The SAGE Handbook of Industrial, Work & Organizational Psycholog,: Volume 2: Organizational Psychology [2 ed.] 1446207226, 9781446207222

Table of contents :
Contents
List of Figures
List of Tables
Notes on the Editors and Contributors
Preface: The Global Science and Practice of IWO Psychology
Acknowledgments
Introduction
Part I: Work Attitude and Values
1 Attitudes: Satisfaction, Commitment and Involvement • Marcus Credé
2 Employee Self-Concept and Identity • Russell E. Johnson, Chu-Hsiang (Daisy) Chang, You Jin Kim and Szu-Han (Joanna) Lin
3 Organizational Justice • Stephen W. Gilliland
Part II: Motivational Perspectives
4 The Building Blocks of Motivation: Goal Phase System • Piers Steel and Justin M.Weinhardt
5 Self-Determination Theory Applied to Work Motivation and Organizational Behavior • Marylène Gagné, Edward L. Deci and Richard M. Ryan
6 Action Regulation Theory: Foundations, Current Knowledge and Future Directions • Hannes Zacher and Michael Frese
7 Goal Setting Theory: Controversies and Resolutions • Gary P. Latham and Edwin A. Locke
Part III: Performance Management
8 Pay Levels and Pay Changes • Jason D. Shaw
9 Revisiting the Social Context of Performance Management: Performance Appraisal Effectiveness • Paul E. Levy, Caitlin M. Cavanaugh, Noelle B. Frantz, Lauren A. Borden and Ariel Roberts
Part IV: Talent management and Development
10 Learning, Training and Development in Organizations: Emerging Trends, Recent Advances and Future Directions • Bradford S. Bell and Ozias A. Moore
11 Employee Development: The Process and Practice of Work-Related Learning • Sarah A. Hezlett and Cynthia D. McCauley
Part V: Leadership, Groups and Teams
12 Leadership in Organizations • Robert Hogan, Gordon Curphy, Robert B. Kaiser and Tomas Chamorro-Premuzic
13 Team Design Characteristics • Greg L. Stewart and Kameron M. Carter
14 From Teams in Organizations to Organizing in Teams • Leslie A. DeChurch, Dorothy R. Carter, Raquel Asencio, Amy Wax, Peter W. Seely, Kathryn Dalrymple, Sidni A. Vaughn, Benjamin R. Jones, Gabe Plummer and Jessica Mesmer-Magnus
15 Multiteam Systems: The Next Chapter • John E. Mathieu, Margaret M. Luciano and Leslie A. DeChurch
Part VI: Work Design and Interv entions
16 Work Design for Performance: Expanding the Criterion Domain • Daniela M. Andrei and Sharon K. Parker
17 Management Interventions • Robert Cardy and T. T. Selvarajan
18 Employee Participation • Melissa Chamberlin, Jeffery A. LePine, Daniel W. Newton and Linn Van Dyne
Part VII: Psychology of Work Relati onships
19 Trust at Work • Ana Cristina Costa, Donald L. Ferrin and C. Ashley Fulmer
20 Politics in Organizations • Gerald R. Ferris, John N. Harris, Zachary A. Russell and Liam P. Maher
21 Toxic Emotions at Work • Michelle K. Duffy and Lingtao Yu
22 Exchange in the Employee–Organization Relationship • Lynn M. Shore, Jacqueline A.-M. Coyle-Shapiro and Chiachi Chang
Subject Index

Citation preview

SAGE was founded in 1965 by Sara Miller McCune to support the dissemination of usable knowledge by publishing innovative and high-quality research and teaching content. Today, we publish over 900 journals, including those of more than 400 learned societies, more than 800 new books per year, and a growing range of library products including archives, data, case studies, reports, and video. SAGE remains majority-owned by our founder, and after Sara’s lifetime will become owned by a charitable trust that secures our continued independence. Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne

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Editor: Delia Alfonso Editorial Assistant: Colette Wilson Production Editor: Rudrani Mukherjee Copyeditor: Jill Birch Proofreader: Sunrise Settings Marketing manager: Emma Turner Cover design: Wendy Scott Typeset by: Cenveo Publisher Services Printed in the UK

Editorial arrangement and introduction © Deniz S. Ones, Neil Anderson, Chockalingam Viswesvaran and Handan Kepir Sinangil 2018 Chapter 1 © Marcus Credé 2018 Chapter 2 © Russell E. Johnson, Chu-Hsiang (Daisy) Chang, You Jin Kim and Szu-Han (Joanna) Lin 2018 Chapter 3 © Stephen W. Gilliland 2018 Chapter 4 © Piers Steel and Justin M. Weinhardt 2018 Chapter 5 © Marylène Gagné, Edward L. Deci and Richard M. Ryan 2018 Chapter 6 © Hannes Zacher and Michael Frese 2018 Chapter 7 © Gary P. Latham and Edwin A. Locke 2018 Chapter 8 © Jason D. Shaw 2018 Chapter 9 © Paul E. Levy, Caitlin M. Cavanaugh, Noelle B. Frantz, Lauren A. Borden and Ariel Roberts 2018 Chapter 10 © Bradford S. Bell and Ozias A. Moore 2018 Chapter 11 © Sarah A. Hezlett and Cynthia D. McCauley 2018 Chapter 12 © Robert Hogan, Gordon Curphy, Robert B. Kaiser and Tomas Chamorro-Premuzic 2018 Chapter 13 © Greg L. Stewart and Kameron M. Carter 2018

Chapter 14 © Leslie A. DeChurch, Dorothy R. Carter, Raquel Asencio, Amy Wax, Peter W. Seely, Kathryn Dalrymple, Sidni A. Vaughn, Benjamin R. Jones, Gabe Plummer and Jessica ­ Mesmer-Magnus 2018 Chapter 15 © John E. Mathieu, Margaret M. Luciano and Leslie A. DeChurch 2018 Chapter 16 © Daniela M. Andrei and Sharon K. Parker 2018 Chapter 17 © Robert Cardy and T. T. Selvarajan 2018 Chapter 18 © Melissa Chamberlin, Jeffery A. LePine, Daniel W. Newton and Linn Van Dyne 2018 Chapter 19 © Ana Cristina Costa, Donald L. Ferrin and C. Ashley Fulmer 2018 Chapter 20 © Gerald R. Ferris, John N. Harris, Zachary A. Russell and Liam P. Maher 2018 Chapter 21 © Michelle K. Duffy and Lingtao Yu 2018 Chapter 22 © Lynn M. Shore, Jacqueline A.-M. Coyle-Shapiro and Chiachi Chang 2018

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ISBN 978-1-4462-0722-2

Contents List of Figures viii List of Tables ix Notes on the Editors and Contributors x Prefacexxiii Acknowledgmentsxxv Introduction xxvi

PART I  Work Attitude and Values

1

1

Attitudes: Satisfaction, Commitment and Involvement Marcus Credé

3

2

Employee Self-Concept and Identity Russell E. Johnson, Chu-Hsiang (Daisy) Chang, You Jin Kim and Szu-Han (Joanna) Lin

25

3

Organizational Justice Stephen W. Gilliland

46

PART II  Motivational Perspectives

67

4

The Building Blocks of Motivation: Goal Phase System Piers Steel and Justin M.Weinhardt

69

5

Self-Determination Theory Applied to Work Motivation and Organizational Behavior Marylène Gagné, Edward L. Deci and Richard M. Ryan

6

7

Action Regulation Theory: Foundations, Current Knowledge and Future Directions Hannes Zacher and Michael Frese Goal Setting Theory: Controversies and Resolutions Gary P. Latham and Edwin A. Locke

97

122

145

PART III  Performance Management

167

8

169

Pay Levels and Pay Changes Jason D. Shaw

vi

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Revisiting the Social Context of Performance Management: Performance Appraisal Effectiveness Paul E. Levy, Caitlin M. Cavanaugh, Noelle B. Frantz, Lauren A. Borden and Ariel Roberts

PART IV  Talent management and Development 10

11

Learning, Training and Development in Organizations: Emerging Trends, Recent Advances and Future Directions Bradford S. Bell and Ozias A. Moore Employee Development: The Process and Practice of Work-Related Learning Sarah A. Hezlett and Cynthia D. McCauley

196

213 215

235

PART V Leadership, Groups and Teams

267

12

Leadership in Organizations Robert Hogan, Gordon Curphy, Robert B. Kaiser and Tomas Chamorro-Premuzic

269

13

Team Design Characteristics Greg L. Stewart and Kameron M. Carter

289

14

From Teams in Organizations to Organizing in Teams Leslie A. DeChurch, Dorothy R. Carter, Raquel Asencio, Amy Wax, Peter W. Seely, Kathryn Dalrymple, Sidni A. Vaughn, Benjamin R. Jones, Gabe Plummer and Jessica Mesmer-Magnus

307

15

Multiteam Systems: The Next Chapter John E. Mathieu, Margaret M. Luciano and Leslie A. DeChurch

333

PART VI  Work Design and Interventions

355

16

Work Design for Performance: Expanding the Criterion Domain Daniela M. Andrei and Sharon K. Parker

357

17

Management Interventions Robert Cardy and T. T. Selvarajan

378

18

Employee Participation Melissa Chamberlin, Jeffery A. LePine, Daniel W. Newton and Linn Van Dyne

405

PART VII  Psychology of Work Relationships

433

19

435

Trust at Work Ana Cristina Costa, Donald L. Ferrin and C. Ashley Fulmer

Contents

vii

20

Politics in Organizations Gerald R. Ferris, John N. Harris, Zachary A. Russell and Liam P. Maher

469

21

Toxic Emotions at Work Michelle K. Duffy and Lingtao Yu

487

22

Exchange in the Employee–Organization Relationship Lynn M. Shore, Jacqueline A.-M. Coyle-Shapiro and Chiachi Chang

499

Subject Index537

List of Figures   2.1   3.1   3.2   4.1   4.2   9.1 11.1 12.1 12.2 17.1 17.2 17.3 17.4 17.5 17.6 17.7 18.1 18.2 19.1 19.2 19.3

The self-concept nexus Organizational justice publications by year Distributive justice × procedural justice interaction on attitudes and behaviors Goal Phase System The relationship between expectancy and value on motivation Appraisal effectiveness model (Levy & Williams, 2004) A process model of workplace development Three phase model of leadership and organizational performance Increasing spending on leadership development, decreasing confidence in leaders Influence of source on intervention effectiveness Intervention breadth Summary of intervention characteristics Potential benefits and costs associated with sources of intervention Intended and unintended intervention signals as influences on effectiveness Conceptual framework for increasing intended signal strength and reducing noise Intervention selection process Conceptualization and grouping of participation-related concepts Employee participation as a process of employee involvement and influence Interpersonal trust: selected determinants and consequences Team trust: selected determinants and consequences Organizational trust: selected determinants and consequences

35 49 54 76 79 197 236 276 281 379 380 381 396 397 398 399 409 416 443 448 452

List of Tables   1.1 Incremental value provided by normative commitment and continuance commitment over affective commitment   3.1 Definitions of organizational justice   3.2 Organizational justice and attitudes and behavior   3.3 Future directions for organizational justice research   8.1 Summary of empirical studies and findings 10.1 Summary of research advances and future directions 11.1 Three lenses on individual development 12.1 Two views of leadership 13.1 Task design elements and relationships with team performance and other constructs 13.2 Team composition elements and relationships with team performance and other constructs 13.3 Team leadership elements and relationships with team performance and other constructs 14.1 Prominent definitions of teams 14.2 Comparing team definitions from two perspectives 14.3 Questions about team inputs that reflect an organizing in teams perspective 14.4 Questions about team process that reflect an organizing in teams perspective 14.5 Questions about team affect that reflect an organizing in teams perspective 14.6 Questions about team cognition that reflect an organizing in teams perspective 14.7 Objective and subjective operationalizations of team performance 15.1 A multi-dimensional framework of multiteam system structural features 16.1 Model of positive work role behaviors 18.1 Summary of the history of participation and participation-related concepts 18.2 Theoretical implications for future research 19.1 Definitions of trust in organizational research: chronological developments over time 19.2 Measures of trust frequently used in IWO psychology 21.1 A summary of definitions of discrete emotions 22.1 Employee–organization relationship constructs 22.2 Issues and research priorities identified by prior reviews of psychological contracts 22.3 Antecedents of social exchange 22.4 Outcomes of social exchange 22.5 Antecedents of economic exchange 22.6 Outcomes of economic exchange

12 48 52 58 171 228 237 282 291 294 298 308 311 314 316 317 318 319 337 361 408 417 437 440 489 500 504 516 517 520 521

Notes on the Editors and Contributors

The Editors Deniz S. Ones  is Professor of Psychology, the holder of both the Hellervik Professorship of Industrial Psychology and the Distinguished McKnight University Professorship at the University of Minnesota. She also holds the prestigious title of Distinguished University Teaching Professor, based on her doctoral student mentoring and training of world-class industrial-organizational psychologists. Several of her students have won best dissertation and early career contributions awards. Her research, published in more than 175 articles and book chapters, focuses on assessment of individual differences for employee selection and measurement of personality, integrity, and cognitive ability constructs for the prediction of job performance, especially counterproductive work behaviors. Her research has been cited over 15,000 times in the scientific literature. Her current H-index is 60 (H-index is the largest number H such that H publications have at least H citations). She has studied and served on research projects and blue ribbon panels focusing on assessment and job performance of law enforcement personnel, engineers, managers (including C suite executives), astronauts, nurses, politicians and R&D teams, among many others. In 2012, she received the Association for Test Publisher’s (ATP) Lifetime Professional Career Contributions and Service to Testing Award. She is ranked in the top 100 most influential management scholars in the past three decades [Aguinis et al. in Academy of Management Perspectives, 2012] as well as ranked in the top 15 most influential management scholars in the world who have received their PhD in the past 20 years. She is ranked in the top ten most-cited authors in popular Industrial-Organizational (I-O) psychology textbooks (highest ranked woman). Neil Anderson is Professor of HRM and Director of Research (Work and Organization Research Centre) at Brunel University, London. Neil is also Director of the Leverhulme Trust funded international centre for research into workplace creativity and innovation research. He conducts research into innovation and well-being, personnel selection, applicant reactions, and the science-practice divide in industrial, work and organizational psychology. He is the Founding Editor of the International Journal of Selection and Assessment and his work has appeared in several outlets including Academy of Management Journal, Journal of Management, Journal of Applied Psychology, Journal of Occupational and Organizational Psychology, and Personnel Psychology. Neil has published a number of edited handbooks in these areas and is a Fellow of the BPS, SIOP, APA, IAAP, and an Academic Fellow of the CIPD. He has advised several organizations in the UK, Europe and the USA on best practice across these topic areas.

Chockalingam Viswesvaran  received his PhD from the University of Iowa and is Professor of Psychology at Florida International University, Miami. His research focuses on personnel selection, job performance assessments, and personality testing. He has served on several editorial boards including those of Journal of Applied Psychology, Personnel Psychology, Journal of Personnel Psychology, and Journal of Organizational Behavior. He was the Associate Editor of the International Journal of Selection and Assessment from 2001 to 2006 and served as its Editor for ten years (2007–2017). He has received the best dissertation award and the early career distinguished scientific contributions award from the

Notes on the Editors and Contributors

xi

Society for Industrial and Organizational Psychology (SIOP). He is a fellow of (1) SIOP, (2) Divisions 14 (I/O) and 5 (Evaluation, Measurement, and Statistics) of the American Psychological Association and (3) the Association for Psychological Science (APS). He has co-edited a special issue of the International Journal of Selection and Assessment on the role of technology on staffing, and a special issue of the journal Human Performance on use of cognitive ability tests. He has served on the awards and fellowship committees of SIOP and on the APA Committee on Psychological Tests and Assessments. He has ­published over 170 journal articles and has made 250 conference presentations besides the six edited volumes. Handan Kepir Sinangil  is Professor Emerita of Work and Organizational Psychology at Marmara University, Organizational Behaviour Graduate Program, and Adjunct Professor Emerita at Bogazici University. She has served as the General Secretary of European Association of Work and Organizational Psychology (EAWOP). She is a member of the American Psychological Association (APA), the Society for Industrial and Organizational Psychology (SIOP, APA Division 14), the International Association of Applied Psychology (IAAP), and the International Association of CrossCultural Psychology (IACCP). Dr Sinangil’s international and national publications exceed 70 as book chapters and conference papers. She also served as Associate Editor of International Journal of Selection and Assessment. Her ongoing research projects, either with international collaboration or alone include expatriate management, organizational culture and change, and performance appraisal and selection.

The Contributors Daniela M. Andrei  is Research Fellow at the Business School, University of Western Australia (UWA) and working across the Centre for Safety (C4S) and the Centre for Transformative Work Design (CTWD). Daniela completed her PhD in Psychology at Babes-Bolyai University in Romania in 2010 where she had also been working as an Assistant Professor, teaching courses and seminars in Work Psychology, Personnel Psychology and Organizational Psychology in the Psychology Department. In 2012 she joined UWA, after she was awarded the competitive GO8 European Fellowship. After the completion of her fellowship Daniela continued as a Research Fellow, working on several research projects within the ALL@UWA and the Centre for Safety. Her current research activities revolve around team leadership and team processes/ emergent states and effectiveness of incident command teams in oil and gas industry as well as work design and antecedents of work design decisions. Raquel Asencio is doctoral candidate in Industrial/Organizational Psychology at the Georgia Institute of Technology, with a Masters Degree in I/O Psychology from the University of Central Florida. She served as the student representative to the INGRoup board, and is the recipient of a Goizueta Fellowship. Her research interests include teams, multiteam systems, collective identity, and social networks. Bradford S. Bell  is Associate Professor of Human Resource Studies in the ILR School at Cornell University. He received his BA in Psychology from the University of Maryland at College Park and his MA and PhD in Industrial and Organizational Psychology from Michigan State University. Dr Bell’s research interests include training and development, team development and effectiveness, and virtual work. His research has been published in journals such as Personnel Psychology, Journal of Applied Psychology, Human Resource Management, Journal of Experimental Psychology: Applied, International Journal of Human Resource Management, and Academy of Management Learning & Education. He has also published numerous chapters in edited volumes. Brad was honored by the HR Division of the Academy of Management with the 2008 Early Career Achievement Award and he currently serves as Editor of Personnel Psychology.

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Lauren A. Borden  is a doctoral candidate at The University of Akron. Lauren works as a Research and Development consultant at Shaker Consulting Group, where she has been responsible for developing prehire assessments for clients across technology, retail, and finance industries. During her time as a graduate student, Lauren has worked as a Psychology instructor, a consultant and student coordinator for the Center for Organizational research, and an intern at a local risk management consulting company. Her research interests include leadership, feedback and performance appraisal, selection, and coaching. Robert Cardy is Professor of Management and Chair of the Management Department at The University of Texas at San Antonio. Bob’s PhD is from Virginia Tech and his undergraduate and Masters degrees are from Central Michigan University. He has teaching and research interests in human resource management, particularly in the areas of employee and student retention, performance appraisal, and the design of effective management systems. His work on management focuses on merging human resource management practices with the contemporary organizational environment. In addition to journal publications, Bob has authored a variety of books, including Performance Management: Concepts, Skills, & Exercises and Performance Appraisal: Alternative Perspectives, and Managing Human Resources. Dorothy R. Carter  is a doctoral candidate in Industrial/Organizational Psychology at the Georgia Institute of Technology. Her research interests include leadership, collaboration, and social networks. Her work has appeared in outlets including the Journal of Applied Psychology, The Leadership Quarterly, and The Oxford Handbook of Leadership. In the Fall of 2015, Dorothy will be joining the faculty of the Psychology Department at the University of Georgia. Kameron M. Carter is a PhD candidate at the Henry B. Tippie College of Business at the University of Iowa. Her research interests include distributed role perspectives across groups and teams as well as research related to interpersonal interactions and work design. She received both a BS and a MBA from Longwood University. Caitlin M. Cavanaugh is a Talent Solutions Consultant with Quintela, where she manages the design and delivery of custom talent technology solutions for a portfolio of consulting organizations and Fortune 500 clients. She earned an MS in Industrial-Organizational Psychology at Indiana University-Purdue University, Indianapolis prior to earning her PhD at University of Akron. During her graduate career, she co-authored several review chapters and focused her research on performance, personality, feedback, and the social context, coaching, and employee self-development. In her professional career, she is interested in the evolving role of data science in I/O work, the reproducibility crisis in published psychology research, and the effects of technology on talent management processes and professionals. Melissa Chamberlin is Assistant Professor of Management at Iowa State University. She earned her PhD in Management from Arizona State University and holds a Master of Arts degree in the Social Sciences from the University of Chicago. Her research interests include employee voice and participation, team processes and communication, and workplace relationships. Tomas Chamorro-Premuzic is an international authority in psychological profiling, talent management, leadership development, and people analytics. He is the CEO of Hogan Assessment Systems, and Professor of Business Psychology at University College London (UCL), and Columbia University. He has previously held positions at New York University and the London School of Economics, and lectured at Harvard Business School, Stanford Business School, and INSEAD. Dr Chamorro-Premuzic has published 10 books and over 150 scientific papers (h index 59), making him one of the most prolific social scientists of his generation. His work has received awards by the American Psychological Association and the International Society for the Study of Individual Differences, to which he was director (2011–2013). Dr Chamorro-Premuzic is also the founding director of UCL’s Industrial-Organizational and Business Psychology program, and the Chief Psychometric Advisor to Harvard’s Entrepreneurial Finance Lab.

Notes on the Editors and Contributors

xiii

Over the past 20 years, he has consulted to a range of clients in financial services (JP Morgan, HSBC, Goldman Sachs), advertising (Google, WPP, BBH), media (BBC, Red Bull, Twitter, Spotify), consumer goods (Unilever, Reckitt Benckiser, P&G), fashion (LVMH, Net-a-Porter, Chanel) and government (British Army, Royal Mail, National Health Service). Dr Chamorro-Premuzic’s media career comprises over 120 TV appearances, including in the BBC, CNN, and Sky, and regular features in Harvard Business Review, The Guardian, Fast Company, Forbes, and The Huffington Post. Dr Chamorro-Premuzic is a keynote speaker for The Economist and the Institute of Economic Affairs. Chiachi Chang  is pursuing his PhD in the Department of Management, London School of Economics and Political Science. His research focuses on psychological contracts, work stress and coping, and employee health. He is currently working on the linkage between psychological needs and psychological contract breach of employees. Chu-Hsiang (Daisy) Chang  is Associate Professor of Organizational Psychology at Michigan State University. She received her PhD in Industrial and Organizational Psychology from The University of Akron. Her research interests focus on occupational health and safety, leadership, and motivation. Specifically, she studies issues related to occupational stress, workplace violence, and the intersection of employee motivation and organizational leadership particularly with reference to employee health and well-being. Her work has been published in Academy of Management Review, Academy of Management Journal, Journal of Applied Psychology, Journal of Organizational Behavior, Organizational Behavior and Human Decision Processes, Psychological Bulletin, and Work & Stress. She has served as an associate editor at Applied Psychology: An International Review and Journal of Organizational Behavior, and is currently serving as an Associate Editor at Journal of Applied Psychology. Ana Cristina Costa  is Senior Lecturer Human Resource Management and Organizational Behavior at Brunel Business School, Brunel University London, UK, and Academic member of the Chartered Institute of Personnel Development (CIPD). Major research interests include the areas of trust in organizations, innovation and psychological well-being, applicant reactions, and employee turnover. She is the co-founder and Investigator for the Leverhulme Trust Centre for Innovation Research (ICRN) in the UK, and Visiting Associate Professor at Valencia University (Spain). She is a member of several editorial boards and serves currently as Associate Editor of the European Journal of Work and Organizational Psychology. Jacqueline A.-M. Coyle-Shapiro  is Professor in Organizational Behaviour in the Department of Management at the London School of Economics and Political Science. Her research interests include employment relationship, psychological contracts, social exchange theory, organizational justice, and organizational citizenship behaviour. She has published in such journals as the Academy of Management Journal, Journal of Applied Psychology and Journal of Organizational Behavior. Marcus Credé  is Assistant Professor of Psychology at Iowa State University. He obtained his PhD in Industrial and Organizational Psychology from the University of Illinois at Urbana-Champaign in 2005. His research interests include attitudes and other determinants of performance in work and educational settings, leadership, and measurement issues relevant to these domains. Gordon Curphy  helps C-suite, business unit, and functional leaders develop business strategies; implement major change initiatives; hire, develop, and promote leadership talent; and build high performing teams. Taking a science-practitioner approach to leadership, Gordy has spent the past 30 years researching, teaching, practicing, and providing consulting advice on leadership. He has written 19 and sold over 100,000 books on leadership and teams and is the co-author of Leadership: Enhancing the Lessons of Experience (8th Ed.). In 2012 he published The Rocket Model: Practical Advice for Building High Performing Teams, a book intended to help leaders transform direct reports into cohesive, goal-oriented

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teams. As a practitioner, Gordy has held numerous leadership positions in the United States Air Force, United States Air Force Academy, Center for Creative Leadership, Blandin Foundation, and Korn/Ferry International. In addition to running his own business since 2002, Gordy has led start-ups, turn-arounds, major organizational change initiatives, and rapid growth companies. As a consultant, he has done over 1,500 executive assessments, worked with over 200 senior teams, and designed and delivered hundreds of executive coaching and leadership development programs to global clients. Kathryn Dalrymple is a PhD student in Industrial/Organizational Psychology at the Georgia Institute of Technology. She holds a Bachelor of Arts in Psychology from the University of South Florida. Her research interests include team assembly, networks, team conflict, and team effectiveness. Leslie A. DeChurch  is Professor of Communication Studies and holds a courtesy appointment in the Department of Psychology, Weinberg College of Arts & Sciences, at Northwestern University. Professor DeChurch’s research seeks to build high-functioning teams that work in scientific innovation, space exploration, healthcare, and the military. Her research is currently supported by the National Science Foundation (NSF), National Institutes for Health (NIH), National Aeronautical and Space Agency (NASA), and Army Research Office (ARO), and has appeared in outlets including the Proceedings of the National Academy of Sciences (PNAS), Journal of Applied Psychology (JAP), Journal of Management (JoM), Organizational Behavior and Human Decision Processes (OBHDP), and Leadership Quarterly (LQ). She is President and Chairperson of the Board of INGRoup, the Interdisciplinary Network for Group Research. She recently served on the National Research Council Committee on the Context of Military Environments, and has contributed to several National Research Council Committees on teamwork issues ranging from measurement to innovation. DeChurch was awarded an NSF CAREER to explore Leadership for Virtual Organizational Effectiveness. Professor DeChurch holds a PhD in Organizational Psychology and is a fellow of the American Psychological Association (APA), Association for Psychological Science (APS), and the Society of Industrial & Organizational Psychology (SIOP). Edward L. Deci is the Helen F. and Fred H. Gowen Professor in the Social Sciences at the University of Rochester. He holds a PhD in Psychology from Carnegie-Mellon University, studied at the University of Pennsylvania, the University of London, and Hamilton College, and was an interdisciplinary post-doctoral fellow at Stanford University. For more than 45 years Deci has been engaged in a program of research on human motivation and is co-founder with Richard M. Ryan of Self-Determination Theory. He has published eleven books, including: Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness (co-authored with R. M. Ryan, Guilford, 2017). He has been a grantee of the National Institute of Mental Health, the National Institute of Child Health and Human Development, the National Science Foundation, and the Institute of Education Sciences. Michelle K. Duffy is the Board of Overseers Professor of Work and Organizations in the Carlson School of Management. She has a PhD in Organizational Behavior and Human Resource Management from the University of Arkansas and a Master’s in Psychology from Xavier University. Her research focuses on: 1) the ways in which employee emotions and affect influences organizational outcomes, 2) the antecedents and consequences of antisocial behavior at work and 3) the role of mindfulness in organizational life. Her current projects include a focus on employee envy, affective balance, resume fraud and mindfulness. She is an Associate Editor for the Journal of Applied Psychology and her work has been published in journals such as the Journal of Applied Psychology, the Academy of Management Journal, Organizational Behavior and Human Decision Processes, and Personnel Psychology. She received the Herbie Award for Excellence in Teaching in 2007 and the Carlson School of Management Award for Service in 2011. She became a SIOP and APA Fellow in 2012. Donald L. Ferrin is Professor of Organisational Behaviour and Human Resources at the Lee Kong Chian School of Business, Singapore Management University. He received his PhD from the University of

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Minnesota’s Carlson School of Management in 2000. Don’s research focuses entirely on trust, including determinants and consequences of interpersonal trust, trust in leadership, trust development processes, trust in the context of networks, trust violations and trust repair strategies, effects of culture on trust, trust in the context of negotiation, trust in e-commerce, and individual-, group- and organization-level trust repair. Gerald R. Ferris is the Francis Eppes Professor of Management, Professor of Psychology, and Professor of Sport Management at Florida State University. Ferris received a PhD in Business Administration from the University of Illinois at Urbana-Champaign. Prior to his current position, he also served on the faculties at Texas A&M University, the University of Illinois at Urbana-Champaign, and the University of Mississippi. He has research interests in the areas of social and political influence in organizations, the nature and consequences of personal reputation in organizations, the underlying dimensions and characterization of work relationships, and particularly how politics, reputation, and work relationships play key roles in human resources management practices. Ferris has been the recipient of a number of distinctions and honors. In 2001, he was the recipient of the Heneman Career Achievement Award, and in 2010 he received the Thomas A. Mahoney Mentoring Award, both from the Human Resources Division of the Academy of Management. Noelle B. Frantz  is an Associate with Shaker, a Cleveland-based consulting firm specializing in talent analytics and custom assessment development. In her time at Shaker, Noelle has served in both researcher and consultant roles, developing science-based solutions designed to meet clients’ selection and assessment needs across a variety of industries and roles. Noelle studied Industrial-Organizational Psychology at the University of Akron in Akron, OH where she researched performance management, feedback, and feedback orientation. Noelle’s interests also include candidate experience and reactions, assessment through science and innovation, gamification, and the use of technology to enhance business m ­ anagement. Michael Frese  has a joint appointment at NUS Business School and at Leuphana University of Lueneburg. He received his PhD from the Technical University of Berlin. His research includes the psychology of entrepreneurship; factors that influence innovation and creativity in people, teams, and organizations; active performance concepts, such as personal initiative; training (e.g., for entrepreneurship in developing countries); and learning from errors and experience. Guiding concepts are evidencebased management and action regulation theory. He is one of the most cited researchers in the field of organizational behavior from outside the United States with 34,000 citations and an H-index of 90 (Google Scholar). C. Ashley Fulmer  is a visiting Assistant Professor at Tippie College of Business, Department of Management and Organizations. She received her PhD in Organizational Psychology from the University of Maryland, College Park and served on the faculty of National University of Singapore. Her research centers on trust in organizations, negotiation and conflict management, cross-cultural organizational behavior, and levels of analysis theory and research. Her work has been published in journals such as the Academy of Management Review, Journal of Applied Psychology, and Journal of Management. Marylène Gagné  graduated from McGill University and received her PhD from the University of Rochester. She is Professor of Organizational Behaviour at the University of Western Australia Business School. Her research examines how organizations, through their structures, cultures, rewards, tasks, and managerial/leadership styles, affect people’s motivational orientations, and in turn the consequences of these orientations for individuals’ performance and well-being, as well as organizational effectiveness. Gagné recently edited the book Oxford Handbook of Work Engagement, Motivation, and SelfDetermination Theory (Oxford, 2015). She is the recipient of many research awards, and has published many articles in organizational behavior and psychology journals. She served as the Associate Editor for the European Journal of Work and Organizational Psychology and is on many other editorial boards.

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Stephen W. Gilliland holds the Peter and Nancy Salter Chair in Healthcare Management and also serves as Executive Director of the Center for Management Innovations in Healthcare. He served as Department Head for the Management and Organizations Department, Associate Dean for executive education, and Vice Dean for the Eller College. Before moving to Arizona, Gilliland received degrees from the University of Alberta and Michigan State University (PhD, 1992) and was on the faculty at Louisiana State University. Professor Gilliland’s research examines the organizational justice of management practices and policies and the impact of fair leadership on employee behaviors. Through this merging of social, legal, and managerial issues, he has authored over 50 journal articles, books, and book chapters. He has served on the editorial boards of the Journal of Applied Psychology, Personnel Psychology, and the Academy of Management Journal. In 2006, Gilliland was elected as a Fellow in the Society for Industrial and Organizational Psychology (SIOP). He was also the 1997 recipient of the Ernest J. McCormick Award for Early Career Contributions from SIOP. Professor Gilliland has taught numerous courses on business ethics, human resource management, leadership, healthcare entrepreneurship, and social entrepreneurship. Gilliland has consulted with small and large, public and private organizations on strategic planning and implementation and executive team development. He also serves on the board of directors of Merchants Information Solutions, a Phoenix-based provider of pre-employment screening, integrity testing, and identity theft management solutions. John N. Harris  is Assistant Professor of Management at Georgia Southern University. He received his PhD from Florida State University in 2017. His research interests include leadership, organizational politics, relationships at work, social effectiveness, and reputation. His research has been published in several outlets, including Journal of Organizational Behavior, Journal of Leadership & Organizational Studies, and Journal of Business and Psychology. Sarah A. Hezlett  has more than 15 years of experience creating talent management solutions, including leadership development tools, 360-degree feedback systems, individual assessments, and employee engagement processes. She serves as a Senior Assessment Scientist with Korn Ferry, leading a team designing and supporting leadership assessments. Her research, which focuses on development, 360-degree feedback, mentoring, and predicting and understanding performance in educational and work settings, has been published in peer-reviewed journals, including Science, Current Directions in Psychological Science, and Advances in Developing Human Resources. She earned her PhD in Industrial/Organizational Psychology from the University of Minnesota, where she is now an Adjunct Faculty Member. Robert Hogan,  President of Hogan Assessment Systems, is an international authority on personality assessment, leadership, and organizational effectiveness. He was McFarlin Professor and Chair of the Department of Psychology at the University of Tulsa for 14 years. Prior to that, he was Professor of Psychology and Social Relations at the Johns Hopkins University. He has received a number of research and teaching awards, and is the Editor of the Handbook of Personality Psychology and author of the Hogan Personality Inventory. Dr Hogan received his PhD from the University of California, Berkeley, specializing in personality assessment. He is the author of more than 300 journal articles, chapters, and books. He is widely credited with demonstrating how careful attention to personality factors can influence organizational effectiveness in a variety of areas – ranging from organizational climate and leadership to selection and effective team performance. He is a fellow of the American Psychological Association and the Society for Industrial/Organizational Psychology. Russell E. Johnson  is Associate Professor of Management in the Broad College of Business at Michigan State University. He received his PhD in Industrial and Organizational Psychology from The University of Akron. His research explores cognitive and affective processes that underlie organizational behavior, including employee self-concept. His research has been published in numerous outlets, including Academy of Management Journal, Academy of Management Review, Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Personnel Psychology, and Psychological Bulletin. He was an

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associate editor at Academy of Management Review and serves on the editorial boards at Academy of Management Journal, Journal of Applied Psychology, Journal of Experimental Psychology: Applied, Journal of Organizational Behavior, The Leadership Quarterly, Organizational Behavior and Human Decision Processes, and Personnel Psychology, among others. In 2013, Dr Johnson received the Distinguished Early Career Contributions Award for Science from the Society for Industrial and Organizational Psychology. Originally from Canada, he still dreams of playing in the National Hockey League one day. Benjamin R. Jones is a PhD student in the Industrial Organizational Psychology program at the Georgia Institute of Technology. His research interests include teams, leadership, the effect of newcomers on team processes, and multiteam systems. Robert B. Kaiser is an author, advisor, and authority on leadership. He began his career at the Center for Creative Leadership and later joined the boutique executive development firm, Kaplan DeVries Inc. In 2013, Rob started Kaiser Leadership Solutions to set a new standard in assessment and development by offering a suite of innovative tools for improving results through leadership. He has published many articles in both the scientific literature and the international business press, as well as five books — including The Versatile Leader (2006), The Perils of Accentuating the Positive (2009), and Fear Your Strengths (2013). He is also the current editor of Consulting Psychology Journal. Rob’s thought leadership is practical and useful because it utilizes the scientific method yet is grounded in his extensive experience as an executive coach, as an evaluator of candidates for executive jobs, and as a strategic talent management advisor to CEOs and HR leaders. You Jin Kim  is Assistant Professor of Human Resource Management in the Fox School of Business at Temple University. She received her PhD from Michigan State University. Her primary research interests include organizational citizenship behavior (OCB), motives, proactive behavior, and emotion in the workplace. Gary P. Latham is the Secretary of State Professor of Organizational Effectiveness in the Rotman School of Management at the University of Toronto, with cross-appointments in the Departments of Psychology and Industrial Relations and the School of Nursing. He is a Past President of the Canadian Psychological Association and the Society for Industrial-Organizational Psychology, and is the President of Work and Organizational Psychology, a division of the International Association of Applied Psychology. He is a Fellow of the Academy of Management, the American Psychological Association, Association for Psychological Science, Canadian Psychological Association, the International Association for Applied Psychology, the National Academy of Human Resources Management, and the Royal Society of Canada. He is the only person to receive both the awards for Distinguished Contributions to Psychology as a Profession and as a Science from the Society for Industrial-Organizational Psychology. He is also the recipient of the Scholarly Practitioner award from the Academy of Management, the Heneman Career Achievement Award from the Academy of Management Human Resource Division, the Lifetime Achievement Award from the Academy of Management Organizational Behavior Division, the Thomas A. Mahoney Mentoring Award from the Academy of Management Human Resource Division, the Harry and Miriam Levinson Award for Exceptional Contributions to Consulting Psychology from the American Psychological Foundation, the Michael R. Losey Award for research that has had a significant impact on human resource management from the Society for Human Resource Management, and the James McKeen Cattell Fellows Award for Lifetime of Outstanding Contributions to Applied Psychological Research from the Association for Psychological Science. He is the co-author of Goal Setting: A Motivational Technique that Works (1984), A Theory of Goal Setting and Task Performance (1990), and New Developments in Goal Setting and Task Performance (2013) with Edwin A. Locke; Increasing Productivity Through Performance Appraisal (1994) and Developing and Training Human Resources (2002) with K. N. Wexley; and Becoming the Evidence-Based Manager (2009). Gary has served on the board of directors for the Center for Creative Leadership and the Society for Human Resource Management. He currently serves on the board of the International Association for Applied Psychology.

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Jeffery A. LePine received his PhD in 1998 from Michigan State University (1998) and is a Professor of Management and the PetSmart Chair in Leadership at the W.P. Carey School of Business, Arizona State University. His current research examines various issues related to team functioning and effectiveness, citizenship and participation behaviors, employee engagement, and stress. He has published his research on these and other topics in most major scholarly outlets. He is a fellow of the American Psychological Association and the Society for Industrial and Organizational Psychology. Paul E. Levy is a Professor and Chair of the Department of Psychology at The University of Akron.

Dr Levy received his PhD in I/O psychology from Virginia Tech in 1989. He is a fellow of the Society for Industrial and Organizational Psychology, the Association for Psychological Science, and the American Psychological Association. His consulting and research interests include performance appraisal, feedback, motivation, coaching, and organizational surveys/attitudes. He is the author of one of the leading I/O textbooks in the field and Associate Editor of Organizational Behavior and Human Decision Processes. He has over 70 refereed publications with many appearing in the top journals in the discipline such as the Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Academy of Management, and Personnel Psychology. Szu-Han (Joanna) Lin  is Assistant Professor of Management in the Isenberg School of Management at the University of Massachusetts Amherst. She received her PhD from Michigan State University. Her research focuses on self-regulation, leadership, and voice. Her research has been published in Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Journal of Business and Psychology, Journal of Occupational and Organizational Psychology, and Personality and Individual Differences, and it has been cited in outlets such as Forbes, Harvard Business Review, Chicago Tribune, and New York Magazine. Edwin A. Locke  is Dean’s Professor of Leadership and Motivation (Emeritus) at the R.H. Smith School of Business at the University of Maryland, College Park. He received his BA from Harvard in 1960 and his PhD in Industrial Psychology from Cornell University in 1964. He has published over 310 chapters, notes and articles in professional journals, on such subjects as work motivation, job satisfaction, incentives, and the philosophy of science. He is also the author, co-author or editor of 11 books, including Study Methods and Study Motivation (Second Renaissance Books, 1998), Goal Setting: A Motivational Technique that Works (Prentice Hall, 1984, with G. Latham), A Theory of Goal Setting and Task Performance (Prentice Hall, 1990, with G. Latham), Handbook of Principles of Organizational Behavior (Blackwell, 2000; and a 2nd edition with Wiley, 2009), The Prime Movers: Traits of the Great Wealth Creators (AMACOM, 2000; 2nd edition Ayn Rand Bookstore, 2008), Post Modernism and Management: Pos, Cons and the Alternative (Elsevier, 2011) and The Selfish Path to Romance (Platform Press, 2011, with Ellen Kenner). A new book on goal setting with Gary Latham, New Directions in Goal Setting and Task Performance was published in 2013. Goal setting theory (developed with Gary Latham) has been rated as #1 in importance among 73 management theories. Dr Locke has been elected a Fellow of the American Psychological Association, of the Association for Psychological Science, and of the Academy of Management. He has won the James McKeen Cattell Award from the Association for Psychological Science, Distinguished Scientific Achievement Awards from both the Academy of Management and the Society for Industrial and Organizational Psychology, and the Lifetime Achievement Award from the OB division of the Academy of Management. He has been cited more than 11,000 times on the Web of Science and over 50,000 times on Google Scholar. He has given more than 115 papers at professional conferences and numerous lectures at universities. He is interested in the application of Ayn Rand’s philosophy of Objectivism to the behavioral sciences and has published several articles and chapters on this topic. Margaret M. Luciano  is Assistant Professor at the W. P. Carey School of Business at Arizona State University. Her research interests include intergroup dynamics, teams, and systems, with a particular interest in healthcare settings. Her work is a vivid example of the scientist/practitioner model, as she has

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partnered with several external entities to conduct leading edge research with real-world implications and benefits. Her research has been published in leading peer-reviewed academic journals, including the Academy of Management Journal, Journal of Applied Psychology, and Journal of Management. She holds a PhD from the University of Connecticut. Liam P. Maher is a doctoral candidate in Organizational Behavior and Human Resources at Florida State University. His research interests include social influence, political will, self-concept, stress, and change management. Liam holds a Bachelor of Arts degree in Business Administration, with a concentration in Finance, from Western Washington University and a Master of Business Administration from Seattle University. John E. Mathieu  is Board of Trustees Distinguished Professor at the University of Connecticut, where he also holds the Friar Chair in Leadership and Teams. His primary areas of interest include models of team and multi-team effectiveness, leadership, training effectiveness, and cross-level models of organizational behavior. He has conducted work with several Fortune 500 companies, the armed services (i.e., Army, Navy, and Air Force), federal and state agencies (e.g., NRC, NASA, FAA, DOT), and numerous public and private organizations. Dr Mathieu has over 100 publications, 200 presentations at national and international conferences, and has been a PI or Co-PI on over $11.1M in grants and contracts. He is a Fellow of the Society for Industrial/Organizational Psychology, American Psychological Association, and the Academy of Management. He serves on numerous editorial boards and has guest edited special volumes of top-level journals. He holds a PhD in Industrial/Organizational Psychology from Old Dominion University. Cynthia D. McCauley is a Senior Fellow at the Center for Creative Leadership. She designs and manages R&D projects, coaches action learning teams, writes for multiple audiences, and is a frequent speaker at professional conferences. As a result of her research and applied work, she is an advocate for using onthe-job experience as a central leader development strategy, for seeing leadership as a product of the collective, and for integrating constructive-developmental theories of human behavior into leader development practice. Cynthia is co-editor of three books for talent development professionals: The Center for Creative Leadership Handbook of Leadership Development (Jossey-Bass, 2010), Experience-Driven Leader Development (Wiley, 2014), and Using Experience to Develop Leadership Talent (Jossey-Bass, 2014). She received a PhD in Industrial-Organizational Psychology from the University of Georgia. Jessica Mesmer-Magnus  is Professor of Human Resource Management in the Cameron School of Business at UNCW. She earned her PhD in Industrial/Organizational Psychology at Florida International University in 2005 and has been certified as a Senior Professional in Human Resources since 2001. Prior to beginning her doctoral program, Dr Magnus worked as a Human Resource Manager and as an HR Consultant for a national consulting firm. Her current research interests include virtual teams, team c­ognition and information sharing, team conflict, work/family conflict, and the role of humor in the workplace. Her work has been published in a variety of academic outlets, including Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Organizational Psychology Review, Human Performance, Journal of Managerial Psychology, Group Dynamics: Theory, Research and Practice, Journal of Vocational Behavior, and Journal of Business Ethics. Ozias A. Moore  is a doctoral candidate at Cornell University’s ILR School in the Department of Human Resource Studies. He received his BS in Business Administration from the University of Pittsburgh, an MS in Engineering from the University of Pennsylvania, and an MS in Industrial and Labor Relations from Cornell University. His major research and teaching interests focus on team and multiteam effectiveness. He is particularly interested in the multilevel effects of multiple team membership on team processes and team outcomes. His research has appeared in the Journal of Applied Psychology and Small Group Research.

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Daniel W. Newton is a doctoral student in Organizational Behavior in the Department of Management at the W.P. Carey School of Business, Arizona State University. His research interests include voice behavior, employee engagement, and informal leadership emergence. Sharon K. Parker  is an Australian Research Council Laureate Fellow and Professor of Organizational Behavior at the UWA Business School, University of Western Australia and an Honorary Professor at the University of Sheffield where she was previously Director at the Institute of Work Psychology. She is also Associate Editor of Academy of Management Annals. Her research interests are focused on proactive behavior, work design, self-efficacy, and employee perspective taking. She has published five books, over 80 internationally refereed journal articles (including publications in top tier journals such as the Journal of Applied Psychology, Academy of Management Journal, and Academy of Management Review), over 60 book chapters and encyclopedia entries, numerous articles in practitioner outlets, and more than 60 technical reports. Professor Parker has attracted competitive research funding worth over $41,000,000, and has worked as a researcher and consultant in a wide range of public and private organizations. Gabe Plummer  is a PhD student in the Industrial Organizational Psychology program at the Georgia Institute of Technology. His research interests include teams, team cognition and the process of switching between team and multiteam system tasks. Ariel Roberts  is a Doctoral Candidate in Industrial/Organizational Psychology at The University of Akron. She is a student affiliate of the Society for Industrial and Organizational Psychology and a student contributor to the Center for Organizational Research at the University of Akron. Her primary research interests are feedback seeking, performance management, implicit person theory, and the Dark Triad. Ariel’s applied experience includes organizational survey creation and analytics, employee engagement, and training and development. Zachary A. Russell received his PhD in 2016 from Florida State University and is Assistant Professor of Management at Xavier University. His research focuses on reputation, organizational politics, human resource policies, and attitudes towards labor unions. His research is published in several outlets, including Human Resource Management Review, Journal of Leadership & Organizational Studies, and Journal of Labor Research. Richard M. Ryan, who holds a PhD from the University of Rochester, is a Professor at the Institute for Positive Psychology and Education at the Australian Catholic University and a Research Professor in Psychology at the University of Rochester. A widely published researcher and theorist in the areas of human motivation, personality, and psychological well-being, he is co-developer of Self-Determination Theory, a theory of human motivation that has been widely researched internationally. Ryan, who has lectured in more than 70 universities around the globe, is a Fellow of the American Psychological Association and the American Educational Research Association, and an Honorary Member of the German Psychological Society (DGP). He was Editor-in-Chief of Motivation & Emotion and has been a James McKeen Cattell Fellow and a visiting scientist at the Max Planck Institute for Development and Education. Peter W. Seely  is a Doctoral Candidate in Industrial/Organizational Psychology at the Georgia Institute of Technology. His research interests include teams, technology in the workplace, collective identity, and multiteam systems. T. T. Selvarajan  is Associate Professor of Management in the College of Business at California State University, East Bay. He received his PhD in Management from Arizona State University. His research interests are in the areas of performance appraisal, business ethics, job engagement and work–family ­balance.

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Jason D. Shaw is Chair Professor and Head of the Department of Management and Marketing, the Yeung Kan Man Endowed Professor, and Director of the Centre for Leadership and Innovation in the Faculty of Business at The Hong Kong Polytechnic University. He is the Editor-in-Chief of the Academy of Management Journal. He received his PhD from the University of Arkansas. His research has appeared or accepted for publication in consensus top-level journals such as the Academy of Management Journal, Academy of Management Review, Journal of Applied Psychology, Organization Science, Strategic Management Journal, Organizational Behaviour and Human Decision Processes, and Personnel Psychology, in addition to practitioner-oriented outlets such as the Harvard Business Review, Compensation and Benefits Review, and World at Work Journal. His research interests include turnover patterns, team effectiveness, and the psychology of financial incentives. He is currently the lead investigator on the IMPACT Project, a global study of reactions to pay changes. Lynn M. Shore  is Chair and Professor of Management at Colorado State University. Her research areas are the employee–organization relationship and work force diversity and inclusion. She has published numerous articles in such journals as Academy of Management Journal, Academy of Management Review, and Journal of Applied Psychology. Dr Shore served as Associate Editor for the Journal of Applied Psychology. Piers Steel  is Professor in the Human Resources and Organizational Dynamics area and is the Distinguished Research Chair in Advanced Business Leadership at the Canadian Centre for Advanced Leadership in Business at the Haskayne School of Business, University of Calgary. Dr Piers’ particular areas of research interest include meta-analysis methodology, personnel selection, culture and motivation, especially procrastination. He is the recipient of several international rewards, including the Raymond A. Katzell Award in I-O Psychology and the George A. Miller Award, given to the best article in psychology in the preceding five years. His work has been published in the premier journals in the social sciences, including Journal of Personality and Social Psychology, Psychological Bulletin, Journal of Management, Journal of Applied Psychology, Personnel Psychology and Academy of Management Review. Greg L. Stewart  is the Mary H. Bell Chair in Leadership at the University of Iowa. He serves as the Director of the VHA VISN 23 PACT Demonstration Laboratory. He received a BS from Brigham Young University and a PhD from Arizona State University. He has published numerous articles related to designing work teams and team leadership. He also conducts research related to organizational staffing, including an emphasis on understanding how individual traits relate to work performance. His research articles have appeared in leading academic journals including Journal of Applied Psychology, Personnel Psychology, Academy of Management Journal, Organization Science, and Journal of Management. Linn Van Dyne  received her PhD from the University of Minnesota with a concentration in Strategic Management and Organization. Van Dyne is an Associate Editor for Organizational Behavior and Human Decision Processes and is on the editorial boards of Academy of Management Journal, Journal of Applied Psychology, Journal of Organizational Behavior, Human Relations, Management and Organizational Review, and Organizational Psychology Review. She has published in Academy of Management Journal, Academy of Management Review, Journal of Applied Psychology, Organizational Behavior and Human Decision Processes, Research in Organizational Behavior, and other scholarly outlets. She is a member of the Academy of Management, the Society for Industrial and Organizational Psychology, the American Psychological Association, the Association for Psychological Science, and she is a Fellow in the Society of Organizational Behavior. Sidni A. Vaughn  is a PhD student in the Industrial Organizational Psychology program at the Georgia Institute of Technology. Her research interests include teams, social networks, social influence, and multiteam systems.

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Amy Wax  is doctoral candidate in Industrial/Organizational Psychology at the Georgia Institute of Technology. Her research interests include team diversity and assembly, and social network analysis. Amy was awarded a National Science Foundation East Asia and Pacific Summer Research Fellowship to conduct her dissertation research on online gaming teams in China. In the fall, Amy will be joining the faculty of the Psychology Department at California State University, Long Beach. Justin M. Weinhardt  is Associate Professor in the Organizational Behaviour and Human Resources at the Haskayne School of Business, University of Calgary. Justin’s particular areas of research interest include computational modeling, dynamic motivation and decision making, multilevel theory and mental illness at work. His research has been published in premier journals such as Journal of Applied Psychology, Journal of Management, Journal of Operations Management, Organizational Behavior and Human Decision Processes, and Organizational Research Methods. Lingtao Yu  is Assistant Professor in the Organizational Behavior and Human Resources Division at Sauder School of Business, University of British Columbia. He received his PhD in Business Administration (OBHR) from Carlson School of Management, University of Minnesota. His research interests include leadership and ethics, workplace deviance, emotions, and mindfulness. His research has been published in Academy of Management Journal. Hannes Zacher  Professor of Work and Organizational Psychology at the University of Leipzig (Germany) and Adjunct Professor in the School of Management at Queensland University of Technology (Australia). He received his PhD in 2009 from the University of Giessen (Germany). His research program investigates sustainability in and of organizations, with foci on successful aging at work, career development, and occupational well-being; action regulation, innovation, proactivity, leadership, and entrepreneurship; and pro-environmental employee behavior. He is an Honorary Research Fellow at the Sloan Center on Aging & Work at Boston College and member of the editorial boards of Group & Organization Management, Journal of Occupational Health Psychology, Journal of Vocational Behavior, and Work, Aging and Retirement.

Preface The Global Science and Practice of IWO Psychology From scientific management to the human relations movement, from cottage industries to craft guilds, from the industrial age to the information society, the issues that have dominated the field of industrial, work and organizational (IWO) psychology have changed over the years. Toward the end of the second decade of the twenty-first century, IWO psychology is a global science and arena for professional practice. Our original, bestselling two-volume Handbook of IWO Psychology (2001) is now a three-volume set that reflects the strides in the field, both in substantive content, expanding knowledge, and evidencebased application. The changes and developments since 2001 have been so fundamental and sweeping that all the chapters in these three volumes are brand new. In preparing these volumes, we have rejected the idea of merely updating the previous version’s chapters with new references and some new content. Instead, we have commissioned chapters that better reflect the current subdomains in the field and incorporate the developments of the past decade and half from the ground up. We have also sought authors with fresh and contemporary perspectives, as it is our hope that the present three-volume set will take IWO psychology to the second quarter of the twenty-first century. Nonetheless, our overarching objectives in this second edition have remained (1) to cover recent research on work and organizational psychology by leading experts around the globe and (2) to develop psychology of work that is applicable globally. Personnel Psychology and Employee Performance volume primarily focuses on individuals in organizations and covers personnel psychology issues. Organizational Psychology volume primarily covers organizational psychology topics. Managerial Psychology and Organizational Approaches volume highlights areas of interest in managerial psychology, with coverage of broader, strategic topics and concerns. It was our intention, as editors of this Handbook, to produce a globally contributed, globally oriented, and globally relevant collection of chapters which comprehensively covered the major and vibrant topics comprising our field into the second quarter of the twenty-first century. We recognize and learn from manifest cross-cultural, socio-economic, and historical differences. But what we stated in the preface of our first edition is still true: We were equally determined not to allow a drift downward into parochial, single nation, local issues and perspectives to dominate this Handbook. The very title, The Handbook of Industrial, Work and Organizational (IWO) Psychology reflects these aspirations on the part of the editors. Credit is due to our esteemed colleague Paul Sackett who proposed this globally-focused title as a combination of Industrial-Organizational (I-O) Psychology in the USA, and Work and Organizational (W/O) Psychology in Europe and other countries ­worldwide.

In the past two decades, IWO psychology has become a field of global science and professional practice. Global psychology of work thrives: IWO research is not solely conducted and published by those in North America and Europe. Knowledge is generated, replicated, and disseminated worldwide. The choice of research questions reflects both the global and regional zeitgeists and concerns. The scientific research

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methods utilized are universal. But, perhaps most importantly, findings of generalization and regional specificity are rigorously tested with regularity. These developments have shifted the perceptual, analytical, and disciplinary boundaries of IWO psychology forever away from the parochial. International concerns and challenges are attended to in all sub areas of IWO psychology. With regard to the practice of IWO psychology, alongside this diversification of scientific focus toward a global conceptualization of the discipline, changes in the practice of organizational psychology have also taken on an increasingly global shape and size. There is greater collaboration and sharing of expertise across countries in IWO psychology. Global consultancies dominate the market with their multinational presence and practice. Consequently, IWO psychology has standardized practices across national boundaries. The chapters in these volumes are geared to consolidate IWO concepts and knowledge on topics studied by IWO psychologists, drawing upon research and practice from across the globe. Hopefully, they reflect and satisfy the demands of a global science and practice of industrial, work, and organizational psychology for many years to come. Handan Kepir Sinangil, Istanbul Deniz S. Ones, Minneapolis Chockalingam Viswesvaran, Miami Neil Anderson, London

Acknowledgments Preparing a major, globally relevant, three-volume Handbook of Industrial, Work and Organizational (IWO) Psychology required much collaboration and effort from everyone involved. First and foremost, thanks are due to the co-editors of these volumes for making this second edition possible. We have great respect and admiration for each other and cannot imagine a better editorial team. Between the first edition and this much expanded second edition, we have collaborated for exactly 20 years, providing each with other intellectual challenge and social support. Each editor’s contribution was somewhat different for this second edition. The chapter reviews were handled by Viswesvaran, Anderson, and Ones. Strategic decisions benefited from Anderson’s leadership. Content benefited from Viswesvaran’s vast expertise and keen awareness of important developments. Structure of the volumes and administrative details were handled by Ones. For the actualization of the Handbook with 69 chapters across three volumes, our gratitude goes to over 100 eminent authors across from over two dozen countries. They accepted our invitation with enthusiasm and devoted a considerable amount of effort to this project. Not only did they produce outstanding chapters, but they also were timely with their revisions, although the publication of the volumes were delayed due to reasons beyond their or our control. The volume of administrative work on this undertaking was much greater than the first edition. Our editorial assistant Melissa Kellen at the University of Minnesota’s Psychology Department, where our Administrative Headquarters was located, was more helpful and important for this project than anyone will ever realize. Her efficiency, professionalism, and enthusiasm made our editorial work a little easier and a little less distressing. Partial financial support for the Handbook editorial office came from Ones’ Hellervik Chair and Distinguished McKnight University Professor research funds at the University of Minnesota. We also would like to extend our sincerest thanks to the Sage (publishing) team. During the various phases of the Handbook, we have had to work with many different Sage publishing editors and their teams; they all were helpful. As the volumes took shape, our colleagues and students at our respective institutions also provided valuable support. In this regard, I cannot overstate the intellectual stimulation, assistance, and care that I received from my past and current doctoral advisees. Thank you! Those closest to us perhaps gave the most and suffered the greatest during the completion of this project. To them, we offer genuine apologies for the neglect they had to endure and for the encouragement they nonetheless provided, while we labored long hours during nights and weekends. In this, I especially would like to acknowledge the encouragement and support of my daughter Daria Haner. We can only hope that the sacrifices made in the preparation of these three volumes will be balanced by the good that they will do for the science and practice of IWO psychology. The ultimate measure of our success will be if the research presented in these volumes can improve the productive work lives of millions around the world. On behalf of the Handbook Editors, Deniz S. Ones Minneapolis March 2017

Introduction Chockalingam Viswesvaran, Deniz S. Ones, Neil Anderson and Handan Kepir Sinangil

In this second edition of the Handbook of Industrial, Work and Organizational (IWO) Psychology, we have greatly expanded the focus and scope compared to the first edition. The fifteen years intervening between the first and second editions have brought to the fore several new areas of research and exploration – environmental sustainability being one example. We have attempted to capture these cutting-edge themes as well as summarize the most recent developments and contemporary literature on traditional topics in IWO psychology, such as individual differences and HR functions (e.g. selection, recruitment, training, designing reward systems). This expanded focus has necessitated the need for a three-volume handbook (as compared to the two volumes of the first edition). However, we have made every effort to retain some of the features that made the first edition well received and globally successful. Across the chapters included in this threevolume set, there are five distinguishing features.

Global Relevance and Representation First, the science and practice of IWO psychology is a global enterprise. Even the organizations that are restricted to local/regional operations have to

contend with global trends. Consider a hair stylist in your local community. While their employees, clientele, etc. are local, even this organization is affected by developments in different parts of the world. Many hair stylists offer their clients the option of booking appointments online in advance. The software for this operation is designed in different parts of the world and, more importantly, consultants can collect enormous amounts of data about their clients (e.g. preferences, patterns of behavior) and enable the local hair stylist to better market their products. They can identify fashion trends across the country and detect regional and cultural patterns. One can expand these examples to every aspect of the stylist’s business (e.g. mom and pop shops competing with national/international chains; globally sourced products used and sold in such businesses). Thus, the business of every organization is affected by trends beyond the local market, and competitive advantage dictates a global outlook in business operations. Each chapter in this Handbook attempts to capture this global impact when summarizing the research and practice in their topic. Globalization, internationalization, and inclusion of cross-cultural issues are also seen in how we practice our science. The leading consulting companies that design and market IWO products are global in scope. Most companies have operations in the USA, UK, and Europe, but also in the

Introduction

Middle East, China, India, Latin America, South Africa, and many other countries and regions. Their products and services are globally accessible. This is also reflected in how our universities prepare future researchers and practitioners. Many professors and universities have collaborations with institutions in other countries and many have cross-cultural topics in the courses they offer. Business and management in general and human resources management and IWO psychology in particular are global enterprises. Given these trends of globalization, the chapters in these volumes were developed to ensure different cultural perspectives are reflected. For example, in considering what we know of the antecedents and consequences of employee– organization relationships (LMX, POS, psychological contracts, etc.), Shore, Cole-Shapiro and Chang (in Organizational Psychology volume) explicitly integrate research from Spain, China, and other countries. The same is true of many other chapters in these volumes. As can be seen from the author affiliations, the contributors to these volumes come from over two dozen countries, yet the international perspectives reflected are global as needs of IWO psychologists worldwide have been kept in mind. In addition to soliciting authors from different cultural and regional backgrounds, we attempted to pair authors on topics such that each chapter had authors representing different experiences (e.g. well established and recently minted scholars; individuals with differing theoretical perspectives or research traditions). We hope the dialogue between co-authors has enriched chapter content and will serve as one of the distinguishing features of this Handbook. Finally, further encouraging dialogue among IWO psychologists from different world regions, Managerial Psychology and Organizational Approaches volume includes chapters summarizing the research and practice of IWO psychology in specific regions of the world. This set of chapters will be a valuable resource to consulting companies as well as academic researchers.

Focus on Findings and Explanations A second characteristic feature of the chapters in this Handbook is the emphasis not only on documenting the major findings but also explicating how and why human resources applications work. IWO psychology started as an applied science, but as a scientific field matures, scientists, researchers, and practitioners move from asking whether something works to how and why it works (Viswesvaran,

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Ones, Schmidt, Le, & Oh, 2014). Developments in psychometric meta-analysis have enabled us to integrate voluminous data and test complex theories involving multiple variables. The chapters on the different topics in this volume summarize the scientific basis of human resources applications and provide knowledge to develop and utilize improved organizational practices. Evidence-based practice can benefit from the knowledge presented in the chapters of these volumes.

Scientist–Practitioner Model: Rigor, Relevance, and Applicability IWO psychology has been an applied endeavor for most of its history. The problems faced by organizations in the management of work has been the focus in IWO psychology, and most of our interventions have been based on scientific testing, experimentation, and study. Accordingly, a third feature of the chapters included in this volume is their attention to the balance between rigor and relevance (Ones, Kaiser, Chamorro-Premuzic, & Svensson, 2017). Given the raison d’être of IWO psychology, while testing effects with rigorous field and lab experiments are needed to sort out precise effects and causality, we also need to ensure that the resultant recommendations remain relevant to organizations and working individuals. Representing this fine balance, Levy, Cavanaugh, Frantz, Borden, and Roberts (this Handbook Chapter 9) review several studies in performance appraisal research but also clearly stress the social context of performance appraisals in organizations. Theory-driven research should not deteriorate to theory as an end in itself. Campbell and Wilmot in their chapter (Personnel Psychology and Employee Performance volume, Chapter 1) caution the field against TFTS (Theory for Theory’s Sake). The fourth major feature of the chapters included in these volumes is the usefulness and applicability of information presented for organizational applications. Developments in psychometric meta-analysis have allowed us to integrate vast quantities of literature to robustly estimate several bivariate relationships. In fact, many of the chapters in the Handbook review, tabulate or draw conclusions from metaanalyses. However, we note that vast research in IWO topics exists in the archives on IWO consulting firms and in organizations where IWO psychological concepts are examined by people-analytics teams. We lament that these data are typically not included in scientific archives of our field (Ones, Viswesvaran, & Schmidt, 2017). This is a great loss. Thus, as far as possible, we have encouraged the

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authors of the chapters in the Handbook volumes to include research and findings from non-academic sources. In discussing the latest innovations in the field, many authors have taken our request to heart and have included practitioner research or views and have noted potential problems, pitfalls, and anticipated progress in applications of their respective content areas.

Methodological Issues and Future Prospects Research methods define what we do as professionals. The final and fifth important feature of this Handbook is its attention to methodological issues and future directions for research and practice. In this regard, as mentioned above, chapters in the Handbook have tended to review, present, or summarize meta-analytic findings and conclusions. They have also pointed out methodological issues relating to their specific topic. IWO psychology is a dynamic and evolving field, and any handbook that summarizes the state of the field has to address future research needs. Each chapter’s attention to future research and application directions is an asset. This aspect of the chapters is poised to provide a roadmap for future research efforts and will be especially appealing to students. The second edition of the IWO Handbook organizes chapters in three volumes. In Personnel Psychology and Employee Performance volume, we focus on issues of selection, training, applicant reactions, and criterion issues, as well as a review of predictors in IWO practice. Organizational Psychology volume presents topics from organizational psychology and behavior, such as work attitudes, teams, leadership, and so forth. Finally, Managerial Psychology and Organizational Approaches volume focuses on macro concerns and social issues that are becoming more salient in the management of workforces and workplaces. Each volume introduces its own coverage.

THIS VOLUME: Organizational Psychology The 22 chapters included in Organizational Psychology volume cover the subfield of IWO psychology described as organizational psychology or organizational behavior. These chapters are organized around several major themes. We describe each in turn.

Work attitudes reflect the affective and cognitive evaluations of the work environment both as perceived and objectively encountered by employees. Credé (Chapter 1) takes on three of the most researched job attitudes – organizational commitment, job satisfaction and involvement. He raises the important question of construct redundancy. Are these postulated attitudes distinct and do they contribute incrementally to our understanding of employee attitudes? Credé finds that although different types of commitment have been postulated, they do not have incremental validity over affective commitment in explaining work behaviors. This chapter calls for future research for understanding within-person variability in job attitudes and the causes and consequences of such variability. We agree: our field will profit from such systematic longitudinal research on attitudes that models daily, weekly, monthly, and even yearly variation. In Chapter 2, Johnson, Chang, Kim and Lin summarize research on how employees form their identity in the workplace. Bidirectional relations between employee self-concept and identity and work attitudes and values are likely. Organizational justice is another employee attitude that has important consequences for individuals and organizations. There is a competition for resources in organizations at all levels, and the distributive justice of allocating scarce resources has ramifications for the smooth functioning of organizations. In addition to distributive justice, the procedures followed (or not followed) influence employee attitudes. A vast literature on procedural justice has argued and researchers have tested whether procedural justice is valued for itself or for its contributions to distributive justice. Gilliland (Chapter 3) integrates the literature on organizational justice from different cultures and discusses the implications for the science and practice of IWO psychology. Individual job performance in organizations is determined by individual differences in abilities, knowledge, and motivation. The first two are covered in Personnel Psychology and Employee Performance volume. Thus, motivation is a major area of thrust in this volume. As there are several theories and perspectives that can inform motivation research and practice in work settings, several chapters were commissioned to reflect mainstays and contemporary integrations. Latham and Locke (Chapter 7) provide a succinct summary of their decades-long research on goal-setting theory. Goals are central to motivation – a person is motivated to reach and maintain that end or optimal state described in the goal. All motivational theories address the concept of goals. Steel and Weinhardt state that to be motivated we need some cognitive representation of an end state (goals) whether we are aware of it or not.

Introduction

Without goals our actions are random. Steel and Weinhardt (Chapter 4) employ the temporal motivation theory as an overarching framework to discuss and contrast different motivational theories. They discuss the three phases to goals: goal choice, goal planning, and goal striving. Whereas Steel and Weinhardt employ the Temporal Motivation Theory to discuss and integrate research on motivation, Gagne, Deci, and Ryan (Chapter 5) focus on the quality and persistence of people’s motivation, performance, and well-being using the selfdetermination theory. Finally, Zacher and Frese (Chapter 6) present the action regulation theory, with its basic concepts of action regulation, hierarchical structure, and foci of action regulation, to understand employee motivation. Employee motivation is a complex topic and the multiple lenses presented here will help the field get a fuller picture of the varied multi-faceted phenomenon studied. Another major area in Organizational Psychology volume is performance management. Specifically, both pay and performance appraisal system effectiveness are covered. Designing pay systems for organizational effectiveness and to link pay to performance has a long history in organizational sciences. Shaw (Chapter 8) provides an overview of 15 years of applied research on designing pay systems. Financial incentives should be linked to performance quality. However, assessing employee performance is not only a psychometric exercise but should also account for the social context in which performance assessments are made. Levy, Cavanaugh, Frantz, Borden, and Roberts (Chapter 9) examine rater errors and biases that affect performance appraisals. They also consider the reactions of different stakeholders in the performance management system, and they summarize the recent developments that capture how rater characteristics like conscientiousness and agreeableness influence ratings. Performance management begins with reliable performance appraisals, and this chapter is a must-read for all IWO researchers and organizational consultants. Talent management and development topics addressed in this volume include learning and employee development. Given the volume of change in the workplace (e.g. rapid technical advances), continuous learning is a prerequisite for success. Researchers have stressed the importance of learning agility for success. Two chapters in this volume cover the recent research and our understanding of employee learning and development. Bell and Moore (Chapter 10) cover the recent research on learning for developing talent. Their chapter provides an overview of contemporary literature. In a related chapter, Hezlett and McCauley (Chapter 11) distinguish development

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from training in that the latter is organizationinitiated and more formal and structured, whereas development is more employee-initiated. They note that training places emphasis on growth relevant to current roles, whereas development aims for long-term professional growth. Hezlett and McCauley then go on to present six popular developmental practices in organizational sciences including multi-source feedback. The concept of leadership has always fascinated the general public and many popular books have been written about the topic, including ones on successful careers of leaders. Leadership has been claimed to make the difference between success and failure of organizations – and even nations. What makes a good leader (individual characteristics, situational factors, etc.) and what derails them have been extensively studied and debated. Hogan, Curphy, Kaiser, and ChamorroPremuzic (Chapter 12) take on this monumentally important topic in IWO psychology and present the burgeoning literature from the novel perspective of evolutionary psychology. Our aim in having these authors take on the long studied, deeply rooted topic of leadership in IWO psychology was to infuse fresh perspectives and new ideas into, frankly, an ossified domain of inquiry. We know that Hogan et al.’s chapter will spur many debates and research in the study of leadership. Several chapters in this volume focus on groups and teams. These include chapters on team-design characteristics, issues around organizing in teams, and multi-team systems. Work design and interventions are addressed in chapters devoted to the design of work, management interventions, and employee participation. The knowledge required for contemporary work has brought the issue of distributed knowledge to the fore. The expertise for completing any work is distributed across individuals, and teams are employed to tackle workplace tasks. Stewart and Carter (Chapter 13) address how team composition is to be conceptualized. They posit that configurational models best capture the asymmetric roles team members play in team functioning. They also discuss issues that arise with virtual teams and globally dispersed teams. Stewart and Carter raise the interesting concept that all team members are not interchangeable but there is a strategic core to team composition. This parallels research on core and temporary employees in organizations. DeChurch et al. in their chapter (Chapter 14) call for a radical shift in our thinking of teams in organizations by considering the potential for organizing in teams. In many industries, these loose coalitions of teams have formed the organization. This chapter will spur much discussion and research in the years to come. Finally, in a third chapter on teams

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in this volume, Mathieu, Luciano and De Church (Chapter 15) present the developments in the past two decades on how teams are linked into multiteam systems. Organizational Psychology volume concludes with a focus on psychology of work relationships. Specific chapters in this area include trust in work settings, politics in organizations, toxic emotions in the workplace, and exchange in employee– organization relationships. Each of these chapters takes on a different aspect of work relations among employees but also those that relate individuals to the organizations. We expect these topics to garner much more research attention in the coming years.

The Future IWO psychology has evolved greatly over the past several decades. We have come a long way. The changing nature of work is perhaps a reflection of our history as a species. In our odyssey as a science, several exciting turns are ahead and we hope this volume captures some of the excitement in our field. Dunnette foresaw the scientist–practitioner model over 40 years ago. Emphasizing the benefits to be gained by both wings of our discipline working in synergy, he professed a cautiously optimistic future: I believe that success for the field … is just around the corner. Industrial and Organizational Psychology is today an academic discipline, an emerging blend of research, theory, and practice. The blend offers great promise, in the years ahead, for further

developing and extending our knowledge of those behavioral processes which are critical to an understanding of interactions between persons and the institutions and organizations of society. (Dunnette, 1976, pp. 11–12)

The ‘great promise’ of blending practice and science is yet to be fully achieved (Ones et al., 2017a), but hope survives. Our sincere hope, in fact, is that the current three-volume set provides the reader – whether a practitioner, established researcher, or student – with a timely overview of our burgeoning field of inquiry into human behavior in the workplace.

References Dunnette, M. D. (Ed.) (1976). Handbook of industrial and organizational psychology. Chicago: Rand McNally. Ones, D. S., Kaiser, T. B., Chamorro-Premuzic, T., & Svensson, C. (2017a). Has industrial-organizational psychology lost its way? The IndustrialOrganizational Psychologist. Retrieved from http:// www.siop.org/tip/april17/lostio.aspx Ones, D. S., Viswesvaran, C., & Schmidt, F. L. (2017b). Realizing the full potential of psychometric meta-analysis for a cumulative science and practice of human resource management. Human Resource Management Review, 201–15. Viswesvaran, C., Ones, D. S., Schmidt, F. L., Le, H., & Oh, I. (2014). Measurement error obfuscates scientific knowledge: Path to cumulative knowledge requires corrections for unreliability and psychometric meta-analyses. Industrial and Organizational Psychology, 7, 507–18.

Part I

Work Attitude and Values

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1 Attitudes: Satisfaction, Commitment and Involvement Marcus Credé

INTRODUCTION Based on classic definitions of attitudes (Eagly & Chaiken, 1993), and more recent definitions of specific job attitudes (e.g., job satisfaction, Judge, Hulin, & Dalal, 2012), this chapter defines job attitudes as ‘affective responses to and cognitive evaluations of job experiences and the job situation’. Because job experiences and the job situation are themselves multidimensional many constructs with attitudinal features have been identified and studied. These attitudinal constructs differ primarily with regard to the specificity of the entity being evaluated: some focusing on broad entities such as the organization in which the job is embedded (e.g., organizational commitment, Mowday, Porter & Steers, 1982; Perceived Organizational Support, Rhoades & Eisenberger, 2002) or the overall job situation (e.g., overall job satisfaction), while others focus more narrowly on the work being performed (e.g., employee engagement, Macey & Schneider, 2008; job involvement; Brown, 1996), ancillary job features (e.g., perceived coworker support; Zhou & George, 2001) or even specific facets of individual job features such as the degree to which supervisors interact

respectfully with subordinates (interpersonal justice, Colquitt et al., 2001). Rather than attempt to discuss the vast literatures related to each of these constructs this chapter focuses instead on the three constructs that are most widely agreed to be attitudinal in nature: job satisfaction, organizational commitment, and job involvement. Five broad issues are discussed. First, the theoretical nature of these constructs is discussed with a focus on how the understanding of each construct has evolved over time. Second, theories and models of how attitudes are formed and how they are related to workplace outcomes are discussed. Third, the measurement of the three constructs is described. Fourth, this chapter discusses two challenges for job attitude research; one regarding the validity of the most widely studied model of organizational commitment, the other relating to the manner in which these attitude constructs are related to each other. The chapter concludes with a discussion of research on these three attitude constructs from an international perspective with suggestions as to how variability across countries and cultures might be further explored and how past findings might be synthesized.

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JOB SATISFACTION Modern conceptualizations of job satisfaction share two features. First, job satisfaction is multidimensional and organized hierarchically such that overall job satisfaction is determined by an aggregation of satisfaction with specific facets of the job (e.g., pay, coworkers). As such overall job satisfaction is a formative (or aggregate) construct and not a reflective higher-order construct (Law, Wong, & Mobley, 1998) like most other higherorder constructs (e.g., core self-evaluations; Judge, Locke, & Durham, 1997). Second, job satisfaction has both affective and cognitive components. Earlier definitions disagreed on whether job satisfaction was primarily cognitive (e.g., Motowidlo, 1996; Weiss, 2002), or affective (e.g., Locke, 1976) in nature. Motowidlo (1996, p. 176) described job satisfaction as a ‘judgment about the favorability of the work environment’, while Weiss (2002, p. 175), seeking to distinguish job satisfaction from affect and aligning it more closely with how job satisfaction is typically measured, defines it as ‘a positive or negative evaluative judgment one makes about one’s job or job situation’. In contrast, Smith, Kendall, and Hulin (1969, p. 7) view job satisfaction as ‘feelings or affective responses to facets of the situation’ and Locke (1976, p. 1304) defined job satisfaction as a ‘a pleasurable or positive emotional state resulting from the appraisal of one’s job or job experiences’, a definition that has been widely echoed by others (e.g., Cranny, Smith, & Stone, 1992). Importantly, those proposing the inclusion of affect in the definition of job satisfaction have typically positioned affect as a consequence of the cognitive appraisal. Thus, an employee may have a positive emotional reaction to his or her job because the cognitive appraisal highlighted the interesting work, wonderful coworkers, and excellent pay that characterize the job. A relatively recent change in this positioning of the affective component of job satisfaction relative to the cognitive/evaluative component comes via Affective Events Theory (AET, Weiss & Cropanzano, 1996; see also Judge, Hulin, & Dalal, 2012; Brief & Weiss, 2002). AET also acknowledges the dual cognitive and affective nature of job satisfaction but reconceptualizes their relationship to each other. That is, workplace events such as an interaction with an angry customer, unfair performance feedback, or a helpful gesture from a coworker drive momentary affective reactions to the job. These affective reactions are part of job satisfaction but also influence the individual’s cognitive evaluations of the job (Weiss, Nicholas, & Daus, 1999). Repeated negative affective events

are integrated into the cognitive evaluation of the job, although this cognitive evaluation of job satisfaction is also influenced by more objective and stable features of the job situation such as the pay and benefits that are received. Importantly, AET also acknowledges that the cognitive/evaluative component of job satisfaction also impacts job affect. Undesirable stable job characteristics might result in negative job affect via a process of rumination. From the AET perspective the affective component of job satisfaction is therefore both an antecedent and consequence of the cognitive/ evaluative component. A number of studies using real-time assessments of affect and job satisfaction (e.g., Illies & Judge, 2002; Weiss, Nicholas, & Dauss, 1999) have highlighted the dual affective and cognitive components of job satisfaction and more recent discussions of the nature of job satisfaction (e.g., Brief, 1998; Dalal & Credé, 2013; Judge, Hulin, & Dalal, 2012) have similarly returned to classical definitions of attitudes (e.g., Thurstone, 1928) and acknowledged the joint cognitive and affective aspects of job satisfaction.

INVOLVEMENT The literature on job involvement is based on findings that individuals can exhibit substantial ego involvement in their approach to work even when the work is artificial and short-lived (e.g., Lewis, 1944). Lodahl and Kejner’s (1965) seminal paper on job involvement described two components of the construct. Job involvement was described both as ‘the degree to which a person is identified psychologically with his work, or the importance of his work in his total self-image’ (p. 24) and as ‘… the degree to which a person’s work performance affects his [sic] self-esteem’. This initial description is problematic because it appears to describe two somewhat dissimilar ideas – the importance of one’s job to self-image and the degree to which self-worth is raised or lowered by variations in job performance – without a formal acknowledgment that the construct is multidimensional, and also because it appears to be somewhat confounded with definitions of work centrality (see Brown, 1996; Diefendorff, Brown, Kamin, & Lord, 2002; Paullay, Alliger, & Stone-Romero, 1994). A clearer definition appears to be that of Paullay et  al. for whom job involvement is ‘… the degree to which one is cognitively preoccupied with, engaged in, and concerned with one’s present job’ (p.  225). This definition of job involvement more clearly distinguishes it from work centrality – as well as the other two attitude constructs discussed in this

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chapter – but suggests substantial overlap with a recently popular attitudinal construct, employee engagement (e.g., Macey & Schneider, 2008).

ORGANIZATIONAL COMMITMENT The concept of commitment has been applied to a variety of foci including unions (e.g., Barling, Wade, & Fullager, 1990), a profession (e.g., Weng & McElroy, 2012) and supervisors (Lapointe, Vandenberghe, & Boudrias, 2013), but by far the most frequently studied focus has been the organization. Early conceptualizations of organizational commitment favored a unidimensional view of the construct (e.g., Becker, 1960) and focused primarily on the employee’s sense of attachment to and identification with the organization. Mowday, Porter and Steers (1982) for example, defined organizational commitment as ‘the relative strength of an individual’s identification with and involvement in a particular organization’ (p. 27). This unidimensional approach to the study of commitment began to shift as researchers began to describe a variety of multidimensional models. These multidimensional approaches included: 1) the distinction between value commitment and commitment to stay (Angle & Perry, 1981), 2) compliance, identification, and internalization (O’Reilly & Chatman, 1986), and 3) moral, calculative, and alienative commitment (Penley & Gould, 1988). Most early researchers focused primarily on either attitudinal commitment as described by Mowday et  al., or calculative commitment which acknowledged that employees can be bound to an organization by the high cost associated with leaving the organization. Indeed, the 1990 meta-analytic review of the commitment literature by Mathieu and Zajac focused only on these two types of commitment. Since 1990 organizational commitment researchers have largely adopted Meyer and Allen’s ThreeComponent Model of Commitment (Allen & Meyer, 1990, 1996) which introduced a third type of commitment to mainstream organizational research. The central insight of this model is that employees can become committed to an organization for a variety of reasons and that these different types of commitment also have unique relationships to employee behavior. The Three-Component Model distinguishes between 1) affective commitment which represents employees’ identification with the organization, sense of belonging, and desire to see the goals of the organization fulfilled, 2) normative commitment, based on work by Wiener (1982), which

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represents a sense of obligation toward the organization, and 3) continuance commitment which largely replicates the calculative commitment construct.

ANTECEDENTS AND DETERMINANTS OF JOB SATISFACTION, COMMITMENT, AND INVOLVEMENT The literature on job attitudes is rich with theories and models that discuss how job attitudes develop. Much of this theoretical literature focuses on job satisfaction but because job satisfaction is often viewed as a proximal determinant of organizational commitment (e.g., Yoon & Thye, 2002) and an outcome of involvement (e.g., Brown, 1996), these theoretical frameworks are also relevant for these other attitude constructs. Collectively most theories and models of job attitude formation emphasize the role of objective situational factors, dispositions, and/or the interaction between these two sets of factors, although the situational factors and dispositional factors that constitute these models vary relatively widely.

THEORIES OF INDIVIDUAL DIFFERENCES AND SITUATIONAL FACTORS The Job Characteristics Model (Hackman & Oldham, 1976) describes five objective characteristics of the job that are positively related to job satisfaction, and how individual characteristics moderate these relationships. These characteristics of the job are: 1) the variety of skills used on the job (skill variety), 2) the degree to which employees complete a job from beginning to end (task identity), 3) the degree to which the job has a positive impact on others (task significance), 4) the degree to which employees control the manner in which the job is completed (autonomy), and 5) the degree to which the job provides employees with accurate and regular feedback about their level of performance (feedback). A meta-analytic review of this literature (Fried & Ferris, 1987) found substantial support for the Job Characteristics Model with correlations between job satisfaction and the five job characteristics ranging from ρ = .26 (for task identity) to ρ = .48 (for autonomy). Indeed, a unit-weighted composite of the five job characteristics (job complexity) was found to correlate ρ = .74 with overall job satisfaction. A more recent theoretical extension

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of this work (and meta-analytic update), by Humphrey, Nahrgang and Morgeson (2007) has identified nine additional job characteristics that also exhibit relatively large relationships with job satisfaction. The largest of the observed effects for these additional characteristics are: task variety (ρ  with job satisfaction = .46), information processing requirements (ρ = .38), interdependence (ρ = .33), feedback from others (ρ = .42), and social support (ρ = .56). Job characteristics have also been linked to job involvement; more interesting and challenging work being thought to increase employees’ interest in and involvement with their jobs. Empirical evidence is supportive of this assertion with the observed meta-analytic relationships between job characteristics and job involvement ranging from ρ = .21 for task identity to ρ = .47 for job challenge (Brown, 1996). The literature l­inking job characteristics to organizational c­ ommitment is sparser, although individual studies (e.g., Elanain, 2009; Joo & Lim, 2009; Liden, Wayne, & Sparrowe, 2000) have reported that job characteristics are related to organizational commitment in a relatively similar manner. According to the Job Characteristics Model the effect of job characteristics on job satisfaction (and other outcomes) is further moderated by a variety of individual difference characteristics. Because the five job characteristics are jointly associated with job complexity the positive impact on job satisfaction is thought to be most pronounced when employees are: 1) high on a desire to learn, achieve, and grow on the job (Growth Need Strength), 2) highly skilled and knowledgeable, so that the complex nature of the job can be satisfying, and 3) highly satisfied with other facets of the job (e.g., coworkers, pay, supervisor, benefits). The hypothesized moderating role of these individual difference characteristics has found only mixed support (e.g., de Jong, van der Velde, & Jansen, 2001; Kemp & Cook, 1983; Loher, Noe, Moeller, & Fitzgerald, 1985), although recent work (Zargar, Vandenberghe, Marchand, & Ayed, 2014) has shown that the moderating role of Growth Need Strategy can also be extended to the impact of job characteristics on commitment. Whereas the Job Characteristics Model hypothesizes a main effect of objective job characteristics on job situation, a relatively large number of other theories and models focus in some way on the discrepancy between what the job provides and what the employee perceives should be provided as an influence on job attitudes. Collectively these have been referred to as Need-Satisfaction Models (Salancik & Pfeffer, 1978) or as manifestation of Lawler’s Discrepancy Theory (Weiss & Cropanzano, 1996).

The Cornell Model (Hulin, Roznowski, & Hachiya, 1985) focuses on the interplay between relatively objective characteristics of the job and individual differences. Specifically, the Cornell Model posits that job satisfaction is influenced by the perceived balance or discrepancy between job inputs (e.g., skills, time, ability, effort) and job outputs (e.g., salary, promotions, job security, recognition) and that the effect of these perceived inputs and outputs on job satisfaction is influenced by employees’ frame of reference that influence how inputs and outputs are viewed. For example, during times of high unemployment employees are predicted to be less critical of the characteristics of their jobs and therefore be more satisfied. This claim has found some support (e.g., Hulin, 1966; Miller, Katerberg, & Hulin, 1979) but findings from others (e.g., Williams, 1999) have been less supportive. Williams found that the job satisfaction of salespeople spread across 25 states and 66 cities was unrelated to unemployment at the state level, city level, or even the unemployment rate of salespeople with the state. Locke’s Value-Percept Model (Locke, 1976) hypothesizes a somewhat different mechanism involving the characteristics of the job and individual differences; employees’ satisfaction with a job facet is a function of the discrepancy between how much of that facet is desired and how much is provided by the job. These different facet satisfactions are then combined to determine overall job satisfaction by using a weighting system that reflects the value or importance given to each aspect of the job. Thus an employee’s substantial dissatisfaction with any one aspect of the job (e.g., coworkers) may have little impact on the level of overall job satisfaction if the employee does not value satisfaction with coworkers very highly. Alternatively, even small differences between the desired level of a job characteristic (e.g., pay, autonomy) and the actual level of that job characteristic can have a dramatic impact on overall job satisfaction if that job characteristic is highly valued. The validity of Locke’s model has proven difficult to assess because of the now well-known difficulties of working with difference scores involving components measured with imperfect reliability (see Edwards, 1994), but those who have examined this issue have generally found support for the framework (e.g., Rice, Gentile, & McFarlin, 1991; Rice, Markus, Moyer, & McFarlin, 1991). Individual differences and discrepancy concepts are also central to the Theory of Work Adjustment (Dawes, England, & Lofquist, 1964) which has garnered some recent support (e.g., Dierdorff & Morgeson, 2013). Here satisfaction is determined by the discrepancy between six different values held by employees (achievement,

ATTITUDES: SATISFACTION, COMMITMENT And INVOLVEMENT

comfort, status, altruism, safety, autonomy), and the degree to which these values are satisfied by the rewards offered by the job or by the nature of the job. Thibaut and Kelley’s Social Comparison Level Model (Thibaut & Kelley, 1959) also relies on discrepancy concepts by describing how an individual’s satisfaction with being a member of a group, or dyad is determined by a comparison of the outputs received as a member of that group or dyad to a standard that is derived from past membership (real or observed) in other groups or dyads. Outputs that exceed this standard are hypothesized to result in satisfaction while outputs that are less than the standard are hypothesized to result in dissatisfaction.

Theories of Social Influences The growing literature on job attitude climate (e.g., Cambre, Kippers, van Veldhoven, & de Witte, 2012) illustrates that job attitudes are often highly similar among employees within a team, department, or organization. One reason for this is, of course, that such employees often do very similar work and operate within very similar job settings. The rude and incompetent supervisor who lowers the commitment or satisfaction of one employee likely also has a similar effect on other employees. Theories of social influence provide another reason for similar job attitude levels among members of a work unit. Salancik and Pfeffer (1978), for example, argue that employees arrive at judgment about seemingly objective work characteristics by interacting with others. Similarly, the weight given to work characteristics and work events is also informed by the social environment, particularly coworkers. If coworkers all agree that a supervisor is incompetent or that the degree of autonomy is too low and that these characteristics are important then the attitude of a new employee with regard to these two job features will be shaped by this judgment of his or her coworkers. Illustrations of emotional contagion in organizational settings (e.g., Dasborough, Ashkanasy, Tee, & Tse, 2009), are also supportive of the role of social influences on attitudes in work settings.

Dispositional Approaches Although dispositional sources of job attitudes have long been acknowledged (e.g., Weitz, 1952), most early theoretical frameworks positioned individual differences primarily as moderators of the

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impact of situational characteristics on job attitudes. More recently authors have increasingly acknowledged the substantial main effect of dispositions on job attitudes and have also attempted to better articulate their moderating role. This focus on dispositions, primarily personality, was in part the result of studies that reported possible genetic determinants of job satisfaction (Arvey, Bouchard, Segal, & Abraham, 1989), relatively high stability in job satisfaction across time even when individuals changed jobs (Staw & Ross, 1985; Steel & Rentsch, 1997), and that dispositions assessed during early adolescence could predict job satisfaction over 40 years later (Staw, Bell, & Clausen, 1986). Although these studies were not without critics (e.g., Gutek & Winter, 1992) they did result in an increasing number of researchers investigating the dispositional correlates of job satisfaction. Trait affectivity, typically conceptualized as positive affect and negative affect (Watson, Clark, & Tellegen, 1988), has proven perhaps to be most predictive of all dispositions. Judge and Hulin (1993) for example, illustrated that affective disposition, defined as the general manner in which one’s environment is interpreted, strongly influences subjective well-being which, in turn, influences job satisfaction. As such ‘job satisfaction is determined to a significant extent by the individual’s general level of happiness and his or her way of looking at the world’ (Judge & Hulin, 1993, p. 413). More recently, a meta-analytic review by Thoresen, Kaplan, Barsky, de Chermont and Warren (2003) reported estimates of the correlation of job satisfaction with positive affect and negative affect as ρ = .34, and ρ = –.34, while the relationship of organizational commitment with positive affect and negative affect were ρ = .35 and ρ = –.27 respectively. Core self-evaluations have also exhibited strong correlations with job satisfaction (e.g., Judge, Bono, & Locke, 2000; Judge, Locke, Durham, & Kluger, 1998), but the relationship of job attitudes with other traits such as the Big Five personality traits have been less strong (see Judge, Heller, & Mount, 2002; Panaccio & Vandenberghe, 2012). Theoretical explanations for the strong relationships between trait affect and job attitudes have assigned main, moderating, and mediating effects to trait affect and related dispositions. Forgas and George’s (2001) Affect Infusion Model proposes that trait affect acts as a lens through which the attitude object (in this case the job) is viewed. That is, those high on positive affect have a general tendency to view the job and its various facets in positive terms while those high on negative affect would have a tendency to view the characteristics of the job in negative terms. Because of the relative

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independence of positive affect and negative affect these two effects would presumably combine in an additive fashion. Judge and Larsen (2001) describe a different role for trait affectivity and related Big Five traits (i.e., extraversion and neuroticism). First, these dispositions may reflect differences in how sensitive individuals are to environmental stimuli. Extroverts and those high on trait positive affect are likely to respond more favorably to rewards and job challenges while those high on neuroticism and trait negative affect would respond more strongly to punishment and negative workplace characteristics and events. Second, trait affectivity and personality are hypothesized to influence: 1) what individuals attend to and how individuals remember and process organizational events, 2) the kind of situations that individuals seek out, 3) how individuals impact the situations that they find themselves in, and 4) how individuals regulate their mood state. In support Judge and Larsen present evidence that extraverts are more attentive to positive stimuli than negative stimuli and are more likely than introverts to seek out jobs and tasks requiring social interaction, while those high on positive affectivity tend to seek out approach goals as opposed to individuals high on negative affect who are more likely to pursue avoidance goals. The literature on job involvement has focused on one additional causal dispositional. This variable termed work ethic endorsement (or sometimes Protestant work ethic, e.g., Elloy & Terpening, 1992), reflects a stable positive orientation to work irrespective of its objective characteristics and is thought to result from socialization experiences that stress the importance and value of work and correlate strongly with job involvement (ρ = .45; Brown, 1996).

The Role of Workplace Events Affective Events Theory (AET; Weiss & Cropanzano, 1996) acknowledges the influence of objective work characteristics and dispositions on job attitude formation but also integrates the role of workplace events. Specifically, workplace events such as interactions with coworkers or customers interact with trait dispositions to determine affective reactions. These affective reactions influence affect-driven behavior (e.g., anger toward a coworker leading to spreading rumors about that coworker) and, over time, also influence the cognitive component of job attitudes. For example, a single instance of a coworker being rude is likely to shift the short-term affective reaction to the coworker suggesting that the affective component

of job satisfaction exhibits substantial within-person variability. At the same time a single negative interaction is unlikely to result in a permanent change in an employee’s judgment about the favorability of his or her coworkers. Repeated negative interpersonal interactions with coworkers on the other hand are likely to shift the evaluation in a negative direction. A growing literature has found support for the core assertions of AET – not only for job satisfaction (e.g., Illies & Judge, 2002; Mignonac & Herrbach, 2004) but also other attitudes such as commitment (e.g., Becker, Ulrich, & van Dick, 2013). Indirect support also comes from the extensive literature linking specific workplace events to job attitudes. Perhaps because the impact of negative events on mood is up to five times as large as the impact of positive events (Miner, Glomb, & Hulin, 2005), much of this work has focused on negative workplace events such as sexual harassment (Fitzgerald, Drasgow, Hulin, Gelfand, & Magley, 1997) and bullying (Rodriguez-Munoz, Baillien, De Witte, Moreno-Jiminez, & Pastor, 2009), although events outside of work such as marital conflict also have an impact (Sandberg et al., 2013).

CONSEQUENCES OF JOB SATISFACTION, COMMITMENT, AND INVOLVEMENT A number of theoretical frameworks address the expected relationships between job attitudes and behaviors on the job. At the most fundamental level is the core assumption of attitude theory that attitudes are related to attitude congruent behaviors (Eagly & Chaiken, 1993) – or at least to behavioral intentions (e.g., Theory of Planned Behavior, Ajzen & Fishbein, 1980). Favorable evaluations of the job or some facet of the job should therefore result in favorable behaviors toward the job or facet. Attitude–behavior relationships are strongest when the attitude is ‘compatible’ with the behavior (Ajzen, 1988) in terms of the target, action, context, and time frame that is referred to. Unfortunately, attitude–behavior compatibility is often low in organizational settings; the attitude measure used may specify a specific target (e.g., coworkers) but does not typically specify a specific action/behavior (e.g., volunteering to help coworkers), context (e.g., when working on a team task), or time (e.g., during the weekend business trip). This lack of compatibility between assessed attitudes and organizational may explain the fact that attitude behavior relationships in organizational settings are typically low (Harrison, Newman, & Roth, 2006).

ATTITUDES: SATISFACTION, COMMITMENT And INVOLVEMENT

Social-Exchange Theory (Blau, 1964) and the Norm of Reciprocity (Gouldner, 1960), also predicts that employees with positive job attitudes will behave positively at work while those with negative attitudes behave negatively. In this case, favorable working conditions that have been supplied by the organization or some other entity (e.g., the supervisor) create a sense of obligation and a desire to maintain the favorable relationship such that the employee is motivated to reciprocate by behaving in a favorable manner toward that entity. These theoretical accounts have been most widely used to explain the relationships between job attitudes and volitional behaviors such as organizational citizenship behaviors and counterproductive workplace behaviors (e.g., Dalal, 2005; Meyer, Stanley, Herscovitch, & Topolnytsky, 2002). The theoretical nature of the relationship between job attitudes and task performance is less clear. Although it is widely believed that changes in job satisfaction cause changes in task performance via a motivational mechanism Judge, Thoresen, Bono, and Patton (2001) describe six alternative models with some empirical support that could plausibly describe the relationship. One such model is that task performance causes job satisfaction via its effect on favorable work outcomes such as reward, recognition, and more challenging work. A second model is that job satisfaction and task performance are reciprocally related, while the third model proposes that the relationship between the two is spurious; that is, the observed relationship is an artifact of some third variable that is causally related to both task performance and job satisfaction. Possible candidates for the cause of such a spurious relationship include core self-evaluations such as self-esteem. The job involvement literature also presents a motivational mechanism to explain the relationship between involvement and workplace behavior (Brown, 1996). From this perspective high involvement reflects an employee’s appraisal that the job is able to satisfy desired needs (e.g., rewards, recognition) if performance is high. This, in turn, activates motivation to achieve the level of performance required to satisfy the desired needs. Job attitudes are, of course, also widely linked to both turnover intentions (often referred to as withdrawal cognitions) and actual voluntary turnover. Voluntary turnover decisions can be precipitated by individual events and can be almost instantaneous (see Sims, Drasgow, & Fitzgerald, 2005), but are more often thought to be a wellconsidered decision driven by a judgmental process that considers the current job experience (i.e., job attitudes) and perceived alternative job opportunities (e.g. Thibaut & Kelley, 1959). The Tett and

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Meyer (1993) meta-analytic review reported that job satisfaction and organizational commitment were respectively correlated r = –.245 and r = –.333 with turnover and r = – .581 and r = –.538 with turnover intentions. The more recent metaanalysis by Griffeth, Hom, and Gaertner (2000) reported meta-analytic estimates for the relationship with turnover of ρ = .22 (job satisfaction), ρ = –.12 (job involvement), and ρ = –.27 (organizational commitment). These relationships also appear to be substantially moderated by economic conditions with stronger attitude-turnover and attitude-turnover intention relationships during times of low unemployment (Carsten & Spector, 1987).

MEASUREMENT OF JOB SATISFACTION, COMMITMENT, AND INVOLVEMENT Job satisfaction, commitment, and involvement are all almost exclusively measured using selfreport inventories, and while ad-hoc (i.e., locally developed) measures of each construct are not uncommon in the literature carefully developed inventories are available for all three constructs. Job satisfaction is most commonly assessed using the Job Descriptive Index (JDI; Smith, Kendall, & Hulin, 1969), the Job in General scale (JIG; Ironson, Smith, Brannick, Gibson, & Paul, 1989), or the Minnesota Satisfaction Questionnaire (MSQ; Weiss, Dawis, England, & Lofquist, 1967). The JDI is a widely translated facet measure of job satisfaction; having been translated into a wide variety of languages including Spanish (McCabe, Dalessio, Briga, & Sasaki, 1980), French (Johns, 1978), Hebrew (Hulin & Mayer, 1986), Arabic (Nasser & Diefenbach, 1996), and Mandarin Chinese (Wang & Russell, 2005). The internal consistency of scores on the JDI is generally good although the meta-analytic review by Kinicki et  al. (2002) reports a large amount of variability in internal consistency estimates for scores on the satisfaction with work scale. Employees are asked to indicate their level of satisfaction with five facets of their job (the work itself, pay, promotion opportunities, coworkers, and supervision) by responding to 72 items (9 or 18 items for each facet). Each item is an adjective or short phrase and employees are asked to use a three-point response scale to indicate whether the adjective or short phrase is an accurate description of the job facet being rated. Because of concerns about including a 72-item inventory in lengthy surveys of employees an abridged 25-item version of the JDI has also been developed (Stanton et al., 2001).

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Some researchers (myself included, Credé et  al., 2007) have combined facet scores to form an indicator of overall job satisfaction. This approach may be unavoidable in some settings (e.g., when using archival data), but is not ideal because it assumes that the facets are equally important to all employees – in contradiction of Locke’s Value Theory. This aggregation approach also assumes that no relevant facets of the job have been left unexamined (Dalal & Credé, 2013), and there is good evidence that the five facets assessed by the JDI do not capture all of the important facets of the job (e.g., Dalal, Bashshur, & Credé, 2011; Probst, 2003). Instead, researchers interested in assessing overall job satisfaction are advised to do so directly with the 18-item JIG scale or the 8-item shortened version of the JIG (Russell et al., 2004). Structured in the same way as the JDI the JIG asks respondents to describe how they experience their overall job; as such concerns relating to the differential weighting of facets and potential facet omission are eliminated. The full and abridged versions of both the JDI and JIG are available for free use by researchers at: http://www.bgsu.edu/arts-andsciences/psychology/services/job-descriptiveindex.html (accessed on 31 July, 2017). Like the JDI and JIG the MSQ comes in both a long and abridged form. The long form assesses satisfaction with 20 facets of the job using five items per facet (100 items) while the abridged form uses 20 items in total. While the long form provides separate scores for each facet the abridged form can be scored to provide either a general satisfaction score or separate extrinsic satisfaction and intrinsic satisfaction scores. More detailed information on the MSQ, including normative information for some occupations can be found at: http://vpr.psych. umn.edu/instruments/msq-minnesota-satisfactionquestionnaire (accessed 31 July, 2017). Other job satisfaction measures that find relatively widespread use include the Faces Scale (Kunin, 1955), the Brayfield-Rothe measure (Brayfield & Rothe, 1951) and the Job Satisfaction Survey (Spector, 1985). Because of the relative neglect of the affective component of job satisfaction in most commonly used measures of job satisfaction some (e.g., Dalal & Credé, 2013) have also advocated measuring job affect directly with measures such as the Positive and Negative Affect Schedule (PANAS-X; Watson & Clark, 1999), or the measure of hedonic tone and activation provided by Shaver, Schwartz, Kirson, and O’Connor (1987). Organizational commitment is predominantly measured using one of two inventories. The Organizational Commitment Questionnaire (OCQ; Mowday, Steers, & Porter, 1979) is comprised of

15 items that primarily capture affective commitment to the organization, has been translated into a wide variety of languages including German, Polish, Hungarian, Spanish, Malay, and Urdu (Abbas & Khanam, 2013; Kanning & Hill, 2013) and typically exhibits a unidimensional structure (e.g., Mowday et al., 1979). Although the OCQ is still widely used by researchers who require only a measure of overall organizational commitment, most commitment researchers utilize the six-item measures of affective, normative, and continuance commitment described by Allen and Meyer (1990) or modifications of these scales (e.g., Meyer, Allen, & Smith, 1993; Powell & Meyer, 2004). Meyer et al. (2002) report that these three scales have been widely translated. The three commitment scales have not been without its critics. Swailes (2002) for example noted that the measure of continuance commitment conflates an inability to leave the job with commitment, while Bozeman and Perrewe (2001) highlighted that the high predictive validity of organizational commitment with turnover intentions is largely due to the fact that turnover intentions are directly assessed in the commitment scales with items such as ‘I would be very happy to spend the rest of my career in this organization’. Recent work by Klein, Cooper, Molloy, and Swanson (2014) presents a promising alternative measure that is shorter (four-items) and that can be used to assess commitment to a variety of targets. Job involvement can also be measured with longer or shorter scales. Lodahl and Kejner (1965) present both a 20-item and 6-item job involvement scale that are both typically aggregated to form a single indicator of job involvement despite Lodahl and Kejner’s finding that involvement is multidimensional. Lodahl and Kejner’s original scale has been criticized (e.g., Kanungo, 1982; Morrow, 1983) for the lack of correspondence between their theoretical articulation of job involvement and the content of these 20-item and 6-item scales and also – perhaps somewhat ironically given modern understandings of attitudes – for assessing both affective and cognitive aspects of the constructs. As a result a preference has emerged for Kanungo’s 10-item questionnaire which focuses on only one aspect of Lodahl and Kejner’s original conceptualization – the centrality of the individual’s job in his/her life. More recently (Diefendorff et  al., 2002) concerns have emerged that existing measures of job involvement have been substantially confounded with work centrality and that newer measures such as the one developed by Paullay et al. (1994) may not only be a purer measure of the job involvement construct but may also produce scores that are more predictive of job performance.

ATTITUDES: SATISFACTION, COMMITMENT And INVOLVEMENT

CHALLENGES FOR JOB ATTITUDE CONSTRUCTS As is the case in many fields of inquiry discrepancies can be observed between the theoretical articulation and the empirical observations of some attitude constructs. Such discrepancies suggest that changes to theory or to the manner in which observations are made may be warranted. This section of the chapter describes two such cases of incongruence that suggest that modifications to attitude theory or measurement may be necessary, the first relating to the validity of the Three-Component Model of Commitment, while the second regards the relationships among attitude constructs.

The Validity of the Three-Component Model of Organizational Commitment The validity of the Three-Component Model is supported by numerous factor analytic studies that have confirmed that indicators of each commitment type load onto their hypothesized factors and that a three-factor solution exhibits significantly better fit than alternative two-factor or single-factor solutions (e.g., Cheng & Stockdale, 2003; Hackett, Bycio, & Hausdorf, 1994). However, the validity of the three-component model rests upon more than mere factorial distinctiveness because the three commitment types are hypothesized to have unique antecedents and consequences (e.g., Allen & Meyer, 1996; Meyer et al., 2002). For example, continuance commitment should be negatively related to turnover but not be related to performance-related variables such as task performance or citizenship behaviors; alternate employment opportunities should be uniquely related to continuance commitment; work experiences should be uniquely related to affective commitment; and socialization experiences should be uniquely related to normative commitment (Meyer et al.). Unfortunately the empirical support for this claim of unique antecedents and consequences is mixed at best. Some individual studies have reported that the three commitment types exhibit unique relationships with antecedents or consequences (e.g., Jaros, 1997; Hackett et al., 1994), but others have noted that continuance commitment is not very predictive of most workplace outcomes (e.g., Swailes, 2002), or have found that normative commitment and affective commitment exhibit largely similar relationships with antecedents and consequences (e.g., Ko, Price, & Mueller, 1997). Indeed, even Meyer et  al. (2002) noted that the relationship between affective commitment and normative commitment

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was so high as to call into question the distinction between these two constructs. Perhaps the greatest problem for the three-component model is that the findings reported by the Meyer et al. (2002) meta-analytic review show that neither normative commitment nor continuance commitment provide meaningful incremental validity over affective commitment for the prediction of most of the antecedents, correlates, and especially the outcomes of commitment. This can be shown if one uses the meta-analytic estimates reported by Meyer et  al. to regress each antecedent, correlate, and consequence onto the three commitment types. The findings of this analysis (see Table 1.1) show that neither normative commitment nor continuance commitment explain a substantial proportion of variance in most of the outcomes and correlates of organizational commitment above and beyond the variance explained by affective commitment – even when the three commitment types are optimally weighted as is done in a multiple regression context. Because the Three-Component Model of commitment is agnostic as to how commitment types are to be weighted in the prediction of the various correlates and consequences Table  1.1 also presents the correlations of a unit-weighted composite of the three commitment types with the correlates and antecedents composite using the formulae presented by Ghiselli, Campbell, and Zedeck (1981). These composites predict the correlates and outcomes more poorly than affective commitment on its own in all but one case. This re-analysis of meta-analytic results suggests that the Three-Component Model may, in fact, represent a step backward for researchers’ and practitioners’ ability to understand and predict workplace behaviors and other important outcomes using organizational commitment scores. Whether this is a failing of the manner in which the three commitment types are typically measured and whether similar findings would be repeated with updated meta-analytic syntheses of the literature remains to be seen. Until then, our preference for parsimony and the necessity to keep surveys of employees as short as possible suggests that researchers and practitioners are unlikely to sacrifice significant explanatory power if measuring only affective commitment or overall organizational commitment. Although these findings are clearly problematic for the validity of the Three-Component Model three alternative lines of inquiry may hold more promise. First, continuance commitment exhibits uniformly poor relationships with workplace criteria, but it may be that continuance commitment should best be broken into the two components of perceived sacrifices and perceived lack of

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Table 1.1 Incremental value provided by normative commitment and continuance commitment over affective commitment Zero-order correlations Antecedents

AC

 Age  Gender  Education   Organization tenure   Position tenure   Marital status   Organizational support   Transformational leadership  Role ambiguity  Role conflict   Interactional justice   Distributive justice   Procedural justice  Alternatives  Investments   Transferability of education   Transferability of skills Correlates   Job involvement   Overall job satisfaction   Pay satisfaction   Coworker satisfaction   Promotion satisfaction   Supervision satisfaction Consequences  Turnover   Overall withdrawal cognition   Overall absence        

Overall job performance Self-rated job performance Organizational citizenship Work–family conflict

ΔR2 over AC

Unit-weighted Worse than composite AC Alone?

NC

CC

NC

CC

Multiple R

Yes/No

.15 −.03 −.02 .16 .07 .09 .63 .46 −.39 −.30 .50 .40 .38 −.07 .24 −.04 .17

.12 −.02 .01 .17 .15 .00 .47 .27 −.21 −.24 .52 .31 .31 −.08 .21 −.07 .13

.14 .01 −.11 .21 .15 .04 −.11 −.14 .10 .13 −.16 −.06 −.14 −.21 .01 −.22 −.31

.002 .000 .001 .008 .018 .000 .009 .000 .002 .004 .070 .006 .009 .002 .005 .003 .001

.018 .000 .012 .043 .019 .001 .020 .026 .014 .021 .034 .006 .026 .043 .000 .047 .102

.19 −.02 −.06 .25 .17 .06 .46 .27 −.23 −.19 .40 .30 .25 −.17 .21 −.15 .00

No Yes No No No Yes Yes Yes Yes Yes Yes Yes Yes No Yes No Yes

.53 .65 .35 .45 .38 .42

.40 .31 .19 .16 .18 .13

.03 −.07 .02 −.11 −.04 −.11

.007 .016 .002 .025 .006 .030

.000 .010 .001 .017 .004 .018

.44 .41 .26 .23 .24 .20

Yes Yes Yes Yes Yes Yes

−.17 −.56 −.15

−.16 −.33 .05

−.10 −.18 .06

.005 .000 .000a

.006 .023

−.20 −.49

No Yes

.16 .12 .32 −.20

.06 .07 .24 −.04

−.07 −.05 −.01 .24

.002 .000 .003 .012

.005 .006 .004 .001 .063

−.02 .07 .06 .25 .00

Yes Yes Yes Yes Yes

Note: AC = Affective commitment, NC = Normative commitment, CC = Continuance commitment, a = increment set to zero because correlation is opposite to the expected direction.

alternatives (McGee & Ford, 1987). Some research (e.g., Herrbach, Mignonac, Vandenberghe, & Negrini, 2009; Lapointe & Vandenberghe, 2011) suggests that this approach is valuable but the burden of proof for adding yet another form of organizational commitment is substantial and may not yet have been met. Second, the interactions among commitment types and/or commitment profiles may help explain workplace behavior. The primary theoretical argument for possible interaction

effects has been that the effect of low levels of any one form of commitment on a behavior can be attenuated by higher levels of another commitment (Meyer & Herscovitch, 2001). For example, the main effect of normative commitment on citizenship behaviors might be low overall, but the effect may be stronger for those employees who are low on affective commitment because the effect of low normative commitment may be drowned out by the impact of high affective commitment

ATTITUDES: SATISFACTION, COMMITMENT And INVOLVEMENT

when it is present at the same time. Some studies have found support for these types of interaction effect. For example, Sinclair, Tucker, Cullen, and Wright (2005) found that the combination of low levels of continuance commitment and moderate levels of affective commitment were associated with the poorest job performance ratings. Other promising findings relating to interactions among commitment types and profile effects have been reported by Gellatly, Meyer, and Luchak (2006), Jaros (1997), Johnson, Groff, and Taing (2009), Somers (1995, 2009), and Wasti, (2005), but the reported interaction effects were either very small in size (Jaros; Johnson, Groff, & Taing; Gellatly, Meyer, & Luchak; Somers, 1995), or only observed for some of the hypothesized interaction effects (Jaros; Gellatly et  al.; Somers). Similarly, profile effects that were observed by Sinclair et al., Wasti, and Somers appear to primarily reflect the main effect of affective commitment. Wasti, for example, identifies six clusters of affective, normative, and continuance commitment scores and shows some differences in outcomes such as turnover intentions and citizenship behaviors among these six clusters but these differences among the groups appear to be largely driven by their differences in affective commitment. A third potentially valuable line of inquiry has focused on different foci of commitment – that is commitment to entities other than just the organization. The most popular of these alternative foci is the occupation, and occupational commitment exhibits relatively strong relationships with job satisfaction, organizational commitment, burnout, stress, and occupational turnover intentions (see the meta-analytic review by Lee, Carswell, & Allen, 2000). Other types of commitment that have been presented as distinct from organizational commitment and that exhibit promising relationships with criteria include commitment to the supervisor, work group, and coworkers (e.g., Vandenberghe, Bentein, & Stinglhamber, 2004; Wasti & Can, 2008). The meta-analytic review by Cooper-Hakim and Viswesvaran (2005) however shows that the value added by these different foci of commitment may be limited because of the strong empirical overlap with organizational commitment and the somewhat similar correlational profiles with many antecedents and consequences.

The Relationship among Attitude Constructs Beyond their similarity to common definitions of attitudes job satisfaction, organizational commitment, and job involvement share a very similar position within the nomological network. All

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three are thought to be some function of objective job characteristics and similar individual dispositions and all exhibit largely similar relationships with these antecedents. Similarly, all three are associated with behavioral outcomes such as task performance, contextual behaviors, turnover, turnover intentions, and well-being in an approximately similar manner. Importantly, all three are also positioned as mediators of the relationship between some set of antecedents and some set of consequences (e.g., Brown, 1996; Credé et  al., 2007; Meyer et al., 2002). While these similarities are supportive of this chapter’s description of all three constructs as attitudes it begs the question of how the constructs are related to each other. The position taken by most organizational researchers – and certainly the position taken by many researchers specializing within one of these three research domains – is that the three constructs are distinct from each other at both the theoretical level and the measurement level. This position finds support in a number of factor analytic investigations (e.g., Brooke, Russell, & Price, 1988; Maier & Woschee, 2002; Mathieu & Farr, 1991; Nystedt, Sjöberg, & Hägglund, 1999) that illustrate the distinction among the constructs. The distinction among the three constructs has been further articulated by attempts to specify a causal ordering; most of this work focusing on the temporal relationship between commitment and satisfaction. Some researchers have been agnostic on the question of temporal ordering of commitment and job satisfaction, preferring to simply refer to satisfaction as a ‘correlate’ commitment (e.g., Mathieu & Zajac, 1990; Meyer et al., 2002) or have even suggested that commitment precedes job satisfaction (e.g., Bateman & Strasser, 1984), but the most common position is that satisfaction precedes commitment (e.g., Harrison & Hubbard, 1998; Mathieu, 1991; Yoon & Thye, 2002), or that the two constructs are related to each other in some reciprocal fashion (Farkas & Tetrick, 1989). The involvement literature has typically viewed job satisfaction and commitment as a consequence of involvement, although Brown (1996) acknowledged that there was no empirical reason to support this ordering. If we add other constructs that also reflect employees’ evaluation of an aspect of their job ‘with some degree of favor or disfavor’ (Eagly & Chaiken, 1993, p. 1) then the implied causal ordering becomes even more complex. POS is a theoretical antecedent of both satisfaction and commitment (Rhoades & Eisenberger, 2002), while justice perceptions are positioned as antecedents of POS, commitment, and job satisfaction (Colquitt et al., 2001), and employee engagement is a predictor of satisfaction and commitment but predicted

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by justice perceptions and POS (Saks, 2006). If each of these relationships is accepted – and each individually is typically presented with theoretically sophisticated justifications – then we end up with a long causal chain of attitude-like constructs that unfold in a presumptively rapid fashion to link non-attitudinal antecedents like workplace events, objective job characteristics, and employee dispositions to employee behavior and outcomes. The following sequence seems to be implied: Antecedent Variables → Justice Perceptions → POS → Employee Engagement → Job Involvement → Job Satisfaction → Organizational Commitment → Consequence Variables.

Whether the theoretical gains involved in such a complex chain of attitudinal constructs warrants the very substantial sacrifice of theoretical parsimony is questionable. A second perspective is reflected in work that has noted the conceptual and empirical similarities among the growing number of attitude constructs. Le, Schmidt, Harter, and Lauver (2010), for example, discussed the overlap between organizational commitment and job satisfaction in terms of their strong relationship with each other and with dispositional antecedents and suggested that the two constructs may be redundant with each other. Other researchers have taken a different approach. Newman, Joseph, and Hulin (2010), for example, noted the overlap between employee engagement and constructs like job satisfaction and proposed a general higher-order attitude (‘a’) factor that might explain the high observed covariation among these constructs. Similar theoretical arguments were also made by Newman and Harrison (2008) based on earlier work by Harrison, Newman and Roth (2006), that had highlighted that a higherorder attitudinal construct (comprised of satisfaction and commitment) is more useful than either satisfaction or commitment on its own when ‘… attempting to understand patterns of work behavior from attitudes such as job satisfaction and organizational commitment’ (p. 316). CooperHakim and Viswesvaran (2005) noted the high correlations among commitment types (including involvement) and also suggested that their metaanalytic data is indicative of a single overall factor which they termed work commitment, although their review also found only a modest relationship between involvement and job satisfaction (ρ = .25) based on data from well over 100,000 individuals. Most recently, Webster, Adams, and Beehr (2014) also proposed a higher-order construct comprised of three lower-order constructs: job satisfaction, organizational commitment, and work engagement. Webster et  al. also show that this

higher-order factor explains work outcomes better than any of the constituent parts. It is the position of this chapter that such a higher-order attitudinal factor can probably be expanded to include other lower-order factors, including involvement as well as related constructs such as POS and justice perceptions, and that the value of considering specific lower-order factors or the higher-order factor is likely to depend on the outcome that the attitude is being related to. That is, broad outcomes are likely to be best predicted by broad attitude predictors while narrower outcomes are best predicted by more specific attitudinal constructs.

