework Study

Country

Hardin, Fuller, & USA Davison, 2007

Team type Global virtual teams

Inputs Collectivism/ individualism

Kotlarsky, van Fenema, & Willcocks, 2008

Europe

Virtual global software projects

–

Zander & Butler, 2010

Europe

Multicultural teams

Multicultural team composition (fault lines, status cues)

Ochieng & Price, 2010

Europe

Multicultural teams

Responsibility; trust; honesty; respect for others; cultural empathy; management techniques

Brodbeck, Guillaume, & Lee, 2011

Europe

Ethnically diverse Societal status of work groups ethnic group; Group ethnic diversity; Individual ethnic dissimilarity

Groves & Feyerherm, 2011

USA

Ethnic and nationality diverse teams

Tadmor, Satterstrom, Jang, & Polzer, 2012 Tröster & van Knippernberg, 2012

USA

Culturally diverse Multicultural teams experience

Europe

Multinational management teams (middle managers)

Leader cultural intelligence (CQ); cultural diversity

Leader openness; leader–member similarity in nationality ; member–team dissimilarity in nationality

Processes Computer self‐efficacy; computer collective‐ efficacy; group self‐ efficacy; virtual team self‐efficacy; virtual team efficacy Knowledge‐based model of coordination: organization design, work‐based, technology‐based, social coordination Team leadership modes

Effective communication

–

–

–

Psychological safety; affective commitment

Outputs

Method

Sample

Findings

Regardless of cultural background, team members reported less confidence in their ability to work in virtual team environments than traditional face‐to‐face environments and that team members from individualistic cultures reported higher self‐efficacy beliefs than team members from collectivist cultures. Managers should consider their organization – Case study SAP case in terms of knowledge processes, not just (successful) and information flows. Technologies are most Baan case useful for amplifying knowledge (unsuccessful) management processes to allow knowledge sharing. – Conceptual – Formulates propositions predicting which team leadership mode (four modes considering focused or distributed, vertical or horizontal) will enhance team outputs given different multicultural team composition. Communications within multicultural project 20 senior project – Individual environments can be effective when project managers with learning managers demonstrate an awareness of regards to dealing performance cultural variation. Several ways to make with cross cultural effective communication: Establish clear lines issues in of responsibility; Institute team effectiveness multicultural (collectiveness); establish trust; implement project teams. honesty; encourage respect for others; introduce cultural empathy; implement value management techniques. Individual‐level ethnic dissimilarity tends to be Laboratory 87 groups of 412 Individual negatively related to the individual learning experiment individuals with learning outcome, but group level diversity is varying degrees of performance positively related to individual learning ethnic diversity outcome. Both levels interact such that the highest learning outcome is shown by non‐Anglo majority members who score low in individual ethnic dissimilarity and are working in high‐diversity groups. Survey 99 work units of Leader CQ contributes to team member Leader 420 respondents perceptions of both leader performance and performance; team performance on work teams characterized Team by significant cultural diversity pertaining performance to team member ethnicity and nationality. 57 diverse teams of Even after controlling for individual creativity, Team creativity Laboratory experiment 114 students dyadic creativity is greatest when both (fluency, dyadic partners had high levels of flexibility, multicultural experience. and novelty) Leader openness and leader‐member Survey 39 teams of 225 Leader‐ similarity both have positive relationships middle managers directed with voice, and these relationships are of a Dutch voice stronger for middle managers who are multinational more dissimilar in nationality to their team logistics company members. The interaction between leader openness and dissimilarity to the team on voice is mediated by affective commitment. –

Multiwave field survey

243 students forming different groups

(Continued )

224

Antecedents to Team Effectiveness

Table 10.1 (Continued) Study Cheng, Chua, Morris, & Lee, 2012

Country

USA

Europe Tenzer, Pudelko, & Harzing, 2013

Nouri et al., 2013

Inputs

Processes –

Cultural diverse teams

Team average uncertainty avoidance; team average relationship orientation; squared team variance in uncertainty avoidance; squared team variance in relationship orientation Cultural diversity; Goal orientation.

Multinational teams

Team CQ; cultural diversity

Team shared values

multinational teams (German‐ and non‐German‐ speaking team members) Multilingual teams

Language asymmetries; power contests

Subgroup dynamics (faultlines); negative team affective state

Language barriers

Perceived trustworthiness; intention to trust; trust formation

Singapore Self‐managing multicultural teams

Europe Pieterse, Van Knippenberg, & Van Dierendonck, 2013 Adair, Hideg, & Canada Spence, 2013

Hinds, Neeley, & Cramton, 2013

Team type

Israel

Europe Guillaume, Van Van Knippenberg, & Brodbeck, 2014

Culturally diverse Cultural diversity; task specificity; dyads task types– (Singapore and convergent and Israel) creative

Anglo and non‐Anglo cultural background

–

Cooperation; conflict

Cultural dissimilarity; Performance monitoring cultural status



225

Cross‐Cultural Teams

Outputs

Method

Sample

Findings

A longitudinal Team lab study performance (including ability, quality, and creativity)

67 multicultural teams (at least three nationalities) of 375 Master of Business Administration students

At the initial stages of team formation, self‐managing multicultural teams with a lower average level of, but moderate degree of variance in uncertainty avoidance performed better. In the later stages, teams with a high average level of relationship orientation performed better. A moderate degree of variance in relationship orientation among team members perform better.

Team task Laboratory performance experiment

79 teams and 312 students; 51% are homogenous teams, the others are heterogeneous 29 homogeneous and 24 heterogeneous teams of 203 undergraduate students 6 software development teams of 96 globally distributed members in one company 15 member‐ nominated trustees of 90 members in 3 German automotive corporations, Two‐person student teams. Experiment 1 : 86 teams (40 diverse, 46 homogeneous; Experiment 2 : 96 teams (50 diverse; 46 homogeneous)

Cultural diversity is more positive for team performance when team members’ learning approach orientation is high and performance avoidance orientation is low

–

Laboratory experiment

–

Interview

–

Interview

Laboratory Team experiment performance: fluency, originality

Individual Laboratory performance experiment

69 teams of 316 upper‐level undergraduates

Behavioral CQ and metacognitive CQ are helpful for the development of shared team values in culturally heterogeneous teams Language asymmetries may cause subgroup dynamics in global teams when power contests also exist.

MNT members’ reactions to language barriers constitute an intervening mechanism mediating the relationship between language barriers and different aspects of perceived trustworthiness and intention to trust. Cross‐cultural teams benefit from specific task instructions when working on an execute task, and they benefit from general task instructions when performing a creative task. Cultural diversity is not crucial under strong situations. Team processes are crucial for tasks that require coordination and cooperation, but they may be less crucial for tasks with a low level of interdependence and a high level of autonomy. Performance monitoring has a curvilinear relationship with individual performance and to mediate between cultural dissimilarity and performance. Cultural dissimilarity has a negative relationship with performance monitoring for high cultural‐ status members, and a positive relationship for low‐status members.

226

Antecedents to Team Effectiveness Information processing theory +

Inputs Cultural diversity; Individual characteristics; Team factors; Organizational factors

Processes

Outputs

Action process Interpersonal process Psychological process

Performance (creative, task) Affective satisfaction

Social identity or social categorization theory

Figure 10.1  Input–process–output model of cross‐cultural team effectiveness.

(Montoya‐Weiss et al., 2001), quality of interpersonal relations (Zellmer‐Bruhn & Gibson, 2006), perception–satisfaction (Hardin et al., 2007), action quality, general commitment, and cohesion (Maznevski & Chudoba, 2000). Generally, the outputs can be organized into two aspects: performance (e.g., task performance, creative performance) and affective reaction (Martins, Gilson, & Maynard, 2004; Mathieu, Heffner, Goodwin, Salas, & Cannon‐ Bowers, 2000).

Performance Task performance  Team task performance is widely used as an outcome of cross‐cultural teamwork in extant researches (Cheng et al., 2012; Groves & Feyerherm, 2011; Matveev & Nelson, 2004; Pieterse et al., 2013; Zellmer‐Bruhn & Gibson, 2006). However, based on our review, most of the studies use quality task performance, in the context of a specific course, task or business simulation, including decision quality and action quality (Maznevski & Chudoba, 2000; Montoya‐Weiss et al., 2001). Creative performance  Team creativity is another popularly used outcome of cross‐cultural teams. Based on an information/decision‐making perspective, cultural diversity in teams provides advantages such as access to diverse ideas and perspectives, enhancing teams’ c­reativity (Ely & Thomas, 2001). Team creativity was measured in different ways in different studies, such as task originality (Nouri et al., 2013), fluency, flexibility, and novelty (Tadmor et  al., 2012). In Tadmor et  al.’s (2012) study, the participants were asked to g­enerate as many uses of a brick as possible in 5 minutes. Fluency was the total number of  distinct uses that participants generated. Flexibility was measured as the number of d­ifferent categories generated. Novelty was the mean of the coders’ ratings of the overall creativity. Affective reaction  Affective reaction is commonly used as an outcome of cross‐cultural team effectiveness. For example, Maznevski and Chudoba (2000) used general commitment and cohesion as the outcome of effective global virtual teams. Watson, Johnson, and Zgourides (2002) used cohesiveness as the outcome of cultural diversity and leader’s behavior in learning teams. Van Der Zee, Atsma, and Brodbeck (2004) examined the



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influence of social identity and personality on team members’ wellbeing among business students who worked together in culturally diverse teams, and showed that cultural d­iversity in teams is negatively related to wellbeing. Finally, in addition to performance at the team level, some studies also looked at individual‐level outcome, such as individual team member wellbeing (Van Der Zee et al., 2004), individual process‐related effectiveness (Thomas, 1999), and individual performance (Guillaume et al., 2014).

Team Processes Team processes have been defined as “how” teams achieve their outputs, which play important roles in teamwork and explain how inputs are transformed into outputs (Mathieu et al., 2000). According to Marks, Mathieu, and Zaccaro (2001), team effectiveness is related to a detailed framework of team processes. Teamwork processes described interdependent team activities that orchestrated task work in employees’ p­ursuit of goals. Thus, they developed a detailed framework and taxonomy of team processes, including transition processes, action processes, and interpersonal processes. Transition processes included mission analysis, goal specification, and strategy formulation and planning. Action processes included monitoring progress toward goals, systems monitoring, team monitoring and backup, and coordination. Interpersonal processes included conflict management, motivating and confidence building, and affect management. In cross‐cultural team effectiveness studies, compared with action and interpersonal processes, transition processes are seldom mentioned. However, there is another type of process related to psychological state, such as psychological safety (Tröster & van Knippenberg, 2012), negative team affective state (Hinds et  al., 2013) and shared team values (Adair et al., 2013), which do not belong in any of the three types of process. We call this type of process “psychological processes,” to distinguish it from the other three types. Thus, team processes in our cross‐cultural team effectiveness model include action processes, interpersonal processes, and psychological processes. We mainly review team coordination (Kotlarsky et  al., 2008; Montoya‐Weiss et  al., 2001) and team learning (Haas, 2006; Zellmer‐Bruhn & Gibson, 2006) in action processes, and review conflict (Montoya‐Weiss et  al., 2001; Nouri et  al., 2013), cooperation (Nouri et  al., 2013), and communication (Ochieng & Price, 2010; Oetzel, 1998) in interpersonal processes.

Action processes Team coordination  Cross‐cultural team coordination has obtained less empirical attention. Based on a comparison between one successful and one unsuccessful case, Kotlarsky et al. (2008) developed a four‐dimension, knowledge‐based coordination model in the context of global virtual software development projects, including organization design, work‐based coordination, technology‐based coordination and social coordination. These coordination mechanisms could improve the success of global teams. Some details of coordination were also mentioned, such as contact person/liaison, mini‐teams, direct contact, making efficient division of work, using specifications to guide the work and using standard tools and methodologies, shared databases and wide range of media, c­ollaborative technology, teambuilding, mutual adjustment and facilitating interactions (Kotlarsky et al., 2008).

228

Antecedents to Team Effectiveness

Montoya‐Weiss and others (2001) examined the effects of temporal coordination on virtual teams. They conducted an experiment in which the experimental variation was the presence or absence of a temporal coordination mechanism. They designed a process structure to serve as a temporal coordination mechanism for organizing team communication, sequencing work, and facilitating problem‐solving activities. The results indicated that the conflict management behaviors had varied effects on team performance and that temporal coordination moderated certain effects. Specifically, the presence of a temporal coordination mechanism significantly weakened the negative effect of both avoidance and compromise conflict management behavior on virtual team performance. However, these two studies are far from understanding the coordination mechanisms in cross‐cultural teams. In ordinary team studies, team coordination is defined as a way of integrating the actions, knowledge and objectives of team members to achieve common goals (Rico, Sánchez‐Manzanares, Gil, & Gibson, 2008). It is obvious that a high level of coordination plays a significant role in a united team, while poor coordination brings about chaos and inefficiency. Coordinated teams manage the dependencies effectively using a number of explicit and implicit mechanisms and processes (Espinosa, Lerch, & Kraut, 2004). Explicit coordination mechanisms are the mechanisms explicitly employed by a team to help manage task dependencies, which usually includes task organization mechanisms and communication (Espinosa et  al., 2004), while implicit mechanisms capture the ability of a team to act in concert by predicting the needs of the task and the team members and adjusting behavior accordingly, without the need for overt communication (Rico et al., 2008). Future studies on cross‐ cultural team coordination studies could follow the explicit and implicit coordination framework. Team learning  Team learning reflects an active set of team processes, which represents an ongoing p­rocess of reflection and action, through which teams acquire, share, c­ombine, and apply knowledge (Mathieu et al., 2008). Zellmer‐Bruhn and Gibson (2006) found a positive effect of team learning on both task performance and the quality of intrateam relations. On the one hand, it showed a direct link between teams learning achieved and task performance. On the other hand, team learning helped to ensure effective internal team functioning, thus enhancing team members’ satisfaction and their ability to work together in the future. Research has shown that cross‐cultural teams are a good platform for learning. Based on a study of 96 project teams at an international development agency, Haas (2006) found that the nature of team members (cosmopolitan or local) played a complex role in project performance, through the effect of knowledge acquiring and applying, generally, cosmopolitan team members offered greater benefits than local members, when considering both internal and external knowledge. Furthermore, Haas (2010) showed that team autonomy and use of external knowledge provided complex conditions for team effectiveness. Teams with high levels of both autonomy and external knowledge completed more effective projects than those with high external knowledge but low autonomy, or high autonomy but low external knowledge. On the other hand, the complementarity between autonomy and external knowledge use depended on the characteristics of the knowledge and the task. Individual performance monitoring is also a process of cross‐cultural teams, which has a curvilinear relationship with individual performance and mediates between cultural dissimilarity and performance. Cultural d­issimilarity has a negative relationship with performance monitoring for high cultural status members, and a positive relationship for low cultural status members (Guillaume et al., 2014).



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Interpersonal processes Team communication  A meta‐analysis of the effects of cultural diversity and team processes and performance examines a null effect of communication (Stahl, Maznevski et al., 2010). However, team members from different cultural backgrounds have different communication styles (Gibson, 1997). For example, communication in Western cultures is particularly direct and explicit, so that a listener does not have to know much about the context or the speaker to interpret it, while communication in Eastern cultures is indirect and embedded in the way in which the message is presented. The differences between direct and indirect communication can cause damage to relationships when team projects run into problems (Brett et al., 2006). It is suggested that effective communication is the key to managing expectations, misconceptions, and misgivings in cross‐cultural teams, which is also a significant factor in the successful completion of heavy construction e­ngineering projects. The effective structure of cross‐cultural teamwork depends on a good, interconnected communication system between the client, the project manager, and the project team. Ochieng and Price (2010) confirmed that when project managers d­ emonstrated an awareness of cultural variation, communication within multicultural project environments could be made effective. Team cooperation  Nouri and his coauthors (2013) attempted to resolve the competing predictions of information/decision‐making versus social categorization theories. On the one hand, they integrated the situational strength theory and the task‐type theory, testing the effects of task specificity and task type on the relationship between cultural diversity and team performance in two experiments with 86 and 96 dyads, respectively. On the other hand, they examined the effects of task specificity on interpersonal processes of team cooperation and team conflict in culturally homogeneous and heterogeneous dyads. Using data from two cross‐national experiments, Chen and Li (2005) confirmed that Chinese people made decisions that were less cooperative than Australians in mixed‐motive business situations in which no formal or informal sanction systems were in place. Chinese people were less cooperative with foreigners than with fellow Chinese when they were in a foreign territory, whereas Australians were equally cooperative with members of both groups. The results showed that the national effects on cooperative decision making were mediated by individual cultural orientation on vertical and horizontal individualism. Cox, Lobel, & Mcleod (1991) also provided empirical evidence that ethnic group differences affected at least some aspects of behavior in task groups and found that, at the individual level, Asian, African American, and Hispanic individuals had a more collectivist–cooperative orientation to a task than did Anglo‐American individuals. Eby and Dobbins (1997) showed that teams with a high percentage of collectivistic members exhibited higher levels of cooperation, which in turn was related to a higher performance. Chen, Brockner, and Katz (1998) suggested that different situational conditions led to cooperation in individualistic and collectivistic cultures. In individualistic cultures, high goal interdependence, enhancement of personal identity, and cognitive‐ based trust fostered cooperation, while in collectivistic culture, goal sharing, group i­dentity, and affective trust fostered cooperation. Team conflict  Team conflict is a commonly used process which mostly explains the n­egative relationship between cultural diversity and team performance based on social identity theory (Pelled, Eisenhardt, & Xin, 1999; Thomas, 1999). Most of the literature is related to the antecedents and outcomes of conflict, and the strategies of conflict management.

230

Antecedents to Team Effectiveness

Conflict is likely to occur in cross‐cultural teams, owing to members’ different values, beliefs, and language systems (Dougherty, 1992), irrespective of emotional conflict (Von Glinow, Shapiro, & Brett, 2004) or process conflict (Van Knippenberg & Schippers, 2007). Pelled et al. (1999) found that race diversity is positively associated with emotional conflict. Despite the cultural differences, Edmondson, Dillon, & Roloff (2007) argued that low levels of task specificity and clarity increase conflicts. In a culturally diversified group, mistrust and miscommunication are among the sources of conflicts; in addition, a  desire for harmony, different standards for social status, different emphasis on group and  fatalism in different cultures may also create conflicts in a multicultural group (Appelbaum, Shapiro, & Elbaz, 1998). Managing conflict in cross‐cultural teams is a common challenge and a matter of importance (Marquardt & Horvath, 2001; Von Glinow et al., 2004). Cultural diversity influences not only the nature of the conflict process itself, but also the strategies adopted to resolve it. Oetzel (1998) found that homogeneous European–American groups have more conflict, use less cooperative conflict tactics, and more competitive conflict tactics than do homogeneous Japanese groups. Groups composed of members with low independent self‐construal are more likely to use more cooperative tactics and less competitive tactics than groups composed of members with high independent self‐construal. The relationship between team conflict and team performance could be changed in different contexts. For example, studies have found, although not consistently, positive relationships between task conflicts and group productivity, especially when norms support discussion, yet negative correlations between relational conflict and group productivity (Amason, 1996; De Dreu & Weingart, 2002; Jehn, 1997). Evidence from Tjosvold, Poon, and Yu (2005) supported the theory that confidence in the group’s interpersonal relationships enhances team effectiveness. In particular, cooperative conflict builds confidence in relationships that, in turn, promotes team effectiveness. Montoya‐Weiss et al. (2001) showed that the way in which virtual teams managed internal conflict was crucial in their success and that temporal coordination had some significant moderating effects.

Psychological processes As mentioned above, psychological processes describe cognitive, motivational, and affective states of teams. Tröster and van Knippenberg (2012) hypothesized that affective commitment and psychological safety would mediate both the interaction between similarity in nationality to the leader and dissimilarity in nationality to fellow team members in leader‐directed voice, and the interaction between similarity in nationality to the leader and dissimilarity in nationality to fellow team members in leader‐directed voice. However, they only found partial support for these predictions. The interaction of leader openness and member–team dissimilarity in leader‐directed voice was mediated by affective commitment but not by psychological safety, whereas the interaction of leader–member similarity and member–team dissimilarity in leader directed voice was mediated by psychological safety but not affective commitment. An interview study investigated how language barriers influenced trust formation in cross‐cultural teams, an important mediator between team inputs and performance outcomes (Tenzer et al., 2013). On the basis of 90 interviews with team members, team leaders and senior managers in 15 multinational teams of three German automotive corporations, they revealed how members’ cognitive and emotional reactions to language barriers influence their perceived trustworthiness and intention to trust, which in turn might affect the formation of trust. This study contributed to diversity research by distinguishing the exclusively negative language effects from the



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more ambivalent effects of other diversity dimensions. Adair et  al. (2013) found that behavioral cultural intelligence and metacognitive cultural intelligence were helpful for the development of shared team values in culturally heterogeneous teams.

Team Inputs According to the IPO framework of team effectiveness (Hackman, 1987), the input factors could be concluded at three levels: 1) individual‐level factors, such as members’ personality, attitudes, and skills; 2) team‐level factors, such as task structure, external leader influences; 3) organizational and context‐level factors, such as organizational design features, environmental complexity. In our review, as cultural diversity can be both an individual and a team level factor, we consider it as a single factor, thus the input factors of cross‐ cultural teams include: cultural diversity (e.g., cultural diversity, language, cultural dissimilarity, time orientation), individual characteristics (e.g., personality, openness, competence), team factors (e.g., team size, team dispersion, team tenure, task complexity, task specificity, task types), and organizational factors (e.g., global integration, group process facilitation, cross‐cultural management).

Cultural diversity Culture is generally defined as a shared meaning system or mental programming (Hofstede, G., Hofstede, G.M., & Minkov, 1991), which implies that members of the same culture share a common meaning, and they are likely to interpret and evaluate situational events and management practices in a similar way. In contrast, members of different cultures who do not share a common way of interpreting and evaluating situational events are more likely to respond in a different way to the same approach (Earley & Gibson, 2002). Cultural characteristics (Hofstede, 1980) germane to team dynamics which have received much attention in cross‐cultural management literature are individualism/collectivism and power distance (or attitudes toward hierarchy and authority; Earley, 1999; Gibson, 1999; Gibson & Zellmer‐Bruhn, 2001; Gómez, Kirkman, & Shapiro, 2000; Harrison, McKinnon, Wu, & Chow, 2000). For example, Harrison and colleagues (2000) found that individualism and low power distance were associated positively with employee’s adaptation to fluid work groups. Gómez et  al. (2000) found that collectivism (measured at the individual level) had a positive effect on the evaluation of a teammate. Different cultural backgrounds generate different types of teams (Gibson & Zellmer‐ Bruhn, 2001), different attitudes toward teams (Kurebayashi, Hoffman, Ryan, & M­urayama, 2012; Ramamoorthy & Flood, 2002; So, West, & Dawson, 2011), and different team behavior (Sanchez‐Burks, Nisbett, & Ybarra, 2000; Yamaguchi, 2013; Zhang & Tsui, 2013). Thus, when people from different cultural backgrounds gather to work in a team, cultural diversity or cultural differences become the basic type of inputs affecting cross‐cultural team effectiveness. It has been suggested that cross‐cultural team performance is influenced by cultural diversity more significantly than by the aggregated level of any particular cultural value or demographic diversity within teams (Kirkman & Shapiro, 2005). Previous studies include two levels of cultural diversity: one is individual‐ level cultural dissimilarity (Tröster & van Knippenberg, 2012), the other is group‐level cultural diversity (Cheng et al., 2012). Studies suggest that these two levels of cultural diversity have different effects and also have interactive effects on team members’ individual performance (Brodbeck, et  al., 2011). That is, individual‐level ethnic dissimilarity is negatively related to the individual learning performance, but group‐level diversity

232

Antecedents to Team Effectiveness

is positively related to individual learning performance. Both levels interact such that the highest learning performance is shown by non‐Anglo‐American majority members who score low in individual ethnic dissimilarity and are working in high‐diversity groups. According to previous empirical studies, there are two ways of measuring cultural diversity. One is to use dummy code (1 and 0) for homogeneous and heterogeneous teams (Adair et  al., 2013; McLeod, Lobel, & Cox, 1996; Nouri et  al., 2013; Pieterse et  al., 2013); the other is to calculate cultural distance based on the Euclidean distance measure developed by Tsui, Egan, & O’Reilly (1992; Guillaume et al., 2014; Van Der Zee et al., 2004). The higher the cultural dissimilarity score, the more different the member is from his or her peers. Researches also focus on the consequences of specific cultural differences for team outcomes. For example, Hinds et al. (2013) and Tenzer et al. (2013) suggested that language differences might cause subgroup dynamics and negative team affective state, and could become barriers to team trust. Hardin et al. (2007) found that team members from individualistic cultures reported higher self‐efficacy beliefs (both group self‐efficacy and virtual team self‐efficacy) than team members from collective cultures. Cheng and coauthors (2012) showed that self‐managing multicultural teams with a lower average level of, but moderate degree of variance in uncertainty avoidance performed better at the initial stages of team formation. In the later stages, teams with a higher average level of, but a moderate degree of variance in relationship orientation among team members performed better.

Individual characteristics Individual characteristics attract more attention in cross‐cultural team studies. Generally, individual characteristics refer to both leader and member characteristics. Team leader characteristics include leader cultural intelligence (Groves & Feyerherm, 2011), cross cultural communication competence (Matveev, & Nelson, 2004), and openness (Tröster & van Knippenberg, 2012). Team member characteristics include network types (e.g., task‐ related, advice‐related, private) and position level (Salk & Brannen, 2000); personality and social identity (e.g., identification with one’s cultural background, identification with the team; Van Der Zee et al., 2004); the nature of members (cosmopolitan or local; Haas, 2006); multicultural experience (Tadmor et  al., 2012); goal orientation (e.g., learning approach, learning avoidance, performance approach, performance avoidance; Pieterse et al., 2013).

Team‐level factors Previous studies also focus on several input factors related to team level. For example, Gibson and Gibbs (2006) explored the effects of geographic dispersion, electronic dependence, and structural dynamism of teams on team performance. So et  al. (2011) found that team structure (i.e., work teams structured in terms of clearly defined roles and objectives) was significantly associated with greater employee satisfaction. Stahl, Maznevski et al. (2010) confirmed that team tenure, team size, team dispersion moderated the effect of cultural diversity on team performance. Studies also show that task design significantly affects team outcomes (Gibson, 1999; Nouri et al., 2013; So et al., 2011). Based on situational strength theory and task type theory, Nouri and coauthors (2013) identified that both task specificity and task type were of particular importance in understanding the effect of team cultural diversity on performance because it could either attenuate or magnify cultural differences. Gibson (1999) argued that task uncertainty significantly moderated the effect of group efficacy on



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team effectiveness. In a meta‐analysis of 108 papers, Stahl, Maznevski and coauthors (2010) found that task complexity moderated the relationship between cultural diversity and team conflict

Organizational‐level factors Whereas the influence of organizational context on team effectiveness has long been recognized (Hackman & Morris, 1975; Snow, Snell, Davison, & Hambrick, 1996), there is surprisingly little research devoted to it. Based on a case study, Snow and his coauthors (1996) suggested that “management levers” (p. 3) could help a team to deal with task complexity, importance and multicultural dynamics, and combined them all as the inputs of cross‐cultural teams. These management levers were mostly conducted at organizational level, such as the link between the team’s mission and corporate strategy, staffing cross‐cultural teams, alignment with organizational structures and systems, communications and decision‐making technologies, group process facilitation, and cross‐cultural management such as performance measurement and reward allocation. In  addition, Zellmer‐Bruhn and Gibson (2006) investigated the influence of macro‐ organizational contextual factors in a multinational company setting, controlling for micro‐contextual factors, such as team type, training, feedback, and autonomy. They found that organizational contexts emphasizing global integration reduced team learning, but those emphasizing responsiveness and knowledge increased team learning.

Input–Output and Input–Process Relationships Many studies of cross‐cultural team effectiveness investigate a direct relationship between inputs and outputs or inputs and processes. Pieterse et al. (2013) examined the combined effects of cultural diversity and members’ goal orientation on team performance. They found that cultural diversity had a stronger positive effect on team performance when team members’ learning approach orientation was high and performance avoidance orientation was low. Van Der Zee et al. (2004) examined the influence of social identity and personality on work outcomes among business students who worked together in culturally diverse teams. As predicted, identification with one’s cultural background had a negative effect on individual wellbeing while identification with the team had a positive effect. Based on the analysis of 37 heterogeneous or homogeneous groups consisting of American and Japanese students, Oetzel (1998) found that heterogeneous groups were more likely to have unequal distribution of turns and to use majority decisions than homogeneous groups. Thomas (1999) suggested that culturally homogeneous groups had higher performance than culturally heterogeneous groups on five group tasks. Gibson and Gibbs (2006) examined a direct negative relationship between team factors (geographic dispersion, electronic dependence, dynamic structure, and national diversity) and team innovation. Brodbeck et al. (2011) found that individual‐level ethnic dissimilarity was negatively related to the individual learning outcome, but group level diversity was positively related to individual learning outcome. Ochieng and Price (2010) suggested that communications within a multicultural project environment could be effective when project managers demonstrated an awareness of cultural variation. Teams with leaders who were knowledgeable and skillful in handling cultural differences could come up with team norms and structure that could facilitate cross‐cultural communication and coordination (Dickson, Den Hartog, & Mitchelson, 2003).

234

Antecedents to Team Effectiveness

Input–Process–Outcome Relationships There are only a few studies conducting a design of a full IPO model, and most of the inputs are still cultural diversity. Guillaume et al. (2014) examined the relationship between cultural dissimilarity and individual performance through the lens of social self‐regulation theory, using self‐performance monitoring as the process variable. Nouri et  al. (2013) examined the mediating effect of conflict and cooperation on the relationship between cultural diversity and team performance. They also examined the competing predictions of information/decision making versus the social categorization theories by integrating two task‐related theories (e.g., situational strength theory and task type theory), suggesting that team processes were crucial for tasks that required coordination and cooperation, but might be less crucial for tasks with a low level of interdependence and a high level of autonomy. Tröster and van Knippenberg (2012) found that affective commitment and psychological safety mediated the relationship between leader openness, leader– member similarity in nationality and leader‐directed voice. Based on a sample of top management teams in international subsidiaries of multinational companies, Elron (1998) found that cultural heterogeneity within the top management team had a positive relation with issue‐based conflict, which in turn affected the top management team’s performance negatively. Zellmer‐Bruhn and Gibson (2006) found an indirect relationship between macro context and team performance through team learning.

Limitations After careful reviewing most of the studies of cross‐cultural teams from 1996 to 2015, it is worth noting that there have already been fruitful achievements in the field. Nevertheless, there are some limitations. First, considering the inputs, organizational factors are ignored. Previous studies mostly focus on individual characteristics (Adair et  al., 2013), cultural diversity (Guillaume et  al., 2014), tasks (Gibson, 1999; Nouri et  al., 2013), and team factors (Gibson & Gibbs, 2006; Stahl, Maznevski et al., 2010). The reason why few studies are related to organizational factors might be the difficulty of collecting data (Zellmer‐ Bruhn & Gibson, 2006), in that most team samples come from either a single division or a single organization, with no variance in macro context. Second, considering the processes, transition processes and coordination are lacking. The majority of studies have focused on differences in communication, conflict, and learning, with the mechanism of social identity theory and information processing theory (Stahl, Mäkelä et al., 2010). However, transition process is missing and information about coordination is confusing. Although there are two studies on coordination (Kotlarsky et al., 2008; Montoya‐Weiss et al., 2001), but the definition and the influence mechanisms are not clear. Third, when considering outputs, behavioral outputs represent a major omission in the literature. In the local team literature, behavioral outputs have been found to impact members’ desire to work together in the future (Kozlowski & Bell, 2003). The lack of focus on behavioral outputs in research on cross‐cultural teams is likely due to most of the studies being conducted in the laboratory with temporary teams. Nonetheless, with cross‐cultural teams becoming more of a norm in organizations, it is important to examine long‐term behavioral outputs, to respond to the integrated measurement of team e­f fectiveness (Hackman, 1987). Fourth, most research focuses on the relationship between only two stages, such as inputs and outputs, inputs and processes, or processes and outputs, while only a few



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c­onsider the entire IPO model for cross‐cultural team effectiveness. Previous empirical studies pay too much attention to whether cultural diversity has a positive or negative effect on team performance, treating cross‐cultural teams as simple and isolated entities, with no past and no future (Arrow, McGrath, & Berdahl, 2000). In a fact, cross‐cultural teams are complex, adaptive, and dynamic systems, and so there is a need for a broader design study. Fifth, considering the methodology, much of the empirical research has been conducted in laboratory settings, using student teams working on short‐term tasks (Adair et  al., 2013; Guillaume et al., 2014; Nouri et al., 2013; Pieterse, et al., 2013) and field studies with real cross‐cultural work team samples are lacking.

Future Research In this section, we suggest a research model (Figure 10.2) for future study, based on our observations of these limitations. First, we combine the three levels of input factors as a nest structure and add further organizational factors. This combination highlights the interactive effects between the different levels of inputs. Organizational factors play important roles in cross‐cultural teams. In multinational companies, different configurations of policy, structure, and support across the organization may simultaneously convey different messages to teams about the value of voluntarily adapting and innovating, and their f­reedom to adapt and innovate. The multinational company context adds such variety and complexity in cross‐cultural teams that it cannot be ignored in terms of team effectiveness. We select most of the organizational factors from Snow et al.’s (1996) “design and management levers” in the model of multinational team effectiveness: •• organization global strategy; •• factors related to a cross‐cultural team building, such as the alignment with company structures, programs, and systems, the link between the team’s mission and corporate strategy, communications and decision‐making technologies; •• staffing, which is a process fraught with tradeoffs and ambiguities, such as the tradeoff between local hiring and the use of expatriates, and consideration of team as well as technical skills among team members; •• performance measurement, such as multiple using of valid performance measurement and post‐project evaluations; •• reward allocation, attaching importance to team financial rewards, knowing exactly the relationship between team effectiveness and company effectiveness; •• the human resources role, where a well‐managed group can help cross‐cultural teams with teambuilding, leader and member selection, and manager education and development. Additionally, two recent formative articles offer some new clues to cross‐cultural team inputs combining individual characteristics and cultural factors; that is, perception of national character (Miller et al., 2015), and global orientations (globalization‐based acculturation; Chen et al., 2016). Perception of national character is different from social values or social beliefs, which could be measured at an individual level to predict one’s interaction with members from other cultures (Miller et  al., 2015). Global orientation indicates individual differences in the psychological processes of acculturating to the globalizing world, which encompasses multicultural acquisition as a proactive response and ethnic protection as a defensive response to globalization. Global orientation could affect

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Team processes Transition process Action process (Coordination) Interpersonal process Psychological process

Team-level outputs Task performance Creative performance Behavioral performance Affective reactions

Individual-level outputs

Contextual factors (Mediated moderation/moderated mediation effect)

Figure 10.2  A multilevel model of cross‐cultural team effectiveness.

i­ndividuating and modest behavior over and above multicultural ideology, and could p­redict attitudes toward ethnocultural groups and other outcomes (Chen et al., 2016). Second, we add a transition process to team processes, and emphasize the coordination variable. Transition processes encompass mission analysis, goal setting, strategy formulation, and other processes related to focusing the group’s efforts. Previous studies have seldom paid attention to the transition process. Coordination plays a very important role in cross‐cultural teams, and future studies are needed to focus more on this process. Third, we add a moderated mediation or mediated moderation effect beside the IPO model. There must be some contextual factors affecting the relationships. In fact, Nouri et al. (2013) examined the mediated moderation effect of task specificity. Future studies could design better models considering the whole process and its contextual effects.



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Fourth, we add behavioral performance to team‐ and individual‐level outputs. With cross‐cultural teams becoming more common in organizations, it is important to examine long‐term behavioral outputs, to respond to the integrated measurement of team effectiveness (Hackman, 1987). Fifth, we design a multilevel model. Previous studies mostly use aggregation, but s­eldom conduct a multilevel analysis (Guillaume et al., 2014). A multilevel design could explain more variances in individual levels, since individuals are very different from each other. Future studies could usefully consider this aspect. Sixth, we suggest that empirical studies move out of laboratory settings and into the field. It is understandably difficult to obtain data on cross‐cultural teams in field studies, but some real contexts, such as the organization context, cannot be adequately tested in a laboratory setting. For example, how do organizational culture and structure affect the functioning of cross‐cultural teams functioning? Future studies could cooperate with m­ultinational companies to conduct more deep observations and interviews.

Conclusion With increasing use of cross‐cultural teams in the current globalization process, understanding and achieving cross‐cultural team effectiveness are key to the success of many multinational companies. In response to this, we have reviewed studies of cross‐cultural teams and cross‐cultural team effectiveness. After a brief introduction of cross‐cultural teams, we reviewed cross‐cultural team effectiveness based on the classical IPO framework. We have described what kind of outputs, processes, and inputs have been studied, considering cross‐cultural team effectiveness, and the linkages such as process–output, input–process, input–output, and IPO in the framework. These inputs included cultural diversity, individual characteristics, team factors, and organizational factors. The processes included action processes (e.g., coordination, learning), interpersonal processes (e.g., cooperation, communication, conflict), and psychological processes (e.g., psychological safety, negative affective state, team shared value), which transform inputs to outputs and explain the “black box” between them. The outputs included performance (e.g., task performance, creativity performance) and affective reactions (e.g., well‐being, satisfaction). Finally, we have identified several gaps as well as research directions in both theory and methods in the light of the IPO analysis, and proposed a more comprehensive multilevel cross‐cultural team effectiveness model, adding input variables such as several organizational factors, process variables such as transition processes, output variables such as behavioral performance, contextual effects such as mediated‐moderation or moderated‐ mediation effects.

Acknowledgements We would like to thank the National Science Foundation of China for supporting this study (#71072055).

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Part III

Team Effectiveness Processes, Emerging States and Mediators

11

Teamwork Processes and Emergent States Rebecca Grossman, Sarit B. Friedman, and Suman Kalra

Introduction In the modern workplace, little can be accomplished without some degree of teamwork. Accordingly, teams have become the backbone of today’s organizations, enabling them to tackle complex problems, adapt to changing demands, and in turn, maintain a competitive advantage (Kozlowski & Ilgen, 2006). In line with their practical importance, teams have been a primary topic of research in recent decades, and continue to receive widespread attention. Various frameworks and reviews have been put forth, both to organize this sizeable literature and to facilitate our theoretical understanding of how teams develop, function, and perform effectively (e.g., Cannon‐Bowers & Bowers, 2011; Ilgen, Hollenbeck, Johnson, & Jundt, 2005; Kozlowski & Bell, 2003; Kozlowski & Ilgen, 2006; Marks, Mathieu, & Zaccaro, 2001; Mathieu, Maynard, Rapp, & Gilson, 2008). The most universally utilized approach to the study of teams is the input–process–outcome (IPO) model (Hackman, 1987; McGrath, 1964), which later evolved to the input–mediator– output–input (IMOI) model (Ilgen et al., 2005), where teams are understood on the basis of their inputs, or antecedents that facilitate or hinder team processes, their mediators, or team processes and emergent states that transform inputs into outcomes, and their outcomes, the desired results of the teams activities. As the transformative mechanism, the mediator component of the model can be considered the heart of the action – where team dynamics are primarily cultivated. Team processes and emergent states, which comprise this aspect, are thus the focus of this chapter. While much is currently known about team processes and emergent states, the w­orkplace, and corresponding research, is changing. Ever‐expanding global markets and technological advances are yielding organizations that are increasingly international in both their scope and composition, triggering a corresponding growth in researchers’ and practitioners’ interest in tackling organizational issues within multinational contexts. For instance, entire journal issues have been devoted to the idea of organizational psychology “going global,” The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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where contributors explore the extent to which consulting principles, as taught in the U.S., can be applied in other nations (Cooper, 2012). Likewise, researchers are recognizing and examining the role of culture in major theories more frequently, and international research is gaining a stronger presence in scholarly journals, reflecting what’s been referred to as a “global paradigm shift” (Gelfand, Leslie, & Fehr, 2008). This shift is becoming critical for our field, so much so, that scholars have argued that if organizational psychology is to prosper, “it must adopt a global perspective” (Gelfand et al., 2008; p. 493). It is fitting then, that a shifting perspective can be seen in the teams literature as well. As teams research continues to flourish, much of it is being conducted entirely by, or in collaboration with researchers outside of the U.S. These changing viewpoints and research samples are yielding findings that need to be incorporated into current theory and knowledge. Thus, the purpose of this chapter is to summarize the major, and most recent literature on  team processes and emergent states, with a particular emphasis on incorporating an i­nternational perspective. Specifically, we highlight variables that have been particularly prominent in the literature, and in relation to these variables, summarize existing knowledge, identify growing research trends, and propose avenues for future research, all while maintaining a focus on integrating the work of international scholars and studies of international samples.

Defining the Basics To lay the foundation for the remainder of our discussion, we must first provide some background information briefly delineating the basics of the groups and teams literature. Teams (and groups  –  terms that are often used interchangeably, Cannon‐Bowers & Bowers, 2011; Sundstrom, McIntyre, Halfhill, & Richards, 2000) have been defined in numerous ways, with most definitions including a set of key characteristics such as task interdependence, shared goals, and specialized roles and responsibilities (e.g., Kozlowski & Bell, 2003; Salas, Dickinson, Converse, & Tannenbaum, 1992; Sundstrom, DeMeuse, & Futrell, 1990). A particularly detailed definition put forth by Kozlowski and Ilgen encompasses each of these features and serves as a strong representation of the terms used in much of the extant literature: (a) two or more individuals who (b) socially interact (face‐to‐face or, increasingly, virtually); (c) possess one or more common goals; (d) are brought together to perform organizationally relevant tasks; (e) exhibit interdependencies with respect to workflow, goals, and outcomes; (f) have different roles and responsibilities; and (g) are together embedded in an encompassing organizational system, with boundaries and linkages to the broader system context, and task environment (Kozlowski & Ilgen, 2006, p. 79).

To understand the complexities of how such entities operate, teams researchers have h­ istorically relied upon the IPO model (Hackman, 1987; McGrath, 1964), briefly m­entioned above, as a guiding theoretical framework. Inputs include such things as team member characteristics (e.g., personality), team‐level factors (e.g., task structure), and contextual factors (e.g., organizational design), while outputs involve performance outcomes (e.g., quality) and team member affect (e.g., satisfaction) (Mathieu et al., 2008). Processes serve as the behavioral mechanisms that convert team inputs into outcomes, often through task‐focused interactions, such as coordination, cooperation, and communication. Although the IPO model has provided a valuable foundation for much of the teams literature, it is not without its shortcomings, and later developed models have aimed



247

Teamwork Processes and Emergent States INPUTS

MEDIATORS

OUTCOMES

Processes and Emergent States Organizational context Team context Members

AFFECT Team cohesion Team confidence Team trust BEHAVIOR Transition processes Action processes Interpersonal processes

Multiple criteria

COGNITION Team mental models Transactive memory systems Team learning

Figure 11.1  Input–mediator–output–input framework (Ilgen et al., 2005; Mathieu et al., 2008), emphasizing the components included in this chapter.

to address them. A major criticism, for instance, is that “many of the meditational factors that intervene and transmit the influence of inputs to outcomes are not processes” (Ilgen, et al., 2005; p. 520). Rather, several mediating mechanisms do not involve team member actions, as team processes do, but instead involve cognitive, motivational, and affective states, referred to by Marks, Mathieu, and Zaccaro (2001), and subsequent researchers as emergent states. These states reflect shared team properties that unfold over time through dynamic interactions among the members of a team. Other drawbacks pertain to the sequencing and nature of relationships between construct types  –  the model implies a single linear path, where one construct type (I, P, or O) proceeds to the next, and the sequence concludes (Ilgen et al., 2005), but research has since expanded to include the idea of a feedback loop, where outcomes can loop back to influence initial inputs, enabling a number of IPO cycles to occur over the course of task completion (Marks et al., 2001). Additionally, research now shows that team dynamics can go beyond a simple linear path from one construct type to the next – conditional relationships, or interactions, between constructs of various types can occur (Ilgen et al., 2005). As such, teams researchers have adopted a modified version of the I‐P‐O framework – the IMOI (input‐mediator‐output‐ input) model (Ilgen et al., 2005) – that addresses these criticisms. As this IMOI model is now the driving force behind much of the current teams research, we adopt a consistent perspective, focusing on both team processes and emergent states in our discussion of the current literature (see Figure 11.1 for a depiction of the IMOI model emphasizing the variables included in this chapter).

Team Processes and Emergent States: The ABCs To date, numerous team mediators have been examined, and the list continues to grow as research advances. As a means of organizing findings, researchers have grouped them based on various factors, such as the stage of team development they correspond with (e.g., Ilgen et al., 2005), or the type of variable – team process or emergent state – they represent (e.g., Mathieu et  al., 2008). While team processes are often grouped into a

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behavioral mechanisms category, emergent states can be further broken down based on whether they are cognitive or affective in nature (Kozlowski & Bell, 2003). Categorizing mediator variables into the ABCs – affective, behavioral, or cognitive mechanisms – provides a useful heuristic for analyzing and integrating the large, often diverse body of research that characterizes the teams literature. Thus, consistent with prior reviews (Kozlowski & Bell, 2003; Kozlowski & Ilgen, 2006), this chapter is organized around these three major types of team mediating variables. At a basic level, affective mechanisms reflect what teams feel, behavioral mechanisms capture what teams do, and cognitive mechanisms encompass what teams think.

Affective mechanisms Affective mechanisms involve team processes and emergent states that reflect relationships among team members, shared motivational characteristics, and affective reactions, such as team moods and emotions (Kozlowski & Ilgen, 2006). Here, we focus on cohesion, team confidence, and trust; not an exhaustive list of all affective mechanisms, but those that have received perhaps the most attention in the literature. Cohesion  Team cohesion is one of the earliest, most widely studied team constructs, with research spanning multiple disciplines (e.g., industrial‐organizational psychology, sport psychology, military psychology, Carron & Brawley, 2000; Dion, 2000). While various definitions have been put forth, most involve a shared attraction or bonding among team members that is grounded in social‐ or task‐based aspects of team membership, and that drives team members to remain together (Casey‐Campbell & Martens, 2009). Further detailing the construct, cohesion has been thought of as multi‐dimensional, comprised of social cohesion (i.e., “a shared liking for or attachment to the members of the group”), task cohesion (i.e., “the extent to which the task allows the group to attain important goals or the extent to which a shared commitment to the group’s task exists”), and group pride (i.e., “the extent to which group members exhibit liking for the status or the ideologies that the group supports or represents, or the shared importance of being a member of the group”; Beal, Cohen, Burke, & McLendon, 2003; Mullen & Copper, 1994). Cohesion has been linked to a number of key team outcomes, most notably team performance. Several meta‐analyses have been conducted over the years, each demonstrating a positive relationship between cohesion and performance, though also identifying some caveats (Beal et  al., 2003; Carron, Coleman, Wheeler, & Stevens, 2002; Chiocchio & Essiembre, 2009; Evans & Dion, 1991; Gully, Devine, & Whitney, 1995; Mullen & Copper, 1994). For example, results show that the cohesion–performance link is stronger when teams are more interdependent, that the type of performance being examined makes a difference (i.e., relationships are stronger when performance measures reflect behaviors and efficiency as opposed to outcomes and effectiveness), and that task cohesion tends to show stronger relationships with performance than do the other two dimensions. Research has linked cohesion to a variety of other important outcomes as well, such as member satisfaction (Forrester & Tashchian, 2006), team viability (Barrick, Stewart, Neubert, & Mount, 1998), and organizational citizenship behavior (Aoyagi, Cox, & McGuire, 2008). Given this large body of support, cohesion is generally regarded as a key contributor to team effectiveness (Carron & Brawley, 2000). While much less is known about cohesion’s antecedents (Kozlowski & Ilgen, 2006), a 2015 meta‐analysis revealed that it can be influenced by a number of variables, such as team member s­imilarity, team building interventions, and task importance, to name a few (Grossman, Thayer, & Salas, 2015).



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Cohesion continues to be a prominent topic of research, with several studies digging deeper into its role in team dynamics. Mathieu, Kukenberger, D’Innocenzo, and Reilly (2015), for example, used meta‐analytic and structural equation modeling techniques to explore the temporal nature of the cohesion–performance relationship, which has not been well understood historically. Findings showed that cohesion and performance demonstrate a reciprocal relationship over time, with the cohesion–performance link being stronger than the performance–cohesion link, suggesting that cohesion plays a larger role in performance than vice versa. Interestingly, another study found that distinct dyadic relationships among team members, where members have unique perceptions of different members that go beyond their perception of the team as a whole, can be detrimental to the formation of strong cohesion (LeDoux, Gorman, & Woehr, 2012). An updated meta‐ analysis found that the link between task cohesion and performance is stronger in sports teams as compared with business‐oriented settings (Castaño, Watts, & Tekleab, 2013). Researchers in Spain have also shed light on cohesion’s temporal nature – a study on project teams revealed that task cohesion emerges more strongly than social cohesion early in such teams’ lifespans, and later mediates the relationship between social cohesion and individual satisfaction (Picazo, Gamero, Zornoza, & Peiró, 2015). Other Spanish work demonstrates that authentic leadership influences cohesion in fire and police departments, a relationship that is partially mediated by members’ identification with the team (López, Alonso, Morales, & León, 2015). Work coming out of the Netherlands has focused more attention on the mechanisms through which cohesion is developed. For instance, De Jong, Curşeu, and Leenders (2014) explored the impact of negative relationships between team members, showing that they can reduce team cohesion, but that high levels of team‐member exchange and task interdependence can act as buffers that neutralize this negative effect. Van Vianen and De Dreu (2001) studied U.S. and Dutch teams to find that minimum levels of conscientiousness and agreeableness among team members relate to stronger task cohesion, while mean levels of extraversion and emotional stability relate to stronger social cohesion. Additionally, studies using samples from Korea and Israel have shown that value similarity can positively influence social cohesion (Seong, Kristof‐Brown, Park, Hong, & Shin, 2015), and that heterogeneity in team members’ attachment orientation can positively influence performance when cohesion is high (Lavy, Bareli, & Ein‐Dor, 2015). Overall then, cohesion continues to be a key team construct, with more recent, and international work shedding light on its temporal nature and its nuanced relationships with other team variables. Team confidence  Like Mathieu and colleagues’ (2008) review, we group team efficacy and team potency together to form a broader, team confidence category. While team efficacy reflects a team’s shared perception that it can successfully perform a specific task, potency captures beliefs about the team’s capacity to be successful across a range of tasks and contexts  –  thus, potency is generic while team efficacy is task‐specific (Gully, Incalcaterra, Joshi, & Beaubien, 2002; Lindsley, Brass, & Thomas, 1995). Key to both constructs is the idea that beliefs are shared by team members – they emerge at the team level, going beyond a simple aggregation of individuals’ self‐efficacy levels (Gully et al., 2002). Thought to influence performance by influencing which goals a team chooses to take on, how much effort team members put forth, and how well the team persists when it experiences setbacks (Bandura, 1982), team confidence has indeed shown consistent links with team performance. A meta‐analysis by Gully and colleagues (2002) found moderate, positive relationships between both team efficacy and potency and team performance, with the efficacy–performance link becoming even stronger when interdependence was high. Later work has uncovered some of the drivers of team confidence.

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For example, meta‐analytic mediation analyses have been used to show that team confidence is influenced by shared leadership, and in turn, influences team performance (Nicolaides et  al., 2014). Another meta‐analysis found that task interdependence, as c­ompared with outcome interdependence, was a stronger predictor of task‐focused team functioning, a construct that included collective efficacy (along with transition and action processes which will be discussed later; Courtright, Thurgood, Stewart, & Pierotti, 2015). In an interesting theoretical piece, DeRue, Hollenbeck, Ilgen, and Feltz (2010) argue that the dispersion of efficacy beliefs is an important consideration. That is, the extent to which efficacy beliefs are shared among members may play a role in efficacy’s antecedents and outcomes – a possibility in need of empirical research. International research on team confidence provides additional insight about its relationship with other team constructs. Hu and Liden (2011), for instance, studied five banks in China, and identified goal clarity, process clarity, and servant leadership as antecedents of team potency. Another China‐based study found that when teams have cooperative goals, they are more likely to develop a strong sense of team potency, and in turn, to achieve higher innovative performance (Wong, Tjosvold, & Liu, 2009). Researchers in Canada have provided support for the roles of leadership type and autonomy levels in the development of team potency (Boies, Lvina, & Martens, 2010; Rousseau & Aubé, 2013). Specifically, while shared transformational leadership demonstrated a positive relationship with potency in a sample of student teams, passive avoidant leadership had a negative impact (Boies et al., 2010). In addition to identifying team autonomy as a positive antecedent, Rousseau and Aubé (2013) found that team potency can reduce levels of a­bsenteeism among team members. Finally, in a collaboration among researchers from Germany, the U.S., and the Netherlands, self‐managed work teams representing 60 different nationalities were studied, revealing that dense task networks among team members had a positive effect on team potency, and this effect became stronger as teams became more culturally diverse (Tröster, Mehra, & van Knippenberg, 2014). In sum, research has moved away from exploring team confidence as a predictor of team performance, and instead has made great progress toward exploring its antecedents, with international research contributing greatly to this progress. Perhaps because it is becoming increasingly common for teams to work on a variety of different tasks, there has been a greater emphasis on team potency, as opposed to the more specific construct, team efficacy. Team trust  For team members to work together effectively, it is critical for them to trust one another. As an individual‐level construct, trust involves 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, Davis, & Schoorman, 1995, p. 712). When this expectation becomes shared among members, it emerges as team trust, a “shared psychological state among team members comprising willingness to accept vulnerability based on positive expectations of a specific other or others” (Fulmer & Gelfand, 2012, p. 1174). While trust is often thought of as key for facilitating team effectiveness in theory (Salas, Sims, & Burke, 2005), team trust has not shared the long history of research as other major affective mechanisms, with much of the work on trust focusing on trust with leadership (e.g. Dirks & Ferrin, 2002) or trust in organizations (e.g., Schoorman, Mayer, & Davis, 2007). Indeed, in their 2006 review, Kozlowski and Ilgen noted that “at the current time…work on team trust [was] underdeveloped” (2006, p. 94). However, team trust research has developed significantly since that time, particularly within international contexts. Researchers in the UK, for instance, developed a multidimensional measure, where team trust was captured by four indicators – propensity to trust, perceived trustworthiness,



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monitoring behaviors, and cooperative behaviors (Costa & Anderson, 2011). The authors chose these behavioral indicators based on the argument that when team members trust one another, they are more likely to openly cooperate and less likely to engage in monitoring behaviors. Support for the reliability and validity of the measure was found in two different samples. Related to this, De Jong & Elfring (2010) proposed a multi‐mediator model to explain the mechanisms through which trust influences team performance. Using a sample from a Dutch tax department, support was found for team monitoring and team effort as mediators of the trust‐performance relationship, but not for the third m­ediator they examined – team reflexivity. Other studies have shed light on the role of leadership in developing team trust. Two longitudinal studies of Chinese and U.S. samples, for example, demonstrated that highly differentiated leader member exchange relationships within a team can be damaging to levels of team trust, which in turn, can be damaging to social interactions among team members (Liu, Hernandez, & Wang, 2014). Another Canada‐based study of student project teams found that transformational l­eadership, when shared among team members, relates to higher levels of within team trust (Boies et al., 2010). Research has also begun to go beyond simply examining mean levels of trust within traditional team settings. Pangil and Chan (2014), for example, explored the role of trust in teams who work together virtually. Data from a multinational organization in Malaysia revealed that three types of trust – cognitive‐based, institutional‐based, and personality‐ based – all positively influenced virtual team effectiveness, with knowledge sharing partially mediating the relationships between institutional‐ and personality‐based trust and team effectiveness. Chyng‐Yang (2013) also studied virtual teams – a study of students in the U.S., Mexico, and Russia showed that trust was positively impacted by the degree of task interdependence, as well as team members’ awareness, defined as their knowledge of the status and actions of various components of their collaborative systems. Interestingly, De Jong and Dirks (2012) argue that it is not just the mean level of intrateam trust that m­atters for team performance. Specifically, they explored the idea of trust asymmetry – the degree to which two parties differ in their level of trust in one another – but extended it to the team level to capture the overall amount of asymmetry across all dyads that make up the team. Two field studies of Dutch teams revealed that trust asymmetry moderated the positive relationship between mean levels of team trust and team performance such that it became weaker as trust asymmetry became higher. In another study that considered the role of dyads, the actor–partner interdependence model (Kenny, Kashy, & Cook, 2006), which analyzes the reciprocal influence members of a dyad have on one another, was used to show that the propensity to trust of one member of the dyad (i.e., the actor) influence both his/her own, as well as the other member’s (i.e., the partner) level of dyadic trust within in new product development teams (Yakovleva, Reilly, & Werko, 2010). Further, propensity to trust had a greater influence on dyadic trust when members worked virtually, as compared with face‐to‐face. Overall, research on team trust has a­dvanced greatly since previous reviews. Much more is known about the antecedents of trust, trust’s impact on team outcomes, and the boundary conditions surrounding these relationships, with international research comprising a large component of this expansion in knowledge.

Behavioral mechanisms Behavioral mechanisms involve what team members do – the activities and interactions primarily focused on accomplishing task objectives (Kozlowski & Ilgen, 2006). While a wide array of team behaviors have been examined, we focus on those that can be categorized

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into one of the three overarching types of team processes identified in a detailed taxonomy by Marks et al. (2001) – transition, action, and interpersonal processes – as these play a central role in team functioning, and accordingly, have served as a foundation for much of the teams literature. Transition processes are defined as “periods of time when teams focus primarily on evaluation and/or planning activities to guide their accomplishment of a team goal or objective” (p. 364), whereas action processes involve “periods of time when teams conduct activities leading directly to goal accomplishment” (p. 366). Underlying both of these are interpersonal processes described as “processes teams use to manage interpersonal relationships” (p. 368). Various, more specific processes comprise each of these categories, as further discussed below. Transition processes  Transition processes occur between performance episodes and focus on both reviewing previous work and preparing for future efforts (LePine, Piccolo, Jackson, Mathieu, & Saul, 2008). These processes include mission analysis, goal specification, and strategy formulation and planning – activities focused on clarifying the team’s mission, delineating specific goals to help achieve that mission, identifying resources that can be utilized, and developing a plan for how work will be accomplished (Marks et al., 2001). As the foundation of other team processes, it is unsurprising that several studies have found support that these behaviors are critical for team effectiveness. Indeed, LePine and colleagues (2008) integrated these studies in a meta‐analysis, finding that transition processes demonstrated moderate, positive relationships with both team performance and team member satisfaction, both at a narrow level (i.e., each transition process demonstrated significant relationships) and as a whole (i.e., all transition processes combined together). Researchers have since focused on gaining a deeper understanding of transition processes, often going beyond those that are specific to the original taxonomy. Mathieu and Rapp (2009), for example, propose a distinction between emergent planning that naturally surfaces as part of team process, and formal planning that takes place resulting from structured team activities. Also distinguishing the focus of planning, their study showed that teams performed best when they used formal planning strategies (e.g., team charter) that focused on preparing for both effective taskwork and teamwork, as opposed to just one or the other. In related research, Fisher (2014) conducted a series of studies to demonstrate support for a two‐factor model of teamwork planning comprised of the taskwork and teamwork dimensions. These factors showed unique relationships with subsequent team processes such that taskwork planning predicted coordination, while teamwork planning predicted interpersonal processes. Other work has explored the notion of team reflexivity, defined as “the extent to which team members overtly reflect upon the group’s objectives, strategies, and processes, and adapt them to current or anticipated endogenous or environmental circumstance” (West, 1996, p. 559). Schippers, Edmondson, and West (2014), for instance, proposed a theoretical model in which they explain that team reflexivity can function as a means to reduce information‐processing failures by allowing teams to discuss and evaluate experiences prior to moving into action phases. Such reflection allows teams to create new, innovative ideas on how to work more efficiently together, and serves as an “antidote” to team‐level biases and error that often make their way into decision‐making processes. Others have also purported that the aim of team reflexivity is to evaluate past actions and performance with the intent to learn from failures and successes, thereby leading to improved future outcomes (Ellis, Carette, Anseel, & Lievens, 2014). As a fundamental aspect of team functioning, transition processes have been, and continue to be studied across the globe. For example, reflexivity was examined as a moderator in a study coming out of the Netherlands, where De Dreu (2002) investigated the effects



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of minority dissent (“when a minority in a group publicly opposes the beliefs, attitudes, ideas, procedures, and policies assumed by the majority of the group,” p. 285), on team innovation and effectiveness. Results showed that minority dissent, which is thought to facilitate divergent and creative thinking, did relate to greater innovation and effectiveness, but only when teams exhibited high reflexivity. Other researchers representing the Netherlands, the U.S., and the U.K., also examined reflexivity, proposing that the development of standards and strategies that encourage critical thought, search for information, and process accountability can promote team reflexivity, and ultimately reduce team information processing failures (Schippers, et al., 2014). In the introduction to a special issue focused on team processes, scholars in Germany briefly reviewed research on the mediating effects of team processes as they relate to team effectiveness (Antoni & Hertel, 2009). One article they discuss that’s included in the issue (Gevers, van Eerde, & Rutte, 2009), found that temporal planning is an important aspect of self‐regulation and performance in teams, more so for project teams bound by deadlines (Antoni & Hertel, 2009). They go on to relay that temporal planning in early project stages helps develop temporal consensus, which increases project teams’ ability to communicate and synchronize actions, allowing them to meet deadlines. Further, they explain that when a team starts working on a new task, team members tend to disagree on strategies, deadlines, and other project plans. They therefore must develop task strategies together and reflect on them collectively. Surprisingly, in their review they also report that two studies (Brav, Andersson, & Lantz, 2009; Gevers et al., 2009) found no significant correlations between team reflexivity and team cooperation, or coordination. Therefore, further research needs to analyze the mechanisms by which team reflexivity affects performance (Antoni & Hertel, 2009). Similarly, existing research has neither established best practices for stimulating reflexivity, nor has it described what interventions are most effective – future work could benefit from such an exploration (Schippers et al., 2014). These developments are important when considering transition processes as they underscore the value of focusing on reflexivity, evaluation, and/or planning activities between action phases. Action processes  Action phases are periods when the team works towards accomplishing its goals and objectives (Marks et  al., 2001). Marks and colleagues’ (2001), taxonomy included four major processes that occur during this phase: monitoring progress toward goals, systems monitoring, team monitoring and backup responses, and coordination, activities focused on tracking resources, progress, and team members throughout the process of task performance. Coordination processes, described as “orchestrating the sequence and timing of interdependent actions” (Marks et al., 2001, p. 368), primarily occur during the action phase (as they often facilitate taskwork), although they also overlap with the transition phase (as they involve the exchange of information, reflection, and mutual adjustment of actions in order to achieve the desired outcome). As an integral component of the taxonomy, these behaviors are crucial to team effectiveness not only in theory, but also as supported through empirical findings. Indeed, LePine and colleagues (2008) reported that action processes also demonstrate moderate, positive relationships with both team performance and team member satisfaction in their meta‐analysis. While we discuss action processes most prevalent in the current literature, we note a continuing need for additional research on many of the action phase dimensions comprising the original taxonomy. Many studies have provided a more nuanced understanding of how action processes influence team effectiveness. Monitoring progress towards goals, for instance involves team self‐regulating behaviors such as tracking progress, assessing what needs to be

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c­ompleted, and communicating progress in order to determine additional requirements for goal attainment (Rapp, Bachrach, Rapp, & Mullins, 2014). Research highlights that these self‐regulating actions promote team awareness and lead to members holding themselves accountable for their contributions to team tasks and success (Marks & Panzer, 2004; Rapp et al., 2014; Salas, Prince, Baker, & Shrestha, 1995). Marks and colleagues (2001) caution, teams who do not monitor goal progress might have a tendency to “drift, procrastinate, or stray off task and lose track of their purpose for extensive periods of time” (p. 367). Monitoring can diminish these threats and appears to be a vital mechanism through which teams can optimize efficacy and performance (Rapp et al., 2014). Interestingly, one study found that goal monitoring can serve as a moderator, such that team efficacy is more beneficial when teams engage in high, as opposed to low levels of goal monitoring, in which case efficacy may even become detrimental (Rapp et al., 2014). Related processes, team monitoring and backup behaviors, involve team members supporting others by a­ssisting in performing tasks, providing feedback, etc. Investigations point out that backup behaviors appear to be beneficial in that they help backup‐recipients, as to be expected (Porter et  al., 2003), yet there are potential pitfalls or drawbacks to be wary of. For example, Barnes et al. (2008) conclude that backup behavior leads those providing support to neglect their own taskwork, especially when team members have equal workloads. As a result, this can become habitual, where those receiving high amounts of backup may indulge in more social loafing. This issue is heightened in cases where team members are able to observe their supporters’ workload and see that teamwork is evenly distributed. Consequently, it is important for researchers and practitioners to consider contextual factors when contemplating the need/use for backup behaviors in the workplace. International research on action processes has provided additional knowledge regarding their importance for team effectiveness. For instance, De Jong and Elfring (2010) c­onducted a study in the Netherlands where they found that team monitoring mediates the relationship between intrateam trust and team performance. They propose that when intrateam trust is high, team monitoring manifests as an awareness of the needs and goals of others that overlaps with backup responses and implicit coordination. Under these conditions, monitoring and backup behaviors are perceived by others as benevolent actions, rather than efforts to control them in a negative manner. This ultimately allows for reduced coordination losses and an increase in team efficiency. In another study, conducted in central India, Mueller and Kamdar (2011) explored help seeking (which can be considered a component of backup behavior) from teammates during creative problem‐solving. Results suggest that seeking help in a balanced manner can have positive influences on creativity, and partially mediates the relationship between motivation and creativity. However, those who sought help also incurred costs  –  because help‐seekers are expected to reciprocate the obtained help, they also provide more help to their teammates, behaviors which were found to have a negative relationship with creativity, revealing that seeking and providing help are not always beneficial. Finally, a study conducted in Switzerland examined two implicit coordination behaviors within high‐risk teams: team member monitoring and ‘talking to the room,’ or information sharing that is not directed to a particular team member, but rather to the room in general (Kolbe et al., 2014). Although high‐ and low‐ performing teams exhibited similar amounts of team member monitoring and talking to the room, their exploration of patterns over time revealed distinct differences – higher‐ performing teams were more likely to speak up, provide assistance, and give instructions following team monitoring, and further, to respond to speaking to the room with additional speaking to the room rather than instructions, than were their lower‐performing counterparts, suggesting that it is not always the absolute levels of action processes that matter, but perhaps the patterns of such processes. In sum, these international studies



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contribute to the overarching theme that teams research is a growing field that is being utilized and developed globally. Nonetheless, while there is a plethora of literature available on team behaviors, we underscore that further research is needed on the action phase processes, particularly within the systems‐monitoring dimension. Interpersonal processes  While transition and action phases are often thought of as separate and distinct periods, interpersonal processes occur throughout both of these stages and establish a core foundation that facilitates effectiveness for other processes. Within this category, Marks et  al. (2001) focus on conflict management, motivating/confidence building, and affect management. A significant amount of research supports the notion that there is a positive relationship between interpersonal processes and team performance across a range of types of teams (Bradley, White, & Mennecke, 2003; LePine et al., 2008; Mathieu et al., 2008), with much of the work in this domain focusing on team conflict and strategies for managing such conflict. A meta‐analysis by De Dreu and Weingart (2003), for instance, concluded that both relationship conflict (conflicts regarding personal taste, political preferences, values, interpersonal styles, etc.) and task conflict (conflicts regarding the distribution of resources, procedures, policies, interpretation of facts, etc.) have negative effects on team performance and average member job satisfaction. However, later research came to differing conclusions – DeChurch, Hamilton, and Haas (2007) studied whether the type of conflict management strategy implemented moderates the relationship between task and relationship conflict. They found that the use of harsh and aggressive management tactics in response to a pure task conflict stimulates relationship conflict. These results are important as they support the idea that task conflict will only be productive if it remains distinct from relationship conflict. Research has continued to expand on knowledge of conflict management and other interpersonal processes. One study used meta‐analysis to clarify which team conflict processes are functional versus dysfunctional, concluding that individualistic processes (avoiding and competing) are negatively related to team performance, while collectivistic processes (openness and collaborating) are positively related (DeChurch, Mesmer‐ Magnus, & Doty, 2013). Additionally, both collectivistic processes were positively related to team affective outcomes, while both individualistic processes were negatively related. These findings highlight the value of understanding interpersonal process behaviors, as such knowledge can inform practitioners on ways to increase positive team performance and team affect. Grounded in the social relations model (used to examine individuals’ behavior and/or perceptions within the context of dyadic interactions), another study investigated the influence of interpersonal perceptions on conflict, cohesion, and team efficacy (LeDoux et al., 2012). Results suggested that the manner in which team members perceive one another on a number of individual characteristics (e.g., creativity, helpfulness) influences their interpersonal processes. Consequently, it seems that enacting conflict management practices at the dyadic level may allow for more effective interpersonal processes, which in turn, can lead to greater team performance (Bradley et al., 2003). On a similar note, Bradley, Klotz, Postlethwaite, and Brown (2013) found that team personality composition moderates the relationship between task conflict and team performance, such that when teams had low levels of emotional stability and openness to experience the relationship was negative, but when levels of these personality traits were high, task conflict actually become positive for team performance. Emotional stability and openness are thought to facilitate more constructive conflict management strategies allowing teams to use task conflict in a healthy manner to better team performance. As a salient component of team functioning, the literature on interpersonal processes continues to expand, and at an international level. In an interesting set of studies c­onducted

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in China, for example, Lam, Van der Vegt, Walter, and Huang (2010) explored the relationship between social comparison processes and interpersonal harming behavior (e.g., interfering with the work of others, treating others with disrespect  –  essentially the opposite of functional interpersonal team processes). Results revealed that a substantial proportion of the variance in team members’ interpersonal harming behavior is located in members’ dyadic relations. In teams with less cooperative goals, team members were more likely to harm other members in upward performance comparison situations (when someone is compared with another individual who is performing better than him/her), but only when their expectations that they would perform similarly to the individual in the future were low. The authors conclude that cooperative team goals might serve as an integral tool to positively influence members’ reactions to comparison information, thereby reducing destructive interpersonal team processes. Highlighting the benefits of strong interpersonal processes, a recent study conducted in the Baltic region found that team interpersonal processes positively related to both team effectiveness, and individuals’ organizational commitment, which in turn, increased individuals’ intent to remain with the organization (Killumets, D’Innocenzo, Maynard, & Mathieu, 2015). These findings r­eaffirm the notion that there are numerous benefits to helping teams manage their interpersonal processes effectively. Finally, a study conducted in Australia sheds light on some of the antecedents of interpersonal processes in teams. Jiang, Jackson, Shaw, and Chung (2012) examined the influence of faultlines, the boundaries that form and split a group into subgroups based on one or more attributes (Rico, Sanchez‐Manzanares, Antino, & Lau, 2012), and found that faultline strength based on team members’ nationality, but not based on their educational specialty, had a negative influence on team social interactions. Overall, from the interpersonal processes literature we can derive that it is paramount to recognize how constructs such as conflict management, team composition, faultlines, and help‐seeking play a fundamental role in team effectiveness, with developing work placing great emphasis on understanding interpersonal processes by examining the dyadic level.