INTERNATIONAL RESEARCH ON JOB ATTITUDES AND THE ROLE OF NATIONAL DIFFERENCES The interest in job attitudes among North American researchers is shared by their counterparts in most other countries where organizational scientists conduct and publish research. Much of this research is based on data from a single country – much like research on attitudes in North America. Single-country research cannot be used to draw strong inferences about the influence of national characteristics on findings but on their own such single country studies can provide valuable information on the generalizability and possible boundary conditions of job attitude theories and models developed (often) in North America. While single-country studies can help establish generalizability an exploration of exactly why some models, theories, or relationships do not generalize across national boundaries requires a different research strategy that goes beyond simple two country comparisons. Any pair of two countries differ on a host of variables, including culture (Hofstede, 1980; House et al., 2004), values (Schwartz, 1999), economic development, form of government, and even climate (e.g., Huang & Van De Vliert, 2004). As such a finding that employees in one country, say China, have different mean job attitude levels to employees in another country, say USA, cannot safely be attributed to cultural variables – let alone any one cultural variable such as individualism-collectivism. Instead, investigations into the impact of national differences require data from many countries as this allows the possible influences of multiple national difference variables to be examined jointly. Such multi-country datasets can be gathered by one individual or group of researchers as the work by Hofstede (1980) and Project GLOBE (House et al., 2004) illustrate, but a more feasible approach may be for researchers

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to conduct quantitative literature reviews and rely on meta-analytic methods to explore the role of national differences. Numerous publically available datasets describe how countries differ from each other, including (but not limited to): country scores on Hofstede dimensions, Project GLOBE cultural dimension scores, various economic development indexes published by the World Bank, International Monetary Fund, and the UN Human Development Report, and other indicators relating to countries’ educational systems, environment, health, political systems, and level of technological development. Researchers interested in exploring the role of national differences in attitude research could assemble a database of the relevant attitudinal data – effect sizes or mean levels – grouped by country, import the relevant scores on the national difference variables being considered – and then regress the mean observed effect size or average score for each country onto the national difference variable. As a field we are nowhere near a unified theory of how national differences impact job attitude findings but this section of the chapter describes six different ways in which national differences can impact attitude findings, and studies of multicountry comparisons of attitude data are briefly reviewed to highlight these six different influences. First, national differences such as culture can influence the mean level and distribution of theoretical antecedents of job attitudes. For example, Gelade, Dobson, and Gilbert (2006) illustrated that affective commitment was highest in countries with high average levels of extraversion and low levels of neuroticism. Because extraversion and neuroticism have been respectively linked to positive and negative trait affect this finding is supportive of the meta-analytic findings by Thoresen et  al. (2003) that commitment is well predicted by trait dispositions. Many other such effects are plausible: for instance, the degree of autonomy granted to employees may, on average, be lower in countries with high levels of collectivism, high levels of power distance, or relatively worse education and training systems. Similarly, the variability of antecedent variables may be affected. Developed countries in which low wage manufacturing occupations are relatively uncommon may exhibit range restriction on antecedent variables such as task identity thereby reducing the size of the observed relationship between job characteristics and job attitudes. Second, national differences may influence the importance of antecedent variables thereby amplifying or attenuating the relationship between the antecedent and job attitudes more directly. Huang and Van de Vliert (2004), for example, found that the relationship between job level (a proxy for

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job complexity) and job satisfaction was stronger in countries that scored highly on individualism, possibly due to the fact that employees from more individualistic societies have been socialized to develop higher-order needs and to value individual goals such as self-advancement. Rockstuhl, Dulebohn, Ang, and Shore (2012) showed that job satisfaction is more strongly related to leader– member exchange in cultures characterized by horizontal individualism where such relationships should be valued more strongly than in those countries characterized by vertical collectivism although a similar effect was not replicated for either affective commitment or normative commitment. Some researchers have also found that the effect of certain antecedents do generalize across cultures. Fisher (2014), using data from over 6,000 employees from 18 countries showed that the negative impact of role overload on organizational commitment did not vary as a function of national culture. Because of the large number of antecedents of job attitudes that have been identified the number of unexplored moderating effects of culture remains large. For example, employees from countries that score high on power distance might care less strongly about high levels of autonomy than employees from countries with lower levels of power distance. Third, national differences may influence how employees respond to measures of job attitudes. Response biases have been linked to culture (Lai, Cummins, & Lau, 2013) and it might be expected that East Asian employees would tend to describe themselves, including their job attitudes, as less positive than Western employees. National differences may also exist in how much jobs themselves are valued – irrespective of their characteristics. Van Der Westhuizen, Pacheco, and Webber (2012) reported higher levels of job satisfaction for individuals from European countries with more traditional values – where jobs were hypothesized to be valued irrespective of their quality – than for European countries with more secular-rational values. In one of the largest cross-cultural review studies ever undertaken Taras, Kirkman, and Steel (2010) reviewed almost 600 studies that had reported the relationship between Hofstede’s original four cultural dimensions and organizational constructs. Individualism was moderately positively related to job satisfaction (ρ = .21) but negatively related to organizational commitment (ρ = –.27); power distance was moderately positively related to job satisfaction (ρ = .21) and most of the commitment types (e.g., ρ = .39 for normative commitment); uncertainty avoidance was, not surprisingly, most strongly related to continuance commitment (ρ = .42) and more modestly related to other job attitudes (e.g., ρ = .24 for job

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satisfaction); while the relationships with masculinity were only weak. Fourth, national differences may impact the relationship among attitude types. Chen, Ford, Kalyanaram, and Bhagat (2012), for example, present evidence that the relationship between job satisfaction and organizational commitment is higher in countries with low levels of in-group collectivism and low levels of institutional collectivism, possibly because employees in such cultures have a greater tendency to differentiate between the organization and the job rather than viewing the entire job situation as a relatively uniform entity. Fifth, national differences such as culture may moderate the relationship between job attitudes and consequences because attitudes may be relatively less important determinants of behaviors in some settings than in others. In more collectivistic countries for example, the job attitude–job performance relationship should be weaker because employees’ level of effort is likely to be more strongly determined by societal norms than by individual attitudes toward the job – a hypothesis supported by the meta-analytic results reported by Ng, Sorensen, and Yim (2009). Similarly, statelevel data from the USA examined by Carsten and Spector (1987) suggests that employees in countries with high levels of unemployment should be less likely to engage in turnover even for jobs that are deeply dissatisfying thereby reducing the job attitude–turnover relationship in those countries. Finally, national differences may also change the distributions of the consequence variables and thereby attenuate the observed relationships of job attitudes with outcome variables. Countries with little voluntary turnover (because of a poor economy) would see the attitude–turnover relationship attenuated. Distributional differences in criterion variables are not restricted to turnover. One of the cultural dimensions identified by Project GLOBE was humane orientation, defined as the degree to which individuals in a culture are rewarded and encouraged to exhibit humane behavior such as altruism, generosity, fairness, and kindness (Javadin, House, & Dorfman, 2004) and it is reasonable to suspect that humane orientation may change the distribution of both citizenship behaviors and counterproductive behaviors. Finally, the distribution of task performance may also differ systematically between countries. Countries in which more organizations have high performance human resource practices should be expected to have relatively fewer employees with very low levels of job performance – again attenuating the relationship between the criterion (task performance) and job attitudes. Exploring how national differences influence job attitude findings will be challenging. Not only

are researchers required to assemble large, multisample datasets but careful attention will have to be paid to two additional factors. First, data on which such comparisons are made should be relatively current. The job attitude literature is old but it probably makes little sense to compare attitude data from the 1950s in the USA to data gathered in China in 2014 if attempting to draw inferences about the role of national culture. Deciding what constitutes ‘current data’ will require careful judgment. Second, a comparison based on translated inventories requires measurement equivalence. A number of authors (e.g., Byrne & van De Vijver, 2010; Little, 1997; Mullen, 1995), have discussed the precise level of measurement equivalence required to facilitate comparisons and attitude researchers interested in making national comparisons should consider these recommendations. It should also be noted that researchers interested in investigating cultural or national differences in attitudes should be cautious about interpreting the impact of any one cultural dimension or national difference variable. As Fisher (2014) noted, countries do not experience a single cultural dimension such as individualism and collectivism in isolation and it may therefore be more informative to consider the influence of multiple national difference variables jointly.

FUTURE RESEARCH This chapter has already noted a number of issues that future research on job attitudes will likely need to address (e.g., the generalizability of theories of job attitudes across cultures; the possible redundancy in some attitude constructs) but at least three additional areas of exploration are likely to be particularly valuable. First, the measurement of all three constructs is likely to benefit from insights from Item Response Theory (IRT). IRT methods could assist in developing scales that are able to accurately assess job attitudes for all levels of job attitudes and also help in the development of scales that are ideally suited for specific assessment purposes such as accurately making the distinction between the very dissatisfied and the merely dissatisfied. This becomes particularly relevant if one considers that job attitude constructs may be non-linearly related to workplace criteria (e.g., Luchak & Gellatly, 2007; Munyon, Hochwarter, Perrewe, & Ferris, 2010), and that most commonly used scales were developed using classical test theory approaches and are often not particularly good at accurately assessing very high or very low levels of each construct. As a result

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the relationships between job attitudes and criteria may be substantially underestimated and misunderstood, especially when researchers hypothesize that relationships are non-linearly magnified at either very low or very high levels of the job attitude. IRT methods could therefore be used to develop scales that better distinguish the extremely dissatisfied from the merely dissatisfied and, similarly, better distinguish the extremely satisfied from the merely satisfied. Our understanding of within-person variability in job attitudes and the causes and consequences of within-person variability in job attitudes also require much better articulation, notwithstanding the excellent initial work in this domain by the likes of Illies and Judge (2002). The growing literature on AET strongly suggests that many workplace behaviors are driven by momentary states that include job attitudes (Dalal, Bhave, & Fiset, 2014), but our understanding of what drives these momentary states and whether some individuals are more predisposed to exhibit high within-person variability in job attitudes is as yet underdeveloped. At least a third of the total variability in job attitudes appears to be within-person (Illies & Judge, 2002) and future research in this domain should expand our understanding of the causes and consequences of this important component of job attitude variance. This chapter noted earlier the growing view among some researchers that the job attitude literature is either characterized by some level of construct redundancy or that it could be organized into a hierarchical model with a general job attitude factor at the apex. A determination of whether this reorganization of the job attitude construct space is valid will require much additional evidence in addition to the work that has already been presented. First, we need to determine whether the strong correlations among scores on measures of theoretically different attitudes reflect true redundancy or simply poor measurement approaches. Many attitude measures were not developed with quite the same care as is evident for measures used in high stakes settings (e.g., selection) and it is likely to be valuable to bring the same level of technical scrutiny to the measurement of job attitudes. Second, researchers will need to examine the immense literature on the antecedents of discussed job attitudes to determine whether or not these three job attitudes have distinct antecedents because distinct antecedents would be supportive of the distinction among the constructs. Some theoretical antecedents of some job attitudes have already been meta-analytically synthesized but the data is largely incomplete. For example, Judge, Heller, and Mount (2002) reported on the relationship of Big Five personality traits with

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job satisfaction but the relationship of Big Five traits with either commitment or involvement has to the knowledge of this author not been meta-­ analytically synthesized.

CONCLUSION Understanding how employees experience both their jobs and the broader context in which these jobs are located is important, irrespective of whether or not job attitudes are strongly predictive of workplace behaviors. Work is a central part of life; as important to many people as their roles as spouse, partner, or parent. As such job attitudes should be regarded as important outcomes in their own right because they reflect the quality of a central life experience. Industrial and organizational psychologists are therefore correct in having spent so much time and effort in attempting to further our understanding of how job attitudes develop, how they can be changed, and how they relate to various workplace behaviors. This chapter has attempted to summarize our current understanding of the field while also drawing attention to the fact that our collective enthusiasm for furthering our understanding of job attitudes may have resulted in some degree of over-elaboration that might require some pruning of the construct space in order to balance theoretical richness with parsimony. Finally, this chapter has also discussed the manner in which the growing literature from outside of North America can be used to further our understanding of job attitudes and their importance in organizational life.

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Judge, T. A., Bono, J. E., & Locke, E. A. (2000). Personality and job satisfaction: The mediating role of job characteristics. Journal of Applied Psychology, 85, 237–249. Judge, T. A., Heller, D., & Mount, M. K. (2002). Fivefactor model of personality and job satisfaction: A meta-analysis. Journal of Applied Psychology, 87, 530–541. Judge, T. A., Hulin, C. L., & Dalal, R. S. (2012). Job satisfaction and job affect. In S. W. J. Kozlowski (Ed.), The Oxford Handbook of Industrial and Organizational Psychology (pp. 496–525). New York, NY: Oxford University Press. Judge, T. A., Locke, E. A., & Durham, C. C. (1997). The dispositional causes of job satisfaction: A core evaluations approach. Research in Organizational Behavior, 19, 151–188. Judge, T. A., Locke, E. A., Durham, C. C., & Kluger, A. N. (1998). Dispositional effects on job and life satisfaction: The role of core evaluations. Journal of Applied Psychology, 83, 17–34. Judge, T. A., Thoresen, C. J., Bono, J. E., & Patton, G. K. (2001). The job satisfaction–job performance relationship: A qualitative and quantitative review. Psychological Bulletin, 127, 376–407. Kanning, U. P., & Hill, A. (2013). Validation of the Organizational Commitment Questionnaire (OCQ) in six languages. Journal of Business and Media Psychology, 4, 11–20. Kanungo, R. N. (1982). Measurement of job and work involvement. Journal of Applied Psychology, 67, 341–349. Kemp, N. J., & Cook, J. D. (1983). Job longevity and growth need strategy as joint moderators of the task-design-job satisfaction relationship. Human Relations, 36, 883–898. Kinicki, A.J., Mckee-Ryan, F.M., Schriesheim, C.A., & Carson, K.P. (2002). Assessing the construct validity of the job descriptive index: A review and metaanalysis. Journal of Applied Psychology, 87, 14–32. Klein, H. J., Cooper, J. T., Molloy, J. C., & Swanson, J. A. (2014). The assessment of commitment: Advantages of a unidimensional, target-free approach. Journal of Applied Psychology, 99, 222–238. Ko, J. W., Price, J. L., & Mueller, C. W. (1997). Assessment of Meyer and Allen’s three-component model of organizational commitment in South Korea. Journal of Applied Psychology, 82, 961–973. Kunin, T. (1955). The construction of a new type of attitude measure. Personnel Psychology, 8, 65–77. Lai, L. C. H., Cummins, R. A., & Lau, A. L. D. (2013). Cross-cultural differences in subjective well-being: Cultural response bias as an explanation. Social Indicators Research, 114, 607–619. Lapointe, E., & Vandenberghe, C. (2011). Organizational commitment, organization-based

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self-esteem, emotional exhaustion, and turnover: A conservation of resources perspective. Human Relations, 64, 1609–1631. Lapointe, E., Vandenberghe, C., & Boudrias, J.S. (2013). Psychological contract breach, affective commitment to organization and supervisor, and newcomer adjustment: A three-wave moderated mediation model. Journal of Vocational Behavior, 83, 528–538. Law, K. S., Wong, C. S., Mobley, W. H. (1998). Toward a taxonomy of multidimensional constructs. Academy of Management Review, 23, 741–755. Le, H., Schmidt, F. L., Harter, J. K., & Lauver, K. J. (2010). The problem of empirical redundancy of constructs in organizational research: An empirical investigation. Organizational Behavior and Human Decision Processes, 112, 112–125. Lee, K. Carswell, J. J., & Allen, N. J. (2000). A metaanalytic review of occupational commitment: Relations with person- and work-related variables. Journal of Applied Psychology, 85, 799–811. Lewis, H. B. (1944). An experimental study of the role of the ego in work, I. The role of the ego in cooperative work. Journal of Experimental Psychology, 34, 113–126. Liden, R. C., Wayne, S. J., & Sparrowe, R. T. (2000). An examination of the mediating role of psychological empowerment on the relations between the job, interpersonal relationships, and work outcomes. Journal of Applied Psychology, 85, 407–416. Little, T. D. (1997). Mean and covariance structures (MACS) analyses of cross-cultural data: Practical and theoretical issues. Multivariate Behavioral Research, 32, 53–76. Locke, E. A. (1976). The nature and causes of job satisfaction. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 1297–1343). Chicago: Rand McNally. Lodahl, T., & Kejner, M. (1965). The definition and measurement of job involvement. Journal of Applied Psychology, 49, 24–33. Loher, B. T., Noe, R. A., Moeller, N. L., & Fitzgerald, M. P. (1985). A meta-analysis of the relation of job characteristics to job satisfaction. Journal of Applied Psychology, 70, 280–289. Luchak, A. A., & Gellatly, I. R. (2007). A comparison of linear and nonlinear relations between organizational commitment and work outcomes. Journal of Applied Psychology, 92, 786–793. Macey, W. H., & Schneider, B. (2008). The meaning of employee engagement. Industrial and Organizational Psychology: Perspectives on Science and Practice, 1, 3–30. Maier, G. M., & Woschee, R.-M. (2002). Die affektive Bindung an das Unternehmen. Zeitschrift für Arbeits- und Organisationspsychologie, 46, 126–136.

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Mathieu, J. E. (1991). A cross-level nonrecursive model of the antecedents of organizational commitment and satisfaction. Journal of Applied Psychology, 76, 607–618. Mathieu, J. E., & Farr, J. L. (1991). Further evidence for the discriminant validity of measures of organizational commitment, job involvement, and job satisfaction. Journal of Applied Psychology, 76, 127–133. Mathieu, J. E., & Zajac, D. M. (1990). A review and meta-analysis of the antecedents, correlates, and consequences of organizational commitment. Psychological Bulletin, 108, 171–194. McCabe, D. J., Dalessio, A., Briga, J., & Sasaki, J. (1980). The convergent and discriminant validities between the IOR and the JDI: English and Spanish forms. Academy of Management Journal, 23, 778–786. McGee, G. W., & Ford, R. C. (1987). Two (or more?) dimensions of organizational commitment: Reexamination of the Affective and Continuance Commitment Scales. Journal of Applied Psychology, 72, 638–642. Meyer, J. P., & Herscovitch, L. (2001). Commitment in the workplace toward a general model. Human Resource Management Review, 11, 299–326. Meyer J. P., Allen N. J., & Smith, C. (1993). Commitment to organizations and occupations: Extension and test of a three-component conceptualization. Journal of Applied Psychology, 78, 538–551. Meyer, J. P., Stanley, D. J., Herscovitch, L., & Topolnytsky, L. (2002). Affective, continuance, and normative commitment to the organization: A meta-analysis of antecedents, correlates, and consequences. Journal of Vocational Behavior, 61, 20–52. Mignonac, K., & Herrbach, O. (2004). Linking work events, affective states, and attitudes: An empirical study of manager’s emotions. Journal of Business and Psychology, 19, 221–240. Miller, H. E., Katerberg, R., & Hulin, C. L. (1979). Evaluation of the Mobley, Horner, and Hollingsworth model of employee turnover. Journal of Applied Psychology, 64, 509–517. Miner, A. G., Glomb, T. M., & Hulin, C. (2005). Experience sampling mood and its correlates at work. Journal of Occupational and Organizational Psychology, 78, 171–193. Morrow, P. (1983). Concept redundancy in organizational research: The case of work commitment. Academy of Management Review, 8, 465–500 Motowidlo, S. J. (1996). Orientation toward the job and organization. In K. R. Murphy (Ed.), Individual differences and behavior in organizations (pp. 175–208). San Francisco, CA: Jossey-Bass. Mowday, R. T., Porter, L. W., & Steers, R. (1982). Organizational linkages: The psychology of

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commitment. Journal of Vocational Behavior, 65, 157–177. Probst, T. M. (2003). Development and validation of the Job Security Index and the Job Security Satisfaction scale: A classical test theory and IRT approach. Journal of Occupational and Organizational Psychology, 76, 451–467. Rhoades, L., & Eisenberger, R. (2002). Perceived organizational support: A review of the literature. Journal of Applied Psychology, 87, 698–714. Rice, R. W., Gentile, D. A., & McFarlin, D. B. (1991). Facet importance and job satisfaction. Journal of Applied Psychology, 76, 31–39. Rice, R. W., Markus, K., Moyer, R. P., & McFarlin, D.B. (1991). Facet importance and job satisfaction: Two experimental tests of Locke’s Range of Affect Theory. Journal of Applied Social Psychology, 21, 1977–1987. Rockstuhl, T., Dulebohn, J. H., Ang, S., & Shore, L. M. (2012). Leader–member exchange (LMX) and culture: A meta-analysis of correlates of LMX across 23 countries. Journal of Applied Psychology, 97, 1097–1130. Rodriguez-Munoz, A., Baillien, E., De Witte, H., Moreno-Jiminez, B., & Pastor, J. C. (2009). Crosslagged relationships between workplace bullying, job satisfaction, and engagement: Two longitudinal studies. Work and Stress: An International Journal of Work, Health, and Organizations, 23, 225–243. Russell, S. R., Spitzmuller, C., Lin, L. F., Stanton, J. M., Smith, P. C., & Ironson, G. H. (2004). Shorter can also be better: The abridged Job in General Measure. Educational and Psychological Measurement, 64, 878–893. Saks, A. M. (2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21, 600–619. Salancik, G. R., & Pfeffer, J. (1978). A social information processing approach to job attitudes and task design. Administrative Sciences Quarterly, 23, 224–253. Sandberg, J. G., Harper, J. M., Hill, E. J., Miller, R. B., Yorgason, J. B., & Day, R. D. (2013). ‘What happens at home does not necessarily stay at home’: The relationship of observed negative couple interaction with physical health, mental health, and work satisfaction. Journal of Marriage and Family, 75, 808–821. Schwartz, S. H. (1999). A theory of cultural values and some implications for work. Applied Psychology: An International Review, 48, 23–47. Shaver, N., Schwartz, J., Kirson, D., & O’Connor, C. (1987). Emotion knowledge: Further exploration of a prototype approach. Journal of Personality and Social Psychology, 45, 513–523. Sims, C. S., Drasgow, F., & Fitzgerald, L. F. (2005). The effects of sexual harassment on turnover in the

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2 Employee Self-Concept and Identity Russell E. Johnson, Chu-Hsiang (Daisy) Chang, Yo u J i n K i m a n d S z u - H a n ( J o a n n a ) L i n

INTRODUCTION The self-concept has emerged as a key construct for understanding employee affect, cognition, and behavior (Johnson & Saboe, 2011; Lord & Brown, 2004; Schaubroeck, Kim, & Peng, 2012). Broadly speaking, the self-concept refers to all knowledge that a person holds about her or his self. It is the ‘totality of an individual’s thoughts and feelings having reference to himself as an object’ (Rosenberg, 1979, p. 7) that organizes and gives meaning to memory and behavior (Kihlstrom & Klein, 1994). Self-referenced knowledge is important to consider because such knowledge is highly accessible in memory and intertwined with other information (Lord & Brown, 2004). The ways in which employees perceive and evaluate themselves also shape how they interpret and respond to the work environment (e.g., Chang, Ferris, Johnson, Rosen, & Tan, 2012; Pierce & Gardner, 2004). The knowledge that comprises the self-concept is multifaceted and can be parsed into different categories (Markus & Wurf, 1987). One general category includes self-evaluations, which encompasses emotion-laden appraisals of one’s overall worth as an employee and of her or his workrelated abilities (Chen, Gully, & Eden, 2004; Pierce & Gardner, 2004). A second category

includes self-construals, which reflect the different ways that people define themselves in relation to others (Brewer & Gardner, 1996; Lord & Brown, 2004). In work settings, employees can see themselves as unique and independent from others, as partners in dyadic relationships with specific persons (e.g., a supervisor or coworker), or as members of larger collectives (e.g., work teams and companies; Johnson & Saboe, 2011). In addition to self-evaluations and self-construals, a third category includes structural and metacognitive aspects of the self-concept. For example, the self-concept can vary in complexity, or the number of distinct self-aspects (e.g., identities, social roles and goals) that comprise people’s self-concepts (Linville, 1985). Self-aspects also vary in how well-understood they are (self-concept clarity; Campbell, 1990), how dissimilar they are from one another (self-concept differentiation; Donahue, Robins, Roberts, & John, 1993), and how malleable they are (self-concept stability; Markus & Kunda, 1986). There is, however, little organizational research pertaining to the role and function of these structural and metacognitive qualities of employees’ self-concepts (Schaubroeck et  al., 2012) and we view them as more peripheral vis-à-vis the actual content of

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employees’ self-concepts. We therefore limit our review to self-evaluations and self-construals. Compared to other individual differences like cognitive ability and personality, self-evaluations and self-construals are more dynamic and heavily influenced by interpersonal interactions and social structures (Lord & Brown, 2004; van Knippenberg, van Knippenberg, De Cremer, & Hogg, 2004). For example, abusive and unfair treatment from a work supervisor harms employees’ evaluations of self-worth and their psychological ties to others (Johnson & Lord, 2010; Tepper, 2000). Self-evaluations and self-construals are fundamental motivation-based variables that are more proximal to behavior than dispositions, and ones that mediate the effects of dispositions on behavior (Bowling, Eschleman, Wang, Kirkendall, & Alarcon, 2010; Lanaj, Chang, & Johnson, 2012). We therefore review person- and situationbased antecedents of self-concept in addition to its work-based consequences. Given the number of existing reviews that cover self-evaluations and self-construals (e.g., Chang et  al., 2012; Chen, Gully, & Eden, 2004; Lord & Brown, 2004; Pierce & Gardner, 2004; Schaubroeck et  al., 2012), we pay special attention to the most recent research concerning them. Besides reviewing the antecedents and consequences of self-concept, we have three additional aims. One aim is to synthesize the voluminous research on self-concept variables. As Schaubroeck et al. (2012, p. 2) noted, ‘Despite [the self-concept’s] importance to behavior in organizations, research is rather scattered and it is often unclear how work with one concept (e.g., self-efficacy) relates to another (e.g., selfesteem)’. Although some research exists exploring the linkages that exist among self-evaluations (e.g., Chen, Goddard, & Casper, 2004) and among self-construals (e.g., Brickson, 2000), there is little cross-pollination across these two sets. To redress this limitation, we present a model that integrates self-evaluations and self-construals within a self-concept nexus that connects relevant person- and situation-factors with employee behaviors. A second aim is to highlight areas where future research on employee self-concept is needed. As mentioned above, there is considerable work on self-evaluations and self-construals in isolation, but disproportionately less attention has been paid to the interplay between the two. Consistent with the notion that the self-concept houses numerous distinct selves (Markus & Wurf, 1987), selfevaluations of worth and efficacy likely differ depending on what self is under current consideration (e.g., the self as a company member vs. a supervisor). An important direction for future research is to move beyond personal-referenced

self-evaluations by paying greater attention to dyad- and group-referenced self-evaluations. Our third aim is to tailor our review specifically to theoretical and empirical developments in organizational psychology. Although research has examined the self-concept in non-work contexts, work settings present a unique blend of achievement and interpersonal domains. There are also phenomena specific to work settings that are particularly relevant for the self-concept, such as economic and social exchanges (Flynn, 2005), leadership (Lord & Brown, 2004), and fairness (Johnson & Lord, 2010). Our coverage of selfevaluations and self-construals therefore targets research on employee self-esteem, self-efficacy, and identity.

DECONSTRUCTING THE SELF-CONCEPT: SELF-EVALUATIONS AND SELF-CONSTRUALS As mentioned above, the self-concept encompasses all knowledge that people have about the self. Included within this knowledge are emotionbased appraisals of self-worth (self-evaluations) and self-definitions that delineate people’s psychological ties to others (self-construals). Selfevaluations are the evaluative component of the self-concept in which the self is the target of assessment. Although the self can be judged across a myriad of facets (e.g., specific skills, physical attributes, etc.), the most fundamental and broad self-evaluations are self-esteem and generalized self-efficacy because they capture global assessments of one’s self and capabilities (Chang et al., 2012; Judge, Locke, & Durham, 1997).

SELF-EVALUATIONS WITHIN ORGANIZATIONAL CONTEXTS Self-Esteem Significant time and energy have been devoted to the study of global evaluations of people’s own worth and value (Schaubroeck et al., 2012). Selfesteem is acknowledged as a positive contributor to psychological adaptation and well-being (Leary & Tangney, 2003). For example, high self-esteem is a predictor of mental health (Furnham & Cheng 2000; Shackelford, 2001), and buffers people from depression (Baumeister, Campbell, Krueger,

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& Vohs, 2003). Rosenberg, Schooler, Schoenbach, and Rosenberg (1995) distinguished between general self-esteem and specific self-esteem, finding that global self-esteem is more relevant for psychological well-being whereas specific selfesteem is more relevant for behavior in specific domains. Given this fact, we focus our attention on employee self-esteem at work, or what is often referred to as organization-based self-esteem (OBSE; Pierce & Gardner, 2004). OBSE reflects the self-perceived value that individuals have of themselves as organizational members and the degree to which they feel efficacious in fulfilling their various organizational roles (Pierce, Gardner, Cummings, & Dunham, 1989). High OBSE is associated with appraising the self as capable, significant, and worthy. However, this begs the question of where these evaluations come from. Early in one’s tenure with an organization, self-evaluations are malleable and subject to change following events that speak to one’s organizational value (e.g., successful project completion, reprimand from an authority). As these experiences accumulate, self-evaluations stabilize and become resistant to change, short of major external events that ‘shock’ employees’ existing perceptions of themselves as organizational members (Pierce & Gardner, 2004).

Antecedents of OBSE

Much of the initial research on antecedents considered general self-evaluations as predictors of work-specific self-evaluations (Bowling et  al., 2010). It is well-established that global selfesteem predicts OBSE, a finding that has replicated across a variety of cultures (e.g., Chen, Goddard, & Casper, 2004; Vecchio, 2000). OBSE is also shaped by other self-evaluations, such as self-efficacy (e.g., Gardner & Pierce, 2001; Kark, Shamir, & Chen, 2003). Recently, core self-evaluation, a higher-order construct comprised of selfesteem, generalized self-efficacy, internal locus of control, and emotional stability (Chang et  al., 2012; Judge et al, 1997), has been linked to OBSE (Pierce & Gardner, 2009). These findings are consistent with distal–proximal frameworks in which general dispositions about the self (e.g., global self-esteem) spill over to shape more contextualized and specific self-evaluations (e.g., OBSE; Bowling et al., 2010; Lanaj et al., 2012). Other non-evaluative traits have been identified as predictors of OBSE. Employees with general propensities to experience distress and other negative emotions (e.g., those high in negative affectivity and neuroticism) tend to report lower OBSE (Heck, Bedeian, & Day, 2005; Royle, 2010), whereas employees with tendencies to seek out

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and experience positive emotions (e.g., those high in positive affectivity and extraversion) experience higher OBSE (Lee, 2003; Royle, 2010). OBSE is also positively related to internal locus of control, Protestant work ethic, and need for achievement (e.g., Tang & Ibrahim, 1998), whereas it is negatively related to Machiavellianism (e.g., Vecchio, 2000). In essence, individual difference variables that help cultivate successful performance (e.g., Protestant work ethic) or interpersonal (e.g., extraversion) episodes at work increase OBSE, whereas variables that elicit negative emotion, distract employees’ from their work tasks, or create social friction harm OBSE. Structural and environmental variables also impact OBSE. Ragins, Cotton, and Miller (2000) and Chattopadhyay (2003) both reported a negative relation of organization size with OBSE. This suggests that employees may develop negative self-evaluations if they do not experience sufficient individualized consideration and recognition at work, which is especially likely in large organizations. In addition to size, whether an organization has a loose, adaptive structure versus a rigid, bureaucratic structure also has implications for OBSE. Two studies (Pierce et  al., 1989; Tan & Peng, 1997) reported positive relations of organic structures with OBSE. One reason why OBSE may fare better in organic environments is because employees have a stronger sense of ownership and involvement in the work. In line with this idea, employee participation and participatory management practices are positively related to OBSE (Elloy, 2005; Lee, 2003). More recently, workgroup composition (i.e., the proportion of standard and temporary workers) and job mobility were found to predict OBSE (George, Chattopadhyay, & Zhang, 2012). The nature of job tasks is another contextual variable that impacts OBSE. Based on the job characteristics model (Hackman & Oldham, 1976), enriched jobs are a key ingredient for employees’ self-evaluations. For example, OBSE is enhanced by job complexity, an aggregate construct comprised of high levels of task identity, significance, and feedback (e.g., Bowling et  al., 2010). Other job characteristics like skill variety and autonomy also contribute to OBSE (Bowling et  al., 2010; Pierce & Gardner, 2009), and these effects have replicated across various cultures (e.g., Lee, 2003). Perhaps the most important sources of OBSE are organizational treatment and interpersonal interactions. According to Leary and colleagues’ (1995) sociometer hypothesis, self-esteem functions as a gauge indicating one’s relational value (i.e., the quality of social ties to others). Increases or decreases in one’s perceived social and relational

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worth bring about concomitant increases and decreases in self-esteem. Organizational variables like the amount of trust, support, pay, and discrimination that employees experience color their perceived value. Empirical evidence consistently reveals that the respect, positive regard, trust, and support from upper management enhance employees’ OBSE (Chattopadhyay & George, 2001; McAllister & Bigley, 2002; Penhaligon, Louis, & Restubog, 2013). The quality of relations with one’s supervisor is a key source of relational value at work, given that supervisors are the embodiment of the organization for many employees (Eisenberger et al., 2010). The quality of leader–follower relations is positively related to employee OBSE (Bowling et  al., 2010; Heck et  al., 2005; Kark & Shamir, 2002; Kark et  al., 2003). A tangible way of showing support is by rewarding employees. To this end, several studies have also established a positive relation of pay level with OBSE (e.g., D. G. Gardner, Van Dyne, & Pierce, 2004; W. L. Gardner, Pickett, & Brewer, 2000). High pay alone, however, is not enough – employees must believe that the compensation and rewards they receive are based on merit (Scott, Shaw, & Duffy, 2008). For example, De Cremer, van Knippenberg, van Knippenberg, Mullenders, and Stinglhamber (2005) found that leader reward behavior enhanced employee OBSE only if the reward process was handled in a fair manner. Fair outcomes, procedures, and treatment have meaningful effects on OBSE. One reason employees care about fairness is because it conveys information about their social and relational value in the eyes of others (Johnson & Lord, 2010; Tyler & Blader, 2003). Employees therefore use fairnessrelated information to infer how trusted and supported they are (Lind, 2001), which impacts their OBSE (McAllister & Bigley, 2002). Empirical evidence is clear on this point, verifying that outcome, procedural, and interaction fairness are all positively related to OBSE (Cohen-Charash & Spector, 2001; Heck et al., 2005).

Consequences of OBSE

Two dominant theoretical frameworks account for the effects of OBSE in predicting employee outcomes. The first – self-enhancement theory (Tesser, 1988) – suggests that people are motivated to maintain positive self-evaluations. The second – self-verification theory (Swann, 1983) – posits that people are motivated to confirm their current self-evaluations rather than enhance them. According to self-verification theory, people minimize uncertainty about the self by creating, seeking, and endorsing information that is consistent

with self-evaluations and by rejecting inconsistent information. Both self-enhancement and self-verification theories predict that high-OBSE employees will exhibit positive work attitudes and high amounts of effort to in-role and extra-role work behaviors, consistent with their favorable selfviews (Bowling et  al., 2010; Pierce & Gardner, 2004). The two theories, however, deviate in their predictions regarding low OBSE. According to self-enhancement theory, individuals with low OBSE will exert greater effort in order to improve their relational and social value (and thus their OBSE). In contrast, self-verification theory predicts that low-OBSE employees prefer situations and interaction partners who impart negative evaluations consistent with their unfavorable selfviews (e.g., Swann & Pelham, 2002; Swann, Johnson, & Bosson, 2009). Such employees withhold effort and self-handicap so as to function at a low level that parallels their unfavorable selfevaluations (Sekiguchi, Burton, & Sablynski, 2008). The evidence to date appears consistent with this latter view. One way that employees verify their self-views to others is via the job behaviors they exhibit. OBSE is positively related to in-role task behaviors (Bowling et al., 2010; Chen, Aryee, & Lee, 2005; Pierce & Gardner, 2004), and has stronger relations with task performance than other self-based evaluations and perceptions (e.g., generalized selfefficacy; Judge & Bono, 2001). High OBSE is also associated with more frequent displays of exemplary work behaviors like citizenship, voice, ethical, and adaptive behaviors (Bowling et al., 2010; Chen et  al., 2005; Liang, Farh, & Farh, 2012; Pierce & Gardner, 2004), and fewer displays of counterproductive behaviors like deviance (e.g., production disruptions, theft) and withdrawal (e.g., absenteeism, turnover; Bowling et al., 2010; Ferris, Brown, & Heller, 2009). Moreover, several studies have found that OBSE mediates relations of various work experiences (e.g., perceived support, fairness, and pay) with employee behaviors (e.g., in-role task behavior, extra-role citizenship, and counterproductive behavior; Bowling et  al., 2010; Ferris et  al., 2009; Heck et  al., 2005; Lee & Peccei, 2007; Liu, Hui, Lee, & Chen, 2013; Pierce & Gardner, 2004; Riordan, Weatherly, Vandenberg, & Self, 2001). OBSE also mediates relations of leader-related perceptions and behaviors with work behaviors (e.g., Chan, Huang, Snape, & Lam, 2013; Jian, Kwan, Qiu, Liu, & Yim, 2012; van Knippenberg et al., 2004). Interestingly, OBSE may in fact have curvilinear relations with the aforementioned work behaviors, given emerging research on employee narcissism and arrogance. Common to both is an inflated evaluation of the self, to the extent

Employee Self-Concept and Identity

that the self is seen as omnipotent (Campbell, Hoffman, Campbell, & Marchisio, 2011; Silverman, Johnson, McConnell, & Carr, 2012). At this extremely high level, relations of selfevaluations with work behaviors reverse directions in that productive behaviors (e.g., task and citizenship) decrease while destructive behaviors (e.g., deviant and withdrawal) escalate (Johnson, Silverman, Shyamsunder, Swee, Rodopman, Cho, & Bauer, 2010; Judge, LePine, & Rich, 2006; O’Boyle, Forsyth, Banks, & McDaniel, 2012). Aggressive interpersonal behaviors are especially likely because any perceived slight against the self is magnified when people hold aggrandized selfevaluations (Bushman & Baumeister, 1998). OBSE also has ties to attitudinal and health outcomes. Employees with high (vs. low) OBSE are more likely to hold positive attitudes about their jobs and organizations (Bowling et  al., 2010; Sekiguchi et  al., 2008). For example, OBSE is positively related to job satisfaction, organizational commitment, and job involvement (Bowling et al., 2010; Judge & Bono, 2001; Pierce & Gardner, 2004). Positive self-evaluations also buffer employees from experiencing physical and psychological strains at work, including illness, anxiety, and depression (Bowling et  al., 2010; Chang et al., 2012). In fact, OBSE mediates negative relations of beneficial situational characteristics like support and autonomy with burnout symptoms (e.g., Kang, Twigg, & Hertzman, 2010; Naus, van Iterson, & Roe, 2007). These beneficial effects manifest in part because high-OBSE employees self-select into interesting jobs and perceive their work circumstances in a positive light (Judge et al., 1997; Sekiguchi et al., 2008).

OBSE as a moderator

Many studies that examine OBSE as a moderator rely on the behavioral plasticity hypothesis (Brockner, 1988), which posits that individuals with low (vs. high) OBSE are swayed more by external cues. Low OBSE employees, who lack confidence, are more dependent on information from outside sources when determining their own thoughts and actions, whereas high OBSE employees are less reactive to external cues. If so, then relations of the work environment with outcomes should be stronger when OBSE is low (vs. high). Empirical findings are largely supportive of the behavioral plasticity hypothesis. For example, Pierce, Gardner, Dunham, and Cummings (1993) found that relations of role stressors (role ambiguity, conflict, and overload) with job satisfaction and performance were stronger when OBSE was low (vs. high). Jex and Elacqua (1999) observed a similar pattern, finding that role ambiguity

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has stronger relations with depression and physical symptoms when OBSE is low (vs. high). The effects of interpersonal stressors are also magnified when OBSE is low (e.g., Kiazad, Restubog, Zagenczyk, Kiewitz, & Tang, 2010). One implication of the behavioral plasticity hypothesis is that employees with low OBSE may have more to gain from favorable organizational experiences. For example, the positive relations of task-based challenges and job transitions with managerial development are stronger when OBSE is low (vs. high; Brutus, Ruderman, Ohlott, & McCauley, 2000). There are, however, examples that run counter to the behavioral plasticity hypothesis (e.g., Hui & Lee, 2000; Lapointe, Vandenberghe, & Panaccio, 2011). Moderator effects have also been interpreted through the lens of self-verification theory (Swann, 1983), such that employees with negative (vs. positive) self-evaluations will react less favorably to positive external cues. For example, Wiesenfeld, Swann, Brockner, and Bartel (2007) found that procedurally fair treatment resulted in lower commitment, greater absenteeism, and fewer citizenship behaviors for employees with low (vs. high) OBSE. Although procedural justice is thought to be desirable because it communicates high relational value, it actually turns off employees who hold negative evaluations about themselves.

Generalized Self-Efficacy Although self-efficacy was originally conceptualized by Bandura (1991) as being task-specific, it has also been examined as a global evaluation of the likelihood of a person’s successful performance across a variety of achievement situations (Gardner & Pierce, 1998). Defining self-efficacy as a global evaluation also blurs its uniqueness vis-à-vis self-esteem. Correlations between generalized self-efficacy and self-esteem exceed .80 (Bowling et  al., 2010; Johnson, Rosen, & Djurdjevic, 2011; Judge, Erez, Bono, & Thoresen, 2002). As noted earlier, generalized self-efficacy is a proximal predictor of OBSE because employees who are successful across a multitude of work activities experience high OBSE as a result (Gardner & Pierce, 1998, 2001). Thus, many of the findings we reviewed concerning OBSE also extend to generalized self-efficacy.

Antecedents of generalized self-efficacy

Generalized self-efficacy is more motivational than OBSE, thus motivational traits are expected to predict generalized self-efficacy (Chen, Gully, & Eden, 2001, 2004; Chen, Gully, Whiteman, &

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Kilcullen, 2000). Indeed, research supports that motivational traits like learning goal orientation, need for achievement, conscientiousness are related to generalized self-efficacy (Bartels, 2007; Carless & Arnup, 2011; Chen et al., 2000; van der Velde & van den Berg, 2003; Yeung, Craven, & Kaur, 2012). A recent meta-analysis by Payne, Youngcourt, and Beaubien (2007) verified that generalized self-efficacy is positively (negatively) related to learning (performance-avoid) goal orientation. In addition to motivational traits, research has explored situational predictors of generalized selfefficacy, especially situational characteristics that cultivate mastery experiences. For example, work autonomy is positively related to generalized selfefficacy (van den Berg & van der Velde, 2005), presumably because autonomous environments encourage employees to explore and identify the best ways to structure their work. Generalized self-efficacy is also bolstered by job complexity (Park, Beehr, Han, & Grebner, 2012), but it suffers when organizational demands exceed employees’ perceived ability to meet them (Park et al., 2012). Transformational leader behaviors, such as setting inspirational goals and emphasizing followers’ high potential, also increase generalized selfefficacy (Nielsen & Munir, 2009). Interestingly, research has been mixed in terms of linking interaction quality with generalized self-efficacy. While some studies observed beneficial effects of positive interactions (e.g., social support: SteeleJohnson, Narayan, Delgado, & Cole, 2010; high interactional fairness: Beauregard, 2012) on generalized self-efficacy, other studies concluded that such experiences have weak or no impact on efficacy beliefs (e.g., van den Berg & van der Velde, 2005).

study on newcomer adjustment, Saks and Ashforth (2000) found that generalized self-efficacy of newly employed workers was positively related to subsequent task performance. In terms of extrarole behaviors, Beauregard (2012) reported a positive relationship of generalized self-efficacy with helping behaviors and loyalty to the organization. Relatedly, Chen, Gully, and Eden (2004) reported positive relations of generalized self-efficacy with behavioral intentions to engage in various citizenship behaviors. Research has also explored the effects of generalized self-efficacy on indicators of employee adaptation. For example, van der Velde and van den Berg (2003) showed that generalized self-efficacy mediated the positive relationship between autonomy and employees’ ability to flexibly apply their knowledge and expertise. Generalized self-efficacy is also related to cultural intelligence and employees’ cross-cultural adjustment and well-being (MacNab & Worthley, 2012; Wu & Bodigerel-Koehler, 2013). Finally, studies have reported benefits of generalized self-efficacy for well-being (Luszczynska, Scholz, & Schwarzer, 2005). High generalized selfefficacy reduces burnout symptoms (e.g., emotional exhaustion, depersonalization) and emotional strains (e.g., anxiety, depression; Park et al., 2012; Schwarzer & Hallum, 2008; Volker et al., 2010). Moreover, generalized self-efficacy mediates the relations of situational (e.g., transformational leadership: Nielsen & Munir, 2009) and individual characteristics (e.g., conscientiousness and openness: Strobel, Tumasjan, & Spörrle, 2011) with well-being indicators (e.g., life satisfaction and happiness). Generalized self-efficacy is also linked to health-promoting behaviors like active coping (e.g., Volker et  al., 2010) and exercising and healthy eating (Luszczynska et al., 2005).

Consequences of generalized self-efficacy

Generalized self-efficacy as a moderator

Similar to OBSE, generalized self-efficacy has been linked to various attitudinal, behavioral, and well-being outcomes. In terms of attitudes, the research is rather limited and sometimes contradictory. While some studies have reported positive relations of generalized self-efficacy with job satisfaction (e.g., Boyar & Mosley, 2007; Carless & Arnup, 2011) and organizational commitment (e.g., van der Velde & van den Berg, 2003), others have reported negative or null relations among these variables (e.g., Saks & Ashforth, 2000; Volker et al., 2010). Research linking generalized self-efficacy to work behaviors has largely focused on task performance, extra-role behaviors, and adaptive behaviors in response to situational changes. In their

Studies find that generalized self-efficacy buffers the detrimental effects of demanding situational characteristics, such as hindrance stressors (Cavanaugh, Boswell, Roehling, & Boudreau, 2000). For example, Siu, Lu, and Spector (2007) found that role conflict and interpersonal conflict had weaker, negative relations with mental wellbeing for employees with high (vs. low) generalized self-efficacy. Similarly, Park et  al. (2012) showed that high generalized self-efficacy attenuated the positive relation of demands–abilities misfit with employee depressive symptoms. Interestingly, Saks and Ashforth (2000) found that role overload had a stronger positive relation with organizational identification for newcomers with high (vs. low) generalized self-efficacy, possibly

Employee Self-Concept and Identity

because workload was viewed as a challenge stressor as opposed to a hindrance stressor (Eatough, Chang, Miloslavic, & Johnson, 2011). When viewed as a challenge stressor, workload feeds employees’ sense of accomplishment and motivation (LePine, Podsakoff, & LePine, 2005). Generalized self-efficacy also moderates the effectiveness of organizational interventions, such that employees with low initial generalized selfefficacy are more likely to benefit from interventions. For example, DiRenzo, Linnehan, Shao, and Rosenberg (2010) found that protégés with low (vs. high) initial generalized self-efficacy benefitted more from a mentoring program designed to improve their ability to gain employment and reach financial independence. Similarly, Eden and Aviram (1993) found that training had a stronger effect on job search intensity and reemployment for participants who reported low (vs. high) initial generalized self-efficacy. The studies reviewed thus far suggest that employees with low (vs. high) generalized self-efficacy are more susceptible to the influence of external events and interventions, however other studies have concluded the opposite. For example, Raub and Liao (2012) found that initiative climate at the unit level has a stronger, positive association with proactive customer service performance for employees with high (vs. low) generalized self-efficacy. Similarly, socialization experiences (e.g., acquiring information concerning organizational practices) produce greater satisfaction in employees with high (vs. low) generalized self-efficacy (Song & Chathoth, 2012). The seemingly contradictory sets of moderator results may be due to differences in foci. Studies that find greater environmental responsiveness when generalized self-efficacy is high focus on sensemaking processes. Employees need to gather information from their environment in order to determine the appropriate reactions. In these relatively ambiguous situations, employees with high (vs. low) generalized self-efficacy appear to be more responsive to environmental influences. In contrast, studies that find greater environmental responsiveness is associated with low generalized self-efficacy tend to focus on clear situations that require employees to reflect internally for responses. According to the transactional model of stress (Lazarus & Folkman, 1984), reactions to stressors are determined by evaluating (a) whether or not the situation is threatening to the self and (b) whether or not one has sufficient resources to cope with the threat. Both appraisals involve self-reflective processes rather than information seeking from the environment. Employees with high generalized self-efficacy are likely to have more positive appraisals in terms of their ability to successfully respond to the stressors and as such, they are less influenced by situational characteristics.

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SELF-CONSTRUALS WITHIN ORGANIZATIONAL CONTEXTS In addition to global self-evaluations of worth and capabilities, the self-concept also encompasses the different ways that employees define themselves in relation to others (Johnson, Chang, & Yang, 2010; Lord & Brown, 2004). In general, there are three levels of self-definition (Brewer & Gardner, 1996). At the individual level, employees see themselves as unique and separate from others, and their focus is on individuating characteristics (e.g., traits, skills) that distinguish them from others. Self-worth is derived from being judged different (and typically better) than one’s work colleagues, and outcomes such as pay, career development opportunities, recognition, and power are especially important (Johnson, Selenta, & Lord, 2006). At the relational level, employees define themselves in terms of their role relationships with specific individuals (e.g., leader–subordinate; Anderson & Chen, 2002). Rather than individuating personal characteristics, the focus is on interpersonal norms and the role expectations of their dyad partners. Self-worth derives from favorable reflected appraisals conveyed by others and from developing high-quality interpersonal relations via the fulfillment of dyadic role expectations (Sluss & Ashforth, 2007). As Sluss and Ashforth (2007, p. 20) noted, when self-definitions are relational in nature, ‘helping one’s relational partner is tantamount to helping oneself’. Motivation at the relational level revolves around the expectations, values, and welfare of employees’ relational partners. At the collective level, self-definitions are based on the groups to which employees belong (Hogg, 2006; Tajfel & Turner, 1986). Employees’ sense of self merges with the group such that individuals see themselves as being similar to other members and they internalize group-defining characteristics (van Knippenberg et al., 2004). Self-worth at the collective level is heavily dependent on the success and social standing of one’s work team or company vis-à-vis other teams and companies. Self-worth also derives from successfully fulfilling one’s group roles and obligations to the collective. Thus, motivation at the collective identity is driven by the values, norms, and goals espoused by the groups that employees belong to. Two additional points about the three selfconstrual levels warrant mention. First, they are relatively orthogonal (Brewer & Gardner, 1996; Johnson, Chang, & Yang, 2010; Lord & Brown, 2004). Thus, defining the self at one level (e.g., collective) does not preclude an employee from

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also defining her or himself at one or both of the other levels (e.g., individual and relational), which is consistent with the empirical evidence (e.g., Johnson & Saboe, 2011; Johnson et  al., 2006; Kashima & Hardie, 2000). It is therefore important to consider all three levels simultaneously and not assume that, for example, a weak individual level signifies a strong relational or collective level. Second, the self-construal levels have both trait and state qualities (Lord & Brown, 2004; Oyserman, 2001). They are trait-like in that the importance of the individual, relational, and collective levels is relatively consistent across time and situations. This stability is the product of a person’s social and cognitive development (Oyserman, 2001) as well as enduring phenomena like culture (Triandis, 2001). They are also state-like in that the relative importance of different self-construal levels at a given point in time can shift temporarily as a function of situational cues (Markus & Wurf, 1987). For example, interpersonal treatment (Johnson & Lord, 2010), especially from leaders (Lord & Brown, 2004), triggers momentary shifts in self-definitions. It is therefore important to consider both between- and withinperson variation in self-construal levels.

Identity and Identification Two streams of self-construal research have emerged. The first – the identity stream – treats self-construal as a general orientation to define the self at individual, relational, and collective levels (e.g., Brickson, 2000; Johnson & Saboe, 2011). The second – the identification stream – stipulates specific foci to which one’s self is merged (e.g., identification with one’s supervisor; e.g., Ashforth & Mael, 1989; van Dick, Wagner, Stellmacher, & Christ, 2004). Although they have developed independently, we see these two streams as complementary. The primary difference between them is one of bandwidth-fidelity (Cronbach & Gleser, 1965). That is, identity orientation is a broad construct that is effective for predicting general behaviors (e.g., relational identity is an antecedent of interpersonal helping), whereas identification is a narrow construct that predicts target-specific behaviors (e.g., supervisor identification is an antecedent of helping one’s supervisor). Identity and identification may also be distinguishable based on their causal precedence because identity orientation likely precedes identification with specific foci (e.g., a strong relational identity leads to identification with one’s supervisor or a coworker). Nevertheless, the differences across the self-construal levels (individual, relational, and

collective) is far greater than the differences between identity and identification at the same level, thus our review combines identity and identification at each level.

Antecedents of self-construal

Research has predominantly focused on contextual antecedents of self-construal, but some work has explored individual differences. Predictors of interdependent (i.e., relational and collective) selfconstruals include the needs for affiliation, belonging, safety, and cognitive closure (Ashforth, Harrison, & Corley, 2008; Pratt, 1998). Positive affectivity may also increase the likelihood of identifying with others because potential targets of identification are seen in a more positive light and experiencing positive emotions causes people to be more cognitively inclusive of those around them (Kreiner & Ashforth, 2004). In contrast, negative affectivity and cynicism elicit the belief that other people cannot be depended on to be trustworthy and sincere (Andersson & Bateman, 1997), which stifles interdependent self-construals (Kreiner & Ashforth, 2004). In terms of distal contextual antecedents, national culture and values shape people’s selfconstruals. Employees in individualistic (vs. collectivistic) cultures are more likely to have prominent individual (vs. interdependent) selfconstruals (Brockner, Chen, Mannix, Leung, & Skarlicki, 2000; Triandis, 2001). Similarly, employees in countries or companies that endorse universalism and benevolence (vs. achievement and power) values are more likely to have prominent interdependent (vs. individual) selfconstruals (Lord & Brown, 2004). Employees also identify more with their company when congruence exists between personal values and company values (Kristof-Brown, Zimmerman, & Johnson, 2005). Besides values, the prestige of the potential target impacts identification. Employees are more likely to identify with companies that have favorable standing and reputation (Bartels, Pruyn, de Jong, & Joustra, 2007; Bergami & Bagozzi, 2000; Mael & Ashforth, 1992). The same is also true of leaders – employees are more likely to identify with high status and power leaders (Farmer & Aguinis, 2005). The most impactful antecedents are discrete experiences that employees have at work, which begin at the point of organizational entry. Several scholars have described how newcomers are socialized into companies and come to identify with their organization and its constituents (e.g., Ibarra, 1999; Swann et al., 2009). It is critical that employees experience support and trust if they are going to identify with the company

Employee Self-Concept and Identity

(Edwards & Peccei, 2010; Schaubroeck, Peng, & Hannah, 2013). Identity-based changes can also occur post-entry following major organizational changes and other triggers (e.g., work role transitions; Conroy & O’Leary-Kelly, 2014; Ibarra & Barbulescu, 2010). Transitioning into leadership roles can be particularly difficult because the increased power and status inherent in the new position, which feeds people’s individual identity, must be balanced by a concern for the wellbeing of one’s subordinates and work team, which requires strong relational and collective identities (Lord & Hall, 2005). Socialization and development processes therefore play an important role in shaping the identities of employees qua organizational leaders (Day & Harrison, 2007; DeRue & Ashford, 2010). Employee self-construals are also shaped by fairness experiences at work. As mentioned earlier, fair treatment communicates to recipients that they have high relational value, whereas unfairness signals the opposite (Lind, 2001; Tyler & Blader, 2003). In response, recipients of fair treatment incorporate the source of the fairness into their own identity. In support of this idea, several studies found that fairness, regardless of whether it involves outcomes, procedures, or interactions, is positively related to the strength of recipients’ collective and relational identities (Blader & Tyler, 2009; Johnson, Chang, & Rosen, 2010; Johnson & Lord, 2010; Olkkonen & Lipponen, 2006). In contrast, receipt of unfairness signals threats of social devaluation and exploitation, causing victims to psychologically dissociate themselves from the perpetrator by assuming an individual self-construal (Johnson & Lord, 2010; Lind, 2001). Leader behavior also has potent effects on follower self-construal levels (Kark & Shamir, 2002; Lord & Brown, 2004; Shamir, House, & Arthur, 1993; van Knippenberg et  al., 2004). Charismatic and transformational behaviors cause followers to identify more strongly with the group (e.g., Conger, Kanungo, & Menon, 2000; Epitropaki & Martin, 2005; Walumbwa, Avolio, & Zhu, 2008) as well as the leader (e.g., Kark et al., 2003; Liu, Zhu, & Yang, 2010; Walumbwa & Hartnell, 2011). Interestingly, such behaviors may also activate followers’ individual identity owing to individualized consideration (Paul, Costley, Howell, Dorfman, & Trafimow, 2001). Other types of leader behaviors, such as ethical leadership (Walumbwa, Mayer, Wang, Wang, Workman, & Christensen, 2011), selfsacrifice (De Cremer & van Knippenberg, 2005), and emphasizing shared values (Shamir, Zakay, Brainin, & Popper, 2000), strengthen followers’ collective self-construal. Although less research has examined the effects of leader behavior on individual self-construal, such effects are likely when

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followers experience differential treatment and abusive or unfair treatment from leaders (Johnson & Lord, 2010; Wu, Tsui, & Kinicki, 2010). Lastly, there is evidence of spillover and trickledown effects involving self-construal. Spillover effects occur when one self-construal level impacts another level. Sluss and Ashforth (2008) described conditions under which employees’ identification with their supervisor can color their identification with the organization. Such spillover effects are likely given that leaders embody the organization (Eisenberger et  al., 2010), especially when they are closely aligned with group prototypes (Sluss, Ployhart, Cobb, & Ashforth, 2012). Trickle-down effects are also possible, such that leaders’ organizational identification transfers to their subordinates (van Dick, Hirst, Grojean, & Wieseke, 2007; van Dick & Schuh, 2010). One way that organizational identification transfers from leaders to followers is via the mediating effects of leader behavior (Schuh, Zhang, Egold, Graf, Pandey, & van Dick, 2012).

Consequences of self-construal

The three self-construal levels are differentially related to outcomes because each level has unique motives and sources of self-worth. For example, although both interdependent levels are positively related to organizational citizenship behavior (OCB) (e.g., De Cremer & van Knippenberg, 2005; Jackson, Colquitt, Wesson, & ZapataPhelan, 2006; Johnson et  al., 2006; Moorman & Blakely, 1995), relational and collective self-construal best predict OCB directed at individuals and the company, respectively (Johnson & Saboe, 2011). The relational and collective levels are also the best predictors of voice behavior directed at one’s supervisor versus peers, respectively (Liu et  al., 2010). The individual level, in contrast, tends not to relate to OCB or voice behavior. With respect to in-role behaviors, all three self-construal levels are positively related to task performance (Jackson et  al., 2006; Riketta, 2005), but the collective level appears to have the strongest effect (Johnson & Saboe, 2011). Accumulating evidence suggests that self-­construal mediates the effects of leader behaviors and fairness on task performance and OCB (e.g., Blader & Tyler, 2009; Johnson & Lord, 2010; Lord & Brown, 2004; van Knippenberg et al., 2004). Not only does leader behavior shape follower self-construal but leader self-construal also shapes leader behavior and the quality of leader–follower relations. Johnson, Venus, Lanaj, Mao, and Chang (2012) found that leaders’ relational identity was positively related to the average frequency of their daily consideration behaviors, whereas collective

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identity was positively related to daily transformational behaviors. Interestingly, abusive leader behavior was most frequent when leaders had a strong individual identity paired with a weak collective identity. In terms of leader–follower relations, they tend to be more positive when at least one dyad partner possesses a strong relational identity (Chang & Johnson, 2010; Johnson & Saboe, 2011). Relationship quality is also higher when leaders and followers have similar interdependent identities and dissimilar individual identities (Jackson & Johnson, 2012). Self-construal also predicts dysfunctional work behaviors. The interdependent levels tend to be negatively related to deviant employee behavior like theft and incivility, whereas individual identity is positively related to them (Jackson et  al., 2006; Johnson & Lord, 2010; Johnson & Saboe, 2011). Interestingly, the positive relation of individual identity with deviant behavior is stronger when paired with weak interdependent identities (Johnson & Saboe, 2011). Given that relational and collective identities reflect psychological attachment to other people and groups, both identities are negatively related to withdrawal outcomes like absenteeism and turnover (Jackson et  al., 2006; Riketta, 2005; van Dick, Christ et  al., 2004; van Knippenberg, van Dick, & Tavares, 2007). In terms of job attitudes, although self-construal has been linked to various attitudes like job satisfaction and involvement (e.g., Johnson et al., 2006; Riketta, 2005; van Dick, Christ et  al., 2004), the most pertinent attitude is commitment. Employees can be committed to their organization for many reasons (Meyer & Allen, 1997), but the nature of the commitment that emerges depends on self-construal (Johnson, Chang, & Yang, 2010). Collective identity gives rise to affective and normative forms of organizational commitment, which are based on desire and loyalty, respectively (Johnson & Chang, 2006; Riketta, 2005). Relational identity elicits commitment to specific individuals within the organization, such as one’s supervisor (Sluss & Ashforth, 2007). Lastly, individual identity cultivates continuance commitment, a self-interested form of commitment owing to desirable personal incentives that derive from organizational membership or a lack of employment alternatives elsewhere (Johnson & Chang, 2006; Taing, Groff, Granger, Jackson, & Johnson, 2011). Lastly, self-construal shapes the nature of exchange relationships that emerge at work (Flynn, 2005). A strong individual identity gives rise to negotiated exchanges in which quid pro quo terms are openly discussed and self-interest is salient. A strong relational identity creates a preference for reciprocal exchanges in which the contributions between dyad partners are unilateral

across separate occasions and the expectation of reciprocity is implicit. Lastly, a strong collective identity cultivates generalized exchanges among group members in which reciprocity is indirect (i.e., it does not necessarily involve the original giver or recipient) and can be unilateral across members.

Self-construal as a moderator

Several studies have examined the accentuating effects of self-construal on relationships involving justice. It is typically found that individual, relational, and collective identity enhance the effects of outcome, interactional, and procedural fairness, respectively, on their consequences (e.g., Brockner, De Cremer, van den Bos, & Chen, 2005; Johnson et  al., 2006; Tyler & De Cremer, 2005), though such effects may differ across cultures (Yang, Johnson, Zhang, Spector, & Xu, 2013). A strong collective identity also accentuates the fair process effect (i.e., the counteracting effect of high procedural fairness on low outcome fairness; Brockner et al., 2000; Holmvall & Bobocel, 2008). Similar moderating effects have been observed for other phenomena. As noted above, self-construal levels are uniquely associated with different forms of organizational commitment. Paralleling this idea, individual identity accentuates the effects of pay satisfaction on continuance commitment, whereas collective identity accentuates the effects of organizational support on affective commitment (Johnson & Chang, 2008). Employees with strong individual and collective identities also report greater commitment to organizations that espouse achievement and benevolence values, respectively (Johnson & Jackson, 2009). Self-construal has been shown to moderate the effectiveness of apologies, such that offers of compensation, expressions of empathy, and acknowledgment of norm violations are especially effective when victims have strong individual, relational, and collective identities, respectively (Fehr & Gelfand, 2010). As a final example, leader evaluations depend mostly on implicit leadership theories (group prototypicality) when followers have strong individual (collective) identities (Lord & Brown, 2004).

INTEGRATING SELF-EVALUATIONS AND SELF-CONSTRUALS – THE SELF-CONCEPT NEXUS Although self-evaluations and self-construals are both key components of the self-concept, theoretical and empirical investigations typically examine

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them separately. Doing so is problematic because they capture complementary aspects of the selfconcept. That is, self-construals determine the self-referenced targets that are appraised as either positive or negative, which comprises the selfevaluative component. In order to fully encapsulate employees’ self-concept, it is necessary to cross self-evaluations with self-construals, such that unique evaluations of self-worth and -efficacy exist at individual, relational, and collective levels. For example, an employee may appraise her personal self-efficacy positively (at the individual level) yet hold a negative evaluation of the efficacy of the partnership she has with her leader (at the relational level). Similarly, self-evaluations at the collective level may differ from the individual and relational levels. Illustrated in Figure 2.1 is the self-concept nexus, which merges self-evaluations with selfconstruals. As shown in the figure, the nexus includes worth, efficacy, and motives for every foci of identification at each of the three levels. At the individual level, there is one focus (the self as

Group-Referenced Antecedents ------------Collective Identity Orientation Affiliation Needs Collectivistic Culture Universalism & Tradition Values Procedural Fairness Organization Prestige Organizational Support

an independent entity), whereas at the relational and collective levels there can be as many foci of identification as there are relationship partners and social groups that a person is involved with. However, in work contexts, we view identification with one’s supervisor and organization as the most salient and influential foci at the relational and collective levels, respectively. Although by no means exhaustive, listed in Figure 2.1 are some of the established antecedents of evaluations and motives at each level. To date, the majority of research concerning evaluations has focused on the individual level (i.e., appraisals of employees’ own self-worth and -efficacy, and personal motives) to the detriment of knowledge about the relational and collective levels. Research outside the organizational sciences, however, speaks to the potential value of considering self-evaluations at interdependent levels. In terms of relational self-esteem, research finds that individuals evaluate themselves based on the significant relationships they have with others (e.g., Du, King, & Chi, 2012). Relational self-esteem is

Efficacy Worth

Motives

Worth

Motives Occupation Identification

Work Team Identification

Organization Identification Efficacy

Efficacy

Motives

Worth

Collective Level

Dyad-Referenced Antecedents ------------Relational Identity Orientation Relatedness Needs Collectivistic Culture Benevolence Values Interactional Fairness Partner Prestige Partner Support

Efficacy Worth

Motives Coworker Identification

Worth

Supervisor Identification

Motives Client Identification

Efficacy

Efficacy

Motives

Worth

Relational Level

Self-Referenced Antecedents ------------Individual Identity Orientation Achievement Needs Individualistic Culture Self-Direction & Power Values Distributive Fairness Career Development Opportunities Recognition & Rewards

Figure 2.1  The self-concept nexus

Efficacy

Self Worth

Motives Individual Level

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positively and negatively related to collectivistic and individualistic cultural values, respectively (Du et  al., 2012). Relational self-esteem derived from evaluations of romantic relationships has been linked to well-being indicators, such as depression symptoms (e.g., Neff & Suizzo, 2006). Finally, experimental evidence suggests that unfair treatment reduces people’s relational self-esteem, particularly when they have an initial positive reputation, giving them more social capital to lose (De Cremer & Sedikides, 2008). Turning our attention to relational self-efficacy, Lent and Lopez (2002) proposed a tripartite model of relational efficacy that distinguishes between two forms of relational efficacy. Other-efficacy reflects people’s beliefs about their partner’s ability to perform specific behaviors, whereas relation-inferred self-efficacy indicates people’s appraisals of how their own abilities are evaluated by their partner. Research on this model has largely focused on dyads in competitive sports, finding that other-efficacy predicts individuals’ commitment to the dyadic relationship (Jackson, Beauchamp, & Knapp, 2007) and performance (Jackson, Myers, Taylor, & Beauchamp, 2012). Relation-inferred self-efficacy, in contrast, has indirect effects on these outcomes through its positive associations with the focal participants’ selfefficacy (Jackson et al., 2007, 2012). Yim, Chan, and Lam (2012) examined the joint effects of self- and other-efficacy among client and financial advisor dyads. Client participation in investment decisions is positively related to satisfaction when clients report high self-efficacy and other-­efficacy in advisor’s abilities to provide high-­ quality advice. Conversely, participation is negatively related to satisfaction when clients reported low self- and other-efficacy beliefs. Unfortunately, little empirical work has examined how significant relationships in the work setting may serve as bases for relational self-esteem and -efficacy, or their implications for work-related outcomes. Future research ought to focus on how relational self-construal intersects with esteem and efficacy evaluations, paying particular attention to how individuals evaluate the capabilities of the dyad as a unit. Similar to relational self-esteem, research on collective self-esteem has been largely conducted in contexts outside of organizational settings. Collective self-esteem is positively associated with individuals’ collectivist cultural values (Bettencourt & Dorr, 1997). High collective self-esteem is associated positively with college students’ psychological well-being (e.g., Crocker, Luhtanen, Blaine, & Broadnax, 1994; Zhang, 2005). Individuals with high (vs. low) collective self-esteem also demonstrate more bias in favor

of in-group members (e.g., Crocker & Luhtanen, 1990; De Cremer, 2001). In the work setting, collective self-esteem mediates the effects of socialization on employees’ organizational identification (Hayashi, 2013). Relatively more research has examined collective efficacy in organizational settings. For example, team-efficacy captures members’ perceptions of task-specific team capability and potency reflects members’ general perceptions of team capability across tasks and situations (Gibson, 1999). Team-efficacy and potency are typically viewed as team-level constructs, thus they are operationalized by aggregating individual members’ perceptions (Gibson, 1999). Meta-analytic results support that team-efficacy and potency are positively related to team performance, especially for interdependent tasks (Gully, Incalcaterra, Joshi, & Beaubien, 2002). Interestingly, while team-efficacy is traditionally considered a shared evaluation among members, recent work on team efficacy has emphasized that the dispersion of members’ efficacy evaluations concerning their teams may be meaningful (DeRue, Hollenbeck, Ilgen, & Feltz, 2010). For example, Mead and Eby (2007) demonstrated that team-efficacy agreement was positively related to team member exchange quality, participation, and cohesion. Hirschfeld, Cole, Bernerth, and Rizzuto (2013) found that team-efficacy dispersion was related negatively to team effectiveness. Future research ought to explore the antecedents and outcomes of collective self-esteem in the work context. Moreover, additional research on collective efficacy may focus on how individual members derive their evaluations of the team’s capability to accomplish team goals, and how these evaluations in turn impact both collective- and individual-level outcomes. In addition to examining appraisals of worth and efficacy at relational and collective levels, another needed direction for research is to examine subconscious content and processes pertaining to the self-concept. There is growing evidence that the self-concept operates outside of people’s awareness and control (e.g., Greenwald, Banaji, Rudman, Farnham, Nosek, & Mellott, 2002; Künhnen & Oyserman, 2002), including work-based self-evaluations and self-construals (e.g., Johnson & Lord, 2010; Johnson & Saboe, 2011; Lane & Scott, 2007). Subconscious content and processes deserve consideration because they have pervasive effects on behavior that are difficult (and in some cases impossible) to override. For example, implicit self-construal has stronger relations with supervisor-rated work behavior than explicit self-construal (Johnson & Saboe, 2011). However, studying implicit self-construal requires measures that can capture content and

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processes outside employees’ awareness and control (Koopman, Howe, Johnson, Tan, & Chang, 2013; Uhlmann, Leavitt, Menges, Koopman, Howe, & Johnson, 2012). Once suitable measures of implicit self-concept have been developed and validated, several research directions are possible. For example, whether or not self-concept variables are congruent across implicit and explicit levels may have implications for employee attitudes and behavior (Bosson, Brown, Zeigler-Hill, & Swann, 2003; Jordan, Spencer, Zanna, Hoshino-Browne, & Correll, 2003). It is also possible that implicit self-concept has indirect effects on outcomes via explicit self-concept (Bing, LeBreton, Davison, Migetz, & James, 2007; Uhlmann et  al., 2012). These and other pursuits are possible by considering implicit self-concept. A final direction for future research is to pay greater attention to structural and metacognitive aspects of the self-concept. Referring back to Figure 2.1, this would include the number of different types of identification at the relational and collective levels, the number of associations that exist between them, and the nature (e.g., activation versus suppression) of associations. Differences in the extent to which identities at separate levels are compartmentalized and the degree of spillover between them also fall into the structural and metacognitive category. Currently, very little is known about the variables in this category (Schaubroeck et  al., 2012). It is possible that variables such as self-concept clarity, complexity, and differentiation have moderating effects on the effects of the self-concept, which is a research question in need of an answer. We look forward with anticipation to future research on employee self-­ concept that addresses these and other questions.

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3 Organizational Justice S t e p h e n W. G i l l i l a n d

INTRODUCTION Philosophers, political scientists, politicians, jurists, and economists traditionally have been the ones concerned with the just distribution of wealth, power, goods, and services in society. Social psychologists and their brethren … have displayed remarkably little professional interest in this, despite the fact that the process of exchange is almost continual in human interactions. (Adams, 1965, p. 267)

With this observation 50 years ago, Stacy Adams (1965) sparked an interest in organizational justice that has continued to grow over the intervening decades and has come to represent one of the dominant theoretical orientations for understanding attitudes, emotions, and motivation in the workplace. From studies of equity in the 1960s and 1970s, to the inclusion of decision process fairness issues (i.e., procedural justice) and formal introduction of the term organizational justice in the 1980s, the evolution of organizational justice has occurred through thousands of studies in psychology and management (Colquitt et  al., 2013; Greenberg & Colquitt, 2005). Contrary to when Adams wrote his seminal chapter on equity theory, organizational scholars in industrial/organizational

psychology and management have demonstrated considerable interest in organizational justice and have written more than 2,500 papers on organizational justice in the past decade alone. Organizational justice is a social construct that refers to subjective perceptions of fairness in an organizational setting (Colquitt, Conlon, Wesson, Porter, & Ng, 2001). The study of antecedents of organizational justice has linked organizational decisions and decision processes with employee (and in some cases third-party) perceptions of fairness (Greenberg & Colquitt, 2005). Consequences of organizational justice perceptions include a wide variety of work attitudes, behaviors, and emotions (Cohen-Charash & Spector, 2001; Colquitt et  al., 2001, 2013). Areas of application include most areas of human resource management (e.g., Folger & Cropanzano, 1998; Gilliland & Steiner, 2012), many areas of organizational behavior (e.g., organizational citizenship behavior, Moorman & Byrne, 2005; counterproductive work behavior, Conlon, Meyer, & Nowakowski, 2005; stress, Judge & Colquitt, 2004; teams, Colquitt, Noe, & Jackson, 2002), and other areas of business (e.g., marketing, Maxham & Netemeyer, 2003; accounting, Libby, 1999; management information systems, Son & Kim, 2008). Given the vast amount of research on the topic of organizational justice, and the existence of many

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good comprehensive reviews (e.g., Greenberg & Colquitt, 2005), this chapter is necessarily focused rather than all-inclusive. The goal of this chapter is to present the current state of the science of organizational justice theories and research. Therefore, an emphasis is placed on current theories and empirical studies, rather than a summary of classics from the past 50 years. Historical overviews are discussed, but primarily to provide context for the current literature. I have also chosen to focus on research that develops organizational justice theories, rather than applies those theories to a specific set of organizational decisions, such as personnel selection (Gilliland & Steiner, 2012), or outcomes, such as employee health (Robbins, Ford, & Tetrick, 2012). Finally, while the primary focus is a review of past research, I also provide forward-looking suggestions for future research directions. By reviewing current research trends and advances, it is possible to note where those trends could and should evolve and also identify gaps and deficiencies in our current understanding. This chapter starts with a brief historical overview of the development of organizational justice constructs. It then reviews the major theoretical streams in organizational justice, tracing how they developed and what theories are currently being tested. It is useful to note that there is no single ‘organizational justice theory’, rather a number of different theories have been developed or adapted to explain both the processes by which fairness perceptions are formed and the impact of justice reactions on employee outcomes. The third section of this chapter reviews attitudinal, behavioral, and affective consequences of justice perceptions. A number of meta-analyses exist that summarize these relationships and these provide a foundation for this review. I then consider themes or trends in recent research and identify five areas that have been particularly productive or insightful over the past 10 years. Finally, I offer suggestions for future research and for the practical application of organizational justice. Although this area of study is 50 years old, there remain important issues that have been neglected by researchers and the application of organizational justice to broader leadership literature and organization level issues is underdeveloped.

A BRIEF HISTORY OF ORGANIZATIONAL JUSTICE As noted in prior historical reviews (e.g., Colquitt, Greenberg, & Zapata-Phelan, 2005), scholarly interest in the concept of justice dates back to Aristotle, with a resurgence of interest among seventeenth-century philosophers Hobbes and

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Locke. However, the study of justice in organizations was primarily launched through the development of equity theory (Adams, 1965). Adams built on Stouffer and colleagues’ (1949) research on relative deprivation and Homans’ (1961) theory of distributive justice to propose that reactions to outcomes (e.g., pay, promotions, etc.) will be based not on the absolute level of those outcomes, but rather on the relative level in comparison to similar others, taking into consideration differences in inputs between oneself and the comparison others. Adams also discussed a number of behavior and cognitive reactions to perceived inequity. Equity theory provided several important assumptions that are still central to organizational justice research. First, reactions are based on perceptions of inputs and outcomes, and any biases in perceptions will shape fairness reactions. Second, justice is a social evaluation process, whereby the specification of what constitutes fairness is relative to the perceived experiences of others. Third, perception of unfairness can provide an important motivator of behavior and attitudes in organizations. Finally, although it was not a central focus in his seminal introduction of equity theory, Adams (1965) suggested that perceived inequity results in negative emotions of anger and guilt. These themes provided the foundation for organizational justice research and continue to guide research today. During the 1970s two groups of researchers added to the distributive justice component of equity theory by proposing the existence of procedural justice – that is the fairness of the decision or allocation process. Thibaut and Walker (1975) examined fairness in the legal dispute resolution context and developed the concept of procedural justice based on control over the decision process. In later work, Thibaut and Walker (1978) distinguished between process control (control of evidence used to make a decision) and decision control (control over the outcome of a dispute). This research established the importance of having voice in decision making as a central feature of procedural justice. Leventhal (1980) approached procedural justice from a reward allocation perspective and proposed a set of six rules that determine fair procedures: consistency, bias suppression, accuracy, correctability, representativeness, and ethicality. These rules have been applied to a variety of different decision processes, such as employee selection (Gilliland, 1993) and form the basis of the most widely used measure of procedural justice (Colquitt, 2001). In 1987, Greenberg proposed a taxonomy formally linking content (i.e., distributive) and process (i.e., procedural) theories and labeling them theories of organizational justice. A third dimension of organizational justice was added when Bies and Moag (1986) observed that

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beyond the formal decision process, the implementation of decisions and procedures can also generate feelings of unfairness. They proposed interactional justice as the fairness of interpersonal communication, with foundational principles being truthfulness, justification, respect, and propriety of statements. Interactional justice was later separated into information and interpersonal components, and with the development of a comprehensive measure of organizational justice, Colquitt (2001) provided support for the construct separation of informational and interpersonal justice. A recent meta-analysis demonstrates that the four justice dimensions are moderately intercorrelated, with corrected population correlations ranging from .43 between interpersonal justice and distributive justice to .74 between interpersonal justice and informational justice (Colquitt et  al., 2013). A final major advancement in the development of organizational justice constructs came when Ambrose and Schminke (2009) developed a measure of overall justice judgments. In a series of experiments, they demonstrated that overall justice mediates the relationships between the different facets of justice and employee attitudes and behaviors. This research suggests that rather than there being four ‘types’ of organizational justice, there instead are four components that contribute to overall feelings of fairness and justice.

Throughout this expansion and ultimate consolidation of organizational justice constructs, it is interesting to note that the definition of organizational justice has remained largely consistent. Table 3.1 provides a summary of definitions at major points in the development of organizational justice. Common across all of the definitions is the focus on perceptions of fairness. Both Adams’ (1965) definition of inequity and Holtz and Harold’s (2009) definition of overall justice highlight perceptions or evaluations based on both personal experiences and the experiences of others. As a side note, it was interesting how few of the papers that introduced new terms to the organizational justice literature actually provided formal definitions of those terms. For example, Greenberg (1987) introduced organizational justice without formally defining this construct. With the constructs established, research on organizational justice took off in the 1990s and justice was applied to a wide variety of organizational decisions such as layoffs (Konovsky & Brockner, 1993), performance appraisal (Taylor, Tracy, Renard, Harrison, & Carroll, 1995), and selection (Gilliland, 1993). Then, with the introduction of valid measures of each dimension of organizational justice (Colquitt et  al., 2001), research flourished in the 2000s. Figure 3.1 presents a summary of the number of organizational justice studies published each year in psychology,

Table 3.1 Definitions of organizational justice Justice construct

Justice construct definition Precursors to organizational justice

Equity theory (Inequity) Procedural justice Distributive and procedural justice Interactional justice

‘Whenever he perceives that the ratio of his outcomes to his inputs and the ratio of Other’s outcomes to Other’s inputs are unequal’. (Adams, 1965, p. 280) ‘The belief that the techniques used to resolve a dispute are fair and satisfying in themselves’. (Walker, Lind, & Thibaut, 1979, p. 1402) ‘A justice judgment sequence estimates the individual’s deservingness based on each [distributional and procedural] rule’. (Leventhal, 1980, p. 27) ‘Concerns about the fairness of interpersonal communication’. (Bies & Moag, 1986, p. 44) Organizational justice formally defined

Organizational justice

Organizational justice

Organizational justice Organizational justice Overall justice

‘Organizational justice’ introduced to the literature as but not actually defined. Discussion focused on ‘matters of justice and fairness in organizations’ (Greenberg, 1987, p. 9) and categorized justice theories along reactive-proactive and process-outcome dimensions. ‘Individuals’ and groups’ perceptions of the fairness of treatment (including, but not limited to, allocations) received from organizations and their behavioral reactions to such perceptions’. (James, 1993, p. 21) ‘An area of psychological inquiry that focuses on perceptions of fairness in the workplace’. (Byrne & Cropanzano, 2001, p. 4) ‘People’s perceptions of fairness in organizations’ (Greenberg & Colquitt, 2005, p. xi) ‘Global evaluations of the fairness of an entity based on personal experiences as well as on the experiences of others’. (Holtz & Harold, 2009, p. 1185)

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350 300 250 200 150 100 50 0

1960s 1970s 1980s 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Published articles, chapters and books

Organizational Justice

Figure 3.1 Organizational justice publications by year Note: Publications include all articles, book chapters, and books in psychology, management, and business that address organizational justice or its subcomponents (i.e., distributive, procedural, interactional, informational, and interpersonal), based on a Web of Science search.

management, and business sources. There has been a steady increase in publications over the past 25 years, with no sign yet of slowing down. Much of this research has been focused on developing theories of organizational justice and examining the impact of justice perceptions on other organizational attitudes and behaviors. I now provide a summary of major theories of organizational justice.

MAJOR THEORIES OF ORGANIZATIONAL JUSTICE When organizational justice was expanded beyond equity theory to include procedural justice, scholars began introducing theories to relate and connect these constructs. Initially, the focus of these theories was on how justice perceptions are formed and why fairness is important to people in organizations. More recent theories focused on explaining the effects of justice on subsequent outcomes. Given that many of these theories evolved (and changed names) over time, I summarize them as clusters of theories.

Group Values to Uncertainty Management Theory One of the first attempts to explain why different components of fairness are important to people

was offered by the Group Value Model (Lind & Tyler, 1988). Building on procedural justice research demonstrating that process control impacts fairness perceptions independent of any influence on the decision outcome (Earley & Lind, 1987), Lind and Tyler (1988) suggested that procedural and interactional justice served to affirm an individual’s standing in a group. Thus, procedural justice elements of process control and bias suppression, and international justice principles are important because they provide evidence of the values of the group and a reflection of one’s position in that group. Lind and Tyler contrast this perspective to an Instrumental Model, in which justice is valued because it leads to long-term beneficial outcomes. This model produced subsequent debate about the extent to which fairness is driven by instrumental versus group value concerns, with some researchers suggesting that all justice reactions are ultimately self-interested (Gillespie & Greenberg, 2005). Very similar to the group value model is the relational model (Tyler & Lind, 1992), which also placed the value of procedural and interactional justice in affirming group values. The extension offered in the relational model is that it focused on the legitimacy of authority figures in a group. Thus, procedural and interactional justice are suggested to lead to judgments about an authority’s legitimacy, which in turn influence the perceiver’s willingness to follow the leadership of the authority figure. Another variation of the group value model was labeled the Group Engagement Model

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(Tyler & Blader, 2003), which was similar in focus but addressed psychological engagement and discretionary behaviors (e.g., OCBs) as outcomes of procedural and interactional justice. In this model, psychological engagement was conceptualized as an intrinsic motivation that leads to positive discretionary behaviors. Although the group value model and its variations provided an explanation for why justice is important to people, all of these models offered little explanation of how justice judgments are formed. Fairness Heuristic Theory (Van den Bos, 2001) addressed this limitation by suggesting that individuals use heuristics (psychological shortcuts) based on fairness information to make judgments about authority figures and the extent to which they should be trusted. Fairness heuristics form early in interactions with an authority figure and initial information is more important in defining the heuristic than later information. Fairness heuristics are also used to resolve a fundamental social dilemma in which contributions to a group may either be valued or exploited. To the extent that fairness heuristics suggest that an authority figure can be trusted, individuals are more likely to conclude their contributions to the group will be valued. Fairness heuristic theory suggests that procedural and interactional justice are most important in establishing trust when individuals lack certainty about the trustworthiness of authority figures. Expanding on this suggestion, Lind and Van den Bos (2002) proposed Uncertainty Management Theory, in which procedural and interactional justice help reduce general uncertainty in organizations, and not just uncertainty associated with trust in authority. Thus, early in one’s experience with groups or when uncertainty is heightened (e.g., during layoffs), fairness information becomes particularly important in dealing with this uncertainty. Recent research in a hospital setting demonstrated that procedural, interpersonal, and distributive justice all predicted trust, which in turn reduced uncertainty and led to stronger job performance (Colquitt et  al., 2012). Other research has demonstrated that the effect of procedural fairness on organizational commitment is greatest under conditions of high organizational uncertainty (De Cremer et al., 2010). Similarly, in a study of 395 employee manager dyads from Hong Kong, Takeuchi, Chen, and Cheung (2012) found that a three-way interaction of interpersonal, procedural, and distributive justice on employee voice behavior was most pronounced when employees felt high uncertainty. This research into uncertainty management theory offer practical suggestions about the importance of ensuring justice during times of uncertainty and change.

Referent Cognitions Theory to Deontic Justice Whereas the group value model started as an explanation of why justice is important to people, Referent Cognitions Theory (RCT; Folger, 1986) addresses how fairness judgments are made. Building on the concept of the simulation heuristic from social cognition research (Kahneman & Tversky, 1982), Folger suggested that people develop ‘if only…’ thoughts based on salient imagined alternative outcomes. RCT brings in elements of procedural and informational justice through the suggestion that justification for received outcomes also impacts fairness perceptions. Perceived unfairness is greatest when an individual can vividly imagine the way things ‘ought to have been done’, with low justification for the actual outcome. Folger and Cropanzano (1998) expanded on RCT in their development of Fairness Theory. Rather than only pertaining to outcomes, fairness theory addresses all negative justice events (e.g., outcomes, procedures, interactions) and suggests people look to assign blame or accountability for those events. Fairness theory suggests that people consider three counterfactual questions in assigning blame: 1) Would I have been better off if a different event had occurred?; 2) Could the authority have acted differently?; and 3) Should the authority have behaved differently? The final question addresses violations of moral or ethical standards. The easier it is to imagine how the event would have turned out better and that the authority figure could and should have acted differently, the more likely the event is to be seen as unfair. Gilliland et al. (2001) provided one of the first tests of fairness theory and demonstrated that employment rejection letters containing explanations that reduced would and could counterfactuals increased perceptions of fairness and increased recommendation intentions toward the organization. RCT and fairness theory address most directly the question of how justice perceptions are formed. Focusing more on the ‘why’ dimension in theorizing, Folger built on the theme of accountability and the moral elements of the should counterfactual to propose a deontic model of justice reactions (Folger, 2001; Folger, Cropanzano, & Goldman, 2005). This model suggests that many types of unfair treatment are seen as morally wrong and produce automatic and emotional anger responses, even among those not directly impacted by the injustice. For example, Turillo, Folger, Lavelle, Umphress, and Gee (2002) demonstrated that people sacrificed financially to punish someone who previously treated another unfairly, even when

Organizational Justice

the punisher had no connection with either party and no possibility of future interactions. Emotions drive an economically irrational response to apply retribution to the perpetrator of the unfairness. Folger and Skarlicki (2008) review evidence to suggest an evolutionary basis for deontic reactions to injustice – that people are wired to react negatively to perceived injustice. Also in support of the deontic model, Skarlicki and Rupp (2010) demonstrated that third-party observers high in moral identity and those primed to use an experiential (rather than rational) processing frame reported higher retribution responses to observed injustices.

Social Exchange and Source Effects The major theories reviewed thus far have examined how and why justice perceptions form, with less attention on the outcomes of these perceptions. Within the last 15 years, there has been increased attention on developing theory for the impact of justice on attitude and behavioral outcomes. Masterson, Lewis, Goldman, and Taylor (2000) investigated the differential effects of procedural and interactional justice on satisfaction, commitment, performance, and OCB. Using a social exchange theory perspective, they suggested and found evidence that procedures tend to be enacted at the organization level and have their strongest effects on organization-focused outcomes (e.g., organizational commitment and organizationdirected OCB), whereas interactional justice tends to originate from supervisors and have their strongest effects on supervisor-related outcomes (e.g., performance, satisfaction, and supervisor-directed OCB). This research highlighted the importance of source effects in organizational justice, namely that responses to experienced (in)justice tend to be most strongly directed toward the source of that (in)justice. This study also demonstrated the applicability of social exchange theory for understanding responses to perceived fairness. Social exchange theory (Blau, 1964) suggests that resources are exchanged based on certain rules (e.g., reciprocity) and these exchanges impact the quality of the relationship between the exchange parties. Applied to organizational justice, social exchange theory involves the exchange of fair treatment for positive attitudes, cooperation, and discretionary behaviors, through mediating mechanisms of relationship quality (e.g., leader–member exchange and perceived organizational support; Masterson et al., 2000; Cropanzano & Rupp, 2008). From this perspective, fair treatment is seen as a benefit deserving of reciprocity. Social exchange theory provided the foundation for many studies

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of effects of justice on attitudes and behaviors, particularly OCB (e.g., Lavelle, Rupp, & Brockner, 2007; Liao & Rupp, 2005; Moorman, 1991). Colquitt et al. (2013) conducted a meta-analysis of outcomes of justice perceptions to test hypothesis derived from social exchange theory. They found support for the effects of justice on outcomes of OCB, performance, CWB and relationship quality indicators of trust, organizational commitment, perceived organizational support, and leader–member exchange. They also found that exchange quality mediated effects of justice on task performance and OCB (but not CWB). However, they failed to find support for source effects predicted by social exchange theory; organization-focused justice did not have stronger effects on organization-targeted outcomes and relationship quality indicators, and supervisor-focused justice did not have stronger effects on supervisor-targeted variables. Rather they found generally strong effects for any focus of justice on supervisor-targeted outcomes. This lack of support for target-similar outcomes stands in contrast to a meta-analysis by Rupp, Shao, Jones, and Liao (2014), which found strong support for the target matching hypothesis. With the exception of OCB directed toward the organization, supervisory justice perceptions more strongly predicted supervisor outcomes and organization-focused justice perceptions more strongly predicted organizational outcomes. Given that these two meta-analyses were conducted at similar points in time on similar sets of studies, it is unclear whether differences are in study coding, analysis methods, or some other factor led to opposite conclusions being drawn. Clearly more work is needed to resolve these discrepancies.

Affect In his presentation of equity theory, Adams (1965) suggested that inequity impacts affect, but he elected to focus on cognitive and behavioral outcomes: ‘Although there can be little doubt that inequity results in dissatisfaction, in an unpleasant emotional state, be it anger or guilt, there will be other effects’ (p. 283). This relative inattention to affect persisted for almost 40 years, and it is primarily in the past 10 years that researchers have integrated affect into justice theories (De Cremer, 2007; Cropanzano, Stein, & Nadisic, 2011). In one of the early studies of justice and affect, Weiss, Suckow, and Cropanzano (1999) demonstrated effects of procedural and distributive justice on discrete emotions of happiness, pride, anger, and guilt. Other studies have demonstrated effects of injustice on negative emotions and the mediating effects of negative emotions on the

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relationships between injustice and retaliatory behaviors (Barclay, Skarlicki, & Pugh, 2005; Rupp & Spencer, 2006). Research on justice and affect has tended to test theories of affect, such as appraisal theory (Weiss & Cropanzano, 1996), which suggests events trigger primary appraisals of harm or benefit (resulting in negative vs. positive emotions) and then secondary contextual appraisals that give rise to discrete emotions. However, affect has also been proposed as a mediating pathway for the effects of justice on behavior. Barclay et al. (2005) demonstrated that outward-focused negative emotions of anger and hostility mediated the relationship between fairness perceptions and retaliation in a layoff context. Rupp and Spencer (2006) found that anger mediated the relationship between interactional justice from customers and emotional labor. In a metaanalysis, Colquitt et  al. (2013) found that affect mediated the relationships between justice and work behaviors, particularly for CWB. The four sets of major theories reviewed here have provided the theoretical foundation for many of the studies of organizational justice. They have also suggested a number of attitudinal and behavioral outcomes of justice reactions. I now summarize research that has examined the consequences of organizational justice perceptions.

CONSEQUENCES OF ORGANIZATIONAL JUSTICE Researchers have examined the influence of justice perceptions on a wide variety of attitudes and behaviors, including job satisfaction, organizational

commitment, trust, performance, OCB, CWB, and withdrawal. In addition to examining main effects of different justice components on these outcomes, researchers have also demonstrated interactions between justice components in predicting outcomes. I start by reviewing the main effects and then turn attention to interactions and ripple effects.

Effects of Justice on Attitudes and Behavior Since the first studies of organizational justice, researchers have examined the effects of justice perceptions on employee attitudes and behaviors (e.g., Alexander & Ruderman, 1987; Moorman, 1991). These results were compiled in a series of meta-analyses published in the early 2000s (Cohen-Charash & Spector, 2001; Colquitt et al., 2001; Viswesvaran & Ones, 2002). In a more recent meta-analysis of almost 500 unique samples, Colquitt et  al. (2013) provide a thorough examination of the attitude and affect mediators of the impact of justice on performance, OCB, and CWB. Table 3.2 provides a summary of the direct effects of each of the four justice dimensions on the most commonly studied attitudes and behaviors. Because the two Colquitt et al. (2001, 2013) meta-analyses are the most comprehensive in examining relationships for each of the four dimensions of organizational justice, I used these studies to produce this table. All dimensions of organizational justice are strongly correlated with trust in supervisor and trust in organization, with distributive justice being a somewhat weaker predictor of trust in supervisor. Recent studies have demonstrated that trust is

Table 3.2 Organizational justice and attitudes and behavior Outcome Trust in supervisor Trust in organization Organizational commitment POS LMX Job satisfaction Task performance OCB CWB Withdrawal 1 2

Colquitt et al. (2013) Colquitt et al. (2001)

Distributive justice Procedural justice Interpersonal justice Informational justice Source .45 .54 .49 .51 .42 .56 .26 .21 −.26 −.50

.65 .63 .53 .59 .50 .40 .24 .30 −.28 −.34

.59 .60 .41 .60 .57 .35 .16 .43 −.24 −.02

.65 .55 .38 .58 .53 .43 .16 .42 −.29 −.24

1 1 1 1 1

2 1 1 1 2

Organizational Justice

a mediator of the relationship between organizational justice and job performance (Colquitt et al., 2012) and between organizational justice and OCB (Erturk, 2007). Organizational commitment is most strongly correlated with distributive and procedural justice, and has a somewhat weaker correlation with interpersonal and informational justice. Two indicators of social exchange quality, perceived organizational support (POS) and leader-member exchange (LMX) are both more strongly correlated with procedural, interpersonal, and informational justice than with distributive justice. On the other hand, job satisfaction is more highly correlated with distributive justice than with the other dimensions of organizational justice. In sum, all dimensions of organizational justice correlate moderately to strongly with major workplace attitudes. Correlations between justice and workplace behaviors are somewhat smaller, but still significant and suggest that justice perceptions provide important sources of motivation for workplace behavior. Excluding research on equity theory, more than 50 studies have examined the relationship between organizational justice perceptions and task performance. Averaging across studies, meta-analyses have demonstrated that all dimensions of organizational justice predict job performance, however the correlations are stronger with distributive and procedural justice than with interpersonal and informational justice. All three dimensions of justice predict discretionary OCB. However, the pattern of correlations for OCB are opposite job performance; OCB is more strongly correlated with interpersonal and informational justice than distributive and procedural justice. The other side of discretionary behaviors are counterproductive work behaviors (CWB) that include theft, sabotage, and a variety of unethical actions. All dimensions of organizational justice have been found to predict CWB. Withdrawal from an organization is also predicted by organizational justice perceptions, but the strongest relationship is with distributive justice, followed by procedural and informational justice. Clearly, organizational justice perceptions are strong predictors of workplace behaviors, with the strongest effects on those behaviors under the greatest discretionary control (OCB and withdrawal). It is also clear that different dimensions of organizational justice drive different behavioral responses. Beyond these major categories of workplace attitudes and behaviors, research has also examined the impact of organizational justice on creativity. George and Zhou (2007) demonstrated that interactional justice interacts with mood to predict creativity. In a study of employees and supervisors in a chemical engineering plant in India,

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Khazanchi and Masterson (2011) demonstrated that supervisor interpersonal and informational justice predicted creativity through a process of trust in supervisors. Research has also demonstrated the impact of organizational justice on health-related outcomes. In a meta-analysis of dozens of studies, Robbins et  al. (2012) found that distributive, procedural, and interactional justice predicted health problems, mental health, burnout, and stress. On the other hand, in a longitudinal study of the relationship between justice perceptions and depression, Lang, Bliese, Lang, and Adler (2011) found that depressive symptoms predicted subsequent justice perceptions, but justice perceptions did not predict depressive symptoms. In one particularly interesting field experiment involving nurses in a hospital setting, Greenberg (2006) found that distributive injustice associated with a pay cut led to greater insomnia among nurses, and this effect was mitigated by training nurse supervisors on principles of interactional justice. This research is part of a larger body of research that has examined the interactive effects of different justice components on work-related outcomes.

Interactive Effects of Justice Components Research has repeatedly demonstrated an interaction between outcome favorability (or distributive justice) and procedural justice on workplace attitudes and behaviors (Brockner & Wiesenfeld, 1996). Generally, the form of this interaction is that outcome favorability has the greatest effect on work attitudes and behavior when procedural justice is low, and procedural justice has the greatest effect when outcome favorability is low (see Figure 3.2). Similar interactions have been found with informational or interpersonal justice and outcome favorability (e.g., Greenberg, 1990, 2006). Skarlicki and Folger (1997) found a three-way interaction between distributive, procedural, and interactional justice on organizational retaliatory behaviors such that the interaction between procedural and distributive justice was only observed when interactional fairness was low. This study suggests that managers have three opportunities to get it right: If even one of distributive, procedural, or interactional justice is fair, the likelihood of retaliation decreases (Cropanzano, Bowen, & Gilliland, 2007). De Cremer and colleagues (2010) provided an interesting extension to this research by examining the effects of uncertainty on the interaction between procedural and distributive fairness. In a series of four studies conducted with different

The SAGE Handbook of Industrial, Work and Organizational Psychology

High Procedural Justice

Attiudes and Behavior

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Low Procedural Justice

Low

High Distributive Justice

Figure 3.2 Distributive justice × procedural justice interaction on attitudes and behaviors international samples (i.e., Dutch, German, and US employees) and different operationalizations of uncertainty, they found the process by outcome interaction under conditions of high uncertainty but not with lower uncertainty. These results suggest that ensuring either procedural or distributive fairness is particularly important when organizations are undergoing change and uncertainty. Brockner and Wiesenfeld (2005) noted that the form of outcome by process interaction is somewhat different when the dependent variable relates to self-evaluations. With high procedural justice, individuals are more likely to internalize outcomes such that outcomes have a greater impact on selfevaluations when procedural justice is high. When procedural justice is low, it is easier to blame the outcome on a poor procedural (an external attribution), such that self-evaluations are less impacted by outcomes.

Ripple Effects of (In)Justice Bowen, Gilliland, and Folger (1999) provided an integration of organizational justice research with marketing literature on customer service delivery and recovery and proposed that when organizations treat employees fairly it has a spillover effect to customers being treated fairly by those employees. Building on this idea, Masterson (2001) demonstrated that instructors’ perceptions of procedural and distributive justice were related to student perceptions of fairness and positive reactions to

their instructor. Maxham, Netemeyer, and Lichtenstein (2008) provided a comprehensive test of these ‘ripple effects’ of organizational justice, in a study of employee fairness perceptions in a large retail store chain. They found that employee perceptions of overall organizational justice were related to supervisor evaluations of employee performance and extra-role behavior, which in turn were related to customer evaluations of satisfaction and repurchase intentions, which were ultimately related to comparable store sales growth. Thus, the ripple effects of organizational justice were found to impact customer evaluations and firm financial performance. Recent research suggests that this ripple effect may also work in reverse. Skarlicki, van Jaarsveld, and Walker (2008) studied the effects of customer interpersonal injustice on sabotage behaviors of customer service representatives at a Canadian call center and found that customer injustice related to employee sabotage over and above organizational sources of fairness. Sabotage was also negatively related to performance ratings. Ambrose, Schminke, and Mayer (2013) studied ripple effects of justice within organizations and demonstrated that supervisors’ perceptions of interactional justice from their bosses were related to group members’ perceptions of interactional justice, which in turn related to group OCB and deviance. The interesting similarity between this study and the ripple effects with customer treatment is that people are not ‘getting even’ with the instigator of the injustice, but rather are passing it along to others. It is also important to note that

Organizational Justice

fair employee treatment can lead to engagement, OCB, and positive customer treatment, and unfair employee treatment can lead to withdrawal, CWB, and negative customer treatment. These dual effects have been described as two sides to the justice coin (Cropanzano et al., 2007). In addition to identifying justice constructs that underlie perceptions of fairness and examining the impact of these perceptions on work attitudes and behaviors, recent research has extended understanding by examining how justice perceptions change over time, asymmetric effects of fairness and unfairness, group level perceptions of justice, and antecedents of fair treatment. I now address these recent trends in justice research.

RECENT TRENDS IN ORGANIZATIONAL JUSTICE RESEARCH As was seen in Figure 3.1, the number of publications of organizational justice research has continued to increase each year. It is not possible to summarize all of the recent research in organizational justice in the scope of this chapter. Instead, I discuss several recent trends in organizational justice research.

Justice Events and Temporal Effects Cropanzano, Byrne, Bobocel, and Rupp (2001) identified two paradigms organizational justice researchers have used to study perceptions of fairness. The event paradigm examines fairness of particular decisions, such as hiring decision (Gilliland, 1994) or performance appraisal (Taylor et  al., 1995). In contrast, the entity paradigm examines the fairness of social entities such as an organization or supervisor. Choi (2008) combined these two paradigms and found that entity justice (both organization and supervisor) moderated the impact perceptions of event justice on organizational commitment and OCB. Specifically, the perceived justice of a recent performance appraisal had a stronger impact on organizational commitment and OCB when entity perceptions were low rather than high. Similarly, Bobocel (2013) found that overall entity justice was positively related to forgiveness and negatively related to revenge reactions in response to an unfair event. These relationships were moderated by participants’ degree of self- and other-orientation. One particular event that has generated considerable organizational justice research is the hiring process (Gilliland, 1993; Gilliland & Steiner, 2012).

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This research has addressed practical questions associated with applicants’ preferences for particular selection procedures and the impact of applicant perceptions on applicant attitudes and behaviors. Consistent with fairness heuristic theory, applicants form strong justice perceptions of the hiring event and these reactions shape attitudes toward the organization, as well as job acceptance decisions and other applicant behavior. However, research has failed to demonstrate a lasting impact of applicant justice perceptions on post-hire employee attitudes and behavior (Gilliland & Steiner, 2012). The focus on justice events has also led researchers to examine temporal effects of justice events. Rodell and Colquitt (2009) examined the impact of anticipatory justice collected in advance of instituting a workplace smoking ban. They found that anticipatory justice was most highly related to experienced justice (after the smoking ban) under condition of high uncertainty and low outcome favorability. Perceptions of justice entities also have been examined across time. Holtz and Harold (2009) examined overall organizational justice perceptions over three points in time and found considerable variation in these perceptions, such that within-person variance accounted for 24% and 29% of the variance in overall organizational and overall supervisory justice perceptions respectively. Hausknecht, Sturman, and Roberson (2011) examined entity justice perceptions on four occasions over the course of a year. They found that justice trends (i.e., increasing or decreasing justice perceptions) explained additional variance in job satisfaction, organizational commitment, and turnover intentions, after controlling for endstate entity justice perceptions. Clearly, the study of changes in justice perceptions over time is an important area for future justice research. Given the interplay between justice event and entity perceptions, it is important to learn more about the frequency and severity of workplace justice events. As Gilliland (2008a) asked, ‘How often do individual employees experience episodes of moderate to severe injustice?’ (p. 305). It is surprising that after 50 years of study, we do not have good data on the baseline occurrence of justice and injustice events at work.

Third-Party Reactions As suggested in the deontic model of justice (Folger, 2001), reactions to injustice are not isolated to recipients, but also are evident in thirdparty observers (Skarlicki, Ellard, & Kelln, 1998). Research on third-party reactions to justice suggest that they are similar to primary experiences of

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justice, only somewhat less impactful (Lind, Kray, and Thompson, 1998). In a study of third-party reactions to injustice with French managers, Skarlicki and Rupp (2010) found that managers high in moral identity demonstrated a stronger relationship between unfairness and retribution responses than those with low moral identity. Third-party reactions are also present when people witness the unfair treatment of coworkers. Spencer and Rupp (2009) found that emotional labor increased as a result of both experienced unfairness from customers and customers’ unfairness directed toward coworkers. Interactive effects demonstrate that if a coworker was treated unfairly by a customer, emotional labor increased even if the individual’s own treatment by customers was fair. These results suggest that justice is a social phenomenon whereby the experiences of others shape one’s own attitudes and behaviors, an interpretation that is consistent with fairness heuristic theory (Van den Bos, 2001). Given the evidence for ‘justice ripple effects’, it would be useful to examine the relative impact of third-party versus first-hand justice experiences on employee attitudes and behaviors. Combined with the previous discussion of justice and injustice, future research should examine the extent to which third parties respond to fair versus unfair treatment of others.

Groups, Unit, and Multilevel Justice Organizational justice has been demonstrated to be an important construct at the group level (Li & Cropanzano, 2009). In one of the original studies of group level justice, Naumann and Bennett (2000) aggregated procedural justice perceptions to a group level and found that group level justice predicted individual level helping behavior, even after controlling for individual level justice perceptions. Liao and Rupp (2005) expanded this idea by examining three dimensions of justice (procedural, interpersonal, and informational) and two foci (organization vs. supervisor) at both the individual and group level. They found that organizationfocused procedural and informational justice and supervisor-focused procedural and interpersonal justice were significantly related to work attitudes and OCB after controlling for individual level justice perceptions. Li and Cropanzano (2009) described these group level shared perceptions as justice climate and present evidence of the existence of justice climate at group and large unit levels of analysis. Indeed, organizational justice can be conceptualized as multilevel phenomena, with distinct justice perceptions existing and interacting at individual, group,

and organization levels of analysis. Cross-level research by Maxham et  al. (2008) demonstrated that individual justice perceptions can impact store unit financial performance through individual employee behavior and customer reactions. Roberson (2006) pointed out that beyond level of perceived justice, the strength of shared perceptions is also an important dimension of justice climate. She demonstrated that a sensemaking process gives rise to shared justice perceptions and justice climate strength. In a recent meta-analysis, Whitman, Caleo, Carpenter, Horner, and Bernerth (2012) demonstrated that distributive justice climate was most strongly linked to unit-level performance and interactional justice climate was most strongly linked to unit-level OCB. Further, they demonstrated that the relationship between justice climate and overall unit effectiveness was stronger with greater climate strength. A somewhat different way of conceptualizing group or team justice is based on individual perceptions of how others in their team are treated (Colquitt, 2004). This is similar to third-party reactions, only as team members the treatment of others on a team is more personally relevant than purely disinterested third parties. Across correlational and experimental studies, Colquitt found that own justice and other team member justice interacted with task interdependence to predict individual performance, conflict, and cooperation. The combined effects of own and others’ justice had the greatest effect under conditions of high task interdependence. Christian, Christian, Garza, and Ellis (2012) examined whether the specific team member experiencing injustice makes a difference to team member perceptions of justice. They found that team members were more likely to respond with lower OCB when a core team member experienced injustice than when a non-core team member experienced injustice. Yet still another way to conceptualize group level justice is the fairness with which group members treat each other. Li, Cropanzano, and Benson (2011) described this as peer justice and found that peer procedural and interpersonal justice perceptions influenced teamwork processes, which in turn influenced team performance and OCBs. It appears that justice is an important construct at group levels of analysis, with a number of distinct ways of conceptualizing and operationalizing group level justice. Future research should give more attention to multilevel and cross-level justice effects.

Justice as a Dependent Variable Given the consistent and pervasive effects of fair treatment on a wide variety of work attitudes and

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behaviors, a question that has generated recent interest from researchers is what makes leaders behave unfairly? In considering fairness as a dependent variable, research has examined antecedents of fair and unfair treatment. In one of the first studies to take this approach, Folger and Skarlicki (1998) examined factors that influenced interpersonal and informational justice in communicating layoff decisions. They found that when the layoff was a result of mismanagement (rather than external factors), managers were more likely to distance themselves in communicating the layoff decision. Gilliland and Schepers (2003) demonstrated there are also practical influences on how leaders choose to communicate layoff decisions. In a survey of more than 500 HR managers, they found that layoff victims were given more advanced notice (informational justice) when the reasons for the layoff were endogenous (e.g., organizational restructuring) than exogenous (e.g., economic downturn). They also found that the use of group layoff notifications that lacks interpersonal justice was more prevalent as the size of the layoff increased. Scott, Colquitt, and Paddock (2009) developed a model of justice rule adherence based on manager cognitive motives (i.e., effecting compliance, creating and maintaining identity, and maintaining a just world view) and affective motives (i.e., maintaining positive affect and reducing negative affect). Scott, Garza, Conlon, and Kim (2014) conducted an experience sampling study over a threeweek period with 90 managers and found support for this model. Cognitive motives were more strongly associated with justice dimension over which managers felt they had less discretion and affective motives were more strongly associated with higher discretionary justice dimensions. In another study of manager characteristics, Patient and Skarlicki (2010) demonstrated that manager empathic concern was positively related to delivering negative news with greater interpersonal and informational justice, and this effect was strongest among managers with high moral development. Finally, Ambrose et  al. (2013) demonstrated a trickle-down model in which supervisors’ perceptions of interactional justice influenced their subordinates’ perceptions of interactional justice. Research has also examined recipient characteristics that influence managers’ application of justice principles. Hoogervorst, De Cremer, and van Dijke (2013) found that managers’ perceptions of an employee’s need for individual control interacted with their perceptions that the employee wants to belong to the organization to predict decisions to grant the employee voice. Although research on organizational justice as a dependent variable has only recently received research

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attention, the results demonstrate that characteristics of the situation, the manager, and the employees all influence the extent to which justice principles are adhered to by managers. Exploring the interactions between these three antecedents of justice seems like an important direction for future research. It would also be useful to determine whether the antecedents of managerial fair behaviors are the same as managerial unfair behaviors. It is possible that fair behaviors are primarily driven by characteristics of the manager, whereas unfair behaviors are driven by the situation or employees.

FUTURE RESEARCH DIRECTIONS If we look beyond the current research trends, it is possible to identify some future directions for organizational justice research. Researchers have done a good job of dissecting justice into different types, foci, and sources, and have developed theories to explain how and why justice reactions form. Justice concepts have also been applied to a wide variety of organizational decisions. The next step for justice research is to integrate organizational justice into other theories of organizational psychology and management. Specifically, I ask researchers to consider organizational justice relative to other motivators of behavior in organization, justice and leadership theories, justice and ethics, and finally justice at an organizational level. These themes and important research question are summarized in Table 3.3.

When is Justice an Important Motivator? Justice is clearly an important motivator of a variety of workplace attitudes and behaviors. However, it is unclear if justice is always important to people, or only when it is violated. Organ (1990) stated, ‘just as we have little reason to recognize comfort except when struck with the reality of discomfort, we have little reason to think much about fairness until demonstrable unfairness obtains’ (pp. 67–8). Rupp and Spencer (2006) make a similar point that fairness is a ‘camouflaged phenomenon’ in the absence of injustice. Gilliland, Benson, and Schepers (1998) provided evidence that injustice has a stronger impact on behavior than justice and found an interaction between justice and injustice such that when injustice reached a violation threshold, just actions could not overcome the negative effects

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Table 3.3  Future directions for organizational justice research Future research themes

Potential research questions

Justice events

• How frequently do employees experience moderate to severe injustice in the workplace? • What proportion of major organization decisions (e.g., hiring, layoff, etc.) are perceived to be unfair? • How are employee attitudes shaped by the combined effects of first-hand and third person justice perceptions? • To what extent are third-party perceptions shaped by justice versus injustice? • How do individual justice perceptions impact organization level phenomena? • To what extent are individual justice perceptions shaped by organization level justice climate? • How do manager, employee, and situation characteristics interact to influence manager fairness behavior? • Do different factors drive managerial fair behaviors versus unfair behaviors? • What is the motivational force of justice versus injustice? • Does ‘more than fair’ represent a positive end of the justice continuum? • What are the relative and interactive effects of justice versus other motivators of workplace behavior? • To what extent is organizational justice embedded in contemporary leadership theories? • How do leader justice behaviors, subordinate characteristics, and organization situations interact to determine leadership effectiveness? • Does a climate of high organizational justice promote ethical behavior? • Does the fairness of ethics complaint handling impact an organization’s ethical climate and future reporting behavior? • Is a business strategy that emphasizes fairness and justice good or bad for sustained competitive advantage? • How can organizational justice research inform and shape public debate on CEO compensation and income disparity? • Are organizations becoming fairer than they used to be? • To what extent are justice principles shaping management practices and organization policies?

Third-party justice

Multilevel justice Justice as a dependent variable Organizational justice as motivation

Justice and leadership

Justice and ethics

Justice at the organization level

Justice in practice

of injustice. A study using functional magnetic resonance imaging (fMRI) demonstrated that violations of procedural and distributive justice rules activated different brain regions than fair treatment (Dulebohn, Conlon, Sarinopoulos, Davison, & McNamara, 2009). Colquitt, Long, Rodell, and Halvorsen-Ganepola (2015) directly examined the asymmetry between justice and injustice in an inductive study and found distinct construct themes emerged in reactions to injustice vs. justice. In a follow-up survey study, they found that measures designed to assess injustice more strongly predicted some outcomes (e.g., CWB), whereas measures designed to assess justice more strongly predicted other outcomes (e.g., OCB). Gilliland (2008b) builds on the theme of justice and injustice but proposes that both tails of justice – either very unfair or more than fair – provide the most motivation. Further, he suggests that justice experiences in the workplace are normally distributed such that most events fall somewhere between these tails of justice. The implication is that for most people, most of the time, justice is

not a particularly important motivator of action. However, it becomes a powerful motivator when violated or when treatment is more than fair. Research has yet to directly examine the more than fair end of the justice continuum. If justice is only a dominant motivation in extreme situations, an important avenue for future research is to examine the motivation force of justice relative to other motivators such as goals, financial incentives, and intrinsic motivation. Although justice in its original form of equity theory was at one time an important theory of motivation, most organizational justice research has tended to be associated with theories of work attitudes rather than motivation. Further, as organizational psychology is a reductionist science, we usually split broad constructs such as motivation into distinct elements and develop theories for each of those elements. I propose that a fruitful avenue for future research is to examine how justice motives combine with other motives to generate task performance, OCB and CWB. It is possible that most of the time work behavior is

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motivated by intrinsic and extrinsic motivators, but when justice is violated, it provides a motivation that trumps all others.

Justice and Leadership Organizational justice research has many natural connections to leadership. Interpersonal and information justice constructs provide a roadmap for how leaders should communicate and interact with subordinates to encourage positive work attitudes and behavior. Thus, it is surprising that there is not yet an organizational justice theory of leadership. A number of contemporary leadership theories contain components of organizational justice. Ethical Leadership Theory has demonstrated connections to organizational justice through identifying the importance of concerns for follower needs, granting voice to followers, and ensuring fair decision processes (Brown, Treviño, & Harrison, 2005). Research on Transformational Leadership has demonstrated that when a leader is perceived to be transformational (as opposed to transactional), followers tend to display higher levels of trust in the leader, and in turn evaluate leaders’ explanations to be more adequate, sincere, and appropriate (Holtz & Harold, 2008). In the first step toward developing an organizational justice theory of leadership, Jouglard and Steiner (2015) developed a model of fair leadership, which proposed that leaders’ values and competencies, along with social norms, influence leaders’ motivation to act fairly with respect to allocation of resources, use of voice, respect for ethics and individuals in decision making, and the informational and interpersonal implementation of decisions. As yet, research has not tested this model of fair leadership, but it offers a number of interesting research questions and highlights important links between organizational justice research and leadership theories. Recent organizational justice research that treats justice as a dependent variable has much in common with contemporary leadership theories by examining characteristics of the decision maker, recipient, and situation that influence how leaders enact justice principles. Current leadership writing also emphasizes the intersection of leader, follower, and the situation to predict leadership effectiveness (Padilla, 2013). Are there some situations or specific types of followers that respond most positively to leadership styles that embrace dimensions of organizational justice? Along this line, Li, Evans, and colleagues (2011) examined the beneficial effects matching a regulatory fit in communicating messaging a negative decision.

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They found that perceptions of fairness were higher when a regulatory focus prime (promotion vs. prevention) was congruent with the gain vs. loss framing of an explanation for a potentially negative decision. It is clear that there are many other potentially interesting and important research questions that can be studied by merging leadership research with organizational justice research.

Justice and Ethics There are strong natural connections between organizational justice and ethics. Organizational justice has been shown to predict a number of counterproductive behaviors that include unethical actions (e.g., Colquitt et al. 2013; Greenberg, 1990). Further, the deontic model of justice suggests a moral underpinning to reactions against injustice. It is surprising there is not more overlap between these two fields. Cropanzano and Stein (2009) observe that organizational justice scholars have been slow to incorporate insights from behavioral ethics research and offer some suggestions as to how the different research focuses of these two disciplines may have prevented crossfertilization of ideas. Research on moral identity and moral selfregulation begins to bridge the divide between justice and ethics. Skarlicki and Rupp (2010) integrate a dual-processing perspective into thirdparty reactions to injustice and demonstrated that people primed to process justice events experientially (i.e., be open to their feelings and intuition) as opposed to rationally, were more retributive in their justice reactions. Further, this effect was moderated by moral identity, such that those higher in moral identity were more retributive regardless of the priming condition. This research suggests that some people are more likely to view situations as unethical, but that having people be more intuitive and responsive to their feelings may also be a means of promoting ethics. Future research should consider whether leaders’ adherence to justice principles can create an ethical climate and culture. Are people less likely to engage in unethical behavior and more likely to report ethical violations when operating in a climate of high organizational justice? Research on other motivators of unethical behavior (e.g., compensation practices) and fair leadership could also provide important insights into these issues. Another potential area of overlap between justice and ethics research is in the organization’s handling of ethical complaints. Does the fairness

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with which organizations are perceived to respond to reports of ethical violations impact future reporting behavior? Does the fairness of ethics complaint handling contribute to an ethical culture or climate?