Cognitive mechanisms Cognitive mechanisms involve what teams think, or cognitive activity that emerges at the team level (Wildman et al., 2012). These mechanisms, as a whole referred to as team cognition, capture the manner in which knowledge important to team functioning is mentally organized, represented, and distributed within the team and allows team members to anticipate and execute actions (Kozlowski & Ilgen, 2006). In a notable meta‐analysis, DeChurch and Mesmer‐Magnus (2010) showed that team cognition contributes incremental variance in team performance beyond that contributed by affective and behavioral mechanisms, providing evidence of its important role in team effectiveness. While research on team cognition has encompassed various constructs (Wildman et al., 2012), we discuss those that have been most prominent, namely team mental models, transactive memory systems, and team learning. Team mental models  Team mental models (TMMs) can be defined as organized mental representations of vital elements in the environment of the team, such as the team’s task, equipment, and roles shared among members of the team (Cannon‐Bowers, Salas, & Converse, 1993; Klimoski & Mohammed, 1994). Early research in this domain primarily focused on the degree to which such mental models are shared among team members, as sharedness is thought to allow team members to anticipate each other’s actions and coordinate behaviors. This is particularly useful when teams are unable to overtly communicate and strategize under time‐restricted or other high pressure conditions, as it allows them



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to rely on preexisting knowledge to predict teammates actions and respond accordingly. In line with these notions, shared mental models (SMMs) have been shown to positively influence team processes, which in turn, enhance team performance (Mathieu, Heffner, Goodwin, Salas, & Cannon‐Bowers, 2000). These relationships held for SMMs related to both task‐focused and team‐focused elements of the teams under study. Later work continued to explore different types of SMMs, while also moving beyond mental model sharedness, and most recently, has begun to identify different antecedents of TMMs. A study by Smith‐Jentsch, Mathieu, & Kraiger (2005), for example, examined the unique influence of two types of SMMs, those focused on positional goal‐interdependencies (teamwork‐oriented) and those focused on cue‐strategy associations (taskwork‐ oriented). Interestingly, neither significantly influenced team performance on their own, but interacted such that SMMs of both types were necessary for team effectiveness, suggesting that important effects can be overlooked if various types of SMMs are not examined. In addition to TMM similarity, a subsequent study also considered TMM accuracy, examining both as predictors of team performance (Edwards, Day, Arthur, & Bell, 2006). The authors found that while both were significant predictors, accuracy was a stronger predictor and partially mediated the relationship between team ability and team performance, while similarity did not, providing new insights about the role of TMMs. While TMMs have been recognized as important drivers of team processes and performance, factors giving rise to these shared cognitive structures have not received as much attention. To this end, Fisher, Bell, Dierdorff, and Belohlav (2012) examined potential antecedents of TMMs, with a specific focus on team composition variables, including various facets of personality and surface‐level diversity. Results indicated that TMM similarity positively predicted implicit coordination – an important outcome of TMMs, which mediated the relationship between TMM similarity and team performance. Results also suggested that team composition, in terms of the cooperation facet of agreeableness and racial diversity were significantly related to team‐focused TMM similarity. International research has played a significant role in developing the literature on TMMs. A study conducted in Switzerland, for instance, investigated how the two properties of TMMs – similarity and accuracy – interacted with monitoring behaviors to predict team performance (Burtscher, Kolbe, Wacker, & Manser, 2011). Results revealed that TMM similarity moderated the relationship between team monitoring and performance in such a way that performance was negatively impacted during low TMM similarity and high team monitoring. This finding suggests that teams may benefit from higher levels of monitoring only when there is high TMM similarity. Further, the interaction also indicated that similarity and accuracy of TMMs both were essential to predicting team performance. In a longitudinal study conducted in Europe, Gevers and colleagues (2009) explored the idea of temporal consensus, the degree to which team members have a shared understanding, or essentially a SMM, regarding the temporal aspects of their task, such as the pacing of activities and the importance of deadlines. Findings showed that teams with greater temporal consensus were more likely to meet deadlines, and this relationship was mediated by improved coordinated action among team members. It was further noted that temporal planning in initial stages, combined with higher exchange of temporal reminders in later stages facilitated temporal consensus in project teams. However, temporal consensus was not affected by temporally‐focused team reflexivity related to task completion. Results of a related study by Mohammed and Nadkarni (2014), conducted in India demonstrated that shared temporal cognition was a positive predictor, as well as a moderator of team performance. The moderating effect occurred due to its mitigating effect on polychronicity diversity (differences related to team members’ work style preference: to multitask versus to focus on a single task at a time) on team performance. Thus, international research is

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extending the scope of current literature, with a broader view toward understanding different types of TMMs and their various characteristics. Transactive Memory Systems  While TMMs primarily involve knowledge that is shared, transactive memory systems (TMS) focus on that which is distributed among team m­embers (Kozlowski & Ilgen, 2006). A TMS comprises the collective knowledge that team members possess, a shared understanding of which member knows what (Austin, 2003), as well as a structure that allows for storage, retrieval, and communication of that knowledge at the team‐level (Wegner, 1995). Use of TMS can decrease cognitive load on any single team member, and expands the existing expertise pool. It can also help reduce effort redundancy while facilitating knowledge sharing, thus optimizing resource allocation to positively impact team effectiveness (Hollingshead, 1998). Indeed, research has shown a positive relationship between TMS and team performance (Austin, 2003; Lewis, 2004), and conversely, evidence that performance suffers under conditions where a strong TMS is not available (e.g., high stress; Ellis, 2006). Digging deeper into these relationships, Austin (2003) conceptualized TMS as having four dimensions: knowledge stock (combined individual knowledge), consensus (agreement about who knows what), specialization (unique expertise across team members), and accuracy (degree to which assessment of who knows what is accurate). Each dimension was deemed to have a significant influence on performance, with TMS accuracy emerging as the most important. Additionally, the study highlighted the importance of considering different types of TMS – in additional to the traditional task‐focused TMS, TMS focused on relationships external to the team that could be used as informational resources were also found to be beneficial for team performance. Much of the literature on TMS has incorporated an international perspective. Work coming out of Turkey, for instance, measured three facets of TMS – specialization (differentiated knowledge), credibility (perceived accuracy of others’ knowledge), and coordination (effective knowledge processing) (Akgün, Byrne, Keskin, Lynn, & Imamoglu, 2005). Findings show that team stability, team member familiarity, and interpersonal trust facilitate these TMS components. TMS also positively influenced various team performance outcomes; as task complexity increased, the relationship became stronger, suggesting that the importance of TMS may change based on circumstances. Providing additional insight about how TMS can be developed, a study of multiple organizations in China revealed that various team characteristics – task interdependence, cooperative goal interdependence, and support for innovation  –  all positively related to TMS, which in turn, influenced team performance, with TMS mediating the team characteristic‐performance link (Zhang, Hempel, Han, & Tjosvold, 2007). It is important to note that TMS may not always be distributed evenly among members of a team. Depending on level of expertise and specialization, knowledge can sometimes be concentrated in one or a few members. To this end, a study conducted in Rotterdam examined metaknowledge, or knowledge of who knows what, and demonstrated that teams with a centralized TMS structure, where such knowledge is concentrated within one central member, can have performance advantages over those with a decentralized structure, where metaknowledge is distributed evenly among members (Mell, van Knippenberg, & van Ginkel, 2014). Results imply that a centralized structure enables central members to trigger the exchange and integration of information among team members, but this is only the case when task information is distributed in a disconnected manner and requires coordination. Interestingly, researchers in the U.K., Australia, the Netherlands, and the U.S. studied a multinational organization spanning four countries (U.S., U.K., Canada, and Sweden), and found that a component of TMS, which they termed



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“knowledge of who knows what (KWKW),” served as a team‐level informational resource that enhanced individual‐level creativity (Richter, Hirst, van Knippenberg, & Baer, 2012). Specifically, within the context of R&D teams, individuals higher on creative self‐efficacy demonstrated more individual creativity, and this relationship was stronger when shared KWKW was greater. The authors argued that because creativity relies considerably on the consideration and integration of various informational perspectives, KWKW is a valuable resource that individuals high on self‐efficacy are likely to take advantage of to facilitate creative outcomes. Team learning  Team learning can be defined as “a process in which a team takes action, obtains and reflects upon feedback and makes changes to adapt or improve” (Edmondson, 2002, p. 129). Because it involves a set of team processes as well as knowledge that occurs at the team level, it has been referred to as blended mediator that has both behavioral and emergent elements (Mathieu et  al., 2008). Though not studied as extensively as other cognitive mechanisms, current research does suggest that team learning is important for team effectiveness, particularly as teams increasingly face dynamic work environments that require continuous adaptation. A study conducted in the Netherlands, for example, examined various team learning activities and concluded that those related to the storing and retrieving of information were most important for both learning and team performance (van Offenbeek, 2001). Other studies have shown that team learning can have a positive influence on efficiency, innovativeness, and interpersonal relations among team members (Wong, 2004; Zellmer‐Bruhn, & Gibson, 2006). International research has played a prominent role in the developing literature on team learning, with much of it focusing on antecedents and mediators of the learning process. For example, researchers in the Netherlands explored team conflict as an antecedent of team learning, and found that relationship conflict, but not task conflict, had a negative influence (van Woerkom & van Engen, 2009). Additionally, team learning emerged as a strong predictor of team performance, and was found to partially mediate the observed relationship conflict‐team performance link. Other work coming out of the Netherlands revealed that situational variables can go a long way in influencing team learning behaviors. Specifically, Walter and van der Vegt (2013) found that team members’ positive mood was positively related to their team‐directed learning behaviors, but this relationship only held when members perceived high levels of team feedback, not when such feedback was lacking. Consistent with prior literature, greater amounts of team learning behaviors were associated with increased team innovation. This study highlights explanations for differences in contributions of individual team members to team learning, offering new insights into the foundations of team learning, and suggesting that member affect plays an important role. In a similar vein, research coming out of Spain investigated characteristics of the interpersonal context as enabling conditions for facilitating team learning (Ortega, S­ánchez‐Manzanares, Gil, & Rico, 2013). Results of a cross‐sectional field study of nursing teams showed that psychological safety, perceived task interdependence, and group potency all positively influenced team learning behaviors, which in turn, enhanced team performance, demonstrating mediating effects. This work highlights the importance of looking beyond task‐focused behaviors for facilitating team learning, and also considering more affective, interpersonal variables. In the above sections we have highlighted the importance and unique contributions of affective, behavioral, and cognitive mechanisms and their impact on team effectiveness. The extant literature underscores the underpinnings of team performance and has paved the way for future research focused on uncovering more specific constructs and boundary conditions within these broader realms. These works have contributed to filling in the

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gaps within existing work by following new and unique lines of inquiry, while increasingly expanding the international perspective. Below, we discuss areas that we believe could benefit from further research.

Future Research This chapter strives to provide a synthesis of knowledge on team processes and emergent states, while recognizing that the workplace, and corresponding research, is changing. As organizations are becoming increasingly global and more expansive, it is important for researchers to continue to explore and develop their understanding of teams within this changing context. Our coverage of topics was not comprehensive, however. We chose to focus on a selection of topics that are most prominent in the literature, while building on prior reviews by including more recent studies, and importantly, an international perspective. Here, we consider growing areas that are becoming progressively more relevant to the workforce and could benefit from future exploration. In order to gain competitive advantage, organizations are becoming more globalized, and as result, they are utilizing virtual teams as an essential function of their workforce (Jang, 2013). Virtual teams allow people to collaborate with one another without having to physically gather in the same location (Jang, 2013). Rather, employees are able to work with one another from any geographical location, using mostly electronic venues (i.e., email, Skype, etc.), making it possible to build worldwide teams (Hertel, Geister, & Konrad, 2005). With virtual teams becoming such an integral aspect of companies, it is vital to further our understanding of effective team processes in this context. While many researchers have conducted studies in order to fill this need (e.g., Fransen, Weinberger, & Kirschner, 2013; Hertel et al., 2005; Jang, 2013, etc.), there is still much to learn. For example, Hoch and Kozlowski (2014) examined degree of virtuality as a moderator, revealing that hierarchical leadership became less important for team performance under highly virtual conditions, while structural supports (e.g., quality of information received) become more important. These findings highlight the notion that it cannot be taken for granted that relationships observed between variables in traditional, face‐to‐face teams will necessarily manifest in the same manner in teams characterized by varying degrees of v­irtuality. To this end, the authors note a lack of research focused on the antecedents of shared team leadership specifically with the virtual context, identifying it as an avenue in need of research attention. More broadly, a review of the literature on virtual teams has been conducted, where the authors identified 10 primary areas in which additional research is needed (Gilson, Maynard, Young, Vartiainen, & Hakonen, 2015), further supporting the need for future work in this area, particularly that which gives consideration to the international environment. Another focus for additional literature should be placed on adaptability. Wiedow and Konradt (2011) explain that team adaptation can be understood as “the production of change (i.e., activities involving action taking such as making decisions or initiating changes)” (p. 36). They also explain adaptation as “goal‐directed behaviors relevant to achieving the desired changes in team objectives, strategies, and processes” (p. 36). Because of the aforementioned globalization of many organizations, it is essential to focus on adaptability behaviors and skills. Interestingly, Wiedow and Konradt (2011) refer to the work of Schippers, Den Hartog, Koopman, & Wienk (2003), and expound that team adaptation has many similarities to team reflexivity, and relate it to Marks and colleagues’ (2001) transition phase processes. They argue that there is a clear distinction between team reflection and team adaptation, but no research has yet to compare how both



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processes are temporally related (Wiedow & Konradt, 2011). However, they hypothesize that adaptation would follow reflection “as it might be the subsequent step after the development of new and changed ideas, objectives, and strategies during the reflection phase, which would indicate a direct causation model” (p. 46). Yet, as this is not a study currently in existence, the field could benefit from additional exploration on this subject, particularly as adaptation demands increase with the growing complexity, internationalism, and dynamism in the modern workplace. We also suggest that research continue to investigate creativity within teams. Creative team outcomes are becoming increasingly valuable as a means of helping organizations maintain a competitive advantage, and thus are often the primary reason for the composition of multinational teams since creativity is thought to be facilitated by diverse informational perspectives. Indeed, researchers in the Netherlands studied top management teams and found that minority dissent promoted innovation, but only under high levels of transformational leadership was such innovation considered to be radical (Nijstad, Berger‐ Selman, & De Dreu, 2014). The authors argued that transformational leadership creates a climate of participative safety, which enables minority dissent to be transferred into innovative outcomes. Interestingly, research coming out of Israel explored what’s been termed “the innovation paradox” – the notion that unique, divergent thinking is required for the development of creative ideas, but that successful integration and implementation of such ideas requires a degree of conformity (Miron‐Spektor, Erez, & Naveh, 2011). Creativity, and two other cognitive styles associated with this implementation process (conformity and attention to detail), were examined, revealing that the presence of creative and conformist team members enhanced team innovativeness, while the inclusion of attentive‐to‐ detail members reduced it. Both creative and conformist members proved important to include, as they demonstrated a balancing out effect where creative members increased task conflict and reduced adherence to standards, while conformist members had the opposite effect. Providing additional insight about how creativity can be facilitated through team composition, a study of research and development teams in Korea found that members’ goal orientation can exert a significant influence (Gong, Kim, Lee, & Zhu, 2013). Specifically, whereas a team performance avoidance orientation negatively influenced team creativity, team learning and team performance approach orientations demonstrated positive relationships, and these links were mediated by information exchange. Thus, as the literature on team creativity is growing, with international research playing a prominent role, current findings demonstrate many key variables with complex relationships, suggesting that much remains to be uncovered. Furthermore, we recommend additional exploration on the topic of diversity, as it will only grow in importance as teams become increasingly international. Although researchers have certainly begun to pave the way for addressing this necessity, much remains to be done. Mohammed and Nadkarni (2011) report on temporally diverse teams engaged in tasks that endure narrow time constraints and are client‐driven. Results underscore how important it is to recognize that individuals differ on how they utilize/manage time and how this might impact decisions regarding team member selection as well as role assignment. They explain the value of developing ways in which to respond to team temporal diversity, while suggesting further analyses on possible mediators of the diversity‐ performance link. In another study, researchers in Rotterdam revealed that the degree to which cultural diversity is beneficial to team performance depends on team members’ goal orientation  –  such diversity is more beneficial when members’ approach orientation is high and avoidance orientation is low (Pieterse, van Knippenberg, & van Dierendonck, 2013). Further, they implore researchers to expand on this work by examining whether effects are more pronounced when looking at different cultural backgrounds of leaders

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(specifically when leaders come from a different cultural background than the rest of his/ her team). Additionally, future research exploring the mechanisms through which diversity influences team mediators and outcomes would be advantageous. As an example, one study made great progress toward understanding how personality‐ and surface‐level diversity influence TMMs, but also served to highlight the need for additional work to elucidate how such influence occurs, and to explore the role of diversity in other deep‐level variables beyond personality (Fisher et al., 2012). For instance, the authors explain that, “the role of early team interactions as the linking mechanism between team composition and TMMs has yet to be confirmed empirically” (p. 836). Therefore, future research might address this by developing deeper into the study of TMMs in order to facilitate understanding how these mechanisms influence the convergence of individual mental models in circumstances characterized by diversity. Lastly, we suggest that researchers should continue to study the constructs described in this chapter, but that they should do so in innovative ways, while considering the influence of contextual variables. For example, because many teams now operate within time‐ sensitive, high‐stakes environments, there is a push in current organizations, and in turn, recent literature, to develop valid approaches to measuring team constructs in manners that can be considered non‐obtrusive (e.g., not requiring team members to continuously fill out self‐report measures). Team cohesion is one construct that has seen great development in this area. Salas, Grossman, Hughes, and Coultas (2015), for instance, describe five emerging methods for measuring cohesion indirectly – big data, sociometric badges, physiological metrics, content analysis, and external observation. In support of the idea that cohesion can be measured effectively by raters external to the team, one interesting paper used a series of studies to show not only that such assessments are viable, but also that they can be done based on very brief exposures to the teams in question; judgments of the cohesion levels of three different types of teams (i.e., rock bands, ultimate frisbee teams, and boards of directors), based on 10‐second video clips, were found to significantly predict team performance (Stillman, Gilobich, & Fujita, 2014). Another study found initial validation evidence of a behaviorally anchored rating scale, where cohesion was measured based on the behavioral interactions, rather than the perceptions and attitudes of team members (Thayer, Gregory, Grossman, & Burke, 2014). Thus, while valid, non‐obtrusive measures of team cohesion are in development, such measures of other team constructs have not received as much attention – we recommend this as an avenue for future studies. Other factors to consider in future research involve the conditions surrounding teamwork that can play a role in how team processes and emergent states manifest, interact, and impact team effectiveness. Salas, Shuffler, Thayer, Bedwell, & Lazzara (2015) have p­resented a simplified heuristic for understanding teamwork (see also Chapter 2 in this volume)  –  in this heuristic, core team processes and emergent states are embedded within what they call influencing conditions, which include context, composition, and culture. A key takeaway here is that team variables do not emerge or operate within a vacuum – they can be heavily influenced by situational factors, bringing into question the ability of many existing findings to generalize to different contexts. While many studies do indeed explore these contextual features as potential moderators of observed relationships, we argue that this should be a primary theme in future studies, particularly as the team context continues to evolve. In sum, while there is a great abundance of future research that we could benefit from, we chose to focus on the need for a better understanding of virtual teams, adaptability, creativity, and diversity, as well as the need to explore existing team constructs in new ways, in light of the fact that team processes and emergent states are continuing to become studied across the globe, and we feel that



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Table 11.1  Future research directions. Topic Virtual teams

Team creativity

Team adaptability

Team diversity

Old constructs, new approaches

Example Research Questions •• Does the degree of virtuality moderate the relationships between various team processes/emergent states and team performance? •• Which team leadership behaviors are most important for facilitating team performance in virtual settings? •• Should team processes/emergent states be measured and aggregated differently in virtual, distributed settings? •• Which team processes/emergent states are most important for facilitating team creativity? •• What combination of team members is ideal for managing the “innovation paradox?” •• Can a team culture be developed to encourage team processes/ emergent states that are necessary for team creativity? •• Which processes/emergent states are most important for facilitating team adaptability? •• How can team adaptability processes/emergent states be trained effectively? •• How does team adaptation relate to team reflexivity? •• When is team diversity beneficial for team processes/emergent states and when is it detrimental? •• Which diversity characteristics are most important to consider? •• How can team leaders help diversity manifest in positive processes/ emergent states and outcomes? •• Which approaches are most valid for measuring team processes/ emergent states in non‐obtrusive ways? •• How do contextual variables influence relationships between team processes/emergent states and team performance? •• How do team processes/emergent states influence each other over time? How often should they be assessed?

these areas are elemental to teams undergoing international expansion. For a summary of our proposed research avenues and specific examples of research questions, see Table 11.1.

Conclusion While teams have been at the heart of various research efforts for several decades now, understanding how they function remains a critical research need, particularly as the context surrounding teamwork undergoes continuous change. To this end, the teams literature is flourishing, with researchers around the globe making great strides toward gaining a deeper understanding of many long‐studied team variables. The purpose of this chapter is to provide an updated summary of current knowledge pertaining to key team processes and emergent states, while incorporating an international perspective. As a whole, the studies reviewed demonstrate growing trends toward considering the role of time and team development, investigating conditional relationships and boundary conditions, and identifying antecedents of processes and emergent states that have been deemed critical for team effectiveness. Additionally, many efforts are focusing on developing more nuanced understandings of team constructs by breaking them down into different dimensions (e.g., examining cohesion as task cohesion, social cohesion, and group pride; exploring

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transition processes focused on taskwork versus teamwork) or aggregating them in different ways (e.g., considering dyadic relationships; calculating variance indices rather than just means). In sum, the literature on team processes and emergent states has continued to grow since previous reviews, and much of this expansion has incorporated multinational samples and the perspectives of multinational researchers, making great strides toward the much needed global paradigm shift in our field.

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12

Team Decision Making Tom W. Reader

Introduction Team decision making refers to “a team process that involves gathering, processing, i­ntegrating, and communication information in support of arriving at a task‐relevant decision” (Cannon‐Bowers, Salas, & Converse, 1993, p. 222). Team decision making is crucial to organizations where performance is dependent upon teams cooperating on diverse and specialized tasks (e.g. sharing expertise and information, critiquing judgments, innovating). Research investigating team decision making has tended to focus on the f­ollowing three types of decisions (Furnham, 2005). First, ‘operational decisions’ with short‐term and immediate effects (e.g. military command and control teams), and where teams make decisions under time pressure, uncertainty, and through combining the expertise of those in different roles (Tziner & Eden, 1985). Second ‘tactical’ decisions with medium effects (e.g. in business firms) where teams must review data, provide options, and participate in discussions (Patchen, 1974). Third, strategic decisions (e.g. in top‐ management‐teams) on long‐term organizational strategy (Bantel & Jackson, 1989). This work has shown that effective decision making is determined by a range of social (e.g. hierarchies), behavioral (e.g. communication), cognitive (e.g. shared mental models), and contextual factors (risk, uncertainty, required expertise, interdependencies for decision making) (Guzzo & Salas, 1995; Hackman, 1987; Ilgen, Hollenbeck, Johnson, & Jundt, 2005; Stewart, 2006). The current chapter considers the key findings (and future research needs) from the social and applied psychology literature on the determinants of effective team decision making, and reviews research on i) the group processes that influence behavior in teams, ii) teamwork and decision making, and iii) interventions for improving team decision making.

The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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Group Processes and Team Decision Making To understand how teams make decisions, it is first necessary to consider the social p­sychology literature on group decision making (Brown, 2000). This outlines various principles critical to understanding how people behave in teams, and underlies conceptualizations of team decision making (Jones & Roelofsma, 2000). Specifically, group processes research has described fundamental tendencies for how individuals behave and think during collaborative decision‐making tasks, and provides the starting point of analysis for investigating team decision making. Although it is beyond the scope of this chapter to provide a full review of the group processes and structures that influence team decision making (see Table 12.1 for an overview), key theories and observations from this literature are considered below.

Group processes and decision making Since the 1940s, psychology research has examined the processes that influence decision making in groups. In terms of understanding team decision making, research on conformity and cohesion in groups has been especially insightful, and shows how group m­embers Table 12.1  Key concepts from the group processes literature relevant for team decision making (Brown, 2000). Concept Communication network Conformity Escalation of commitment Group cohesion Group norms Group polarization Groupthink Hidden profile Intragroup conflict Leadership Minority influence Obedience Voice

Definition Hierarchies and linkages within groups through which information and opinions are shared for group decision making (Bavelas, 1950). Where group members comply with the behaviors and opinions of an individual or the majority (Asch, 1951). Where groups make decisions to support a previous decision despite it not being successful (Staw, 1981). The attraction of a group to its members, and subsequent tendencies to desire to remain in a group, and the influence of this upon attitudes and behavior (Hogg, 1992; Schachter, 1951). Values for acceptable and unacceptable attitudes and behaviors within a group (Sherif, 1966). Where groups make decisions that are more extreme than the initial judgments of group members (Moscovici & Zavalloni, 1969). Instances when groups make poor decisions (e.g. not adequately appraising alternative courses of action) in order to maintain unanimity (Janis, 1972, p. 9). The bias for groups to use shared information over unshared information when making decisions (Stasser & Titus, 1985). Where group members believe that their goals and interests are incompatible or in opposition (Korsgaard, Jeong, Mahony, & Pitariu, 2008). Where a leader influences the behavior and attitudes of group members during decision making (Lewin, Lippitt, & White, 1939). Where an individual or minority group influences the majority to accept their beliefs or behavior (Moscovici & Lage, 1976). Behaving in accordance to instructions or actions from an authority figure (Milgram, 1963). Opportunities and ability of group members to present information relevant to a decision (Folger, 1977).



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often agree with the judgments of other group members (even when those judgments are obviously incorrect) due to social pressure. In particular, a series of seminal social psychology studies have shaped our understandings of how decisions are made in groups and teams. First, seminal laboratory research by Solomon Asch (Asch, 1951) demonstrated that, through a series of perceptual judgement tasks (where participants compare the lengths of black lines on two cards and specify which are the same) using one subject and several confederates, people will tend to report the same judgments as other group members even when those judgments are clearly wrong. This indicates that the desire to conform to a consensus opinion shapes decision making in groups to the point that participants would rather give a wrong answer than contradict the group. Furthermore, and outside of the laboratory, seminal field research by Hofling et al. (1966) within hospitals showed that nurses would often obey the directives of senior doctors (e.g. to administer unapproved drugs) even though those directives were known to be wrong. This illustrates seminal research by Stanley Milgram (1963) on obedience to authority figures, which shows that the extent to which a participant is willing to punish a confederate (even when they do not want to do so) depended in part on their deference to the instructions and responsibilities of authority figures. This finding can be understand from the paradigm of group power, where it is argued that the more “power” a group member has, e.g. in terms of position, expertise, personality; Raven, 1992), the more influence they have over the judgments and points of view of other group members. Such findings are essential understanding team decision making, as they highlight the influence of authority and conformity in group decision making. In particular, group decision making researchers have focused upon the desire to maintain “group cohesion” as a key influencer of how members collaborate together to reach a decision. Group cohesion has been described as “the ‘cement’ binding together group members and maintaining their relationship to one another” (Schachter, 1951, p. 229). It is both argued to improves the solidarity and commitment of members to a group (and thus performance), and also to be a product of shared success (Mullen & Copper, 1994). For example, Festinger’s (1954) social comparison theory argues that we often refer to other people’s beliefs in order to assess the correctness of our own beliefs. Where a decision is important, difficult and requires uniformity, group members have tendencies to refer to the majority judgement as this is judged likely to be more consistent and correct (Baron, Vandello, & Brunsman, 1996). Thus, in making decisions, group members have tendency to behave and act in a way that will maintain group cohesion. For example, research on ‘group polarization’ (Moscovici & Zavalloni, 1969) shows that the judgement of a group (e.g. investment decision, or jury sentencing: Barber, Heath, & Odean, 2003; Bray & Noble, 1978) tends to become intensified as a result of group discussions. Group members favor options that are to the extreme (e.g. in terms of risk‐taking that is willing to be tolerated) of their initial tendencies. This is explained, in part, as a social phenomenon whereby group members desire to be perceived favorably by others through suggesting options that are in the direction of the majority of a group (e.g. a financial investment), but are to the extreme of the average (and their own) belief (Isenberg, 1986). Group polarization often results in groups focusing on information that supports their decision, which negatively influences the future review and analysis of information. This phenomenon has been repeatedly demonstrated through “hidden profile” experiments (Stasser & Titus, 1985), which investigates how groups share information when making decisions (e.g. personnel selection). This paradigm involves giving group members shared information (e.g. on the qualifications of potential job candidates) and unshared information (i.e. unique to each participant, for example on job experience), and examining

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how and whether they combine their knowledge to make an optimal decision. Research tends to show that groups focus on information that is shared in order to reach group consensus, and information that is unshared often remains little discussed because it contradicts or disrupts the consensus on decision making. This leads to suboptimal decisions based on a lack of full appraisal of information (with groups that communicate unshared information making the best decisions), and the effect can be exaggerated through group members knowing one another’s perspective on a decision (as members aim to reach consensus with each other), whereas encouraging group dissent can result in a more thorough analysis of information and better decisions (Mojzisch & Schulz‐Hardt, 2010; Schulz‐ Hardt, Brodbeck, Mojzisch, Kerschreiter, & Frey, 2006). Linked to group polarization and information sharing is the notion of escalation of commitment: where decisions are made (e.g. investments) to support a previous decision despite it resulting in a negative outcome (Staw, 1981). Research shows that groups tend to be more likely than individuals to escalate commitment to failing strategies (e.g. in policymaking, economic investment, gambling; Whyte, 1993), and the explanations for this tendency are again social, with group members indicating continued support for a decision in order to show their c­ommitment to the group. The influence of conformity and cohesion upon team decision making in group settings is arguably brought together through theory on groupthink, which is used to understand decision failures. Groupthink is described as: “a mode of thinking that people engage in when they are deeply involved in a cohesive in‐group, when the members’ striving for unanimity overrides their motivation to realistically appraise alternative courses of action … Groupthink refers to a deterioration of mental efficiency, reality testing, and moral judgement that results from in‐group pressures” (Janis, 1972, p. 9).

Groupthink theory was developed through case study (e.g. the Bay of Pigs fiasco, the Watergate scandal), and range of behaviors have been identified as indicative of groupthink: for example, illusions of invulnerability in decision making, pressures to conform to the group consensus, marginalization of people with alternative viewpoints, self‐censorship, “mindguards” who protect the leadership from hearing information that might cause doubt, and confirmation bias whereby group member seek out information that supports the chosen course of action (and ignore information that contradicts it). Groupthink occurs due to the desire to maintain cohesion and positive relationships (Baron, 2005), and is more likely to occur when there is strong directive leadership, time pressure, and complex decisions with high stakes. The level of empirical support for groupthink is diverse, and researchers have been somewhat conflicted on the validity of the model and the extent to which it explains poor decision making. Experimental research is mixed in terms of supporting groupthink theory, and the strongest evidence is argued to be qualitative and based on case‐studies (Aldag & Fuller, 1993; Esser, 1998). In particular, laboratory research has only partially successfully tested the groupthink mode. Many experimental studies compare the decision making of cohesive and non‐cohesive groups (e.g. which require partial or total support for a decision; Ahlfinger & Esser, 2001; Flowers, 1977; Kameda & Sugimori, 1993; Leana, 1985). These indicate that groups with participative leaders tend to consider more decision options and have group members more involved in decision making. They show cohesive groups are also more willing to agree with a leader’s decision if that decision was presented early in the process of making a decision (i.e. the group becomes locked onto one decision option), and where group opinions are homogenous at the beginning of a



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discussion they are less likely to change or adapt (because they are not challenged adequately within the group). Furthermore, experimental research indicates the importance of group identity as an underlying predictor of groupthink in threatening situations (Flowers, 1977; Turner & Pratkanis, 2014; Turner, Pratkanis, Probasco, & Leve, 1992). Various strategies for reducing groupthink have been proposed, for example deliberative discussion strategies have been show to improve how groups consider different decision options, alongside ensuring group members are in some way individually accountable for decision‐making processes and outcomes (Kroon, Hart, & Van Kreveld, 1991; Turner & Pratkanis, 1998). Yet, critique for the groupthink model is substantial, and is based on the observation that studies supporting one aspect of the groupthink model often indicate minimal support for another aspect of the model (Aldag & Fuller, 1993). In effect, the groupthink model outlined by Janis (1972) has not been fully and successfully tested. For example, research also shows that members of non‐cohesive decision groups are more likely to self‐censor in putting forward information, that greater cohesion does not lead to poor decision making, and that there are contradictory findings on whether groupthink is more likely to occur when leaders indicate early in a decision process that they prefer a particular solution to a decision problem (Ahlfinger & Esser, 2001; Flowers, 1977; Leana, 1985). This has led to some researchers concluding that the groupthink phenomenon is only partially demonstrated, and that the influence of individual components of groupthink (e.g. authority, cohesion) upon decision making is highly situational (Esser, 1998; Leana, 1985). Thus, it can be argued that groupthink is a highly situational phenomena (e.g. it involves risk, high stakes decisions, real power distances and relationship), and its key utility has been that it reflects and synthesizes different phenomena related to social psychology (e.g. example consensus seeking, group polarization, and obedience to authority) into a more holistic decision making framework, and is a catch‐all term to account for when groups make poor decisions (Baron, 2005). To this end, the groupthink paradigm is widely applied to understand decision making in a range of settings. For example, in understanding financial decision making and beliefs about asset markets and investments (Bénabou, 2012). It is still widely used to understand foreign policy decisions, for example those in the lead up to the Iraq war (Schafer & Crichlow, 2013). In addition, groupthink theory is also used to understand challenges within the field of applied psychology, for example in terms of explaining poor patient decision making in medical teams (Mannion & Thompson, 2014). It is particularly important for understanding team decision making, as it illustrates the social processes that lead teams to misunderstanding a situation, inadequately evaluating decision options, or making poor decisions in order to maintain group unity. Indeed, within team settings, the term “team‐think” has been put forward to describe how groupthink can occur in self‐managed work teams (Manz & Neck, 1997).

Group structures and decision making Group process research has also investigated how group structures influence decision making. This is important for understanding team decision making in different and situations teams (e.g. autonomous workgroups, expert teams). For example, research has examined how group decision making is shaped by communication structures. Groups with centralized communication structures tend to perform better on simple tasks (where a single decision making collates information and reaches a decision), and those with decentralized communication structures perform better on complex tasks (Leavitt, 1951; Shaw, 1964). Research using Bales’ (1950) interaction process analysis (where group behaviors are examined in minute detail) shows group size and duration of decision making to influence

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how decisions are made (e.g. in larger groups leaders become more dominant, information‐ sharing lessons over the course of a group discussion; Hare, 1976; McGrath, 1984). Such work has led to the focus by teamwork researchers on the importance of interdependencies (e.g. understanding who depends on who for information within a team), specialized team roles (e.g. technical, expert), and shared knowledge structures (e.g. shared situation awareness) for effective team decision making. One of the most cited models for understanding how group structures influence decision making is the normative decision model (Vroom & Jago, 1978; Vroom & Yetton, 1973). This posits that the effectiveness of team decision making is dependent upon the appropriateness of the structure of team to the situation being faced. For example, the extent to which information and insight critical to effective decision making is dispersed among a team is theorized to determine whether the structure for decision making should be i) autocratic (e.g. team leaders gather information from team members, but make decisions alone), ii) consultative (e.g. team leaders ask opinions and ideas of others, but makes the final decision), or iii) shared (e.g. team members reach consensus with the group and adopt an inclusive style). Furthermore, the requirement for a decision to be accepted by all team members (e.g. for implementation) also influences the optimal structure of a team for enabling effective decision making. Where decisions require acceptance by the team and input from all team members, a consultative or group structure is more effective. Where decisions do not require input (e.g. under high time pressure), autocratic approaches can be more effective. Research has indicated some support for the Vroom‐ Yetton‐Jago model (e.g. through experimental work examining the suitability of team leadership approaches to different problems: Field, 1982), although critics have highlighted that the model is overly prescriptive, quite complex to use, and that factors such as time pressure, task requirements, stress, training, and resources are not fully considered within the model (Yukl, 2010). Alongside research into the team structures that influence decision making, investigations have focused on the variety and types of team member roles necessary for an effective work group. Belbin’s (1981) theorization on team roles posits that teams must enact eight (later nine) roles to perform and make decisions effectively (e.g. a leader, a creative thinking, a completer–finisher, workers). This is critiqued (e.g. for lack of validity), yet reflects the intuitive observation that effective teams require team members with a range of abilities and duties to make decisions effectively (Fisher, Hunter, & Macrosson, 2001; Furnham, 2005; Senior, 1997). Research on team roles for effective decision making has arguably become more nuanced in order to reflect the complex situations and types of groups that perform decision‐making tasks. For example, studies on ‘dynamic delegation’ in acute medical teams show that the roles of team members within pressurized and highly skilled teams are often restructured and fluid (e.g. for the team member leading decision making) according to the situation and skills required (Klein, Ziegert, Knight, & Xiao, 2006). Furthermore, research indicates that alternating leadership roles in teams improves creativity in decision making (e.g. during marketing strategy tasks; Aime, Humphrey, D­eRue, & Paul, 2014), that team decision making is often dependent upon temporary group members (Tushman & Scanlan, 1981), and that team functioning is influenced by factors such as temporal stability (Hollenbeck, Beersma, & Schouten, 2012). This reflects the complexity and transient nature of organizational teams, with applied researchers a­ttempting to account for this when describing the factors that lead to effective decision making in teams. Research in applied settings has examined this. For example, investigations of decision making in intensive care unit teams (which provide life‐saving care to critically ill patients) has highlighted how environmental factors (e.g. patients, resources, time pressure) shape



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the structure and roles of team members during daily ‘decision rounds’ (Reader, Flin, & Cuthbertson, 2011). This is where multidisciplinary teams (senior doctors, trainee doctors, nursing staff) must make decisions on diagnosing patient’ illnesses, developing treatment plans, and delegating tasks for providing urgent care. To achieve this, senior doctors (who lead the team) tend to adopt a more consultative style of decision making, with input and opinions from team members being used to form decisions. Furthermore, where appropriate, decision making is delegated to junior doctors, who are tasked with leading decision making as part of their training (with senior doctors providing guidance) and because they may have had more contact or knowledge about a particular patient. However, senior doctors must also lead team decision making during emergency situations, for example a patient experiencing septic shock. In these situations, team decision making is required to be far more autocratic, with senior doctors making rapid decisions, and using the team as a resource for information and conducting tasks. Team members have clearly defined roles, and these change during the course of a task. This exemplifies how the structure of a team for making decisions is altered by the task (does it require information from a variety of team members), the team (who is qualified and best placed to make a decision), and the situation (e.g. time pressure). This type of team decision‐making research resonates with contingency models of leadership, which argue that effective teamwork and decision making is highly specific to the problem being solved and the skills and experience of leaders and team members (Yukl, 2010). For example, research in trauma resuscitation teams show that empowering forms of leadership (i.e. giving teams decision‐making autonomy) are most effective when the severity of a situation is low, and the experience of a team is high (Yun, Faraj, & Sims, 2005). Where the severity of a situation is high, and the experience of a team is low, more directive forms of leadership and decision making are required. Thus, it can be concluded that research investigating group processes has shown decision making in group settings to be influenced by both social processes and group structures related to a task. The desire to maintain cohesion has been shown to influence how groups review and share information, and alter group member judgments in order to ensure that they are consistent with others. Tendencies to defer to those in authority roles have also been shown to influence group decision making. In terms of understanding how teams in organizations make decisions, this work is foundational, and nuanced in how it is applied to explain team decision making. For example, research shows the importance to effective team decision making of speaking‐up behaviors in groups (to overcome tendencies to be obedient to authority, to self‐censor in order to maintain group cohesion), and communicating information that is unshared or poorly understood (Edmondson, 2003; Mesmer‐ Magnus & DeChurch, 2009). It also shows that, in some circumstances, team decision making needs to be highly directive (which provides less scope for speaking‐up and c­ommunication; Reader, Flin, & Cuthbertson, 2011; Yun et al., 2005). We consider this further in the following section.

Teamwork and Team Decision Making Social psychology research on group dynamics has been highly instructive for understanding the social influencers of team decision making. Yet it does not specify the team behaviors and cognitions integral to effective decision making in organizational settings. For example, team decision making often occurs within situations characterized by severe time pressure, rapidly changing environments, uncertainty and ambiguity, complexity, and multicomponent decision tasks (Orasanu, 1990; Orasanu & Salas, 1993). From this perspective, team

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decision making can be less about reaching consensus and involving team members, and instead focused on ensuring that teams effectively “process and filter ‘raw’ data, apply individual expertise, communicate relevant information, and (often) make recommendations to other team members” (Cannon‐Bowers et al., 1993, p. 222). Thus, team decision making is both a group task (i.e. sharing information to support a decision), and also an individual task (e.g. a leader must integrate this information and make a final decision). Effective team decision making is therefore dependent upon the ability of team members to work well together in preparing, selecting, and enacting a decision, and the fusion of different skills, expertise, and knowledge of team members required for a task or decision. To understand this, teamwork researchers have extensively applied social psychology theory to investigate the teamwork skills that underpin effective team decision making, and the shared knowledge structures (team cognition) that lead to teams combining their knowledge and skills effectively.

Teamwork skills and team decision making A team refers to “a distinguishable set of two or more people who interact … towards a common goal … and who have specific roles or functions” (Salas, Dickinson, Converse, & Tannenbaum, 1992, p. 4). As discussed above, teamwork – the ways in which team members function and coordinate to produce a “synchronized” output – has become the focus of much research investigating decision making in organizational settings (Klimoski & Jones, 1995; Paris, Salas, & Cannon‐Bowers, 2000). This work has extended and applied the group dynamics literature in order to understand effective decision making in teams (Guzzo & Dickson, 1996). Investigations in laboratories, management settings, and high‐ risk industries have shown a myriad of factors to affect team decision making. These include team size (Campion, Medsker, & Higgs, 1993); team member composition (Tziner & Eden, 1985); team cohesiveness (Mullen & Copper, 1994); team autonomy and empowerment (Cohen & Ledford, 1994; Pearson, 1992); team member personalities (Barry & Stewart, 1997); team member participation (Simard & Marchand, 1995); ability of the team to adapt to changing circumstances (LePine, 2003); shared goals and team member motivation to meet those goals (Weingart, 1992); group planning (Stout, Cannon‐Bowers, Salas, & Milanovich, 1999); teamwork norms for communication (Edmondson, 1999); and group hierarchies and distributed expertise (Hollenbeck et al., 1995). Research on teamwork has attempted to identify the specific skills required by individual team members to effectively support team decision making (Flin, O’Connor, & Crichton, 2008; West, 2012). This includes team member skills for supporting each other (e.g. sharing workloads, providing information support), resolving conflict (e.g. to generate constructive debate on decision making, and avoid interpersonal conflict), exchanging information important to a decision (e.g. sharing awareness regarding the stage of task), and coordinating activity (e.g. in terms of constructing the analysis for making a decision, allocating roles). In a review of models used to explain team performance, Militello, Kyne, Klein, Getchell, & Thordsen (1999) outlined a range of behavioral dimensions critical for effective team decision making. These included ensuring that teams i) envision the goal of decision making (i.e. that a goal is defined and shared), ii) maintain dynamic focus (i.e. on the time span of a decision, and the breadth of concepts and information to be used), iii) form a shared and accurate understanding of the situation and stage of decision making (e.g. for collecting information), iv) articulate expectations (i.e. on how team members should behave), and v) envision the course of a decision or decision options (i.e. visualizing how a decision will be enacted by the team, and where problems might occur). Furthermore, Salas, Wilson‐Donnelly, Sims, Burke and Priest (2007) have



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synthesized findings from teamwork research in order to outline a set of principles underpinning effective teamwork (Table 12.2). These capture the overarching themes found within the teamwork literature conducted over the past 40 years, and account for various behaviors and skills that underpin effective team decision making. To understand how the teamwork principles outlined in Table  12.1 influence team decision making, team performance frameworks have been developed. These outline the mechanisms underlying team performance by using systems theory. Systems theory describes teams as complex, adaptive, and dynamic systems that evolve over time (McGrath, Arrow, & Berdahl, 2000), and describes team decision making as an output of teamwork within an input–process–output (IPO) framework (Hackman, Pallak, & Perloff, 1986; McGrath, 1984; Steiner, 1972). This assumes that certain inputs (e.g. team member knowledge) affect group outputs (e.g. decision making) via the interactions (e.g. team communication) that take place in the team (Figure 12.1). The IPO model outlined in Figure 12.1 can be used to describe team performance both during a specific task and also throughout a team’s lifecycle. A range of inputs have been described as affecting team processes, including the attitudes and abilities of team members, and the degree to which the team leader can influence team members to complete both their individual and team objectives (Unsworth & West, 2000). Consistent with group research, also important is the structure of the team in terms of size, the norms of acceptable behavior, the roles of team members during specific tasks, status differences and influence between team members, and the cohesiveness of team members (Steers, 1988). Furthermore, the properties of the organizational setting and the tasks being performed by the team must also be considered when formulating models of team decision making (Kent & McGrath, 1969). For example, the complexity and importance of tasks is likely to affect how decisions are made during the task and the level of communication and coordination needed between team members for completing the task. Despite critiques of the use of IPO models to understand team performance, increasingly complex models have been used to explain team decision making. For example, IPO models have been developed to describe and understand how teams adapt decision making according to changing task and team conditions (Burke, Stagl, Salas, Pierce, & Kendall, 2006). Furthermore, conceptual models have focused on the role of emergent states (e.g. team cognition) upon team performance and decision making (Ilgen et  al., 2005). This is considered in the ­following section.

Team cognition and team decision making To understand how teamwork influences decision making, applied psychology research has also focused on team cognition (Kerr & Tindale, 2004). Team cognition broadly refers to the shared knowledge and awareness of a team that emerges through team member interactions and collaborative activities in the pursuit of a shared goal or task (Reader, 2007). Numerous constructs within the team psychology literature have been labeled as facets of team cognition; including ‘team mental models’ (Cooke, Salas, Cannon‐Bowers, & Stout, 2000), ‘shared cognition’ (Peterson & Swing, 1985), ‘emergent cognition’ (Ilgen et al., 2005), ‘shared mental models’ (Mohammed, Klimoski, & Rentsch, 2000), ‘team situation awareness’ (Salas, Prince, Baker, & Shrestha, 1995), and ‘distributed cognition’ (Nemeth, Cook, O’Connor, & Klock, 2004). Cooke et al.’s (2000) review of the team cognition and team performance literature concludes that much of the research has focused on ‘team mental models’ for ‘teamwork’ (e.g. expected team member interactions, team member roles, individual team member knowledge) and ‘taskwork’ (e.g. task goals, procedures and strategies for completing the goals).

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Table 12.2  Principles of effective teamwork (adapted from Salas et al: 2007, p. 806). Domain

Principle

General

•• Teamwork and taskwork represent different components of team performance. •• Teamwork is influenced by various external and internal factors. •• Some teamwork skills (e.g. communication) are generic. •• Effective teams optimize and distribute resources. •• Teams develop and transform over time, and combine for divergent periods. •• ‘Mature’ teams have members who understand and predict one another’s needs. •• Mature teams require less overt communication to perform effectively. •• Teamwork is influenced by various external and internal factors. •• Team leadership influences team performance. •• Teamwork involves active participation and engagement by team leaders. •• Team leaders clearly define team structures, attribute roles, and encourage open communication. •• Leaders should clearly define team goals and expectations for performance. •• Team leaders maintain the focus of team members on tasks. •• Team leaders should plan, structure, and coordinate the team. •• Team leaders should develop coherent teams. •• Team leaders communicate effectively to members, and provide them feedback on performance. •• Leaders adjust their role as the team progresses through a task. •• Team members must convey information using appropriate terminology (e.g. depending on technical expertise of team members). •• Team members must convey information to appropriate others (i.e. those interdependent on them). •• The effectiveness of new communication tools depend on the structure of the team. •• Closed‐loop communication is essential for effective teamwork. •• Communication frequency affects performance depending on the nature of the task and the composition of the team. •• Teams that are well coordinated will be more successful. •• Team members can ask for assistance when needed. •• Team members coordinate to collect information systematically. •• Team members should assist team members who experience difficulties. •• Team members can ask for assistance when needed. •• Team members coordinate to collect information systematically. •• Team members who feel integral to the team will report being more satisfied. •• Team motivation and performance are supported through positive feedback. •• Teams should recognize success depends on the interactions of team member. •• Team members should accept and value the input of other members. •• Team motivation and performance are supported through positive feedback. •• Team members should monitor one another’s performance.

Team leadership

Communication/ coordination

Interpersonal



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Table 12.2 (Continued) Domain

Principle

Performance monitoring

Adaptability‐related

•• Teamwork involves members providing feedback to one another. •• Teamwork involves being willing, prepared, and inclined to back‐up teammates in achieving their goals. •• Teamwork involves “inter‐member reinforcement” behaviors that encourage future performance. •• Team members should understand how they must adjust their behaviors and information sharing activities according to the stage of a task. •• Team members that are versatile in how they perform a task can adapt better to changing circumstances. •• Team members should be prepared to be versatile. •• Team members should provide information for identifying and correcting their mistakes and the mistakes of others.

INPUTS

PROCESSES

Individual-level factors e.g. Team member skills, personality, knowledge, attitudes, training, experience

Team-level factors e.g. Team structure, interdependencies, cohesiveness, size, norms, team member roles Task-level factors e.g. Task structure, requirements for coordination, risk, complexity, stress, uncertainty

OUTPUTS

Performance outcomes e.g. Decision making, error, speed of output Team interactions e.g. Leadership, communication, coordination Team outcomes e.g. Satisfaction, morale, cohesiveness

Figure  12.1  Input–process–output model of team decision making adapted from McGrath’s (1964) model of group interaction and performance.

The concept of the team mental model is used to explain the fluid and implicit coordination observed in effective teams, and to better understand how teams make decisions in complex, dynamic and ambiguous situations (Cannon‐Bowers et al., 1993). Team mental model research hypothesizes that the processes and effectiveness of team decision making can be explained by the degree to which the members of a team hold similar and accurate organized expectations regarding the team and task (Cannon‐Bowers et al., 1993). This is because convergent team mental models will ensure that i) information critical to a decision is shared, ii) the goals of task or decision are aligned amongst team members, iii) teams have a shared understanding of the roles of individual members within a decision‐making process, and iv) knowledge interdependencies (e.g. for coordination, or information sharing) are shared and acknowledged.

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Empirical research supports this perspective, with team mental models being found to influence performance and decision making in a range of domains (Burtscher & Manser, 2012). For example, laboratory research with team members cooperating in helicopter simulation tasks shows that the convergence of team member knowledge for various pieces of mission related information (e.g. task goals) is associated with performance (Stout et al., 1999). Specifically, in‐depth pretask planning is shown to result in team members developing more similar knowledge structures, and improved behaviors essential for decision making (e.g. information sharing, coordination of team activities). Similarly, M­athieu, Heffner, Goodwin, Salas, & Cannon‐Bowers (2000) have shown (using an F‐16 flight combat simulator) higher convergence of teamwork mental models (e.g. understanding team member interdependencies) to be being significantly associated with team performance, with the relationship mediated by teamwork processes essential to effective decision (e.g. strategy formation and coordination, cooperation and efficient communication). Furthermore, convergence of temporal team mental models (e.g. on the scheduling of work) has been associated with problem solving in simulated emergency crisis management (Mohammed, Hamilton, Tesler, Mancuso, & McNeese, 2015). In terms of developing team mental models, Marks, Mathieu, and Zaccaro (2001) have shown (within a low‐fidelity tank simulator) enhanced briefings and team interaction training to result in the development of more similar and accurate knowledge structures. This is demonstrated to lead to (via improved communication) enhanced performance and decision making during novel scenarios. Similarly, research examining the effect of cross‐training (where team members are trained in the duties of their fellow team members: Volpe, Cannon‐Bowers, Salas, & Spector, 1996) shows that teams which receive high levels of cross‐training develop more similar and accurate mental models, which in turn leads to more effective team coordination, communication, and performance (Marks, Sabella, Burke, & Zaccaro, 2002). Outside the laboratory, field research has also indicated team mental models to underlie effective team decision making. For example, research conducted in the military has examined the effect of team mental model similarity for taskwork and teamwork during simulated combat circuit. Shared mental models for team procedures, equipment, and tasks and expected interaction processes were found to be associated with enhanced performance and decision making (Beng‐Chong & Klein, 2006). In addition, the relationship between team mental models and safety has been investigated in air traffic control towers (Smith‐Jentsch, Mathieu, & Kraiger, 2005). This shows team mental models of air traffic controllers for taskwork (e.g. assessing strategies for coping with various emergency scenarios) and teamwork (e.g. different goals of individuals performing various job roles) to be associated with the efficiency and safety rates of air traffic controllers. Finally, in anesthesia, task monitoring behaviors have been shown to be influenced by the accuracy and sharedness of team mental models (e.g. for understanding of task), with implications for decision making (Burtscher, Kolbe, Wacker, & Manser, 2011). Alongside team mental models, the team cognition literature has also emphasized the importance of team situation awareness for effective team decision making (Cooke et al., 2000). Endsley (1995, p. 39) describes team situation awareness as the “degree to which every team member possesses the SA required for his or her responsibilities,” and team situation awareness is built through team processes during a task (e.g. team members sharing information on their understanding of a situation as it is updated). In particular, the degree to which the members of a team have a shared and accurate perception, comprehension, and anticipation of their future task environment is theorized as crucial for effective team decision making (Endsley, 1995). This is especially the case where team members must work collectively together to maintain a shared understanding of a dynamic



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situation (e.g. healthcare teams, aviation crews, military teams). Forming shared and accurate team situation awareness is crucial for team members to be able to coordinate their work effectively, to adapt behaviors in response to changes in their working environment, and to make effective decisions that address the evolving nature of a task or situation (Brannick, Salas, & Prince, 1997). Without this shared awareness, teams are more likely to behave in a non‐coordinated fashion, owing to a lack of common understanding or m­ismatching perceptions of events. Much of the research investigating the importance of team situation awareness for decision making emerges from analyses of decision‐making failures within teams. For instance, analyses of airline accidents have attributed mishaps to factors such as crew members l­osing sight of the “big picture” owing to being preoccupied with minor information and not sharing more important information (Wellens, 1993). An analysis of aviation incidents collected by the Aviation Safety Reporting System (ASRS) has shown loss of team situation awareness by flying teams to be a primary source of decision‐making errors (Jentsch, B­arnett, Bowers, & Salas, 1999). This most often occurs in situations where the captain is the pilot flying, and first officers do not recognize loss of situation awareness in the c­aptain, or are reticent (owing to lack of experience and assertiveness) to update the captain (and thus form shared team situation awareness) if they do recognize a problem. Such findings are illustrated through analyses of aviation accidents examining how aspects of group dynamics have led to the loss of team situation awareness in flight crews. Famously, the runway crash of two Boeing 747 aircrafts at Tenerife Airport in 1977 (r­esulting in 583 deaths) is argued to have been caused by problems in speaking up and listening, which resulted in a lack of shared awareness for threats to safety (Weick, 1990). In this case, the Captain of KLM flight 4805 overruled the concerns of the flight deck engineer that Pan Am Flight 1736 was not clear of the runway. The runway was obscured by heavy fog, and the KLM flight entered taking off procedures without clearance from air traffic control, while the Pan Am flight was still on the runway. The lack of shared situational awareness for the various parties contributed to the collision, and highlighted how basic social process such as speaking up, rule following, and listening, influence team situation awareness and decision making in flight teams. Within the aviation industry, team training is used extensively to avoid problems in team decision making. Nonetheless, recent aviation accidents highlight that even where this is available, mishaps still occur. For example, the accident investigation into Korean Air Cargo flight 8509 (which crashed at London Stansted Airport on the 22 December 1999, with four fatalities) indicated that the unwillingness of a first officer to speak up to the captain (for correcting the altitude of the plane) led directly to an accident. Other examples of loss of team situation awareness leading to poor team decision making include military friendly fire incidents (e.g. where decisions have been made on inaccurate understandings of vehicle positions), and major industrial accidents such as the Deepwater H­orizon explosion (where drilling teams made decision based on an incomplete understanding of risk; Rafferty, Stanton, & Walker, 2013; Reader & O’Connor, 2014). Conversely, research also shows team situation awareness to underpin effective team decision making. For example, research with airline crews has indicated that when flight status information is shared among crew members, fewer errors occur as a result of mistakes in misreading instruments or mishandling engines being identified and corrected before decisions are made (Foushee, 1984; Foushee & Helmreich, 1988). Endsley (2004) has stressed the importance of teams both shared and accurate interpretations of a situation. For example, a study of situation awareness incidents in commercial aviation by Jentsch, Barnett and Bowers (1997) found that 60% of flying incidents (i.e. flying at incorrect altitude) involved loss of situation awareness by both crew members, with both pilot and

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co‐pilot making decisions on a shared but incorrect understanding of the flight situation. while experimental research investigating the association between team co‐pilot and decision making is not as extensive as within the team mental model literature (due, in part, to the complicated nature of measuring team situation awareness), links between team situation awareness and team decision making have been established. For example, research using student pilots in flight simulators have shown team situation awareness (e.g. for technical information) during flight to be correlated with ratings of team performance during simulated emergencies (Prince, Ellis, Brannick, & Salas, 2007). F­urthermore, field research in critical care medicine has shown that clinical teams often have divergent team situation awareness for understanding the condition of patients (e.g. severity of illness), with implications for how decisions are made and enacted (e.g. information sharing, prioritization of tasks; Reader, Flin, Mearns, & Cuthbertson, 2011). Thus, a key component of research investigating team decision making has been the identification of teamwork skills and behaviors that underpin effective decision making. This is because team decision making is dependent upon the ability of team members to coordinate effectively during decision‐making tasks, and to combine their skills, expertise, and knowledge. Teamwork research has extensively identified the skills and behaviors associated with effective decision making, and as described in the following section, these have been used to develop interventions. In addition, team cognition research has indicated that effective team decision making (and the behaviors that lead to this) is shaped by the extent to which teams form shared and accurate team mental models and situation awareness. This ensures that team members cooperate to make decisions with a coherent understanding of the task (e.g. information requirements, goals), team (e.g. roles for supporting decision making), and situation (e.g. the status of the task environment). Yet, arguably, research has not fully demonstrated the mechanisms that mediate the relationship between team cognition (and in particular team situation awareness) and team decision making, and this should be the focus of future research.

Interventions for Improving Team Decision Making Using the findings of research into group dynamics and team decision making, a variety of interventions have been developed to support and improve team decision making. These tend to aid groups in overcoming aspects of group dynamics that can impede team decision making, and to support them in developing the knowledge, skills, and attitudes for e­f fective team decision making (e.g. reluctance to contradict authority).

Restructuring teams to improve decision making In terms of influencing group dynamics in order to improve team decision making, interventions often focus on restructuring how teams cooperate to make decisions (Hardman, 2009). For instance, decision rules have been developed to change how teams make decisions (Hastie & Kameda, 2005). Examples include applying a majority/plurality rule (whereby all team members have a vote on a decision, and the majority choice is applied) to ensure that decisions are not dominated by one individual, and that a team functions as democratic unit (e.g. in project management teams). Alternatively, in tasks where expertise is key to team decision making (e.g. design tasks), ‘leader’ or ‘best member’ rules can be applied in order to ensure that the individual team member most critical to a judgement leads decision making. Where appropriate, more complex decisions rules can be applied to thoroughly decision options (e.g. for business strategies). For example, the



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‘Condorcet majority rule,’ where all decision options are compared and voted on by a team in pairwise fashion (with the best decision being that which survives this process). Where quantitative decisions are being made by a team (e.g. for calculating risk in investment decisions), Delphi‐style techniques can be applied (Rowe & Wright, 1999). This involves compiling the individual judgments of team members relating to a decision (e.g. on likelihood of an event occurring, probability of a best course of action), with the team being provided statistical feedback on the preferences for the group. This is used to facilitate discussion, with further Dephi‐style rounds being used to collate perspectives on decision making until variability between team members is reduced. While the application of the Dephi‐technique can improve the decision‐making accuracy of teams, they can tend to result in a regression to the mean. To overcome this, methods such as social judgement analysis (where team members explore in depth differences in the weight and importance they place upon information underlying their decision preferences) can be found to reduce disagreements between team decision makers (Rohrbaugh, 1979). For scenarios where teams are applying expertise to examine and solve difficult, uncertain and complex problems, decision making can be supported through applying structured methods for evaluating decision options. For example, to support managerial teams in reaching complex decisions, methods such as devil’s advocacy and dialectical inquiry have been extensively developed (Schweiger, Sandberg, & Ragan, 1986). These are used to encourage critique and rigor in how teams evaluate decision options (e.g. for a business strategy decision), and aim to overcome the tendency of groups to avoid conflict and be overly consensual. Devil’s advocacy has groups critique a single decision option or assumption. A team member (or subteam) is tasked with examining a decision option selected by the group, and to subject it to a thorough critical analysis (which is then incorporated into the final decision). Dialectical inquiry has groups debate between diametrically opposed sets of options, and involves a subgroup defending an option and its underlying assumptions against the challenge of another group (and vice versa). Depending on which assumptions survive the debate, the groups reach a decision on which option to take (or an amalgamation of them). In comparison with teams that must simply reach consensus on decision making, these techniques are found to result in improved decision making (e.g. quality of recommendations) and reduced time to take decisions (Schweiger et al., 1986; Schweiger, Sandberg, & Rechner, 1989). However, compared with consensus groups, team members tend report being less satisfied accepting of the decision. This highlights that methods for improving team decision making can positively influence the quality of decision making (through forcing teams to critically evaluate their options), yet negatively influence the satisfaction of team members within the group. Furthermore, they are found to have a lesser influence upon decision making during tasks with unclear structures (Schwenk, 1990).

Training team decision‐making skills Alongside altering team structure for decision making, interventions have focused on training skills for team decision. In particular, within high‐stress, risky, and complex industries (e.g. aviation, health care, nuclear power, military), team training programs have been developed to improve team decision making. These programs draws on the teamwork literature, with perhaps the most well‐known example of this being ‘crew resource management’ (CRM) training. CRM is defined as “a set of instructional strategies designed to improve teamwork in the cockpit by applying well‐tested tools (e.g., performance measures, exercises, feedback mechanisms) and appropriate training methods (e.g., simulators, lectures, videos) targeted at specific content (i.e., teamwork knowledge, skills, and attitudes)”

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(Salas et al., 1999, p. 163). It primarily relates to ‘operational’ decisions being made by teams (i.e. decisions being made by teams during a complex task), and was initially used to  prevent and mitigate the consequences of error in aviation (Helmreich, Merritt, & Wilhelm, 1999). CRM focuses on training and assessing skills essential for effective team decision making, and has been highly influential in shaping how skills for managing safety in high‐risk organizations are identified, trained, and taught (e.g. behavioral based safety; Geller & Robinson, 2015). For example, information acquisition and processing, communication and coordination among team members, leadership skills for motivating and guiding team members, conflict resolution, team self‐critique, and stress and fatigue management. Team members are trained through lectures, practical exercises, role‐playing, and case studies. This ensures that individual team members are aware of the key skills underpinning effective team decision making, and have the opportunity to practice and receive feedback on those skills. Assessments are made through structured observations, reflective practice, and debriefing. CRM training is used to improve team decision making in a myriad of industries, and meta‐analyses of the influence of CRM have shown it to positively influence attitudes and behaviors towards team decision making (O’Connor et al., 2008). This is due to it both teaching skills and behaviors critical for decision making in team settings (information gathering and assessment), and enabling team members to better share information and perspectives (e.g. on risk) and work under stress (Mearns, Flin, & O’Connor, 2001). Yet, there remains a need to better establish whether investment in CRM (which is costly and time consuming for staff) positively impacts upon decision m­aking and performance within organizations (Salas, Burke, Bowers, & Wilson, 2001). Other training methods for improving team decision making include approaches such as the advanced team decision model (ATDM; Zsambok, 1997; Zsambok, Klein, Kyne, & Klinger, 1993). This was developed to improve strategic team decision making in the m­ilitary, and the ATDM model highlights 10 key behaviors for effective team decision making, and these relate to developing team identity (e.g. defining roles and functions), self‐monitoring (e.g. team management), and conceptual understanding (e.g. forming a shared understanding of a problem, outlining plans and goals). While models such as ATDM overlap considerably with CRM (e.g. in the skills being trained), they focus more on strategic (i.e. planning and execution) decision making than operational decision m­aking (Flin et  al., 2008). Yet, as with CRM training, further research is required to e­stablishing the effectiveness of the ATDM. Furthermore, it is notable that although aspects of team training are generic (e.g. the focus on communication skills), effective team training identifies the fine‐grained teamwork skills required for decision making in a given domain or environment. For example in health care, team training programs focus on the specific demands and needs of teams facing different scenarios and problems (O’Dea, O’Connor, & Keogh, 2014). For i­nstance, in terms of training team skills for managing emergencies and routine care in surgery, anesthesia, cardiac care (Awad et  al., 2005; Fletcher et  al., 2004; Østergaard, Østergaard, & Lippert, 2004; Sevdalis et al., 2008; Walker et al., 2011). This work shows that the decision‐making skills that lead to effective patient care in different scenarios vary according to the demands of patient care. For example, in cancer diagnosis teams, leadership that fosters input from those in junior roles and carefully manages time for each d­iagnosis is indicated to underlie effective decision making (Lamb, Sevdalis, Mostafid, Vincent, & Green, 2011; Lamb, Sevdalis, Arora et al., 2011). In domains such as anesthesia, the ability of team members to anticipate one another’s needs, and in particular for anesthetic assistants to understand the decision‐making needs of anesthetists (and support them when necessary; Rutherford, Flin, & Mitchell, 2015).



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Thus, it can be observed that team decision making interventions attempt to both overcome problematic aspects of group behavior that influence decision making (e.g. desire for consensus), and also to train the skills and behaviors that have been shown to lead to effective team decision making. Approaches can involve restructuring how a team approaches a decision (e.g. devil’s advocacy), or instituting a set of rules to improve decision making. However, such interventions can be difficult to practically apply in organizational settings, and teamwork researchers have focused on developing interventions to ensure teams and team members have the competencies that allow them to effectively contribute to team decision making. Team training interventions such as CRM have been adapted and are used to improve decision making in a range of industries, although the extent to which team training methods such as CRM have been shown to demonstrably and positively influence decision making in teams remains under investigation. Critically, the power and utility of a team training intervention is determined by the extent to which it addresses the specific demands and skills required by team members to make decisions effectively and successfully.

Future Research The above review of the team decision making literature leads to a number of pertinent and important research questions for the future. These cluster around the following issues. The first is the need to better understand the interactions between social psychology, team cognition, and team decision making. While there is considerable research establishing that team functioning (e.g. communication and coordination) influences team mental models (Mathieu et al., 2000; Mathieu, Heffner, Goodwin, Cannon‐Bowers, & Salas, 2005), and through this team performance and decision making (e.g. in simulation), there may be benefits in greater engagement with more classical social psychological concepts. For example, how do aspects of group cohesion and tendencies for obedience and conformity (e.g. in terms of sharing information in project team) influence team mental models (e.g. understanding of project task), and through this team decision making (i.e. final decisions)? The social psychological literature on group think in‐effect argues that the behaviors and beliefs that lead to groupthink can result in quite disjointed team mental models (e.g. on the appropriateness of a decision plan), and in turn poor decision making. Traditionally, team mental model research has focused on how communication and coordination influence team mental models, and less the wider context of a group. Using team mental model theorem to understand how groupthink might influence team decision making would expand our understanding of how precisely social psychological concepts such as group cohesion influence team decision making, and also develop knowledge on how team mental models are developed and influential upon team decision making. The second key issue for future research into team decision making is to develop a more systematic understanding of how the context of a team and task influence team decision making processes. The literature review above demonstrates how in both laboratory and field settings team decision making is influenced by a number of contextual variables. Furthermore, owing to the transient nature of tasks and teams, and the need of teams to adapt to variations in risk, task complexity, and team composition (Burke et al., 2006), teamwork scholars have highlighted the need to better understand the contextual influencers of effective teamwork (Morgeson, DeRue, & Karam, 2010). Thus, future research may wish to capture and further theorize the interactions between team decision making and context. For example, through systematically reviewing the constraints and demands of a situation that influence how teams do and should reach decisions. This might be done

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through adapting current frameworks on team competencies to take into account contextual variables (e.g. risk, time pressure, experience of team members, behaviors required to make a successful decision; Cannon‐Bowers, Tannenbaum, Salas, Volpe, & Guzzo, 1995; Wildman et al., 2012). Such an approach would allow for knowledge on team decision making to be better grounded in terms of task context, and the composition of members within a team. Thirdly, and linked, knowledge on the interaction between context and forms of team decision making might be augmented through applying a naturalistic decision‐making framework to explain team decision making. This traditionally has focused on the cognitive processes within a work environment (e.g. sensemaking, situational awareness) that allow decision makers to develop and use expert decision making skills (Gore, Banks, Millward, & Kyriakidou, 2006). Naturalistic decision‐making research has diverged from classical approaches to decision making (e.g. the rational actor model), and through qualitative techniques (e.g. interviews, case studies) focuses on the interaction between situations (e.g. risk, complexity, time pressure), individuals (e.g. experience, expertise), and interdependencies (e.g. where decision makers rely on one another) to explore decision making (Cannon‐Bowers & Bell, 1997; Carroll, Hatakenaka, & Rudolph, 2006; Lipshitz, Klein, Orasanu, & Salas, 2001; Lipshitz & Strauss, 1997; Reader & O’Connor, 2014). This l­iterature has led to key theoretical developments in decision‐making research (e.g. on intuitive decision making; Klein, 1993), and has increasingly focused on decision making in team settings. For example, in terms of examining decision inertia (where individuals and teams struggle to choose between equally bad options) in the police services, strategies used by athletes to manage stress and fatigue, and preferences for surgeons to use deliberative or intuitive decision strategies while collaborating with surgical and anesthetic teams (Alison et  al., 2015; Macquet & Skalej, 2015; Pauley, Flin, Yule, & Youngson, 2011). Through applying naturalistic decision‐making theory and practice to team decision making, the contextual drivers and optimal strategies of decision making in a range of applied settings can be outlined, with benefits for both theory and practice. Finally, team decision making research has been conducted predominantly in relatively heterogeneous contexts. For example, in Western countries with relatively well‐educated populations (e.g. university students, professionals). Much of our knowledge about how teams function and collaborate to make decisions has emerged from these environments. Yet, research in organizational psychology has increasingly highlighted the importance of understanding how broader societal factors (e.g. national culture) influences collaborative behavior (e.g. national norms for power distance and speaking‐up behaviors: Reader, Noort, Shorrock, & Kirwan, 2015). This indicates an urgent need to consider how broader cultural factors might shape how teams collaborate to make decisions (e.g. are there differences in what makes an effective team in different cultural contexts?), and in particular to consider how this might influence decision making in multicultural teams where diverse groups members collaborate together (Brett, Behfar, & Kern, 2006; Gassmann, 2001). To  date, this remains a relatively unexplored domain within the team decision‐making l­iterature.

Conclusions Team decision making has been systematically researched by psychologists since the mid‐ twentieth century. Initially, social psychologists were interested in how aspect of group dynamics (e.g. desire to maintain cohesion, obedience to authority) shaped behavior and decision making in groups. This research was foundational, and highlighted the behaviors



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(e.g. speaking‐up) and structures of groups (e.g. expert leadership) that led to optimal and suboptimal decision making in range of tasks and settings. Applied psychologists have used these findings to outline a range of principles on the skills and behaviors crucial for team members to contribute effectively to decision making in organizations. These outline a range of generic principles (e.g. on information sharing) and also contextual principles (e.g. on the role of leaders during routine and emergency situations) on how teams should function during decision‐making scenarios. In particular, to explain the link between group dynamics, teamwork skills, and decision making, concepts from the team cognition literature have been applied. These suppose that the extent to which teams form shared understandings of the task they are performing (e.g. information, goals), their roles within team (e.g. coordination, sharing information), and the current situation (e.g. stage of decision) predicts team decision making, and are a consequence of teamwork behaviors and group dynamics. This remains a highly promising area of research, as it integrates the social and cognitive aspects of group functioning to explain how teams make decisions. Yet, further research and theorization is required to establish how team cognition m­ediates the relationship between group process, teamwork, and team decision making. Furthermore, a more nuanced and systematic understanding of how team decision making is shaped by context is required, and new areas of research might include applying naturalistic decision‐making models to understand team decision making, and investigating the influence of societal factors (e.g. national culture, multicultural group members) upon decision making in teams.

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13

Teamwork under Stress Aaron S. Dietz, James E. Driskell, Mary Jane Sierra, Sallie J. Weaver, Tripp Driskell, and Eduardo Salas

Introduction In an ideal world, teams would perform in a vacuum where the outcome of their performance would be solely determined by their effort, ability and strategy. Unfortunately, this is not the case. Teams perform in specific task, organizational, and environmental contexts that may include stressors or demands that can tax even the best‐performing teams. Stressors permeate the work environment and, as a result, team performance is dependent on the capacity to perform effectively within this real‐world context. The real‐world environment that teams perform in is challenging, ever‐changing, and unpre­ dictable. Given that teams have become the central approach for tackling complex tasks, it is essential to understand how stress may affect team performance. For example, stress may result in increased anxiety, negative emotion, distraction, conflict, and loss of team orientation – all of which may individually and/or collectively have a detrimental effect on team effectiveness. In this chapter, we provide the reader with a review of research on the relationship between stress and teams. To accomplish this, we address several topics. First, we present an overview of what the concept of stress means, especially in relation to the team context. Second, we examine the impact of stress on team performance. Third, we describe research on teams in extreme environments and review issues in measuring stress at the team level.

Understanding Teamwork under Stress In one of the earliest empirical examinations of stress and team performance, entitled “Group behavior under stress,” Lanzetta (1955) noted that there was only one prior experimental study of groups under stress, referring to the work of French (1944).

The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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Thus, for Lanzetta, the task of reviewing research within this domain was quite simple. Some 60 years later, this task is a bit more complex; yet there is still a fairly limited and manageable body of research on this topic. To begin, we first wish to describe what we mean by the terms “team” and “stress.” By team, we refer to two or more persons characterized by high task interdependency and shared and valued goals (Salas, Cooke, & Rosen, 2008). In this chapter, we use the terms team and group interchangeably. However, we will exclude from consideration groups that are merely collectives (including research on panic and collective behavior), groups whose primary purpose is recreational rather than task completion (such as friendship groups), and therapeutic groups. Thus, we use the terms team or group to refer to what others have termed work groups or task teams (Devine, 2002). The term “teamwork” refers to the ability to coordinate and interact to achieve task objectives though a shared understanding of the team’s resources, the team’s goals and objectives, and the constraints under which the team works (Salas, Sims, & Burke, 2005). Defining the construct of stress is a bit more difficult. According to Salas, J. E. Driskell, and Hughes (1996), stress is a process whereby environmental demands evoke an appraisal process in which perceived demand exceeds resources, and that results in undesirable physiological, psychological, behavioral, or social outcomes. Similarly, team stress has been defined as the “relationship between the team and its environment, including other team members, that is appraised as taxing or exceeding their resources and/or endangering their well‐being” (Weaver, Bowers, & Salas, 2001, p. 86). Teamwork stressors are stimuli, or conditions, that influence the team’s capability to interact interdependently or capacity to achieve their goals. Figure  13.1 presents a high‐level model delineating the mechanisms through which stressors may impact team performance. This model is intentionally presented within a linear input–mediator/processes–output frame, a common framework for depicting team phenomena (Salas, Rosen, Burke, & Goodwin, 2008), in order to provide a parsimonious representation of major components and relationships. As depicted in Figure 13.1, this

• Duration of exposure • Number of stressors

Individual effects of stress Stressors

• Cognitive • Emotional • Behavioral • Physiological

Team effects of stress • Decreased cooperation • Ineffective communication • Decreased coordination

Figure 13.1  A model framework of the effects of stress on performance.

Team performance effectiveness



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model represents a simplified depiction of the multilevel impact of stressors as their effects manifest up through both individual and team processes to ultimately impact team performance effectiveness. Additionally, this model incorporates two focal modera­ tors: duration of exposure and the cumulative, interactive effect of exposure to multiple stressors simultaneously as a means to illustrate the complexity of the stress experience. We begin by defining each of the core model components and then discuss the theoretical impact of these moderators in greater detail.

Stressors Historical definitions of stress focused on the presence of certain environmental or social stimuli which can cause affective, cognitive, or behavioral effects that lead to task performance decrements (Cox, 1978). While early mechanistic models assumed that the presence of these environmental factors or physiological responses (e.g. elevated heart rate) automatically implies that an individual will experience subjective perceptions of stress, transactional models define stress as the product of an appraisal processes in which stressors are perceived as exceeding one’s resources (Lazarus & Folkman, 1984; Salas et al., 1996; Stokes & Kite, 2001). Table 13.1 presents a representative subset of stressors that may be present in team contexts (Driskell, J. E., & Salas, 1996; Driskell, J. E., Salas, & Johnston, 2006).

Table 13.1  A subset of stressors present in team contexts. Stressor Time pressure Task load Role conflict Role ambiguity Threat Performance pressure Command pressure Coordination requirements Uncertainty Complexity/ difficulty Ambiguity Novelty Fatigue Environmental stressors

Definition Defined as a restriction in time required to perform a task. The increased pace of military operations is often termed operations tempo. Defined as performing two or more tasks concurrently. Conflicting task demands stemming from the nature of the task or from conflicting supervisor or subordinate demands. Lack of clarity in job demands or procedures. Refers to the anticipation or fear of physical or psychological harm. Refers to the increased consequences for error in a high‐stress environment. Increased demands stemming from evaluation from superiors or senior personnel. The increased demands of coordinating task performance with multiple others. A property of the task environment defined by unclear, shifting, or ill‐defined goals. Complexity or difficulty of the task. A property of the task environment defined by missing, unreliable, or inaccurate information or task data. A property of the task environment in which events occur that are unique or unanticipated. Physical or cognitive fatigue resulting from sleep deprivation or continuous operations. Immutable features in the task environment such as noise, temperature, vibration, motion, etc.

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Individual effects of stress This component of the model subsumes the effects of stress on the individual cognitive, affective, physiological, and behavioral processes underlying individual performance. For example, stress activates affective reactions including anxiety, fear, and frustration (Driskell, J. E., & Salas, 1991). Further, stress demonstrates well‐documented effects on cognitive processes such as attention, memory, problem‐solving, judgment, and decision making (Staal, 2004), all critical components of individual team‐member performance. For example, individuals narrow their attention, rely more heavily on heuristics and biases, decrease vigilance, and demonstrate performance rigidity and reduced problem‐solving ability (Salas et al., 1996). These processes, in turn, impact individual‐level task and social behaviors. Individuals manage stress by adapting their physical, emotional, or cognitive exertion (de Jonge & Dormann, 2006). For instance, cognitive resources are actively allocated to control for how individuals conduct a task in relation to their current goals, changing demands, and energetic resources (Hockey, 1997). This allocation of effort is associated with the depletion of additional resources, subjective effort, and subsequent loss of behavioral and physiological capacities necessary to manage future demands (Hancock & Warm, 1989). Alternatively, individuals may choose to reduce effort or change their performance goals as a means to maintain stability rather than allocate additional effort or deplete further resources (Cannon‐Bowers & Salas, 1998; Hockey, 1997).

Team effects of stress Teams, like individuals, have knowledge (i.e. what they think), skills (i.e., what they do), and attitudes (i.e. how they feel; Salas, Cooke et al., 2008) that can be negatively impacted by stress. At the team level, the individual effects of stress may individually and collectively impact team processes (Burke, Priest, Salas, Sims, & Mayer, 2008). This is demonstrated by the linkage between the individual effects of stress and team effects of stress, as shown in Figure  13.1. For instance, stress has been shown to lead to individual cognitive impair­ ments, which result in suboptimal performance (Petrac, Bedwell, Renk, Orem, & Sims, 2009). Such individual performance impairments can emerge at the team level to hinder team processes underlying effective team performance, such as communication, coordi­ nation, and cooperation. The narrowing of attention that occurs at the individual‐level (i.e., intra‐member) can lead team members to abandon their team‐level focus and adopt more individualistic orientations that degrade team performance outcomes (Driskell, J. E., Salas, & Johnston, 1999). Similarly, stress that emerges directly at the team level (i.e., inter‐member) also influences team processes and performance. This type of stress is demonstrated by the linkage between stressors and team effects of stress, as shown in Figure 13.1. Intramember stress is present in a team when interpersonal processes or states between two or more team members become disrupted (e.g., disrupted motivational/ affective states, cognitive states, and/or behavioral processes; Sierra, Smith‐Jentsch, & Dietz, 2012). For example, Kamphuis, Gaillard, and Vogelaar (2011) found that teams performing under threat evidenced restrictions in information processing, more controlling leadership, fewer group discussions, and a reduction in coordinating and supporting behavior. Ellis (2006) reported that acute stress negatively affected team performance in a decision mak­ ing task, and that this effect was mediated by the impact of stress on degrading team mental models and transactive memory. Pfaff and McNeese (2010) observed that another way that stress impacted team performance was through an increase in negative affect. Just as individuals demonstrate adaptive capacities in response to stress (Hancock & Warm, 1989), teams also depend on similar adaptive capacities to maintain effective



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performance (Weaver et al., 2001). For example, J. E. Driskell et al. (1999) observed a narrowing of attention in teams under stress, which is generally viewed as an adaptive response to stress demands in which attention is maintained on the core features of the task. Yet it is important to note that in this case, the resultant loss of team perspective resulted in poorer performance in a team context.

Team performance effectiveness Team performance effectiveness is defined as the relative degree to which the outcomes of team performance (i.e. what teams actually do) meet relevant performance goals and objectives (Salas, Stagl, Burke, & Goodwin, 2007). Practically, team performance effec­ tiveness represents an evaluation regarding the degree to which products of team effort meet specific goal‐related criteria such as quality or efficiency. For example, team performance effectiveness could capture whether or not a team meets specified objectives, whether they produced high‐quality outcomes in a timely manner, or some combination of these or other criteria. Conversely, team performance refers to the actual cognitive, behavioral, and affective processes teams engage in (e.g. communication, cooperation, coordination, leadership; Campbell, McCloy, Oppler, & Sagar, 1993).

Moderators: Time and interaction among multiple stressors The increased complexity of today’s organizational systems requires constant adjustment to a continuous stream of environmental demands (Dooley & Van de Ven, 1999). Models of stress and performance (e.g., Driskell, J. E., Mullen, Johnson, & Hughes, 1992) underscore the notion that the effects of environmental stressors such as time pressure, noise, and circadian disruptions on performance outcomes are moderated by several factors, including: intensity, predictability, controllability, duration of exposure, and task type. Recent conceptualizations underscore that models of stress must do a better job accounting for continuous exposure to multiple, often asynchronous, stressors (Hancock & Szalma, 2008; Sikora, Beaty and Forward, 2004). Stressors have been linked with important affective, behavioral, and cognitive processes at both the individual and team level of analysis (Driskell, T., Driskell, J. E., & Salas, 2014). When stressors are viewed solely as acute phenomena, however, the magnitude of their impact on the individual and the team is bound to be underestimated. Extended exposure to a stressor can amplify its impact at both levels of analysis, suggesting that the stress experienced by individuals and teams in response to the presence of a stressor will depend greatly on the duration of their exposure to it. Further, most stressors do not operate in isolation, but instead, they interact with a number of other co‐existing stressors to determine the degree of stress that is experienced at both the individual and team levels. Given the paucity of research on the summative effects of stress interac­ tions (Hancock & Szalma, 2008), it is difficult to pinpoint which stressors may ultimately influence performance in real‐world scenarios.

Teams in Extreme Environments Much of the empirical research on team performance under stress originated with the con­ cern of teams operating in extreme or hazardous environments. As Kamphuis et al. (2011) have noted, teams offer many capabilities that encourage their use in complex and dangerous settings, yet they are not invulnerable to the demands of high stress situations.

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In perhaps the earliest work on groups and stress, French (1944) examine how six‐ person groups of Harvard undergraduates responded to conditions of frustration (stemming from working on unsolvable problems) and fear (smoke pouring into the locked labo­ ratory room and a fire truck siren in the distance). The results were ambiguous because some groups believed the manipulation and some did not; however, French observed that those groups who were skeptical displayed more aggression toward the experimenter. This study also brings to mind ethical requirements that must be met in current stress research. French noted that some research subjects were stressed to the extent that they attempted to tear the locked door down. In current stress research, ethical guidelines must be followed such that research participants’ welfare is paramount. This is a challenge for the stress researcher because a research goal may be to simulate stress in a realistic manner in a laboratory or field setting, and yet the stress manipulation cannot be such that it poses an actual threat to the research participants’ wellbeing. It is entirely likely that the smoke and fire scenario would not pass modern Institutional Review Board protocols. Subsequent work on teams in extreme environments was stimulated by real‐world requirements. For example, there is a rich legacy of research on team performance in high‐demand settings such as submarines and other undersea environments (e.g., Beare, Bondi, Biersner, & Naitor, 1981; Radloff & Helmreich, 1968; Weybrew, 1961), the Arctic and Antarctic (e.g., Gunderson & Nelson, 1963; Nelson, 1965; Taylor & McCormick, 1985), military field settings (Berkun, Bialek, Kern, & Yagi, 1962; Driskell, J. E., & Olmstead, 1989; Torrance, 1954), isolated and confined environments (Altman & Haythorn, 1967; Palinkas, 2003; Smith & Haythorn, 1972), space exploration (Harrison & Connors, 1984; Helmreich, 1983; Kanas, 1988), and the aviation cockpit (Foushee, 1984; Mila­ novich, Driskell, J. E., Stout, & Salas, 1988). A more recent challenge has spurred research on crew performance in long duration spaceflight and the requirements for human exploration of Mars (Salas, et al., 2015). The National Aeronautics and Space Administration (NASA)’s planned mission to Mars in the 2030s will involve a crew of four to six individuals on a 3‐year journey in a small, confined habitat. Moreover, the crew is likely to be composed of male and female crewmembers of various nationalities. They will be undertaking a journey so far that Earth will fade out of view, and even communication with those on Earth will be delayed up to 20 minutes each way. They will have limited personal space and will be separated from family and friends. Finally, there is considerable inherent stress associated with such a demanding and dangerous environment. It is worthwhile to note that whereas the transit from Earth to the International Space Station is an approximate 6‐hour trip for current astronauts; the demands of long‐duration exploration missions are of a vastly different magnitude (Schmidt, Keeton, Slack, Leveton, & Shea, 2009). Considerable research is underway within realistic analog environments, including NASA’s Human Exploration Research Analog, and the underwater NASA Extreme Envi­ ronment Mission Operations habitat, to examine the effects of high‐demand conditions on crew performance and to examine countermeasures to overcome these effects (Vessey, Palinkas, & Leveton, 2013). The challenges are many. For example, research is underway to examine the effects of stress on crew communications (Driskell, T., Burke, Driskell, J. E., Salas, & Neuberger, 2014); research is examining the concept of team resilience (Alliger, Cerasoli, Tannenbaum, & Vessey, 2015), research is being conducted to exam­ ine the effect of high‐demand conditions on the structure and stability of team roles (Burke, Driskell, T., Driskell, J. E., & Salas, 2016), and research is examining the impact of long‐duration spaceflight on interpersonal tension and cohesion (Kozlowski, DeShon, Biswas, & Chang, 2012). This nascent, yet state‐of‐the‐art, research is focused on team performance under the types of high‐demand stressors referenced in Table 13.1.



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Optimizing Teamwork under Stress In applied research, efforts to understand the effects of stress on team performance go hand‐in‐hand with research to counter these effects. That is, it is informative to under­ stand both how stress may operate to impact team performance and how counter­ measures may be developed to overcome the detrimental effects of stress. There are several challenges, however, in developing a comprehensive stress training strategy that can be applied to team contexts. Three broad challenges are: (a) identifying and measuring the job stressors that exist for a given task, (b) linking these stressors to the core psychological mechanisms that are engaged, and (c) identifying training approaches to target the mechanisms that are affected. These topics are discussed in the following sections.

Defining the task environment We should first note the difference between assessing task requirements in isolation and assessing performance requirements in context. In other words, there is a significant difference in what it takes to perform a task such as a team decision‐making task in a benign environment and what it takes to perform that same task in a hazardous or high‐demand environment. This difference is the contextual environment  –  the organizational, environmental, and task demands that are imposed upon the operator or team. The primary goal of stress training is transfer to the real‐world operational environ­ ment. Therefore, stress training should be context specific and designed to provide pre‐ exposure to the stress conditions that are likely to be encountered in the operational environment. Accordingly, Johnston and Cannon‐Bowers (1996) noted the impor­ tance of designing stress training based on an analysis of the specific task environment. This analysis becomes the basis for the development of training content, including the specific tasks to be trained and the types of real‐world stressors that are incorporated into the training intervention. The first challenge is that current task analysis tools do a poor job of measuring stress, or defining the characteristics of the task environment that impose stress or demand. For example, some existing models simply use broad descriptors of overload and underload (Frankenhaeuser & Gardell, 1976). As Hogan and Lesser (1996) conclude, “Current job analysis methods are not very helpful for studying hazardous performance” (p. 218). Therefore, the first fundamental problem is that we have no adequate tool to define the environmental stressors or “work context factors” that impose demands on the team in hazardous or stressful environments. Any such classification must include typical stressors that have been identified as ­relevant to the team performance environment, such as those presented in Table 13.1. To assess these stressors in any given context requires the development of an instrument to mea­ sure the extent to which these demands are relevant to a particular task. J. E. Driskell, Wadsworth, and Krokos (2009), for example, have developed the Work Hazards Analysis Scale that can be administered to team members or to external raters to identify the contextual variables or stressors in the work environment that impose demands on the operator. The Work Hazard Analysis Scale provides separate items to assess time pressure, task load, threat, role conflict, role ambiguity, coordination requirements, task ambiguity, task uncertainty, novelty, fatigue, noise, performance pressure, command pressure, and other environmental factors such as heat, light, or vibration.

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Linking stressors to core psychological mechanisms One means for simplifying a long list of task stressors such as those shown in Table 13.1 is by categorizing them into higher‐level categories. For example, J. E. Driskell et al. (2009) defined the following four stressor domains: •• Task demands: Demands related to time pressure, performing multiple tasks, and shifting from one task to another. Stressors include time pressure, task load, and fatigue. •• Coordination demands: Demands related to coordinating tasks with others as well as coordinating up and down the command chain. Stressors include role ambiguity, coordination requirements, and command pressure. •• Threat demands: Demands related to performing under threat and maintaining composure under demanding environmental conditions. Stressors include threat and related combat stressors. •• Novelty demands: Demands related to adapting to novel situations and uncertain task environments. Stressors include novelty, task uncertainty, and task ambiguity. A potential drawback with such an organizing structure is that a large number of discrete stressors within these categories may be salient in a particular task setting. It has been noted that stress is not unidimensional, but is a multidimensional construct comprising a number of distinct components, including specific stressors such as noise, time pressure, and social demands. It is useful to speak of “stress” in general terms, in referring to high‐ demand task environments, but it is less useful to try to predict performance, or to design training, at this broad or unidimensional level. It is likely, however, that stress effects can be defined in terms of a core set of psychological mechanisms that are engaged or are impacted by stress. For example, Figure 13.1 suggests that while there may be an extensive array of stressors inherent in team contexts, these stressors result in a narrower range of cognitive, behavioral, and attitudinal consequences (e.g., attention narrowing, poor situation awareness, and loss of team perspective) with the potential to degrade team performance effectiveness. The challenge, then, is to define these core mechanisms – to define the effects of specific stressors in terms of the underlying psychological mechanisms that are engaged and that impact individual and team performance. For example, Poulton (1978) has argued that the detrimental effects of noise on performance are primarily the result of distraction. Others have argued that the detrimental effects of noise on performance are primarily the result of increased task load. In fact, noise can have either effect: Noise that is relevant to the task (i.e., sound that has a bearing on the task) can place an increased task load on the operator, whereas noise that is irrelevant to the task (that carries no task‐related information) can serve primarily as a distraction. In other words, we would argue that the primary effect of task‐relevant noise on the operator is that it increases cognitive load, and we would argue that the primary effect of task‐irrelevant noise on the operator is that it distracts from the task. By identifying the psychological mechanisms through which noise may impact performance in a given task setting (such as via distraction or task load), we can then identify training approaches most relevant to these dimensions. The evidence suggests that there are a limited number of cognitive, emotional, and social mechanisms through which stress impacts team performance. According to T. Driskell, J. E. Driskell, and Salas (2015), the “Big Five” stress mechanisms are: 1  Stress increases distraction and decreases attentional focus. 2  Stress increases cognitive load and demand on capacity.



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3  Stress increases negative emotions and frustration. 4  Stress increases fear and anxiety. 5  Stress increases social impairment. 1 Stress increases distraction and  decreases attentional focus  One of the more well‐ established findings in the stress literature is that as stress or arousal increases, the individual’s breadth of attention narrows (Combs & Taylor, 1952; Easterbrook, 1959). Perhaps the earliest statement of this phenomenon was William James’s (1890) belief that the individu­ al’s field of view varied from a broader perspective under normal conditions to a more narrow, restricted focus under stress. For complex tasks, in which the individual must attend to a relatively larger number of salient task cues, this narrowing of attention may result in the elimination of relevant task information and task performance will suffer. The narrowing of attentional focus under stress may have both cognitive and social effects. As important social or interpersonal cues (such as attention to others’ requests or actions) are neglected, team performance suffers (see Driskell, J. E. et al., 1999). Thus, one effect of stress is to narrow attentional capacity, and the narrowing of attention may lead to a neglect of relevant task or interpersonal cues. Related research shows that stress results in narrowing of attention or perceptual tunneling (Easterbrook, 1959), reduced working memory (Huey & Wickens, 1993), and performance rigidity (Staw, Sandelands, & Dutton, 1981). Dorner (1990) found that individuals under stress were prone to “ballistic” decision making; that is, making decisions without checking the consequences of their decision. 2  Stress increases cognitive load and demand on capacity  Task load is defined as performing two or more tasks concurrently. However, this construct is related to a number of other terms, including multitasking, dual‐task performance, and workload. Typically, the term workload refers to the individual’s perception of the work demands imposed by a task environment, although the term has also been used to describe the demands of the task environment itself in terms of the volume and pace of the work to be performed (Spector & Jex, 1998). Time‐sharing, or multitasking, can be defined as the capacity to perform concurrent tasks or to interleave multiple tasks (Fischer & Mautone, 2005). High‐stress environments often involve an increase in task load, stemming from the imposition of additional tasks (e.g., a radar operator whose task suddenly expands from monitoring several targets to monitoring multiple targets while answering outside queries and requests for information) or may result from having to attend to novel or unfamiliar stimuli (e.g., an operator may engage one target while scanning for additional threats). 3 Stress increases negative emotions and  frustration  Emotional reactions to stress may include subjective feelings of anger, annoyance, tension, frustration, and increased con­ cern for the wellbeing of self and others. Effective performance under stress requires the capacity to maintain one’s composure and emotional control while remaining task‐focused under demanding and threatening conditions. In past studies of military teams, Haythorn (1953) and Greer (1955) reported that emotional stability was positively related to mili­ tary team effectiveness. Other researchers have claimed that emotional stability is a significant factor in any task that requires cooperative behavior (Driskell, J. E., Hogan, & Salas, 1987; Mount, Barrick, & Stewart, 1998). 4  Stress increases fear and anxiety  Evidence from a broad range of studies indicates that the threat of dangerous or novel environments may result in impaired performance, an

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increase in subjective stress, and increased physiological reactivity. A threatening situation is one in which dangerous, hazardous, or novel environmental events pose the potential for pain or harm. A number of military studies have examined the effects of stress in combat. Many of these studies were conducted during World War II and documented the impact of battlefield threats on military performance. For example, Reid (1945) analyzed the calculation and plotting errors involved in measuring wind vectors by navigators on operational sorties during World War II. He found that compared with errors made over England, errors increased significantly once bombers crossed the enemy coast and increased even further as the bombers approached the target. When the bombers crossed the coast on the return journey, errors declined (suggesting that the initial dip in performance was probably not attributable to fatigue). Other investigators have examined demanding environments, such as parachuting. Not surprisingly, most investigators have found increases in subjective anxiety and impaired performance either preceding or during early jumps. Hammerton and Tickner (1969) found that novice military parachutists showed a decrement in tracking performance immediately before and after their descent from a balloon. Burke (1980) examined army jumpmaster training in order to search for variables predictive of ability to perform under threat. He found a strong negative corre­ lation between jumping performance and perceived stress. 5  Stress increases social impairment  Social effects of stress may include a reduction in the tendency to assist others, increased interpersonal aggression, neglect of social or interpersonal cues, and less cooperative behavior among team members. Tasks that require interaction among other team members or co‐workers to accomplish require that activities be coordi­ nated to achieve the team goal. Larson and Schaumann (1993) noted that for routine tasks, coordination requirements are relatively simple  –  team members must coordinate their behavior to the extent required to carry out predetermined plans and procedures. However, for stressful or dynamic tasks, in which behavior is difficult to predict in advance, performance requires more complex interdependence, such that coordination can only be achieved by mutual adjustments among team members as the task is performed.

Candidate stress training approaches There are a number of stress training approaches that have been successfully implemented in high‐demand environments. Table 13.2 illustrates several candidate training approaches that may target the primary stress mechanisms described above. Table 13.2  Candidate stress training approaches. Big Five Stress Mechanisms 1 2 3 4 5

Distraction/attentional focus Cognitive load/capacity Negative emotions/ frustration Fear/anxiety Social impairment

Training Approach Attentional Dual task Mindfulness Stress exposure Team dimensional

Reference (Singer, Cauraugh, Murphey, Chen, & Lidor, 1991) (Bherer et al., 2005) (Jha, Stanley, Kiyonaga, Wong, & Gelfand, 2010) (J. E. Driskell, Salas, Johnston, & Wollert, 2008) (Smith‐Jentsch, Cannon‐Bowers, Tannenbaum, & Salas, 2008)



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1  Decrease distraction and increase attentional focus (exemplar: attentional training)  Singer et al. (1991) proposed an attentional training approach that attempts to train individuals to maintain attentional focus on task‐relevant stimuli in the face of external distractions. This approach includes training that describes how attention may be distracted during task performance, followed by practice in performing the task under stress, focusing attention, and refocusing attention after distraction. Empirical results indicated that this type of training, by focusing directly on enhancing attentional focus, could overcome the distrac­ tion and perceptual narrowing that occurs in stress environments (see also Driskell, J. E., Johnston, & Salas, 2001). 2  Decrease cognitive load and demand on capacity (exemplar: dual‐task training)  Dual‐ task training occurs when participants are trained through individualized adaptive feedback and task prioritization instructions to concurrently perform multiple tasks (Bherer et al., 2005). Research has shown that dual‐task training can improve executive control skills required to coordinate multiple tasks (Kramer, Larish, & Strayer, 1995). 3 Decrease negative emotions and  frustration(exemplar: mindfulness training)  Mindfulness training has been implemented among predeployment military personnel to reduce the effects of stress on cognitive functioning and emotional disturbance (Jha et  al., 2010). This program of mindfulness‐based training was implemented in a group context to enhance stress resilience skills and regulate emotional reactivity. Related research has demonstrated success in implementing mindfulness‐based training for high‐stress environ­ ments (Erisman & Roemer, 2010; Goldin & Gross, 2010). 4  Decrease fear and anxiety (exemplar: stress exposure training)  Stress inoculation training is a three‐stage intervention in which trainees learn about stress effects, effective coping mechanisms, and practice learned coping mechanisms through role plays and simula­ tions in environments that increasingly approximate the performance environment. Stress inoculation training has been found to be effective at both reducing anxiety and enhancing performance (Driskell, J. E., Salas, & Johnston, 2001; Driskell, J. E., Salas, Johnston, & Wollert, 2008). Providing preparatory information to individuals regarding the likely symptoms associated with stress, environmental causes of stress, and effective coping mechanisms has also been found to reduce feelings of stress and enable them to  perform at high levels in stressful environments (Inzana, Driskell, J. E., Salas, & Johnston, 1996). 5  Decrease social impairment (exemplar: team dimensional training)  Team dimensional training is a type of guided team self‐correction found to increase teamwork processes and effective outcomes through the development of more accurate shared mental models (Smith‐Jentsch, Cannon‐Bowers, Tannenbaum, & Salas, 2008). During team dimensional training, a debrief facilitator asks team members to generate positive and negative examples of their own performance using an “expert model” of teamwork as an organizing framework. This expert model was shown to predict team performance outcomes in a navy command and control environment (Smith‐Jentsch, Johnston, & Payne, 1998). In team dimensional training, errors are framed as necessary opportunities to learn. Prior research has demonstrated that such error framing facilitates regulatory activity and ultimately promotes adaptive transfer to novel task environments (e.g., Bell & Kozlowski, 2008).

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Measuring Team‐Level Stress A number of methodological approaches exist for measuring stress at the individual level of analysis (Langan‐Fox, Sankey, & Canty, 2009), yet guidance for measuring stress at the team level of analysis is less clear. A measurement system capable of measuring team‐level stress is critical for the diagnosis of stress effects as well as for evaluation of stress training effectiveness. Such tools can also be used to dynamically monitor team stress levels during real‐time performance. In the following, we describe some of the research issues involved in measuring stress effects at the team level.

Team stress should be measured at multiple levels of analysis Sierra et al. (2012) described differences between intrateam member stress (i.e., the stress experienced by an individual team’s member) and interteam member stress (i.e., stress that exists among or between team members). Intramember stress is present within a team when one or more team member’s experiences negative internal states (e.g., negative physical, affective, and/or cognitive states; de Jonge & Dormann, 2006). Conversely, intermember stress originates and exists only at the team level. This type of stress is present in a team when interpersonal processes or states between two or more team members become disrupted (e.g., disrupted motivational/affective states, cognitive states, and/or behavioral processes; Sierra et  al., 2012). Because both intra‐ and intermember stress occur within the team, these types of stress can be conceptualized as broad classes of team stress. Therefore, both intra‐ and intermember stress must be assessed to accurately measure team stress. Intramember stress should be measured at the individual level of analysis because it originates within individuals. After individual‐level measurements are taken, however, in­ tramember stress can be transformed into a team‐level variable by summing or averaging the intramember stress levels of all team members (i.e., aggregating intramember stress scores; e.g., Ellis & Pearsall, 2011). Because it originates and exists only at the team level, intermember stress should be measured by taking global measures of intermember stress states (i.e., having an individual make a global evaluation about the entire team regarding its level of intermember stress; Kozlowski & Klein, 2000).

Team stress should be measured in multiple forms Team stress should be measured in multiple forms because it exists in multiple forms. This principle suggests that any measurement system designed to assess team stress should be capable of capturing information about physical, affective, and cognitive team member states as well as motivational/affective, cognitive, and behavioral emergent states and team processes. Measurement systems that fail to capture one or more of these forms are defi­ cient and are likely to underestimate the degree to which team members are experiencing stress (Borman, 1991).

Team stress should be measured in situ The most accurate measurements of team stress are likely to take place when team stress is assessed in situ, during teams’ natural performance episodes (Baker & Salas, 1992; 1997). In real‐world settings in which questionnaires, scales, and direct observations may be of limited use, automated data collection methods provide a good alternative for accurately measuring team stress in an unobtrusive manner. For example, devices that automatically



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collect information about team members’ physiological stress indicators and patterns of interaction can serve as unobtrusive measures of team stress (Driskell, J. E., King, & Driskell, T., 2014). Such an approach does not require team members to halt performance for measurement purposes, can follow teams into any environment, and has information processing resources that greatly exceed those afforded to human raters.

Team stress should be measured over time One time/context‐related factor that impacts team stress measurement relates to the fact that teams are exposed to different types and numbers of stressors at various points in time and in various situations (Driskell, J. E. et al., 1992). Because teams experience different forms and levels of team stress, team stress measurements can vary substantially over time. A further time/context‐related factor is that teams go through different phases in their lifespan (e.g., Kozlowski, Gully, Nason, & Smith, 1999; Morgan, Salas, & Glickman, 1993) and these phases influence team processes and performance in a variety of ways (Baker & Salas, 1997). In order to obtain information about the nature and level of team stress over an extended period of time, multiple measurements are needed. Multiple mea­ surements allow for the observation of how team stress indicators vary across time and situations. This information provides those interested in understanding team stress with greater insight regarding how and why changes in team stress occur.

Future Research Understanding the effects of stress on team performance and mitigating the negative effects of stress on teams are enormous challenges. The optimist would note that these challenges also represent a host of opportunities for future research. Perhaps the most overarching need is for continued research on stress and teams that bridges the gap bet­ ween theory and application. That is, research is needed that builds upon the existing body of research on stress and team performance and that applies and tests this research in real‐ world contexts. Driskell et al. (2014) note the value of applied experimental research that takes the findings and knowledge drawn from basic and theoretical research and applies it to solve practical, real‐world problems. The dynamic interplay between basic and applied research recalls this famous anecdote: When the British physicist Faraday was asked by the Finance Minister Gladstone in the 1850s whether his fledging research on electricity had any practical value, Faraday replied “One day Sir you may tax it.” We emphasize the need for both basic and applied research on stress and team performance with practical applica­ tions in mind. Further research is needed to develop and test approaches for assessing stress effects in real‐world contexts. Driskell et al. (2014) refer to this problem as assessing team performance “at a distance.” Many teams in real‐world contexts operate autonomously, apart from direct observation and supervision, are not amenable to the intrusion of the psychologist’s typi­ cal array of questions and questionnaires. This suggests the value of non‐obtrusive means of assessing stress and other cognitive/emotional deficits without disrupting ongoing performance. For example, Driskell, T., Salas, Driskell, J. E., Burke, and Neuberger (2015) have developed a lexical approach to assessing stress, termed “STRESSnet,” to provide a non‐obtrusive means of detecting stress and related deficits in long‐duration spaceflight through the assessment of spontaneous verbal output in real‐time crew communications. A third fertile area for future research is the further development of stress training interventions in team contexts. J. E. Driskell et al. (2006) have noted that early research

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on stress took place within a medical/psychoanalytic paradigm, which led to a preoccupa­ tion with disordered behavior and illness. Thus, the bulk of stress research over the past decades has emphasized disordered behavior and coping behaviors, and almost ignored the effects of stress on performance, effectiveness, and productivity in real‐world environments. The result is that the term stress carries a lot of semantic baggage, and many people in applied operational communities may be hesitant to embrace stress training interventions because they view them as irrelevant to productivity. Further research is needed to develop stress training interventions that can produce demonstrative effects on enhancing team performance and productivity.

Conclusion This chapter has provided an overview of research on the effects of stress on teams. We have examined ways in which stress may negatively impact individual and team processes underlying team performance effectiveness. This chapter has also addressed the challenges faced when seeking to optimize teamwork under stress. This research is of considerable theoretical and practical value. We note that the original research on teams and stress originating in the 1940s and 1950s has now evolved to address issues on the frontier of science in the 2030s and beyond. We hope this review stimulates interest in this journey.

Acknowledgement This work was supported by supported by funding from the National Aeronautics and Space Administration (Grant# NNX09AK48G) and the National Science Biomedical Research Institute (NCC‐9‐58‐401/NBPF03402).

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14

Conflict in Teams Lindred L. Greer and Jennifer E. Dannals

Introduction Conflict is inevitable when groups of people work together. Different opinions may arise about the goals and means of task accomplishment, interpersonal tensions may escalate, and struggles over leadership and power may derail team collaboration. Understanding where such conflicts come from, how they impact team outcomes, and how they may be best managed has been a central focus within the literature on small groups and teams for the last 20 years. Since the seminal work in the mid‐1990s on task versus relational conflicts in teams (e.g., Amason, 1996; Jehn, 1995; Pelled, 1996), interest in conflict research has soared. In this chapter we provide an integrative overview of the large and growing body of research on conflict in teams. We begin with an overview of the largest body of work in this area – on the effects, moderators, and antecedents of the different types of conflict in teams. We highlight work on traditional distinctions between task conflicts over the ideas and goals of task work, and relationship conflicts about personalities and interpersonal issues, and we also give attention to work on two additional types of conflict in teams: process conflicts about the logistics of task accomplishment, and status conflicts about disagreements over prestige and hierarchy within the team (e.g., Bendersky & Hayes, 2012; Jehn, 1997). We review and discuss meta‐analytic conclusions (e.g., De Wit, Greer, & Jehn, 2012; O’Neill, Allen, & Hastings, 2013) about the general effects of conflict on team outcomes, and then we delve into depth in reviewing and organizing the emerging plethora of work that has arisen on the moderators and antecedents of the different types of conflict in teams. We systematically review and draw conclusions on how three key facets of the team environment (team composition, team conflict management style, and team atmosphere) determine when each of the different conflict types arise and whether they help or hurt team performance.

The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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Secondly, we identify and review what we see as promising emergent areas of research on conflict in teams, which do not necessarily rely on traditional distinctions between the conflict types. We highlight three emergent research areas which we feel may be especially critical in understanding the role of conflict in teams: the work on conflict transformation (when one type of conflict is likely to cause another type of conflict; e.g., Arazy, Yeo, & Nov, 2013; Simons & Peterson, 2000), multilevel models that integrate individual conflict behaviors with team level processes over time (e.g., Jehn, Rispens, Jonsen, & Greer, 2013; Korsgaard, Jeong, Mahony, & Pitariu 2008), and research on compositional models of team conflicts (which investigate the possibility that not all members in the team may perceive, experience, or engage in conflicts in the same manner; e.g., Jehn, Rispens, & Thatcher, 2010; Sinha, Sirvanathan, Greer, Conlon, & Edwards, 2014). We review the key theories and findings that have emerged in each of these three areas, and then we move on to our general discussion. Finally, we conclude our review with a discussion section in which we highlight what we view as being the key conclusions in the team conflict literature at this point in time. We posit that many findings on team conflict, whether in regards to the effects of the conflict types or even to findings in the new emergent models of conflict in teams, can be tied back to two essential factors in teams – team norms (particularly concerning safety, participation, and emotionality) and team status concerns. When team norms can direct group behavior towards work‐focused task conflicts and fewer emotional and personal debates in teams, conflicts are likely to be more beneficial for team performance (e.g., Bradley, Klotz, Postlethwaite, & Brown, 2013; Curşeu, Boros, & Oerlemans, 2012; Jehn, Greer, Levine, & Szulanski, 2008). In contrast, we suggest that status and power disagreements may often be underlying drivers of the negative effects of conflict in teams (Greer, 2014), and that learning how to disentangle status issues from other forms of expressed conflict in teams is critical for research in this area. In sum, we aim with this chapter to provide a state‐of‐the‐art review of the past and present research on intragroup conflict, and we hope by extracting key conclusions from this large and varied literature to refocus conflict research going forward to get at the essence of what’s driving conflict in teams – team norms surrounding emotions and participation and underlying status concerns.

Intragroup Conflict Intragroup conflict is defined as the degree to which members have real or perceived incompatible goals or interests (De Wit et al., 2012; Korsgaard et al., 2008). The last 20 years of research on conflict in teams focused mainly on understanding how different types of conflicts, or conflicts about different topics, may differentially affect team outcomes. Four key types of conflict have arisen in the literature: Task, relationship, process, and status conflicts. The two most researched types – task and relationship conflict – stem from the classic distinctions of personal versus work‐related conflicts, as made famous by a set of classic studies in the late 1990s (i.e. Amason, 1996; Jehn, 1995; Pelled, 1996). Task conflicts are defined as disagreements about task content, while relationship conflicts are defined as interpersonal incompatibilities and tensions (De Wit et al., 2012; Jehn, 1995). Because this dichotomy did not adequately capture the range of conflicts that can occur in teams, in 1997, Jehn introduced a third type of conflict in teams – process conflict, defined as disagreements about the logistics of task completion, including roles, responsibilities, and work arrangements. In 2012, Bendersky and Hays added the fourth type of conflict, status conflict, which they defined as disputes over members’ relative positions of respect in the team’s social hierarchy.



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In the following sections, we provide a high‐level overview of current conclusions in the literature on how each of these conflict types impacts team outcomes. For each type of conflict, we review the moderators of the relationship between the conflict type and team outcomes, as well as the antecedents of each conflict type. We group the moderators and antecedents into three primary categories, reflecting member composition (e.g., demographic traits, personalities), member behaviors (e.g., conflict management strategies, emotional coping styles), and team atmosphere (e.g., intrateam trust, respect). For each conflict type, we conclude by providing a summary of what we feel are the key underlying issues, and as well as discussing possible solutions and research directions, in order to truly unpack and understand the nature and effects of each conflict type.

Task Conflict The most researched form of intragroup conflict is task conflict (De Wit et al., 2012). The debate on the functionality of task conflict has been one of the more vigorous and contested debates in the teams’ literature. Early work by Jehn (1994; 1995) suggested that task conflict could improve team performance. The debates and exchange of information that accompanies task conflict can improve member understanding of the task at hand (Amason, 1996; Choi & Sy, 2010), and lead to higher quality and more creative team outcomes (Matsuo, 2006). However, task conflicts can also become emotional and thus can distract members from the task at hand (Jehn, Greer, Levine, & Szulanksi, 2008) and resolving such escalated task conflicts can consume considerable time (Jehn et al., 2013). In the first meta‐analysis of this literature, De Dreu and Weingart (2003) found support for the negative side of task conflicts: all forms of team conflict, including task conflict, negatively impacted all forms of team outcomes, including team performance. However, emerging research on task conflict complicates this somewhat simplistic picture of the effects of conflict. Interestingly, in the meta‐analysis by De Wit and colleagues (2012), task conflict had neither a positive nor negative effect on team performance – rather, it was entirely dependent on the context and, more specifically, on the degree to which task conflicts co‐occurred with relationship conflicts. In a subsequent analysis, in which the authors only examined studies containing both conflict types and conflict management behaviors, the authors actually even find a significant positive effect of task conflict on performance, and even satisfaction (DeChurch, Mesmer‐Magnus, & Doty, 2013). Rather than continuing to examine the main effect of task conflict in teams, this research s­ uggests that it is prudent to consider factors that may moderate task conflict’s relationship with team outcomes.

Moderators of the effects of task conflict In an effort to understand the exact conditions under which task conflict is likely to help or hurt team performance, a number of studies have begun to examine moderators of the relationship between task conflict and performance. At the meta‐analytic level, De Wit and colleagues (2012) found that a primary moderator of the task conflict–team outcome relationship (including both satisfaction and team performance) was the degree to which relationship conflicts co‐occurred with task conflicts. When task conflicts did not become personal, they found that task conflicts had the potential to be more positive for team performance. Similarly, Shaw and colleagues (2011), examining work teams in Taiwan and Indonesia, found that relationship conflict moderated the effects of task conflict on performance, such that task conflict linearly harmed performance when relationship

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conflict was high, but had a curvilinear relationship with performance when relationship conflict is low, such that very low and very high levels of task conflict harmed team performance at low levels of relationship conflict. Their results provided some suggestion that these effects would also hold for team member satisfaction as well. Why might the co‐occurrence of task and relationship conflicts nullify the benefits of task conflicts? De Wit, Jehn, and Scheepers (2013) found that when relationship conflicts were present during task conflicts, members were more rigid in holding onto suboptimal decision processes and engaged in more biased usage of information. This shift in team processes in turn led to negative effects on team decision outcomes. Taken together, the effects of task conflict on team outcomes are highly dependent on the degree to which relationship conflicts are present and task conflicts become personal. The benefits of task conflict seem to be only found in situations where personal, relationship concerns can be kept completely outside the conflict. To identify when task conflicts become inflamed and personal, researchers have looked at several aspects of teams, including the team composition, team behaviors, and the team atmosphere. In terms of team composition and behaviors, both member personalities and coping styles matter in determining the effects of task conflict and team outcomes. Curşeu, Boros, & Oerlemans (2012) find that the personalization of task conflicts can be reduced in teams that are effective at emotion regulation, and that employ problem‐focused coping strategies (Pluut & Curşeu, 2013). Similarly, task conflicts are also more likely to be productive for team performance when members have a high average level of emotional stability and openness, as Bradley et al. (2013) demonstrated in a study 117 student teams. Conflict management strategies are also relevant. Task conflicts are best for team performance when teams let the task conflict play out and do not engage in high levels of conflict management (Tekleab, Quigley, & Tesluk, 2009). These teams show improved group cohesion and, indirectly, thus show improved group outcomes. However, other research suggests that task conflicts are more positive for group outcomes when task conflicts are actively managed and members engage in agreeable behaviors (DeChurch & Marks, 2001). There has also been much research on the role of team atmosphere in moderating the effects of task conflict on group outcomes. For example, when team interests in the task conflict are high – such as when the issue is of high importance to the team (Rispens, 2012), or when intrateam trust is high (Choi & Cho, 2011; Simons & Peterson, 2001), conflicts are less likely to become emotional and personalized (Rispens, 2012). Members are then more likely to focus on the task at hand and prioritize team needs above individual emotional concerns. This increases the likelihood that task conflicts can be decoupled from relationship conflicts, and thereby makes task conflict more beneficial to team performance. Interestingly, De Clercq, Thongpapani, and Dimov (2009) find that while trust may reduce the emotionality associated with task conflict, it can also reduce the expression of wilder ideas and thus may reduce group creativity. Finally, the task type has been shown to matter – Jehn (1995) found that task conflicts were more positive for performance on nonroutine, rather than routine, tasks in her classic study of manufacturing teams. Similarly, Puck and Pregernig (2014) found that task conflicts were more useful (less negative) on nonroutine, creative brainstorming tasks than on decision‐making tasks. Task conflicts are also of more benefit to groups when groups have norms that encourage open communication (Jehn, 1995; Jehn, Greer, Levine, & Szulanski, 2008) and positive social interactions (De Clercq et al., 2009). Fairchild and Hunter (2014) found in a field study of 55 design teams that when participative safety is high, task conflicts are most likely to promote team creativity. Similarly, Bradley and colleagues (2013) found task conflict most benefited group performance when psychological safety was high. Lastly, Bendersky



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and Hayes (2012) found that task conflicts were more likely to benefit performance in the absence of status conflicts. When status conflicts co‐occurred with task conflict, task debates escalated and became personal, owing to the higher personal stakes involved for members. However, when members could separate ego and reputation concerns from their task debates, task conflicts had the potential to improve team functioning. Antecedents of task conflict  Given the potentially performance enhancing effects of task conflict, a number of researchers have sought to identify when task conflicts are most likely to occur. Informational (sometimes called functional) diversity is positively related to task conflict (Jehn, Chadwick, & Thatcher, 1997; Jehn, Northcraft, & Neale, 1999; Mooney, Holahan, & Amason, 2007; Pelled, Eisenhardt, & Xin, 1999). Ayub and Jehn (2014) found national variety to positively relate to task conflict, and in earlier work in 2010, they also found national diversity, particular when members had nationalistic attitudes, to positively relate to task conflict. Vodosek (2007) found cultural diversity to be positively related to task conflict, as did Elron (1998). And Jehn (1994) found value diversity to be positively related to task conflict (note that this effect did not hold in later work, such as Jehn et al., 1999). Chun and Choi (2014) found that teams in which members have a high need for achievement were more likely to have task conflicts. Bono, Boles, Judge, and Lauver (2002) found that teams that had differences in members’ level of extraversion were more likely to have task conflicts. And Barsade, Ward, Turner, and Sonnenfeld (2000) found that the mean level of trait negative affect in a team was positively related to the level of task conflict in the team. Finally, Mooney et al. (2007) found that group size was positively associated with task conflict in teams. Other forms of team composition have also been examined as antecedents of task conflicts. For example, demographic faultlines, or the simultaneous alignment of multiple member demographic characteristics in such a way that clearly demarcated subgroups form, have been linked to task conflict. Some have found faultlines to be positively related to task conflict (e.g., Choi & Sy, 2010; Li & Hambrick, 2005), while others have shown faultlines to negatively relate to task conflict (e.g., Thatcher, Jehn, & Zanutto, 2003). Thatcher and Patel (2011) conclude in their meta‐analysis that, on average, demographic faultlines are most likely to positively relate to the occurrence of task conflict in teams. Further research examines the role of team atmosphere in provoking task conflict. For example, Spell, Bezrukova, Haar and Spell (2011) find an association between distributive injustice and task conflict, and Mooney et al. (2007) find an association between team goal uncertainty and task conflict. Teams with more positive group states, such as trust, respect and cohesion (Jehn & Mannix, 2001) or group identification (Mooney et al., 2007), are also associated with lower levels of task conflict in teams. In terms of the team and organizational context, in a study of 193 sales departments in Japanese firms, Matsuo (2006) found customer orientation to be positively related to the likelihood of task conflicts occurring in the groups. In a study comparing virtual teams to co‐located teams, Hinds and Mortensen (2005) found more task conflicts in distributed rather than collocated teams, and that shared context and spontaneous communication reduced task conflicts, particularly in distributed settings. Chen (2006) found that task conflicts were more common (and more positive for performance) in technology‐driven project teams than in service‐driven project teams. Conclusions on task conflict  Task conflicts remain an interesting venue for future research. Of all the conflict types, task conflicts continue to show the strongest possibility to benefit team outcomes (De Wit et al., 2012), particularly team performance. However,

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the conditions under which task conflicts are able to live up their potential are admittedly narrow (De Dreu, 2008). Solving this mystery – of identifying exactly when task conflicts may benefit performance – has sparked an enormous sure of research in the last few years. Across this growing body of work, a key conclusion seems to be that in order to achieve the benefits of task conflict, the effective management of task conflict is critical: task conflicts are most positive for teams when they are less personal and emotional (e.g., Bradley et al., 2013; Choi & Cho, 2011; Curşeu et al., 2012) and when teams have open, psychologically safe norms surrounding communication (Bradley et al., 2013; Fairchild & Hunter, 2014; Jehn, Greer, Levine, & Szulanski, 2008). In future research on task conflict, continuing to pinpoint the exact situations and conditions where task conflict may benefit team outcomes is important. Additionally, theoretical and empirical work on the multilevel dynamics of how task conflicts are expressed, and how this impacts team outcomes is promising. For example, Jehn et al. (2013) propose that task conflicts are most likely to benefit performance when they are resolved when only members who authentically disagree with the task strategy are involved. Once members join in the task conflict because of heightened emotions or for political siding, the benefits of task conflict are lost. As such, this suggests that multilevel, temporal views of task conflict may provide valuable insight into extracting the benefits of task conflict moving forward.

Relationship Conflict The second most commonly studied form of conflict is relationship conflict (De Wit et al., 2012). In general, the vast majority of the literature has proposed and found that relationship conflicts harm team outcomes. Relationship conflicts are often highly emotional (Chen & Ayoko, 2012) and damage ongoing relationships and cohesion within the team (De Dreu & Weingart, 2003), which in turn can harm team outcomes (Greer, 2012). Relationship conflicts can worsen individual mood and can even have physiological consequences, such as increases in somatic complaints (Meier, Gross, Spector, & Semmer, 2013). Moderators of the effects of relationship conflict  Given that a key reason why relationship conflicts can harm group outcomes is the emotionality associated with relationship conflicts, Jehn, Greer, Levine, and Szulanski (2008), examined what would happen when relationship conflicts varied in their emotionality. They found in a study of student teams that relationship conflicts were only negative for group climate and viability when emotionality in the team was high. When relationship conflicts were not surrounding by a high level of negative emotions, they no longer harmed the group. What makes relationship conflict more or less harmful for team outcomes? In terms of team composition, Duffy, Shaw, and Stark (2000) found that in interdependent student groups, low self‐esteem made the effects of relationship conflict on individual performance and absenteeism worse. In terms of member behaviors, De Dreu and Van Vianen (2001) looked at the role of conflict management behaviors in reducing the negative effect of relationship conflict on group outcomes – they found in a study of 27 organizational teams that avoiding responses were the best way to manage relationship conflicts. Collaborating or contending during relationship conflicts only served to derail teams more from effective task accomplishment (De Dreu & Van Vianen, 2001). Similarly, Jehn (1995) found that when teams had conflict‐avoidant norms, the negative effects of relationship conflict on group satisfaction and member liking were reduced. This may explain why employing the emotion regulation strategy of distraction can reduce the negative outcomes associated with relationship conflicts (Griffith, Connelly, & Thiel, 2014).



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In contrast to these findings on the benefits of avoiding relationship conflicts, Auh, Spyropoulou, Menguc, and Uslu (2014) found that the negative effects of relationship conflict were actually improved when teams used a collaborative conflict management approach, as relationship conflicts were less likely to impair information processing in the team. Tekleab and colleagues (2009) similarly found relationship conflict was less harmful in teams that were more effective at conflict resolution because those teams experienced improved team cohesion. Lastly, in terms of team atmosphere, De Clercq and colleagues (2009) found that high levels of positive social interactions in 232 Canadian firms buffered teams from the negative effects of relationship conflict allowing teams to maintain their levels of innovation. Antecedents of  relationship conflict  A growing line of research has sought to identify when relationship conflicts are most likely to occur. One line of research focuses the effects of team composition on relationship conflict. Given the close link between relationship conflicts and emotionality, it is unsurprising that groups with high trait negative affect are more likely to have relationship conflicts (Barsade et al., 2000). Similarly, teams with high emotion recognition along with low mean levels of agreeableness and extraversion are more likely to appraise conflicts as relationship conflicts (Bechtoldt, Beersma, Rohrmann, & Sanchez‐Burks, 2013). In another set of studies on individual differences in personality and demographics, Bono and colleagues (2002) found that differences in neuroticism in the team, and high mean levels of extraversion and conscientiousness were more likely to predict relationship conflicts. Relationship conflict is similarly more likely when members have a lower need for affiliation (Chun & Choi, 2014). However, Ensley and Pearson (2005) found that familial top management teams, which one would expect to have a higher need for affiliation, had more relationship conflicts than non‐familial top management teams. This suggests that the need for affiliation deserves further research, as research has shown that it can both increase and decrease relationship conflicts. In other research on team compositions, other needs and motivations have been shown to predict relationship conflict. For example, both Greer, Caruso, & Jehn (2011) and Buchholtz, Amason, and Rutherford (2005) found high‐power teams, in which both the team and its members, had high levels of power in the organization (and ostensibly higher levels of power motivation), had more relationship conflicts than low power teams. Lastly, as a final indicator of group composition, group size has been positively related to relationship conflict in teams (Mooney et al., 2007). Further research explores the role of diversity in provoking relationship conflicts. Mohammed and Angell (2004) found in a study of student teams that gender‐diversity predicted relationship conflict, particularly when team orientation was low and in the early phases of team life. Jehn et al. (1997) found sex and age diversity to positively relate to relationship conflict, and found value congruence to decrease relationship conflict. Similarly, Jehn et al. (1999) found social category diversity (sex and age) and value diversity to increase relationship conflict (the relationship between value diversity and relationship conflict was also shown by Jehn, 1997). In contrast to the Jehn findings on age diversity and conflict, Pelled et  al. (1999) actually found age diversity to decrease relationship conflict. They did find race and tenure diversity to increased relationship conflict. Related to the race findings of Pelled and colleagues, Ayub and Jehn (2010) found in a study of teams in the information technology industry in Pakistan that national diversity, particularly when members had nationalistic attitudes, positively related to relationship conflict. Similarly, Vodosek (2007) found cultural diversity to be positively related to relationship conflict. Interestingly, Ayub and Jehn (2014) found when national diversity was conceptualized in terms of variety, rather than separation, relationship conflict decreased.