Organizational Justice at the Organization Level Perhaps one of the most surprising omissions in organizational justice research is consideration of justice at the organizational level. Schminke, Cropanzano, and Rupp (2002) examined the relationship between organizational structure and fairness reactions and found that centralization and formalization exerted main effects on perceptions of organizational justice. While this research examined the influence of organizational structure on individual justice reactions, it did not examine the extent to which organizations act fairly or unfairly. Recent research on corporate social responsibility (CSR) begins to extend justice concepts to organization level actions. Rupp, Shao, Thornton, and Skarlicki (2013) used a deontic justice perspective to examine reactions to CSR initiatives and demonstrated that relationships between CSR perceptions and applicant job pursuit intentions and employee citizenship behaviors were attenuated by first-hand distributive justice perceptions. Organizational justice theories may help us understand strategic management concepts of strategic competition and executive compensation. When is competitive strategy seen as good business versus unfair competition? Clearly, some people felt Microsoft crossed this line when an antitrust lawsuit was brought against them for bundling their internet explorer into their operating system, thereby providing an unfair competitive advantage to their internet browser. However, Microsoft has also maintained a position of sustained competitive advantage for several decades. Is justice ultimately beneficial or harmful for strategic positioning? Principles of distributive, procedural, and informational justice may offer a useful theoretical perspective for examining reactions to and effectiveness of competitive strategy. It is somewhat ironic that the growth of organizational justice research has coincided with a widening gap between CEO compensation and the pay of average workers (Gilliland, Gross, & Hogler, 2014). As researchers have identified factors of what makes organizational decisions fair, top executive compensation had become more unfair, at least in the eyes of some in the general public. It would be interesting to see whether organizational

justice research could help inform this debate and help create a more equitable distribution of wealth in organizations. Gilliland and Anderson (2011, 2014) developed an organizational justice model of perceptions of greed that may contribute to this discussion. They suggest that entities are perceived to be greedy when they are actively seen as taking more of a scarce resource than they need and deserve, violating principles of distributive and procedural justice to the detriment of others. It is possible that this model could be used to understand third-party reactions to CEO compensation and influence societal debate on growing wage disparities in the USA. It would be good to see organizational justice research have this broad public impact.

Implication for Practice Organizational justice research has clear implications for managers and leaders in organizations. Principles of distributive, procedural, interpersonal, and informational justice provide a roadmap for treating employees fairly in general and during specific justice events (e.g., layoffs or disciplinary decisions). Further, research on the consequences of justice provides a compelling business case for treating employees fairly. Finally, uncertainty management theory and associate research demonstrate that justice is particularly important early in a work relationship and during times of greater uncertainty. Both Cropanzano et  al. (2007) and Brockner (2006) published managerial-oriented articles highlighting the what, why, and how of organizational justice. Despite the obvious practical implications and publications in practitioner journals, it is unclear to what extent managers and organizations are becoming fairer. There are no longitudinal studies demonstrating trends in fairness perceptions across time. In fact, there is no systematic baseline data demonstrating levels of justice across organizations. Anecdotally, it appears that more information is being shared with employees and organization decision processes are more systematic than in the past. On the other hand, since organizational justice theories were introduced in the 1980s, at least in the United States, employee wages have stagnated, employee benefits have been cut, and work hours have increased. Therefore, it is not clear whether organizations have actually become fairer. It would be useful to have systematic evidence on ways in which organizational justice concepts are being implemented in organizations in both developed and developing countries.

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CONCLUSION Organizational justice has emerged as one of the more significant areas of research in organizational psychology. Perceptions of fairness influence a wide variety of work attitudes and behaviors, and have also been shown to impact worker health and wellness. Current research examines how justice perceptions emerge and change over time, thirdparty reactions to justice, how groups experience justice, and why and when managers act fairly and unfairly. I have suggested that future research should take justice research beyond the realm of organizational justice constructs to understand broader issues of motivation, leadership, ethics, and strategic management. I also suggested that we need to give greater attention to the adoption of justice principles by managers and organizations. I suspect Stacy Adams would be very surprised to see how his ideas of equity have been developed over the past 50 years. It will be interesting to see if organizational justice research continues to evolve or if researchers move on to different topics. Given the potential for organizational justice to impact important issues of leadership, business ethics, strategic competition, and societal equity, I hope organizational justice continues to garner research attention.

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relations between organizational justice perceptions and depressive symptoms. Journal of Applied Psychology, 96, 602–618. Lavelle, J. J., Rupp, D. E., & Brockner, J. (2007). Taking a multifoci approach to the study of justice, social exchange, and citizenship behavior: The target similarity model. Journal of Management, 33, 841–866. Leventhal, G. S. (1980). What should be done with equity theory? New approaches to the study of fairness in social relationship. In K. J. Gergen, M. S. Greenberg, & R. H. Willis (Eds.), Social Exchange: Advances in Theory and Research (pp. 27–55). New York: Plenum. Li, A., & Cropanzano, R. (2009). Fairness at the group level: Justice climate and intra unit justice climate. Journal of Management, 35, 564–599. Li, A., Cropanzano, R., & Benson, L. (2011). Peer justice and teamwork process. Group and Organization Management, 36, 567–596. Li, A., Evans, J. M., Christian, M. S., Gilliland, S. W., Kausel, E. E., & Stein, J. H. (2011). The effects of managerial regulatory fit priming on reactions to explanations. Organizational Behavior and Human Decision Processes, 115, 268–282. Liao, H., & Rupp, D. E. (2005). The impact of justice climate and justice orientation on work outcomes: A cross-level multi-foci framework. Journal of Applied Psychology, 90, 242–256. Libby, T. (1999). The influence of voice and explanation on performance in a participative budgeting setting. Accounting Organizations and Society, 24, 125–137. Lind, A. E., & Tyler, T. R. (1988). The social psychology of procedural justice. New York: Plenum. Lind, A. E., & Van den Bos, K. (2002). When fairness works: Toward a general theory of uncertainty management. Research in Organizational Behavior, 24, 181–223. Lind, A. E., Kray, L., & Thompson, L. (1998). The social construction of injustice: Fairness judgment in response to own and others’ unfair treatment by authorities. Organizational Behavior and Human Decision Processes, 75, 1–22. Masterson, S. S. (2001). A trickle-down model of organizational justice: Relating employees’ and customers’ perceptions of and reactions to fairness. Journal of Applied Psychology, 86, 594–604. Masterson, S. S., Lewis, K., Goldman, B. M., & Taylor, M. S. (2000). Integrating justice and social exchange: The differing effects of fair procedures and treatment on work relationships. Academy of Management Journal, 43, 738–748. Maxham, J. G., & Netemeyer, R. G. (2003). Firms reap what they sow: The effects of employee shared values and perceived organizational justice on customer evaluations of complaint handling. Journal of Marketing, 67, 46–62.

Maxham, J. G., Netemeyer, R. G., & Lichtenstein, D. R. (2008). The retail value chain: Linking employee perceptions to employee performance, customer evaluations, and store performance. Marketing Science, 27(2), 147–167. Moorman, R. H. (1991). Relationship between organizational justice and organizational citizenship behaviors: Do fairness perceptions influence employee citizenship? Journal of Applied Psychology, 76, 845–855. Moorman, R. H., & Byrne, Z. S. (2005). How does organizational justice affect organizational citizenship behavior? In J. Greenberg & J. A. Colquitt (Eds.), Handbook of organizational justice (pp. 355–380). Mahwah, NJ: Erlbaum. Naumann, S. E., & Bennett, N. (2000). A case for procedural justice climate: Development and test of a multilevel model. Academy of Management Journal, 43, 881–889. Organ, D. W. (1990). The motivational basis of organizational citizenship behavior. In L. L. Cummings & B. M. Staw (Eds.), Research in organizational behavior (pp. 43–72). Greenwich, CT: JAI. Padilla, A. (2013). Leadership: Leaders, followers, and environments. New York: Wiley. Patient, D. L., & Skarlicki, D. P. (2010). Increasing interpersonal and informational justice when communicating negative news: The role of managers’ empathic concern and moral development. Journal of Management, 36, 555–578. Robbins, J. M., Ford, M. T., & Tetrick, L. E. (2012). Perceived unfairness and employee health: A meta-analytic integration. Journal of Applied Psychology, 97(2), 235–272. Roberson, Q. M. (2006). Justice in teams: The activation and role of sensemaking in the emergence of justice climates. Organizational Behavior and Human Decision Process, 100, 177–192. Rodell, J. B., & Colquitt, J. A. (2009). Looking ahead in times of uncertainty: The role of anticipatory justice in an organizational change context. Journal of Applied Psychology, 98, 989–1002. Rupp, D. E., & Spencer, S. (2006). When customers lash out: The effects of customer interactional injustice on emotional labor and the mediating role of discrete emotions. Journal of Applied Psychology, 91, 971–978. Rupp, D. E., Shao, R., Jones, R. S., & Liao, H. (2014). The utility of a multifoci approach to the study of organizational justice: A meta-analytic investigation into the consideration of normative rules, moral accountability, bandwidth-fidelity, and social exchange. Organizational Behavior and Human Decision Processes, 123, 159–185. Rupp, D. E., Shao, R., Thornton, M. A., & Skarlicki, D. P. (2013). Applicants’ and employees’ reactions to corporate social responsibility: The moderating

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effects of first-party justice perceptions and moral identity. Personnel Psychology, 66, 895–933. Schminke, M., Cropanzano, R. S., & Rupp, D. E. (2002). Organization structure and fairness perceptions: The moderating effects of organization level. Organizational Behavior and Human Decision Processes, 89, 881–905. Scott, B. A., Colquitt, J. A., & Paddock, E. L. (2009). An actor-focused model of justice rule adherence and violation: The role of managerial motives and discretion. Journal of Applied Psychology, 94, 756–769. Scott, B. A., Garza, A., Conlon, D., & Kim, Y. J. (2014). Why do managers act fairly in the first place? A daily investigation of ‘hot’ and ‘cold’ motives and discretion. Academy of Management Journal, 57, 1535–1543. Skarlicki, D. P., & Folger, R. (1997). Retaliation in the workplace: The roles of distributive, procedural, and interactional justice. Journal of Applied Psychology, 82, 434–443. Skarlicki, D. P., & Rupp, D. E. (2010). Dual processing and organizational justice: The role of rational versus experiential processing in third-party reactions to workplace mistreatment. Journal of Applied Psychology, 95, 944–952. Skarlicki, D. P., Ellard, J. H., & Kelln, B. R. C. (1998). Third-party perceptions of a layoff: Procedural, derogation, and retributive aspects of justice. Journal of Applied Psychology, 83, 119–127. Skarlicki, D. P., van Jaarsveld, D., & Walker, D. D. (2008). Getting even for customer mistreatment: The role of moral identity in the relationship between customer interpersonal injustice and employee sabotage. Journal of Applied Psychology, 93, 1335–1347. Son, J. Y., & Kim, S. S. (2008). Internet users’ information privacy-protective responses: A taxonomy and nomological model. MIS Quarterly, 32, 503–529. Spencer, S., & Rupp, D. E. (2009). Angry, guilty, and conflicted: Injustice toward coworkers heightens emotional labor through cognitive and emotional mechanisms. Journal of Applied Psychology, 94, 429–444. Stouffer, S. A., Suchman, E. A., DeVinney, L. C., Star, S. A., & Williams, R. M. (1949). The American soldier: Adjustment during Army life. Princeton, NJ: Princeton University Press. Takeuchi, R., Chen, Z., & Cheung, S. Y. (2012). Applying uncertainty management theory to employee voice behavior: An integrative investigation. Personnel Psychology, 65, 283–323.

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Taylor, M. S., Tracy, K. B., Renard, M. K., Harrison, J. K., & Carroll, S. J. (1995). Due process in performance appraisal: A quasi-experiment in procedural justice. Administrative Science Quarterly, 40, 495–523. Thibaut, J., & Walker, L. (1975). Procedural justice: A psychological analysis. Hillsdale, NJ: Erlbaum. Thibaut, J., & Walker, L. (1978). A theory of procedure. California Law Review, 66, 541–566. Turillo, C. J., Folger, R., Lavelle, J. J., Umphress, E., & Gee, J. (2002). Is virtue its own reward? Selfsacrificial decisions for the sake of fairness. Organizational Behavior and Human Decisions Processes, 89, 839–865. Tyler, T. R., & Blader, S. L. (2003). The group engagement model: Procedural justice, social identity, and cooperative behavior. Personality and Social Psychology Review, 7, 349–361. Tyler, T. R., & Lind, E. A. (1992). A relational model of authority in groups. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 25, pp. 115–191). San Diego, CA: Academic Press. Van den Bos, K. (2001). Fairness heuristic theory: Assessing the information to which people are reacting has a pivotal role in understanding organizational justice. In S. W. Gilliland, D. D. Steiner, & D. P. Skarlicki (Eds.), Theoretical and cultural perspectives on organizational justice (pp. 63–84). Greenwich, CT: Information Age. Viswesvaran, C., & Ones, D. S. (2002). Examining the construct of organizational justice: A meta-analytic evaluation of relations with work attitudes and behaviors. Journal of Business Ethics, 38, 193–203. Walker, L., Lind, E. A., & Thibaut, J. (1979). The relation between procedural and distributive justice. Virginia Law Review, 65, 1401–1420. Weiss, H. M., & Cropanzano, R. (1996). Affective events theory: A theoretical discussion of the structure, causes and consequences of affective experiences at work. Research in Organizational Behavior, 18, 1–74. Weiss, H. M., Suckow, K., & Cropanzano, R. (1999). Effects of justice conditions on discrete emotions. Journal of Applied Psychology, 84, 786–794. Whitman, D. S., Caleo, S., Carpenter, N. C., Horner, M. T., & Bernerth, J. B. (2012). Fairness at the collective level: A meta-analytic examination of the consequences and boundary conditions of organizational justice climate. Journal of Applied Psychology, 97, 776–791.

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Part II

Motivational Perspectives

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4 The Building Blocks of Motivation: Goal Phase System Piers Steel and Justin M. Weinhardt

INTRODUCTION Motivation all begins with a goal. To be motivated, we need some cognitive representation of an end-state, of what is to be acquired, conducted, or achieved (Austin & Vancouver, 1996). We may or may not be aware of this goal, it may be explicit or implicit, but without it, our behavior is effectively random. Motivation gives directions to our actions. While this direction is inherently important, a series of researchers make the case that motivation will become among the most important fields of social science. As civilization advances and prosperity becomes widespread, we remove most of the external maladies that were once major contributors to our misery (e.g., predation, starvation, lack of shelter). By default, the source of our failures increasingly becomes ourselves. Ainslie (2005) argues, ‘We smoke, eat and drink to excess, and become addicted to drugs, gambling, credit card abuse, destructive emotional relationships, and simple procrastination, usually while attempting not to do so’ (p. 635). Stanovich (1999) believes it is even worse; we exist in an increasingly artificial or built environment that has sporadic overlap with the environment of evolutionary adaption. Since, our motivational impulses are fine-tuned to the latter, rather than the former, we increasingly

find ourselves motivationally adrift, knowing what to do but not being motivated to do it. Steel (2010) stresses that this built environment is not necessarily motivationally neutral; free market capitalism ensures it is constructed with considerable design, including features that can coax maladaptive behaviors, particularly overconsumption. Putting candy and lottery tickets by the checkout counter is an example of an insidious but common motivational praxis. Building on this line of reasoning, Heath (2014) makes an extended case that this will likely get worse, that ‘absent conscious guidance, cultural evolution will produce an environment that is more hostile to human rationality’ (p. 146). We need conscious, rational guidance, which must be based on a firm understanding of our motivational foundation. Unfortunately, the importance of motivation has led to an unwieldy number of theories on the topic, making understanding or advancement difficult. For example, Zeidner, Boekaerts, and Pintrich (2000) note, ‘the fragmentation and disparate, but overlapping, lines of research within the selfregulation domain have made any attempt at furthering our knowledge an arduous task’ (p. 753). Diefendorff and Lord (2008) express a similar sentiment, ‘the most important future development in self-regulation research will involve integrating these approaches so as to develop a more

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comprehensive understanding of goal-directed behavior’ (p. 163). Locke and Latham (2004), writing about the future of motivational research, conclude, ‘there is now an urgent need to tie these theories and processes together into an overall model’ (p. 389). And Schmidt, Beck, and Gillespie (2013), while acknowledging these previous calls, argued that integration has become ‘all the more important as the motivational sciences continue to mature and expand their focus’ (p. 332). Despite the observed complexity of behavior, the unity we are seeking as a field could be obtained because the underlying major motivational mechanisms are relatively simple and few in number. Chaos theory has shown that considerable system complexity can arise from rather simple underpinnings (e.g., Mandelbrot sets). Following up on this insight, Navarro and Arrieta (2010), while examining the chaotic nature of work motivation, concluded that ‘no more than three or four variables would be required to explain the dynamics of work motivation’ (p. 253) and argued that ‘mega-models of motivation developed from a large number of potential explanatory variables’ were misguided. While three variables might be an overly aggressive reduction, our goal here is not to catalog all previous motivational perspectives, but to focus on these likely motivational fundamentals. We start with temporal motivation theory (Steel & König, 2006), a meta-theory that Anderson (2007) concludes, ‘may prove successful … in reducing the number of theories into a smaller subset of theories that have a wider range of applicability’ (p. 763). We then extend this with an examination of goal phases, which indicates fundamentally different formulations may be required to map different steps in the motivational process (Gollwitzer, 1990). Finally, we incorporate cybernetic or control theories of motivation, particularly the computational theory of multiple-goal pursuit (Vancouver, Weinhardt, & Schmidt, 2010a; Vancouver, Weinhardt, & Vigo, 2014), which was specifically designed ‘to continue the process of integrating motivational theories’ (Vancouver et al., 2010a, p. 1002). We consider each in turn as well as combine them, calling the resulting framework the Goal Phase System (GPS). It has three major phases (Goal Choice, Goal Planning, and Goal Striving), in which motivation unfolds differentially across each stage. After integration, we review how most self-regulatory or motivational interventions can be understood as modifying specific elements of the motivational process during discrete goal phases.

Temporal Motivation Theory One strategy for determining what is fundamental to motivation is simply to identify repeating themes. If multiple investigations all agree, this is an excellent example of consilience (Wilson, 1998), a strong form of scientific proof. This was Steel and König’s (2006) approach for developing temporal motivation theory, a meta-theory explicitly designed to integrate the key features of other motivational formulations. Examining hyperbolic discounting and the matching law (with origins from behaviorism), expectancy × value formulations (with origins from economics), cumulative prospect theory (with origins in psychophysics), and need theory (with origins in personality research), they found several reoccurring features (Hodgkinson & Healey, 2008). In all, Steel and König argue, ‘motivation can be understood by the effects of expectancy and value, weakened by delay, with differences for rewards and losses’ (p. 897). The strengths of these four motivational elements are influenced by both the individual and the situation and can be examined from both perspectives (e.g., personality versus behaviorism). Aside from integration, a benefit of temporal motivation theory was to explicitly incorporate time into our motivational models, something that field had been lacking (e.g., Fried & Slowik, 2004; Locke & Latham, 2004; Mitchell & James, 2001; Schmidt et al., 2013). When one is evaluating among multiple possible outcomes for an act, some positive and others negative, a more complete but also more complex version of temporal motivation theory is recommended, which Steel and König (2006) cover in detail. For the purposes of this review, we will focus on the simplified form of temporal motivation theory, where people are evaluating one possible outcome instead a multiplicity of them, which consequently does not require modeling different functions for losses versus gains. Before considering each component in detail, we briefly review the overall equation: Motivation =

Expectancy × Value 1 + Impulsiveness × Delay

Expectancy occurs in each theory, except in some forms of matching law (e.g., Mazur, 1987). It represents the perceived probability that an outcome will occur, dependent on both the situation and individual differences. Trait pessimism and challenging obstacles, for instance, will drive expectancy downwards. Value occurs in all theoretical formulations and reflects the attractiveness of an event. Like expectancy, this is influenced by both the situation and individual differences. The denominator of the equation captures time or

The Building Blocks of Motivation: Goal Phase System

temporal discounting, which appears in hyperbolic discounting, behaviorism, and need theory (i.e., press). Being on the bottom of the equation, as their combined effects increase, motivation decreases. There are three components of temporal discounting. The first is Impulsiveness, which refers to three or four different types of individual differences (Sharma, Markon, & Clark, 2014; Whiteside & Lynam, 2001) but here focuses on the urgency or people’s sensitivity to delay use of the term (Cyders & Smith, 2008). The second is the Delay itself, which represents the perceived nearness or time required to realize an outcome. The third is the constant ‘1’. This defines the upper limit of temporal discounting as it prevents motivation becoming infinite when delay is effectively zero. As Lord, Diefendorff, Schmidt, and Hall (2010) note, temporal motivation theory has been particularly useful for modeling ‘the motivating power of approaching deadlines’ (p. 550), especially procrastination. This partly reflects that the development of the theory drew upon a meta-analytic review of procrastination (Steel, 2007), and we use this selfregulatory failure here to demonstrate the theory’s workings. Temporal motivation theory is atypical in that it does not assume work motivation is always optimal but at times can operate dysfunctionally. Procrastination is inherently irrational, defined as the voluntary delay of an intended course of action despite expecting to be worse off for the delay (Klingsieck, 2013) that is putting off despite expecting to be worse off. Despite numerous theories of perfectionism or anxiety being the primary source of irrational delay (e.g., Pychyl & Flett, 2012), it is largely a function of temporal discounting. Gustavson, Miyake, Hewitt, and Friedman (2014), using twin research to test temporal motivation theory, found 100% of the genotypic variance in procrastination was due to impulsiveness. Procrastination arises when we make long-term plans of actions, such as to start saving or writing next month. Since all possible actions for next month are similarly discounted, delay has minimal impact and goal choices are primarily made by expectancy and value. However, when next month becomes today, civilization has furnished us with an impressive array of temptations that are immediately available. If the intended task can be delayed, a slippery deadline, or if the firm end date is still far in the future, a hard deadline, the choice often becomes between one minimal discounted temptation and one significantly discounted task. It is often not until the final hour, when both temptation and task have imminent consequences, that we become fully engaged with the work at hand. Having given an overview of temporal motivation theory, we proceed to examine each component: Expectancy, Value, Impulsiveness, and

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Approach versus Avoidance Orientation. In keeping with an integrative focus, we review each of these constructs broadly, where the plurality of variation across each component is considered minor or superficial.

Expectancy Expectancies refer to beliefs about contingencies and are often described as the belief of some outcome occurring based on some action (Lewin, 1951; Tolman, 1938). Regarding work motivation, expectancy played an essential role in early theories (Vroom, 1964) and still plays an indispensable part in current theories of work motivation (e.g., Vancouver et al., 2010a, 2014). However, the nature and definition of expectancy has expanded over the years (Vancouver et al., 2013). Self-efficacy is the most prominent concept in the expectancy family. Bandura (1977) proposes that self-efficacy is different from expectancy because the focus is on the belief in the capacity to achieve a goal and if they do not have a high belief in their capacity, they will be less likely to achieve their goal. Other concepts related to expectancy include confidence/overconfidence (Dunning, Heath, & Suls, 2004; Moore & Healy, 2008), optimism (Carver & Scheier, 1998), optimism bias (Sharot, 2011) and risk seeking or aversion (Zuckerman, 2007). The field of work motivation has largely overlooked the multitude of concepts relating to expectancy. We propose that in most situations, these terms can be used interchangeably, often are (e.g., Locke & Latham, 2002), and can then be largely considered old wine with new labels (Kirsch, 1985; Vancouver et  al., 2013). In addition, there has been a large discussion about the validity of the concept and its relationship with motivation (Bandura, 2012; Bandura & Locke, 2003; Sitzmann & Yeo, 2013; Vancouver, 2005, 2012; Yeo & Neal, 2013). We propose that how and when expectancy is measured matters more than what it is called (Yeo & Neal, 2006). As we will explore in more detail during subsequent sections, expectancy (and related concepts) have different effects depending on the goal phase (Vancouver, More, & Yoder, 2008). Generally, expectancy has a positive effect on goal choice, a negative effect on goal planning, and a mixed effect on goal striving.

Value It is actually surprisingly difficult to define in a meaningful way what we value, has utility, find

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rewarding, or reinforcing. For example, Rescher (1982) summarized philosophical and social science’s considerable attempts at providing a coherent definition of value as having ‘failed’ (p. 1). The difficulty lies in that value, utility, rewards, and reinforcers are essentially unseen entities that are inferred to exist from the effects they produce. Consequently, despite inauspicious beginnings in a nursing journal, the adage ‘what gets rewarded, gets done’ (Holle & Armocida, 1988, p. 84) concisely captures our understanding. It is essentially identical to the behaviorists’ position, who define positive reinforcers as stimuli ‘that increase the likelihood of the behavior that precedes them’ (Schwartz, 1989; p. 28). Or consider economists. Hodgson (2011), when reviewing economic utility, notes that the concept fits everything yet explains nothing as it is an unobservable ‘blank cheque’ that can explain any behavior without fear of refutation. It is circular logic if we explain our behavior by unseen preferences and then identify these same preferences by what is revealed by our behavior. Attempts to escape this tautology do not get far beyond the prison walls, such as Cameron and Pierce’s (1994) definition: ‘Rewards are stimuli that are assumed to be positive events, but they have not been shown to strengthen behavior’ (p. 364). Actually, we cannot even go so far as this, stating that rewards are positive events. Addiction research has demonstrated we can want what we do not like, and feel compelled to pursue paths that bring us pain (Berridge, 2009). Presently, rewards can only be definitively revealed by seeing their effects on people’s actions, or as economists like to say ‘De gustibus non est disputandum’ (Stigler & Becker, 1977, p. 76): There is no accounting for tastes. Despite the challenge that preferences may need to be revealed, the construct of value is indispensable. All motivational theories contain a form of value, for without it there can be no ‘why’. The dominant framework for understanding the issue of why has been to break down rewards into intrinsic and extrinsic, with Reiss (2004) tracing this division back to Aristotle and his discussion of motives as being intrinsically an end, done for its own sake, or extrinsically a means, done instrumentally to enable some other outcome. Consequently, we are extrinsically motivated when we are driven by the outcome of our actions, such as for financial incentives, but intrinsically motivated when driven by rewards inherent in the task itself (Ryan & Deci, 2000). The difference between the two is not always readily apparent. For example, is raising a fork to one’s mouth instrumental, so one can experience the pleasure of eating, or intrinsic, part of the process of eating? Given this ambiguity, Thierry (1990) concluding

that ‘the intrinsic–extrinsic motivation distinction is based on a delusion’ (p. 80); all rewards, both intrinsic and extrinsic, create an internal state of motivation spurred by some external stimuli. Despite the tension between the two, the distinction of intrinsic–extrinsic has proven useful. We use rewards to incent a wide variety of behaviors and there can be conflict among types of rewards. It is possible that incentives we purposefully tack on to tasks interfere with those already naturally or intrinsically occurring. Cerasoli, Nicklin, and Ford (2014) conducted a meta-analysis that examined the interrelationship among intrinsic motivation, incentives and performance. In addition, they examined two key moderators, how performance was measured (quantity versus quality) and the salience of the incentives (directly tied to performance versus indirectly tied to performance). Cerasoli et  al. found that intrinsic motivation is positively related to performance across a number of domains (e.g., work, school and physical activity). They also found that when incentives were added in combination with intrinsic motivation, this actually increased performance. However, when incentives were directly tied to performance, the effect of intrinsic motivation was depressed. On the other hand, when incentives were indirectly tied to performance, intrinsic motivation was an important determinant predictor of performance. This result supports motivational crowding (Frey & Jegen, 2001), where extrinsic incentives can overshadow other motivational forces. Finally, intrinsic motivation was a better predictor of quality performance, whereas incentives were a better predictor of quantity performance. This work shows that incentives and intrinsic motivation can work together in concert to increase motivation and performance, but researchers and managers need to be careful about what type of performance they want (quantity or quality) and how the incentives are tied to performance (directly or indirectly). While motivation researchers know that there is much more to rewards than financial incentives, more could be done here. Personality and need theory has done the most work on explicating what type of rewards are associated with individual profiles, beginning with Murray’s (1938) catalog of needs. Presently, a large part of personality’s predictive power comes from assessing three profiles of needs – need for affiliation, need for achievement, need for power – and using them to suggest other behavior of this kind is likely forthcoming (Winter, 1996). For example, DeShon and Gillespie (2005) consider fundamental categories of goal content, the ‘why’ of motivation, are to address: agency (power), esteem (achievement), and affiliation. Still, these three primary needs are not an exhaustive list and there are other

The Building Blocks of Motivation: Goal Phase System

well-supported taxonomies to consider, such as Haidt’s (2012) six elements comprising Moral Foundation Theory or Reiss’ (2004) argument for sixteen primary motives. For example, ethics, respect, or justice are important for job seekers selecting organizations (Ogunfowora, 2014) and are unlikely to be fully subsumed under needs for affiliation, achievement, and power. What is the exact relationship between behavior and the type of rewards? To what degree is one type of motivator fungible with that of another? By expanding our motivational palette, we should be rewarded with improved prediction and understanding of behavior.

Impulsiveness Over two millennia ago, Buddha likened his mind to a wild elephant, full of lust and desire but kept in check by a trainer (Haidt, 2006). Plato describes our motivations as a chariot being pulled by two horses, one of reason, well-bred and behaved, and the other of brute passion, ill-bred and reckless. At times, the horses pull together and at other times, they pull apart. Adam Smith, in his book The Theory of Moral Sentiments, wrote about the balance between ‘the passions’ and ‘the impartial spectator’, where the latter tries to moderate the former’s excesses of lust, hunger, and anger (Ashraf, Camerer, & Loewenstein, 2005). And Sigmund Freud continued Plato’s equestrian analogy by comparing us to a horse and rider. The horse is desire and drive personified, powerful but needs direction from the rider, who represents reason and commonsense. This division has been rediscovered by dozens of other investigators, each with their own angle, emphasis, and terminology for the same multiple self. Along with 25 other terms documented by Stanovich (2011), these include: emotions versus reason, habit versus planned, experiential versus rational, hot versus cold, affective versus deliberative, impulsive versus reflective, viscerogenic versus psychogenic, Dionysian versus Apollonian, intuitive versus reasoning (Baumeister, 2005; Bechara, 2005; Bernheim & Rangel, 2002; Redish, Jensen, & Johnson, 2008). Among all these possibilities to describe this split, and evidently drawing on the full poetry of science, the blandest seems to have gained the most traction: System 1 versus System 2 (Kahneman, 2003). More recently, with the onset of fMRI studies that reveal thinking in situ, we come to see this duet as less of an analogy and more of an accurate account of how we make decisions. Falling under the larger rubric of dual process theories

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(Evans, 2008), the interplay between System 1 and System 2 is reflected in our brain’s very architecture. System 1 or the limbic system (more specifically, the mesolimbic dopaminergic system such as the amygdala and nucleus accumbens) is the evolutionarily older, and its purview is often the here-and-now. It is aroused by sensations of sight, smell, sound, or touch that usually indicates ‘immediately available’, and this often results in heightened cravings and impulsiveness. System 2 is part of the neocortex or ‘new bark’, specifically the prefrontal cortex, and it enables long-term plans and consideration of our extended future. As might be expected by the numerous analogies to a rider atop some great beast, System 1 with its direct line to amygdala (the source of our strong emotions) tends to be stronger and quicker than System 2. Gifford (2002) provides a particularly good account of the resulting conflict (p. 129): It is this divergence between the cultural and biological rates of time preference that creates a potential internal nature versus nurture conflict leading to self-control problems [like procrastination]. The higher level prefrontal working memory system allows the agent to consider possible events in the extended future and to discount those events at a rate appropriate to the individual’s current environment. The lower level [limbic system] does not have access to events not yet experienced, and as a result, ignores these purely abstract events; it also incorporates the high level discount rate similar to that used by non-human primates and some other mammals that is a product of natural selection.

In the parliament of the mind, it is common for the carefully laid plans of the prefrontal cortex to be trumped by the cravings from the limbic system (Bechara, 2005). When our decision-making becomes limbic heavy or prefrontal light, we refer to this as impulsive. Reflecting impulsiveness’ System 1 versus System 2 roots, Sharma et  al. (2014) show that this dichotomy shows up at a measurement level. Some impulsiveness assessments, such as the Premeditation scale of the UPPS (Whiteside & Lynam, 2001), focus on the limbic Disinhibition vs. Constraint aspect. Other measures, such as the UPPS’ Perseverance scale, focus on prefrontal Will vs. Resourcelessness. The work motivational field studies impulsiveness, though often divorced from the biological underpinnings and unintegrated with parallel programs conducted under different terms. For example, Schouwenburg (2004) notes that ‘Various studies show a very distinct clustering of related traits: trait procrastination, weak impulse control, lack of persistence,

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lack of work discipline, lack of time management skill, and the inability to work methodically’ (p. 8). Given the number of work studies that include conscientiousness, where many of these impulsive related facets are nested, particularly those Will vs. Resourcelessness-related, the field has indeed been researching this, but predominantly as personality traits, which tend to be atheoretical or descriptive. Their connection to the process or explanatory model of the limbic-prefrontal cortex duet is often overlooked, which if attended to would considerably reduce construct proliferation.

Approach vs. Avoidance Whereas motivation can be divided by whether it is System 1 or System 2, it can also be divided by gains and losses or approach and avoidance. Are we seeking an outcome or seeking to avoid it? During the construction of temporal motivation theory, this feature was found in prospect theory and need theory, but it was extremely well supported by a variety of other investigations (Carver, Sutton, & Scheier, 2000; Elliot & Thrash, 2002; Higgins, 1997; Ito & Cacioppo, 1999). Since this time, support has further deepened to justify a dedicated handbook on the topic (Elliot, 2008), who in a historical review also makes an excellent case that this distinction is ‘one of the oldest ideas in the history of psychological thinking about organisms’ (Eilliot, 2006, p. 111). At its basic level, the approach versus avoidance dichotomy means that expectancy, value, and impulsiveness will be modified depending upon whether we have framed our goal as something to be lost rather than something to be gained. We will become more risk prone or risk averse, more avaricious or less, and more impulsive or more patient. The appearance of an approach or avoidance mindset is influenced both by personality traits (e.g., attributional style, Weiner, 1991) and by what the task itself evokes (e.g., when mistakes are very costly, we might find ourselves focusing on avoiding them). Temporal motivation theory unevenly reviews how expectancy, value, and impulsiveness change with an approach versus avoidance mindset. Consistent with prospect theory, a threat of a loss is considered more motivating that the equivalent gain. Consequently, the frame with which we make decisions matters, as we tend to be risk seeking to avoid a loss but risk avoidant to keep a gain. Behavioral economics and neuroeconomics, which have blossomed over recent years, provides support and refinement. On average, we tend to adopt the avoidant mindset and tend to be risk

averse. For example, two large natural field experiments are especially evocative. Using data from the internationally successful television shows, Who Wants to be a Millionaire? and Deal or No Deal (Hartley, Lanot, & Walker, 2013), contestants show a degree of risk aversion on average. When, for example, posed between two equally likely outcomes, one of possibly loosing $99,999 but at the chance of gaining $400,000 more, contestants are more likely to choose the ‘sure thing’, and stick with their present winnings of $100,000, despite this being inferior in terms of expected value. Supporting this finding is the neurobiology of decision-making, sometimes termed neuroeconomics, which is proceeding at a rapid pace and ‘aiming to develop a unified theory of value and choice’ (Levy & Glimcher, 2012, p. 1036). Their goal is determined exactly the way our brain processes subjective value, with intense focus on elements of prospect theory. While decisions among choices have shown eventually to convert to a ‘common neural currency’, there is evidence that neural components are activated differentially depending on whether it is a loss or a gain (Brooks & Berns, 2013). The approach versus avoidance dichotomy extends to impulsiveness as well, though Steel and König (2006) leave the matter somewhat open: ‘Differences between positive and negative impulsiveness have not yet been definitively established, although they do appear to differ’ (p. 898). Since this time, some consensus has emerged. Cyders and Smith (2008) review the issue under the term ‘urgency’. Drawing from a variety of research streams, they concur that impulsiveness can be further split into two components, depending on whether the emotional state reflects positive affect or negative affect. In addition, Mogilner, Aaker and Pennington (2007) show evidence from consumer research that avoidance goals are discounted more steeply, meaning that rewards may be most motivating ahead of time while costs become more salient in the short term. Notably, this is almost the exactly same conclusion that Dollard and Miller (1950) drew over a half-century earlier: ‘The strength of avoidance increases more rapidly with nearness than does that of approach. In other words, the gradient of avoidance is steeper than that of approach’ (p. 352).

Goal Phase System (GPS) New Year’s resolutions are one of the better of our modern day institutions. Originally, a holiday to celebrate Janus the two-faced god (i.e., January is where we have an opportunity to look forward and

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back over the past year and on to the new), Christians tried to reform the holiday into a holy day by making it a time to redouble one’s commitment to God through religious resolutions. Presently, we get the benefit of both, where after a New Year’s party many make a resolution to live their lives, in some way, for the better. In a pair of papers, Norcross and colleagues determined how successful were these resolutions (Norcross, Mrykalo, & Blagys, 2002; Norcross, Ratzin, & Payne, 1989). As our experiences might confirm, there was massive drop off, with only 71% holding true by week two, 64% by February, and only 50% by April. This drop off is not uncommon and is more broadly studied as intention-action gaps or preference reversals (Frederick, Loewenstein, & O’Donoghue, 2002; Green & Myerson, 2004). Despite making a clear choice or intention to act, the action does not follow because we fail to pursue effectively our earlier goals despite circumstance remaining ostensibly the same. Clearly, the forces that help determine what goal we chose were not operating to the same degree or perhaps even in the same way later on. There must be multiple goal stages. As Diefendorff and Lord (2008) note, there have also been multiple phase theories, stretching back to Lewin, Dumbo, Festinger, and Sears (1944). Lewin et  al. posit two stages: goal setting, where we deliberate, wish, or establish what goals we will pursue, and then goal striving, during which we pursue them. Over the subsequent decades, every model contains at least these two, and at times nothing more. For example, regulatory mode theory divides action orientation into an assessment mode and a locomotion or doing mode (Kruglanski et  al., 2000). In her review of motivation, Kanfer (2012) also settles on essentially the same two: goal choice and goal pursuit or striving. During goal choice, we decide which goal we will allocate resources. During goal striving, we attempt to implement steps that will help realize our goal. Importantly, we may fail in our attempts here, and though we are doing nothing that advances our goal, we are in this second stage. Will two goal stages suffice? To develop GPS, we want to identify goal phases where there are fundamentally different principals operating. Gollwitzer (1990), whose work in this area has become among the most influential, recognized the importance of goal choice and goal striving, but argued for two more. Drawing on Heckausen and Gollwitzer’s (1987) ‘Rubicon Model’ of action phases (named to stress little overlap between stages), he posits these two additional phases: goal planning and goal evaluation. During goal planning, we decide on the when, where, and how we will act. During goal evaluation, we

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reflect on the degree we have achieved the desired goal, and whether we should continue to pursue it. Importantly, each phase is associated with a different mindset, which is potentially a way of evaluating motivationally relevant criteria. This four-stage model (i.e., choice, planning, striving, evaluation) has waned somewhat over the years, with the differences between when goal evaluation stops and goal choice begins once again becoming unclear (Diefendorff & Lord, 2008). Functionally, we must evaluate our goals to know we have completed them, but the motivational properties appear to be largely the same as goal choice. Gollwitzer (2012), for example, now almost exclusively focuses on the qualitative differences between a deliberative mindset, associated with goal choice, and an implemental mindset, associated with goal striving, effectively reverting to the two-stage model. The status of goal planning, however, remains less certain. Neuroscience is proving helpful to understand how goal phases work and their number. Whatever theories we develop, they can be informed by what we know about our brain’s mechanisms. Consequently, Diefendorff and Lord (2008) applied neuroscience to help explicate how the separate goal phases operate. O’Reilly, Hazy, Mollick, Mackie, and Herd (2014) extend this strategy by using neuroscience to establish the very number of goal phases. Relying on expert knowledge of brain areas and their workings, they develop a detailed computational model of goal behavior. Their conclusion is familiar, that there are two primary phases: goal selection and goal engagement. Crucially, ‘these states have strongly dissociable properties’ (p. 4), meaning that they must be modeled separately. However, they also suggest that goal planning might be a third stage that is ‘episodically-driven reevaluation and planning of future goal states’ (p. 37). Similarly, Stanovich (2011) makes an extended argument for a tripartite model that includes a separate ‘reflective’ mind, which is supervisory in nature, involving simulating possible futures and determining goal priority and regulation. Finally, Vancouver and colleagues (2008) have broken down motivation into choice, planning, and striving, finding that expectancy and value demonstrate disparate findings across the motivational process. Based on this work, GPS incorporates a threestage framework of motivation: goal choice, goal planning, and goal striving. As simplified in Figure 4.1, the typical effect of the building blocks of motivation often switches as one passes through these three stages. Though inevitably some goal choice initiates the phases, the exact order of them is not consistently in sequence. During a larger and longer-term goal, especially with multiple

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• Expectancy (+) • Value (+) • Impulsiveness (+)

Goal Planning • Expectancy (–) • Value (+) • Impulsiveness (–)

Goal Choice

• Expectancy (+/–) • Value (+/–) • Impulsiveness (–)

Goal Striving

Figure 4.1  Goal Phase System Note. The approximate effect of the building blocks of motivation during each goal phase

sub-goals, each of these stages may be revisited many times (Bateman & Barry, 2012). For example, goal abandonment comes from revisiting goal choice and finding insufficient commitment. From a goal phase perspective, there can be no single theory of motivation. As Norem (2012) states, during her own integration of person and situation motivational perspectives, ‘Attempts to stay within the historical or prototypical boundaries of personality and social psychology are doomed to either distort those boundaries beyond recognition, or to ignore significant and arguably relevant material’ (p. 287). Consequently, we consider how motivation manifests during each of GPS’ three goal phases.

Motivation during Goal Choice Right from the beginning, Lewin et  al. (1944) thought that expectancy × value models do best here, during goal choice. At this initial stage of GPS, we have a deliberative mindset, focusing on the feasibility (i.e., expectancy) and desirability (i.e., value), in a largely rational and therefore multiplicative manner (Gollwitzer, 1990). Inevitably, this is where expectancy × value theories and their derivations, such as Vroom’s VIE theory or Fishbein and Ajzen’s (1975) theory of reasoned action, should show their highest verisimilitude. There is little disagreement on this point. Economists, management researchers, philosophers, and psychologists concur that expectancy and value are strongly and positively related to goal choice (Anand, 1993; Bandura, 1997; Carver & Scheier, 1998; Edwards, 1954; Locke & Latham, 2004; Ramsey, 1931; Vancouver, 2008; Vroom, 1964). If an individual is faced with a decision between choosing between goals, they

are more likely to choose the option that gives them more of what they want and with a higher probability of doing so. Though expectancy × value theories are favored during goal choice, they are not perfect. This class of motivational theories are often considered representative of the rational model and given the copious critiques of classical economics (e.g., Camerer & Fehr, 2006; Hodgson, 2011; Kahneman, 2003), which has doubled-down on rationality, it is important not to overreach. At all times, we are best described as quasi-rational (Thaler, 1994), meaning there are several caveats. Many of these deviations from the rational model can be understood as motivational ‘hardwiring’ for our ancestral hunting grounds, heuristics that had previously maximized our chances of reproductive success. For value, Tversky and Kahneman’s (1992) cumulative prospect theory adds another level of refinement, demonstrating that there is not a oneto-one correspondence between perceived and actual rewards and probabilities. Consistent with the psychophysics of ‘Just Noticeable Differences’, we tend to require exponentially more of a reward to feel more rewarded. Also, a potential loss of a reward is more motivating than the potential gain of the same amount. As discussed, we have a motivational system fine-tuned for an environment of evolutionary adaption, which we no longer inhabit. Exponential discounting of rewards, for instance, is adaptive if such rewards (e.g., food) can be consumed only at a limit rate and the excess will effectively disappear (e.g., spoil). For expectancy, we tend to overestimate for ourselves the likelihood of positive events and underestimate the likelihood of negative events, an optimism bias (Sharot, 2011). Despite disappointing base rates, we get married, start businesses, and adopt risky lifestyles. When asked about our chances of divorce, bankruptcy, or

The Building Blocks of Motivation: Goal Phase System

health complications, we underestimate. McKay and Dennett (2009) discuss how these optimistic judgments could have evolved. Going further, Johnson and Fowler (2011) created a simulation of the role inflated expectancy beliefs could have in survival. Summarizing these positions, in evolutionary environments where rewards were highly valuable compared to the costs of fighting for those resources, it is advantageous to have inflated expectancy beliefs to obtain the resources because veridical beliefs may be self-limiting. Without the inflated beliefs, we would not go out for risky hunts, travel to new regions, or try new food resources. In today’s environment, this optimism bias is not as warranted but not necessarily unbeneficial. As Sharot (2011) concludes, while ‘classic theories in economics and psychology assert that correct beliefs will maximize reward and minimize loss, many sources of evidence point to the conclusion that optimism is nonetheless advantageous compared to unbiased predictions’ (p. 944). Returning to Johnson and Fowler (2011), inflated beliefs can still be handy when there is competition over contested resources. For example, in academia, the top journals in our field all have an acceptance rate of 5% or lower. However, submissions to the journals for the year are often above 1,000. This high submission rate appears inconsistent with the low acceptance rate, so to submit to the top journals we need to have somewhat inflated expectancy beliefs. On the other hand, those with realistic expectations never submit and invariably never publish there, so irrational exuberance can be adaptive. The same logic plays out in many different situations. Then, there is the issue of time. While expectancy × value models rarely include time as a variable (Locke & Latham 2004; Sonnentag, 2012), when they do, they typically incorporate an exponential function and a low discount rate, which is consistent with the rational model. However, hyperbolic discounting, as used in temporal motivation theory, and a high discount rate tends to fit our choices better, reflecting the evolutionary kluge composed of the limbic and the prefrontal cortex (Fudenberg & Levine, 2006; Inbinder, 2006). As Marcus (2008) writes, ‘over hundreds of millions of years, evolution selected strongly for creatures that lived largely in the moment’ (p. 84). Fortunately, poor modeling of time is not always an issue regarding goal choice. When deciding between outcomes that are in the same relative time envelope or horizon, the effect of impulsiveness or delay becomes a constant and can be dropped from the equation. It is important to stress that it is not the absolute difference in time between the choices that are attended to but the relative difference. The difference in motivation

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between two choices, one that can be realized in a year and another in a year and a day, is fractional. On the other hand, the motivational differences between two options, one that can be realized immediately and another only tomorrow, are monumental. It is in the latter of these two cases that we need to incorporate temporal discounting (e.g., temporal motivation theory), or find our models poorly predict. Finally, as Scholer and Higgins (2011) review, expectancy and value are affected by approach and avoidance. When people are promotion or approach focused, they more closely fit the rational model, seeking to maximize outcomes. However, when prevention or avoidance focused, they become remarkably less sensitive to expectancy. For example, the odds of an American citizen dying by a terrorist event, foreign or domestic in origin, in the last years are approximately 1 in 20 million. As per the National Safety Council’s data, this can be compared with more prosaic deaths, such as dying in a car accident (1 in 18,585) or accidentally suffocating yourself in bed (1 in 807,349). With over $1 trillion dollars spent in anti-terrorist security in ten years (not including the cost of military actions and conflicts), this type of expenditure is perverse, estimated at roughly $400 million per life saved (Mueller & Stewart, 2011). Aside from these four general trends, the judgment and decision-making field has a litany of other biases or ‘anomalies’ that affect how we estimate expectancy and value during goal choice. Bazerman and Moore (2013) organize these around three heuristics: Availability, Representativeness, and Confirmation. For example, one aspect of the Representative heuristic is that we at times judge two events occurring in sequence as being more likely than either event. Rather than review all these biases, their purpose here is simply to stress that expectancy × value models, which includes temporal motivation theory, are approximations and provide only a reasonable account for most goal choice situations. Such discrepancy is normal, however, and not necessarily cause for concern, a point that has been made numerous times, such as Box and Draper’s (1987) famous ‘all models are wrong, but some are useful’ (p. 424) or Fox’s (2011) paraphrasing of the same point, ‘All scientific models are oversimplifications. The important test is whether they’re useful’ (p. xiv). In most situations, these ‘biases’ can be considered heuristics that conserve cognitive resources and enable a close to optimal choice among many commonly occurring situations. Consequently, an option is indeed more likely to be chosen with increases in expectancy and value or, to some degree, their interaction.

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While additive models will often suffice (Schmitt & Chan, 1998) since these interactions need ratio scales (i.e. a meaningful zero point) to be properly observed (Anderson, 1970), a blatantly impossible task will still curtail choosing a goal regardless of the possible reward. Likewise, an option usually diminishes in luster when we are impulsive and the longer we must wait for a reward.

Motivation during Goal Planning The failure of expectancy × value theories to account for motivation across all goal phases led to criticism, disillusionment, and disuse, at least within psychology (Kanfer, 2012). Latham (2011) chronicles the decline, which culminates with Van Eerde and Thierry’s (1996) meta-analysis on the topic. Attempts to test expectancy × value theories found that the components predicted better when allowed to operate separately rather than multiplicatively. Though the sporadic use of ratio scales contributed (Anderson, 1970), part of the problem is that of overextension. While expectancy and value remain important variables throughout the motivation process, their psychophysics does not remain constant. Going from goal choice to goal planning and goal striving in GPS, one passes through ‘the looking glass’, and one motivational principle in particular radically changes in direction: expectancy. We know that from work on the planning fallacy and the illusory superiority effect that individuals are poor planners (Buehler, Griffin, & Ross, 1994; Kahneman & Lovallo, 1993; Kahneman & Tversky, 1979). Individuals falsely believe they will be able to complete a task in a shorter amount of time than it actually takes to complete the task. There are numerous examples of the planning fallacy, as it occurs in almost all projects, from the small to the very large. For example, the Denver International Airport was delayed 16 months with costs 300% more than initially planned (Buehler, Griffin, & Ross, 2002). Our over optimism regarding the time it takes is often predicated on a tendency to simplify, failing to incorporate the inevitable deviations from our imagined ideal set of events (Roy, Christenfeld, & McKenzie, 2005). For big planning projects, for example, we fail to anticipate sick days, shipment delays, or union problems. Similarly, academics may believe that they can write an academic paper in three months because, when they make this plan, they only think about how long it will take to analyze the data and write up the project. They neglect to consider the intense competition for their time, which comes from service committees, teaching

requirements, family commitments, and graduate student obligations. Although, the majority of research and theory regarding expectancy has focused on the positive effect, Bandura (1997) proposed that in a preparatory context that self-efficacy might be negatively related to performance, implying that self-doubt might be a good thing. For example, when discussing athletes, Bandura states, ‘In approaching learning tasks, athletes who perceive themselves to be highly efficacious in their capabilities have little incentive to invest much effort in tedious preparatory practice. Some uncertainty clearly benefits preparation’ (p. 405). When individuals have high self-efficacy, they may not allocate resources towards preparing for an upcoming task because they believe their ability will already suffice. Similarly, Bandura has been cautious about selfefficacy’s positive role in an educational preparatory context. For example, Bandura (1997) states, ‘Students who greatly underestimate the difficulty of academic course demands and remain blissfully free of self-doubt are more likely to party than to hit the books to master the academic subject matter’ (p. 76). In a classroom setting, where individuals need to prepare for an upcoming exam, low rather than high self-efficacy may be preferred. In line with Bandura (1997), Vancouver (2008) proposes that expectancy negatively relates to motivation while planning for a goal they have already adopted. In that case, the higher individuals’ expectancy, the fewer resources they will allocate towards the goal because they believe it can be achieved more easily (e.g., requires fewer resources). For example, once a student starts taking a course, expectancy will negatively relate to motivation because when individuals have high expectancy that they will get an A, they believe fewer resources (i.e., study time) are needed to get an A compared to when self-efficacy is lower. These two processes (i.e., goal choice and resource allocation) create a complex relationship between self-efficacy and motivation. Vancouver (2008) has proposed that the relationship between expectancy and motivation is non-monotonic and discontinuous. Figure 4.2 depicts this relationship. Kukla (1972) first discussed the non-monotonic, discontinuous relationship and later Carver and Scheier (1998) integrated it into their self-regulation theory. Generally, this relationship reflects the notion that when people believe they have no chance of achieving the goal (i.e., extremely low expectancy), they are not motivated to strive for that goal (i.e., allocate no resources) and effectively abandon it. However, if the individual believes the task is somewhat achievable, but still difficult, they will plan to allocate considerable resources towards realizing it. Likewise, as

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Motivation

The Building Blocks of Motivation: Goal Phase System

High value Low value Low

High Expectancy

Figure 4.2  The relationship between expectancy and value on motivation perceived ability (i.e., expectancy increases), they will plan to allocate fewer resources. In the same way we are often cognitive misers, using the shortcut of heuristics, we are also motivational misers, not wanting to spend more energy and time on a task than necessary. Vancouver et al. (2008) provided empirical support for this model. On the other hand, the effect of value and time prove to be more consistent with goal choice, operating in the same fundamental manner. While several theories of motivation predict no effect for value on planning or resources independent of goal level (e.g., Klein, 1991; Locke & Latham, 1990; Wright & Brehm, 1989), other theories and research predict a positive effect (Hyland, 1988; Pritchard & Curts, 1973; Terborg & Miller, 1978; Yancey, Humphrey, & Neal, 1992). Recently, Sun, Vancouver, and Weinhardt (2014) find support for the latter by examining the discontinuous non-monotonic model (Figure 4.2) in regards to value. Across two studies, they found that value increased planned resource allocation. As can be seen in Figure 4.2, an increased value will make the discontinuity happen sooner and more resources are allocated. Like for goal choice, we are still quasi-rational regarding how we assess value. For example, planning is adversely influenced by affective forecasting errors regarding the capabilities and desires of our future selves (O’Donoghue & Rabin, 2008; Wilson & Gilbert, 2005). Where expectancy and value may bias our planning, impulsiveness can make planning disappear. Overall, individuals who are high on impulsiveness are poor or even absent planners (Sharma et al., 2014), and they may go straight from goal

choice to goal striving. Instead of thinking about how to achieve their goal and develop strategies, they just start working towards the goal (or not). This can happen even for the less impulsive when their choices can be immediately realized, such as for an impulse purchase (Kalla & Arora, 2011), as there is effectively no time between choice and implementation. Why does this impulsiveness do this to us? The explanation will be familiar as it draws on the previously described limbic system/ prefrontal cortex duet. Specifically, the prefrontal cortex is the brain region where planning arises. The more developed the prefrontal cortex, the better we are at planning. One of the more poignant examples of this is our teenagers, who are worse at planning than adults because they have a less developed cognitive-control system in the brain (Steinberg, 2007). The application of this neuroscience to organizational science is still underdeveloped itself, though there are advocates (e.g., Volk & Köhler, 2012); continued application of this knowledge base should be pursued. At its basic level, the approach and avoidance dichotomy results in modification of expectancy, value, and impulsiveness. At the planning stage, this is best discussed in terms of regulatory fit. Higgins (2005) has extensively researched ‘the value of fit’, that is the benefits that occur when regulatory frame is aligned with the type of goal we are pursuing. Higgins proposes two types of regulatory frames, a promotion focus where we are concerned with realizing positive outcomes and a prevention focus where we are concerned with avoiding negative outcomes. To maximize their motivation benefits, in signal detection terms, a promotion focus should pursue goals constructed

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around maximizing hits and avoiding misses while a prevention focus should pursue goals constructed around ensuring correct rejections and avoiding false alarms. For example, a technology company with a promotion focus would reward creativity and the generation of new products while if it had a prevention focus it would emphasize the quick elimination of unprofitable lines and extensive justification for any new investment. In short, promotion leads to eager strategies while prevention promotes vigilant strategies (Higgins, 1997; Scholer & Higgins, 2011). The nature of goal influences the planning to achieve it. In addition, prevention focus leads to quicker initiation of a goal and those who employ it are better at maintaining a plan than promotion focused individuals (Freitas, Liberman, Salovey, & Higgins, 2002; Poels & Dewitte, 2008; Scholer & Higgins, 2011). Prevention focused people see their goals as more important obligations (i.e., higher value) than promotion focused individuals and consequently allocate more resources to it. Also, prevention focused individuals may be better at resisting temptation because they remove themselves from enticements as well as develop escape routes that deliver themselves from temptation. For example, an employee may set a goal of accomplishing some work task by the end of the day and, to do so, they may temporarily block their email or access to distracting Internet sites. Notably, these tactics tend to be used by those who are themselves pessimistic regarding their ability to resist temptation, while those more confident tend to expose themselves to more temptations and subsequently lose control (Nordgren, van Harreveld, & van der Pligt, 2009).

Motivation during Goal Striving Similar to the goal planning, expectancy × value theories fail to fully translate to the goal striving phase of GPS. For example, Klinger (1977), in early work notes, ‘expectancy × value theories have been only very modestly successful in predicting vital aspects of goal striving, such as work and quality of performance’ (pp. 329–330). Where during goal planning, it was principally just expectancy that changes form and function, during goal striving, value, impulsiveness, as well as approach versus avoidance orientation also need goal phase specific consideration. Every major aspect of motivation has different functioning, emphasis, or impact during goal striving. After we have chosen a goal, the feasibility and desirability, that is expectancy and value, tends to further increase (Gollwitzer, 2012). An example of

this is the endowment effect (Apicella, Azevedo, Fowler, & Christakis, 2014), where we value our belongings and acquisitions more after obtaining them. Given goal evaluation and goal switching themselves have costs, this can be considered a useful form of goal shielding (Shah, Friedman, & Kruglanski, 2002), allowing us to inhibit competing choices and more fully engage in singular goal pursuit. However, during goal striving, excessive expectancy can work against timely goal completion in multiple ways. During goal choice, we are indifferent to where the source of our expectancy comes from. If the outcome is sufficiently likely, and the reward competitively desirably, we will make pursuit intentions. During goal striving, the effects of expectancy transform, largely as described during goal planning. Reminiscent of Aesop’s Tortoise and the Hare cautionary tale, those who are confident of the outcome, are less likely to invest in resources, which manifests itself here during goal pursuit. We take it easy, perhaps too easy. Also as mentioned, those overconfident in their impulse control beliefs tend to expose themselves to tempting situations, whereupon the impulse multiples in power and overrides the long-term goal (Nordgren et al., 2009). Choosing to work with a television nearby or take ‘a quick break’ to have a drink with friends are potential examples. In addition, Polivy and Herman (2002) describe what they call the false hope syndrome. Overconfidence regarding the speed, size, or ease of life changes is actually associated with lower levels of striving and perseverance. When extremely positive expectations are not met, people become disillusioned and are more likely to give up entirely. Minor lapses or setbacks are seen in catastrophic terms, hastening disengagement or dysfunctional forms of self-regulation. It would have been better if they started with a more modest outlook (Aspinwall, 2005). To prevent this, expectancy has to be crafted in a particular way, contingent on hard work or perseverance. In other words, expectancy should not be centered on the outcome being likely, which sufficed during goal choice, but that despite inevitable and repeated obstacles and setbacks, we can be confident that we have the internal resources and depth of character to meet and overcome them (Aspinwall & Taylor, 1997; Baumeister, Heatherton, & Tice, 1994; Schwarzer, 2008). Success will come, but after sometimes considerable but not insurmountable effort. One way to achieve this mindset is to increase task difficulty, a core tactic of goal setting theory (Locke & Latham, 2002). Similarly, forces can switch for value. Ryan and Deci’s (2000) self-determination theory and Frey and Jegen’s (2001) motivational crowding theory

The Building Blocks of Motivation: Goal Phase System

argues extrinsic rewards can crowd out or replace intrinsic ones. Extrinsic rewards are likely to be large and associated with goal choice, such as an incentive plan. Intrinsic rewards are more subtle and experienced by the individual during the very act of goal striving, such as during a flow state (Nakamura & Csikszentmihalyi, 2002). Consistent with Allport’s (1937) functional autonomy of motives, what reward originally makes us select as a goal is not necessarily what propels us towards achieving it. As reviewed by Steel and MacDonnell (2012) or Freund and Hennecke (2015) under the rubric of process versus outcome goals, there are a variety of problems with treating the rewards that helped establish goal choice as ones that should be stressed during goal pursuit. Focusing on extrinsic rewards can be a form of positive fantasy, which can be likened to motivational pornography (Kappes & Oettingen, 2011). Fantasizing diminishes motivational energy as the image partially satiates and reduces the need for the act itself. In addition, since extrinsic rewards are received at goal completion, emphasizing them not only shifts focus from intrinsic rewards but also from the task at hand. As Kanfer and Ackerman (1996) describe in their resource allocation theory, when we are thinking about more than what we are doing, we are introducing competition for our limited cognitive capacity. For any cognitively complex task, emphasizing the rewards at completion is experienced simply as a distraction (e.g., Ariely, Gneezy, Loewenstein, & Mazar, 2009). In a related manner, impulsiveness and delay do not change in function during goal pursuit but definitely in standing. It helps explain the importance of intrinsic motivators. While they may be small compared to extrinsic motivators emphasized during goal choice, they are experienced during the act of goal pursuit and benefit from our impulsive nature. Being immediately generated and consumed, these intrinsic rewards are usually highly valued. Impulsiveness and delay also largely accounts for intention–action gaps and procrastination (Gustavson et  al., 2014; Steel, 2007). Where during goal choice and goal planning we decide upon what we will do, impulsiveness often gets in the way of doing it as planned. During previous phases, the time until implementation may have been distant enough that temporal discounting among competing tasks and temptations may have seemed a non-issue. However, during goal pursuit, it often becomes the choice between an immediate alternative, often instantly pleasurable, and the larger but later extrinsic reward. While there are several factors that influence the degree of implementation, such as the availability of temptation, it is not until the task deadline nears that forces start to favor consistency with goal

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planning. The time remaining shortens, naturally increasing the difficulty of the task, which in turns heightens the amount of resources we allocate for task completion. In addition, the target task’s outcomes become increasingly short-term, meaning that motivation is escalating moment by moment. In some cases, this window of peak motivation shrinks sufficiently fast that by the time people start to act, the ever increasing task difficulty quickly outstrips our capability, making the task effectively impossible within the time available so that it is abandoned or standards are sacrificed and drastically lowered. Finally, with goal choice and goal planning, we were somewhat neutral regarding whether goals were framed as approach or avoidance. There should be a fit between regulatory focus and goal (Scholer & Higgins, 2011), which is consistent with behavioral theory (Schultz, 2006). Punishers are best used to stop a behavior, while rewards are better at creating action. To the extent our goals represent what we want to achieve, there tends to be an advantage to framing goals with an approach orientation (Elliot & Friedman, 2006; Howell & Watson, 2007). Also, inhibition or avoidance goals can trigger ironic-processes, where we obsess about what we are trying to prevent (Wenzlaff & Wegner, 2000). In addition, avoidance goals are resistant to forming specific deadlines, and rarely benefit from temporal discounting (e.g., otherwise one must be soon not be doing something). Accordingly, techniques such as Applied Behavioral Analysis (ABA) recommends that instead of just trying to stop or punish a problem behavior, try to also establish a replacement behavior that takes its place (Hagopian, Dozier, Rooker, & Jones, 2013). For example, focusing on starting early is a preferable frame to not procrastinating. Particularly relevant during goal striving is effortful control, colloquially referred to as willpower, which often manifests in our attempts to remain consistent with our original intention or plans. Because the role of cognitive resources in goal striving and in particular self-control is beginning to emerge in organizational psychology, we provide an overview of the issues regarding this work more generally in psychology. The general model stipulates that motivational resources may be depleteable and that exerting self-control itself saps the individual of motivation. This is particularly evident when we attempt to deploy cognitive resources among a number of tasks simultaneously (Evans, 2008; Miller, 1956; Simon, 1955). As a result, when individuals multi-task, they experience fatigue and performance deficiencies (Baumeister, Vohs, & Tice, 2007; Kurzban, Duckworth, Kable, & Myers, 2013), though certain tasks may exacerbate these effects more than

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others. In addition, Muraven and Baumeister (2000) have proposed that this depletion effect is analogous to muscular development, that after exhaustion, given time, self-control will recover and potentially at a heightened level of strength. What resource is being depleted when people exert self-control? Gailliot et al. (2007) proposed and found initial support for the hypothesis that glucose is being drained. While this has become a dominant explanation for explaining self-control and the experience of subjective effort in goal striving, recent work has called aspects of the model into question. A series of studies re-examined Gailliot et al.’s (2007) glucose explanation for depletion and found it unreliable. Kurzban (2010) found the effect only occurs for subjects who have fasted. Molden et al. (2012) found across four experiments no support for the glucose-model. Notably, these experiments used more precise measures and tighter controls than Gailliot et  al.’s investigations. Job, Walton, Bernecker, and Dweck (2013), replicating their earlier work, confirmed that subsequent performance only decreased for people who believe selfcontrol is a limited benefit and can be depleted, indicating a nocebo (the opposite of a placebo) effect. Converse and DeShon (2009) did not find support for ego depletion when a three-task design was used rather than the traditional twotask design. Specifically, they propose that if individuals are given the opportunity to adapt to their situation, depleting effects will disappear. Because of these empirical findings, a number of people have called into question the glucose theory and proposed alternative theories that are motivationbased, including Beedie and Lane (2012), Inzlicht and Schmeichel (2012), and Kurzban et al. (2013). We believe the model by Kurzban et al. (2013) provides the best motivational account for understanding the phenomenology of effort because it utilizes expectancy and value. Specifically, they proposed, ‘that the sensation of “mental effort” is the output of mechanisms designed to measure the opportunity costs of engaging in the current mental task’ (p. 13). Therefore, as individuals engage in one task, they are intermittently calculating the cost/benefit of engaging in other tasks (i.e., goal choice). When there are valuable alternatives to the focal task, individuals perceive that task as more effortful and fatiguing because the alternative task has higher value. As discussed in the next section (i.e., dynamic theories of multiple-goal pursuit), this is similar to Vancouver et al. (2010a) who proposed that expectancy and value are changing dynamically as individuals strive for multiple competing goals. Building on this model by incorporating impulsiveness, individuals who are high impulsiveness may find striving towards their long-term

goals more fatiguing and effortful because alternative tasks that offer immediate rewards appear more valuable. We recommend moving beyond the depletion model and adopt a motivational account for understanding the phenomenology of effort in self-control and goal striving.

DYNAMIC THEORIES OF MULTIPLE-GOAL PURSUIT: THE ROLE OF COMPUTATIONAL MODELS So far, motivational components are largely treated as static or at the trait level. To fully develop the GPS framework, this is not enough. As van Gelder and Port (1994) stressed ‘Cognitive processes and their context unfold continuously and simultaneously in real time’ (p. 2). Yet, motivational theories and research designs are largely snapshots of dynamic phenomena with a single criterion. The world outside is not stationary but in flux, where individuals constantly interact with their environment, and static theories may not explain behavior adequately when applied to this dynamic context. To address this gap in the literature, there have been numerous calls in the literature advocating a dynamic approach to understanding organizational behavior (e.g., Dalal & Hulin, 2008; Mitchell & James, 2001; Sonnentag, 2012). These calls are beginning to be answered by researchers examining work behavior using longitudinal designs (e.g., Becker & Cropanzano, 2011; Colquitt, LePine, Piccolo, Zapata, & Rich, 2012; Ilies, Wilson, & Wagner, 2009; Kammeyer-Mueller, Wanberg, Glomb, & Ahlburg, 2005; Schmidt & DeShon, 2007; Yeo & Neal, 2006). In addition, multilevel modeling is prevalent in all major journals. There is an intimate relationship between statistical tools, empirical design, and theory. As our field uses more dynamic tools, this will influence what theories we rely on and how we collect data to adequately test these theories. Accordingly, Gigerenzer (1991) proposed that the tools (often our statistical procedures) researchers use to account for some phenomenon become our theories for that phenomenon. He calls this the tools-to-theories-heuristic. There are numerous examples of this throughout the history of psychology. During Freud’s era, the steam engine was used as a metaphor for how the mind works. Then with the development of the computer, von Neumann (1958) proposed that the brain could be considered a digital computer. Gigerenzer suggests that not only do these larger metaphors shape how we think about the brain but that our statistical

The Building Blocks of Motivation: Goal Phase System

tools also shape how we think about psychological phenomenon. For example, he discusses how signal detection theory is theoretically grounded in Neyman-Pearson hypothesis testing. Signal detection theory was based on new tools (statistics) not widely used within the field at the time and therefore required new data (Tanner & Swets, 1954). Thus, data did not drive the theory, but rather the tools and the theory required new data. In the organizational literature, with the increased use of multilevel modeling (tools), our theories and research designs can now account for nested and repeated measures data, which was out of our reach 30 years ago. From this perspective, over the next 20 years computational models (tools) will influence our theories greatly. With the increased use of computational modeling, our theories will become more dynamic, more precise and with multilevel modeling, our research designs will be able to account for such complex and dynamic theories. Consequently, we briefly define computational models, why they are necessary, and what advantages they have. After this foundation, we review computational models in motivation and make suggestions of theories that should be integrating with current computational models. Computational models are algorithmic descriptions of process details, typically operationalized as computer programs that are dynamic and can be simulated (Taber & Timpone, 1996). The goal of computational modeling is to create a representation of the system-in-context that approximates the underlying process of the phenomenon we as researchers are trying to understand (Myung, 2003). Perhaps more importantly, computational models can be simulated, allowing researchers to examine how phenomena evolve over time. This is difficult to duplicate using other tools because of limitations in verbal language and the human mind. When developing a computational model, it is necessary to describe the relationship between the variables mathematically. Unlike verbal theories, there is little ambiguity regarding the theory. And through our own mental modeling we may believe we can easily understand dynamic phenomenon, research shows that even well-educated individuals from STEM degrees at some of the best institutions in the United States of America do not effectively understand dynamic systems (Cronin, Gonzalez, & Sterman, 2009; Navarro & Arrieta, 2010; Sterman, 1989; Weinhardt, Hendijani, Harman, Steel, & Gonzalez, in press). Necessarily then, as our field begins to develop dynamic theories and use dynamic empirical designs, computational models are required to help researchers test the validity of their theories and designs. As computational modeling is adopted, a number of benefits will accrue. Specifically,

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computational models can be used for theory building (Ilgen & Hulin, 2000; Vancouver et  al., 2010a), formally describing and testing parts of an existing present theory (Vancouver, Putka, & Scherbaum, 2005), resolving conflicting theoretical issues (Vancouver & Scherbaum, 2008; Vancouver & Weinhardt, 2012), integrating theories (Steel & König, 2006; Vancouver et al., 2014), and resolving conflicting empirical findings (Vancouver, Tamanini, & Yoder, 2010b). Aguinis and Vandenberg (2014) have provided a review of steps researchers should do before data collection. We propose that computational models should be one of the steps researchers embrace. Now that we have outlined broadly the importance of computational models, we will turn to computational models regarding motivation. While a large recent proportion of motivational computational models has been done by Vancouver and colleagues (e.g., Vancouver & Scherbaum, 2008; Vancouver et  al., 2005; Vancouver et  al., 2010a; Vancouver & Weinhardt, 2012; Vancouver et al., 2014), there are several other contributors, such as DeShon and Gillespie’s (2005) motivated action theory which integrates aspects of control theory with expectancy × value. Rather than discussing each model in detail, we focus on computational models dealing with dynamic multiple goal regulation and propose how work on affect could be integrated with these models to further motivation research. In our daily lives, we are constantly regulating multiple goals over time and must regularly choose where to allocate resources among short-term and long-term goals. While some excellent work has been done, such as by Schmidt and colleagues (Schmidt & DeShon, 2007; Schmidt, Dolis, & Tolli, 2009), the field is rather limited in regards to research on multiple-goal motivation. To rectify this, Vancouver et  al. (2010a) developed a dynamic computational model that addresses how individuals strive for two competing goals over time, which included dynamic expectancy, value, and temporal components. Notably, not only are there reversals for expectancy, which has positive effects for goal choice and negative effects for goal planning, there is competition among these tasks. In the model, as individuals begin to work on a goal, their expectancy is changing due to a reduced discrepancy between where they are now and what they want to achieve; having accomplished more, they have higher expectancy that the goal will be reached. Meanwhile, their expectancy for the other goal is low but the relative need (i.e., value) is increasing as more remains to be done. Therefore, they switch to the goal with the greater need, despite having the lower expectancy. This back-and-forth switching continues, until the

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deadline approaches. Near the deadline, assisted by hyperbolic discounting, it becomes clear that it is difficult to achieve both goals, whereupon expectancy dominates as individuals favor the task they are most certain of completing to ensure at least one goal is achieved. As outlined above, one of the advantages of computational models is that they can easily incorporate different motivational components and formulations. Vancouver et al. (2010a) were able to model dynamically expectancy and value using a control theory framework and integrated this with hyperbolic discounting from temporal motivation theory. For example, one of the regulatory agents in the model was called a time agent, which continuously monitored the time until deadline. They also modeled people’s impulsiveness, their sensitivity to deadlines. Individuals who are more sensitive to deadlines will be more likely to switch earlier to the goal that is more easily achievable. Demonstrating its flexibility, Vancouver et  al. (2014) later expanded on the model by integrating cognitive psychology theories of learning with theories of motivation. In this expansion, biases in time perception were incorporated and how they may arise as individuals regulate multiple competing goals. The resulting model was able to account for how an individual learns to regulate multiple competing goals over time and can account for various differences in regulation of multiple competing goals, including the planning fallacy (Buehler et al., 1994). Although these models have led to greater understanding of dynamic multiple goal regulation, there is still room for further expansion. Specifically, these models do not incorporate work from the affect literature. Consequently, a natural next step would be to incorporate Carver and Scheier’s (1998) ideas about the affective system, who propose that an affective self-regulatory system runs in parallel with the behavioral selfregulatory system. This affective system has two main functions. The first function uses affect as a signal in the behavioral system about the rate of discrepancy reduction between the current state and the goal state (i.e., goal progress). If the rate of progress is below a certain criterion, the individual experiences negative affect, which signals the need for more resources to be allocated to achieving a goal. If goal progress is above a certain criterion the individual experiences positive affect which indicates that fewer resources need to be applied to the focal goal, which they refer to as coasting. The other function of the affective selfregulatory system is to signal goal reprioritization, as postulated by Simon (1967). Where negative affect serves as a signal to allocate more resources towards a goal, positive affect on the other hand

serves as a signal that resources do not need to be applied to this goal, and therefore the individual is more likely to engage in another goal. Carver (2003), in a more thorough examination of goal prioritization, theorized that positive affect is the mechanism that leads an individual to switch from one goal to another. However, very little empirical research has been done regarding this second function of the affective system. To better address affect regulation, more work should be done in line with Beal, Weiss, Barros, and MacDermid’s (2005) dynamic episodic process model, which examines several interconnected elements that simultaneously impact performance. In general, emotion is inherently suited to a computational modeling approach as affect can be both an antecedent of motivation, such as influencing perceptions of risk (Slovic, Peters, Finucane, & MacGregor, 2005), and an outcome, requiring a feedback loop (Schmidt et al., 2013). In addition, there should be continued efforts to link motivation with emotion regulation and emotional labor. There are strong parallels between motivational and emotional regulation, though the latter may be quicker. For example, Vancouver and Weinhardt (2012) computationally modeled the regulation of well-being and stress using the same self-regulatory framework found in theories of motivation. Also, researchers often discuss the outcome of emotional labor as emotional exhaustion, which is the same nomological net of the ‘depletion’ effects described by self-control researchers (Beal et  al., 2005). Consequently, computational models should help integrate the work on emotional regulation and labor with motivation to better account for the dynamic processes unfolding throughout the day. Motivation is a dynamic and complex process and can be examined both from its operations as well as its outcomes. Though there are constrained numbers of major components, they interact and change over time, making computational models an especially useful tool for capturing motivational dynamics. If we want the field of work motivation to be considered in the same conversation as other sciences, it appears that we need to develop and test our theories computationally (Harrison, Lin, Carroll, & Carley, 2007; Ilgen & Hulin, 2000; Vancouver & Weinhardt, 2012; Weinhardt & Vancouver, 2012).

MOTIVATIONAL INTERVENTIONS In our efforts to improve motivation, there generally has been little understanding of the complete

The Building Blocks of Motivation: Goal Phase System

array of motivational components, phases, or its dynamic nature. This is most easily seen in our attempts to design incentive programs. As Fryer (2010) discovered while attempting to motivate student achievement across 250 urban schools, ‘In stark contrast to simple economic models … incentives tied to output are not effective’ (p. 2). He suggests several reasons, including that there were failures during goal striving (i.e., ‘lack of self-control’). The major point being is that expectancy × value models operate best at determining goal choice and are less appropriate for subsequent goal phases. Similarly, in a pair of studies, Pepper and Crossman evaluated how long-term incentives for executives, which comprise almost 50% of their total earnings, were being implemented compared with the underlying motivational forces. Typically, as they note, this compensation scheme relies on a very basic or stripped-down rational or expectancy × value model. Adopting the more complex temporal motivation theory, they found the way ‘senior executives assess probabilities and value is significantly affected by risk aversion, time discounting and uncertainty aversion’ (Pepper, Gore, & Crossman, 2013, p. 48), a finding they replicated cross-culturally and concluded that despite their widespread use, ‘long-term incentives are not an efficient way of motivating senior executives, irrespective of national culture’ (Pepper & Gore, 2014, p. 359). To effectively apply GPS, we need to take a more sophisticated approach to motivational interventions. To do this, we need to understand where motivational interventions are best applied by connecting them to the appropriate goal phase and subcomponent as well as considering their dynamic nature, all of which can be done (Vancouver, 2008; Vancouver, et  al., 2014). As Gröpel and Steel (2008) argued, this includes moving ‘beyond motivational main effects and towards customizing interventions to the individual’ (p. 410). They recommend developing diagnostic procedures, such as the Motivational Diagnostic Test (Steel, 2011), that identify people’s particular motivational vulnerabilities (e.g., impulsiveness) or gaps in skill repertoires (e.g., poor goal setting). This will enable precise matching of the intervention to the individual, increasing the efficacy of our motivational treatments. Also, this integrative approach has important implications for coordinating employee-based interventions. A variety of motivational training programs, despite having different theoretical origins, are overlapping in practice, advocating essentially identical techniques. For example, interventions for emotional intelligence, aside from the construct’s disputed theoretical or

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measurement independence (Joseph & Newman, 2010; O’Boyle, Humphrey, Pollack, Hawver, & Story, 2011; Walter, Cole, & Humphrey, 2011), often stress improving the fundamental motivational elements of impulse control and self-­ confidence (Clarke, 2006; Goleman, 1998). Almost regardless of a self-regulatory intervention’s espoused heritage, the core and most effective features can be largely classified as operating on a particular goal phase and motivational component (i.e., expectancy, value, time, and approach versus avoidance), often in a dynamic fashion. We review these interventions here.

During Goal Choice Attempts to improve decision-making come under the rubric of ‘Choice Architecture’. While we have been very successful at identifying sources of biases during goal choice and exploiting them during consumption, creating ‘patches’ for our faulty decision-making software has not kept pace. As Payne, Bettman, and Schkade (1999) discuss, we have yet to develop a ‘building code’, which is a set of universally accepted guidelines that help construct optimal preferences. Still, basic heuristics have been developed, particularly removing the effect of impulsiveness through the mechanism of enforced delays. This class of selfcontrol techniques that delay action belongs to is precommitment. Precommitment may also occur during goal planning, but given pre-commitment’s commonality with commitment, we discuss it here. Precommitment has a long history, stretching back at least to the ancient Greek story of Ulysses (Bryan, Karlan, & Nelson, 2010). In a form of anticipatory self-command, we act now in order to prevent ourselves from acting otherwise later. Ulysses, for example, used this principle to bind himself to his ship’s mast so he couldn’t later respond to the Sirens’ song. There are a variety of modern-day variations, such as freezing a credit card in a block of ice (which must be thawed) or Clocky, an alarm clock on wheels that evades its owner (Steel, 2010). Notably, the very structure of most governments is built to prevent impulsive choice, such as the use of bicameralism, which requires legislation to pass through two houses (e.g., a senate and a congress). This structure was explicitly designed to foster more deliberative and cooler decision-making (Hamilton, 2004; Steel, 2010). More comprehensive processes to improve goal choice have proved elusive, but work has been done. There are two active support protocols that

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assist in making decisions more consistent with our values, even in situations of considerable complexity. First, multiple criteria decision-making or multi-attribute utility theory, which has origins in operations management, is explicitly designed to help us determine our preferences in situations where the classical or rational expectancy × value model has less hold (Figueira, Greco, & Ehrgott, 2005). Most typically, this happens when there are multiple conflicting criteria with a variety of tradeoffs, creating goal conflict (Emmons, King, & Sheldon, 1993). Second, incorporating and building on multi-attribute utility theory, structured decision-making is a bundle of techniques that include steps that mitigate cognitive biases, such as the tendency to adopt the present state of affairs (i.e., status quo bias) as being the best possible (Gregory et  al., 2012). Both techniques require a degree of preparation that make them difficult to casually use, though many applications exist for more pivotal and established life events, such as choice of pension plan, with adoption further eased, as the International Society on Multiple Criteria Decision Making documents, through dozens of decision-making software programs. Through a combination of these two factors, for example, they have also made significant inroads into environmental decision-making, such as choice of energy source or sustainability plans (Arvai, Campbell-Arvai, & Steel, 2012).

During Goal Planning Allocation of appropriate resources during goal planning is compromised by the planning fallacy, where we underestimate the amount of resources necessary for goal success. This is not easy to remedy and the majority of interventions that sought to prevent it have failed (Roy et al., 2005). However, it can be significantly reduced if we acknowledge our internal mental biases and compensate for them with explicit external processes. Roy et al. propose a method that improves estimation by keeping strict objective records of how long projects take and relying on these to determine tasks time. Similarly, Bishop and Trout (2008) recommend when determining how long it takes to write a paper to look back at one’s CV and determine how many articles we have previously published per year. Another suggested mechanism for guarding against over-commitments is to take the ‘outside view’, where we assess how long it would take for someone else to finish the project (Lovallo & Kahneman, 2003). Goal planning is not limited to simply scheduling, however, where we just rationally allocate

time and resources so we can tackle the task at hand. It also has meta-cognitive aspects, where we anticipate our motivational weaknesses and plan how to overcome them (Gollwitzer & Oettingen, 2011). Unfortunately, these same weaknesses reduce our capacity to plan. Those overconfident in their self-regulatory strength and their degree of certainty are less likely to take steps to mitigate problems they don’t even acknowledge existing. Those who don’t value self-regulation aren’t likely to self-regulate. And, while there are many techniques available to reduce impulsivity, those most impulsive are least likely to use them. Of the available techniques, many of them have been codified under the inductive theory of goal setting. This is almost a form of motivational reverse-engineering as we know that motivation coalesces just before most naturally occurring deadlines. The question then becomes: What are these attributes that create this motivation and can we input them into artificially created goals of our own choosing? Goal setting is consistent and derivable from the motivational fundamentals outlined in temporal motivation theory (Gröpel & Steel, 2008). It counsels us to make goals that: we are committed to, are difficult but achievable, are specific and proximal (Locke & Latham, 2002). Notably, the theory has one area of tension, between goal commitment and difficulty, as per ‘expectancy is said to be linearly and positively related to performance. However, because difficult goals are harder to attain than easy goals, expectancy of goal success would presumably be negatively related to performance’ (Locke & Latham, 2002, p. 706). This conflict can be readily resolved through the use of goal phases. Goal commitment, which has positive relationships with expectancy and value, applies to goal choice while goal difficulty, which by making the task harder decreases expectancy, applies to the subsequent two phases. Expectancy is central to two other motivational interventions. Notably, goal commitment moderates one motivational technique during goal planning. If there is sufficient goal commitment, goal planning is further assisted by mental contrasting (Kappes, Singmann, & Oettingen, 2012). Mental contrasting is where we vividly imagine our goal completion and its benefits, similar to positive goal fantasy. It differs from the latter by adding the additional step of reflecting with equal vigor on our present situation and its accompanied challenges and obstacles. If commitment remains strong, planning typically ensues. The false hope syndrome can also be mitigated during goal planning. Sitkin’s (1992) strategy of small losses, whereby we learn from failure but construct our goals to contain failure so as to incur only small

The Building Blocks of Motivation: Goal Phase System

losses, is a useful framework for guarding against disengagement caused by minor setbacks. Other goal setting techniques primarily deal with our excessive impulsivity and can be largely interpreted as translating goals chosen with a deliberative mindset, associated with the prefrontal cortex, into motivational terms receptive to the implemental mindset, associated with the limbic system. Goal setting theory states that specific and proximal tasks tend to be pursued with more vigor than vague and distal ones, an observation that it largely shares with construal theory (McCrea, Liberman, Trope, & Sherman, 2008). This is exactly the type of phenomena that the limbic system attends to, the nearby and the concrete. So, the goal of ‘writing a book’ may result in delay but the goal of ‘writing 300 words today on chapter three’ is much more likely to result in action. This will result in effort being more spread out, which should help to alleviate the stress involved in working close to the deadline. Several techniques used during motivational planning prevent the limbic system from re-evaluating goal choice, helping to ensure that goal striving continues. Automatization or routine building is a particularly successful application of this principle. We are susceptible to impulsivity when making decisions so one way to avoid impulsiveness is to avoid making decisions altogether. While almost half of our daily behaviors are really well-rehearsed routines that we re-enact with little thought (Ouellette & Wood, 1998), these scripts can be usefully cultivated to prevent impulsive choice, enabling us to persevere towards our goals instead of succumbing to temptation (Baumeister, Muraven, & Tice, 2000). Implementation intentions have proven to be particularly useful in this regard (Gollwitzer & Oettingen, 2011). They resemble stimulus–response conditioning, where we specify actions to be done contingent on a trigger or situation. Often portrayed in a mad-lib fashion, we state, ‘If (situation X), then I will do (action Y)’. A particularly useful autological example of them is ‘If I am pursuing a goal, then I will use implementation intentions’. Notably, these techniques have been successfully combined with mental contrasting (i.e., the MCII technique). Consistent with them operating on separate components of motivation, used together they are better than each alone (Oettingen, 2012).

During Goal Striving Successful goal striving is often predicated on steps taken during goal planning. When actually engaged in the task, fewer options remain.

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To differentiate these from goal planning, these techniques must be initiated and applied currently or in parallel with the actual pursuit. What aspects of ongoing events one focuses on and how these events are interpreted influence the perception and effects of expectancy, value, and impulsiveness. Broadly, there are two classes of motivational interventions that accomplish this: cognitive and attentional. Cognitive therapy aims to shift how we think and feel in order to change behavior. There are a variety of different forms, but two are particularly relevant in a work motivational context, both drawing on attribution theory and explanatory style. The first is learned optimism (Seligman, 2011), where we improve expectancy in the face of failure by attributing setbacks to temporary, situation specific, and external forces (rather than permanent, pervasive, and internal causes). The second is cognitive evaluation theory, a subset of selfdetermination theory (Deci & Ryan, 1985). To the extent we can emphasize feelings of competence and autonomy during goal striving, intrinsic motivation is enhanced. These feelings are influenced by our general causal attributional style, so we can improve our intrinsic motivation by emphasizing volition, even if it is simply choosing between two work-related tasks, and connecting what we do to our more fundamental needs, such as achievement or affiliation. The second class of goal striving motivational interventions can be labeled attentional control. This has features similar to stimulus control, where external cues trigger specific behaviors. However, with attentional control, these cues are either enhanced or redirected cognitively away from distracting temptations and/or towards our target goal. Highlighting how basic or fundamental is this technique, the foundational research done in this area is with children and chimpanzees. Mischel and Baker’s (1975) seminal work on cognitive reappraisals examined how children can use them to assist in delaying gratification. Instead of avoiding thinking about temptations (i.e., irrelevant non-goal options), we can mentally distance ourselves by focusing on their ‘cooler’ non-consumptive aspects (Mischel & Ayduk, 2004). To do this, temptations are framed in terms of their abstract and symbolic features, such as Mischel having children focus on pretzels’ shape and color, (e.g., ‘the pretzels are long and thin like little logs’), rather than their texture and taste. In a strikingly similar study, the anthropologist Deacon (1997) trained chimps to use lexigrams to make food choices. Lexigrams are symbolic representations, such as kiwis being depicted by a black square with a blue ‘Ki’ and strawberries depicted by a red square with two horizontal white lines.

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The chimps had to point to one of these options and, importantly, get the fruit they didn’t select. Chimps trained with lexigrams soon learned how the game worked and chose the less desirable options but if confronted with actual bowls of fruit, not their symbolic stand-ins, were unable to override their initial instincts. In Deacon’s words, this symbolic representation is vital for self-control, for without it ‘being completely focused on what they want, they seem unable to stand back from the situation, so to speak, and subjugate their desire to the pragmatic context’ (1997, p. 414). Basically, symbols tip our choices away from the concrete and stimulus-driven limbic system and back to the abstract plans of the prefrontal cortex (Gifford, 2002).

CONCLUSION We used this chapter to take a moment of pause, to reflect on what major motivational elements our field has consistently found, and then assembled these pieces together into a coherent whole: the Goal Phase System (GPS). This framework moves us towards a ‘boundaryless’ science of motivation (Locke & Latham, 2004), one that can be informed from multiple disciplines and applied to a wide variety of problems. In absence of this broader view, our present perspectives are both too simplistic and too complicated. Too simplistic in that each motivation theory only accounts for a small part of the motivational process, and we tend to have little appreciation of these limits. Too complicated in that we have far too many theories, with many of them being functionally identical or a recombination of more basic elements. Considerable confusion has arisen as we treat theories that focus on motivational subsections (e.g., expectancy × value) or goal phases (e.g., goal choice) as being more comprehensive than warranted and then inappropriately overextend them. While the integrated view provided here is a remedy, we will still need to employ it strategically, balancing parsimony with completeness. As Einstein suggested ‘Make everything as simple as possible, but not simpler’. When drawing on the motivational features here, it is unlikely that we need to use all aspects of GPS for all research questions or applied situations. We may be focusing on a particular goal phase and specific motivational aspects, such as impulsiveness or time, may be effectively constants in our situation, allowing them to be dropped from the analysis. Or, we may be dealing with a participant group that is selected for a precise motivational weakness, such

as lower self-confidence or impulsiveness. Our motivational interventions should be appropriately targeted to their motivational weaknesses, a matching presently we rarely consider (Gröpel & Steel, 2008). Other times, a more sophisticated battery of concepts is needed. For example, Richardson and Taylor (2012) used almost all aspects discussed here – temporal motivation theory, ­ goal phase theory, and computation modeling – to help understand how employees respond to requests for their input. While not all aspects of motivation are always needed, we do need to always consider all motivational aspects. To justify simplification, we need first to cogently consider and argue which features of GPS are operating and which are not. As we stressed at the start, this is part of the conscious guidance that Heath (2014) was calling for. And, based on this understanding, perhaps we can avoid the accelerating slide towards dysfunctional decision-making that Heath and all the others warned us about. We can learn self-regulatory techniques, create motivational schemes, and build environments that coax us towards better goal choices, better goal plans, and superior goal striving.

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The Building Blocks of Motivation: Goal Phase System

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5 Self-Determination Theory Applied to Work Motivation and Organizational Behavior Marylène Gagné, Edward L. Deci and Richard M. Ryan

INTRODUCTION

SELF-DETERMINATION THEORY

Self-determination theory (SDT) is a broad theory of human motivation that has developed over the last forty years to become an important framework in many fields of psychology, including organizational psychology (Gagné & Deci, 2005). Initially developed by Deci and Ryan (1985b), it is now used by thousands of researchers and practitioners around the world to help foster work environments that cultivate engagement, performance, and wellbeing in their employees. SDT employs organismic principles regarding human nature, and as such it views human beings as active organisms and relies on human experience as an important source of data to understand motivation and barriers to engagement. Yet SDT is well grounded in empirical endeavors, including evidence from well-validated questionnaires (e.g., Gagné, Forest, Gilbert, Aubé, Morin, & Malorni, 2010), organizational interventions (e.g., Deci, Connell, & Ryan, 1989), experimental manipulations (e.g., Grolnick & Ryan, 1987), brain imaging studies (e.g., Murayama, Matsumoto, Izuma, & Matsumoto, 2010), research with biological markers such as cortisol secretion (e.g., Reeve & Tseng, 2011), and studies using implicit (unconscious) procedures (e.g., Lévesque & Pelletier, 2003; Weinstein, Deci, & Ryan, 2011).

The development, testing, elaboration, and refinement of SDT has proceeded for more than forty years, initially focusing on intrinsic motivation and examining its antecedents and consequences. This led quickly to examining the relations between intrinsic and extrinsic motivation and in turn to differentiating types of extrinsic motivation in terms of the concept of autonomy. As well, its research soon began examining personality components of self-determination, as well as social-psychological ones. Over time many other phenomena were explored as the theory grew. Consequently, as a long-developing framework, SDT has numerous facets. Formally, the theory has been organized into six ‘mini-theories’ each of which deals with different aspects of motivation, engagement, performance, and well-being, and each of which has relevance to motivation within organizations, whether they be for-profit or not-for-profit. In what follows we review each of the mini-theories of SDT, along with research illustrating their applications to organizations.

Basic Psychological Needs Theory Although Basic Psychological Needs Theory (BPNT) was not the first mini-theory to be

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developed within SDT, we begin with it because of its theoretical centrality. BPNT proposes that human beings have a small set of fundamental or basic psychological needs, the satisfaction versus frustration of which strongly determines the people’s level of wellness and the degree to which they are functioning optimally. Deci and Ryan (1985b) argued that just as trees need sunshine, water, and certain minerals to grow and be healthy, human psychological growth and health requires satisfaction of the needs for competence, autonomy, and relatedness. The nutrients sun, water, and temperature do not ‘make’ trees grow; instead they represent essential nourishments that facilitate growth processes inherent in the living entity. So too, SDT posits that competence, autonomy, and relatedness satisfactions are the psychological nutrients that individuals must have in order to do what is natural for them – namely develop their capacities and realize their potentials. More specifically, SDT argues that in environments where their three basic needs are met, people are most fully functioning and engaged. In these contexts people feel: (1) competent, meaning that they feel they can master the challenges and demands they face, (2) autonomous, such that they have a sense of psychological freedom, volition, and choice, as opposed to feeling like a pawn that is being pressured and coerced, and (3) related to others so they have meaningful social interactions that involve both giving and receiving (Ryan, 1995). One criterion used to determine whether a need is basic has been to examine whether its presence or absence is reliably and incrementally associated with increases and decreases in well-being, respectively. Studies using a variety of empirical methods have indeed demonstrated that satisfaction of each of SDT’s three basic psychological needs relates to enhanced well-being, as indexed by higher vitality and life satisfaction, and less illbeing, as indexed by less anxiety and depressive symptoms (Sheldon, Elliot, Kim, & Kasser, 2001; Vansteenkiste, Lens, Soenens, & Luyckx, 2006). Such findings have been observed at both the between-person and within-person levels, with more need-satisfying days being associated with higher daily well-being and need-frustrating days relating to drops in well-being (e.g., Reis, Sheldon, Gable, Roscoe, & Ryan, 2000). In one such study, Ryan, Bernstein, and Brown (2010), used an ­ experience-sampling method with full-time workers from various occupations. Assessed three times a day, these researchers found that in situations where need satisfaction was high, indicators of both psychological and physical health were also high. Sadly, for most workers their well-being peaked on weekends and during evening hours

when they were away from work. In fact, the data revealed that most workers found low autonomy and low relatedness in work, which was associated with increases in ill-being and somatic symptoms. Given that this study was conducted using a heterogeneous sample of workers, it would be interesting for future research to evaluate moderators of the ‘weekend effect’, including job characteristics such as work autonomy and social support (Humphrey, Nahrgang, & Morgeson, 2007), task significance, and relational job design (Grant, 2007, 2008a), all of which are likely to facilitate basic psychological need satisfaction. Research on job design clearly points to autonomy as a major design component for enriched jobs across industries and cultures (see Gagné & Bhave, 2011 for a review). Nonetheless some theorists have criticized SDT’s assertion that autonomy is a universal psychological need, seeming to base their arguments on the definition of autonomy as independence and individualism. For example, Iyengar and DeVoe (2003) and Markus and Kitayama (2003) argued that autonomy is a Western concept, and that it is not relevant in East Asian collectivistic cultures where interdependence and deference are valued. Yet SDT has clearly differentiated its conception and operationalization of autonomy from constructs such as independence, individualism, defiance, or detachment (e.g., Deci & Ryan, 2000; Ryan & Lynch, 1989), and researchers have empirically distinguished ‘reflective’ or volitional autonomy from reactive or independent autonomy (Koestner & Losier, 1996). More importantly, numerous crosscultural studies have shown that autonomy as well as the needs for competence and relatedness are indeed essential for well-being in Eastern as well as Western countries (Bao & Lam, 2008; Chirkov, Ryan, Y. Kim, & Kaplan, 2003; Jang, Reeve, Ryan, & A. Kim, 2009; Nie, Chua, Yeung, Ryan, & Chan, 2014; Vansteenkiste, Lens et  al., 2006; Vansteenkiste, Zhou, Lens, & Soenens, 2005; Zhou, Ma, & Deci, 2009). Part of the controversy concerning autonomy is that some see autonomy as being antagonistic to relatedness – again confusing autonomy with independence (e.g. Iyengar & DeVoe, 2003). Yet in SDT we suggest that not only are autonomy and relatedness not antagonistic – they tend to be very positively related and synergistic (e.g., Hodgins, Koestner, & Duncan, 1996). People often feel most autonomous in settings where they also feel connection and belonging. Accordingly, SDT has provided much empirical evidence that autonomy and relatedness satisfactions are dynamically complementary (Deci & Ryan, 2000) unless the environment is structured in such a way as to turn them against each other, as is the case in the

Self-Determination Theory

provision of conditional regard (e.g., Assor, Roth, & Deci, 2004). Both autonomy and relatedness needs are as critical as the need for competence in workplace settings, although competence has perhaps been more widely researched in the workplace and elsewhere using both SDT (e.g., Koestner & McClelland, 1990) and other theoretical perspectives (Bandura, 1986; Csikzentmihalyi, 1975; Locke & Latham, 1990; Rotter, 1954). That is, research on self-efficacy, flow, goals, and locus of control have all shown the importance of expecting to competently attain desired outcomes for functioning optimally, although these other perspectives do not speak of a need for competence, nor address either the relative autonomy of people’s competent efforts or the relationships within which these efforts are embedded (Rodgers, Markland, Selzlet, Murray, & Wilson, 2014). The importance of psychological needs in a thriving workplace cannot be overstated. A growing body of research in the field of organizational psychology has shown that need satisfaction, as defined within SDT, is key to promoting positive work outcomes (see Gagné & Deci, 2005, for a review). These outcomes include both better engagement and job performance (e.g., Baard, Deci, & Ryan, 2004; Deci, Ryan, Gagné, Leone, Usunov, & Kornazheva, 2001) and greater wellbeing at work (e.g., Ilardi, Leone, Kasser, & Ryan, 1993; Van den Broeck, Vansteenkiste, Lens, & De Witte, 2010). Studies have also shown need satisfaction at work to be associated with decreased risk of burnout (Fernet, Gagné, & Austin, 2010; Nie et al., 2014); higher commitment to the organization (Gagné, Chemolli, Forest, & Koestner, 2008); decreased career indecision (Guay, Ratelle, Senécal, Larose, & Deschênes, 2006); and lower turnover (Gagné, 2003; Richer, Blanchard, & Vallerand, 2002), among other outcomes. Need satisfaction is important even for very specific goals and issues within unique work environments. For example, need satisfaction was shown to mitigate the negative effects of threats to safety of staff working in a volatile psychiatric environment (Lynch, Plant, & Ryan, 2005). In a training context, Roca and Gagné (2008) found that need satisfaction at work was related to the perceived usefulness, perceived ease of use, and enjoyment of an e-learning program, which in turn were related to future training intentions. In sum, basic psychological need satisfaction has broad positive effects on employees’ work motivation and adjustment in both broad and nuanced ways. Basic psychological needs theory also fits well with research on psychological empowerment, which has been defined in terms of a Gestalt measure of four feelings, namely meaning,

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self-­ determination, competence, and impact (Thomas & Velthouse, 1990). Clearly, it is easy to link the two empowerment components of selfdetermination and competence to two of the psychological needs proposed in SDT, while impact can be related to the need for relatedness (Grant, 2007, 2008a) and meaning is linked to satisfaction of all three SDT needs (Weinstein, Ryan, & Deci, 2012). The SDT needs also have some relations to Hackman and Oldham’s (1980) model of job design, which has been empirically related to intrinsic work motivation in paid workers, and to autonomous motivation in volunteer workers (Gagné, Senécal, & Koestner, 1997; Millette & Gagné, 2008). The concept of basic psychological needs has therefore been useful in making both direct and indirect predictions regarding work environments. Yet in a practical sense it is important to highlight that needs are most often supported through very specific and concrete strategies in organizations. We would advance that work environments in which people are offered optimal challenges, as well as training and developmental opportunities, and where constructive feedback is given, the people are more likely to feel competence need satisfaction. In work environments where strategic goals are communicated to employees, participative methods are used, people are supported to take initiative and make decisions and voice their opinions are likely to foster workers’ autonomy. Finally, work environments in which social interactions are encouraged through team work, and managers take the time to listen to their employees, empathize with them, and show concern and care for the employees’ well-being are more likely to foster high relatedness satisfaction. Indeed, many of these practices have been shown to ‘move the needle’ on these psychological needs, which in turn leads to greater engagement and commitment to the organization (Marescaux, De Winne, & Sels, 2013). Interesting in this regard is research showing that when individuals experience high basic need satisfaction, they are less alienated from their surroundings. For example, Chirkov, Ryan, and Willnes (2005) showed among both Brazilian and Canadian young adults that those who had higher general need satisfaction felt more aligned with and less estranged within their cultures. Analogously, employees whose values fit with those of their organization and group, and whose knowledge and abilities fit their job requirements, are more likely to experience high need satisfaction, which in turn is associated with better performance and commitment to the organization (Greguras & Diefendorff, 2009). Not only has basic psychological need satisfaction been used to show how managerial behaviors

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that support the three psychological needs influence performance and well-being (Baard et  al., 2004; Deci et  al., 2001), it has also helped clarify why transformational leadership positively influences work motivation, performance, commitment to organizations, and job satisfaction of subordinates (Bono & Judge, 2003; Hetland, Hetland, Andreassen, Pallesen, & Notelaers, 2011; Kovjanic, Schuh, Jonas, van Quaquebeke, & van Dick, 2012; Wang & Gagné, 2013). The concept of transformational leadership describes a leader as an individual who effectively addresses the needs of those they lead. Burns (1978), in fact, argued that a transformational leader ‘seeks to satisfy higher needs and engages the full potential of the follower’ (p. 4). Kovjanic et al. (2012) suggested that it is indeed their focus on employees’ psychological needs that differentiates transformational leaders from transactional leaders. Based on this, Kovjanic et  al. did two studies in European companies demonstrating that the effectiveness of transformational leaders at fostering job efficacy, satisfaction, and organizational commitment was indeed mediated by their ability to facilitate autonomy, competence, and relatedness need satisfactions. Research has even recently been applied to occupational health and safety inspectors, showing that those who support psychological needs when dealing with workplace conflicts are more effective in getting organizations to adhere to regulations (Burstyn, Jonasi, & Wild, 2010). On the other side of the coin, abusive supervision has been shown to negatively impact employees’ need satisfaction, which makes them more likely to engage in deviant work behaviors (Lian, Ferris, & Brown, 2012). Moreover, such effects trickle down through organizations. To foster transformational leadership styles, leaders need to be supported themselves and develop autonomous work motivation (Deci, Spiegel, Ryan, Koestner, & Kauffman, 1982; Pelletier, Lévesque, & Legault, 2002; Trépanier, Fernet, & Austin, 2012). The next question of interest is whether needsupportive managerial behaviors are trainable, and the answer is yes. Studies have indeed shown that training managers to be more supportive of the three needs enhances employee motivation, trust in management, commitment to the organization, and job satisfaction (Deci et  al., 1989; see Su & Reeve, 2011 for a recent meta-analysis). There may, of course, be individual differences in how managers react to training, so it would be important to evaluate training effects on both intra-individual and inter-individual changes in need-supportive behaviors as a function of individual differences such as general causality orientations (Deci & Ryan, 1985a) and personal aspirations (Ryan,

Sheldon, Kasser, & Deci, 1996), both of which are discussed below. Indeed, Su and Reeve (2011) reported that trainees who were high on the autonomous causality orientation showed greater changes in their autonomy supportive behavior following training than those high on the controlled causality orientation. It is also possible that other individual differences might influence the effects of needsupportive training on the trainees, although there is relatively little evidence supporting that possibility (Bono & Judge, 2004). Regardless, training is important to facilitate managers becoming more need supportive, but training is not enough. Managers also need to experience satisfaction of their own basic psychological needs if they are to be autonomously motivated themselves and supportive of their employees’ psychological needs (Pelletier et  al., 2002; Pelletier & Sharp, 2009), which means that organizational structure, culture, and practices are also important. Job design (variety, challenge, feedback, decision-making latitude) also significantly influences work motivation through need satisfaction (Fernet, Austin, Trépanier, & Dussault, 2013; Gagné et al., 1997; Millette & Gagné, 2008; Van den Broeck, Vansteenkiste, De Witte, & Lens, 2008). Job autonomy has been a particularly important factor to foster proactive work behavior (proactive idea implementation and problem solving), innovative work behaviors (Parker, Williams, & Turner, 2006; Ramamoorthy, Flood, Slattery, & Sardessai, 2005), training intentions (Roca & Gagné, 2008), and knowledge sharing (Cabrera, Collins, & Salgado, 2006). The famous buffering effects of job control on the relation between job demands and stress (Karasek & Theorell, 1990) is only found in people with high autonomous work motivation (Fernet, Guay, & Senécal, 2004), and can be explained through an increase in feelings of autonomy (Van den Broeck et al., 2008). Task significance (defined as the perceived impact of one’s work on others) has also been shown to be a powerful driver of performance, supposedly because it increases identified motivation (Grant, 2008b). This effect may be partly explained by the effects of task significance on feelings of competence and relatedness, which can be enhanced by allowing for direct contact between workers and beneficiaries (Grant, 2007; Grant, Campbell, Chen, Cottone, Lapedis, & Lee, 2007).

Cognitive Evaluation Theory Most certainly the SDT sub-theory that has made the most ink flow within organizational psychology is cognitive evaluation theory (CET; Deci, 1975;

Self-Determination Theory

Deci & Ryan, 1980), which Ambrose and Kulik (1999) referred to as one of the seven traditional theories of work motivation. This is not because CET addresses all aspects of work motivation – in fact as just one of the mini-theories within SDT, CET is exclusively focused on intrinsic motivation and its contextual antecedents. However, the groundbreaking research within CET on intrinsic motivation opened up many controversies about the nature of extrinsic rewards, calling into question the commonly accepted idea that they always have positive effects on motivation, and it also led to several other lines of research and theorizing within SDT that have more recently been considered by organizational psychologists. Intrinsic motivation is defined as doing something for its own sake, out of interest and enjoyment, and CET maintains that people need to feel both competent and autonomous in order to be intrinsically motivated. As well, some research has indicated that relational satisfaction also contributes to intrinsic motivation (Anderson, Manoogian, & Reznick, 1976). When discussed, intrinsic motivation is often contrasted with extrinsic motivation, in this case defined as doing something for a separable consequence such as obtaining a reward or avoiding a punishment. Based on White’s (1959) early theorizing about the importance of ‘effectance’ motivation, and de Charms’ (1968) theorizing about the importance of ‘personal causation’, Deci (1971, 1972) set out to examine whether intrinsic and extrinsic motivation were additive or interactive and to see how the needs for competence and autonomy might play a role in the relations of the two types of motivation. He gave a group of students a monetary reward for completing an enjoyable task while a control group did the same task but got no reward. Deci found that the contingent tangible rewards undermined intrinsic motivation, indicating that the two types of motivation were negatively interactive. Only when the rewards were non-contingent and unexpected during task activity were the two types of motivation additive. Deci suggested that the contingent rewards prompted a shift in perceived locus of causality for the behavior from internal (i.e., feeling like an origin with respect to the behavior) to external (i.e., feeling like a puppet or pawn). Underlying this shift in perceived causality, was that the contingent rewards (even though desired) left people feeling controlled, thwarting their need for autonomy, while the non-contingent or unexpected rewards did not diminish autonomy. Subsequently, studies examining the effects of rewards and feedback further parsed the functions of these inputs. CET was then refined to organize findings showing that rewards and performance feedback could either undermine or

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enhance intrinsic motivation depending on how they were administered and thus perceived (Deci & Ryan, 1980). Specifically CET argues that rewards and feedback affect intrinsic motivation through their functional significance as experienced by the recipient. These inputs can be salient either controllingly, in which case they interfere with perceived autonomy, undermining intrinsic motivation, or informationally, in which case they are perceived as supporting a sense of competence and initiative. Moreover, the theory points to specific ways in which the same input, for example, a financial bonus, can be differentially administered so as to have controlling or informational salience, thus undermining or facilitating intrinsic motivation, respectively. The idea that rewards could ever yield an undermining effect was of course, in an era of behavior modification, initially controversial, but has now been well researched. For example, more than 15 years ago a meta-analysis of 128 laboratory experiments strongly supported the CET account of these effects (Deci, Koestner, & Ryan, 1999), showing that the net effects of all rewards on intrinsic motivation (assessed using behavioral ‘free-choice’ paradigms) was in fact negative (d = –0.24). When just tangible rewards were examined, the effect was somewhat stronger (d = –0.34), whereas, when only verbal rewards (positive feedback) were examined, the effect was positive (d = .33), as CET predicted. An additional moderator of reward effects pertaining to the link between tangible rewards and intrinsic motivation was the type of contingency. Obtaining a reward for engaging with a task or for completing a task had greater detrimental effects (d = –0.40 and –0.44, respectively) than obtaining a reward contingent on attaining a specified level of good performance (i.e., a performance-contingent reward; d = –0.28). These weaker effects for performancecontingent rewards can be explained by considering the impact of rewards on need satisfaction. As noted, tangible rewards can negatively affect feelings of autonomy, but when they are performance contingent they can also positively affect feelings of competence by providing information about behavioral effectiveness. These two simultaneous effects may to some degree offset each other in influencing intrinsic motivation (Ryan, Mims, & Koestner, 1983). Indeed, recent research has shown interactive effects of competence and autonomy on intrinsic work motivation (Dysvik, Kuvaas, & Gagné, 2013). Based on these findings, three conclusions have been offered regarding the use of performancecontingent rewards (Deci, Koestner, & Ryan, 2001). First, the precise impact of a performancecontingent reward appears to depend on whether

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its controlling or informational aspect is made more salient by the interpersonal context. So rewards given by a controlling agent should have stronger negative effects than rewards given by a supportive agent (Ryan et al., 1983). Second, the use of performance-contingent rewards in reallife settings are often accompanied by additional controlling features such as deadlines, surveillance, evaluations, and competition, all of which have been shown to negatively impact intrinsic motivation (Amabile, DeJong, & Lepper, 1976; Deci, 1972; Deci, Betley, Kahle, Abrams, & Porac, 1981; Enzle & Anderson, 1993; Mossholder, 1980). In contrast, the factors that have been shown to enhance intrinsic motivation, such as choice (Patall, Cooper, & Robinson, 2008: Zuckerman, Porac, Lathin, Smith, & Deci, 1978) and perspective taking (Koestner, Ryan, Bernieri, & Holt, 1984) are less frequently present. Finally, the use of performance-contingent rewards in real-life settings often results in many individuals failing to receive the reward because of insufficient performance, and there is clear evidence that such outcomes are highly damaging to motivation (Deci et al., 1999). The factors that diminish feelings of autonomy and that negatively affect intrinsic motivation mentioned above are omnipresent in work organizations. People frequently work with imposed goals and deadlines, and are monitored and evaluated by supervisors. They often compete for work opportunities, for rewards, or for promotions. The horde of controlling factors used in organizations is cause for concern, and it is therefore not surprising that employers often struggle to keep employees engaged and mentally healthy. There have indeed been sharp increases in mental health issues that organizations and governments are now more concerned about (e.g., Stephens & Joubert, 2001). Organizations spend a considerable amount of money paying consultants to make their employees more engaged and productive. Consultants, in turn, often advise organizations to use participative methods, give employees more autonomy and development opportunities, and recognize good work, which would serve to satisfy the three basic needs. Yet consultants and human resource specialists still often advocate for the use of contingent pay systems on the basis of principles from operant theory, expectancy theory, and agency theory (Jensen & Meckling, 1976; Lawler, 2000; Rynes, Gerhart, & Parks, 2005). Although it is true that people will exert effort to get money, in general, the quality tends to be inferior when the money is their reason for doing the task (Cerasoli, Nicklin, & Ford, 2014). This has led some scholars to warn organizations about the dangers of incentive pay,

using arguments compatible with findings from cognitive evaluation theory (Kerr, 1995; Kohn, 1993; Pfeffer, 1998). The response from many avid incentive enthusiasts is that the studies showing undermining of intrinsic motivation are from laboratory settings and therefore are not relevant to real-life work situations. However, studies by researchers such as Kuvaas, Dysvik, and Buch (2014) have shown that, in work settings, ranging from gas stations to financial-company offices, extrinsic motivation was negatively correlated with intrinsic motivation among these employees, a finding that supports the undermining phenomenon. Following the initial research on the effects of rewards on intrinsic motivation, there has been considerable debate about the research and its meaning for management (e.g., Calder & Staw, 1975; Deci, 1976; Deci, Cascio, & Krusell, 1975; Scott, 1975), which led some management specialists and managers to ignore the theory, for example, because some have misinterpreted it as implying that employees should not be well paid or that their level of pay should in no way reflect their contributions to the organization. To understand why this is not the case, we need to consider various points. First, research has long shown that employees need to be equitably compensated for effective performance and job satisfaction (e.g., Adams, 1963). CET would maintain that rewards are more likely to be experienced as informational if they are equitable, and BPNT would suggest that equitable rewards are likely to be more need supportive and thus more likely to have positive relation to intrinsic motivation and work engagement. As these hypotheses have not been tested, they represent an important future direction for research. Second, many factors in the workplace affect intrinsic motivation and engagement, perhaps the most important being the interpersonal climate of the workplace. The effects of compensation systems are likely to be strongly affected by the degree to which the work climate is need-supportive versus controlling, as was shown in experimental studies (e.g., Ryan et al., 1983). In short, the evidence is highly compelling that using monetary rewards to motivate performance represents a significant risk as the rewards can have a negative impact on autonomy, prompting the undermining of intrinsic motivation as specified by CET, especially when they are designed and administered in a way that is pressuring and controlling. In fact, the little research that exists on the effects of incentive pay systems on intrinsic motivation and performance has yielded mixed results, as we might expect from the complexity of pay and its potential to have both positive and negative effects

Self-Determination Theory

on psychological needs. High proportions of variable pay in the form of performance-contingent rewards have been positively related to turnover (Harrison, Virick, & William, 1996). However, a positive relation has been found between bonus-tobase ratios (bonuses in the form of stock options) and organizational return on assets (Gerhart & Milkovich, 1990). Still, research has shown that when monetary rewards are given for outcomes, there is a tendency for people to take a short route to the outcome (Shapira, 1976), and the short route chosen is sometimes deceptive and dishonest as we saw so clearly in the infamous case of Enron executive bonuses (Deci & Ryan, 2013). In terms of performance, psychological research has found that rewards tend to enhance algorithmic performance but to diminish heuristic performance (e.g., McGraw & McCullers, 1979), and a meta-analysis of economics and psychological studies showed that rewards tended to improve performance on dull boring tasks but to impair it on complex and interesting ones (Weibel, Rost, & Osterloh, 2007). A recent, more extensive metaanalysis by Cerasoli et al. (2014) considered 183 effects and found that intrinsic motivation did positively predict performance, but, in line with the idea of the undermining effect (Deci et  al., 1999), intrinsic motivation was a weaker predictor of performance when rewards were in effect. Further, when both intrinsic motivation and extrinsic incentives were considered together, intrinsic motivation predicted quality of performance, whereas incentives predicted quantity of performance, a finding in line with an earlier metaanalysis (Jenkins, Mitra, Gupta, & Shaw, 1998). Further, still another meta-analysis found positive effects of incentives in manufacturing firms, but not in service firms (Stajkovic & Luthans, 1997), which seems quite consistent with other conclusions because the work in manufacturing firms tends to be more algorithmic, whereas that in service firms tends to be more heuristic. In sum, then, these results show that reward effects tend to be moderated by the type of task or work activities: if it is boring and algorithmic, rewards may have a positive effect, but if it is interesting and heuristic, rewards are more likely to have a negative effect. This is particularly important given that so many of the types of problems facing contemporary work organizations increasingly require heuristic problem solving. One thing to note is that much of the compensation research has been done by accounting and finance researchers with little regard to intervening variables such as employee motivation and performance. Nonetheless, some behavioral economists have recently adopted the self-­determination theory perspective. For example, Frey and Osterloh

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(2005) argued that agency theory relies solely on the concept of extrinsic motivation, and that incentive pay does not, inevitably, lead to the expected goal alignment between employer and employee. These economists show that the relative price effect hypothesized by agency theory, whereby increases in payment lead to increases in effort, is counteracted by a ‘crowding out’ effect, which we refer to as the undermining effect, on intrinsic motivation (Frey, 1993). To avoid the crowding out effect, Frey and Osterloh advocate the use of fixed pay systems (i.e., non-contingent) coupled with a governance structure that fosters need satisfaction. To study compensation systems in a way that takes into account their complex characteristics, Gagné and Forest (2008) have proposed the breakdown of compensation systems into their basic components to study their effects on the need satisfaction of employees. These components include: (1) the absolute amount of pay in monetary value; (2) the perceived equity of the compensation, operationalized in terms of distributive justice; (3) the ratio of fixed amount of pay versus the amount of pay that varies according to a certain criterion, such as performance; (4) the objectivity of the performance appraisal that determines compensation; and (5) the number of people whose performance is used to determine one’s compensation, in other words, whether it is a group or individual incentive. Such a model would shed light on the motivational effects of compensation systems.