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In addition to looking at specific characteristics in determining conflict, research has also looked at the aggregate effects of member composition. For example, research on demographic faultlines has shown faultlines to be negatively related to relationship conflict (e.g., Choi & Sy, 2010; Lau & Murnighan, 2005; Thatcher, Jehn, & Zanutto, 2003), but research has also shown faultlines to be positively related to relationship conflict (e.g., Chen, Wang, and Lee, 2009; Li & Hambrick, 2005; Pearsall, Ellis, & Evans, 2008; Polzer, Crisp, Jarvenpaa, & Kim, 2006). On average, Thatcher and Patel (2011) conclude in their meta‐analysis that faultlines are most likely to be positive related to relationship conflict. Team atmosphere and behavioral processes can also impact relationship conflict. For example, Peterson and Behfar (2003) found that early negative performance feedback in teams increased the likelihood of relationship conflict, particularly in teams lacking trust, and Amason and Mooney (1999) found that prior poor performance was associated with relationship conflicts in top management teams. Trust, respect, and cohesion in teams can all decrease the occurrence of relationship conflict, while intrateam competition often increases it (Jehn & Mannix, 2001). This may explain why, in a classic field intervention, when 15 medical teams received a training on perspective taking, they were less likely to see conflict as people‐oriented as compared with teams that received a control training session (Sessa, 1993). Time urgency and previously established effective team interactions can also help to decrease relationship conflict in teams (Mohammed & Angell, 2004). Hinds and Mortensen (2005) found that shared identity, shared context, and spontaneous communication helped to reduce relationship conflicts, particularly in distributed teams who otherwise had higher levels of relationship conflicts than collocated teams. Hobman Bordia, Irmer, and Chang (2002) similarly found that computer‐mediated groups, compared with face‐to‐face groups, expressed more relationship conflicts, especially in the early stages of team interaction. Chen (2006) also found that relationship conflicts were more likely to occur in service‐driven project teams than technology‐driven project teams. Finally, as previously discussed relationship conflicts are highly associated with task conflicts that have become personal and emotional. Task conflicts can frequently give rise to relationship conflicts in teams, particularly when task conflicts occur in teams with low trust (Kerwin & Doherty, 2012; Peterson & Behfar, 2003; Simons & Peterson, 2000; Tidd, McIntyre, & Friedman, 2004), high performance (as opposed to learning) orientation (Huang, 2010), negative diversity climates, including perceived subgroups and ingroup– outgroup distinctions (Xie & Luean, 2014), competitive conflict management behaviors (DeChurch, Hamilton, & Haas, 2007), where members interact face to face and emotions are visible (Martínez‐Moreno, Zornoza, Gonzáles‐Navarro, & Thompson, 2012), and where members have high emotion recognition and low agreeableness and/or extraversion (van den Berg, Curşeu, & Meeus, 2014). The nature of the conflict also matters – relationship conflicts are more likely to arise during tasks conflicts over issues with low importance for members (Rispens, 2012), high emotionality (Yang & Mossholder, 2004) and low resolution potential (Greer et al., 2008). Additionally, process conflicts have also been shown to be direct predictors of relationship conflicts (Greer, Jehn, & Mannix, 2008; Martínez‐ Moreno et al., 2012; van den Berg et al., 2014) – the emotional and heated nature of process conflicts likely carry relational concerns that quickly escalate relationship tensions and spark relationship conflicts in teams. Conclusions on relationship conflict  In contrast to task conflicts, relationship conflicts exhibit a more stable negative effect on team outcomes. As such, research has largely focused on how to mitigate or prevent relationship conflicts. In terms of mitigation, avoidance may be a useful strategy for managing relationship conflicts (De Dreu & Van Vianen, 2001).



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Reducing emotionality can also help to mitigate the negative effects of relationship conflict on team outcomes (Jehn, Greer, Levine, & Szulanski, 2008). In contrast to task conflict, where much research has focused on the moderators of task conflict, relationship conflict research has focused more on the antecedents than the moderators: how can a team prevent relationship conflict before it begins? Diverse teams, particularly teams with faultlines (Thatcher & Patel, 2011), are likely more susceptible to relationship conflicts (Jehn, Greer & Rupert, 2008). Additionally, teams with a history of conflict (even task conflicts) and negative events, such as negative performance feedback, are likely to be at risk of developing performance‐detracting relationship conflicts (e.g., Amason & Mooney, 1999; Greer et  al., 2008; Kerwin & Doherty, 2012; Martínez‐Moreno et  al., 2012; Peterson & Behfar, 2003; van den Berg et  al., 2014). Future research on relationship conflict moving forward could examine, for example, how in difficult situations in teams, teams can take steps to make sure that conflicts and performance setbacks do not directly have to translate into escalated relationship conflicts in teams. Additionally, relationship conflicts could offer teams valuable insights into ways of interacting. Future research could focus on finding moderators for when teams might extract such insights from relationship conflicts. Research could also shift focus to more nuanced dependent variables, beyond team performance, such as social learning.

Process Conflict While process conflict is a more recently introduced type of conflict than task and relationship conflicts, there is already a large body of work surrounding it. A potential reason for this interest may be the huge effects of process conflict on teams – effects often much larger than either task or relationship conflict. In their meta‐analysis, De Wit and colleagues (2012) found process conflict to explain more variance in team outcomes than any of the other conflict types, and to be, by far, the most negative form of conflict for team performance. These large and negative impacts of process conflict were echoed by work by Greer and colleagues (2008) who found process conflicts to be the most long lasting form of conflict in teams. In a study of student teams, process conflicts at the start of the semester, particularly when unresolved, predicted all other types of conflict for the duration of the team, but the effects of initial task or relationship conflicts were not nearly as long lasting (Greer et al., 2008). Process conflicts are thought to be so strong and detrimental teams for several reasons. First, they are central to feelings of justice and equity, and thereby also strongly tied to negative emotions (Greer & Jehn, 2007), such as guilt (Chen & Ayoko, 2012). Indeed, Kerwin and Doherty (2012) found that process conflict was the only type of conflict (compared with relationship and task conflicts) to significantly correlate with negative affect in teams. Second, they are tied to power and resource control, as process conflicts are often bout the delegation of valued resources and responsibilities (Greer et al., 2011). And three, they often are not transparent – what people are verbalizing as the main issue is often not the real issue (Greer al., 2011). Process conflict is reliably harmful for teams  –  as noted in the De Wit et  al. (2012) meta‐analysis, all studies they included on process conflict showed a negative effect of process conflict on group outcomes. For example, work by Behfar and co‐authors (2011) shows that process conflict is negative for group coordination, group performance, and member satisfaction. This relationship also holds for different subtypes of process conflicts – both conflicts about logistics as well as conflicts about contributions were negative for all forms of group outcomes. Process conflicts have also been linked to lower quality group

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climate (trust, respect, cohesion; Jehn, Greer, Levine, & Szulanski, 2008), lower decision quality (Passos & Caetano, 2005), lower group creativity and innovation (Kurtzberg & Mueller, 2005; Matsuo, 2006), worse group productivity (Jehn et al., 1997), and reduced group viability (Jehn, Greer, Levine, & Szulanski, 2008). Despite these negative findings on process conflict, a few studies have identified a certain contexts in which positive effects of process conflict might be beneficial for groups – namely, during the early phases of group life. Goncalo, Polman, and Maslach (2010) proposed and found that process conflict that emerged within teams whom were not overly confident in the beginning phases of task work benefitted group outcomes. Similarly, Martínez‐ Moreno, González‐Navarro, Zornoza, & Ripoll (2009) found process conflict in computer‐mediated teams, also in the early phases of group life, benefitted group performance, as did Jehn and Mannix (2001) in their study of Master of Business Administration (MBA) student teams. And Jehn (1997), in her classic qualitative piece that introduced the notion of process conflict, noted that small amounts of process conflict, which promoted effective role assignment, could benefit group outcomes.

Moderators of the effects of process conflict In an effort to understand when process conflicts can be minimized, a number of studies have investigated moderators of the relationship between process conflict and team outcomes. For example, research suggests that subgroups within teams can exacerbate the negative effects of process conflicts (Greer & Jehn, 2007). Research has also looked at the role of member behaviors in moderating the relationship between process conflict and group outcomes. Greer and Jehn (2007) posited that process conflict harms team outcomes because of its effects on team emotionality. They find that process conflicts become less emotional when key emotional triggers in the team are reduced or managed – members are given high levels of voice, members do not perceive one another to be obstructing their goals, and members see themselves as a team rather than polarized subgroups. Other work also points to the importance of effectively managing and resolving process conflicts. Greer et al. (2008) find that conflict resolution efficacy can reduce the long‐ lasting negative effects of process conflict, and Jehn, Greer, Szulanski, and Levine (2008) also find conflict resolution efficacy to reduce the negative effects of process conflict on group emergent states (trust, respect, cohesion) and group viability. Lastly, group atmosphere has also been examined. One important part of group atmosphere comes from the setting in which interactions take place. Martínez‐Moreno and coauthors (2009) found both time and form of media usage to moderate the effects of process conflict – namely, they found process conflicts to be positive in face‐to‐face teams, and in computer‐mediated teams, they found process conflict to benefit performance in early stages, but to hurt performance when occurring in later team stages.

Antecedents of process conflict Several antecedents of process conflict have been identified. In terms of team composition, the most research has linked diversity to process conflict. Teams with value diversity (Jehn et al., 1997; Jehn & Mannix, 2001) or cultural diversity (Vodosek, 2007) tend to have higher levels of process conflict. In contrast, Ayub and Jehn (2014) found national variety (categorical differences in number of nationalities, as opposed to national distance or separation) to decrease process conflict. In investigations of other types of team composition, Greer and colleagues (2011) found teams composed of all high‐power individuals



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to have more process conflicts than teams composed of lower‐power individuals. This was especially likely when members did not have congruent perceptions about the intrateam hierarchy. In terms of team atmosphere and context, several aspects of the team environment have been shown to predict process conflict. Matsuo (2006) found that low customer orientation was associated with high levels of process conflict in sales departments in Japan, and Hobman et al. (2002) found that computer‐mediated groups, compared with face‐to‐face groups, expressed more process conflicts, especially in the early stages of team interaction. Jehn and Mannix (2001) found process conflicts to be lower when groups had higher levels of trust, respect, and cohesion, and higher when groups had high levels of competition.

Conclusions on process conflict Process conflict can be the most important form of conflict to manage in teams. As shown in meta‐analyses, process conflicts are the most detrimental form of conflict for team outcomes. They are also one of the most difficult to manage. Namely, process conflicts often arise because of underlying issues in the team members do not feel able to speak about. Frequently, process conflicts are a venue in which value disagreements or leadership contests play out, as members may express their frustrations or make their plays for power in more day‐to‐day discussions of logistical agreements, rather than in open discussions of deep and difficult issues. For example, debates over role allocations may often signify a lack of consensus about hierarchical roles in the team, and disagreements over work arrangements may stem from underlying differences in value sets. In a first initial study of this idea, van den Berg et al. (2014) find that process conflicts mediate the relationship between task conflict and relationship conflict. As task conflicts become emotional, members start to use process conflicts as a way to express their negative emotions and frustrations with the team, and this in turn gives rise to relational tensions in the team. In such situations, true, authentic disagreement with the process was not there – rather, process issues provided a venue where members could express their frustration and challenge team norms and structures. As such, process conflicts are often conflicts in disguise – the expressed content of process conflicts often does not reflect the real, underlying issues. Effectively managing such highly loaded conflicts, and unpacking the conflicts to resolve the real underlying issue, be it status concerns, equity, or spillover emotions from other conflicts, is critical.

Status Conflict Status conflict, conceptualized as a fourth type to add to Jehn’s (1997) tripartite typology, is defined as disputes over the relative status positions in a team’s social hierarchy (Bendersky & Hayes, 2012). Although a later addition to the typology, disputes over positions in the social hierarchy have long been a fundamental part of our understanding of teams. Status is unique from other forms of hierarchical rank in that it is socially conferred and thus an individual’s relative ranking relies upon consensus (Goldhamer & Shils, 1939; Emerson, 1962). Status hierarchies are thus simultaneously complex negotiated social orders (Owens & Sutton, 2001; Strauss, Schatzman, Ehrlich, Bucher, & Sabshin, 1963) and yet ubiquitous to team interactions (Gould 2003; Tiedens, Unzueta, & Young, 2007). Given the comparable benefits of high status (e.g. influence, information and resource access, work recognition; Berger, Rosenholtz, & Zelditch, 1980; Foschi, 2000; Friedkin, 1999; Ridgeway & Correll, 2006), it seems only logical that when individuals perceive

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the hierarchy as in flux, challenges to relative positions ensue (Bendersky & Hayes, 2012; Greer & van Kleef, 2010; Groysberg, Polzer, & Elfenbein, 2011; Hargadon & Sutton, 1997; Owens & Sutton, 2001; Porath, Overbeck, & Pearson, 2008). While research on the effects of status conflict is still emerging, several studies have documented the effects of status and power conflicts on team outcomes, finding highly consistent negative effects on team outcomes. For example, Chun and Choi (2014) found, in a study of 145 Korean work teams, that status in conflict was negatively related to team performance. Greer and Van Kleef (2010) found that power conflicts impaired conflict resolution in organizational teams as well as negotiating dyads. Relatedly, Tiedens and Fragale (2003) found that dominance competition heightened team emotionality. Taken together, these studies exhibit remarkable consistency in showing that status and power conflicts have adverse effects on team outcomes. Lastly, in the foundational piece on status conflicts, Bendersky and Hayes (2012) demonstrated the discriminant validity of status conflict from the traditional tripartite typology of task, process and relationship conflict across a series of studies, and found that status conflicts were negative for team performance – in their case, the grades MBA student teams received in their course.

Moderators of the effects of status conflicts Several moderators have been investigated for the effects of status conflict on performance. Early work on status competition demonstrates that while status obtained via political maneuvering harmed team performance, while status perceived as meritocratic served as an incentive (Loch, Huberman, & Stout, 2000). Sutton and Hargadon (1996) similarly found that status auctions served as an incentive for IDEO employees to generate more creative ideas during brainstorming sessions. IDEO’s status auctions may be more successful not only because they are perceived as more democratic and legitimated but also because the behavior status is used to reward is prosocial and task oriented. Individuals compete for status not only by asserting dominance, as captured in the status conflict scale, but also by demonstrating their competence, ingroup generosity and task commitment (Anderson & Kilduff, 2009), which suggests that the normative status striving strategy might act as a powerful moderator for the relationship between rank‐infused conflicts and team performance.

Antecedents of status conflicts Conflicts over power and status have been posited to be most likely to arise when members are motivated to protect or obtain positions of power and status (Greer, 2014). For example, Chun and Choi (2014) found that mean level of need for power was positively associated with status conflict in teams. Similarly, research on top management teams suggests that having too many high power individuals can generate more conflicts (Greer et al., 2011; Ronay, Greenaway, Anicich, & Galinsky, 2012), as individuals are more motivated to protect their valued high‐power positions. These conflicts are especially likely when there are subtle power differences among high‐power teams, such as management teams, where the combination of motivation (high‐power holders) and opportunity (a  hierarchy in which people can advance) exists. Greer and Van Kleef (2010) indeed found in a combination of field and in‐lab negotiation studies that high power teams with hierarchies had particularly high levels of intrateam power struggles. In dyadic interactions, when an individual has higher power but lower status, he or she is more likely to debase their partner when given the option, as compared to partners with both power and status or status alone but no power (Fast, Halevy, & Galinsky, 2012).



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This suggests that status differences can lead to greater interpersonal friction than power differences alone and that power without status is particularly damaging. This perhaps explains why, when a team member claims leadership, an assertion of high power, but this claim is not reciprocated via a grant from other team members, thus indicating the claimant’s low status, teams are theorized to similarly descend into conflict (DeRue & Ashford, 2010). Furthermore, the loss of status can be as instigatory as the absence of it. High‐status baseball players who lose in salary negotiations tend to perform worse in the season after the arbitration decision, even compared to lower status players with comparable negotiation losses (Marr & Thau, 2013). Similarly, when high‐status individuals are randomly subjected to lose their team’s respect in the laboratory, they perform worse than low‐status individuals losing similar amounts of respect (Marr & Thau, 2013).

Conclusion on status conflicts Although status conflict is a comparatively new form of conflict, it builds on a strong literature on the intersection of hierarchy and conflict in organizational contexts. Salient and contended status differences seem to be particularly problematic for team performance. Because status can fill fundamental individual motivations for esteem, standing, and belonging in a group (Anderson, John, Keltner, & Kring, 2001), conflicts over status are likely to be highly personal (indeed, Bendersky & Hayes, 2012, note a particularly high correlation between relationship and status conflicts). Status conflicts may, similarly to relationship conflicts, be best avoided or prevented. Research into how exactly status conflicts can be avoided or mitigated would be an important future direction for this research area. Additionally, more research is also needed into how status conflict fuels and feeds into other forms of conflict in teams. As Anderson, Srivastava, Beer, Spatarao, and Chatman (2006) show, overt claims to status are often avoided in groups, for fear of retributive punishment. Instead, status conflicts may instead be waged more indirectly, such as through conflicts over visible roles in the team or the control of valued resources, suggested that status conflicts may be a key driver and escalator of process conflicts in teams. This may explain in part why process conflicts are so difficult to resolve – the real issue, status, is not what is being explicitly discussed. Thus, future research into how status conflicts promote and escalate other forms of conflict, and how these root status conflicts can be successfully prevented or resolved would be a key important research direction for this area.

Alternative Perspectives on the Study of Intragroup Conflict While much has been written about the conflict types, there are also critiques of the types, including their close correlations, their focus on perception rather than behavior, and various measurement issues (e.g., whether the group mean of perceived conflict is meaningful; see Bendersky et al., 2014; Hamilton, Shih, Tesler, & Mohammed, 2014; Speakman & Ryals, 2010). A large body of research has emerged which investigates alternative approaches to the study of the conflicts in teams that go beyond simply looking at the effects of the level of a conflict type on team outcomes. Instead, this alternative body of work examines the inter‐relationships among the conflict types and the implications of individual‐level differences in behaviors and perceptions for the development and consequences of team‐level conflicts.

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Conflict transformation One of the most frequently discussed and researched problems with the conflict types is their high inter‐correlations (i.e., Bendersky et  al., 2014; De Dreu & Weingart, 2003; Simons & Peterson, 2000). As a result, the effects of one conflict type on a particular outcome are hard to distinguish from the effects of other conflict types on team outcomes. For example, the effects of task conflict are contingent on the presence of relationship conflict, with task conflict being positive for performance when relationship conflict is low (see the meta‐analysis by De Wit et al., 2012; also see an interesting study of conflicts over Wikipedia articles, which similarly shows task conflicts to become negative for performance at the point that they transform to relationship, or also process conflict  –  Arazy et  al., 2013), and negative when relationship conflict is high. Similarly, the effects of relationship conflict on group outcomes are also contingent on the presence of task conflict – relationship conflicts are worst for member affective outcomes when they occurred in isolation from task conflicts. When relationship conflicts co‐occurred with task conflicts, team members actually judged them as less upsetting, and experienced fewer physiological consequences for members (Meier et al., 2013). A growing body of research has begun to examine the situations in which the types are more or less likely to co‐occur. For example, task conflict has been shown to be less likely to co‐occur with relationship conflict in teams with high intragroup trust (Kerwin & Doherty, 2012; Peterson & Behfar, 2003; Simons & Peterson, 2000; Tidd et al., 2004), or when individual conflict episodes can be easily resolved (Greer et al., 2008), the conditions in which a ‘pure’ conflict exists, where there is only a single, clearly identifiable issue at hand being discussed are very narrow.

Individual behavior and the development of team conflicts Another key critique of the literature on the conflict types has been the lack of attention to the role of actual member behavior in conflicts in teams, and a too extreme focus on conflict types (i.e., Bendersky et al., 2014; Speakman & Ryals, 2010). In support of this standpoint, DeChurch and colleagues (2013), in a meta‐analysis, found that conflict behaviors explain important variance in team outcomes above and beyond the conflict types. They conclude that the conflict process – how teams fight – is just as, if not, as we argue, more, important, than the topic over which teams fight. They find that the effects of team conflict on team outcomes is highly determined by whether team members engage in collectivistic or individualistic behaviors during disagreements. When individuals manage conflicts in a collectivistic fashion, this appears to help team performance while individualistic conflict management strategies harm team performance. Several other articles have made similar points – team conflicts can be best understood by not just the content, but also the format of interaction during the conflict (Behfar, Peterson, Mannix, & Trochim, 2008; Weingart, Behfar, Bendersky, Todorova, & Jehn, 2014). Given this, a growing line of research has begun to focus on theory and research on the role of individual behaviors and emergent patterns of interactions in understanding group conflicts. By better focusing on how individuals think, feel, and behave when disagreements occur within their team and how these individuals processes compile into dyadic and eventually group‐level processes over time, researchers hope to gain more fine‐grained and predictive insights than is gleaned by only studying the content of conflict via the conflict typology. One of the first theoretical frameworks to emerge in this line of work is the process‐state model proposed by Korsgaard and colleagues (2008). They posit that group conflicts



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reflect a compilation process of individual differences, states, and behavior, interpersonal context, interaction, and sense‐making, and group‐level context, interaction, and sense‐ making. Their theoretical article was one of the first to suggest that intragroup conflicts are inherently multilevel processes, rather than just purely group phenomena. In their ensuing work, authors have begun to embrace this more multilevel perspective on the emergence of group conflicts, both theoretically and empirically. For example, in a theoretical article, Jehn and colleagues (2013) develop a model of conflict contagion in teams  –  the authors posit that dyadic conflicts spread to other members and come to ‘infect’ all team members through processes of emotional contagion, siding, and coalition formation. They propose that conflicts are most easily resolved and most likely to benefit performance when they are confined to the initial individuals with authentic disagreement, and before other members are swept up into the conflict because of spreading emotions or defending friends and allies. In related empirical work to this, Paletz, Schunn, and Kim (2011) suggest that microconflicts – short‐term, behavioral disagreements within a larger conversation – have less emotion and are more easily to resolve than full‐blown, long‐lasting team conflicts. Speakman and Ryals (2010) also speak to the multilevel nature of conflict in their theoretical paper describing how individual behavioral strategies impact conflict episodes over time in a team, and how these interactions are in turn impact by environmental conditions. In related empirical work, Jamieson, Valdesolo, and Peters (2014) find that when an individual expresses a dissenting task opinion – initiates a task conflict – the group members being disagreed with feel threatened and have avoidance cardiovascular responses. This suggests one mechanism by which conflicts may spread in teams, as well as a reason why task conflicts may also become personal. In related empirical work, Paletz, Schunn, and Kim (2013) examine the words used by individuals during group conflict episodes. They found that when individuals used within‐domain analogies when referencing their work, this highlighted representational gaps in multidisciplinary teamwork and sparked intrateam conflicts. In an interesting line of work linking the minority dissent literature with the team conflict literature, Jamieson et al. (2014) find that when one person engages in a task conflict in a team, this makes other group members feel more threatened and makes them exhibit restrictive, avoidant cardiovascular responses, showing support for the idea of Jehn et al. (2013) that conflicts may spread to the group level, and transform into other forms of conflict, through the spread of emotions after an initial conflict is expressed.

Compositional views of conflict A third critique of past studies of the conflict types has been that not all conflicts are experienced by all team members equally (Jehn et al., 2010), and that conflicts instead may emerge over time, spreading from dyadic disagreements to team‐wide conflicts (Jehn et al., 2013; Korsgaard et al., 2008). This suggests that both compositional and temporal perspectives are useful in understanding conflict in teams, and as the DeChurch et  al. (2013) meta‐analysis points out, individual behavioral choices may play a key role in determining not only how conflicts impact team outcomes, but how conflicts develop over time and come to be perceived, experienced, and engaged in by all team members. These emerging multilevel views on conflict raise the possibility that not all members in a team may experience a conflict equally – in fact, different compositions of conflict perceptions may exist in a group at any given point (Korsgaard, Ployhart, & Ulrich, 2014). A new and growing line of research has begun to explicitly examine this possibility, and its repercussions for team performance. Jehn and colleagues (2010) look at the effects of

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variance in perceptions of intragroup conflict on team outcomes in a study of MBA student teams. They find that asymmetric conflict perceptions within the team were associated with lower levels of team creativity and performance. They suggest that this is because when groups have asymmetric conflict perceptions, they are unable to resolve conflicts and thus, in order for conflicts to be resolved, perceptions of conflict in the team must first be symmetric. Similarly, Jehn and Chatman (2000) also find variance in within‐team conflict perceptions to negatively impact team outcomes in two organizational samples. Sinha and colleagues (2014) take the notion of asymmetric conflict perceptions a step further and introduce the notion of ‘skewed’ conflict, in which a minority of group members perceive much more conflict than the other group members. They find that such skewed task conflicts are actually positive for group performance in field studies conducted both in India and in the Netherlands. Namely, they theorize and find that when only a minority of members, or even a single member sees a conflict in a team  –  a positively skewed conflict – the member(s), due to the minority position, are forced to express their dissenting opinion in a careful and sensitive manner. This situation – in which there is carefully expressed minority dissent – they show may be the optimal form of task conflict in teams to achieve higher performance outcomes. In addition to looking at variance and skew, researchers could also examine the idea of kurtosis in conflict perceptions and behaviors (Sinha, et al., 2014). Kurtosis could provide information on the degree to which subgroups exist in terms of conflict perceptions in the teams, which may also be a potentially interesting form of conflict composition to study in the team.

Meta‐themes in the study of team conflicts Research on team conflict has a rich history. Few topics in the organizational behavior literature have generated as much research interest as intragroup conflict. We have considerable insight into the nature and dynamics of conflict in teams, and its effects on team outcomes. Conflict of all forms harms team affective outcomes (for meta‐analytic reviews, see De Dreu & Weingart, 2003; De Wit et  al., 2012). When considering performance outcomes, task conflict has the potential to be helpful under certain conditions, while process and status conflicts are more likely than not to harm all forms of team performance, across most types of teams and situations (for meta‐analytic reviews; see De Wit et  al., 2012; O’Neill et al., 2013). This research provides important insights for the teams literature, and suggests that researchers should focus on how to extract value from task debates, and minimize the co‐occurrence of other forms of conflict. In the past few years, a number of researchers began to address this question: how can teams bring out the benefits of conflict and avoid its pitfalls? Two key streams of research seek to answer this question. One line of research seeks to understand the moderators of conflict and team outcomes, including identifying the conditions under which the relationship between task and relationship conflict is likely to be reduced and task conflict is most likely to benefit team outcomes. Another related line of research seeks to identify the antecedent conditions of each types of conflict, with the guiding goal to find situations in which task conflicts are most likely to arise and other forms of conflict are less likely to occur. In this chapter, we have built on key conclusions from meta‐analyses of team conflict (e.g., De Wit et al., 2012; O’Neill et al., 2013) in order to review both the growing literature on conflict’s moderators and antecedents, as well as the new and emerging research directions. Two key themes emerged from our review of the work on the antecedents and ­moderators of the conflict types. First, research suggests that removing the personal and emotional components from team conflicts, can take the sting out of conflict’s



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effects on team outcomes. When conflicts, especially task conflicts, can be discussed without engaging personal feelings, and thus remaining less emotional, conflicts are more likely to benefit team outcomes (e.g., Bradley et al., 2013; Curşeu et al., 2012; Jehn, Greer, Levine, & Szulanski, 2008). Relatedly, conflicts that are inherently personal and emotional in nature, such as relationship conflicts, will always harm team outcomes (e.g., see the De Wit et al., 2012 meta‐analysis), and they should be avoided if possible in teams (De Dreu & Van Vianen, 2001). A key theme that emerges then across this growing body of research is the role of team norms in promoting work‐focused task conflicts and reducing emotional and personal debates in teams. We discuss the role of conflict norms in more depth below. A second key theme that we identify based on our review of the moderators and antecedents of conflict in teams is the underlying role of status disagreements and power struggles in giving rise to and moderating the effects of conflict on team outcomes. The meta‐analysis by De Wit et al. (2012) finds that process conflicts are more negative for team outcomes than other forms of conflict, such as task and relationship conflicts. One reason process conflicts may be so harmful to team outcomes is that process conflicts can serve as a venue for expressing other underlying issues in the team, such as status disagreements (Greer, 2014). Given the fundamental role of power and status concerns in social interactions (Greer, 2014) and in conflict in particular (Greer & Bendersky, 2013), we argue here that power and status concerns often instigate and guide conflict in teams. Research has already begun to identify and investigate exactly how power and status guide conflicts, including work by Bendersky and Hayes (2012) on the role of status conflicts in teams, but more attention to the critical role of power and status in team conflicts is needed. We highlight the key directions surrounding this theme below.

The role of norms: Encouraging task debates and  minimizing relationship conflicts The first key theme we focus on as emerging from our review of the moderators and antecedents of team conflicts is the role of team conflict norms, and particularly, norms which encourage work‐related conflicts and discourage behaviors or topics which could lead conflicts to become emotional and personal. Several research streams have touched on the importance of a team’s normative climate in situations of team conflict. In particular, research has focused on the effects of whether the team environment allows for open conflict expression (e.g. Amason & Sapienza, 1997; Jehn, 1995; Jehn, Greer, Levine & Szulanski, 2008), and the cultural framing of the conflict (Gelfand et al., 2001), as well as looking at the effects of team conflict processes and behaviors rather than states alone (DeChurch et al., 2013). However, much of intrateam conflict research often uses a team’s normative climate as an unspoken actor in the research context. Intrateam conflict research relies on teams holding relatively unified perceptions of what constitutes a conflict and how to correctly express conflict in the team setting. Learning about these team norms could help to better understand how conflict perceptions affect team outcomes. One team member’s intense argument can be another’s lively discussion. Team members must try to learn and adapt to each other’s schemas of “conflict” in order to share perceptions of team dynamics. For example, imagine a group meeting in which a disagreement takes place between two group members. This disagreement could be somewhat ambiguous to other group members and could be encoded in each member’s memory differently depending on the team member’s conflict schema. One team member might have a conflict schema that requires raised voices in order for a conflict to have occurred, while another might be so sensitive to conflict that anything other than full agreement qualifies as a conflict‐laden meeting. Research has begun to investigate asymmetries in

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conflict perception (Sinha et al., 2014) but research could further explore how different schemas of conflict interact in affecting team member performance and satisfaction. For example, future research could examine whether teams that are less conflict sensitive, which is to say, have a schema that sets a high bar for what disagreements constitute a conflict, perform better, due more open expression of conflict, or whether they perform worse, due to a likely higher instance of expressed disagreements. In addition, little research has examined how new team members learn the existing teams’ schema for conflict and how successfully new group members learn to find the correct style of expressing disagreement. When one team member disagrees with another, he or she has a variety of ways of expressing (or censoring) that disagreement, some of which will be more normatively acceptable to the team. Research has shown, for example, conflicts framed as based on concern for the team (collectivistic) have better outcomes than those framed in a more self‐oriented fashion (individualistic; DeChurch et al., 2013). Future research could examine further normative differences in conflict expression. For instance, it is an open question whether more direct expression of disagreement is helpful to group performance. On the one hand, groups that judge direct expression of disagreement as valuable may benefit from more clarity and authenticity during discussions. On the other hand, groups that value more polite and indirect expressions of disagreements, (e.g. Asking “Isn’t that expensive?” rather than saying the more direct, “I think that’s too costly an option.”) could have better affective relations between members because they express disagreement less confrontationally.

The role of status concerns: Understanding the heart of process and status conflicts A second key theme that emerges from our review of the moderators and antecedents of team conflict is the omnipresent role of status concerns in teams. Research findings indicate that process conflicts are potentially even more negative for team performance than other conflict types (De Wit et al., 2012), and potentially the most difficult to manage and contain (Greer et al., 2008). We build here upon empirical evidence to argue that this is because process conflicts are often a venue in which underlying status concerns are expressed in teams. Process conflicts about role assignments and meeting times often are about issues that are symptomatic of larger concerns of equality and equity in the team (Greer & Jehn, 2007), and as such provide a work‐related topic in which members can vie for better position in the team or make clear their dissatisfaction with the team hierarchy. In order for such process conflicts to be resolved, the nature, role, and dynamics of these underlying status concerns in teams needs to be understood. However, the large literature on status and status hierarchies is only just starting to be integrated into research on team conflict (Greer & Bendersky, 2013). The most notable examination of the interplay between status hierarchies and team conflict comes from Bendersky and Hayes’ (2012) conceptualization of status conflict. Status conflict in  teams might arise for two different and distinct reasons: status competition or status  misperception. Given that both determinants would lead to a decline in team performance, it would be tempting to disregard the distinctions in determinants so long as we can accurately capture the downstream consequences in the form of conflict, but this impulse would seriously hamper efforts at ameliorating team dynamics. A  team with a status misperception problem would need a very different intervention from one with a status competition problem; the first might simply need clarity about the order of command, while the s­ econd needs to deal with questions of ambition and the basis for eventual advancement within the team. An interesting research question



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for future research on team ­conflicts might therefore be how well individuals within the team perceive the intrateam status hierarchy. Previous research has suggested that people often self‐aggrandize on a variety of dimensions, including their intelligence (Kruger & Dunning, 1999), physical abilities (Dunning, Meyerowitz, & Holzberg, 1989), and physical attractiveness (Heine & Lehman, 1997). In evaluating one’s position in the hierarchy, an individual must read social cues from their team members indicating how much the team likes the individual interpersonally and how much the team perceives the individual to have contributed to the valued goals of the team (Anderson & Kilduff, 2009; Fiske, Cuddy, Glick, & Xu, 2002). For example, when an individual is placed in charge of a team project, this serves as a positive social signal that one is of high status. Similarly, if one is frequently interrupted when speaking, this could serve as a negative social signal that one is of lower social status. In addition, in order to perceive the full status hierarchy, individuals need to not only perceive signals relevant to themselves, but also signals given and received by other team members. But empirical correlations between objective measures such as rank in an organization or positional power in a negotiation, has only shown small, albeit significant, correlations with subjective ratings of self‐perceived rank (Anderson & Spataro, 2005). This suggests that status misperception, or the incorrect aggregation of status signals, can occur in along two orthogonal dimensions. The first dimension describes whether the misperception is one of one’s own position or the position of other members in the network. The second dimension captures whether the misperception was an under or overestimation. Self‐effacement thus describes misperception in which the focal actor believes he or she is lower in the team status hierarchy than he or she actually is. Self‐ enhancement describes the opposite extreme, a misperception in which the focal actor believes that he or she is higher in the team status hierarchy than he or she actually is. Other‐effacement or other‐enhancement describes the parallel processes of misperception of a third party (or parties) by the focal actor. Previous research however has only focused on self‐enhancement misperception. When an individual oversteps his or her position, other team members send negative social cues to punish individuals the team believes act immodestly or rudely (Anderson et  al., 2006). But self‐effacement and other‐based misperception might be equally as damaging. Research suggests that status perception is linked with confidence and self‐esteem (Anderson & Spataro, 2005) and as such it is possible that those who underestimate their status will be less‐productive team members because they will not take on sufficient leadership duties. In addition, third‐party misperception could hinder coordination tasks because team members who misperceive others’ status will fail to employ their team members effectively. Status misperception along both dimensions can lead to incidents of team conflict and lowered task performance. Understanding status perception in teams and its relationship to team conflict, particularly the strongly negative form of process conflict, is an important future direction for research on ­intrateam conflicts.

Future Research While substantial progress has been made in understanding intragroup conflict in teams, there remains much to learn. Namely, as our understanding of conflict has expanded, new avenues of research have opened up. We highlight here several such fruitful avenues for future investigations of intra‐team conflict, including multilevel models, conflict asymmetry, conflict norms and the role of status in team conflict.

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One key area of research on intragroup conflict that is opening up involves multi‐level conceptualizations of conflict. Research has only just started to harness new methods of examining perceptions and structures of conflict in groups. Increasing our knowledge in this area is critical as team conflicts take place within multilevel systems, yet they have frequently been only examined as a team‐level construct in isolation from individual‐level variables and firm‐level structures. Future research can expand the nascent literature on multilevel modeling of team conflict in order to facilitate a broader, multisystem understanding of conflict in teams. Relatedly, perceptions of conflict are rarely uniform within teams. Often, individual perceptions of conflict in teams may be asymmetric, such that individual members within a team perceive the same conflict differently. Research has begun to examine how varied perceptions of conflict (i.e. high variance in individual‐level perceptions) or skewed perceptions of conflict (i.e. high skew in individual‐level perceptions) can influence teams’ outcomes. By better understanding different perceptions of conflict and the forms and patterns these distributions of individual perceptions can take a the team‐level, research can gain insight into how to best address these different perspectives for improved team functioning. In addition to these multilevel perspectives on conflict, we also strongly advocate for what we see as two key emerging directions for the study of team conflict at the conceptual level: namely, the role of social norms and status in understanding the nature, causes, and effects of conflict in teams. Research has only begun to examine how norms around conflict are created within teams, but research does suggest that norms encouraging open expression of conflict can be beneficial to team performance, such as by improving how conflicts are expressed and thereby impact team dynamics (e.g., Jehn, Greer, Levine, & Szulanski, 2008). However, norms may also play a role in determining the types and nature of conflict that arises in teams in the first place, and may also have implications for the degree to which conflict types transform to other types during group discussions. By examining how norms around conflict take shape and evolve over time, research can shed light on how teams can most productively handle naturally occurring disagreements and avoid the arise of unproductive disagreements, such as process and relationship conflicts. Social norms are powerful forces in social interactions and thus a better understanding of their dynamics can help to positively shape relationships within teams. Finally, research on the role of status in team conflicts could greatly benefit our understanding of conflict in teams. A better understanding of status in teams could help us understand the emergent construct of status‐conflicts, including understanding how status structures and perceptions and teams can give rise to status conflicts, and how status structures may exacerbate or ameliorate disagreements over status in teams. Additionally, a better of understanding of status in teams can also help to understand conflicts on a variety of other topics as well. For example, status can exacerbate otherwise minor disagreements or cause conflicts of other forms to arise (i.e., process conflicts) as a venue to joust over status. Understanding how status can give rise to such conflicts may help give insight into how to ultimately resolve the traditionally most destructive and most difficult to resolve conflicts in teams.

Conclusion Intragroup conflict is arguably one of the most important behavioral processes in teams. A large and prospering body of research has consistently shown that conflict can explain important variance in team outcomes. While conflicts about personal tensions or underlying status concerns can harm team outcomes, debates over work‐related matters can improve



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team outcomes. In our chapter, we reviewed the proliferation of articles that have aimed to understand the moderators and antecedents of team conflicts, with the hopes of understanding the conditions that give rise to productive team conflicts and minimize destructive team conflicts. We find two key themes to have emerged in the literature. Firstly, teams which have evolved norms, either stemming from the composition of individual members, member behaviors in conflicts, or the team atmosphere, which encourage the expression of open, cooperative, non‐emotional task debates are more likely to be reap the benefits of conflict in teams. Secondly, status concerns are an insidious challenge to teams, and often may explain why more destructive conflict forms arise in teams, such as process conflicts. Teams that can identify and manage such status concerns are also more likely to successfully navigate the perils and opportunities of conflict in teams. As research in this area moves forward, with more attention to the development of such conflicts from individual motivations to group level processes (with potentially varying levels of member involvement), the role of individual conflict behaviors, and the inter‐relation among the conflict types, we hope that researchers will continue to build on the key themes we have identified here, and that we as a field can reach consensus on the understanding and management of conflict in teams.

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15

Team Leadership Daan van Knippenberg

Introduction Leadership has a long history in behavioral research. Despite the huge volume of research in leadership, however, there are surprisingly few studies that explicitly engage with leadership of teamwork. A review of the 1985–2009 body of leadership research by DeChurch, Hiller, Murase, Doty, and Salas (2010) showed for instance that only 11% of leadership studies focused on the team level of analysis – and this is likely to be an overestimation for the leadership field as a whole, given that this excludes roughly 90 years of leadership research before the review period in which the team level of analysis likely was even less common (cf. Humphrey & Aime, 2014). Most leadership research is either agnostic about whether the individuals targeted by leadership engage in teamwork or treats teamwork as little more than a context where a group of people needs to be collectively led. Only a relatively modest number of leadership studies explicitly engage with teamwork to ask what is required from leadership to motivate effective team process and performance. It is this body of research with an explicit team focus that the current chapter aims to review. Roughly speaking, this body of research can be divided into two main streams of research. On the one hand, there is a stream of research that applies generic leadership models that are not specific to teams and that mostly concentrates on charismatic‐transformational leadership. On the other hand, there is a stream of research that has been developed with a more specific focus on teams and that revolves largely around empowering leadership and shared leadership. The potential advantage of the former approach over the latter to team leadership is that it may contribute more to broader ranging theories of leadership that apply to team and non‐team contexts alike. The potential advantage of the latter approach over the former is that it may generate a more complete picture of the requirements of team leadership by explicitly addressing team‐specific aspects of leadership that fall outside of the scope of more generic leadership models. I therefore aim to take stock of research in team leadership with an eye on reaching conclusions not just about what we know The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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from empirical research but also about the relative merits of both approaches. Following from this, a key element of this chapter is to identify a way forward – to boldly pass judgment and identify some approaches as more promising than others, and some questions in particular need of research attention. Presaging things to come, the perhaps inevitable conclusion is that generic models have their value up to a point – motivating and inspiring teams is not so different from motivating and inspiring individuals in non‐team contexts – but that team‐specific leadership models have value added above and beyond generic models in particular for those team contexts that seem to hold most excitement for most team researchers and practitioners – complex, knowledge‐intensive work with clear decision making, problem solving, and creative ­components. As important directions for future research I highlight the further development of the team‐specific perspective including empirical attention to issues that currently have mainly been addressed conceptually.

Teams and Leadership Effectiveness: Setting the Stage Teams are groups of people collectively responsible for the performance of a job or task  –  an output at the team level. Teams are increasingly popular in organizations (Ilgen, Hollenbeck, Johnson, & Jundt, 2005; West, Tjosvold, & Smith, 2003). A core reason, perhaps the core reason, for this growing popularity of team‐based work seems to be the expectation that synergies can be achieved through teamwork that result in levels of performance that could not be reached when organization members work more independently – diverse knowledge and perspective and unique contributions of members with different roles are expected to lead to higher‐quality performance (e.g., greater creativity and innovation in research and development teams, better decision making in management teams, superior health care in primary care teams). This suggests the simple observation that teamwork is different from individual work and that therefore the leadership that is most effective to motivate and guide effective team processes may not be the same as the leadership that is most effective in motivating and guiding individual performance (cf. Hackman, 2002) – and even it if was, this is not so self‐evident that we should not address the question by looking at the empirical evidence. Indeed, the growing popularity of teamwork renders the question of what makes for effective team leadership of growing importance. As behavioral research goes, leadership is a research area with a long tradition, with research into leadership effectiveness dating back to the 1900s (e.g., Judge, Bono, Ilies, & Gerhardt, 2002). Even so, leadership research traditionally has paid only modest attention to teamwork, either studying leadership effects at the level of the individual follower, or studying team outcomes (e.g., team performance) without any attention to the processes unique to teamwork (i.e., team member relationships, knowledge exchange and integration, etc. e.g., Yukl, 2002). In part, this probably reflects the more recent popularity of team‐ based organization of work (i.e., a substantive body of research in leadership predates this emphasis on teamwork). In part, it may also reflect the traditional emphasis on teamwork as relatively autonomous – by implication leaderless (e.g., Cohen & Ledford, 1994; Manz & Sims, 1993). Moreover, with an established body of research in non‐team leadership, it is perhaps only reasonable to take leadership models developed outside of the team contexts as a starting point for the study of team leadership. Yet, even relatively autonomous, self‐managing teams are subject to a leadership structure (i.e., with a leader external to the team; e.g., Kirkman & Rosen, 1999). ­Moreover, many teams have some level of internal leadership structure. Indeed, a­ uthority



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differentiation is one of the key dimensions underlying differences between teams (Hollenbeck, ­Beersma, & Schouten, 2012). Understandable as perhaps it may be that team leadership was t­ raditionally not high on the agenda in leadership research or team research, the question what makes team leadership effective is a highly relevant one given the growing attention to teamwork. At an abstract level of analysis, there at least is evidence that team leadership matters. In a meta‐analysis, Burk et al. (2006) showed that the range of l­eadership behaviors covered (transformational, transactional, initiating structure, consideration, motivational, boundary spanning, empowering) were all related to team effectiveness suggesting that clearly it is preferable that team leaders show leadership in one form or another (even when the subjective survey ratings of leadership also open up the possibility that leader ratings are caused at least in part by team effectiveness; cf. Lord & Maher, 1991). In this chapter, I aim to take stock of what we can say about team leadership effectiveness on the basis of the published empirical research in team leadership. To this end, I understand evidence as pertaining to team leadership when some aspect of leadership – leader characteristic or behavior – is related to team‐level outcomes. That is, the level of analysis plays a key defining role in this understanding of team leadership, and studies of leadership processes in team contexts that target individual or dyadic outcomes are not considered as studies of team leadership in this review (e.g., Nahrgang, Morgeson, and Ilies’s 2009 study of the development of leader–member relationships in teams would not be considered as a study of team leadership) nor are studies where leadership is considered as the outcome of interest (e.g., emergent leadership; Judge et al., 2002). This is not to say that such studies cannot be relevant to team leadership, but from that broader perspective any leadership study can be relevant to team leadership and it is beyond the scope of this chapter to review the entire leadership literature through the lens of implications for team leadership. Leadership effectiveness is understood as indicators of leaders’ success in mobilizing and motivating followers to contribute to the achievement of collective objectives (e.g., Yukl, 2002). Team leadership effectiveness would also be understood in those terms, with the qualification that I focus here on indicators of leadership effectiveness at the team level of analysis. Such indicators can include shared psychological states such as collective efficacy and team processes such as cooperation, but typically – or ideally – these would be seen more as mediating variables than as the outcome of ultimate interest. The latter would be more behavioral such as team performance, creativity, or decision quality (cf. Kaiser, Hogan, & Craig, 2008; van Knippenberg, 2012) – behaviors that would flow from team process and shared psychological states. In the following section, I review empirical evidence concerning team leadership effectiveness. This review has no claims to being exhaustive. For one, it only includes published work, and even there it does not claim to have included all studies that could be included. What I do hope to achieve with the review is an assessment that is representative of the state of the science in team leadership. Following the review, I focus on the key conclusions from the review, with explicit attention to the research agenda suggested by these conclusions  –  where in particular is more research needed? To structure the review, I follow a rough distinction between generic leadership approaches that were not developed with an eye on understanding team processes specifically and team‐specific approaches that were developed to speak to the challenges of teamwork (e.g., stimulating cooperation, coordination, integration of contributions, etc.). I am the first to recognize, however, that to some extent this generic versus team‐specific distinction is sometimes somewhat of a forced‐fit approach with studies mixing elements of both. The distinction should be regarded as a convenient structure and not as a conceptual insight.

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Generic Approaches to Team Leadership Charismatic–transformational leadership Since the 1980s or so, leadership research has been heavily dominated by studies of charismatic–transformational leadership (Bass & Riggio, 2006; Conger & Kanungo, 1987; Shamir, House, & Arthur, 1993). Some researchers argue that these are different types of leadership, but both conceptually and empirically their similarity is so overwhelming to conclude that essentially they are the same thing – and also encompass such alternative labeling as inspirational and visionary leadership (van Knippenberg & Sitkin, 2013). Generic approaches to team leadership are no exception in this respect, and largely revolve around the label of transformational leadership. At first review, there seems to be a good case that transformational leadership is an ­effective form of team leadership. This is perhaps most concisely seen from a series of meta‐analyses consistently linking transformational leadership to team effectiveness (Burke et al., 2006; Stewart, 2006; Wang, Oh, Courtright, & Colbert, 2011). On closer inspection, however, research in transformational leadership is close to worthless in understanding team leadership (or indeed leadership more generally). The problems with ­charismatic–transformational leadership research that lead to this conclusion are well documented. Most recently, van Knippenberg and Sitkin (2013) outlined these as including the following. First, the only available definitions of charismatic–transformational leadership include the leadership effectiveness outcomes it is proposed to predict. That is, charismatic–transformational leadership is effective by definition, rendering the study of its relationship to leadership effectiveness conceptually meaningless. Second, models of charismatic–transformational leadership are multidimensional but fail to specify what the different dimensions have in common that together makes them charismatic– transformational and what differentiates these dimensions from other aspects of leadership that are seen as not charismatic–transformational. Moreover, these models do not specify how the different dimensions combine to form charismatic–transformational leadership. The measurement practice of simply taking their average suggests the not obviously most logical model in which the absence of leadership along the one dimension can be compensated by abundance of leadership along another dimension for unclear and unarticulated reasons. Third, all available measures of charismatic–transformational leadership are invalid in that they confound leader behavior with the effects they are supposed to predict, fail to reliably reproduce the proposed multidimensional structure, and fail to achieve empirical distinctiveness from aspects of leadership that are argued to be not charismatic–transformational (most striking here is the r = .80 meta‐analytic correlation between transformational leadership and the prototypical non‐transformational  –  but transactional  –  leadership dimension of contingent reward; DeRue, Nahrgang, Wellman, & Humphrey, 2011). These problems in combination led van Knippenberg and Sitkin (2013) to conclude that research in charismatic–transformational leadership is a total loss: there is no valid theory to develop valid measurement, and because of the invalid measurement there is no useful empirical knowledge base from which to more inductively build better theory. In view of these problems, there is little point in an attempt to define charismatic– transformational leadership (or its supposed counterpoint, transactional leadership) here. It seems more to the point to note that the existing base of published field studies in charismatic–transformational leadership can perhaps best be understood as concerning the subjective perception that a leader is effective (indeed, as a case in point, correlations between charismatic–transformational leadership measurement and subjective evaluations of leadership are so high to suggest they are by and large the same thing; a conclusion that is



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corroborated by the fact that leadership items include the very leadership evaluations they are used to predict; van Knippenberg & Sitkin, 2013). The following review is therefore included to present a representative picture of empirical research in team charismatic– transformational leadership and not as a basis for conceptual conclusions. Given the meta‐analytic evidence linking transformational leadership to team effectiveness, it is not surprising to find a series of empirical studies advancing that conclusion for such ­effectiveness indicators as team performance and ratings of team effectiveness (Braun, Peus, Weisweiler, & Frey, 2013; Colbert, Kristof‐Brown, Bradley, & Barrick, 2008; Flood et al., 2000; Hur, van den Berg, & Wilderom, 2011; Pillai & Meindl, 1998; Schaubroeck, Lam, & Peng, 2011; Schippers, Den Hartog, Koopman, & van Knippenberg, 2008; Wang & Howell, 2010; Williams, Parker, & Turner, 2010; but see Eisenbeiss & Boerner, 2010; Hambley, O’Neill, & Kline, 2007; also see Pirola‐Merlo, Hartel, Mann, & Hirst, 2002, applying conventional levels of significance). Likewise, studies link transformational l­eadership to subjective ratings and behavioral process measures that could not only be seen as indicators of effectiveness but also as states or processes mediating leadership effects on team performance outcomes. Examples include trust in team members, team efficacy, and team commitment (Arnold, Barling, & Kelloway, 2001), s­ervice climate (Liao & Chuang, 2007), safety climate strength (Luria, 2008; cf. Zohar, 2002), team behavioral integration, decentralization, risk‐taking propensity, long‐term compensation, and corporate entrepreneurship (Ling, Simsek, Lubatkin, & Veiga, 2008), and ambidexterity and learning culture (Nemanich & Vera, 2009). Van Knippenberg and Sitkin (2013) noted that an apparent consequence of the absence of a sound conceptualization of charismatic‐transformational leadership seemed to have resulted in a true proliferation of moderating and mediating variables being identified in the relationship between charismatic‐transformational leadership and indicators of leadership effectiveness with little or no attempt at integration across studies. Their non‐exhaustive review identified 52 different mediating variables and 58 different moderators. This is a state of affairs that could be criticized for lack of parsimony if not for lack of plausibility. The latter is a concern indeed because most mediators and many moderators identified are subjective‐evaluative ratings that are likely to be correlated substantially with mediators and moderators identified in other studies. Mediating variables  Team transformational leadership research is representative of this broader picture in these respects. Mediating variables identified include empowerment, cohesion, and collective efficacy (Jung & Sosik, 2002), team potency (Schaubroeck, Lam, & Cha, 2007), value congruence (Brown & Treviño, 2006), goal importance congruence (Colbert et  al., 2008), shared vision and team reflexivity (Schippers et  al., 2008), team creative efficacy (Shin & Zhou, 2007), team commitment and information elaboration (Kearney & Gebert, 2009), interpersonal norms (Williams et  al., 2010), cognitive and affect trust, team potency, psychological safety (Schaubroeck et  al., 2011), cooperative approach to conflict and team coordination (Zhang, Cao, & Tjosvold, 2011), trust in team (Braun et  al., 2013), support for innovation (Eisenbeiss, van Knippenberg, & Boerner, 2008; Chen, Farh, Campbell‐Bush, Wu, & Wu, 2013), advice network density (Zhang & Peterson, 2011), and participative safety (Nijstad, Berger‐Selman, & De Dreu, 2014). Some of the variation in mediating variables identified can be understood from a focus on different outcome variables, and some concepts go by different names even when they are strongly overlapping. Collective efficacy and team potency for instance can be understood to by and large concern the same psychological state, and a focus on collective efficacy/potency in studies of team performance (e.g., Schaubroeck et al., 2007) is consistent with a focus on team creative efficacy in a study of team creativity (Shin & Zhou, 2007).

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Even acknowledging such similarities (e.g., also between shared vision and goal importance congruence; between psychological safety and participative safety; between different aspects of trust) and diverging outcomes of interest (e.g., the focus on support for innovation is only found in studies of team innovation), it seems fair to say that there is not a clear mediation model emerging from these studies with both a range of psychological states and a range of behavioral processes identified as mediators that cannot be accounted for only, or even largely, by conceptual overlap or differences in the outcome variables of interest. From this perspective then, even when measures of transformational leadership are essentially uninformative about leader behavior, these studies do clearly identify a need for more developed process models of team leadership influences. It may be tempting to suggest that attitudinal responses to leadership feed into team behavioral responses that in turn feed into team outputs like task performance, creativity, and innovation. Indeed, studies of multistep mediation tend to follow this logic. Schippers et al. (2008) for instance identified shared vision as a shared state as the first step mediator and the interactive process of team reflexivity as the second stage mediator between transformational leadership and team performance, Kearney and Gebert (2009) focused on the attitudinal concept of commitment as the first stage mediator and the team interaction process of information elaboration as the second stage mediator, and Zhang et al. (2011) identified the more attitudinal cooperative approach to conflict as the first step mediator and the more behavioral team coordination as the second step mediator between leadership and team performance. Yet, conceptual analyses of the dynamics of team emergent states and team interaction processes suggest that the influence needs not be unidirectional from attitudinal responses to behavioral responses. Rather, team emergent states (e.g., shared understanding of the team and its task) and team interaction processes may mutually influence each other such that the nature of team interaction shapes emergent states as much as emergent states shape the nature of team interaction (Morgeson & Hofmann, 1999). A more dynamic way to understand team leadership, then, would be one in which leadership shapes both team emergent states and team interaction processes as well as the way they mutually impact each other (cf. van Knippenberg, van Ginkel, & Homan, 2013). Obviously, these suggestions are not uniquely tied to charismatic‐transformational leadership. Indeed, as per van Knippenberg and Sitkin (2013), they are better followed up through another focus than charismatic‐transformational leadership. Yet, the proliferation of mediators found here is particularly illustrative of the need for better team process models to understand how team leadership may influence team effectiveness. Moderating variables  Research on charismatic–transformational team leadership is also illustrative of the lack of integration and guiding theory in the moderators considered. Moderating variables studied in team charismatic‐transformational leadership research include power distance and collectivism (Jung & Avolio, 1999; Schaubroeck et al., 2007), typical versus maximum performance (Lim & Ployhart, 2004), research versus development projects in research and development teams (Keller, 2006), educational and demographic diversity (Kearney & Gebert, 2009; Kunze & Bruch, 2010; Shin & Zhou, 2007; cf. Greer, Homan, De Hoogh, & Den Hartog, 2012), age difference between leader and team (Kearney, 2008), climate for excellence (Eisenbeiss et al., 2008), virtual versus face‐to‐face teams (Purvanova & Bono, 2009), geographical dispersion (Joshi, Lazarova, & Liao, 2009), instant messaging versus virtual world communication mode (Kahai, Huang, & Jestice, 2012), physical proximity (Varella, Javidian, & Waldman, 2012), contingent performance rewards and social integration (Jansen, George, van den Bosch, & Volberda, 2008), weak or poor organizational climate (Zohar & Luria, 2010), agreement in leadership



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perceptions (Cole, Bedeian, & Bruch, 2011), member core self‐evaluations (Zhang & Peterson, 2011), and minority dissent (Nijstad et al., 2014). Again, we may recognize that there are certain overlaps here such as in the study of different moderators related to communication mode (e.g., virtuality, geographical dispersion) or of diversity (e.g., different forms of diversity, minority dissent) and that there are differences that could be explained by a focus on different outcome variables (e.g., innovation versus performance). Again, however, these similarities and differences do not tell the whole story and the diversity of moderators studied seems illustrative of the lack of integrative theory about the team processes leadership should influence  –  and for which moderating influences should thus be identified. Stronger process theory to link team leadership to team effectiveness thus also seems key in developing theory about moderating influences. Again, this is a conclusion that is not unique to charismatic–transformational leadership even when research in charismatic‐transformational leadership gives most obviously rise to it. Again, it is also a conclusion that would be better followed up with a focus other than charismatic– transformational leadership. In sum then, even when research in charismatic‐transformational leadership is not informative about the nature of team leadership (as per van Knippenberg & Sitkin, 2013), the body of team charismatic‐transformational leadership research gives rise to the conclusion that more integrative team process theory is needed to advance team leadership research. Part of the problem here is that this is not just a challenge of team leadership research, but of team research at large – a strong team performance theory is currently lacking.

Other generic approaches Directive‐participative leadership  Even when charismatic–transformational leadership is the road most traveled in generic approaches to team leadership, there are a number of studies applying other generic behavioral approaches to team leadership. Several of these studies focus on a contrast between leadership that gives more agency to team members such as participative, supporting, or coaching leadership and leadership that puts agency firmly with the leader such as directive leadership and performance management. This focus is potentially important, because the presumed benefits of teamwork are associated with a relatively high level of team agency and proactivity (e.g., Kirkman & Rosen, 1999). Durham, Knight, and Locke (1997) experimentally compared directive versus coaching leadership and found that teams with coaching leaders performed better; the relationship was mediated by the quality of tactics used. In related vein, DeRue, Barnes, and Morgeson (2010) found that directive leadership led to higher team performance than coaching when leader charisma was low but lower performance when charisma was high, and that directive was more effective with high team member self‐efficacy and less effective with low efficacy; relationship that were mediated by effort. Somech (2006) found that participative leadership was more effective in stimulating reflexivity and innovation with functional diversity, but less so in stimulating in‐role performance, whereas directive leadership was more effective with low diversity in stimulating reflexivity. Larson, Foster‐Fishman, and Franz (1998; also see Larson, Christensen, Franz, & Abbott, 1996) showed that participative leadership as compared with directive leadership resulted in more information exchange. Focusing on the related concepts of coaching and facilitative leadership, Edmondson (2003) found that team leader coaching stimulated speaking up and preparation for team meetings, and Hirst, Mann, Bain, Pirola‐Merlo, and Richter (2004) observed that facilitative leadership predicted team reflexivity which mediated leadership’s relationship with team performance.

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The potential benefits of participative over directive leadership in mobilizing diverse informational resources are consistent with early work by Vroom and colleagues even when the Vroom models themselves are poorly supported. Vroom and Yetton (1973) and Vroom and Jago (1988) suggested that the effectiveness of more participative or more directive leadership would be contingent on such factors as knowledge differences between leader and followers and leader‐follower agreement. Research reported by Vroom and colleagues showed some support for these models, but the stronger tests that do not rely on managers’ self‐reports and dichotomous comparisons like Vroom’s work tend to yield weaker evidence (e.g., Field, 1982; Field & Andrews, 1998, Field & House, 1990) to the extent that it seems fair to conclude that support for the Vroom models is largely contingent on poor‐quality evidence (cf. van Knippenberg, 2014). Wendt, Euwema, and van Emmerik (2009) found that directive leadership negatively and supportive leadership positively related to team cohesiveness, and more so in individualistic as compared with collectivistic national cultures. Drach‐Zahavy (2004a) observed that leader support was more effective in engender team performance in richer jobs mediated by team support (interestingly, the same interaction based on the same data seems to be reported in Drach‐Zahavy, 2004b). Sauer (2011) found that directive leadership was more effective with low leader status, whereas participative leadership was more effective with high leader status. Gibson and Vermeulen (2003) found that leader performance management was more strongly positively related to team learning when there were either strong subgroupings in a team or no or weak subgroupings. There is thus evidence that either more directive or more participative leadership may be more effective in teams. There is also some consistency over studies in the perceived charisma and status effects in DeRue et  al. (2010) and Sauer (2011), and in the notion that teams with greater knowledge integration requirements (functional diversity, distributed information, richer jobs) may benefit from participative leadership (Drach‐Zahavy, 2004a; Larson et al., 1998; Somech, 2006). The latter in particular is consistent with the notion that teamwork for more knowledge‐intensive tasks benefits from more empowered team members (also see below). The former seems to reflect a precondition for such participative or empowering leadership – the perception that the leader is not participative out of weakness or incompetence. Even so, it may also be evident from this concise review of studies of participative and directive leadership and the contingencies of their effectiveness that there is no clear guiding framework to understand their influence – two propositions as summarized here do not constitute a theory of team leadership. Other behavioral approaches  Focusing on another classic distinction in leadership research  –  that between task‐focused leadership (or initiating structure) and person‐ focused leadership (or consideration), Klein, Knight, Ziegert, Lim, and Saltz (2011) found that task‐focused leadership reduced conflict with greater value diversity (i.e., greater differences between team members in personal values), whereas person‐focused leadership enhanced conflict with greater value diversity. Homan and Greer (2013) observed that leader consideration interacted with team tenure diversity such that consideration appeared unrelated to team performance with high diversity but negatively related to performance with low diversity. Tenure diversity and value diversity should not be equated, and neither should team conflict and team performance, but it may be noted that if anything the findings of these two studies are not consistent. Not obviously related, Keller (2006) found that initiating structure was more strongly positively related to research and development team performance for development projects than for research projects.



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Focusing on more recent leadership approaches, Nohe, Michaelis, Menges, Zhang, and Sonntag (2013) found that leader change‐oriented behavior predicted team performance mediated by charisma and commitment to change. Gil, Rico, Alcover, and Barrasa (2005) found that change‐oriented leadership predicted team climate for innovation moderated by group potency. Rego, Vitória, Magalhães, Ribeiro, and Pina e Cunha (2013) observed that authentic leadership predicted team potency, and Hannah, Walumbwa, and Fry (2011) that leader authenticity predicted team authenticity, which interacted with variance in team authenticity to predict teamwork behavior which predicted team performance. Hu and Liden (2011) report that servant leadership predicted team potency which predicted team performance and team citizenship, and strengthened the positive influence of goal and process clarity. Schaubroeck et  al. (2011) found that servant leadership predicted respectively affective trust which predicted team potency and psychological safety, which predicted team performance. Mohammed and Nadkarni (2011) showed that temporal leadership predicted team performance especially with team diversity in traits related to timing preferences. For none of these other generic behavioral approaches there is a sufficient body of research to pass judgment on their merits in understanding team leadership. It is for future research to determine whether these are viable perspectives. What does seem fair to note, however, is that none of these studies invokes a well‐articulated theory of team process and performance to ground their analysis of team leadership. As per my observations in the previous, it would thus seem critical that future research along those lines would work from a stronger, more integrative team process model to link further developments of these approaches to team leadership to an integrative understanding of team dynamics. Leader–member exchange  Leader–member exchange (LMX) captures the quality of the relationship between leader and follower. LMX theory is a dyadic leadership theory (Graen & Scandura, 1987)  –  it revolves around the dyadic relationship of the leader with each individual follower. The basic logic is that LMX represents the quality of the social exchange relationship between leader and follower and that with higher relationship quality the leader can expect greater contributions of the follower to that relationship – commitment, effort, performance, etc. As a dyadic theory, LMX theory cannot directly be applied to team leadership. A straightforward extension of the LMX perspective, however, is to aggregate LMX to the team level – taking the team mean LMX – and predict that on‐average better LMX relationships are positively related to indicators of team leadership effectiveness (presumably because each team member is individually more motivated to contribute to the team because it is associated with the leader). Evidence for this is found in Ford and Seers (2006) for team climate, in Gajendran and Joshi (2012) for team innovation, who also observed that this effect was stronger with frequent leader‐member communication in interaction with team dispersion, and in Chen and Tjosvold (2013) for team‐rated effectiveness and mediated by constructive controversy. In a more team‐specific extension, research has also investigated the influence of LMX differentiation – the extent to which LMX relationship vary between team members (Henderson, Liden, Glibkowski, & Chaudhry, 2009). From a social exchange perspective researchers have argued for a positive motivating effect of LMX differentiation. Research has also found evidence in support. Naidoo, Scherbaum, Goldstein, and Graen (2011) for instance found a positive relationship between LMX differentiation and team performance, as did Liden, Erdogan, Wayne, and Sparrow (2006) but only with higher task interdependence, and LeBlanc and González‐Romá (2012) but only with low median LMX – whereas reversing angles Guan et al. (2013) found that mean team LMX was more weakly related to team identification with higher LMX differentiation.