Organismic Integration Theory Intrinsically motivated behavior, which is propelled by people’s interest in the activity itself, is prototypically autonomous. However, daily work life for many people is not comprised of highly intrinsically motivated or inherently interesting activities. An important aspect of SDT addresses the question of how non-intrinsically motivated behaviors can be internalized, such that the person is actively motivated to engage in them autonomously. This is the territory of organismic integration theory (OIT), a third SDT mini-theory. Internalization is defined as people taking in values, attitudes, or regulatory structures, such that the external regulation of a behavior is transformed into an internal regulation (Ryan & Deci, 2000). The internalization can, however, take several forms, with resulting variability in the degree of autonomy. Very poor internalization is associated with being externally regulated – that is, by being motivated by externally controlling rewards or punishments. This classic type of extrinsic motivation is considered a controlled form of

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motivation within OIT. People can also introject a value or regulation and use it to measure their own worth. With this type of regulation, the person feels controlled by internalized pressures and standards that have been taken in but not really accepted by the person. Examples of introjected regulation include contingent self-esteem, which pressures people to behave in order to feel worthy, and ego involvement, which pressures people to behave in order to buttress their fragile egos (Ryan, 1982). On the other hand, if the context supports the people to feel autonomous and agentic, they are likely to identify with the value of the activity for themselves and integrate it into their core self-regulatory system, which in SDT is labeled the ‘self’. With identified regulation, people feel greater freedom and volition because the behavior is more congruent with their personal goals and identities. At the extreme, this form of internalization involves the integration of a given identification with other aspects of oneself – that is, with other identifications, interests, and values, resulting in the most fully internalized extrinsic motivation. With integrated regulation, people experience the behavior as an integral part of who they are, as an action that emanates from their sense of self. Integrated regulation does not, however, become intrinsic motivation but is still considered extrinsic motivation because the motivation is characterized by the activity’s being instrumentally important. Of course people tend to be motivated to different degrees by each of these motives. For example, people may both be interested in and value an activity; or they could be both intrinsically motivated and ego-involved for a given task. Nonetheless, the more a person is motivated by forms of controlled motivation (e.g., rewards; internal pressures) relative to autonomous forms (e.g., value; interest) the lower the quality of their engagement. Indeed, the autonomous forms of motivation (identified, integrated, and intrinsic) generally yield more positive behavioral, cognitive, and affective outcomes than the controlled forms (external and introjected). Still, all of these concepts refer to types of motivation even though some are in general more effective than others, and as such they stand in contrast to amotivation, which refers to lacking intentionality and motivation. According to OIT, internalization functions most effectively when individuals experience satisfaction of the basic psychological needs in the contexts where internalization is occurring. Many studies have shown, for example, in various domains that need support leads to greater internalization and autonomous self-regulation (e.g., Deci, Eghrari, Patrick, & Leone, 1994; Grolnick & Ryan, 1987; Williams & Deci, 1996; Williams, McGregor et  al., 2006), and it is interesting that

need support has yielded the greatest benefits for people who were more controlled in their initial motivation for the target activity in both school and work settings (Black & Deci, 2000; Fernet, Gagné, & Austin, 2010). Scales have been developed to capture the various types of workers’ intrinsic and extrinsic motivation. Based on an approach developed by Ryan and Connell (1989) to assess children’s academic and prosocial motivation, the first scale was developed in French by Blais et al. (1993). It asks workers to rate reasons for doing their jobs, each reason representing a form of motivation. Revisions and English translations of this scale exist (Gagné et al., 2010; Tremblay, Blanchard, Taylor, Pelletier, & Villeneuve, 2009). Most recently, Gagné et al. (2015) developed a scale that asks workers to rate reasons for why they put effort into their jobs. The scale attempts to capture the contingencies between the various sources of motivation for people’s work and their behavioral intensity/duration. Research has validated this approach in seven languages, showing its convergent and discriminant validity with many work-related antecedents and outcomes (Gagné et  al., 2015). There are also scales that measure task-level motivation for certain professions, such as school teachers and school principals (Fernet, 2011; Fernet, Senécal, Guay, March, & Dowson, 2008). Such scales have been used to demonstrate the powerful mediating role that motivation plays in relations between job design and work outcomes (Fernet, Austin, & Vallerand, 2012), between organizational/supervisor support and job satisfaction/retention (Gillet, Gagné, Sauvagère, & Fouquereau, 2013), between supervisor support and the retention of police officers (Otis & Pelletier, 2005), and between partner/employer support and work–family conflict as well as emotional exhaustion (Senécal, Vallerand, & Guay, 2001). Intrinsic motivation has also been shown to help employees break through negative burnout spirals, while external regulation aggravates them (Ten Brummelhuis, Ter Hoeven, Bakker, & Peper, 2011). In parallel, research has examined employee reactions to the introduction of new information technology (IT). Mitchell, Gagné, Beaudry, and Dyer (2012) designed a scale to measure the motivation for using new information technology at work, and found that the more autonomous forms of motivation were related to greater enjoyment of the IT as well as greater usage of its features. New research has also looked at the motivation to share knowledge in organizations, something that organizations increasingly consider crucial to their success (Gagné, 2009). Once again, autonomous motivation for sharing knowledge is the type of motivation most strongly related to sharing

Self-Determination Theory

behaviors (Foss, Minbaeva, Pedersen, & Reinholt, 2009; Reinholt, Pedersen, & Foss, 2011). Interestingly, even the motivation behind unemployed people’s job search activities has been a focus of research (Vansteenkiste, Lens, Dewitte, De Witte, & Deci, 2004; Vansteenkiste, Lens, De Witte, & Feather, 2005). The researchers examined autonomous and controlled reasons for searching, as well as autonomous and controlled reasons for not searching for a job. Findings suggested that those more autonomously motivated to search were more persistent and successful in the job search and evidenced greater well-being. Further, those who were more autonomously motivated to be unemployed showed less negative effects from that status. These findings once again show the importance of autonomous motivation. Work engagement is a very popular concept in organizational psychology because of its established link to organizational effectiveness (Macey & Schneider, 2008). Conceptualizations of work engagement that are used by researchers as well as practitioners vary widely. Some conceptualize engagement as a trait, such as proactive personality and conscientiousness, others conceptualize it as a state, such as feelings of energy or organizational commitment, and still others conceptualize it as a behavior, such as organizational citizenship behavior or personal initiative (see Macey & Schneider, 2008, for a review). Meyer and Gagné (2008) argued that autonomous motivation is actually a good representation of the state of work engagement because it is defined as motivation through enjoyment and meaning. They also proposed need satisfaction as an essential ingredient for fostering work engagement, suggesting that need satisfaction could actually serve as an explanatory mechanism for the effects of organizational interventions aimed at increasing work engagement. Schaufeli and colleagues’ conceptualization of work engagement is close to what would constitute an experiential outcome of autonomous motivation in self-determination theory (Schaufeli, Salanova, González-Romá, & Bakker, 2002). Their measure includes three components: vigor, which is essentially the same as vitality in self-determination theory (Ryan & Frederick, 1997) and constitutes a core aspect of eudaimonic well-being (Ryan, Huta, & Deci, 2008); dedication, which likely emerges when people have fully internalized the importance of their assigned job tasks (Deci et al., 1994); and absorption, which has been used extensively as an indicator of intrinsic motivation and flow (Kowal & Fortier, 1999). The opposite of work engagement is burnout (González-Romá, Schaufeli, Bakker, & Lloret, 2006), and again, autonomous motivation has been shown to protect people against burnout (Fernet et al., 2010).

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More recently, Spreitzer and her colleagues have developed a model of thriving at work based partly on self-determination theory (Spreitzer, Sutcliffe, Dutton, Sonenshein, & Grant, 2005). Thriving is defined as a feeling of vitality combined with a sense of growing or developing in one’s work, its opposite being languishing or being stuck. Thriving could be considered another good indicator of eudaimonic well-being and optimal functioning, and it has been related to innovative work behavior (Carmeli & Spreitzer, 2009). Spreitzer et al. (2005) have argued that thriving can only occur when one is autonomously motivated, which is fostered by need satisfaction, although that remains to be tested. Proactivity is also a desirable characteristic or behavior that has drawn much attention lately in organizational research. The concept emerged out of research on functional flexibility and role breadth (Cordery, Sevastos, Mueller, & Parker, 1993; Parker, Wall, & Jackson, 1997), which focused on how broadly workers define their role in an organization. Proactivity is one of the basic assumptions about human nature in SDT. Specifically, all humans are assumed to be proactive by nature, and supports for the basic needs for competence, autonomy, and relatedness have been found to maintain or enhance this inherent tendency (Deci & Ryan, 2000). Recent research on proactive work behavior corroborates this view. Proactive work behavior is operationalized as being self-directed, change oriented, and focused on the future (Parker, Bindl, & Strauss, 2010). This model of proactive motivation by Parker and colleagues includes ‘can do’ motivation based on self-efficacy, ‘reason to’ motivation based on the motivational regulations proposed in SDT, and ‘energized’ motivation based on emotions. With regard to ‘reason to’ motivation, it is argued that proactive work behavior is likely to emerge when people are autonomously motivated in their work. In fact, Parker et al. (2010) specifically said that proactive behavior is by definition autonomously regulated. But they proposed further that engaging in proactive behavior can also help fulfill psychological needs. For example, engaging in proactive behavior can bring additional challenges that can increase one’s feelings of mastery. Research by Parker et al. (2006) has shown that autonomy, trust, and support are important antecedents of proactive work behavior, three factors that overlap significantly with the three basic psychological needs. In addition, Wu and Parker (2014) have shown that supportive leadership has effects on proactive behavior through its influence on autonomous motivation. Finally, Grant, Nurmohamed, Ashford, and Dekas (2011) found that having substantial personal initiative combined with having

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high autonomous work motivation and low controlled work motivation fostered proactive behaviors. Job applicants received more job offers and call center employees generated more revenue when they displayed such characteristics. To reflect the importance of proactive work behavior for organizations, Griffin, Neal, and Parker (2007) have re-conceptualized individual work performance into three types, namely proficiency, adaptivity, and proactivity. While the first, proficiency, has been found to be as strongly associated with both controlled and autonomous work motivation, the latter two have been more strongly associated with autonomous work motivation (Gagné et al., 2015). These findings support Gagné and Deci’s (2005) proposition that algorithmic tasks could be prompted by either controlled or autonomous motivation, but heuristic tasks require autonomous motivation. It also fits well with the idea that more complex and demanding activities require autonomous motivation to be successfully regulated (Cerasoli et  al., 2014; Green-Demers, Pelletier, & Ménard, 1997). With regards to adaptivity, research has also shown how supporting the three psychological needs helps workers cope with and accept organizational change (Gagné, Koestner, & Zuckerman, 2000), and how autonomous motivation to use a new IT system is related to its adoption and usage (Mitchell et al., 2012).

Causality Orientations Theory Self-determination theory also assumes that not everyone reacts in the same way to the same environment. Some people are generally more engaged and volitional in their behavior, treating environments as useful information, whereas others are more sensitive to controlling factors in the environment, feeling pressured by them. Still others have low intentionality and tend to be quite amotivated. A fourth minitheory of SDT deals with such individual differences, and is called causality orientations theory. Deci and Ryan (1985a) devised a measure, the General Causality Orientations Scale, to capture these generallevel personality differences. A great deal of research using this scale has shown that some people tend to experience more choice when initiating and regulating their behavior, they tend to be more proactive than others, and they tend to use environmental cues as ways of figuring out the best way for them to behave. These people, who are high in the autonomy orientation, are more likely to select jobs that allow for initiative, interpret feedback as more informational, and make choices based on their own interests and values. They tend to be more self-aware and to behave

in ways that are consistent with their attitudes (Koestner, Bernieri, & Zuckerman, 1992). In contrast, some people are very sensitive to environmental controls, so they feel and behave in controlled ways quite often. These people, high in the controlled orientation, look for controls in the environment that will tell them what is expected of them, and feel pressured to initiate and regulate their behaviors accordingly (Deci & Ryan, 1985a). At times, however, they tend to defy rather than just comply by doing the opposite of what they were told to do. Still, they generally rely on environmental controls, such as deadlines and threats, to regulate their behavior, and they are more likely to select jobs based on status and pay. Studies have shown that individuals high on the controlled orientation tend to be defensive in their interpersonal relations, to behave in more machiavellian ways, and to be more aggressive in sports and while driving (Goldstein & Iso-Ahola, 2008; Hodgins et al., 1996; McHoskey, 1999; Neighbors, Vietor, & Knee, 2002). They are also more likely to dehumanize others and to consider them as obstacles to their goal attainment (Moller & Deci, 2010; Vansteenkiste, Mouratidis, & Lens, 2010). Still other people often feel lost when at work, unsure about how to attain desired outcomes, and unable to do their work effectively. These people, who are high in impersonal causality, tend to be low in intentionality and high in amotivation. Causality orientations are theorized to emerge out of early need-supportive or need-thwarting experiences with significant others, such as parents, teachers, and peers. When the early environment satisfies the basic needs for autonomy, competence, and relatedness, people tend to develop a stronger autonomous orientation, whereas, when developmental contexts thwart the basic needs, people will tend to develop stronger controlled and impersonal orientations. The theory suggests that everyone has some level of each of the three orientations, so all three can be used in predicting people’s behaviors, cognitions, and affect. Research has related the autonomy orientation positively to trait self-esteem and more mature ego development (Deci & Ryan, 1985a), as well as to the tendency to support the needs of others (Williams & Deci, 1996). In contrast, the control orientation has been positively related to public self-consciousness and the Type-A coronary prone behavior pattern, and therefore to being pressuring rather than supporting of others and to being at greater risk for health problems (Deci & Ryan, 1985a). It has also been positively related to self-monitoring (Zuckerman, Gioioso, & Tellini, 1988). The impersonal orientation has been positively related to an external locus of control, to self-derogation and depression, and to social

Self-Determination Theory

anxiety. It has also been negatively related to trait self-esteem and ego-development. Other research has shown that people high in the autonomy orientation tended to interpret monetary rewards informationally, with an increase in intrinsic motivation, whereas those high in the control orientation tended to interpret them as controls and showed a decrease in intrinsic motivation (Hagger & Chatzisarantis, 2011). As concerns the workplace directly, the autonomy orientation has been positively related to autonomous work motivation, work-related wellbeing, high performance, and work engagement, while the control orientation has been negatively related to autonomous work motivation and has not predicted work effectiveness (Baard et  al., 2004; Deci, Ryan, & Gagne et al. 2001; Gagné, 2003; Lam & Gurland, 2008). Accordingly, causality orientations constitute individual differences that, like contextual factors, affect need satisfaction and motivation and, in turn, performance and wellness. Some research in organizations has assessed both the degree of autonomy support of the managerial climate and the autonomous causality orientation of the employees as predictors of the employees’ need satisfaction (e.g., Baard et  al., 2004; Gagné, 2003). In the Baard et al. study both the contextual factor and the causality orientation had significant, independent positive relations to basic psychological need satisfaction, although the variance accounted for in need satisfaction by the autonomy-supportive work climate was far greater than that accounted for by the autonomy causality orientation. In turn, need satisfaction predicted both work performance and psychological well-being. Studies by Olesen and colleagues (Olesen, 2011; Olesen, Thomsen, Schnieber, & Tønnesvang, 2010) using both college students and working adults demonstrated that the causality orientations are empirically distinct from the Big Five personality traits (McCrae & Costa, 2008), representing independent variance, although there are some relations between causality orientations and the personality traits. Specifically, the autonomy orientation related positively to extraversion and openness to experience; the control orientation was negatively related to agreeableness; and the impersonal orientation was positively related to neuroticism and negatively related to extraversion (Olesen, 2011). In light of the control orientation finding, it would be interesting to see whether the control orientation would relate with the honesty/integrity subscale of the HEXACO model (Lee & Ashton, 2004), given that this orientation has been related to some antisocial behaviors in past research reviewed earlier (e.g., McHoskey, 1999; Neighbors et al., 2002; Vansteenkiste et al., 2010). Olesen (2011) views the relations between

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causality orientations and personality traits as an indication that ‘causality orientations are characteristic adaptations of personality’ (p. 464), meaning that personality traits may influence, in part, the development of the motivational orientations.

Goal Content Theory A fifth mini-theory of SDT deals not with the ‘why’ but with the ‘what’ of people’s motivation. Goal Content Theory (GCT) suggests that the content of the goals people hold is systematically associated with well-being, such that some goals are more beneficial than others because of their relations with basic psychological needs. Specifically GCT posits categories of intrinsic versus extrinsic life pursuits that differentially predict well-being. GCT grew from early research by Kasser and Ryan (1993) revealing that people who strongly held materialistic values tended to experience low levels of well-being. The researchers speculated and found that holding such materialistic values distracted people from activities and goals that would be more likely to satisfy their psychological needs. Kasser and Ryan (1996) subsequently proposed a model of intrinsic and extrinsic aspirations and showed that strong intrinsic aspirations (e.g., self-acceptance, affiliation, community, and fitness) were related to higher levels of well-being than were strong extrinsic aspirations (e.g., financial success, appealing appearance, and social recognition). What’s more, despite the fact that individuals with strong extrinsic aspirations believe that the attainment of extrinsic goals will increase their well-being, the attainment of intrinsic aspirations was found to lead to greater well-being, but the attainment of extrinsic aspirations led to more ill-being (Niemiec, Ryan, & Deci, 2009; Sheldon, Gunz, Nichols, & Ferguson, 2010). Again, need satisfaction was found to explain these effects. More recently, the model was extended into a circumplex model underlying an intrinsic/extrinsic dimension and a self-transcendence dimension that have been shown to be reliable across samples from many nations (Grouzet, Kasser, Ahuvia, Dols, Kim et al., 2005). Moreover, it has been shown that intrinsic-goal content has positive relations to learning, persistence, performance, and well-being, that are independent of both whether the motivation for pursuing the goal is autonomous versus controlled, and whether the environment in which the goal is being pursued is need-supportive or needthwarting (Sheldon, Ryan, Deci, & Kasser, 2004; Vansteenkiste, Simons, Lens, Sheldon, & Deci, 2004). Research has suggested that the development of materialistic values can often emerge out of early

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thwarting of the psychological needs (Kasser, Koestner, & Lekes, 2002; Kasser, Ryan, Zax, & Sameroff, 1995; Williams, Cox, Hedberg, & Deci, 2000), and as such, developing materialistic values may act as substitutes for the unfulfilled needs. Education has also been shown to impact on the development of values among adults. Law and business education students tend to adopt more extrinsic values over the course of their studies, whereas undergraduate students in arts and science tend to adopt more intrinsic values during their education (Kasser & Ahuvia, 2002; Sheldon & Krieger, 2007; Vansteenkiste, Duriez, Simons, & Soenens, 2006). Interestingly another study by Sheldon and Krieger (2014) showed that lawyers who said they were in the profession ‘for the money’ were less happy than lawyers who said they were in it ‘to do good’, even though the income of the latter group was lower. Such aspirations can have repercussions on optimal functioning. People with intrinsic values have tended to learn better and persist more, whereas those with extrinsic values cooperate less, hoard resources, act more prejudicially, and take more health risks (for an overview see Vansteenkiste, Soenens, & Duriez, 2008). Intrinsic work values have also been related to interest for training, the ability of unemployed people to find a job, job satisfaction, engagement, and well-being (Van den Broeck et al., 2010; Vansteenkiste et al., 2007), and as such would seem to be important to examine in work settings. To take these arguments further, we could also explore the role that money plays in people’s lives, which may constitute another way in which to understand the effect of compensating people for their work. Stone (2014) presents an interesting framework on people’s functional relations to money that is based on self-determination theory. He argued that it is possible to have a relation to money that is ‘non-materialistic’ if one views money as a means of achieving non-financial or more transcendent goals in one’s life. He further argued that the belief that one is able to manage one’s money (competence), that one’s financial decisions are volitional (autonomy), that one’s financial resources can be used to achieve community goals (relatedness) will lead to higher levels of well-being so long as one is not spending an inordinate amount of time pursuing the money and thus ignoring need satisfaction in other domains of one’s life such as family and personal health.

Relationships Motivation Theory The most recent addition to SDT’s mini-theories is Relationships Motivation Theory (RMT), which

summarizes a large body of research on the ways in which relatedness needs become most fully satisfied through high-quality relationships. While many theories suggest relationships are important to motivation and well-being, RMT examines more closely what comprises a high-quality relationship. In line with SDT more generally, RMT suggests that those relationships in which people are most connected are not just those where there is warmth, protection, and care, which provide supports for relatedness, but also where there are also supports for autonomy and competence. For example, La Guardia, Ryan, Couchman and Deci, (2000) showed that in adults, security of attachment with other adult figures fluctuated with perceived autonomy and competence satisfaction when interacting with those other individuals. In fact, SDT research showed that in adult relationships, mutuality of autonomy support was highly need satisfying (Deci, La Guardia, Moller, Scheiner, & Ryan, 2006). Thus, it is not only when other people support us for feeling related, autonomous, and competent that we feel well; providing supports for the other people’s feeling more related, autonomous, and competent also has benefits for our own well-being, probably because relationship quality is enhanced when we give to other people, thereby increasing our own feelings of relatedness to them. RMT helps further explain how managers can foster a high quality of connection, loyalty, and congeniality in work contexts. Managers who both convey feedback to scaffold growth and who support autonomy are more likely to have employees who feel connected both to them and to the organization as a whole (e.g., Deci et al., 1989). Indeed, quality of communication with supervisors predicts satisfaction of all three of the employees’ needs, and these in turn relate to both job-related vitality and organizational commitment (Graves & Luciano, 2013). RMT could also be used to better understand and use teamwork in organizations. By examining patterns of need support between team members, it may be possible to better understand the development of team cohesion, satisfaction, and performance.

APPLYING SDT TO BETTER UNDERSTAND OTHER PHENOMENA Motivation is at the heart of organizational behavior. Whether one studies leadership, teams, job design, performance, strategy, or compensation, motivation is always an important explanatory process. Yet motivation is a complex construct, as

Self-Determination Theory

there are varied types of motives, and varied types of work and performance environments. Selfdetermination theory is comprised of multiple mini-theories addressing these different facets of motivation, performance, and wellness. SDT research has shown that not only the amount of motivation matters, but that its type or quality often matters even more. Furthermore, SDT provides a framework for understanding how internalized values affect optimal functioning, and how a variety of contextual and personal factors influence individual performance and well-being. The concept of psychological needs plays a pivotal role in explaining and predicting what will lead to the different types of motivation, and it helps us understand how to design organizations in ways that promote optimal outcomes. For the researcher who wants to study how organizational practices and individual characteristics influence the motivation, performance, and well-being of workers, a key issue to consider is how these practices and characteristics are likely to affect the workers’ feelings of autonomy, competence, and relatedness.

Employee Monitoring For example, SDT principles could be employed to better understand employee monitoring systems such as computer monitoring, GPS systems on cars, and mobile phones. As organizations use such technologies more and more (American Management Association, 2008) different stakeholders have questioned the consequences of their use. Monitoring is very often intended to control employees’ behavior, and research has shown that this will likely have negative consequences (Amabile, 1979; Enzle & Anderson, 1993; Plant & Ryan, 1985), but monitoring could be used to help the employees by making it informational rather than controlling. In fact, some past research has shown that performance monitoring can increase stress levels and lead to health problems (Aiello, 1993; Carayon, 1993; Smith, Carayon, Sanders, Lim, & LeGrande, 1992), but it can sometimes increase work performance if it is used in a supportive rather than controlling way (Komaki, 1986; Komaki, Desselles, & Bowman, 1989; Larson & Callahan, 1990). If monitoring is used in a genuinely supportive and non-pressuring way, with the intent to help employees grow in their jobs, it may well have more positive effects (Aiello & Shao, 1993; Griffith, 1993; Wells, Moorman, & Werner, 2007), but it will require developing a high level of trust from providing ongoing need support (Deci et al., 1989). Some researchers have indeed argued

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and shown that the effects depend on how autonomous people feel under such monitoring systems, which relates to their degree of need satisfaction (Ball & Wilson, 2000; Carayon, 1994; De Tienne & Abbott, 1993; Stanton & Barnes-Farrell, 1996). In other words, from an SDT perspective, the difference in outcomes would be a function of the degree to which the employees experience satisfaction versus thwarting of the basic psychological needs, which could be a powerful mediator of the effects of monitoring and other managerial practices on employee motivation, performance, and well-being.

Health, Safety and Aggression in Organizations SDT can also inform the process of building better occupational health and safety practices in organizations. Scott, Fleming, and Kelloway (2014) have shown that the safety-culture approach to promoting safe behaviors in the workplace makes assumptions about human motivation and self-regulation that are similar to those made in SDT, and that safety programs based on this approach yielded better results than behavior-based approaches, which rely on reinforcement processes. Further, SDT could also be used to better understand the causes and consequences of workplace violence. In fact, research by Przybylski, Deci, Rigby, and Ryan (2014) supported the SDT hypothesis that thwarting the basic psychological needs in video game play led to greater aggression on the part of the players. Complementarily, Assor, Kaplan, Feinberg, and Tal (2009) and Lynch et al. (2005) showed that it is possible to intervene in organizations to decrease the level of aggression by facilitating greater need satisfaction, thus providing further support for the proposition that basic need thwarting underlies aggressive behavior. In a similar vein, Dagenais-Desmarais and Courcy (2014) presented a model in which psychological need thwarting explained the downward spiral of workplace violence, and suggested that using interventions supporting basic psychological needs, rather than those using traditional sanction methods, would be more likely to cull violence.

Regulation of Emotions and Behaviors Emotional labor, which constitutes a form of selfregulation that often has to be exercised at work, could also possibly be better understood from an

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SDT point of view. Cossette (2014) has argued that our reasons (motivation) for engaging in emotional labor may affect the regulatory choices we make (surface versus deep acting), which may have repercussions on our energy levels and our wellbeing. What organizations could do to facilitate the right type of emotional regulation for different work contexts (e.g., contexts in which one must display positive emotions versus contexts in which one must display neutral emotions) could be studied using this framework so that the employees experience autonomy rather than control in their regulation of emotions as well as behaviors.

FUTURE RESEARCH At several places throughout this chapter we have spoken about future research on a variety of issues and topics pertinent to effective functioning in organizations. Clearly, there are many promising directions for SDT research, reflecting the powerful role played by psychological needs in the workplace, the many factors that affect the needs, and the ways the needs affect outcomes. For example, there are various other phenomena in addition to those discussed in the last section where SDT concepts might be used to help interpret findings. But there are also specific areas where SDT would benefit from testing propositions. We now discuss a few.

Compensation Carefully controlled laboratory experiments have been plentiful examining the effects of monetary and other tangible rewards on intrinsic motivation, and the conclusions from that research are clear and highly relevant to the workplace, as they highlight the risks associated with using monetary rewards as a way to motivate people. Previous SDT research (Deci et al., 1999; Ryan et al., 1983) has specified different reward-contingencies that moderate the reward effects, and these findings are very important as well. However, there have been only a few studies done in the workplace examining the relations among rewards, compensation, intrinsic motivation, and important work outcomes. We cited one by Kuvaas, Dysvik and Buch (2014) for example that showed negative relations between intrinsic and extrinsic motivation in varied work settings, but additional studies also taking account of the contingencies would be very helpful. Especially interesting would be more research on the role of compensation of rewards on

internalization, because from an SDT point of view, although rewards can be functionally significant as external control, when carefully applied they can also convey competence and value and therefore facilitate internalization (Ryan & Brown, 2005). Studies of the sort mentioned in this section would seem to be important for informing compensation strategies.

Moderators At various points in the chapter we have discussed the issue of moderation. For example, we saw that autonomy support in college classrooms had a more positive impact on students who were initially relatively controlled in their motivation than it did on students who were initially relatively autonomous (Black & Deci, 2000). Further, we reported that individuals who were high on the controlled causality orientation evidenced an undermining effect when receiving a monetary reward, whereas those who were high on the autonomous causality orientation showed an enhancement effect (Hagger & Chatzisarantis, 2011). However, very few individual-difference moderation effects have emerged from the research, as main effects of greater need support predominate across settings and cultures (e.g., see Chen et  al., 2015). Further, attempts to isolate moderation effects may be useful and our belief is that it is most important to use variables within the theory to examine for moderation. Were variables from other theories found to moderate effects of need-supports versus need-thwarts, for example, it would be important to examine whether those other variables are correlated with SDT variables so we would know whether it may in fact be the SDT variables that are the important moderators. Perhaps even more important than individualdifference moderators are the interpersonal-climate variables as possible moderators. Various SDT studies have examined the need-supportive versus need-thwarting interpersonal climates within organizational settings and found them to be very powerfully influenced by managerial styles and to have powerful influences on a range of important performance and well-being work outcomes (Baard et al., 2004; Deci et al., 1989; Deci, Ryan, & Gagne et al. 2001). Yet, some variables can moderate these effects. For example, Schultz, Ryan, Niemiec, Legate and Williams (in press) recently found that employees’ mindfulness could buffer the negative effects of a need-thwarting organizational context on workers’ well-being and burnout. This suggests that mindfulness interventions may be helpful in creating a more resilient workforce. We also know

Self-Determination Theory

that the interpersonal climate can moderate the effects of both tangible rewards (Ryan et al., 1983) and competition (Reeve & Deci, 1996) on intrinsic motivation. Thus additional research examining work climate as a possible moderator of organizational phenomena would be helpful.

Interventions Given the importance of various contextual factors such as need-supportive work climates for work outcomes, it will be important for future research to examine how to improve these climates. An early study (Deci et al., 1989) tested a SDT-based, management-training intervention in a Fortune 500 company and found positive results in terms of employees’ job satisfaction and trust in the management. Other, smaller-scale interventions have also been studied in various work settings (e.g., Lynch et al., 2005), but further work is necessary. As Dysvik and Kuvaas (2014) put it, it is crucial to transform trainees from passive recipients into active participants by making training programs into a source of need satisfaction and engagement. They then proposed ways of supporting employees’ motivation to attend training programs and of supporting their motivation to put their learnings from the training programs into practice in their daily work lives. In the domain of education, Su and Reeve (2011) did a meta-analysis showing that interventions with teachers did enhance the teachers’ autonomy support as assessed with student perceptions and observer ratings. Further, in other studies (e.g., Reeve & Jang, 2006) the researchers isolated specific teacher behaviors that promote autonomous motivation in their students. Rigby and Ryan (2011) reviewed findings suggesting that the increasingly technological workspace of employees can be made more or less need satisfying through principles of ‘gamification’ and motivational design. Such findings, although not yet integrated into the literature of work, could inform intervention development for management and job design, with beneficial results for the organizations where they are tested and implemented.

CONCLUSION To summarize, SDT represents a comprehensive theory of human motivation that differentiates types or qualities of motivation using the distinction between autonomous and controlled motivation.

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Autonomous motivation comprises intrinsic motivation and fully internalized extrinsic motivation, while controlled motivation comprises regulations that are external or introjected. Considerable research has shown that when employees are autonomously motivated rather than controlled, their performance, especially on heuristic tasks, is better, their psychological adjustment and well-being are also higher, and they are more likely to give to and be supportive of others. The theory also employs the concept of basic psychological needs to explain how interpersonal contexts can either diminish or enhance autonomous motivation, engagement, and well-being in organizational settings. Specifically, people have three basic psychological needs – for competence, autonomy, and relatedness – and satisfaction of these needs is essential for autonomous motivation, effective functioning, and wellness. Interpersonal contexts created, for example, by managerial styles, which support satisfactions of the employees’ basic psychological needs also facilitate effective performance and psychological health. This chapter reviewed research and theory that used the motivational differentiation to provide insight into a range of phenomena that have been observed in workplaces and other organizational venues. More specifically, we presented six mini-theories that make up SDT, reviewed empirical support for each of the mini-theories, and discussed their relevance to the workplace. Simply stated, SDT has manifold applications in business settings, especially because of its ready translations to practice.

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findings. Psychological Bulletin, 134, 270–300. doi: 10.1037/0033-2909.134.2.270 Pelletier, L. G., & Sharp, E. C. (2009). Administrative pressures and teachers’ interpersonal behaviour in the classroom. Theory and Research in Education, 7, 174–183. doi:10.1177/1477878509104322 Pelletier, L. G., Lévesque, C. S., & Legault, L. (2002). Pressure from above and pressure from below as determinants of teachers’ motivation and teaching behavior. Journal of Educational Psychology, 94, 186–196. doi:10.1037/0022-0663.94.1.186 Pfeffer, J. (1998, May). Six dangerous myths about pay. Harvard Business Review, 108–119. Plant, R., & Ryan, R. M. (1985). Intrinsic motivation and the effects of self-consciousness, self-­ awareness, and ego-involvement: An investigation of internally controlling styles. Journal of Personality, 53, 435–449. doi: 10.1111/j.1467-6494. 1985.tb00375.x Przybylski, A. K., Deci, E. L., Rigby, C. S., & Ryan, R. M. (2014). Competence-impeding electronic games and players’ aggressive feelings, thoughts, and behaviors. Journal of Personality and Social Psychology, 106, 441–457. doi: 10.1037/ a0034820 Ramamoorthy, N., Flood, P. C., Slattery, T., & Sardessai, R. (2005). Determinants of innovative work behaviour: Development and test of an integrated model. Creativity and Innovation Management, 14, 142–150. doi: 10.1111/j.1467-8691.2005.00334.x. Reeve, J., & Deci, E. L. (1996). Elements within the competitive situation that affect intrinsic motivation. Personality and Social Psychology Bulletin, 22, 24–33. doi: 10.1177/0146167296221003 Reeve, J., & Jang, H. (2006). What teachers say and do to support students’ autonomy during a learning activity. Journal of Educational Psychology, 98, 209–218. doi: 10.1037/0022-0663.98.1.209 Reeve, J., & Tseng, C.-M. (2011). Cortisol reactivity to a teacher’s motivating style: The biology of being controlled versus supporting autonomy. Motivation and Emotion, 35, 63–74. doi: 10.1007/ s11031-011-9204-2 Reinholt, M., Pedersen, T., & Foss, N. J. (2011). Why a central network position isn’t enough: The role of motivation and ability for knowledge sharing in employee networks. Academy of Management Journal, 54(6), 1277–1297. doi: 10.5465/ amj.2009.0007 Reis, H. T., Sheldon, K. M., Gable, S. L., Roscoe, J., & Ryan, R. M. (2000). Daily well-being: The role of autonomy, competence, and relatedness. Personality and Social Psychology Bulletin, 26, 419–435. doi: 10.1177/0146167200266002 Richer, S., Blanchard, C. M., & Vallerand, R. J. (2002). A motivational model of work turnover. Journal of Applied Social Psychology, 32, 2089–2113. doi: 10.1111/j.1559-1816.2002.tb02065.x

Rigby, C.S., & Ryan, R.M. (2011). Glued to games: The attractions, promise and perils of video games and virtual worlds. New York: Praeger. Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in human behavior, 24, 1585–1604. Rodgers, W. M., Markland, D., Selzlet, A., Murray, T. C. & Wilson, P. M. (2014). Distinguishing perceived competence and self-efficacy: An example from exercise. Research Quarterly for Exercise and Sport, 85, 527–539. doi 10:1080/02701367.2014.961050 Rotter, J. (1954). Social learning and clinical psychology. Englewood Cliffs, NJ: Prentice Hall. Ryan, R. M. (1982). Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology, 43, 450–461. doi: 10.1037/ 0022-3514.43.3.450 Ryan, R. M. (1995). Psychological needs and the facilitation of integrative processes. Journal of Personality, 63, 397–427. doi: 10.1111/ j.1467-6494.1995.tb00501.x Ryan, R. M., & Brown, K. W. (2005). Legislating competence: The motivational impact of high stakes testing as an educational reform. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence (pp. 354–374). New York: Guilford Press. Ryan, R. M., & Connell, J. P. (1989). Perceived locus of causality and internalization: Examining reasons for acting in two domains. Journal of Personality and Social Psychology, 57, 749–761. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78. doi: 10.1037/ 0003-066X.55.1.68 Ryan, R. M., & Frederick, C. M. (1997). On energy, personality and health: Subjective vitality as a dynamic reflection of well-being. Journal of Personality, 65, 529–565. doi: 10.1111/ j.1467-6494.1997.tb00326.x Ryan, R. M., & Lynch, J. (1989). Emotional autonomy versus detachment: Revisiting the vicissitudes of adolescence and young adulthood. Child Development, 60, 340–356. doi: 10.2307/1130981 Ryan, R. M., Bernstein, J. H., & Brown, K. W. (2010). Weekends, work, and well-being: Psychological need satisfactions and day of the week effects on mood, vitality, and physical symptoms. Journal of Social and Clinical Psychology, 29, 95–122. doi: 10.1521/jscp.2010.29.1.95 Ryan, R. M., Huta, V., & Deci, E. L. (2008). Living well: A self-determination theory perspective on eudaimonia. Journal of Happiness Studies, 9, 139–170. doi: 10.1007/s10902-006-9023-4 Ryan, R. M., Mims, V., & Koestner, R. (1983). Relation of reward contingency and interpersonal context

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to intrinsic motivation: A review and test using cognitive evaluation theory. Journal of Personality and Social Psychology, 45, 736–750. doi: 10.1037/0022-3514.45.4.736 Ryan, R. M., Sheldon, K. M., Kasser, T., & Deci, E. L. (1996). All goals are not created equal: An organismic perspective on the nature of goals and their regulation. In P. M. Gollwitzer & J. A. Bargh (Eds.), The psychology of action: Linking cognition and motivation to behavior (pp. 7–26). New York: Guilford Press. Rynes, S.L., Gerhart, B., & Parks, L. (2005). Personnel psychology: Performance evaluation and pay for performance. Annual Review of Psychology, 56, 571–600. Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3, 71–92. doi: 10.1023/ A:1015630930326 Schultz, P. P., Ryan, R. M., Niemiec, C. P., Legate, N., & Williams, G. C. (in press). Mindfulness, work climate, and psychological need satisfaction in employee well-being. Mindfulness. Scott, N., Fleming, M., & Kelloway, E. K. (2014). Understanding why employees behave safely from a self-determination theory perspective. In M. Gagné (Ed.), Oxford Handbook of work motivation, engagement and self-determination theory (pp. 276–294). New York, NY: Oxford University Press. Scott, W. E., Jr. (1975). The effects of extrinsic rewards on ‘intrinsic motivation’: A critique. Organizational Behavior and Human Performance, 14, 117–129. doi:10.1016/0030-5073(76)90032-5 Senécal, C., Vallerand, R. J., & Guay, F. (2001). Antecedents and outcomes of work–family conflict: Toward a motivational model. Personality and Social Psychology Bulletin, 27, 176–186. doi: 10.1177/0146167201272004 Shapira, Z. (1976). Expectancy determinants of intrinsically motivated behavior. Journal of Personality and Social Psychology, 34, 1235–1244. doi: 10.1037/0022-3514.34.6.1235 Sheldon, K. M., & Krieger, L. K. (2007). Understanding the negative effects of legal education on law students: A longitudinal test and extension of selfdetermination theory. Personality and Social Psychology Bulletin, 33, 883–897. doi: 10.1177/ 0146167207301014 Sheldon, K. M., & Krieger, L. K. (2014). Service job lawyers are happier than money job lawyers, despite their lower income. Journal of Positive Psychology, 9, 219–226. http://dx.doi.org/10.108 0/17439760.2014.888583 Sheldon, K. M., Elliot, A. J., Kim, Y., & Kasser, T. (2001). What is satisfying about satisfying events? Testing 10 candidate psychological needs. Journal

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of Personality and Social Psychology, 89, 325–339. http://dx.doi.org/10.1037/0022-3514.80.2.325 Sheldon, K. M., Gunz, A., Nichols, C. P., & Ferguson, Y. (2010). Extrinsic value orientation and affective forecasting: Overestimating the rewards, underestimating the costs. Journal of Personality, 78, 149–178. doi: 10.1111/j.1467-6494.2009.00612.x Sheldon, K. M., Ryan, R. M., Deci, E. L. & Kasser, T. (2004). The independent effects of goal contents and motives on well-being: It’s both what you pursue and why you pursue it. Personality and Social Psychology Bulletin, 30, 475–486. doi: 10.1177/0146167203261883 Smith, M. J., Carayon, P., Sanders, K. J., Lim, S., & LeGrande, D. (1992). Employee stress and health complaints in jobs with and without electronic performance monitoring. Applied Ergonomics, 23, 17–27. doi: 10.1016/0003-6870(92)90006-H. Spreitzer, G., Sutcliffe, K., Dutton, J., Sonenshein, S., & Grant, A. (2005). A socially embedded model of thriving at work. Organization Science, 16(5), 537– 549. http://dx.doi.org/10.1287/orsc.1050.0153 Stajkovic, A. D., & Luthans, F. (1997). A meta-analysis of the effects of organizational behavior modification on task performance, 1975–1995. Academy of Management Journal, 5, 1122–1149. Stanton, J. M., & Barnes-Farrell, J. L. (1996). Effects of electronic performance monitoring on personal control, task satisfaction, and task performance. Journal of Applied Psychology, 81, 738–745. doi: 10.1037/0021-9010.81.6.738. Stephens, T., & Joubert, N. (2001). The economic burden of mental health problems in Canada. Chronic Diseases in Canada, 22, 18–23. Stone, D. (2014). What is a functional relationship to money and possessions? In M. Gagné (Ed.), Oxford Handbook of work motivation, engagement and self-determination theory (pp. 374–385). New York, NY: Oxford University Press. Su, Y., & Reeve, J. (2011). A meta-analysis of the effectiveness of intervention programs designed to support autonomy. Educational Psychology Review, 23, 159–188. doi 10.1007/ s10648-010-9142-7 Ten Brummelhuis, L. L., Ter Hoeven, C. L., Bakker, A. B., & Peper, B. (2011). Breaking through the loss cycle of burnout: The role of motivation. Journal of Occupational and Organizational Psychology, 84, 268–287. doi: 10.1111/j.2044-8325.2011.02019.x Thomas, K. W., & Velthouse, B. A. (1990). Cognitive elements of empowerment: An ‘interpretive’ model of intrinsic task motivation. Academy of Management Review, 15, 666–681. doi: 10.5465/ AMR.1990.4310926 Tremblay, M. A., Blanchard, C. M., Taylor, S., Pelletier, L. G., & Villeneuve, M. (2009). Work extrinsic and intrinsic motivation scale: Its value for organizational psychology research. Canadian

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Journal of Behavioural Science, 41, 213–226. doi: 10.1037/a0015167 Trépanier, S. G., Fernet, C., & Austin, S. (2012). Social and motivational antecedents of perceptions of transformational leadership: A self-determination theory perspective. Canadian Journal of Behavioural Science, 44(4), 272–277. doi: 10.1037/a0028699 Van den Broeck, A., Vansteenkiste, M., De Witte, H., & Lens, W. (2008). Explaining the relationships between job characteristics, burnout, and engagement: The role of basic psychological need satisfaction. Work & Stress, 22(3), 277–294. doi: 10.1080/02678370802393672 Van den Broeck, A., Vansteenkiste, M., Lens, W., & De Witte, H. (2010). Unemployed individuals’ work values and job flexibility: An explanation from expectancy-value theory and self-determination theory. Applied Psychology: An International Review, 59, 296–317. doi: 10.1348/096317909X481382 Vansteenkiste, M., Duriez, B., Simons, J., & Soenens, B. (2006). Materialistic values and well-being among business students: Further evidence for their detrimental effect. Journal of Applied Social Psychology, 36, 2892–2908. doi: 10.1111/ j.0021-9029.2006.00134.x Vansteenkiste, M., Lens, W., Dewitte, S., De Witte, H., & Deci, E. L. (2004). The ‘why’ and ‘why not’ of job search behavior: Their relation to searching, unemployment experience, and well-being. European Journal of Social Psychology, 34, 345– 363. doi: 10.1002/ejsp.202 Vansteenkiste, M., Lens, W., De Witte, H., & Feather, N. (2005). Understanding unemployed people’s job search behaviour, unemployment experience and well-being: A comparison of expectancy-value theory and self-determination theory. British Journal of Social Psychology, 44(2), 269–287. doi: 10.1348/014466604X17641 Vansteenkiste, M., Lens, W., Soenens, B., & Luyckx, K. (2006). Autonomy and relatedness among Chinese sojourners and applicants: Conflictual or independent predictors of well-being and adjustment? Motivation and Emotion, 30, 273– 282. doi: 10.1007/s11031-006-9041-x Vansteenkiste, M., Mouratidis, A., & Lens, W. (2010). Detaching reasons from aims: Fair play and wellbeing in soccer as a function of pursuing performance-approach goals for autonomous or controlling reasons. Journal of Sport & Exercise Psychology, 32, 217–242. Vansteenkiste, M., Neyrinck, B., Niemiec, C. P., Soenens, B., de Witte, H., & Van den Broeck, A. (2007). On the relations among work value orientations, psychological need satisfaction, and job outcomes: A self-determination theory approach. Journal of Occupational and Organizational Psychology, 80, 251–277. doi: 10.1348/096317906X111024

Vansteenkiste, M., Simons, J., Lens, W., Sheldon, K. M., & Deci, E. L. (2004). Motivating learning, performance, and persistence: The synergistic effects of intrinsic goal contents and autonomy-supportive contexts. Journal of Personality and Social Psychology, 87, 246–260. doi: 10.1037/0022-3514.87.2.246 Vansteenkiste, M., Soenens, B., & Duriez, B. (2008). Presenting a positive alternative to materialistic strivings and the thin-ideal: Understanding the effects of extrinsic relative to intrinsic goal pursuits. In S. J. Lopez (Ed.), Positive psychology: Exploring the best in people (Vol. 4, pp. 57–86). Westport, CT: Greenwood Publishing Company. Vansteenkiste, M., Zhou, M., Lens, W., & Soenens, B. (2005). Experiences of autonomy and control among Chinese learners: Vitalizing or immobilizing? Journal of Educational Psychology, 97, 468–483. doi: 10.1037/0022-0663.97.3.468 Wang, Z., & Gagné, M. (2013). A Chinese–Canadian cross-cultural investigation of transformational leadership, autonomous motivation and collectivistic value. Journal of Leadership and Organization Studies, 20, 134–142. Weibel, A., Rost, K., & Osterloh, M. (2007). Crowdingout of intrinsic motivation: Opening the black box. Unpublished manuscript, IOU Institute for Organizational and Administrative Science, University of Zurich. Weinstein, N., Deci, E. L., & Ryan, R. M. (2011). Motivational determinants of integrating positive and negative past identities. Journal of Personality and Social Psychology, 100, 527–544. doi: 10.1037/a0022150 Weinstein, N., Ryan, R. M., & Deci, E. L. (2012). Motivation, meaning and wellness: A selfdetermination perspective on the creation and internalization of personal meanings and life goals. In P. T. P. Wang (Ed.), The human quest for meaning (2nd edition, pp. 81–106). New York: Routledge Publishers. Wells, D. L., Moorman, R. H., & Werner, J. M. (2007). The impact of the perceived purpose of electronic performance monitoring on an array of attitudinal variables. Human Resource Development Quarterly, 18, 121–138. doi: 10.1002/hrdq.1194. White, R. W. (1959). Motivation reconsidered: The concept of competence. Psychological Review, 66, 297–333. doi: 10.1037/h0040934 Williams, G. C., Cox, E. M., Hedberg, V., & Deci, E. L. (2000). Extrinsic life goals and health risk behaviors in adolescents. Journal of Applied Social Psychology, 30, 1756–1771. doi: 10.1111/j.1559-1816.2000. tb02466.x Williams, G. C., & Deci, E. L. (1996). Internalization of biopsychosocial values by medical students: A test of self-determination theory. Journal of Personality and Social Psychology, 70, 767–779. doi: 10.1037/0022-3514.70.4.767

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6 Action Regulation Theory: Foundations, Current Knowledge and Future Directions Hannes Zacher and Michael Frese

INTRODUCTION Action is goal-oriented behavior and it needs to be regulated. Most behavior is goal-oriented (exceptions are reflexes). Action regulation theory is a theory on this regulation of goal-oriented behavior. It is not a mid-range theory with a finite number of hypotheses but a meta-theory which provides a framework that allows to integrate mid-range theories; its roots are in German and some Russian psychological, cognitive, and human factors research (Frese, 2006; Hacker, 1994; H. Heckhausen, 1980), with additional roots in systems theory and activity theory (Leontjev, 1978; Miller, Galanter, & Pribram, 1960). Action regulation theory was developed in response to behaviorist theories which conceptualized human behavior as mere reactions to environmental stimuli. In contrast, action regulation theory is a molar theory that connects actions to purpose (Silver, 1985). Moreover, the theory was conceptualized as opposite to solipsistic cognitive theories (‘it is all in the mind’); action regulation theory assumes that humans approximate their imagery to reality by (inter-)acting upon and with reality (Frese & Sabini, 1985). Action regulation theory is not a monolithic theory, but a set of theories that rally around its basic tenets: assuming humans as active agents,

examining how actions evolve, interact with, and change (social) reality, taking actions ontologically as primary for the development of the human psychological system, and conceptualizing (faulty) actions as learning devices and as enablers of developing a concept of reality (Knoblich & Prinz, 2001). The theory has been particularly influential in applied psychology, including industrial, work, and organizational psychology (Frese & Sabini, 1985; Frese & Zapf, 1994; Hacker, 2003). Some accounts of the basic concepts and propositions of action regulation theory exist in the literature (e.g., Frese, 2009; Frese & Zapf, 1994; J. Heckhausen & Heckhausen, 2008). We will therefore not review the foundations of the theory in great detail in this chapter; instead we first provide a short introduction to four of its most important basic concepts: the sequence, hierarchical structure, and foci of action regulation, as well as the action-oriented mental model. Second, we distinguish action regulation theory from four other cognitive-behavioral theories: control theory, social cognitive theory, goal setting theory, and self-determination theory. Third, we review current research based on action regulation theory in three established areas: proactive work behavior, work-related learning and error management, and entrepreneurship. We close the chapter

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with a discussion of how action regulation theory can be applied in future research to gain a better understanding of six emerging areas: occupational strain and well-being, reciprocal influences between personality and work, innovation, team work, career development, and successful aging at work.

BASIC CONCEPTS IN ACTION REGULATION THEORY Action is goal-directed behavior that is influenced by stable and dynamic person factors (e.g., personality, orientations, affect) and by signals and feedback from the physical and social environment (e.g., job characteristics, supervisors, coworkers; Hacker, 1985). Actions, in turn, lead to changes in the person and the environment. In contrast to behaviorist theories, action regulation theory explains how individuals regulate their behavior through cognitive processes such as goal development, planning, and feedback processing. However, action regulation theory differs from purely cognitive and information processing theories in that it places a stronger focus on the links between cognitive processes, behavior, the objective environment, and objective outcomes. In the following, we describe four basic concepts of action regulation theory: the sequence, hierarchical structure, and foci of action regulation, as well as the action-oriented mental model (for further details, see Frese, 2009; Frese & Zapf, 1994).

Sequence of Action Regulation The action sequence includes the phases of (a) goal development and selection, (b) orientation or mapping the environment, (c) planning (plan development and plan selection), (d) monitoring of execution, and (e) feedback processing (Frese, 2009). Importantly, individuals do not have to follow this idealized sequence in a rigid way; human action can resemble a rather chaotic process that sometimes requires individuals to go back and forth between steps or omit steps in the action sequence (e.g., if later steps in the action sequence lead to changes in goals). Moreover, certain phases may have to be repeated (e.g., if goals change in the process) and, in the case of multiple goals, certain action phases may happen simultaneously.

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Development and selection of goals

Goals are internal representations of desired states in the future (Austin & Vancouver, 1996), standards that guide the goal achievement process, and set points that signal that one has achieved the goal. According to Hacker (1985), an action is the smallest unit of behavior that is linked to a goal. Originally, action regulation theory assumed that there were only conscious goals for molar behavior. However, modern research has corrected this to also include unconscious goals (Bargh & Barndollar, 1996; Latham & Locke, 2007; Latham & Piccolo, 2012). This means that environmental stimuli may trigger actions by priming individuals. Possibly, (organizational) cultural factors often produce actions via this process. In the goal development and selection phase, goals are either internally generated and selfselected or assigned (e.g., by a supervisor, the management of the organization, or by models). Individuals can develop goals based on wishes that have their origin in individual difference characteristics such as values, motives, and expectations (H. Heckhausen & Kuhl, 1985). In the external goal setting case, employees are told by others to pursue certain goals. Individuals may or may not internalize these assigned goals. The internalization process includes some degree of redefining tasks based on individual characteristics such as attitudes, experience, and knowledge (Hacker, 1985). The redefinition process is also influenced by task characteristics such as clarity, and may be conscious and intentional or automated. Conflicts may arise if two parties (e.g., an employee and his or her supervisor) have different redefinitions of tasks. Generally, goals motivate employees to expend effort because they provide employees with a standard for comparison with the current state (i.e., goal performance discrepancy) and action progress (i.e., velocity; Johnson & Howe, 2013). The efficiency and effectiveness of the action sequence depends to a great extent on the nature and coordination of goals. Goals can be decomposed into several, more or less specific sub-goals that contribute to a higher-order goal. For instance, the life goal ‘balancing success in work and family domains’ involves sub-goals, sub-sub-goals, and so forth related to success at work (e.g., achieving tenure) and success in the family domain (e.g., achieving marriage satisfaction). Research on goal setting theory has shown the strong effects that difficult and specific goals can have on performance, if certain conditions are fulfilled (e.g., goal commitment; Locke & Latham, 2013). This also implies that vague goals, too easy or too difficult goals, multiple competing or contradictory goals,

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and concerns about the social desirability of goals may lead to negative performance effects. It is particularly important that future research examines the pursuit of multiple goals because people usually are confronted with multiple, and often even conflicting, goals (Sun & Frese, 2013). Managers and coworkers can facilitate the pursuit of multiple goals by assisting employees with goal prioritization and selection, and by helping out when goal progress is slow or when goal-relevant resources are lacking (i.e., optimization and compensation; Baltes & Dickson, 2001). Goals can range from relatively long-term (e.g., life goals) to short-term goals (e.g., what to accomplish in the next five minutes at work) – most people focus on mid-range goals (Vallacher & Wegner, 1987). Moreover, individuals differ in their characteristic way of developing and pursuing goals, such as action styles and approach and avoidance tendencies (DeShon & Gillespie, 2005; Frese, Stewart, & Hannover, 1987). The type of goals that are selected and pursued is also closely related to the experience of positive and negative emotions at work (Hacker, 2003). Gollwitzer (1993) differentiated between goal intentions and implementation intentions. This differentiation has advanced our knowledge of how thoughts are translated into actions. A goal intention is typically characterized by rational deliberation on the pros and cons of action alternatives. Once a plan has been generated, an implementation intention is developed and the mindset changes to one of putting the action into effect. The Rubicon has been crossed and people no longer deliberate about the pros and cons of an intention; instead, everything is put into the service of achieving the goal (Gollwitzer, 1999; Gollwitzer, Heckhausen, & Steller, 1990). We shall describe a number of studies that have shown this interaction of goals and plans in applied settings later in this chapter.

Mapping the environment (orientation)

Action regulation theory conceptualizes action as firmly situated in the objective physical and social environment (Frese & Zapf, 1994; Suchman, 1987). Individuals regulate their action based on information or signals they receive from the environment, and they also change their environment through action. Once a goal has been established, individuals orient themselves in the work environment by searching for action-relevant information (e.g., execution opportunities and constraints, availability of relevant tools and methods), and by detecting signals that enable them to make predictions about successful goal attainment. This includes the possibility to coordinate their own actions with the

actions of supervisors as well as colleagues during team work (Hacker, 2003). The search for information can be more or less active, ranging from a deliberate and conscious search process to an unconscious and intuitive understanding of situations. The action sequence is more efficient and effective to the extent to which individuals develop adequate, action-relevant mental representation of the goal and its boundary conditions. Individuals’ recognition and understanding of relevant signals is dependent on their knowledge and experience with a task, as well as on the transparency of available signals (Dörner & Schaub, 1994; Perrow, 1984). Possible problems in this phase include inaccurate retrieval of information, too much information search but no resulting action (i.e., encapsulation), and biased processing of information (i.e., dogmatization; Dörner, 1996; van der Linden, Sonnentag, Frese, & van Dyck, 2001). Managers and coworkers can assist employees during the orientation phase by providing employees with relevant, clear, and structured information about tasks, goals, and the goal context (London & Smither, 1995; Morrison, 1993).

Planning (plan development and selection)

This phase involves the creation of and selection among different plans that are instrumental in attaining a goal. Miller et al. (1960) characterized plans as bridges between thought and action and, as the above discussion of the function of plans for implementation intentions has shown, this is indeed the case (Gollwitzer, 1999). Plans are mental simulations of an action (German ‘Probehandlung’ or trial action) and consist of more or less detailed cognitive representations of behavioral steps that are needed to attain a goal. A list of sub-goals may constitute a simple plan (Latham & Arshoff, 2015; Locke & Latham, 2002). The planning process may involve the development of one or more back-up plans that can be put in place in case an initial plan fails. Detailed planning is an effective strategy in most work contexts; nevertheless, it can come at some costs, for instance, when detailed plans are followed in an overly rigid manner (Baker & Nelson, 2005), or when planning involves a disproportionate investment of cognitive effort (Mumford, Mecca, & Watts, 2015). Indeed, planning fallacies particularly about time allotments to plans are quite frequent and difficult to deal with (Buehler, 2015). The usefulness of detailed planning depends on the complexity of goals, the experience of employees with a given task, and on the situation. A less detailed plan may be beneficial in complex situations as it allows employees to deal flexibly

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with additional information they receive during the action process. On the other hand, employees may encounter problems in subsequent phases of the action sequence if they do not plan at all or develop unrealistic plans. According to action regulation theory, so-called ‘best-workers’ or ‘superworkers’ possess more elaborate and proactive task-oriented plans that allow them to better deal with and learn from errors and other challenging situations (Frese & Zapf, 1994; Frese et al., 2007). In addition, they can actively manage different situations based on their long-term planning. In contrast, ineffective workers produce plans that are not tied adequately to the goal and have problems choosing among several plans.

Monitoring of execution

People need to monitor their actions. This involves a comparison between the goal and the associated plan and the actual execution of behavior. This comparison is highly dependent on individuals’ working memory capacity, and therefore general mental ability is important here. During execution, employees can respond more or less flexibly to unexpected situations, adapt their goals and plans, and coordinate action efficiently with regard to the time available and other, possibly competing, tasks (Frese & Zapf, 1994). Possible problems in this phase include intrusion of distracting thoughts, cognitive overload, and attention problems that reduce the accuracy of plan execution.

Processing of feedback

Feedback provides people with information from the environment about their current performance or progress toward attaining the goal. Moreover, feedback enables learning which types of plans can be most successfully used in the future. Once they have received feedback, individuals compare their current state or goal progress with the desired result of action or intended goal progress. They then regulate their behavior to reduce any discrepancies and to achieve the goal. Goal disengagement (or change of a goal) can happen as well; however, the Rubicon theory of Gollwitzer (1993) argues and shows empirically that disengagement is difficult and takes energy. Feedback can be more or less detailed and timely, and it can be either positive (e.g., praise) or negative (e.g., error). According to action regulation theory, positive feedback is mainly beneficial in terms of maintaining or repeating behavior, whereas negative feedback is considered a more important predictor of learning and personal development, as long as it is task-related and carries clear information (Frese & Keith, 2015). A meta-analysis on feedback interventions showed that both positive and negative

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feedback interventions can trigger self-related thoughts which may undermine employees’ selfesteem, learning, and subsequent performance (Kluger & DeNisi, 1996). Thus, when giving feedback, managers and coworkers should ensure that the information they want to convey is clear, wellstructured, and task-related.

Hierarchical Structure of Action Regulation Action regulation theory argues that goal development and selection, as well as the associated processes of mapping the environment, planning, monitoring of execution, and feedback, are occurring on multiple levels of action regulation. These levels are hierarchically organized, and action sequences on lower levels can be regulated by higher levels, such that the sequence of observable behaviors at the lowest level (e.g., typing letters on a keyboard) follows from a higher-order goal and planning (e.g., writing a manuscript). However, these action regulation hierarchies are relatively weak in that higher levels do not completely determine action regulation on lower levels, action sequences on lower levels can be adapted to situations without the involvement of higher levels, and results obtained at lower levels may lead to bottom-up changes at higher levels (Frese & Zapf, 1994; Hacker, 2003). Generally, individuals’ focus of action regulation moves up the regulation hierarchy when it is important to understand why an action is done (e.g., after an error occurred or constraints are encountered), and their focus moves down the hierarchy to understand how something is done (Lord & Levy, 1994). With practice in a redundant environment, actions tend to be regulated at lower levels of the hierarchy. The four levels of action regulation are (a) the sensorimotor or skill level, (b) the level of flexible action patterns, (c) the intellectual or conscious level, and (d) the heuristic or meta-level. These levels can be characterized by two dimensions, one ranging from conscious thought to unconscious and automatized control of actions, and the other ranging from thought processes to muscular action (Frese & Zapf, 1994). Action regulation at lower levels in the hierarchy is more situation specific, requires less cognitive effort, decisions, and feedback, and movements become more parsimonious and elegant.

Sensorimotor or skill level

At the lowest level, highly automatized movement patterns, stereotypic routines, and non-physical cognitive automaticities are regulated. These elementary processes are triggered by higher level

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regulation processes and do not require independent goals and conscious attention. However, changing these automatized action programs through conscious regulation requires effort because once a certain behavioral pattern has been automatized it tends to run its course if we do not attend to it. Feedback at this level takes the form of internal and external, automatically generated feedback on motor movement coordination (i.e., kinesthetic and proprioceptive signals); on this level, feedback at the end of an action cycle is often too late to actually interrupt the action flow.

Level of flexible action patterns

The next higher level involves action regulation that is based on sub-goals and schemata or scripts which can be either conscious or semi-conscious. At this level, information from the environment is processed according to rules. Usually only a few signals from the environment are necessary to activate these flexible action patterns from memory, which are then typically carried out as a whole. These action programs can be used quite flexibly to fit the parameters of the specific situation. For instance, the warning signal of a machine may trigger a routinized action sequence that allows experienced employees to solve a problem. In contrast, workers with less experience first have to invest effort to consciously develop an understanding of the situation and initiate appropriate actions.

Intellectual or conscious level

On this level, employees consciously regulate complex or unaccustomed actions, including the development and selection of conscious goals and the control of more or less detailed action plans. Feedback at this level involves the analysis and combination of novel and complex information. The action sequences on this level require attention, and the use of problem solving strategies but, with practice and experience, they can be routinized by shifting them to lower levels of action regulation. Novel, challenging, and important goals are typically regulated on this level, and regulation is rather slow, serial, and effortful (Vallacher & Wegner, 1987). However, the thought processes on this level involve more object- and task-­oriented regulation than the next higher level.

Level of meta-cognitive heuristics

At the highest level of action regulation, people use meta-cognitive templates, strategies, and abstract heuristics to guide their goal and plan development, feedback processing, as well as understanding the environment (Frese & Zapf, 1994). These meta-cognitions allow individuals to approach similar problems in an effective way.

For instance, people may consciously reflect on the prioritization of multiple competing goals, compensate for losses in goal-relevant means, or ensure consistency and coordination of different action plans (P. B. Baltes & Baltes, 1990). The use of such action regulation strategies and heuristics is associated with particular action styles (Frese et  al., 1987) and successful development and adaptation (Freund & Baltes, 2000).

Foci of Action Regulation According to action regulation theory, three different foci can be in the foreground of action regulation: task, social context, and self (Frese, 2007). A focus on the task implies that the individual considers the task content (e.g., goal) and context (e.g., time and resources available to complete the task), and conducts an analysis of task redundancy, the number of crucial decision points, and degrees of freedom during task execution (Frese & Zapf, 1994). A strong task focus is generally associated with high efficiency and effectiveness of action regulation as it links task-related goals with relevant plans and behavior, and goal striving is regulated by task-relevant feedback. When the focus of action regulation is on the social context, past, current, and expected interactions with other people (e.g., supervisors, coworkers, customers) influence the processes of goal development and selection, mapping the environment, planning, monitoring of execution, and feedback processing. For instance, an employee might pursue the goal of helping others, develop different plans to achieve this goal, and seek feedback on goal progress primarily from his or her coworkers. Depending on the situation and the quality of interpersonal relationships in the workplace, a focus on the social context can facilitate or constrain the efficiency and effectiveness of action regulation (Duffy, Ganster, & Pagon, 2002). Consistently, research on self-monitoring (i.e., ‘self-­observation and -control guided by situational cues to social appropriateness’; Snyder, 1974, p. 526) and emotional labor (i.e., showing socially expected emotions during interpersonal situations at work; Ashforth & Humphrey, 1993) has suggested that a social focus during action regulation may increase task performance in customer service contexts, but may also lead to emotional dissonance and alienation of the self (Zapf, 2002). Finally, when the self is the focus of action regulation, the impact of individuals’ actions on their self (e.g., their emotions and self-beliefs), and vice versa, are in the foreground. On the one hand, self-regulation and particularly improvement of

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self-regulatory capacity can have very positive effects on work outcomes (Baltes & Dickson, 2001; Latham & Locke, 2007). On the other hand, a self-focus can have a disruptive function during action regulation (Kluger & DeNisi, 1996; Sonnentag, 1998) because this focus distracts from the task. For instance, when a researcher is thinking too much about the self-implications of writing a manuscript, he or she might be distracted from the actual task of thoroughly reviewing the literature. In personnel selection and appraisal contexts, encouraging test takers and interview candidates to focus on the concrete problem to be solved instead of focusing on the potential social and personal implications of the selection or appraisal situation should reduce anxiety, enhance performance, and improve the validity of assessments. Consistently, research on stereotype threat in work and organizational contexts has argued that foci of action regulation mediate the effects of identity-threatening conditions (e.g., a low proportion of minority group members in the board of directors) on undesirable work outcomes such as poor performance, unfavorable job attitudes, and diminished occupational health and wellbeing (Kalokerinos, von Hippel, & Zacher, 2014; Roberson & Kulik, 2007). A specific practical implication is that personnel selection and performance evaluation systems should not put selfrelated personality descriptors in the foreground but concrete task-related behaviors (Kluger & DeNisi, 1996).

Action-Oriented Mental Model Employees develop action-oriented mental models that include long-term cognitive representations of input conditions, goals, plans, and expected and prescribed results of action, as well as knowledge about boundary conditions of action and the transformation procedures that turn goals into expected results (Frese & Zapf, 1994; Hacker, 1985). Across the four levels of action regulation, an action-­ oriented mental model contains unconscious movement schemata, routinized yet flexible action schemata, representations of complex and conscious intellectual processes, and generalized metaplans and heuristics. These elements constitute the knowledge base of action regulation, which is necessarily selective due to its focus on relevant tasks. Action-oriented mental models guide employees until the action sequence is completed and the goal is attained. The accuracy and level of detail of such action-oriented mental models is closely associated with the efficiency and effectiveness of action regulation (Frese & Zapf, 1994).

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DIFFERENTIATION FROM OTHER COGNITIVE-BEHAVIORAL THEORIES Several cognitive-behavioral theories attempt to explain goal-directed behavior as well as its antecedents and consequences in work and organizational contexts. There are a number of important agreements between these theories – all of them have learnt from empirical studies and, therefore, have moved toward some degree of convergence. However, there are also a number of differences between action regulation theory and other theories that have not always been emphasized in prior research. In the following, we differentiate action regulation theory from control theory and more recent approaches to self-regulation, from social cognitive theory, from goal setting theory, and from selfdetermination theory.

Control Theory A key concept in control theory is the negative (i.e., discrepancy reducing) feedback loop, which includes a goal (or referent standard), an input function (or current state), a comparator, and an output function (Carver & Scheier, 1981, 1982; Powers, 1973). The cybernetic hypothesis (Wiener, 1948) proposes that the output function (i.e., behavior) is the result of a discrepancy between the perceived input function and the goal detected by the comparator. Multiple negative feedback loops are hierarchically organized, such that the output of a higher-order feedback loop is equivalent to the goal of a lower-order feedback loop (Lord & Levy, 1994; Miller et  al., 1960). Contemporary extensions of control theory in the form of self-­regulation approaches assume that these negative feedback systems operate across different levels of abstraction and time periods (Johnson, Chang, & Lord, 2006; Lord, Diefendorff, Schmidt, & Hall, 2010; Lord & Levy, 1994), and that they can explain the amount of time and effort individuals allocate to multiple competing goals (Schmidt & DeShon, 2007; Vancouver, Weinhardt, & Schmidt, 2010). However, changes in goals and increases in levels of ambition of goals after goal attainment are very difficult to explain from a control theory perspective. Thus, control theory is tied to its starting point of a machine metaphor and, therefore, reproduces a mechanistic theory. Because action regulation theory was more open to the influence of Lewin, Piaget, Leontjev, and other theorists who espouse non-mechanistic concepts, there is less of a mechanistic bias in this theory (Nuttin, 1984). Control theory and action regulation theory differ particularly with regard to the action sequence.

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Control theory focuses on a mechanistic negative feedback loop, and assumes that motivated behavior is discontinued when a match (or zero discrepancy) between the goal and the current state is achieved (see Locke, 1991, for a critique of control theory and comparison with goal setting theory). In contrast, action regulation theory also emphasizes a positive (i.e., discrepancy creating) feedback loop, and assumes that goals have functions that go beyond being mere comparison standards (set points). Specifically, due to its theoretical relationships with achievement and goal setting theories (H. Heckhausen, Gollwitzer, & Weinert, 1987; Locke, 1991; McClelland, 1987), action regulation theory postulates that individuals who actively develop, select, revise, and set more challenging goals are more likely to develop themselves and become more effective in terms of changing their work environment. In contrast, proponents of control theory suggest that the active creation of goal-performance discrepancies leads to instability in the action regulation process and performance (Lord et al., 2010). Moreover, control theory is silent on the processes of active information search, the development of action plans, the development of pre-feedback, and the development of expertise (Ericsson & Charness, 1994). Control theory and action regulation theory agree on the idea of hierarchical levels of action regulation, in particular the distinction between conscious and automatic levels of regulation. However, control theory does not theorize about the dynamic interplay between different levels of regulation which may lead to the development of routines and expertise.

Social Cognitive Theory Social cognitive theory posits reciprocal relationships between individuals’ cognitions (i.e., their expectations, beliefs, and goals), behavior, and the environment (Bandura, 2001). A central difference between social cognitive theory and action regulation theory is that social cognitive theory assumes that outcome expectations and self-­ efficacy (i.e., individuals’ beliefs that they can successfully accomplish a specific task; Bandura, 1977) constitute the major bridges between cognition and action. For action regulation theory, outcome expectations and self-efficacy determine primarily goal choice; however action regulation theory also focuses on all of the issues discussed above as important for action regulation after goal choice (e.g., plans, development of routines, and feedback). The concept of ‘action’ is primary for action regulation theory and it, therefore,

emphasizes more strongly that self-efficacy is not only a predictor of task performance, but also an outcome of actions and performance-relevant feedback. Consistent with this assumption, a metaanalysis of the within-person relationship between self-efficacy and performance concluded that the effect of past performance on self-efficacy is stronger than the effect of self-efficacy on future performance (Sitzmann & Yeo, 2013). A final key difference between the two theories concerns the question how individuals learn most effectively. Whereas social cognitive theory proposes that people learn effectively from observing the behavior of others (Bandura, 2001), action regulation theory assumes that active behavior (e.g., deliberate practice; Ericsson, Krampe, & Tesch-Römer, 1993; Unger, Keith, Hilling, Gielnik, & Frese, 2009) and both positive and negative feedback are important prerequisites for learning (in addition to role models which, according to action regulation theory, need to be enacted to produce learning). Further, the function of errors is seen differently. Social cognitive theory conceives of negative feedback and errors as disruptive and frustrating events. Bandura argued that dealing with errors and active exploration are time-consuming and needless: ‘Without informative guidance, much of one’s efforts would be expended on costly errors and needless toils’ (Bandura, 1986, p. 47). In contrast, action regulation theory conceptualizes disruptions of smooth actions positively because they facilitate the development of new ideas and learning, as well as changes in how one deals with the environment (Frese & Keith, 2015).

Goal Setting Theory There are many more similarities than differences between goal setting theory and action regulation theory. As a matter of fact, the most important tenet of goal setting theory, suggesting that specific and challenging goals, both self-set and assigned by others, lead to high task performance when individuals accept the goals and their goal commitment is high (Locke & Latham, 1990, 2002, 2013), is typically integrated seamlessly into action regulation theory. If at all, action regulation theory emphasizes more the differences of results when goal setting is done on different levels of action regulation. Action regulation theory further emphasizes that employees need to actively internalize assigned goals (redefinition); it also places a strong focus on the development of active and elaborate planning and feedback compared to specific and challenging goals as direct predictors of performance.

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Self-Determination Theory Self-determination theory is a meta-theory that focuses on the effects of the social environment on intrinsic motivation, personality development, and well-being (Ryan & Deci, 2000). The theory suggests that the fulfillment of three psychological needs leads to positive life and work outcomes (Gagné & Deci, 2005). Specifically, situations that support individuals’ needs for competence, autonomy, and relatedness increase intrinsic motivation (i.e., pursuing an activity out of inherent interest, satisfaction, and enjoyment; Ryan & Deci, 2000) and self-determined action, whereas extrinsic factors such as material rewards or threats can diminish these outcomes. Moreover, by increasing one’s level of identification and active regulation, extrinsic motivated behavior can become more self-determined over time. According to Gagné and Deci (2005), a key difference between self-determination theory and action regulation theory is that the latter does not make separate predictions regarding intrinsic and extrinsic motivations for behavior but instead focuses on personal initiative as a single motivational factor. Conversely, action regulation theorists have argued that three conceptual problems make it difficult to apply the concept of intrinsic motivation in work settings. Specifically, Fay and Frese (2000) noted that (a) in most cases work involves externally assigned and not self-developed tasks, (b) extrinsic rewards are ubiquitous at work (e.g., salary, promotions, power, approval) and thus work can never be fully intrinsically motivated, and (c) research showing that negative emotions can motivate work behavior stands in contrast to the notion of intrinsic motivation. Fay and Frese (2000) proposed that the concept of personal initiative (i.e., self-starting, proactive, and change-oriented behavior), which was derived from action regulation theory, is more useful than self-determination theory to understand motivated behavior in work settings, because it allows for the simultaneous occurrence of extrinsic and intrinsic motivational causes of behavior. Also, these researchers argued that the dichotomy of extrinsic and intrinsic motivation proposed by self-determination theory does not offer an explanation for the occurrence of selfstarting and proactive behavior in the work context.

RESEARCH AREAS BASED ON ACTION REGULATION THEORY Over the past two decades, empirical research based on action regulation theory has made

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progress in three areas: proactive work behavior, work-related learning and error management, and entrepreneurship. In this section, we review current knowledge and outline ideas for future research in each of these areas.

Proactive Work Behavior Researchers increasingly acknowledge that many behaviors in dynamic work environments are better characterized by active performance concepts instead of passive concepts (Fay & Sonnentag, 2010; Frese, 2008; Grant & Ashford, 2008; Griffin, Neal, & Parker, 2007). Based on action regulation theory, Frese, Fay, and colleagues (Fay & Frese, 2001; Frese & Fay, 2001; Frese, Fay, Hilburger, Leng, & Tag, 1997; Frese, Kring, Soose, & Zempel, 1996) developed the construct of personal initiative, which entails future-oriented and persistent behaviors that contribute to individual and organizational effectiveness. The different aspects of personal initiative align with the steps of the action sequence (cf. facet model of personal initiative; Frese & Fay, 2001). People showing high levels of personal initiative set challenging goals for themselves, are not pursuing the most obvious paths to success, scan their environment for opportunities and potential problems in the future, and adapt their plans based on feedback to overcome problems and barriers that arise during goal pursuit (Frese et al., 1996). Moreover, research has shown that employees with high personal initiative change aspects of their work environment, such as their jobs’ levels of complexity and control (Frese, Garst, & Fay, 2007). In contrast, people with low personal initiative tend to do what they are told, are more oriented toward the present than the future, give up in the face of difficulties, and passively react to the work environment instead of changing it (Frese & Fay, 2001). Zhao, Frese, and Giardini (2010) showed that personal initiative in the area of networking positively predicted the size of government and business networks among small business owners in China. Government, network size, in turn, positively predicted business growth. Personal initiative behavior is typically assessed with supervisor ratings on a reliable and well-validated 7-item scale (Frese et al., 1997), or with ratings of information collected in structured interviews by trained researchers (Fay & Frese, 2001). For instance, Frese et  al. (2007) operationalized personal initiative using (a) interviewer evaluations of participants’ behavior during the entire interview (3 items with semantic differentials; e.g., ‘behaves actively … passively’), (b) interviewer ratings of qualitative and quantitative

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initiative (8 items; e.g., effort and level of proactivity shown when talking to a supervisor about a work problem), and (c) behavioral scores from a situational interview (4 items; e.g., rating of persistence and level of proactivity in dealing with a fictional work problem, such as a colleague always doing his or her work sloppily). The measures had internal consistency estimates of .75 or greater, and had high loadings on a latent personal initiative factor (Frese et al., 2007). A meta-analysis by Tornau and Frese (2013) demonstrated that personal initiative b­ ehavior – assessed with either supervisor ratings or interviews – is positively related to, but distinct from, the trait-like concept of proactive personality (i.e., the generalized tendency to affect change in the environment; Bateman & Crant, 1993). Specifically, the sample-size weighted and reliability-corrected mean correlation between personal initiative behavior and self-rated proactive personality was rwc =.39 (k = 3 studies). In contrast, personal initiative assessed with a 7-item self-report scale overlapped substantially with a standard measure of proactive personality by Bateman and Crant (1993; rwc = .83 based on k = 5 studies; Tornau & Frese, 2013, 2015). Furthermore, personal initiative was shown to be positively and moderately related to other proactive behaviors such as taking charge (rwc = .54, k = 10) and voice behaviors (rwc = .51, k = 9), and to possess incremental validity in predicting performance in work settings, above and beyond the Big Five personality traits (Tornau & Frese, 2013). Specifically, personal initiative behavior explained 5.2% of additional variance on top of the Big Five personality dimensions in objective job performance, and 21.4% of additional variance in supervisor-rated job performance. Further research is needed to gain a better understanding of different individual and contextual antecedents as well as both positive and negative consequences of personal initiative (Fay & Sonnentag, 2010; Rank, Pace, & Frese, 2004). For instance, it would be interesting to investigate how the nature, antecedents, and consequences of proactivity change across the working lifespan, and how managers and coworkers can facilitate personal initiative of employees. Moreover, future research should continue to examine proactivity on multiple levels (e.g., intraindividual, between-­person, team, and organizational levels) and evaluate training to enhance personal initiative. At the company level, Baer and Frese (2003) showed that an organizational climate for initiative positively predicted future return on assets after controlling for prior return on assets, firm size and industry, and process innovativeness. Results of a randomized controlled long-term training experiment showed that (a) an

intervention based on the facet model of personal initiative increased personal initiative of small business owners and managers, (b) those business people who participated in the training improved their business performance, and (c) the increase in business performance was completely dependent on the mediator of increased personal initiative (Glaub, Frese, Fischer, & Hoppe, 2014).

Work-Related Learning and Error Management Action regulation theory assumes that people are active learners and that the development of knowledge and skills requires action. The theory has several implications for learning in work and organizational contexts (Frese, Beimel, & Schoenborn, 2003), particularly with regard to learning from errors (Frese & Keith, 2015; Frese & Zapf, 1994). According to action regulation theory, learning and training lead to improved and differentiated action-oriented mental models on different levels of action regulation which, in turn, are important prerequisites for efficient and effective work behavior. At the beginning of learning processes, actions are regulated on the conscious intellectual and meta-cognitive levels and, over time and with practice in redundant work environments, shift to lower levels for more automatized processing. Such routinized actions do not require much cognitive effort and movements are more parsimonious (Johnson et  al., 2006). Moreover, routines allow for additional cognitive operations to be performed at higher levels (e.g., creative thinking), because automatized operations do not require a lot of attention (Gielnik, Frese, & Stark, 2015; Ohly, Sonnentag, & Pluntke, 2006). Action regulation theory suggests that training and learning turn average workers into superworkers who have superior knowledge and skills, a deeper understanding of tasks (‘intellectual penetration’; Hacker, 1985), use more active and longterm strategies, and structure situations so that they receive useful feedback. In addition, superworkers are more sensitive to signals from the environment and perceive signals and differences between signals as well as errors more accurately (Hacker, 1985). For instance, action regulation theory would argue that emotional intelligence (i.e., the abilities to perceive and regulate one’s own and others’ emotions; Van Rooy, Viswesvaran, & Pluta, 2005) is a form of social competence that can be developed across the working lifespan, particularly through experience and errors in the interpersonal domain. Related to the idea of superworkers is the concept of expertise (Ericsson & Lehmann, 1996).

Action Regulation Theory

Here, self-regulated learning processes in the form of deliberate practice (often helped by coaches) are important. Deliberate practice involves self-­ regulated activities aimed at improving one’s level of performance (Ericsson et  al., 1993). For instance, Unger et al. (2009) examined predictors and outcomes of deliberate practice among small business owners. They found that cognitive ability and education positively predicted deliberate practice, which, in turn, influenced entrepreneurial knowledge and business growth. For action regulation theory, errors are an important part of negative feedback. More importantly, errors help the individual shift processing to a higher level of regulation and more conscious approaches (Lord & Levy, 1994).Therefore, errors are the prime material that stimulates people to start learning – more so than positive feedback (Frese & Keith, 2015; Joung, Hesketh, & Neal, 2006). Errors can only appear in goal-directed actions – thus, a machine does not make errors, only the producer or programmer of the machine can make errors (leading to faults in the machine). Action errors are defined as ‘unintended deviations from plans, goals, or adequate feedback processing, as well as incorrect actions resulting from lack of knowledge’ (Frese & Keith, 2015, p. 3). Errors are ubiquitous – a natural consequence of the human mind regulating actions in ambiguous environments – and errors can never be completely prevented (Brodbeck, Zapf, Prümper, & Frese, 1993; Hofmann & Frese, 2011). Errors can be differentiated according to their position in the sequence and structure of action regulation as well as in the action-oriented mental model (Zapf, Brodbeck, Frese, Peters, & Prümper, 1992). Knowledge errors are linked to the actionoriented mental model, whereas errors due to a lack of self-reflection occur at the meta-cognitive heuristics level; errors in goal setting, orientation, memory, and judgment occur at the conscious intellectual level; habit errors (i.e., the correct action is performed in the wrong situation), omission errors (i.e., execution of sub-plan is missing), and recognition errors (i.e., a relevant signal is missed) occur at the level of flexible actions patterns; finally, movement errors are linked to the sensorimotor or skill level (Zapf et al., 1992). Errors can have positive functions when they are managed well – they facilitate the construction of more realistic models of the objective work environment, and lead to a re-intellectualization of tasks and a greater differentiation of actionoriented mental models (e.g., about potential pitfalls and problems in action execution). In addition, errors enhance learning, lead to improvements in performance, and help develop skills in error handling and dealing with frustration

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due to errors (Frese et  al., 1991; Frese & Keith, 2015). Exclusive error prevention programs, on the one hand, are used in organizations to avoid errors in the first place. Error prevention influences employee action (i.e., employees are generally more careful) as well as the effects of actions on errors (i.e., the effect is weaker in companies with high error prevention). On the other hand, error management programs involve dealing with errors once they have occurred in order to avoid negative error consequences, allow for quick damage control (including error detection, correction, and recovery), reduce error cascades, and maximize the positive consequences of errors (e.g., learning from errors and innovations; Frese & Keith, 2015). Error management training encourages participants to learn from errors (Frese et al., 1991). In these training programs, participants are told to actively explore the training content and to make errors in order to learn from them (Heimbeck, Frese, Sonnentag, & Keith, 2003). Error management instructions (also called heuristics in earlier publications) conveyed in training include, for instance, statements such as ‘There is always a way out of any error situation’, ‘The more errors you make, the more you learn’, and ‘Errors are a natural part of any learning process’ (Frese et al., 1991). Meta-analytic research has shown that error management training is more effective in predicting training and transfer outcomes than other forms of training, such as error avoidance training or exploratory training without error encouragement (Keith & Frese, 2008). Specifically, Keith and Frese (2008) found that the effect of error management training across 24 studies with more than 2,000 participants was positive and significant in comparison to non-error management training. Effect sizes were larger for post-training transfer than for within-training performance, and for tasks that were different from training compared to tasks that were similar to training. Keith and Frese (2005) investigated the regulatory processes that explain the positive effect of error management training on training transfer performance. They found that the effect was mediated by participants’ meta-cognitive activity (i.e., planning, monitoring, and evaluating goal progress) and emotion control (i.e., down-regulating negative emotions). With regard to the boundary conditions of error management training, one line of research has shown that the effectiveness of error management training is higher among participants with high cognitive ability compared to those with low cognitive ability (Loh, Andrews, Hesketh, & Griffin, 2013). Carter and Beier (2010) found that particularly older adults with high cognitive ability benefited from error management instructions in highly structured training. However, Keith,

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Richter, and Naumann (2010) reported that error management training had positive effects on transfer performance for participants either high or low in motivation and cognitive ability. Errors and attitudes toward errors can be conceptualized at different levels, and further research is needed that links these levels. At the company level, van Dyck, Frese, Baer, and Sonnentag (2005) demonstrated that approximately 20 per cent of firms’ profitability is determined by error management culture. An error management culture is characterized by quick error discovery, quick recovery from errors, help in discovery and recovery, organizational routines for dealing with errors, open communication about errors, and an emphasis on learning from errors (van Dyck et al., 2005). Example items from the highly reliable error management culture scale used by van Dyck et  al. (2005) are ‘For us, errors are very useful for improving the work process’ and ‘Our errors point us at where we can improve’. More recently, Rosenbusch and colleagues provided evidence that error management climate was related to performance via the mediator of ambidexterity – a state of producing innovations by both strong exploration but also by basing the innovation on existing products (i.e., exploitation; Rosenbusch, Rosing, Bledow, & Frese, 2014). Future research should also focus on the role of managers, coworkers, and teams in the error management process. For instance, it would be important to know how these people and certain team characteristics can enhance positive learning and transfer effects of error management training in the work context.

Entrepreneurship Entrepreneurs have to act independently of their environment, develop new ideas, perform many things for which they have not been prepared and, thus, they have to learn through actions; moreover, they have to act in an uncertain environment (Frese, 2009). All of these factors contribute to being challenged in the area of action regulation. Therefore, action regulation theory has been applied to entrepreneurship, answering research questions on planning, personal initiative, innovation, emotional processes, and how intentions lead to actions. Entrepreneurship research has been traditionally ambivalent on planning (Gielnik, Frese, & Stark, 2015). On the one hand, there are metaanalyses demonstrating the superiority of planning (Brinckmann, Grichnik, & Kapsa, 2010). On the other hand, the environment for entrepreneurs clearly demands to be able to deviate from plans and to improvise (Baker & Nelson, 2005). Action

regulation theory argues that both is true – good planning is important for success but, at the same time, people have to be able to deviate from their plans as long as they defend the overall goal of independence and innovativeness. Action regulation theory proposes that intuition is based on experience and routinization; most entrepreneurs do not have these when they start a company for the first time. Therefore, particularly beginners have to plan a lot (Cha, Ruan, & Frese, 2014). Planning helps to translate both long- and shortterm goals into actions, prioritize multiple competing goals, decrease distraction, prepare for future opportunities, and to maintain persistence even when facing difficulties in the present. In addition, actions are running more smoothly and cognitive load is reduced when actions have been planned in advance. Finally, flexible plans allow owners to switch plans in case something goes wrong during plan execution (‘Plan B’). Wiesner and Hacker (1994) stated that ‘Plans as retrospective descriptive concepts of actions can easily be confused with plans as causal psychological mechanisms of the generation of actions’ (p. 652; cf. Frese, Krauss et al., 2007). Therefore, empirical entrepreneurship research based on action regulation theory has used a broad pluralistic approach to measure small business owners’ planning and action strategies. This approach emphasizes the solicitation of qualitative interview responses which are subsequently transcribed and coded by trained raters (Frese, Krauss et al., 2007). In the interview, participants are first asked to prioritize their business-related goals, and then to describe how they want to achieve their two most important goals. Subsequently, two independent raters evaluate the level of elaboration (e.g., detail of plan, number of sub-steps identified) and proactivity (e.g., produces changes, is not a copy of others in the same environment) of planning. These interview measures have been shown to possess good reliability and construct validity (Frese, Krauss et al., 2007; Frese, van Gelderen, & Ombach, 2000; Van Gelderen, Frese, & Thurik, 2000). Research on business owners’ planning showed that a reactive strategy (the opposite of planning – just reacting to environmental stimuli) was negatively related to firm success (Frese, Brantjes, & Hoorn, 2002; Frese et  al., 2000; Van Gelderen et  al., 2000). A reactive strategy involves being driven by the situation and there is no proactive and planned use of information, little planning, and little proactivity, little goal orientation, and no long-term planning. In addition, these studies point to the importance of complete planning and critical point strategies. Complete planning means that business owners develop and implement strategies with high goal orientation, high long-term

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planning, high knowledge and deep understanding, and high proactivity. The critical point strategy involves planning for the most important task first and then planning other issues after the first issue has been dealt with. Both complete and critical point planning positively predict entrepreneurial success (Van Gelderen et al., 2000). Research on the predictors and outcomes of elaborate and proactive planning strategies among small business owners in three African countries showed that cognitive resources (i.e., cognitive ability and human capital) positively predicted elaborate and proactive planning which, in turn, was positively related to business success (Frese, Krauss et  al., 2007). These results suggest that business owners and managers could be selected based on their cognitive ability; more importantly however, they could be trained in elaborate and proactive planning strategies. Another recent study reported the results of an action-based entrepreneurship training conducted in Africa which focuses on action principles and active learning (e.g., participants engage in the start-up of a business during the training). Gielnik, Frese, KaharaKawuki et al. (2015) evaluated the impact of their training over the period of one year using a randomized control group design and found that the training increased entrepreneurial goal intentions and self-efficacy as well as action planning and action knowledge. These action-regulatory factors subsequently predicted entrepreneurial action, business opportunity identification, and business start-up. Consistent with propositions of action regulation theory (Gollwitzer, 1999), results further showed that entrepreneurial intention needs to be complemented by action plans to be effective for new firm creation. Action regulation theory is particularly useful in terms of investigating the temporal dynamics and change in entrepreneurship over time, and further research is needed in this area. Gielnik, Barabas et  al. (2014) recently drew on action regulation theory to examine the effects of entrepreneurial goal intentions, positive fantasies, and action planning on new venture creation over time. Data from 96 entrepreneurs collected over 30 months showed that action planning moderated the positive effects of entrepreneurial goal intentions and fantasies on new venture creation, and these effects became weaker over time. Finally, action regulation theory has long suggested that emotions have three functions – they help people to get started and to continue actions when actions are interrupted. But they also can be the result of actions (Pekrun & Frese, 1992). Gielnik and colleagues showed that passion for entrepreneurship may be more strongly the result of effort rather than the emotions that propel

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people forward (Gielnik, Schmitt, Spitzmuller, Klemann, & Frese, 2015). Specifically, passion for entrepreneurship was predicted very well by entrepreneurial effort but itself did not predict entrepreneurial effort. Further research is clearly needed that addresses the role of positive and negative emotions for action regulation in different work settings (cf. also Bledow, Rosing, & Frese, 2013).

DIRECTIONS FOR FUTURE RESEARCH Research on proactive work behavior, active learning and error management, and entrepreneurship conducted over the past two decades demonstrates that action regulation theory can be fruitfully applied to understand important phenomena in work and organizational contexts. As a meta-theory, action regulation theory also has appeal to other topics in the field because it has an appropriate level of decomposition of behavior for applied topics compared to theories that focus more on micro- or macro-levels (e.g., purely cognitive or purely sociological theories). In addition, action regulation theory is less fine-grained than most cognitive theories and therefore has integrative potential as it can help bridge gaps between basic topics such as cognition and motivation and more practical workplace issues (Hacker, 1994). It is also broad enough to be able to integrate findings based on middle- and small-range theories. In this section, we discuss six areas in which some research based on action regulation theory has already been conducted but, in our opinion, should be further developed in the future. The areas are occupational strain and well-being, reciprocal influences between personality and work, innovation, team work, career development, and successful aging at work.

Occupational Strain and Well-Being Action regulation theory argues that ‘human beings usually try to actively cope with their environment. In this sense, job design should support this active approach by providing challenging (i.e., sufficiently complex) tasks (regulation requirements) and control (regulation possibilities), while reducing the stressors (regulation problems)’ (Zapf, 2002, p. 254). Stressors tend to disturb the regulation of action. Since humans have a natural tendency to act, disturbances of action regulation lead to negative affect. Thus, action regulation theory explains the development of occupational strain and well-being using the concepts of action regulation problems

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and positive regulation opportunities (Greiner, Ragland, Krause, Syme, & Fisher, 1997; Leitner & Resch, 2005; Resch, Volpert, Leitner, & Krogoll, 1984). Within this tradition, a classification of stressors is based on action regulation problems (Frese & Zapf, 1994). Regulation obstacles refer to task-related events or conditions that make it harder or impossible to attain the goal, such as interruptions or lack of information and equipment. When people encounter regulation obstacles, they could repeat steps in the action sequence, take detours to achieve the goal, or give up the task. Regulation uncertainty can be caused by a lack of knowledge on how to achieve a goal, which plan is most likely to be successful, and whether feedback is useful. Finally, overtaxing regulation refers to a straininducing situation in which external demands are too high (e.g., time pressure) or signals too intense (e.g., information overload) and thus disrupt the action sequence (Frese & Zapf, 1994). Action regulation theory further suggests that action regulation at higher levels is more difficult and effortful. Yet it is also necessary that work is challenging enough to engage both high as well as low levels of regulation. If there are too few challenges, action is incomplete – leading to feelings of emptiness and boredom. Thus, the most stressful jobs should be jobs that require constant vigilance but that also do not engage high-level intellectual processing of work. This is work where constant attention is needed but where the decisions are completely routinized; for instance, quality control of beer bottles or electric bulbs involves the detection of small differences but the decisions are trivial (i.e., throw away the defective bulb or take out the dirty or discolored beer bottle; Hacker & Richter, 1984). Additionally, disruptions of routines (e.g., incoming emails, sudden changes in goals) are frustrating and disturbing (Baethge, Rigotti, & Roe, 2015; Semmer, 1982). In service, knowledge, and managerial work, many of such disruptions occur (Zapf, 2002; Zijlstra, Roe, Leonora, & Krediet, 1999). A number of studies on occupational strain and well-being have been conducted based on action regulation theory (Frese, 1985, 1987; Schönpflug, 1985; Zapf, 1993). For instance, Zijlstra et  al. (1999) examined the effects of interruptions on occupational well-being, and how workers regulate these experiences. Results showed that interruptions lead to a decline in well-being; at the same time, employees expended higher levels of effort following interruptions and developed action strategies that allowed them to deal effectively with the interruptions. Using longitudinal data, Fay and Sonnentag (2002) showed that job stressors had positive effects on change in personal initiative behavior over time. Thus, while most stress

research assumes negative relationships between stressors and work behavior, participants in this study increased their proactive behavior after encountering stressors. However, action regulation theorists have also suggested that active behavior such as personal initiative may aggravate stress in the long-term, because it requires cognitive effort and consumes cognitive resources (Frese & Zapf, 1994). More recently, Schmitt, Zacher, and Frese (2012) conducted a daily diary study to examine the effects of self-reported action regulation strategies (i.e., goal selection, optimization of effort, and compensation for potential barriers; P. B. Baltes & Baltes, 1990) on the relationship between problem solving demands at work and occupational wellbeing. Problem solving demands were positively related to fatigue, and action regulation strategies mitigated this relationship. Overall, studies on occupational strain and well-being based on action regulation theory are still relatively sparse and scattered; future research could attempt to move the field forward by integrating concepts and propositions of action regulation theory with other, more mainstream approaches in the stress literature (e.g., conservation of resources theory; Hobfoll, 1989).

Reciprocal Influences between Personality and Work In action regulation theory, personality is conceived as both an antecedent and an outcome of work, and action is assumed to mediate these effects (Frese & Zapf, 1994). On the one hand, personality predisposes individuals to act in a certain way which, in turn, impacts on the work environment. On the other hand, the perspective of occupational socialization suggests that the work environment stimulates active learning and behavior which, over time, may lead to positive changes in personality (Frese, 1982). Obviously genetics produce limits on how much personality can be changed through work experience (Ilies, Arvey, & Bouchard, 2006; Zyphur, Narayanan, Arvey, & Alexander, 2009). However, consistent with propositions of action regulation theory, contemporary theories and research on personality development suggest that there are reciprocal influences between personality traits such as conscientiousness and workplace characteristics and behavior (Judge, Simon, Hurst, & Kelley, 2014; Roberts, Caspi, & Moffitt, 2003; Woods, Lievens, De Fruyt, & Wille, 2013). Action regulation theory argues that personality consists of generalized tendencies to act (Frese et al., 1987). However, the work situation produces

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norms, signals, and cognitive primes for certain behaviors that can also be automatized. Once they are automatized, they may produce changes in personality (Li, Fay, Frese, Harms, & Gao, 2014). It is highly unlikely that people start to behave opposite to their prior personality, but it is likely that work increases behavioral tendencies that are already present prior to entering such a workplace (Woods et al., 2013). A few recent studies seem to confirm these assumptions. Jackson, Thoemmes, Jonkmann, Lüdtke, and Trautwein (2012) reported that individuals with low levels of agreeableness were more likely to join the military, and that time spent in the military led to further decreases in agreeableness over time. Li et al. (2014) used longitudinal data across three years to show that work characteristics (i.e., job demands and job control) led to increases in proactive personality. Moreover, proactive personality increased job demands, job control, and supervisory support, and decreased organizational constraints over time. It is important to note that in another study the same first author described the genetic side of proactivity (Li, Song, Arvey, & Zhang, 2014) – this illustrates that it makes little sense to play genetics against the implications of the impact of work on the person. Further research using longitudinal designs and advanced analytical approaches such as latent change score models is needed in this area.

Innovation Innovation refers to ‘the intentional introduction and application within a role, group or organization of ideas, processes, products, or procedures, new to the relevant unit of adoption, designed to significantly benefit the individual, the group, organization or wider society’ (West & Farr, 1990, p. 9). There is increasing recognition among organizational researchers that active behavior plays an important role in the complex processes leading to individual, team, and organizational innovation. For instance, Gebert, Boerner, and Kearney (2010) suggested that leaders’ opposing yet complementary action strategies related to idea generation and implementation facilitate team innovation. Similarly, Rosing, Frese, and Bausch (2011) argued that leaders need to facilitate high levels of both active exploration and exploitation behaviors among employees to enhance individual and team innovation. Action regulation theory suggests that problem solving, ‘connecting the dots’ between different environmental signals, and other cognitive operations related to innovation take place at the conscious intellectual level of regulation. However,

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regulation at lower levels may also contribute to innovation. For instance, the automatization of action patterns frees cognitive resources at higher levels which, in turn, may benefit creative thinking (Ohly et al., 2006). Hacker (2003) proposed that mental work leading to innovation (e.g., product development) requires a hybrid model of action regulation because the innovation process does not entail clearly defined goals and results. This model combines knowledge-based opportunistic sequences with systematic goal development and planning, which facilitate creativity and implementation of ideas, respectively. An innovative action style involves that self-set goals are flexibly changed and accomplished, the identification of important problems, and the development of mental models regarding possible changes and alternative procedures for goal attainment (Hacker, 2003). In addition, Hacker (2003) argued that innovative work requires dynamic reciprocal influences between higher level intellectual regulation and lower level psychomotor operations. This is consistent with research in social cognition showing that movements (e.g., walking around the office) facilitate creativity. Most psychological research on innovation investigates stable personality and work characteristics while active behavior has been rather neglected. In an early exception, Frese, Teng, and Wijnen (1999) showed that initiative at work was positively related to employees’ development of ideas for improvements at work, submitting suggestions, and quality of suggestions. Consistent with action regulation theory, Ohly et  al. (2006) argued that routinization at work saves time and cognitive resources and, therefore, may free resources that can be used to develop new and useful ideas. Results indeed showed that routinization positively predicted creativity and innovation at work, above and beyond work characteristics. Holman et  al. (2012) investigated employees’ learning strategies as a mediator of the relationships between job design and different aspects of innovation. They found that learning strategies mediated the relationship between job control and problem solving demands on the one hand, and idea generation on the other hand. Finally, consistent with Rosing et al.’s (2011) ambidextrous leadership theory, Zacher and Wilden (2014) showed that leaders’ daily engagement in both opening behavior (e.g., encouraging experimentation) and closing behavior (e.g., emphasizing goal achievement) was associated with the highest level of employee innovation. Future research should continue to study different cognitive processes and strategies on multiple levels of action regulation, as well as dynamic and active behaviors as antecedents of workplace innovation.

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Team Work ‘Work action is something that shapes people collectively and individually’ (Frese & Zapf, 1994, p. 326). Although in most cases the work context includes other people such as supervisors, coworkers, and customers, action regulation theory has traditionally focused on the individual and action regulation that occurs within persons over time. Social processes such as coordinating one’s actions with those of others have so far received relatively little attention (Hacker, 2003). An important exception is research by von Cranach, Tschan, and colleagues, who systematically applied principles of action regulation theory to study small groups such as medical teams (McGrath & Tschan, 2004; Tschan & von Cranach, 1996; von Cranach, 1996; von Cranach, Ochsenbein, & Valach, 1986). Based on the assumption that teams exist primarily through their actions and interactions with their environment, these researchers proposed that team action, analogous to individual action, is sequentially and hierarchically organized. The hierarchical-­ sequential activity cycle of teams involves goal development, selection, and resource allocation, as well as planning and execution (McGrath & Tschan, 2004). In addition, a team’s performance is determined by the team’s action-oriented mental model containing task-relevant knowledge as well as by the extent to which the group structure fits the task structure (von Cranach, 1996). Empirical research based on action regulation theory by Tschan showed, for instance, that the quality of task-related communication predicted team productivity (Tschan, 1995, 2002). Specifically, teams with an ‘ideal communication cycle’, which includes multiple phases of the action regulation sequence ranging from goal development and orientation to evaluation of feedback, had the highest productivity. Another study demonstrated that guided reflection had positive effects on team processes and performance (Gurtner, Tschan, Semmer, & Nagele, 2007); the effects were mediated by improved team communication, development of implementation strategies, and increased similarity of shared mental models. These findings suggest that in order to enhance team performance, managers and organizations should facilitate employees’ engagement in guided reflection and effective taskrelated communication. Another exception to the dearth of empirical research on action regulation in teams is a study by Weber (1999), who considered the role of collective action regulation in teams, including the development of common goals, collective planning, collective decision-making, and collective task orientation. When working together in teams, individuals can produce shared action-oriented

mental models (similar to shared mental models) based on individual regulation processes that are linked through communication processes (Weber, 1999). For instance, team members may comment on others’ ideas, correct others’ errors, and provide others with feedback. Results of an interview study with members of 17 teams showed that attitudes toward cooperation and collaboratively developed knowledge were higher in teams with high compared to low autonomy for collective planning and decision-making (Weber, 1999). More recently, Hacker and Melzer (2009) examined whether task design based on action regulation theory principles could also be applied to the context of interactive jobs. They argued that the concept of task identity or completeness of tasks can also be applied to the social context. Specifically, task identity can be conceived as the extent to which work functions are allocated between different employees (i.e., division of labor). For instance, some employees may complete the whole action sequence at different levels of regulation, whereas others experience only partial completeness as different steps of the action sequence and operations on different levels are divided among employees. Consistent with propositions on individual task completeness, complete tasks should lead to more favorable perceptions of task characteristics and well-being (Hacker & Melzer, 2009). Overall, existing research on action regulation in and of teams can be used as a fertile ground for further research in this important area.

Career Development and Successful Aging at Work Managing one’s career requires high levels of proactivity and adaptability on the part of an individual employee. Most research on career development to date has focused on relatively stable personality characteristics (Ng, Eby, Sorensen, & Feldman, 2005). However, action regulation theory suggests that individuals can regulate their career development by proactively setting and pursuing career goals. So far, only few empirical studies exist that examined career development from an action regulation perspective. In one study, Brandstaetter, Heimbeck, Malzacher, and Frese (2003) applied the theory to the area of continuing education. They showed that individuals’ goals and planning related to continuing education positively predicted participation in vocational retraining two years later. Raabe, Frese, and Beehr (2007) investigated effects of a career management intervention based on action regulation theory. Their intervention increased employees’ self-knowledge, career goal

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commitment, and career plan quality which, in turn, predicted career self-management behaviors and career satisfaction 10 months after the intervention. Further research is needed that aligns the dominant trait paradigm in the career literature with major concepts and propositions based on action regulation theory. For instance, researchers could examine whether active behavior and regulation strategies mediate the relationships between traits and subjective and objective career outcomes. As the workforce is aging rapidly, researchers in work and organizational psychology have become increasingly interested in the role of age and lifespan development in the work context (Hedge & Borman, 2012; Zacher, 2015). Thirty years ago, Frese and Stewart (1984) suggested that ‘analyses of the tasks confronting an individual and of the structure of action, as well as the interplay of these two, have implications for the understanding of development across the life span’ (p. 145). These researchers put forward several propositions regarding action regulation and successful aging at work that await empirical investigation. First, Frese and Stewart (1984) offered a different perspective on the ‘use it or lose it hypothesis’ (cf. Hertzog, Kramer, Wilson, & Lindenberger, 2008; Salthouse, 2006) – the assumption that losses in cognitive functioning with age can be buffered by an intellectually stimulating environment. Specifically, they argued that impaired cognitive performance is due to increased routinization of skills with age, which results in the disuse of the conscious intellectual level of regulation, for instance, to monitor the environment and to develop new plans. Second, Frese and Stewart (1984) argued that automatized tasks may result in ‘skill traps’ among older employees. In these situations, certain welllearned skills are no longer relevant and the high level of routinization makes it difficult to adjust quickly to new tasks and the task environment (‘re-intellectualization’). Inadequate responses to new environmental demands are likely to increase strain which, in turn, may lead to further regulatory problems. Skill traps may surface gradually over time or due to career transitions such as job changes or retirement. Thus, action regulation theory can shed new light on person–environment fit approaches to aging at work, which are concerned with changes within individuals and across work environments over time (Zacher, Feldman, & Schulz, 2014). Finally, Frese and Stewart (1984) proposed a novel perspective on the notion of life and career stages based on action regulation theory. They argued that such stages are characterized by specific sub-goals (e.g., finding bridge employment) that are part of broader life or career goals (e.g., work activity as an important source of meaning in life). Their approach suggests that

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moving to a new life or career stage entails a qualitative change in sub-goals, while the higher-order goals produce continuity among the sub-goals. One area in which action regulation theory has been fruitfully applied to study successful aging is individuals’ use of higher level, meta-cognitive action regulation strategies. This research has mainly focused on general life or work goals and self-management across the lifespan. Specifically, Baltes and colleagues conceptualized selection, optimization, and compensation (SOC) strategies from an action theoretical perspective, and they developed and validated self-report scales to assess employees’ general tendencies to make use of these strategies in different domains (e.g., work, family; P. B. Baltes & Baltes, 1990; Freund & Baltes, 2000, 2002). According to Baltes and Baltes (1990), the use of SOC strategies leads to successful adaptation and well-being because these strategies help individuals make the most of their increasingly limited resources across the lifespan. The broader idea that individuals need to both actively shape their environment and adjust to unavoidable losses and constraints in order to age successfully is also evident in more recent theoretical developments in lifespan psychology that make references to action regulation theory (Heckhausen, Wrosch, & Schulz, 2010). Two empirical studies have demonstrated the beneficial effects of SOC strategy use among older employees (see also B. B. Baltes & Dickson, 2001). First, Zacher and Frese (2011) used Freund and Baltes’ (2002) reliable and well-validated SOC strategies scale and demonstrated that particularly older employees in low complexity jobs benefited from SOC use with regard to their perceived occupational opportunities in the future. Second, Weigl, Müller, Hornung, Zacher, and Angerer (2013) found that SOC strategy use and job control buffered the negative relationship between employee age and supervisor ratings of work ability (i.e., the extent to which job demands can be met by employees’ physical and mental capabilities). Future research should integrate these metacognitive strategies with other components of the action sequence (e.g., planning and feedback) and examine how they may impact on lower levels of action regulation (cf. Zacher, Hacker, & Frese, 2016). Finally, it would be important to investigate the ways in which coworkers, managers, teams, and organizational practices can facilitate employees’ effective strategy use.

CONCLUSION Action regulation theory is an important basic theory for applied psychology, as it provides an

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integrative framework to examine the sequence, structure, and foci of action regulation, as well as action-oriented mental models in work and organizational contexts. Most research based on action regulation theory over the past two decades has focused on proactive work behavior, work-related learning and error management, and entrepreneurship. However, concepts and propositions of action regulation theory can also be fruitfully applied to other areas, such as occupational strain and well-being, reciprocal influences between personality and work, innovation, team work, career development, and successful aging at work. We hope that this chapter inspires researchers to base their conceptual models and hypotheses on action regulation theory in order to gain a better understanding of topics in industrial, work, and organizational psychology that are, at least in part, influenced by human action.

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7 Goal Setting Theory: Controversies and Resolutions G a r y P. L a t h a m a n d E d w i n A . L o c k e

INTRODUCTION Goal setting research has been proliferating since the beginning of the twentieth century and continues unabated in the present century. Hence, there have been numerous reviews of this body of work (e.g., Latham & Locke, 2007; Latham & Yukl, 1975a; Locke & Latham, 2002; Locke, Shaw, Saari, & Latham, 1981). The purpose of this chapter is ten-fold. First, studies of goal setting conducted prior to the 1960s are selectively reviewed. Second, the inductive development of the theory of goal setting is explained in terms of the discovery of the causal relationships it specifies, the moderators or boundary conditions, and the mediators that explain the theory’s effectiveness for increasing job performance and satisfaction. Third, attention is given to explaining the similarities and differences among performance, behavioral and learning goals. Fourth, the interrelationships between goal setting as a state and individual differences, namely, the Big-5, goal orientation, and core self-evaluations are discussed. Fifth, goal setting is discussed in relation to societal culture. Sixth, goal setting controversies, specifically external validity, feedback, the method for setting goals, and competing theoretical frameworks are examined. Seventh, limitations to using goal

setting as a technique for performance improvement are discussed including the allegation that the setting of a specific high performance goal results in unethical behavior. Eighth, applications of goal setting to the external world are pointed out. Ninth, the effect of priming goals in the subconscious on organizational behavior is reviewed. The chapter closes with an explanation for the endurance of goal setting as a theory and a technique, namely, the result of inductive rather than deductive theory-building.

EARLY GOAL SETTING RESEARCH As noted above, the effect of goal setting on performance has been of interest to psychologists since the beginning of the twentieth century (Latham, 2012). For example, during the era of behaviorism (Watson, 1913), McDougall (1908) strongly objected to the stimulus–response view of behavior. He was so struck by the goal-seeking quality of behavior that he later described himself as a purposive psychologist (McDougall, 1930). Tolman (1932) also wrote about purposive behavior. In that same time period, F. W. Taylor (1911),

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an industrial engineer, advocated giving each employee a specific difficult amount of work to complete, of a certain quality, within a specific time period, on the basis of a time and motion study, as the means for ensuring a highly productive work-force. A monetary reward was advocated for goal attainment. In the 1930s, a series of laboratory experiments conducted in the United Kingdom by Mace (1935) showed that the standard (i.e., goal) that was set has a positive influence on an individual’s performance. But, this occurred only when an individual’s skill had developed to the extent that the person had a reasonable expectation that the standard was attainable. Setting a performance standard for an employee, Mace concluded, is most effective when it is adjusted to take into account an individual’s ability. Ability, as is discussed below, is a moderator in goal setting theory (Locke & Latham, 1990, 2013a). A field study conducted in the United Kingdom by Wyatt, Frost, and Stock (1934) revealed that factory workers reduced their boredom by setting ‘definite aims’ to complete a certain number of units within a predetermined time period. Two dependent variables of primary concern in goal setting theory, as noted above, are job performance and job satisfaction (Latham, Locke, & Fassina, 2002). In their review of the Hawthorne field studies conducted between 1927 and 1933, Ryan and Smith (1954) concluded that the results show that when employees are given the opportunity to express their preferences, are free of overly strict supervision, and are given standards that take into account their ability, employees work effectively. Ryan and Smith, Locke’s mentors when he was pursuing his doctoral studies, also stated: Whether a means activity is initiated, and the degree of effort which is devoted to it, are functions of (a) the attractiveness of the goal, (b) the attractiveness of the means activity itself and of its surrounding conditions, (c) the uniqueness of the goal (as perceived by the individual), (d) the directions of the relationship between the means and the end result, also as perceived or understood by the subject, and (e) the individual’s estimates of his ability to perform the means activity well enough to achieve the goal. (Ryan & Smith, 1954, pp. 387–388)

Clinical psychologists in the early to mid twentieth century also acknowledged the importance of goals. For example, Adler broke away from Freud over the latter individual’s emphasis on sexual instincts as an explanation of behavior. Adler viewed individuals as goal-directed, motivated by expectations of the future: ‘Causes, powers, instincts, impulses, and the like cannot serve as explanatory principles.

The final goal alone can explain man’s behavior’ (Adler, 1930, p. 400). The usefulness of goal setting in psychotherapy has been described in detail by Matre, Dahl, Jensen and Nordahl (2013). The above studies and observations were not used to build a theory of the effects of goals on performance. Although Lewin (1951) conducted a series of experiments, guided by theory, on goals which he called levels of aspiration, his focus was on goals as dependent variables, as affected by previous successes and failures, rather than as independent variables. When Locke was attending graduate school, his mentor, T. A. Ryan (1970) was completing his book on a theory of intentions. This theory, the immediate precursor to goal setting theory, states that an intention is an individual’s mental representation of a planned action. Furthermore, the theory states that an intention is the most immediate and important cause of behavior. In short, behavior is intentional. The pre-publication draft of this book stimulated Locke’s (1964) choice of a doctoral dissertation. Ryan’s theory, however, garnered relatively little attention.1

GOAL SETTING THEORY Goal setting theory was developed inductively over a 25-year period. It is based on systematically accumulated empirical experiments and correlational studies conducted between 1964 and 1989. Among the advantages of inductive theory building is that it avoids the danger of concluding too much too soon, and over-formalizing ideas at the early stage of theory development.2 When goal setting theory appeared (Locke & Latham, 1990), it was based on approximately 400 studies, involving close to 40,000 participants, performing 88 different tasks in laboratory and field settings for time spans ranging from one-minute to several years. These studies focused on assigned, participatively set, and self-set goals. C. Lee and Earley (1992) concluded that the most valid and practical theory of motivation in organization settings is goal setting. Based on the quantity and quality of the research underlying this theory, both Miner (1984) and Pinder (1984) had reached the same conclusion in the previous decade. A goal is the object or aim of an action (Locke & Latham, 1990; 2002; Latham & Locke, 2007). The core tenets of the theory are that (1) a specific, high goal leads to higher performance than an easy goal, a vague goal such as ‘to do your best’, or no goal setting at all; (2) the higher the goal, the higher an individual’s performance; and (3) such

Goal Setting Theory: Controversies and Resolutions

variables as performance feedback, participation in decision-making, and competition have no effect on behavior unless they lead to the setting of and commitment to a specific, high goal. A theory requires not only the specification of causal relationships, it requires the identification of the moderators and mediators of the causal relationships. It would be absurd for someone to embrace this theory and then set a specific, high goal to make a billion dollars on the stock market within the following 12-month period unless that person has very high financial acumen. As noted earlier, a moderator in goal setting theory is ability, that is, task-relevant knowledge or skills. Unless people derive some sense of achievement for partial success on goal progress, setting goals that are unattainable is unlikely to motivate people over the long term, especially if the goals are tied to incentive bonuses (Lee, Locke, & Phan, 1997; Mawritz, Folger, & Latham, 2014). A second moderator is commitment. It is the sine qua non in that if an individual is not committed to pursuing a specific goal, by definition the goal does not exist for that person. Commitment, which varies on a continuum, is especially important when the goal is difficult to attain (Klein, Cooper, & Monahan, 2013; Seijts & Latham, 2011). A third moderator is feedback. An individual is likely to experience great, if not insurmountable, difficulty in attaining a specific, high goal if performance feedback is lacking on what the person should continue, start, stop, or consider doing differently to ensure goal attainment. The fourth moderator is situational constraints. An individual must have the requisite resources (e.g., finances, technology), or the goal is unlikely to be attained. Moreover, organizational decision makers must ensure that organizational policies do not hinder goal pursuit. The causal mediators that explain why goals are such an effective source of motivation are fourfold, namely, choice, effort, persistence, and strategy. Goals give people focus. Setting a goal is a choice that an individual makes among alternative courses of action, including that of setting no goals and meandering aimlessly. In short, people make choices as to whether to set goals, the difficulty level of the goals, and if they choose to commit to them, they choose courses of action to attain them. A specific, high goal results in an increase in effort over and above easy, vague, or non-existent goals. Given goal commitment, people persist until the goal is attained. Finally, a specific, high goal cues the cognitive retrieval, discovery of, or search for strategies to attain it (Latham & Arshoff, 2015). Strategy is both a mediator and a moderator of the goal–performance relationship (Wood, Whelan, Sojo, & Wong, 2013).

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Based on these findings, Locke and Latham published their inductively developed theory in 1990. Job performance increases, the theory states, because a specific, high goal enables an employee to observe, monitor, evaluate, and adjust behavior so as to attain a desired outcome. It is the specificity and difficulty level of a goal that delineates the conditional requirements for positive self-evaluation. Satisfaction with one’s performance is defined in terms of the number of successes attained, or degree of success attained. In fact, when people perform well in relation to the goals to which they are committed, they not only feel satisfied with their performance, they generalize this positive affect to the task. That is, they like the task more than they did initially (Locke, 1967; Locke & Latham, 1990; Locke, Cartledge, & Knerr, 1970).3 Goal attainment induces a sense of pride and achievement (Mento, Locke, & Klein, 1992). Goal setting theory applies to the individual, group, departmental, and organizational levels, at least for relatively small organizations. To our knowledge, there have been no field experiments on the effects of goal setting on job performance in relatively large organizations. However, goal setting is the central variable in management by objectives (MBO), a technique frequently used in large organizations. A meta-analysis conducted by Rodgers and Hunter (1991) revealed that when top management supported this technique, the average gain in productivity was 56%.

Types of Goals The vast majority of experiments on goal setting involve performance goals. Performance goals focus attention on desired outcomes. Examples of outcomes include cost-related measures and what are traditionally referred to in the I-O psychology and HRM-OB literature as ‘hard criteria’. Not every criterion of interest in the workplace, however, can be assessed in cost-related terms. Goals can also be set for criteria that are behavioral in nature (e.g., team-playing, ethical behavior, scores on the dimensions of a 360º survey). The behavioral criteria may be assessed using behavioral observation scales (BOS; Latham & Wexley, 1977) or behavioral expectation scales (BES; Smith & Kendall, 1963). The behavioral items on those scales are typically identified through a systematic job analysis. A field experiment on goals set by engineers and scientists, discussed later in this chapter, involved BOS (Latham, Mitchell, & Dossett, 1978). Kanfer and Ackerman (1989) found that when people were in the declarative stage of learning,

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that is, when they lacked the ability to perform effectively on an air-traffic control simulation, urging them to do their best led to higher performance than that of peers randomly assigned to the condition where a specific, high performance goal had been set. Mone and Shalley (1995) obtained similar results using a complex human resources staffing simulation that participants performed over a three-day period. Worse, the dysfunctional effects of a specific high performance goal increased with each passing day. In both of these experiments, the importance of ability or task knowledge, a moderator in goal setting theory, had been overlooked. Participants in the do-your-best condition searched systematically for effective task strategies whereas those with a specific high goal failed to do so. Winters and Latham (1996) tested their belief that the above findings do not reflect a limitation of goal setting theory. Rather, these results reflect a problem in the type of goal that had been set. Using a scheduling task that people lacked the ability to perform effectively, they replicated the findings of both Kanfer and Ackerman (1989) and Mone and Shalley (1995). People who were urged to do their best had higher performance than those who had a specific high performance goal. But, the highest level of performance was attained by participants who were randomly assigned to the condition where a specific, challenging learning goal had been set. It is not known for certain when is the optimum time to set a learning versus a performance goal. Clearly the extant evidence suggests that a learning goal should be set when an individual or team has yet to acquire the knowledge or skill to master a task. This is because a learning goal shifts attention from the end result (e.g., a golf score of 90) to discovering/developing a specific, challenging number of strategies, procedures, or systems for task mastery. So one could argue that only when an individual has the requisite knowledge, skill, or ability to perform the task should a performance goal be set, and only when a job analysis will identify the behaviors critical to a particular endeavor (e.g., team-playing) should behavioral goals be set. When the knowledge of the behaviors required for performing a task effectively does not exist, a specific high learning goal should be set (Brown & Latham, 2002; Seijts, Latham, Tasa, & Latham, 2004). However, one experiment found that people can pursue learning and performance goals simultaneously (Masuda, Locke, & Williams, 2015). Williams (2013) reported similar findings in goal setting studies involving athletes. But, Noel (2012) found that this is not necessarily the case. The setting of both goals hurt rather than helped performance. Thus, additional research is needed on this issue. Learning goals have the same properties as performance and behavioral goals in terms of

the importance of specificity and difficulty level (Latham, Seijts, & Crim, 2008; Seijts & Latham, 2005). Consistent with goal setting theory, specific, high learning goals lead to higher performance than easy learning goals (Seijts, Latham, & Woodwark, 2013). Performance is high when commitment to the learning goal is high rather than low (Seijts & Latham, 2011). However, a specific, high performance goal is more beneficial for people with higher than lower cognitive ability. This is because those with high ability are more capable of drawing upon extant strategies for goal attainment than are those with less cognitive ability (Locke, 1965). The opposite has been found to be the case for learning goals (Latham, Seijts, & Crim, 2008). This is because a learning goal focuses the attention of those lower in cognitive ability to do what those with high cognitive ability do naturally, namely, take the time to systematically think of ways to master the task at hand. Seijts and Latham (2001), using a task that was complex for people, showed that the learning goal–performance relationship is mediated by the discovery of appropriate strategies, a mediator in goal setting theory. Their repeated measures design showed that self-efficacy decreased significantly across trials for those with a performance goal and increased for those with a learning goal. Goal commitment, another moderator in goal setting theory, was higher in the learning than in the performance goal condition. Latham and Brown (2006) found that student satisfaction with the MBA program was highest in the learning goal condition rather than in the performance or do best goal conditions. The setting of a learning goal appears to have focused attention on ways of addressing the challenges of pursuing an MBA degree that in turn increased student satisfaction with the program. R. Porter and Latham (2013) surveyed managers and their employees in various industries (e.g., financial, manufacturing, technology) during the 2009 financial and economic crisis. Learning goals explained more of the variance in a department’s performance than did performance and doyour-best goals. With regard to feedback, a moderator in goal setting theory, Cianci, Klein, and Seijts (2010) reported that individuals who had learning goals responded more productively to negative feedback than those who had performance goals.

GOALS AND PERSONALITY Goal setting is a state variable that effectively increases performance. Effect sizes typically

Goal Setting Theory: Controversies and Resolutions

range from .42 to .82 (Locke & Latham, 1990). This is because the goals that are set are task and situationally specific. This is in sharp contrast with measures of personality traits that typically correlate (uncorrected) .20 or less with a Cohen’s d of .41 or less with performance. Until recently (Latham, Ganegoda, & Locke, 2011), goal setting theory ignored individual differences in personality variables. This is because goal setting, a state, creates a strong situation (Meyer, Dalal, & Hermida, 2010; Mischel, 1973). Goal specificity results in a common understanding among employees of what is expected of them, thus masking the influence of a trait (Adler & Weiss, 1988). In their respective reviews of the literature, both Mitchell (1979) and Schneider (1985) concluded that personality variables explain little of the variance in organizational behavior. However, the development of the Big 5 as well as advances in the measurement of personality traits has led to a different conclusion with regard to their relationship to goal setting, goal difficulty level, goal commitment, and goal attainment. The Big 5 includes measures of conscientiousness, openness to experience, agreeableness, extroversion, and neuroticism. A meta-analysis of studies on self-set goals shows that all five measures are related to an individual’s goal setting motivation (Judge & Ilies, 2002). This is especially the case for goal difficulty level. On the basis of correlations corrected for unreliability, agreeableness was shown to have the strongest relationship with goal setting (r = –.51) followed by conscientiousness (r =.35) and neuroticism (r = –.31). Extroversion (r =.15) and openness to experience (r =.18) had a significant, but weak relationship with goal setting motivation. The negative correlation between agreeableness and goal difficulty level may be due to the small number of correlations analyzed (k = 4), or due to the fact that agreeable people set less difficult goals because they are motivated more by the desire to fit in with their work group than they are for high performance (Judge & Ilies, 2002). Yukl and Latham (1978) found that need for achievement scores of word processing operators correlated positively with the setting of high goals. Measures of conscientiousness assess an achievement orientation. Several studies show that people who score high on conscientiousness are predisposed to set and commit to high goals when they have jobs with relatively high autonomy. For example, Barrick, Mount, and Strauss (1993) reported that sales representatives, who scored high on this trait, also set high goals. A trait theory that assesses differences among individuals in their appraisal of people, events, and things in relation to themselves was developed by

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Judge, Locke, and Durham (1997). This theory, core self-evaluations, consists of four inter-­correlated traits, namely, self-esteem, locus of control, neuroticism, and ‘general’ self-efficacy. The theory states that individuals make generalized conclusions about themselves, especially their worthiness, competence, and capabilities. Erez and Judge (2001) found that core self-evaluations explained 17% of the variance in goal setting behavior and 32% of the variance in goal commitment. Locus of control, one of four variables in core evaluations theory, has a positive relationship with the level of goal difficulty. Word processing operators who scored high on internal locus of control set higher goals than those who scored high on external locus of control (Yukl & Latham, 1978). An internal locus of control also moderates the self-set goal performance relationship in that the goals are more predictive of performance when an individual scores high on internal locus of control (Lambert, Moore, & Dixon, 1999). At least three studies have shown that selfesteem is related to goal attainment. Employees who scored high on a measure of self-esteem were more likely to attain a difficult goal than were those with low self-esteem (Dossett, Latham, & Mitchell, 1979; Yukl & Latham, 1978). Similarly, Tang and Reynolds (1993) reported that individuals with high self-esteem set higher goals and performed more effectively than those who scored low on this trait. Goal orientation, a trait rather than a state (Dweck, 1999), is a moderator of the type of goal, a state, that an individual chooses (Brett & VandeWalle, 1999). People with a learning goal orientation tend to self-set difficult goals; those with a performance goal orientation tend to selfset goals they believe they can easily attain in order to impress others as opposed to attaining a specific, challenging level of performance. But, a learning goal, a state, masks the effect of goal orientation on the performance of a task that is complex for an individual (Seijts et al., 2004).

Societal Culture In their review of the literature, Locke and Latham (1990) reported strong evidence that goal setting theory is applicable in at least eight countries: Australia, Canada, the Caribbean, England, Germany, Israel, Japan, and the United States. Using survey data on employees in 10 different countries, Sue-Chan and Ong (2002) found that self-efficacy mediated the effect of goal assignment on the job performance of those low in power distance. Nevertheless, the act of putting

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goal setting into practice requires adaptation to the context in which it will be applied. For example, we have no knowledge of the most appropriate ways of setting goals in the workplace in Arab countries. But Triandis and Gelfand (2012) reported that in collectivistic cultures, people tend to give priority to in-group goals whereas those in individualistic cultures tend to give priority to their own goals even if their goals are in conflict with their group’s. Research conducted in organizational settings in China between an in-group (i.e., the company) and an out-group (i.e., the supplier) revealed that a superordinate goal or vision gave the members in both groups a common cause to rally around which in turn fostered cooperative interdependence (Wong, Tjosvold, & Yu, 2005).

GOAL SETTING CONTROVERSIES Science advances as a result of constructive criticism emanating from rigorous empirical studies. Goal setting theory has been the subject of several controversies.

External Validity Among the earliest criticisms of goal setting theory is that it lacked external validity. In his Annual Review of Psychology chapter, Hinrichs (1970) questioned whether the findings from laboratory experiments on goal setting ‘will carry through in the complex behaviors required in organizations’ (p. 125). Similarly, Heneman and Schwab (1972) noted that research on ‘goal setting theory usually involved students working on laboratory tasks in experimental settings. The cost of external validity has been of course, a general inability to make causal inferences’ (p. 8). This criticism was removed with Latham’s programmatic research in the forest products industry involving longitudinal experiments with loggers where the goal was set in terms of number of trees to be cut down (e.g., Latham & Baldes, 1975; Latham & Kinne, 1974; Latham & Locke, 1975; Ronan, Latham, & Kinne, 1973). Follow-up interviews with these employees, who were paid on a piece-rate basis, revealed that: Goal setting had instilled a sense of purpose, challenge, and meaning into what had been perceived previously by them as a tedious and physically exhausting task. In short, goal pursuit and attainment led to enhanced task interest, pride in performance, and a heightened sense of personal

effectiveness as well as an increase in pay. The problem with urging people to do their best, even when they are paid on a piece-rate basis, is that they do not, in fact, do so. The exhortation is too vague, too abstract. There is no meaningful referent for evaluation of one’s performance. Consequently, it is defined idiosyncratically. It allows for a wide level of performance levels that are acceptable to different people. Setting a specific high goal, in contrast, makes explicit for employees what needs to be attained. (Latham, 2012, p. 64)

Psychologists (e.g., L. Porter, 1976), as well as senior managers at the Weyerhaeuser forest products company, questioned whether something so straightforward as goal setting would increase the performance of higher-level employees than loggers. A subsequent field experiment put this issue to rest (Latham, Mitchell, & Dossett, 1978). The job behavior of scientists and engineers, with master’s and doctoral degrees, was examined in a 3 (assigned goal, participatively set goal, do your best) × 3 (praise, public recognition, and a monetary bonus) factorial design over a six-month period. In addition, there was a 10th cell, a comparison group. None of the individuals in this latter group were aware that they were in this experiment. Consistent with goal setting theory, those employees who were urged to ‘do their best’, and in addition received praise, public recognition, or a monetary bonus performed no better than those in the comparison group. Goal commitment in the participative goal condition was not significantly different from those who were assigned goals. But, the difficulty level of the goals that were set were significantly higher in the participative than in the assigned goal condition. Consistent with the theory, the higher the goal, the higher the scientists’/engineers’ performance. Both goal setting conditions led to higher performance than that of the do-best and comparison group conditions.

Feedback Behaviorists (e.g., Komaki, 1981) argue that performance is largely, if not solely, a function of its consequences. If performance feedback is positive, the behavior increases in frequency; if the feedback is negative, performance decreases in frequency. However, this is simply not true. Feedback is only information; its effects depend on how one evaluates it and what one chooses to do about it. If the feedback tells one, or implies how to perform better, and one already has a specific goal, then feedback will facilitate performance. However, if no specific goal has been set,

Goal Setting Theory: Controversies and Resolutions

then the effect of feedback depends on whether a goal is subsequently set in response to it (Latham, Mitchell, & Dossett, 1978; Nemeroff & Cosentino, 1979; Locke, 1980; Locke, Cartledge, & Koeppel, 1968; Locke & Latham, 1990).4 In such cases, goals mediate feedback effects. If a specific goal has been set, task knowledge aside, feedback is a moderator of goal effects. In a laboratory experiment, Erez (1977) showed the moderating effect of feedback on goal setting by giving half of her participants feedback on their scores in the first work period before setting goals for the second period of work. The other participants set goals in the absence of performance feedback. In the second work period, there was a significant goal–performance relationship only for the participants who had been given performance feedback relevant to the goal. Ironically, Komaki, Warwick, & Scott (1978), behaviorists, showed that when feedback regarding safety behaviors was posted on a chart along with a line showing the goal, safety performance increased dramatically. However, when this chart was removed, performance dropped to the baseline level even though the employees were still pursuing their safety goal. In short, feedback was once again shown to be a moderator of the goal–­ performance relationship. Feedback following the setting of a goal allows individuals to know whether to increase their effort and/or to modify their strategy for goal attainment. Ilies and Judge (2005) discovered that affect, namely, the emotions a person experiences, mediates the effect of feedback on the goal–­performance relationship. Goals are lowered when the feedback is perceived by an individual as negative; when the feedback is perceived as positive, the goals are increased.

Assigned Versus Participatively Set Goals The necessity for employee participation in decision-making to gain goal commitment, including the choice of a goal, was considered to be a moral imperative in the 1960s–1980s (Locke, Schweiger, & Latham, 1986; Sashkin, 1984). This was arguably due to participation in decisionmaking being embedded in democracy, and thus being viewed as an antidote to fascism. Goal setting theory is silent as to the optimal method for setting a goal. However, consistent with the inductive method, a series of 17 experiments (e.g., Latham & Marshall, 1982; Latham & Saari, 1979; Latham & Steele, 1983) showed that when an assigned and a participatively set

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goal are yoked for difficulty level, the two goals do not differ significantly in their causal effect on performance relative to a ‘do-your-best’ placebo condition. Moreover, regardless of whether the goal is assigned or set participatively, the higher goal, consistent with the theory, leads to higher performance (Latham & Yukl, 1975b; Latham, Steele, & Saari, 1982). Erez (1986), however, reported that in her experiments participatively set goals consistently led to higher performance than assigned goals. Working together, she and Latham conducted a series of experiments, with Locke acting as a mediator (Latham, Erez, & Locke, 1988), to discover the reason for these contradictory findings. They found that an assigned goal was shown to be as effective as a participatively set goal in increasing performance when it is accompanied by a rationale or logical explanation as was done in Latham’s experiments. A curt or tersely worded assigned goal, as was done in Erez’s experiments, is not effective for improving performance. Another issue involving the importance of participation concerned whether the benefit, if any, is motivational or informational. Latham, Winters and Locke (1994) experimentally separated the cognitive from the motivational effects of participation by manipulating participation in the goal setting process from that of developing strategies for performing the task. A mediation analysis revealed that it is not goal commitment, but rather the quality of the strategies that are used that is responsible for the high performance of the people in the participative goal condition. In short, researchers had been on the wrong track. The benefit of participation in the goal setting process is primarily cognitive rather than motivational. However, Scully, Kirkpatrick, and Locke (1995) found that the cognitive benefit of participation in decision-making occurs only when the subordinate has knowledge the supervisor lacks.

Self-Set Goals Self-set goals and performance feedback in relation to those goals form the core of self-regulation (Day & Unsworth, 2013; Sonnentag, 2002; Vancouver & Day, 2005). Frayne and Latham (1987) were among the first to apply this technique in an organizational setting. The dependent variable was the job attendance of unionized state government employees. Training in self-regulation involved eight one-hour weekly group sessions followed by eight weekly one-on-one individual sessions. In addition to goal setting and feedback, the treatment package included each employee writing a behavioral contract that self-identified

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rewards and punishers to be self-administered for pursuing/not pursuing the attendance goal. The assumption underlying a treatment package, Azrin (1977, p. 144) has argued, is to ‘include as many component procedures as seem necessary to obtain ideally, a total treatment success’. Job attendance improved immediately relative to the control group. Nine months later, the employees who had been randomly assigned to the control group received the same training in the self-regulation of job attendance. Their attendance increased to that of the original trained group (Latham & Frayne, 1989). Subsequent programmatic research on changing dysfunctional to functional self-talk through verbal self-guidance conducted over days/ weeks has been shown to be highly effective in enabling people to attain their goal of becoming meaningfully re-employed (Latham & Budworth, 2006; Millman & Latham, 2001; Yanar, Budworth, & Latham, 2009). For the past 10 years, Travers (2013) has been conducting a self-regulation course for university students. Each student sets three specific, challenging behavioral goals (e.g., time management, health and fitness, and stress management), records them in a diary, and specifies the action steps for goal attainment. The course concludes with the student writing a 3,500-word report on the affect experienced and the behaviors involved in goal pursuit. The results, albeit anecdotal, have been extremely favorable, even life changing, for many students. The critical variables in the self-regulation of learning have been identified through a meta-analysis (Sitzman & Ely, 2011). The difficulty level of the goal, and the goal theory mediators of effort and persistence, as well as self-efficacy, have the strongest effects on performance after controlling for an individual’s cognitive ability and pretraining knowledge. Travers’ (2013) program, described above, suggests that extensive cognitive processing in tying one’s goals to one’s values also may be a critical factor in bringing about fundamental life changes. Although most studies on self-regulation are conducted in field settings, Gino, Schweitzer, Mead, and Ariely (2011) conducted four laboratory experiments, each lasting only a few minutes. They hypothesized that self-regulation is a finite resource and that the setting of a goal and then monitoring one’s performance depletes that resource and hence leads to an inability to refrain from unethical behavior, namely, overstating one’s actual performance (e.g., on a problem-solving task). Their hypothesis was supported for individuals who scored low on moral identity. However, Inzlicht, Schmeichel and Macrae (2014) have criticized the self-regulation depletion hypothesis on the grounds

that changes in motivation simply reflect changes in goal priorities. The motivational-priorities view point suggests that self-regulation can become resilient against fatigue by an individual focusing on the personal benefits of the self-control goal and, in addition, providing a meaningful rationale for pursuing it (Inzlicht, Legault, & Teper, 2014). If Gino et al.’s finding generalized to other dependent variables as well as one’s daily pursuits in the workplace, individuals would constantly act impulsively and be unable to complete a day’s work. This is clearly not the case.5 Building on the Gino et  al. (2011) experiments, Welsh and Ordoñez (2014a) examined the effect of consecutive high goals on alleged self-regulation depletion. The latter was defined, once again, as overstating one’s performance on laboratory tasks. Once again, consistent with goal setting theory, the higher the performance goal, the higher the person’s actual performance. Only a marginally significant correlation between depletion and the over-statement of performance was found across all traits and goal structures (r =.15). Nevertheless, the authors concluded that the consecutive setting of high performance goals diminishes self-regulatory capacity which in turn leads to unethical behavior, namely exaggerating one’s performance. The authors also concluded that ‘it may be time to consider replacing goalsetting theory’s 90th percentile framework with a more nuanced approach that considers ethical, self-regulatory, and temporal factors when setting a goal’ (p. 87). They advanced this conclusion despite the fact that in the vast majority of the field experiments in this domain, the goals that were set were difficult yet attainable. Hence the acronym, emphasized by most, if not all, goal setting practitioners, SMART, that is goals should be specific, measurable, attainable, relevant with a timeframe (Mealiea & Latham, 1996). The recommendation for a goal difficulty level of 90% was advocated by Locke and Latham (1990), Appendix C, entitled, ‘Guidelines For Conducting Successful Goal Setting Studies in Laboratory Settings’. There was never an implication that this level of goal difficulty should be used outside the laboratory. This is likely a key reason why unethical behavior involving goal setting in a field setting is by far the exception rather than the rule.6 Moreover, there is a fundamental question that has yet to be addressed. If a goal in a 10-minute study causes cognitive depletion, why is it manifested only in the exaggeration of one’s performance? Why is there no lapse in the individual’s actual performance? The latter would appear to be a logical prediction. Yet there is no evidence that this occurs. Kerr and LePelley (2013) described the use of ‘stretch goals’ at GE to motivate innovation.

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Stretch goals are frequently impossible to attain. But no penalty is contingent on failure to fully attain a stretch goal. Whatever their magnitude, the specific quantities of the goals that are set invariably turn out to be less important than the conversations, energy, and activities they stimulate The immediate purpose of stretch goals in GE was to try to achieve the designated targets (without punishing failure), but the broader purpose was to get employees to conceive of their jobs and perform their tasks in fundamentally different and innovative ways. The message that GE was trying to convey was that most people typically access only a small portion of their creative energy – that most people have a huge capacity to do things quicker, better, and cheaper. (Kerr & Landauer, 2004, p.136)

Longitudinal Studies Welsh and Ordoñez have (2014a) asserted that: ‘One of the limitations of the goal setting literature is that the consecutive use of goals, which is common in many organizations, has been overlooked by research using a single-trial, single goal paradigm’ (p. 11). The voluminous field experiments cited thus far in this chapter, not to mention those reviewed elsewhere (Latham & Kinne, 1974; Latham & Locke, 2007; Latham & Yukl, 1975b; Locke & Latham, 1990, 2002, 2013b) should disabuse even the harshest critics of goal setting theory of this erroneous belief. Pritchard et  al.’s (2013) PROMES model, consisting primarily of goal setting and feedback in organizational settings shows positive increases in performance over many years. Howard (2013) found that self-set job promotion goals by managers at AT&T predicted actual job promotion over a 25-year period.

COMPETING THEORETICAL FRAMEWORKS Achievement Theory Both Atkinson (1958) and his mentor, McClelland (1961), argued that task difficulty, measured as the probability of one’s success, is related to performance in a curvilinear fashion, an inverted U. This assertion is in contradiction to goal setting theory which states that given the existence of the four moderators (i.e., ability, commitment, feedback, resources), there is a linear relationship between

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the difficulty level of the goal and an individual’s or team’s job performance. Atkinson’s theory of task motivation asserts that motivation is maximized on tasks that are moderately difficult, that is, when the probability of success (PS) is approximately .50. This is because the theory further states that the incentive value (I) of success is inversely proportional to task difficulty, and that task motivation is a product of the two. Thus at .50, the probability of success, the incentive value, is 1 – .50 = .50. The product is .25 higher than any other product if the probability of success and the incentive value is inversely related (e.g., the product of .75 and .25 is .19). This means that performance is highest when PS = .50. Locke and Shaw (1984) were unable to obtain support for this theory. The subjective probability of success and the incentive value (the rated valence of success in winning a monetary prize) correlated .16 rather than –1.0. Self-set goal level, incentive value, and commitment to winning the prize correlated positively with performance, but the probability of success did not correlate with performance. The effects of goals and incentive value on performance were mediated by goal commitment. Thus there are at least three problems with Atkinson’s theory. First, the probability of success and incentive value do not appear to be inversely related. Certainly they are not r = –1.0. Second, too many motivational variables (e.g., goal commitment) are not taken into account. This may explain the mixed support the theory has received (e.g., Locke & Shaw, 1984). Nor does it appear that the probability of success and incentive value should be multiplied together rather than being analyzed independently. Third, a task in itself is not a performance goal. A task is a piece of work to be accomplished. A performance goal refers to a desired performance level on a task. Thus a task and a goal are separate variables. With motivation held constant, task difficulty is typically inversely related to performance. But, this is an oversimplification because difficulty is a concept of relationship. Experienced difficulty is a combination of task characteristics as well as one’s ability. What is difficult for one person may be easy for another. This led to another controversy, namely, the relationship between probability of success and performance.

Expectancy Theory Vroom’s (1964) expectancy theory is a theory regarding choices. As is the case with Atkinson’s, it is a multiplicative theory. An employee’s

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performance level is based on an individual’s expectancy that effort (E) will lead to performance × valence (V), that is, the value of success or a reward × instrumentality (I) to the individual, and the individual’s perceived relationship between the performance outcome and a reward. Expectancy theory states that the higher the E × V × I product, the higher an individual’s performance. Holding valence and instrumentality constant, expectancy theory predicts a positive, linear relationship between expectancy and task performance. Consistent with the theory, most studies have indeed found a positive association between expectancy and performance. It is this result that brought expectancy theory into conflict with goal setting theory. Goal setting theory states that the higher the goal (given the four moderators), the higher an individual’s or team’s performance – despite the fact that the higher the goal the lower the probability of success. This conflict between the two theories was readily resolved by holding goal level constant. Thus within any given goal group, the higher the expectancy the higher the performance. When expectancy crosses goal levels, the referent for the expectancy rating is different between goal groups. Thus, there is a confound (see Locke & Latham, 1990, pp. 64–70). This is because the various expectancy ratings have different referents. This confound does not exist in typical VIE experiments because the goal is often general rather than specific. Aside from holding goal level constant, this controversy was further resolved by measuring an individual’s expectancy in a different way, using a different concept, namely, self-efficacy (Bandura, 1997). Self-efficacy strength is one’s belief that one can attain a specific outcome, not based solely on effort, but on the ability to orchestrate one’s abilities, resourcefulness, and adaptability. Selfefficacy is measured not by a one-item measure, but rather by having people rate their confidence in being able to attain each of a number of performance outcome levels. For school grades, the items would be: How confident are you that you can attain a grade of D or higher in this course; then C or higher; then B or higher; then A or higher. Self-efficacy strength is the sum of all the confidence ratings. This means that this measure is not tied to only one outcome level, but to all outcome levels combined. This prevents the type of confounding involved if expectancy alone is rated without regard to a common outcome. Self-efficacy is positively related to task performance, even when controlling for an individual’s ability or past performance. Self-efficacy affects performance through its effects on self-set goals and goal commitment. Assigned goals affect

self-efficacy which in turn affects performance directly, or through the self-set goals that are set in response to assigned goals (Locke & Latham, 1990). Self-efficacy also has been found to have a reciprocal effect on strategy (Seijts & Latham, 2001).

Control Theory Arguably the most puzzling controversy around goal setting theory involves its predictions versus those of control theory (Carver & Scheier, 1981). The two theories are similar in their emphasis on the beneficial effects on performance for an individual or team to set goals. The critical flaw in control theory is that it ignores the fact that performance increases because goal setting is first and foremost a goal– performance discrepancy production process. Control theory, for inexplicable reasons, is based on a machine model. It adopted a machine metaphor, the thermostat, to explain the beneficial goal–performance relationship. Inanimate objects, however, do not have goals. Nevertheless, just as a thermostat stops when a predetermined temperature is reached, so does an individual, the theory states, when a goal is attained. The theory fails to recognize that ‘notable attainments bring temporary satisfaction, but people enlist new challenges as motivators for further attainment’ (Bandura, 1997, p. 130). Goal setting theory, unlike control theory, is based on a biological model. The theory states that goal pursuit is the core motivating principle of human beings. People set goals to further benefit their lives and in doing so continually create goal– performance discrepancies. The ultimate motive of human beings cannot be solely discrepancy reduction because this can be attained merely by abandoning all goals. Discrepancy reduction is a correlate of goal pursuit, not a motive. As Bandura (1989, p. 38) noted, the setting of a specific, high goal typically creates rather than reduces performance discrepancies: ‘Self motivation thus involves a dual cyclic process of disequilibrating discrepancy production followed by equilibratory reduction.’ Similarly, Ilies and Judge (2005, p. 463) concluded from their research that ‘the present results suggest that, in general, after meeting or exceeding their goals, individuals do not maintain their goal-level and decrease effort in order to minimize the positive discrepancy between performance and goals, but rather set higher goals that motivate them to increase performance’. Their findings are supportive of goal setting theory which states that

Goal Setting Theory: Controversies and Resolutions

the beneficial effect of setting a specific, high goal is that it involves discrepancy production between one’s performance and one’s goal. This leads to the choice to exert effort and persist until the goal is attained. To put the issue succinctly, life is a process of goal-directed action.

GOAL LIMITATIONS Phillips (1987) argued that any theoretical position can be supported by citing selective evidence. What counts he said, is how well a theory can stand up to vigorous assault, namely, to the totality of the empirical evidence. It would appear that goal setting theory has done rather well on this criterion. That is not to say that as a technique, goal setting has no limitations. The limitations have been detailed elsewhere (Latham & Locke, 2006; Locke & Latham, 1984, 2013a). Among them are the following: There is a context to virtually all choices. The choice to focus on goal A may preclude focusing on goals B and C. A focus on a specific challenging goal on quality may result in an individual doing so at the expense of quantity. A correlational study revealed that a sole focus on a bottom-line measure without regard to the behaviors exhibited in goal attainment correlates with unethical behavior (Greenbaum, Mawritz, & Eissa, 2012). Using factor analysis of a survey conducted in the logging industry, Ronan, Latham and Kinne (1973) found that supervisory presence, goal setting, and an objective measure of a group’s productivity (cords per employee hour) loaded on the same factor. Supervisory presence without goal setting loaded on a second factor that did not include productivity. But, goal setting, without the presence of supervision, loaded on the same factor as employee injuries. In a field experiment involving word-processing operators, goal attainment that was not accompanied by an employee’s perceived growth/ competence led to job dissatisfaction (Latham & Yukl, 1976). Seijts and Latham (2000) developed a job simulation based on an alliance of Canadian provincial telephone companies. They found that in a social dilemma, people in larger groups (i.e., n = 7) were more likely to pursue their goal at the expense of their team’s goal than were those in smaller groups (i.e., n = 3). Hrabluik, Latham, and McCarthy (2012) investigated whether there is a possible dark side of goal setting, namely, perfectionism. Perfectionists are predisposed to setting goals for themselves

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that are excessively high and making their selfworth contingent upon attaining them. The results showed that these self-set goals correlated positively with maximum performance (Cronbach, 1970), namely, promotional examination scores of police officers, as was the second predictor, contingent self-worth on goal attainment. The mediating variable was perceived effort. In their second study involving police officers, goal difficulty level also predicted typical performance, namely, performance appraisals. However, perceived high goals, and self-worth contingent upon their attainment, also had a positive relationship with emotional exhaustion. A large survey of employees across myriad industries revealed that supervisors, who perceived the goals that they were assigned as excessively high, manifested their stress by abusing their subordinates. This study shows the danger of ignoring a theory’s moderators, in this instance, a supervisor’s ability, and the availability of the resources needed to attain the goals (Mawritz, Folger, & Latham, 2014). Despite the voluminous empirical studies showing a positive goal–performance relationship relative to those that have shown the contextual limitations of setting goals, Ordoñez and her colleagues, as noted earlier, have alleged that setting a specific high performance goal leads to unethical behavior, as defined by overstating one’s performance. She consistently cites Barsky (2008) to buttress her charge, but neither he nor she (e.g., Ordoñez, Schweitzer, Galinsky, & Bazerman, 2009) pointed out that Barsky’s doctoral dissertation, on which his 2009 article is based, reveals that the data he collected did not support the contention that a high performance goal results in unethical behavior. In a laboratory experiment, Ordoñez and her colleagues found that only 1.5%, 4.7%, and 9% respectively of the college students in the doyour-best, goal setting alone, or goal setting plus monetary incentive conditions lied about goal attainment if they were close to attaining their goal (Schweitzer, Ordoñez, & Douma, 2004). This finding is consistent with the observations by the present authors that some students who receive an 88 or 89 on an examination, insist they should receive an A, as well as golfers whose ball lands near the hole insist on a ‘gimme’. But, the mean ‘overstatement score’ by the students in her experiment was only 0.11. This is a very low score given that the means in these three conditions ranged from 5.46 to 6.17. Most importantly, not a single college student in any one of her three conditions took money undeservedly for goal attainment. This finding is noteworthy because the experiment was designed so that a student could do so without seemingly getting caught.

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Nevertheless, Ordoñez et al. (2009) later wrote an essay based primarily upon events reported in the news media where she and her co-authors made causal inferences to conclude that ‘goal setting should not be an over-the-counter salve for boosting performance’ or a ‘prescription-strength medication’ (p. 6). Despite her choice of satire rather than the scientific method to present her conclusions, it is certainly true that any technique, whether in medicine or in psychology, is subject to misuse. In the taxonomy of motivation (Latham, 2012; Locke & Henne, 1986), values precede goals. The goals people set and how they go about pursuing them reflect their values (Latham & Locke, 2009). Similarily, in organizational settings, goals do not operate in a vacuum. Rather, they interact with an individual’s values and an organization’s culture. As Kahneman (2011, p. 220) stressed, behavior ‘in the real world is determined by many factors that are specific to the particular situation’. Similarily Johns (2006) has emphasized the importance of context on behavior. In their review of the literature on (un)ethical behavior in organizations, Trevino, Nieuwenboer and Kish-Gephart (2014) stressed the ‘powerful influence of peers, leaders, significant others, rules, laws, and codes, all of which guide employees’ ethical decision making and behavior’ (p. 637). At the macro level, an ethical culture moderates the relationship between cognitive moral development and ethical behavior (Trevino, 1986). If an organization’s culture encourages leaders and employees to behave unethically, a starting point for correcting this situation would appear to be here rather than focusing solely on the goal setting process because an unethical culture will affect adherence to virtually all of an organization’s processes and procedures. That being said, Ganegoda, Latham, and Folger (2016) assessed fairness in a competitive negotiation context, in which all participants were asked to attain a specific high performance goal. A negotiation task was used because it tempts selfaggrandizing efforts to capture the ‘lion’s share’ of available outcomes. The results of this experiment showed that the pairing of a behavioral goal for fairness with a challenging performance goal minimized this tendency. So, the question remains as to why events reported in the news media would lead to wild claims about the dangers of goal setting? A plausible answer lies in the decision-making literature, namely, availability bias. In his review of the decision-making literature, Kahneman (2011) reported that estimates of an event, no matter how rare, are warped by media coverage. Unusual events attract disproportionate attention. Consequently, they are perceived as less unusual

than they are in fact. Expectations about the frequency of events are distorted by the prevalence and emotional intensity of the messages to which people, such as Ordoñez and her colleagues, are exposed. Moreover, Kahneman found that behavioral scientists, although sophisticated in statistical methods, make judgments about risk no better than the general public. The amount of their concern is not adequately sensitive to the probability of harm. This is because people tend to focus on the numerator without thinking about the denominator (e.g., approximately 1,000 studies on goal setting). ‘The combination of probability neglect with the social mechanism of availability cascades inevitably leads to gross exaggeration on minor threats, sometimes with important consequences’ (Kahneman, 2011, p. 114). In this instance, the consequence could lead to the abandonment of goal setting, a technique that increases both job performance and satisfaction. This is because ‘people can maintain an unshakable faith in any proposition, however absurd, when they are sustained by a community of like minded believers’ (Kahneman, 2011, p. 217). Thus ‘this seems to be a common pattern: people who have information about an individual case rarely feel the need to know about the statistics of the class to which the case belongs’ (Kahneman, 2011, p. 249).

APPLICATIONS OF GOAL SETTING THEORY Applications of goal setting in field settings are described in detail in Locke and Latham (2013b). Among the myriad topics are teams, leadership, sports, and education.

Teams In their review of the literature, Kramer, Thayer, and Salas (2013) found that goal setting theory’s specification of causal relationships, mediators, and moderators are as applicable for teams as they are for an individual. However, they also reported a plethora of additional factors that come into play when goals are set by or for teams. One factor is that an individual’s and the team’s goals may be in concert, or they may be in conflict (Seijts & Latham, 2000). Second, team members work interdependently. Thus there should be information exchange. Information sharing is beneficial to task performance when a team’s members know what they are doing and useful information is

Goal Setting Theory: Controversies and Resolutions

acknowledged and acted upon. However, team members sometimes withhold information. Worse, they may demotivate each other through undermining collective efficacy. A variety of team processes may mediate or moderate a team’s effectiveness. Coordination is especially critical, as are a team’s norms for cooperation. A team’s leader needs to ensure that a team’s goals are specific, and that an individual’s goals are congruent with those of the team. Learning goals are beneficial for teams, especially if combined with feedback in the form of team debriefings. Active participation in the goal setting process by team members not only helps members exchange information, it may help build a team’s collective efficacy and commitment to the goals a team sets. Moreover, employee participation in setting a team’s goals can lead to higher goals being set that in turn leads to higher team performance (Latham & Yukl, 1975b).

Leadership Goal setting is a core variable in most theories of leadership (Piccolo & Buengeler, 2013). This is because the very process of leadership involves assigned, participatively set, as well as self-set goals. Lou Gerstner, a former CEO of IBM, had a specific, challenging self-set goal to improve his personal productivity by 30% annually (Saari, 2013). A leader’s vision (e.g., Win, Execute, and Team; a global bank with a local touch) serves as a superordinate goal. Specific, challenging goals for teams and individual employees typically mediate vision effects (Kirkpatrick & Locke, 1996).

Sports In his review of the literature on the application of goal setting theory in sports, Williams (2013) found that the causal relationships, as well as the theory’s moderators and mediators are the same in this domain as they are in industrial-organizational settings. However, Williams differentiated three types of goals, namely, process, performance, and outcome goals. Process goals are highly similar to learning goals (Winters & Latham, 1996) in that the focus of an individual or team is on discovering and mastering techniques and strategies for enhancing performance. Performance goals are set in terms of personal achievement (e.g., time to complete a race), and are similar to performance goals set in the workplace. Outcome goals in sports refer to the ‘endpoint’ of activities such as the results of a competition (e.g., winning versus losing, finishing first place in a race, making the varsity team).

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As is the case in the workplace (Hrabluik et al., 2012), Williams found that perfectionism, a trait that predisposes people to set exceedingly high goals, leads to high performance. And again, as is the case in the workplace, research is needed to examine how these three types of goals can be effectively combined. Williams reported that the extended use of process goals increases the performance of beginners. But, the debate continues on whether process goals should be sequential or whether multiple process goals should be set. And at what point should performance and outcome goals be set? The performance discrepancy reduction process predicted by control theory has not been supported in this domain. That is, athletes are not content with their performance once their goal has been attained. Rather, Williams (2013) reported that athletes typically set goals to create a goal–performance discrepancy. A distal goal, along with proximal goals, is set that is higher than what the individual or team attained previously. Including proximal goals with a distal goal on a task that is difficult to perform increases performance (Latham & Seijts, 1999).

Education Morisano (2013) has provided a review of the literature on the application of goal setting theory in the academic arena. In the Morisano, Hirsh, Peterson, Pihl, and Shore (2010) field experiment, sophomore to senior students who had grade point averages B– or lower were randomly assigned to a goal intervention or a control condition. Those in the former group completed a single-session 2.5 hour on-line narrative personal goal-setting program. Those in the control group completed a 2.5 hour placebo on-line intervention. Among the steps required in the goal intervention were: describing and ranking 7–8 personal goals, the outcomes expected from attaining them, strategies for goal attainment, and assessing their commitment to goal pursuit. The results showed that their GPA increased 30% at the end of the semester. In addition, these students reported a significant reduction in their negative affect, a reduction they attributed to the goal-setting intervention.

PRIMING GOALS IN THE SUBCONSCIOUS In the 1990s, research on the priming of goals in the subconscious, and their subsequent effect on behavior proliferated in the social psychology

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literature (e.g., Bargh 1994; Bargh & Chartrand, 1999; Dijksterhuis & van Knippenberg, 1998). Based on this research, Bargh (1994; Bargh, Chen, & Burrows, 1996; Bargh, Gollwitzer, LeeChai, Barndollar, & Trotschel, 2001) proposed an automaticity model that states a goal is a mental representation that can be activated by subtle situational cues or naturally occurring environmental stimuli through priming. An individual is neither aware of, nor able to control the influence of a prime on his or her behavior. Finally, this model states that a conscious and a subconscious goal affect behavior through the same processing stages, and predict and produce the same outcomes. Nevertheless, this model, and the literature on which it is based, has been largely ignored by I-O psychologists. Among the reasons for doing so is the small time gap between the manipulation of the independent variable and the measurement of the dependent variable – typically only a few minutes. Such a short-lived effect lacks practical significance for influencing organizational behavior, especially for increasing job performance (Latham, Stajkovic, & Locke, 2010). In the present century, there have been at least three literature reviews that have called this conclusion into question (Friedman, 2013; Latham et al., 2010; Latham & Locke, 2012). The genesis of these reviews is a laboratory experiment by Stajkovic, Locke, and Blair (2006) that primed a goal for achievement. Their experiment, consistent with Bargh’s methodology, required participants to make words from scrambled letters. Those in the priming group were given achievement-related words within 60% of the word sets (e.g., win) while those in the control group were given only neutral words (e.g., sun). In the subsequent 2-minute brainstorming task, those in the primed condition generated significantly more ideas than did the participants in the control group.7 The priming effects were independent of significant, conscious goal effects, though priming had no effect for the consciously set easy goal. This finding led to a series of field experiments. Call center employees in the experimental group were primed with a performance goal through the use of a photograph of an individual winning a race. They subsequently raised significantly more money from donors than those in the control condition during their work shift (Shantz & Latham, 2009). This result was replicated in two additional call centers (Shantz & Latham, 2011). The length of time between the manipulation of the independent variable and the measurement of the dependent variable was 3–4 hours rather than 3–4 minutes. Latham and Piccolo (2012), working in a fourth call center, assessed the effect of two primed goals,

one context-specific (i.e., a photograph of call center employees in the workplace) and the other general (i.e., the photograph of the racer) over an even longer time period, a four-day work-week. In addition, steps were taken to eliminate the rival hypotheses that the results could be attributed to experimenter bias or to demand effects. The results were consistent with goal setting theory. The amount of money pledged by donors was highest in the context-specific condition. In terms of practical significance, the amount of money raised was 16% more in the context-specific condition than in the general condition, and 85% more than that collected in the control group. Employees in the general prime condition raised 60% more money than those in the control group. As in the Shantz and Latham field experiments, none of the employees were aware of the effect of either prime on their job performance. In fact, none of them noticed the primes even though both were presented supraliminally – that is, they were in plain sight. In follow-up interviews, the employees stated that they were too busy attempting to raise money to pay attention to a photograph. Shantz and Latham (2009) administered the TAT (Morgan & Murray, 1935) to students and scored it using the Linguistic Inventory Word Count (Pennebaker, Chung, Ireland, Gonzales, & Booth, 2007) to determine the effect, if any, of the prime on one or more implicit motives. The stories written, subsequent to seeing the racer photograph, contained significantly more achievement-related words than the stories written regarding neutral stimuli by those who did not see this photograph. Latham and Piccolo (2012) replicated this finding with employees. Both of the primes used in that experiment resulted in stories containing a significantly greater number of achievement-related words than the stories written by employees in the control group. Those who saw the context-specific prime also wrote stories containing a significantly greater number of work-related words. It should be noted however, that the implicit need for achievement was not shown statistically to mediate the primed goal–performance relationship. In both the laboratory and the four field experiments on organizational relevant behavior (i.e., brainstorming ideas; money), people already knew how to perform the tasks before a goal had been primed. Consequently, Chen and Latham (2014) conducted an experiment to determine whether performance increases on a task that requires the acquisition of knowledge. Thus a learning goal was primed using a photograph of Rodin’s ‘The Thinker’. A 2 (primed learning goal vs control) × 2 (primed performance goal using the picture of a racer vs control) × 3 (trials) revealed a significant main effect only for the learning goal. Their

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finding supports both goal setting theory and the automaticity model. Ganegoda et al. (2016) found that a goal can be primed supraliminally in the subconscious for fairness on a negotiation task. Zdaniuk and Bobocel (2013) used a subliminal prime to successfully prime fairness in communicating a job dismissal on an in-basket test. Welsh and Ordoñez (2014b) found that a goal can also be primed in the subconscious for inducing honest behavior. The above studies reveal that priming is a real phenomenon, though one that is far from understood. Future research should look for both moderators and mediators of priming effects, such as need for achievement and affective arousal, that explain the primed goal–performance relationship, as well as job characteristics (George, 2009) that may be determinants of goal activation. This is necessary since there has been considerable controversy regarding the failure of some priming studies in social psychology to be replicated. Another major problem concerns the logical relationship between a given type of prime and the dependent variable that is measured. For example, totally different types of primes have been used to affect performance, but we don’t know the common element. Finally, a few priming researchers have drawn questionable conclusions from their narrowly focused work that the conscious mind is basically irrelevant to life. Such overgeneralization does a disservice to science. It would be more productive to examine how conscious and subconscious goals work together, as shown by the impressive research of Gollwitzer and his colleagues (e.g., Oettingen, Wittchen, & Gollwitzer, 2013).

CONCLUSION If we take as the starting point Locke’s laboratory experiments in the mid-1960’s and Latham’s field experiments in the early 1970s, the process of building and refining goal setting theory has continued for more than 50 years. We know of no theory-building efforts that have endured as long in organizational psychology as goal setting theory. We attribute the theory’s longevity to the method through which it was developed, namely, using induction to answer five questions: (1) Does goal setting work? If yes, what are the causal mechanisms? What are the mediators? (2) Do the findings generalize to different populations of employees, different tasks, different settings, and different countries? (3) What about lateral integration? How are goals related to feedback, participation in

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decision-making, self-efficacy, and satisfaction? (4) What about vertical integration? How are goals related to individual differences such as intelligence, personality, and values? (5) Are there moderators where their presence strengthens and absence weakens the goal–performance relationship? It is virtually an axiom in the behavioral sciences that theory building should proceed deductively. That is, the starting point is the formulation of a theory. Second, experimental predictions or hypotheses are deduced. Third, the hypotheses are tested. Fourth, if the hypotheses are not supported, the process typically ends. If the hypotheses are supported, the process may also end because the theory has already been formulated. This shortcuts the theory building process. Many journal editors today reject inductive experiments. Researchers understand how this ‘system’ works. They find or develop theories after the fact to deduce hypotheses, even though their research findings were not originally based on a theory. Recently it has been suggested that authors submit their hypotheses to a central system before doing their experiments so that they cannot make up hypotheses after the fact. But the prevention of HARKing is treating the symptom not the problem.8 Under an inductive approach, there is no reason why a hypothesis has to be based on a theory versus previous findings, or why a research study cannot begin with a question. The problem is editors requiring a wrong approach to scientific discovery (Hambrick, 2007; Locke, 2007). Science progresses fundamentally from induction rather than deduction. Deduction presupposes induction. In the famous Socrates syllogism, the premise that all men are mortal is an inductive discovery. Deduction only makes explicit what is already implicit in a premise. Many, if not most, scientific discoveries occur by systematically testing ideas based on one’s existing context of knowledge. If a cause–effect relationship is discovered among variables, one replicates the study, ideally with variations in the procedures to ensure that the original finding was not a chance occurrence or idiosyncratic to a unique set of ungeneralizable or disqualifying circumstances. Then researchers build on that finding by systematically varying the parameters. Again, causal mechanisms are sought. More experiments are subsequently conducted to systemically rule out rival hypotheses and confounds. Moderators are investigated to determine the limits of generality to other populations and dependent variables. Contradictions are identified and resolved. In short, theory building is a gradual process of programmatic research that cumulates into an integrated whole. Goal setting theory, developed inductively, can now be used to make deductions (Locke & Latham, 2013a).

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However, the theory also remains open to discoveries and thus refinements based on induction. Ignoring the necessity for systematic research as a basis for developing a theory has led to another problem. Many researchers are tempted to overgeneralize their results before they have identified moderators and mediators. When these claims are promulgated to the public, the limitations are soon exposed, and the public becomes cynical about psychological research. Hopefully the inductive method that led to the development of goal setting theory will inspire researchers to abandon or at least de-emphasize deduction as a basis for building new theories.

 6  The requirement to attain 90% on a test in order to earn an A is the norm in most schools in North America. Some students cheat in order to attain this grade, but this is the exception, dependent on a student’s values. Consequently, few people have urged the lowering of this goal for attaining an A.  7  In their 2 × 2 factorial design, the primed goal had a positive main effect in the prime/high conscious goal and prime/do best condition. There was no effect in the prime/conscious easy goal condition because participants stopped working as soon as the easy goal was attained.  8  HARKing is the unethical practice of developing hypotheses after the results are known.

Notes

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Porter, R., & Latham, G. (2013). The effect of employee learning goals and goal commitment on departmental performance. Journal of Leadership and Organizational Studies, 20, 62–68. Pritchard, R. D., Young, B. L., Koenig, N., Schmerling, D., & Dixon, N. W. (2013). Long term effects of goal setting on performance with the productivity measurement and enhancement system (ProMES). In E. A. Locke and G. P. Latham (Eds.), New developments in goal setting and task performance (pp. 233–245). New York, NY: Routledge. Rodgers, R., & Hunter, J. E. (1991). Impact of management by objectives on organizational productivity. Journal of Applied Psychology, 76, 322–336. Ronan, W. W., Latham, G. P., & Kinne, S. B. (1973). The effects of goal setting and supervision on worker behavior in an industrial situation. Journal of Applied Psychology, 58, 302–307. Ryan, T. A. (1970). Intentional behavior. New York: Ronald Press. Ryan, T. A., & Smith, P. C. (1954). Principles of industrial psychology (pp. 353–427). New York: Ronald Press. Saari, L. M. (2013). Goal setting and organizational transformation. In E. A. Locke & G. P. Latham (Eds.), New developments in goal setting and task performance (pp. 233–245). New York, NY: Routledge. Sashkin, M. (1984). Participative management is an ethical imperative. Organization Dynamics, 12, 4–22. Schneider, B. (1985). Organizational behavior. Annual Review of Psychology, 36, 573–611. Schweitzer, M., Ordóñez, L. D., & Douma, B. (2004). The dark side of goal setting: The role of goals in motivating unethical behavior. The Academy of Management Journal, 47, 422–432. Scully, J. A., Kirkpatrick, S. A., & Locke, E. A. (1995). Locus of knowledge as a determinant of the effects of participation on performance, affect and perceptions. Organizational Behavior and Human Decision Processes, 61, 276–288. Seijts, G. H., & Latham, G. P. (2000). The effects of goal setting and group size on performance in a social dilemma. Canadian Journal of Behavioural Science, 32, 104–116. Seijts, G. H., & Latham, G. P. (2001). The effect of learning, outcome, and proximal goals on a moderately complex task. Journal of Organizational Behavior, 22, 291–307. Seijts, G. H., & Latham, G. P. (2005). Learning versus performance goals: When should each be used? Academy of Management Executive, 19, 124–131. Seijts, G. H., & Latham, G. P. (2011). The effect of commitment to a learning goal, self-efficacy, and the interaction between learning goal difficulty and commitment on performance in a business simulation. Human Performance, 24, 189–204.

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Winters, D., & Latham, G. P. (1996). The effect of learning versus outcome goals on a simple versus a complex task. Group and Organization Management, 21, 236–250. Wong, A., Tjosvold, D., & Yu, Z. (2005). Organizational partnerships in China: Self-interest, goal interdependence, and opportunism. Journal of Applied Psychology, 90, 782–791. Wood, R. E., Whelan, J., Sojo, V., & Wong, M. (2013). Goals, goal orientations, strategies, and performances. In E. A. Locke & G. P. Latham (Eds.), New developments in goal setting and task performance (pp. 90–114). New York, NY: Routledge. Wyatt, S., Frost, L., & Stock, F. G. L. (1934). Incentives in repetitive work. Industrial Health Research Board (Great Britain), Report No. 69. Yanar, B., Budworth, M. H., & Latham, G. P. (2009). The effect of verbal self-guidance training for overcoming employment barriers: A study of Turkish women. Applied Psychology: An International Review, 58, 586–601. Yukl, G. A., & Latham, G. P. (1978). Interrelationships among employee participation, individual differences, goal difficulty, goal acceptance, goal instrumentality and performance. Personnel Psychology, 31, 305–324. Zdaniuk, A. & Bobocel, R. A. (2013). The automatic activation of (un)fairness behavior in organizations. Human Resource Management Review, 23, 254–265.

Part III

Performance Management

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8 Pay Levels and Pay Changes Jason D. Shaw

INTRODUCTION A paradox confronts industrial and organizational psychologists studying compensation and financial incentives in the workplace. On one hand, academic and practitioner circles widely discuss pay-related issues. They find, for example, strong evidence that financial incentives are positively related to individual performance (Jenkins, Mitra, Gupta, & Shaw, 1998) and retention (Dineen & Williamson, 2012; Gerhart & Rynes, 2003; Shaw & Gupta, 2007), and that financial incentives typically have stronger effects than other organizational interventions for enhancing motivation and performance (Aguinis, Joo, & Gottfredson, 2013). Also, practitioner-oriented circles frequently spark heated media debates about compensationrelated issues. Even in the United States, long a bastion of pay-for-performance, much controversy surrounds debate about whether to grant financial incentives to public school teachers and government workers (e.g., Fryer, Levitt, List, & Sadoff, 2012; Gupta & Shaw, 2014). Lively discussions about cultural applicability of merit pay have also accompanied trends toward merit pay in places such as Finland and Hong Kong, where longevity- and seniority-based provisions were long the norm (Mitra, Tenhiälä, & Shaw, 2016).

Resistance to performance-based pay persists despite many cross-culturally stable findings in the compensation literature (e.g., Du & Choi, 2010; Mattson, Torbiörn, & Hellgren, 2014). Finally, compensation levels for top management teams and executives, and pay levels for celebrities and athletes are endlessly fascinating (e.g., Trevor, Reilly, & Gerhart, 2012). On the other hand, compensation research has been on a downward trend in applied psychology (Cascio & Aguinis, 2008; Gupta & Shaw, 2014). At the 2013 Society for Industrial and Organizational Psychology conference, for example, only 3 of 1,200 sessions were devoted to compensation-related topics (Gupta & Shaw, 2014). To reinvigorate research in this area, I undertook an exhaustive review of the literature related to pay levels and pay changes in the domains of work psychology, I/O psychology, organizational behavior, and human resource management. Given the downward research trends, I cast a broad net for papers related to pay levels and pay changes and evaluated the literature over a 15-year period, from 2000 to 2014. I reviewed and evaluated the studies in terms of their conceptual and empirical antecedents and consequences. My review of the extant empirical and conceptual literature allowed me to draw some general conclusions about the

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findings and about the state of the literature. Based on these conclusions, I outline several areas of need and promising research directions. In addition to the time frame, this review has some boundary conditions. I focused on individual-level research, those areas closest to the domain of industrial, work, and organizational psychology, although some of the papers appear in journals not typically associated with applied psychology. I mostly excluded research on the team-level and organizational consequences of compensation levels and structure, such as organizational performance outcomes associated with pay structure (e.g., Shaw & Gupta, 2007; Shaw, Gupta, & Delery, 2001, 2002; Trevor et al., 2012) and executive compensation, because those studies have been reviewed extensively elsewhere in recent years (e.g., Conroy, Gupta, Shaw, & Park, 2014; Gerhart & Rynes, 2003; Gerhart, Rynes, & Fulmer, 2009; Gupta, Conroy, & Delery, 2012; Shaw, 2014). My review includes only studies in applied psychology that include information about pay levels or pay changes, and excludes studies that examine non-monetary and other forms of rewards. I also excluded studies that examined consequences of compensation attitudes and that omitted data on actual pay levels of changes partly because of recent meta-analytic activity (Williams, McDaniel, & Ford, 2007; Williams, McDaniel, & Nguyen, 2006). Finally, I ignored the growing ‘money prime’ literature that appears mostly in social (e.g., Zhou, Vohs, & Baumeister, 2009) and economic psychology (e.g., Pfeffer & DeVoe, 2009) outlets. I organized the review into two major sections – (1) the antecedents and consequences of pay levels and (2) the consequences of pay changes. There were no studies, to my knowledge, that examined antecedents of pay changes during the review window. Table 8.1 includes summary information for the empirical papers reviewed under each section. I followed the review with a conclusion section that includes directions for future research.

PAY LEVELS Antecedents The major contributions to the empirical literature on the determinants of pay levels are a series of studies that primarily use the exhaustive, publicly available National Longitudinal Surveys of Youth (NLSY79) data set from the United States (e.g., Judge & Cable, 2011; Judge & Hurst,

2008; Judge, Hurst, & Simon, 2009; Judge, Klinger, & Simon, 2010; Judge & Livingston, 2008). Perhaps the dominant theme in the literature on pay level determinants relates to the persistent pay gap between men and women. For example, in a study of government employees in the United States, gender was a stable predictor of pay levels (women earned less than men), even after controlling for a set of job-related factors that explained two-thirds of the variance in pay levels (Harris, Gilbreath, & Sunday, 2002). Perhaps more surprising – gender related to pay increases such that women earned higher raises than men. The authors provided two potential explanations for the pay increase relationships. First, women in the sample earned higher performance evaluations than men did, which might explain the relationship between gender and pay changes. The second argument and finding was structural: women more frequently occupied lower levels within the organization’s pay grades and received higher percentage pay increases as a result. The two factors – higher performance and lower pay grade positions – accounted for the relationship between gender and pay changes in their study. The applied psychology literature, however, has moved well beyond direct examinations of the gender pay gap and toward nuanced explanations of why the gap persists. In a sample of more than 2,000 managers, the gender pay gap persisted in the presence of human-capital-based control variables (Ostroff & Atwater, 2003). But, as the authors predicted, characteristics of the managers’ reference group also significantly influenced pay levels: managerial pay levels were lower when managers’ referent groups were predominantly female and when coworkers were younger. Working with predominantly female referent groups negatively affected managerial pay levels and most prominently when subordinates were primarily women. Their findings also demonstrated substantial pay level penalties when a manager’s subordinates were either younger or older than prime working age (defined as around 40 years) or when a manager’s supervisor was younger than prime working age. The issue of mentoring and gender was examined in terms of total cash compensation in Ramaswami, Dreher, Bretz, and Wiethoff (2010). Taking a signaling theory view, they argued that women in male-dominated industries benefit most in terms of pay when they have a mentor who has high signal visibility. In line with their theory and controlling for a number of correlates to pay levels (e.g., age, prior career interruption, career priority, number of children, education), women who had a senior male mentor in male-gendered

Sample(s)

Formal theory

Antecedents/Consequences

Managers (USA)

Belliveau (2012)

Judge, Klinger, & Simon (2010)

Judge, Hurst, & Simon (2009)

Participants in the Boston In-Depth Study of Management Processes in Midlife (USA) NLSY (USA)

Harris, Gilbreath, & Government employees Sunday (2002) (USA) Judge & Cable (2011) Socio-Economic Panel (Germany) and National Longitudinal Survey of Youth (NLSY) (USA) Judge & Hurst (2008) NLSY (USA)

NBA players (USA)

Barnes, Reb, & Ang (2012)

Human capital

General mental ability (GMA)

General mental ability (GMA); physical attractiveness; education; CSE

Core self-evaluations (CSE)

Self-verification; human capital Transactional stress; implicit leadership

Physical weight

Cultivation

Gender

Typical (mean) performance; performance trend; performance variability; the performance increase or decrease in new contract value Gender

Antecedents of pay levels Avey, West, & Supervisors and subordinates Relational demography Ethnic congruence Crossley (2008) in a Fortune 100 organization Barnes & Morgeson National Basketball Equity Mean performance; performance (2007) Association (NBA) variability; maximum players (USA) performance

Study

Table 8.1 Summary of empirical studies and findings

(Continued)

The trajectory between human capital and pay level was steeper for high-GMA individuals. High-GMA individuals had steeper earnings growth and human capital accumulations over time. Education and job complexity, but not training, mediated the relationship between time and pay levels.

Typical performance (positive), variability (negative), and maximum performance (positive) each related to pay levels (total contract value), but only typical performance and performance variability remained significant in an omnibus equation. Performance mean and trend were positively related to the change in contract value, but variability was not related. The relationship between mean performance and pay change disappeared after controlling for future performance. But the relationship between performance trend and pay change persisted in the presence of future performance control. Women were paid less than men when allocators could provide a social account for the decision; men were paid less than women when no social account opportunity was available. The differences were stronger among allocators who had more managerial experience. Gender predicted of pay levels (women earned less than men) and pay increases (women earned higher raises than men). Among German employees, weight was negatively related to annual pay level among women and curvilinear in the shape of an inverted U among men. Among American workers, weight and pay levels were negatively related among women, but positively related among men. Pay levels increased more rapidly over time among those higher in CSE. Higher educational attainment partially explained steeper pay level trajectories and fewer self-reported health problems among those high in CSE. Educational attainment mediated the relationship between GMA and pay levels; CSE partially mediated the relationship between attractiveness and pay levels.

Subordinates in same-ethnicity supervisor–subordinate dyads had higher pay levels compared to subordinates in incongruent dyads.

Key findings

Gardner, Van Dyne, & Construction workers (USA) Pierce (2004) Harris, Anseel, & Cross-industry employees Lievens (2008) (USA); Chemical industry employees (Belgium) Irving & Montes Short-term contract (2009) employees (Canada)

Compensation satisfaction; job satisfaction; turnover intentions

Mood-as-information; affective events

Met expectations

Employee satisfaction

Equity; social Organizational-based self-esteem exchange; reflection (OBSE) Equity Pay satisfaction

Job satisfaction

Job characteristics

Gender; age

Social networks

Mentoring; gender

Managers (USA)

Ostroff & Atwater (2003)

Base salary and subjective value related positively to pay satisfaction, but the economic value of other negotiable items did not. Subjective value positively related to overall job satisfaction and negatively related to turnover intention, but base salary and economic value did not relate to those general attitudes. Pay levels related positively to OBSE and supervisor-rated performance. OBSE mediated the relationship between pay levels and performance. Pay level was positively related to pay satisfaction. Upward comparisons (the perceived pay levels of a higher-paid group) were negatively related to pay satisfaction in both samples. Delivered compensation levels were positively related to satisfaction. Expected compensation levels were not significant predictors of satisfaction at conventional levels. Delivered and expected compensation did not interact to predict satisfaction.

Pay level was not significantly related to job satisfaction.

Gender role orientation was positively related to pay levels among men and negatively related to pay levels among women. Women earned less than men, in general, but ‘high potential’ women earned more than ‘high potential men’ among a sample of employees from a Fortune 500 organization. A follow-up laboratory experiment revealed that participants viewed high-potential women as valuable for achieving diversity goals which explained higher recommended pay levels. Social network dimensions related to bonus levels beyond controls for banker performance and prior bonus levels. Interpretations of interactions between network density (or sparseness) and tie strength on pay levels were unclear. Managerial pay levels were lower when managers’ referent groups were predominantly female, especially when subordinates were women. Pay levels were also lower when managers’ coworkers were younger. Women with a senior male mentor in male-gendered industries earned over $70,000 more per year than women without a senior male mentor in the same industries.

Gender role orientation Gender

Key findings

Antecedents/Consequences

Signaling

Commercial bankers

Mizruchi, Stearns, & Fleischer (2011)

Ramaswami, Dreher, University alumni (USA) Bretz, & Wiethoff (2010) Consequences of pay levels Aletraris (2010) Household, Income and Labour Dynamics in Australia (HILDA) survey (Australia) Curhan, Elfenbein, & MBA graduates (USA) Kilduff (2009)

National Longitudinal Survey Gender role; social of Youth (NLSY) (USA) role; role congruity Managers in a Fortune 500 Supply and demand organization; graduate students

Judge & Livingston (2008) Leslie, Manchester, & Dahm (2014)

Formal theory

Sample(s)

Study

Table 8.1 Summary of empirical studies and findings (continued)

Manufacturing and serviceindustry employees (Korea) Energy company employees (Norway) Undergraduate students (USA)

Brosi, Spörrle, Welpe, & Shaw (2013)

Begley & Lee (2005)

Consumer products company employees Students (Germany) Signal sensitivity

Ease and desirability of movement framework Signal sensitivity

Public school teachers (USA) Relative deprivation

Consequences of pay changes Allen & Griffeth Medical service workers (2001) (USA)

Trevor & Wazeter (2006)

Pay satisfaction

Bonus satisfaction; distributive justice Subjective magnitude of rewards

Turnover

Pay equity perceptions

Distributive and procedural justice; turnover

Turnover rates

Pay satisfaction

Pay satisfaction

Cognitive evaluation; Affective commitment; self-determination work effort Psychological contract; Employee satisfaction discrepancy; equity; need Regulatory focus Pay equity perceptions

Social cognitive

Cross-industry employees (Korea); undergraduate students (USA) Salimäki, Hakonen, & Healthcare workers (Finland) Reflection Heneman (2009) Shaw & Jenkins Working MBA students Reflection (1995) (USA) Shaw, Delery, Jenkins, Trucking companies Employee–organization & Gupta (1998) relationships Tekleab, Bartol, & Liu MBA students (USA) Justice; (2005) multidimensional pay satisfaction

Park (2014)

Lambert (2011)

Kuvaas (2006)

Kim, Mone, & Kim (2008)

(continued)

The relationship between potential bonus reductions and attitudinal reactions was stronger among those low in negative affectivity. The relationship between rewards and perceptions of the magnitude of the payment was stronger among high positive affectivity (PA) individuals when rewards were small to moderate; the relationship was weaker among high-PA individuals when rewards were moderate to large. High-PA individuals also evaluated their own rewards as being larger than the rewards of others.

The negative indirect relationship between performance and turnover via job satisfaction was stronger when pay levels were tied to performance.

Pay knowledge, instrumental meaning of pay, and symbolic meaning of pay related positively to pay satisfaction, but pay levels did not significantly relate. Pay level, pay knowledge, and reflection theory dimensions (instrumental and symbolic meaning) related positively to pay satisfaction. Pay levels were negatively related to quit rates but not significantly related to discharge rates. Distributive justice mediated the relationship between pay and both pay level satisfaction and pay raise satisfaction. Distributive justice related more strongly to pay level satisfaction than procedural justice; procedural justice related more strongly to pay raise satisfaction than distributive justice. Teachers with pay levels low in the internal pay structure reported pay inequity when pay dispersion was high and when the school was in a low-paying district (low in external standing).

Base pay, but not bonus levels, related to affective commitment and work effort; the relationships were partially mediated by intrinsic motivation. Delivered pay levels were positively related to satisfaction, while promised pay was negatively related. Effects sizes for promised and delivered pay were larger than those for promised and delivered work. High (low) equity perceptions were reported when pay differentials were wide and individuals were promotion (prevention) focused.

Pay level related positively to pay level satisfaction, but not to other aspects of pay satisfaction (benefits and structure/administration).

Undergraduate students (Australia)

Army SOF personnel (USA)

University alumni; undergraduate students (USA) Undergraduate students (USA)

Cadsby, Song, & Tapon (2007)

Dierdorff & Surface (2008)

Eisenberger & Aselage (2009)

Lee, Edmondson, Thomke, & Worline (2004)

Kwon, Kim, Kang, & Kim (2008)

Janssen (2000)

Hospital employees (USA)

Combinational

Non-management Person–environment employees (Dutch) fit; social exchange Company employees (Korea) Equity

Experimentation

Pay satisfaction; job satisfaction

Innovative work behavior

Insomnia

Job satisfaction

Cross-occupation workers Cognitive evaluation (USA); undergraduate students (USA); insurance sales agents (Taiwan) Nurses (USA) Justice

Greenberg (2006)

Cheating

University students (UK)

Performance

Gill, Prowse, & Vlassopoulos (2013) Grandey, Chi, & Diamond (2013)

Agency

Trust

Creativity

Rates of skill change

Productivity

Antecedents/Consequences

Undergraduate students (USA)

Attribution

Prospect; selfdetermination

Behavioral management

Agency

Formal theory

Fong & Tosi (2007)

Ferrin & Dirks (2003)

Sample(s)

Study

Table 8.1 Summary of empirical studies and findings (continued)

Performance-contingent rewards related positively to satisfaction that workers experienced when performing emotional labor (surface acting). Deep-level acting was positively associated with satisfaction regardless of whether financial incentives were used. Pay reductions were associated with more insomnia among nurses. The positive relationship between pay cuts and insomnia was weaker when interactional justice was high. Job demands were positively related to innovative work behavior when the payfor-performance system was judged to be fair. The relationship between gainsharing and job attitudes (pay and job satisfaction) was mediated partially by distributive and interactional justice, but not procedural justice. Individuals experimented less when normative values and instrumental rewards were not congruent and when evaluative pressure was high. When evaluative pressure was low, inconsistent conditions were associated with increased experimentation.

Prior performance related positively to preference for pay-for-performance. Payfor-performance was associated with higher productivity, especially among those low in risk aversion. Receipt of skill-based pay was related to individual skill change and maintenance. Individuals who earned skill-based pay on their initial attempt had higher rates of learning. Higher frequency of skill-based pay increased and total earnings were associated with skill development and maintenance. Pay-for-performance related positively to intrinsic interest and creativity. The relationships operated via perceived pressure to perform and higher selfdetermination. Lying and information withholding were higher in competitive reward conditions than in cooperative reward conditions. When initial trust was high, participants emphasized cooperative aspects of the reward structure. When initial trust was low, competitive aspects were emphasized. Financial incentives related positively to performance among low-conscientious individuals, but did not predict performance among those high in conscientiousness. Those who received random bonuses cheated more than those in fixed pay conditions.

Key findings

Undergraduate students (USA)

McLeod (2011)

Insurance company employees (USA)

Nyberg, Pieper, & Trevor (2016)

Undergraduate students (USA)

Hospital employees (USA)

Cross-occupation workers (USA) Hospital employees (USA)

Schmidt & DeShon (2007)

Scott, Shaw, & Duffy (2008)

Shaw, Duffy, Jenkins, & Gupta (1999) Shaw, Duffy, Mitra, Lockhart, & Bowler (2003)

Pascual-Ezama, University students (Spain) Prelec, & Dunfield (2013) Schaubroeck, Shaw, Hospital employees (USA) Duffy, & Mitra (2008)

Insurance employees (USA)

Nyberg (2010)

Signal sensitivity

Signal sensitivity

Reflection

Self-regulation

Psychology of surprise

Self-concept maintenance

Contingency

Expectancy; equity

Mitra, Gupta, & Shaw Cross-industry organizations Relative deprivation; (2011) (USA) signaling; adaptation

Social comparison

Nuclear power plant workers Agency (Sweden)

Mattson, Torbiörn, & Hellgren (2014)

Safety behaviors tended to be higher when bonuses were linked to safety-related performance goals. Differences in bonus system design appeared to affect behavioral outcomes. High effort levels were observed when reward recipients could be identified and when rewards could be compared with others. There was weak evidence that anonymous rewards were associated with more creative ideas. Workers under skill-based pay plans, when compared with market-based pay plans, were found to have better workforce membership behaviors and more positive job attitudes.

(Continued)

The relationship between job performance and turnover was weaker when pay growth was high. Performance Bonuses were more strongly related to performance among those who received small merit pay increases. Bonuses were more strongly related to performance among short-tenured employees. Both types of incentives were more strongly related to performance if past incentives levels were low. Performance; cheating behavior Unsupervised participants performed better when financial incentives were offered. Incentives associated positively with motivation, especially when workers liked their tasks. Pay raise happiness; pay level Merit pay raises were positively related to attitudinal reactions when workers satisfaction; turnover intentions had high raise expectations and believed that pay raises were related to performance. The effects of met expectations interactions were mediated by perceptions of raise generousness and reward happiness. Goal choice Participants pursued a goal furthest from completion when incentives were offered for all goals. When incentives were offered for only one task, the rewarded task was given the highest priority. Organization-based self-esteem Merit pay increases were not related to OBSE among younger individuals. (OBSE) Among older individuals, merit raises related positively to OBSE when pay-forperformance perceptions were high and negatively when pay-for-performance perceptions were low. Pay satisfaction The relationship between pay levels and job attitudes was weaker among those high in positive affectivity. Pay level satisfaction; pay raise Merit pay raises were more strongly and positively related to attitudinal outcomes reactions among those low in positive affectivity.

Workforce flexibility; workforce attitudes; superior workforce membership behaviors; productivity Turnover

Task effort; idea quality

Safety-related behavior

University employees (Finland)

Medical doctors (USA)

Tenhiälä & Lount (2013)

Young, Beckman, & Baker (2012)

Shaw, Gupta, Mitra, & Ledford (2005)

Agency

Affective events

Organizational commitment; helping behaviors; employee voice; turnover Performance

Trucking companies Motivation; Workforce performance; accident and concrete pipe institutional; justice rates manufacturing facilities (USA) Cross-industry organizations Productivity; flexibility; cost(USA) effectiveness; survival probability

Antecedents/Consequences

Shaw, Gupta, & Delery (2002)

Formal theory

Sample(s)

Study

Table 8.1 Summary of empirical studies and findings (continued)

Organizational support variables related positively to workforce flexibility and skill-based pay survival. Skill-based pay plans had longer survival in manufacturing facilities than in service facilities and shorter survival rates in facilities pursuing a technical innovation strategy. Pay changes were positively related to helping behaviors and voice, but these associations were not significant in the presence of positive and negative affective reactions to a pay-system change. Introduction of financial incentives was positively related to physician performance and the relationship was stronger when physician autonomy was high.

Pay dispersion related positively to performance-related outcomes when it was accompanied by the use of performance-based pay.

Key findings

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industries earned over $70,000 more per year than women without a senior male mentor in the same industries. Judge and Livingston (2008) offered an alternative perspective on the gender pay gap. In their study a variety of characteristics such as demography, intellect, and family background were found related to gender role orientation, or beliefs about proper roles for women and men at work and at home. The authors predicted that beliefs about proper gender roles would predict different pay levels over time for men and women. Among participants in the NLSY79 data set, gender role orientation was positively related to pay levels among men and negatively related to pay levels among women. One fascinating study shows the dark side of procedural justice in explaining gender pay gaps. Belliveau (2012) argued that social accounts, explanations, apologies, or justifications for a decision could activate gender stereotypes prior to pay decisions. Managers are likely to believe that women will value procedural justice more deeply than men will, whereas men will attend to and value distributive justice more than women will. Consequently, managers ‘are likely to assume that men do not value procedural justice enough for social accounts to substitute effectively for high pay’ (Belliveau, 2012, p. 1156). In a laboratory experiment and a scenario-based study with practicing managers as participants, the study found that women were paid less than men when allocators could provide a social account for the decision; men were paid less than women when no social account opportunity was available. In addition, the differences were even larger among allocators who had more managerial experience. A study of the relationship between physical weight and pay levels for men and women among samples of German and American workers (Judge & Cable, 2011) found that among German employees, weight was negatively related to annual pay level among women and curvilinear in the shape of an inverted U among men. Men suffered substantial wage penalties for being very thin or very heavy, but the penalty for thinness was much more substantial. Very thin women enjoyed a strong wage advantage and very heavy women suffered a strong wage penalty. The results were similar for women in the US sample: weight and pay levels were negatively related among women, but the weight–pay level relationship was strongly positive and slightly attenuated at high weight levels. The results were observed after accounting for job characteristics and personal characteristics such as height, age, race, education, and family background; habits such as smoking and drinking;

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and family status such as number of children and spouse’s wages. A final study on gender and pay levels showed rare evidence of reversals of the gender pay gap. Leslie, Manchester, and Dahm (2014) examined whether the emphasis placed on achieving diversity goals would cause ‘high-potential’ women in the upper echelons of organizations to reach higher pay levels. The authors predicted that when organizations deemed women to have high potential for performance, the organizations would value those women more highly than they valued high-potential men. As a result, the pay system in general would favor normal-potential men, but high-potential women would be favored over high-potential men. In a field study among managerial employees of a Fortune 500 organization, the authors found that leadership performance and potential ratings from organizational records were related to pay levels, although task performance ratings were not. They found further that gender interacted with potential ratings in predicting pay levels. Among low-potential employees, women earned $.92 for every dollar earned by men. But, among employees the organization rated as having high potential, women earned $1.07 for every dollar earned by men. In a second experimental study, the authors found a similar reversal of the gender pay gap among high-potential women. Furthermore, participants’ ratings of the perceived value of diversity in higher-level positions explained the relationship. In an examination of ethnicity issues and pay levels, Avey, West, and Crossley (2008) drew on social categorization theory to investigate whether ethnicity in supervisor–subordinate dyads would predict organizational advancement and pay levels and whether congruence in ethnicity and subordinate pay levels would be more strongly related in merit-pay rather than seniority-pay systems where supervisors have more control over subordinate pay levels and promotions. They found support for the interaction of ethnic congruence and pay systems in predicting organizational advancement but not for predicting overall salary level. In particular, they found that the achieved organizational level of subordinates was higher in ethnic-congruent dyads. They found further that the level difference was wider in merit-based systems than in seniority-based systems. Their findings tended to support pay level differences as well. Subordinates in congruent dyads made more than $3000 over the salaries of subordinates in incongruent dyads, but the difference was statistically nonsignificant at conventional levels. Three additional studies examined the relationship between individual characteristics and pay levels. In the first study, Judge and Hurst (2008)

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examined the relationship between the meta-construct core self-evaluations (CSE) and work success. CSE comprises the personality dimensions of self-esteem, self-efficacy, neuroticism, and locus of control (Judge, Erez, Bono, & Thoresen, 2002). Using the NLSY79 data set, the authors argued and found that pay levels increased more rapidly over time among those scoring higher on a CSE measure (as did job satisfaction and ratings of occupation status). Furthermore, for high-CSE individuals compared with low-CSE individuals, higher educational attainment partially explained steeper pay level trajectories and fewer selfreported health problems. In the second study, Judge et al. (2009) showed a somewhat different causal sequence. They expected general mental ability (GMA) but not CSE to be associated with pay levels through educational attainment as a mediator. In contrast, they expected physical attractiveness to be positively associated with pay levels via CSE as a mediator. Their two-wave sample of workers in the northeastern United States indicated partial mediation: educational attainment explained a significant portion of the relationship between GMA and pay levels, and CSE scores explained part of the direct relationship between attractiveness and pay levels. In the third study, Judge et al. (2010) argued that GMA would moderate the relationship between elements of accumulated human capital and pay levels over time. They expected more intelligent individuals to be better able to capitalize on or translate their human capital, conceptualized as educational attainment, training, and job complexity, into higher pay. The authors found steeper trajectories between human capital and pay levels for high-GMA individuals. Over time, higher-GMA individuals had steeper earning growth and human capital accumulations, with education and job complexity, but not training, mediating the relationship between time and pay levels. Two studies examined performance-related issues and pay levels among players in the US-based National Basketball Association (NBA). One study considered three conceptualizations of performance – mean performance, performance variability, and maximum performance (Barnes & Morgeson, 2007). When entered into separate equations, typical performance (positive), variability (negative), and maximum performance (positive) each related to pay levels (total contract value), but only typical performance and performance variability remained significant in an omnibus equation. The second study (Barnes, Reb, & Ang, 2012) was an examination of the relationship between typical (mean) performance, performance trend (whether a player’s points per game increased or decreased over the study’s time

window), and performance variability and the increase or decrease in new contract value. They found performance mean and trend to be positively related to the change in contract value, but variability was not related. In additional analyses, the authors found that the relationship between mean performance and pay change disappeared after controlling for future performance (average points scored in the duration of the subsequent contract). But the relationship between performance trend and pay change persisted in the presence of future performance control. The authors reasoned that NBA owners place too much emphasis on trends when assigning new contract value. A study of the social networks of commercial bankers moved beyond individual characteristics and performance levels. Mizruchi, Stearns, and Fleischer (2011) reasoned that bankers who had sparse information networks among their peers could access better information, especially if the sparse connections were strong ties. The authors offered a similar set of predictions for networks with superiors, which they labeled approval networks. Tests using an ego-centric approach for measuring banker networks yielded significant interactions. The authors did not provide graphs of their significant interactions, but plots of the coefficients involved in the interactions showed that those who had strong ties and dense, rather than sparse, networks received the highest bonuses, which was contrary to the authors’ predictions. Their results are interesting in that the social network variables predicted bonus levels beyond controls for banker performance or prior bonus levels. But the direction of the effects and the conclusions to be drawn about network density or sparseness and tie strength remain unclear. Future researchers should address those issues.

Consequences For pay level, studies of pay level consequences concerned mostly work-related attitudes, including compensation or pay satisfaction and more general attitudes such as job satisfaction and organizational commitment. These studies are few in number, perhaps because of two meta-analyses of the predictors of pay satisfaction which appeared during the time frame of this review (Williams et al., 2007; Williams et al., 2006).

Pay levels and compensation attitudes

The literature offers consistent evidence that pay levels are related to pay satisfaction, other compensation-related attitudes (e.g., pay fairness), and

Pay Levels and Pay Changes

forms of employee withdrawal (Shaw, Delery, Jenkins, & Gupta, 1998; Williams et  al., 2006). For example, the meta-analytic correlation between pay level and pay satisfaction was .29 in Williams et al.’s (2006) analysis. As a result, the studies focusing on attitude-related outcomes of base pay tended to focus on the mechanisms between base pay levels and compensation attitudes, or contextual moderators. In line with Williams et  al.’s (2006) metaanalysis, Harris, Anseel, and Lievens (2008) examined the relationships among base pay, social comparisons, and pay level satisfaction. They expected base pay to relate positively to pay satisfaction and also that social comparisons about pay levels would explain variation in pay satisfaction above and beyond base pay and also interact with pay in predicting satisfaction. They sampled US and Belgian workers and found that pay level was positively related to pay satisfaction. Upward comparisons (the perceived pay levels of a higher-paid group) were negatively related to pay satisfaction in both samples. Among US participants, pay level and upward comparisons interacted to predict pay satisfaction: pay satisfaction was highest among those whose actual pay level matched the level of the perceived upward comparison group. In line with traditional equity theory arguments, pay satisfaction was lower when pay levels fell short of upward comparisons and when they exceeded upward comparisons, but the former effect was stronger. In a similarly ambitious study, Trevor and Wazeter (2006) examined a large sample of US school teachers to determine whether pay levels, pay relative to internal referents, and the overall pay structure combined to relate to pay equity perceptions. They found that internal standing – individual pay level divided by the average pay in the district – moderated the relationship of pay structure to pay equity. For widely dispersed structures, those with high internal standing reported high equity perceptions; those low in internal standing reported low equity perceptions. Results also revealed how individuals evaluate pay equity in terms of their internal standing (pay relative to the average in a teacher’s district) and external standing (pay relative to the average pay levels of teachers in adjacent districts). The results suggested that internal pay standing matters more in terms of pay equity when the district is low-paying on average than when the district is high-paying on average. Park (2014) showed further that individuals’ reactions to pay differentials were moderated by whether they were promotion (concerned with gains) or prevention (concerned with losses) focused. High (low) equity perceptions were reported when pay differentials were wide and individuals were promotion (prevention) focused.

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Curhan, Elfenbein, and Kilduff (2009) studied later impacts on compensation satisfaction, job satisfaction, and turnover intentions emanating from the subjective and economic value of job offer negotiations including base pay levels, relocation allowances, debt refinancing, training, and vacation. They defined subjective value of negotiations as a broad construct of perceptions as to whether the negotiations were fair, whether individuals lost face or behaved appropriately in the negotiations, whether the negotiation was desirable and legitimate, and whether the negotiators were trustworthy. Base salary and subjective value related positively to later pay satisfaction levels, but the economic value of other negotiable items did not. Subjective value also positively related to overall job satisfaction and negatively related to turnover intention, but base salary and economic value did not relate to those general attitudes. Because aspects of justice and pay outcomes are strongly correlated (Shaw, 2014), it is not surprising that a subjective value including a strong justice component was strongly associated with pay satisfaction. The differential pattern of results between pay satisfaction and general attitudes such as job satisfaction and intention to quit are provocative in any event. Future research should perhaps disentangle the subjective value components and examine whether fairness, self-esteem, process, and instrumentality independently or only jointly relate to compensation and general work attitudes. Two other studies tried similarly to link paylevel variables to outcomes such as fairness, commitment, performance, and turnover (Tekleab, Bartol, & Liu, 2005). The studies correlated pay levels with dimensions of justice and a distal behavioral outcome – turnover. They argued that distributive justice perceptions would be more strongly related than procedural justice perceptions to base pay levels. They also suggested that pay level satisfaction would mediate distributive justice perceptions, and pay raise satisfaction would mediate procedural justice perceptions. Finding considerable support for their hypothesized model, the authors advanced the literature by partially testing a differential process model and elucidating the relationships between pay levels, pay raises, and important workplace outcomes such as turnover. The study was limited in that pay raise information was not available, so they could not examine one of the first steps in the implied causal sequence: the relationship between pay raises and procedural justice. Departing from psychological studies, Kuvaas (2006) reasoned that pay levels rather than performance-based pay could relate positively to work performance through intrinsic motivation as

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mediator. The choice of pay levels rather than pay changes as a motivational tool is interesting, but so too is the mediating mechanism. Cognitive evaluation theory (Deci & Ryan, 1985) has long implied that extrinsic rewards such as pay levels or incentives reduce intrinsic motivation and work interest, although recent research entirely debunked that contention (Gerhart & Fang, 2014). Kuvaas (2006) argued that high base pay signals that employees are ‘trusted to continue to perform well without being externally regulated or controlled by shorter-term performance contingencies, which may strengthen their perceptions of autonomy and self-regulation’ (p. 369). He sampled employees in a Norwegian energy company and found that base pay, but not bonus levels, related to affective commitment and self-reported measure of work effort. Furthermore, reports of intrinsic work motivation partially mediated the relationships. A final study in this set examined whether base pay and perceptions of self-efficacy would relate to pay satisfaction in a sample of employees in several Korean companies (Kim, Mone, & Kim, 2008). Pay level was found to be positively related to pay level satisfaction, but not to other aspects of pay satisfaction (benefits and structure/administration). Controlling for pay level, self-efficacy perceptions related negatively to pay satisfaction dimensions. Self-efficacy also interacted with perceptions of the link between pay and performance (PFP perceptions), although the results showed the highest satisfaction levels among low self-efficacy individuals who believed that pay was not related to performance. Those results were not as predicted.

Pay levels and general job attitudes

Lambert (2011) and Irving and Montes (2009) took met-expectations approaches to the study of pay levels and job attitudes. Irving and Montes (2009) evoked Warr’s (1987) vitamin model in predicting that pay levels in excess of expected levels would continue to relate positively to employee satisfaction. Warr (1987) explained that very high levels of vitamins such as C and E and certain minerals have beneficial, or perhaps nonnegative, effects on humans, but vitamins such as A and D can be harmful in excess. Irving and Montes (2009) offered pay levels and support as analogous to vitamins C and E, and skill development opportunities as analogous to vitamins A and D: they predicted that satisfaction would increase as pay levels approached expected levels but that reported satisfaction levels would be even higher as pay levels exceeded expectations. When decomposed, their reasoning fails to suggest an interaction between expected and received pay levels, but rather independent or additive effects

with opposite signs. Received pay levels should be positively related to job satisfaction, while expected pay levels should be negatively related. They found that pattern in their study, although compensation and satisfaction had a positive relationship that was much stronger than the negative relationship between expected compensation and satisfaction. Lambert (2011) examined the same issues in an experimental study using psychological contract breach as the organizing framework, with results yielding a very similar pattern of findings – delivered pay was positively related to participant reports of satisfaction, and promised pay was negatively related. Aletraris (2010) returned to gender study in the context of pay levels, but focused on the satisfaction-related outcomes of temporary versus permanent work positions. Using a large nationally representative panel of workers in Australia, she found that temporary workers reported lower levels of satisfaction than did workers in permanent positions, as expected. Various work characteristics such as skill utilization, control, autonomy, job stress, and job security mediated the relationship. Pay level did not significantly predict satisfaction, nor did it mediate the relationship. In supplemental analyses, however, she showed that pay level was negatively related to job satisfaction among women and was one of multiple mediators of the relationship between temporary work status and satisfaction. She reasoned that pay levels may have served as a proxy for ‘bureaucracy, more peer-competition, or larger workloads’ (p. 1147) in her sample, which may have resulted in a negative relationship between pay and satisfaction. Gardner, Van Dyne, and Pierce (2004) looked at the relationship between pay levels and organizational-based self-esteem (OBSE), or individual’s self-perceived value as a member of a specific organization. In a multi-wave study of employees at a construction company, they found that pay levels obtained from worker records related to OBSE and supervisor-rated performance. Furthermore, OBSE mediated the relationship between pay levels and performance. Their results go beyond most research on consequences of pay levels, which tend to focus on job attitudes as the distal outcome. They show some potential performance benefits via more favorable job attitudes. Their results also align with Thierry’s (2001) reflection theory of pay which suggests that pay levels inform individuals about themselves on various dimensions; that is, pay serves somewhat as a mirror. The organizational literature has rarely tested this theory (for exceptions see Salimäki, Hakonen, & Heneman, 2009; Shaw & Jenkins, 1995), but, conceptually, it advances our understanding of the relationship between pay level and performance-related outcomes.

Pay Levels and Pay Changes

CONSEQUENCES OF PAY CHANGES Pay Changes and Performance A comprehensive meta-analysis (Jenkins et  al., 1998) resolves a long-standing debate about the relationship between financial incentives and performance. Several decades of research cumulatively revealed that financial incentives and performance quantity were significant, positive, and moderate in magnitude (rho = .34). Only six studies in the meta-analysis addressed the relationship between financial incentives and the quality of performance. The estimated correlation for performance quality from this small set of studies was not significantly different than zero, but had a positive sign. Recently, an updated meta-analysis included more recent research and a slightly less restrictive set of inclusion criteria (Garbers & Konradt, 2014). This brought considerably more studies into the analysis. The results were, however, nearly identical for performance quantity – the meta-analytic correlation was .28 between financial incentives and performance quantity. The study’s major contribution was the demonstration of a larger correlation (rho = .39) between financial incentives and performance quality, although the total number of studies analyzed remained rather modest (k = 10). Thirty-five additional studies had a performance metric that could not be classified as based on quantity or quality. The financial incentives association with these ‘mixed’ performance outcomes was also robust (rho = .38). In line with Gerhart and Fang (2014), the results of these meta-analyses should quell suggestions that financial incentives are not positively related, or even negatively associated, with work motivation and performance. They demonstrate significant and stable positive associations with quantity, quality, and mixed performance outcomes. A set of additional studies extended the basic proposition that financial incentives and performance are positively related and uncovered new theoretical and practical moderators of the relationship. Most of the studies took an agency theory view, which assumes that managers can choose between offering incentives or monitoring behavior to elicit high performance. Young, Beckman, and Baker (2012) studied whether payfor-performance would increase performance among a sample of practicing medical doctors. Tracking performance regarding the use of a new screening tool for diabetic patients, they found that financial incentives were positively related to physician performance, but the effects were stronger among physicians who believed the new system still allowed them autonomy and among

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those who believed the incentive had a ‘clinically meaningful’ outcome (Young et al., 2012, p. 973). An experimental laboratory study (Fong & Tosi, 2007) touched on similar themes of incentives, performance, and personal control. The authors argued that incentives would be more strongly related to performance among participants who were low in conscientiousness. Highly conscientious individuals are characterized as achievementoriented dependable, persevering, and deliberate. The authors argued that agency theory’s incentive alignment effects would be weaker among highly conscientious participants. They found effort and performance levels to be high across conditions among highly conscientious participants, but financial incentives were associated with significant effort and performance improvements among less-conscientious participants. They concluded that incentive alignment is more important for less-dependable or less achievement-oriented individuals. Also from the perspective of agency theory, Cadsby, Song, and Tapon (2007) examined the sorting effect of performance-related outcomes and worker preferences for such systems. An experimental study of students solving anagrams supported predictions that prior performance would relate positively to preference for pay-forperformance and that pay-for-performance would yield higher productivity. They predicted further that risk aversion would moderate both relationships: risk aversion would mitigate the relationship between prior performance and preference for pay-for-performance and between pay-for-­ performance and future performance. Their results supported the future performance attenuation, but not the selection effect attenuation. In particular, those operating under pay-for-performance generally performed better, but the highly risk-averse performed worse. Nyberg, Pieper, and Trevor (2016) sought to integrate economic and psychological theories on the relationship between financial incentives and performance. They examined moderators of relationships between two types of pay-for-performance – merit pay and bonus pay – and job performance. They provided several interesting insights into how individuals react to financial incentives. First, they found that bonuses had stronger incentive effects for those who received small merit pay increases (annual increases to base pay). Also, bonuses had stronger positive effects on employee performance among short-tenured employees, and incentives (merit pay and bonuses) had stronger effects among employees who lacked substantial bonuses or merit increases in the past (i.e., the bonus/merit trend was low). The findings underscore that financial incentives and performance

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are positively related, but suggest two important implications for theory and compensation practices. First, multiple incentive systems operating concurrently may add little value because merit pay and bonuses have consistent substitution effects. Second, interactions between incentives and prior trends suggest that employees who have received larger raises or bonuses in the past (stronger prior trends) may view incentives as entitlements, so the motivational power diminishes with time.

Pay Changes and Creativity/ Innovation A few studies have heeded calls for additional studies on the relationship between incentives and creativity. The accumulated evidence here remains rather sparse and, in general, the studies reported below do not examine directly the relationship between merit increases or bonuses and employee creativity. The results are suggestive, however, and can be used as a foundation for future studies. Lee, Edmondson, Thomke, and Worline (2004) examined whether perceptions of rewards for experimentation and organizational normative values for experimentation would predict actual experimentation behavior. They argued that evaluative pressure – the degree to which salient others were judging rather than enabling performance – would further moderate the effect. High evaluative pressure had a congruence effect; highest levels of experimentation behavior occurred when rewards and organization values encouraged experimentation. Low normative pressures and misalignment of rewards and values encouraged higher experimentation. Also low normative pressures with concurrently low rewards and values yielded very low experimentation levels. Eisenberger and Aselage (2009) sought to understand the mechanisms affecting performancebased pay and individual creativity. Across three studies, they tested an increasingly more elaborate path model. In an initial study, they argued and found that perceptions of pay-for-­ performance were associated with greater pressure to perform as well as self-determination and that these perceptions, in turn, were related to higher intrinsic interest in work. In a second study, they observed support for the initial path model, but also showed that self-reported intrinsic interest was positively associated with a supervisor-rated measure of creativity. They replicated the full path model in a third experimental study. Overall the results provide evidence of dual pathways between payfor-performance, intrinsic interest, and creativity, via higher perceived pressure to perform, but also

increased self-determination. Although Janssen (2000) did not focus specifically on pay changes, perceptions of the fairness of pay-for-performance moderated the relationship between an omnibus measure of job demands (e.g., pacing, time pressure, workload) and innovative work behavior. The interaction was such that job demands were positively related to innovative work behavior under high perceptions of fairness of pay-for-performance. This pattern held for both self- and supervisor-rated measures of innovation. McLeod (2011) conducted a clever study of electronic brainstorming as a function of whether experimental participants received low, medium, or high levels of performance-based rewards and whether the rewards were anonymous or could be compared with others in the experiment. She found high effort (the number of electronic brainstorming suggestions) when reward recipients could be identified and even higher when rewards could be compared with others. She also rated the suggestions for feasibility and creativity. The predicted positive effects for the outcomes were not supported; indeed, some evidence weakly indicated that anonymous reward conditions encouraged more creative ideas. The findings for creativity were perhaps not surprising given that study instructions encouraged participants to generate ideas, but did not encourage them to offer feasible or creative suggestions. In line with these findings, future research on financial incentives and innovation could also be viewed from the perspective of learning and informed potentially from the literature on learning effects, for example, from studies of reactions to skill- and competencybased pay programs (Dierdorff & Surface, 2008; Mitra, Gupta, & Shaw, 2011; Shaw, Gupta, Mitra, & Ledford, 2005).

Pay Changes and Job Attitudes In a series of field and experimental studies, Grandey, Chi, and Diamond (2013) found that performance-contingent rewards were positively associated with the satisfaction that workers experienced when performing emotional labor, or the positive emotions that individuals must display while working in service-oriented jobs. Another set of studies focused on how dispositional affectivity – namely positive and negative affectivity – influenced reactions to performance-based pay changes. The studies were based on Gray’s (1970) theory, which suggests that behavioral activation and behavioral inhibition systems reflect dispositional tendencies to be sensitive to reward and punishment signals, respectively. Positive (PA)

Pay Levels and Pay Changes

and negative affectivity (NA) (or extraversion and neuroticism) are seen as primary markers of the sensitivity to reward and punishment signals (Larsen & Ketelaar, 1991). In an early study, Shaw, Duffy, Jenkins, and Gupta (1999) evoked Gray’s (1970) theory and argued that because those higher in PA are more sensitive to reward signals, the between-persons relationship between pay levels and subsequent job attitudes would be weaker than the relationship between pay levels and attitudes among those low in PA. In essence, high-PA individuals ‘pick up’ or notice even small reward signals in the environment and react positively to them, whereas low-PA individuals need a larger reward signal to detect and react positively. Shaw et al. (1999) found some support for these interactions using base pay level as a proxy for reward signal, but noted that a truer test would involve pay changes. Shaw, Duffy, Mitra, Lockhart, and Bowler (2003) built on the prior study and examined reactions to merit pay increases among a sample of employees at a university hospital. In line with the signal sensitivity perspective, they found a generally high relationship between pay raises and attitudinal outcomes (and self-reported work effort) across pay raise levels among those high in PA. In contrast, a positive and significant relationship occurred between pay increases and outcomes among those low in PA – low-PA individuals failed to react positively to weak reward signals; only large reward signals elicited their positive reactions. In a recent extension of the signal sensitivity perspective, Brosi, Spörrle, Welpe, and Shaw (2013) argued that reward signal sensitivity could be further refined by examining reactions to small and large rewards using a within-person design. They found that when high-PA individuals received very small rewards, they had amplified perceptions regarding the magnitude of the payments, but as reward size increased, their reactions hit a ceiling. For low-PA individuals, reward size and subjective magnitude perceptions had a much weaker relationship across the range of very small rewards, but a stronger relationship across the range of large rewards, consistent again with the signal sensitivity perspective. Those authors advanced the signal sensitivity perspective further by examining whether PA moderated the relationship between the rewards others received and subjective magnitude. They also found support for this ‘rose-colored glasses’ prediction: highPA individuals evaluated their own rewards more favorably than they evaluated rewards received by others. The PA interaction effect was ‘mainly driven by low-PA individuals, who pessimistically evaluated others’ rewards as being larger than their own’ (p. 110).

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Begley and Lee (2005) further extended the findings on signal sensitivity theory by examining how dispositional NA moderated the relationship between potential pay reductions – a type of punishment signal – and attitudes as the organization transitioned to a new pay system. In line with Gray’s (1970) theory, they predicted that PA would have no bearing on potential pay reductions because, as a form of reward signal sensitivity, it would operate on a separate continuum than punishment signals. Rather, NA would moderate the relationship between pay-at-risk and attitudinal reactions: the relationship would be stronger among low-NA individuals. In essence, high-NA individuals, being sensitive to punishment signals, would ‘pick up’ and react negatively to even small punishment signals, while low-NA individuals would need larger punishment signals to evoke negative reactions. Their results from a large sample of employees of a consumer products company supported the predictions. Shaw and colleagues (Schaubroeck, Shaw, Duffy, & Mitra, 2008; Scott, Shaw, & Duffy, 2008) conducted two studies of the relationship between merit pay and job-related attitudes using the same multi-wave sample of employees at a university hospital. Schaubroeck et al. (2008) developed and tested a met-expectations model of reactions to merit pay increases. The authors drew on social psychological theories of surprise and argued that perceptions of control – operationalized by pay-for-performance perceptions – would determine whether individuals would react strongly or weakly to undermet and overmet expectations. The theory suggested that when individuals lack control over their circumstances, overmet expectations are more surprising and hence evoke strong positive reactions (albeit short-lived), but reactions to undermet expectations are muted. In situations of personal control, undermet expectations were predicted to be devastating, but overmet expectations were expected to be met with only pleasant surprise. In essence, overmet expectations (windfall gains) are more surprising when outcomes are believed to be chance occurrences, but undermet expectations are more surprising when outcomes are believed to be tied to actions. The tests were conducted before and after the administration of merit pay increases with measurements separated by eight months. They found support for the predictions that when individuals had high perceptions of control, undermet expectations would be associated with negative job attitude and effort reports several months after pay increases were awarded. The predictions related to surprise and lack of control were not supported; overmet expectations were not associated with euphoria when individuals lacked control.

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Scott et al. (2008) examined the interactive relationships among pay raises, pay-for-­performance perceptions, and employee age in predicting organization-based self-esteem (OBSE), or individuals’ evaluations of their worth within the organizational context. They predicted, based on sociological theory, that older individuals would have a greater need to derive meaning from the work domain because they have narrower life interests and horizons compared with younger workers. From this perspective, among older individuals, high raises from legitimate or fair systems would have strong positive implications for OBSE, and raises presumed to be unfair or not legitimate would have negative implications. For younger individuals, raise levels were not expected to predict OBSE levels, regardless of procedural justice concerns. The findings were in line with predictions. Among older individuals in the sample, merit raises were positively (negatively) associated with OBSE when pay-for-performance perceptions (the procedural justice of the pay system) were high (low). Younger individuals showed no discernible pattern between raises and OBSE. A final study in this section examined whether perceptions of justice mediated the relationship between gainsharing – bonuses based on grouplevel productivity – and general job attitudes (Kwon, Kim, Kang, & Kim, 2008). The authors conducted cross-level tests of the prediction among employees in five companies that used gainsharing to supplement a seniority-based system and five companies that did not. The variance on pay changes – the presence or absence of a gainsharing plan – was admittedly quite coarse and did not include variance on bonus sizes. Nonetheless, the authors found some support for the predicted mediation model. They reported that distributive and interactional justice, but not procedural justice, partially mediated the relationship between gainsharing and two job attitudes – pay satisfaction and job satisfaction.

Pay Changes and Antisocial Behavior Salin (2003) contributed a broad-ranging conceptual paper on the organizational and structural antecedent of bullying, or repeated and persistent negative acts toward one or more individual(s) that involve a perceived power imbalance and create a hostile work environment (Zapf, Knorz, & Kulla, 1996). She described a set of motivating structures and processes that would be positively associated with bullying and, by extension, other forms of antisocial behavior. She suggested that certain reward practices encourage antisocial

behavior through rational rent seeking processes – behavior that allocates more benefits to certain individuals. In particular, depending on how extensively the reward system distinguished among individuals based on relative ranking, certain individuals would obtain more rents by engaging in bullying behavior. Beyond individual incentives, she suggested that certain team-based financial incentives could also lead to bullying via punishment of low-performing members. Samnani and Singh (2014) expanded on Salin’s (2003) initial ideas and developed a more specific conceptual model of the relationship between payfor-performance and bullying behavior. In their model, they focused on what they termed ‘zerosum’ pay-for-performance systems, in which higher rewards for some mean necessarily lower rewards for others. Under these conditions, they argued that increased work stress and higher individual competition will cause individuals to proceed along different emotional and behavioral pathways. Reward systems that induce higher stress and competition are likely to generate more bullying. The resulting performance effects in the model are positive for the bullying perpetrators, but negative for targets. The performance implications are likely to create a feedback loop beginning in the administration process for performancebased rewards in the next cycle. The applied psychology literature rarely offers tests of the conceptual models from Salin (2003) and Samnani and Singh (2014). Ferrin and Dirks (2003), however, conducted an experimental study of reward structures, interpersonal and antisocial behavior, and trust. They argued that competitive reward structures would generate a variety of processes and biases, including correspondence bias (the tendency to draw inferences about personal characteristics based on situational factors), suspicion, cognitive schemas, and expectations. They found that competitive rewards positively related to lying behaviors, for individuals and their partners, in a decision-making task, and a tendency for both parties to withhold information. Furthermore, behavior on the individual level affected perceptions of dyadic trust, but not on the partner level. They also examined whether initial trust perceptions played a moderating role; they found that under high initial trust, trust levels in mixedreward situations (a combination of competitive rewards and cooperative rewards) were similar to the trust patterns observed for pure cooperative rewards. In essence, in a mixed-reward situation, individuals who initially trust one another will emphasize the cooperative aspects of the rewards system, but when they initially distrust each other, they attend to the competition-based aspects of the structure.

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Two recent studies of economic behavior also provide insights relevant to the antisocial behavior models. Gill, Prowse, and Vlassopoulos (2013) compared productivity in two fixed pay level conditions and a bonus pay condition in an experimental design. They assigned bonus pay randomly, and therefore the condition was similar to the notion of unexplained pay variation shown clearly to have negative implications for worker attitudes and behaviors (Shaw, 2014; Shaw et  al., 2002). Although individuals in the random bonus condition would be expected to perform better than those in fixed pay or control conditions given that the bonuses were not tied to performance, they did not. In addition, those who received random bonuses cheated more than those in other conditions. The design did not tie performance levels to bonuses, so the results say little about the relationship between financial incentives and performance, or about the zero-sum compensation designs and internal competitions that Salin (2003) and Samnani and Singh (2014) discussed. They do show, however, that individuals react negatively to unexplainable compensation decisions, a finding well established in the applied psychology and organizational literature for many years. This paper contributes, however, by showing cheating behavior to be among the likely outcomes of unfair compensation designs. Pascual-Ezama, Prelec, and Dunfield (2013) performed an experimental study examining incentives, performance, and cheating. Unsupervised study participants who were offered financial incentives or social rewards (being announced as competition winners) cheated more (declaring work done when it was not) and performed better. Contrary to agency theory explanations for individual reactions to incentives, unsupervised participants performed better in reaction to rewards, but only when they reported high levels of intrinsic motivation. Those findings are again counter to CET’s popular notion that financial incentives reduce intrinsic motivation. Generally, individuals reacted more dishonestly to financial incentives if part or all of the incentives encouraged cheating or other antisocial acts. Also, cheating was higher among those receiving social rewards or recognition than among those receiving financial rewards. Those two conceptual and empirical studies offer provocative propositions and findings. However, researchers appear to be just now scratching the surface in terms of understanding how financial incentives schemes and pay changes relate to forms of antisocial behaviors such as bullying and dishonesty. I return to those issues in my conclusions.

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Pay Changes and Other Outcomes A small set of studies examined the relationship between financial incentives and other workrelated outcomes at the individual level. Schmidt and DeShon (2007) examined whether incentives changed the priorities that experimental participants gave to concurrent goals. They found that when incentives were provided for completion of two tasks, individuals tended to prioritize the goal that was furthest from completion, but when incentives were provided for only one task, the rewarded task took priority. Those general findings align with research showing that individuals tend to focus on narrow sets of behaviors that are rewarded (Gupta & Shaw, 2014). Schmidt and DeShon (2007) found further that incentives had differential effects on goal prioritization when the goals were framed as avoidance-related (undesired end states are to be avoided) or approachrelated (desired end states are to be gained). In particular, when incentives were offered for goal completion, participants spent more time on the tasks with avoidance framing than they spent on tasks framed as approach-oriented, which is consistent with a stream of psychological and economic literature showing that individuals weigh potential losses more heavily than they weigh potential gains. Tenhiälä and Lount (2013) examined the relationship between pay changes and voice behaviors and helping after a system change in two universities in Finland. They found that pay changes positively related to helping and voice assessed at a later point, but these relationships were not significant when affective reactions (positive and negative) to the pay-system change were entered into the equation. Mattson et al. (2014) researched the relationship between bonuses and safety behaviors. Their qualitative study was notable in its setting: three Swedish nuclear power plants. The study compared the potential safety implications of bonus systems with different plant features. For example, Plant A’s program emphasized quality goals over economic goals at the plant level (75%). Plant B emphasized economic and quality goals in equal proportions and mostly at the group level rather than giving plant-wide or individual-level bonuses. Plant C heavily emphasized individual quality goals (40%). In each plant, employees were rewarded for behavior that ‘focused on competence and process development as well as safetyrelated issues’ (p. 21). The authors used a set of transcribed semi-structured interviews and conducted a theory-led thematic analysis. They drew several conclusions about safety-related concerns after systematically evaluating the evidence. First, employees in Plant A were primarily indifferent

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to the bonus plan, but the authors concluded that indifference posed little threat to safety. Plant B, emphasizing group-level goals, reported the highest levels of satisfaction with the system and the lowest levels of conflict. The authors reasoned that increased cooperative behaviors would promote safety. Plant C’s individual-level focus had the potential for creating discontent, frustration, and perhaps workplace envy. But the authors concluded that system characteristics, specifically the individual-level bonus payment, were perhaps too small to be noticed (Mitra et  al., 2016), mostly negating the day-to-day impact. Nyberg (2010) and Allen and Griffeth (2001) examined how pay-related variables moderated the relationship between job performance and turnover behavior. Nyberg (2010) tested whether actual pay growth, perceptions of whether pay and performance were related, and promotions weakened the relationship between performance and turnover behavior. Analyzing a large sample of insurance company employees over a three-year period, he found that pay growth strengthened the relationship between job performance and the survival rate of employees. Employees were least likely to quit when they performed well and had substantial pay growth (percentage increases) over the study window. A similar relationship was found when employees were promoted within the study window. Contrary to expectations, perceived pay-for-performance did not moderate the performance–turnover relationship. Nyberg (2010) found further that job satisfaction mediated the direct relationship between performance and turnover, although it did not appear to mediate the moderated effects of pay growth and promotions. Similarly, Allen and Griffeth (2001) proposed that job satisfaction would mediate the relationship between job performance and turnover behavior. Furthermore, reward contingencies, or the belief that pay and performance were related, would moderate the relationship between job performance and satisfaction. Unlike the Nyberg (2010) study, Allen and Griffeth (2001) found support for the moderation of perceived performance-based rewards. Although their study predates new techniques for testing moderated mediation, the results imply that the negative indirect relationship between performance and turnover via job satisfaction should be stronger when pay levels are tied to performance. Greenberg (2006) extended the study of pay changes into the realm of behaviors outside work. In a quasi-field experiment, he tracked sleep patterns for nearly 500 nurses in four hospitals, two of which underwent pay reductions after a nursing policy change. Supervisors in two of the hospitals received training in ways to promote interactional justice prior to the pay change. After developing

a stress-based theory of reactions to the payment, he found higher levels of insomnia among nurses in the two hospitals where pay levels had been reduced, as expected. Also as predicted, he found that the positive relationship between pay cuts and insomnia was partially alleviated among nurses in the hospitals where supervisors had been trained in ways to improve interactional justice. Insomnia levels were low in the two sites that escaped pay cuts, regardless of whether employees had received interactional justice training.

CONCLUSION Summary and Observations More than 20 years ago, Lawler and Jenkins (1992) described the compensation literature as uneven, noting that certain areas were wellresearched and other areas were essentially ignored. In many ways, the same description can be applied to the literature today. Researchers have offered various reasons for the downward trend in pay-related applied research. One possibility is that researchers may view certain research questions in the financial incentives literature as being self-evident and may pursue other research questions that appear to be more interesting. A second observation on the downward trend is that most studies appearing in this review were conducted by researchers who would not necessarily be called compensation researchers. Rather, they appear to be interested in general research questions or theories that they test in the arena of pay or financial incentives. Relatively few individuals from the major applied psychology associations such as the Society for Industrial and Organizational Psychology and the Academy of Management seem to identify themselves as compensation researchers. A final explanation is that researchers face difficulty in collecting payrelated data in organizational settings. Although pay secrecy in the United States is legally disputable (e.g., Bamberger & Belogolovsky, 2010), pay secrecy is formally and informally common. Negotiating with organizations for release of payrelated data can be especially challenging. Pay data may be obtained from employee reports of their pay levels, but individuals generally tend to be sensitive about disclosing pay-related information. A number of studies have been conducted in public university settings or among public school teachers where pay data are publicly available (e.g., Schaubroeck et al., 2008; Shaw et al., 2003; Trevor & Wazeter, 2006), but these contexts are

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limited in terms of the types and sizes of incentives offered. The difficulty of obtaining data, in toto, may cause more researchers to focus on topic areas where data collections are more feasible. Despite the relative paucity of research on pay levels and pay changes compared with topics in other areas of industrial, work, and organizational psychology, this review confirms that progress has been made in the last 15 years. In particular, we have much more extensive knowledge of the determinants of pay level, largely on the weight of Judge and colleagues’ investigations of personal characteristics, personality, and attributes in largescale, longitudinal studies across the working lives of individuals (e.g., Judge & Hurst, 2008; Judge, Hurst, & Simon, 2009; Judge, Klinger, & Simon, 2010; Judge & Livingston, 2008). The literature also provides much needed, nuanced explanations for gender pay differentials including detailed accounts of how workplace social contexts contribute to the explanations of pay levels: relationships with coworkers and supervisors (Ostroff & Atwater, 2003) and mentors (Ramaswami et al., 2010). Research in this review period provides additional evidence confirming prior meta-analytic findings (cf., Gupta & Shaw, 1998; Jenkins et  al., 1998) that financial incentives are positively related to how much workers produce, but also provides the first compelling evidence that financial incentives relate positively to performance quality as well (Garbers & Konradt, 2014). Although additional studies are needed to estimate the magnitude of the relationship more precisely, Garbers and Konradt’s (2014) finding that financial incentives had a stronger positive relationship with performance quality than with performance quantity is provocative. In another meta-analysis that examines the relationship between incentives and performance, Cerasoli, Nicklin, and Ford (2014) found that extrinsic rewards and intrinsic motivation predicted mixed performance outcomes with identical regression weights. In this meta-analysis, the relationship between extrinsic incentives and performance quantity was stronger than the relationship between intrinsic motivation and performance quantity. The reverse was true for performance quality; intrinsic motivation was more strongly related to quality, although the relationship between extrinsic rewards and quality remained positive. A final finding of interest from their study was that financial incentives related more strongly to performance when the rewards were tied directly to performance, but intrinsic motivation mattered more when rewards were only indirectly related to performance. These findings correspond well with the results of a number of the studies reported above in that the success of

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rewards systems hinges on whether performance outcomes are legitimate, measured accurately, and link directly to controllable employee behaviors. The differences between the Garbers and Konradt (2014) and the Cerasoli et al. (2014) findings could be explained by the different selection criteria for the analyses; the former examined only workrelated samples whereas the latter included workrelated samples, plus studies involving children and other non-work settings. But, the general pattern of findings, now across three separate metaanalyses is quite consistent. Although it remains popular to suggest otherwise, these studies seem to separate fact from fancy. They show robust positive relationships between financial incentives and performance (quality and quantity). They also dispel the myth espoused by popular writers and speakers (e.g., Kohn, 1993; Pink, 2009) that financial incentives erode intrinsic motivation and performance quality. I suggest that it is time to put to rest the debate about whether financial incentives are negatively related to these outcomes. The evidence now clearly shows that pay changes relate positively to performance quantity and quality, have no negative effects on intrinsic motivation, and can be used in conjunction with intrinsic motivation to produce the highest performance outcomes. The empirical literature in this time frame shows important boundary conditions of the relationships between pay changes, merit pay, bonuses, performance, and attitudinal-related outcomes. Individual differences in sensitivity to reward and punishment signals (Begley & Lee, 2005; Brosi et  al., 2013; Shaw et  al., 2003), prior expectations and pay growth (Nyberg, 2010; Schaubroeck et  al., 2008), and position in the salary structure (Trevor & Wazeter, 2006) all play important roles in determining how individuals react to financial incentives. Finally, Nyberg et  al.’s (2016) finding of potential substitution effects for different types of financial payments (e.g., bonuses and merit pay) has important theoretical implications and also seems counter to a trend in organizational practice of layering different types of reward systems. Future research on the utility of performance-based pay (e.g., Sturman, Trevor, Boudreau, & Gerhart, 2003) would be well served to consider the value of using several types of incentives concurrently. A note on method- and design-related issues is in order. As noted above researchers studying pay levels and pay changes face some difficulties in obtaining data on these variables from organizational records. But, when it is possible to obtain, the use of archival pay data may improve estimation by minimizing error. It has been many years since researchers tackled the issue of whether

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individuals can accurately estimate their own pay changes and the pay changes of others. Studies that seek to understand the conditions under which individuals can and will accurately report their pay changes would be useful, especially for researchers who must rely on self-reported pay data. There may be reason to believe that employees can report their annual or monthly pay levels more accurately than their pay increases, although whether they will report these accurately to researchers is another question altogether. For pay increases, employees may be influenced by their own inaccurate anchors for pay increases. In Schaubroeck et al. (2008), the merit pool (i.e., the average pay increase) was 5%. This budget pool was widely publicized in electronic and print forms and communicated directly to employees by supervisors before the administration of increases. The average expected pay increase reported by employees, however, was only 3%, despite the fact that nearly all employees rated themselves as performing well above average. In sum, a greater understanding of the extent to which reports of pay variables are accurate and the factors that drive accurate and inaccurate reporting would be useful tools for researchers using self-reports in future studies. Whether experimental designs can be used effectively in compensation studies has long been debated. The evidence suggests that external validity concerns may not be severe, although experimental settings may, in general, understate pay-change effects. Jenkins’ and colleagues’ qualitative (Jenkins, 1986) and quantitative (Jenkins et  al., 1998) reviews, for example, revealed that the direction of the relationship between financial incentives and performance is in the same direction (positive) for laboratory and field studies, although stronger in field than in the lab. Other studies which have compared experimental findings to field researchers have also reached similar conclusions (Mitra et  al., 2016). Researchers have also frequently suggested that any study that purports to study pay levels and pay changes must include or manipulate actual monetary payments (e.g., Gupta & Shaw, 1998). This proves somewhat difficult in short-term laboratory experiments as it is frequently not feasible to pay participants for several hours of ‘work’, not to mention that it can be expensive to do so. One caveat is that some of the more unusual findings in the literature have appeared after researchers offered unusually large payments to participants in the laboratory (e.g., Ariely, Gneezy, Loewenstein, & Mazar, 2009). The suggestion to use actual payments may be reasonable advice, in general. But, a balanced approach to evaluating the veracity of certain research designs must be taken. For example, the burgeoning social psychological literature on

priming effects of money has rarely used actual money as a manipulation, but has provided a number of potential insights into how individuals react in the context of payments (e.g., Zhou et al., 2009). My recommendation is that researchers pair experimental designs with field studies designs where possible. In addition, I would advocate a careful and detailed evaluation of validity threats and the design features most susceptible to these threats. My sense is that evenhanded evaluations of validity threats are somewhat of a lost art, replaced instead with an emphasis on highlighting design strengths. A better way forward would be to isolate the internal and external validity threats associated with a researcher’s current design, then to conduct a follow-up study in a situation where those threats are addressed directly.

Future Research Directions Threshold effects and ‘just noticeable differences’

I encourage a revival of a small but important area of financial incentives research: the study of minimum thresholds or ‘just noticeable differences’ in pay increases or pay changes. The evidence concerning the general positive relationship between financial incentives and individual performance is now well established (Gerhart & Rynes, 2003; Gupta & Shaw, 2014), and the literature has moved in several directions. One potentially fruitful path would be to explore further the point at which pay increases convey meaning (behaviorally and attitudinally) for individuals and the point at which positive effects may begin to subside. The positive relationship notwithstanding, individuals are very likely to be most sensitive to pay changes within a certain range; below a certain threshold, changes are essentially undetectable (Gneezy & Rustichini, 2000; Mitra, Gupta, & Jenkins, 1997); above the threshold, changes are decreasingly meaningful. The literature’s scant evidence on these effects suggests a minimum threshold of about 7 percent in between-person tests (Mitra et al., 1997), a finding that seems to have some cross-cultural stability (Mitra et  al., 2016). I judge that at the current time we know much more about minimum thresholds for reactions to pay changes than we know about upper boundaries of employee reactions. Studies that purport to test reactions to large rewards tend to include experimental conditions that seem beyond believable ranges. For example, high-performing participants in Ariely et  al.’s (2009) experiment could be awarded half of a yearly salary, which seems beyond a reasonable level for experimental

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participation. Some very good possibilities still exist for field studies of upper-threshold reactions to pay changes, especially in growing economies like China’s where average pay levels increases are much higher than in the United States and Europe in recent years. As a final note on thresholds, the literature tends to focus on behavioral and affective reactions to pay changes, but perspectives such as Thierry’s (2001) reflection theory suggest that individuals learn much information about themselves from organizational pay decisions. Small pay increases may elicit few, if any, behavioral reactions, but ‘below the threshold’ reactions may be observed if the scope is extended to other dimensions of pay meaning (Shaw & Jenkins, 1995).