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Even when research in LMX differentiation seems consistent with the notion that differences in leader‐follower relationship quality are good for team performance, it is important to note that a broader relationship perspective might suggest differently. Wu, Tsui, and Kinicki (2010), for instance, show that leadership treating different team members differently as compared with group‐focused leadership created divergence in leader identification and member self‐efficacy, and thus resulted in lower collective efficacy and team effectiveness. Hogg et  al. (2005) speak to this issue by showing that differentiated, personalized leadership is only more effective under conditions of low team identity salience and team identification. With higher team identity salience and identification, group‐focused leadership treating team members more equally is more effective. This is not a trivial proposition, because conditions of high team identification typically seem to be the ones organizations desire to create for team performance. An obvious observation here is that the evidence for the LMX differentiation argument flows from studies using measures of LMX, whereas the counterpoint flows from operationalizations of leadership that are not LMX but focus on differentiation between followers. It seems obvious that the potential for integration of these perspectives lies in these divergent treatments of leadership (e.g., perhaps LMX differentiation is a not unwelcome outcome, but it should not derive from leadership proactively differentiating between team members?). An obvious implication for future research then is to develop this most team‐specific aspect of the LMX – leader‐follower relationships analysis towards more integration. Leader–follower similarity  In a sense a different twist on leader–follower relationships is the study of leader–follower similarity. There are two basic perspectives on leader– team similarity. The first focuses on the psychological effect of similarity per se. The logic here is that similarity attracts, and that therefore leaders are better positioned to motivate their team when they are more similar to their team. Such similarity could in principle take many different forms. Kirkman, Tesluk, and Rosen (2004) for instance focused on leader–team race similarity and found that similarity was positively related to team empowerment and team effectiveness. A second perspective focuses on how similarity in work‐related values and cognitions are conducive to smoother leader–team interaction because they make such interactions more predictable and understandable. Gibson, Cooper, and Conger (2009) for instance observed that leader–team dissimilarity of perceptions of goals and constructive conflict negatively related to team performance and more strongly so when team perceptions were more positive than leader perceptions, and Cole, Carter, and Zhang (2013) found that leader–team similarity in power distance value predicted team procedural justice climate, which predicted team performance and team citizenship. Even when not unique to team leadership – leader–follower similarity can and does also play out at the dyadic level (e.g., Tröster & van Knippenberg, 2012), and team level similarity is an aggregation of dyadic similarities – the importance of the similarity perspective lies in the recognition that the influence of leadership characteristics is not independent of team member characteristics. Leader psychological traits and states  A series of studies looks at the influence of leaders’ psychological traits and states. Traits and states can only influence teams when they express themselves one way or the other – through leader behavior – so the difference between behavioral approaches on the one hand and trait and state approaches on the other is not so much that the latter would not concern behavior as it is that the latter often does not speak to the mediating leader behavior involved. Characteristics considered include personality traits as well as affective and cognitive states.



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Peterson, Smith, Martorana, and Owens (2003), for instance, studied Big‐Five personality traits of chief executives to predict top management team process, which in turn was taken to predict firm performance. Their analysis linked openness to experience, agreeableness, and conscientiousness positively and neuroticism negatively to team processes that predicted performance. Tost, Gino, and Larrick (2013) found that leader sense of power was bad for team performance, because it leads to leader verbal dominance, which in turn results in reduced team communication and thus reduced performance. Focusing on leader skill rather than leader psychological state, Ahearne, Ferris, Hochwarter, Douglas, and Ammeter (2004) report that leader political skill positively related to team performance. What these diverse studies have in common is that they show that leader traits may have both good and bad effects depending on the leader behavior they inspire. An obvious step forward in the development of such trait approaches would thus be to integrate them with behavioral approaches to team leadership. A number of studies focused on leader affect. Sy, Côté, and Saavedra (2005) showed for example that leader mood predicted follower mood, and thus team cooperation (positive mood) and persistence (negative mood). Van Kleef et  al. (2009) compared the effects of leader displays of anger versus happiness on team performance. They found that leaders displaying happiness were more effective in stimulating team performance mediated by social contagion for teams with members high in need for structure, whereas leader displays of anger were more effective mediated by appraisals of performance feedback. Also comparing angry and happy displays, Van Kleef, Homan, Beersma, and van Knippenberg (2010) found that team member agreeableness moderated team performance effects such that happy leaders were more effective for teams high in agreeableness and angry leaders for teams low in agreeableness. Chi, Chung, and Tsai (2011) found that leader positive mood predicted perceptions of transformational leadership and member mood which in turn predicted performance. In similar vein, Seong and Choi (2014) report that leader positive affect predicted group positive affect which in turn predicted lowered conflict and in turn performance. A tentative conclusion thus may be that both leader positive affect and leader negative affect may have their benefits, and an understanding of the role of leader affect in team leadership requires a contingency approach (cf. van Knippenberg, van Knippenberg, Van Kleef, & Damen, 2008). Research also covered leader characteristics that could be qualified as leader cognition. Hoyt, Murphy, Halverson, and Watson (2003), for example, studied leader self‐efficacy, and found that it predicted leader team efficacy, which in turn predicted follower team efficacy, which predicted team performance. Dragoni and Kuenzi (2012) found that leader goal orientation (learning, prove, avoid) predicted team goal orientation which in turn predicted team performance, and for both paths more so in organic versus mechanistic team structures. What these studies have in common with studies of leader affect is that they show that leader states may predict shared team member states and thus shape team process and performance. An important qualification of this conclusion is added by the Van Kleef et al. (2009) and Chi et al. (2011) studies that show that team members adopting the same psychological state as the leader is one mechanism through which leader psychological states can affect team process and performance, but not the only one – leader states may also influence leadership perceptions, performance perceptions, and a presumably a range of other team‐relevant perceptions. Here too, it would seem important to develop these state perspectives to be more explicit about the leader behavior through which leader states affect followers, and to do this in a way that integrates these perspectives more with existing behavioral perspectives.

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Team‐Specific Approaches to Team Leadership Empowering leadership behavior As noted in the previous, the potential benefits of teamwork should be contingent on a proactive and empowered approach by team members – synergetic teamwork benefits are unlikely to materialize when team members only mechanistically follow leader orders. The concept of empowering leadership was developed in recognition of this – although interestingly what is probably its most influential instantiation, the conceptualization and measurement proposed by Kirkman and Rosen (1999) was introduced under the label external leader behavior. Here, I take the liberty of calling it empowering leadership. Empowering leadership consists of the combination of giving responsibility to the team, encouraging the team to use this responsibility, and conveying a sense of trust in the team. Stewart’s (2006) meta‐analysis shows that empowering leadership is positively related to team performance, even when there is substantial variability in effect sizes that suggest that it is important to consider moderators of the influence of empowering leadership. In line with these meta‐analytic findings, it is not surprising to see evidence of both the effectiveness of empowering leadership and of the contingent nature of this effectiveness. Speaking to the core of the notion of empowering leadership, Kirkman and Rosen (1999) and Chen, Kirkman, Kanfer, Allen, and Rosen (2007) show that empowering leadership predicted team performance through team member psychological empowerment (i.e., a psychological state not to be confused with the leadership intended to induce that state). Speaking to the notion that empowering leadership is important in reference to the synergetic benefits of teamwork, Srivastava, Bartol, and Locke (2006) found that knowledge sharing (and team efficacy) mediated the relationship between empowering leadership and performance (also see Carmeli, Schaubroeck, & Tishler, 2011). Luciano, Mathieu, and Ruddy (2014) showed that in addition the extent to which the own team is more empowered than other teams also predicts team performance (mediated by psychological empowerment). Research also identified a variety of moderators of the effects of empowering leadership. These include environmental dynamism, which strengthens the effectiveness of empowering leadership (mediated by behavioral integration and team potency; Carmeli et  al., 2011), and the qualification in further moderation by team diversity such that empowering leadership is better for diverse teams in stable environments and for homogeneous teams in turbulent environments (Hmieleski & Ensley, 2007). Other research has identified moderators in variance in empowering leadership perceptions as reducing its effectiveness (mediated by team potency, helping, and effort; Ahearne, Mackenzie, Podsakoff, Mathieu, & Lam, 2010), overload and improvisation such that empowering leadership is a more positive influence with improvisation and low load (Magni & Maruping, 2013). Studies have also drawn comparisons between empowering leadership and directive leadership from a contingency perspective. Yun, Faraj, and Sims (2005) found that empowering leadership was more effective in trauma teams when trauma severity was low or the team experienced, whereas directive leadership was more effective with high trauma severity or team inexperience. Lorinkova, Pearsall, and Sims (2013) looked at development over time and found that performance was better initially for teams with a directive leader but, over time, teams with an empowering leader improved more (and this was mediated by team learning, coordination, psychological empowerment, and mental model development). Martin, Liao, and Campbell (2013) observed in a field experiment that directive leadership enhanced proactive behaviors only for teams that were highly satisfied with their leaders, whereas empowering leadership had stronger effects on both



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task proficiency and proactive behaviors for teams that were less satisfied with their leaders (cf. findings cited above that leader status moderates the effectiveness of directive versus participative leadership). These findings are consistent with the conclusion that empowering leadership can be effective in stimulating synergetic team benefits. However, even though empowering ­leadership may be more effective with greater challenges in integrating information or the environment, there are limits to the extent to which these challenges are conducive to the effectiveness of empowering leadership – e.g., diversity or environmental turbulence, but not both; improvisation, but not combined with high load; in trauma teams, but not with severe trauma or inexperienced teams; and time is required to let the positive effects of empowering leadership materialize. What also emerges from this concise review is that here too more integrated team process theory would be helpful. Empowering leadership as a team‐specific approach to leadership has the advantage that it is directly tied in some of the processes seen as key to effective team functioning, but this nonexhaustive review identified a series of mediators without an obvious integrating theory  –  mental model development, psychological empowerment, team efficacy/potency, behavioral integration, knowledge sharing, team learning, coordination, helping, and effort.

Shared leadership Related to empowering leadership, but in a sense taking it one step further is the notion of shared leadership (Pearce & Conger, 2003; Pearce & Sims, 2000). Shared leadership describes a situation in which all team members at least to a certain extent share in fulfilling the leadership role  –  guiding and leading each other. To a certain extent, then, shared leadership could be seen as the fully developed consequence of empowering leadership – the team leads itself. Following from the same logic underlying the focus on empowering leadership then, shared leadership is closely tied in with the kind of proactive engagement of team members that would underlie teams’ potential to achieve synergetic teamwork benefits. At the same time, however, this notion of team self‐leadership runs the risk of turning shared leadership into old wine in new bottles in that shared leadership could be reduced to a new term for an older concept – team self‐management or self‐managing leadership (Manz & Sims, 1993), where the absence of formal leadership defines the team leadership context (cf. leadership as the outcome of team process; Day, Gronn, & Salas, 2004). Complicating the development of the concept further, shared leadership has been alternatively operationalized as the extent to which team members display a certain type of leadership behaviors (e.g., transformational leadership), as the extent to which members display an aggregation of different types of leadership, and in a more abstracted way as the extent to which team members display leadership as an otherwise undefined concept. Some shared leadership research thus speaks to what kind of leadership is shared (although arguably this is a conclusion that does not apply to ratings of transformational leadership; van Knippenberg & Sitkin, 2013), whereas other research only speaks to the perception that leadership is shared. With these caveats in place, we may note that meta‐analytic evidence speaks to the ­effectiveness of shared leadership. Wang, Waldman, and Zhang’s (2014) meta‐analysis showed that overall shared leadership is positively related to team effectiveness overall. When a ­distinction is made between shared “traditional” forms of leadership (e.g., initiating structure and consideration) the relationship is weaker than when the focus is on either shared “newer” forms of leadership (e.g., charismatic–transformational leadership), shared aggregated leadership, or shared undefined leadership. Illustrative of these meta‐analytic

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f­ indings is research such as that by Hiller, Day, and Vance (2006); Mehra, Smith, Dixon, and Robertson (2006); Carson, Tesluk and Marone (2007); Small and Rentsch (2010); and Bienefeld and Grote (2014); finding that shared leadership predicted team performance. Related findings show that shared leadership was positively related to trust, consensus, and cohesion and negatively to conflict (Bergman, Rentsch, Small, Davenport & Bergman, 2012), positively to innovative behavior (Hoch, 2013). Comparisons of shared and vertical leadership (i.e., from leader to members) also show that shared leadership makes an independent and potentially bigger contribution to team effectiveness. Pearce and Sims (2002) looking at different forms of vertical and shared leadership effectiveness – transformational, transactional, empowering, aversive, and directive, and the most consistent finding over effectiveness rating sources (manager, team, and costumers) was that both vertical and shared transformational leadership was positively related to effectiveness and vertical and shared directive leadership negatively related to effectiveness. Also comparing vertical and shared leadership, Ensley, Hmieleski, and Pearce (2006) looked at directive, transactional, transformational, and empowering leadership in startups and found that shared leadership was more consistently a positive influence than vertical leadership. Mediation findings point to such varied variables as team positive affect (Hmieleski, Cole, & Baron, 2012), and team mental model similarity and accuracy (McIntyre & Foti, 2013). Moderation evidence concerned for instance the proportion of women in the team (associated with a stronger effect for higher proportion; Neubert, 1999) and job variety (stronger effects with higher variety; Liu, Hu, Li, Wang, & Lin, 2014). Drawing a comparison with vertical leadership in a contingency approach, Hoch and Kozlowski (2014) found that shared leadership was more important and vertical leadership less important with higher virtuality, although it should be noted that their comparison confounded vertical versus horizontal leadership with type of leadership behavior. In sum, then, there is a clear case that shared leadership can be a positive influence independent from more traditional vertical leadership. At the same time, it may be noted that shared leadership and empowering leadership are so naturally related (i.e., when viewing shared leadership as the developed state empowering leadership aims to achieve) that far greater integration of the two perspectives seems warranted. Do the same mediators apply to both forms or leadership? Do the same moderators? And if the answer to these two questions is affirmative, what theory of team process underlies both perspectives on team leadership? In addition, when seeing shared leadership as a developed state of empowerment, an important question to address is how empowering leadership evolves into shared leadership.

Other team‐specific approaches In addition to empowering leadership and shared leadership as the “Big Two” of team‐ specific approaches to leadership, there are a number of other perspectives that are more or less related to empowering/shared leadership. Some of these are more like isolated studies, such as Nembhard and Edmondson (2006) study of leader inclusiveness – leadership valuing member contributions (cf. participative and empowering leadership) – that showed that it predicted team learning which in turn predicted engagement with quality, especially for those who do not already feel safe by virtue of their professional status. Or Morgeson’s (2005) study showing that leader interventions led to higher team perceptions of leadership effectiveness with better leader preparation as events were more novel and with more active intervention as events were more disruptive.



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Another perspective engages with the growing awareness of the importance of shared cognition in team effectiveness (cf. Mohammed, Ferzandi, & Hamilton, 2010). Marks, Zaccaro, and Mathieu (2000) showed that leader briefings to create shared mental models lead to better team performance (cf. Randall, Resick, & DeChurch, 2011) and, in a related vein, van Ginkel and van Knippenberg (2012) showed that leader advocacy and role modeling of task understanding resulted in shared team task representations that sequentially predicted team information elaboration and team decision quality. Whereas the number of studies like this that conceptualize leadership explicitly in terms of building team member mental representations (mental models, task representations) is limited, there is a potential for integration with other perspectives highlighting the mediating role of mental representations in team leadership effects (e.g., Lorinkova et  al., 2013; cf. Dragoni & Kuenzi, 2012). Given the central focus that team mental representations increasingly take in models of team effectiveness, the focus on leadership’s role in developing shared cognition in teams seems promising in further developing our understanding of team leadership. Such development would ideally also include leaders’ role in engendering adaptive shifts in understanding to respond to changing situational demands (Alexander & van Knippenberg, 2014; Kozlowski, Watola, Jensen, Kim, & Botero, 2009). Recognizing that the network of interpersonal relationships in a team is not homogeneous, Balkundi and colleagues demonstrated that leaders are more effective when they hold a more central position in the team’s network, both in terms of team performance (mediated by perceived charisma; Balkundi, Kilduff, & Harrison, 2011) and in terms of reduced team conflict and higher team viability (Balkundi, Barsness, & Michael, 2009). This social network perspective is more team‐specific than dyadic approaches to interpersonal relationships like LMX (i.e., because the social network perspective focuses on the position within the network as a whole), but even so it is a relational perspective just like research on LMX and other perspectives on leader‐follower relationships, and it would be valuable for future research to build towards more integrative accounts of team leader‐ member relationships.

Future Research One of the most important conclusions to emerge from this review is that team leadership research is fragmented in its understanding of mediating processes and contingencies of leadership effectiveness. It seems a fair conclusion that this fragmentation is not so much due to a focus on different aspects of leadership as it is to the absence of an integrative process theory of team performance. Perhaps the most important advice to the field to follow from this review then is to put the development of a strong process theory center‐ stage, and to let the analysis of team leadership theory follow from this process theory. A second observation is that a dominant approach in the field is to focus on generic leadership approaches. To a certain extent, this seems to be a case of a field betting on the wrong horse. The dominant generic perspective is the charismatic‐transformational leadership framework – a framework that is conceptually and empirically bankrupt. An important part of the other generic approaches concerns the domain of directive leadership versus participative, coaching, and supporting leadership. This is a perspective more closely tied in with an understanding of team process benefits, but at the same time, this process perspective seems better represented by the focus on empowering and shared leadership that is more explicitly connected with the proactive self‐management of team members that seems key to synergetic teamwork benefits. In terms of the dominant perspectives in team leadership research then, the most promising way forward seems the further development

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of the empowering‐shared leadership framework. As per the earlier discussion of these perspectives above, important development of this framework would work towards more integrative treatment of these aspects of leadership, and a better grounding in strong team process theory. At the same time, it would be important if further development of such a framework could do more justice to other perspectives that are arguably important, such as the shared cognition perspective (arguably easily integrated with an empowering‐shared leadership perspective by seeing developing shared cognition as key to successful team self‐leadership; cf. van Knippenberg et al., 2013) and the leader‐follower relationship perspective (LMX, social networks). On a different note, it seems also a fair observation that some issues have received little or no empirical follow‐up yet even when they have been recognized as important to team leadership. The functional leadership perspective sees the role of team leaders as doing whatever the team cannot do itself to make high performance possible, such as making the right decisions in composing the team (Hackman, 2002; Hackman & Wageman, 2005; Kozlowski, Gully, McHugh, Salas, & Cannon‐Bowers, 1996; McGrath, 1962; Morgeson, DeRue, & Karam, 2010; Zaccaro, Rittman, & Marks, 2001). Many team leaders may not find themselves in a situation where such team design choices are up to their discretion, but when they are, such choices may be more important to team success than leadership during team task performance (Wageman, 2001; cf. Hackman 2002; Hackman & Wageman, 2005). Hackman (2002) identified setting and communication the team’s mission as an important element in setting the stage for good team performance, and it is interesting to note that this element too is underdeveloped in team leadership research (note that charismatic–transformational leadership research despite its claims to include vision communication cannot claim to speak to this because of its invalid measurement). Indeed, what is missing from visionary leadership research (i.e., the leadership that would most obviously speak to the issue of setting and communicating the team mission) is a focus on how visionary leadership can effectively engender the collective pursuit of a shared vision (Stam, Lord, van Knippenberg, & Wisse, 2014), and here lies an obvious and important challenge for team leadership research. A final issue that I would highlight here is the increasing recognition that teams do not function in isolation but are embedded in a network of teams where performance at least in part is intergroup performance  –  the achievement of objectives for which teams rely on interteam collaboration (DeChurch & Marks, 2006; Hogg, van Knippenberg, & Rast, 2012). To the extent than that team effectiveness would also be understood in terms of such intergroup performance, effective leadership of teams may require elements not included by perspectives on leadership that have an exclusive focus on intra‐team dynamics (Hogg et al., 2012). An important future development of team leadership research would thus be to more explicitly recognize the multilevel nature of team performance.

Conclusion With the growing emphasis on team work, team leadership is an issue of ever greater importance. The conclusion of the current review is that team leadership research needs to refocus, and prioritize the development of team‐specific leadership theory closely tied in with integrative theory of team processes (i.e., rather than apply generic leadership models to team leadership). This is no small challenge – the development of integrative process theory is probably the main challenge of the team research field as a whole. Arguably,



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however, exactly because of the broad‐ranging importance of such integrative process theory and the key role team leadership may play in team performance, no subfield of team research will yield a greater return on investment for developing such integrative theory than team leadership research.

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Team Cognition Team Mental Models and Situation Awareness Susan Mohammed, Katherine Hamilton, Miriam Sánchez‐Manzanares, and Ramón Rico

Introduction One of the most exciting developments in team research has been the proliferation of team cognition research across disciplinary and international boundaries. Team cognition is a broad construct reflecting how knowledge is collectively represented in a team, enabling team members to interpret information in a similar manner, share expectations, and e­ffectively coordinate actions (Cannon‐Bowers & Salas, 2001; Klimoski & Mohammed, 1994). Originating in the cognition of individuals, team c­ognition represents a bottom‐up construct which emerges from member interactions in a dynamic context (Kozlowski & Chao, 2012). This chapter focuses on team mental models (shared, organized understanding of teamwork and taskwork knowledge; Klimoski & Mohammed, 1994) and team situation awareness (TSA, shared know­ledge about current and future environmental events; Wellens, 1993) because both capture unique aspects of team cognition, and derive from distinct literatures and methodologies. Extending prior work, the main purpose of this chapter is to provide an integrative review across team mental model (TMM) and situation awareness domains, answering the call for greater synthesis across team cognition constructs (e.g., DeChurch & M­esmer‐Magnus, 2010a; Mohammed, Frezandi, & Hamilton, 2010; Salas & Wildman, 2009). After providing a brief overview of team cognition, TMMs and situation awareness, we present an integrative framework and identify future research needs. We conclude that several significant opportunities exist at the intersection of TMM and situation awareness research that can advance the science of team science as a holistic entity.

The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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The Importance and Maturation of Team Cognition Conceptually and empirically differentiated from motivational and behavioral team processes because of its focus on the team’s knowledge architecture (DeChurch & Mesmer‐Magnus, 2010a), the importance of team cognition has been demonstrated practically, theoretically, and empirically. Practically, the lack of team cognition has been implicated in notable team performance breakdowns in space missions (Bearman, Paletz, Orasanu, & Thomas, 2010), surgical errors (e.g., Santos et al., 2012), airline accidents (e.g., Bell & Kozlowski, 2011; Jentsch, Barnett, Bowers, & Salas, 1999), and fratricide (Rafferty, Stanton, & Walker, 2010; Wilson, Salas, Priest, & Andrews, 2007). Theoretically, team cognition has been recognized as one of the hallmarks of expert teams (Salas, Rosen, Burke, Goodwin, & Fiore, 2006) and is featured prominently in models of shared leadership (e.g., Burke, Fiore, & Salas, 2004), team adaptation (Burke, Stagl, Salas, Pierce, & Kendall, 2006), and implicit coordination (e.g., Rico, Sanchez‐Manzanares, Gil, & Gibson, 2008). Empirically, a meta‐analysis of 65 studies concluded that team cognition contributed positively and uniquely to team performance beyond other emergent states and team processes (DeChurch & Mesmer‐Magnus, 2010a). The maturation of team cognition research is apparent through the growing number of books/edited volumes (e.g., Letsky et al., 2008; Salas, Fiore, & Letsky, 2012; Stahl, 2006), qualitative reviews (Akkerman et  al., 2007; Cooke, Gorman, & Winner, 2007; Mohammed & Dumville, 2001; Uitdewilligen, Waller, & Pitariu, 2013; Wildman, Salas, & Scott, 2014), and meta‐analyses on the topic (DeChurch & Mesmer‐Magnus, 2010a, b). In addition, there has been a significant increase in empirical research, as measured by a trend analysis (Wildman et al., 2012). The rising influence of team cognition research is also demonstrated in the number of fields represented, including medicine (e.g., Burtscher & Manser, 2012), engineering (e.g., Patterson & Stephens, 2012), computer science (Schrieber & Engelmann, 2010), sports (Gershgoren, Filho, Tenenbaum, & Schinke, 2013), aviation (Hauland, 2008), project management (Hsu, Chang, Klein, & Jiang, 2011), psychology (e.g., Randall, Resick, & DeChurch, 2011), information sciences and technology (Carroll, Rosson, Convertino & Ganoe, 2006), communication (Poole, 2012), entrepreneurship (e.g., West, 2007), human factors (Kiekel & Cooke, 2005), the military (Espevik, Johnson, & Thayer, 2006), and management (e.g., Maynard & Gilson, 2014). The far‐reaching impact of work on team cognition is further illustrated by the increasing international representation of research settings (e.g., Hsu et al., 2011; Huang, 2009; Zhou & Wang, 2010), and publication outlets (e.g., Hauland, 2008; Hsu et al., 2011; Jo, 2011; Westli, Johnsen, Eid, Rasten, & Brattebo, 2010). As a multidimensional construct, team cognition comprises many specific exemplars (e.g., Mohammed & Hamilton, 2012), and we chose to focus on TMM and situation awareness, which emphasize knowledge content (as opposed to belief structures; Mohammed & Hamilton, 2012). To avoid redundancy with comprehensive reviews of the research on team mental models (TMMs; Mohammed et  al., 2010) and situation awareness (situation awareness; Salmon et al., 2008), our individual coverage of each construct emphasizes current research.

Team Mental Models Conceptualization Representing one of the more developed (Mathieu, Maynard, Rapp, & Gilson, 2008, p. 429) and the most commonly studied forms of team cognition (Wildman et al., 2012), TMMs are “team members’ shared, organized understanding and mental representation



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of knowledge about key elements of the team’s relevant environment” (Mohammed & Dumville, 2001, p. 90). The central assumption underlying this research is that team members who are “on the same page” regarding team knowledge are better poised to anticipate the needs and actions of other members, thereby increasing coordination and effectiveness (Cannon‐Bowers, Salas, & Converse, 1993). TMMs are conceptually distinct from other types of team cognition in that they include a broader range of cognitive content, encompassing both taskwork (work goals and performance requirements) and teamwork (interpersonal interaction requirements of members) domains (Mohammed et  al., 2010). Although studies measuring generic taskwork and teamwork categories remain dominant, there is an increasing trend to operationalize more specific types of TMM content, including strategic (Randall et  al., 2011; Resick, Murase, Randall, & DeChurch, 2014), temporal (Mohammed, Hamilton, Tesler, Mancuso, & McNeese, 2015; Mohammed, Tesler, & Hamilton, 2012), situational (Hamilton, 2009; Waller, Gupta, & Giambatista, 2004), and team membership (Mortensen, 2014) TMMs. TMMs have two primary properties: sharedness (the degree to which members’ mental models overlap with one another) and accuracy (the degree to which members’ and experts’ mental models are consistent). Because members’ mental models may be similar, but erroneous, the greatest team performance benefits are predicted from shared and accurate TMMs (e.g., Edwards, Day, Arthur, & Bell, 2006; Mathieu, Heffner, Goodwin, Cannon‐Bowers, & Salas, 2005). Despite being well understood that team members hold multiple models simultaneously (Cannon‐Bowers et al., 1993), most TMM studies continue to examine just one type of TMM content (e.g., Fisher, Bell, Dierdorff, & Belohlav, 2012; Pearsall, Ellis, & Bell, 2010; Resick, Dickson, Mitchelson, Allison, & Clark, 2010; Resick et al., 2014). However, in the studies that examine multiple content types, it is often the case that different predictors and/or outcomes emerge for taskwork and teamwork TMMs (e.g., Cooke, Kiekel, & Helm, 2001; Gorman & Cooke, 2011; Santos & Passos, 2013; Mathieu et al., 2005; Zhou & Wang, 2010). A similar state of affairs exists for studies that examine both TMM similarity and accuracy (e.g., Marks, Zaccaro, & Mathieu, 2000; McIntyre & Foti, 2013; Resick et al., 2010), so careful analysis of the distinctions between multiple content domains and TMM properties is needed, in addition to exploring interactions between TMM types and properties.

Measurement Measuring TMMs tends to be more challenging than other types of team cognition in several ways. There is no consistent methodology to rely on as a default option (e.g., Cooke, Salas, Cannon‐Bowers, & Stout, 2000; Langan‐Fox, Code, & Langfield‐Smith, 2000) because the context‐dependent nature of TMMs requires tailoring to the specific task and teams under investigation (Mohammed & Hamilton, 2012). Therefore, conducting a team task analysis is a key part of assessing TMMs (Mohammed & Hamilton, 2012). Most notably, however, TMMs are operationally differentiated from other forms of team cognition by requiring the additional step of examining the relationship between concepts (Mohammed et al., 2010; Rentsch, Small, & Hanges, 2008). Whereas all team cognition metrics capture cognitive content, TMMs also measure cognitive structure or how c­oncepts are organized in individuals’ minds (DeChurch & Mesmer‐Magnus, 2010a; Mohammed, Klimoski, & Rentsch, 2000). The most popular TMM measurement technique is pairwise ratings (also referred to as paired comparison or similarity ratings), in which respondents rate the similarity between pairs of concepts (e.g., Randall et al., 2011; Zhou & Wang, 2010). The structure of pairwise

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ratings is represented by means of computerized scaling algorithms, the most prevalent of which is Pathfinder (e.g., Gorman & Cooke, 2011; McIntyre & Foti, 2013). However, the UCINET network analysis program (e.g., Santos & Passos, 2013; Uitdewilligen et al., 2013), and multidimensional scaling (e.g., Rentsch & Klimoski, 2001) have also been used. The second most used TMM measurement approach is concept mapping, in which respondents place concepts in a hierarchical structure (e.g., Burtscher, Kolbe, Wacker, & Manser, 2011; Pearsall et al., 2010). Two other categories of TMM measurement include card sorting (e.g., Smith‐Jentsch, Campbell, Milanovich, & Reynolds, 2001) and qualitative methods (e.g., McComb, Kennedy, Perryman, Warner, & Letsky, 2010; Waller et al., 2004; Westli et al., 2010). Because survey items fail to capture cognitive structure, there is growing consensus that they do not qualify as true TMM measures (e.g., DeChurch & Mesmer‐Magnus, 2010a; Mohammed et al., 2000, 2010; Rentsch et al., 2008). Multiple studies have also shown stronger results for structural over questionnaire measures (e.g., Cooke et  al., 2001; Cooke et  al., 2003), including a meta‐analysis (e.g., DeChurch & Mesmer‐Magnus, 2010b). Nevertheless, surveys (e.g., Ellwart, Konradt, & Rack, 2014; Hsu et al., 2011; Johnson et al., 2007) and survey‐based metrics (e.g., Biemann, Ellwart, & Rack, 2013) continue to be developed and labeled as measuring TMMs.

Empirical research Research contexts  Empirical research on TMMs has continued to accumulate at a strong pace since 2000 and shows no signs of waning. TMM studies commonly include students performing various computer simulations, most of which are military‐based (e.g., Gorman & Cooke, 2011; Pearsall et al., 2010; Marks et al., 2000; Mathieu, Heffner, Goodwin, Salas, & Cannon‐Bowers, 2000), but also include medical (Burtsher et al., 2011), firefighting (e.g., Uitdewilligen et  al., 2013), and business (Fisher et  al., 2012; Santos & Passos, 2013) contexts, as well as commercially available programs like SimCity (Randall et al., 2011; Resick et al., 2014) and Freelancer (Resick et al., 2010). However, various field contexts are equally represented (Wildman et  al., 2012), including emergency management command and control teams (van der Haar, Segers, Jehn & van den Bossche, 2015), nuclear power plant crews (Waller et al., 2004), software development and high‐tech teams (Mortensen, 2014; Reuveni & Vashdi, 2015), military teams (Ayoko & Chua, 2014; Lim & Klein, 2006; Smith‐Jentsch, Cannon‐Bowers, Tannenbaum, & Salas, 2008), air traffic control towers (e.g., Mathieu, Rapp, Maynard, & Mangos, 2009; Smith‐Jentsch, Mathieu, & Kraiger, 2005), faculty decision‐making teams (Kellermanns, Floyd, Pearson, & Spencer, 2008), and government employee teams (e.g., Rentsch & Klimoski, 2001; Smith‐Jentsch et al., 2001). There is growing recognition of the importance of TMMs in healthcare collaboration (e.g., Burtscher & Manser, 2012; McComb & Simpson, 2013), and while most of the extant research is qualitative (e.g., Custer et al, 2012; Mamykina, Hum, & Kaufman, 2014; Westli et al., 2010), a quantitative study examined anesthesia dyads performing a simulation (Burtscher et al., 2011). In addition, the need for research on TMMs in virtual contexts has been underscored conceptually (Maynard & Gilson, 2014) and empirically (e.g., McComb et al., 2010). TMM similarity outcomes  Across diverse settings, the most consistent and strongest finding to emerge is that TMM similarity positively predicts team performance, as supported by multiple meta‐analyses (e.g., DeChurch & Mesmer‐Magnus, 2010a, 2010b). Higher taskwork (e.g., Cooke et  al., 2001) and teamwork (e.g.,  Rentsch & Klimoski, 2001)



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TMM  similarity have been associated with higher performance. However, results for ­taskwork tend to be stronger than teamwork in studies measuring both types of content (e.g., Cooke et al., 2001, 2003; Gorman & Cooke, 2011; Lim & Klein, 2006; Mathieu et al., 2005, 2009). Although team performance has been the most popular criterion by far, ­viability (members’ willingness to work together in the future) has been positively associated with teamwork (Rentsch & Klimoski, 2001; Santos & Passos, 2013) and ­taskwork (Resick et al., 2010) TMM similarity. In addition, Reuveni and Vashdi (2015) reported that higher teamwork TMM similarity (content only) predicted higher innovation. Growing attention is being paid to team adaptation as a key indicator of team effectiveness for TMMs, both conceptually (e.g., Burke et  al., 2006; Uitdewilligen, Waller, & Z­ijlstra, 2010; Zajac, Gregory, Bedwell, Kramer, & Salas, 2014) and empirically (e.g., Randall et  al., 2011; Uitdewilligen et  al., 2013). To illustrate, Randall and colleagues (2011) found that strategy‐focused TMM similarity and accuracy positively predicted reactive strategy adaptation (altering existing strategies in response to unexpected changes), which in turn positively predicted decision effectiveness. Uitdewilligen and colleagues (2013) were the first to demonstrate that taskwork TMM updating (changes in alignment with the situation) was positively related to post‐change performance in a simulation requiring adaptation. This relationship was mediated by teammates developing interaction patterns to match the new task situation. These studies build on previous research indicating that TMM development was more salient during novel than routine contexts (Marks et al., 2000; Waller et al., 2004). In addition to performance, viability, and adaptation, TMM similarity has also been positively associated with team processes (DeChurch & Mesmer‐Magnus, 2010a, b), including backup behavior (Marks, Sabella, Burke, & Zaccaro, 2002), strategy implementation (e.g., Gurtner, Tschan, Semmer, & Nägele, 2007), coordination (Fisher et  al., 2012; Marks et  al., 2002), information elaboration (Resick et al., 2014), and communication (Gorman & Cooke, 2011; Marks et al., 2000; Waller et al., 2004). Investigating a dysfunctional team process, Santos and Passos (2013) found that teamwork TMMs reduced relationship conflict. Team processes have frequently been found to mediate the relationship between TMM sharedness and team performance (e.g., Fisher et al., 2012; G­orman & Cooke, 2011; Gurtner et  al., 2007; Marks et  al., 2002; Mathieu et  al., 2000, 2005; Santos  & Passos, 2013). TMM similarity has also moderated the relationship between team processes and performance (e.g., Burtscher et  al., 2011; Zhou & Wang, 2010). Although less studied, higher TMM convergence has resulted in increased emergent states such as  collective efficacy (Ayoko & Chua, 2014; Mathieu et  al., 2010), transactive m­emory systems (Mortensen, 2014), and engagement (Miles & Kivlighan, 2008). TMM accuracy outcomes  TMM studies have traditionally emphasized similarity (e.g., Kellermanns et al., 2008; Smith‐Jentsch et al., 2005), but studies are increasingly including both similarity and accuracy properties (e.g., McIntyre & Foti, 2013; Mortensen, 2014; Randall et  al., 2011; Uitdewilligen et  al., 2013). Whereas most studies reported that greater TMM accuracy was associated with higher performance (e.g., Cooke et al., 2001; 2003; Edwards et  al., 2006; Ellis, 2006; Lim & Klein, 2006; Marks et  al., 2000; McIntyre & Foti, 2013; Pearsall et al., 2010; Resick et al., 2010), others failed to find such a relationship (e.g., Mathieu et al., 2005; Webber, Chen, Payne, Marsh, & Zaccaro, 2000). In studies measuring both accuracy and similarity, some authors have reported that TMM accuracy was a stronger predictor of team performance (e.g., Edwards et  al., 2006; McIntyre & Foti, 2013; Resick et  al., 2010), whereas others found that similarity was more important (e.g., Marks et al., 2000; Webber et al., 2000). Although some authors

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report a significant interaction between TMM similarity and accuracy on team outcomes (e.g., Burtscher et  al., 2011; Marks et  al., 2000; Mathieu et  al., 2005), others did not (e.g., Lim & Klein, 2006; McIntyre & Foti, 2013). Divergent from TMM similarity results, support has generally not been found for linear effects of TMM accuracy and team processes (e.g., Mathieu et al., 2005), including communication (e.g., Marks et al., 2000) and information elaboration (Resick et al., 2010). TMM antecedents  Because establishing a relationship between TMMs and team outcomes has dominated the research focus, less attention has been devoted to examining TMM antecedents (Mohammed et  al., 2010). However, increasing attention has been given to predictors of TMM emergence (e.g., Fisher et al., 2012; McIntyre & Foti, 2013; Mortensen, 2014; Randall et al., 2011; Resick et al., 2010). Earlier research found that various types of training had positive effects on TMM similarity and/or accuracy, such as cross training (Cooke et  al., 2003; Marks et  al., 2002), computer‐based training (e.g., Smith‐Jentsch et al., 2001), guided team self‐correction (Smith‐Jentsch et al., 2008), and experimental/hands‐on learning (Van Boven & Thompson, 2003). In addition to training, other team interventions such as planning (Stout, Cannon‐Bowers, Salas, & Milanovich, 1999), reflexivity (Gurtner et al., 2007), and leadership (e.g., Marks et al., 2000; Gurtner et al., 2007) were facilitative of TMM development. Extending prior work, Randall and colleagues (2011) found that leader external sense giving was positively related to strategy‐ focused TMM similarity and accuracy. Revealing a similar pattern of results, McIntyre and Foti (2013) reported that teams with distributed‐coordinated leadership (shared perceptions of leadership) had greater similarity and accuracy of teamwork TMMs than teams with no leader or distributed–fragmented leadership (lack of mutual recognition of leadership). Transformational leadership has also been found to positively predict taskwork and teamwork TMM similarity (Ayoko & Chua, 2014). Scaling up to cognitive structures of between team activities, leaders’ multiteam interaction mental model accuracy increased between team coordination through strategic communication and fostering accuracy in members’ mental models (Murase, Carter, DeChurch, & Marks, 2014). Introducing storytelling as a new TMM antecedent, an experiment found that the combination of presenting important information in story format and giving members time to reflect upon their strategies had a positive effect on TMM similarity and subsequent team performance (Tesler et al., 2011). In other studies, team interaction mental model accuracy was positively predicted by role identification behaviors (Pearsall et al., 2010) but negatively predicted by stress (Ellis, 2006). In addition, membership model divergence (misalignment about who are team members) was positively related to variance in team interaction patterns, but negatively related to the mean percentage of time that members spent in the team and the mean level of interaction in the team (Mortensen, 2014). More research has also been conducted on the compositional determinants of TMMs, including personality, ability, and demographics. Mean agreeableness was positively associated with taskwork TMM similarity (Resick et al., 2010), whereas the cooperation facet of agreeableness was positively related to teamwork TMM similarity (Fisher et al., 2012). Demonstrating a consistent pattern of results, cognitive ability has been shown to be positively predictive of taskwork TMM similarity and accuracy (Edwards et al., 2006; Resick et  al., 2010) and strategy‐focused TMM similarity and accuracy (Randall et  al., 2011). Fisher and colleagues (2012) found that higher racial, but not gender, diversity was associated with lower teamwork TMM similarity. Prior work revealed that tenure (Smith‐Jentsch et al., 2001), experience (Rentsch & Klimoski, 2001), and educational and organizational‐ level similarity (Rentsch & Klimoski, 2001) generally enhanced TMM development. A­ssessing the dynamic nature of team composition, Gorman and Cooke (2011) manipulated



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whether teams returned for a follow‐up session after 3–6 or 10–13 weeks (retention interval) with the same or different members (team composition). The effect of changing team membership depended on the length of the retention interval in that intact teams were more effective than teams with new members after the shorter interval, but teams with new members experienced increased knowledge, process, and performance after the longer interval.

Team Situation Awareness Conceptualization Situation awareness has been most popularly conceptualized as a multifaceted construct that consists of three levels (Endsley, 1995a). Level 1 situation awareness represents p­erception, which involves being aware of elements in the external environment that are relevant to one’s task performance. Level 2, also known as comprehension, involves being able to decipher the meaning of changes in the external environmental. Finally, Level 3, or projection, is focused on predicting how elements in the environment will change in the future. These levels are theorized to be hierarchical such that an individual cannot attain the highest level of situation awareness without first acquiring the lower levels (Endsley, 1995a). Individuals who develop high situation awareness are argued to be able to process information more accurately and quickly than individuals with low situation awareness (Endsley, 1995a). Given this trait, the study and application of research on situation awareness has been most popular in contexts in which individuals work in c­omplex and changing environments, such as those found in aviation and the military (Salmon et al., 2008). Despite widespread agreement on the nature of individual situation awareness, the conceptualization of TSA has been much more diverse. Early conceptual debates focused on how knowledge was distributed in the team. On the one hand, researchers suggested that TSA involved having overlapping representations of situational knowledge among team members (Endsley, 1995a; Salas, Prince, Baker, & Shrestha, 1995). In this viewpoint, TSA is considered to be isomorphic to individual situation awareness and emerges to the team level through compositional processes. On the other hand, researchers argued that TSA is best conceptualized as representing differentiated knowledge profiles among team m­embers because overlapping representations lead to inefficiencies due to information redundancy (Artman & Garbis, 1998; Hutchins, 1995). This viewpoint assumes that situation awareness emerges from the individual to the team level through compilational processes and is more discontinuous. Since 2000, research has extended the concept of distributed situation awareness beyond representations of complementary knowledge profiles at the team level. These extensions include having complementary levels of situation awareness among team members, such that some individuals may be responsible for the perception of events, others for comprehension, and others for projecting the meaning of the events (Stanton et  al., 2006). This model is inconsistent with Endsley’s (1995a) conceptualization of situation awareness, which suggested that individuals in teams would move through the hierarchical levels of information processing within‐person as opposed to between‐person. Despite this inconsistency, distributed situation awareness is based on military command and control teams, which often have team members whose roles are to scan the environment and relay information, whereas other team members higher in the chain of command use the intelligence gathered to identify patterns and project their future status

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(Salmon, Stanton, Walker, Jenkins, & Rafferty, 2010; Stanton et al., 2006). Other extensions propose that situation awareness is distributed across both human and nonhuman agents in the system, such that technological artifacts and intelligent aids are also considered to be a part of the team’s situation awareness (Stanton, Salmon, Walker, & Jenkins, 2010). However, the concept of distributed situation awareness has been criticized for having little relevance for teams working in non‐specialized roles, as such teams may need to have a shared understanding of their environment in order to make better team decisions (Chiappe, Rorie, Morgan, & Vu, 2014; Chiappe, Vu, & Strybel, 2012). Chiappe and colleagues (2012; 2014) have also argued that external tools are functional in aiding TSA but do not constitute a fundamental part; nonhuman agents should therefore be excluded from models of TSA. Building on these ideas, the authors argue that situated situation awareness develops through individuals in the team perceiving elements in the external environment and developing a shared understanding through sharing their interpretations of the situation (Chiappe et al., 2012; 2014).

Measurement There is a wide diversity of measures that can be used to assess situation awareness (see Salmon, Stanton, Walker, & Green, 2006 for a review). These measures vary based on whether situation awareness is evaluated as a process or a product. Process‐oriented m­easures of situation awareness capture the means through which individuals achieve and maintain situation awareness, whereas product‐oriented measures capture the resultant knowledge (Graham & Matthews, 2000). The most popular product measure of situation awareness is the Situation Awareness Global Assessment Technique (SAGAT; Endsley, 1995b). SAGAT evaluates knowledge of the external environment through the use of objective questions. The questions are scored based on accuracy and are typically aggregated to the team level using the average (see Saner, Bolstad, Gonzalez, & Cuezas, 2009 for an exception). Advantages of SAGAT include the objectivity of the measure and its ability to capture multiple levels of SA (Endsley, 1995b). Disadvantages of SAGAT include its complexity in design and administration. An important step in the design of SAGAT questionnaires is a hierarchical task analysis of the team task prior to administration (Endsley, 1995b). A critical step in the administration of SAGAT is being able to freeze the team task at random intervals during performance and ask participants’ questions related to their perception of environmental cues. Given these limitations in design and administration, SAGAT is best administered when teams are operating in simulated task environments. In field settings, subjective and popular situation awareness product measures include the Crew Awareness Rating Scale (McGuinness & Foy, 2000) and the Mission Awareness Rating Scale (Matthews & Beal, 2002). These measures involve aggregating team members’ self‐ratings of perception, comprehension, and projection of the status of events to the team level. Interestingly, researchers have found little to no correlation between SAGAT and subjective product scales (Endsley, Selcon, Hardiman, & Croft, 1998; Endsley, Sollenberger, Nakata, & Stein, 2000; Jones & Endsley, 2004; Lee, 1999; Lichacz, 2008; Rousseau, Tremblay, Banbury, Breton, & Guitouni, 2010; Salmon et al., 2009a), and each contributes uniquely to the prediction of performance (Rousseau et al., 2010). This has led to the proposition that SAGAT best represents situational knowledge/cognition, whereas self‐ratings of situation awareness capture an awareness of that knowledge or situational metacognition (Rousseau et al., 2010). However, these studies have been largely conducted at the individual level of analysis and have yet to demonstrate the link between SAGAT and self‐ratings for TSA.



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Process measures of TSA focus on capturing the behaviors that team members engage in to coordinate their shared knowledge of the situation. Process measures move beyond the typical conceptualization of situation awareness as being in the mind of the operator to being an interaction‐based construct. These measures often involve the collection of data through the use of video recordings, interviews, and team observations, which are typically transcribed and then coded (e.g., Patrick & Morgan, 2010). Process measures can therefore be time and labor intensive to analyze but can provide meaningful information on the development of situation awareness over time. A notable process‐oriented measure is the Coordinated Awareness of Situations by Teams, which assumes that TSA is best observed when a team is working in a non‐routine environment (Cooke & Gorman, 2006, 2009; Gorman, Cooke, Pedersen, Connor, & DeJoode, 2005; Gorman, Cooke, & Winner, 2006). This measurement approach involves first identifying potential team roadblocks and then observing and making note of how the team coordinates their actions to resolve the roadblock. Another notable process measure focuses on evaluating distributed situation awareness through the use of propositional networks (Salmon, Stanton, Walker, & Jenkins, 2009b; Salmon et al., 2010; Sorensen & Stanton, 2011). Evaluating propositional networks first involves conducting a frequency analysis on the content of team communication, then uses social network analysis to examine the structure of communication in relation to the centrality, density, and sociometric status of information concepts in team chat (Sorensen & Stanton, 2011). Other forms of process measures include recording and coding eye tracking movements while working in a simulated environment (e.g., Hauland, 2008) and the use of observer ratings of TSA from subject matter experts (e.g., Patrick James, Ahmed, & Halliday, 2006; Proctor, P­ankno, & Donovan, 2004).

Empirical research Research contexts  TSA has mainly focused on the study of action teams working in c­omplex and dynamic environments that are characterized by high role differentiation and high need for coordination among team members (Sundstrom, de Meuse, & Futrell, 1990). The types of action teams examined in the literature have been quite diverse. These include teams working in mission control (e.g., Onken, 2013), aviation (e.g., Chiappe et al., 2012; Liang, Lin, Hwang, Wang, & Patterson, 2010), traffic operations (e.g., Glynn, 2011), the military (e.g., Chen & Barnes, 2012; Fischer, 2011), emergency response (e.g., Heard, Thakur, Losego, & Galluppi, 2014; Lewis et al., 2011; Taber, McCabe, Klein, & Pelot, 2013), sports (e.g., De Keukelaere, Kermarrec, Bossard, Pasco, & De Loor, 2013), nursing (e.g., Deckers, 2011), and surgery (e.g., Blavier & Nyssen, 2010). Situation awareness outcomes  The positive effects of TSA on performance have been primarily demonstrated in the context of aviation and air traffic control. Laboratory studies have shown that Reserve Officer Training Corps (ROTC) cadets flying simulated unmanned air vehicles (Cooke et al., 2001), air traffic control students completing an air traffic simulation (Hauland, 2008), and military fighter pilots using a flight simulator (Sulistyawati, Wickens, & Chui, 2009) all have higher levels of team performance when their TSA is high. Reasons for these increases in performance include fewer errors and faster response rates. These findings are consistent with field studies that have shown the positive effects of TSA on team performance in both military aviation (Prince, Ellis, Brannick, & Salas, 2007) and air traffic control (Soraji et al., 2012). A similar pattern of results have been found among teams working in nuclear power plants both in simulated laboratory environments (Banks & McKeran, 2005) and in field settings (O’Connor, O’Dea, Flin, & Belton, 2008).

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Situation awareness antecedents  TSA is a dynamic concept, varying widely both across different performance episodes and even within a single‐performance episode (Patrick & Morgan, 2010). Understanding and controlling the factors that impact TSA can be useful in improving the consistency of situation awareness in the team both within and across performance episodes. Four categories of antecedents of TSA have been studied by researchers: technological artifacts, team communication, team training, and cultural/ individual differences. Given the foundation of situation awareness in the aviation and human factors literatures, a wide variety of studies have been conducted to illustrate how technological artifacts can improve TSA. The majority of these studies propose the use of context‐specific tools that can be used as cognitive aids. For example, Big Board is a geospatial tool p­roposed to improve TSA among emergency response teams (Heard et al., 2014), the Da Vinci Robotic system has been proposed to improve TSA among surgical teams (Blavier & Nyssen, 2010), and RoboLeader is an intelligent agent proposed to aid TSA in military environments (Chen & Barnes, 2012). Other than system design and evaluation studies, research in this area has focused on identifying common attributes of technological a­r tefacts that enable the formation of situation awareness. These attributes include using displays that are interactive (Taber et  al., 2013), reducing cognitive load by enabling a common operational picture among team members (Banks & McKeran, 2005; Funke & Galster, 2009; Seppänen, Mäkelä, Luokkala, & Virrantaus, 2013), and automating the retrieval of regularly accessed information (Glynn, 2011). The most prominent team process that has been found to impact the formation of TSA is communication. In a qualitative study of search and rescue teams, Seppänen and colleagues (2013) observed that two of the major factors that reduced the formation of TSA were gaps in information and lack of fluent communication among team members. S­imilarly, qualitative data retrieved from observing orthopedic surgical teams showed that surgeons who communicated little with their team or used one‐way communication were more likely to encounter lower TSA (Bleakley, Allard, & Hobbs, 2013). Wauben and colleagues (2011) found that surgeons were least likely than nurses and anesthetists to engage in the information gathering necessary to facilitate the development of TSA. Bleakley and colleagues (2013) suggested that these communication dynamics can be attributed to the presence of hierarchical and authoritative cultures in the organization. This idea is consistent with Saner, Bolstad, Gonzalez, & Cuevas (2009) who found that information flow and TSA are significantly impacted by one’s role and position in the organization in relation to the organizational hub. The virtuality of the team is another structural component found to impact communication flow. In an experimental study manipulating communication types, Funke and G­alster (2009) showed that TSA was significantly lower in the ‘no communication’ and ‘text communication’ conditions as compared with oral and oral and text communication conditions. Among railroad workers positioned at different locations who could only communicate by using a radio, Roth, Multer, and Raslear (2006) observed that teams that used proactive and informal communication were better able to develop TSA than teams whose members relied on formal communication processes. The administration of training interventions to improve TSA by developing communication and situation assessment skills has long‐standing roots in aviation (Salas, Burke, Bowers, & Wilson, 2001; Salas, Nichols, & Driskell, 2007). Research has shown that both task‐based (e.g., Proctor et al., 2004) and team‐based training (e.g., Jankouskas, 2010) help to facilitate the formation of TSA. After critically evaluating the performance of nuclear plant control room teams, Patrick and colleagues (2006) recommended that future team training interventions focused on developing TSA should hone skills and



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behaviors in the area of planning, team coordination, problem‐solving, communication, attention, and knowledge. Individual differences that have been linked to the formation of TSA include experience and national culture. Empirical data gathered from military fighter pilots working in a simulated air combat mission showed that having high diversity in flight experience among team members contributed to lower TSA (Sulistyawati et al., 2009). National culture has also been found to impact the formation of TSA, with nonnative English‐speaking countries (in particular France and Germany) scoring lower on TSA than native English‐speaking countries (Lichacz, 2009). Organizational culture has also been proposed to impact the formation of TSA, with cultures that have a sense of shared ownership and prioritization of situation awareness and a proactive model of workload management having higher situation awareness among team members (Mackintosh, Berridge, & Freeth, 2009).

Integration of Team Mental Models and Situation Awareness In comparing across the two types of team cognition, the TMM and situation awareness literatures have complementary strengths and weaknesses. The TMM literature is more mature and coherent, as well as larger, as evidenced by TMMs accounting for 50% of studies, whereas situation awareness represented 9% in a trend analysis of team cognition research between 2000 and 2009 (Wildman et al. 2012). However, situation awareness research better addresses the dynamic nature of team environments, whereas TMM studies have been largely static in nature (Uitdewilligen et al., 2013). With calls for more team‐ level studies on situation awareness and the need for TMM research to better incorporate situational content, and dynamic contexts, much can be gained from integration across disciplinary boundaries. Below, we discuss parallel research needs as well as ways to assimilate both literatures through testing the relationship between TMMs and TSA and measuring the notion of a team situation mental model.

Parallel research needs Both types of team cognition share several future research needs, including the need to understand the process by which TMMs and situation awareness evolve over time. McComb (2007) proposed a three‐step process by which TMMs evolves: orientation (becoming familiar with the situation), differentiation (identifying unique viewpoints of the situation), and integration (allowing individual perspectives to develop into a collective focus). Other conceptual developments include a model of macrocognition detailing the process by which individual knowledge is learned by team members, internalized, shared, and transformed into externalized team knowledge (Fiore et al., 2010; Letsky & Warner, 2008). Beginning to test these models qualitatively, McComb and colleagues (2010) found that distributed teams communicated less and experienced TMM convergence later in their development than face‐to‐face teams. Addressing the under‐researched topic of how TMMs are revised over time, Uitdewilligen and colleagues (2013) examined changes in underlying structure of taskwork TMMs to assess whether they aligned with situation changes. Adopting a team interactive framework in which team member communications are examined as cognitive processes in their relevant context would facilitate a clearer understanding of TMM convergence (Cooke, Gorman, Myers, & Duran, 2013). Far more common in the situation awareness literature, interaction‐based, process measures that capture observed behaviors provide a wealth of rich data to dissect how cognition evolves

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over time. Nevertheless, given the paucity of team‐level situation awareness research, a­dditional research is also needed that evaluates the formation of TSA over time. According to Endsley (1995a), situation awareness operates in working memory and can change from moment to moment. However, research on expertise and situation awareness imply that situation awareness development is linked to deeper information processing, such that teams may become more adept at developing SA over time through forming expert‐like mental models (Walker et al., 2010). Therefore, future research should continue to explore the emergent mechanisms through which TMMs and situation awareness converge, are maintained, and are altered throughout the team’s lifecycle. Closely related to the need to better understand TMM and situation awareness development is the ubiquitous call for more longitudinal research. Although it is commonly assumed that TMMs will converge over time, owing to increased interaction (e.g., Rentsch & Hall, 1994) and some support has emerged (e.g., Marks et al., 2000), most studies have found no significant differences over time (e.g., Edwards et al., 2006; M­athieu et  al., 2000, 2005; Santos & Passos, 2013; Smith‐Jentsch et  al., 2001). However, the limited TMM longitudinal work is generally measured two or three times over a multi‐ hour period during a team simulation (e.g., Edwards et  al., 2006; Marks et  al., 2000; Mathieu et  al., 2000, 2005) rather than over days (Smith‐Jentsch et  al., 2001), weeks (Santos & Passos, 2013), or months (Levesque, Wilson, & Wholey, 2001). Situation awareness also suffers from a similar, but more acute, research gap as TMMs in that longitudinal research is uncommon, but is theorized to be a highly context‐ specific construct that may fluctuate widely both within and across performance episodes for each team (Patrick & Morgan, 2010). Ironically, situation awareness is often evaluated only once within longitudinal studies in which other variables are evaluated over time (e.g., Funke & Galster, 2009; Mackintosh et al., 2009). Moreover, in studies that measure situation awareness over multiple time points, the data are often averaged during analyses (e.g., Sulistyawati et al., 2009; Taber et al., 2013). In a notable exception, Saner and c­olleagues (2009) examined changes in SAGAT across five different scenarios within 2  hours. Although expecting situation awareness to increase over time, results showed that it remained steady across scenarios 2 and 3, peaked in scenario 4, and showed a significant decrease in scenario 5. Another study, in which situation awareness was e­valuated using video recordings of nuclear control room teams participating in simulator training, found that temporal fluctuations in situation awareness varied across teams, and that awareness tended to be highest in the third and most complex scenario (Patrick et al., 2006). In addition to knowledge convergence processes and longitudinal studies as research needs, both TMM and situation awareness scholars have grappled with the longstanding issue of how much knowledge should be overlapping and how much knowledge should be distributed among team members. “Sharing” ambiguously encompasses the notion of “having in common” as well as “dividing up” (Klimoski & Mohammed, 1994). C­onceptually, both the TMM (Cannon‐Bowers et al., 1993; Mohammed et al., 2010) and situation awareness (e.g., Chiappe et al., 2012, 2014; Endsley, 1995a; Salas et al., 1995) literatures have emphasized sharing as overlapping knowledge. Likewise, reviews of TMM (DeChurch & Mesmer‐Magnus, 2010a) and situation awareness (Salmon et  al., 2008) studies confirm that more similar knowledge is associated with higher performance. However, in both areas, there is increasing awareness that “more overlapping knowledge is better” is too simplistic and that we need to answer the more nuanced questions of who and what types of knowledge should be shared. Although empirical work is needed, researchers have proposed that a TMM comprising mostly distributed taskwork knowledge, but mostly overlapping teamwork knowledge, may be optimal for heterogeneous teams



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with distinct roles requiring specialized knowledge (Mohammed & Dumville, 2001; Rentsch, Delise, & Hutchison, 2009). The situation awareness literature has addressed this issue to a greater extent through the concept of distributed situation awareness (e.g., Artman & Garbis, 1998; Hutchins, 1995; Salmon et  al., 2010; Stanton et  al., 2006) and specific measures of distributed situation awareness that help to address under what conditions situation awareness should be similar and dissimilar (e.g., Sorensen & Stanton, 2011). The realities of modern‐day teams also call for greater sophistication regarding whether team knowledge should be overlapping or distributed. Team boundaries are becoming more permeable and difficult to identify because many employees are members of multiple teams simultaneously, work in multiple geographies and/or time zones, may join or leave teams at different times, and are expected to self‐govern (Tannenbaum, Mathieu, Salas, & Cohen, 2012). Therefore, future TMM and situation awareness research should challenge implicit, traditional assumptions underlying the nature of collaboration by considering how members achieve knowledge convergence and divergence amidst multiple team affiliations, geographically dispersed teammates, changing team members, and self‐managed teams.

The relationship between TMMs and TSA Research streams from both constructs suggest an intersection between TMMs and TSA. Early conceptual situation awareness research proposed that TMMs should facilitate the development of TSA (Bolstad & Endsley, 1999; Cooke et al. 2001; Endsley, 1995a; Prince & Salas, 2000). For example, Durso and Gronud (1999) suggested that TMMs should diminish the load on working memory, allowing team members to develop effective situation awareness. Similarly, the formation of expert‐like mental models through deep information processing has been proposed as the mechanism by which teams develop situation awareness over time (Walker et al., 2010). In contrast, others have argued that the relation between the two constructs may operate in the opposite direction (i.e., situation awareness →TMM), or be reciprocal (i.e., TMM→ situation awareness →TMM; Neisser, 1976). In this sense, Jones and Endsley (1996) described how “a Level 1 SA error, such as failure to monitor or observe data can lead to a lack of or incomplete mental model, which is a Level 2 or Level 3 SA error” (p. 508). Although several authors have alluded to the relationship between mental models and situation awareness (e.g., Durso & Gronlund, 1999; Jones & Endsley, 1996; Saner et al., 2009; Uitdewilligen et  al., 2010), direct empirical tests are lacking at the team level. Despite numerous calls for studies to simultaneously investigate multiple forms of team cognition (e.g., DeChurch & Mesmer‐Magnus, 2010a; Mohammed & Dumville, 2001; Mohammed et al., 2010; Wildman et al., 2012), there has been little movement in this regard, especially with regard to TMMs and situation awareness. However, in one exception, Saetrevik and Eid (2014) tested a new measurement approach in an emergency preparedness center in which team members answered multiple‐choice probe questions at various points during scheduled training exercises. On the same probe questions, similarity between team members was used to measure TMMs (did not capture knowledge structure), and similarity between team members and the best‐informed member was used to assess TSA. As the simultaneous measurement of multiple forms of team cognition would facilitate building unified theories of team knowledge, additional research that measures both TMMs and situation awareness in the same study is highly recommended. Not only do the causal linkages between situation awareness and TMMs need to be empirically tested, but studies should investigate how each form of team cognition differentially p­redicts processes and performance.

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Future Research In addition to the future research needs already discussed regarding the parallel needs of the TMM and situation awareness literatures and the relationship between TMMs and TSA, we advocate that studies should also be conducted on the notion of a team situation model (TSM). Whereas the situation was originally identified as a key component of task TMMs (Cannon‐Bowers et al., 1993), the importance of this specific type of cognitive content has gotten lost in the generic teamwork and taskwork categories that are commonly operationalized. In addition, multiple scholars have called for research to capture the dynamism and interaction inherent in how team cognition emerges (Cooke et  al., 2013; Fiore et al., 2010; Kozlowski & Chao, 2012). Responding to these two research needs, attention is being given to TSMs, defined as “the mental representation associated with a dynamic understanding of the current situation that is developed by team members moment by moment” (Rico et al., 2008, p. 167). Whereas TMMs are team‐level stable knowledge representations that are acquired before any task execution and stored in long‐ term memory (e.g., knowledge about infectious diseases), TSMs are the dynamic mental representations that team members develop when engaging in a particular task (e.g., understanding by the medical team of the infectious process of an inpatient who does not respond to standard treatment). In comparing TSMs and TSA, both are dynamic and situation driven, operate on team members’ working memory, and use long‐term knowledge to make sense of the situations at hand. However, a key difference between both constructs is that TSA describes the cognitive operations/processes involved in gathering and organizing information (i.e., perception, comprehension, and projection), whereas the TSM may be considered the knowledge structure (including both content and relationships among concepts) that results as a product of situation awareness development. Although research remains in its infancy, the TSM has been linked to increased team processes and performance, both conceptually (Rico et al., 2008) and empirically (Hamilton, 2009; van der Haar et al., 2015). Hamilton (2009) collected data from student teams working in an emergency management simulation and found that the sharedness and accuracy of TSMs evaluated using paired comparison ratings had independent positive effects on team performance. Also examining emergency management teams, but in a multidisciplinary field simulation, van der Haar and colleagues (2015) similarly found that TSM sharedness predicted higher team effectiveness as measured by action quality at the scene of incidents. In addition, co‐construction (process by which team members facilitate information exchange and build meaning) was positively related to TSM sharedness when constructive conflict was high (van der Haar et al., 2015). Building on prior conceptual and empirical research, we propose that TSMs could be the cognitive mechanism through which dynamic situation awareness influences static TMM and vice versa. In a particular team performance episode, TMMs represent the base of stable knowledge that team members hold in their long‐term memory systems that is relevant for facing the current situation. These TMMs would operate as the cognitive inputs for the team‐level process of situation awareness that occurs dynamically in team members’ working memory systems when they perform specific tasks. Thus, the TMM would fuel the three situation awareness‐related processes: (1) perception by signaling team members what relevant contextual information to attend to and gather; (2) comprehension by interpreting the meaning of changes in the external environment; and (3) projection by making predictions on how elements in the environment are likely to change. If the TMM is shared and of high quality, team members would be able to quickly and accurately process the critical elements in their environment (e.g., recognizing worsening symptoms in a cardiac inpatient) and



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accordingly manage the event. Compared with teams of novices, this would be more apparent in teams of experts capable of developing higher situation awareness when facing domain‐related problems because of more shared and accurate TMMs (Uitdewilligen et al., 2013). As a result of the collective situation awareness process, a TSM would emerge in real time as the team‐level dynamic understanding of the situation at hand. The TSM would impact stable TMMs by updating, expanding, reinforcing or refining pre‐existing knowledge (both content and structural relations), which in turn would impact TSA in future performance episodes. Overall, we argue that in a particular team performance episode, long‐term TMMs would operate as a cognitive input, dynamic situation awareness as a cognitive process, and TSMs as an output of that process, which in turn will impact (directly) TMMs and (indirectly) situation awareness in a cyclical, iterative manner. However, empirical research needs to confirm these proposed relationships, the role played by sharedness and accuracy, and the implications for team effectiveness.

Conclusion Team cognition has been recognized as one of the most noteworthy developments in team research (Salas, Cooke, & Rosen, 2008; Salas & Wildman, 2009), and our review demonstrates that this research has maintained an upward trajectory with no signs of waning. Within the TMM and situation awareness literatures, we have identified several important future study needs that will continue to extend these research streams. However, across TMM and situation awareness research, there are significant opportunities for intersection and integration that would not only enhance these respective literatures, but also advance the science of team cognition as a holistic entity. As such, we believe that some of the most exciting developments in the future will result from merging concepts from multiple team cognition literatures, identifying causal linkages between different forms of team knowledge, and testing how each differentially predicts various team processes and effectiveness indicators.

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17

Team Trust Ana Cristina Costa and Neil Anderson

Introduction Trust has long been recognized as a critical factor in the functioning, effectiveness and wellbeing of individuals and teams in organizations. Its importance has grown considerably over the years, 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, 2012). The present chapter builds on and extends prior literature and research on team trust in three important ways. First, we seek to clarify the definition of trust and its conceptualization specifically at the team or workgroup level, as well as discussing the similarities and differences between interpersonal and team level trust. In doing so, we develop a more integrative understanding of the conceptualization of team trust. Second, we comprehensively review the extant literature and empirical research findings into working team trust. Here, we identify a number of consistent themes, key findings, and advances made in team level trust research over recent years. Third, and finally, we delineate a multilevel framework with individual, team and organizational level determinants and outcomes of team trust. Our aim is to clarify core variables and processes underlying team trust and to develop a better understanding of how these phenomena operate in a system involving the individual team members, the team self and the organizational contexts in which the team operates. We conclude this chapter by reviewing and proposing a number of directions for future research and future‐oriented methodological recommendations.