Pay secrecy

Research on pay secrecy issues enjoyed what seemed to be a long hiatus after founding studies decades ago (e.g., Lawler, 1966). But this research area has been rejuvenated in recent years on the basis of new theory (Colella, Paetzold, Zardkoohi, & Wesson, 2007) and empirical testing. Bamberger and Belogolovsky (2010) argued that perceptions of justice as well as instrumentality perceptions would mediate the relationship between pay policy (open versus secret) and outcomes. But their experiment did not uncover a direct or mediated relationship. They argued further that the relationship would be uncovered by accounting for individuals’ tolerance for inequity. They found support for the moderated-mediation model. Instrumentality perceptions mediated a positive (negative) relationship between pay policy and individual performance among those with high (low) tolerance for inequity. Their findings show the highest performance levels among those with low tolerance for inequity in open pay systems and among those highly tolerant for inequity in secret pay systems. In a follow-up study, Belogolovsky and Bamberger (2014) found that performance was higher in an open pay policy condition than in a secret pay policy condition, especially when the open system included objective performance criteria. Their results also suggest that pay secrecy policies have sorting effects such that secret pay conditions and lower pay-for-performance perceptions increase turnover intentions, especially when performance evaluation criteria are based on relative, rather than objective, standards. Holzen and Gupta (2014) extended those empirical findings in a recent conceptual paper designed to map the domain of pay secrecy and openness. They developed a pay secrecy matrix by juxtaposing two continua of information about people (distributive) and processes (procedural).

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The intersection of policies about the distributions of pay and the degree of procedural information that is shared with employees creates a 3 × 3 matrix of pay secrecy types and should allow for more specific predictions and tests than are possible with single-continuum approaches. Another advantage is that it creates a window of opportunity for linkages with literature on pay-system communication appearing in a parallel plane in the literature. Pay communication policies (at the organizational level; viz., Shaw & Gupta, 2007) and pay-system knowledge (at the individual level; e.g., Fulmer & Chen, 2014) have some overlap with the procedural information dimension in Holzen and Gupta’s (2014) model. Individuals typically have positive affective reactions to procedural knowledge about the pay system (Fulmer & Chen, 2014; Moisio & Vartiainen, 2011; Shaw & Jenkins, 1995), and communication policies play a significant role in differential turnover patterns for good and poor performers (Shaw, Dineen, Fang & Vellella, 2009; Shaw & Gupta, 2007). Future investigators are strongly encouraged to test and extend this new conceptual perspective on pay secrecy.

Financial incentives and the behaviors of others

Pohler and Schmidt (2016) offered a new perspective on the use of financial incentives for managers and the trickle-down effects on behavior. They offered three different theoretical positions on the relationship between managerial bonuses and turnover patterns among subordinates. From an agency theory view, they argued that bonusreceiving managers would pay more attention to selection and set higher expectation levels for subordinates. Bonus provision and subordinate turnover was predicted to have a stronger relationship when the workforce had low overall performance. From an equity theory view, Pohler and Schmidt (2016) reasoned that managerial bonuses and subordinate turnover relationships would be stronger when lower-level employees were ineligible for bonuses. From a strain perspective, the authors argued that the two variables would be more strongly associated when managers were not evaluated on their treatment of subordinates. In a two-wave sample of Canadian organizations, the authors found a stable positive relationship between managerial bonuses and quit rates of lower-level employees. They found further support for the strain-based theory; managerial bonuses were most strongly related to quit rates when managers were not evaluated on their treatment of employees. Supplemental analyses showed that employees mentioned compensation

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issues and disagreements with management as reasons for quitting more frequently when managers received bonuses. Although Pohler and Schmidt (2016) conducted their study at the establishment level, it provides an interesting example in an area that has yet to be explored thoroughly, namely how incentives provided to one individual impact the attitudes and behaviors of others. Social psychological research has demonstrated further that reminders of money can increase self-sufficiency, decrease altruistic and generous behavior, and even create more physical distance between individuals (Vohs, Mead, & Goode, 2006, 2008). It is reasonable to anticipate that offering financial incentives to individuals will alter their behavior in ways that impact others. Future research that explores trickle-down incentive effects from supervisors to subordinates and the implications of other reactions to financial incentives on the attitudes and behaviors of others would be a major step forward.

Cross-cultural differences

Finally, although the studies reviewed here were conducted in locations around the world, the individual-level literature on the antecedents and consequences of pay changes lacks systematic tests of cross-cultural differences in the antecedents and consequences of pay levels and pay changes. Sweeney and McFarlin (2004) conducted surveys aimed at elucidating how individuals in different cultures made social comparisons with others in the country, with others with similar educational background, and with others in similar jobs. Although some differences emerged, specifically with employees in certain Eastern bloc countries, the patterns seemed more suggestive than definitive. A few other studies have examined whether reward preferences differ across countries (for exceptions, see Chiang & Birtch, 2007, 2012) and within countries across different organizational cultures such as stateowned versus private enterprises in China (He, Chen, & Zhang, 2004; see also, Werner & Ones, 2000). Some differences emerged in these studies, but as Chiang and Birtch (2007) noted, cultural differences ‘may be diminishing or giving way to a range of other contextual forces’ (p. 1293). Studies are needed to undertake explicit crosscultural replications of established findings, such as Mitra et  al.’s (2016) test of pay raise threshold in Finland, or to compare underlying perspectives in concurrent tests across several countries or cultures. One potential avenue for moving forward may be to utilize the pan-nation values data such as the World Values Survey (http://www.worldvaluessurvey.org). For example, Bond and Lun (2014) used items

reflecting national socialization goals to identify two independent dimensions of national goals. The first dimension was self- versus other-­ directedness with factors such as ‘independence’ reflecting the former end of the continuum and ‘obedience’ and related issues occupying the latter end. A separate continuum was labeled the civility versus practicality dimension with factors such as ‘tolerance and respect for other people’ being consistent with civility and ‘thrift, saving money, and things’ representing the practical end of the band. Although examinations of these facets represent only one possible direction for examining cultural-value-based differences in reactions to pay levels and pay changes, it seems straightforward to anticipate that reactions to pay decisions and the bases for pay changes will differ as a function of the implicit and explicit socialization goals. Moreover, we know relatively little about how individuals react to normatively accepted versus culturally unique dispersion-creating practices and methods for differentiating pay. For example, Shaw et  al. (2002) theorized that reactions to pay dispersion would differ based on whether the dispersion could be explained by normatively accepted dispersion-creating practices. In their study, conducted among trucking and manufacturing organizations in the United States, they operationalized the acceptable practice using pay-for-performance schemes. Their argument was that the cultural context was accepting of pay-for-performance as a legitimate dispersion-­ creating practice. Similar arguments and f­ indings – albeit using different operationalizations of performance-based pay – were presented by Kepes, Delery, and Gupta (2009) and Trevor et al. (2012). But, in other cultural situations, the normatively accepted practices for creating pay dispersion may vary considerably and may include seniority (see also, Shaw & Gupta, 2007), experience, family situation, or personal need. Future studies on the culturally relevant antecedents of pay levels, pay changes, and pay structure would be a step forward.

Conclusions My goals in this review were to take stock of the pay level and pay change findings over the past 15 years, to identify areas where progress has been made, and to identify areas of critical research needs. It is clear that much progress has been made in terms of identifying personal characteristics and other factors that are related to pay level. In stark contrast, the literature on the antecedents

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of pay changes is nonexistent. The other area with a number of notable findings relates to the consequences of pay change, especially related to the strong conclusion that can now be drawn linking financial incentives positively to performance quantity and quality. My suggestion is that we move beyond this debate and focus our efforts on uncovering new theoretical mechanisms, contingencies, and boundary conditions that factor in to the relationship between pay changes and outcomes. Finally, I encourage future researchers to continue the pursuit of understanding issues related to pay-raise and pay-cut thresholds, the dynamics of pay secrecy, cross-cultural issues, and how financial incentives for one play a role in terms of the attitudes and behaviors of others.

ACKNOWLEDGmENTS I wish to thank Shuisheng Shi for research assistance and Jacquelyn Thompson for editorial assistance.

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9 Revisiting the Social Context of Performance Management: Performance Appraisal Effectiveness P a u l E . L e v y, C a i t l i n M . C a v a n a u g h , N o e l l e B . Frantz, Lauren A. Borden and Ariel Roberts

INTRODUCTION In 2004, Levy and Williams proposed that the social context in which performance appraisal (PA) takes place was of paramount importance in understanding PA and in developing effective PA systems. Further, they argued that this notion of the social context was the framework that drove the PA research in the 1990s. Therefore, the authors reviewed the PA research that was conducted in the 1990s and early 2000s as a way of highlighting and understanding the role played by this social context. One major change that we’ve seen since this time is an emphasis on performance management (PM) as a broader conceptualization that includes what we have traditionally thought of as PA, but also other elements such as employee development. Aguinis (2013) defines PM as ‘a continuous process of identifying, measuring, and developing the performance of individuals and teams and aligning performance with the strategic goals of the organization’ (p. 2). PM is a broader concept than PA as it includes more than just the formal, often annual, evaluation of employees’ performance. This chapter will use the broader term where appropriate, but will use PA when that is the context for the research studies we are reviewing.

One of the key pieces that emerged from the Levy and Williams (2004) review was the notion of ‘Appraisal Effectiveness’. Drawing on some earlier work by Cardy and Dobbins (1994), Levy and Williams (2004) proposed a heuristic framework that described appraisal effectiveness as having three spokes: rater errors and biases, rating accuracy, and appraisal reactions. Figure 9.1 (taken from Levy and Williams, 2004) shows the three spokes of appraisal effectiveness and many of the specific elements that load onto each spoke. For instance, we list nine rating errors or biases within that first spoke which detract from appraisal effectiveness. Accuracy is also an important element of the effectiveness of PA systems and that spoke lists many ways that accuracy has been conceptualized in the research. Finally, how raters and ratees react to the PA is also a key indicator of appraisal effectiveness and many of those key reactions are listed in this spoke. All in all, Figure 9.1 presents a comprehensive look at appraisal effectiveness. The purpose of this chapter is to pick up where Levy and Williams left off in reviewing the literature around PA. There are a few important advancements made by the current chapter. First, we are not limiting ourselves to discussions of PA, but will include PM where appropriate. Many of the research studies that are included here are

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Appraisal effectiveness

Rater errors and biases

• • • • • • • • •

Halo Severity Central tendency Leniency Similar-to-me Liking Friendship First impression Recency

Rating accuracy

• • • • •

Differential elevation Differential accuracy Stereotype accuracy Elevation Borman’s rating accuracy • Classification accuracy • Behavioral accuracy

Appraisal reactions

• • • • • • • •

System satisfaction Session satisfaction Perceived utility Perceived accuracy Procedural justice Distributive justice Interactional justice Motivation to use feedback • Acceptability

Figure 9.1 Appraisal effectiveness model (Levy & Williams, 2004) PA studies, but some talk about PM and fit their work into the broader context – we will try to do the same. Second, we will focus on work published since the Levy and Williams (2004) piece, which means that we went back to the early 2000s in our review of the literature and include work that has taken place as recently as 2014. Third, we organize the chapter around the three spokes in Figure 9.1 and discuss the research in that same order – errors/ biases, accuracy, and reactions. Finally, we will conclude the chapter with some ideas about where we think the research needs to go in the future and we will also weigh in on the very common view these days that PM is broken. Is it broken because we don’t know how to do it better or is it broken because we aren’t relying on the existing research and implementing it as we should? We will conclude with some thoughts in this regard.

HOW WE ARRIVED HERE: A BRIEF REVIEW OF EARLY PA RESEARCH Historically, research on PA first focused on psychometric issues associated with the subjective process of assigning ratings (Austin & Villanova, 1992; Bretz, Milkovich, & Read, 1992; Farr &

Levy, 2007; Landy & Farr, 1980). Initial ratings attempted to identify types of measurement and rater-related errors present in making these ratings. In addition, research advocated for strategies to remove or reduce the effect of these errors in an effort to make ratings more accurate. Two streams of research emerged, stemming from Classical Test Theory, where every score on a test or measure is comprised of some degree of ‘true score’ reflecting a person’s true nature, and error (Landy & Farr, 1983). Most of the PA research adopted a perspective known as the ‘Test Metaphor’, which treats PA as a form of measurement (Farr & Levy, 2007; Folger, Konovsky, & Cropanzano, 1992) and focused on the psychometric properties of appraisal tools, presence of error, and the accuracy of the ratings they produced (Landy & Farr, 1980). During this period of time, the focus was on developing tools that reduced measurement error and improved rater accuracy, especially by improving appraisal tool format and design (Farr & Levy, 2007; Levy & Williams, 2004). The second stream of research emerged a bit later and was called the ‘Due Process’ metaphor which argued that PM was not simply a measurement issue, but that fairness and justice were integral parts of PM (Folger et al., 1992). Stemming from the ‘Test Metaphor’, specific errors, including halo (i.e., the tendency to allow

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overall impressions of an individual to influence ratings of each separate, unique component of performance) and leniency (i.e., inflation of performance ratings), were considered evidence of ineffective rating formats or appraisal structures. However, research consistently demonstrated very little reduction of error as a result of changes to formatting (Landy & Farr, 1980), suggesting that the current understanding of error as a result of rating format was likely incomplete. A lesser-known theory of rating error, proposed by Wherry (1952) and brought to light by Landy and Farr (1980), took the classical theory a step further by arguing that each component of the rating process was comprised of a systematic element that included a true component, a bias, and a random element (i.e., random error). In Wherry’s approach, bias refers to the rater’s experiences with the individual to be rated, and the effect of the rater’s own ‘lens’ altering his or her perception of a ratee’s true performance. This notion of bias influenced our understanding and interpretation of rater errors and accuracy after 1980 by introducing different influences within the cognitive processes of observing, storing, and retrieving performance information. Thus, the study of PA measurement evolved from a focus on rating formats to understanding the cognitive processing of performance information required to make a rating decision (Ilgen, Barnes-Farrell, & McKellin, 1993; Landy & Farr, 1980). Ilgen et al. (1993) summarized the work on cognitive processes and drew several important conclusions regarding rater error and bias. First and foremost, their review of the literature identified and corrected the false assumption that rating errors are evidence of rating inaccuracy – something that plagued early research in PA. Second, they collected evidence suggesting that rater error training did very little to improve overall rating accuracy, and occasionally made ratings less accurate. Once rater error and rating accuracy were disentangled, there became less need to emphasize specific types of rater error in PA, and instead to focus on identifying the circumstances of PA that could lead to error or inaccuracy. Regarding their conclusions on rater accuracy, extensive research emphasizing rating scale formats suggests that, contrary to popular belief, raters do not passively report their observations of ratee behavior in the form of evaluations (Ilgen et  al., 1993). Instead, raters may exhibit cognitive distortions that influence the accuracy of their ratings. While studies have indicated that characteristics of rating scales may have a small effect on these distortions, their overall impact is negligible compared to other factors, such as rater motivation and

other social variables (Ilgen et al., 1993; Landy & Farr, 1980). During this time, PA research focused on the effects of rater frame of reference training on accuracy (Levy & Williams, 2004), as well as the techniques that can prevent errors at each stage of cognitive processing (Austin & Villanova, 1992). This research has indicated that rater training does improve accuracy (Ilgen et al., 1993). In fact, a recent meta-analysis conducted by Roch, Woehr, Mishra, and Kieszczynska (2012) indicated that frame of reference training improves rating accuracy, with an average effect size of .50. Along with other prominent works published around the same time (e.g., Bretz et  al. 1992; Ferris, Judge, Rowland, & Fitzgibbons, 1994; Murphy, 1991), Ilgen and colleagues (1993) ultimately proposed a shift in focus from individual cognitive processing to the social environment within which the PA process inherently takes place as the next frontier for improving PA.

RATER ERROR As discussed previously, Levy and Williams (2004) summarized the field’s progress in examining the social context and provided a framework to help organize current and future research. Their review of the extant literature included a treatment of rater error issues framed in a new light: raters are not always motivated to make accurate ratings, and rater error or bias may stem from these alternative motivations. Recent research on halo and leniency has explored the role the social context plays in the presence of these traditional rater errors. We discuss each in turn.

Halo Recent work on halo has (1) defined halo in the modern PM context, (2) identified characteristics associated with increased presence of halo, and (3) evaluated solutions for reducing the influence of halo. With regard to the first stream of research, a meta-analysis by Viswesvaran, Schmidt, & Ones (2005) clarified the previous literature’s tendency to consider the presence of high intercorrelations of factor scores as halo itself, rather than as evidence of the underlying, psychological processes resulting from a rater making an evaluation of the ratee. Viswesvaran et al. (2005) argued that: Between-rater reliabilities and between-rater interdimension correlations are reduced by halo error, whereas within-rater reliabilities and correlations

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are inflated by halo error; that is, the psychological process underlying halo error – idiosyncratic rater impressions of each ratee – is symmetrical: It inflates all within-rater correlations and deflates all betweenrater correlations in comparison to values that would be observed in the absence of halo (p. 112).

Separating the cognitive processes underlying halo from the statistical evidence typically cited as ‘being’ halo has moved appraisal error research into the social context. Regarding the effects of halo on ratings, recent work has examined halo in the context of modern approaches to PM, including situations with multiple raters. Meta-analytic findings demonstrate that, on average, the presence of halo inflates supervisor ratings by 33%, while peer ratings are inflated by 64% (Viswesvaran et al., 2005). Halo in a multiple rater system was further explored in the context of multisource feedback (MSF) by Lance and colleagues in a series of publications using the multi-trait, multi-method matrix approach to parse out variance observed in 360-degree performance ratings (Lance, Baranik, Lau, & Scharlau, 2009; Lance, Hoffman, Gentry, & Baranik, 2008). Their work suggested that, while there is a ‘general’ performance factor associated with multidimensional performance ratings, each individual also contributes an idiosyncratic element that should not be treated as error, but rather, as meaningful variation in each rater’s experience with the ratee. Taken together, this stream of research on halo error has moved the field forward by helping researchers and practitioners alike to more thoroughly understand how individual perceptions of performance can influence ratings on dimensions of PA. Other work on halo has continued the efforts to understand which rater and ratee characteristics might lead to the presence of halo. Wong and Kwong (2007) found that raters who genuinely wanted to help improve subordinates’ weaknesses and expand upon their strengths were less likely to provide lenient or halo-driven ratings. Further, subordinates who notice a strong power distance and identify as collectivists are more likely to demonstrate rating patterns associated with halo (Ng, Koh, Ang, Kennedy, & Chan, 2011). Additionally, there is evidence that individual demographics, including race and gender, may bias raters (Heilman & Chen, 2005; Maas & Torres-González, 2011; Stauffer & Buckley, 2005). These studies demonstrate that the presence of error or bias in PA ratings can be driven by rater motivation and individual characteristics of both raters and ratees, supporting the notion that the social context plays a critical role in PA. A final stream of research has focused on reducing halo. Palmer and Feldman (2005) found that the presence of halo was reduced when there were

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context cues allowing for performance ratings to be clarified and elaborated. Raters observing examples of deficient performance (poor context) were more likely to pay attention to good behaviors, and therefore, rate individuals above average. However, by priming raters with examples of good behaviors (good context) prior to rating a performance incident, raters were more likely to target poor behavior when giving ratings, and less halo was observed. Rater accountability for ratings also reduced halo, particularly when positive context information was given. Lastly, rater accountability interacted with the need for cognitive processing such that raters with a low need for cognition, but who were still held accountable, exhibited less of a halo effect (Palmer & Feldman, 2005). This suggests that holding raters accountable for their ratings may be one way to demonstrably reduce the presence of halo in PA ratings. Recently, work exploring the relationship between halo and rater accuracy has received attention. Conflicting evidence has suggested that halo can increase accuracy, while other research argues that halo decreases accuracy (Thomas, Palmer, & Feldman, 2009). Fisicaro (1988) posited that there are two types of halo; positive in which there is more covariation between performance dimensions than there should be and negative in which there is less covariation between performance dimensions than there should be. He argued that the relationship between halo and accuracy was likely curvilinear, with accuracy decreasing as either negative or positive halo increase. Thomas et al. (2009) tested Fisicaro’s (1988) hypothesis and established that rater accuracy does change as a function of halo: the presence of both positive and negative halo errors decreases rater accuracy, with the most accurate ratings occurring when ratings display only slight negative halo. Thomas et al. (2009) also found more variation in rater accuracy in reports of positive halo than in reports of negative halo, suggesting that some instances of positive halo may result in more accurate ratings while in other instances it may not.

Leniency Research on leniency in the last decade has focused on how the social context influences rater behavior. The relationship between rater and ratee has demonstrable influence on the rater’s ability to make ratings free from error. Bernardin, Tyler, and Villanova (2009) found that rater personality influenced tendencies towards leniency, with more conscientious raters being less lenient and more accurate, while more agreeable raters tended to be

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more lenient and less accurate. This is not surprising, as one might expect agreeable raters to be motivated to maintain positive rapport with ratees. However, these findings extended the work of Bernardin, Cooke, and Villanova (2000) by replicating the effects of personality in a context of no rater accountability, suggesting that personality influences leniency regardless of accountability. While the rater’s personality can influence how he or she makes ratings, the effect of the supervisor–subordinate relationship on PA errors has also been well documented. Supervisor leniency can also be a result of positive leader– member exchange (LMX) relationships or due to rater–ratee similarity (Laird, Zboja, & Ferris, 2012; Latham, Almost, Mann, & Moore, 2005). Conversely, rater discomfort and fear have shown to increase the likelihood of rating leniently, especially when raters aimed to avoid disapproval or backlash from ratees (Bamberger, 2007; Jawahar, 2010). Based on these findings, allowing anonymous ratings may weaken the propensity for raters to be lenient (Spence & Keeping, 2010). In the context of multisource feedback (MSF), perceptions of legitimate power have been linked to a supervisor’s tendency to rate less leniently than other types of raters (Ng et al., 2011). Additionally, high power distance and collectivism were shown to increase the likelihood that subordinates rated their supervisors more leniently (Ng et al., 2011). Taken together, these studies have provided additional evidence for the influence of social context on PM. To effectively deal with rater leniency, some researchers have proposed the use of Frame of Reference (FOR) training (Latham et  al., 2005; London, Mone, & Scott, 2004). This is based on the argument that FOR training curtails rater errors and biases. Training should include relevant criteria for evaluating performance, specific behaviors to rate, and actions to take so that errors such as leniency, halo, or similar-to-me are diminished. Kline and Sulsky (2009) recommend rater-error training when performance is expected to be normally distributed. Raters are informed about the kinds of errors that may be made in order to reduce the risk of errors and biases such as halo and also increasing psychometric soundness. Beyond training, some research has suggested that cultural, structural, and contextual elements at the organization level can diminish the prevalence of rater error or bias. In terms of cultural changes to reduce leniency, implementing organizational norms around accuracy and introducing systems for holding raters accountable may decrease the tendency for raters to rate leniently (Mero, Guidice, & Brownlee, 2007; Spence & Keeping, 2010). Structurally, a final suggestion made by

Ng et al. (2011) is that recipients of MSF should be debriefed about the potential consequences of errors such as leniency and halo. Adding this element to MSF may enhance appraisal effectiveness by educating recipients on errors and helping them to better understand the rating process.

Conclusions In sum, work on rater errors in the last decade has continued to move the field beyond basic issues of measurement-based error, and into an understanding of how social interactions between raters and ratees can influence the PM process. Several streams of research have combined to clarify what individual and contextual characteristics might lead to rater errors and bias, to enhance the field’s statistical understanding of the effects of error and bias, to move research and practice around PA into the multi-level realm, and to suggest practical solutions for ameliorating the effects of rater error on performance ratings using the social context as a critical element. The relationship between rater error and rater accuracy is as complex as ever. As the previous review noted, providing rater training focused on understanding and avoiding errors is largely ineffective at improving rater accuracy unless true performance is distributed normally (Kline & Sulsky, 2009). FOR training, on the other hand may be more effective as a way to educate raters about various levels of performance and how to identify them as poor, acceptable, or exceptional (London et al., 2004). We now move to a review of accuracy research.

RATING ACCURACY A wide array of literature has supported the idea that rating accuracy is one of the integral elements of PA and rating effectiveness (Levy & Williams, 2004; Roch et  al., 2012). While early research relied heavily on the assumption that raters were primarily motivated to produce accurate ratings (Ilgen et  al., 1993), it has become increasingly apparent that this is not the case. A multitude of studies have suggested that rating accuracy is shaped jointly by the ratees’ performance and by rater motivation (e.g., Wang, Wong, & Kwong, 2010). In this sense, some have suggested that the literature needs to move away from focusing on accuracy, instead considering the possibility that accuracy may not be the primary goal that raters have in mind (Spence & Keeping, 2011).

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Accuracy Defined Although rating accuracy has been conceptualized in a variety of ways, each conceptualization compares the rater’s evaluation to some standard or ‘true score’, which is believed to represent the rating recipient’s actual level of performance, free from error. For example, Cronbach (1955) presents the most common indices of appraisal accuracy, which measure the difference between ratings produced by a specific rater, and another set of ratings provided by some expert (Kline & Sulsky, 2009). In this sense, ratings that are closer to the value of the ‘true score’ are assumed to be more accurate. The Cronbach accuracy indices decompose the distance between the rating and the true score into four elements: elevation accuracy, differential elevation, stereotype accuracy, and differential accuracy (Cronbach, 1955; Kline & Sulsky, 2009). Elevation accuracy assesses the average accuracy of a rater’s ratings over all ratees and dimensions. Differential elevation assesses the accuracy of the rater in discriminating among ratees averaging over dimensions. Stereotype accuracy refers to accuracy in discriminating among performance dimensions, averaging over ratees. Lastly, differential accuracy refers to accuracy in detecting ratee differences in patterns of behavior (i.e., identifying ratee strengths and weaknesses). While Cronbach’s accuracy measures are some of the most widely used in the extant literature, several other indices have also been utilized. Specifically, Borman’s (1977) conceptualization of rating accuracy involves correlating ratings with true scores for each dimension and then averaging those correlations. Additionally, Lord (1985) proposed a measure of behavioral accuracy, which assesses the extent to which ratees’ correct behaviors are identified by the rater, recalled, and properly differentiated from incorrect behaviors. Lastly, some researchers have utilized measures of classification accuracy (e.g., Murphy, Garcia, Kerkar, Martin, & Balzer, 1982), which defines accuracy as the positive correlation between the true score and ratings of overall performance.

Recent Accuracy Research One major conclusion that can be drawn from the extant literature is that accuracy should not be considered the only primary criteria for appraisal effectiveness (Ilgen et al., 1993; Levy & Williams, 2004). Accuracy has been shown to be associated with a variety of positive outcomes, such as improvements in motivation and fairness perceptions (DeNisi & Smith, 2014). Therefore, accuracy

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is a necessary component of PA effectiveness (Ilgen et al., 1993). However, additional constructs, such as rater motivations, cognitive distortions, and social factors, are important to consider in assessing appraisal effectiveness. Nevertheless, given the importance of accuracy in the literature, as well as the positive outcomes associated with rater accuracy, a variety of recent work has continued to examine this construct. Specifically, several studies have examined the concept of accuracy within the realm of MSF in an attempt to understand whether variation among sources is meaningful or denotes inaccuracy. Recent research has also examined the broader concept of accuracy, examining its antecedents. In particular, some research has examined the effects of accountability, rater individual differences, as well as relational characteristics, such as the raters’ familiarity with the target. To provide a flavor and sampling of this recent accuracy-related work, we review many of these papers below.

Agreement, congruency, and discrepancy

A variety of studies have been conducted examining rating accuracy in MSF and 360-degree feedback. In particular, this area of research examines source effects in ratings, and whether discrepancies between sources’ ratings are problematic. This is a relevant concern, because the empirical literature clearly demonstrates that discrepancies between raters occur (Viswesvaran, Schmidt, & Ones, 2002, 2005). For example, Patiar and Mia (2008) conducted a survey of managers from 164 Australian resorts. The study sought to determine whether significant differences exist in subordinates’ self-ratings compared to their supervisors’ ratings of their performance, and whether gender accounted for these differences. Their results indicated that participants’ self-ratings were significantly more lenient than the ratings generated by their supervisors. Additionally, gender seemed to play an important role. Specifically, males’ self-ratings were significantly higher than their supervisors’ ratings of their performance; however, women’s ratings did not differ significantly from their supervisors’ ratings. These findings suggest that subordinates’ gender plays a critical role in determining the magnitude of the discrepancy between self- and supervisor-ratings such that men are more vulnerable to these differences. These findings clearly demonstrate that source effects occur in organizational contexts. Self-ratings may be an especially idiosyncratic perspective, but source effects also include the frequent rating differences found among supervisors, peers, customers, and clients.

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Given the frequency of these source effects in organizational settings, it is not surprising that there has been some disagreement in the literature regarding whether discrepancies between raters in MSF contexts is a problem, or more specifically, whether it constitutes rating error. Two primary perspectives have been proposed regarding this issue. First, according to the normative accuracy model, differences in ratings between raters (i.e., rater source effects) represent unwanted, systematic error in ratings between sources, and should therefore be avoided as much as possible (Lance et  al., 2008). This perspective maintains that, in an ideal world, raters would exhibit perfect agreement in their ratings of a particular target, and any deviation between raters is an indication of a flaw in the rating system. Proponents of this perspective maintain that these discrepancies are an inherent drawback of multisource rating systems. Conversely, the ecological perspective views rater disagreement in a more positive light. According to this approach, rater source effects are not error, but merely represent differences in rater perspectives (Lance et al., 2008). In this sense, it is expected, and even desired, that raters will provide differing evaluations of a target, and these source effects actually represent an overall assessment of different elements of the target’s job performance. This perspective maintains that these differing opinions actually enhance the accuracy of the rating system by providing a more complete view of the target’s behavior. Lance and colleagues (2008) reviewed the literature in this area and concluded that the vast majority of empirical research supports the ecological perspective, as different raters tend to provide non-redundant perspectives on ratee performance. Ultimately, they argued that supervisory and peer ratings may represent two distinct perspectives on a target’s job performance, and are therefore equally valid. Follow-up studies have confirmed the existence of source factors (i.e., supervisor, peer) in addition to idiosyncratic rater factors from MSF ratings, and have found that they meaningfully contribute to the PA process (Hoffman, Lance, Bynum, & Gentry, 2010). Many others have also ascribed to this view, arguing that MSF actually improves appraisal accuracy by incorporating multiple viewpoints (Atwater, Brett, & Charles, 2007; Latham et al., 2005). The basic premise of MSF is that sources have unique perspectives to share, and that including them provides more diverse and varied feedback, strengthening the evaluation process. From a purely conceptual standpoint, expecting different rating sources to strongly agree on their ratings would suggest that MSF is a wasted initiative – why have multiple sources do the rating if they have the same perspective and always agree on the ratings?

Additionally, research has indicated that discrepancies in ratings between sources may have a positive influence on recipient motivation. For example, Atwater et al. (2007) suggested that the presence of discrepancies between individuals’ self-ratings of performance and others’ ratings of their performance can increase motivation. Elicker, Levy, and Hall (2006) found a similar pattern: the discrepancy between self- and supervisor-performance ratings was positively associated with individuals’ motivation to improve. When individuals become aware that they have rated their own performance more favorably than their supervisors rated their performance, they feel a greater sense of motivation to reduce that discrepancy. Although we have highlighted the benefits of rating discrepancies, it is important to note that many authors still argue that these discrepancies represent measurement error (Viswesvaran et al., 2002, 2005) and may produce adverse effects. DeNisi and Pritchard (2006) argued that disagreement between sources may have negative consequences for recipients’ motivation, performance, and attitudinal outcomes. Brutus (2010) suggested that discrepancies between narrative comments in PAs may be especially salient to rating recipients, ultimately causing confusion or frustration. Tying the issue of feedback source discrepancy back to the role of the social context, we find it interesting that Atwater et  al. (2007) conclude that the successful implementation of an MSF program is substantially tied to the organization’s feedback environment – a construct that is most prototypical of the social context as it has been defined. Thus, the success or failure of MSF may be dictated, in part, by the social context within which the evaluation takes place.

Accuracy

As discussed previously, extensive research has examined specific factors that increase rater accuracy and reduce error, especially the effects of frame of reference training for raters (e.g., Latham et  al., 2005; London et  al., 2004; Roch et  al., 2012). However, there have been calls for further research on other antecedents of appraisal accuracy (e.g., Ferris, Munyon, Basik, & Buckley, 2008). In accordance with this suggestion, a variety of research studies have evaluated how accountability mechanisms influence rating accuracy. Specifically, several theoretical reviews have concluded that various forms of accountability enhance rating accuracy (e.g., Dhiman & Singh, 2007; Lewis, 2011). However, research has also shown that the effects of accountability may

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depend on the nature of the accountability strategy, or more specifically, the status of the person holding the rater accountable, as well as the context in which the rater will be held accountable. For example a study conducted by Mero et al. (2007) examined the effects of accountability in a sample of MBA students. Participants rated the performance of individuals depicted in a video, and were either told that they would have to justify their ratings to a high-status individual (i.e., the session administrator), to a lower-status individual (i.e., the rating recipient), or to both of these individuals simultaneously. Their results demonstrated that when participants were told that they had to explain their ratings to someone with a higher status, they provided more accurate ratings. These individuals were also more likely to take high quality notes during the appraisal session, and to engage in preemptive self-criticism, anticipating critiques they might encounter during the conversation and adjusting their ratings accordingly. Alternatively, when raters were told they would have to justify their ratings to the rating recipient, they relied on an acceptability heuristic, focusing on the elements of the target’s performance that supported giving them higher ratings, and then using this information as justification for rating inflation. Additionally, Mero and colleagues’ results indicated that the form of accountability moderated this relationship, such that when they were required to justify their ratings to ratees in person, rather than in writing, they were more likely to record a greater proportion of positive behaviors, exhibit less accuracy, and provide more inflated ratings. Alternatively, when they were held accountable with a face-to-face meeting with the high-status session administrator, participants exhibited higher levels of accuracy. Ultimately, these findings highlight the superiority of faceto-face meetings with high-status individuals in holding raters accountable and increasing accuracy over written feedback delivered to the recipient. In a very recent study Mero, Guidice, and Werner (2014) examined both antecedents and consequences of accountability in a field sample. Results indicated that managers’ use of monitoring (e.g., probing about progress on a task) is related to perceptions of accountability from employees and these accountability perceptions are related to performance. The researchers argue that employees use manager monitoring as a cue to identifying organizational expectations and behave accordingly. However, research has highlighted an additional boundary condition to the effects of accountability, specifically, the performance level of the person being evaluated. Palmer and Feldman (2005)

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conducted a study on 120 undergraduate students. Participants in the accountability condition were told their ratings would be compared to those generated by an expert rater, while those in the nonaccountable condition were told that their ratings would remain confidential. Participants were then shown a video of a videotaped lecture depicting high and low performers. Their results indicated that those in the accountable condition demonstrated higher levels of accuracy in their ratings of poor performers; however, accountability did not significantly affect the accuracy of ratings of good performers. This suggests that, while accountability does increase rating accuracy, this is primarily the case for rating targets who exhibit low levels of performance, as raters of ‘good performers’ already tend to be more accurate and accountability seems less important. Recent research has also evaluated the effects of group ratings and group discussion on rating accuracy. Given the tendency for accountability to high-status individuals to provoke higher levels of rating accuracy (Mero et al., 2007), it might be expected that group discussions would have a similar effect. However, research has indicated that this is not the case. Specifically, Palmer and Loveland (2008) examined the effects of engaging in a group discussion on rating accuracy by having 360 undergraduate students rate the performance of individuals delivering lectures of good, average, or poor quality. Their results indicated that ratings made by groups after engaging in a group discussion were less accurate than ratings made by individual raters. More specifically, ratings became more extreme after group discussion and this pattern was especially prevalent if the rater already had an extreme view of the individual’s performance. In this sense, it appears that group discussion does not have the effect of holding raters accountable, and may actually make ratings less accurate. These results, that performers at the extremes are rated even more extremely after group discussion than without group discussion, is consistent with the group polarization phenomenon that is well-established in social psychology (Isenberg, 1986). In addition to whether or not raters are held accountable for their evaluations, recent research has also evaluated whether rating accuracy varies as a function of rater personality characteristics. As noted earlier, Bernardin et al. (2009) demonstrated that rater personality influences rating accuracy. Specifically, highly agreeable individuals tend to provide less accurate ratings, whereas highly conscientious individuals exhibited higher levels of accuracy. The worst accuracy was found among those who were very agreeable, but not particularly conscientious.

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Research has also argued that a rater’s ability to produce accurate ratings may depend on their familiarity with the target. Specifically, in a sample of 104 Air Force officers, Van Scotter, Moustafa, Burnett, and Michael (2007) examined whether ratings of acquainted ratees were more accurate than ratings of unacquainted ratees. Participants were asked to rate the performance of officers who delivered research presentations. Their results indicated that raters who had previously met the target were better at distinguishing between performance dimensions, and produced more accurate ratings. These findings demonstrate that information gathered outside of the rating context may help improve rating accuracy – an interesting idea and one that surely generalizes to many workplaces where relationships and social interactions may be included as data when making performance judgments.

Conclusions Several important conclusions can be gleaned from the accuracy literature reviewed above. First, although there is still disagreement in this area, more recent literature suggests that multisource feedback (MSF) provides a unique opportunity to increase rating accuracy by assessing multiple, equally valid perspectives (Lance et  al., 2008), which runs counter to the more frequently endorsed view that MSF lacks acceptable levels of reliability and that ratings gathered from sources aside from supervisors are likely to introduce error to the appraisal measure (Atwater et  al., 2007; Atwater, Ostroff, Yammarino, & Fleenor, 1998; Latham et al., 2005). Additionally, research has indicated that source effects might serve as an incentive for feedback recipients to change their behavior (e.g., Atwater et al., 2007; Elicker et al., 2006), suggesting that disagreement between sources may actually be beneficial in a performance appraisal context. Another clear implication that emerges from the literature reviewed above is that utilizing strategies that keep raters accountable can generally augment appraisal accuracy. However, the efficacy of these strategies will depend on the performance level of the rating target (Palmer & Feldman, 2005), who the raters are accountable to, and how they are held accountable (Mero et al., 2007). In addition to accountability, rater personality characteristics, as well as their familiarity with the target also appear to play a role in appraisal accuracy. In particular, raters who are high on agreeableness and low on conscientiousness (Bernardin et al., 2009), or who are unfamiliar with the ratee (Van Scotter et al., 2007) may be especially vulnerable to producing inaccurate

ratings. In this sense, social variables play a clear role in determining the accuracy of their ratings. Ultimately, these findings provide further support to the claim that ratings occur in a social context. Rating accuracy appears to depend on a variety of social forces, such as accountability mechanisms, familiarity with the ratee, and personality variables that make raters more or less likely to fall prey to these social forces. We now move to a review of appraisal reaction research.

APPRAISAL REACTIONS Bernardin and Beatty (1984) suggested that employee reactions to PAs are critical criteria to consider when evaluating PA effectiveness. However, over 10 years later, research on appraisal reactions was still so scarce that Murphy and Cleveland (1995) referred to appraisal reactions as the ‘neglected criteria’ (p. 310). Between 1995 and 2004, over 20 articles on ratee reactions to PA or feedback had been published in the applied psychology literature (Levy & Williams, 2004). A review of the literature presented since this time suggests continued growth and interest in research on ratee reactions to PA. We have grouped the recent work on appraisal reactions into three categories: (a) satisfaction and utility, (b) acceptability, accuracy, and motivation, and (c) fairness and justice.

A Brief Review Satisfaction and utility

Prior to the 1990s, satisfaction had been the most researched appraisal reaction, typically conceptualized as satisfaction with the PA system, the PA session, and with PA ratings (Keeping & Levy, 2000; see also Giles & Mossholder, 1990). Perceived utility, conceptualized as the usefulness of the appraisal session and the feedback provided, also received research attention in the 1990s. Although there were some measurement issues around satisfaction (e.g., confounds with job satisfaction, accuracy, etc.), the measurement of utility was generally consistent and not confounded with other variables (Keeping & Levy, 2000). Utility has typically been operationalized as perceived usefulness of the feedback or the PA process.

Acceptability, accuracy, and motivation

As an appraisal reaction, acceptability can be defined as the ratee’s acceptance of the PA and the resulting appraisal feedback. Perceived accuracy

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of the PA can be defined as an employee’s perception of how accurate another’s ratings are. Accuracy has often been linked with satisfaction, as well as fairness and justice. One of the most commonly accepted definitions of motivation to use feedback comes from Steelman, Levy, and Snell (2004): ‘an individual’s desire to perform better based on the feedback received’ (p. 170).

Fairness and justice

Within the appraisal context domain, researchers have recognized three common conceptualizations of fairness: procedural justice (i.e., fairness of the appraisal’s rules and process), distributive justice (i.e., fairness of allocated appraisal outcomes), and interactional justice (i.e., fairness of treatment received during the PA process). These constructs have long been the focus of both empirical and theoretical work on organizational justice and are well represented in the PA literature (Keeping & Levy, 2000; Levy & Williams, 2004). Based on these definitions, a procedurally just PA system is one in which the rules and details of the system are well articulated and perceived by the ratee as fair; a PA system that exhibits distributive justice is one in which the ratee perceives the outcomes received as a result of the system as fair; and an interactionally just PA system is one in which the ratee feels as if the treatment he or she received is fair. Folger et al. (1992) took a slightly different perspective on fairness and justice, applying the due process metaphor from legal scholarship and theory to PA. The authors stated that greater perceptions of appraisal fairness should occur when employees perceive the PA process provides adequate notice, a fair hearing, and judgment based on evidence, and research has generally supported this assertion (Erdogen, Kraimer, & Liden, 2001; Taylor, Masterson, Renard, & Tracy, 1998; Taylor, Tracy, Renard, Harrison, & Carroll, 1995; see also Levy, Cavanaugh, Frantz, & Borden (2015) for a specific treatment of the due process approach).

Recent Research on Appraisal Reactions Levy and Williams (2004) concluded that the area of appraisal effectiveness that had grown the most between 1995 and 2004 was appraisal reactions. Given the early paucity of appraisal reactions research noted by Murphy and Cleveland (1995), this growth demonstrates that researchers have made great progress in understanding the once ‘neglected criteria’. In our current review of the research since

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the publication of Levy and Williams (2004), studies on appraisal reactions have mostly focused on measuring reactions as a function of appraisal-related structural and contextual variables. In recent years, appraisal reactions have been operationalized as one way to evaluate PA effectiveness. The following sections review some major additions to the research in the three main areas.

Satisfaction and utility

An individual’s expectations are likely to play a role in his or her level of satisfaction with a PA experience. Whiting, Kline, and Sulsky (2008) found a significant, positive relationship between congruency and system satisfaction as well as perceived utility. In other words, their findings suggest that an employee is more satisfied with the PA system at one’s current job and perceives it to be more useful when it is congruent with one’s expectations for a PA system. Other researchers have examined how the interpersonal relationships between subordinate and supervisor impact PA reactions. Elicker et al. (2006) found perceptions of voice in the PA process mediated the relationship between supervisor– subordinate relationships and system satisfaction, session satisfaction, and perceived utility. Therefore, appraisal satisfaction and perceived utility of the appraisal are indirectly related to the relationship employees have with their supervisor, but perceptions of voice and fairness of the process serve as the explanatory mechanisms for this effect. That is, favorable relationships lead to opportunities for voice and perceptions of justice, which lead to favorable reactions on the part of the employees. Similarly, a vignette study found that, while providing voice in performance feedback conversations was more demanding for managers, both managers and employees perceived PA situations where employees were invited to contribute (i.e., provide voice) to be more effective (Lizzio, Wilson, & McKay, 2008). Researchers have also explored the relationship between reactions to feedback and subsequent performance improvement. For example, authors have demonstrated that a favorable reaction to feedback is more likely to result in increased performance, although the process is less straightforward (Kinicki, Prussia, Wu, & McKee-Ryan, 2004; Kuvaas, 2006). More recently, Jawahar (2010) sought to test the effect of employees’ appraisal reactions on their use of feedback to improve performance. Satisfaction with feedback was a mediator of the influence of various feedback-related characteristics on employee performance. These findings provide additional support for the notion that the effectiveness and benefits of PA depend on ratee reactions.

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Acceptability, accuracy, and motivation

Recently, accuracy has received the most attention, while less published research has focused on acceptability and motivation to use feedback. Jawahar’s (2010) research found perceived accuracy of the PA mediated the relationships of rater job knowledge, job relatedness of criteria, and goal setting with performance. As Jawahar described it, ‘when raters are credible (job knowledge), use job relevant criteria to evaluate performance, refrain from criticism, engage in goal setting, and suggest ways to improve performance, ratees react positively and perceive the feedback to be accurate’, motivating the employee to use the feedback to enhance performance (p. 516). Some earlier research examined source effects as influences of perceived accuracy of feedback in PM and set the tone for more recent work and theorizing. Brett and Atwater (2001) found that rating source and discrepancy interacted to influence the target employee’s perceptions of accuracy in a 360-feedback context. First, the study revealed that employees who rated themselves higher than their supervisor or direct reports did, perceived the feedback ratings they received as less accurate. Second, employee perceptions of accuracy were positively related to positive reactions for both peers and direct reports, whereas perceptions of accuracy for supervisor ratings were negatively related to positive reactions, meaning the less accurate the ratings were perceived, the more negative the employee’s reactions. Finally, the authors reported that the more accurate the employee perceived his or her supervisor and direct report ratings to be, the more useful they perceived the feedback to be. With these findings, Brett and Atwater not only demonstrated that employees perceive ratings from various sources differently, but they also demonstrated that perceptions of accuracy should be considered when studying appraisal reactions. Bell and Arthur (2008) also found that affective reactions to feedback and acceptance are largely interwoven. Their study of developmental assessment center participants suggested that participant reactions to feedback and personality interacted to influence whether or not participants accepted the feedback. No such effect was observed for participant’s self-ratings, reinforcing Brett and Atwater’s (2001) findings of source-related influences in acceptability. Kinicki et al. (2004) used the model proposed by Ilgen, Fisher, and Taylor (1979) to determine whether or not cognitive variables mediate an employee’s response to PA feedback. At the beginning of the model, a feedback-rich environment and source credibility positively relate to perceived accuracy, suggesting that employees

perceive feedback as more accurate when it is specific, frequent, positive, and delivered by a trustworthy and competent manager. Perceived accuracy was also found to positively relate to desire to respond to feedback, linking the motivation to use feedback to the accuracy of that feedback. The next and final cognitive component of their model, intent to respond to feedback, mediated the relationship between desire to respond to feedback and performance (measured 11 months after feedback was provided to the employee), further supporting the importance of employees’ motivation to use the feedback they receive (Kinicki et al., 2004). Research has also highlighted the importance of the supervisor–subordinate relationship as an influence of employee acceptance and acceptability of PA. Elicker et  al. (2006) found perceived accuracy and motivation to improve were higher when employees had a favorable relationship with their supervisor. This research argues that PA takes place in a social context largely defined by the relationship between the subordinate and supervisor. Further, the results clarify that a positive subordinate–supervisor relationship is likely to be associated with the subordinate’s opportunity for voice and his or her perception of fair treatment, supporting previous research (Korsgaard & Roberson, 1995; Peterson, 1999; Whiting, Podsakoff, & Pierce, 2008). Their results indicate that voice and fairness perceptions are related to perceived accuracy and motivation to use feedback.

Fairness and justice

Researchers have found that the more congruent an employee’s current PA system was with his or her expectations of what a PA system should be, the more the employee perceived the system as fair (Whiting et al., 2008). A study of 367 Finnish public sector employees showed that the political context – especially perceptions of favoritism and fairness in the PA process – resulted in lower perceptions of system effectiveness (Salimäki & Jämsén, 2010). Perhaps the most interesting finding is that voice only had expected favorable effects on perceptions of system effectiveness when politics were relatively low. When the environment was politically charged employees did not seem to believe that voice was going to matter and, therefore, viewed the system less favorably. Kavanaugh, Benson, and Brown (2007) looked at what practices within a PA system would enhance employees’ perceptions of fairness. Their results suggested that perceptions of PA fairness were positively influenced by employee participation in the appraisal process, perceptions of supervisor neutrality, and knowledge of the

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PA system. In addition, the authors believe that it is possible for practitioners to develop PA systems that are perceived as fair by employees. Perhaps this seems a simple point but remember that many have been arguing that PM is broken (e.g., Pulakos & O’Leary, 2011), and therefore, we believe that it is important to highlight research that suggests there are ways to make PM fair and accepted by employees. Elicker et al. (2006) investigated the effects of employee voice and perceptions of justice, known correlates of LMX, on the link between LMX and appraisal reactions. Specifically, based on their opinion that PA is a critical ‘justice event’ (Elicker, et al., 2006, p. 532; Folger & Cropanzano, 1998) the authors tested a mediational chain linking LMX to voice perceptions to justice judgments to appraisal reactions. Their findings suggest that the higher an employee’s perceptions of justice, the higher the employee’s satisfaction with both the PA system and session, the higher their perceptions of the system as useful and accurate, and the higher their motivation to improve performance. This study helps illustrate the importance of not only exchange relationships on appraisal reactions, but also the importance of voice and postappraisal justice judgments (Elicker et al., 2006). In 2009, Chory and Westerman studied the effects of negative feedback on the three types of organizational justice (procedural, distributive, interactional). They found that while the four feedback dimensions of evaluative consistency, feedback constructiveness, clarity, and cognizance all influenced perceptions of organizational justice, consistency and constructiveness were the strongest predictors. These results suggest that the more consistent the PA process, standards, and rules are, and the more careful and sensitive the supervisor is when communicating negative, but constructive feedback, the higher the justice perceptions of employees. The practical implications of this research suggest that managers should focus on consistency and constructiveness to elicit perceptions of justice, thus creating a just feedback environment in which constructive feedback can be well-received and in turn, potentially increasing appraisal effectiveness.

Conclusions Of the three second-order constructs that operationalize appraisal effectiveness in Levy and Williams’ (2004) model, appraisal reactions have seen the most growth in the PA literature over the last 25 years. A recent meta-analysis reports strong relationships between supervisor–subordinate

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relationship quality measures and employee reactions (Pichler, 2012). Relationship quality impacted appraisal reactions through both rating favorability and appraisal participation, but also directly without mediation. It’s interesting to note that a somewhat parallel research agenda exists in the realm of applicant reactions to selection systems. For instance, recent work has examined applicant reactions within an organizational justice framework (Walker, Helmuth, Field, & Bauer, 2014). One potentially interesting future direction might be to pursue an integration of the appraisal reactions work that we have reviewed here and the selection system reactions work that continues to be of interest to selection researchers. Our review of the appraisal reactions literature further substantiates our argument for the importance of the social context in PM, but we wonder if the social context is just as important for examining applicant reactions. Although research on these appraisal reactions has continued to expand, much more research is needed to understand how appraisal reactions and the social context of PA systems uniquely fit into the performance management literature and in practice.

CONCLUSION What We Know, and What’s Next There has been much written in recent years about PM being wrought with problems, ineffective, and disliked by raters and ratees alike. In fact, recent articles have concluded that PM is ‘broken’ (Pulakos, Mueller Hanson, Arad, & Moye, 2014; Pulakos & O’Leary, 2011). We certainly agree that PM doesn’t work as well as we’d like it to work in as many situations or organizations as we’d like. However, we wonder if PM is truly broken or if we simply aren’t implementing it correctly. To say that PM is broken suggests to us that there is something inherently wrong with PM, and that it is, in and of itself, flawed. We don’t take that perspective. We believe that its purpose and the important process variables that are integral to its effectiveness are poorly understood by many in organizations. We also believe that where it often fails is in the implementation of the PM system itself. To say that it’s broken suggests to us that the problem is with PM and we don’t believe that. In fact, we think it’s too easy to say that PM is broken because that tends to absolve both researchers and practitioners of the problems and allows us to say, ‘Oh well, it doesn’t work and it’s not really our problem’. We disagree – it is very much our responsibility to better

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understand the antecedents and outcomes of PM systems, to develop better PM systems, and to implement them more effectively. We argue that an important first step is recognizing the importance of the social context in which PM takes place. In this chapter we have reviewed a great deal of research published in the last 10 years that should help us understand, develop, and implement more effective PM systems. PM is important and it works; however, it will appear to be broken if we don’t continue doing high quality research and then develop and implement PM systems based on that research. Research has been carefully conducted on all the spokes of the Appraisal Effectiveness Model (see Figure 9.1). It’s important that we continue to research a variety of issues – PM is a broad and important process that is incredibly complex. Therefore, we need to continue to broaden the way we see it and continue to research varied important elements. Our review indicated that the social context has become a foundational fixture in the way in which researchers view PM and, similarly, we found that much of what practitioners identify as important to the PM process are social context variables as well. We believe that to scientifically explore and employ PM requires the recognition that it takes place in a social context and that that context plays a major role in the process. Three particular elements have received a great deal of attention and have been strongly linked to appraisal effectiveness. First, accountability continues to gain the attention of researchers and we continue to see how important accountability is to the process. The recent research also shows how complex accountability is, identifying many variables that impact accountability and the role that accountability plays. Second, justice continues to be very important. Our review identified many research studies that focused on justice and voice as integral components of PM. We certainly agree that justice/voice are extremely important things to consider in the development and implementation of PM and the research supports this convincingly. Third, MSF continues to be very popular in PM systems and we believe that we are better prepared to make use of the benefits of MSF as a result of the recent research that has argued and supported the importance of disagreement among rating sources (see the work of Lance and colleagues). We believe that future research needs to continue teasing apart the various elements of the social context including characteristics of the rater, ratee, message, and environment. These all interact in exciting and complex ways to impact on PM effectiveness, but the interaction is what is so important because PM does not take place in a vacuum. It happens in a complex and motivationally

charged environment in which a rater brings idiosyncrasies and characteristics that may overlap and may differ from those brought by a ratee. The environment may change and the players may change which just complicates the process all the more. It is important that we continue furthering scholarship in this area and expanding our methodologies and our focal variables. It’s really not broken, but we need to do it better.

REFERENCES Aguinis, H. (2013). Performance management. Upper Saddle River, NJ: Pearson. Atwater, L. E., Brett, J. F., & Charles, A. (2007). Multisource feedback: Lessons learned and implications for practice. Human Resource Management, 46(2), 285–307. doi:10.1002/ hrm.20161 Atwater, L. E., Ostroff, C., Yammarino, F. J., & Fleenor, J. W. (1998). Self-other agreement: Does it really matter? Personnel Psychology, 51(3), 577–598. Austin, J. T., & Villanova, P. D. (1992). The criterion problem: 1917–1992. Journal of Applied Psychology, 77, 836–874. Bamberger, P. A. (2007). Competitive appraising: A social dilemma perspective on the conditions in which multi-round peer evaluation may result in counter-productive team dynamics. Human Resource Management Review, 17(1), 1–18. doi:10.1016/j.hrmr.2006.11.001 Bell, S. T., & Arthur, Jr., W. (2008). Feedback acceptance in developmental assessment centers: The role of feedback message, participant personality, and affective response to the feedback session. Journal of Organizational Behavior, 29, 681–703. Bernardin, H. J., & Beatty, R. W. (1984). Performance appraisal: Assessing human behavior at work. Boston, MA: PWS-Kent. Bernardin, H. J., Cooke, D. K., & Villanova, P. (2000). Conscientiousness and agreeableness as predictors of rating leniency. Journal of Applied Psychology, 85(2), 232–234. Bernardin, H., Tyler, C. L., & Villanova, P. (2009). Rating level and accuracy as a function of rater personality. International Journal of Selection and Assessment, 17(3), 300–310. doi:10.1111/ j.1468-2389.2009.00472.x Borman, W. (1977). Consistency of rating accuracy and rating errors in the judgment of human performance. Organizational Behavior and Human Performance, 20, 238–252. doi: 10.1016/ 0030-5073(77)90004-6 Brett, J. F., & Atwater, L. E. (2001). 360° feedback: Accuracy, reactions, and perceptions of usefulness. Journal of Applied Psychology, 86(5), 930–942.

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performance appraisal discussions. Journal of Management, 21(4), 657–669. Kuvaas, B. (2006). Performance appraisal satisfaction and employee outcomes: Mediating and moderating roles of motivation. International Journal of Human Resource Management, 17, 504–522. Laird, M., Zboja, J. J., & Ferris, G. R. (2012). Partial mediation of the political skill–reputation relationship. The Career Development International, 17(6), 557–582. doi:10.1108/13620431211280132 Lance, C. E., Baranik, L. E., Lau, A. R., & Scharlau, E. A. (2009). If it ain’t trait it must be method: (Mis) application of the multitrait-multimethod design in organizational research. In C. E. Lance & R. J. Vandenberg (Eds.), Statistical and methodological myths and urban legends: Doctrine, verity, and fable in the organizational and social research (pp. 337–360). New York: Routledge. Lance, C. E, Hoffman, B. J, Gentry, W. A., & Baranik, L. E. (2008). Rater source factors represent important subcomponents of the criterion construct space, not rater bias. Human Resource Management Review, 18(4), 223–232. Landy, F. J., & Farr, J. L. (1980). Performance rating. Psychological Bulletin, 87, 72–107. Landy, F. J. & Farr, J. L. (1983). The measurement of work performance: Methods, theory, and applications. Orlando, FL: Academic Press. Latham, G. P., Almost, J., Mann, S., & Moore, C., (2005). New developments in performance management. Organizational Dynamics, 34, 77–87. Levy, P. E., & Williams, J. R. (2004). The social context of performance appraisal: A review and framework for the future. Journal of Management, 30(6), 885–909. Levy, P. E., Cavanaugh, C. M., Frantz, N. B., & Borden, L. A. (2015). The role of due process in performance appraisal: A 20-year retrospective. In R. Cropanzano and M. Ambrose (Eds.), Handbook of justice in work organizations. Oxford, England: Oxford University Press. Lewis, R. E. (2011). Accountability is key to effective performance appraisal systems. Industrial and Organizational Psychology, 4, 173–175. Lizzio, A., Wilson, K., & McKay, L. (2008). Managers’ and subordinates’ evaluations of feedback strategies: The critical contributions of voice. Journal of Applied Social Psychology, 38, 919–946. London, M., Mone, E. M., & Scott, J. C. (2004). Performance management and assessment: Methods for improved rater accuracy and employee goal setting. Human Resource Management, 43(4), 319–336. Lord, R. G. (1985). Accuracy in behavioral measurement: An alternative definition based on raters’ cognitive schema and signal detection theory. Journal of Applied Psychology, 70(1), 66–71.

Maas, V. S., & Torres-González, R. (2011). Subjective performance evaluation and gender discrimination. Journal of Business Ethics, 101(4), 667–681. doi:10.1007/s10551-011-0763-7 Mero, N. P., Guidice, R. M., & Brownlee, A. L. (2007). Accountability in a performance appraisal context: The effect of audience and form of accounting on rater response and behavior. Journal of Management, 33(2), 223–252. doi:10.1177/0149206306297633 Mero, N. P., Guidice, R. M., & Werner, S. (2014). A field study of the antecedents and performance consequences of perceived accountability. Journal of Management, 40(6), 1627–1652. Murphy, K. R. (1991). Criterion issues in performance appraisal research: Behavioral accuracy versus classification accuracy. Organizational Behavior and Human Decision Processes, 50, 45–50. Murphy, K. R., & Cleveland, J. (1995). Understanding performance appraisal: Social, organizational, and goal-based perspectives. Thousand Oaks: Sage. Murphy, K. R., Garcia, M., Kerkar, S., Martin, C., & Balzer, W. K. (1982). Relationship between observational accuracy and accuracy in evaluating performance. Journal of Applied Psychology, 67(3), 320–325. Ng, K., Koh, C., Ang, S., Kennedy, J. C., & Chan, K. (2011). Rating leniency and halo in multisource feedback ratings: Testing cultural assumptions of power distance and individualism-collectivism. Journal of Applied Psychology, 96(5), 1033–1044. doi:10.1037/a0023368 Palmer, J. K., & Feldman, J. M. (2005). Accountability and need for cognition effects on contrast, halo, and accuracy in performance ratings. The Journal of Psychology, 136(2), 119–137. Palmer, J. K., & Loveland, J. M. (2008). The influence of group discussion on performance judgments: Rating accuracy, contrast effects, and halo. The Journal of Psychology, 142, 117–130. Patiar, A., & Mia, L. (2008). The effect of subordinates’ gender on the difference between self ratings, and superiors’ ratings, of subordinates’ performance in hotels. International Journal of Hospitality Management, 27, 53–64. Peterson, R. S. (1999). Can you have too much of a good thing? The limits of voice for improving satisfaction with leaders. Personality and Social Psychology Bulletin, 25(3), 313–324. Pichler, S. (2012). The social context of performance appraisal and appraisal reactions: A meta analysis. Human Resource Management, 51, 709–732. Pulakos, E. D., & O’Leary, R. S. (2011). Why is performance management broken? Industrial and Organizational Psychology, 4, 146–164. Pulakos, E. D., Mueller Hanson, R., Arad, S., & Moye, N. (2014). Performance management can be fixed: An on-the-job experiential learning approach for complex behavior change. Industrial and

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Van Scotter, J. R., Moustafa, K., Burnett, J. R., & Michael, P. G. (2007). Influence of prior acquaintance with the ratee on rater accuracy and halo. Journal of Management Development, 26(8), 790–803. Viswesvaran, C. Schmidt, F. L., & Ones, D. S. (2002). The moderating influence of job performance dimensions on convergence of supervisory and peer ratings of job performance: Unconfounding construct-level convergence and rating difficulty. Journal of Applied Psychology, 87(2), 345–354. Viswesvaran, C., Schmidt, F. L., & Ones, D. S. (2005). Is there a general factor in ratings of job performance? A meta-analytic framework for disentangling substantive and error influences. Journal of Applied Psychology, 90, 108–131. Walker, H. J., Helmuth, C. A., Field, H. S., and Bauer, T. N. (2014), Watch what you say: Job applicants’ justice perceptions from initial organizational correspondence. Human Resource Management, 54, 999–1011. doi: 10.1002/hrm.21655 Wang, X. M., Wong, K. F. E., & Kwong, J. Y. Y. (2010). The roles of rater goals and ratee performance levels in the distortion of performance ratings. Journal of Applied Psychology, 95(3), 546–561. Wherry, R. J. (1952). The control of bias in ratings: VIII. A theory of rating. PRB Report No. 922, Contract No. DA-49-083 0SA69, Department of the Army. Whiting, H. J., Kline, T. J. B., & Sulsky, L. M. (2008). Performance appraisal congruency: An important aspect of person–organization fit. International Journal of Productivity and Performance Management, 57, 223–236. Whiting, S. W., Podsakoff, P. M., & Pierce, J. R. (2008). Effects of task performance, helping, voice, and organizational loyalty on performance appraisal ratings. Journal of Applied Psychology, 93(1), 125–139. Wong, K. F. E., & Kwong, J. Y. Y. (2007). Effects of rater goal on rating patterns: Evidence from an experimental field study. Journal of Applied Psychology, 92, 577–585.

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Part IV

Talent Management and Development

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10 Learning, Training and Development in Organizations: Emerging Trends, Recent Advances and Future Directions Bradford S. Bell and Ozias A. Moore

INTRODUCTION In recent years, learning, training, and development in organizations has changed dramatically. Once considered a tangential activity, training and development initiatives are now viewed as playing a critical role in the development of human capital resources and an organization’s ability to gain competitive advantage (Noe, Clarke, & Klein, 2014). A recent survey of chief learning officers (CLOs) found that, over the next year, 82 percent expect training to become even more aligned with company objectives and 70 percent project that it will grow more valuable to the success of their organization (Anderson, 2015). This increase in strategic importance has been accompanied by greater spending on training and development. The American Society for Training and Development (ASTD), for example, estimated that in 2012 organizations spent $164.2 billion on employee training, or about 1.3 percent of their revenue (Miller, 2013). Over half of CLOs also expect their budget to increase in the coming year (Anderson, 2015). The evolution of training and development in organizations has created both opportunities and challenges. Although more resources than ever are being directed toward training and development,

there exists greater pressure to extract maximum value from these investments. It has been estimated, for example, that upwards of 80 percent of training costs is spent on getting employees to training, maintaining them while there, and absorbing their lost productivity (Kozlowski, Toney, Mullins, Weissbein, Brown, & Bell, 2001). Thus, organizations are increasingly turning to technology as a way to deliver training to employees anywhere and anytime, thereby avoiding or greatly reducing these costs. In fact, it is estimated that about a third of all training today in organizations is delivered through technology (Miller, 2013). In addition to reducing costs, organizations are searching for ways to enhance the impact of their training and development initiatives, including increasing the transfer of competencies developed through formal training to the job and leveraging the informal learning that naturally takes place in the workplace (Bear et  al., 2008; Blume, Ford, Baldwin, & Huang, 2010). As organizations continue to shift to team-based work structures, there is also greater recognition that training and development can serve as a valuable tool for influencing team effectiveness (Kozlowski & Bell, 2013). Finally, training departments are increasingly being asked to provide organizational stakeholders with evidence of the impact of their initiatives. As a result,

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assessment and evaluation have become one of the highest priorities for CLOs (Anderson, 2014). Our goal in the current chapter is to examine how these trends are shaping, and being shaped by, research on learning, training, and development. It is important to note that we do not provide a comprehensive review of the training and development literature (see Noe et al., 2014; Salas, Weaver, & Shuffler, 2012 for excellent reviews of the field). Rather, our chapter focuses on the implications of the current trends in learning, training, and development for four areas of research: training design and delivery, team training and development, training transfer, and training evaluation. We believe the research in these four areas has been most heavily impacted by changes in training practice and also has the most to offer in terms of guiding the field as it responds to emerging opportunities and challenges. We also orient our chapter around more recent research developments rather than reviewing the history of research in these areas. We focus on more recent research in part because it provides a tighter connection to current trends, but also because we aim to build upon and extend the chapter published by Campbell and Kuncel (2001) in the first edition of the Handbook. In the sections that follow, we discuss recent advances in the four areas of learning, training, and development research and also highlight directions for future research in each area.

TRAINING DESIGN AND DELIVERY Research has provided considerable evidence of the benefits of training and development for individuals, teams, and organizations (Aguinis & Kraiger, 2009). A meta-analysis by Arthur, Bennett, Edens, and Bell (2003) also revealed that the effects of organizational training are comparable to or greater than other popular organizational interventions, such as feedback and goal setting. However, the benefits of training and development are certainly not guaranteed. Morrow, Jarrett, and Rupinski (1997), for example, examined the utility of 18 managerial and sales/technical training programs in a Fortune 500 pharmaceutical company. Although they found that, on average, the effect and utility (i.e., return on investment) of the training programs were positive, they also discovered significant variation between the effectiveness of programs. In fact, two of the managerial training programs had negative effects and five programs were estimated to have negative utility values. As they conclude, ‘it would be simplistic to claim that “training is a good investment” or

that “training is a waste of time and money’’’ (Morrow et al., 1997, p. 112). Effective training requires careful consideration of a variety of factors that occur before, during, and after training (Salas, Weaver et al., 2012). During training, important considerations involve training design and in particular the selection of an instructional strategy that can deliver the training experience necessary to achieve targeted learning objectives (Kozlowski & Bell, 2007). Over the years, substantial research attention has been directed at the methods and instructional principles (e.g., feedback, practice opportunities) that underlie various instructional strategies. In the following sections, we discuss two important and interrelated developments within this research that align with the themes that opened this chapter. In particular, we examine the emergence of a more learner-centered approach to training design and discuss the evolution of research on technologybased training. We conclude with a discussion of future research directions in the area of training design and delivery.

Learner-Centered Training Design Over the years, training research and practice has tended to treat the learner as a passive recipient to various training interventions or designs (Ford & Kraiger, 1995). This passive orientation was, in part, a product of the behavioral learning paradigm that argued that errors should be avoided during training since they serve as aversive stimuli that reduce motivation and lead to the adoption of erroneous habits (Skinner, 1953). As a result, traditional approaches to training were designed around the idea that the instructional system (e.g., instructor, computer program) should retain control over important learning decisions and guide individuals step-by-step through the complete task and its concepts, rules, and strategies (Smith, Ford, & Kozlowski, 1997). Programmed instruction and early forms of computer-assisted instruction, for example, were designed to break the procedure for achieving a training goal into a series of steps and then guide trainees through these steps so as to maximize success and minimize the number of errors committed (Ford & Kraiger, 1995; Ivancic & Hesketh, 1995). These and other proceduralized approaches to training proved effective for developing routine expertise and supporting the transfer of skills to problems or situations similar to those encountered during training (i.e., analogical transfer; Frese, 1995). Although passive, proceduralized learning has historically dominated training research and

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practice, recent changes in the nature of work and learning have called this approach into question. As jobs have grown increasingly complex and dynamic, greater attention has been focused on developing adaptive expertise, or competencies that are specialized but also flexible enough to be generalized to novel problems and situations (Bell & Kozlowski, 2009). Research suggests that although proceduralized instruction can be effective for analogical transfer, it may impede the development of generalizable skills (e.g., Devine & Kozlowski, 1995). In addition, the growth in technology-based training and informal, job-embedded learning has provided individuals with greater control over their learning (Bell & Kozlowski, 2002; Brown, 2001). The result has been the emergence of a more learner-centered approach to training design that views learners as active participants in the learning experience (Bell & Kozlowski, 2009; Keith & Wolff, 2014). There exist a number of educational philosophies and approaches, such as experiential and action learning, built around the idea that the learner should be an active participant in the learning process (Kolb, 1984; Revans, 1982). However, much of the recent work in this area has focused on active learning approaches, which not only shift control to the learner but also use formal training design elements to systematically engage the cognitive, motivational, and emotional processes that underlie self-regulated learning. Selfregulation plays a critical role in active learning because research suggests that trainees often do not make effective use of the control they are given over their learning (DeRouin, Fritzsche, & Salas, 2004). Furthermore, research has identified metacognition, self-efficacy, and other self-regulatory processes as critical mechanisms in not only learning but also the development of adaptive expertise (Kozlowski, Toney et al., 2001; Sitzmann & Ely, 2011). Thus, training strategies that selectively influence these processes can be effective tools for promoting adaptive learning. Researchers have developed a number of active learning interventions. One of the first and most widely studied interventions is error management training, which involves active exploration and explicit encouragement for trainees to make errors during training and to learn from them. A recent meta-analysis by Keith and Frese (2008) revealed that error management training leads to better training outcomes than methods that do not encourage errors during training. In addition, they found that error management training is more effective for adaptive transfer than analogical transfer and that both active exploration and error encouragement are important elements of the strategy. Research has also provided evidence to support

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the effectiveness of a number of other active learning interventions, including mastery training (e.g., Kozlowski & Bell, 2006; Kozlowski, Gully, Brown, Salas, Smith, & Nason, 2001) and guided exploration (Debowski, Wood, & Bandura, 2001). A recent study by Bell and Kozlowski (2008) aimed to integrate and extend this research by identifying the core training design elements that cut across different active learning interventions and examining their effects on trainees’ learning and transfer via distinct self-regulatory pathways. The findings revealed that two of the core training design elements, exploratory learning and error framing, had a positive effect on trainees’ performance, in particular their adaptive transfer. In addition, exploratory learning and error framing interacted with cognitive ability and dispositional goal orientation to influence trainees’ metacognition and state goal orientation. The third training design element, emotion-control, did not have a direct impact on trainees’ performance but did reduce their state anxiety. Overall, this research suggests that by shaping the nature of instruction, influencing trainees’ motivational orientation, and helping trainees to manage their emotions, active learning interventions can influence trainees’ cognitive, motivational, and emotional self-regulatory processes and enhance their learning and adaptive performance. In addition, the interplay of the training design elements with individual differences suggest that it is important to consider trainees’ characteristics when implementing active learning interventions.

Technology-Based Training As noted earlier, the utilization of technologybased training has expanded significantly in recent years. The American Society for Training and Development, for example, estimated that in 2012 about a third of all training in the workplace was delivered through technology (Miller, 2013). In addition, colleges and universities are increasingly using technology to train their students. Babson Survey Research Group estimated that between 2002 and 2010 online course enrollments grew at a rate of 18.3 percent (Allen & Seaman, 2011). This growth has spurred thousands of studies aimed at evaluating the effectiveness of technology-based training. Research on technology-based training has been characterized by several notable limitations. First, there currently exists a fragmented understanding of what technology-based training means and how it should be defined. In their recent review, Brown, Charlier, and Pierotti (2012) identified 46 distinct

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terms that have been used in this area, including e-learning, online learning, technology-based instruction, and computer-based simulation. Often these terms are used inconsistently across studies, which presents a challenge to those seeking to make sense of the field. Also, research in this area has suffered from a number of methodological limitations. Studies often rely on single-group pretest, posttest designs, which can inflate effect sizes (Lipsey & Wilson, 1993). Also, studies that use two-group designs often do not randomly assign participants to conditions and frequently confound differences in delivery media with differences in instruction. For example, there may be differences in curriculum materials, time spent in learning, or activity level across the technology-based and comparison conditions. The result is that it is often challenging to tease apart whether differences in achievement are due to differences in the delivery media or differences in instructional features (Bell & Federman, 2013). These limitations notwithstanding, several recent meta-analyses have provided valuable insight into the effectiveness of technology-based training. Sitzmann, Kraiger, Stewart, and Wisher (2006), for example, compared the effectiveness of classroom and web-based instruction. They found that web-based instruction was 6 percent more effective than classroom instruction for teaching declarative knowledge but produced similar results in terms of procedural knowledge and trainee satisfaction. However, they also note that in studies where equivalent instructional methods were used across conditions, web-based instruction and classroom instruction were equally effective for teaching declarative knowledge. Thus, they conclude that the advantage of web-based instruction may be due to the use of more effective instructional methods rather than the media per se. Another meta-analysis by Sitzmann (2011) found that training conducted through simulation games, as opposed to other methods, led to significant gains in post-training self-efficacy, declarative knowledge, procedural knowledge, and retention. However, the results varied depending on the type of instruction provided to the comparison group, characteristics of the simulation game, and the instructional context. In particular, when instructional methods were matched in terms of activity level, simulation games and alternative instructional methods produced similar results. Overall, these and other reviews suggest that technology-based training is, on average, as effective as more traditional forms of instruction. Perhaps more importantly, however, these reviews seem to support the position advocated by Clark (1994) and others over the past several decades, which is that learning is influenced by pedagogy,

not delivery media. That is, it is the characteristics of the instructional design, such as the instructional methods used and the activity level of the trainees, that influences learning, and media are simply the vehicles through which these instructional conditions are delivered to the trainee. Given this, numerous observers have suggested that studies focused on evaluating the effectiveness of different training technologies are of limited value (Bell & Federman, 2013; Brown et al., 2012). Indeed, any technology can be effective for delivering training. The more important concern is understanding the conditions that influence the effectiveness of technology-based training.

Future Research Directions Although recent research has advanced our understanding of learner-centered training design, more work is needed in this area. For example, there is a need to consider a broader range of individual differences that may influence how learners interact with active learning approaches (Bell & Kozlowski, 2009). Some individual differences, such as cognitive ability, have been examined in a number of active learning studies, whereas others, such as personality, have received much less attention. In addition, the few studies that have examined personality have sometimes produced conflicting findings (e.g., Cullen, Muros, Rasch, & Sackett, 2013; Gully, Payne, Kiechel, & Whiteman, 2002). This may be because past research has tended to focus on multifaceted active learning interventions, in which case the effects of individual differences will depend on the specific design elements that comprise the intervention and the emphasis these elements receive during training. For instance, individuals high in conscientiousness, who tend to be selfdisciplined, careful, and thorough, may be well equipped to take advantage of the control offered by exploratory learning environments. At the same time, these individuals may react negatively if the training emphasizes that they should make errors as they explore the task. Thus, we recommend that future research adopt a more nuanced approach that examines how individual differences interact with specific training design elements, which can provide greater insight into how to design active learning interventions to meet the needs of different learners. It will also be important for research to further elaborate the process pathways through which active learning approaches have their effects. There are a number of process mechanisms, such as mental models, that have been discussed as playing an important

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role in active learning but have thus far received limited empirical attention (for an exception, see Kozlowski, Gully et al., 2001). Future research is also needed to provide insight into the conditions under which the different training design components are more or less important to the success of active learning interventions. For example, Bell and Kozlowski (2008) found that although emotioncontrol reduced trainees’ state anxiety, it did not have an effect on their performance. They suggest, however, that this may be due to the moderate levels of anxiety experienced by trainees in their experiment and that emotion-control strategies may be critical for performance in situations where trainees experience more extreme levels of stress. Finally, Keith and Wolff (2014) note that most studies have focused on active training as opposed to active learning more generally and call for research that examines other forms of active learning. For instance, recent work has focused on gaining a better understanding of experiencebased learning, including examining interventions (e.g., structured reflection) that may enhance the developmental value of different experiences (DeRue, Nahrgang, Hollenbeck, & Workman, 2012). Integrating research on active learning and experience-based learning may serve to advance our of understanding of how organizations can leverage learner-centered design. In the area of technology-based training, future research needs to shift attention from the ‘does it work’ question to examining the instructional features and strategies that influence effectiveness. Kozlowski and Bell (2007), for example, developed a typology that specifies four categories of instructional features – content, immersion, interactivity, and communication – that can be configured to create instructional experiences in technologybased learning environments. They argue that the importance of these features in a given context depends on the instructional environment that is required to achieve the targeted learning objectives. Future research that examines the ability of different technologies to deliver these features can help ensure that the technological platform chosen for a particular training program is aligned with the learning objectives. Recent research has also provided insight into instructional strategies that can be integrated into technology-based training to support critical learning processes. For example, studies have demonstrated that prompting self-regulation (e.g., Sitzmann, Bell, Kraiger, & Kanar, 2009; Sitzmann & Ely, 2010) and providing adaptive guidance (Bell & Kozlowski, 2002; Kanar & Bell, 2013) during technology-based training can enhance learning and reduce attrition. Additional research is needed on strategies that can be used to help individuals make better use of

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the learner control afforded by technology-based training. Finally, from a practical standpoint it will be important for future research to consider not only the benefits but also the costs associated with different forms of technology-based training. For example, although simulations offer a number of potentially valuable instructional capabilities (e.g., high levels of immersion and interactivity), the significant costs associated with simulation development has limited their utilization in practice, particularly in smaller organizations (Bell, Kanar, & Kozlowski, 2008). Thus, future research is needed that provides insight into the cost–benefit tradeoffs associated with different configurations of technology-based training (Bowen, Chingos, Lack, & Nygren, 2012).

TEAM TRAINING AND DEVELOPMENT In recent decades, the nature of work has shifted from individual jobs to team-based work systems (Kozlowski & Bell, 2013). Teams enable organizations to bring together the diverse skills, expertise, and experience needed to solve complex problems and to stimulate creativity and innovation. Teams also allow organizations greater flexibility in how they allocate their resources, particularly as individuals increasingly belong to multiple teams simultaneously (Tannenbaum, Mathieu, Salas, & Cohen, 2012). They can be formed rapidly to respond to organizational challenges and can be quickly reconfigured as project requirements evolve or more pressing needs arise. Moreover, virtual teams – whose interaction occurs primarily through technology – allow organizations to connect individuals who are distributed across the globe so they can collaborate on assignments and share best practices (Bell & Kozlowski, 2002). Given these many benefits, it is not surprising that teams have become ‘the critical unit that “gets things done” in today’s world’ (Marks, 2006, p. i). This is especially true in settings where quality and high reliability are paramount, since team members can serve as redundant systems to help mitigate errors (Salas, Wilson, Murphy, King, & Salisbury, 2008). Yet, it is also widely acknowledged that teams can and often do fail (Bell & Kozlowski, 2011). As Salas, Kosarzycki, Tannenbaum, and Carnegie (2004) observe, ‘On the one hand, it is clear that teams can accomplish great things, but it is equally apparent that subfunctioning teams have the potential to fail spectacularly’ (p. 98). The proliferation of teams combined with the evidence of their fallibility has stimulated

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significant research aimed at understanding and influencing team effectiveness. A recent review of the literature by Kozlowski and Ilgen (2006) identified three interventions or levers – team design, training and development, and leadership – that can be used to shape and align team processes and thereby improve team effectiveness. In the next section, we review what we know about the effectiveness of a range of team training and development interventions, highlighting recent developments as well as future research needs.

Team Training and Development Interventions Team training is defined as ‘a set of strategies that create a context in which team skills can be practiced, assessed, and learned’ (Salas & CannonBowers, 1997, p. 274). The effectiveness of team training depends, in part, on the type of team training, training content, delivery method, and training task that an organization employs (Kozlowski & Ilgen, 2006). Training researchers and practitioners alike are interested in knowing the differential impact of training interventions on organizational outcomes. Hence, significant research attention has been focused on identifying the most effective type of team training interventions for improving various team process and outcomes. In what follows, we describe four of the most prevalent team training and development interventions and review the research evidence of their effectiveness.

Cross-training

Although there are many different types of crosstraining (see Blickensderfer, Cannon-Bowers, & Salas, 1998 for a review), in general the approach is designed to help a team achieve operational readiness and promote teamwork by training team members to take on the tasks, roles, responsibilities, and functions of their teammates (CannonBowers, Salas, Blickensderfer, & Bowers, 1998; Kozlowski & Ilgen, 2006). This goal is achieved through the creation of interpositional knowledge (IPK). According to Volpe, Cannon-Bowers, Salas, and Spector (1996), ‘IPK can be described as a type of “role” knowledge held by team members’ (pp. 87–8). Additionally, this approach focuses on developing shared team mental models and skills for enhanced team coordination. For example, Marks, Sabella, Burke, and Zaccaro (2002) empirically examined the effect of three types of crosstraining (e.g., positional clarification, modeling, and rotation) on shared team-interaction mental models, coordination (i.e., back-up behavior), and

performance. They found across two studies that cross-training helped to facilitate the development of shared team-interaction models. In addition, the more in-depth forms of cross-training (positional modeling and rotation) resulted in more shared mental models than positional clarification, although there were no significant differences between the positional modeling and positional rotation approaches. Additionally, they also found that the effect of cross-training on team performance was mediated by shared team-interaction mental models and team backup and coordination processes. Similarly, a study by Cooke et al. (2003) found that teams exposed to full cross-training exhibited greater gains in taskwork and teamwork knowledge than teams exposed to a conceptual version of cross-training. Several meta-analyses have also examined the effects of cross-training, although they have sometimes yielded mixed findings. Salas, Nichols, and Driskell’s meta-analysis (2007) indicated that cross-training does not have a significant effect on team effectiveness (r = –.09), whereas Salas et al. (2008) found that cross-training has a moderate positive effect on team outcomes (ρ = .44). The difference in findings may be due to the fact that many of the studies included in Salas et al. (2007) examined multiple components of team training, making it difficult to tease apart the effects of a particular strategy. Salas et al. (2008) appear to provide a cleaner test of the effects of crosstraining, although the number of effect sizes (k = 14) is small. Ultimately, the authors call for more empirical research that examines the effectiveness of this team training strategy.

Adaptation–coordination– CRM training

Within this category, there are two team training strategies. The first strategy, team adaptation and coordination training (TACT), is used in an effort to develop teamwork skills needed to enhance coordination and decision-making processes and to teach team members how to take advantage of idle periods to anticipate and discuss potential problems (Salas et  al., 2007). This training approach is commonly used in military tactical decision-making training programs (Cannon-Bowers et al., 1998). Two metaanalytic studies provide evidence for the effectiveness of this team training strategy. Salas et  al.’s (2007) meta-analysis showed that TACT training was effective at improving team performance (r = .61). However, they note that studies that employed high levels of TACT tended to also employ relatively high levels of team self-correction training (r = .71), again making it somewhat difficult to tease apart the effects of a particular strategy. Salas et al.’s

Learning, Training and Development in Organizations

(2008) meta-analysis also demonstrated that TACT has a positive effect on team outcomes (ρ = .56), although this finding is based on a small number of effect sizes (k = 6). The second training approach within this category is crew resource management (CRM) training. CRM training interventions have been widely adopted in the military and aviation environments (see Flin, O’Connor, & Mearns, 2002) and are increasingly being used in medical settings. This method takes into account a wide range of KSAs such as interpersonal communication, situational awareness, problem-solving, leadership, decision-making, and teamwork (Salas, Prince et al., 1999). The ultimate goal of this method is to improve safety by enhancing teamwork, skill integration, and coordination. Numerous researchers have demonstrated that CRM is effective (e.g., Fisher, Phillips, & Mather, 2000; O’Connor & Flin, 2003; Salas, Wilson, Burke, & Wightman, 2006). O’Connor, Campbell, Newton, and Melton et  al. (2008) demonstrated that CRM training had a moderate positive effect on trainee knowledge evaluation (dmean = 0.59) and a strong positive effect on trainee attitudes (dmean = 0.94) and behaviors (dmean =1.18). However, they could unequivocally confirm a significant effect of CRM training only for trainee attitudes since the confidence intervals for behavior and knowledge bounded zero. Coordination/CRM training was the most popular training strategy in Salas et  al.’s (2008) database (k = 33) and they found it had a positive effect on team outcomes (ρ = .47). Overall, these findings support the effectiveness of CRM training.

Team self-correction training

The focus of this training technique is to develop a team’s KSAs to diagnose and self-correct team attitudes, behaviors, and cognitions (Blickensderfer, Cannon-Bowers, & Salas, 1997). The goals of this training approach are to enhance a team’s ability to reflect on prior performance, identify errors, solve problems, and plan for future improvement (DeChurch & Haas, 2008). Although the bulk of relevant research has been conducted on guided or facilitator-led self-correction training, Blickensderfer et  al. (1997) compared unguided with guided team self-correction training. Their findings revealed that guided team self-correction training was more effective at developing teamwork processes and shared task expectations than unguided team self-correction. However, team performance was not significantly different for either unguided or guided team self-correction training. A recent study by Smith-Jentsch, CannonBowers, Tannenbaum, and Salas (2008) found that teams that participated in facilitator-led guided team self-correction organized around an expert

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model of teamwork had more accurate mental models and greater teamwork processes and performance than teams that participated in more traditional military briefings and debriefings. The meta-analyses by both Salas et  al. (2007) and Salas et al. (2008) provided evidence of the effectiveness of self-correction training (r = .45 and ρ = .27, respectively), although the number of studies examining this form of training were small (k = 2 in Salas et al., 2008).

Team Building

Klein et al. (2009, p. 183) define team building as ‘a class of formal and informal team-level interventions that focus on improving social relations and clarifying roles, as well as solving task and interpersonal problems that affect team functioning’. As this definition suggests, team building encompasses a diverse array of developmental interventions, although there is consensus that there exists four basic approaches or models of team building: goal-setting, interpersonal relations, role clarification, and problem-solving (Salas, Rozell, Mullen, & Driskell, 1999). A metaanalysis by Klein et al. (2009) revealed that team building had an overall positive effect across all team outcomes (ρ = .31). In addition, they found that team building had stronger effects on affective (ρ = .44) and process (ρ = .44) outcomes than cognitive (ρ = .13) or performance (ρ = .26) outcomes. There were also some differences across the four types of team building, with goal setting (ρ = .37) and role clarification (ρ = .35) components generally having stronger effects than either interpersonal relations (ρ = .26) or problem-solving (ρ = .24) components. Finally, they found that team building tended to be more effective for large teams (> 10 members; ρ = .66) than either medium teams (5–10 members; ρ = .27) or small teams (< 5 members; ρ = .28). Overall, these results suggest that team building training interventions are effective for improving team effectiveness.

Future Research Directions The empirical and meta-analytic evidence reviewed above for the relative impact of the various training interventions on team outcomes is promising and should provide researchers with fruitful directions for future research. As noted by Salas and colleagues (2007), we have a relatively small body (but growing in aviation, military, and medical domains) of empirical results that hint at the components of team training that drive enhancements to team perfor-

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mance (Salas et  al., 2006). Researchers interested in the problems of bolstering team performance might direct and channel primary level research efforts into these promising directions. (pp. 485–6)

These results also point to the fact that not all training interventions are effective for all teams and that different types of interventions lead to different team outcomes; thus those interested in improving team performance would benefit from more research on specific training interventions that would enable decisive comparisons (Salas et  al., 2007, 2008). For example, in spite of the popularity of cross-training in organizations, relatively few empirical studies have examined its effectiveness and a number of these studies used laboratory settings or student samples. Given the variety of extant teamwork arrangements (i.e., virtual teams, multinational teams) future research needs to evaluate the effectiveness of cross-training and other team training interventions for influencing the functioning of different types of teams. Recent work by Tannenbaum et al. (2012) also highlights recent changes in the nature of teams that have potentially important implications for team training and development. For instance, Tannenbaum and colleagues note that modern teams increasingly are characterized by a dynamic composition. Whereas in the past teams had well-defined and stable memberships, today they are much more fluid and changes in membership are common. In addition, teams are more diverse, highly specialized, and globally distributed, and increasingly they utilize multiple-team memberships and other more contemporary teamwork arrangements. These progressive and ongoing changes in the nature of teams (e.g., the way teams are designed, implemented, used, and transitioned) require concurrent advances in research on team training and development. For example, the trend toward dynamic team composition suggests that greater attention may need to be devoted to strategies that can be used to develop generic teamwork skills that members can transport across different team environments (Ellis, Bell, Ployhart, Hollenbeck, & Ilgen, 2005). Also, methods that encourage more self-directed learning, such as team-led debriefing techniques, may enable teams to respond more quickly to developmental needs created by changes in team membership (Eddy, Tannenbaum, & Mathieu, 2013).

TRAINING TRANSFER In an effort to improve employee work performance and as a strategy for gaining competitive

advantage, organizations make significant investments in training and development (Adler & Kwon, 2002; Barney & Wright, 1998; CutcherGershenfeld & Ford, 2005). The intent of this investment is to develop capabilities that employees will transfer to their jobs, improving performance and quality of service (Zumrah, Boyle, & Fein, 2013). However, despite major investments in training, many organizations report that the bulk of training expenditures seemingly do not transfer to the job (Baldwin & Ford, 1988; Burke, 2001; Cheng & Ho, 2001; Ford & Weissbein, 1997; Goldstein & Ford, 2002; Grossman & Salas, 2011) or improve organizational performance (Broad, 1997). In fact, some estimates suggest that only 10 percent of training expenditures result in transfer to the job (Georgenson, 1982). Increasing organizational investments in training combined with the evidence that much of what is trained is not applied in the work setting has resulted in growing interest in what is known to researchers and practitioners alike as the transfer problem (Baldwin & Ford, 1988). Understanding transfer provides an important nexus of training research since it is considered the point where training influences individual, group, and organizational outcomes (Goldstein & Ford, 2002; Kozlowski, Brown, Weisbein, Cannon-Bowers, & Salas, 2000). Such research focuses on identifying the factors that influence the extent to which newly acquired KSAs developed during training transfer effectively to the job and result in positive changes in work performance (Grossman & Salas, 2011). For example, numerous reviews (e.g., Baldwin & Ford, 1988; Baldwin, Ford, & Blume, 2009; Burke & Hutchins, 2007; Cheng & Hampson, 2008; Cheng & Ho, 2001; Grossman & Salas, 2011), meta-analyses (e.g., Blume et al., 2010), and models (e.g., Burke & Hutchins, 2008; Kozlowski & Salas, 1997; Thayer & Teachout, 1995) have examined predictor–transfer relationships. We have made meaningful progress, but a number of important and unanswered questions remain. In the next section, we review what we know about training transfer and where we need to go. First, we define training transfer and discuss the evolution of the concept over time. Second, we review research that has examined the predictors of training transfer. Lastly, expanding on prior research, we identify future directions that should further enhance our understanding of the transfer of training.

The Evolution of Training Transfer Baldwin and Ford (1988) defined transfer as ‘the degree to which trainees effectively apply the

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knowledge, skills, and attitudes gained in a training context to the job’ (p. 63). Transfer has traditionally been conceptualized as a function of two conditions: (1) generalization of learning to settings/situations on the job and, (2) maintenance of the learning over a period of time (Baldwin & Ford, 1988; Cheng & Hampson, 2008; Ford & Weissbein, 1997). In recent years, a number of researchers have extended Baldwin and Ford’s (1988) conceptualization of transfer. For example, Yelon and Ford (1999) proposed a multi-­ dimensional perspective of training transfer which suggests that transfer is dependent on the nature of training content, skills trained, and degree of supervision on the job. In particular, they conceptualize transfer along two dimensions: open versus closed skills (task adaptability) and heavy supervision versus autonomous supervision (degree of supervision). Training for open skills is focused on developing skills that allow for greater variability in how they are performed, whereas training for closed skills is highly prescribed and offers limited variation in how the task is performed. Barnett and Ceci (2002) developed a taxonomy consisting of two global factors – content (i.e., what is transferred) and context (i.e., when and where it is transferred from and to) – which are subdivided into nine dimensions along which transfer can occur. They argue that the application of the taxonomy provides greater clarity about whether and what form of transfer has occurred. Finally, a few researchers have discussed the concept of upward or vertical transfer across organizational levels (Kozolowski, Brown et  al., 2000; Tharenou, Saks, & Moore, 2007). More specifically, vertical transfer focuses on how individual training outcomes emerge to the team and organizational level (Kozlowski, Brown et al., 2000). To date, most work has focused on horizontal transfer, or transfer across different settings or contexts within the same level.

Predictors of Training Transfer Over the years a number of models and conceptual frameworks have been developed in an effort to understand the factors that influence the transfer of training. For example, Baldwin and Ford’s (1988) seminal model of training transfer identified the importance of three categories of traininginput factors as critical for influencing transfer: (1) trainee characteristics (e.g., motivation), (2) training design (e.g., sequencing), and (3) work environment (e.g., support). The majority of research on the predictors of training transfer has focused on variables that fall within these three

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categories. More specifically, according to Blume et  al. (2010), the nine training inputs most commonly examined are individual (i.e., locus of control, self-efficacy), motivational (i.e., career/ job attitudes, organizational commitment, decision/reaction to training, post-training interventions), and environmental (i.e., supports in organization, continuous-learning culture, task constraints) variables. In this section, we review the current evidence for the relationship between these predictors and transfer.

Trainee characteristics

Trainee characteristics include such factors as ability, personality, skill, and motivation. According to Grossman & Salas (2011), there is research evidence to demonstrate that cognitive ability, self-efficacy, motivation, and perceived utility of training have the strongest relationship with training transfer. For example, numerous researchers have shown that cognitive ability positively impacts transfer through its effects on skill acquisition and post-training self-efficacy (e.g., Colquitt, LePine, & Noe, 2000). It has also been consistently demonstrated that self-efficacy is a predictor of training performance and transfer (e.g., Ford, Smith, Weissbein, Gully, & Salas, 1998; Gist, Stevens, & Bavetta, 1991; Mathieu, Martineau, & Tannenbaum, 1993). Additionally, there is considerable research that has demonstrated the influence of motivation to learn on training transfer (Colquitt et  al., 2000). A recent meta-analysis by Blume et  al. (2010) found evidence of moderately strong relationships between transfer and cognitive ability (ρ = .37), voluntary participation (ρ = .34), conscientiousness (ρ = .28), pre-training self-efficacy (ρ = .22), trainee motivation (ρ = .23), and neuroticism (ρ = –.19).

Training design

According to Blume et al. (2010), training design factors have received more attention in the transfer literature than any other training input. For example, meta-analytic reviews have examined the relationship between transfer and different training delivery methods (Arthur et al., 2003) and learning principles (e.g., overlearning; Driskell, Willis, & Copper, 1992). In addition, recent metaanalyses have provided evidence that behavior modeling (Taylor, Russ-Eft, & Chan, 2005) and error management training (Keith & Frese, 2008) are effective training methods for facilitating transfer. However, as noted by Blume et al. (2010), ‘missing from current meta-analytic studies on training design and transfer is consideration of the training objectives or goals of the training program on transfer’ (p. 1069). Blume et  al. (2010)

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also examined the effects of three transfer interventions, optimistic preview, goal-setting, and relapse prevention, and found them to have small to moderate effects on transfer. However, they caution that relatively few studies have examined these interventions and there may be moderators that influence their effectiveness.

Work environment

The work environment includes such factors as climate and support. The post-training work environment has been shown to have a major influence on the transfer of training to the job (Tracey, Tannenbaum, & Kavanagh, 1995; Velada, Caetano, Michel, Lyons, & Kavanagh, 2007). Blume et al. (2010) found evidence for a positive relationship between transfer and the overall work environment (ρ = .23), and the work environment had a stronger effect on the transfer of open skills (ρ = .26) than closed skills (ρ = .04). Furthermore, they found that transfer climate (ρ = .27) had the strongest relationship with transfer when compared with other elements of the work environment. In addition to transfer climate, researchers have demonstrated that peer, social, subordinate, and supervisor support affects transfer (e.g., Burke & Baldwin, 1999; Tracey et al., 1995). For example Blume et  al., (2010) found overall support (ρ = .21) had a positive impact on transfer, and subsequent analyses suggested that supervisor support (ρ = .31) may be more important for transfer than peer support (ρ = .14). Additionally, providing trainees with the opportunity to use what they learn in training on the job has been shown to positively influence transfer (Ford, Quiñones, Sego, & Sorra, 1992; Gaudine & Saks, 2004; Lim & Morris, 2006). Blume et  al. (2010) found a weak relationship between organizational constraints (ρ = .05) and transfer, although it was based on only two studies.

Future Directions Looking ahead, it will be important for future research to address some of the limitations that have characterized prior research on the transfer of training. Blume et al. (2010), for example, provide evidence that the relationships observed in a number of transfer studies have been biased by same-source and same-measurement-context effects. Thus, future research should collect predictor and transfer variables from different sources and should incorporate time lags between training and transfer measures. Blume et  al. (2010) also note that the median time between training and the transfer measures for the studies they examined

was 1 day for lab studies and 7.5 weeks for field studies. This suggests that much of what we currently know about transfer is based on an examination of a relatively brief window of time following training. Future research that examines transfer over an extended period may reveal that some predictors become more or less important as time passes. In addition to addressing these issues, there are several new directions that hold considerable potential for advancing our understanding of the transfer of training. Below we discuss two that we see as most promising.

Multilevel perspective on transfer

Kozlowski and Salas (1997) developed a multilevel model for training implementation and transfer that distinguishes among the different levels that comprise the organizational system (i.e., individual, team, organization) and specifies linkages between the factors and processes at the different levels. The model considers not only how the effects of training at the individual level aggregate to affect higher level outcomes but also how higher levels serve as a context that shapes the effects of training at lower levels. Kozlowski, Brown et  al. (2000) further developed the concepts of horizontal (same-level) and vertical (cross-level) transfer and the top-down contextual effects and bottom-up emergent processes that are implicated in the transfer of training across levels. As discussed earlier, the vast majority of research has focused on horizontal transfer, although there have been numerous calls to focus more attention on vertical transfer. Future research that adopts a multilevel perspective of training transfer is needed to test and refine the cross-level linkages between training activities, trainees’ learning processes, work environment factors, and outcomes at different levels of the organizational system (team, unit, or organization).

Social network perspective on transfer

Given the importance of social support to the transfer of training, a social network perspective may provide valuable insight into trainees’ organizational network relationships in the work context (Hatala, 2006). To date, a limited number of scholars have used social network analysis (SNA) to examine learning (e.g., Borgatti & Cross, 2003; Cross, Parker, Prusak, & Borgatti, 2001; Reffay & Chanier, 2000; Van den Bossche, Segers, & Jansen, 2010). However, social network analysis examines the interpersonal mechanisms and social structures (e.g., centrality, position, strength of ties, cohesion, division) that connect interacting units (i.e., small groups, large groups, departments,

Learning, Training and Development in Organizations

units, within organizations, between organizations) and thus may serve as a useful lens for exploring how relationships between actors, subgroups of actors, or groups affect learning outcomes at various levels of analysis (Scott, 2011; Wasserman & Faust, 1994). By measuring and mapping the connections and interactivity between trainees and others within the organization, researchers can examine the network structure prior to, during, and after a training intervention and the exchange patterns that lead to differential transfer outcomes (Hatala, 2006; Ibarra & Andrews, 1993). In addition to serving as a methodological approach, SNA also holds promise for theory building (Hatala, 2006; Wasserman & Faust, 1994). For example, the use of both attribute and relational data via SNA should help to answer a number of open research questions regarding the level of centrality and connectedness and the type of post-training interaction patterns that best facilitate positive transfer. Moreover, longitudinal transfer studies could leverage SNA to examine the effect of network changes on transfer over time.

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2009). These findings may help explain why only 43 percent of CLOs report being satisfied with their organizations’ training evaluation and learning measurement practices (Anderson, 2012). However, it is also clear that there exists a growing desire to reverse these trends. For example, 30 percent of CLOs report plans to develop measurement programs to track the impact of learning on employee capability and nearly 80 percent intend to increase evaluation of their learning and development programs (Anderson, 2012). Additionally, numerous observers have called for greater research attention to issues surrounding training evaluation (e.g., Edkins, 2002; Littrell, Salas, Hess, Paley, & Riedel, 2006; Kraiger, McLinden, & Casper, 2004). In the following sections, we discuss the two major types of training evaluation – formative and summative – and review the dominant frameworks and models within each area. In addition, we discuss recent advances in each area and conclude with a discussion of potential future research directions.

Types of Training Evaluation TRAINING EVALUATION Evaluation is a necessary and critical component of effective training programs. It provides input for making decisions about training, including determining whether a program should be retained and identifying changes that can be made to improve current and future programs (Kraiger, 2002). In addition, evaluation data can be used to market training to both internal and external stakeholders, including upper management, future trainees, and outside agencies. The importance of these decision-making and marketing activities becomes even more salient when recalling the magnitude of organizational spending on employee training (Miller, 2013). It is surprising then that only a small percentage of organizations succeed in establishing effective internal training evaluation processes (Brown & Gerhardt, 2002; Swanson, 2005). In fact, evaluation continues to be one of the most neglected aspects of the training process. For example, a recent study by ASTD found that although 91.6 percent of organizations assess trainee reactions and 80.8 percent assess learning, only 54.5 percent evaluate behavior (i.e., transfer) and 36.9 percent evaluate results, even though the data collected through assessments of behavior and results was perceived to be of significantly greater value than that collected through assessments of reactions and learning (Wentworth, Tompson, Vickers, Paradise, & Czarnowsky,

Training researchers typically classify evaluation into two broad categories, formative evaluation and summative evaluation (Brown & Gerhardt, 2002; Rossi, Freeman, & Lipsey, 1999). Each approach provides a unique conceptualization of evaluation (i.e., purpose, timing, and outcome). More specifically, in an effort to achieve the intended training objectives, formative evaluation provides information (i.e., quantitative and qualitative data) about how to improve the quality of a training intervention that is being developed (Beyer, 1995; Scriven, 1991). Formative evaluation occurs throughout the training design and development process. For example, a formative evaluation would identify weaknesses in learning objectives, training materials, or methods with the goal of developing solutions during training design and development (Brown & Gerhardt, 2002). In contrast, summative evaluation practices are used to evaluate the effectiveness of completed training interventions with the goal of providing suggestions about their use (Brown & Gerhardt, 2002; Scriven, 1991). Summative evaluation occurs after design and development, and it can be further classified into short-term and long-term outcome evaluation (Wang & Wilcox, 2006). This approach centers on identifying individual and organizational training outcomes and supports decision-making for future training and development investments (Alvarez, Salas, & Garofano, 2004; Kraiger, 2002). For example, a summative

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evaluation would utilize measurements (i.e., prepost metrics) to assess training outcomes and report an overall conclusion regarding the effectiveness of the training.

order to fully understand how to utilize formative evaluation procedures and to determine the conditions under which the different approaches are most effective.

Formative Evaluation Models

Summative Evaluation Models

There are several perspectives on how best to conduct formative evaluation and each highlights a number of potentially important considerations (e.g., involvement of stakeholders in training design and development, identification of the critical training components, the use of established scales). In general, there are three main models to consider (Brown & Gerhardt, 2002). The Geis Method Model by Geis (1987) uses two main methods, developmental testing and expert review, to gather feedback for improving a training program. For example, development testing would utilize test subjects who possess characteristics (i.e., cognitive ability) similar to those of the target training group to evaluate training material. In contrast, the expert review method involves selecting experts of various types (eight types of experts are identified) to evaluate and critique training materials with a focus on errors, omissions, and improper emphasis. The Stage Model by Dick and Carey (1996) outlines three steps of formative evaluation (i.e., one-on-one, small group, and field test) and provides the specific purpose, criteria, methods, and procedures for each stage of evaluation. Finally, the Component Model by Weston, McAlpine, and Bordonaro (1995) outlines the evaluation goals and practical constraints for each of the major components during formative evaluation: participants (i.e., nature and level of expertise), roles (i.e., evaluator, learner, critic, reviser), methods (i.e., techniques ant tools), and situation (i.e., the evaluation context). Brown and Gerhardt (2002) suggest that each of these models has both benefits and limitations and they propose an integrative model that leverages the strengths of the different perspectives on formative evaluation. In particular, their model focuses on four stages of the design and development process – concept, design, prototype, and pilot – and describes what to measure in each stage as well as who should be involved in the effort. They argue that their integrative approach to formative evaluation offers a number of potential practical and scientific benefits and may be most effective when combined with summative evaluation. Unfortunately, very little research has tested or compared the utility of these different approaches for improving the effectiveness of training in organizations. Thus, more research is needed in

The summative evaluation approach has dominated both training research and practice (Brown & Gerhardt, 2002). Most summative evaluation efforts have used or extended Kirkpatrick’s fourlevel framework (1994, 1996). Reactions evaluation (Level 1) provides information on participants’ thoughts about training. Learning evaluation (Level 2) is measured during training and focuses on the acquisition of desired knowledge and skills. Behavior evaluation (Level 3) is concerned with the transfer of knowledge and skills to the job and thus is measured at some point after training. The final level, Level 4, measures the tangible results of a program in terms of reduced costs, improved quantity or quality of output, or other valued organizational outcomes. In practice, it is often assumed that each level is caused by the previous level (e.g., improving reactions will increase learning; Kraiger, 2002). However, there is limited empirical evidence for casual relationships among the four levels, which is cause for concern when using one of the levels as the sole criterion of training effectiveness or inferring changes at one level from changes in another level of the hierarchy (Alliger, Tannenbaum, Bennett, Traver, & Shotland, 1997; Alliger & Janak, 1989; Mathieu, Tannenbaum, & Salas, 1990). Kirkpatrick’s four training criteria are still consistently used in training evaluation research (Salas, Tannenbaum, Kraiger, & Smith-Jentsch, 2012), although there have been a number of extensions, improvements, and clarifications over the years. Kraiger, Ford, and Salas (1993), for example, utilized cognitive theory in an effort to broaden existing models of training evaluation. In particular, they developed a classification scheme that positioned learning as a multidimensional construct that could be evaluated through cognitive, skill-based, and affective outcomes. Their article underscored the importance of being clear about, and choosing appropriate measures to assess, not only the objectives of training (e.g., declarative knowledge) but also the processes required to achieve them (e.g., mental models, metacognition). Kraiger’s decision-based model (2002) built on this earlier work and emphasized that evaluation efforts should be guided by a consideration of why the evaluation is being conducted (i.e., how the information will be used). Depending on the

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reasons for doing the evaluation, trainers may target one or more of the following areas: training content and design (e.g., design, delivery, and content validity), changes in learners (e.g., affective, cognitive, and behavioral), and organizational payoffs (e.g., transfer, performance, and results). Despite being the most frequently used evaluation criterion in research and practice, trainee reactions have limited theoretical grounding and are also poorly understood (Brown, 2005; Colquitt et al., 2000). However, recent meta-analyses have helped to clarify the antecedents and consequences of trainee reactions. Brown (2005) found that reactions have a hierarchical structure, such that different reaction facets (e.g., relevance, technology satisfaction) are related through an overall satisfaction construct. In addition, reactions were influenced by both person constructs (e.g., content interest) and situation constructs (e.g., delivery media) and related to the learning process (e.g., engagement) and outcomes (e.g., learning). More recently, Sitzmann, Brown, Casper, Ely, and Zimmerman (2008) found that reactions primarily captured perceptions of the training environment (e.g., instructor style) but were also influenced by trainee characteristics and perceived organizational support. Further, their results demonstrated that reactions were most strongly related to affective learning outcomes (e.g., motivation, self-efficacy), although they also exhibited moderate relationships with cognitive learning outcomes (e.g., declarative and procedural knowledge). Overall, these findings provide insight into the factors that influence trainees’ reactions and also suggest that the influence of reactions on other learning outcomes may be greater than previously thought.

Future Research Training evaluation research, although not necessarily training evaluation practice, has evolved from its reliance on Kirkpatrick’s four-level model of outcome evaluation to a multidimensional framework of learning outcomes (Kraiger et  al., 1993). Consistent with this advancement in training evaluation is a more construct-oriented approach that incorporates psychological constructs (Kraiger, 2002), casual relationships (Holton, 1996) and more precise evaluation methodologies (e.g., Kraiger et al., 1993; Tannenbaum & Woods, 1992) to understand whether or not a given training intervention has worked. This shift is attributed largely to Kraiger et al.’s (1993) multidimensional learning taxonomy. Kraiger et  al. (1993) argued that ‘to advance the science and practice of training evaluation, it is necessary to

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move toward a conceptually based classification scheme of learning based on such a multidimensional perspective’ (p. 312). Ford, Kraiger, and Merritt (2010) reviewed training evaluation research that was conducted since Kraiger et  al. (1993) introduced the multidimensional perspective and identified four trends as well as directions for future research. The first trend involved acknowledging that a majority of researchers have adopted a multidimensional approach in their empirical examination of training evaluation. Second, evaluation researchers have sharpened their focus on cognitive and affective learning outcomes relative to skill-based outcomes (e.g., Kraiger & Ford, 2007; Yeo & Neal, 2004). Third, researchers are measuring not only declarative knowledge but also more cognitive learning outcomes (e.g., strategic knowledge and knowledge structures). Lastly, in an effort to expand our understanding of trainee reactions, researchers have started using alternative measures of affective outcomes (e.g., Hsieh & Chao, 2004) and examining learning across levels of analysis. Despite these advancements, Ford et al. (2010) argue that future research is needed to expand our understanding of cognitively and affectively based learning outcomes. In particular, they suggest examining metacognition as a learning outcome and its changes over time as well as changes in trainee motivational disposition and attitudes (e.g, measures of explicit and implicit attitudes) as a function of training. As Kraiger (2002) notes, there are often alternative methods available to evaluate a particular target and these methods can vary in terms of how easy they are to implement, time and resources required, and validity. We believe that future research that examines different evaluation methods or approaches can help researchers and practitioners navigate these options. For example, a recent metaanalysis by Sitzmann, Ely, Brown, and Bauer (2010) examined the construct validity of self-assessments of knowledge. Given that it can be time-consuming and challenging to develop objective measures of learning, it is common for organizations to rely on self-report measures that ask trainees to estimate how much they know (i.e., knowledge level) or have learned about (i.e., knowledge gain) a specific domain (Lopker & Askeland, 2009). However, the meta-analytic findings of Sitzmann et  al. (2010) suggest that trainees generally have difficulty selfassessing their learning. Specifically, they found that learners’ self-assessments of their knowledge level were only moderately correlated with measures of cognitive learning, and self-assessments of knowledge gain did not correlate with cognitive learning. Further, they found that self-assessments of knowledge related more strongly to affective training outcomes (e.g., satisfaction, motivation)

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Table 10.1 Summary of research advances and future directions Training design and delivery Research advances • Support for the effectiveness of several active learning interventions; core training design elements identified and some process mechanisms mapped • Meta-analytic support for the effectiveness of technology-based training Future directions • Identify individual differences that interact with active learning approaches and elaborate process pathways; examine alternative forms of active learning • Focus on the instructional features and strategies that influence the effectiveness of technology-based training Team training and development Research advances • Meta-analytic support for the effectiveness of several team training and development interventions Future directions • Continue to evaluate and refine team training strategies across different types of teams; explore new training and development strategies that can respond to changes in the nature of teams Training transfer Research advances • Expanded conceptualization of transfer; meta-analytic support for influence of numerous trainee, training design, and work environment factors on transfer of training Future directions • Examine transfer over extended periods; adopt a multilevel perspective of training transfer; apply social network analysis to the study of training transfer Training evaluation Research advances • Multidimensional and decision-based models of training evaluation; clarification of structure and nomological network of trainee reactions Future directions • Expand understanding of cognitive and affective learning outcomes; examine different formative and summative evaluation methods

than cognitive training outcomes. Overall, these findings raise serious concerns about using selfassessments to measure learning, although Bell and Federman (2010) note that before a definitive conclusion can be reached additional research is needed on several issues, including differences among the various types of self-assessments and the conditions that may enhance or inhibit the accuracy of self-assessments.

CONCLUSION The past decade has witnessed significant changes in learning, training, and development in organizations. Training and development initiatives are now seen as critical to achieving company objectives and training expenditures have increased accordingly. The costs and challenges associated with offering formal training have prompted greater utilization of

technology to deliver training and have sparked interest in harnessing the potential of informal and selfdirected learning. Team training and development interventions that were once used almost exclusively in aviation and the military are now being deployed across a wide range of industries to influence collective performance. And now more than ever there is interest in bridging the gap between learning and on-the-job performance and in evaluating the impact of investments in training and development. In this chapter, we have examined how these trends and changes are shaping research in the field of training and development. Table 10.1 provides a summary of our key conclusions and highlights that significant advances have been made in each of the four research areas we reviewed. However, the table also points to the need for future research to continue to evolve if the science of training is to remain relevant to the realities of contemporary organizations. Fortunately, we believe there is a strong foundation on which to build and look forward to future progress in both training research and practice.

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Kozlowski, S. W. J., & Salas, E. (1997). A multilevel organizational systems approach for the implementation and transfer of training. In J. K. Ford (Ed.), Improving training effectiveness in work organizations (pp. 247–287). Mahwah, NJ: Lawrence Erlbaum. Kozlowski, S. W. J., Brown, K., Weisbein, D., CannonBowers, J., & Salas, E. (2000). A multi-level approach to training effectiveness: Enhancing horizontal and vertical transfer. In K. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations (pp. 157–210). San Francisco: Jossey-Bass. Kozlowski, S. W. J., Gully, S. M., Brown, K. G., Salas, E., Smith, E. M., & Nason, E. R. (2001). Effects of training goals and goal orientation traits on multidimensional training outcomes and performance adaptability. Organizational Behavior and Human Decision Processes, 85(1), 1–31. Kozlowski, S. W. J., Toney, R. J., Mullins, M. E., Weissbein, D. A., Brown, K. G., & Bell, B. S. (2001). Developing adaptability: A theory for the design of integrated-embedded training systems. In E. Salas (Ed.), Human/technology interaction in complex systems (Vol. 10) (pp. 59–123). Greenwich, CT: JAI Press. Kraiger, K. (2002). Decision-based evaluation. In K. Kraiger (Ed.), Creating, implementing, and maintaining effective training and development: Stateof-the-art lessons for practice (pp. 331–375). San Francisco, CA: Jossey-Bass. Kraiger, K., & Ford, J. K. (2007). The expanding role of workplace training: Themes and trends influencing training research and practice. In L. L. Koppes (Ed.), Historical perspectives in industrial and organizational psychology (pp. 281–309). Mahwah, NJ: Lawrence Erlbaum Associates. Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive, skill-based, and affective theories of learning outcomes to new methods of training evaluation. Journal of Applied Psychology, 78(2), 311–328. Kraiger, K., McLinden, D., & Casper, W. J. (2004). Collaborative planning for training impact. Human Resource Management, 43(4), 337–351. Lim, D. H., & Morris, M. L. (2006). Influence of trainee characteristics, instructional satisfaction, and organizational climate on perceived learning and training transfer. Human Resource Development Quarterly, 17(1), 85–115. Lipsey, M. W., & Wilson, D. B. (1993). The efficacy of psychological, educational, and behavioral treatment: Confirmation from meta-analysis. American psychologist, 48(12), 1181–1209. Littrell, L. N., Salas, E., Hess, K. P., Paley, M., & Riedel, S. (2006). Expatriate preparation: A critical analysis of 25 years of cross-cultural training research. Human Resource Development Review, 5(3), 355–388.