The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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Conceptual Issues and Clarification Definitions of trust For decades, the field has witnessed much debate and disagreement over the precise definition of trust. While some scholars have approached trust as a risk‐taking behavior and have operationalized it as a cooperative choice in the context of experimental cooperation and competition (e.g., Deutsch 1958; Gambetta, 1988), others have defined trust essentially as a “trait” that individuals carry from one situation to another and develop in varied degrees depending on their personal experiences and prior socialization (e.g., Rotter 1980). Still others have emphasized conditions of dependence, reliance or confidence between individuals (e.g., Cook & Wall, 1980; Golembiewski & McConkie, 1975), while others have identified different trust bases (i.e. cognition‐ and affect‐base trust; McAllister, 1995). Finally, some other scholars have viewed trust as a synonym of trustworthiness and focus on the personal characteristics that inspire positive expectations on the part of other individuals (e.g., Butler & Cantrell, 1984). In contrast to this earlier work, recent literature has seen increasing consensus about how trust should de conceptualized and measured. This is largely due to the seminal articles by McAllister (1995) and Kramer (1999) and the integrative definitions proposed by Mayer, Davis, and Schoorman (1995) and Rousseau, Sitkin, Burt and Camerer (1998). Specifically, these authors define trust essentially as a psychological state consisting of two interrelated components: (1) the willingness to accept vulnerability, and (2) positive expectations trustworthiness (Fulmer & Gelfand, 2012). This definition has been widely adopted by researchers studying trust at different levels including the work team level (e.g., Costa & Anderson, 2011; Cummings & Bromiley, 1996; De Jong & Elfring, 2010). The widespread consensus around this two core component definition has contributed to the integration of the trust literature in two major ways. First, it has helped scholars to identify what trust is, and to structure the literature in terms of antecedents and consequences of trust. Second, it has helped distinguishing trust from other related variables such as trust propensity, trustworthiness, and risk‐taking behaviors all of which have been studied as indicators of trust in prior research. These distinctions are briefly discussed below.

Trust and other related constructs By explicitly describing trust between two specific parties: a trusting party (trustor) and a party to be trusted (trustee), Mayer et al.’s (1995) model of trust in organizations was fundamental to clearly distinguish trust from other related constructs by identifying the antecedents of both the trustor and the trustee. On the side of the trustor, propensity to trust refers to a relatively stable individual disposition that affects the likelihood that one person will trust another (Rotter, 1980) and constitutes an important antecedent of trust. Kee and Knox (1970) argued that trust depends not just on past history between the trustor and the trustee but also on dispositional factors such as personality. To explain the origins of such dispositional trust, Rotter (1980) suggested that individuals extrapolate from their early trust‐related experiences to build up general beliefs about other people, which eventually assume the form of a relatively stable personality characteristic. On the side of the trustee, perceived trustworthiness refers to the attributes and actions of the person to be trusted which lead that person to be more or less trusted. Perceptions of others trustworthiness can be formed across three dimensions: ability, benevolence, and ­integrity (Mayer et al., 1995). Meta‐analytic evidence from Colquitt, Scott, and LePine (2007)



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shows that both trust propensity and trustworthiness have a direct effect on trust and also influences a number of other outcomes both directly and indirectly via trust. Trustworthiness has shown to be a more relevant antecedent of trust in ongoing relationships, whereas trust propensity is mostly important when there is little information to form expectations about others (Colquitt et al., 2007; Mayer et al., 1995). The importance of trust propensity has been particularly acknowledged in newly formed organizational relationships (e.g., McKnight, ­Cummings, & Chervany, 1998). While trust propensity and perceived trustworthiness are often seen as antecedents of trust, risk taking is viewed as an outcome of trust. Risk taking can lead different behaviors depending on the context and levels of the trust relationship at study. For example, at the individual and team levels, trust has been associated with cooperation between individuals, information sharing, organizational citizenship behaviors, reliance on another’s work and expertise, lack of interpersonal surveillance and monitoring (e.g., Colquitt et al., 2007; Costa, 2003; Cummings & Bromiley, 1996; Currall & Judge, 1995; Gillespie & Mann, 2004; Smith & Barclay, 1997; Zand, 1972). Although trust often leads to risk‐taking behavior, trust is not risk taking but rather the willingness to assume risk (Mayer et al., 1995). This is an important distinction, as to define trust as risk taking is to confuse the focal construct with its logical consequence. Moreover, evidence suggests that risk taking may take place in the absence of trust, and risk taking may not occur even if trust is present (e.g., Bohnet, Greig, Herrmann, & Zeckhauser, 2008). The distinction between trust, trustworthiness, and risk‐taking behavior is important as it clarifies the nature of the trust concept and at the same time identifies the mechanism through which trust emerges in a particular relationship. Although some of these constructs can be more critical to a particular context or be more central to address a particular research question than the others, it is imperative that the appropriate construct is selected, defined, and measured accordingly.

Team Trust: Similarities and Distinctions with Interpersonal Trust Team trust refers to the collective trust that is shared among team members; i.e., “a shared psychological state among team members comprising willingness to accept vulnerability based on positive expectations of a specific other or others” (Fulmer & Gelfand, 2012, p. 1174; Rousseau et  al., 1998). Similar to conceptualizations of trust reflecting an individual’s “psychological state” (Fulmer & Gelfand, 2012:1174; Rousseau et al., 1998), team trust comprises of the same underlying dimensions; i.e., positive expectations of trustworthiness and willingness to accept vulnerability. The key difference is that these are assumed to be shared among team members. Consistent with Chan’s (1998) consensus composition models, the basic content of the construct remains unchanged across levels; i.e., from an individual psychological state to a team collective state. As constructs at a team level are only meaningful when shared among members (Kozlowski & Klein, 2000), within‐group agreement is then necessary to justify the aggregation of individuals’ collective trust perceptions at the team level (Chan, 1998). Thus, team trust is essentially a collective phenomenon that entails generalized expectations of trustworthiness and the willingness to accept vulnerability to all members (Costa & Anderson, 2011; Costa, Ferrin & Fulmer, in press; De Jong & Elfring, 2010; Kramer, Hanna, Su, & Wei, 2001; Langfred, 2004, 2007). However, team members may share a high or low collective trust and yet individually maintain different levels of interpersonal trust between them (Chan, 1998). For example, De Jong and Dirks (2012) found that

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consensus of collective perceptions of trust within teams can coexist together with a number of trust asymmetries between team members. In this sense, the construct of interpersonal trust is different than the team trust construct.

Team Trust: A Multilevel Perspective Team trust is believed to emerge from ongoing interaction between members and team membership (Williams, 2001) in which collective sense making about shared experiences emerges (Shamir & Lapidot, 2003). This suggests that trust is an emergent phenomenon (e.g. Kiffin‐Petersen, 2004; Burke, Sims, Lazzara & Salas, 2007), which develops from individual cognitions, emotions, and interactions which aggregate to form meaningful collective constructs (Kozlowski & Klein, 2000; Marks, Mathieu & Zaccaro, 2001). Social exchange theory (e.g. Blau, 1964) and the principle of positive reciprocity (Zand, 1972) have been used to explain the development and maintenance of trust in teams. Bliese (2000) proposes that attitudes among team members are non‐independent, such that one member’s trust in the team is expected to affect and be affected by other ­members’ trust in the team. Although collective team trust is different than interpersonal trust between team members, the emergent nature of trust suggests that individual trust ­ ­dispositions, perceived trustworthiness between members of the team and risk‐taking behaviors influence the emergence of a collective shared trust, which in turn is likely to  influence individual behaviors, trust dispositions and perceptions of trustworthiness (Kramer, 2010). These cross‐level relationships reflects both “bottom up” and “top down” processes through which individual members influence their team and the team influences their members, respectively (Chen & Kanfer, 2006; Chen & Tesluk, 2012). Research on trust development has emphasized the reciprocity between team members to explain the development of trust through self‐reinforcing paths. Zand (1972), for example, found that initial experiences of high trust within groups of managers led members to increase their vulnerability by exchanging more relevant information, accept each other’s influence and decrease efforts to control other members’ behavior, which then supported and reinforced the initial high trust, whereas initial low trust experiences made members hesitate in accepting vulnerability, and be less open to exchange information and less willing to accept the influence of others, which then reinforced the initial low trust experience (Zand, 1972). In the context of construction projects, Munns (1995) also showed that cooperation was anticipated by trust between members which then was reciprocated with behavior validating that trust. Conversely, Simons and Petersen (2000) found that initial low team trust can lead to relationship conflict between individual team members which can increase defensiveness and withholding of information which only lowers the collective trust level even further. Thus, the notion of the self‐reinforcing interpersonal feedback loops in the development of trust in teams has demonstrated that trust (or the lack of it) tends to induce behaviors that are similar to or anticipated by the earlier action. This also suggests that antecedents affecting team level trust are likely to influence interpersonal trust and vice versa. In addition, teams and work‐groups operate in a broader organizational context and are exposed to organizational level influences (Ilgen, 1999). In particular contextual ­features of the organization, such as human resource management (HRM) practices, top management and climate can facilitate or impede the emergence of collective trust at the team level. For example, the degree of organizational centralization (or decentralization) in decision making has been found to impact the level of trust within and between teams (Hardin, 1996). This is important because it suggests that the team level trust literature



397

Team Trust Organizational-level antecedents

Organizational-level outcomes

Social oriented • Organization climate

• Better communication • Knowledge exchange between individuals and groups

Structure oriented • Organizational structure • Human resources management practices Team-level antecedents Social oriented • Interpersonal ties • InteractionaI processes • Team leadership Structure oriented • Team structure • Communication medium

Team level trust

Team-level outcomes • Team satisfaction • Risk-taking behaviors • Cooperation • Information sharing • Knowledge creation • Psychological safety • Citizenship behaviors • Conflict resolution • Team performance Individual-level outcomes

Individual-level antecedents • Trustor characteristics • Trustee characteristics • Trustor–trustee relationship

• Positive job attitudes • Risk-taking behaviors • Information sharing • Knowledge transfer

Figure 17.1  Multilevel antecedents and outcomes of team trust.

can be expanded to include a multilevel perspective. To date, there has been very little attempt to examine multilevel influences on team trust. Basically, scholars continue to maintain separate micro and macro streams of research, failing to acknowledge the broader and inescapable aggregates in which individuals, teams and organizations are embedded. Therefore, we argue that a multilevel perspective is needed to better understand the how team trust evolves as a function of individual, team and organizational level influences. Having reviewed the rather vexed issue of reaching some consensus over different definitions of the concept of trust, we move on in the following section to consider and review research into the major antecedents to team trust. Here, and in accordance with other authors in this field, we conceptualize trust development as being a longitudinal process, with key antecedents having been found to influence trust development at different stages over time. We review individual level, team level, and organizational level antecedents and outcomes in turn. Figure 17.1 sets out these multilevel antecedents and outcomes of team level trust.

Multilevel Antecedents of Team Trust Teams consist of social systems of interdependent individuals working together for a common goal and are inherently multilevel (Kozlowski & Bell, 2003). Consequently, team trust is affected by top‐down, intrateam, and bottom‐up influences. In this section, we discuss multilevel (individual‐, team‐ and organizational‐level) antecedents of team trust but note that there are unavoidably some overlaps present, for instance between individual level and team‐level antecedents.

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Individual Level Antecedents Individual level antecedents can exert influence on team trust in two different ways. First, they can influence interpersonal trust between team members, which then aggregates to influence shared or collective trust at team level. For example, there is empirical evidence that interpersonal trust and team trust are positively related (DeJong & Dirks, 2012). Second, antecedents can aggregate at team level and then directly influence team trust. This is likely to occur when antecedents involve individual differences on a number of relevant characteristics (e.g., demographic diversity) or attributes related to trust dispositions, perceptions of trustworthiness and past history of the relationship. Trust research has categorized individual level antecedents according to factors associated with the trustor, the trustee(s), and the relationship between trustor and trustee(s). Trustor characteristics  Factors associated with the trustor include, as discussed earlier, trust propensity which is particularly important in explaining variations in initial trust levels when other potential sources of information about trustworthiness are limited in such situations (Colquitt et  al., 2007). The importance of trust propensity has been acknowledged both in determining interpersonal trust (e.g., Colquitt et  al., 2007) and team trust in newly formed project teams (Costa, Bijlsma‐Frankema, & De Jong, 2009) and in virtual teams (Jarvenpaa, Knoll, & Leidner, 1998). People differ in their propensity to trust. Different experiences, personality types, cultural backgrounds, education, and several other social‐economic factors influence one’s propensity to trust (Mayer, et  al., 1995). For example, Ferrin and Gillespie (2010) noted that mean country levels of trust propensity vary greatly from country to country, such that individuals in Nordic European countries plus China, Vietnam, New Zealand, and Saudi Arabia have demonstrated higher level of trust propensity than individuals in Malaysia, Indonesia, Turkey, Brazil, and a number of African countries (Delhey & Newton, 2005). Thus, such individual differences are likely to affect individual’s trust judgments in the workplace. Trustee(s) characteristics  Trustee characteristics are among the most studied determinants of trust (Dirks & Ferrin, 2002). Three trustee factors have been identified by Mayer et al. (1995) as core of trustworthiness; i.e., ability, benevolence, and integrity. Ability or ­competence captures the knowledge and skills needed to do a specific job along with the interpersonal skills and general wisdom needed to succeed (Colquitt et al., 2007; Mayer et al., 1995). Ability refers to what trustee(s) can do or perform and has become one of the most discussed components of trustworthiness in organizational contexts (Barber, 1983; Butler, 1991; Butler & Cantrell, 1984; Gabarro, 1978; Kee & Knox, 1970; Mayer et al., 1995). Benevolence reflects a positive orientation towards the trustor; i.e., a sense that the trustee wants to “do good” to the trustor, which can create an emotional attachment and foster a sense of positive affect towards the trustee (Colquitt et  al., 2007). Integrity reflects an adherence by the trustee(s) to a set of acceptable principles or a set of shared values, such as honesty, reliability, and fairness, which can provide the kind of long‐ term predictability and can help individuals cope with uncertainty (Lind, 2001). Meta‐analytical evidence by Colquitt et al. (2007) has demonstrated that each of these trustworthiness dimensions has a direct and unique relation with trust. How these three factors are combined into establishing someone’s trustworthiness is unique both between individuals and between situations (Mayer et al., 1995; Mayer & Davis, 1999). In some situations, someone’s ability may be much more important than the other two factors. For example, in situations of knowledge exchange and transfer the perceived trustworthiness of someone’s competence or ability can be a determinant factor for trusting that person



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(Dirks & Skarlicki, 2009). Trust in another’s competence affects the perceived usefulness of knowledge received and trustors are more likely to listen to, absorb, and take action on the knowledge received from a trustworthy source (Levin & Cross, 2004). Other work situations may involve simpler tasks but be politically sensitive. In such cases, the trustee’s integrity may have a greater impact on trust than does ability (Mayer & Davis, 1999). Colquitt et al.’s (2007) meta‐analysis revealed that interpersonal trust between co‐workers and leaders had similar patterns on the three trustworthiness dimensions. The only exception was the relation between integrity and trust, which was significantly stronger for leader‐based referents. Within teams, ability and integrity showed the greatest influence on team trust in the case of temporary work groups (e.g., Aubert & Kelsey, 2003; Jarvenpaa et al., 1998; Robert et al., 2009), supporting the argument that members of a task‐focused temporary team consider benevolence to be less important to short‐term performance than ability or integrity (Meyerson et al., 1996). Mayer et al. (1995) argue that judgments of ability and integrity are formed relatively quickly in the course of a relationship, while benevolence judgments will take more time as they require the development of emotional attachment. Studying the effects of strong and weak ties in knowledge transfer within teams Levin and Cross (2004) found that benevolence was an important source of trust in knowledge transfer. Trustor‐trustee(s) relationship  A third set of individual‐level antecedents involves the relationship between the trustor and trustee(s). This relationship is influenced by both past history (process‐based) and degree of similarity (characteristic‐based) between the trustor and the trustee(s)(Kramer, 1999; Mayer et al., 1995; Zucker, 1986). Research on interpersonal trust has shown that individual perceptions of others trustworthiness and their willingness to engage in trusting behavior when interacting with them are largely history‐dependent processes (e.g., Boon & Holmes 1991; Deutsch, 1958). Thus, trust between two or more interdependent individuals develops as a function of their cumulative interaction. This past history comprises not only trustee behaviors, but also trustors’ past experiences of placing trust and then seeing whether that trust is honored or violated (Kim, Ferrin, Cooper, & Dirks, 2004). Studies of repeated trust games typically show that honoring of trust by the trustee encourages future placement of trust by the trustor (e.g. Berg, Dickhaut, & McCabe, 1995; Deutsch, 1960). The competing assumption is that relationships begin with a baseline level of moderate or even high trust, and then that level of trust is calibrated over time based on outcomes of trusting and then updating trust related expectations (see Lewicki, Tomlinson, & Gillespie, 2006, for a review). In newly formed project teams, Costa, et  al. (2009) found that teams in which members had substantial experience working together had higher team trust, both initially and throughout the team tenure, and higher levels of performance, compared with teams in which members lacked prior experience of working together. In this regard, history‐based trust can be construed as an important form of knowledge‐based or personalized trust in organizations (Lewicki & Bunker 1996; Shapiro, Sheppard, & Cheraskin, 1992). Another facet is that the degree of similarity between the trustor and trustee. Trust is believed to develop more naturally in contexts where individuals have more commonalities, are more similar affectively, and are more comfortable with each other’s interpersonal interaction than in contexts where members are more diverse (e.g. Blatt, 2009). Research studying the effects of demographic diversity between team members on trust has provided support for this idea. For example, Zolin, Hinds, Fruchter, and Levitt (2004) found that nationality diversity was negatively associated with team trust and perceptions of trustworthiness between team members in a sample of global student teams. Curşeu and Schruijer (2010) found similar results in a study with student teams working in one location.

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Also, in a field study with entrepreneurial teams, Chowdhury (2005) found that diversity in terms of age and functional background was negatively related to perceived trustworthiness between team members. As Brewer (1981) noted, shared membership in a given category can serve as a rule for defining the boundaries of low‐risk trust. Furthermore, individuals tend to attribute positive characteristics such as honesty, cooperativeness, and trustworthiness to other ingroup members (Brewer, 1981). As a consequence, individuals may confer a sort of depersonalized trust on other group members that is predicated simply on awareness of their shared category membership. In summary, research into individual level antecedents has delineated a whole range of variables and processes found to be associated with team level trust outcomes. A particular focus has been relationships between trustor and trustee, and research developments in this area have been particularly noteworthy over more recent years. As previously mentioned, the boundary between interindividual effects and team‐level antecedents is somewhat porous, with studies addressing similar or identical issues in several cases. However, this caveat duly noted, we now move on to discuss advances in team‐level antecedents.

Team‐level antecedents Given that trust has been studied across a varied range of teams from ongoing (long‐ cycled) to project‐based (short‐term) teams, including cross‐functional and virtual teams, and also within diverse contexts such as research and development, health care, to customer service and student teams, there is some degree of fragmentation in terms of the antecedents identified. While we recognize that there are many important factors that potentially can impact trust in teams, we focus on those that have been empirically studied. In line with sociotechnical approaches to organizational behavior (Salancik & Pfeffer, 1978) and other reviews on team‐level constructs which emphasize both social‐ and structural‐oriented inputs (e.g., Chen & Tesluk, 2012). Social‐oriented antecedents include variables such as interpersonal ties, process, climate, and leadership practices which exert social influences on teams. Structural‐oriented antecedents refer to the relevant structural aspects of teamwork and job characteristics as well as the communication technologies in which teams operate. In the following sections, we review empirical research in support on different team‐level antecedents of trust. Interpersonal ties  A number of classical studies have highlighted the influence of interpersonal ties in the development and maintenance of trust within teams (e.g., Burt & Knez, 1996; Granovetter, 1985; Putnam, 1993). Trust and tie strength are often positively correlated (Levin & Cross, 2004). Teams with strong ties are shown to have greater shared perceptions of trust than teams that lack such strong ties, because members who interact more frequently are more emotionally attached and cohesive (e.g., Krackhardt, 1992; Portes & Sensenbrenner, 1993). Teams with strong ties tend to share common values and to have a shared vision which also encourage the expectation that members will work for collective goals and not for self‐interest (Tsai & Ghoshal, 1998). Such teams allow for greater risk taking, as members will be more willing to trust one another because they will expect that these will be returned (e.g., Edmondson, 1999; Kramer, et al., 2001). Evidence from Balkundi and Harrison’s (2006) meta‐analysis shows that teams with strong and dense ties not only have higher levels of trust, but also attain their goals more effectively, as compared to teams with weak and sparse configurations. Levin and Cross (2004) also found that tie strength had a significant positive impact on benevolence‐ and competence‐based trust in teams, which in turn influenced knowledge exchange. In studying the impact of social capital dimensions on creativity in R&D project teams,



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Chen, Chang and Hung (2008) found that interpersonal ties and mutual trust within teams were positively related. Thus, teams with strong ties not only increase team‐level trust but also diminish the risk for opportunistic behavior which reduces the need for costly monitoring and consequently transaction costs (Nahapiet & Ghoshal, 1998). Interaction processes  The trust literature widely recognizes that trust is not a static phenomenon. Yet research identifying trust patterns and shifts over time is scarce. As discussed earlier, several studies have emphasized the self‐reinforcing cycle of trust, which imply that the behavior consequences of trust lead to a recreation or amplification of the original trust (Ferrin, Bligh & Kohles, 2008). The most straightforward self‐reinforcing path identified in the literature is between trust and cooperation. The implicit idea is that ‘trust lubricates cooperation and cooperation itself breeds trust’ (Nahapiet & Ghoshal, 1998, p. 255). Much of this work portrays trust as building incrementally over time as a result of the choice to reciprocate cooperation and declining drastically when the choice is not to reciprocate (Deutsch, 1960; Jones & George, 1998). Evidence of the cycle between trust and cooperation within teams can be found in the earlier work of Zand (1972) with groups of manages and the more recent work by Munns (1995) complex projects and by Chowdury (2005) in entrepreneurial teams. Important to note is that cooperation can exist also without trust (Mayer et al., 1995). Trust can only lead to cooperation in circumstances where other means of producing cooperation (e.g., the use of coercion) are not possible (Serva, Fuller & Mayer, 2005). Another reciprocal effect identified in the literature is between trust and monitoring in teams. Findings on the trust‐monitoring relationship in teams are mixed. Several studies have demonstrated that monitoring and trust are positively reinforcing. For example, De Jong and Elfring (2010) found that monitoring was positively related to team trust in a study involving ongoing tax teams. With a study sample of 57 research intensive teams, Bijlsma‐Frankema, De Jong and Van de Bunt (2008) showed that monitoring can enhance trust by providing information about other members’ actions upon which expectations of trustworthiness were formed. Other studies, however, have demonstrated that close mon­ aillieu, itoring between team members leads to low trust within teams (e.g., Costa, Roe & T 2001; Langfred, 2004). Although these divergent findings may be contingent upon the nature of the teams studied, they also indicate that the reciprocal effects of trust and monitoring need further investigation. The team development literature suggests that the trust‐monitoring relationship may also be dependent upon how teams approach their tasks at each phase of the project. Indeed, empirical evidence suggests that the relation between trust and monitoring is not static (e.g., Costa et  al., 2009). Gersick’s (1988, 1989) model of punctuated equilibrium suggests that when a team reaches the midpoint, a sense of urgency to complete the project triggers a transition in the way teams approach tasks. Monitoring team processes are known to dominate the action phases of team goal accomplishment, particularly in terms of assisting team members to perform their tasks (Marks, et al., 2001). This is consistent with McAllister’s (1995) evidence that individuals expressing high affect‐based trust increase their monitoring by looking out for and helping their colleagues to get their performance back on track. Thus, it appears that the valence of this relation is contingent upon how monitoring is experienced by team members. Conflict is another critical process to the development of team trust. Conflict within teams can arise from diversity such as differences in beliefs (Olson, Parayitam, & Bao, 2007), unequal power distribution (Rau, 2005) or geographical and cultural distances as in the case of virtual teams (Jarvenpaa & Leidner, 1999; Newell, David & Chand, 2007). Conflict can be critical in undermining team trust (Lewicki & Bunker, 1996). For example,

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Simons and Peterson (2000) found that groups with higher trust suffered less‐destructive relationship conflict. Relationship conflict in particular has been found to decrease team trust in teams (Langfred, 2007) while task conflict may not directly contribute to reduce team trust. In addition, the way in which conflict is managed influences team trust within teams. For example, while conflict management strategies such as cooperation are able to promote team trust in teams, in contrast, competition approach results in imposed resolutions that fragment relationships and therefore decrease team trust (Hempel, Zhang, & Tjosvold, 2009). Similarly, research by Simons and Peterson (2000) found that top management teams low in interpersonal trust tended to attribute conflict to relationship‐based issues, whereas top management teams high in interpersonal trust tended to attribute conflict to task‐based disagreements. Thus, interpersonal trust may be an important variable to consider when managing conflict in teams. Team climate  A number of recent narrative reviews and meta‐analytical summaries have noted the connections and overlaps between team trust and team level climate (e.g., Costa et al., in press; Hulsheger, Anderson, & Salgado, 2009). Some of these reviews conceptualize trust as a sub‐facet of overall team climate (e.g. Hulsheger, et al., 2009), whereas others conceive of trust as being a related but separate construct from wider team climate (e.g., Edmondson, 1999; Holton, 2001). For instance, aspects of trust climate, including participative safety and shared norms, have been found in several primary studies and meta‐analyses to relate to team level innovation (Anderson, Potocnik, & Zhou, 2014; Hulsheger, et al., 2009; West & Anderson, 1996). Conceiving of trust as an emergent feature of team climate, authors have argued that the multiple dimensions comprising trust are fundamentally important and ongoing aspects of a team’s sustainability and capacity toward continuance functioning, especially in a changing organizational environment (see Anderson & West, 1998, for example). Finally, conceptualizing trust as part of a wider nomological network of team climate dimensions allows researchers to locate trust in relation to other concomitant dimensions important for team functioning and ongoing viability (Zornoza, Orengo & Peñarroja, 2009). Trust research has made important advances in this regard, and we return later in this chapter to consider these processes and outcomes, especially those regarding team performance in the workplace. Team leadership  Leaders play a primary role in establishing and developing trust in teams (Dirks, 2000; Dirks & Ferrin, 2002; Gillespie & Mann, 2004). Several leadership theories view trust as an essential component of team leadership. For example, the leader–member exchange theory (Graen & Uhl‐Bien, 1995) includes trust as a major component of the quality of the dyadic relationships between the leader and team members. Empirical evidence by Carmeli, Tishler and Edmondson (2011) demonstrated that team trust can be nurtured by leadership behaviors that are relationally driven, such as helping and willing to convey openness and emotional accessibility, which builds a foundation for high‐quality relationships with team members. When leaders signal sensitivity to the relational dynamics within the team, they create conditions for mutuality and trust, which them motivates team members to reciprocate and accept vulnerability (Dutton, 2003). Leaders who promote sincere behaviors cultivate team members’ beliefs that others are reliable, thus engendering willingness to be vulnerable and a sense that they can rely on each other (Doney, Cannon, & Mullen, 1998). In addition, leaders who engage in high‐quality leader–member exchange are perceived as being more trustworthy and approachable and providing greater role clarity to subordinates (Graen & Uhl‐Bien, 1995). Other leadership theories, including charismatic leadership theories, posit trust in the leader as an essential component of leadership (e.g., Conger & Kanungo, 1998; House, 1977),



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and transformational leadership theories place trust at the center of the relationship such leaders have with their followers, and suggest that it is through followers’ trust and respect in their leader that they are motivated to perform beyond expectations (e.g., Yukl, 1998). In their meta‐analysis of empirical research on trust in leaders, Dirks and Ferrin (2002) report a strong, positive association between transformational leadership and trust in the leader. Empirical evidence on leadership practices shows that providing individualized support and fostering acceptance of group goals are positively associated with trust in the leader (e.g., Butler, Cantrell, & Flick, 1999). In a study with research and development teams, Gillespie and Mann (2004) found that transformational leadership practices including consultative leadership, common values and idealized influence were strong predictors of trust in the leader. Also the trust a leader conveys to team members through these actions encourages the reciprocation of trust by team members (Gillespie & Mann, 2004). Research by Mayer and Davis (1999) and by Mayer and Gavin (2005) supports the theory that subordinates’ trust in their leader depends on the leader’s perceived levels of ability, benevolence and integrity. In sum, trust in team leadership is important in that it allows teams to be willing to accept the leader’s activities, goals, and decisions and work hard to achieve them (Dirks, 2000). Team structure  The degree of functional diversity is one relevant structural factor in the formation of team level trust. Cross‐functional teams, in particular, are believed to have more difficulty in developing trust between members, owing to unshared and sometimes conflicting goals, reporting relationships and perceived differences in professional allegiance (Jasswalla & Sashittal, 1999). For example, Newell, et al., (2007) reported that an “us vs. them” attitude often prevails and is detrimental to trust in cross‐functional teams. However, researchers have also found that it is mainly during the initial team formation that functional diversity can be a hindrance to trust development (e.g., Webber & Donahue, 2001). Taking another approach, Kirkman, Rosen, Gibson, Tesluk, and McPherson (2002) argued that functionally diverse teams are more likely to develop trust based on team member reliability, consistency and responsiveness (i.e. cognitive‐based or competence‐based trust) rather than similarity or affect‐based trust. While studying antecedents of team creativity, Barczak, Lassk and Mulki (2010) found that cognitive‐based trust together with a collaborative culture were significant predictors of team creativity while affect‐based trust was not. Similarly, in a study surveying 85 top‐management hospital teams, Olson, et  al. (2007) found that functional diversity was positively related to competence‐based trust. These results suggest that while similarity between may lead to a quick development of trust earlier on in the team formation, to have a positive and significant impact on team performance cognitive‐based trust is more important. Communication medium  The medium of communication between individuals has been found to be another important contextual determinant of team trust. Computer‐mediated teams (e.g. virtual teams) have been found to be lower than in face‐to‐face teams at the start of projects; however, over time trust levels in computer‐mediated teams can reach levels comparable to those of face‐to‐face teams (Wilson, Straus, & McEvily, 2006). Computer‐ mediated teams take longer to develop trust because it requires more time for members to exchange social information (e.g. Walther, 1992). The detrimental effect of computer‐ mediated communication on team trust can be limited if members employ additional communication strategies including taking initiative, expressing enthusiasm, responding in a timely and meaningful manner, providing transparent information, and focusing on the tasks rather than on procedures (Alge, Wiethoff, & Klein, 2003; DeRosa, Hantula, Kock, & D’Arcy, 2004; Jarvenpaa & Leidner, 1999; Palanski, Kahai, & Yammarino, 2011).

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To summarize, antecedents of team trust consist of a confluence between social and structural factors that together encourage and enable team members of share collective perceptions of trust. Given that these antecedents operate at team level they are most likely to exert direct effects of team trust than other interpersonal organizational antecedents do. This is consistent with multi‐level principles which argue that that direct influences are likely to emanate from within the same level where the phenomenon occurs than those emanating from antecedents that originate at different levels of analysis (Kozlowski & Klein, 2000).

Organizational level antecedents As teams operate in a broader organizational context is most likely that organizational level antecedents also influence team‐level trust. Features of the organization create a broader context that can further support or inhibit team level trust within and between multiple teams across the organization. First, employees continually observe the organizational environment when they consider whether or not to trust their team members (Creed & Miles, 1996). Secondly there is evidence that organizational variables impact team functioning and effectiveness (Ancona & Caldwell, 1992). Similar to the team‐level antecedents, organizational level antecedents can also be classified in terms of social‐­ oriented including management style and organizational climate, and structural oriented inputs such as organizational structure and HRM practices. Organizational climate  Organizational climate refers to the shared perceptions of the events, practices, and procedures and the kinds of behaviors that are rewarded, supported and expected within an organization (Klein & Sorra, 1996; Victor & Cullen, 1988). Research has indicated that climate can exert meaningful influences at different organizational levels (Ostroff, Kinicki & Tamkins, 2003). Therefore, organizational climate in addition to team climate can influence trust at the team level of the organization (Anderson & West, 1998; West & Anderson, 1996). Of course, the precise border between what is organization level and what is team‐level climate is moot; each influences the other in reciprocal ways. Consistent with this view, Barney and Hansen (1994) argue that only when the central values and beliefs of the organization are supported by organizational practices and decision‐making process across the organization, makes it possible that a particular climate emerges. Shared perceptions evolve over time and are important to the creation of trust at different levels of the organization (Hosmer, 1995). When the positive values are expressed but not enacted, trust can fall (Jahdi & Acikdilli, 2009). Chatman and Barsade (1995) suggest that organizational climates emphasizing individualistic values focus on individual achievement and self‐interests and therefore can have a negative influence on the trust climate, whereas climates emphasizing cooperative action may reward joint contributions to organizational accomplishments. Hosmer (1995) emphasizes the “right,” “just” and “fair” treatment of employees as determinants of how trust is established and evolves in organizations. Particularly, transparency, fairness, and consistency of organizational policies and practices can contribute to establish trust at different levels of the organization (Gillespie & Dietz, 2009; Pučėtaitė & Lämsä, 2008; Schnackenberg & Tomlinson, 2014). In contrast, legalistic approaches and formalization such as keeping a paper trail and disclosing only the necessary information can lower trust in the organization (Li, Bai & Xi, 2011; Sitkin & Roth, 1993). Organizational structure  The structure of the organization determines the degree of centralized or decentralized decision making and therefore impacts the level of trust within



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and between groups and organizations (Hardin, 1996). Mechanistic structures characterized by centralization of power where formal rules and regulations predominate decision making, and by rigid communications follow and hierarchical channels will constrain the interactions necessary for the emergence of a high trust environment (Creed & Miles, 1996). By contrast, organic organizations characterized by loose and decentralized power, and by flexible and open channels of communication structures provide employees with the autonomy necessary to succeed and are more likely to produce trust (Shaw, 1997). A study by Moorman, Deshpande, and Zaltman (1993) assessing different levels of hierarchy and trust, found that organizational level trust was directly related to how much influence the employee felt he or she had in the organization. These researchers also found that participative structures and management style improved the level of trust in the organization, while bureaucratic structures negatively affected trust (Moorman, Deshpande, & Zaltman, 1993). Human resource management practices  HRM practices and, in particular, how employees perceive and experience such practices is an important factor affecting their job attitudes and behaviors at different levels of the organization. Teams play an important role in such interpretations, as employees discuss their views and concerns with other team members (Guzzo & Noonan, 1994). High performance or high commitment HRM systems typically comprise practices that enhance competence or ability to perform (e.g., through integrated job analysis, recruitment, selection and training) and motivation to perform (e.g., through well‐aligned compensation, job design, performance management and career advancement programs) (Huselid, 1995). Whitener (1997) argues that team level trust increases as HR practices are implemented to select and train team members’ communication and influence skills and to assign clearly defined work roles. For example, teamwork skills regarding conflict resolution, collaborative problem solving and communication facilitate effective interactions between team members, with trust playing a central role in these processes (Stevens & Campion, 1999). Similarly, HR practices that motivate team members to contribute to the team’s objectives and that recognize and reward the desired activities should increase cooperation and team effectiveness (Lawler, 2003) and at the same time align the interests of individuals with those of their team and the organization (Gottschalg & Zollo, 2007). In addition, HRM systems that provide opportunities for team members to connect with others within and outside the team can support and facilitate teamwork by building the team’s social capital (Collins & Clark, 2003; Joshi & Jackson, 2003). For example, supporting the formation of cross‐functional teams and communities of practice can serve this purpose. By supporting such sharing contexts, HR practices encourage the development and extension of social capital and boundary spanning (Collins & Smith, 2006; Jackson, Colquitt, Wesson & Zapata‐Phelan, 2006). Such networks provide organizations with the necessary stability and within which trust, commitment, and reciprocity can develop in support of team coordination (Sydow & Staber, 2002). In a case study analysis of four project‐intensive firms, Söderlund & Bredin (2006) found that HR practices that focused on facilitating employee mobility, setting up project teams, designing role structures (Ancona, Bresman & Kaeufer, 2002), and facilitating personal networks among employees and consultants to support well‐functioning communities of practice (Brown & Duguid, 2001), led to a higher climate of trust within and between project teams. To summarize, in addition to individual‐level and to team‐level antecedents, organizational‐ level antecedents can also influence trust at team level. Given their broader range, some organizational‐level antecedents can exert a direct effect on team‐level trust while others will have an direct effect on team‐level antecedents and thus an indirect effect on team‐level trust.

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Multi‐Level Outcomes of Team Trust Given that teams operate in multilevel systems, team trust not only is influenced by multilevel antecedents but is likely to have an impact that transcends different levels of the organization. Several studies have reported a number of positive individual‐, team‐ and organizational‐level outcomes of team trust. For the individual team member, team trust has been found to increase job satisfaction (Braun, Peus, Weisweiler & Frey, 2013) and organizational commitment, as well as decrease job stress (Costa et  al., 2001; Costa, 2003) and to promote organizational citizenship behaviors (Hodson, 2004). At the organizational level, team trust has shown to lead to better communication and knowledge exchange between individuals and groups (Collins & Smith, 2006; Trapp, 2011). Team‐level outcomes of trust include increases in team satisfaction (Costa, 2003), information sharing (Howorth, Westhead, & Wright, 2004), team learning (Bogenrieder & Nooteboom, 2004), and team OCB (Joshi, Lazarova, & Liao, 2009; Hempel et al., 2009; Walumbwa, Luthans, Avey & Oke, 2011). Edmondson (1999) found trust in teams to be important in developing a climate of psychological safety which induces learning. Klimoski and Karol (1976) and Lee and Choi (2003) found support for the relationship between trust and knowledge creation within teams. MacCurtain (2005) and Farrell et al. (2005) found that intrateam trust was related to knowledge sharing within the team and beyond. Intrateam trust has also been found to play an important role in the management and resolution of conflicts (Simons & Peterson, 2000). Interteam trust has been found to increase resources and knowledge exchange as well as team innovation (Szulanski, Cappetta, & Jensen, 2004; Tsai & Ghoshal, 1998) and team OCB (Hempel et al., 2009; Joshi, et al., 2009; Walumbwa et al., 2011). Despite the increasing support for the overall positive effect of trust in teams, results have been less consistent regarding team performance. In a recent meta‐analysis, the relationship between team trust and team performance was found to be positive and significant (De Jong, Dirks & Gillespie, 2016). However, while some primary studies have found team trust to be directly linked to team performance (e.g. Costa et al., 2001; Walumbwa et al., 2011), others have failed to demonstrate a significant relationship (e.g. Aubert & Kelsey, 2003), and still others reported a negative relationship between team trust and performance (e.g. Langfred, 2004). Following Dirks and Ferrin (2001), several researchers have explored indirect effects between team trust and team performance. For example, De Jong and Elfring (2010) found that intrateam trust affects performance through mediated effects by team effort and team monitoring. De Jong and Dirks (2012) found that team trust influences positively team performance only under conditions of low trust asymmetry between team members. Interteam trust has been found to increase resource and knowledge exchange, which in turn boosts the innovation of the teams (Tsai & Ghoshal, 1998), but the trust and knowledge exchange relationship is weaker if the knowledge being transferred is unspecified and its function is ambiguous (Szulanski et al., 2004). Trust in teams has also been examined as a moderator. For instance, Langfred (2004) found that high team trust was associated with higher team performance when individual autonomy was low but with lower team performance when individual autonomy was high. Cognitive‐based trust in teams has been found to moderate the relationship between collaborative culture and team creativity (Barczak, et al., 2010). In relation to conflict, team trust has been found to moderate the attribution between task and relationship conflict such that when team trust is low members tend to misattribute task conflicts to be relationship ­conflicts (Simons & Peterson, 2000). Similarly, Parayitam and Dooley (2007) found that teams high on affect‐based trust display less strong correlations between cognitive and affect conflict.



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In a meta‐analysis, De Dreu & Weignart (2003) concluded that at relatively high levels of intrateam trust, openness, and psychological safety task conflict can have any positive effects on team performance. In virtual teams, Jarvenpaa, Shaw and Staples (2004) found that team trust affects virtual teams differently depending on the level of team structure. For example, in a situation of weak structure, increases in trust are likely to have a direct, positive impact on a team member’s attitudes and perceived outcomes, whereas in situations with moderately strong structure, increases in trust are likely to have contingent impacts through other factors and in situations with strong structure, increases in trust are likely to have little or no effect on work outcomes.

Future Research Although team trust is still a developing area, the empirical evidence to date suggests that team trust is an important driver for effective functioning and performance of individuals, teams and organizations. There consequently remain a number of important directions and possibilities for future research to address in this nascent area, four of which we highlight as being of particular promise to inform our understanding of how team trust operates and influences key outcomes: (i) quasi‐isomorphic and cross‐level studies; (ii)  antecedent studies examining the relative importance of different multiple‐level variables; (iii) real‐life longitudinal studies into how trust develops and changes over time; and (iv) negative influences of team trust. We consider each in turn below. First, the conceptualization of team trust here presented is based on a quasi‐isomorphic view of trust in which the same core components of trust can be identified across levels; i.e., the willingness to accept vulnerability, and positive expectations trustworthiness (Fulmer & Gelfand, 2012; Mayer et al., 1995; Rousseau et al., 1998). It is therefore important that future research investigates whether this view of trust is supported by examining the factor structure of trust across levels of analysis to confirm if these two dimensions are comparable across levels. Adopting consistent practices regarding the operationalization and measurement of trust at multiple levels is crucial in examining whether interpersonal and team trust share these dimensions. In addition, research studying collective trust in teams should reflect the appropriate consensus (i.e. agreement) among members, as conceptualizations at team level are only meaningful when the attributes (e.g. perceptions, attitudes) are shared between members (Kozlowski & Klein, 2000). Moreover, the relationship between team‐level trust and unit outcomes such as performance may only be realized when there is a high degree of agreement among team members (see Walumbwa et al., 2011). Future trust research can also improve upon using reliable and valid instruments that measure these components to generate a cumulative body of work and facilitate comparisons across studies and across‐levels to build on further knowledge. Secondly, future research should provide a finer analysis of the impact of the different antecedents at multiple levels discussed here, as well as identifying other antecedents that potentially influence the impact of trust at different levels. For example, research has demonstrated that levels of trust in teams are affected by levels of trust in specific individuals (e.g., leaders) (Dirks & Ferrin, 2002). Dirks & Ferrin (2001) also discuss the possibility that trust at one level may amplify and/or suppress factors at other levels. Thirdly, the trust literature is overall consistent that trust is not a static phenomenon; however research on trust as a longer‐term process remains in its infancy. Existing models of trust development modeled the evolution of trust over time following a simple, linear cause and effect model (e.g., Jones & George, 1998; Lewicki & Bunker, 1996). However, work relationships and teams in particular have very complex interactive histories that lead

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to particular trust dynamics, which have yet to be studied systematically. It seems reasonable to expect that teams vary in how they develop trust depending on the social and contextual pressures. For example, at team level the dynamics of trust between members have shown not to be linear (e.g. Costa et al., 2009). While such dynamics reflect the reality of trust in organizations, they also challenge existing methods and assumptions for understanding the development of trust in teams. Therefore, a better integration of theory and empirical research is necessary. In particular, longitudinal studies are highly needed. Such studies have a more systematic approach and also include different time frames – probably longer time frames rather than the typical short‐cycled project teams – in order to identify critical transition stages and relevant predictors at each stage. Likewise, further development of cross level literatures such as the trust repair literature will be an important contribution to further understand the dynamics of trust. Finally, the great majority of trust research has emphasized the multiple benefits of trust, rather than its shortcomings. The implicit assumption is that the higher the level of trust, the better. However, considering that in reality some situations may warrant lower levels of trust than others (e.g. Langfred, 2004), it may be more appropriate to consider whether there are optimal levels of trust in given situations, rather than simply aiming to increase trust (Lewicki, et al., 2006). This view highlights the importance of research on trust calibration rather than trust enhancement and suggests that not all aspects of trust are unconditionally positive in their antecedents, processes, or outcomes. Here, future research is called for to open up what has remained a Pandora’s box of potentially and actually negative aspects of trust in teams, and to counter what has become perhaps an unchallenged view that trust in exclusively a positive concept possessing linear and uncapped outcomes wherein inevitably and in all situations more trust results in better performance outcomes.

Conclusion In the present chapter, we have reviewed a large body of literature and empirical findings relevant to team level trust. As our Figure 17.1 and our preceding review comments show, there has been a concerted effort among trust researchers to uncover and quantify the substantial number of antecedents held to be related to team level trust, and then in turn, onto other more distal outcomes, such as team performance and team innovativeness. Developments in some areas have been considerable and this body of knowledge now allows a far more comprehensive and fine‐grained understanding of relationships between individual, team, and organizational level variables and team trust as a pertinent outcome. Research has become more multilevel in recent years, and the present chapter highlights this trend as being key to advancing our understanding of trust at work phenomena. Given the role that team trust plays in a host of outcomes at the individual, team, and organizational levels of analysis, it is our hope that future research will continue this vital trend and that this chapter sets out a comprehensive and timely narrative review of the state of our understanding of team trust in workplace settings.

Acknowledgments This research was supported by grant number IN‐2012‐095 from the Leverhulme Trust, U.K., awarded to both authors.



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Psychological Contracts in Teams Carlos‐María Alcover, Ramón Rico, William H. Turnley, and Mark C. Bolino

Introduction While the idea of the employment relationship as an exchange between individuals and organizations can be traced back to the writings of Barnard (1938) and March and Simon (1958) (Coyle‐Shapiro & Parzefall, 2008), the construct of the “psychological contract” first appeared in the organizational literature in the 1960s, when it was used by Argyris (1960), Levinson, Price, Munden, and Solley (1962), and Schein (1965, 1978), among others. In general, these scholars conceptualized the psychological contract as the sum of the mutual expectations between the individual and the organization, identifying the terms and conditions that define the employment relationship (Herriot & Pemberton, 1995). Expectations may be conscious and explicit like those related to wages and benefits, or they may be unconscious and implicit, as in the case of long‐term employment and ­promotion opportunities; however, both types of expectations govern the exchange relationship (Thomas & Anderson, 1998). According to Kotter (1973), the psychological contract may be viewed as an implicit agreement between the worker and the organization, composed of everything that each party expects to give and receive over the course of the relationship. As Blau (1964) theorized, then, the notion that social relationships consist of unspecified obligations (Cullinane & Dundon, 2006) is central to the construct of the psychological contract. It was not until the end of the 1980s, however, that Denise Rousseau would pick up and expand the term, with the construct subsequently becoming the focus of increasing interest and importance in the organizational literature. According to Rousseau (1989), a psychological contract is defined as an individual’s beliefs about the terms and conditions of a reciprocal exchange agreement with another party. Hence, a psychological contract emerges when one party believes that an implicit or explicit agreement has been reached in which the contributions of one party obligate the other party to reciprocate with future rewards. Rousseau (1989, 1990) initially distinguished between transactional psychological The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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contracts (i.e., those in which terms were concerned mainly with financial and material matters) and relational psychological contracts (i.e., those in which social or symbolic content is prominent). Later, she would add the category of balanced psychological contracts, which she defined as combining financial or material components with social and symbolic elements in varying degrees (Rousseau, 1995). Since its reintroduction by Rousseau (1989), the psychological contract has played a central role in research in the field of the employee–organization relationship, con­tributing to our understanding of this crucial aspect of organizational life (Shore, Coyle‐­Shapiro & ­Tetrick, 2012). More recent, integrated views of the topic treat the psychological contract as comprising the beliefs and perceptions held by employees, but shaped by the organization, with regard to the implicit and explicit promises and obligations that make up the employment relationship (Conway & Briner, 2005; Rousseau, 1995; Rousseau & McLean Parks, 1993). As such, the psychological contract defines what employees believe they have been promised by the organization and what they believe they must con­tribute in return (Dabos & Rousseau, 2004; Parzefall, 2008; Rousseau, 2001). Thus, the psychological contract refers to an implicit exchange agreement between the individual and the organization (Rousseau, 1995). Contracts of a psychological nature are usually presented in the literature as being rooted in social exchange theory (Blau, 1964; Homans, 1961) and equity theory (Adams, 1965), describing mutual exchanges between employees and the organization (Bal, Chiaburu, & Jansen, 2010; Suazo, Martínez & Sandoval, 2009), and setting out each party’s obligations towards the other. In general, employees and employers strive to maintain a fair balance in terms of the reciprocal inducements and considerations they offer each other (Rousseau, 2005; Taylor & Tekleab, 2004), and the psychological contract describes the types of obligations that both parties implicitly agree to perform (Janssens, Sels, & Van den Brande, 2003). Thus, both positive instances of reciprocity (“you fulfill your side of the bargain, and I’ll fulfill mine”) and negative instances (“fail to fulfill your side of the bargain, and I won’t fulfill mine”) must be considered in order fully to understand the psychological contract. Using a social exchange perspective, Cropanzano and Mitchell (2005) argue that norms of reciprocity can help employees and organizations establish positive exchanges. As such, positive treatment from the organization is related to individual responses linked to effort and performance, affective work commitment, organizational citizenship behavior, and felt obligations toward the organization. In short, psychological contracts lend structure to expectations concerning future exchanges, thereby reducing uncertainty between parties (e.g. by defining roles and specifying future courses of action) and creating mutual obligations that define the employment relationship (Schalk & Roe, 2007). The contract also plays a role in creating social units (e.g., partnerships, joint ventures) and managing interdependencies between individuals, groups, and organizations (Rousseau & McLean Parks, 1993). Despite, or perhaps due to, its popularity and widespread use, the psychological contract has been the target of both conceptual and methodological critiques (Marks, 2001). Researchers have raised conceptual concerns about the use of a legal metaphor to refer to an implied construct (Conway, 1996; Guest, 1998a) and the lack of a universally accepted definition, since the term psychological contract may refer to the implicit obligations of one or both parties, to the expectations of the parties regarding the expected content of the exchanges implicit in the employment relationship, and to the reciprocal mutuality regulating relations between the employee and the organization (Cullinane & Dundon, 2006). Additionally, methodological concerns related to the assessment of psychological contracts have also been raised. In particular, researchers often use different scales to measure the psychological contract, and this has caused methodological confusion and undermined



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the cumulative value of empirical research, as the term can refer to d ­ ifferent processes and variables (Guest, 1998a, 1998b). In spite of these limitations, the psychological contract has generated a wealth of highly relevant research, paving the way for significant progress in the study of the organization relationship (Shore et al., 2012). However, the question of exactly who represents the organization is also one of the key criticisms associated with conceptualizations of the psychological contract (Turnley & Feldman, 1999). Indeed, how might an employee have a direct tie to something as abstract as an organization (Ashforth & Rogers, 2012)? Several theorists have argued that perhaps the relationship between the employee and his or her organization is largely mediated by relationships with more proximal and tangible entities (Shore, Tetrick, Coyle‐ Shapiro, & Taylor, 2004; Silva & Sias, 2010). While it may be true that the line manager or immediate supervisor is the organization’s most visible representative, this merely highlights the fact that there are, or can be, multiple agents to a psychological contract. Thus, there are ­different people who represent the organization, and with whom each employee may establish psychological terms forming part of the contract as a whole. The immediate supervisor plays a particularly prominent role in the employee–organization exchange (Kozlowski & Doherty, 1989) because he/she is usually the organization’s most visible representative (Conway & Briner, 2002, 2005; Liden, Bauer, & Erdogan, 2004; Robinson & Morrison, 2000; Tekleab & Taylor, 2003) and its primary agent in psychological contracts with employees (Shore & Tetrick, 1994). Thus, the quality of the working relationship between the supervisor and the employee is a key aspect of the development and fulfillment of the psychological contract. As such, the integration of leader–member exchange (LMX) theory into the psychological contracts framework may provide valuable insights into the ways in which employees process information about the organization’s fulfillment of perceived obligations (Henderson, Wayne, Shore, Bommer, & Tetrick, 2008). Hence, it would be useful to consider an approach that is capable of integrating multiple sources vested with differing degrees of responsibility as organizational agents with whom the employees enter into psychological contracts. In this light, the multiple foci exchange relationships approach (Cropanzano & Rupp, 2008) provides a very useful conceptual framework because it explains how individuals distinguish between different sources in their perceptions of organizational justice and trust (e.g. Frazier, Johnson, Gavin, Gooty & Snow, 2010) and in the personal attachments they form, influencing identification, commitment and organizational citizenship behaviors (e.g., Lavelle et al., 2009; Riketta, & Van Dick, 2005). Therefore, consideration of multiple foci should shed light on the formation and fulfillment of psychological contracts at the team level. As previous research has shown, the psychological contract is a key construct for understanding of how employees’ perceptions and assessments of the fulfillment of obligations by organizational agents influence their attachment to and engagement with in‐role and task behaviors and with extra‐role behaviors (Dulac, Coyle‐Shapiro, Henderson, & Wayne, 2008; Henderson et al., 2008; Turnley, Bolino, Lester, & Bloodgood, 2003). The prevalent approach to research on the psychological contract in organizations was locating it at the individual level, regardless the consideration of the relational nature of organizational contexts (Coyle‐Shapiro & Parzefall, 2008; Takeuchi, 2012). In this sense, research is needed that addresses issues associated with the team or group level. Considering the ever‐increasing use of work teams to perform core processes in organizations, a team‐level examination of the psychological contract is critical for understanding the ways in which relationships with colleagues and the team leader, and perceptions of their behavior and attitudes, will shape both the formation and development of the psychological contract and perceptions of fulfillment or breach.

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This chapter begins by raising the question of multiple foci social exchange relationships at work and their implications for the psychological contract. Next, we review research that has considered the team‐level implications of the psychological contract and its potential link with LMX processes. Further, we examine the relationships between the psychological contract, fairness and peer justice, and social support in teams. Finally, we conclude with some reflections and suggestions about the implications of the psychological contract in work teams.

Multiple Foci Social Exchange Relationships and Psychological Contracts As mentioned earlier, investigations of the psychological contract have burgeoned over the last two decades, providing researchers with important insights into the employee‐organization relationship (Shore et  al., 2012; Zhao, Wayne, Glibkowski, & Bravo, 2007). However, the existing literature still fails to satisfactorily address the key issue of identifying the organizational agent (Alcover, Rico, Turnley, & Bolino, 2016); thus, it is often unclear who represents the organization in social exchange and employment relationships. Indeed, the failure to address interdependencies and relational effects in social exchange networks, and not just dyads, has been a major limitation of previous employee–organization relationship research (Takeuchi, 2012). The exchange relationships are embedded in a context (Chaudhry, Wayne, & Schalk, 2009). Employment relations in today’s workplace tend to have multiple foci, and this is likely to remain so for the foreseeable future (Lapalme, Simard, & Tremblay, 2011). This means that employees simultaneously depend on several agents representing one or possibly more organizations, who assign tasks and goals, supervise work, and offer rewards (or impose sanctions) depending on results. Prior research has often implied that each employee establishes one psychological contract with the “organization.” As a result, it is generally taken for granted that the organization is represented by a single person – most often the employee’s direct supervisor or team leader (Lester, Turnley, Bloodgood, & Bolino, 2002; Liden et al., 2004; Tekleab & Taylor, 2003) –who has the power to make all decisions affecting the employee and the psychological contract. Some researchers (Cropanzano, Byrne, Bobocel, & Rupp, 2001; Lavelle, Rupp, & Brockner, 2007) have argued that people often attribute human‐like qualities to the organization as a whole, and in this sense, the supervisor is assumed to speak for the organization. Again, however, this dyadic employee–organization relationship fails to fully capture the social context arising from interactions with different agents and the resulting processes that influence the formation and development of the psychological contract (Henderson et al., 2008). For instance, in contemporary organizations, employees are likely to maintain multiple relations with diverse organizational agents (Dawson, Karahanna, & Buchholtz, 2014; Lapalme et al., 2011), each of whom possesses a different degree of power and decision‐ making authority (Conway & Briner, 2009). Researchers in both the organizational behavior and social exchange fields have recently recognized that employees hold differing perceptions of multiple foci social exchange relationships involving various organizational agents like supervisors, higher level administrators and teammates (Cropanzano & Rupp, 2008; Rupp & Cropanzano, 2002). In terms of the psychological contract, the idea of multiple agency focuses attention on the openness of content and simultaneity of action in response to various competing demands (McLean Parks, Kidder & Gallagher, 1998). Contributors to the psychological contract include



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recruiters, supervisors, representatives of human resources departments, top managers, coworkers, teammates, and mentors (Scandura & Williams, 2002), while s­ ocialization ­rituals and the larger organizational environment also play a part. Psychological contracts do not develop in a vacuum (Haggard & Turban, 2012; Ho & Levesque, 2005), so employees pick up perceptions of mutual obligations from a whole range of sources, and their actions may simultaneously fulfill obligations to more than one agent. A further problem with multiple agency is the difficulty of identifying who is the representative organizational agent, and it must also be recognized that the messages sent by organizational agents to employees are often inconsistent. According to Schalk and Rousseau (2002), “the organization cannot be considered a single party to the psychological contract, and it does not always speak with one voice. Recruiters, managers, personnel policies/handbooks, and colleagues may all send different messages to employees” (p. 136). March and Simon (1958) argue that organizational roles are defined by the nesting of roles within the firm, the department, and the specific work team. Extending this argument, we propose that each of these roles involves demands, obligations, and expectations, which will also be nested and will define the content of the psychological contract in multiple agency relationships. The degree of overlap between the various obligations perceived by employees will depend on the relative congruence between their perceived obligations towards different agents and the perceived entitlements which they expect to receive from those agents. In this way, these nested perceptions help to define multiple agency relationships (McLean Parks et al., 1998). Other scholars (e.g. Herriot & Pemberton, 1995; Marks, 2001) have even suggested the possibility that an employee could develop multiple psychological contracts with the different organizational agents. As Marks (2001) argues, psychological contracts are complex and multifaceted, and they may be made with various constituencies within the organization. Thus, “the number of constituencies with which individuals holds contracts depends on the nature of the organization in terms of structure and work processes” (Marks, 2001, p. 460). The growth of team‐based organizations in recent decades has changed work structures and processes for the majority of today’s employees (Kozlowski & Bell, 2003; Van der Vegt & Bunderson, 2005), whose tasks, goals, and rewards depend to a very considerable extent on collective processes. According to the rule of proximity formulated by Lawler (1992), individuals develop stronger affective links to subgroups with in a social system than they do to the system itself, and thus, to work teams rather than to the organization as a whole. This means that the members of the subgroup intensify their commitment towards and identification with those to whom they are closest, so that colleagues and the team leader will appear as the principal models in relation to attitudes and behavior, while other more distant organizational agents will be less influential (Lawler, 1992; Mueller & Lawler, 1999). Similarly, self‐categorization theory and the social identity approach also suggest that individuals in a given comparative organizational context are more likely to identify with distinctive collectives such as their work team than with broader collectives like the larger organization to which they belong (Ellemers, De Gilder & Haslam, 2004). Consistent with this notion, Richter, West, van Dick and ­Dawson (2006) point out that in team‐based organizational contexts “the team rather than the organization emerges as the primary focus of identification” (p. 1252). As a result, the differentiation of roles, relationships of interdependence, the formation and development of shared mental models, emerging affective states, and the attainment of common goals all foster commitment, psychological involvement, and the identification of members with the team’s work (Solansky, 2011; Van der Vegt & Bunderson, 2005). Hence, positive relations between employees and other members of their team, and with their line manager or team leader are directly associated with desirable behaviors and

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a­ ttitudes for the achievement of favorable team and organizational outcomes (Gerstner & Day, 1997; Humphrey, Nahrgang, & Morgeson, 2007; Morgeson, DeRue, & Karam, 2010). Since the psychological contract reflects the affective state of trust, fair treatment, and reciprocity, teams that are perceived as being responsible for positive affective tone, high performance, and psychological safety are likely to have the most impact on any collective psychological contract (Marks, 2001). To sum up, the formation and development of the psychological contract depends on multiple agents who are not confined solely to the representatives of the organization, whether team leader, line manager or top manager, but also include other team members. Hence, a multiple foci approach should be highly relevant to studies of how the dynamics between team members and the team leader contribute to the development and fulfillment of psychological contracts. A final interesting issue related with multiple agency is the possible dual role played by some line managers in the formation of their subordinates’ or team members’ psychological contracts. As argued by Rousseau (1995), managers may sometimes act as agents representing the organization, making a psychological contract with a group of employees in its name, while at the same time singling out certain subordinates directly to make a private psychological contract in pursuit of personal interests and goals. These dual managerial roles and the psychological contracts made with chosen employees may properly align personal and organizational objectives (Coyle‐Shapiro & Shore, 2007), or they may be inconsistent, causing conflict where personal goals clash with the objectives of the organization (Rousseau, 1995). Based on this distinction, a recent paper by Lee and Taylor (2014) proposes a theoretical model of the dual role of line managers in establishing psychological contracts with employees. The 2 x 2 matrix proposed by these researchers differentiates between the agent role (representing the organization) and the principal role (representing personal interests), and between the possible perform or renege behaviors inherent in both to distinguish four archetypes (juggler, organizational exploiter, employee exploiter, and all about me) consisting of behavior patterns influenced by both external and personal factors (cf. Lee & Taylor, 2014, p. 101) and the possible psychological contract types they may generate. In summary, then each employee’s psychological contract can develop out of relations with multiple organizational agents belonging to different levels in the hierarchy and enjoying varying degrees of authority. Accordingly, a multiple foci approach could prove highly effective for capturing the ways in which relations between the different organizational agents shape the formation and development of psychological contracts, and perceptions of fulfillment. Given the objective of this chapter, we concentrate on the level of the work team, analyzing how interactions and relations between team members, and especially with the team leader or supervisor, contribute to the formation of the psychological contract and the development of its content.

LMX and Psychological Contracts LMX has become one of the most influential leadership models used to predict the behaviors, attitudes, and performance of employees (Dulebohn, Bommer, Liden, Brouer, & Ferris, 2012). The theory was initially based on the dyadic relations established by leaders with their followers and proposed that leaders developed unique relationships with each subordinate. The LMX literature distinguishes between low‐quality and high‐quality LMX relationships or exchanges; whereas the former are based on principles of economic or transactional exchange, the latter are driven by relational or social exchange processes (Liden, Sparrowe, & Wayne, 1997). Employees who have relatively high‐quality exchange



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relationships with their leaders tend to have higher levels of job satisfaction and organizational commitment, report lower levels of stress and turnover intentions, and are more likely to engage in organizational citizenship behavior and have better in‐role job performance (Bolino & Turnley, 2009; Gerstner & Day, 1997; Ilies, Nahrang, & Morgeson, 2007; Maslyn, & Uhl‐Bien, 2005). In addition, research on LMX suggests that the quality of leader–employee relationships acts as a form of social support that is capable of buffering or minimizing the effects of negative employment experiences at work (Erdogan, Kraimer, & Liden, 2004). Recent work has focused on identifying the factors that influence the nature of dyadic and triadic LMX relationships, co‐worker exchanges (CWX; Sherony & Green, 2002; Takeuchi, 2012), and team‐level relationships (Naidoo, Scherbaum, & Goldstein, 2008). In the opinion of Henderson et  al. (2008), however, it is necessary to look in greater depth at how LMX differentiation operates to shape team members’ evaluations of psychological contract fulfillment and affect their subsequent motivation to contribute to the attainment of organizational goals. Based on the theoretical propositions and empirical results described in prior research, Henderson et al. (2008) argue that LMX may operate simultaneously on multiple theoretical levels to influence perceptions, attitudes and behaviors in the employee–organization relationship. Specifically, LMX may operate: (a) at the individual level, through exchanges with a specific coworker, supervisor, or teammate (Dulac et al., 2008), based on the propositions of social exchange theory (Blau, 1964) and the norm of reciprocity (Gouldner, 1960); (b) at the individual‐within‐group level, through the individual’s perceptions of his/her treatment by the supervisor compared with the treatment of other teammates (relative LMX; Schriesheim, Castro, Zhou, & Yammarino, 2001); and (c) at the team level as a result of team‐level variability in LMX quality as a factor determining the extent to which meaningful differences in employee–organization (i.e., employee–supervisor) exchanges may arise from comparisons (Henderson et al., 2008). Senior management can use team leaders to transmit the organization’s expectations to employees (in terms of performance, positive attitudes, extra‐role behaviors, etc.) and to inform those individuals of the rewards they will receive for their efforts (e.g., wages and benefits, job security, recognition, promotion opportunities, training). This constitutes the content of the psychological contract. Moreover, employees are often vulnerable to their supervisors’ attempts to induce them to form the desired psychological contract because of their power to control salaries, assignments, performance appraisals, training, career opportunities, and other inducements and benefits (Sparrowe & Liden, 1997). Previous research has highlighted the powerful role played by the immediate boss in negotiating and establishing the psychological contract, and in perceptions of fulfillment or breach of the contract (e.g. Dabos & Rousseau, 2004; Lester et al., 2002; Rousseau, 1995; Rousseau, Ho & Greenberg, 2006; Shore & Tetrick, 1994; Tekleab & Taylor, 2000, 2003). Despite evidence to support the reciprocal character of the supervisor–employee relationship (e.g. De Cremer & Van Dijk, 2008), existing power differences in the dyad can limit such reciprocity (Hüffmeier & Hertel, 2011). Given that team leaders are among the direct organizational agents involved in the formation and development of psychological contracts, LMX theory is highly relevant to our understanding of how the psychological contract operates at the group level. Once the LMX relationship is established, it constitutes the primary channel through which employees and the organization exchange the contributions and compensation making up the psychological contract (Dulac et al., 2008). Given that the quality of the LMX relationship differs from one team member to the next, it is to be expected that the team leader will tend to offer higher compensation and rewards to those team members with whom he/she has established a higher‐quality LMX relationship (Henderson et al., 2008).

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Likewise, team members enjoying a high‐quality LMX relationship will behave proactively towards their leader by displaying high levels of job performance and producing desirable or beneficial results for the supervisor. This behavior will, in turn, oblige the team leader to recognize and reward the contributions made by high‐quality LMX team members (Liden et al., 1997; Gerstner & Day, 1997). Finally, Henderson et al. (2008) note that “social exchange motives in the LMX dyad would lead to a positive relationship between subordinates’ individual‐level perceptions of LMX quality and PC fulfillment” (p. 1210). Thus, these exchange evaluations operate at the individual level. LMX processes may also influence team members’ perceptions of fulfillment of the psychological contract at the individual‐within‐group level. In particular, each member of the team observes not only the team leader’s responses to his/her own performance and contribution, but also the leader’s behavior towards other members. According to the tenets of equity theory (Adams, 1965), each member of the team draws comparisons and makes assessments of how fairly the psychological contract is being fulfilled. Based on such social comparison processes, Henderson et al. (2008) propose the concept of relative LMX quality to refer to an employee’s LMX quality relative to the average LMX quality found within a work team. Perceptions of fulfillment or non‐fulfillment of the psychological contract expand in this way, because team members draw comparisons not only between the level of equity in reciprocal obligations between themselves as individuals and the organization (in this case represented by the team leader), but they also compare their own treatment with the perceived equity relations of other team members. Given that the members of a team may perform similar roles, and that levels of performance and contributions made by colleagues are easily perceivable even where roles differ, what each team member receives in return for his/her efforts and what others receive will constitute twin points of reference from the standpoint of equity theory (Adams, 1965) for perceptions of both the treatment received from the organization and fulfillment of the psychological contract. However, team members may make distinctions between instances in which the fulfillment or non‐fulfillment of the psychological contract is attributed directly to the team leader. Given that the contents of psychological contracts are defined and developed through multiple foci social exchange relationships, it is possible that a leader will reciprocate the contributions made by the members of his/her team even while other organizational agents fail to fulfill their part of the bargain. This situation is most likely to occur in organizations where decisions are made at different levels of authority and approvals must be obtained from different sources until a final decision is reached (Alcover et al., 2016). Such cases may not necessarily have a negative effect on the LMX relationship, because the team members clearly perceive that any eventual failure to fulfill obligations must be ascribed to other organizational agents and not to their team leader. Furthermore, the LMX relationship will act positively as a form of social support to cushion or minimize the adverse effects of a breach of psychological contract (Erdogan et al., 2004; Restubog, Bordia, Tang & Krebs, 2010). Hence, the quality of LMX relationships and relationships with other team members may attenuate or diminish the negative consequences associated with a perceived breach of the psychological contract, where organizational agents located in the upper reaches of the hierarchy are held responsible for the failure to abide by the obligations in question. For example, Turnley and Feldman (1998) found that perceptions of psychological contract breach by other organizational agents did not necessarily result in the neglect of in‐role responsibilities or in any increase in turnover intentions when employees could count on the support of their team leader and colleagues. Conversely, if breach of the psychological contract is the direct responsibility of the team leader, his/her LMX with the team members involved will be impaired, thereby



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diminishing the quality of the supervisor–subordinate relationships. In this case, it is also necessary to establish whether non‐fulfillment by the team leader also adversely affects the psychological contracts of all or only some of the other team members, as the quality impairment will differentially affect various team members. Differences in employees’ reactions are likely to be associated with relative LMX quality relationships (Henderson et al., 2008). In cases where there is a of failure to fulfill the psychological contract, team members will begin to make attributions about the team leader (or other organizational agents, where applicable) and the motives underlying the breach, which can have negative consequences for various performance‐related and attitudinal dimensions (Turnley et al., 2003). According to Rousseau (1995), breaches of the psychological contract may be: 1) inadvertent, meaning that the organization was able and willing to fulfill the psychological contract but simply interpreted the conditions of employment differently than did the employee; 2) the result of disruption, meaning that the organization was willing but unable to fulfill the contract due to circumstances beyond their control; or 3) deliberate breaches of contract, meaning that the organization was able but unwilling to fulfill the psychological contract and, thus, intentionally reneged on the agreement. Where bad faith is apparent, the consequences of breach can be severe, particularly when one of the parties concerned feels betrayed. Betrayal is a serious violation of the norms and expectations of a relationship (Elangovan & Shapiro, 1998), and the consequences are often intensely negative, even dramatic, due to the emotional reactions of members who feel betrayed (Morrison & Robinson, 1997). Such situations are linked to the construct of perceived organizational cruelty proposed by Shore and Coyle‐Shapiro (2012). Clearly, then, LMX relationships will be negatively affected when the leader is unambiguously to blame for the breach, whether it involves all or only some members of the team. Another key issue is the quality of LMX relationships and the possibility of paradoxical negative effects (Restubog et al., 2010). Because high LMX relationships involve mutual trust, professional respect, loyalty, and obligations, they may impose greater expectations and heightened pressure on the leader in a work team context. A high‐quality LMX relationship involves greater exchange of effort, resources, and support between the parties (Graen & Uhl‐Bien, 1995). Thus, team members enjoying high level LMX relationships with their supervisors may experience more intense reactions to a perceived lack of reciprocity than those with only moderate quality LMX relationships (Harris & Kacmar, 2006). Based on this premise, Restubog et al. (2010) propose that team members with high‐quality relationships with leaders may respond more negatively when psychological contracts are breached. That is, when a team leader violates employees’ expectations in relation to the trust, obligations, and support supposedly inherent in high‐quality LMX relationships, the consequences can be dramatic and employees are likely to feel angry, resentful, bitter, mistreated, and even betrayed (Bordia, Restubog, & Tang, 2008). They may also feel especially disheartened by the breach, which could produce a negative effect and a sharp decline in in‐role performance, commitment, and organizational citizenship behavior, or a spike in intentions to quit the organization (Alcover, Martínez‐Íñigo, & Chambel, 2012; Conway, Guest, & Trenberth, 2011). Indeed, Restubog et  al. (2010) found that the behavioral consequences of a perceived psychological contract breach are more severe under conditions of high LMX relationships; however, their study also showed a strong negative relationship between psychological contract breach and LMX, suggesting that employees with higher‐quality LMX relationships generally perceive lower levels of breach. In a similar vein, Bal et al. (2010) found strong support for the intensifying hypothesis (versus the buffering hypothesis), where social exchange accentuates the relationships

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­ etween contract breach and work performance. Specifically, these researchers found that b the negative relationship between psychological contract breach and work performance was moderated by social exchanges (measured by social exchange relationships, perceived organizational support, and trust), so that the relationship was stronger among employees who reported higher levels of quality social exchange relationships, perceived organizational support, and trust. These results are consistent with previous propositions about betrayal (Elangovan & Shapiro, 1998; Restubog & Bordia, 2006), showing that psychological contract breach causes serious damage to the employment relationship, and it is therefore those members who experience high levels of social exchange who are especially likely to feel betrayed and to respond by decreasing their effort and work performance. Finally, another key factor is the degree of alignment between the leader’s perceptions of relationship quality and those of the team members. Given that LMX relationships are dyadic, the members of the dyads should, theoretically, perceive the situation in similar terms. However, meta‐analytic reviews (Sin, Nahrgang & Morgeson, 2009) reveal that agreement about LMX between leaders and members is only moderate (P = .37), which suggests the existence of asymmetries in assessments of the quality of exchanges between the parties. These asymmetries may also apply to perceptions of psychological contract fulfillment, which would vary in the case of dyadic comparisons made by each team member with respect to the leader on one hand, and the comparisons drawn by each team member with respect to the leader’s fulfillment of obligations towards colleagues on the other. Similarly, perceptions may vary between the parties over the course of the relationship, both with regard to the quality of LMX exchanges (Narhgang, Morgeson & Ilies, 2009; Naidoo et  al., 2008) and to the degree of psychological contract fulfillment or breach (Conway & Briner, 2005; Conway et al., 2011; Solinger, Hofmans, Bal, & Jansen, 2015; Turnley & Feldman, 1999).

Fairness, Peer Justice and Psychological Contracts Like the quality of LMX relationships, the relations between the members of a work team can affect the behavior and attitudes of employees. Indeed, there is considerable empirical evidence that, under certain conditions, people are highly motivated when working as part of a team and may achieve higher performance than they would working individually (Baron & Kerr, 2003). In addition, other evidence supports the claim that positive intermember relations reduce group motivation losses (Karau & Williams, 1993). The beneficial consequences of group climate have been widely documented in the literature (e.g., Eisenbeiss, van Knippenberg & Boerner, 2008; Gil, Rico, Alcover, & Barrasa, 2005; Tse, Dasborough & Ashkanasy, 2008); a positive climate can enhance the cognitive, emotional and behavioral functionality of the group, enhance the performance of team members, and increase the team’s ability to innovate. More recently, research on work teams has begun to make use of the concept of peer justice (Cropanzano, Li, & Benson, 2011), which “refers to a shared perception regarding how individuals who work together within the same unit and who do not have formal authority over each other judge the fairness with which they treat one another” (p. 568). Researchers had previously referred to such collective judgments as the “intra‐unit justice climate” (e.g. Cropanzano, Li & James, 2007; Li & Cropanzano, 2009), but the term “peer justice” has gained currency because it avoids the unnecessary confusion with the older concept of the “justice climate,” which refers to group‐level cognition about how a work group as a whole is treated (Naumann & Bennett, 2000) or members’ shared



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perceptions as to how their unit/group/team has been treated by an organizational authority ­figure (Colquitt, Noe, & Jackson, 2002; Ehrhart, 2004). Peer justice, in contrast, is linked to co‐workers as a source of fairness. Research on multi‐foci justice has shown that employees may discriminate between justice perceptions originating from different organizational sources (Liao & Rupp, 2005). Thus, it is important to distinguish between coworkers and supervisors (Lavelle et al., 2007), in turn differentiating peer justice from LMX relationships or exchanges. The concept of organizational justice has a long research history. The cumulative evidence shows that employees who perceive their treatment by the organization, or by organizational agents with decision‐making power over them, as unfair or unjust are likely to develop more negative attitudes towards their work, suffer more stress, perform worse, exhibit fewer organizational citizenship behaviors, and be more prone to engage in counterproductive work behavior (Cohen‐Charash & Mueller, 2007; Cropanzano & Wright, 2011). In short, perceptions of unfairness have negative implications for both employees and organizations, and it has been found in the specific case of the psychological contract that perceptions of procedural and interactional justice moderate employee responses to PC breach (Kickul, Lester, & Finkl, 2002). Most research has focused on the perceptions of justice emanating from authority figures, which is consistent with the predominance of hierarchical organizations where decisions are handed down through vertical structures (Cropanzano et al., 2011). As mentioned earlier, however, the flattening of organizational hierarchies and increased use of work teams as the basic structures of organizations have brought colleagues to the fore as key referents for perceptions of justice (Lavelle et al., 2007). As a result, it has become necessary to develop a different construct of fairness in work teams from the traditional concept of organizational justice. The main characteristics of this construct are, in the first place, that peer justice pertains to employees who do not have formal authority over each other (Aquino, Tripp, & Bies, 2006), even when they do not have identical role or status in the organizational structure, and in the second, that it is a unit‐level construct. Accordingly, Cropanzano and colleagues (2011) conceptualize peer justice as a specific facet of organizational climate, which refers to the perception that members within an organization/unit/team develop and share cues about their social environment, shaping members’ attitudes and behavior at work (Kuenzi & Schminke, 2009). Building on organizational justice research (Cohen‐Charash & Spector, 2001), Li, Cropanzano, & Benson (2007) found empirical evidence to support a three‐factor structure of peer justice. First, distributive peer justice is defined as the shared perception that team members get what they deserve based on their contribution to achieving team outcomes. Second, procedural peer justice is defined as the extent to which team members use fair procedures in the decision‐making process. And third, interactional peer justice is related to interpersonal fairness in the team members’ dealings with one another. In the domain of organizational justice, interactional justice is sometimes split into two subdimensions – namely, interpersonal and informational justice. Interpersonal justice refers to degree of respect and dignity inherent in individuals’ treatment of each other, while informational justice concerns the sharing of timely and accurate information (Colquitt, 2001). Colquitt proposes a four‐factor framework or model of justice (distributive, procedural, interpersonal, and informational) for which empirical support has been found (Colquitt, Conlon, Wesson, Porter, & Ng, 2001; Masterson, Byrne, & Mao, 2005). The theoretical model proposed by Cropanzano et al. (2011) addresses the procedural and interpersonal dimensions of interactional justice because justice research at the group level tends to focus on these two dimensions. Procedural peer justice boosts task teamwork processes (communication, coordination, and balance of member contributions) and

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interpersonal teamwork processes (cohesion, effort, and support). Interpersonal peer justice, which is less directly relevant to task teamwork processes, is only expected to affect interpersonal teamwork processes. The model predicts that the two team process variables influence effective work behaviors. Task teamwork processes enhance team performance, whereas interpersonal teamwork processes enhance team citizenship behaviors (helping and loyalty). Preliminary empirical findings (Cropanzano et al., 2011) generally support the theoretical model, attesting to the importance of peer justice in both team processes and outcomes. Just as employees’ perceptions of organizational justice may be crucial to their perception of psychological contract fulfillment (DiMatteo, Bird, & Colquitt, 2011), it should be expected that peer justice will also be relevant to team members’ assessments of the state and degree of fulfillment of their respective psychological contracts. While little research has investigated the relationship between peer justice and the psychological contract, a number of exploratory studies address the links between these two constructs. For instance, Restubog, Hornsey, Bordia, and Esposo (2008) draw on the group value model (Tyler, Degoey & Smith, 2008) to postulate that fair treatment by team members communicates symbolic messages about the relationship between the organization and the employee, and has implications for employees’ pride in belonging to the team. In addition, pride and respect can foster group identification, and the group engagement model built upon on this identity‐based model postulates that the social identities employees form around their work groups and organizations are strongly related to psychological engagement, behavioral engagement, and cooperation in groups (Tyler & Blader, 2003), as well as engagement in extra‐role behaviors (Blader & Tyler, 2009). Conversely, unfair treatment can undermine team members’ trust in the organization and cause them to disidentify from the team. This may in turn lead to a certain unwillingness among employees to engage in organizational citizenship and other extra‐role behaviors (Restubog et al., 2008). This promising line of research should advance our knowledge of the complex relationships that exist between the different forms of peer justice in intrateam relationships, the development of the psychological contract and perceptions of fulfillment, the behavior and attitudes of members, and team performance and success. All of this will, in turn, allow integration at the team level of constructs which have hitherto only been examined separately.

Social support in teams and psychological contracts As explained above in relation to the psychological contract, the theory of organizational support (Rhoades & Eisenberger, 2002; Shore & Shore, 1995) maintains that perceived organizational support (POS) emerges from the general tendency of employees to assign humanlike characteristics to their organizations (Aselage & Eisenberger, 2003). Therefore, POS will be valued by employees when it meets key socioemotional needs, providing an indicator of the organization’s readiness to reward increased work effort and higher performance, and of the extent to which the organization values employees’ contributions and cares about their well‐being (Eisenberger, Huntington, Hutchison, & Sowa, 1986). One of three major work‐experience antecedents of POS (along with organizational rewards and organizational justice) is perceived supervisory support, which refers to employees’ beliefs that their supervisor cares about them and values their contributions to the attainment of organizational goals (Aselage & Eisenberger, 2003). Previous research in this area has found that employees tend to identify treatment by their supervisor as indicative of organizational support and, interestingly, perceived supervisor support leads



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to POS rather than the other way around (Eisenberger, Stinglhamber, Vandenberghe, Sucharski & Rhoades, 2002). The integration of organizational support theory with psychological contract theory proposed by Aselage and Eisenberger (2003), and empirical studies linking the two constructs (e.g. Coyle‐Shapiro & Conway, 2005; Zagenczyk, ­Gibney, Few & Scott, 2011) have focused on the organizational level of analysis. Consequently, to date, there is neither a theoretical framework nor empirical findings that might shed light on the psychological contract in teams or on perceptions of support held by the supervisor and by other team members, except for the implications of LMX theory for the psychological contract in teams that were described earlier. Nevertheless, some studies have begun to explore relationships which could be of use in any future research seeking to link these constructs. For example, Shanock and Eisenberger (2006) found that when supervisors perceive organizational support, their subordinates perceive support from their supervisor, and this, in turn, is positively associated with their POS, in‐role performance, and extra‐role performance. Based on these results, we may hypothesize that joint perception of supervisor POS and fulfillment of the psychological contract by higher‐ranking organizational agents will be positively associated with team members’ perceptions of supervisor support and fulfillment of the psychological contract, which will, in turn, feed into their POS, perceptions of psychological contract fulfillment by high‐ranking organizational agents, and in‐role and extra‐role performance. Another key issue is the role played by POS as a moderating factor in employees’ reactions to the perception of failure by other team members to perform their obligations (i.e. to fulfill their psychological contracts). In particular, the findings of a study by Eder and Eisenberger (2008) showed that high POS eliminated the relationship between the work group and individual tardiness, suggesting that POS weakens the link between work group withdrawal and individual withdrawal. Another study by Van Emmerik, Euwema and Bakker (2007) found a positive effect from peer support in the case of perceptions of environmental stress. Hence, peer support was associated with a higher level of job investments (i.e. affective organizational commitment and job dedication, strong psychological contracts) and buffered reactions to an unsafe workplace climate and employees’ experience of threats. On the other hand, Ho (2005) argued that employees’ referent choices when evaluating psychological contract fulfillment will be contingent on the domain of the promise evaluated. In this regard, Ho and Levesque (2005) found that employee referents in evaluations of the fulfillment of organization‐wide promises were primarily couched in terms of relational others, which is to say people with whom an individual has direct interactions and enjoys close social proximity (Burkhardt, 1994). For instance, relational others would include coworkers with whom an individual has close direct ties, such as friends and advice givers (the main sources of social support). Conversely, the referents in evaluations of the fulfillment of job‐related promises were mainly positionally similar others, which is to say people who occupy positions similar to that of the employee in the informal social structure (Burkhardt, 1994). Positionally similar others would thus include fellow workers who could stand in for the employee and people with whom he or she has multiple relationships. Ho and Levesque (2005) found that the effects of social influence also varied with the domain of promise evaluated. In the case of organization‐wide promises, employees’ fulfillment evaluations were similar to those of their friends, who provided mutual social support to the team. In the case of job‐related promises, however, their fulfillment evaluations differed from those of coworkers, who played a dual role as both friends and substitutes. These findings reveal new and important informal constituents in the realm of psychological contract evaluation, broadening the existing focus on recruiters, ­managers,  coworkers, mentors, and top management (e.g., Rousseau & Greller, 1994;

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Scandura & Williams, 2002; Turnley et  al., 2003), as well as reinforcing the reasoning that third parties to the psychological contract can influence fulfillment evaluations (Ho & Levesque, 2005). A final interesting aspect is explored by Bingham, Oldroyd, Thompson, Bednar and Bunderson (2014), who examine how perceptions of psychological contract fulfillment by colleagues influence attributions of coworker status. Based on previous findings about the ways in which individuals’ fulfillment of psychological contract obligations might influence how a member is perceived by others (Bolino, Turnley, & Bloodgood, 2002; Ho & Levesque 2005), these researchers argue that the fulfillment of obligations essential to an individual’s psychological contract with the organization will shape coworkers’ attributions of his/her social status either at the team or at the organizational level. Bingham et al. (2014) suggest that co‐workers who fulfill relational (i.e. socioemotional) obligations to the organization will be viewed by peers as friends and, we suggest, as potential sources of social/relational support inside the team, whereas those who fulfill ideological obligations (i.e., “true believers”) will be viewed by peers as influential (Thompson & Bunderson, 2003) and, in our opinion, as potential sources of social/instrumental support within the team. In contrast, the model suggests that co‐workers who fulfill transactional obligations to the organization will be named by peers much less frequently as friends or as having influence within the group or the organization (Bingham et al., 2014). To sum up, the integration of the POS and psychological contract constructs at the team‐level of analysis provides an interesting and important topic for future research. The possibility of analysis at the organizational level is already being explored (Aselage & Eisenberger, 2003; Coyle‐Shapiro & Conway, 2005), which will in turn advance our understanding of the multiple agency processes involved in the formation and fulfillment of psychological contracts.

Future Research The psychological contract construct has enhanced the focus on the analysis of relations between employees and organizations in the last two decades (Alcover, Rico, Turnley, & Bolino, 2016; Shore et al., 2004). To the extent that employment relations are redefined by the transformations affecting the world of work, research into the psychological contract should continue to evolve and expand, seeking to capture the specific features of new forms of individual–organization relations. In this chapter, we have reviewed research into the psychological contract in work teams. While this research is not particularly extensive, we have endeavored to paint a broad canvas, including the multiple agency context and multiple‐foci social exchange relationships in the development and fulfillment of the psychological contract, and we have also considered the links between LMX theory, peer justice, social support in teams, and psychological contracts. The results of our review highlight a field in which research is still at an early stage and where promising lines of inquiry exist to advance our knowledge of the multiple individual–organization relations existing in contemporary work contexts. First, the integration of LMX theory and psychological contract theory may provide valuable insights into the role played by the team leader or immediate manager in perceptions of psychological contract content and the fulfillment or breach of the resulting obligations, given that such figures are typically the organization’s main representatives in dealings with employees (Conway & Briner, 2005, Henderson et al., 2008; Liden et al., 2004; Tekleab & Taylor, 2003). This line of research may also shed light on the possible dual role of the leader in transmitting the organization’s psychological contract and,



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at the same time, establishing a specific psychological contract with each member of the team (Lee & Taylor, 2014). In the same way that LMX theory has grown more complex in order to capture differences in the quality of leader‐member relations, analysis of the psychological contract at the team level should also address such relational differences (Dulac et al., 2008) and expand the current focus on dyadic relations to relations existing at the team level (Naidoo et al., 2008). This could prove relevant to the examination of such processes in teams as shared leadership (Carson, Tesluk & Marrone, 2007), and to the exploration of their implications for team members’ psychological contracts. In our opinion, future research on the psychological contract at the team level should adopt a multi‐foci approach which takes into account differences in the quality of leader–member relations, as well as different relations shared by the members of the team themselves. Second, it has been shown that employees’ perceptions of organizational justice are very important to perceptions of psychological contract fulfillment (DiMatteo et  al., 2001), and perceptions of peer justice may prove equally relevant to the development and performance of the psychological contract and to perceptions of performance at the team level. Thus far, very little research has sought to link these two constructs, so such explorations could provide new and valuable insights into the analysis of individual–organization relations. Considering the increasing role of work teams of different kinds of organizations, it will be necessary to integrate theory regarding perceptions of justice and the psychological contract at the team level if we are to advance a multilevel analysis of these processes, adding to our existing understanding of organizational justice and the psychological contract at the macro level based on a multi foci social exchange relationships approach (Cropanzano & Rupp, 2008; Rupp & Cropanzano, 2002). Third, and as mentioned in relation to justice, the proposals advanced at the organizational support level (e.g. Aselage & Eisenberger, 2003; Coyle‐Shapiro & Conway, 2005) need to be supplemented by an analysis of leader and co‐worker social support within the team in relation to the formation, development, and fulfillment of the psychological contract. Once again, recent research has begun to explore these relations. Therefore, future research should consider the relations existing within teams, as well as the different kinds of social support and their direct, mediating, or buffering effects upon the perception of psychological contract fulfillment both by the team leader and other organizational agents. In this regard, we suggest integrating theoretical frameworks like the model of social support in teams proposed by Hüffmeier and Hertel (2011), which differentiates between affective support (social recognition and social encouragement) and task‐related support (information‐related and behavioral task support) in order to better understand their relations with the formation, development and fulfillment of the psychological contract. Fourth, the operationalization and measurement of the psychological contract has so far been problematic, as shown by previous research (see Freese & Schalk, 2008 for a critical review). However, it is still necessary to develop specific measures that include perceived organizational obligations, perceived employee obligations, and perceived fulfillment/ breach, as well as the respective perceptions of team members and the team leader. While some existing measures may be able to be adapted for this purpose, further adaptions will probably be necessary to gain a better understanding of how the psychological contract works in a multi‐focal social context. Finally, ongoing investigations of the psychological contract at the team level have practical implications for the management of work teams, because such studies enhance our understanding of processes which are currently overlooked, such as perceptions of peer justice between team members and social support between colleagues, in addition to differentiation in LMX relationships. These processes can be facilitated directly by the team leader and by other organizational agents, strengthening the quality of relations

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throughout the team system and reinforcing the psychological contract, which at this level will improve the cohesion and potential of the team (Chang & Bordia, 2001; Beal, Cohen, Burke, & McLendon, 2003), thereby raising its effectiveness and contributing to the attainment of beneficial outcomes for the organization.

Conclusion In summary, we believe that the research presented in this chapter and the results of future research on the psychological contract in work teams underscore the importance of the demand voiced in the literature for further work on the relational nature of employee– organization relationships in organizations (Shore, Coyle‐Shapiro & Tetrick, 2012; Takeuchi, 2012) and highlight the relevance of incorporating the embedded nature of the employee–organization relationship into theory and research.

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Affect and Creativity in  Work Teams March L. To, Neal M. Ashkanasy, and Cynthia D. Fisher

Introduction Work‐related creativity in the context of teamwork refers to the processes by which employees generate novel and useful ideas to solve problems related to team productivity and effectiveness (George, 2007; Hennessey & Amabile, 2010; Zhou & Hoever, 2014). Engagement in creative processes within this situation concomitantly involves cognitive activities such as reframing the problem, searching for and encoding information, generating and modifying alternatives, and extending initial ideas (e.g., Drazin, Glynn, & Kazanjian, 1999; To, Fisher, & Ashkanasy, 2015; To, Fisher, Ashkanasy, & Rowe, 2012; Zhang & Bartol, 2010). While these cognitive activities can be conducted by individuals working alone, researchers such as Hargadon and Bechky (2006) and To, Tse, and Ashkanasy (2015) stress that creative synergy is best achieved through team members working together to share their knowledge with the intention to build innovative solutions to work‐related problems (see also Shin & Zhou, 2007; Zhang & Bartol, 2010). Achieving success through creative synergy is not automatic, however; it requires meaningful task and interpersonal interactions among team members. Further, team creative efforts may be full of ups and downs (Amabile, Barsade, Mueller, & Staw, 2005; Tsai, Chi, Grandey, & Fung, 2012). While team members working together can sometimes feel inspired and experience new and useful insights, they may just as often feel they are stuck; they then are likely to feel frustration and maybe even express hostility towards other team members (Barsade & Knight, 2015; George, 2007). Indeed, such problems with managing emotions in teams may explain why so many teams fail to realize their potential. It appears that knowing how to increase and sustain creativity is an important challenge for project leaders and supervisors. The need to understand how and when collective emotional states experienced at the group level may impact critical team functions such as creativity is especially imperative given the ­ ilson, 2008). rise of team‐based structures in organizations (Mathieu, Maynard, Rapp, & G The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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Further, there is evidence that affect can play a major role in team functioning (see Collins, Lawrence, Troth, & Jordan, 2013 for a review). In this regard, most research on creativity in teams borrows from the much larger literature on mood and individual creativity (see, e.g., Anderson, Potočnik, & Zhou 2014) to inform work on group affect and creativity. In this chapter, we seek to make a key conceptual extension from individual to group creativity, noting that this entails an extra degree of complexity. We begin with a review of research findings on affect and its effects at the level of individual creativity, and follow up by describing the research that has extended individual phenomena to the group level, including discussion of the dynamic nature of creativity in groups. Finally, we identify the inadequacies of the conceptual extension in current group research and offer recommendations for future research.

Affect and Individual Creativity Over the past few decades, researchers have produced complex findings regarding the effects of affect on individual creativity. In this section, we review the major perspectives and findings documented in the literature. This will serve in turn as a building block for our later discussion on the affect‐creativity nexus in teams. Research consensus until recently (see Baas, De Dreu, & Nijstad, 2008, for a review) has been predicated on a general assumption that positive (rather than negative) affect facilitates individual creativity. This idea is largely grounded in Alice Isen’s laboratory research (Isen, 1999; Isen, Daubman, & Nowicki, 1987), suggesting that positive feelings prime people to access more extensive and complex materials stored in memory and thus to promote the cognitive flexibility necessary to enable creativity. Moreover, according to Fredrickson’s (1998) “broaden‐and‐build” theory, positive feelings serve to broaden people’s thought‐action repertoires. This is in contrast to negative feelings, which cause people to narrow their focus in order to solve a particular problem. According to this theory, positive feelings do the opposite: they widen an individual’s scope of thoughts and therefore create an urge to explore and to take in new information and experiences. Other scholars, such as George and Zhou (2002) and Kaufmann (2003), have nonetheless raised concerns about the positive valence hypothesis. Their argument is informed by the mood‐as‐input account, which holds that positive feelings signal good progress or sufficient effort. Interpreted from this perspective, positive feelings have potential to lead people to reduce their efforts and instead try to find a quicker solution (see also Johnson & Tversky, 1983; Kavanagh & Bower, 1985; Martin & Stoner, 1996). In contrast, negative moods can function as a signal to the individual that her or his current efforts to solve a problem are insufficient, which then might motivate harder and longer work effort in order to generate better alternatives and higher‐quality solutions (e. g., see George & Zhou, 2002, 2007; Kaufmann, 2003). These competing perspectives led scholars such as De Dreu, Baas, and Nijstad (2008) and George and Zhou (2007) to consider the complementary facilitating roles of positive and negative affect in creativity. Specifically, George and Zhou (2007) put forward the dual tuning perspective, which holds that the combined experience of both positive and negative affect valences facilitate creativity (see also George, 2011). Grounded in Schwarz and Clore’s (2003) “affect‐as‐information theory” (p. 296), the core idea of the dual‐ tuning model is that feelings of both valences have different but potentially complementary effects on the cognitive processes producing creativity. Thus, on the one hand, positive affect facilitates creativity by prompting looser information processing, greater



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use of integrative top‐down strategies, and more expansive divergent thinking (George & Zhou, 2007; Schwarz & Clore, 2003). Negative affect, on the other hand, alerts individuals to shortfalls in their problem‐solving progress, thereby producing a more analytical, effortful thinking mode and reducing reliance on preexisting mental scripts or assumptions (George & Zhou, 2007; Martin & Stoner, 1996; Schwarz & Clore, 2003). Indeed, subsequent empirical research evidence has tended to support the notion that experiencing a blend of positive and negative affect can benefit individual creativity – which requires both divergent thinking and evaluative and persistent processing (Bledow, Schmitt, Frese, & Kuehnel 2011; Fong, 2006; George & Zhou, 2007). Consistent with the dual tuning account, De Dreu and his colleagues (De Dreu et al., 2008; Nijstad, De Dreu, Rietzschel, & Baas, 2010) propose further that creativity is achieved via two pathways: (1) enhanced cognitive flexibility engendered by positive affect; and (2) increased persistence promoted by negative affect (Baas et al., 2008; To et al., 2012). This line of research stresses that affect valence needs to be considered in concert with the other affect dimension – activation– in order to understand affect‐creativity relationships. In this view, moods must be high arousal or activating if they are to provide the cognitive energy beneficial for creativity by either the flexibility or the persistence route (De Dreu et al., 2008). In a series of laboratory studies, De Dreu et al. (2008) and Nijstad et al. (2010) found support for the valence activation predictions of their dual pathway model (see also Baas et al., 2008, for a review). More recently, in a study using an experience sampling design, To et  al. (2012) found that momentary activating feelings of both valences promoted concurrent engagement in creative processes within‐person among individuals working on a demanding long‐term project. Conversely, deactivating positive and negative feelings were negatively related to creative engagement. These inconsistent relationships between affect and creativity at individual level have also subjected to two independent meta‐analyses (Baas et al., 2008; Davis, 2009). In the first of these, Bass and his colleagues looked specifically at effect sizes relating specific mood states to creativity and examined the moderating effects of mood activation (activating or deactivating) and regulatory focus (promotion or prevention focus; see Higgins, 1998). The authors found that positive mood resulted in more creativity compared with neutral mood controls, but not compared with negative mood. Importantly, they also found that creativity was most enhanced when actors were in an activating mood state associated with approach motivation. Creativity (on the cognitive flexibility dimension) was found to be lower when mood states were activating and negative (e.g., anxiety) with a prevention focus. In the other meta‐analysis, Davis (2009) found effects similar to those reported by Bass et al. (2008) but reported in addition that affect intensity was non‐linearly related to creative performance. Specifically, Davis found a curvilinear relationship between positive affect and creativity such that creativity was most likely to be exhibited at moderate levels of positive affect (see also Isen et al., 1987). Davis also concluded from his meta‐analytic results that context plays a key role as a determinant of the relationship between positive affect and creative behavior. Although the foregoing meta‐analyses focused mainly on the results of laboratory studies, their results support on the one hand a pervasive and consistent role for activating positive affect in facilitating individual creativity. On the other hand, the effects of negative activating feelings on creativity seem in comparison to be more complex and sensitive to context (see also George & Zhou, 2007). Consistent with this observation, Binnewies and Wörnlein (2011) and To et al. (2015) recently demonstrated in separate experience‐sampling studies that high arousal positive affect promotes employees’ creativity on a day‐to‐day basis. Binnewies and Wörnlein found that contextual factors such as job

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control moderate the effects of negative activating affect on creativity. To and his collaborators found that negative affect can be “unleashed” as energy for greater creative process engagement, but only for individuals who are high on trait learning goal orientation and who experience high psychological empowerment in the workplace. In sum, the latest research evidence indicates that activating affects are more effective than deactivating affects in fostering individual creativity. The effects of negative activating affect on creativity appear however to be less straightforward than those of positive affect.

Affect and Creativity in Teams Since 2005, researchers (e.g., Jones & Kelly, 2009; Tsai et  al., 2012; see also Zhou & Hoever, 2014, for a recent review) have begun to investigate affect as an influence on collective creativity at the group level. This line of research is guided by the affective convergence perspective regarding affect as a meaningful group‐level construct. In this perspective, group affective tone (GAT) is typically measured by aggregating individual members’ trait or state affect in a team (Barsade & Gibson, 2012; George, 1990). This approach has resulted in somewhat mixed findings concerning the relationship between GAT and team creativity. While some researchers have found that GAT is related to team creativity, others have struggled to replicate this effect. In the following, we review research findings in this regard (see Table 19.1 for a summary).

Positive GAT and Team Creativity The idea that positive GAT should produce greater team creativity essentially mirrors the effects of positive affect found at the individual level. Grawitch and his colleagues (Grawitch, Munz, Elliott, & Mathis, 2003; Grawitch, Munz, & Kramer, 2003) found in two laboratory studies that ad hoc groups whose members were induced to feel positive affect demonstrated greater creativity. Grawitch and his collaborators suggest that their results support the notion that happy individuals are more cognitively flexible and are therefore more effective in combining each other’s ideas, especially those flowing from their expanded thought and action propensities. More recently, Tsai et al. (2012) conducted a field study to investigate the relationship between GAT and creativity in work groups. Using a cross‐sectional design, Tsai and his colleagues measured GAT by aggregating individual feelings at group meetings in sixty‐eight research and development teams involving 270 team members. Based on the assumption that happy team members would make more novel suggestions and would thus be better able to follow and build upon each other’s ideas, the authors expected to find that positive GAT would produce greater team creativity. The expected positive main effect of positive GAT did not materialize, however. Moreover, other researchers, such as George and King (2007), argue that positive GAT may even be detrimental to team creativity. In particular, members sharing positive feelings might experience a pleasant and harmonious team atmosphere, which might discourage them from raising opposing opinions that may endanger the pleasant atmosphere (cf. Wegener & Petty, 1994). Specifically, George and King argued that collective positive affect has potential to promote groupthink, conformity to norms, suppression of dissent, and resistance to change. If so, then individuals in a positive mood (who might tend to feel more favorably about the status quo) may adopt a less discerning cognitive mode and rely on pre‐existing understandings or schema. George

Longitudinal field research (military teams); aggregation of individual feelings (about the team interactions)

Knight (2015)

Group Arousal Knight & Baer (2014)

Knight (2015)

Tsai et al., (2012)

Laboratory research; detection of group members’ nervous system activity

Cross‐sectional field research (R&D teams) using aggregated individual mood scores Longitudinal field research (military teams); aggregation of individual feelings (about the team interactions)

Laboratory research using mood induction

Cross‐sectional field research (R&D teams); aggregation of individual feelings (felt at team meeting)

Tsai et al., (2012)

Negative GAT Jones & Kelly (2009)

Laboratory research; mood induction

Laboratory research; mood induction

Group Affect Measures (GAT)

Grawitch, Munz & Kramer (2003)

Positive GAT Grawitch, Munz, Elliot & Mathis (2003)

Studies

Effects on Team Creativity or Near Terms

Effect: + Outcome: Group performance in the creative task. Mechanism (found): Information elaboration.

Effect: + Outcome: Team creativity in the idea generation task. Mechanism (found): Persistence. Effect: – (marginally significant) Outcome variable: Team creativity at work Effect: + Outcome: Exploratory search in (the second half of a team’s life) Mechanism (proposed): Mood‐as‐input/stop‐rule effect

Effect: + Outcome: Creativity in the team brainstorming task. Mechanisms (proposed): Cognitive flexibility and processing efficiency. Effect:+ Outcome: team creativity in the structure‐building tasks. Mechanisms (proposed): Broaden‐and‐build effect and cognitive flexibility. Effect: – (for teams with high trust); + (for teams with low trust and high negative GAT). Outcome variable: Team creativity at work. Mechanism (proposed): Dual‐tuning effect. Effect: – Outcome: Exploratory search (in the second half of a team’s life). Mechanism (proposed): Mood‐as‐input/stop rule effect.

Table 19.1  Recent findings regarding the relationship between group affect and creativity.

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and King posit further that these collective effects of positive feelings should tend to be self‐reinforcing, thus leading to a uniform view concerning objectives, problems and opportunities, and cause‐effect relations. This in turn is likely to discourage group members from considering novel ideas.

Negative GAT and team creativity Thus far, we have argued that there are many questions surrounding the idea that positive GAT fosters team creativity; but what about the effect of negative GAT? Again, we find controversy and inconsistent research findings. Kelly and her colleagues (see Kelly & Spoor, 2007; Jones & Kelly, 2009) suggest that negative group affect might in certain circumstances serve to foster creativity in groups. In particular, Jones and Kelly argue that negative feelings (suggesting dissatisfaction with the status quo) can prompt members of a group to continue to strive for better solutions rather than to prematurely settle for an inferior solution. Jones and Kelly stress in particular that the persistence gained from negative affect may be more helpful to a group than to an individual because the group benefits from abundant resources (cf. “three heads are better than one”). The authors collected laboratory evidence to show that, when negative affect was induced in groups, these groups performed more creatively than did their individual members on an idea generation task. The same group synergy effect did not occur in happy groups. Jones and Kelly found further, that the groups in their study spent more time on task than individuals, but that only negative affect groups took advantage of the increased time to improve creativity. Overall, results of this research support the thesis that negative GAT may sometimes increase team creativity (via persistence). On the other hand, there is evidence that negative GAT may also be detrimental to team functioning. For example, Cole, Walter, and Bruch (2008) found in a field study of 61 work teams that negative GAT mediated the relationship between dysfunctional team behavior and team performance, especially when negative affect expressivity was high. In particular, they found that negative group feelings may have unproductive consequences such as increasing interpersonal tension, which undermines completion of task goals (Kelly & Spoor, 2007). As Janssen, van de Vliert, and Veenstra (1999) note, personal incompatibility may limit cognitive processing of new information, which in turn may reduce receptiveness to ideas advocated by others who are disliked. This might in turn serve to foster hostile attributions concerning other’s intentions and behaviors  –  and so reduce communication and cooperation.

Applying Individual‐Level Effects to Multiple Levels So far in this chapter, we have looked at the relationship between positive and negative affective states and creativity at individual and group levels of analysis. While it is clear that affect can be related to creative behavior at both levels of analysis, it is not so clear how these effects manifest as multilevel phenomena. In the following sections of the chapter therefore, we first address three questions that arise when applying individual‐ level creativity concepts at the group level: (1) Why are the effects of affect on creativity operating at individual and group levels not parallel? (2) What are the implications for creativity of the combined effects of the affect dimensions of valence and activation? (3) What is the effect of group affective diversity on creativity? Following this discussion, we then turn our attention to two additional questions that arise from the dynamic nature of creativity in groups.



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The differential effects of affect at individual and group levels Borrowing perspectives from individual‐level research, current research on group affect and team creativity seems by and large to be based in the assumption that group affective tone influences team creativity in the same way that affect influences individual creativity (Zhou & Hoever, 2014). This perspective ignores the complex social and interpersonal issues occurring in teams, however. Affect experienced collectively in teams may not supply the same adaptive cognitive resources for information processing as it provides for individuals working independently (Cole et al., 2008; George & King, 2007). Rather, affect that arises from relational or identity‐oriented issues in teams may serve to distract members from their shared task (for reviews, see Beal, Weiss, Barros, & MacDermid, 2005; van Knippenberg, De Dreu, & Holman, 2004). Consistent with this idea, although Tsai et al. (2012) were unable to find a main effect of positive GAT on team creativity, they nonetheless found that positive GAT (measured in terms of high arousal) was positively related to team creativity when team trust was low, but was negatively related to team creativity when team trust was high. The authors reasoned that this is because experiencing homogenous positive affect in a trusting team may be detrimental to team creativity owing to group‐centrism effects (George & King, 2007), such as enhanced conformity and reduced divergent thinking among team members, which seem likely to have occurred in the groups they studied. Notably, the detrimental effect of positive GAT that Tsai and colleagues (2012) found appears to contradict individual‐level findings that suggest a more pervasive role of positive affect in facilitating individuals’ flexible thinking and creativity in the context of teamwork (George & King, 2007). It may be that the detrimental effect of positive GAT in groups might be explained by the “stop‐rule principle” (e. g., George & Zhou, 2002; Martin & Stoner, 1996), in which collective pleasant feelings prevent creative attempts via reduced persistence (De Dreu et al., 2008). Based on the foregoing, it seems the effects of affect on the mechanisms leading to creativity are not necessarily parallel across individual and group levels. In this case, there would appear to be a case for extending the dual tuning model (De Dreu et al., 2008; George & Zhou, 2007; Nijstad et al., 2010) to include the idea that activating feelings might produce varied behavioral responses to creativity depending on an individual or team goal preferences (e.g., To et al., 2012, 2015). In particular, the presence of a shared cognitive framework within a team might play a vital role in directing appraisal of affective cues (i.e., what is going on and why it is happening?) and thus informing behaviors among team members (Klimoski & Mohammed, 1994; Mohammed, Ferzandi, & Hamilton, 2010). As identified in recent group research, team variables such as transformational leadership (e. g., Shin & Zhou, 2007), team learning goal orientation (e. g., Gong, Kim, Lee, & Zhu, 2013), and team reflexivity (e. g., Pieterse, van Knippenberg & van Ginkel, 2011) may provide a collective mental representation shared by group members that serves to channel group affect into team creativity. For instance, in the case of a shared learning goal orientation, where a team is experiencing a positive GAT, happy team members might feel more freedom to express their new ideas flowing from the flexibility associated with positive mood (Grawitch Munz, Elliott, & Mathis, 2003; Grawitch, Munz, & Kramer, 2003; Tsai et al., 2012). Fellow team members may be motivated to follow through on creative suggestions, and then to explore additional possibilities when learning is the shared objective of the team. In contrast, the facilitating effects of positive GAT may not occur in a team where team members are not motivated by learning goals. Feeling that things are going well and that the environment is unproblematic, happy members may become more satisfied

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with their initial solutions. Positive GAT may then intensify the groupthink syndrome as George and King (2007) posited, resulting in the team ceasing efforts to refine ideas or to seek out new solutions. Similarly, in a high learning goal team, negative GAT might be seen to transmit a problematic signal or a sense of insufficiency that prompts members to reexamine current strategies, explore alternatives, persist, and thus arrive at a better solution. In a negatively aroused group where a shared learning goal is absent, however, members may be tempted to settle on more obvious solutions. Without consensus that learning and exploration is the key, a team may be less effective in reaching meaningful agreement during disputes. The resultant interpersonal conflict, biases, or even hostility surrounding the negative affective experience are likely to be intensified in such a team. Team creative processes that require task‐focused discussion, persistence, and constructive collaboration are likely to be hindered. Given this scenario, we suggest that, in teams where members focus their attention to on task and information exchange activities, learning goals might help teams to capitalize on their collective affect as a resource for creativity.

Combined effects of valence and activation The second question relating to multilevel effects concerns the potential for combined effects of the two affect dimensions (valance and activation) on creativity at the individual and group levels of analysis. As we reviewed earlier in this chapter, evidence from both laboratory and field research shows that activating affect is more likely to promote individual creativity than is deactivating affect (e. g., see Binnewies & Wörnlein, 2011; De Dreu et al., 2008; To et al., 2012). More recently, Knight and Baer (2014) found in a laboratory study that group arousal can produce higher creativity in brainstorming groups through facilitating group members to combine and to integrate ideas. In their study, the authors measured individual arousal objectively using sensors that recorded each group member’s nervous system activity. They aggregated the individual sensor scores to form a group arousal score. The results of this study support the view that group arousal may promote group performance in creativity via enhanced information elaboration. Extending this line of research, it is plausible that both positive and negative GAT may have different ramifications for team creativity depending upon the level of activation or arousal. For instance, it is possible that the occurrence of groupthink might be more likely under low‐arousal positive GAT (such as feeling calm and relaxed) than with high‐ arousal GAT (such as feeling inspired and excited). When low‐arousal positive affect converges to become homogenous in a team, team members might tend to reinforce each other’s opinions, and therefore develop automatic trust or even a false sense of certainty. This automatic trust (or false sense of security) may then reduce alertness when questionable ideas or proposals are floated. Team creativity requires contributors to break sets and question assumptions, and these behaviors may be suppressed in low arousal positive GAT teams (George & King, 2007). On the other hand, negative GAT teams might engage in constructive task conflict, which can enhance team performance (De Dreu & Weingart, 2003). In contrast, interpersonal clashes associated with a negative group affective tone might be intensified under high arousal states. For instance, activating feelings such as anger and frustration may be more likely than deactivating states such as sadness to strengthen relational conflict, thus blocking constructive idea exchange and testing needed for team creativity.



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Group affective diversity The third question we address concerns the effect of group affective diversity. Group research to date has focused largely on how homogeneous affective reactions shared by team members may influence team creativity. In recent reviews, however, Barsade and Knight (2015) and Collins et al. (2013) pointed out that groups are not necessarily affectively homogeneous. In this regard, a small but growing body of research by scholars such as by Barsade et al. (2000), Kaplan, LaPort, and Waller (2013), and Magee and Tiedens (2006) has shown that affective diversity can be represented as a compositional model that helps to explain group behavior. Barsade et  al. (2000) undertook one of the first field studies of affective diversity in top management teams. The authors found that diversity in team members’ trait positive affect was positively related to top management team conflict and negatively related to team cooperativeness. Moreover, the effects of affective diversity on team processes and outcomes were also moderated by the average trait positive affect of the team, such that affectively diverse, low mean trait positive affect groups experience more conflict and less cooperation. In another study of nuclear power plant crews engaging in a crisis simulation, Kaplan et al. (2013) found that diversity in trait‐positive affect (implying less interpersonal bonding) was associated with more negative emotion during the crisis simulation that, in turn, was negatively related to group effectiveness. Barsade and Knight (2015) suggest further that the detrimental effects of affective diversity may be explained in terms of a similarity–attraction perspective, in which people prefer to work with others who share similar attributes with themselves (see also Williams & O’Reilly, 1998). Team members’ affective dissimilarity may thus produce a sense of interpersonal strain or stress between team members, thereby hindering group functioning (Barsade, Ward, Turner, & Sonnenfeld, 2000). Consistent with this perspective, Magee and Tiedens (2006) found, in a series of laboratory studies, that groups displaying comparatively more diverse facial expressions were judged by observers to share less similarity and to have lower group cohesion. On the other hand, and as Barsade and Knight (2015) and George and King (2007) noted, heterogeneous feelings may sometimes be beneficial to team creativity by supporting asymmetric, divergent thinking styles among team members. In summary, the results of individual‐level research from the dual‐tuning perspective seem to demonstrate that the combined effects of positive and negative affect can promote access to different kinds of information and different information processing styles, thus stimulating more creative and unusual ideas (e.g., Bledow et al., 2011; Fong, 2006; George & Zhou, 2007). George and King (2007) suggest that this phenomenon might extend to the group context in that diversity in affect among team members permits the processing styles associate with both positive and negative affect to be applied to a problem at the same time. Positive feelings may help the team to break out of existing mind sets and flexibly generate new alternatives, while negative affect may lead to careful, effortful, and persistent work on the problem. On the maladaptive side, however, the natural interpersonal tension implied by affective differentiation might be detrimental to creative processes as described above (Barsade & Gibson, 1998). In view of this, it is important for researchers and practitioners to understand why some teams are more or less effective in harnessing the different thinking styles and energy associated with affective diversity to improve their creative performance. We suggest in particular that leadership, group norms, creative process stage may serve either to neutralize or to enhance the effects of affective diversity on team creativity (e. g., Likowski, Muhlberger, Seibt, Pauli, & Weyers, 2011; Tiedens, Sutton, & Fong, 2004).

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Affect and Creativity in Teams as a Dynamic Process For decades, group researchers have called for the investigation of group dynamics, while noting that the vast majority of studies are of “group statics” (Cronin, Weingart & Todorova, 2011), where the focus is on stable properties of teams at a single point in time. Cronin and his colleagues thus call for researchers in future to investigate the evolution, emergence, and/or fluctuation of states and relationships within teams over time. There have also been calls for research into the role of time in creativity (Gilson, Litchfield, & Gilson, 2014). We know that teams are not always at their creative best. We know that affect, both at individual and group levels of analysis, varies over time within groups. Research in this regard (e. g., see Amabile et al., 2005; Binnewies & Wörnlein, 2011; To et al., 2012) tells us that affect is a potential contributor to understanding fluctuations in team creativity as we discussed earlier in this chapter. To complete our review therefore, we discuss two additional questions specifically related to understanding of the role of affect in facilitating of hindering work‐related team creativity in group settings as a dynamic process: (1) What are the effects of conceptualization at different levels from a dynamic perspective? (2) What is the potential for dynamic reciprocal effects?

Level of conceptualization from a dynamic perspective Thus far in this chapter, we have discussed mean positive and negative GAT in teams, as well as diversity of affect within teams. Each of these variables, however, can be conceptualized and measured at three different levels under a dynamic perspective, which we d ­ iscuss next. First, and characteristic of much of the research to date, both GAT and affective diversity can be conceptualized at trait level, based on the affective dispositions of team members. This makes the construct stable over time, with the only variance occurring between teams or if the membership of a team changes. This stability need not imply that GAT or affective diversity always have consistent and stable relationships with team creativity over time. Indeed, creativity varies over time within teams, and demands on teams may vary as a function of the stage of a creative project, with different stages requiring different approaches and cognitive styles (Allen & Thomas, 2011). Stage models include exploration (requiring more creativity) and then exploitation or implementation. The exploration stage may be further broken down into steps such as problem finding, incubation, illumination, and verification. Problem finding and verification may be well served by the careful and critical persistence generated by negative GAT, while incubation and illumination may be facilitated by the broad and flexible thinking associated with positive GAT. Since individuals are stable in affective disposition, it may well be that affectively diverse teams are most effective overall in the exploration stages, benefiting from dual‐tuning effects to serve the needs of different aspects of the creative task. Once the exploration stages are passed, converging upon and implementing a solution may be easier in teams with more homogeneous and positive affect (Knight, 2015). Second, GAT and affective diversity may be conceptualized and measured at the week or daily level for teams that work together regularly over a period of time. Teams may vary in GAT from day to day based on the moods that members bring to the team from other areas of their lives or from their experiences in the team on that day (Weiss & Cropanzano, 1996). Again, both mean GAT and affective diversity within the team on the day may be related to team creativity that day, or the next day, e.g., if positive affect today facilitates incubation over night as it does at the individual level (Amabile et al., 2005).



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It may also be helpful to consider the dynamics of GAT and affective diversity in teams over time. Paralleling the work on intra‐individual variation over time (Nesselroade, 1991), there may be two types of variation in affective experiences in teams from day to day or week to week. The first is intragroup fluctuation, short‐term reversible ups and downs in mean GAT and perhaps by chance, in affective diversity. The second is intragroup change, a more stable progression along a trajectory which may be due to developmental processes. For instance, affective diversity may decline as team members spend more time together and come to share norms and world views, or positive GAT may increase over time as the team comes closer to a successful resolution of their creative task. Again, mean GAT and affective diversity may have different effects depending upon the stage of the creative process. While quite rare, there have been a few studies of the effects of group affect on creativity over time. An example can be found in the work of Knight (2015), who found that affect predicts the amount of exploratory search over the course of a team’s life, and that such search is functional during the first half of a project but problematic in the second half when the team instead should turn to implementation of agreed ideas. Third, GAT and affective diversity may change within a single team meeting or day. Individuals bring affect with them to work, and this affect changes as a result of experiences during the day (Fisher & Noble, 2004). Within a team, mean GAT may change or affective diversity may decline during the meeting as affect contagion occurs (Barsade, 2002). Whether or not increasing homogeneity of affect and momentary mean positive or negative GAT are functional for concurrent creativity may depend on the stage of the creative process or the point in the task cycle (Knight, 2015).

Studying reciprocal dynamic relationships Finally, we note that another question involving dynamics concerns the potential for reciprocal relationships between GAT and creativity. For much of this chapter, we have assumed that GAT or affective diversity produces effects on creativity via cognitive or motivational means, but it is also likely that successful (or unsuccessful) creative efforts feedback to influence individual and collective affect in teams (Amabile et al., 2005). To explore such complex processes, organizational scholars are increasingly turning to more sophisticated means of collecting longitudinal data to explore dynamic relationships between constructs that vary over time between and within teams and individuals. Daily diary and experience sampling methods are used to collect frequent and repeated real‐time reports as individuals (and teams) go about their daily business (Beal, 2015; Fisher & To, 2012; Mehl & Conner, 2011). Computers and mobile devices have made gathering such data more feasible, and statistical methods for analyzing multilevel data have improved greatly. These techniques are likely to prove invaluable in enhancing understanding of the dynamic relationships between affect and creativity within teams over time and across project stages.

Future Research Based on the foregoing analysis, it is clear that our understanding of the group affect–creativity nexus is far from complete. While some research progress has been made to extend the individual phenomenon to the group level, the current literature appears to be fragmented and replete with inconsistent findings. Furthermore, empirical research investigating the hypothesized intervening variables in the relationship between GAT and team creativity is still limited, so the underlying mechanisms of the phenomena remains unclear. Clearly, any

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conceptual extension from individuals to teams requires additional and more integrative thinking to clarify the complexity demonstrated in the extant research. In view of this inconsistency, we next revisit the six issues that emerge from our analysis and discuss their potential as topics for future research. The first question concerns the apparent disconnect between individual‐level and group‐ level effects of affect on creativity. As we noted in our earlier discussion of this issue, research (e.g., Cole et al., 2008; George & King, 2007) has conclusively established that affect experienced in teams does not necessarily aggregate to influence teams in the same way that affect affects individuals who make up the team. Based on this concern, and taking our lead from the Tsai et al. (2012) study, we recommend that future group affect researchers consider both the information processing and social consequences of members’ affective experiences, and investigate the boundary conditions of team context under which the resources and energy provided by group affect will be harnessed to improve team creativity. The next multilevel effect we discussed concerns the potential for effects at the individual and group levels of analysis to combine to produce a symbiotic outcome that takes account of both the valence and activation dimensions of affect (Baas et al., 2008; Barsade & Knight, 2015; De Dreu et al., 2008). This aspect needs further work, however, and researchers in future may wish to investigate the differential effects of group affective tones based on the valence and activation dimensions. A further suggestion is to explore how team context (e.g., leadership and team norms) might play a moderating role in determining how teams make use of the resources provided by affect to become creative. The third area for future research we identify in this chapter can be seen in the recently developed idea of affective diversity. Thus, rather than focusing on affect as a relatively homogenous phenomenon within teams, researchers such as Barsade and Knight (2015) and Collins et al. (2013) have shown that there is a need also to consider that individuals in a group may experience different affects in the same situation or in response to the same stimulus. More research is needed in this regard, however, to ascertain whether such affect heterogeneity is detrimental or beneficial to team creative behavior. As we discussed earlier, affective dissimilarity can serve either to impede group creativity through the resulting sense of interpersonal stress between team members or, as Eden King and her colleagues found (see Barsade & King, 2015; George & King, 2007), can help to enhance creativity though the resulting increased diversity of views. The next potential area for future research we identify concerns the dynamic nature of team functioning. Indeed, nothing could be further from the truth than the idea that groups are static entities; indeed, groups, group affect, and group performance have been shown to emerge, to evolve, and to fluctuate over time (Cronin et al., 2011). We suggest that there is therefore a need to study group dynamics via nuanced and longitudinal work – investigating temporal variation in affect and creativity within teams over time. Also earlier in this chapter, we identified three aspects of level of conceptualization as issues in studying affect and team creativity that might warrant additional research attention in future. First, GAT is more than just a phenomenon that reflects team members’ affective traits. In particular, different dynamics can apply in different stages (Knight, 2015). Second, GAT can vary daily or weekly, or even diurnally both within and between groups. To date, only Knight’s study has specifically addressed just one aspect of this issue, so clearly there is great potential for future research in this regard. The final issue we identified relates to the potential for reciprocal effects between GAT and creativity. As Amabile and her associates (2005) found, while affect can impact creative behavior, the accomplishment or non‐accomplishment of creative outcomes can cause affective responses. In this regard, there would seem to be scope for researchers to



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use sophisticated methods such as experience sampling methods (Beal, 2015; Fisher & To, 2012; Mehl & Conner, 2011) to track the dynamic and reciprocal nature of such effects.

Conclusions In this chapter, we sought to provide an up‐do‐date survey of the current state of research into the nexus of affect and creativity. We first looked at how positive and negative affect can influence creativity and creative behavior at the individual level. In this regard, there is an established literature showing that positive affect is associated with creativity (Isen, 1999; Isen, Daubman, & Nowicki, 1987). More recently, however, researchers (e.g., see George & Zhou, 2002; Kaufmann, 2003) have found that, under particular circumstances, creativity also may be associated with negative affect. Arising from this idea, George and Zhou (2007) proposed a dual‐tuning perspective, in which positive and negative affect combine to facilitate creativity (see also George, 2011, De Dreu et al., 2008; Nijstad, De Dreu, Rietzschel, & Baas, 2010). Turning next to examine the effects of GAT on group creativity, we see that the research findings in this area are even more complex than they are for individual creativity, with some researchers (Grawitch, Munz, Elliott, & Mathis, 2003; Grawitch, Munz, & Kramer, 2003) finding that positive GAT promotes creativity and others such as Tsai et al. (2012) reporting contrary results. Similar mixed findings have emerged in respect of negative GAT and group creativity, where Kelly and her associates (Kelly & Spoor, 2007; Jones & Kelly, 2009) found evidence that negative GAT can promote creativity, while Cole et al. (2008) reported that negative GAT can suppress team functioning. In the previous sections of the chapter, we discussed in more detail the issues that serve to complicate our understanding of GAT and team creativity. Research progress to date is still in the early stages of mapping the complications of affect–creativity links operating at different levels. In this chapter, we have addressed various conceptual aspects as guidance for future researchers who wish to extend the individual phenomena to the collective level. First, the behavioral implications of affective experiences are complicated insofar as their effects on creativity are not necessarily parallel across the levels. Untangling the complexity requires a more complete understanding of not only the information‐processing functions but also the social functions of affective experiences. Second, it is important to distinguish the impacts of activating affect from those of deactivating affect. The valence and activation dimensions of affect as a collective experience in teams may produce differentiated behavioral responses influencing creativity in teams. Third, divergence in affect may play an additional role in influencing effective team functioning and creativity. Further, neither group affect nor team creativity are stable over time. It is important for researchers to explore the dynamics of the relationship between affect and creativity over time. Finally, we noted that issues of conceptualization level and reciprocal causality are further areas for potentially fruitful future research. We hope that, by addressing the aspects of group affect we have identified in this chapter, we have been able to further scholarly efforts to develop a more integrative theory concerning the (complicated) effects of affect on creativity in contemporary workplace settings.

Acknowledgement The authors wish to acknowledge the contribution of Ms. Alana Dorris, who assisted with proof reading and reference checking for this chapter.

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20

Team Reflexivity and Innovation Michaéla C. Schippers, Michael A. West, and Amy C. Edmondson

Introduction In competitive and demanding environments, teams and organizations must innovate to succeed over time (De Dreu, 2002; Hammedi, van Riel & Sasovova, 2011; West & Anderson, 1996). A small but growing body of research suggests that teams can con­ sciously develop reflexivity as a way of enhancing their innovation processes (e.g., Carter  & West, 1998; De Dreu, 2002; Hammedi et  al., 2011; Schippers, West & Dawson, 2015; West, 2002; West & Anderson, 1996; for reviews, see Schippers, Edmondson & West, 2014; Widmer, Schippers & West, 2009). Indeed, reflexivity has been identified as a key factor for teams to stay innovative, especially in a demanding environment (Schippers et al., 2015), because it prompts reconsideration of strategies, processes and environments of the team, and an identification of discrepancies between current and desired states (Locke & Latham, 1990; Schippers, Den Hartog, Koopman & van Knippenberg, 2008). Hence, discovering what factors prompt innovation is of vital importance for organizations. However, to date, research investigating the link between team reflexivity and innovation is scarce. A review by Moreland and McMinn (2010) reveals that the relation between team reflexivity and team outcomes (such as innova­ tion) is not simple, but rather that team reflexivity has positive consequences under the right conditions. The aim of the current chapter is to review the literature on team reflexivity and inno­ vation, to spur research on this important topic. In the following sections, we first review the literature on reflexivity and innovation. We then propose a model of antecedents and outcomes of team reflexivity (Figure  20.1). Our model is necessarily incomplete in its attempt to describe an important and complicated team phenomenon. Our objective is to depict the most important influences on reflexivity and information processing effective­ ness in teams as suggested by our review of prior research; as with all models, it is not exhaustive. Moreover, relationships are simplified; for instance, the relationship between The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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Team Effectiveness: Processes, Emerging States and Mediators Diversity beliefs

Interdependence Task complexity

Expertise diversity

Team leader coaching

Psychological safety Clear team goal

Team reflexivity

Team innovation Team performance

Minority dissent Task conflict Time pressure

Figure 20.1  A model of antecedents and outcomes of team reflexivity.

expertise diversity and other variables in the model is more complicated than can be d­epicted by a single arrow and a single type of diversity. We propose that several team characteristics promote team reflection: A clear team goal, psychological safety, team leader coaching, and expertise diversity. Also, structure variables that predict team reflection in the model are (moderate) time pressure, task complexity, and task interdependence. We argue that our model of team reflection applies to many types of teams, but especially intact and co‐located teams. After presenting our model, we review evidence from the literature for the effect on team innovation. In this chapter, we thus present a theoretical model to motivate and structure needed research in  this area. Finally, we point to avenues for future research and discuss implications for p­ractice.

Team Reflexivity In prior work, team reflexivity has been defined as the extent to which group members overtly reflect upon and communicate about the group’s objectives, strategies (e.g., decision making) and processes and make changes accordingly (Schippers et  al., 2015; West, 2000). Although the original construct comprised three parts, namely reflection, planning, and action/adaptation, most work today views team reflexivity as one con­ struct, with a focus on team reflection (Schippers, Den Hartog & Koopman, 2007; Schippers et  al., 2008; Schippers, Den Hartog, Koopman & Wienk, 2003; Schippers, Homan & van Knippenberg, 2013; Widmer et al., 2009; for an exception, see Konradt, Schippers, Garbers, & Steenfatt, 2015). In the current literature, many different terms are used that all seem to be similar or the same as team reflexivity. These include terms such as: after action/after‐event reviews (e.g., DeRue, Nahrgang, Hollenbeck & Workman, 2012) briefing–debriefing (Vashdi, Bamberger, Erez & Weiss‐Meilik, 2007), action team learning (to indicate the iterative nature of team reflexivity; Vashdi, Bamberger & Erez, 2013), team learning (e.g., Edmondson, 1999; Van der Vegt, De Jong, Bunderson & Molleman, 2010), team learning behaviors (Savelsbergh, van der Heijden & Poell, 2009), team cognition (Andres, 2013), and task debate (Johnson, van de Schoot, Delmar & Crano, 2015). The use of so many different terms to indicate a similar construct hinders progress in this area, and many different papers are published with similar findings not citing each



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other because of the use of different terms. The main overarching construct seems to be team learning (Schippers et al., 2014). According to Schippers et al. (2014, p. 736): team learning has remained a fairly undifferentiated, or encompassing construct ‐ comprised variously of engaging in learning behaviors that emphasize communication between team members and others and range from asking questions and admitting mistakes within the team, to boundary spanning activities that gather information or expertise from others outside the team (Edmondson, Dillon & Roloff, 2007).

Team reflexivity can be seen as an essential aspect of team learning. We propose to review the field and also cite papers that use a term similar to team reflexivity and explore this construct in relation with innovation. Reflexive behaviors, such as “questioning, planning, exploratory learning, analysis, d­iversive exploration, making use of knowledge explicitly, planfulness, learning at a meta‐ level, reviewing past events with self‐awareness, and coming to terms over time with a new awareness” (West, 2000, p. 4) are especially useful in recognizing if current ways of working are obsolete and can aid in either replacing those ways of working or developing innovation (Tjosvold, 1991).

Team reflexivity and innovation Innovation can be defined as: the intentional introduction and application within a job, work team or organization of ideas, processes, products or procedures which are new to that job, work team or organization and which are designed to benefit the job, the work team or the organization (West & Farr, 1990, p. 9).