Lopker, G., & Askeland, R. (2009). More than a smile sheet: Using level 1 evaluation effectively. T+ D, 63(9), 74–75. Marks, M. A. (2006). The science of team effectiveness. Psychological Science in the Public Interest, 7(3), i. Marks, M. A., Sabella, M. J., Burke, C. S., & Zaccaro, S. J. (2002). The impact of cross-training on team effectiveness. Journal of Applied Psychology, 87(1), 3–13. Mathieu, J. E., Martineau, J. W., & Tannenbaum, S. I. (1993). Individual and situational influences on the development of self-efficacy: Implications for training effectiveness. Personnel Psychology, 46(1), 125–147. Mathieu, J. E., Tannenbaum, S. I., & Salas, E. (1990). A causal model of individual and situational influences on training effectiveness measures. In M. Teachout (Chair), Understanding and evaluating training effectiveness: Multiple perspectives. Symposium conducted at the Fifth Annual Conference of the Society for Industrial and Organizational Psychology, Miami. Miller, L. (2013). State of the Industry. ASTD Research, Alexandria, VA. Morrow, C. C., Jarrett, M. Q., & Rupinski, M. T. (1997). An investigation of the effect and economic utility of corporate-wide training. Personnel Psychology, 50(1), 91–117. Noe, R. A., Clarke, A. D. M., & Klein, H. J. (2014). Learning in the twenty-first-century workplace. Annual Review of Organizational Psychology and Organizational Behavior, 1, 245–275. O’Connor, P., & Flin, R. (2003). Crew resource management training for offshore oil production teams. Safety Science, 41, 591–609. O’Connor, P., Campbell, J., Newton, J., Melton, J., Salas, E., & Wilson, K. A. (2008). Crew resource management training effectiveness: A meta-analysis and some critical needs. International Journal of Aviation Psychology, 18, 353–368. Reffay, C., & Chanier, T. (2000). How social network analysis can help to measure cohesion in collaborative distance-learning. Dordrecht, The Netherlands: Kluwer. Revans, R. W. (1982). The origins and growth of action learning. Bromley, UK: Chartwell-Bratt. Rossi, P. H., Freeman, H. E., & Lipsey, M. W. (1999) Evaluation: A systemtic approach. London: Sage. Salas, E., & Cannon-Bowers, J. A. (1997). Methods, tools, and strategies for team training. In M. A. Quinones & A. Ehrenstein (Eds.), Training for a rapidly changing workplace: Applications of psychological research (pp. 249–279). Washington, DC: American Psychological Association. Salas, E., DiazGranados, D., Klein, C., Burke, C. S., Stagl, K. C., Goodwin, G. F., et  al. (2008). Does

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team training improve team performance? A meta-analysis. Human Factors, 50(6), 903–933. Salas, E., Kosarzycki, M. P., Tannenbaum, S. I., & Carnegie, D. (2004). Principles and advice for understanding and promoting effective teamwork in organizations. In R. Burke & C. Cooper (Eds.), Leading in turbulent times: Managing in the new world of work (pp. 95–120). Malden, MA: Blackwell. Salas, E., Nichols, D. R., & Driskell, J. E. (2007). Testing three team training strategies in intact teams: A meta-analysis. Small Group Research, 38(4), 471–488. Salas, E., Prince, C., Bowers, C. A., Stout, R. J., Oser, R. L., & Cannon-Bowers, J. A. (1999). A methodology for enhancing crew resource management training. Human Factors: The Journal of the Human Factors and Ergonomics Society, 41(1), 161–172. Salas, E., Rozell, D., Mullen, B., & Driskell, J. E. (1999). The effect of team building on performance: An integration. Small Group Research, 30(3), 309–329. Salas, E., Tannenbaum, S. I., Kraiger, K., & SmithJentsch, K. A. (2012). The science of training and development in organizations: What matters in practice. Psychological Science in the Public Interest, 13(2), 74–101. Salas, E., Weaver, S. J., & Shuffler, M. L. (2012). Learning, training, and development in organizations. In S. W. J. Kozlowski (Ed.), The Oxford handbook of organizational psychology (Vol. 1, pp. 330–372). New York: Oxford University Press. Salas, E., Wilson, K. A., Burke, C. S., & Wightman, D. C. (2006). Does crew resource management training work? An update, an extension, and some critical needs. Human Factors: The Journal of the Human Factors and Ergonomics Society, 48(2), 392–412. Salas, E., Wilson, K. A., Murphy, C. E., King, H., & Salisbury, M. (2008). Communicating, coordinating, and cooperating when lives depend on it: Tips for teamwork. Joint Commission Journal on Quality and Patient Safety, 34(6), 333–341. Scott, J. (2011). Social network analysis: Developments, advances, and prospects. Social Network Analysis and Mining, 1(1), 21–26. Scriven, M. (1991). Evaluation thesaurus (4th edn). Thousand Oaks, CA: Sage. Sitzmann, T. (2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64(2), 489–528. Sitzmann, T., & Ely, K. (2010). Sometimes you need a reminder: The effects of prompting self-regulation on regulatory processes, learning, and attrition. Journal of Applied Psychology, 95(1), 132–144. Sitzmann, T., & Ely, K. (2011). A meta-analysis of selfregulated learning in work-related training and

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11 Employee Development: The Process and Practice of Work-Related Learning Sarah A. Hezlett and Cynthia D. McCauley

INTRODUCTION Industrial-organizational psychologists have long investigated employees’ knowledge and skill acquisition. The majority of work on the topic has focused on training, particularly the design and evaluation of individual training programs. Although organizations place over half of their development budgets into formal training programs, the proportion going to alternative methods has grown in recent years, particularly when the target audience is at higher organizational levels (O’Leonard & Loew, 2012). In surveys, human resource professionals and organizational leaders regularly report that developmental assignments, rotational job assignments, coaching from external and internal coaches, mentoring, action learning, and executive assessments are effective development methods (Abel, Ray, Roi, & Nair, 2013; Developmental Dimensions International, 2014; HayGroup, 2014). Given their growing importance, it is critical for industrial-organizational psychologists to have a strong understanding of these alternative developmental tools. The purpose of this chapter is to review the current state of research and practice related to development in the workplace. The chapter is organized in three major sections. In the first section, we present a conceptual framework of development in the workplace. The

framework is informed both by broader theories of human learning and development and by practice-specific models of workplace development. In the second section, this framework is used as a platform for synthesizing and comparing recent research on practices currently being used by organizations to promote development in the workplace. We review six popular practices: 360-degree feedback, developmental assessment centers, executive coaching, formal mentoring, developmental job assignments, and action learning. In the final section of the chapter, we look at the changing landscape of development practices and offer recommendations for future research. Throughout the chapter, we focus on what has been learned about the development of employees, including leaders, since 2000. Relevant articles and chapters have appeared in a number of related disciplines, including psychology, human resource development, and human resource management. Within this literature, a variety of related concepts have been defined and studied. Employee development (Noe & Tews, 2014), leader development (Day, 2001), continuous learning (Kuznia, Kerno, & Gilley, 2010), continuous employee development (Garafano & Salas, 2005), informal learning (Tannenbaum, Beard, McNall, & Salas, 2010), self-development (Boyce, Zaccaro, & Wisecarver, 2010), and employee development activity (Maurer,

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Lippstreu, & Judge, 2008) are some of the terms that have been used to describe work-related learning and development. We have drawn broadly from theory, research, and practice in these overlapping domains, explicitly excluding only work on what classically has been defined as training. There is considerable similarity between some definitions of training and development. Both involve improvements in individuals’ capacity to perform current or future work (McCauley & Hezlett, 2001). Key differences between the two have historically included: (1) formal education and training often occur offsite, or off-the-job; (2) training typically is more greatly structured by the organization; and (3) training tends to place greater emphasis on growth relevant to current roles, while development encompasses long-term professional and personal growth (McCauley & Hezlett, 2001). Changes in training and development (Bell & Kozlowski, 2010) are increasingly blurring even these differences. We see formal education and training programs as one kind of developmental experience, but exclude it from our review due to the substantial size of the literature on training. In Chapter 10 in this Handbook, Bell and Moore address this topic, delving into key advances in training design and delivery, team training, training transfer and training evaluation.

DEVELOPMENT FRAMEWORK There is a generous buffet of theory and research offering insight into the process of development in the workplace (Noe & Tews, 2014). The diverse, cross-disciplinary options can make it challenging to choose, digest, and apply the available ‘dishes’ or ‘ingredients’. Organizing variables related to workplace development into a process model offers a useful framework for summarizing past practice and research, as well as guiding future work (see Figure 11.1). The proposed framework includes concepts in common across major theoretical approaches, ‘or lenses’, to understanding work-related development. These include the behavioral change, self-directed learning, and adult development perspectives on development (See Table 11.1; McCauley & Hezlett, 2001). This framework is designed to complement, rather than supplant, related models, such as those describing informal learning (Tannenbaum et al., 2010), continuous employee development decisions (Garafano & Salas, 2005), and employee involvement in work-related learning and development activity (Maurer, Lippstreu, & Judge, 2008; Maurer, Weiss, & Barbeite, 2003).

Antecedents and moderators • Individual attributes • Learning strategies • Situational variables

Motivational states • Awareness of developmental needs • Self-efficacy for development • Valuing of development

Developmental experiences • Education and training

Individual development • Cognitive • Skill-based

• Developmental relationships

• Affective

• Stretch experiences

Organizational outcomes • Employee performance • Employee engagement • Leader ‘bench strength’

• Organizational performance

Figure 11.1 A process model of workplace development

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Table 11.1  Three lenses on individual development Behavior change

Self-directed learning

Adult development

Focus

Replacing currently ineffective behaviors with more effective ones

The development of more complex capacities over time

Emphasized outcomes Theoretical roots

Changed behaviors

Deliberate, sustained pursuit of formal and informal learning to increase careerrelevant knowledge and skills Enhanced knowledge and skills

Awareness of development needs Self-efficacy for learning

Support from others New experiences

Reflection

Social learning theory, behavior change in clinical psychology, training

Continuous learning, career management, participation in development activities, updating professional competence Key elements of development Clear articulation of desired Awareness of gaps between behaviors current capabilities and current or future job requirements High expectations that one can Motivation and ability to close perform the behaviors and the gap doing so will lead to valued outcomes A work context that reinforces Support from coworkers and desired, new behaviors; supervisors, including provides feedback feedback Opportunities to see the Opportunities to apply new behavior, and try out the knowledge and skills behavior Self-monitoring and feedback Feedback and situational cues to apply new skills

Transformed understanding that guides future action Experiential learning, cognitive theories of learning, socialconstructive theories of adult development

Reacting to disequilibrating experiences by re-examining assumptions Belief that new meaning structures can be created

Relationships with more developed mentors or guides Experiences that are new or different from previous ones Intentional reflection during and on experiences

Source: Adapted from McCauley & Hezlett (2001)

Individual Development Development involves ‘the expansion of an individual’s capacity to function effectively in his or her present or future job and work organization. In other words, an instance of individual development is an intra-individual change that results in better work performance, today or in the future’ (McCauley & Hezlett, 2001, p. 314). Since work performance is multi-dimensional, development is not limited to changes that affect task or technical performance. Development, therefore, entails stable changes in individual determinants of job performance, such as knowledge, skills, and motivation (Campbell, 2012). More specifically as illustrated in Figure 11.1, our development process framework includes three areas of development derived from Kraiger, Ford, and Salas’s (1993) classification of learning outcomes: cognitive, skill-based, and affective learning. Although this classification scheme was originally

delineated to advance understanding of training effectiveness and improve training evaluation (Ford, Kraiger, & Merritt, 2010), it is highly relevant to development as well. Cognitive learning includes enhancements in verbal knowledge, knowledge organization, or cognitive strategies. For example, employees may acquire new verbal knowledge (e.g., facts about products sold by their company, knowledge of the company-approved steps in selling the products, information about when to use which steps), expand their job-related mental models (e.g., models of the competencies they need to apply to sell the products), or learn new cognitive strategies (e.g., meta-cognition, self-regulation). Skill-based learning involves improving the ability to execute a set of organized behaviors smoothly and efficiently. As individuals become more proficient after initial skill acquisition, skills are compiled and ultimately become automatic. A variety of skills may be enhanced, ranging from technical (e.g., developing

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the specifications for a new product) to interpersonal (e.g., building relationships with customers) to motor skills (e.g., using a sales aid on a tablet, carrying a sample case). Affective-based learning includes attitudinal (e.g., changes in self-awareness or values) and motivational (e.g., enhanced self-­ efficacy) changes. As depicted in Figure 11.1, we propose that development has several major outcomes. First, development has an impact on individuals’ future motivation to learn and develop (Noe & Tews, 2014; Tharenou, Saks, & Moore, 2007). These links highlight the core premise that development is an ongoing process. Second, over time, development shapes individuals’ attributes and learning strategies.

Organizational Outcomes Multiple models explaining how training and development might yield positive organizational outcomes have been proposed. One integration of these models suggests that training drives human resource outcomes, including attitudes, behaviors, and human capital (i.e., performance-related knowledge, skills, and abilities of the workforce). Organizational performance outcomes, such as performance and productivity then mediate the relationship between HR outcomes and financial outcomes (Tharenou et al., 2007). Although these mediating effects have not been directly tested in many studies (Tharenou et al., 2007), the pattern of results observed across studies are consistent with this proposal. Meta-analyses have estimated moderate to large effects of training (Arthur, Bennett, Edens, & Bell, 2003) and management development programs (Collins & Holton, 2004) on employees’ learning and job-related behavior. Improvements in behaviors, skills, and competencies have been associated with better employee performance, promotions, and career success (Noe & Tews, 2014). These gains should improve the overall proficiency of an organization’s workforce, increasing the organization’s ‘bench strength’. In addition, expansion in organizations’ human capital has been linked with stronger organizational performance, particularly for operational measures of performance (Crook, Todd, Combs, Woehr, & Ketchen, 2011). We propose these relationships will hold when developmental practices (not just training) promote individuals’ cognitive and skillbased development in the workplace. That is, cognitive and skill-based development will mediate the relationship between developmental experiences, broadly defined, and the organizational outcomes of employee performance, leader ‘bench strength’, and organizational performance.

Ties also have been observed between participation in developmental experiences and affective outcomes, such as job satisfaction, organizational commitment, and willingness to take on new roles (Noe & Tews, 2014; Tharenou et al., 2007). These affective outcomes ultimately also may affect organizations’ ‘bench strength’ and organizational performance by improving workforce retention. In one large, high-technology manufacturing firm, employees who participated in more on-the-job training had higher organizational commitment and were less likely to intend to leave the organization. In contrast, employees who were reimbursed by the firm for tuition had higher intentions to turn over, unless they were promoted within a year after earning a degree (Benson, 2006). Although additional research is needed to fully specify the nature of the mediating and moderating relationships involved (Tharenou et al., 2007), the accumulated evidence indicates affective development, along with cognitive and skill-based development, are related to multiple organizational outcomes.

Developmental Experiences Experiences are at the heart of the development process (McCauley & Hezlett, 2001). As highlighted in Table 11.1, all major conceptual approaches of development include some form of experience as a key antecedent of development. The behavioral change perspective emphasizes learning situations that provide opportunities to see the desired behavior, to try out the behavior, and to get feedback and reinforcement. These learning situations may be the same as the one in which the new behaviors ultimately will be performed (e.g., the workplace) or may be different (e.g., a training program, a volunteer experience). Self-directed learning approaches propose that employees seek out opportunities that permit them to build their skills. These opportunities include activities required to execute one’s job or other assigned responsibilities and interactions with others (e.g., peers, customers, higher management). Adult development theories also emphasize experience – purposive interaction with one’s environment – as the medium through which learning occurs. Development is sparked by unfamiliar situations that are not well-explained by an individual’s existing mental models and by relationships with others who display more complex mental models. There are three broad categories of developmental experiences: education and training, developmental relationships, and developmental assignments. Education and training are structured

Employee Development: The Process and Practice of Work-Related Learning

off-the-job experiences with targeted learning objectives and a curriculum and instructional design methodology to support those objectives. Developmental relationships are connections with other people that stimulate and support learning via an array of processes, such as knowledge sharing, observing skilled behavior, receiving feedback and advice, serving as a sounding board, and providing encouragement to engage in new behaviors or take on new perspectives. Stretch experiences are ones that require new capabilities in order to succeed; they include challenging tasks, new roles and responsibilities, uncertain situations, and difficult goals.

Motivational States Motivational states are a key precursor to individuals’ participation in developmental experiences. Although a variety of motivational concepts have been examined in research on development, we highlight three that stand out in common across the major theoretical approaches to development: awareness of development needs, selfefficacy for learning, and valuing of development (McCauley & Hezlett, 2001).

Awareness of development needs

Individuals with a high awareness of their development needs know how their capabilities need to be changed. According to behavior change perspectives on development, attaining this awareness involves several steps that move from a broad assessment of development needs through establishing specific behavioral objectives to commitment to achieving these behavioral changes (Hellervick, Hazucha, & Schneider, 1992). Selfdirected learning approaches to development also propose that awareness of development needs is part of a complex motivational process preceding development. Recognizing the need to learn continuously and setting goals for learning are examples of awareness variables included in self-directed learning models (London & Smither, 1999). Theories within the adult development tradition suggest that awareness involves detecting discrepancies between new information and one’s current mental models. These discrepancies trigger the motivation to learn. Thus, across a variety of approaches to understanding development, awareness of development needs plays a key role.

Self-efficacy for learning

Self-efficacy broadly refers to individuals’ confidence in their ability to perform successfully in a

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particular domain. In the context of development, self-efficacy refers to belief that one can expand one’s capabilities in a particular learning context or domain (i.e., cognitive, skill-based, or affective learning). It is a key element of development in the behavior change, self-directed learning, and adult development perspectives on development. According to the behavior change perspective, development is fueled by employees having high expectations that they can achieve the desired behavioral objective. Within the self-directed learning literature, self-efficacy for learning is one of the malleable individual differences characteristics that has been examined as a determinant of participation in developmental experiences. Adult development approaches conceptualize self-efficacy as the belief that new meaning structures can be created in response to disequilibrating experiences. Research has consistently found that self-efficacy is positively related to important developmental variables, including motivation to learn (Noe & Tews, 2014; Tannenbaum et al., 2010).

Valuing of development

Also reflected in major theoretical approaches to development is the idea that development is valued by employees who are developing and those who work with them. One way that valuing of development influences learning is through employees’ expectations that achieving desired behavioral objectives will lead to valued outcomes. These expectations help individuals initiate developmental activities and sustain their motivation to develop over time. Employees’ perceptions of the intrinsic, extrinsic, and organizational benefits of participating in development activities also have been positively associated with attitudes toward learning and development, which in turn affect intentions to engage in developmental experiences (Maurer et al., 2008). Similarly, components from Vroom’s Expectancy theory have been positively linked to leaders’ interest in self-directed learning (Boyce et al., 2010).

Antecedents and Moderators Theory and research on work-related development has also focused on identifying individual and situational factors that shape development. These concepts have been particularly prominent in selfdirected learning approaches to development where individual and situational variables have been investigated as potential antecedents that determine participation in developmental experiences (McCauley & Hezlett, 2001). Individual and situational characteristics also have been

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studied as potential moderators of the relationship between participation in developmental experiences and development. Learning strategies, such as creating a development plan and systematic reflection, may also serve as key antecedents and/ or moderators in the development process. Learning strategies are prominent in organizational efforts to promote development.

Individual attributes

There is a burgeoning literature on how individual attributes affect development. A number of possible roles played by human capital variables (e.g., age, education, management level), personality traits (e.g., core self-evaluations, proactive personality, conscientiousness, and openness to experience), and a variety of attitudinal and motivational variables (e.g., perceptions of feedback, self-­ efficacy) have been investigated. In subsequent sections of this chapter, we summarize key, practice-specific findings on individual differ­ ences and the roles they play in the development process. Looking broadly at the literature on development, two individual attributes stand out: learning-goal orientation and learning agility. Learning-goal orientation shines as one of the most heavily investigated individual difference variables in research on development. It has been examined in the context of 360-degree feedback, mentoring, executive coaching, and development experiences. Learning-goal orientation is a key factor affecting both motivation to learn and participation in developmental activities (Noe & Tews, 2014). It is positively associated with improvement after upward feedback (Smither, London, & Reilly, 2005) and been shown to moderate the relationship between the degree of challenge in developmental experiences and supervisors’ ratings of leaders’ competencies (Dragoni, Tesluk, Russell, & Oh, 2009). In contrast, learning agility is a relatively recent concept that has been popular in work on leader development (Hezlett & Kuncel, 2012). Although the appropriate definition of learning agility is under debate (DeRue, Ashford, & Miles, 2012), the concept involves individuals’ ability and willingness to adapt to and learn from new experiences, as well as effectively apply the lessons learned to improve future performance (De Meuse, Dai, & Hallenbeck, 2010). Recent research has found support for learning agility as an antecedent of stretch experiences (Dai & Hezlett, 2017). In addition, learning agility is proposed to moderate the relationship between stretch assignments and development, such that those higher in learning agility will achieve greater cognitive, skill-based, or affective learning than those lower in learning agility. An

individual attribute that could consistently be used to identify individuals, particularly leaders, likely to successfully learn from developmental experiences would be a valuable tool for organizations striving to efficiently maximize the skills of their workforce. Additional research is needed to determine if the potential offered by learning agility is realized (Hezlett & Kuncel, 2012).

Situational variables

Two broad categories of situational variables have been examined in the context of work-related development: support from others and organizational culture. Support from others has been positively associated with a variety of development-related variables (McCauley & Hezlett, 2001; Tannenbaum et  al., 2010). Support is multifaceted, including instrumental and affective components (Wanberg, Welsh, & Hezlett, 2003). Individuals who provide support for development, such as peers and managers, may facilitate employee development through a variety of mechanisms. Several examples illustrate how support may moderate the relationship between developmental experiences and development. First, support providers may enhance learning from developmental experiences by serving as role models (Hezlett, 2005; Lankau & Scandura, 2002). Second, psychosocial support may enhance development from challenging developmental experiences by reducing the stress, increasing the cognitive resources available for learning (DeRue & Wellman, 2009; Gregory, Levy, & Jeffers, 2008). Third, support providers may accelerate development from developmental experiences by encouraging reflection (McCall, 2010). The broader organizational context also may influence the process of development in a variety of ways. A number of factors, including business strategy, developmental tools, HR policies, time constraints, and the organizational climate, shape organizations’ culture for development. Research on structured training has shown that organizations’ transfer of training climate affects training effectiveness (Colquitt, LePine, & Noe, 2000; Tannenbaum et al., 2010). Research on the impact of organizational practices and policies has been more limited (Tannenbaum et  al., 2010), but has yielded some favorable results (McCauley & Hezlett, 2001).

Learning strategies

According to multiple theories of individual development, development is improved when individuals use self-regulatory, supplemental learning strategies. Setting goals, seeking feedback, and reflecting on experiences are examples of development strategies expected to moderate the relationship between developmental experiences and development

Employee Development: The Process and Practice of Work-Related Learning

(Ashford & DeRue, 2012; McCauley & Hezlett, 2001; Tannenbaum et al., 2010). One of the most robust findings in organizational psychology is that individuals who are committed to specific, difficult goals perform better than those without goals or those who set easy goals. Goal-setting is central to behavioral change theories of development (Hellervick et al., 1992), as well as a core part of models of development following particular interventions, such as 360-degree feedback (Nowack, 2009; Smither, London, & Reilly, 2005). In practice, it is common for individuals working on their development to be instructed to create an individual development plan that specifies their developmental goals. Both theories of behavior change and selfdirected learning highlight that ongoing feedback should help leaders as they apply new knowledge, behaviors, and skills in new situations (McCauley & Hezlett, 2001). Feedback may be critical for employees to learn appropriate lessons from developmental experiences, rather than simply experience an unfamiliar situation or activity (Tannenbaum et al., 2010). Practice-specific models of development also indicate that feedback may be a key moderator of the relationship between developmental experiences and successful development. For example, within coaching relationships, feedback is posited to enhance development by helping employees set goals and assess their progress (Gregory et al., 2008). Although appearing less frequently in models specifying how individuals develop in the context of particular developmental practices, the concept of reflection is prominent in multiple theoretical perspectives on development (McCauley & Hezlett, 2001). For example, according to behavioral change approaches to development, self-monitoring helps individuals determine if their new or changed behaviors are meeting their goals for their desired behaviors. Within theories of adult development, intentional reflection on experiences helps individuals build new meaning structures. Therefore, reflection is another moderator likely to accelerate development during developmental experiences.

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to change behaviors and enhance their skills. The other four practices are aimed at providing experiences that directly stimulate development. Executive coaching and formal mentoring are two variations of developmental relationships. Developmental assignments are a type of stretch experience, and action learning typically combines a stretch experience with relationship-based learning strategies. Some developmental assessment centers also are designed to offer opportunities to practice and build skills. Individuals may experience each of these practices multiple times over the course of their employment lifecycle with an organization. For example, onboarding programs for new employees often include a formal mentoring component in addition to formal education about the organization and its processes and systems. For some employees, there may be an extended onboarding period during which they rotate through developmental assignments across multiple functions. Each move that an employee makes to a higher level in the organization is often supported by an in-depth feedback experience (either via 360-degree feedback or an assessment center) and access to a coach or mentor. Some organizations embed formal development practices in their annual processes, for example, conducting an organization-wide 360-degree feedback process each year, identifying developmental assignments for high potentials during annual succession management sessions, or including action learning experiences as part of each leadership development programs. In reviewing the development practices, we include research from all points in the employment lifecycle. For each practice we provide a brief description and summary of the current state of the practice. We also note the theoretical roots of the practice and current theory that guides the practice. Using the development process model as an organizing framework, we review research on the impact of the practice on individual development (and when available on organizational outcomes), individual and situational moderators that enhance impact, and antecedents that motivate individuals to engage in the practice. Finally, we note important research gaps that need to be addressed.

DEVELOPMENT PRACTICES We now examine six formal development practices commonly used by organizations to enhance employee learning and development. Two of the practices – 360-degree feedback and developmental assessment centers – are primarily used to increase individuals’ awareness of their developmental needs and to motivate and guide their efforts

360-Degree Feedback Organizations use 360-degree feedback (also called multisource or multirater feedback) to systematically collect ratings of an individual’s workrelated skills and behaviors from a wide range of coworkers, and then summarize and feed back the data to the individual. The individual’s boss,

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peers, and direct reports typically rate the individual. Additional coworkers and external customers may also contribute ratings in some organizations. Organizations may use 360-degree feedback for development purposes only (i.e., to help individuals gain a broad awareness of their strengths and weaknesses and identify areas for targeted improvement efforts) but may also use the data generated to make administrative decisions about individuals (i.e., as input on promotion or pay increase decisions). Multisource feedback proliferated in the 1990s and is now a core HR tool (Bracken & Rose, 2016). Surveys regularly report that two-thirds or more of major organizations use some form of 360-degree feedback (3D Group, 2013; Church & Rotolo, 2013), and managers around the globe have been recipients of 360-degree feedback (Brutus et  al., 2006; Eckert, Ekelund, Gentry, & Dawson, 2010). Organizations may use 360-degree feedback by itself as a regularly occurring, company-wide process or combine it with other development practices. For example, 360-degree feedback may be embedded in a formal leader development program or executive coaching initiative. The design and implementation of 360-degree feedback is grounded in theories of behavior change, which highlight the need to understand development objectives and sustain motivation to change (Bracken & Rose, 2011; Nowack, 2009; Smither, London, & Reilly, 2005). For example, well-designed 360-degree feedback generates an awareness of the need to change by providing feedback on performance dimensions important for success in the organization and from coworkers who are in the position to observe the individual’s behavior. Feedback on skills and behaviors enables the individual to set specific goals for improvement. And including accountability mechanisms into the process strengthens the individual’s commitment to pursue the goals. Multisource feedback practices also are based on the assumption that different rater groups provide unique and meaningful information on the individual’s behaviors, a view grounded in theories of leader effectiveness that take into account the context and the leader–follower relationship (rather than only the skills of the leader). The prevalence of 360-degree feedback processes in organizations has stimulated a large and growing body of research on this developmental intervention. A central question has been whether 360-degree feedback positively impacts individual development. A meta-analysis of 24 longitudinal studies indicated an overall mean positive improvement in skills and behaviors after feedback, but the magnitude of improvement was small and there was noteworthy variability (Smither, London, &

Reilly, 2005). A subsequent longitudinal study designed to overcome some of the limitations of earlier studies found that improvement after feedback is greater on the competencies the individual selected for improvement (compared to those not selected) and for competencies that are less difficult to improve (Dai, De Meuse, & Peterson, 2010). Improvements also persisted across multiple administrations of the 360-degree assessment. Little attention has been given to evaluating the broader organizational impact of an employee’s post-feedback improvement. An exception is a study by Atwater & Brett (2006) that found positive post-feedback changes in supervisors’ leadership skills were related to increases in subordinates’ satisfaction and engagement and to decreases in their intent to leave. Given the variation in impact of 360-degree feedback on development, research that identifies factors affecting that impact becomes particularly important for advancing practices that make use of this tool. Models of feedback-generated change point to several critical post-feedback variables: feedback acceptance, intent to change, accountability for change, and support for change (Bracken & Rose, 2011; Smither, London, & Reilly, 2005). Researchers have examined individual and situational factors hypothesized to impact each of these variables and thus act as moderators affecting development. Credible data is assumed to contribute to feedback acceptance, and individuals who perceive their feedback as accurate do report more behavior change after feedback (McCarthy & Garavan, 2006). The organizational context may also play a role in acceptance: Individuals improve more after feedback when they are clear about how the process supports organizational goals and are generally less cynical about the organization (Atwater, Waldman, Atwater, & Cartier, 2000; Waldman & Atwater, 1998). Because positive attitudes about the feedback process are also assumed to contribute to feedback acceptance, researchers have examined the role of individual differences in these attitudes. Individuals with high self-esteem, high self-efficacy, internal locus of control, and a learning-goal orientation have more positive attitudes toward the feedback process (Brett & Atwater, 2001; Funderburg & Levy, 1997; Maurer & Palmer, 1999; Maurer, Mitchell, & Barbeite, 2002; Smither, London, & Richmond, 2005). The feedback recipients’ intent to change is reflected in the goals set and development activities engaged in after feedback. Individuals receiving negative feedback set more improvement goals than those who receive positive feedback (Brutus, London, & Martineau, 1999; Mesch, Farh, & Podsakoff, 1994; Smither, London,

Employee Development: The Process and Practice of Work-Related Learning

Flautt, Vargas, & Kucine, 2003). This may help explain findings of greater improvement following 360-degree feedback by individuals who are rated lower by their coworkers (Soomyung, Zong-Tae, & Seung-Yoon, 2012; Walker & Smither, 1999) despite results indicating that those receiving higher ratings view the process as more accurate and useful than do those with receiving lower ratings (Brett & Atwater, 2001). In terms of individual differences, self-efficacy is positively related to engagement in development activities after feedback (Atwater & Brett, 2005; Maurer et  al., 2002). However, feedback ratings and recipient self-efficacy may interact in more complex ways when predicting intent to change. Bono and Colbert (2005) examined the role of core self-evaluations (i.e., self-esteem, generalized self-efficacy, locus of control, and neuroticism) in goal commitment after feedback. Individuals with high core self-evaluations were most committed to developmental goals when self and others’ ratings were discrepant. In contrast, individuals with low core self-evaluations were most committed to developmental goals when self and others’ ratings were in agreement. Other people are the primary source for accountability and support for change. Individuals who perceive support from coworkers and supervisors for development activities report more positive attitudes toward the feedback system and were involved in more development activities (Maurer et al., 2002). Individuals who meet with their raters as a group after receiving feedback are more likely to improve (Goldsmith & Underhill, 2001; Walker & Smither, 1999) as are individuals who work with a coach after receiving feedback (Luthans & Peterson, 2003; Smither et al., 2003). Properties of the assessment survey itself, plus a host of factors that can vary in the implementation of a 360-degree feedback process, may also impact the degree to which improvement is seen post-feedback (Hezlett, 2008). For example, feedback recipients who receive numeric scores react more positively to the feedback than those who received text feedback (Atwater & Brett, 2006). And a feedback process not aligned with the organization’s core competencies or other development plans can fail to produce desired results (Morgan, Cannan, & Cullinane, 2005). There are a number of useful articles available that review research relevant to the design of 360-degree assessment surveys, feedback reports, and feedback processes (see Atwater, Brett, & Charles, 2007; Morgeson, Mumford, & Campion, 2005; Nowack & Mashihi, 2012). The growing body of research on 360-degree feedback has a number of positive features. The research is cumulative rather than piecemeal:

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researchers build on one another’s work and link their studies to models of the change process that feedback is hypothesized to stimulate. Studies have employed more powerful longitudinal designs to better understand the dynamics and impact of 360-degree feedback over time. Research has been conducted in multiple countries and with diverse samples. And research findings have been integrated and translated into recommendations for those designing and implementing feedback systems. Multisource feedback research should continue to examine the combined effects of individual differences and organizational support on the recipients’ use of feedback to change behaviors and improve skills. However, this research would benefit by supplementing its use of repeated measures designs (i.e., multiple administrations of the 360-degree assessment) with additional methods to evaluate impact on individual development and by using qualitative methods to better understand how individuals are processing and using feedback. In addition, extending questions of impact to the organizational level are needed. For example, to what degree does the regular use of 360-degree feedback in the organization contribute to an enhanced feedback culture in the organization or to a stronger pipeline of leaders in the organization?

Developmental Assessment Centers In an assessment center, multiple trained assessors evaluate participants on job-related dimensions based on their performance on multiple exercises that simulate job-related tasks. Assessment centers should have ten essential elements: (1) systematic analysis to define job-relevant behavioral constructs; (2) classification of participants’ observed behaviors into the behavioral constructs; (3) multiple assessment components, including (4) behavioral simulation exercises; (5) a mapping between the behavioral constructs and assessment center components; (6) multiple assessors, who have received (7) training; (8) systematic recording and scoring of behaviors; (9) data integration; and (10) standardized administration procedures (The International Taskforce on Assessment Center Guidelines, 2014). Assessment centers may be conducted in person or virtually (Collins & Hartog, 2011). Common simulations include role-plays, in-baskets, planning exercises, group discussions, and presentations (Eurich, Krause, Cigularov, & Thorton, 2009; Lievens & Thorton, 2005; Thorton & Krause, 2009). The relative popularity of different kinds of exercises varies by

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the purpose of the assessment center (Thorton & Krause, 2009). Originally used for selection and promotion decisions, assessment centers were initially implemented in the United States and the United Kingdom. More recently, interest in assessment centers has spread in Europe, Asia, South America, and the Middle East (Thorton & Krause, 2009). The purpose of assessment centers also has broadened to include developmental purposes (The International Taskforce on Assessment Center Guidelines, 2014; Collins & Hartog, 2011; Eurich et al., 2009; Lievens & Thorton, 2005). Developmental assessment centers vary in their design and purpose. When used for diagnostic purposes, participants are given detailed feedback identifying their areas of strength and development needs for the purpose of development planning (The International Taskforce on Assessment Center Guidelines, 2014; Collins & Hartog, 2011; Lievens & Thorton, 2005). With this use, acceptance of the feedback is critical. Self-motive theories, such as selfenhancement and self-verification, have been used most frequently to understand acceptance of assessment center feedback (Bell & Arthur, 2008; Rupp et al., 2006; Woo, Sims, Rupp, & Gibbons, 2008). These theories posit that reactions to feedback depend upon individuals’ motives regarding self-­perceptions. For example, according to selfenhancement theory, feedback reactions are shaped by individuals’ desire to improve their feelings of self-worth and maintain positive feelings about themselves. Several feedback theories also have been used to understand responses to assessment center feedback. For example, feedback-seeking theories, which propose that individuals wish to avoid receiving negative feedback, have been used to explain acceptance of developmental assessment center feedback (Abraham, Morrison, & Burnett, 2006). Developmental assessment centers may also be used to promote skill development (The International Taskforce on Assessment Center Guidelines, 2014; Collins & Hartog, 2011; Lievens & Thorton, 2005; Rupp et  al., 2006). Immediate feedback, practice, reinforcement, transfer of training instructions, and follow-up support are used to stimulate skill-based development during and after the center (Lievens & Thorton, 2005). The theoretical approaches applied to understanding the impact of assessment centers on skill development have been eclectic. Initial studies of assessment centers as a learning and development intervention considered the malleability of the behavioral dimensions assessed (Jones & Whitmore, 1995; Engelbrecht & Fisher, 1995). More recently, behavioral change perspectives and

taxonomies of training program evaluation have been applied (Rupp et al., 2006). A variety of criteria have been used in the small number of published studies evaluating the impact of assessment centers on individual development. These include change in understanding of performance dimensions, intentions to take action to develop, actual engagement in developmental activities, skill improvement, behavior change onthe-job, career advancement, and improvement in organizational effectiveness (Lievens & Thorton, 2005). Results have varied somewhat across these criteria and research methodologies. In one of the first studies evaluating the impact of developmental assessment centers, Engelbrecht and Fisher (1995) found supervisors who had participated in a center were rated significantly higher on six out of eight behavioral dimensions by their managers three months after the center compared to individuals in a control group who were on a wait list. The effect size was large (d = .85). In contrast, in a more recent investigation of two developmental assessment centers using pre- and post-ratings collected over a similar timeframe, relatively little change was observed (Rupp et al., 2006). Participants in one center perceived themselves as improving on half of the assessed dimensions. Pre- and post-ratings were not significantly different for participants in the second center. No changes over time were significant based on subordinate or supervisor ratings. The apparent differences across these studies may partially have been a function of the design of the developmental assessment centers. In the study where a large effect was observed, the design included participants’ pursuing on-the-job development based on recommendations and action plans from a feedback session following the center. In contrast, the study where few changes were observed was focused on providing developmental experiences within the center. This study did find some evidence of the centers’ participants changing their behavior during the second half of the experience in response to feedback they received after the first three exercises. Using a third research design (pre-, post-, and retrospective ratings), Broderson and Thorton (2011) evaluated the impact of a developmental assessment center on several types of change. Results suggested that participants more frequently re-conceptualized constructs being assessed (gamma change) or recalibrated their use of the measurement scale (beta change), than they perceived a change in their proficiency on the dimensions assessed in the center (alpha change). The results suggest that assessment centers can promote development in the form of skill-based change, but may be more likely to result in changes in understanding what certain skills are or mean.

Employee Development: The Process and Practice of Work-Related Learning

Overall, research on the effectiveness of developmental assessment centers has so far yielded results similar to other training programs, as well as 360-degree feedback interventions. At times, developmental assessment centers have been successful in stimulating improvements, but in other cases, sustained behavioral change on-the-job following participation in developmental assessment centers has been elusive. A handful of empirical studies provide initial insights into moderators that may explain some of this variation. As with 360-degree feedback research, feedback acceptance is viewed as an important step in moving from feedback to development. Participants who perform better in developmental assessment centers are more likely to accept their feedback (Bell & Arthur, 2008; Jones & Whitmore, 1995). Individuals who anticipate negative feedback from a developmental assessment center, may avoid it. In one developmental assessment center, newly hired supervisors who performed poorly on interpersonal skills were less likely to complete a feedback telephone call with an assigned coach (Abraham et  al., 2006). This relationship between feedback favorability and acceptance appears to be mediated by participants’ affective reactions to the feedback (Bell & Arthur, 2008). Personality variables also play a complex role in feedback acceptance, with extraversion being linked to affective reactions to feedback and agreeableness moderating both the relationship between affective reactions and feedback acceptance and the direct relationship between assessor ratings and feedback acceptance (Bell & Arthur, 2008). On the other hand, Jones and Whitmore (1995) observed that feedback acceptance was unrelated to development activity and had a slight negative relationship with attaining division-level management responsibilities. In contrast, a more recent study found positive associations between assessors’ ratings and center participants’ behavioral engagement (Woo et al., 2008). Participants with less favorable feedback were less engaged in feedback sessions, less likely to engage in improving their performance on a specific center exercise, and less likely to engage in follow-up activities. In addition, participants who rated themselves lower than assessors rated them had higher feedback and exercise engagement. Additional research is needed to evaluate if the key relationship between motivational states and participation in developmental experiences in our framework holds in the context of developmental assessment centers. Research on developmental assessment centers is in the early stages of development. Studies to date have focused on establishing developmental assessment centers as a phenomenon. That is,

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initial studies simply examined whether or not developmental assessment centers encouraged positive employee and organizational outcomes. More research is needed to understand the dynamics of development during and after participation in a developmental assessment center. For example, stronger empirical evidence of the connection between motivational states following assessment center participation and engagement in development activities is needed. Such a program of research would include the role of discrepancies between participants’ ratings and assessor ratings, motivational variables (e.g., self-efficacy and expectancies), and potential individual difference moderators (e.g., learning orientation); and would include multiple indicators of feedback acceptance. Additional research that examines the impact of program features on development also would be valuable. Research to date hints that features such as providing development recommendations and setting expectations to work with one’s supervisor on development post-participation, may affect the size of developmental impact. Design features of diagnostic developmental assessment centers may promote the use of learning strategies (e.g., goal-setting, reflection) and situational resources (e.g., supervisor support) that are likely to affect identification and completion of relevant stretch experiences following participation in the assessment center. For assessment centers designed to promote development during the center, design features may enhance learning during the center, as well as follow-up that promotes subsequent transfer and development on-the-job. One aspect of assessment center design that appears particularly important is the role played by the number of exercises and dimensions included. One concern that has been raised about using assessment centers for development purposes is that they may lack sufficient construct validity to offer effective guidance on what behavioral dimensions need improvement (Fleenor, 1996). In order to provide meaningful feedback to participants, the dimensions on which participants are rated must have robust divergent and convergent validity. Recent work (Kuncel & Sackett, 2014) suggests the key to accumulating sufficient dimension-specific variance to reasonably differentiate feedback dimensions is having sufficient exercises. Approximately 6–7 exercises may be needed to evaluate 4–5 behavioral dimensions. Benchmarking surveys suggest 4–5 exercises are used in the majority of assessment centers, with 8 or more dimensions assessed in the majority of developmental assessment centers (Eurich et  al., 2009; Thorton & Krause, 2009). Increasing the typical number of exercises (and decreasing the

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number of dimensions) may be challenging given pressure to reduce the cost and length of assessment centers (Lievens & Thorton, 2005). Research examining the impact of different numbers of exercises and/or dimensions on development would reveal if longer centers with fewer dimensions pay off in terms of development and organizational outcomes.

Executive Coaching Executive coaching involves a series of one-onone interactions between a client (typically in a leadership role) and a professional coach in order to further the development of the client. A coaching engagement typically begins with setting expectations for the coaching relationship and gathering data about the client via multiple methods, such as coworker ratings, interviews, and observation. Summarized data is shared and discussed with the client to generate insights about development needs and a plan for addressing specific needs. The coach then works with the client over time in a structured process to monitor and reflect on progress, as well as to discuss issues that arise and strategies for dealing with those issues. The coach and client may also opt to meet with other stakeholders to foster an environment which facilitates the client’s development efforts. A coaching engagement typically concludes with some form of evaluation of its success. The use of executive coaches has increased substantially in organizations over the last two decades, with estimates that more than 70% of organizations with formal leader development initiatives include executive coaching in their mix of offerings (Zenger & Stinnett, 2006). Several organizations have emerged to certify coaches, including the International Coach Federation (ICF, 2014). Professional and academic publications on the topic have grown exponentially. Consulting Psychology Journal has devoted five special issues to executive coaching, far more than for any other topic. And two new journals focus exclusively on the topic: the International Coaching Psychology Review and Coaching: An International Journal of Theory, Research, and Practice. As the practice of executive coaching has grown, attention has been given to clarifying its boundaries, for example, how it is different from other relationship-based interactions for development such as counseling or advising. Peterson (2011) offers eight criteria for defining executive coaching that aid in differentiating it from other relationships: (1) one-on-one, as opposed to group or team coaching; (2) relationship-based,

as opposed to content-based (e.g., tutoring); (3) methodology-based, drawing on specific techniques in a structured process, as opposed to conversations with a trusted advisor; (4) provided by a professional coach, as opposed to coaching by a manager or peer; (5) scheduled in multiple sessions over time; (6) goal-oriented for both organizational and individual benefit; (7) customized to the person; and (8) intended to enhance the manager’s ability to learn and develop independently, as opposed to long-term reliance on the coach. Although skill development and behavior change is the goal of most executive coaching, the specific approaches taken by different coaches varies depending on their theoretical orientation. Five main theoretical approaches to coaching have been described (Davis & McKenna, 2011; Feldman & Lankau, 2005; Peltier, 2001): • Psychodynamic Theory. People are assumed to have limited awareness of the underlying drivers of their thoughts, feelings, and actions. The coach’s role is to help clients explore unconscious thinking, particularly defenses that negatively affect performance, and break out of dysfunctional cycles of behavior. • Behaviorism. Coaches focus on observable behavior rather than underlying psychological dynamics. They help clients set behavioral goals, rehearse desired actions, and use cues and reinforcement to maintain focus on goals. • Cognitive Behavioral Theories. Coaches focus on clients’ way of making sense of the world and how those thoughts influence behavior. They help clients notice and change their own thoughts, including their emotional reactions. • Person-Centered Psychology. People are assumed to be capable of learning on their own; they do not need an expert to tell them how to change. The coach’s role is to be an empathetic partner, helping the client discover what changes are needed and being a listener and sounding board on the learning journey. • Systems Theory. Coaches focus less on intrapsychic forces to understand behavior and more on the social system within which the client is embedded. They help clients examine how role demands, interactions with others, and intergroup relations impact behavior, and experiment with new ways of responding to these system forces. Despite these variations, differences in how coaching is practiced between psychologists and non-psychologists or among psychologists from

Employee Development: The Process and Practice of Work-Related Learning

different disciplines is generally small (Bono, Purvanova, Towler, & Peterson, 2009). Although research on executive coaching has struggled to keep pace with the growing practice, a meta-analysis does provide evidence for the impact of executive coaching on individual development. Theeboom, Beersma, and van Vianen (2013) identified 18 studies with research designs that yielded effect sizes for executive coaching interventions. The measured outcomes in these studies were categorized as changes in performance/skills, well-being, coping, work attitudes, or goal-directed self-regulation. The results showed that coaching had significant positive effects on all outcomes with effect sizes ranging from .43 (coping) to .74 (goal-directed selfregulation). However, it is important to note that the majority of the outcome measures were based on self-reports and that the effects of coaching varied considerably across studies. In addition to the studies included in the meta-analysis, there is a considerable body of less rigorous research (e.g., self-reports from clients and their managers post-coaching, case studies, surveys of organizational purchasers of coaching, ROI evaluations) that document positive outcomes from coaching (Peterson, 2011). Ely, Boyce, Nelson, Zaccaro, Hernez-Broome, and Whyman (2010) propose a framework for evaluating executive coaching that includes four categories of potential moderators of the coaching-development relationship: • Coach Characteristics. Coach characteristics deemed important by clients and client organizations include good communication and listening skills, credibility and experience, integrity, empathy, intelligence, psychological mindedness, assessment knowledge, motivational skills, and ability to develop rapport (Blackman, 2006; Dean & Meyer, 2002; Hall, Otazo, & Hollenbeck, 1999; Stevens, 2005). • Client Characteristics. Research on the role of client characteristics is less extensive. In a study of client-coaching pairs, de Haan, Duckworth, Birch, and Jones (2013) found that client selfefficacy predicted perceptions of the outcomes of the coaching engagement. Baron and Morin (2009) found that the client’s motivation to transfer newly acquired skills and perceptions of supervisory support were related to the quality of the client–coach relationship. • Client–Coach Relationship. The most consistently identified factor seen as contributing to the success of a coaching engagement is the quality of

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the relationship between the coach and client (Passmore & Fillery-Travis, 2011; Peterson, 2011). Quality of relationship has been found to be significantly related to coaching outcomes (Baron & Morin, 2009; Boyce, Jackson, & Neal, 2010; De Haan et al., 2013). • Coaching Process. There is little research on how variations in the coaching process impacts coaching outcomes. Ely et al. suggest examining how various strategies for providing assessment, challenge, and support during the coaching process impacts outcomes. Despite its rapid growth, the field of executive coaching is still in many ways early in its development, working to distinguish itself from related practices such as counseling and consulting while at the same time relying heavily on theory and tools from those practices. Yet, there is clear evidence that the field is advancing: an active practitioner community that regularly shares best practices and supports research, enough published controlled studies to conduct a meta-analysis, and the emergence of coaching frameworks to guide research and practice (see, for example, Boyce & HernezBroome, 2011; Ely et  al., 2010; Gregory et  al., 2008; Passmore, 2007). Although continued impact research is needed to expand beyond self-report data, research on executive coaching has examined a broad variety of outcome measures, including changes in attitudes and enhanced well-being. And researchers are beginning to look at links between development outcomes from coaching and organizational benefits, such as employee turnover (Ladegard & Gjerde, 2014) and organizational performance (Levenson, 2009). There remains a great need for research examining moderators and studies focused on the process of coaching. And finally, as with any developmental relationships, it is important to also include as part of the research agenda a focus on the impact of the relationship on the ‘developer’ – in this case, the coach.

Formal Mentoring Mentoring traditionally has been defined as a oneon-one relationship between a senior, more experienced individual (the mentor) and a junior, less experienced person (the protégé or mentee) that is intended to advance the personal and professional growth of the less experienced individual (Noe, Greenberger, & Wang, 2002). Mentoring is characterized by career (sponsorship, protection, exposure, challenging assignments, and coaching) and psychosocial (acceptance/confirmation,

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counseling, friendship) support provided by the mentor. Role modeling is also a key part of mentoring relationships, although there is mixed evidence on whether it is a distinct mentoring function or simply another form of psychosocial support (Wanberg et al., 2003). Over the past fifteen years, a number of metaanalyses (Allen, Eby, Poteet, Lentz, & Lima, 2004; Eby, Allen, Evans, Ng, & DuBois, 2008; Eby et  al., 2013; Kammeyer-Mueller & Judge, 2008; Underhill, 2006), reviews (Allen, Eby, O’Brien, & Lentz, 2008; Noe et al., 2002; Wanberg et al., 2003), and edited handbooks (Allen & Eby, 2010; Ragins & Kram, 2007) on mentoring have been published, signaling the maturation of research on this key developmental relationship. Although more longitudinal and experimental studies are needed to fully understand the causal dynamics of mentoring (Allen et  al., 2008; Wanberg et  al., 2003), having a mentor and receiving mentoring support is associated with key subjective and objectives outcomes beneficial to individuals and organizations. Although less thoroughly investigated, mentoring also provides benefits to mentors (Ghosh & Reio, 2013), including learning (Allen & Eby, 2003). Mentoring research initially focused on informal relationships that evolved between individuals. Seeking to leverage the accumulating evidence of the benefits of mentoring, organizations began creating formal programs that match mentors and mentees. Sufficient attention has been given to these formal programs that theory (Wanberg et al., 2003), research (Allen, Eby, & Lentz, 2006), and practical guidance (Allen, Finkelstein, & Poteet, 2009) about them are available. While recognizing that other types of developmental relationships have been distinguished from mentoring (Higgins & Kram, 2001) and are valuable to individuals and organizations (Bozionelos, 2003, 2006), we focus on mentoring in this chapter due to its prominence in practice and research. Within the mentoring literature, individual development has received relatively less attention compared to other outcome variables, such as career satisfaction and success (Hezlett, 2005; Lankau & Scandura, 2007). To some extent, the relationship between mentoring and learning historically has been assumed, rather than studied. More recent conceptual approaches offer insight into the role of mentoring in learning and development. Both broad models of formal mentoring (Wanberg et al., 2003) and more focused models of personal learning from mentoring have been articulated (Lankau & Scandura, 2007). Most simply, social learning theory (Gibson, 2004) has been applied to understand learning in mentoring relationships. Mentees may learn by observing

their mentors’ behaviors and the reinforcements or consequences that follow (Hezlett, 2005). Synthesizing a small number of studies on the relationship between mentoring and development, Eby and her colleagues (2008, 2013) estimated the effect size of having a mentor on socialization and skill-development across three major areas of mentoring research: youth, academic, and workplace learning. Their results indicate having a mentor has a small (rc = .13), generalizable relationship with skill development (Eby et al., 2008). Greater perceived career support and psychosocial support also have small, positive associations with mentee learning and socialization in the workplace, as well as attitudinal (situational satisfaction, sense of affiliation) and motivational (self-efficacy) outcomes (Eby et al., 2013). Dysfunctional relationship characteristics, such as distancing behavior, manipulative behavior, mismatch, lack of mentor expertise, and general dysfunctionality have a negative impact on learning (Eby, Butts, Lockwood, & Simon, 2004). At the extreme, lack of learning may contribute to mentee withdrawal from formal mentoring relationships (Beech & Brockbank, 1999). Scant attention has been given to investigating the organizational outcomes of development from a mentoring relationship. Encouraging results were found by Lankau and Scandura (2002), who observed that relational job learning fully mediated the relationship between career-related mentoring and two outcomes: perceived role ambiguity and job satisfaction. Few studies have investigated individual variables, situational variables, or learning strategies as moderators of the relationship between mentoring and individual development. One exception is a study examining the relationship career-related mentoring and short-term, context-specific personal learning. Fleig-Palmer and Schoorman (2011) observed that trust moderated this relationship, with career mentoring having a stronger relationship with knowledge transfer when mentees’ trust for their mentors was low. Relatively little knowledge transfer occurred when both career mentoring and trust were limited. More extensive research attention has been given to the personal and situation antecedents to having a mentor and receiving mentoring functions. In most cases, cross-sectional designs have been used, making the direction of causality between some personal and situational variables and mentoring unknown. Mentee characteristics associated with at least some mentoring functions include proactivity, core self-evaluations, self-monitoring, and learning-goal orientation (Kammeyer-Mueller & Judge, 2008; Ghosh, 2014). In contrast, demographic variables (gender,

Employee Development: The Process and Practice of Work-Related Learning

race) and human capital variables (education, tenure) do not consistently or substantially relate to mentoring received (Kammeyer-Mueller & Judge, 2008). Organizational support for mentoring is an example of a situational variable associated with higher levels of mentoring (Ghosh, 2014). Research on mentors has included both qualitative studies seeking to understand motivation to mentor, as well as empirical investigation of the roles played by mentors’ characteristics in mentoring relationships. Although the latter are sometimes framed as personal characteristics that differentiate more and less effective mentors, they also may be construed as motivational states shaping mentors’ willingness to offer support to mentees and thereby influence mentees’ development. Mentors’ learning-goal orientation and transformational leadership are associated with mentoring functions (Ghosh, 2014). Explicit investigation of motivation to mentor has identified learning as one reason that individuals choose to mentor others (Allen, Poteet, & Burroughs, 1997). Dyad and relational characteristics also have been examined as antecedents to mentoring functions. The combination of mentee and mentor characteristics affects mentoring functions, and may be viewed as antecedents that create a sense of comfort with or motivation to engage fully in the mentoring relationship. Both values on the same characteristic (deep-level similarity) and relationships on different characteristics (mentor trust and mentee locus of control) have been associated with the amount of support mentees receive from their mentors (Eby et al., 2013; Wang, Tomlinson, & Noe, 2010). Trust and self-disclosure are examples of relational characteristics that are associated with mentoring functions (Ghosh, 2014). Comparing the mentoring literature to our development framework suggests several possible directions for future research. First, directly measuring mentors’ and mentees’ motivational states relevant to development may provide additional insight into how to maximize affective, skill-based, and cognitive learning in mentoring relationships. Although personal antecedents to mentoring have been examined, more narrowly focused motivational antecedents of development have not been studied. Second, research on how moderator variables affect mentoring dynamics is just emerging. There is particularly a gap in investigating the role of learning strategies, such as goal-setting, feedback, and reflection. An intriguing exception is a study investigating how mentees’ feedback seeking and acceptance mediated the relationship between mentees’ attachment style and the mentors’ feedback frequency and feedback quality (Allen, Shockley, & Poteat, 2010). Third, further investigation on the types of development

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effectively fostered by mentors would be valuable. Qualitative research has suggested mentoring may help mentees acquire a variety of knowledge, skills, and attitudes (Hezlett, 2005). Additional empirical research would extend the promising insights gained from prior research on mentoring and socialization (Chao, Walz, & Gardner, 1992) and mentoring and personal and relational learning (Lankau & Scandura, 2002). Finally, additional tests of development as a mediator between mentoring and outcomes are needed. Some benefits of being a mentee may stem from gains in social capital (Kirchmeyer, 2005) or changes in perceived organizational support (Baranik, Roling, & Eby, 2010), rather than cognitive, skillbased, or affective learning. Additional research would further build understanding of what linkages to expect between specific mentoring functions, types of mentee development, and particular organizational outcomes.

Developmental Assignments Capitalizing on the knowledge that a great deal of workplace learning occurs on the job as individuals deal with the challenges inherent in their roles and responsibilities, organizations have increased their efforts to be more intentional and systematic in using job assignments to enhance employee learning and development (McCauley, DeRue, Yost, & Taylor, 2014; McCauley & McCall, 2014). This practice takes various forms: (1) identifying stretch assignments that match individuals’ development needs or that prepares them for higher-level positions; (2) staffing with development in mind, that is, filling assignments with the individual’s on-the-job learning as a desired outcome (in addition to his or her job performance); (3) creating new types of assignments so that more people in the organization have the opportunity to take on and learn from important challenges (e.g., working across cultures, working in a different function); (4) enhancing employees’ ability to better learn while in a developmental assignment, for example, through better access to feedback and coaching; and (5) embedding support for experience-based development in the organization’s systems and culture. The practice of developmental job assignments is largely grounded in experiential theories of adult development which depict learning as an ongoing process of engaging in experiences, making observations and reflections on those experiences, forming concepts and generalizations based on those reflections, and testing those ideas in new situations (Argyris & Schön, 1974; Knowles, 1984;

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Kolb, 1984). However, links are also made to cognitive theories of learning (Ausubel, 1968; Kanfer & Ackerman, 1989) which posit that knowledge structures grow and develop when they are challenged by novel information obtained via experience. More recent work has pointed to the expertise literature (Ericsson, Charness, Feltovich, & Hoffman, 2006), with its emphasis on the development of superior performance through repeated practice in handling the important situations that arise in a performance context, as another theoretical frame for advancing the practice (see Day, Harrison, & Halpin, 2009; McCall & Hollenbeck, 2008; McHenry & McKenna, 2014). There is a wealth of research focused on the characteristics of assignments that develop managerial and leadership competencies – research that organizations can use to identify and create developmental experiences: • Exploratory research conducted in the UK on how managers learn from their day-to-day experiences identified several features of high-­development job experiences, including the novelty of the experience for the individual, high decision-making responsibility and latitude, lack of goal clarity, the need to innovate, a changing environment surrounding the job, and supervisory pressure (Burgoyne & Hodgson, 1983; Davies & EasterbySmith, 1984; Kelleher, Finestone, & Lowy, 1986). • A series of studies of key developmental experiences in managerial careers (as identified by the managers) found that the majority of these experiences were challenging assignments. Types of challenging assignments included starting something from scratch, turning around a troubled business, key projects and task forces, increases in job scope, negotiations, joint ventures and alliance, cross-functional and cross-cultural moves, and rotations at corporate headquarters (McCall, Lombardo, & Morrison, 1988; McCall & Hollenbeck, 2002; Yip & Wilson, 2010). Although there are some differences in the frequency with which different types of assignments are reported by managers in different parts of the world, there is surprising global consistency in the basic types of challenging assignments. • Research to develop an assessment of the developmental components of managerial jobs yielded five major components: unfamiliar responsibilities, creating change, high levels of responsibility, managing across boundaries, and dealing with diversity (McCauley, Ruderman, Ohlott, & Morrow, 1994). Subsequent studies have found

small to moderate relationships between job incumbent ratings on these components and supervisor ratings of leadership skill development from the assignment (DeRue & Wellman, 2009), supervisor ratings of managerial competencies (Dragoni et  al., 2009), supervisor evaluations of promotability (De Pater, Van Vianen, Bechtoldt, & Klehe, 2009), and peer-rated job performance (Carette, Anseel, & Lievens, 2013). However, skill development may diminish for some competencies at very high levels of challenge, presumably because of the cognitive overload it creates (DeRue & Wellman, 2009). • Building from a series of company-specific research studies of developmental assignments, Kizilos (2012) developed a two-dimension model of the drivers of on-the-job development: intensity (e.g., time pressure, risk, visibility) and stretch (e.g., lack of expertise, interaction with diverse perspectives, new context). Intensity enhances mastery within an already-established area of expertise, and stretch broadens capability through new types of work and situations. Clearly, there are similar findings across these sets of studies. In developing a framework to use for identifying developmental assignments in their own organizations, learning and development practitioners often use the frameworks from one or more of these studies or have conducted their own internal research to develop a company-­specific framework (for example, see Bhide, Wilson, Kakkar, & Misra, 2014; Bly & Kizilos, 2014). Research examining the characteristics of individuals who are more likely to be in a developmental assignment has yielded insight into the antecedents of this development practice. Managers with higher levels of learning-goal orientation report having higher access to developmental assignments and engaging more in challenging tasks than do those with lower levels of learning-goal orientation (Dragoni et al., 2009; Preenen, Van Vianen, & De Pater, 2014). And when supervisors have authority over the tasks of their employees, performing challenging tasks are negatively related to supervisors’ performance-goal orientation. One study has also looked at personality traits, finding that extraversion and openness to experience were positively related to diversity of accumulated work experience (Dragoni, Oh, Van Katwyk, & Tesluk, 2011). In addition, learning agility has been positively linked to diversity of job challenges and work experiences (Dai & Hezlett, 2017; Dries, Vantilborgh, & Perpmans, 2012). There is also strong evidence that women managers are less likely than men to be

Employee Development: The Process and Practice of Work-Related Learning

given the challenging assignments critical for development and career advancement (De Pater, Van Vianen, & Bechtoldt, 2010; Ohlott, Ruderman, & McCauley, 1994; Silva, Carter, & Beninger, 2012). Researchers have begun to examine moderators of the relationship between job challenge and on-the-job learning. Such research is important for discerning effective tactics for enhancing learning during developmental assignments. In addition to being examined as a characteristic of individuals in developmental assignments, learning-goal orientation has also been examined as a moderator. Dragoni et al. (2009) found that learning-goal orientation moderated the relationship between developmental quality of managerial assignments and supervisor ratings of competencies: the positive relationship is stronger for those with higher levels of learning-goal orientation. Although DeRue and Wellman (2009) hypothesized a similar moderator role for learning-goal orientation in examining developmental challenge and leadership skill development, their results did not support the hypothesis. They did, however, find that access to feedback offset the diminishing returns associated with high levels of challenge. Carette et al. (2013) found that career stage moderated the relationship between challenge in recent assignments and job performance. The relationship was positive for early career employees but exhibited diminishing returns for mid-career employees, perhaps due to changes in learning motivation at different career stages or to higher challenge intensity at mid-career. A particular area for continued research is the potential negative consequences of developmental assignments. Both DeRue and Wellman (2009) and Carette et  al. (2013) found curvilinear relationships between job challenge and outcome measures of learning and performance for subsets of their samples, presumably because high challenge can lead to levels of stress that interfere with learning and performance. And several of the components of developmental assignments (e.g., unfamiliar responsibilities, need to create change) are positively correlated with negative stress emotions (McCauley et  al., 1994). Recent research demonstrated that although challenging assignments can increase managers’ pleasant feelings which, in turn, increase their advancement potential and decrease their intent to leave their current job, these assignments can also increase negative feelings which decreases the managers’ advancement potential (Dong, Seo, & Bartol, 2014). These researchers also found that challenging assignments were related to increased turnover intentions only for those low on emotional intelligence. In addition, Courtright, Colbert, and Choi (2014) found that experiencing job challenges was associated with higher work engagement which

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was associated with higher levels of transformation leadership behaviors. However, experiencing these challenges was also associated with higher emotional exhaustion which was associated with higher levels of laissez-faire leadership behaviors. This indirect relationship between job challenge and laissez-faire leadership behaviors held only for those managers with low leadership self-efficacy. Finally, recent theorizing and research on one type of experience that can be ­developmental – expatriate assignment – also reinforces that job challenges may not always be accompanied by negative consequences on all outcomes. Expatriates with higher levels of job challenges maintained higher work adjustment over time and consequently, more positive attitudinal outcomes; expatriates with lower levels of job challenge experienced steeper declines in work adjustment over time and less favorable attitudinal outcomes, including intentions to prematurely return from their assignments. In this case, challenges may have helped expatriates maintain their motivation, exert extra effort to cope with stress, and overcome obstacles (Firth, Chen, Kirkman, & Kim, 2014). Clearly, further research is needed to determine how to identify and manage what degree of job challenge yields the most beneficial combination of outcomes. Although there is strong evidence for the important role that developmental assignments play in management and leadership development, there is little published research on the efficacy of efforts to make the use of developmental assignments more intentional and systematic in organizations. Organizations that develop special initiatives to create developmental assignments for targeted groups (typically high potential managers) often evaluate the effectiveness of these initiatives using surveys and interviews to capture the perspectives of participants and key stakeholders as they look back on the initiative. These evaluations typically report learning and growth for participants, increased engagement and commitment to the organization, and benefits to recipients of the participants’ work assignments. However, such initiatives are not without problems, such as covering the participants’ regular work during temporary assignments and drops in motivation after returning to ‘normal’ work after a challenging special assignment (McCauley & McCall, 2014). Expatriate assignment repatriation issues have received special attention given the significant number of employees who leave the organization within two years of returning from an expatriate assignment (Bailey & Dragoni, 2013). Research points to the importance of the type of job secured upon return, organizational career support, and utilization of the returning employee’s expertise

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for increasing retention after such assignments (Bailey & Dragoni, 2013; Kraimer, Shaffer, & Bolino, 2009; Suutari & Brewster, 2003). These findings are consistent with relationships observed between tuition reimbursement, intentions to turnover, and promotion following degree-attainment (Benson, 2006), highlighting the need for careful practice and further research on factors affecting retention following developmental assignments. Research on developmental assignments has been a useful reminder that individual development is happening all the time in organizations as a natural part of doing work. Useful advances in recent years have been the broadening of both research samples (to include more global and non-managerial employees) and methodologies (beyond retrospective reports of experiences and self-ratings of learning). However, researchers have only scratched the surface in examining the role of individual difference and contextual moderators of the challenge–learning relationship (Hezlett, 2010). Of particular interest are strategies leaders can use to maximize learning from developmental assignments, such as learning goals, feedback seeking active experimentation, emotional regulation, and reflection (Ashford & DeRue, 2012). More research is also needed to better link particular on-the-job experiences with the development of specific competencies, to examine optimal combination and sequencing of experiences, and to understand how developmental assignments differ by organizational level (Hezlett, 2010; Yost, 2014).

Action Learning Action learning is an approach to development that involves small groups taking action on real problems in their own work settings and learning from that action by taking time to reflect with peers. Although there are numerous ways that action learning is practiced, the approach typically has six main components: (1) an important and urgent task or problem; (2) a diverse group of six or so members; (3) processes for questioning and reflecting; (4) the development and implementation of action strategies; (5) commitment to learning; and (6) a learning coach (Marquardt, Leonard, Freedman, & Hill, 2009). Action learning has become a widely used tool for leadership and organizational development. It may be utilized as a component of a formal development program, employed to simultaneously advance an organizational change initiative and leadership development, or embedded in the organization as an ongoing strategy for organizational

learning. In a global survey of senior leadership development and talent management professionals, action learning was rated as most impactful among various approaches to accelerating leadership development (Abel et  al., 2013). The pragmatic approach of integrating learning with real work on organizational problems is appealing to organizations seeking tangible results from their investment in employee development. The popularity of action learning led to the creation of a journal, Action Learning: Research and Practice, in 2004. Action learning comes in a variety of forms, but as O’Neil and Marsick (2007) note, these forms ‘grew out of a common interest in different parts of the world in an action-based approach to learning from experience that values the knowledge people develop by doing things’ (p. xvii). Although all of the approaches to action learning are heavily influenced by experiential theories of adult learning, O’Neil and Marsick have identified four ‘schools’ of action learning that vary somewhat in their view of how learning takes place and thus in their design and implementation of action learning interventions: • Scientific school. This approach is rooted in the work of Revans (1982), who is considered the father of action learning and who based his approach on the scientific method. Revans conceptualized learning as a function of programmed knowledge (gained through traditional instruction) and questioning insight (arrived at through reflective questions). He saw ‘questioning insight’ as the more powerful but underutilized part of the learning equation. In a typical program, participants are exposed to knowledge using instructional methods and then apply this knowledge (and knowledge from previous experiences) to a project that has potential value to the organization. Each individual is engaged in his or her own project; the group is a set of mutual learners whose role is to question and stimulate reflection on each project. A learning coach may be used initially with the goal of having the group take over its own coaching role over time. • Tacit school. The tacit approach is associated with Dotlick and Noel (1998) and with Tichy (2002). General Electric’s Work Out process is an oft-cited example. In this approach, teams of peers address a real and challenging business issue together. The focus is on taking action with the assumption that learning will take place if a diverse set of colleagues work together on

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an unfamiliar problem, gathering and analyzing data, and with the freedom to experiment and try new ideas and approaches. The learning is tacit and incidental. A coach may work with the team, initially on team-building and framebreaking activities and then to provide feedback and encourage reflection, but is not an essential component of the approach. • Experiential school. Many proponents of action learning make use of Kolb’s (1984) learning cycle of immersing oneself in the task, then reflecting on the experience, drawing new concepts and ideas from the reflection, and planning changes in action based on those new concepts. Implementing those plans starts the cycle again. Learning is very intentional in this approach with focus given to learning processes and achievements. The learning coach plays a central role in facilitating team learning throughout the cycle, primarily using questions to surface and examine mental models. A goal is to develop members’ ability to learn how to learn. • Critical reflection school. This approach goes beyond the reflection-on-action of the experimental school to focus on basic premises that underlie thinking. Mezirow (1991) calls this reflection on underlying premises ‘critical reflection’. As they engage in critical reflection, people see how their perceptions are filtered through uncritically accepted beliefs which can distort their understanding of problems and situations. This approach emphasizes questioning the rationale underlying actions, challenging norms, and examining problems through multiple perspectives. Learning coaches, who often come from outside the organization’s culture, play a central role in helping the team to learn how to critically reflect. Leonard and Marquardt (2010) reviewed 21 studies examining the impact of action learning initiatives on the participants. The vast majority of these studies relied on post-initiative surveys and interviews to capture participants’ perceptions of impact, although several studies did employ prepost multirater assessments to measure skill improvements. Across studies, there was evidence of action learning program participants developing broad managerial leadership skills, developing collaborative skills, improving their ability to develop win–win solutions in conflict situations, and improving coaching skills. The elements of action learning experienced as the drivers of these outcomes included: the questioning process,

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taking action, learning from each other, listening to each other, diversity of team membership, the safe environment, and a learning coach. The studies also pointed to organizational-level variables important for conducting successful action learning programs: communicating the importance of the problem, support of top decision makers, ability to leverage resources across the organization, and ensuring implementation of solutions. One of the key challenges in action learning initiatives is balancing action and learning. Cho and Egan (2009) reviewed 50 empirical articles on action learning published between 2000 and 2007. Each article was categorized as predominantly focused on action, predominantly focused on learning, or balanced in examining action and learning. Action-focused studies concentrated on organizational problem solving and evaluated project results. Learning-focused studies concentrated on individual problem solving and learning and evaluated learning outcomes. The role of reflection and the learning coach was only weakly recognized in both types of unbalanced studies. Balanced studies focused on both organizational problem solving and individual learning, and they fully recognized the role of reflection and the learning coach. Nineteen of the 50 studies (38%) were categorized as balanced, and the majority of the unbalanced studies (25) were learningfocused, suggesting that achieving an action and learning balance can be challenging. Although practitioners often warn that action learning programs can foster action at the expense of learning (Raelin, 2008), this study confirmed earlier findings that action learning has been used more often for individual development than organizational problem solving. The widespread use of action learning has generated and been supported by a robust practitioner community. Expertise and best practices related to the design and implementation of action learning initiatives are readily available (see Marquardt et  al., 2009; O’Neil & Marsick, 2007; Raelin, 2008; Rimanoczy & Turner, 2008). And the practice continues to evolve and spread to new contexts, including cross-organizational learning (Boshyk, 2014), community development (Fick-Cooper & Clark, 2014), and virtual work (Waddill, 2006). However, research has not kept pace with this growing use. There is a limited amount of rigorous action learning research. Only 17 of the 50 studies examined by Cho & Egan (2009) involved what they labeled ‘common features for quality research’ (i.e., use of a conceptual framework; detailed reporting on study participant demographics and contexts, study design, and analytic methods; and the precise description of study procedures). The majority of studies use

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case study methodology and rely on self-reported learning or skill improvement. As Leonard and Marquardt (2010) note, ‘in many cases, the data has been gathered primarily to justify the expense of the program rather than to promote scientific knowledge about action learning. As a result, compromises in design or procedure are adopted that would not be acceptable in more scholarly research’ (p. 122).

FUTURE DIRECTIONS The Changing Landscape of Development Practices Organizations are increasingly asking learning and development professionals: How can we accelerate the development of critical employees? The question is often driven by one or a combination of the following business needs: (1) fast growth in the company, a business unit, or region; (2) the need to have more versatile employees who can help the organization be nimble in the face of an uncertain marketplace and changing industry dynamics; (3) perceived gaps in leadership bench strength; or (4) concerns about losing talented employees who want opportunities to advance their careers. One answer to the acceleration question is to make effective use of multiple development practices, including formal training. Different practices bring different strengths to the development process, so using them in combination engenders a more robust process overall. Some organizations are taking this one step further and employing multiple practices in an integrated way to address a targeted development need. They are working to accelerate development by creating intentional periods of intense development. These periods of intense development are most likely targeted for recently hired individuals moving into the organization, for those advancing to a new role or new level of responsibility in the organization, for individuals identified by the organization as having high potential for moving to more senior levels of management, or when changes in the organization are calling for new capabilities among a broad set of employees. An example of an integrated development process for individuals moving to a new role is Heineken’s development initiative for new firstlevel supervisors (Plunkett & Daubner, 2014). For the organization, the first-level supervisor role is critical because of its impact on the performance and satisfaction of frontline employees. For the

individual, moving into this role is often the biggest career transition one experiences, impacting day-to-day responsibilities, skill requirements, and professional identity. Heineken wanted a more consistent approach to developing individuals during the transition, but one that could be adapted to local needs across its global operations. The organization started by recognizing that moving into a supervisory role for the first time is a developmental assignment. Individuals taking on this assignment become part of a local ‘continuous learning group’ of first-time supervisors. Modeled after Revans’ original action learning approach, members bring current job challenges to periodic meetings of the group, and an action learning coach guides the group through a structured process to reflect on and discuss challenges. The coach also has access to a set of e-learning modules on topics relevant to the supervisory role. He or she can design a formal training curriculum from the modules or use them as needed, depending on the challenges team members bring forward. Clearly, this initiative is integrating learning from stretch experiences, developmental relationships, and formal training. The Robert Wood Johnson Foundation’s Ladder to Leadership Program for communitybased public health leaders is another example of an integrated development process. The program’s aims are both to accelerate the development of high potential leaders and to collectively equip them with skills to work more collaboratively across organizations – a capability seen as increasingly important for advancing social agendas (Fick-Cooper & Clark, 2014). The program is run within local communities in the US for groups of 20–25 early to mid-career professionals in public health organizations who are seen as having the potential to move into significant leadership roles. As a cohort, the group attends a formal training program held in three sessions over a 12-month period. As part of the initial session, individuals receive 360-degree feedback and work with an executive coach to set development goals. The coach continues to work with the individual throughout the year in virtual coaching sessions. During the year the cohort also divides into several action learning teams with each team designing and implementing a cross-organization project based on their common interest in a particular public health issue. Each action learning team has a learning coach and a senior public health leader who serves as the team’s sponsor. At the end of the formal program, participants identify a formal mentor in the community to support their continued development, and together they attend a mentoring workshop. A second answer to the acceleration question is to support and enhance informal, day-to-day

Employee Development: The Process and Practice of Work-Related Learning

learning in the workplace. Informal learning is predominantly learner directed and takes place outside of formal learning settings structured and managed by the organization (Tannenbaum et al., 2010). It includes learning from everyday experiences and reflection on those experiences; from peers, supervisors, and mentors; and from reading print or online material (Noe, Clark, & Klein, 2014). The development process model introduced earlier in the chapter (see Figure 11.1) highlights points in the process where organizations can support and enhance self-directed learning: (1) enhancing motivation to learn by building an organizational climate that clearly values learning, (2) increasing the availability of informal development opportunities, and (3) equipping individuals to maximize the learning they derive from these opportunities by providing tools and training that encourage individuals to apply effective learning strategies, such as reflection. An organization with a strong climate for learning demonstrates to employees the importance of learning and development. Certainly the presence of formal development practices is one way to communicate the importance of learning. Other ways for organizations to signal the value they place on learning is to measure and track employee development, to recognize and reward learning, to recognize and reward leaders who support and encourage others’ learning, to hire and promote individuals who exhibit high ability to learn, to communicate regularly about learning, and to consider the time spent on learning activities to be worthwhile (McCauley, Lafferty, & Kanaga, 2010). For example, organizations can make setting and working toward development goals a central part of every employee’s performance management process. They can make the degree to which managers develop their direct reports a significant part of their bonus pay. They can celebrate employees who develop themselves. They can encourage senior leaders to talk about their own learning journeys. These types of actions communicate that learning is a necessary part of organizational life and activates employees’ motivation to learn. Organizations can increase the availability of informal development opportunities in several ways: • Supporting the development of diverse informal networks within the organization. These social networks connect individuals to others from and with whom they can learn. Practices that afford employees the opportunity to work with different coworkers over time (e.g., cross-­functional teams, rotational assignments, short-term ‘loaned’

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employees) encourage the development of diverse networks. Communities of practice that link experts from across an organization are also supported in a number of organizations as a vehicle for employee development, transfer of best practice, and quick problem solving (Wenger & Snyder, 2000; Raelin, 2008). The growth of social media has increased opportunities for individuals to connect online and participate in virtual learning communities (Allen, 2014; Winter, 2014). • Increasing the developmental potency of supervisor–employee relationships. Supervisors ­ are an obvious coaching resource in organizations (Hunt & Weintraub, 2007); however, the role expectations of a manager and a coach are often quite different (Waldroop & Butler, 1996). Managers often achieve success by being quick to judge and act, focused on near-term results, and adept at pointing out problems. Effective coaching encourages reflection before action, is focused on the longer term, and seeks to understand rather than critique. Supervisors can learn coaching skills through training and practice, but also need to be motivated to coach by setting expectations and holding them accountable for coaching, recognizing and rewarding effective coaches, and articulating the connection between coaching employees and meeting the business needs of the organization. • Making the pursuit of on-the-job stretch experiences easier. Although developmental assignments are often orchestrated by the organization, self-directed learners also pursue on-the-job opportunities to advance their expertise and expand their skill sets. One way that organizations make this pursuit easier is through the use of ‘project marketplaces’, intranet-based spaces for managers to post projects looking for help on certain tasks or in need of particular skills, shortterm assignments, jobs needing to be filled on an interim basis, and job rotation openings (Davis, 2007). Another tactic is processes that allow employees to express their interest in gaining a particular type of experience; managers monitor these interests and look for opportunities to match work with interests (Boyd, 2014). A final avenue for increasing informal learning is to equip individuals with the strategies and tools to be active learners (e.g., setting learning goals, seeking feedback, reflecting on experience). These capabilities can be addressed in formal

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education and training programs. They can also be supported via talent management practices, access to online and mobile tools, and the organization’s culture. For example, setting learning goals can become a standard practice when taking on a new role or assignment. Tools that encourage regular reflection on experience can become embedded in the regular routines of employees (see Hill, 2014; Nowak, 2014). And the regular seeking of feedback can be supported by an organizational culture that values open and honest relationships.

Future Research As areas of research mature, they pass through a series of phases. In the first phase, the phenomena of interest is explored. Practices are shared among practitioners and defining boundaries of the phenomena are debated. Attention shifts in the second phase to theory building and testing. Initially, qualitative research is conducted with researchers adapting models from relevant domains. Later in this phase, rigorous quantitative studies are conducted with large samples. Ultimately, these primary studies are cumulated with meta-analysis (Passmore & Fillery-Travis, 2011). In the final phase, the primary concern is to identify exceptions to established theories. Although over the past 15 years research on development has generally evolved from early to later phases, research on specific development practices has progressed at somewhat different rates. Research on 360-degree feedback, mentoring, and executive coaching have matured to the point that meta-analyses are being used to synthesize findings and practice-specific models have been proposed. Research on developmental assessment centers, developmental experiences, and action learning also has grown to include theory-driven, empirical investigations, but has not progressed to the point meta-analytic research has been conducted. These differences highlight the potential for cross-pollination of ideas across development practices. Two strategies may be helpful in guiding this transfer. First, scientists can apply conceptual approaches across similar kinds of developmental practices. Research on 360-degree feedback already has informed studies of developmental assessment centers (Rupp et al., 2006; Woo et al., 2008). Similarly, approaches to conceptualizing support and relationship dynamics from mentoring could be applied to research on executive coaching and vice versa. And the breakthrough finding that more challenge in stretch assignments is not always better (DeRue & Wellman, 2009) could be

scrutinized in developmental assessment centers and action learning. Second, within a development practice, research on topics where there has been a dearth of research may benefit from an injection of ideas from development practices where the same topic has received substantial attention. For example, studies of the effects of pre-project motivation, its antecedents, and moderators on development in action learning interventions could be informed by the roles played by individual differences, such as learning-goal orientation, core self-evaluations, and self-efficacy, in 360-degree feedback systems, formal mentoring, and stretch assignments. Echoing the recommendations outlined for practice, attention also should be given to studying combinations of development practices. As discussed earlier, the average effect sizes observed for some developmental practices have been small. Combinations of practices (e.g., 360-degree feedback and coaching) have been effective. Further research identifying what practices work well together is critical. For example, it would be very valuable to know what combinations of practices are particularly effective in fostering the development of specific types of cognitive, skill-based, or affective learning. And, although aptitude by treatment interactions have proved elusive in the context of education and training, knowledge of whether individuals with some characteristics benefited more (or less) from particular developmental practices would help organizations match development methods to individuals. Research on employee development also could benefit from the transfer of concepts from other areas of psychology. General mental ability is a critical individual difference affecting learning outcomes in education and training (Colquitt, LePine, & Noe, 2000), but it has been examined infrequently as a factor affecting the success of developmental relationships or stretch experiences. Its inclusion in future research could inform the selection of individuals for developmental experiences, as well as clarify the nature of and role played by learning agility. The concepts of implementation intentions from the motivation literature (Novelli, 2014) and deliberate practice from the expertise literature (McHenry & McKenna, 2014) also would be valuable to investigate in the context of development. Social media and computer-mediated communications have transformed how individuals connect and communicate with each other. The capacity to deliver content via the internet has diversified how development practices are implemented, ranging from electronic delivery of 360-degree feedback reports to virtual administration of developmental assessment centers, and

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from e-mentoring to virtual action learning group meetings. To date, relatively little research has investigated the impact of these changes on individual development or organizational outcomes. Both initial conceptual and empirical work suggest that communication methods and forums may meaningfully change developmental practices. For example, interesting conceptual work has highlighted how electronic communication may overcome organizational, individual, and interpersonal barriers that impede mentoring (Hamilton & Scandura, 2002). Focusing on individual attributes, e-mentoring may initially diminish the salience of demographic characteristics, enabling mentors and mentees to focus on deep level similarity in values, attitudes, and/or goals as they initiate their relationships, rather than surface-level appearances that may trigger stereotypes and impede relationship development. On the other hand, a lack of demographic awareness may later limit open conversations about diversity, restrict the growth of diversity awareness, and impede the development of competencies related to building diverse relationships (Kram & Ragins, 2007). Other unintended consequences of e-mentoring may include challenges incorporating all the defining mentoring functions into the relationship. Role modeling, for example, may be more difficult to do virtually than in more traditional face-to-face relationships (de Janasz & Godshalk, 2013; Hamilton & Scandura, 2002). Although initial research with students suggests support received via e-mentoring is associated with selfreported learning (de Janasz & Godshalk, 2013), more research is needed to understand the process and outcomes of e-mentoring. As this unfolding literature on e-mentoring illustrates, the nature of virtual development practices may be qualitatively different than their more traditional counterparts. Social media may also serve as a substitute for some aspects of currently popular development practices, such as getting advice from a mentor or coach. Finally, we encourage the continued replication and testing of concepts and evaluation of practice across cultures. Fifteen years ago, a substantial amount of the research that had been conducted on development was based on data collected from individuals working in the United States, Australia, and the United Kingdom (McCauley & Hezlett, 2001). Examples of empirical studies published over the last few years include samples from Israel (Bozer & Sarros, 2012), Korea (Joo, Park, & Oh, 2013; Soomyung et  al., 2012), the Netherlands (De Pater et  al., 2009), Norway (Lysø, Mjøen, & Levin, 2011), and Singapore (Ng, Koh, Ang, Kennedy, & Chan, 2011), as well as multi-­country comparisons (Eckert et al.,

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2010; Yip & Wilson, 2010). Extending this trend will not only provide valuable guidance to practitioners, but also contribute to the identification of boundary conditions and exceptions that is needed to fully mature research on development in the workplace.

Note

The first author notes that this chapter represents the views of the authors and does not necessarily represent the opinion of Korn Ferry or its affiliates, including Korn Ferry Institute, Hay Group, Futurestep, or any other organization with which Korn Ferry may be affiliated.

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Part V

Leadership, Groups and Teams

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12 Leadership in Organizations R o b e r t H o g a n , G o r d o n C u r p h y, R o b e r t B . K a i s e r a n d To m a s C h a m o r r o - P r e m u z i c

INTRODUCTION The success of schools, sports teams, businesses, armies, governments, and nations depends on the performance of their leaders. Thus, leadership is one of the most consequential subjects in human affairs. However, despite the immense literature on the topic, there is no consensus regarding how to define, identify, or develop leadership. This chapter provides a theoretical framework for organizing the vast body of interdisciplinary research on leadership, and is aimed primarily at scientist-practitioners; for a review of mainstream academic thinking on leadership, see Barling, Christie, and Hoption (2011).

DEFINING LEADERSHIP Our discussion of leadership rests on two assumptions: (1) All significant human achievement, from the building of the Egyptian pyramids to the NASA space program, depends on coordinated group effort; (2) Inter-group differences in the success of coordinated group effort can be explained in terms of five generic precepts from

evolutionary psychology. These themes, all human universals, are as follows. First, people evolved as group living animals; as a result, they have deep needs for attention and approval, and find criticism and rejection stressful. Accordingly, people devote a great deal of effort to gaining social approval and avoiding disapproval. People in leadership positions receive more positive attention than their subordinates – such are the rewards of high status. Second, every human group, from pre-school toddlers to prison inmates, has a status hierarchy. People seem innately motivated to seek power and status; conversely, they find the loss of power and status stressful. Much everyday behavior concerns gaining, or avoiding losing, status. People in leadership positions enjoy more power and status than those lower in their hierarchies. Third, religion has been a cultural universal from the beginning of human history. This suggests that people have deep needs for meaning, structure, and order – all needs that religion serves. Conversely, people find inexplicable and unpredictable circumstances highly stressful. High status people, like leaders, have more structure and meaning (and less stress) than low status people (Marmot, 2004), and one of the critical functions of leadership is to provide a sense of meaning and purpose to others, much as religion does.

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Fourth, the powerful human needs for acceptance and status are inherently in conflict (Campbell, 1965). Being accepted depends on ingratiating ourselves with others – who enjoy it; acquiring status, however, depends on outperforming other people – who resent it. As Oscar Wilde noted, ‘If you are successful, you will win false friends and true enemies’. To the degree that people seek acceptance, they risk losing status (being downgraded); to the degree that people seek status, they risk losing acceptance (being resented). Good leaders are able to balance the tension between these conflicting demands. Fifth, people within groups constantly compete for attention and status, something most employed adults understand. However, there is also constant competition between groups for resources and even survival, something most anthropologists understand (Chagnon, 1988; Wrangham, 1999). Evolution within groups is driven by sexual selection (Miller, 2000); evolution between groups is driven by warfare. The fact that warfare is a major force in human evolution has been recognized for a long time (cf. LeVine & Campbell, 1972, Chapter 6), and the inevitability of warfare has implications for understanding leadership. Specifically, people are inherently self-centered, but when their groups come under attack, the members must organize themselves for defense. Leadership evolved as a mechanism for organizing group effort, and it is most indispensable when groups face crises. The distinction between within- and betweengroup competition parallels the distinction between leadership emergence and leadership effectiveness (Kaiser, Hogan, & Craig, 2008). Leadership emergence involves standing out in a group, and modern organizations tend to reward the winners of the within-group competition with ‘high potential’ nominations and promotions (e.g., salary raises, more senior titles, and bonuses). In contrast, leadership effectiveness concerns forming and managing a winning team, unit, or organization. Effective leaders are able to organize collections of people to outperform other groups, and most organizations are surprisingly inept at recognizing effectiveness. What is good for a group is always good for its members, but what is good for individuals may or may not be good for their fellow group members. Kaiser et al. (2008) point out that most leadership research concerns identifying the characteristics of individuals who are perceived as being leader-like (i.e., people who stand out). But, as Lord, DeVader, and Alliger (1986) note, leadership research typically operates under the misconception that the attributes associated with standing out are the same as those that help organizations prosper: ‘Understanding the

characteristics associated with how leaders are perceived … is primarily relevant to the careers of individual managers’ (Kaiser et al., 2008, p. 99). Organizations confuse leadership emergence and effectiveness; moreover, the leadership literature contains a critical methodological confound that adds to this confusion. Most published research defines leader emergence and leader effectiveness using observer ratings. However, factor analytic studies consistently find only one dimension underlying ratings for both emergence and effectiveness (Russell, 2001; Scullen, Mount, & Goff, 2000). That single underlying dimension concerns the degree to which raters approve of the person being rated (Brown & Keeping, 2005). We think rated effectiveness is a poor index of true effectiveness; we prefer to define effectiveness exclusively in terms of the impact a leader has on employee motivation, team dynamics, and tangible results, notably team performance. It is important to note that the majority of the published literature defines leadership in terms of emergence (Kaiser et al., 2008). Following the above, we define leadership as ‘a solution to the problem of collective effort – the problem of bringing people together and combining their efforts to promote success and survival’ (Kaiser et al., 2008, p. 96). Simply put, leadership concerns building teams that win, either by surviving or outperforming rival teams – which, in the long run, leads to success. Two aspects of this definition are unique. First, leadership is essential for organizational effectiveness, and we think effectiveness should be the bottom line in all organizational research. Nonetheless, the organizational behavior literature largely ignores organizational effectiveness. Second, academic researchers typically define leadership in terms of the people who are in charge. But the behavioral characteristics associated with being promoted to top positions are often uncorrelated with the behavioral characteristics associated with building and maintaining high performing teams. For example, in a large-scale investigation of the determinants of executive career success in Europe and the US, Boudreau, Boswell, and Judge (2001) found that disagreeable behaviors (e.g., being selfish, selfcentered, and arrogant) are positively linked to managers’ promotion and compensation – yet the opposite characteristics are necessary to inspire high performing teams (Collins, 2001). We want to emphasize the fact that being promoted – managing one’s career – is different from being a leader – managing a team effectively. As Luthans, Hodgetts, and Rosenkrantz (1988) note, ‘One of the dominant myths surrounding the field of management is that “successful managers” are equated with “effective managers”’ (p. xv); their research debunks this myth. They studied 457

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managers, 44 by ‘free observation’ on the job, 248 systematically on the job, and 165 using interviews, and they collected standardized questionnaire data from the managers’ subordinates. They defined two groups: (1) successful managers – people who had been rapidly promoted in their organizations; (2) effective managers – people whose subordinates expressed satisfaction and commitment and whose organizational units were judged to be effective. They then catalogued the behavior of the two groups. Based on hard data, their three key findings are: (1) successful managers spend most of their time ‘networking’, defined as ‘interacting with outsiders’ and ‘socializing and politicking’ (p. 38); (2) effective managers spend most of their time ‘communicating’, defined as ‘keeping people informed’, and ‘human resource management’, defined as ‘managing conflict’ and ‘persuading people to cooperate’ (p. 70); and (3) 10% of their sample were both successful and effective. This research shows those who want to stand out and get promoted act differently from those who want to beat the competition by boosting their team’s performance, yet most leadership competency models fail to distinguish between leader success and effectiveness. This has profound implications for employee engagement, leader assessment and development, and organizational culture.

LITERATURE REVIEW We now turn to a review of the empirical literature based on our definition of leadership. The review reveals several interesting themes.

Implicit Leadership Theory Because intra-group warfare is and has been a constant theme in human affairs, leadership was important in our evolutionary history (Van Vugt, Hogan, & Kaiser, 2008). It may be the case, then, that people are pre-programmed to: (a) need leadership; and (b) have certain innate expectations regarding the characteristics of leaders. Freud (1960) argued that most people need external direction (he thought this was the reason for religion, art, and philosophical inquiry), a need that is potentiated during crises; certain other people opportunistically co-opt these needs and assume leadership roles. Freud described these people as narcissistic, psychopathic, egoistic, and demanding, and the followers who need direction, ‘fall in love’ with them. As Nicholson (2013, p. 20) notes:

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When leaders evoke such visceral feelings in followers, we are in the territory of pure animal instinct. The leaders know what they are doing – they get a deep animal satisfaction from their dominance – Hitler, Pol Pot, Gaddafi, Idi Amin, Saddam Hussein, Stalin, and many more …. It is a model of leadership that has its roots in the deepest areas of animal response – where the most basic emotions of fear and need reside.

This leadership style is also held in high regard in modern business, as in the case of Steve Jobs and Jack Welch (cf. Isaacson, 2011). The idea that, at an unconscious level, most people need direction appears in the modern literature as ‘the romance of leadership’ (Meindl, 1995). This model interprets followers’ ratings of leaders’ performance as a kind of wish fulfillment or ‘sense making’ rather than as veridical accounts of actual leader performance. This line of analysis is important because it focuses on followers’ reactions to leader performance, rather than on the characteristics of high ranking people. If leadership concerns effectively mobilizing group effort to outperform other groups, then it is crucial to understand the psychology of those who are led, and particularly how they respond to the characteristics of would-be leaders. This leads directly to implicit leadership theory (Schyns & Meindl, 2005) – how followers define effective leadership. Kouzes and Posner (2010) devised a disarmingly simple paradigm for studying leadership from the followers’ perspective: ask people to describe the best and the worst bosses they have ever had using a standardized format. This research reveals that followers evaluate leaders in terms of four broad categories of behavior (Hogan & Kaiser, 2005). The first concerns integrity (Kaiser & Hogan, 2010). Followers need to be able to trust their leaders, to know that they are honest, even handed, keep their word, and avoid self-dealing; followers’ ratings of the degree to which they trust their leaders are the best single predictor of engagement and team performance (Dirks & Ferrin, 2002). The second thing followers hope to see in potential leaders is evidence of good judgment; Lyndon Johnson’s decision to attack North Vietnam and George Bush’s decision to invade Iraq were the two worst foreign policy decisions in modern American history and permanently damaged their reputations for leadership. Third, followers expect their leaders to be competent, to know something about the business they are in and have recognized expertise in that area. Terry Semel, the failed CEO of Yahoo!, was a Hollywood producer and the only chief executive in Silicon Valley without a background in technology. Leo Apothecker, the failed CEO of the computer hardware manufacturer,

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Hewlett-Packard, came to the company from SAP with a background in software development and lasted 11 months. The fourth thing that followers want to see in potential leaders is ‘vision’ – the ability to explain why a team is doing what it is doing, where it is going, and why it matters. Vision provides teams with a meaningful mission and a sense of purpose, inspiring them to work hard to achieve their goal. This speaks to people’s needs for meaning and purpose in their lives. These four themes emerge in descending order – integrity is the most important perceived leader attribute and vision is the least important – but all four are crucial components of leaders’ reputations (Lord, Foti, & DeVader, 1984). Followers hope to see integrity, good judgment, competence, and vision in their leaders; conversely, leaders who lack integrity, good judgment, competence, and vision will surely lead their followers to failure.

Personality and Leadership Freud (1960) believed that: (1) leadership powerfully shapes the course of human affairs; (2) leaders have distinctive personalities; and (3) followers’ personalities shape the way they respond to leaders. Thus Freud thought that leadership and personality are inextricably intertwined, a view that has not been popular among academic researchers. From the late 1940s until fairly recently, behaviorism and situationism dominated American psychology; these views maintain that people’s (including leaders’) behavior depends more on situational contexts than stable personal characteristics. In the early 1990s, researchers finally agreed that personality does have significant real-life consequences (Barrick & Mount, 1991); ten years later researchers agreed that personality is related to leadership emergence (Judge, Bono, Illies, & Gerhardt, 2002). Across organizations, industry sectors, hierarchical levels, and thousands of managers, the personality dimensions of Emotional Stability, Conscientiousness, Extraversion, and Openness predict leader emergence – managers who seem confident, socially appropriate, outgoing, dutiful, and bright stand out from the crowd. Collins’ (2001) seminal book is another milestone in leadership research. As noted above, most of academic research involves comparing individuals who make it to the top with those who don’t, which is a within-groups approach to leadership – i.e., why some individuals outperform others inside a group. In contrast, Collins compared successful versus mediocre companies, a betweengroups approach that is unique in this field and

enables us to understand the determinants of organizational effectiveness – i.e., why some groups outperform others. Collins’ research team studied the 1,435 companies listed on the Fortune 500 list between 1965 and 1995. They then identified 126 companies that had a period of below average returns followed by a period of above average returns or vice versa. Then they focused on 11 companies that showed 15 years of above average performance, followed by a transition year, and then 15 years of performance substantially above their industry average. Finally, they identified the factors that separated the 11 winning companies from all the others. We want to emphasize four findings from Collins’ research. First, the transition event that turned good companies into great companies was the arrival of a new CEO, a finding that the researchers did not seem to expect. Second, the 11 highly effective CEOs had the same personalities; they were: ‘Self-effacing, quiet, reserved, even shy’ (p. 12); at the same time they were ‘incredibly ambitious – but their ambition [was] first and foremost for the institution, not themselves’ (p. 21). Thus, they were a blend of extreme personal humility and ‘intense professional will’ – a fierce and relentless drive to win. Third, the new CEOs all did the same thing: in Collins’ terms, they got the right people on the bus, the wrong people off the bus, and the right people in the right seats – i.e., they built high performing teams starting with proper employee selection. Finally, Collins’ data demonstrate that effectiveness always trumps emergence. Time and again he contrasts his humble, self-effacing, but highly effective CEOs with their high-profile, publicity-seeking (emergent) counterparts in poorer performing companies. We can summarize this final (counter-intuitive but highly significant) finding as, ‘Say good-bye to the myth of the ego-driven and charismatic CEO’. Consistent with the preceding section of this chapter, we also believe that these 11 highly effective CEOs were perceived as having integrity, competence, good judgment, and vision, but this is not part of Collins’ message.

EQ and the Soft Skills of Leadership Modern leadership research provides compelling evidence for the importance of ‘soft skills’ – e.g., intrapersonal, interpersonal, management, or people skills – in leaders. Hard skills (technical expertise) primarily predict task performance, but soft skills are critical for managing people and building winning teams. Emotional intelligence (EQ), usually defined as the ability to identify and

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manage one’s and other people’s emotions (Chamorro-Premuzic & Furnham, 2010; Mayer, Caruso, & Salovey, 2000), is an important component of the soft skills discussion. Research shows those who are better able to read others’, and control their own, emotions are better at engaging and empowering employees. Sivanathan and Fekken (2002) found that EQ predicted subordinates’ ratings of transformational leadership; Palmer and Gignac (2012) showed that managers’ EQ explained a substantial amount of variance in subordinates’ engagement levels; Leary et  al. (2013) reported that employees whose bosses score lower on EQ are significantly more disengaged. In addition, a meta-analytic study synthesizing the results of 62 independent studies reported a strong correlation (.59) between ratings of transformational leadership and EQ scores (Harms & Credé, 2010). EQ not only predicts employee engagement, it also plays an important role in leadership emergence. A recent model of employability and career success (Hogan, Chamorro-Premuzic, & Kaiser, 2013) proposes that the ability to gain and maintain desirable jobs and to climb the organizational hierarchy is largely a function of the degree to which people are rewarding to deal with; people with higher EQ scores are more rewarding to deal with. In line, meta-analytic research shows that EQ is positively related to supervisory ratings of job performance, even after holding constant IQ and the Big Five personality dimensions (O’Boyle, et al., 2010). Because supervisory ratings of performance are more indicators of approval versus effectiveness, EQ is important for those wanting to appear on high potential lists (i.e., leadership success). The relationship between EQ and leadership effectiveness is less clear, as there has been little research in this area. Despite the positive correlations between EQ, employee engagement, and leadership success, it should be noted that there are positive and negative characteristics associated with both high and low EQ scores. For example, studies suggest that EQ and narcissism are related, even at the genotypic level (Petrides, Vernon, Schermer, & Veselka, 2011), and that being able to recognize emotions in others and empathize with them is positively related to narcissistic exploitativeness (Konrath, Corneille, Bushman, & Luminet, 2014). It is also clear that too much positive self-belief leads to self-enhancing delusions; thus under-confidence is less harmful than over-confidence (Colvin, Block, & Funder, 1995). Concerning the benefits of low EQ, research suggests that people who worry about their performance tend to outperform those who don’t (Huy et al., 2011), and lower EQ scores predict people’s receptiveness to negative

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feedback (Baumgardner, Kaufman, & Levy, 1989), which should result in greater coachability. Unfortunately, the EQ concept has been hijacked by the (pseudo-scientific) positive psychology movement, which means that most popular articles concerning EQ are colored by the self-indulgent spirit of the self-help industry. High EQ scores, like any other aspect of personality, are adaptive in some environments but counterproductive in others. A good example of the ambivalent nature of EQ would be US President Barack Obama, who would surely score highly on any EQ test. Thus, he is steady, calm, and a good communicator, but he also resists negative feedback, and is somewhat aloof, smug, and self-satisfied. Thus, it is probably more useful to think of EQ in terms of emotional sensitivity and reactivity than an actual ‘intelligence’.

Intelligence and Leadership Evolutionary psychology also suggests that intelligence plays an important role in between-groups competition. Ancestral groups that could predict weather patterns, anticipate attacks, develop superior weapons, and exploit their competitors’ weaknesses had an adaptive advantage. Intelligence can be defined as the ability to think clearly, which entails reviewing assumptions, reflecting on performance, and making changes as needed (Hogan, Barrett, & Hogan, 2009). Successful entrepreneurs constantly review assumptions and change their business models based on feedback from customers and competitors; those who do not adapt to business realities usually fail. Intelligence may also be defined as the ability to detect and exploit relevant covariations in external events. Intelligent people can identify reliable sequences of events (e.g., falling customer satisfaction ratings are followed by declining revenues) and recognize when these sequences are reoccurring. Less intelligent leaders fail to note how events are interconnected and cannot accurately predict outcomes. Thus intelligence can be seen as a twostep process, where the first step is problem detection. Do leaders recognize problems or identify the causes underlying a variety of challenges? The second part of the process is problem resolution. Once leaders have identified problems, can they fix them? Leaders who can recognize and correctly prioritize issues and develop sound solutions will seem to have good judgment. They are also more likely to develop visions of the future that make sense to followers. Those who misdiagnose issues or develop sub-optimal solutions will be perceived as having bad judgment and lacking vision.

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Leaders are constantly required to recognize conflicts, resolve resource constraints, make trade-offs, and manage talent; thus problem detection and resolution are important for organizational performance. It could be that the success of the organization depends on their leaders’ day-today problem solving and decision-making competence (Hogan et  al., 2009). It is interesting to note that this definition of intelligence resembles Spearman’s (1927) concept of g, which he defined as the ability to solve a wide variety of problems correctly. Most standardized tests of intelligence are designed to predict academic performance, but they have been used to select leaders since World War I. Research reviews (cf. Stogdill, 1948, 1974; Bray & Howard, 1983; Lord et  al., 1986; Bass, 1990; Fiedler, 1992; Judge, Colbert, & Illies, 2004) report correlations between standardized intelligence tests and leadership in the r = .3 to .5 range. Most of this research used rated leadership performance as criteria, which means that doing well on standardized tests helps individuals gain approval, stand out, and win the withingroups competition (i.e., be successful leaders). How standardized intelligence test scores relate to building winning teams is not clear. Assessments that measure problem detection and resolution may be better predictors of leadership effectiveness than the standardized intelligence tests currently in use. It is interesting to speculate how successful versus effective leaders approach problem identification and resolution. Successful leaders are concerned with winning the within-group competition and may focus their problem detection to the challenges posed by their peers. Problem resolution may be directed toward developing programs that gain them recognition rather than resolving the issues affecting team performance. Effective leaders are more concerned with winning the between-groups competition and identifying challenges posed by market and customer trends, competitive threats, and internal capabilities. They develop and implement solutions that leverage team strengths and exploit competitors’ weaknesses in order to win customers.

associated covariations. This is why organizations rotate high potential candidates through various functions and geographic locations. However, exposure does not entail learning; some people learn much faster on the job than others (Hughes, Ginnett, & Curphy, 2014). The speed of learning, the talent to apply that learning to novel situations, and the ability to unlearn false covariations are important qualifiers of experience (DeRue, Ashford, & Myers, 2012; Kuncel, 2013). Personality also predicts the ability to learn from experience versus learning to perform (Dweck, 1986). Eichinger and De Meuse have developed measures of the ability to learn and apply the lessons from experience and their measures correlate in the r =.3 to .4 region with supervisors’ ratings of leadership competence, performance, and potential (De Meuse, Dai, & Hallenbeck, 2010; De Meuse et. al, 2012; Eichinger & Lombardo, 2004). However, the evidence indicates that ‘learning agility’ predicts leadership success not effectiveness. It may be that those individuals who seem better able to learn from experience are also adept at managing upwards and attracting attention to themselves. The action-observation-reflection model provides a framework for understanding how leaders learn from experience (Hughes et  al., 2014; Curphy, 2014c). For learning to occur, leaders need to observe the impact of their actions on staff, team, and organizational performance. If they fail to act or to note the consequences of their actions, then they will be unable to learn from experience. Reflection helps leaders codify lessons learned, adapt insights to new situations, and unlearn lessons that are no longer valid. Leaders who fail to reflect are apt to continue repeating mistakes (Argyris, 1991). The irony is that reflection is least likely to occur when leaders deal with crises, complex challenges, high stakes/visibility projects, and demanding workloads – the situations offering the greatest potential for learning (Bennis & Thomas, 2002).

Derailment Experience and Leadership Experience also affects leader competence. Managers who seem to lack the relevant experience (e.g., newly commissioned military officers) are not perceived as competent. Experience is related to problem detection and resolution – the more exposure people have to organizational challenges, the more likely they are to learn the

In another milestone paper in the history of leadership research, Bentz (1967, 1985) reported on a 30-year study of managerial performance at Sears. During the study, Sears used a well-developed assessment battery to hire all new managers, thereby assuring that they were bright, well adjusted, and socially skilled; nonetheless, half of them were fired. Bentz’ research is important for three reasons. First, he broke the mold – his research shows that the failure rate for managers

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in American business is substantially higher than anyone expected. Second, he showed that managers fail for personality-based reasons. And third, his results, which consistently replicate (see Hogan, Hogan, & Kaiser, 2011), launched the modern study of managerial derailment. This derailment research answers three questions: (1) what percentage of managers can be expected to fail?; (2) what are the consequences of bad management?; and (3) what causes managers to fail? Regarding the relative frequency of bad managers in everyday organizational life, Hogan et al. (2011) identified 12 published estimates of the base rate of management failure. The percentages ranged from 30% to 67%, with an average of about 50%. On the one hand, the estimates come from a variety of sources and are somewhat consistent; on the other hand, these estimates concern the number of managers who are fired. We suspect that the number of managers who alienate their subordinates but are not fired is substantial. For instance, according to Gallup (2013), the bulk of employees worldwide (70%) are disengaged at work, and the data come from organizations that ask their employees how they feel. We believe that about two-thirds of existing managers are ineffective, but because many are good at internal politics, fewer than half will be caught. There are substantial costs to organizations and employees when managers fail. DeVries and Kaiser (2003) report that a poll of senior HR executives estimated the cost of derailment to be between $750,000 and $1.5 million per executive – a number that must be adjusted up over time. But there are also hidden costs associated with managerial failure, including lost intellectual and social capital, damaged reputations, missed business opportunities, and the low productivity associated with alienated employees. Then there are the moral consequences that accompany the misery that bad managers create for their staff. Climate surveys routinely report that 75% of working adults say the most stressful aspect of their jobs is their immediate boss (Hogan, 2007, p. 106). The National Institute for Occupational Safety and Health (NIOSH) reports (NIOSH, 1999) that: (1) 40% of American workers think their jobs are very or extremely stressful, and (2) stress at work is the major cause of health complaints in American life, more than finances or family problems. Skogstad, Einarsen, Torsheim, Aasland, and Hetland (2007) identify the empirical links between bad managers and employee stress. Kelloway, Sivanthan, Francis, and Barling (2005) provide a good review of the literature on the effects of bad management on employee health. Bad managers are a mental health menace that imposes huge medical costs on society and ruins the quality of life of many working people.

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Finally, bad managerial behavior primarily originates in ‘the dark side’ of personality (Hogan & Hogan, 2001). As Bentz (1967) noted, managers fail for a surprisingly consistent set of reasons: emotional immaturity, arrogance, micro-management, dishonesty, indecisiveness, poor communications, etc. Hogan and Hogan (2001) proposed a taxonomy containing the most common counterproductive managerial behaviors. Although the behavior patterns differ, they have the same effect on employees – they erode trust and increase stress in their subordinates. Emergent leaders – those who stand out – are less concerned about their relations with their subordinates and, at the same time, certain dark side personality traits actually help them stand out.

Financial Consequences of Leadership There are well-established financial consequences associated with individual differences in leadership performance. Although Collins doesn’t emphasize the point, his research clearly demonstrates that well-led companies are more profitable than those with average leadership. Research on managerial succession over the past 25 years consistently shows a relationship between leadership and organizational performance (Barney, 1991; Barrick, Day, Lord, & Alexander, 1991; Bertrand & Schoar, 2003). Although the estimates vary – from Joyce, Nohria, and Roberson’s (2003) estimate of 14% to Mackey’s (2008) 29% to Day and Lord’s (1988) 40% – several studies conclude that CEOs account for a sizable proportion of variance in the financial performance of large organizations. In comparison, McGahan and Porter (1997) estimate that the industry in which a firm competes accounts for about 19% of the variation in financial performance. Again using organizations as units of analysis, Huselid (1995) and Van Reenen and Bloom (2007) provide strong evidence for the links between leadership and organizational performance. In a study of 700 manufacturing firms in the US, the UK, France, and Germany, Van Reenen and Bloom evaluated each firm in terms of management practices (internal communications, transparency of goal setting, monitoring individual performance, links between pay and performance) and company performance (profitability, sales growth). Country and industry effects explained about half the variance in firm performance; the remaining explainable variance (after accounting for measurement error) was attributed to the quality of the management within the organization rather than the top executives themselves (see Collins, 2001). They subsequently replicated these results with 4,000 Asian manufacturing firms (Bloom, Sadun, & Van Reenen, 2012).

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Employee engagement

Personality

Team functioning

EQ Leadership behavior

Organizational performance

Intelligence

Organizational culture

Experience

Organizational strategy

Figure 12.1  Three phase model of leadership and organizational performance Adapted from Robert Hogan and Robert B. Kaiser (2005), What we know about leadership, Review of General Psychology, 9, 169–180. (Figure 1. How Leader Personality Affects Organizational Performance.)

HOW LEADERS IMPACT ORGANIZATIONAL EFFECTIVENESS Much of the psychological study of leadership has focused on how leaders influence individual followers (Bass, 1990). Although building relationships with followers is important, leaders impact organizational effectiveness in other ways that are important. Figure 12.1 suggests that leaders influence organizational performance through four channels and at three different levels of analysis: (1) at the individual level, they motivate and engage employees; (2) at the group level, they foster constructive team dynamics among the employees; and (3) at the organizational level they shape culture and formulate strategy (Hogan & Kaiser, 2005).

Leadership and Employee Engagement Over the past ten years, employee engagement has become an important topic in I/O psychology and much of this interest is based on the strong empirical links between employee engagement and important organizational outcomes. In two landmark studies, Huselid (1995) and Harter, Schmidt, and Hayes (2002) show that: (a) managers’ behavioral

style predicts employee engagement at the business-unit level; and (b) employee engagement predicts business-unit performance (absenteeism, turnover, productivity, client satisfaction, and financial results). Engagement is a function of how people are treated by management; when engagement is low, productivity, client satisfaction, and unit financial performance are low, and vice versa. Employee engagement can be assessed easily and reliably using any of a number of commercially available surveys. Engagement can be defined as a relatively persistent psychological state associated with attitudes and behaviors that are beneficial to the organization (Macey & Schneider, 2008). Engagement is evaluated at the aggregate level (e.g., teams, units, organization), but it is created at the individual level; engagement ultimately is about the relationships between managers and subordinates, and that is the subject of leadership–member exchange (LMX) theory and Transformational Leadership. LMX theory (Dansereau, Graen, & Haga, 1975; Gerstner & Day, 1997) argues that the quality of the relationship between leaders and followers significantly determines organizational outcomes. Good relationships are characterized by trust and loyalty (Schriesheim, Castro, & Cogliser, 1999) and create engagement; Gerstner and Day (1997) summarize the positive consequences of such relationships for

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organizational performance and Christian, Garza, and Slaughter (2011) show that relationships are the aspect of leadership most correlated with engagement. Conversely, Townsend, Phillips, and Elkins (2000) show that low quality relationships not only degrade performance but lead subordinates to retaliate against bad leadership. The transformational leadership model (Bass & Riggio, 2006) also maintains that effective leadership depends on the relationships between leaders and followers. The model identifies four kinds of relationship-building behaviors as follows. Leaders should: (1) Work for the good of the organization as opposed to self-dealing; this is called Idealized Influence and it promotes follower trust; (2) Inspire subordinates to adopt high standards of performance, which is called Inspirational Motivation and promotes effort; (3) Encourage subordinates to think for themselves, which is called Intellectual Stimulation and promotes empowerment; and (4) Consider subordinates’ personal needs, which is called Individualized Consideration and promotes commitment. The Christian et  al. (2011) meta-analysis shows a direct relationship between transformational leadership and employee engagement; Barling et  al. (2011) review data showing how transformational leadership creates trust in the leader, commitment to the organization, responsiveness to change initiatives, identification with the leader and organization, organizational citizenship behavior, and follower creativity, and reduces turnover and cynicism.

Leadership and Team Functioning Sometimes individual motivation is not enough to impact organizational performance. The political unrest in Thailand, Egypt, and Ukraine shows that well-organized teams made up of moderately engaged members can overwhelm disorganized but highly motivated teams. Teams are composed of individuals, but effective team performance is an aggregate phenomenon. At the aggregate level of analysis, leaders must also persuade individuals to work together and then promote constructive team dynamics among the different interests and conflicting egos. Building on the work of Luthans et  al. (1988), Katzenbach and Smith (1994), Collins (2001), and Hackman (2002), Curphy and Hogan (2012) state that effective leaders: (a) ensure team members share the same assumptions about the job; (b) create a set of measurable team goals; (c) select and develop the talent needed to do the work; (d) clarify team member responsibilities; (e) gain team member buy-in; (f) acquire

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adequate resources; (g) manage conflict; (h) monitor progress; and (i) teach teams how to win. This framework can be used to launch new teams as well as improve the performance of existing teams. As stated earlier, most leadership research concerns the impact of leaders on individual followers. Research concerning how leadership affects team dynamics focuses on narrow aspects of team functioning, such as intra-group conflict (Chun & Choi, 2014), optimal structures (Hoch & Kozlowski, 2014), or de-briefing strategies (Eddy, Tannenbaum, & Mathieu, 2013). This literature is fragmented, making it difficult to discern patterns; in turn, this has produced two unintended consequences. First, many leaders don’t know how to create or maintain high performing teams – because the research-based answers are not readily accessible. Second, marketers have exploited this knowledge gap by promoting simple but unproven models of team performance (e.g., Lencioni, 2002). Curphy and Hogan’s (2012) Rocket Model attempts to build an integrative model based on team research. Effective leaders build strong relationships with direct reports and get them working as cohesive units; successful leaders spend little time attending to employee engagement and team dynamics.

Leadership and Organizational Culture All leaders need to engage employees and build teams, but senior leaders can also use organizational culture and strategy to drive performance. Organizational culture can be defined as the collection of beliefs, norms, values, rituals, and stories shared by members of an organization (Schein, 1985). The Catholic Church, the United States Army, and the Mayo Clinic have cultures that specify what tasks need or don’t need attention, and which behaviors are rewarded and which are punished. Culture affects how leaders exercise power, make decisions, treat staff, or get into trouble, and the local jargon and behavioral norms tell members how to behave (Kilmann & Saxton, 1983). In many ways organizational culture satisfies peoples’ needs for meaning and structure by explaining why, what, and how things get done in organizations. The rise of multinational corporations and the increasing globalization of business raises the question of whether values and cultural identity impact leader effectiveness – e.g., can Americans lead French work groups; is leadership talent similar in the West and the East (Hofstede, 2001; House, Hanges, Javidan, Dorfman, & Gupta, 2004; Trompenaars & Hampden-Turner, 2012)? Social

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identity theory (Tajfel & Turner, 1986; Hogg, 2001) provides some answers to these questions. As noted above, group membership is a human universal. Social identity theory explains why individuals identify with their social groups and how that affects their perceptions and behavior. The model argues that identifying with their groups gives people a sense of identity and self-esteem. Social identity theory also argues that emergent leaders must embody their group’s values and norms in order to be accepted. Leaders who understand and endorse a group’s values are likely to support that group’s collective interests (can be trusted), and that in turn will reinforce the group’s collective identity. Although social identity theory has been evaluated primarily with laboratory studies, it has intuitive appeal and some interesting support (e.g., leaders who embody the group’s values receive higher ratings for effectiveness – cf. Hains, Hogg, & Duck, 1997). Barling et  al. (2011, p. 215) note that there are no data showing that a match between leaders’ values and the groups’ values enhances group effectiveness – which for us is the bottom line in leadership research. Nonetheless, the idea that leaders should share the values of their followers is intuitively appealing. We close this discussion with four observations. First, similarity of values is the most powerful predictor of interpersonal attraction yet discovered (Hogan, Hall, & Greif, 1972); to the degree that leaders and followers share values, they will necessarily interact more productively. Second, we believe that a leader’s perceived integrity, competence, good judgment, vision, and relationship skill will predict effective team performance more powerfully than value match. Third, it would be easy to test social identity theory in the context of the Holland (1985) model. The Holland types are constellations of values; thus, for example if managers promise to treat Realistic/Conventional groups (combat unit, athletic team, police department) with compassion and understanding, they would lose credibility – because Realistic/Conventional groups are concerned with competence and effectiveness. Finally, for managers to be successful working in cultures other than their own, they need to understand and appreciate the values of that culture, but they don’t necessarily need personally to endorse those values. Senior leaders should be able to answer three questions: (a) what is their organization’s culture?; (b) does the culture promote organizational performance?; and (c) if not, can it be changed? Concerning the first question, incumbents often have difficulty describing their organization’s culture; what is obvious to outsiders can be invisible to insiders, particularly if they came up through

the ranks (Tichy & Devanna, 1986; Hughes et al., 2014). Concerning the second question, leaders who don’t understand their organizational culture will be unable to evaluate its impact on performance. Concerning the third question, leaders who want to change a culture must assess the current culture, specify the new culture, and develop and implement plans to close the culture gaps (Kotter & Heskett, 1992; Work Effects, 2014). These plans might include new organization structures, operating rhythms, policies, procedures, reward systems, dress codes, and titles. Implementing these changes takes time and effort. Many organizations are unable to make this happen, which explains why many mergers and organizational change initiatives fail (Hughes et al., 2014; Kotter & Heskett, 1992; Work Effects, 2014).

Leadership and Organizational Strategy Peter Drucker argued that culture trumps strategy; nonetheless, flawed strategies will doom organizations more quickly than dysfunctional cultures. Culture describes how things are done in organizations, whereas strategy describes what organizations need to do to succeed. Strategy can be defined as a set of coordinated actions needed to overcome a critical organizational challenge (Rumelt, 2011). Two aspects of strategy parallel our discussion of intelligence. The first concerns problem detection. Organizations are constantly bombarded with customer, competitor, regulatory, supply chain, technology, financial, and labor challenges. Effective leaders accurately identify the top problems facing their organizations. Problem detection itself depends on curiosity and a sober view of the facts (Collins, 2001, 2009; Kim & Mauborgne, 2005; Hogan et al., 2009). The second parallel to intelligence concerns problem solving. Effective leaders develop solutions for properly identified challenges. Making choices is a critical component of strategy (Collins, 2009; Rumelt, 2011; Wickman, 2007). Effective leaders make tough calls, which means there are winners and losers in their strategic plans. Departments, initiatives, products, and people that can solve the organization’s key challenges get supported; those having little impact do not. Two other aspects of organizational strategy are worth noting. First, the process of strategy development is rarely an individual effort. Sometimes strategy consulting firms make strategy recommendations; other times CEOs and their staff review customer feedback, market trends, and com­ petitive threats in order to create strategic plans.

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Second, strategy formulation is fairly easy; strategy execution is difficult (Marcus & Weiler, 2012; Wickman, 2007). Middle managers are often responsible for translating strategic plans into actions which themselves may run counter to organizational culture. Successful strategy execution is a problem in many organizations (Bossidy & Charan, 2002; Wickman, 2007).