Compared with creativity, innovation not only entails the generation of new ideas, but also includes the implementation of these ideas (West, 2002; West, Hirst, Richter & Shipton, 2004). The need for team level innovation is often a result of problems and challenges that teams encounter in the pursuit of work goals (Hirst, van Knippenberg & Zhou, 2009) and teams reflexivity is needed to realize the innovation dividend (Schippers et  al., 2015). However, a review of team‐level predictors of innovation concludes that inconsistent findings characterize the field and points to the need for contingency models (Hülsheger, Anderson & Salgado, 2009). This meta‐analysis concluded that team reflexivity was related to team innovation, but that it was measured as being part of the broader concept of task orientation (Hülsheger et al., 2009). In general, past research has shown reflexivity to be related to innovation (De Dreu, Nijstad & van Knippenberg, 2008; Hülsheger et al., 2009; Schippers et al., 2015; Wong, Tjosvold & Su, 2007). For instance, Tjosvold, Tang & West (2004), in their study of 100 work teams in China, found that teams which reflected on their tasks were more innova­ tive, as rated by their managers. Another study, among BBC TV production teams, showed a positive relation between team reflexivity and external management ratings of creativity (Carter & West, 1998). In this research, the authors followed the performance of 19 BBC TV production teams over an 18‐month period. These interdependent teams worked in an uncertain, unpredictable environment, with developing technology, and had a highly creative task. The teams had to come up with new ideas for television programs, and also produce them. Innovation was measured by an audience appreciation index and executive producer ratings. The relation between reflexivity and these innovation measures was

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especially strong, accounting for about 50% of variance, and reflexivity was a stronger p­redictor than climate for innovation or team size. Research among 98 healthcare teams showed a strong and direct effect between team reflexivity and innovation, besides m­oderated effects (discussed below; Schippers et al., 2015). In a study among 136 primary‐ care teams, an especially strong relationship was found between team reflexivity and i­nnovation (β = .41) and reflexivity mediated the moderated relationship of functional diversity with participative and directive leadership (Somech, 2006). An area where reflexivity is especially likely to be effective is the area of product development. A study among 145 software development teams who were required to develop innovative solutions for clients, showed that team reflexivity was positively related to team effectiveness, measured as the technical quality of the software solution, but not efficiency, measured as the extent to which the project remained within schedule and b­udget (Hoegl & Parboteeah, 2006). Social skills and project management skills were identified as predictors of team reflexivity in this study. A study of 107 Turkish new product/project development teams indicated that team reflexivity was related to product team success for teams working in a highly turbulent environment (Dayan & Aydin, 2010). Finally, a study among 77 new product development teams from 15 companies showed that reflexivity was related to unlearning of current beliefs and routines, and in turn p­ositively impacted innovation (Lee & Sukoco, 2011). Research at the organizational level with 96 senior executives of technology‐based s­ervice‐sector organizations involved in new‐product screening showed that reflexivity was related to effective and efficient screening decision making (Hammedi et  al., 2011). A study among top management teams from 39 Irish software firms revealed that team reflexivity was highly related to new product performance (MacCurtain, Flood, Rama­ moorthy, West & Dawson, 2010). Research in the context of strategic alliances in China suggested that task reflexivity enhanced resource exchange between partners and in turn innovation, as rated by customer organizations representatives (Wong et al., 2007). This provides initial compelling evidence of the relationship between team reflexivity and i­nnervation across sectors.

Moderators of the team reflexivity–innovation relationship Recent research focuses more on moderators. For instance, Schippers et al. (2015) showed that a sizable direct effect of team reflexivity on innovation (β = .35), was moderated by work demands and the physical working environment. The research focused on healthcare teams, and these teams often had a high work load, because they had on average 3,146 patients (i.e. the patient to doctor ratio). Thus, they had to deal with a high quantity of patients on a daily basis and the challenge these teams faced was to deal efficiently and effectively with such large numbers of patients. In order to cope with these high numbers, an innovative response was required, by means of reflexivity. It was proposed that team reflection would produce a general innovation orientation and that this would lead to a functional response over other possible responses such as working harder or hiding as a reaction. The idea was that reflection would create a shared cognitive space in which the preparedness for action spurred by demands would create the opportunity for innovation (Bunce & West, 1994; Janssen, 2000; West, 1989). Moreover, the healthcare teams in the described study had to deal with the physical working environment, and often teams were inclined to adapt or complain about the quality of the work environment rather than actively make changes. However, by reflecting and responding innovatively, teams may become more effective (cf. Elsbach & Pratt, 2007; Vashdi et  al., 2007), for instance by actively making adjustments to the physical layout of the room (Vashdi et al., 2007),



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or  making optimal use of the equipment and space (Schippers et  al., 2015). This was indeed what was found: Controlling for team size and psychological strain, teams with a combination of a low quality of physical work environment and a high level of reflexivity were most innovative. Also, a suppression effect for the control variable, psychological strain, showed that strain was more positively related to innovation when reflexivity was added to the equation. This was attributed to the greater sense of urgency to innovate as a coping response, owing to the proportion of team members experiencing increases in strain (Bunce & West, 1994). The relationship between team reflexivity and team innova­ tion was contingent upon team level work demands, such that for teams with high objective job demands (i.e., many patients), team reflexivity was associated with the development of new and improved ways of working (Schippers et al., 2015). This prior research showed that the relation between team reflexivity and innovation is important for team functioning and that reflexivity is especially functional when the teams’ work load is high and when the team has to deal with a low‐quality physical working envi­ ronment. This is in line with earlier research showing that reflexivity is most functional when prior performance is low, indicating that the “investment” of time and energy into a reflexivity process may pay off when team functioning is low, or when faced with a demanding environment (for a review, see Schippers et al., 2014; Schippers et al., 2013; see also Widmer et al., 2009). Interestingly, however, this study used a measure of innova­ tion where external raters were used to rate the innovations listed by team members on four dimensions derived from West and Anderson (1996): magnitude – how great would be the consequences of changes introduced; radicalness – to what extent the status quo would change as a consequence; novelty  –  how new in general were the changes; and impact – to what extent changes would improve the teams’ effectiveness. As was noted by Hülsheger, et  al. (2009), in the past researchers have often relied on self‐ratings when measuring team innovation, leading to an overestimation of effect sizes. This is an impor­ tant issue in the field of innovation, and it would be advisable that researchers use more similar measures of innovation as well as more objective measures (Hülsheger et al., 2009). In general, we expect several variables to moderate the relationship between antecedents and outcomes of team reflexivity, as outlined below.

A Model of Team Reflexivity Several team characteristics are likely to promote team reflexivity: A clear team goal, psychological safety, team leadership coaching, and expertise diversity. Second, structural variables that predict team reflexivity in the model are (moderate) time pressure, task com­ plexity, and task interdependence. We argue that our model of team reflexivity applies to many types of teams, especially those that are intact and co‐located. These variables are described in more detail below (Figure 20.1).

Time pressure The time teams have available to engage in discussion will affect the extent of team reflex­ ivity. Research on time pressure (e.g., Darley & Batson, 1973; De Dreu, 2003; Jones & Roelofsma, 2000; Klein, Calderwood & Clinton‐Cirocco, 1988; Parks & Cowlin, 1995; Verplanken, 1993) showed that under conditions of high time pressure, decision makers tend to restrict their search for information and consider fewer alternatives, resulting in lower quality decisions (Chirumbolo, Livi, Mannetti, Pierro & Kruglanski, 2004; De Dreu, 2003; Tjosvold, 1984), especially for people low in need for cognition – the tendency to

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engage in thinking and cognitive efforts (e.g., Petty & Cacioppo, 1986; Verplanken, 1993; for a review, see De Dreu et al., 2008). Time pressure seems to have an overriding effect, and is stronger, for instance, than the effect of priming. In their seminal research, (Darley & Batson, 1973), designed an experiment in which individuals were asked to help the experimenter by giving a short talk on either the Good Samaritan p­arable, or a topic unrelated to helping. Just before the talk, they were directed to another building to give the talk, and the experimenter told them they were expected a few minutes ago (high‐ hurry condition), the assistant was waiting in the other building (intermediate‐hurry condition), or that they had plenty of time (low‐hurry condition). On their way to the other building, the participants saw a “victim in distress.” Under conditions of high time pressure, they were less likely to help a victim in distress, regardless of whether they were giving a talk on the Good Samaritan parable or the unrelated topic. In hindsight, many participants admitted that this person probably needed help, however at the time of passing by they did not reflect on this. Research shows a consistent tendency for people under time pressure to regress to over‐ learned behavior (e.g., De Dreu, 2003; De Grada, Kruglanski, Mannetti & Pierro, 1999; Weick, 1990). Under severe time constraints, less information can be shared, and elabo­ rated upon, and a breakdown of the team mental model is likely to occur (Ellis, 2006). We propose that, as with individuals under time pressure, groups also regress to a well‐ learned response. The group accentuation pattern – the tendency of groups to exaggerate individual level failures if that particular failure is likely among individuals (Hinsz, T­indale, & Vollrath, 1997) – would predict this. For example, when the workload in a breast cancer care team is high (e.g., there are nearly twice as many referrals as normal) – the team would have less time available to discuss each case. Time pressure leads groups to focus on task completion, causing members’ initial preferences to have more influence on group discussion and completion, whereas moderate time pressure allows teams to focus on the quality of the decision (Karau & Kelly, 1992) and consider a range of alternatives (Schippers et  al., 2014). In the condition of time abundance, groups focused more on social and non‐task activities, causing them to perform less well. Although not directly put to the test in their research on triads working on planning tasks, Karau & Kelly (1992) argue that it is likely that teams in the optimal time condition reflected more on the pros and cons of a chosen solution, whereas in the restrictive time conditions solutions were accepted w­ithout much discussion. Where teams have an abundance of time to achieve objectives, process information, make decisions and take action, there is no sense of urgency. Consequently, the spur for reflexivity will be low. Reflexivity is always likely to reveal a discrepancy b­etween the current and desired state. It is therefore a potentially aversive process because reflexivity will indicate a need for new effort, changed behavior or inadequate performance. If there is little time pressure for achieving outcomes then reflex­ ivity is more likely to be avoided therefore. Hence, we propose that under high time pressure, teams will be less innovative (Schippers et al., 2015).

Clear team goal Reflexivity is effortful, and hence team members must be motivated to exert effort for reflexivity to occur. A compelling shared goal can motivate them by establishing positive pressure toward a desired future state. A clear, meaningful goal has been shown to moti­ vate team learning, including active team reflection, in prior research. For example, in one study of team learning in cardiac surgery departments, some of the surgeon leaders pre­ sented the new technology as an opportunity to help patients (by dramatically reducing the size of the surgical wound) and stressed the difficulty of the challenge, explaining that



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it would require everyone on the team’s active participation to pull it off (Edmondson, Bohmer, & Pisano, 2001). This emphasis on the goal and on the outcomes of their work helped the team go through the arduous learning process, including engaging in active team self‐reflection. Having a shared team goal or vision of the future is likely to make it easier for teams to reflect effectively on their functioning and progress towards their goal. The motivational power of goals is well established in the literature (e.g., Locke & Latham, 1990; Locke & Latham, 2002). Research has also shown that goal interdependence enables efficiency in group problem solving (Tjosvold, 1990). Goals keep a team “on track” by establishing a benchmark against which its members can measure progress. Goals must be reasonably well defined and understood by all team members to foster reflexivity. For example, in a study of geographically dispersed product development teams (Sole & Edmondson, 2002), one team was working to develop a radical new material for a large Asian manufac­ turer. Distant team members had had no direct contact with this customer yet needed to understand its market strategy to estimate the longer‐term commitment required for the team and its company. Other team members were located near the customer site and seemed to be in a good position to have the necessary information. An intermediary familiar with both companies became involved and through his probing discovered that the customer itself had not yet established sales, marketing, or distribution plans, nor identified people responsible for these activities. We thus expect that teams with a clear team goal will be more motivated to reflect, because without an explicit goal for a team to work on together, it is difficult to assess how well the team is doing (cf. Locke & Latham, 1990; Senge, 1990). For instance, a team with a clear goal of developing a car navigation system can reflect on whether the team is on the right track to getting the design accomplished and adapt if necessary. Support for this proposition comes from research on team mental models (Klimoski & Mohammed, 1994). Team members’ shared, organized understanding and mental representation of knowledge about key elements of the team’s task environment (Klimoski & Mohammed, 1994), and they facilitate coordination and communication about team processes. S­imilarly, Scott and Kameda (2000) coined the term social sharedness as a unifying theme representing the degree to which “preferences, attitudes, motives, norms, identities, e­thnicities, etc., as well as cognitions and cognitive processes” (p. 124) are shared and are being shared within groups. Having a clear team goal also requires social sharedness, because a goal is neither clear nor team‐based if members do not share it. Recent labora­ tory research showed that the collaboration mode (face‐to‐face versus technology‐mediated communication) impacted team reflexivity, team learning and team mental models, with face‐to‐face communication being superior in facilitating team cognitions such as team reflexivity (Andres, 2013). Research among new product development teams operating in  turbulent environments showed that goal clarity was related to product success, a­long with to transactive memory system, team empowerment, and interactional justice (Dayan & Aydin, 2010).

Minority dissent, psychological safety and conflict A climate in which team members feel free to speak up is an important prerequisite for team reflexivity because of the interpersonal risks involved in raising sensitive issues in a team (Edmondson, 1999; for a review, see Edmondson & Lei, 2014). Psychological safety is defined as “a shared belief that the team is safe for interpersonal risk‐taking” (Edmondson, 1999, p. 354), and was shown to be positively related to team learning behaviors. Edmondson’s (1999) conceptualization of team learning includes reflexivity as defined

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here, in addition to external learning behaviors, and more specific behaviors such as feedback seeking, experimentation and discussion of errors. Psychological safety is needed for teams to cope with the potentially threatening nature of the content of their reflexivity. For example, if reflecting on past performance suggests that there are problems with how the team is working together, it is likely to require psychological safety to discuss the accompanying challenges productively. Some empirical evidence supports this relationship. Specifically, in a study of 51 work teams in a manufac­ turing firm, psychological safety was the most important predictor of both observer‐rated and self‐reported team learning behavior, and psychological safety explained variance above and beyond other factors such as team efficacy, context support and team leader coaching. Further, team‐learning behavior mediated the effect of psychological safety on team performance (Edmondson, 1999). Recent research suggests that psychological safety enhances the effectiveness of minority dissent and task conflict, and in turn innovation. In an environment characterized by high psychological safety, task related conflict will be related to debate and consideration of alternative interpretations and in turn team innovation (West & Richter, 2008). Van Dyne and Saavedra (1996) argued: Nemeth (1986) has theorized that the primary effect of minority influence is a change in group problem‐solving processes that leads to higher quality decisions and more innovative group outcomes. In other words, the focus is on improved group decision‐making processes that increase originality and not on whether the minority convinces the majority to change their opinions (p. 154).

As such, minority viewpoints are important in stimulating divergent attention and thinking. Nemeth proposes that: “As a result, even when they are [minority groups] wrong they contribute to the detection of novel solutions and decisions that, on balance, are qualita­ tively better. The implications of this are considerable for creativity, problem solving and decision making” (1986, p. 23). In conclusion, these divergent cognitive processes allow for novel solutions and for innovation (Nemeth, 1986). Recent research suggests that over time, dissenting in‐group members, sharing information via debate (i.e., team reflexivity) improve team performance by means of innovation (Johnson et al., 2015). This study adds to the idea that the timing of team reflexivity is important (Ford & Sullivan, 2004). Another study among 36 top management teams showed that minority dissent was positively related to the number of innovations implemented by the teams. However, the psychologically safe climate created by transformational leaders, led to more radical innovations under conditions of minority d­issent (Nijstad, Berger‐Selman & De Dreu, 2014).

Team leader coaching Several meta‐analyses show the impact of leadership behaviors on team outcomes such as motivation, satisfaction and performance (Hiller, DeChurch, Murase, & Doty, 2011; Judge & Piccolo, 2004; Judge, Piccolo & Ilies, 2004), and also research has shown a (moderated) link with innovation (e.g., Nederveen Pieterse, van Knippenberg, Schippers, & Stam, 2010; for a review, see Rosing, Frese, & Bausch, 2011). Evidence for the effects of team leadership behavior on team process, and more specifically team reflexivity, is limited however. Preliminary evidence that team leader coaching promotes team reflexivity may be found in a study by Hirst, Mann, Bain, Pirola‐Merlo, and Richter (2004), who found that facilitative leader behaviors were positively related to team reflexivity, which in turn



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affected customer ratings of team performance. Somech (2006), also found, in a sample of healthcare teams, that directive and participative leadership both moderated the relation­ ship between functional heterogeneity and team reflexivity (measured with a reflexivity scale), such that team reflexivity was highest when both participative leadership and functional heterogeneity were high. Team reflexivity was also high when directive leader­ ship and low functional heterogeneity coexisted in a team. Team reflexivity was in turn positively related to team innovation. Furthermore, research in medical settings s­uggests that leaders have an information management role in problem‐solving discussions (Larson, Christensen, Franz & Abbott, 1998). Team leader coaching, defined as “direct interaction with a team intended to help m­embers make coordinated and task‐appropriate use of their collective resources in a­ccomplishing the team’s work” (Hackman & Wageman, 2005, p. 269) can be impor­ tant in three ways. First, a team leader can create a climate in which team members feel free to speak up and reflect on performance. When a team leader creates a warm, positive and inclusive team environment that encourages information sharing, partici­ pants involvement in decision making, excellence in task performance and the development of ideas for new and improved products, services or ways of working, reflexivity will be much more evident. Field research showed that the team leader coaching plays an important role in conveying the message that team members can speak up. Conversely, when psychological safety was low, lower‐status team members were unwilling to risk speaking up for fear of censure by superiors (Edmondson, 2003; Edmondson et  al., 2001). As mentioned above, psychological safety within teams is positively related to team learning behavior (E­ dmondson, 1999). The underlying mechanism is not clear, however. Several explanations have been offered, namely: (1) psychological safety and positive affect influence courage and exploration of novel stimuli (Edmondson, 2003); consequently, team members may be more likely to iden­ tify variations from the norm in their functioning and performance; (2) in a team high in positive affect, there is more preparedness to react favorably to feedback about the team’s performance (Lee, Edmondson, Thomke & Worline, 2004); or (3) positive affect could be associated with interpersonal risk taking more than negative affect (cf. Aspinwall & Staudinger, 2003; Aspinwall & Taylor, 1992). The potential mechanisms through which this effect occurs are thus multiple, and so we propose that psychological safety is an important antecedent of team reflexivity and that team leaders play a role in enhancing team psychological safety. Second, team leaders can facilitate the development of a clear team goal. Just as leaders can develop and communicate a vision in organizations, to motivate followers to try to attain it (e.g., Bass, 1985), team leaders also help articulate a clear goal for their teams. In research on product development teams, Sarin and McDermott (2003) identified ­clarifying team goals by the team leader as a predictor of team learning, and Schippers et al. (2008), in a study of 32 teams, found that transformational leadership was related to the adoption of a shared vision, which was in turn related to team reflexivity and ultimately team performance. Third, team leaders can actively intervene and lead the discussion so as to enhance reflexivity (Hackman & Wageman, 2005). For example, the team leader who typically asks, “What can we learn from this?” following errors is directly encouraging reflexivity. Gersick and Hackman (1990) suggested that a team leader can help the team develop meta‐routines, which prompt members to initiate reevaluation of first‐level routines regu­ larly. Team leadership style encouraging followers to see problems from different angles will therefore stimulate reflexivity among team members (Schippers et al., 2008). Research among 54 work teams from different companies and with different tasks suggested that

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the team leader can play an important role. A transformational leader seemed to set the stage for shared vision (i.e. social sharedness) in a team, which led to enhanced reflexivity, and this in turn led to enhanced team performance (Schippers Den Hartog, Koopman & van Knippenberg, 2008). Team leaders who themselves reflect are also likely to encourage each member to share and discuss their information, scan for new information, challenge framing, reveal and dis­ cuss heuristics, draw attention to potential biases, and generally encourage the team to discuss their decision‐making processes (Hackman & Wageman, 2005; Schippers et al., 2014; Schippers et al., 2008). This will enhance the level of team innovation, as teams will be better able to scan the team environment, and make use of available information (cf. Schippers et al., 2015). Indeed, team reflexivity (and psychological safety) was shown to mediate the relationship between transformational leadership and creative problem solving (Carmeli, Sheaffer, Binyamin, Reiter‐Palmon & Shimoni, 2014).

Expertise diversity Many studies have examined effects of team composition, particularly in terms of demo­ graphic diversity, on team process and performance. Extensive research shows that demo­ graphic diversity affects team process and team performance, but the observed relationships are rarely straightforward (for recent reviews, see Cronin & Weingart, 2007; van Knippenberg & Schippers, 2007). First, diversity effects, positive and negative, tend to be moderated by other variables. Second, there are numerous aspects of team composition to consider. Indeed, many types of diversity have been studied, including demographic, expertise, and value diversity. Although we focus primarily on expertise diversity, most types of diversity lead people to have different knowledge and values (Cronin & Weingart, 2007; van Knippenberg & Schippers, 2007; Williams & O’Reilly, 1998). Information processing theories suggest that divergent viewpoints will invite more careful consideration of the team’s functioning and thus stimulate team reflexivity, that in turn results in team learning and improved team process (van Knippenberg & Schippers, 2007; West, 1996, 2002; for a review see Schippers et al., 2014). The most salient dimension of diversity in relation to task‐relevant reflexivity is expertise diversity. Teams are created to combine the skills of different individuals in order to p­erform a team task that the individuals alone could not perform. The greater the range of skills required to perform the task, the higher the level of expertise diversity. Where expertise diversity is high, team members will necessarily require high levels of communi­ cations in order to exchange the divergent information necessary for team member coordination and integration on inputs to achieve successful task performance. Because the level of diversity in expertise is high, effective communication will require longer and more intensive interaction. This inevitably creates more opportunities for reflexivity. Con­ currently however, expertise diversity will lead to process losses and also, potentially, higher levels of conflict. Reviews suggest that high levels of diversity produce higher levels of intrateam conflict. So, unless there are positive views within the team about the value of the expertise of others, expertise diversity could be associated with interpersonal conflict, which, in turn, will be related to lower levels of reflexivity (cf. van Knippenberg & S­chippers, 2007). Research evidence reveals support for the propositions outlined in relation to expertise diversity. Schippers et  al. (2003) found that team reflexivity mediated the (moderated) relationships between a general measure of diversity and team performance, commitment, and satisfaction. Providing further support for this perspective, a field study of multidisci­ plinary teams showed that expertise diversity was related to team learning, moderated by



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collective team identification (Van der Vegt & Bunderson, 2005). In their research, the moderated relationship between diversity and team learning was also curvilinear, such that these relationships were U‐shaped for teams with low levels of collective identification, and inverted U‐shaped for high levels of collective team identification. Furthermore, these relationships were also mediated by team learning. A study of top management composi­ tion of new product performance in the software industry showed that educational level, tenure and age diversity had indirect effects on team reflexivity and knowledge sharing, and task reflexivity and knowledge sharing were related to new product performance (MacCurtain et al., 2010). From these and other studies, we can conclude that the relationship between team com­ position, in terms of diversity, and team reflexivity is not a simple one (van Knippenberg & Schippers, 2007). Recent research suggests that the extent to which team members value diversity, or diversity beliefs, is an important moderator between diversity and group p­rocess variables (Homan, van Knippenberg, Van Kleef & De Dreu, 2007).

Moderators Diversity beliefs People may differ in their beliefs about and attitudes toward diversity (Hostager & De Meuse, 2002; Strauss, Connerley & Ammermann, 2003; van Knippenberg & Haslam, 2003). Diversity beliefs refer to ideas about diversity within work groups, namely the extent to which group members value homogeneity or heterogeneity on some group aspect such as gender, age, nationality or expertise (Ely & Thomas, 2001; Homan, van Knippenberg, Van Kleef & De Dreu, 2004; Homan et al., 2007; van Knippenberg, Haslam & Platow, 2007). Team members may believe that diversity is beneficial in the sense that it improves group functioning and performance. For example, team members may think that by having different experts, the team will be more creative, or more interesting to work in. Consequently, they will value diverse groups more than homogeneous groups. The premise of this theory is that when groups value (expertise) diversity, this will buffer the negative effect of diversity on group process and performance. More specifically, if people hold a positive view of the different skills of team members, they will ask for more advice, discuss issues more thoroughly to get a good view of the problem at hand, and invite more expert opinions from each other. Research findings showed that when m­embers of diverse teams adopted an integration‐and‐learning perspective, (as opposed to an access‐and‐legitimacy perspective or a discrimination‐and‐fairness perspective) they benefited more from each other’s knowledge and skills, and performed better (Ely & Thomas, 2001). Also, pro‐diversity beliefs and task motivation have been shown to buffer the negative effects of overcome faultlines (Meyer, & Schermuly, 2011). We thus propose that diversity beliefs will moderate the relationship between expertise diversity and reflexivity.

Task complexity and task experience A complex task, as opposed to a simple one, is characterized by high rather than low information processing requirements (Gist, Locke & Taylor, 1987; West, 1996). The uncertainty inherent in a complex task will spur teams to broaden their knowledge base, for instance through reflexivity. Where the task is complex, the stimulus to exchange information and collectively process information will be strong. Because of the relatively

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high level of uncertainty inherent in complex situations, team members will be more likely to seek out each other’s information, opinions and recommendations. Several authors have hypothesized the moderating effects of team task characteristics such as task interde­ pendence (Gladstein, 1984), and routineness or task complexity (Gist et  al., 1987; Gladstein, 1984; Stewart & Barrick, 2000). Stewart and Barrick (2000) found that task type (conceptual vs. behavioral) moderated the relationship between team processes (com­ munication, conflict, shirking and team member flexibility) and performance. Other authors failed to show the expected moderating effect of team task (e.g., Gladstein, 1984). Gladstein explained this by suggesting that there was insufficient task variability. Recent research by Vashdi, Erez, Bamberger, and Weiss‐Meilik (2007) showed that task com­ plexity moderated the relationship between structured reflexivity (i.e., briefing–debriefing) and performance in surgical teams, such that high task complexity was associated with a stronger positive relationship between reflexivity and performance. Finally, a laboratory study among teams working on a product development task showed that direct task expe­ rience was related to higher levels of team creativity than indirect task experience, and that this effect was mediated by transactive memory systems (Gino, Argote, Miron‐Spektor & Todorova, 2010).

Task and outcome interdependence Another aspect of the task that could influence reflexivity and ultimately innovation is the extent to which team members are dependent on each other (Hülsheger et  al., 2009). Interdependence can be defined as the extent to which team members are dependent on each other to accomplish the overall task at work. Several authors have distinguished between task and outcome interdependence (Johnson & Johnson, 1989; Saavedra, Early & Van Dyne, 1993; Shea & Guzzo, 1987; Van der Vegt, 1998; Van der Vegt & Emans, 2000; Wageman & Baker, 1997). Outcome interdependence refers to the extent to which team members are provided with group goals and receive group feedback (Van der Vegt, Emans & Van de Vliert, 2001; Van der Vegt & Janssen, 2003). Task interdependence refers to the extent to which team members must exchange information, expertise and materials in order to get the job done (Cummings, 1978; Susman, 1976; Van der Vegt & Emans, 2000; Wageman & Baker, 1997). When teams are highly task interdependent, there will be a greater need for coordination, communi­ cation and cooperation (Early & Northcraft, 1989; Galbraith, 1978), again encouraging behaviors among team members of eliciting each other’s information, opinions and r­ecommendations. Schippers et al. (2003) found that outcome interdependence moderated between diversity and reflexivity. Outcome interdependence focuses team member’s attention on the group’s progress towards achieving group goals (Wageman & Baker, 1997). The gap between current and required performance will therefore be apparent, creating awareness of the need to consider how to close the gap. Such a situation is likely to stimulate discussions about goals, team processes and team effectiveness. A study by De Dreu (2007) showed that cooperative outcome interdependence was related to information sharing, learning and effectiveness, especially under conditions of high task reflexivity. A recent meta‐analysis found that outcome interdependence was a stronger predictor of team innovation than task interdependence, the latter being not a significant predictor. However, it is likely that goal and task interdependence interact to predict both reflexivity and innovation. Furthermore, the relationships between both task complexity and task interdependence on the one hand and team reflexivity on the other hand, will be strengthened by psychological safety. Both features of the task will make reflexivity useful, but only if the



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team has high psychological safety will team members be comfortable enough engaging in reflexivity at team level. The same holds for a clear goal: reflexivity will be higher if task complexity and task interdependence co‐occur with a clear goal. Finally, it can be argued that both task complexity and interdependence will moderate the relationship between expertise diversity and team reflexivity. In support of this propo­ sition, Jehn, Northcraft and Neale (1999) found that for less‐routine tasks, informational diversity was more positively related to group performance, and a meta‐analysis by Bowers, Pharmer and Salas (2000) showed that, on more complex tasks, diversity was positively related to group performance, but was negatively related on simpler tasks. Schippers et al. (2003) found that outcome interdependence moderated between diversity and team reflexivity, and Van der Vegt and Janssen (2003) found that when team members in ­heterogeneous teams experienced high task and outcome interdependence, individual innovative behavior was also higher. When task interdependence was high and outcome interdependence low, no relation with innovative behavior was found. Tjosvold et  al. (2004) found that cooperative goals, but not competitive or independent goals were related to reflexivity and in turn innovation. Competitive and independent goals were neg­ atively related to team reflexivity. As outlined in Figure 20.1, but not explicitly hypothesized, we propose in general that reflexivity mediates between these antecedents and team innovation. In the next section, we discuss the implications for practice.

Implications for Practice Practitioners who wish to structure and lead groups in ways that foster the benefits of reflexivity, may wish to start with training both team leaders and team members to engage in focused, evaluative discussion of goals, processes, and outcomes. Both should also regularly discuss whether they need to seek out new information of different kinds to ensure the effectiveness of their decision making. They should also reflect on whether they have suitable procedures in place to ensure that sufficient attention is paid to uniquely held information. Reflexivity about common information‐processing failures and their manifestations in their teams is also important such as escalation of commit­ ment, failure to update conclusions, confirmation bias and habitual routines (Schippers et al., 2014). Team members should also be encouraged to reflect on their objectives – their appropriateness, clarity, specificity and their commitment to them. They should also be encouraged to regularly review decision process and changes in their work envi­ ronment that have implications for the team’s work (West, 2000). Overall, teams that regularly review their performance and reflect on how it can be improved are likely to be more innovative. Reflexivity, we propose, is the most important intervention a team can routinely use in order to improve performance and enhance innovation. However, reflexivity must also translate into action or adaptation. West (1996, 2000) proposed that when reflex­ ivity leads to planning and subsequent action by team members, team innovation and effectiveness is likely to be significantly improved. Planning is the step between reflex­ ivity and adaptation (Gollwitzer, 1996), and the more detailed the planning, the more likely the adaptation is to lead to improved team performance and higher levels of innovation. Adaptation takes the form of changing objectives, strategies, team processes or altering the team’s environment (e.g., technologies, increasing autonomy vis à vis senior managers). Adaptation also will lead to new and improved teams, p­r oducts and services.

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Because teams are inclined to quickly create comfort‐enhancing routines, often at their first encounter or meeting (Gersick & Hackman, 1990), it is important to develop a norm encouraging reflexivity very early in a team’s life. Also, regular interventions aimed at enhancing team reflexivity will be needed as a team may be inclined to move to a comfort zone of relying on habitual routines at other points in time as well. Regular interventions in the form of team training may prevent teams from choosing and sticking to routines and help them stay reflexive instead (Schippers et al., 2014).

Future Research As described, research among laboratory teams suggested that reflexivity, in the sense of “stop and think”, could be enhanced by formal instructions in teams with a complex problem‐solving task (Okhuysen, 2001). However, this research was done using a sample of student teams with a limited task of 60 minutes. Other research (Schippers, 2003), suggested that in a field setting, reflexivity could be enhanced even through a relatively modest intervention. Teams received 4 hours of training aimed at enhancing reflexivity in everyday interaction at work. This had a positive effect on their level of reflexivity 6 months later. Thus, more extensive training combined with regular follow‐ up, might have even more effect. Other work suggests that the timing may be crucial: around the midpoint creative sparks are welcome and may increase innovation, whereas after the midpoint transition towards implementation too much creativity may become dysfunctional (Ford & Sullivan, 2004). Similarly, recent work shows that teams can be instructed to be creative and this seems to work especially well for teams that are collec­ tivistic rather than individualistic (Goncalo & Staw, 2006). Future work combining reflexivity with creativity interventions would be especially helpful in establishing the link between reflexivity and innovation. Overall, there is a need for well‐designed inter­ vention studies to determine how reflexivity can be enhanced to ensure team effective­ ness and innovation. This requires the development of theoretically based interventions that also have face validity and which can then be compared in terms of their effects with other credible interventions, such as training in team information processing and decision making. Equally pressing, is the need for research to explore the processes and mechanisms of team reflexivity in detail. Ethnographic research studying reflexive processes in team meet­ ings or away days would help to explicate the rich texture of reflexivity in the work of teams. It would also allow researchers to determine under what conditions, reflexivity elicits ideas for new and improved processes, products and procedures. Studying team away days where there was an explicit focus on team objectives, performance, processes and dynamics would be one way to help to elucidate the mechanisms of the relationship between reflexivity and innovation. Such ethnographic research would also help to reveal the variation in types and contents of team behaviors that are bundled together within the broad term “reflexivity.” There would also be huge value in examining reflexivity as a temporal process both in terms of the relationship between reflexivity and immediate and long‐term outcomes (for example, adjustments to team processes and team performance, respectively) and in terms of the development of reflexivity over time. For example, some teams might develop greater depth of reflexivity with practice and experience (see West, 2000, for a description of shallow versus deep reflexivity). Investigating the extent to which depth of reflexivity leads to innovation and other desirable outcomes, and under which circumstances, would also be valuable for developing this area of teams research.



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Conclusion We have emphasized throughout the paper that team reflexivity can help innovation and thus aid processes of teams that operate in a demanding, knowledge‐intensive context. We also proposed a model of antecedents and consequences of team reflexivity intended to help researchers and practitioners further explore and apply team reflexivity. Teamwork is important in many areas of human endeavor, and mistakes can be costly or even fatal. Reflexivity can be a powerful way of overcoming the problems inherent in team‐based knowledge work. The human capacity to reflect is a valuable but often underused resource. Using this capacity to overcome group information processing problems can enable team productivity, innovation and effectiveness, but focused research on team reflexivity is in its infancy. This chapter is a call to study the conscious use of reflexivity in teams or other settings in which people are working to achieve shared goals. We hope that the arguments and model presented here will spur new research and new understanding of the m­echanisms that underlie team reflexivity and its role in enhancing team innovation.

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Part IV

Team Effectiveness Tools and Outputs

21

Team Performance Measurement Michael A. Rosen and Aaron S. Dietz

Introduction In many industries, teams are (or are rapidly becoming) the fundamental building blocks of organizations. In addition to the traditional tightly coupled, physically co‐located, and synchronously interacting social structures that typically come to mind when thinking of ‘teams,’ new and more complex forms of organizing are emerging (Cross, Rebele, & Grant, 2016). These collaborative organizational designs are enabled by technology and responsive to pressures in many industries for rapid adaptation to changing external environmental and market forces. They are characterized by fluid membership and inter­ action patterns, physical and temporal distribution, and dynamic task structures. Teams are more important than ever, but their form and function is evolving. Consequently, team performance measurement, as a way to understand and improve team functioning, must evolve and mature as well. Team performance measurement provides a tool set for researchers to understand what differentiates high‐ and low‐performing teams as well as for practitioners’ to diagnose strengths and deficits in performance or competency and intervene with appropriate improvement strategies. With the growing popularity of “social computation” (Lazer et al., 2009; Pentland & Heibeck, 2010) made possible by emerging technology and analytics, team performance measurement is all the more relevant for modern organizations. In this chapter, we summarize the existing literature germane to developing a team performance measurement system. Specifically, we address four objectives. First, we introduce and define fundamental concepts from the science of teams and performance measurement. Second, we advance a set of requirements for team performance measurement systems. These requirements can be thought of as key questions that must be answered throughout the team performance measurement system development process. Domains of requirements include the purpose, content, location (or context), timing, and method of measurement. Third, we

The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes, First Edition. Edited by Eduardo Salas, Ramón Rico, and Jonathan Passmore. © 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.

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discuss emerging areas of unobtrusive measurement methods for team performance. These methods remain nascent, but hold great promise to complement existing methods in both laboratory and field settings. Fourth, we discuss strategies for implementing team performance measurement systems. These strategies represent approaches to balancing the strengths and weaknesses of different measurement methods for capturing team performance in complex organizations.

The Science of Teams and Performance Measurement In the following sections, we define key terms and concepts necessary for clearly articu­ lating team performance measurement system design. We draw first from the science of teams to outline the construct space or content for team performance measurement. Second, we describe critical concepts from the performance measurement and evaluation literatures.

Core concepts from the science of teams This section provides an overview of key terms and concepts from the science of teams in order to establish a shared nomenclature for the remainder of the discussion. A team is defined as “a distinguishable set of two or more people who interact dynamically, interdependently, and adaptively toward a common and valued goal/object/mission, who have each been assigned specific roles or functions to perform, and who have a limited life span of membership” (Salas, Dickinson, Converse, & Tannenbaum, 1992, p.  4). While frequently taken for granted, team composition is one of the most fundamental attributes of a team. This includes who is on the team, as well as mixtures of the characteristics of individual team members. In field settings, understanding exactly who is on a team is a challenging yet critical component for developing an effective team performance measurement system. Teams can be partially or wholly distributed in space (i.e., collocated vs. virtual teams; Kirkman, Rosen, Tesluk, & Gibson, 2004) and time (i.e., using synchronous vs. asynchronous communication technologies; Fiore et  al., 2010). To illustrate, surgical teams are primarily collocated and synchronous while conducting a surgical procedure. These teams include clinicians with varying professional backgrounds and roles (e.g., surgeon, anesthesiologist, nurse) that execute interdepen­ dent tasks (e.g., anesthesia precedes incisions); the team’s goal is to safely and efficiently complete a surgical procedure (Baker, Day, & Salas, 2006). Conversely, teams in an intensive care unit may be composed of a more complex configuration of members, including collocated and synchronous interaction (i.e., multidisciplinary patient rounds) as well as distributed interaction (i.e., use of synchronous phone and paging systems; use of asynchronous electronic health records as a means of communication). This contrast is not unlike comparing a tightly coupled co‐present commercial aviation crew to a globally distributed virtual team. These differences in team structure and patterns of interaction have dramatic differences in how team performance measurement systems are designed and implemented. The term “teamwork” refers to the actions team members engage in (e.g., communication, coordination) to yield team outcomes (Salas, Cooke, & Rosen, 2008). Underpinning effective teamwork are competencies: the knowledge (e.g., shared mental models), skills (communication, planning), and attitudes (e.g., mutual trust, collective efficacy) that drive effective teamwork (Cannon‐Bowers, Tannenbaum, Salas, & Volpe, 1995). In contrast to teamwork, taskwork describes the tasks each member completes without input from other



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team members (Baker & Salas, 1992). Understanding this distinction is important because teams can be composed of members with exceptional technical proficiency in their respective areas, but deficient in teamwork skills. There is a large multidisciplinary science of teams, and a rich theoretical and empirical knowledge‐base of the factors that drive effective performance outcomes in teams (Salas, Rosen, Burke, & Goodwin, 2009; Kozlowski & Ilgen, 2006; Ilgen, Hollenbeck, Johnson, & Jundt, 2005). A leading framework that underlies much of this research describes teams in terms of their inputs, mediators (processes or emergent states), and outcomes (Ilgen et al., 2005). Team inputs are relatively stable features of the team, its members, the task and environment. Examples of team inputs include the skill set of its members, the layout of their workspace, and the technological infrastructure that influences team member interactions. Team mediators are dynamic team member interactions (i.e., processes) or transient products of interactions (i.e., emergent states) that translate team inputs into team outputs such as effectiveness, viability (i.e., the ability of team members to work together in the future), and learning. Team processes are frequently the focus of training and measurement. Three key categories of team processes have been identified in the literature: action, transition, and interpersonal (Marks, Mathieu, & Zaccaro, 2001; LePine, Piccolo, Jackson, Mathieu, & Saul, 2008). Action processes, such as coordination, monitoring, and backup behavior occur during the periods of time when teams conduct activities leading directly to goal accomplishment. Transition processes, such as mission analysis, goal specification, and strategy formulation occur during planning activities  – phases of the team’s work characterized by preparation for, or reflection on their performance. Interpersonal processes – conflict management and affect management – occur in both transition and action phases. These interpersonal processes focus on relationships between team members, critical for building trust and achieving team viability (Snyder & Stukas, 1999). This input–moderator–output framework is illustrated in Figure  21.1, together with examples of important factors for each component of the framework. As demonstrated in Figure 21.1, teams are either transitioning between tasks – preparing for performance or reflecting on performance – executing the task, or managing inter­ personal relationships. A final distinction with implications for measurement concerns the differences between team performance and team performance effectiveness. Team performance represents the sum of both taskwork and teamwork activities (Salas et al., 2008). This means that measures of team performance capture in a collective sense what the team does in relation

Inputs Team member skills Task structure Task complexity Team familiarity Team diversity Temporal and physical distribution Organizational culture

Mediators Action: communication, performance monitoring Transitional: planning, sensemaking Interpersonal: conflict management Emergent States: team situational awareness

Figure 21.1  Overview of the input–mediator–output framework.

Outcomes Task outcomes: error rates, completion time Member satisfaction Learning outcomes: enhanced knowledge, skills, and attitudes

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to their personal and team goals. Last, team performance effectiveness is an assessment of the quality or quantity of team performance outcomes in relation to specified standards (i.e., an overall evaluation of how well the team performed; Salas et al., 2008). Measures of team performance effectiveness include outputs such as error rates, compliance, com­ pletion time, team member satisfaction and viability, and learning.

The fundamentals of measurement and evaluation We provide a brief review of key terminology from the measurement and evaluation literatures to further frame our discussion of team performance measurement systems. Fundamentally, measurement is a process of assigning numbers to events, objects, occur­ rences, or other phenomena, or of classifying those phenomena into categories or groups (Nunnally & Bernstein, 1994). The quality of any measurement system is a function of the characteristics of rules used to assign numbers or categories to phenomena (i.e., the construct validity of the measurement system) as well as the consistency by which those rules can be applied across units or occasions of measurement (i.e., the reliability of the measurement system). From this, we can define a team performance measurement system as the systematic application of a set of rules for quantifying different aspects of a team’s functioning, including teamwork and taskwork processes (Rosen, Sciebel et  al., 2012). The terms evaluation and assessment refer to applications of the data resulting from measurement processes. Use of these terms varies widely, but they both involve comparing measurements with a pre‐specified criterion, threshold, or set of expectations for effective or ineffective teamwork. Assessment generally refers to some type of summative judgment (i.e., a team does or does not pass some threshold of competence), and evaluation generally refers to a formative process of identifying relative strengths and weaknesses that can be used to guide feedback, remediation, and more generally team development. Performance diagnosis refers to a process of identifying causes of ineffective or effective team performance (Salas, Rosen, Burke, Nicholson, & Howse, 2007). As with general diagnosis, the goal is to infer underlying conditions responsible for the observed phenomenon  –  team performance in this case. Team performance diagnosis is most commonly discussed in the context of learning and development programs, where under­ standing why a team performed as it did is valuable for identifying deficiencies in team­ work competencies, reinforcing positive aspects of performance, and making decisions about what types of learning opportunities the team should engage in in the future to maximize improvement. To enable diagnosis, a wide range of measurements is required. This is referred to as a team performance profile (Salas et al., 2007). Table 21.1 provides a description of common characteristics of a team performance profile as well as rationales for why they support the diagnostic process. In sum, these concepts from the science of teams and performance measurement and evaluation literatures provide invaluable guidance for understanding the options of what to measure (i.e., the content of the team performance measurement system) and how to go about designing a measurement system. Given the complexity of team performance and the breadth of factors that can influence performance levels, it is unlikely that any one system will capture the full spectrum of team performance related constructs. Measurement is a valuable and limited resource, both for the researcher and practitioner. Consequently, its use must be focused and aligned with the priorities and purpose behind the development of the measurement system. In the following section, we outline requirements for team performance measurement systems. These requirements represent fundamental decisions that must be made while developing an approach for capturing team performance.



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Table 21.1  Description of a team performance profile (adapted from Salas et al., 2007). Characteristic

Description and rationale

Capable of capturing and discriminating performance at multiple levels of analysis Include contextualized and task‐relevant content Include focused competency‐based or theory driven content Provide a description of team performance

Collect data from multiple sources through multiple methods Capture team performance over time

A more complete understanding of the reasons for an observed level of performance outcomes can be inferred by distinguishing between individual and team level processes (i.e., teamwork and taskwork). In a training context, this allows feedback to be directed to the appropriate level (i.e., a specific individual, or the team as a whole). By accounting for unique characteristics and teamwork demands of the focal work domain, overly generic or abstract measures of teamwork can be avoided. These abstract measures can complicate rater training for observational measures and leave room for divergent interpretations. Measurement content should be based in an assessment of relevant teamwork competencies and connected to the science of teams. However, the science of teams is large, too large to capture all team related constructs at once. A team performance profile should have a prioritization scheme for focusing on the most critical content. A measurement system should be capable of providing a detailed account of what happened in a team performance episode, and not just a high level summary score. Fine‐grained capture of performance processes affords identification of specific performance issues and process‐ oriented feedback. Using multiple sources of data (e.g., team members, observers) and methods of collection (e.g., observation, self‐report, unobtrusive systems) affords triangulation – a process of understanding performance from multiple perspectives. These approach allows for a more complete representation of the construct space and the balancing of strengths and weaknesses of different methods. Using a measurement approach with a high time resolution can allow for more fine grained and descriptive understanding of variation in team performance within a performance episode. When possible, capturing team performance over multiple performance episodes is advised in order to gain a more reliable picture of team functioning.

Requirements for Team Performance Measurement System Design A number of factors shape the development and implementation of team performance measurement systems. This section provides an overview of these issues to set the stage for a discussion on the relative strengths and shortcomings of specific measurement approaches. Table 21.2 summarizes team performance measurement system requirements and critical decision points.

Purpose of measurement Establishing the purpose of measurement at the onset of the measurement system development process is paramount (Rosen, Wildman, Salas, & Rayne, 2012). Team performance measurement systems are developed to elicit information; data are collected to inform decisions surrounding teamwork competencies, draw inferences, or test hypotheses. As described later in this chapter, approaches to measuring team perfor­ mance (e.g., surveys, observations, sensor‐based technology) have inherent tradeoffs.

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Table 21.2  Summary of requirements for a team performance measurement system. Requirement Purpose of measurement

Measurement content Location of measurement Frequency of measurement

Key considerations •• Formulate an explicit statement that explicates why data are needed. •• Think through who will use the results of measurement and the type of information that will be helpful. •• Determine whether measurement is needed for formative or summative purposes. •• Ensure the measurement system captures team processes in addition to performance outcomes. •• Align measurement content with the knowledge, behavioral, and attitudinal competencies underpinning effective performance. •• Match the location of measure with the type of performance required for analysis. Typical performance is best captured on‐the job while maximal performance is best captured during simulated exercises or training. •• Measure performance longitudinally to capture trends and provide points for comparison. •• Measure performance before, during, and after team training and quality improvement efforts.

Maximizing the effectiveness of the measurement system and balancing these compromises requires a well‐defined and perspicuous rationale for why performance is being measured (Brannick & Prince, 1997). Defining why team performance will be measured early on drives the selection of what content (i.e., team competencies) will be targeted for measurement, the specific measures that will be employed, and how collected data will be utilized. Team performance can be measured for a variety of reasons, including testing hypotheses in research, and certification, selection, and training in applied contexts. These purposes can be broadly dichotomized into formative and summative categories (Rosen, Wildman et al., 2012). Formative measurement seeks to foster team learning and the development of team competencies or modification of work processes or structural factors (e.g., team composition, technology enabling interaction) influencing team performance. Conversely, summative measurement elicits an assessment of a proficiency area or team outcome in relation to performance criteria. Team performance measurement can also be applied to program evaluation of quality improvement initiatives. In the context of training, for example, different populations may require different information. Learners depend on measurement in the form of feedback that is timely and accurate. Trainers require measurement to evaluate proficiency and in turn, provide feedback to trainees that is consistent no matter who the trainer or trainee is. Those who are implementing team training and organizational leadership may seek data relating to the overall effectiveness of the endeavor and whether there are components in need of improvement. Articulating the purpose of measurement is crucial because it provides a framework that will ultimately govern the entire measurement process: who will be using the data, how the data will help inform decisions, the attributes of teamwork that require measurement and the level of granularity needed to be captured, and the methods used to capture this information.

Measurement content A comprehensive team performance measurement system incorporates measures of teamwork competencies and outcomes (Brannick & Prince, 1997; Salas, Rosen, Held, & Weissmuller, 2009). Outcome measures inform what the team was able to accomplish,



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such as whether a mission was completed successfully, error rates, or completion time. While outcome measures help capture results of interest, relying on them alone is insuf­ ficient. Performance measurement systems must frequently provide information on why something happened. The measurement of teamwork processes and emergent states establish the explanatory link between team inputs and outcomes, as illustrated in Figure 21.1. Equipped with the knowledge of why outcomes register at a certain level, corrective action can be taken should the need arise. Determining exactly what compe­ tencies and outcomes that will be measured is contingent of the purpose of why team performance is measured. Table  21.3 provides an example set of team performance competencies and related behavioral markers (i.e., indicators of effective and ineffective team performance). These competencies and markers frequently form the basis of team performance measurement tools (Rosen et al., 2011).

Location of measurement Measurement contexts vary with respect to the fidelity of the measurement environment and control over opportunities to exhibit how team processes unfold. Fidelity concerns how closely the measurement location matches the conditions of the work environment; that is, the degree to which the measurement context looks and feels like the work setting of team members and the extent to which the same knowledge, behaviors, and attitudes are exhibited (Bowers & Jentsch, 2001; Curtis, DiazGranados, & Feldman, 2012; Kozlowski & DeShon, 2004). Naturally, measurement can take place on the actual job, or in a high‐fidelity simulation, but can also take place in simulated settings that are lower in fidelity (e.g., computer‐based training) or in classroom settings. Control refers to the degree to which conditions in the measurement context and team behaviors can be mani­ pulated or anticipated. For example, much like the presentation of a test item stem in survey research provides a measurement opportunity for the respondent (i.e., a stimuli to react to), task conditions within the measurement context must create an opportunity for teams to exhibit effective or ineffective performance before that performance can be measured (Fowlkes, Dwyer, Oser, & Salas, 1998; Salas et al., 2007). These opportunities can be observed naturally, or introduced into the workplace or simulation environment. To illustrate, a tightly controlled simulation can be designed to elicit specific team behav­ iors and to regulate the sequence in which they occur. By ensuring there are predictable opportunities for team members to engage in certain behaviors, observers can easily iden­ tify important events and judge performance (Rosen, Salas, Wilson et al., 2008). This also provides some standardization and increased comparability of data across teams being measured. If the measurement context is too contrived, however, it can be difficult to appreciate how other factors (e.g., stressors, other team members) would impact team­ work in the actual work environment. The location of measurement also influences the type of performance exhibited by team members: Maximal or typical performance (Klehe & Anderson, 2007; Rosen, Schiebel et al., 2012). Maximal performance occurs when team members are performing at their best and usually tends to take place when people know they are being evaluated or observed. Maximal performance often manifests in simulated settings because team members know they are being evaluated, are often told to perform at their best, and the scenarios are short enough in duration to where peak performance can be sustained. Max­ imal performance measures are especially useful during periods of initial skill development and routine assessments to ensure team members are capable of performing at high levels and that skills do not degrade and deviate from a desired threshold over time. Typical performance measures provide insight into what is routinely exhibited on a daily basis

Interpersonal processes: Team processes that focus on the management of interpersonal and social relationships between team members.

Transition processes: Team processes focused primarily on the preparation for or reflection on the pursuit of a goal within an episode of interdependent task performance.

Action processes: Interdependent actions taken that are focused directly on the accomplishment of a shared team goal.

Types of team process

•• Seek solutions that have mutual gains for all interests. •• Openly discuss task‐related conflict. •• (Find it acceptable to) change their minds and express their doubts.

Conflict resolution/management: “Preemptive conflict management involves establishing conditions to prevent, control, or guide team conflict before it occurs. Reactive conflict management involves working through task and interpersonal disagreements among team members” (Marks, et al., 2001, p. 363).

Intrateam feedback: The provision of information about team or individual performance either before, during, or after a performance episode.

•• Explicitly articulate expectations for how a proposed course of action should unfold. •• Explicitly define desired outcomes. •• Collectively visualize how a planned course of action will be carried out, and where it can go wrong. •• Seek out information and feed it to fellow team members. •• Share unique information. •• Engage in a cycle of pre‐brief, performance, debrief. •• Provide pre‐performance information (feed forward). •• Develop and integrate lessons learned from past performance. •• Provide information to correct deficient performance during a performance episode. •• Provide constructive and specific comments to other team members.

•• Clearly communicate problem definitions. •• Acknowledge messages when they are sent. •• Crosscheck information with the sender to ensure that the message meaning is understood. •• Articulate “big picture” to one another as appropriate. •• Proactively pass information without being asked. •• Proactively step in to assist fellow team members when needed. •• Communicate the need for assistance. •• Can identify unbalanced workload distributions. •• Redistribute workload to underused team members.

Example generic behavioral makers

Planning: The generation of a proposed sequence of actions intended to accomplish a set goal.

Backup/supportive behavior: “Ability to anticipate other team member’s needs through accurate knowledge about their responsibilities. This includes the ability to shift workload among members to achieve balance during high periods of workload or pressure” (Salas, Sims, & Burke, 2005, p. 560).

Team communication: The exchange of information among team members emphasizing appropriate content, form, structure, and timing.

Example process definitions

Table 21.3  Summary of team competencies and example behavioral markers (adapted from Rosen et al., 2011).

Jordan & Troth (2004) De Dreu & Weingart (2003) Simons & Peterson (2000)

Smith‐Jentsch et al. (1998) Inzana, Driskell, Salas, & Johnston (1996)

Klein & Miller (1999) Mathieu & Schulze (2006) Stout, Cannon‐Bowers, Salas, & Milanovich (1999)

Porter et al. (2003) Marks, Mathieu, & Zaccaro (2001)

Smith‐Jentsch, Johnston, & Payne (1998) Fleishman & Zaccaro (1992)

Example citations



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when team members are not aware they are being overtly evaluated. Such measurements are especially useful for illuminating whether gains made through team performance improvement efforts (e.g., team training) actually translate to the work environment; they gauge whether trained competencies are actually being used on the job.

The frequency and timing of measurement A team performance measurement system must specify when and how often to measure. Best practices for the frequency of team performance measurement call for longitudinal measurement, or repeated measurements within a team over time (Gregory et al., 2013; Salas et al., 2015). Team performance is dynamic and a single measurement of perfor­ mance only yields data concerning how well or poorly the team performed at that specific measurement occasion. Capturing longitudinal team performance data serves to illumi­ nate trends that offer a more realistic, dependable, and informative account of a team’s strengths and shortcomings. Further, obtaining multiple measurements provide reference points to compare different pieces of information. In laboratory research settings, team performance can be measured at a very high temporal resolution through communication and interaction coding (Cooke, Gorman, Myers & Duran, 2013; Gorman, Amazeen, & Cooke, 2010); however, teams in laboratory research usually do not have a long lifespan or multiple performance episodes. High temporal resolution is less frequently an option in field studies, but teams in real organizations can have much longer lifespans, so longitudinal data is still relevant and meaningful (Rosen, Wildman et  al., 2012). An important consideration for the timing of team performance measurement relates to the context of team training, other organizational interventions, or experimental manipula­ tions. Measurement is needed: (1) before the intervention to establish a baseline in performance; (2) during the intervention to determine whether it is having its desired effect; and (3) following the intervention to ensure the competencies are transferred to the work context and sustained over time (Anguinis & Kraiger 2009; Salas, Tannenbaum, Kraiger, & Smith‐Jentsch, 2012).

Method of measurement Method of measurement refers to the manner in which data is collected. In general, two methods of measuring teamwork have dominated the research literature and applied settings: self‐report, and observation. We summarize these below, and discuss emerging unobtrusive methods of measurement in a following section. Self‐report methods involve asking team members to provide ratings about themselves as individuals, the team, or the entire organization. These methods can be used to collect a broad range of measures of team performance related constructs. It is well suited for captur­ ing the attitude competencies (e.g., mutual trust, belief in the importance of teamwork, collective orientation, psychological safety) because these constructs are inherently subjective in nature. However, self‐report methods are applied to perceptions of teamwork as well (e.g., Wageman, Hackman & Lehman, 2005). The limitations of self‐report methods for assessing performance or competence are broadly recognized (Dunning, Johnson, ­Ehrlinger, & Kruger, 2003; Kruger & Dunning, 1999). Specifically, there is a tendency for self‐perceptions of performance to be inflated (i.e., the “Lake Wobegon effect”), and this tendency is greater for novices in a domain than it is for experts. This means that respondents with lower levels of competence with teamwork behaviors will be more likely to have inflated self‐ratings. This creates challenges when attempting to detect change over time. Additional challenges involve achieving an adequate survey response rate so results can be interpreted appropriately, and aggregating individual survey responses to a team‐level construct.

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Observational measures of teamwork are considered the gold standard because they avoid many of the self‐report biases. Observational measurement systems of teamwork pose at least three unique challenges: Labor costs for data collectors associated with establishing and maintaining good reliability and collecting data, temporal resolution, and behavioral specificity. First, any observational system requires raters to be trained and their calibration monitored over time. This can be a large investment in effort, up to 20 hours per rater for some systems implemented within health care (Dietz et al., 2014; Feldman, Lazzara, Vanderbilt, & ­DiazGranados, 2012). Additionally, these raters must have dedicated time to collect the data. These costs limit observations largely to funded research studies and makes their widespread adoption in routine practice highly unlikely. While these systems can serve critical roles in focused education and training as well as periodic peer review or feedback processes, the ­frequency with which team members will receive feedback will likely be low. Second, different measurement approaches capture teamwork competencies at different levels of descriptive behavioral specificity with some being highly abstract and others being much more concrete. Within a tightly defined task, very specific actions and utterances can be defined for team members. For example, in healthcare settings very specific markers of team functioning have been developed for crisis teams that respond to trauma, cardiac, or other time urgent conditions (Dietz et  al., 2014). This idea of behavioral specificity relates to an early framework in the team competency literature  –  team‐ and task‐ specific, or team‐ and task‐generic competencies (Cannon‐Bowers & Salas, 1997). Team‐ and task‐generic competencies apply across a broad range teams and tasks, and team‐ and task‐specific competencies are unique to a specific collection of individuals or set of tasks. Typically, the more general a competency is, the more abstract it is to measure. The more team or task specific it is, the less abstract and more behavioral it is. There are inherent tradeoffs between the behavioral specificity to the tool, its generalizability, and ease of rater training. Abstract measures are more generalizable, but also more difficult to achieve interrater reliability because there is more room for interpretation of the meaning of measurement items. Specific measures are easier to train, more useful as feedback, but apply in limited contexts. These tensions complicate the process of developing widely scalable team performance measurement systems. Third, the temporal resolution or granularity of a tool refers to the degree of summation or collapsing across time that raters are required to do. Ratings that collapse or summate across time have a low temporal resolution; that is, they do not provide insight into the vari­ ations in performance over time. For example, global ratings scales may ask an observer to make a set of ratings at the end of a team’s performance episode. These global ratings may suffice for comparing between different teams, but do not provide information about a team’s functioning over time. Frequency counts collected over time and event‐based mea­ sures where observations are tied to controllable conditions requiring some type of team response are methods for adding temporal resolution to observational ratings (Salas, Rosen, Held, & Weissmuller, 2009). In general, higher levels of temporal resolution are useful for diagnosing performance and providing developmental feedback. Conversely, tools with lower temporal resolution are better suited for summative assessments such as overall effec­ tiveness of a teamwork improvement interventions. These tools can indicate the presence or absence of a problem, but do not provide much insight into the causes of those problems or how to correct them. Table 21.4 provides an overview of observational scale types commonly used for team performance measurement (see Salas, Rosen, Held, & Weissmuller, 2009). The preceding discussion has focused primarily on the application of self‐report and observational methods to elicit data related to attitudinal and behavioral competencies, respectively. Methods used to capture information regarding team cognition rely on both



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Table 21.4  Overview of common scale types used for observational measures of team performance (adapted from Rosen, Salas, Wilson, et al., 2008). Scale type Event‐based measures

Description

Advantages

Disadvantages

Example citations

Fowlkes et al. Event‐based systems A general method Maintains explicit (1994) can be time connections between for linking Fowlkes et al. consuming to measurement structural (1998) develop relative to opportunities and behavioral Rosen, Salas, other scale types. measurement checklists to Wu et al. Requires predictability content. critical (2008) (and ideally Focuses observer’s events–task controllability) of attention on situations events experienced predefined events. experienced by by the team. Reduces amount of the team. Measurement tools are judgement raters tied to a specific have to make by context; do not focusing on specific generalize well. behaviors.

Uses descriptions Behaviorally of behaviors as anchored anchors for rating scales rating scales. (BARS)

Kendal & Observers may fixate Can be a flexible Salas on the specific approach in that (2004) behaviors in the behavioral anchors Murphy & anchors and miss can be modifiable for Pardaffy broader (yet related) different contexts. (1989) behaviors not Concrete examples of Yule et al. explicitly team behaviors (2008) represented in the facilitate accurate anchor. ratings.

Uses a Likert‐ Behavior type scale to observation rate the scales frequency of team process behaviors.

Avoids problems in BARS related to rating isolated, atypical, or exceptional performance by forcing ratings of typical performance.

Taggar & Ratings may be Seigits influence by primacy (2003) and recency effects Driskell & while estimating Salas frequencies. (1992) Malec et al. (2007)

approaches. Team cognition refers to the knowledge structures (e.g., mental models) and cognitive processes (e.g., sensemaking, situational assessment) within teams (Wildman, Salas, & Scott, 2013). The investigation, measurement, and improvement of these cognitive competencies have become salient priorities across research and applied communities given the escalation of complexity in today’s work settings (Salas et al., 2007; Cooke, Gorman, Duran, & Taylor, 2007; Cooke, Salas, Kiekel, & Bell, 2004). Wildman and colleagues (2013) reviewed approaches to measuring team cognition and provided a relative comparison of these methods. Self‐report methods can rely on perceptual surveys regarding cognitive constructs, but also text analysis, knowledge tests, relatedness ratings, card sorting, and individual probes during a task. These methods vary with respect to the richness of data that are gleaned and costs associated with data collection. For example, analysis of textual data from interviews (or focus groups) is advan­ tageous because interviews allow flexibility for how content is discovered (e.g., can be structured, semi‐structured, or unstructured) and can be designed to elicit information

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across a wide array of cognitive constructs. That said, responses from interviewees are subject to their own biases, memory failures, and the interviews themselves can be time consuming. Task probing has been used to identify team member(s) knowledge of a current situation, but inherently disrupts performance because the task (or simulation) is interrupted to elicit information. Observation can also be used to capture behavioral examples of cognitive constructs. For example, Team members form common expectations of additional task and information requirements and Team members articulate plans and strategies for solving problems are observable markers of a construct related to problem identification and conceptualization (Salas et al., 2007, p. B81). The benefits and challenges associated with real‐time obser­ vations of behavior were already noted, but videos are advantageous because performance episodes can be repeated by rewinding the footage for analysis unlike naturalistic obser­ vation where only one opportunity to observe performance exists. The equipment needed for video analysis can be expensive, however, and the usage of recording technology may be precluded depending on the sensitivity of the task being observed (e.g., protected health information; Wildman et al., 2013). The previous sections provided a necessarily high level summary of decades of method­ ological research and practice in teams. This work is foundational, and practices are well established and embedded within overarching psychometric test development processes (AERA, APA & NCME, 2014). However, technological and computational advances have created opportunities to augment these tried and true methods with new approaches to team performance measurement.

Emerging Methods of Unobtrusive Team Performance Measurement Unobtrusive measurement is defined as “measures that do not require the cooperation of a respondent and that do not themselves contaminate the response” (Webb, Campbell, Schwartz, & Sechrest, 1966, p 2). Researchers have repeatedly called for the development of more robust approaches to unobtrusive measurement methods within organizational science over the past half century (Webb et al., 1966; Webb & Weick, 1979), yet their application remains uncommon (Hill, Kern, & White, 2014; Hill, White, & Wallace, 2014). Recent advances in embedded and mobile technology, as well as the computing power needed to quickly analyze large amounts of data, have created a renaissance of sen­ sor‐based measurement (e.g., Vinciarelli, Pantic, & Boulard, 2009). While still nascent, the existing scientific literature clearly demonstrates the potential of these novel approaches to measuring behavior in organizations (Rosen, Dietz, Yang, Priebe, & Pronovost, 2015), but continued development of sound measurement and analysis prac­ tices around these new technologies is needed (Chaffin et al., 2015). Sensor‐based technology offers a novel method for augmenting the current approaches to better meet the need for measurement of teamwork in dynamic domains. We use the terms sensors/sensor‐based technology for human and team performance to describe automated data collection tools including radio‐frequency identification tags, infrared sensors, video and audio recording devices, and accelerometers implemented for the purpose of capturing real‐time sociometric data (e.g., behavior, speech analysis, and proximity to other team members, devices, and workplace location). Accordingly, sensor‐based measurement refers to the use of sensors to capture team performance data. Unlike the survey and observa­ tional approaches to team performance measurement described above, sensor‐based measurement is automated, objective, and activity data are collected in real time. While



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this area is methodologically nascent, intriguing results support the feasibility of measuring constructs related to team composition, team and taskwork performance processes, and team outcomes. For team composition, the personality traits and attitudes of individual team members influence patterns of interaction and significantly predict team‐level performance outcomes (Bell, 2007; Bowers, Pharmer, & Salas, 2000; Horwitz & Horwitz, 2007). Several studies have found strong associations between how individuals respond to traditional personality measures and indices derived from sensor systems. In a sample of college students, infor­ mation about a person’s interactions, locations, activities, mood and language use coded from a relatively small sample of audio recordings (two minutes per hour over a two day period) significantly predicted Big Five personality traits; however, patterns were different for males and females (e.g., the amount of self‐talk was significantly predictive (r = –.38) of extraversion for males, but not for females; group conversations were predictive (r = .49) for females, but not males; Mehl, Gosling, & Pennebaker, 2006). Similarly, in a sample of 67 post‐anesthesia care unit nurses, sensor‐based measurement of physical activity, speech activity, face to face interaction and physical proximity collected over the course of a month predicted four of the Big Five personality traits (e.g., r = .41 between neuroticism and face to face interaction time; r = –.39 between proximity and extroversion; r = –.36 between openness and speech activity and r = –.32 with proximity; r = –.43 between agreeableness and speech activity; Olguín Olguín et al., 2009). Several studies highlight the potential of sensor‐based measures of team and taskwork performance processes. Parlak, Ayyur, Liu and Marsic (2014) demonstrated the feasibility of environmental sensors for tracking performance processes of trauma resuscitation teams. Researchers fitted equipment the team used during resuscitations with radio‐ frequency identification tags to capture work processes and task completion times based upon the movement of objects. This feasibility study highlights the need for careful sensor placement and refined analysis strategies, including zone‐based location, motion, and contact cues for drawing useful inferences from patterns of object use. In a related stream of research, Vankipuram, Kahol, Cohen, and Patel (2011) achieved a high level of reliability in classifying trauma team activities in simulated environments using motion and location sensors and a hidden Markov modeling analysis. They identified 15 key tasks, generated sequential behavioral descriptions of processes used to complete the task, and detected these patterns in proximity sensor data collected in simulations, with a mean 87.5% accuracy across all tasks. Kannampallil and colleagues (2011) implemented both location detection sensors and human observers within a trauma center and found a significant correlation between data sources (r = .96, P