Organizational Effectiveness and the Apple Paradox The data clearly show that personality and intelligence influence leadership behavior; certain leader behaviors affect employee engagement, team dynamics, and organizational culture and strategy; and all of these factors impact organizational effectiveness. The data are also clear that some CEOs (e.g., Jack Welsh, Steve Jobs) treat their subordinates badly and yet preside over high-performing organizations. These people violate basic psychological principles about leadership and by doing so challenge the view that there is a right way, and many wrong ways, to treat subordinates. We call this the Apple Paradox because of the incongruity between the excellent performance of Apple Inc. and CEO Steve Jobs’ abusive behavior (Kaiser, McGinnis, & Overfield, 2012; Reichman et al., 2013; Curphy & Kimball, 2013). Our explanation for the Apple Paradox concerns how the tasks of leadership tend to vary with hierarchical level. It is generally understood that managerial tasks differ across hierarchical level (Charan, Drotter, & Noel, 2001; Katz & Kahn, 1978). First line supervisors work directly with employees to implement decisions from senior management; a key task for first line supervisors is building employee engagement. Middle managers are mediators whose job involves interpreting decisions from senior management and passing them on to first line supervisors; middle managers specialize in building the consensus needed to drive decisions, conflict resolution, and coping with role-related stress. Senior managers make and communicate decisions – about strategy, structure, staffing, and culture – and deal with the fallout from bad decisions. In this chain of tasks and responsibilities, relationship maintenance is most important for first line supervisors and least important for senior managers. If first line supervisors work their way up to senior management, their relationship and team building skills are often reasonably well developed. But if people are promoted primarily based on results, if they parachute into senior management from a consulting firm, or if they assume leadership

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roles by buying or starting companies, then their relationship and team building skills may be undeveloped. In these cases, it is their vision, strategy, decision-making, and selling abilities that matter. Steve Jobs had a compelling vision, formulated productive strategies, and made good decisions about products that created considerable wealth for shareholders, and this created the credibility and latitude to behave badly. Conversely, the equally abusive Carlton Fiorina, failed CEO of Hewlett-Packard, made bad decisions about strategy, culture, and products and suffered the consequences. So one explanation of the Apple Paradox concerns the degree to which senior managers gain credibility and respect by making good decisions and projecting a positive vision, even if they treat their staff badly. In our view, there are lots of Apple Paradoxes in modern business.

THE ROLE OF INDUSTRIAL & ORGANIZATIONAL PSYCHOLOGY IN LEADERSHIP Leadership Competency Models Job analysis, the traditional cornerstone of industrial-organizational psychology, is used to identify the knowledge, skills, and aptitudes needed to perform a job. If a leadership competency model is based on a job analysis, then it will describe the performance capabilities that an organization expects from its managers. Every organization has a competency model but the models are all interchangeable. In fact, the various well-known leadership competency models differ only in their level of specificity. Yukl, Gordon, and Taber (2002) propose three categories of competencies: Task-, Interpersonal-, and Change-oriented behaviors. Hogan and Warrenfeltz (2003) suggest four categories: Self-management; Relationship management; Business skills; and Leadership skills. Bartram (2005) identifies ‘the great eight’ competencies which expand on Hogan and Warrenfeltz. Lombardo and Eichinger (2001) propose a list of 67 competencies. Recently, Campbell (2012) has proposed a synthesized taxonomy of basic factors comprising management and leadership performance. Many organizations define leadership competency models for different geographic regions, functions, and hierarchical levels (Hollenbeck, McCall, & Silzer, 2006; Hughes, et al., 2014). Most global companies use competency models to guide their leadership selection, development, performance appraisal, and succession planning processes.

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Despite the widespread use of these models, most suffer from two critical flaws. First, many organizations lack explicit definitions of leadership; consequently, the personal views of their senior leaders, human resources, or marketing departments drive their competency models, and these often specify idiosyncratic leadership behaviors. Second, competency models are often developed by asking incumbents for their opinion on what it takes to be a good leader. But research shows that these opinions often have little to do with winning the between-groups competition. Table 12.1 shows that many leadership competency models concern getting promoted rather than building effective teams (Curphy & Kaiser, 2014; Kaiser & Curphy, 2013). These competency models often cause organizations to select the wrong people for leadership positions and teach the wrong content in leadership development programs. Thus, competency models based on local job analysis may inadvertently contribute to the high levels of managerial incompetence discussed earlier.

Leadership Assessment Leadership assessment serves two purposes: (1) selecting individuals for leadership roles; or (2) evaluating individual performance in leadership roles. For selection, the problem usually concerns forecasting performance in a new context – in a new company or in a higher-level job with increased responsibility – for which past performance data are either unavailable or unhelpful. In such cases, personality assessment can provide a basis for comparing candidates because the empirical literature shows strong links between personality and leader emergence, effectiveness, and failure across the board. Judge et  al. (2002) summarize the research on personality and leader emergence. People who seem articulate, self-­ confident, socially appropriate, and visionary (e.g., Bill Clinton) stand out and seem to have leadership potential. Personality characteristics that contribute to a reputation for integrity, decisiveness, competence, vision, and interpersonal skill signal the kinds of qualities that employees prefer in their leaders. Similarly, Collins’ research shows that humility and a competitive drive distinguish effective senior leaders. Finally, personality assessment should also consider the dark-side factors associated with troubled relationships and corrupted judgment that are discussed in the literature on derailment (Hogan et al., 2011). When evaluating performance in current roles, behavioral samples and personality assessment can be combined with coworker feedback

about observed performance. This raises questions about what behavior to sample and how to evaluate it. Leadership behavior assessments can come from research-based questionnaires – such as the Multifactor Leadership Questionnaire (Bass & Avolio, 1997) – or from ad hoc competency models. Unlike personality, there is no consensus regarding an adequate taxonomy of leader competencies. We propose that a proper assessment will consider three general domains: (a) interpersonal style, representing the themes in the literature on implicit leadership theory and leader–member exchange; (b) judgment, representing the themes in the literature on leader problem solving and strategic decision-making (Fleishman et al., 1991); and (c) team building and facilitation, representing the leadership tasks outlined by Curphy and Hogan (2012). Assessments should also be tailored to the appropriate hierarchical level, because the behaviors associated with effectiveness differ for frontline supervisors, middle managers, and executives (Kaiser & Craig, 2011). Assessments for front-line supervisors should emphasize technical skills and building employee engagement whereas assessments for senior executives should emphasize strategic thinking and business judgment. Concerning measurement method, multisource or 360-degree feedback provide a well-rounded view of performance by supplementing the conventional, and incomplete, boss perspective with subordinate and peer perspectives. The ratings provide useful quantitative behavioral feedback, but evidence suggests that qualitative feedback (interview transcripts or written responses to open-ended questions) provide rich and compelling information, especially about behaviors that need to change (Smither & Walker, 2004). Further, research indicates that ratings made with the common five-point rating scale often yield a single underlying dimension defined by how well the rater likes the person being rated (Brown & Keeping, 2005; Varma, DeNisi, & Peters, 1996). This underscores the importance of qualitative feedback and alternative rating formats (e.g., Kaiser & Kaplan, 2005). For instance, a key theme in derailment research is that ‘strengths can become weaknesses’ when overused, and evidence suggests that ratings made with response scales that range from ‘too little’ to ‘too much’ capture this phenomenon and discriminate among different dimensions of performance (Kaiser, LeBreton, & Hogan, 2013). Researchers have introduced few new leadership assessment methods over the past ten years; the methods currently in use (e.g., personality, EQ, and work values inventories; structured interviews; standardized intelligence tests; business simulations; 360-degree feedback) account for

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15 $

Confidence in leadership

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$ $ Annual spending on leadership development 1997

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$Billions USD

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Figure 12.2 Increasing spending on leadership development, decreasing confidence in leaders Source: Reproduced from Robert B. Kaiser and Gordon Curphy (2013) Leadership development: The failure of an industry and the opportunity for consulting psychologists. Consulting Psychology Journal: Practice and Practice, 65, 294–302. (Figure 1. Trends in Confidence in Leadership and Annual Spending on Leadership Training and Development in the US)

25–45% of the variance in leadership performance (Scott & Reynolds, 2010). However, most of this research concerns predicting leader emergence rather than effectiveness. Distinguishing between leader emergence and effectiveness might lead to better predictors, but objective indices of effectiveness are often hard to define. The movement toward big data – the growing trend for organizations to quantify performance and make evidencebased management decisions – might improve the prediction of leadership effectiveness (ChamorroPremuzic, 2014). The adoption of assessments that measure the ability to detect and resolve problems across a variety of organizational situations might also result in some predictive improvement (Hogan et  al., 2009). Although I-O Psychology may have reached the upper limit on predicting performance with its current battery of assessment tools, adopting better measures of leader effectiveness may increase the predictive power of existing assessment tools.

Leadership Development The ability to build winning teams seems to be a learned skill, and few new managers know how to do it (Curphy & Hogan, 2012; Curphy, 2014a). Even if they know how to perform at one organizational level they can be uncertain about what to do when moving into a new role. Well-validated assessment methods can improve the odds of

selecting/promoting effective leaders, yet the most valid techniques for identifying leadership talent are often the least frequently used (Hughes, Ginnett, & Curphy, 2006). Between faulty competency models and bad selection and promotion practices, relatively few people in positions of authority know what it takes to be an effective leader – but almost everyone has an opinion. Some senior leaders recognize this problem. Since 1996, annual spending on leadership development by US corporations has grown from $7 billion to $14 billion (see Figure 12.2). Consider the thousands of executive coaching, executive education, in-house classroom training, action learning, experiential learning, and e-learning programs that have been delivered over the past 20 years. The content of these programs is typically based on leadership competency models that vary by organizational level and function. Training programs for front-line supervisors focus on the knowledge and skills needed to set goals, delegate tasks, monitor performance, and provide feedback to direct reports. Executive level programs focus on strategic thinking, marketing, decisionmaking, and organizational change skills. Some e-learning modules can be completed in an hour whereas executive-MBA programs are multi-year undertakings. Some programs use assessment to provide developmental feedback to participants. In the battle for business, leadership consulting firms provide training on topics such as authenticity, mindfulness, synchronicity, and horse whispering.

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Table 12.1  Two views of leadership Topic

Standard view

Our view

Definition Goal Role of subordinates Attributes Legitimacy Desired behavior Role of training Focus on team Role of ambition Role of personality Good leadership Client

Persons who are in charge Get to the top Serve the leader Charisma Conferred (title or status) Structure and consideration Optional Rarely Getting ahead Irrelevant Normative CEO/Manager

Persons who build teams Build a winning organization Help the leader Humility Earned (subordinate approval) Effectiveness Essential Always Beating the competition Central focus Exception Organization

Despite this increased emphasis on leadership training, public confidence in corporate leadership has declined by 30% (see Figure 12.2) and a recent Harvard Center for Public Leadership poll showed that 70% of Americans think we have a leadership crisis that is leading to national decline (Rosenthal, 2012). A 2013 Brandon Hall Group survey of 329 organizations reported that 75% of the respondents thought the leadership training programs in their organizations were ineffective. What might account for this gap between the money spent on leadership training and the sense that leadership in most organizations is inadequate? As recently noted by Kaiser and Curphy, there are six reasons why organizations get poor returns on their leadership development investments (Curphy, 2014b; Curphy & Kaiser, 2014; Kaiser & Curphy, 2013). First, many organizations define leadership using idiosyncratic competency models, and when organizations don’t know what they are looking for, they rarely find something meaningful. Second, organizations often send the wrong people to leadership development ­programs – including people who are primarily interested in being promoted and unwilling to listen to feedback. Third, most programs teach the wrong content; training in public speaking, building relationships, and influencing others is about learning to stand out and not about building a team. Fourth, most leadership development programs are targeted at individuals; if organizations want higher performing teams, they also need programs targeted at intact teams. Fifth, many organizations offer leadership development programs for the wrong reasons; senior leaders read trendy business books and decide to send key staff through related programs to save their organizations. Finally, training participants are not required to apply what they learn and the training

programs are rarely evaluated. None of this is likely to change soon because many people are powerfully invested in maintaining the leadership development status quo.

CONCLUSION The arguments in this chapter can be summarized rather quickly (see Table 12.1). Our most important point is that the academic literature defines leadership in terms of the persons in charge, and the popular leadership literature advises individuals on how to get ahead. In contrast, we define leadership in terms of creating team, unit, or organizational effectiveness, and our practical advice concerns team building not career advancement. We also argue that most academic leadership research confuses emergence with effectiveness. We believe the leadership literature doesn’t converge because of these two conceptual confusions: (1) defining leadership in terms of who is in charge; and (2) conflating emergence and effectiveness. Finally, we believe that defining leadership in terms of effective group or team performance leads to the following seven conclusions: 1 The data clearly show that leadership impacts the success and well-being of teams and organizations. As Collins (2001) and Bloom et  al. (2012) demonstrate, well-led and well-managed companies are significantly more profitable than poorly led and managed companies. Good leadership is better for everyone associated with an organization. Identifying and developing good leaders should be the number one priority of

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2

3

4

5

organizations that want to succeed. This is true for business organizations, schools and universities, hospitals, athletic teams, police and fire departments, military organizations, and city, county, state, and federal governments. How should organizations identify and evaluate leadership? Collins and his research team (2001) show that effective CEOs are not charismatic; they are humble, self-effacing, amazingly persistent, and fiercely ambitious for the success of their organizations. They also pay attention to talent and team dynamics. Implicit leadership theory (Schyns & Meindl, 2005) supports this view; it shows that subordinates prefer bosses who display integrity, good judgment, competence, and vision. These aspects of leader performance should be part of any competency model or evaluation process. The manner in which employees react to their bosses is an index of employee engagement. Harter et al. (2002) show that managers’ behavior creates or destroys employee engagement, and engagement predicts business unit performance. Engagement is the ‘g’ factor in organizational performance; it correlates with every index of effectiveness, positive or negative: productivity, customer service ratings, absenteeism, turnover, and safety violations. It is important to note that the ‘C-suite’ is largely clueless about engagement. Senior managers typically think they are responsible for engagement; in fact, it is long-serving first line supervisors who create or destroy employee engagement, independent of the actions of senior leaders. Because leadership training focuses on the C-Suite and largely ignores first line supervisors, we suspect the problems will persist. Judge et al. (2002) show that personality predicts leadership emergence better than any other individual difference variable including IQ. Luthans et  al. (1988) demonstrate that leader emergence is not the same as leader effectiveness. Emergent leaders (high potentials) specialize in politics; effective leaders specialize in building high performing teams. Thus, different patterns of behavior underlie leader emergence and leader effectiveness; emergence is largely a political outcome; effectiveness is the result of team building. Bentz (1985) shows that at least 50% of newly hired managers fail. We believe this underestimates the base rate of managerial incompetence in the corporate world, but the true base rate is an empirical question. In our view, good management and leadership are statistically

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rare, and bad management is the norm. Bad management creates unnecessary costs due to employee turnover, absenteeism, sabotage, and reduced productivity and customer service ratings. The behaviors associated with managerial incompetence are well understood and can be identified with well-validated assessment tools. To the degree that organizations ignore the arguments in this chapter, they act contrary to their own financial best interests. But there is also the moral problem of allowing bad managers to abuse, exploit, harass, intimidate, and bully their subordinates. 6 We also think it is important to appreciate the fact that there is no news in good news – organizations must acknowledge their shortcomings, failures, and mistakes in order to improve their performance. From the beginning, management consultants have specialized in telling clients how great their organizations are, but to ignore the data regarding the base rate of managerial incompetence in corporate life does everyone a disservice. 7 We propose a three phase model of leadership and organizational performance. In the first phase, personality, EQ, intelligence, and experience affect leadership behavior. In the second phase, leadership behavior affects employee engagement, team dynamics, and organizational culture and strategy. In the third phase, engagement, team dynamics, culture, and strategy affect organizational performance. This model has important implications for leadership recruitment, selection, development, promotion, and research. Finally, we would like to comment on the overall state of leadership research in the modern world. First, it is important to note that most of the points made in this paper were (or still are) contrary to received academic opinion. From about 1968 until the publication of the Barrick and Mount (1991) meta-analysis, academic psychology maintained that personality was either irrelevant or unable to predict occupational performance; in essence personality didn’t exist before 1991. In addition, it was only with the publication of the Judge et al. (2002) meta-analysis that academic psychologists began to believe that personality impacts leadership performance. And then the publication of the Harter et  al. (2002) meta-analysis made the case that there are financial consequences associated with managerial behavior. The academic community seems still to resist the notion that over half of all existing managers are alienating their subordinates. And finally, there is virtually no attention to the subject of organizational effectiveness in the

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modern literature. This means that most of the claims in this chapter would have been rejected out of hand 20 years ago and some are still controversial. We would also like to point out that most of our discussion was prefigured in Freud’s (1923 [1960]) book Group Psychology and the Analysis of the Ego. Freud argued that personality and leadership are related. He also stated clearly that leaders and subordinates have different personalities, that followers respond most powerfully to a circumscribed set of leader personality characteristics, and that demonic leaders understand this very well. Finally, Freud characterized the psychodynamics of both leaders and followers in unattractive terms, but terms that seem pretty valid today. All of which is to say that little in the chapter would have surprised Freud, which speaks volumes about the overall state of leadership research in the modern world.

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Kuncel, N. (2013). Where we are at with learning agility. In R. Stewart (Chair), Alternative Approaches to Assessing Learning Agility. Presentation given at the 28th Annual Conference for the Society of Industrial and Organizational Psychology, Houston, TX. Leary, T. G., Green, R. K., Denson, K., Schoenfeld, G., Henley, T., & Langfeld, H. (2013). The relationship among dysfunctional leadership dispositions, employee engagement, job satisfaction, and burnout. The Psychologist-Manager Journal, 16, 112–130. Lencioni, P. (2002). The five dysfunctions of a team: A leadership fable. San Francisco: Jossey-Bass. LeVine, R. A., & Campbell, D. T. (1972). Ethnocentrism: Theories of conflict, ethnic attitudes, and group behavior. New York: Wiley. Lombardo, M. M., & Eichinger, R. W. (2001). The leadership machine: Architecture to develop leaders for any future. Minneapolis: Lominger Limited. Lord, R. G., DeVader, C. L., & Alliger, G. (1986). A meta-analysis of the relationship between personality traits and leader perceptions. Journal of Applied Psychology, 71, 402–410. Lord, R. G., Foti, R. J., & DeVader, C. L. (1984). A test of leadership categorization theory. Organizational Behavior and Human Performance, 34, 343–378. Luthans, F., Hodgetts, R. M., & Rosenkrantz, S. A. (1988). Real managers. Cambridge, MA: Ballinger. Macey, W. H., & Schneider, B. (2008). The meaning of employee engagement. Industrial and Organizational Psychology, 1, 3–30. Mackey, A. (2008). The effect of CEOs on firm performance. Strategic Management Journal, 29, 1357–1367. Marcus, R., & Weiler, R. (2012). Aligning organizations: The foundation of performance. Camden, ME: Brimstone Consulting. Marmot, M. (2004). The status syndrome. New York: Times Books. Mayer, J. D., Caruso, D. R., & Salovey, P. (2000). Selecting a measure of emotional intelligence: The case for ability testing. In R. Bar-On & J. D. A. Parker (Eds.), Handbook of emotional intelligence (pp. 320–342). New York: Jossey-Bass. McGahan, A. M., & Porter, M. E. (1997). How much does industry matter, really? Strategic Management Journal, 18, 15–30. Meindl, J. R. (1995). The romance of leadership as a follower-centric theory. Leadership Quarterly, 6, 329–341. Miller, G. F. (2000). The mating mind: How sexual choice shaped the evolution of human nature. New York: Doubleday. National Institute for Occupational Safety and Health (NIOSH). (1999). Stress at work. (DHHS Publication No. 99–101). Washington, DC: Author. Nicholson, N. (2013). The ‘I’ of leadership. London: Jossey-Bass.

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O’Boyle, E. H., Humphrey, R. H., Pollack, J. M., Hawver, T. H., & Story, P. A. (2010). The relation between emotional intelligence and job performance: A meta-analysis. Journal of Organizational Behavior, 32, 788–818. Palmer, B. R., & Gignac, G. (2012). The impact of emotionally intelligent leadership on talent retention, discretionary effort and employment brand. Industrial and Commercial Training, 44, 9–18. Petrides, K., Vernon, P. A., Schermer, J. A., & Veselka, L. (2011). Trait emotional intelligence and the dark triad traits of personality. Twin Research and Human Genetics, 14, 35–41. Reichman, W., Saltzman, J., Peterson, D. B., Byham, B., & Curphy, G. J. (2013). Steve Jobs leadership style: Good or not? Panel presentation given at the 28th Annual Conference for the Society of Industrial and Organizational Psychology, Houston, TX. Rosenthal, S. A. (2012). National Leadership Index 2011: A national study of confidence in leadership. Center for Public Leadership, Harvard Kennedy School, Harvard University: Cambridge, MA. Rumelt, R. (2011). Good strategy, bad strategy: The difference and why it matters. London: Profile Books. Russell, C. J. (2001). A longitudinal study of top level executive performance. Journal of Applied Psychology, 6, 510–517. Schein, E. H. (1985). Organizational culture and leadership: A dynamic view. San Francisco: Jossey-Bass. Schriesheim, C. A., Castro, S. L., & Cogliser, C. C. (1999) Leader–member exchange (LMX) research: A comprehensive review of theory, measurement, and data-analytic practices. Leadership Quarterly, 17, 21–38. Schyns, B., & Meindl, J. R. (2005). Implicit leadership theories: Essays and explanations. Charlotte, NC: IAP. Scott, J. C., & Reynolds, D. H. (Eds.) (2010). Handbook of workplace assessment. San Francisco: Jossey-Bass. Scullen, S. E., Mount, M. K., & Goff, M. (2000). Understanding the latent structure of job performance ratings. Journal of Applied Psychology, 85, 956–970. Sivanathan, N., & Fekken, G. C. (2002). Emotional intelligence, moral reasoning and transformational leadership. Leadership & Organization Development Journal, 23, 198–204. Skogstad, A., Einarsen, S., Torsheim, T., Aasland, M. S., & Hetland, H. (2007). The destructiveness of laissez-faire leadership behavior. Journal of Occupational Health Psychology, 12, 80–92. Smither, J., & Walker, A. G. (2004). Are the characteristics of narrative comments related to improvement in 360-degree feedback ratings over time? Journal of Applied Psychology, 89, 575–581.

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13 Team Design Characteristics Greg L. Stewart and Kameron M. Carter

INTRODUCTION Research at the team level of analysis has exploded over the past two decades. This development coincides with practice, as teams rather than individuals are increasingly touted as the fundamental building block of work organizations (Mathieu, Tannenbaum, Donsbach, & Alliger, 2014). Teams occur everywhere from the executive suite to the production line, and in all industries ranging from automobile manufacturing to healthcare. However, teams are not all equally productive, nor do their members experience equal levels of job satisfaction. The way that teams are organized, or designed, is one overarching feature that has been identified as a source of differences in both productivity and satisfaction. In this chapter we thus discuss team design as a broad set of characteristics related to team effectiveness, team processes, group emergent states, and worker satisfaction. Team design characteristics correspond to the input portion of the ubiquitous input–process–­ output (IPO) framework for understanding teams. In this framework inputs are defined as factors that can be manipulated by leaders (Cohen & Bailey, 1997; Stewart, 2006). Processes are defined as cognitive, verbal, and behavioral activities that organize interdependent interactions (LePine,

Piccolo, Jackson, Mathieu, & Saul, 2008; Marks, Mathieu, & Zaccaro, 2001). Outputs include not only team effectiveness and team member satisfaction but also viability of the team to continue working together (Hackman, 1983). This basic IPO model has been expanded to include things like emergent states such as cohesion, a feedback loop from outputs returning to inputs, and coordination and boundary spanning with the external environment (Cohen & Bailey, 1997; Ilgen, Hollenbeck, Johnson, & Jundt, 2005; Marks et al., 2001). The basic category of inputs is, nevertheless, relatively consistent across perspectives, and thus serves as the broad boundary for our discussion of design features. Following the classification scheme put forth by Stewart (2006), we explore team design factors in three broad categories: task design, team composition, and team leadership. Task design captures task-driven prescriptions for coordinating work. Team composition focuses on the mix of individuals included in the team. Leadership captures the effect of both formal managers and emergent influencers, including rewards. Our objective is not to provide an extensive list of all research published in each area but rather to describe recent exemplary work in each category and then identify emerging themes and discuss forward-looking

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ideas for both research and practice. For each section we also provide a table that summarizes definitions and relationships, as well as highlights key studies.

TASK DESIGN Task design concerns guidelines and prescriptions for coordinating various work issues across group members. Teams perform better when they have a sense of certainty about the tasks they are expected to perform (Cordery, Morrison, Wright, & Wall, 2010). However, a frequently advocated advantage of structuring work around teams is improved ability to adapt and cope with uncertain environmental demands (Manz & Stewart, 1997). Two areas of focus that capture differences in coordination and clarity about completing responsibilities are autonomy and interdependence. Team autonomy captures the extent to which the team as a whole has freedom to determine its tasks and course of action, and is primarily concerned with how team responsibilities are organized within the broader organizational context. In contrast, team interdependence concerns the extent to which individuals within the team work together to complete tasks, thus capturing intrateam interactions.

Team Autonomy Higher levels of team-level autonomy have been generally linked to increased performance and satisfaction. The localized control of autonomous teams allows them to more quickly and effectively respond to environmental changes. Autonomous teams experience a greater sense of independence that creates a stronger sense of group potency (Rousseau & Aubé, 2013). Autonomy can, however, increase isolation and make it more difficult for teams to coordinate with other parts of the organization. The net effect of team autonomy on performance thus appears to depend largely on contextual factors, particularly the nature of the work with which the team is engaged. Autonomy is most beneficial when team tasks are uncertain (Rousseau & Aubé, 2013; Stewart & Barrick, 2000). This value of autonomy for completing uncertain tasks aligns with research suggesting a positive effect on team creativity (Liu, Chen, & Yao, 2011). Autonomy has also been found to be most beneficial when it is combined with knowledge obtained from sources outside the team (Haas, 2010), which is consistent with a recent study that found feedback to be an important

mechanism for assuring coordination of autonomous teams (Gonzalez-Mulé, Courtright, DeGeest, Seong, & Hong, 2016). Team members also derive greater satisfaction from working in autonomous groups, and they are more likely to remain members of such teams (Liu, Zhang, Wang, & Lee, 2011; Rousseau & Aubé, 2013). Teams themselves, as well as the individuals who compose them, thus generally benefit from increased autonomy. However, there is a lingering question about the organization-level effect of increased autonomy at the team level. One context where such autonomy seems to have a positive effect on organization performance is outpatient healthcare. In a currently popular approach to provide ongoing care to patients with chronic conditions, relatively autonomous teams consisting of a primary care provider such as a doctor, a registered nurse, a practical nurse, and a clerical worker are organized to proactively assess and meet the needs of patients (Bodenheimer & Laing, 2007; Schram, 2012; True, Stewart, Lampman, Pelak, & Solimeo, 2014). In this context, team-level autonomy is beneficial for customizing care delivery and adapting to the unique needs of individual patients. Such adaptation has led to greater organizational performance in areas such as reduction in patient time to obtain appointments and use of emergency care and hospital visits (Raskas et al., 2012; Yoon et al., 2013). Greater team autonomy does, nevertheless, decrease centralized control, making it potentially more difficult to coordinate efforts across teams. Some progressive organizations, such as W. L. Gore (Manz, Shipper, & Stewart, 2009), appear to achieve such coordination through informal links and relationships. Yet few theoretical models and rigorous quantitative studies have directly explored methods for coordinating clusters of autonomous teams. In summary, as shown in Table 13.1, the metaanalytically derived relationship between autonomy and performance is positive and moderate in size (ρ = .25). The relationship is, nevertheless, moderated with autonomy seemingly most beneficial when teams perform tasks that are dynamic and complex. Similar to what has been found at the broader organizational level, autonomy seems to not only increase motivation but also provides a mechanism for localized control that allows units greater flexibility and capacity for adaptation. Yet, exploring autonomy at the team level of analysis also highlights important multilevel effects. Greater autonomy for individuals may impede team-level autonomy, or from the other side greater autonomy for teams may impede individual autonomy. Although limited research has looked at such possibilities (Langfred, 2005; van Mierlo, Rutte, Vermunt, Kompier, & Doorewaard,

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2006), there is much more that needs to be done to understand the complex relationships of autonomy that evolve across levels. As highlighted above, more work also needs to be done to understand how different combinations of autonomy across levels influence performance outcomes. Given what has been done so far, it seems safe to conclude that autonomy at the individual level does not simply aggregate in an isomorphic fashion to create higher level constructs.

Team Interdependence Team members who depend on one another for information, materials, and reciprocal inputs are highly interdependent. Interdependent teams display a high degree of within-team coordination. Studies such as Kearney, Gebert, and Voelpel (2009) have found positive relationships between task interdependence and leader and supervisor performance ratings. This positive effect is thought to occur because higher task interdependence and intrateam coordination increase shared expectations, create norms for appropriate behavior, improve team communication, and decrease social loafing. For example, Somech, Desivilya, and Lidogoster (2009) found team interdependence to be positively related to a cooperative conflict management style and in turn team performance. Interdependence has also been shown

to moderate the negative effects of demographic diversity on social integration such that under high interdependence conditions, the effect of demographic diversity across members is less detrimental to the team’s social integration (Guillaume, Brodbeck, & Riketta, 2012). Greater within-team interdependence is not, however, universally beneficial for teams. For example, Barrick, Bradley, Kristof-Brown, and Colbert (2007) found higher interdependence to impede management teams that had little need to coordinate their actions. In a related finding, Maynard, Mathieu, Rapp, and Gilson (2012) found interdependence to be most beneficial when team members allocate a higher portion of their time to the particular team. Building and maintaining high interdependence requires substantial effort and resources. Expenditure of extensive time and effort is counterproductive when the nature of the task requires less ongoing coordination. Indeed, moderate interdependence, such as the sequential coordination associated with assembly lines is best when tasks are clearly established and consistent, whereas high interdependence seems optimal when tasks are uncertain and dynamic (Saavedra, Early, & Van Dyne, 1993; Stewart & Barrick, 2000). As summarized in Table 13.1, the effects of interdependence are somewhat difficult to unravel. The meta-analysis by Hülsheger, Anderson, and Salgado (2009) found interdependence to have a negligible effect on innovation (ρ = .03). In general higher interdependence appears to correspond

Table 13.1  Task design elements and relationships with team performance and other constructs Predictor

Definition

Relationship(s) with Relationship(s) with other team performance constructs

Primary source(s)

Team autonomy

Capacity allowed to make decisions concerning the accomplishment of work

team performance (ρa = .25)

Haas (2010); Liu, Chen, & Yao (2011); Liu, Zhang, Wang, & Lee (2011); Rousseau & Aubé (2013)

Task interdependence

Extent to which team member needs information, materials, and support from other team members

inverted-U relationship with team performance

a

meta-analytic correlation; b simple correlation from a single study

collective team satisfaction (rb = .26); strategic effectiveness (r = .24); operational effectiveness (r = .06); increased team potency (r = .58); time lost (r = −.28) innovation (p = .03); cooperative conflict management (r = .18); team identity (r = .17); task conflict (r = .19); relationship conflict (r = −.29)

Hülsheger, Anderson, & Salgado, (2009); Somech, Desivilya, & Lidogoster (2009); Stewart (2006)

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with greater intrateam coordination; yet, the effect of interdependence on performance is difficult to clearly assess and summarize with quantitative tools such as meta-analysis given that the relationship appears to be curvilinear. Perhaps one key for improving our understanding of interdependence is the development of a more clear perspective of the nature of the construct. In a broad sense interdependence concerns the level of coordination among teammates; yet, a variety of specific forms such as task interdependence and outcome interdependence have been identified. Developing a better understanding of each potential type of interdependence can guide theoretical efforts to understand the various mechanisms through which coordination among team members influences not only processes but also outcomes such as innovation and overall performance. A recent example of such work is the theoretically driven meta-analysis conducted by Courtright, Thurgood, Stewart, and Pierotti (2015) who found task interdependence to operate primarily through task-focused functioning (i.e., transaction/action processes, collective efficacy) and outcome interdependence to operate primarily through relational team functioning (i.e., interpersonal processes, cohesion).

Future Directions Related to Task Design In summary, research evidence suggests that structure has a contingent effect, wherein its effects depend a great deal on the nature of the tasks that a team must complete. Autonomous teams that are highly interdependent are best when tasks are uncertain and dynamic, whereas tasks that are clear and static are more effectively carried out by teams with low autonomy and limited interdependence. This broad conclusion provides important practical guidance for team designers: match team design features of autonomy and interdependence to the task type. Of course research knowledge in this area needs to be updated and extended as teams continue expanding into globally dispersed and virtual settings. As cross-cultural, technologically linked teams become more common, these taskrelated variables will become more important to consider during team formation. One such effort is the inclusion of technological interdependence in team interdependence and coordination (Bailey, Leonardi, & Chong, 2010). More emphasis is also needed to understand the longitudinal effects of task design. Hollenbeck, Ellis, Humphrey, Garza, and Ilgen (2011) found moving from a decentralized structure to a centralized structure to be more

difficult than moving in the opposite direction. In this same sense, moving from lower to higher levels of autonomy and interdependence may be easier than moving from higher to lower levels. Such an effect might make it particularly difficult for entrepreneurial teams to adapt if their tasks become standardized and the emphasis shifts from exploration and creativity to efficiency and cost reduction. Future research should therefore focus more attention on longitudinal effects such as this to clearly understand methods of optimizing team task structure. In this sense, team task design, as well as most other areas of inquiry, is in need of increased understanding about its effects across time. Even though longitudinal studies are difficult to conduct, and often suffer from deficiencies related to sample size and attrition, research evaluators such as journal reviewers and editors can likely advance the field as they place greater emphasis on the potential benefit of longitudinal studies relative to the inherent shortcomings.

TEAM COMPOSITION Individual members comprise one of the most important inputs to team design. The constellation of individual team member characteristics is often referred to as group composition. Looking at group composition within a team provides the opportunity to examine how individual dispositions, knowledge, skills, and abilities combine within the team. Mathieu et al. (2014) recently synthesized group composition research by first categorizing efforts as either individual or team models. Individual models emphasize characteristics of people who happen to be working in team settings. In essence, these models seek to determine characteristics of individuals who are most likely to contribute in any team context regardless of the attributes of teammates. Desirable characteristics include general traits such as cognitive ability and conscientiousness (Bell, 2007), as well as characteristics more specific to the team context, such as agreeableness and knowledge of how teams operate (Morgeson, Reider, & Campion, 2005). Team models extend perspectives of group composition to explore complex combinations of team members. These models build on compilation concepts of aggregation (Kozlowski & Klein, 2000) and explore mixes of individual characteristics. The emphasis moves from the attributes that any specific team member brings to the group and seeks to understand how the constellation of individual attributes combines to create a grouplevel construct. Given space constraints, our focus

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here is these team-level models. We first focus on what Mathieu and colleagues (2014) call the team profile model, which looks at distributions of characteristics across team members. In particular, we begin by exploring team member heterogeneity and then move to faultlines and subgroups research. We follow these discussions with an examination of the strategic core and distributed role perspectives as examples of the Mathieu et al. (2014) team profile model, which acknowledges that not all team members have equal weight on collective composition. We also summarize key findings related to team size.

Team Member Heterogeneity Competing hypotheses suggest opposite effects for heterogeneity across team members. One argument looks at diversity as a positive input that leads to increased flexibility, creativity, and competitive advantages for teams. An example of this perspective is cognitive diversity which has been linked to increased creativity, innovation, and problem solving ability (Horwitz & Horwitz, 2007; Shin, Kim, Lee, & Bian, 2012). Stachowski, Kaplan, and Waller (2009) similarly found increased diversity in experience to allow for increased adaptability across nuclear power plant teams engaged in a crisis simulation. Moreover, cultural diversity has been linked modestly to team creativity, task conflict, team member satisfaction, and social integration (Stahl, Maznevski, Voigt, & Jonsen, 2010). On the other hand, an opposing perspective suggests that large differences among group members in characteristics such as age, ethnicity, and personality negatively influence group processes and outcomes (Horwitz & Horwitz, 2007). In this vein, a meta-analysis by Bell, Villado, Lukasik, Belau, and Biggs (2011) found negative effects for education-level diversity and team tenure diversity on team performance. Their findings also identified negative relationships with team performance for diversity of ethnicity, sex, and age. A different meta-analysis also found negative relationships with age, ethnicity, nationality, gender, and educational level diversity when it comes to in-role performance and innovation (van Dijk, van Engen, & van Knippenberg, 2012). Diversity in personality traits has similarly shown mixed results. Extraversion, neuroticism, and openness to experience diversity between members have shown positive effects with group task performance, whereas diversity in member agreeableness and conscientiousness has demonstrated negative effects with task performance (Bell, 2007).

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Given the mixed findings related to team member heterogeneity, researchers have focused on determining whether specific types of diversity have differential effects. Joshi and Roh (2009) meta-analyzed the effects of gender, race, age, education, tenure, and functional diversity across teams. They discovered that relations-oriented diversity traits (gender, race, age) were negatively related to team performance, whereas among task-oriented traits (education, tenure, functional diversity) only educational diversity was negatively related to team performance. In comparison, tenure and function diversity were both positively related to team performance. These findings support the notion that task-oriented diversity is more beneficial than relations-oriented diversity. However, the effect sizes were all relatively small with confidence intervals frequently including zero. Examining diversity in terms of type has thus been somewhat beneficial, but the overall pattern of results only weakly supports team member heterogeneity as an important design factor affecting team processes and outcomes. Research related to team diversity thus has very limited guidance for identifying compositional differences between successful and unsuccessful teams. As shown in Table 13.2, meta-analytic results suggest that team member heterogeneity has a relatively weak relationship with team performance (ρ ranges from .02 to .14 depending on type of diversity). Relationships with heterogeneity are slightly stronger for team processes (ρ ranges from .10 to .18). Although numerous attempts have been made to identify stronger relationships by refining measurement and defining different forms of diversity, evidence continues to suggest that the overall effects of heterogeneity, at least when captured simply as variance among team members, are relatively weak. More recent efforts to capture complex patterns of diversity, such as those discussed below, thus seem to hold the most promise for understanding how differences in individual characteristics combine to influence teams.

Faultlines and Subgroups Given the lack of clear conclusions related to team member heterogeneity, researchers have begun to further explore patterns of diversity. A particular emphasis has been placed on the concept of faultlines. Faultlines are hypothetical dividing lines that exist in relation to diversification of attributes. Social categorization theory (Joshi & Roh, 2009; Thatcher & Patel, 2012), social identity theory, and the similarity–attraction paradigm all provide insight into the formation of faultlines where

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Table 13.2  Team composition elements and relationships with team performance and other constructs Predictor

Definition

Team member Diversity of team heterogeneity member attributes

Faultlines and subgroups

Strategic core

Team size

a

Hypothetical dividing lines that exist related to the diversity of attributes; lead to subgroups in teams Members who have greater relative effect because they (a) encounter more problems, (b) have greater exposure to the team’s tasks, and (c) are more central to the team’s workflow Number of individuals included in the team

Relationship(s) with team performance

Relationship(s) with other Primary source(s) constructs

diversity of gender creativity and innovation Bell et al. (2011); Joshi & (ρa = −.06), race with diversity of Roh (2009) (ρ = −.14), education gender diversity (ρ = −.03); (ρ = −.16), race team tenure (ρ = .02); (ρ = −.18), education functional diversity diversity (ρ = .23); (ρ = .12) team tenure (ρ = .10); functional diversity (ρ = .18) team effectiveness satisfaction Cronin, Bezrukova, (rb = −.26) (rb = −.30) Weingart, & Tinsley (2011)

task performance (rb = .32)

Summers, Humphrey, & Ferris (2012)

innovation (ρ = .30); information sharing (r = −.11)

team effectiveness (r = .16); team performance (ρ = .04)

DeDreu (2007); Hülsheger, Anderson, & Salgado (2009); Mueller (2012); Stewart (2006)

meta-analytic correlation; b simple correlation from a single study

individuals classify, identify, and are attracted into subgroups and to people similar to themselves. In essence the faultline perspective suggests that team members identify with subgroups of individuals whom they perceive as most similar to themselves. Carton and Cummings (2012) classified faultline subgroups into three distinct types: identity-based, resource-based, and knowledgebased. In the formation of these subgroups they also identified three faultline types: separationbased, disparity-based, and variety-based. Rico and colleagues (Rico, Sánchez-Manzanares, Antino, & Lau, 2012) expanded upon these different faultline types and concluded that situational activation is necessary for these diverse member inputs to result in faultline creation. Without a situational cue, diversity between members has less chance of resulting in faultline and subgroup formation.

Stronger demographic faultlines within a group have been linked to weaker cohesion amongst team members (Flache & Mäs, 2008). Cronin et al. (2011) investigated affective and cognitive integration as mechanisms explaining the effects of faultlines. Affective integration – capturing the overall quality of interpersonal relationships – mediated a negative relationship between faultlines and satisfaction. Cognitive integration – capturing heterogeneity of thought and information – mediated the negative faultline-performance relationship such that it is attenuated when cognitive integration is high. The faultline perspective thus appears to hold promise for identifying critical effects of diversity within teams. Specifically, the formation of faultlines decreases team cohesiveness and team identity as members find greater similarity and identification with their fellow subgroup members

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than with the team as a whole. Diversity thus manifests itself most negatively when team members are divided into more discrete subgroups. Groups are therefore at highest risk when team members fall into subgroups that share a large number of characteristics. For example, diversity in an engineering team might be most problematic when there is one faction of team members who are male, educated at a nearby state university, and over 40 years of age; whereas the other faction is female, educated at a common coastal university, and under 30 years of age. Such clear lines of demarcation along faultlines seem more detrimental to team function than an overall level of diversity that is captured through a simple analysis of member heterogeneity. The faultline perspective thus captures complex patterns of composition and, although research is still emerging in this area, has the potential to capture diversity effects in a way that transcends simple analyses of team member heterogeneity. Although it is still an emerging area of research, the summary shown in Table 13.2 suggests important relationships with performance and satisfaction when team member diversity is captured in terms of faultlines (r = –.26 and –.30, respectively). Although the number of primary studies in this area is still somewhat limited, the preliminary results seem very promising and identify an important area for future research. Thinking of teams in terms of faultlines can also provide potentially valuable contributions to practice. For example, if a team of engineers is composed of three men and one woman, it might be beneficial to assure that the woman is similar to the men on characteristics such as education, race, and age. Specifically, greater potential for difficult interactions seems likely if the men are similar on these characteristics and the woman is not. Perhaps teams can be facilitated, and underrepresented individuals will be able to contribute more fully, when team formation takes into account not only specific traits but also patterns of characteristics.

Strategic Core The introduction of the strategic core by Humphrey and colleagues (Humphrey, Morgeson, & Mannor, 2009; Summers, Humphrey, & Ferris, 2012) adds to the team composition equation by highlighting the fact that in many circumstances team members are not interchangeable. The effect of some team members is greater than the effect of others. Individual team members making up the strategic core have greater relative effect on the team

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because their roles: (a) encounter more problems; (b) have greater exposure to the team’s tasks; and (c) are more central to the team’s workflow. As these core roles are more important to team functioning and task performance, their fulfillment has a stronger impact on team performance. In designing a team, these strategic core members can have a relatively large impact on both overall team performance and individual member performance. As Summers and colleagues (2012) found, the strategic core can have moderate to large correlations with task performance in spite of changes in the team’s composition of non-core members. Similar to the effects of faultlines, this strategic core perspective once again captures the importance of examining team composition in relation to complex patterns rather than simply looking at aggregate statistics. Although this is a new area of inquiry with limited evidence, the summary in Table 13.2 does suggest that there is potentially important insight that can be gained by looking at team composition in terms of core roles. Achieving a greater understanding of such asymmetrical patterns of team member roles represents an important area of emerging research and has the potential to provide substantial guidance for improving team composition.

Distributed Expertise Much of the team literature is based on studies of student study teams or production teams where individual members are mostly interchangeable. Individual roles are not prescribed in these teams, and team members can generally complete the tasks performed by their teammates. However, as Hollenbeck and colleagues (1995) pointed out two decades ago, individuals in many teams have expertise that is unique from their teammates. Team members cannot substitute for each other, and composition must be assessed in terms of the individual roles. The healthcare team example introduced earlier represents an example of such teams. Each member of the team (doctor, nurse, clerk) has a unique skill set and predetermined role. Knowledge and training – and in many cases legal scope of practice requirements – necessitate certain tasks being completed only by certain team members. In this particular example, roles are made even more complex in that there is a dominant social hierarchy. Doctors have higher status than nurses, who in turn have higher status than clerks. A clear understanding of the effects of team composition thus requires treating each team member as unique. Team members with lower status and less specialized skill experience teams

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very differently than doctors (Solimeo, Ono, Lampman, Paez, & Stewart, 2015; Solimeo, Stewart, Stewart, & Rosenthal, 2014). Distributed expertise and differential status make it so that team composition has variable effects depending on the individual characteristics of each individual within each role. Additional theory and models of group composition that go significantly beyond the compilation perspective of interchangeable team members are thus needed. In many professional and service settings the frequent assumption of team members as interchangeable components must be revisited and group composition must be explored in terms of more complex patterns of expertise.

Team Size The actual size or number of members on the team serves as an additional factor when focusing on the composition of teams. Finding the balance between the optimum number of group members and the group’s performance can be difficult to determine. From an organization’s economic standpoint the larger the group the more resources that are needed to support the group’s functions. However, group members can see benefit in larger groups as there are increases in time, energy, money, expertise, and perhaps creativity. Yet, larger groups often face issues with coordination between members and therefore experience process losses. Mueller (2012) found indication that when an employee perceives less support due to the large number of team members, there is a negative relationship between team size and individual performance, even when controlling for extrinsic motivation. De Dreu (2007) found a weak correlation between team size and team effectiveness, as well as a weak negative relationship between team size and information sharing. Additional research has found team size to have negative relations with elaboration of information and collective team identification (Kearney et  al., 2009). Although they have access to more resources, larger teams thus seem to struggle, supporting the notion that team size should be kept as small as possible while assuring sufficient knowledge and resources. As summarized in Table 13.2, meta-analytic evidence suggests that team size has a larger impact on innovation (ρ = .30) than overall team performance (ρ = .04). This is perhaps not unexpected given that group innovation depends on the inclusion of creative individuals, with the chance of success increasing as more individuals are included. Yet, when it comes to overall performance, more team members likely strain relationships and reduce efficiency. Of course, efficiency

and innovation can be simultaneously improved when selection procedures assure that highly skilled employees are included in the team. This notion emphasizes the underlying value of basing team composition on the maximization of individual skill and ability rather than sheer number of people or variance among those individuals.

Future Directions for Team Composition Perhaps the most consistent conclusion associated with team composition research is the difficulty of assessing and understanding complex combinations of team member attributes. The characteristics of individuals combine with the characteristics of teammates to create higher order team-level properties. The potential benefits and liabilities of specific attributes are thus dependent on the specific team context, which includes teammate attributes. Recent advances from applying the faultline and the strategic core notions are helpful for understanding these differences. Indeed, future research should focus on techniques such as these that capture patterns of relationships rather than simply assessing variance among individuals. However, improved theoretical models that better capture the overall constellation of individuals are needed. Being able to predict an individual’s impact on a team is an area where traditional research conducted at the individual level must be extended and revised to represent complex relationships in multifaceted social environments. The spread of hierarchical linear modeling techniques is helpful, as these methods capture the nesting of individuals within larger groups. Consistent with this multilevel perspective of hierarchical modeling, Guillaume and colleagues (Guillaume, Dawson, Woods, Sacramento, & West, 2013) reviewed the impact that diversity has on team functions and called for more research on how diversity at multiple levels affects work outcomes; the development of integrative theory to take into account that diversity might impact separation, variety, and disparity; and the need for more empirical attention to the climates or cultures that facilitate the positive effects of diversity. Recent applications of social network analysis also provide a promising avenue for future studies that seek to capture more complex configurations of team composition (Crawford & LePine, 2013). Such advance methods capture differential relationships among team members and explicitly recognize that each individual has a potentially unique effect. As with other team design factors, more longitudinal research is needed to understand the

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implications of member composition effects on processes and performance across a team’s lifespan. The value of deep-level diversity along member psychological characteristics has been shown to increase as teams are together longer, whereas the impediment of demographic diversity weakens (Harrison, Price, Gavin, & Florey, 2002). More longitudinal research such as this is needed. Also, team membership is not static, and recent models of group-level turnover (Hausknecht & Holwerda, 2013; Nyberg & Ployhart, 2013) highlight the need to examine changes in team composition over time. How does turnover affect group-level performance levels and trends? Consistent with the strategic core and distributed expertise perspectives, turnover of some team members may have a larger impact on the group than turnover of others. The effects of continual turnover may also aggregate over time such that a team member’s leaving might harm a team more when there has been previous turnover. Capturing composition as a dynamic property thus has great potential to expand our understanding of team design.

TEAM LEADERSHIP As an input to teams, the leader is granted the power to influence the direction, function, behaviors, and outputs of the collective group. Additionally, leaders form relationships with team members and are often responsible for the allocation of resources, information, responsibility, and cooperation. The meta-analysis by Stewart (2006) identified two form of leadership then frequently advocated for teams: transformational and empowering. We include these two forms, as well as leader–member exchange and shared leadership, which have become increasingly popular in both research studies and practical application. We also examine the impact of rewards, which has often been labeled as a transactional style of leadership.

Transformational Leadership Transformational leadership was first introduced by Bass (1985) and focuses on encouraging and motivating individuals and team members to perform at higher levels for the betterment of the group. This perspective has evolved into a theory of four factors: idealized influence, individualized consideration, intellectual stimulation, and inspirational motivation. Transformational leadership has been linked to many performance outcomes both at the individual and group levels (e.g., Colbert,

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Kristof-Brown, Bradley, & Barrick, 2008; Piccolo & Colquitt, 2006). The transformational approach has been theoretically and empirically linked to a number of factors associated with better functioning teams. It has been hypothesized to impact team performance through shaping team goal orientation and group affective tone (Chi & Huang, 2014), as well as through increased team reflexivity (Schippers, Den Hartog, Koopman, & van Knippenberg, 2008). The focus of transformational leadership on team members working collectively toward a shared end-goal results in an understanding within the team of the purpose, direction, and desired outcome. Meta-analytic results have shown transformational leadership to be positively related to team potency and team collectivism across individualistic and collectivistic societies (Schaubroeck, Lam, & Cha, 2007). The transformational approach has also been meta-analytically linked with creative performance and team performance (Wang, Oh, Courtright, & Colbert, 2011). Recent criticism has, however, been directed toward transformational leadership research. Specifically, van Knippenberg and Sitkin (2013) have argued that the conceptual definition of transformational leadership is too vague, and that current measurement instruments are not precise. Their general criticism being that transformational leadership is so broad that it cannot be distinguished from other forms of leadership. It seems that studies of transformational leadership might capture a broad picture of effective leaders, but the exact nature of what it is that these leaders do remains unclear. Transformational leadership has thus been a dominant perspective over the past couple of decades but perhaps will be less prevalent in future research and practice endeavors. Despite recent criticism, the summary shown in Table 13.3 does show a moderately strong positive relationship between transformational leadership and performance (ρ = .33). Leadership directed toward creating a vision and encouraging team members to transcend their individual interests does therefore seem to be a critical feature of team design. More precise theoretical development and clarification of differences with other forms of leadership thus seem like important avenues for future work. The current evidence does, nevertheless, suggest that helping leaders create and communicate effective visions can be an important practical focus.

Empowering Leadership Empowering leadership builds on empowerment theory whereby leaders pass power to individuals

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Table 13.3  Team leadership elements and relationships with team performance and other constructs Predictor

Definition

Relationship(s) with team performance

Relationship(s) with other constructs

Primary source(s)

Transformational leadership

Leadership in which leaders motivate followers to move beyond self-interest and work for the collective good Leadership in which the leader shares power and gives more responsibility and autonomy to employees

team performance (ρa = .33)

team potency (rb = .28); team collectivism (r = .14)

Schaubroeck, Lam, & Cha (2007); Wang, Oh, Courtright, & Colbert (2011)

overall team performance (ρ = .51); team task performance (ρ = .19)

helping (r = .28) and extra-role behaviors (r = .26); creative processes (r = .24); knowledge sharing (r = .39); team efficacy (r = .47); team cohesion (r = .37)

Raub & Robert (2010); Seibert, Wang, & Courtright (2011); Srivastava, Bartol, & Locke (2006); Tung & Chang (2011); Zhang & Bartol (2010)

Exchange relationships (TMX)

Representation of the quality of reciprocal exchanges among peers within a team

team member performance (ρ = .25)

Shared leadership

Emergent and dynamic team performance team phenomenon (ρ = .21) whereby leadership roles and influence are distributed among team members

Banks et al. (2014) satisfaction (ρ = .43); organizational commitment (ρ = .45); turnover intentions (ρ = −.16) innovative behaviors Carson, Tesluk, & (r = .37); Marrone (2007); internal team D’Innocenzo, environment (r = .33) Mathieu, & Kukenberger (2016); Hoch (2013)

Empowering leadership

a

meta-analytic correlation; b simple correlation from a single study

through increased responsibility and decisionmaking authority. Meta-analytic results by Seibert, Wang, and Courtright (2011) found team-level empowerment to be positively related to overall team performance and team task performance. They further found individualized empowerment to be related to attitudinal and behavioral consequences such as job satisfaction, organizational commitment, strain, turnover intentions, organizational citizenship behaviors, and innovation. Empowerment provides team members the ability to accomplish work tasks as well as the support needed to handle additional responsibility effectively. The increased responsibility allocated to team members allows for increased intrinsic motivation within team members and the ability of members to lead themselves. Successful empowering leadership also increases knowledge sharing, team efficacy (Srivastava, Bartol, & Locke, 2006), and team cohesion (Tung & Chang, 2011). Lorinkova, Pearsall, and Sims (2013) found that although directive leadership improves team

performance in the short run, long-term performance is best facilitated by empowering leaders. This occurs because teams with an empowering leader excel at learning, coordination, and mental model development. These results are echoed by other findings where empowering leadership has been positively linked to creative processes and intrinsic motivation in teams (Zhang & Bartol, 2010) as well as team member proactivity (Martin, Liao, & Campbell, 2013). Empowering leadership has also been correlated with helping and extrarole behaviors (Raub & Robert, 2010), trust, and uncertainty avoidance (Zhang & Zhou, 2014). Empowering leadership thus continues to be one of the most promising approaches for guiding teams. This style can, nevertheless, be difficult for leaders to enact. A common perception held by leaders at all organizational levels is the belief that they themselves could do more if the leaders above them would provide them greater freedom and responsibility, while at the same time denying increased empowerment to their own subordinates

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because of a belief that those individuals lack the skills and motivation to direct their own work (Solimeo et  al., 2015). This empowerment paradox illustrates the difficulty of actually sharing power with teams. In contrast to leader-centric models associated with transformational concepts, the empowering perspective suggests that the focus should be taken off the leader and placed more on followers. This requires a fundamentally new set of leadership behaviors that move away from directing and controlling toward coaching, coordinating, and providing resources. Although difficult to enact, an empowering approach to leadership is theoretically consistent with commonly advocated reasons for adopting team-based structures and thus seems to hold considerable promise as a critical design factor. As summarized in Table 13.3, meta-analytic results suggest a particularly strong relationship between empowering leadership and overall team performance (ρ = .51). This form of influence has also been shown to be beneficial for improving more specific outcomes such as creativity and helping among team members. Nevertheless, effective empowering leadership takes time to develop, and may not be appropriate in all situations. Additional research is thus needed to clearly identify conditions where empowerment works best, as well as issues that facilitate or hinder movement between empowering leadership and other forms of influence. For example, is it possible for a leader to credibly act in an empowering manner at some times but then become more directive in other situations? Does such a contingent approach illustrate effective adaptation or does it confuse team members? Once again this is an area where longitudinal research is clearly needed.

Exchange Relationships An alternative perspective of leadership that has increasingly become popular is leader–member exchange (LMX) and its associated concept of team–member exchange (TMX). Both TMX and LMX focus on the reciprocal relationships amongst individuals where LMX is focused on vertical leader–subordinate relationships and TMX examines horizontal team–member relations. These reciprocal exchanges are influenced by the need for information, resources, effort, and cooperation between individuals (Nahrgang, Morgeson, & Ilies, 2009). From a leader’s perspective, there are limited resources available to provide to team members and therefore limited individuals are able to gain ‘high-quality exchange relationships characterized by a high degree of

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mutual trust, respect, and obligation’ (Graen & Uhl-Bien, 1995, p. 227). Furthermore, those individuals who engage in high-quality exchange relationships will feel obligated to reciprocate with increased effort and performance. TMX, on the other hand, is a characteristic of team members’ reciprocity and contributions between one another (Seers, 1989). Meta-analytic results provide evidence of a moderate, positive correlation between LMX and TMX (Banks et al., 2014), supporting the notion that they are indeed distinct constructs. Banks and colleagues also found correlations between LMX and team member performance, satisfaction, organizational commitment, and turnover intentions. TMX exhibited weaker, but still significant relations with team member performance, satisfaction, organizational commitment, and turnover intentions. As the power distance between leader and member is much greater than that between team members, stronger relationships for LMX with individualized variables should not be surprising. As mentioned, both LMX and TMX are viewed as reciprocal relationships and increased interest from a more powerful individual, the leader, should lead to higher levels of reciprocity from team members. As an input, the leader’s ability to form strong LMX relationships with all members can increase output, feelings of member belonging, and decrease team conflict (Boies & Howell, 2006). Additionally, strong TMX relationships parallel OCB behaviors and provide indications of cooperation and equal contribution from all members. The exchange relationship perspective highlights how important it is for leaders to develop positive relationships with team members. Interestingly, notions about the extent to which such relationships can be built with team members have evolved. Early LMX research suggested that only a few team members form high-quality relationships with a leader, and wanting to be part of the inner group serves as a strong motivational force. Recent revisions to the theory have moved more toward the perspective that all team members can potentially have high-quality relationships (Northouse, 2013), acknowledging a relationship bond as the primary motivational force. Group settings present an optimal context for building relationships and bonds, making LMX and TMX well-suited for understanding leadership effects in teams. As summarized in Table 13.3, meta-analysis has thus revealed a moderately positive relationship between TMX and performance (ρ = .25) and somewhat stronger relationships with team member satisfaction (ρ = .43) and organizational commitment (ρ = .45). One area of potential interest for

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future research is variability of TMX within groups. Recent studies of LMX have captured variance among team members as a focal construct. In a similar way, variance in TMX may provide information that extends beyond measurement error. Consistent with the strategic core and distributed expertise of team composition, the perceptions of key team members may be more critical than perceptions of more peripheral members. Nevertheless, current findings seem adequate to encourage the development of positive relationships not only with leaders but also among team members.

Shared Leadership Shared leadership is built upon the notion that leadership duties and responsibilities can be spread among multiple team members. This sharing of power is another form of social exchange between members where all are able to partake in the power, responsibility, and leadership duties normally relegated to a single individual (Carson, Tesluk, & Marrone, 2007). Leadership can be shared among multiple individuals at any given time, or leadership responsibilities can move from one individual to another across time. More specifically, shared leadership has been viewed as an aggregate form of leadership across all members (e.g., Ensley, Hmieleski, & Pearce, 2006), as the centralization level of leadership measured through network ties (e.g., Mehra, Smith, Dixon, & Robertson, 2006), or as a density-dependent form of leadership based on distribution throughout the group (e.g., Carson et  al., 2007). Although the number of empirical studies is limited, research has found correlations between shared leadership and team innovation (Hoch, 2013), cooperation and trust, and team empowerment (Carson et  al., 2007). Additionally, D’Innocenzo, Mathieu, and Kukenberger (2016) linked all three views of shared leadership positively with team performance. Shared leadership thus represents perhaps the newest perspective on leading teams. The metaanalytic results shown in Table 13.3 suggest a modest link between shared leadership and team performance (ρ = .24), with a potentially stronger effect for innovation (r = .37). The number of studies assessing shared leadership is, nevertheless, quite small and additional work is needed. Although the concept has generated substantial interest, and initial empirical findings are somewhat positive, there is still a lot that we do not know. Is shared leadership best when different individuals step forward and lead at different times, or is the benefit in having multiple leaders at any given time? How are shared efforts

coordinated? Who excels in this new leadership role? Understanding shared leadership is critical for future research endeavors geared toward understanding team design. A good starting point for such endeavors might be clearly establishing acceptable measures. Is the most appropriate measure the extent to which individual team members are each rated as leaders, or is a referent-shift measure wherein perceptions of global assessments about group-level sharing are captured more appropriate? In the end, shared leadership represents an extremely intriguing theoretical concept that is in need of much greater research emphasis.

Transactional Leadership Although most leadership studies focus on social forms of influence, some attention has been paid to transactional influence. Transactional perspectives emphasize using rewards to motivate individuals and teams. Within the realm of teams, two somewhat opposing types of reward systems have been acknowledged: competitive and cooperative (Johnson et  al., 2006). Competitive incentives emphasize individual contribution and reward team members differentially. Some team members receive greater rewards than others, which can create struggles wherein teammates attempt to excel at the expense of others. In contrast, cooperative incentives reward the group as a whole. All team members receive the same reward, which is often contingent on collective outcomes. Unfortunately, cooperative rewards often result in withholding of individual effort, as individuals have a tendency to free-ride. In essence, competitive rewards encourage individuals to maximize personal contributions somewhat at the expense of teamwork, whereas cooperative rewards encourage cooperation somewhat at the expense of high individual contribution. A potential solution to the cooperative-­ competitive reward dilemma is mixing approaches to create a hybrid reward system. With such systems some rewards are based on collective performance, but others are based on individual contribution. This seems like a simple solution; however, the implications of such hybrid rewards are unclear. Two recent studies conducted by separate researchers but using the same laboratory activity found opposing results. Pearsall, Christian, and Ellis (2010) found hybrid rewards to improve informational allocation and reduce social loafing, which in turn increased team performance. In contrast, Barnes, Hollenbeck, Jundt, DeRue, and Harmon (2011) found a mixed-reward manipulation to increase speed but negatively impact accuracy, primarily because individuals in hybrid

Team Design Characteristics

incentive conditions focused on their own work and did not assist and back each other up. A subsequent meta-analysis by Garbers and Konradt (2014) did, nevertheless, provide support for the practice of combining cooperative and competitive rewards. An important finding of Johnson and colleagues (2006) was a difference in ability to change reward systems. They found that teams could effectively switch from a cooperative reward system to a competitive system. However, change in the other direction was difficult. Introducing a cooperative reward structure into a team that has a competitive structure did not increase cooperation. The negative effects were neutralized when team members engaged in discussion about their roles, which helped them to better coordinate actions and meet expectations regarding conflict (Beersma et al., 2009). In a parallel stream of research, Stewart, Courtright, and Barrick (2012) examined peer involvement in rewards as an approach for simultaneously increasing both individual and group performance. They specifically explored the effects of allowing teammates to have a say in the rewards received by one another. In this way, teammates can be recognized for high individual contribution; yet, peers are not likely to recommend rewards for teammates who achieve personal success at their expense. Empirical findings did indeed illustrate that teams utilizing a peer reward system had both improved individual contribution and higher team-level performance. This effect was most pronounced in less cohesive teams, suggesting that the impact of rewards is strongest when normative pressure among teammates is not strong. Although recent trends in leadership research have focused most on concepts other than the transactional perspective, rewards do continue to be seen as important motivational forces in individual performance settings. As with most other areas of research and practice, applying the individual level motivator to the team level of analysis presents a more complex picture. Individual motivation is often not parallel with collective motivation. A clear understanding of the proper balance between team and individual incentives is still being developed, as well as a balance between this form of transactional motivation and more normative factors such as group cohesion. Optimization of transactional rewards thus appears to be a potentially fruitful avenue for future work.

Future Directions for Leadership and Organization Context Overall, the design factor of leadership seems to be entering a phase of redirection. The relative

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dominance of the transformational approach seems to be giving way to newer approaches that emphasize more equal relationships between leaders and followers. Methods of empowering collective teams and sharing leadership thus seem to be gaining favor but are in need of substantial research and clarification. It is doubtful that the importance of leadership as a design feature will diminish, but the emphasis does appear to be shifting. Recent work by Mohammed and Alipour (2014) and Lord and Dinh (2014) also highlights the importance of increasing our understanding of team leadership over time. Given that empowering leadership was found to be more beneficial for teams with longer tenure, it seems that alternative approaches to leadership might be needed in early stages of team development. Perhaps teams benefit from a transformational leader early but eventually are best when leadership becomes more empowering. Also, how do approaches such as LMX combine with empowering leadership? Is there a synergistic relationship, or does one form of leadership substitute for the other. The same question can be asked for shared leadership. Does it supplement or contradict traditional forms of influence? In this case, preliminary evidence suggests a synergistic effect, as horizontal leadership is not a replacement for vertical leadership (Yammarino, Salas, Serban, & Shuffler, 2012). Indeed, some qualitative evidence suggests that combinations of transformational and empowering leadership have the potential to provide teams with overall guidance while developing feelings of ownership and commitment (Stewart, Manges, & Ward, 2015).

CONCLUSION Team design features continue to be an important factor for understanding what separates high and low performing teams. The last decade has clarified a variety of aspects associated with task design, team composition, and team leadership. Optimal task design appears to depend substantially on the type of work being performed by teams. Team composition has been shown to be complex and best captured through configurational models that capture asymmetric roles of team members. Leadership has been shown to have a substantial impact on group processes and performance, but the emphasis appears to be shifting from a transformational approach to emerging approaches such as empowering leadership, exchange relationships, and shared leadership. Future work that captures the effects of design features over time also appears to be of particular

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importance. As we continue to move forward in the implementation of teams throughout different types of organizations, it is important to remember the impact of the team’s design characteristics on current and future performance. As teams advance, forward-looking ideas also need to be explored to better understand and motivate collective groups. Task design, team composition, and team leadership will continue to serve as the building blocks of teams as the application of teams as a primary structure of work continues to evolve. As we close this chapter we first provide a brief review of the practical implications for effective team design. We then provide some future directions that we hope guide future research endeavors.

Practical Implications The first category of design feature – task characteristics – demonstrates contingent relationships with team performance. High levels of interdependence and autonomy are best when team tasks are dynamic and complex. In this case, greater coordination and within-unit freedom encourage innovation and customization. Lower levels of interdependence and autonomy are best when tasks are mundane and simple, making efficiency inherently valuable. When it comes to team composition, assessing critical individual skills and complex patterns of diversity appear to be more important than simple variance on demographic characteristics. The effects of individual team member traits depend on overall team composition. Inclusion of individuals with underrepresented characteristics such as race and gender can likely be facilitated by making sure that these individuals share alternative characteristics such as education with other team members. Team designers should also pay particular attention to the effects of key members whose influence is likely stronger than others who enact more peripheral roles. When it comes to the input of leadership, the preponderance of evidence supports the benefits of both transformational and empowering approaches. Team leaders should develop an overall vision that transcends the interests of individual members but also allows team members freedom to determine how to carry out the vision. Leaders who can balance these two perspectives can motivate teams by not only providing direction but also tapping into internal sources of energy.

Future Directions Although we have provided direction for future research efforts in each section, there are a couple

of overarching areas that seem worthy of emphasis. One is the ongoing need for longitudinal research. The other is the evaluation of combinations of design factors. As with most areas of inquiry, team design research has rarely examined how factors play out over time, particularly in ongoing teams with time horizons that exceed a few weeks. Most ongoing teams likely benefit from adopting different levels of interdependence and autonomy at different points in time. Take the act of developing a strategic forecast. Teams likely excel by first adopting reciprocal interdependence to coordinate the complex task. Efficiency can then likely be gained by moving to sequential interdependence wherein each team member conducts individual research and analysis. The individual efforts can then be combined through once again adopting reciprocal interdependence. Although this sequence of events makes sense for optimizing performance, researchers have not explored how variations in interdependence unfold over time. Future efforts to understand movement between task design features over time thus presents a valuable avenue for future research. Longitudinal efforts also seem to hold promise when it comes to team composition. Most work to date has captured static pictures of team member characteristics. However, membership is often in flux in ongoing teams. Little is known about the cumulative effects of team members joining and leaving the team. Anecdotal experience suggests that changing a single team member can have a large impact for either good or for bad on a team. Researchers need to develop long-term relationships with organizations in order to better capture these effects over time. Understanding of the third feature – leadership – can likewise benefit from longitudinal study. Of course leaders do not adopt one form of leadership without also engaging in activities consistent with other approaches. Many models of contingent leadership also highlight the necessity of adapting to changing conditions. Yet, few studies have actually examined the extent to which a single leader can effectively adopt different approaches across time. This complexity of leadership is difficult to capture but likely critical to advancing our understanding of how leaders influence teams. The second broad direction for future efforts relates to exploring relationships among different design factors. For instance, how might team interdependence be best combined with composition and leadership? Does high interdependence require a more active leader who assures coordination? Is high interdependence more difficult to carry out when teams have deep faultlines that create distinct subgroups? Answers to questions such

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as these are critical for understanding complex relationships that span multiple levels of analysis. Overall, the field of team design characteristics has received substantial research attention over the past few years. Substantial knowledge has been gained, but additional questions have also been identified. Looking at the field as a whole suggests that the future looks bright for endeavors aimed at focusing on team design as a way to develop greater understanding of complex organizational phenomena.

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Team Design Characteristics

Lord, R. G., & Dinh, J. E. (2014). What have we learned that is critical in understanding leadership perceptions and leader–performance relations? Industrial and Organizational Psychology, 7, 158–177. Lorinkova, N. M., Pearsall, M. J., & Sims, H. (2013). Examining the differential longitudinal effects of directive versus empowering leadership in teams. Academy of Management Journal, 56, 573–596. Manz, C. C., & Stewart, G. L. (1997). Attaining flexible stability by integrating total quality management and socio-technical systems theory. Organization Science, 8, 59–70. Manz, C. C., Shipper, F., & Stewart, G. L. (2009). Shared influence at W. L. Gore & Associates. Organizational Dynamics, 38, 239–244. Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and taxonomy of team processes. Academy of Management Review, 26, 356–376. Martin, S. L., Liao, H., & Campbell, E. M. (2013). Directive versus empowering leadership: A field experiment comparing impacts on task proficiency and proactivity. Academy of Management Journal, 56, 1372–1395. Mathieu, J. E., Tannenbaum, S. I., Donsbach, J. S., & Alliger, G. M. (2014). A review and integration of team composition models: Moving toward a dynamic and temporal framework. Journal of Management, 40, 130–160. Maynard, M. T., Mathieu, J. E., Rapp, T. L., & Gilson, L. L. (2012). Something(s) old and something(s) new: Modeling drivers of global virtual team effectiveness. Journal of Organizational Behavior, 33, 342–365. Mehra, A., Smith, B. R., Dixon, A. L., & Robertson, B. (2006). Distributed leadership in teams: The network of leadership perceptions and team performance. Leadership Quarterly, 17, 232–245. Mohammed, S., & Alipour, K. K. (2014). It’s time for temporal leadership: Individual, dyadic, team, and organizational effects. Industrial and Organizational Psychology, 7, 178–203. Morgeson, F. P., Reider, M. H., & Campion, M. A. (2005). Selecting individuals in team settings: The importance of social skills, personality characteristics, and teamwork knowledge. Personnel Psychology, 58, 583–611. Mueller, J. S. (2012). Why individuals in larger teams perform worse. Organizational Behavior and Human Decision Processes, 117, 111–124. Nahrgang, J. D., Morgeson, F. P., & Ilies, R. 2009. The development of leader–member exchanges: Exploring how personality and performance influence leader and member relationships over time. Organizational Behavior and Human Decision Processes, 108, 256–266.

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14 From Teams in Organizations to Organizing in Teams L e s l i e A . D e C h u r c h , D o r o t h y R . C a r t e r, R a q u e l A s e n c i o , A m y W a x , P e t e r W. S e e l y, K a t h r y n D a l r y m p l e , S i d n i A . Va u g h n , B e n j a m i n R . J o n e s , G a b e P l u m m e r a n d J e s s i c a M e s m e r- M a g n u s

INTRODUCTION The computer and the Internet are among the most important inventions of our era, but few people know who created them … It’s … a narrative of how they collaborated and why their ability to work as teams made them even more creative. (Isaacson, 2014, p. 1)

In his latest book, The Innovators: How a Group of Hackers, Geniuses and Geeks Created the Digital Revolution (2014), Walter Isaacson reflects on the many biographies he has written on great minds – Albert Einstein, Benjamin Franklin, Steve Jobs. The Innovators relays Isaacson’s realization that these individuals’ success stories can be re-interpreted as team success stories. Isaacson recalled Steve Jobs’ answer to his question of which product he was most proud of: ‘you know, making a product is hard, but making a team that can continually make products is even harder. The product I’m most proud of is Apple and the team I built at Apple’ (Big Think Editors, n.d.). The study of small groups and teams has a long history in Psychology. What is now known as teams research grew out of a research tradition on small groups. Some of this earlier work focused on questions of how groups form and

their developmental phases (Tuckman, 1965), or their social interaction processes and sources of process loss (Steiner, 1972). Early scholars were intrigued by groups as a context that exerted forces on the individual (Allport, 1962; Festinger, 1950; Lewin, 1947). A literature flourished exploring the various ways small groups affect individuals’ thoughts, feelings, and actions (e.g., Asch, 1956; Janis, 1971). A significant transition occurred as groups became teams, and research evolved from posing questions of group development and the effects of groups on individuals, to a focus on understanding how patterns of interactions, dubbed team processes, drive team performance. This was a focus brought on by the more applied and performance emphasis of Industrial/Organizational (I/O) Psychology. This shift was eloquently announced by Levine and Moreland (1990): ‘small group research is alive and well, but living elsewhere’ (p. 620). Since that time, I/O and Organizational Behavior (OB) scholars have amassed a large body of theory and research that describes team interactions and explains the factors, or enabling conditions (Hackman, 2012), underpinning their success. Yet, there are clear signs that we are at the cusp of another important shift in the area of teams research. Teams have now caught the attention

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of scholars across a wide variety of disciplines. Computer scientists, information scientists, sociologists, and communication scholars are considering how to best design group technologies to enable new ways of working together (Ackerman, 2000; Galegher, Kraut, & Egido, 2014). Engineering scholars are posing new and interesting questions about the design and optimization of teams, and are exploring the social and organizational processes that enable systems engineering and design (Hinds, 2002; Horii, Jin, & Levitt, 2005). Scholars interested in translational research, evaluation science, management, health, other areas of psychology, and communications recently formed a new community – the science of team science (Stokols, Hall, Taylor, & Moser, 2008) – to understand and enable collaborative, team-based scientific research. In short, the team is a powerful vehicle for harnessing and integrating individual activity to produce collaborative outcomes. Despite the maturity of teams research as a field of study, the dominant paradigm rooted in I/O and OB is aimed at understanding teams in organizations. This frame implies that teams perform tasks

in organizations. There is an implied formality and deliberateness in this characterization of teams. However, today’s collaborations are occurring within as well as across and without formal organizational boundaries. Many of the collaborations highlighted in Isaacson’s The Innovators were not formally appointed, nor were they contained within a single formal organization. Many teams form within online communities and exist for the sake of the team without serving an organizational purpose. These fluid collaborations reflect a type of team that is, somewhat ironically, increasingly prevalent in the digital age that Isaacson’s innovators, those who invented the internet, brought on. We use the phrase organizing in teams to describe the expanding array of collaborative arrangements that are comprising this third era of teams research. In Table 14.1, we summarize some prominent definitions of teams. Scanning these definitions, five themes are apparent in how teams have been conceptualized in prior work: size, goals, interdependence, formation, and specialization (or differentiation). In terms of size, Bales’ (1950) classic definition set the range at 2 to 20 individuals.

Table 14.1 Prominent definitions of teams Authors (Year) Bales (1950)

Definition of a team

‘… ranging in number from two to something around twenty, then may be classified together as “small groups”… direct face to face interaction takes place in all of these groups.’ (p. i) McGrath (1984) ‘The central feature, the “essence,” of a group lies in the interaction of its members – the behaving together, in some recognized relation to one another, of two or more people who also have some past and/or future relation to each other. So group interaction process is the centerpiece of the model.’ (p. 13) Sundstrom, De Meuse, & ‘Work teams are defined as interdependent collections of individuals who share responsibility Futrell (1990) for specific outcomes for their organizations.’ (p. 120) Salas, Dickinson, ‘A team is a set of two or more people who interact dynamically, interdependently, and Converse, & adaptively toward a common and valued goal, each having specific roles or functions to Tannenbaum (1992) perform and a limited life span or membership.’ (p. 4) Katzenbach & Smith ‘A team is a small number of people with complementary skills who are committed to a common (1993) purpose, performance goals, and approach for which they are mutually accountable.’ (p. 112) McGrath, Arrow, & ‘We view groups as bounded, structured entities that emerge from the purposive, Berdahl (2000) interdependent actions of individuals. Groups bring together individuals who carry their pasts with them, and groups create their own history, guided by members’ sense of the future, as they operate in time.’ (p. 95) Marks, Mathieu, & ‘Teams are multitasking units that perform multiple processes simultaneously and sequentially Zaccaro (2001) to orchestrate goal-directed taskwork.’ (p. 356) Kozlowski & Bell (2003) ‘Work teams and groups: (a) are composed of two or more individuals, (b) who exist to perform organizationally relevant tasks, (c) share one or more common goals, (d) interact socially, (e) exhibit task interdependencies (i.e., workflow, goals, outcomes), (f) maintain and manage boundaries, and (g) are embedded in an organizational context that sets boundaries, constrains the team, and influences exchanges with other units in the broader entity.’ (p. 415) Ilgen, Hollenbeck, Johnson, ‘Teams are complex, dynamic systems existing in larger systemic contexts of people, tasks, & Jundt (2005) technologies, and settings.’ (p. 519)

From Teams in Organizations to Organizing in Teams

This minimum size of 2 has been echoed by subsequent definitions (e.g., Kozlowski & Bell, 2003; Salas, Dickinson, Converse, & Tannenbaum, 1992). The notion of a shared goal that unites individuals is a second common theme. Sundstrom, De Meuse, and Futrell (1990) call it shared ‘responsibility for specific outcomes for their organizations’ (p. 120). Salas and colleagues add the phrase ‘a common and valued goal’ (p. 4; emphasis added), recognizing that members not only share, but also internalize the team’s goal. The third theme is interdependence. McGrath (1984) eloquently expressed, ‘the “essence” of a group lies in the interaction of its members – the behaving together’ (p. 13). The fourth theme, formation, is one present in early but conspicuously absent in modern definitions of teams. Again quoting McGrath, groups ‘have some past and/or future relation to each other’ (p. 13). And a later definition of McGrath and his colleagues emphasizes that ‘groups bring together individuals who carry their pasts with them, and groups create their own history, guided by members’ sense of the future, as they operate in time’ (p. 95). This notion of time-boundedness, and its accompanying attention to how and why the team comes together, is missing from many of the later definitions that came about when groups became teams. In contrast to McGrath’s emphasis on individuals coming together to form groups, many subsequent definitions of teams de-emphasize formation and focus on the already bounded entity. For example, Marks and colleagues refer to teams as ‘multitasking units’ (2001, p. 356), and Ilgen and colleagues describe them as ‘complex, dynamic systems’ (2005, p. 519). Fifth, these definitions recognize that members posses different skills and develop differentiated role structures. Katzenbach and Smith capture this with the expression ‘complementary skills’ (1993, p. 112); Salas and his colleagues note that individuals perform ‘specific roles or functions’ (p. 4; 1992). Later we return to these themes and definitions as we consider the shift from conceptualizing teams in organizations to organizing in teams. In this chapter, we mark the transition from explaining the functioning of teams in organizations toward an emphasis on understanding organizing in teams. Research on teams in organizations has much to contribute to this broader inquiry into organizing in teams, but requires that we revisit some of the simplifying assumptions routinely made about teams in organizational research, and adopt a more encompassing definition of what constitutes a team. First, we introduce the perspective of organizing in teams in order to encourage future research to better account for the wide array of collaborative organizational forms

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that involve teamwork. Then, we discuss recent theoretical and empirical work published in top outlets that has begun to tackle the challenge of studying organizing in teams. We end by considering new frontiers in data sources and analytic approaches poised to advance this era of organizing in teams.

ARE THESE TEAMS? Let us consider some examples of collaborative organizational forms that involve teamwork. When reading each example, consider the question, ‘Are these teams?’. Big science ‘teams’: The European Organization for Nuclear Research known as CERN, is a massive collaboration among experts who are striving to understand the fundamental structure of the universe (CERN, 2014). At present, CERN runs 26 large-scale projects, each of which has its own set of collaborators. For example, in ATLAS, a CERN project that began in 2009, researchers used the Large Hadron Collider, in which beams of particles collide together to form new particles (ATLAS Experiment, n.d.), to identify the presence of the Higgs Boson particle. As of February 2012, the ATLAS project involved over 3,000 scientists and collaborators from 174 institutions in 38 countries. These individuals work together in person at the facility in Switzerland and/or from across the world using virtually mediated communication tools. Plans for additional ATLAS experiments span the next 20 years. Open source software: GitHub (GitHub, 2014) is an online code-sharing community in which programmers collaborate to write and publish code. The programmers provide the expertise; GitHub provides the interface and collaboration resources. Group boundaries might be defined before groups begin coding, meaning that a group of individuals decides to work on a coding project together and use the GitHub interface to manage the project. However, group boundaries might also be defined or re-defined within the GitHub interface. GitHub has over 7 million users, each of which has the opportunity to contribute to any project on the site. Programmers wanting to help with an existing project can download the source code, make edits locally, then email the project owner with suggested changes. The project

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owner determines whether to merge the changes with the existing code or reject the contribution (Finley, 2012). Because anyone has the potential to contribute, project membership can be quite fluid. At any given moment, each of the over 18 million projects on GitHub may have a minimum of 1 contributor and a maximum of over 7 million (the entire GitHub community). Peer production: Wikipedia (Wikipedia, n.d.) is a free and open web-based collaborative encyclopedia. Any individual user can begin a new article on Wikipedia and anyone in the world can edit and update existing articles. Wikipedia pages are always open for editing, so new contributors might make edits to a page at any time. Wikipedia provides a ‘talk’ feature for contributors to exchange messages. These ‘talk pages’ are archived so new users can review previous discussions. Like GitHub collaborations, the number of contributors on a given Wikipedia page can range from one to millions of people who work together to construct an encyclopedia page simultaneously or over time. The contributors to a Wikipedia page jointly determine who collaborates, the amount of interaction involved in page creation and editing, the duration of the collaboration, and the degree to which individuals work together on different sections of the page. Are these examples of teams? Certainly, each example involves teamwork, and there is a thriving literature that investigates teamwork in these contexts (Dabbish, Stuart, Tsay & Herbsleb, 2012; Holloway, Bozicevic, & Börner, 2007; Kalliamvakou, Damian, Singer, & German, 2014 Krige, 1993; Spek, Postma, & Herik, 2006). To some degree, each example aligns with commonly accepted definitions of teams in organizations such as: 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)

However, the teams nucleating within these and other novel IT-enabled forms of organizing such as GitHub, Wikipedia, and CERN are pushing the

bounds of traditional teams research in several important ways. First, whereas the minimum size of a team has long been debated in organizational research, with some suggesting two people can constitute a team and others arguing that teams must include at least three people, teams research has not effectively dealt with the question of what is the upper bound on team size. Newly emerging collaborative forms are challenging teams researchers to consider this question. For example, as noted above, a collaborative project on GitHub might involve anywhere from two to over 7 million collaborators. Second, today’s collaborative forms challenge our assumptions of what constitutes a common goal. Shared goals have long been used to define the boundaries of groups and teams. Certainly, scientists at CERN, and within the GitHub and Wikipedia communities work toward common goals. However, some of these goals are quite distal such as CERN’s ‘understand the fundamental structure of the universe’, suggesting that contributors must balance their efforts within a hierarchical goal structure by working both toward proximal goals (e.g., conduct experiments) as well as toward broader superordinate objectives. Furthermore, shared goals in novel forms of organizing are often collectively determined rather than assigned by a manager or formal organization. For example, contributors begin new projects and pages in GitHub and Wikipedia as their interests and experiences shift. Many of these teams fall at the extreme end of the self-managing continuum. Third, novel forms of organizing encourage a reconsideration of the definition of interdependence. In organizational research, interdependence is typically depicted as a property of the task, reflecting the extent to which interaction is required to accomplish it (Hackman & Oldham, 1976). Certainly, task-based interdependence is present in the examples above. In GitHub, individuals recognize this interdependence and interact in ways that permit transparency in order to enable more streamlined collaboration (Dabbish et  al., 2012). However, in many of today’s collaborative forms, interdependence is a more fluid concept, where any given collection of individuals determines its own interdependence through their patterns of interactions. For instance, on Wikipedia, contributors jointly negotiate the degree to which tasks will involve interdependent interaction through the ‘talk’ function and their combined edits. Two nearly identical tasks could be performed by two teams, one interdependently and one independently. The same task can witness a shift in interdependence as the work evolves or as membership shifts over time.

From Teams in Organizations to Organizing in Teams

Fourth, teams in organizational research assumes that teams are appointed. As the definition quoted earlier states, teams ‘are brought together to perform organizationally relevant tasks’ (Kozlowski & Ilgen, 2006, p. 79). However, there is a growing awareness that individuals exhibit significant personal agency in deciding who to work with and what to work on (Harrison & Humphrey, 2010; Humphrey & Aime, 2014). Fifth, definitions and empirical investigations of teams often make the assumption that members are ‘assigned specific roles’ (Salas et  al., 1992, p. 4), for example, by managers or formal team leaders. However, these often hierarchical assumptions of task assignment are becoming less the norm in today’s collaborations. Although there are project ‘leads’ in GitHub, leadership in these contexts is more about demonstrating expertise and harnessing social capital and less about asserting formal authority over others or assigning followers to specific roles or tasks. In summary, although the examples provided above do meet the minimal criteria of a team, they also challenge dominant notions in mainstream I/O and OB research about what constitutes a team. These novel IT-enabled forms of organizing force us to consider the upward size, the fluidity of interdependence, and the emergent nature of hierarchy, roles, and shared goals. We introduce the phrase organizing in teams to better account for these features of collaboration.

ORGANIZING IN TEAMS Organizing in teams describes purposive collaborative interaction among a set of individuals. Table 14.2 presents a summary of how research on organizing in teams differs from the traditional ‘teams in organizations’ perspective. We use the term purposive instead of goaldirected. A goal implies a desired outcome or end

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state. Many teams come together with a similar reason for doing something, but not necessarily a clearly defined goal. Consider collaborations on WikiProjects, an online collaboration space where individuals come together to coordinate editing tasks and form meaningful social groups (Kittur & Kraut, 2008; Morgan, Gilbert, McDonald, & Zachry, 2014). Here individuals produce content (e.g., encyclopedia entries) and also serve task coordination roles. Although it may be too strict to argue WikiProject members have a shared goal, or end state, they clearly have a similar purpose around which their entitativity (Campbell, 1958) is based. We use the word collaborative instead of interdependence. Beginning with Lewin (1935), groups and teams research has considered interdependence as a key feature of teams that arises in conjunction with common objectives and members who are motivated to achieve them (Johnson & Johnson, 2005). Certainly teams exhibit interdependence. But problematically, the term interdependence is often used to refer to a property of the task itself, rather than, as is often the case in many teams, a property of the team determined by the individuals who interact in the team. This task-based view stems, in part, from the task design literature, where interdependence refers to a potentially motivating feature of organizational tasks (Grant & Parker, 2009; Hackman & Oldham, 1976; Van de Ven, Delbecq, & Koenig Jr., 1976). In essence, the prevailing notion of interdependence implies that the task comes before the team; assuming there is a task that needs to be performed, and then a team of individuals is assigned to complete it. In fact, in many teams, the team may come before the task. Such is the case when a group of scientists comes together to pursue a new idea and then seeks out the funding to support it. Finally, we use the word interaction as the centerpiece of the definition because it is the interactions occurring within the context of a shared purpose and collaborative intentions that

Table 14.2  Comparing team definitions from two perspectives Two views of teams Feature

Teams in organizations

Organizing in teams

Size Common goal vs. Common purpose Task interdependence vs. Collaborative interactions Formation Role specialization

Focus on minimum size Members share a common goal

Focus on maximum size Members share a similar purpose

Interdependence is a property of the task Teams are appointed Differentiated roles

Interdependence is a property of the task and individuals’ collaborative interactions Teams can be appointed or can self-organize Emergent, dynamic role structures

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ultimately characterize teamwork. Purposive interaction is what all teams share, whether they collaborate in a traditional organization or an online community (Kittur, Chi, Pendleton, Suh, & Mytkowicz, 2007; Kittur, Suh, Pendleton, & Chi, 2007; Zijlstra, Waller, & Phillips, 2012). The central role of interactions in defining teams makes dyadic, relational data and social network analytic approaches particularly appropriate for teams research. We touch on this topic in the section on new methodologies. The organizing in teams perspective is clearly visible in Amy Edmondson’s notion of teaming (2012). Teaming includes the set of interpersonal actions and behaviors needed for individuals to collaborate quickly and adaptively. Whereas the notion of teaming characterizes what individuals need to be able to do to work together flexibly and adaptively, the organizing in teams perspective also includes a range of multilevel processes, encompassing the individual, dyadic, team, and contextual perspectives in which the team phenomenon is rooted. Importantly, the organizing in teams perspective is reflected in international work on teams. These reflect a focus on emergent and dynamic roles, as opposed to the traditionally differentiated roles (Cleveland, Finez, Blascovich, & Ginther, 2012; Li & Roe, 2012; Schraub, Michel, Shemla, & Sonntag, 2014). For example, Li and Roe (2012) introduced an intrateam, longitudinal approach to investigate how roles and emergent patterns change within teams over time. This work also investigates formation. Work on self-assembled teams during times of emergency (Zijlstra et al., 2012), as well as self-assembled family grown businesses (Sharma, De Massis, & Gagne, 2014) run contrary to the traditional conception of appointed team membership. Finally, virtual teams and multiteam systems (see Mathieu, Luciano, & DeChurch, in press [this volume]) have intrigued a global community of researchers (Bienefeld & Grote, 2014; DeVries, Walter, Van der Vegt, & Essens, 2014; Rico, Bachrach, Sánchez-Manzanares, & Collins, 2011). The view of organizing in teams has many potential levels of explanation, each of which is essential to understanding purposive collaborative interaction among a set of individuals. From an individual perspective, research has looked to people’s personal characteristics (e.g., Bell, 2007), their thoughts, feelings, and actions, to understand their entree into and interaction within teams. From a dyadic or relational perspective, research has looked to the affective, motivational, and cognitive relationships, and the behavioral interactions that connect members of teams to one another (e.g., Crawford & LePine, 2013; Schraub

et  al., 2014). From a team perspective, researchers have looked for explanations in the emergent patterns of behavioral processes that characterize how a team works together (e.g., Marks et al., 2001) or toward emergent psychological states such as cohesion which characterize members’ attraction to the group (e.g., Festinger, Schachter, & Bach, 1950; Mullen & Copper, 1994; Picazo, Gamero, Zornoza, & Peiró, 2015). At the contextual level, we can look for explanations in the design of collaborative technologies (e.g., Fussell, Kiesler, Setlock, & Scupelli, 2004; Majchrzak, Rice, Malhotra, King, & Ba, 2000), or embeddedness in multiteam systems (see Mathieu et al., this volume). Having introduced the broader perspective of organizing in teams, we now consider how this vantage point opens up new theoretical directions and methodological opportunities for a more interdisciplinary science of understanding teams. In terms of theory, this vantage point allows us to see new team phenomena that may not be visible to the researcher looking only at teams in organizations. In terms of data and methods, this vantage point also makes new sources of data and some methodologies that have been traditionally underutilized in teams research, more attractive. In the next section we present a high level overview of recent research on teams with an eye toward two questions: (1) How does the voluminous literature on teams in organizations inform the broader view of organizing in teams?; and (2) How does the organizing in teams perspective shift the nature of the research questions we ask and answer about teams?

EXTENSIONS TO TEAM THEORY Studies of teams in the I/O and OB literatures are framed using the well-established input–process– output (I-P-O) model of team effectiveness (Hackman, 1987; McGrath, 1984; Kozlowski & Ilgen, 2006) or the more recent and expansive input mediator output input framework (IMOI; Ilgen et  al., 2005). Broadly, these models view teams as purposive collaborations that transform inputs, such as team composition, team design, and team leadership, into valued outcomes through various mediators (e.g., team processes, emergent psychological states). Recent reconceptualizations of these models advance a temporal view of teams whereby teams cycle through phases of action and transition, and outputs in one phase become inputs or mediators in another (Ilgen et al., 2005; Marks et al., 2001).

From Teams in Organizations to Organizing in Teams

Relying on this fundamental architecture, the field of teams in organizations, has generated a great breadth of theory and empirical research on team effectiveness. Over the past decade, scholars have synthesized this work in many excellent reviews. These include general reviews of team effectiveness (e.g., Ilgen et al., 2005; Kozlowski & Ilgen, 2006; Mathieu, Maynard, Rapp, & Gilson, 2008) as well as reviews focused on specific team topics such as composition (Bell, 2007; Mathieu, Tannenbaum, Donsbach, & Alliger, 2014), leadership (Morgeson, DeRue, & Karam, 2010), cognition (DeChurch & Mesmer-Magnus, 2010a, 2010b; Mohammed, Ferzandi, & Hamilton, 2010), virtuality (de Guinea, Webster, & Staples, 2012), and adaptability (Baard, Rench, & Kozlowski, 2014). We refer the interested reader to those sources for a fuller treatment of these topics. In our review, we use the I-P-O model as an organizing framework to consider how recent work on teams is already ushering in the transition from a focus on teams in organizations to one of organizing in teams.

Team Inputs Team inputs are the antecedent factors that impact team functioning (Gladstein, 1984), and can stem from the individual-level (e.g., members’ personalities or competencies), dyad-level (e.g., interpersonal ties; Balkundi & Harrison, 2006), team-level (e.g., team task structure, external leadership), or originate in the team’s external context (e.g., organizational boundaries; Mathieu et  al., 2008). For example, research on team composition has explored the compositional inputs of teams that have implications for team process and performance (e.g., cognitive ability, personality, sex, race, etc.). A clear sign of the shift to a focus on organizing teams is the growing interest in team self-­ organization. Research on team self-organization signals a shift in understanding team composition as determined, to one of team composition as selfdetermined (Harrison & Humphrey, 2010). This view is reflected in large-scale investigations of teams who come together in the creative industries (e.g., Broadway musical teams) and those who come together to tackle scientific problems (Guimera, Uzzi, Spiro, & Amaral, 2005; Jones, Wuchty, & Uzzi, 2008). Studies of team self-­organization view team composition as self-determined, and explore the natural processes that unfold within team ecosystems to determine the size and diversity of the team. Findings reveal that, over time within ecosystems such as Broadway performers or scientific

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disciplines, teams naturally evolve toward and settle on an optimal size that is large enough to provide ample expertise and specialization, but not so large as to become unmanageable. Teams also self-determine their diversity, and how they do this affects their ultimate success or failure – teams that include newcomers tend to outperform teams of people that habitually regroup with one another (Guimera et al., 2005; Jones et al., 2008; Nijstad, Berger-Selman, & De Dreu, 2014). This work reflecting the organizing in teams perspective has clear implications for investigations of team inputs such as composition and diversity. There is no doubt that many organizational teams exhibit significant agency in shaping who’s on the team. Guimera et al. and Jones et al.’s work, and other recent conceptual work (Harrison & Humphrey, 2010), point to the importance of understanding self-determined team inputs. One future direction for this genre is to uncover the mechanisms that explain these effects, i.e., how do newcomers change the dynamics within teams (Chen, 2005; Choi & Levine, 2004)? For example, research shows that scientific teams are more likely than solo authors to insert novel combinations of citations into their papers, and this combination of conventionality combined with atypical combinations of citations ultimately increases the chances the team’s paper will be highly cited (Uzzi, Mukherjee, Stringer, & Jones, 2013). More investigations like Uzzi and his colleagues’ that uncover the underlying mechanisms of team performance are essential to advance a science of organizing in teams. This is where research conducted within the teams in organizations tradition can prove especially useful. Research on teams in organizations includes a well-developed conceptual framework for understanding the processes and properties that characterize the mechanisms of teams. A new theoretical advancement gets right to the issue of understanding this team self-determinacy and its mechanisms for team effectiveness. This area, dubbed team assembly explores the agency individuals have in deciding who to work with on what (Contractor, 2013; Zhu, Huang, & Contractor, 2013). Self-assembled teams come together of their own accord. They not only choose who to work with but also determine the size of the team as well as the team’s goals, interdependencies, and lifespan. Self-assembled teams experience greater initial cohesiveness (Strong & Anderson, 1990), greater satisfaction (Bacon, Stewart, & Silver, 1999; Chapman, Meuter, Toy, & Wright, 2006), and fewer logistical concerns (Bacon, Stewart, & Anderson, 2001) than individuals with less agency in making teammate selections. However, these experiential benefits may come at a significant

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cost: self-assembled teams tend to be less heterogeneous (Bacon et al., 1999; Butterfield & Bailey, 1996), report more conflict (Chapman et  al., 2006), be more prone to groupthink (Bacon et al., 1999), and perform more poorly (Butterfield & Bailey, 1996; Chapman et al., 2006) than typical deliberately designed teams. Team self-assembly is the dominant mode of team formation in environments like GitHub and Wikipedia. Even in an environment like CERN, team assembly plays out as scientists chose one another either directly or because they gravitate toward common interests. The context of selfassembling teams stands in stark contrast to the manager-assembled teams studied in the teams in organizations perspective. Furthermore, the idea underlying team assembly challenges IPO and IMOI models by positing dynamics that occur well before traditionally studied team inputs are of great importance to subsequent processes and outcomes. Questions about team assembly seek to understand the natural organizing processes that explain teammate attraction: who comes together and why? and; what effects do the mechanisms of team assembly have on team functioning? As these examples illustrate, there are clear signs, visible in the types of research questions being asked, that show research on team inputs is shifting toward an organizing in teams vantage point. Table 14.3 presents a summary of some exemplar questions that signal this shift, along with a tagging of the definitional features of teams exemplified in the article. Harrison and Humphrey’s (2010) conceptual paper explores the chicken–egg argument surrounding tasks and work groups; should tasks or work teams be specified first, and in what ways should the input that takes precedence be allowed to impact the input that does not? The authors address team formation from a number of different angles, discussing seldom-mentioned topics such as rolling team membership. Similarly, Mariotti and Delbridge’s (2012) research veers from the traditional team

formation literature. The authors find a host of complex relational structures – such as redundant, latent, and ‘potential’ relationships – impact work group composition. Haas (2010) directly investigates emergent, dynamic role structures in a sample of self-managing teams from a multinational organization. Finally, Guimera et  al. (2005) studied team size from a multifaceted perspective, emphasizing maximum rather than minimum team size, and viewing size as selfdetermined by the team.

Team Mediators: Team Processes and Emergent States In models of team effectiveness, mediators refer to the linkages between team inputs and outputs. Often, mediators constitute emergent team phenomena – constructs that originate at lower levels of analysis (e.g., within psychological characteristics of or interactions among team members), but over time, come to characterize the team as a whole (Kozlowski, Chao, Grand, Braun, & Kuljanin, 2013; Kozlowski & Klein, 2000; Morgeson & Hofmann, 1999). Commonly studied emergent mechanisms include team behavioral processes, affective and motivational team states, and team cognition. The shift from teams in organizations to organizing in teams is less evident in the study of mediators than it is in the study of inputs like team composition and especially team assembly. We review this ‘team process’ literature with an eye toward how these mechanisms of teamwork could feature more prominently in investigations of teams conducted outside of I/O and OB research. In fact, much of the research on teams outside of those disciplines tends to focus only on inputs and outcomes with little, if any, attention paid to the rigorous theory and measurement of team dynamics apparent in Psychology and OB. This is an area where prior teams in organizations research has

Table 14.3  Questions about team inputs that reflect an organizing in teams perspective Authors (Year) Harrison & Humphrey (2010)

Research question

Which input should take precedence: work group composition or task design? Mariotti & Delbridge (2012) How do redundant, latent, and potential relationships impact work group composition? Haas (2010) Do self-managing teams with high autonomy perform effectively? Guimera, Uzzi, Spiro, & Amaral How do changes in the size of a team over time impact (2005) performance?

Definitional features Formation Formation Role specialization Size, formation

From Teams in Organizations to Organizing in Teams

much to offer within the new organizing in teams perspective.

Team processes

Team behavioral processes reflect ‘what teams do’ (Kozlowski & Ilgen, 2006, p. 95) and are the basic mechanisms that explain how members combine their skills and expertise toward the accomplishment of team objectives (Cohen & Bailey, 1997). Understanding processes is the chief avenue through which researchers can elucidate why and how teams accomplish certain outcomes (Hülsheger, Anderson, & Salgado, 2009; Kozlowski & Ilgen, 2006; LePine, Piccolo, Jackson, Mathieu, & Saul, 2008; Mathieu et al., 2008; Marks et al., 2001). Research in the area of team process is advancing on a number of fronts: multilevel, time, structure, and materiality. We briefly review these advancements, not because they necessarily reflect an organizing in teams perspective, but because they reflect the rigor and sophistication of conceptualizing team process that could prove valuable in future work on organizing in teams. The first advancement in work on team process is the reincorporation of the individual and the context into multilevel models of team processes. Teams reflect a meso level of inquiry (Mathieu, Maynard, Taylor, Gilson, & Ruddy, 2007), and explaining them requires at least some recognition that individual thoughts, feelings, and actions are the building blocks from which many team level processes regularize and emerge (Kozlowski et al., 2013). This view was central in the era of small group research (Emery & Trist, 1960; Katz & Kahn, 1966), though initial work on teams in I/O has tended to focus on both theory and measurement exclusively at the team level. Recent work is again using multilevel frameworks as a way to bracket team phenomenon (Hackman, 2003; Kozlowski & Klein, 2000; Schraub et al., 2014), to look at teams and their drivers one level up (i.e., rooted in contextual-level phenomena) and one level down (i.e., rooted in individuallevel phenomena). Bracketing across the individual- and team-levels is common in I/O and OB. For example, Chen, Kanfer, DeShon, Mathieu, and Kozlowski (2009) tested a multilevel model of motivation and performance in teams, showing the quality of team interactions affects individual motivation to work for the team; and team action processes affect how hard and how effectively individuals work to achieve their goals. Bracketing of the team and contextual levels is less common (Hackman, 2003), but the literatures on multiteam systems (see Mathieu, Luciano, & DeChurch, this volume) and team boundary

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spanning (Joshi, Pandey, & Han, 2009) represent two notable departures where teams are being studied in context. A second advancement in work on team process is greater attention to the role of time in team functioning (e.g., Humphrey & Aime, 2014). Time is a centerpiece in two dominant perspectives of teams: developmental and episodic (Mathieu et  al., 2008). The former perspective considers how teams mature over time as they approach impending deadlines (Gersick, 1988) or become more skilled in conducting taskwork and teamwork (Kozlowski, Gully, Nason, & Smith, 1999). The latter perspective examines the task-driven cycles teams experience as they work together (e.g. Marks et  al., 2001). Recent work in this domain has sought to examine when and why teams shift between transition and action behaviors and identify key moderating variables of the recurring phase model (Arman & Adair, 2012; Kennedy & McComb, 2014). Results suggest process team interventions that encourage teams to delay action processes can improve performance. A third advancement in work on team process is greater attention to the role of structural patterning. Whereas the Marks et  al. (2001) model classified the content of interactions needed for effective team functioning, Crawford and LePine (2013) suggest that the pattern of these interactions adds something more to our understanding of team functioning. Crawford and LePine’s (2013) configural theory of team process posits that different patterns of processes – closure, centralization, and subgrouping – differentially impact team functioning. A fourth advancement in work on team process is greater attention to the role of the materiality of team process which reflects the physical characteristics and the capabilities of a technology within which interactions occur (Leonardi & Barley, 2008; Orlikowski & Scott, 2008). This work emphasizes the inextricable linkage between the social (human-centric forces) and material (technology-centric forces) aspects of teamwork. Hence the sociomaterial perspective is one wherein technology, or its materiality (e.g. Leonardi, 2011), is conceptually integrated into the very notion of team process (e.g., Cramton, 2001; Hakonen & Lipponen, 2008; Hinds & Mortensen, 2005; Kirkman & Mathieu, 2005; Orlikowski, 2009). Whereas these advancements in team process research are relevant to future work on organizing in teams, there are also some recent investigations of team processes that mark the changing vantage point. Table 14.4 summarizes some research questions that suggest an organizing in teams perspective, and the ways in which they define the team phenomenon somewhat differently than is

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Table 14.4  Questions about team process that reflect an organizing in teams perspective Authors (Year)

Research question

Definitional features

Summers, Humphrey, & Ferris (2012) Kalliamvakou, Damian, Singer, & German (2014) Keegan, Gergle, & Contractor (2013)

How does team member role change affect team coordination? What are the mechanisms of efficient coordination in self-organized, virtual collectives? Does collaboration structure differ in self-assembled teams operating under threat and time pressures compared to those that are not? What role structures enable efficient coordination in self-organizing teams?

Role specialization

Hoda, Noble, & Marshall (2010)

customary in the literature on teams in organizations. For example, Summers, Humphrey and Ferris (2012) find dynamic role structures can lead to high levels of flux in team coordination. Their central assertion is that team membership is fluid, and as such, so are the patterns of collaboration in many modern teams. Further emphasis on the perspective of organizing in teams can be found in the work of Kalliamvakou et al. (2014). Findings from their qualitative analysis of GitHub teams revealed that members predominantly work independently, while relying on self-organization to coordinate work when necessary. A defining feature of these teams was the lack of a predetermined task structure; rather, they relied on a self-determined pattern of interdependence to accomplish teamwork. Additional work embodies the organizing in teams’ perspective by examining the coordination dynamics of online communities that come together to serve an overarching purpose, but do not possess a clearly defined goal in the traditional sense. For instance, Keegan, Gergle, and Contractor (2013) examined the coordination dynamics of self-assembled groups of Wikipedia collaborators, and found that breaking article groups exhibit similar coordination patterns over time to that of non-breaking article groups. Lastly, an additional marker of the organizing in teams perspective can be found in the work of Hoda, Noble, and Marshall (2010). Their perspective on informal role structure emphasizes the emergent nature of collective interaction as a defining characteristic of self-organizing software teams. The conceptual foundation for team process is well established empirically (LePine et al., 2008), and multiple promising avenues for future research have arisen aimed to account for the increasingly complex nature of modern teamwork. Whereas much of the research on mechanisms of teamwork focuses on these behavioral interactions, research also explores two broad classes of emergent states, affective and cognitive.

Collaborative interactions Formation

Role specialization

Affective/motivational emergent states

Affective and motivational team states are emergent team properties originating in members’ motives, emotions, or attachment to the team. These properties include team cohesion, collective efficacy, affect, mood, emotion, and team conflict, each of which has important implications for team functioning. As a set, affective and motivational properties of teams are some of the most robust predictors of team viability and performance (c.f., Beal, Cohen, Burke, & McLendon, 2003; DeChurch, Mesmer-Magnus, & Doty, 2013; Stajkovic, Lee, & Nyberg, 2009), and so greater integration of them within the organizing in teams perspective is clearly warranted. Team cohesion captures members’ ‘bonding to the team and its task’ (Kozlowski & Ilgen, 2006, p. 87), and is related to team performance, specifically in highly interdependent tasks (Beal et al., 2003; Evans & Dion, 1991; Gully, Devine, & Whitney, 1995; Mullen & Copper, 1994). Collective efficacy refers to the confidence a team has in its ability to be effective (Bandura, 2000), and is also a strong predictor of team performance (Stajkovic et  al., 2009). Recent work on efficacy has also uncovered some of its antecedents, revealing that leaders, managers, and even the composition of the team each play unique roles in shaping collective efficacy (Priesemuth, Schminke, Ambrose, & Folger, 2014; Wu, Tsui, & Kinicki, 2010; Zyphur, Narayanan, Koh, & Koh, 2009). Team affect, mood, and emotion capture a team’s ‘affective processes and reactions’ (Kozlowski & Ilgen, 2006, p. 87), and are related to common team performance antecedents such as communication, group perceptions, and information sharing (Baysinger, Scherer, & LeBreton, 2014; Lehmann-Willenbrock & Allen, 2014; van Knippenberg, Kooij-de Bode, & van Ginkel, 2010). Finally, team conflict captures a team’s ‘fractures, frictions, and disagreements’

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Table 14.5  Questions about team affect that reflect an organizing in teams perspective Authors (Year)

Research question

Definitional features

Dabbish, Stuart, Tsay, & Herbsleb (2012) Hogg, van Knippenberg, & Rast (2012)

What is the value of transparency in distributed collaborations? How do leaders in intergroup collaborative contexts develop members’ intergroup relational identities and encourage collective performance? How does team self-management influence team conflict? How does the mix of content and administrative team members affect team performance?

Collaborative interactions

Bunderson & Boumgarden (2010) Arazy, Nov, Patterson, & Yeo (2011)

(Kozlowski & Ilgen, 2006, p. 87), and has important implications for both team performance and satisfaction (DeChurch et  al., 2013; De Dreu & Weingart, 2003; de Wit, Greer, & Jehn, 2012). Research on team affect shows some signs of moving toward the organizing in teams perspective; Table 14.5 shows some exemplar studies that reflect this new orientation. For example, Dabbish et  al. (2012) found that acting in ways that promoted transparency helped online teams streamline their coordination. Their view of transparency emphasizes that teams shape their own interdependence. Recent research also highlights the agency of teams, particularly team leaders, in creating a sense of purpose. In their theory of intergroup leadership, Hogg, Van Knippenberg, and Rast (2012) explain the process through which leaders communicate in ways that activate an intergroup relational identity between interdependent groups. Another marker of the organizing in teams approach shows up in a question posed by Bunderson and Boumgarden (2010), who considered how self-management affects the experience of conflict. Their work explicitly recognizes that role structures can be team-determined. Lastly, Arazy, Nov, Patterson, and Yeo (2011) examined the effects of how a team organizes itself on team performance; finding teams need to maintain a balance of content and administrative team members.

Team cognition

Team cognition is another robust predictor of team performance (DeChurch & Mesmer-Magnus, 2010b; Mohammed et al., 2010). Team cognition characterizes the patterns of team members’ teamand task-related thoughts, beliefs, and expectations (Klimoski & Mohammed, 1994). Two commonly investigated constructs in the team cognition literature are team mental models (TMMs) and team transactive memory systems (TMS). Whereas TMMs are ‘organized mental representations of the key elements within a team’s

Common purpose

Role specialization Formation; Role specialization

relevant environment that are shared across team members (Mohammed et al., 2010, p. 877), TMS refers to the set of information possessed by each member of a group combined with a shared awareness of who knows what within the group ( Lewis, 2003; Wegner, Giuliano, & Hertel, 1985). Greater attention to team cognitive processes are also a priority for future works on organizing in teams. Research on team cognition shows some signs of moving toward the organizing in teams perspective. Table 14.6 displays exemplar studies that signal this new orientation. Research on team cognition has started to consider the agency teams have regarding with whom to work (i.e., formation) in their formulations of cognitive constructs. For example, Lewis, Belliveau, Herndon, and Keller (2007) consider how team membership changes can degrade TMS and impede the transfer of information within the team. Mortenson (2014) also grapples with the issue of team formation and boundaries directly, introducing the concept of a membership model to characterize individuals’ mental representations of who is and is not a team member. Research on team cognition is also dealing with the issue of emergent, dynamic cognitive role structures in teams. For example, Mell, van Knippenberg, and van Ginkel (2014) investigated differing structures of TMS (centralized vs decentralized) in decision-making teams, finding teams need to communicate their evolving roles to help others gain an understanding of the knowledge retrieval process. Similarly, Pearsall, Ellis, and Bell (2010) found role identification behaviors contribute to the formation of TMMs and TMSs.

Team Outputs At the end of the day, researchers and practitioners alike are interested in predicting outcomes of collaboration. Despite this objective, more attention

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Table 14.6  Questions about team cognition that reflect an organizing in teams perspective Authors (Year)

Research question

Definitional features

Lewis, Belliveau, Herndon, & Keller (2007) Mortenson (2014)

How does team membership change affect transactive memory and information exchange? How does membership model divergence affect team performance? How does TMS structure impact team transactive retrieval processes and decision-making? How do role identification behaviors foster team cognition?

Formation

Mell, van Knippenberg, & van Ginkel (2014) Pearsall, Ellis, & Bell (2010)

has been spent on questions of who and how and less attention on understanding the end results (Mathieu et al., 2008). The result is a large body of literature with vast differences in how team ‘effectiveness’ is operationalized. For example, team success is variously defined across industries (e.g., health care teams use objective measures such as patient health and mortality; West et  al., 2002; scientific and product development teams use innovation; De Dreu & West, 2001; Hülsheger et al., 2009; customer service teams use ratings of customer satisfaction; Gilson, Mathieu, Shalley, & Ruddy, 2005; Kirkman, Rosen, Tesluk, & Gibson, 2004). As we transition to research on organizing in teams, performance metrics should be designed so they are conceptually related to the function and tasks of the teams being studied, sensitive enough to capture the important aspects of performance rather than global assessments, and rely on a formally articulated combination algorithm such as that employed in balanced scorecard techniques (Mathieu et al., 2008). Another caveat with team performance measurement is that team self-report should be avoided as a way to operationalize team performance. Numerous meta-analyses of team construct relations find team self-report operates differently than either supervisor ratings or objective metrics of team performance (e.g., Hulsheger et al., 2009; Mesmer-Magnus & DeChurch, 2009). Furthermore, although IPO frameworks tend to broadly define team outcomes as performance or affective reactions (e.g., such as viability or satisfaction; Mathieu et al., 2008), other outcomes may be of relevance as we shift toward an organizing in teams perspective. In Table 14.7 we present some examples of how team performance has been operationalized in the literature. These typically involve either objective or subjective indicators of the degree of team goal accomplishment. But as we broaden our conception of the team, the relevant criterion space expands. For example, team adaptability may be the best marker of effectiveness for teams that frequently encounter novel and complex environments (Baard et  al., 2014) and

Formation Role specialization; Collaborative interactions Role specialization

may be increasingly important as we transition to studying when, and how effectively individuals organize collectively. Having explored the ways in which the organizing in teams perspective is and can continue to shape theory on teams, we now consider data sources and analytics that are particularly valuable for exploring organizing in teams.

Leveraging New Data Sources and Analytics to Understand Organizing in Teams Rapid advancements in technology are changing the way people collaborate as well as the methodological approaches researchers use to understand these collaborations. In this final section, we consider new sources of data and increasingly prevalent analytic approaches that can be harnessed to understand the purposive collaborative interactions that constitute ‘organizing in teams’.

New data sources

To begin, research on organizing in teams can capitalize on digital trace data – records of activity gathered through virtual systems – to understand purposive collaborative interaction. Digital trace data offer many advantages for teams research. These data are often voluminous, automatically recorded, and readily available (Howison, Wiggins, & Crowston, 2011; Williams, Contractor, Poole, Srivastava, & Cai, 2011). Digital traces offer researchers a more precise window into real-world interactions. For example, users leave digital traces of their interactions as they engage with one another through social networking websites (e.g., Facebook, Twitter, LinkedIn), massively multiplayer online role-playing games (e.g., Sony’s Everquest II), large-scale online collaborations (e.g., Wikipedia, GitHub), or other virtual communication tools (e.g., email, chat logs, ‘smart’ phones, video conferencing). By capturing the nature of interpersonal interactions

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Table 14.7 Objective and subjective operationalizations of team performance Authors (Year)

Operational definition

Objective indicators Gilson, Mathieu, Shalley, & Ruddy (2005)

Team effectiveness: ‘We used three archival measures gathered by the organization to track team effectiveness … Machine reliability was the average number of copies made by machines between service calls … Response time was the average length of time between a customer’s call and the technician’s (or technicians’) arrival … Parts expense was the percentage of budget associated with replacing machine parts … three performance indexes collectively constituted an aggregate construct.’ (p. 525) Kirkman, Rosen, Tesluk, & Team effectiveness: ‘One independent component of the balanced scorecard was “process Gibson (2004) improvement,” an assessment of reductions in cycle time for each team based on the number of days between an order for a travel reservation system and the effective operation of a system … process improvement was viewed as an objective assessment of team learning, or a team’s ability to continually refine, processes and develop innovative solutions to shorten the cycle time needed to deliver and install reservation systems.’ (p. 181) West, Borrill, Dawson, Team performance: ‘…Six measures of health outcomes were obtained. These were deaths Scully, Carter, Anelay, … following emergency surgery, deaths following non-emergency surgery, deaths following & Waring (2002) admission for hip fractures, deaths following admission for heart attacks, re-admission rates and a mortality index.’ (p. 1303) Subjective indicator De Dreu & West (2001) Team innovation: ‘Innovation was assessed through interviews with the team supervisors … Interviewers subsequently asked supervisors to indicate for each of the innovations they identified whether the innovation was primarily the result of (a) the entire team; (b) one individual team member; (c) outside sources, including other teams in the postal service; or (d) the supervisor himself or herself.’ (p. 1194)

at exceptionally high levels of resolution, researchers can observe indicators of emergent team phenomena (e.g., trust, cohesion) as these constructs manifest over time. As an example, Zhu, Kraut, and Kittur (2013) used a sample of approximately 182 million digital trace indicators of interaction to identify emergent leadership processes among collaborators on Wikipedia pages. Teams researchers can also capitalize on digital traces obtained in more traditional laboratorybased or quasi-field study team settings. These settings continue to aid knowledge accumulation on teams by enabling causal inferences based on controlled experimentation. Furthermore, laboratory and quasi-field studies are particularly helpful in construct validating digital metrics against established scales and observer ratings. Laboratories also allow for the collection of technology-mediated automated observations that continuously measure individual behavior (e.g., physical location, active task work), information availability (e.g., sharing, utilization), and interpersonal processes (e.g., communication) without interruption to enable direct investigation of team dynamics. For example, Cooke and her colleagues (Cooke, Gorman, Myers, & Duran, 2013) used digital traces of communication in the lab to study team cognition as a ‘dynamic flow of team

interaction’ (p. 266). A significant open question is the extent to which these digitally derived metrics adequately capture the team behavioral, affective, and cognitive constructs described earlier. At this point, rigorous construct validation studies based on triangulated data streams are critical to teams research. Another promising source of data stems from sensors. Sociometric badges (e.g., Kim, Chang, Holland, & Pentland, 2008) and neural sensors (e.g., Derks, Inzlicht, & Kang, 2008) collect large amounts of information about individual actions and interactions. Sociometric badges worn by study participants generate at least one-hundred data points per minute which can include information about participants’ patterns of speech and body movement, tone of voice, spatial positioning, gestures, frequency of speech, or tendencies to listen or interrupt during conversation. Sociometric badges are useful for teams research in both lab and field settings. Using sociometric badges in field settings allows researchers a relatively unobtrusive window into team dynamics. Using sociometric badges in traditional laboratory studies provides researchers with rich high-resolution data in controlled settings. These badges represent a viable alternative to labor-intensive methods of studying teamwork such as hand-coding

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of video or audio recordings. Prior laboratorybased studies of teams using sociometric badges demonstrate that teams whose members engage in more evenly distributed communication patterns tend to be more effective (Pentland, 2012; Woolley, Chabris, Pentland, Hashmi, & Malone, 2010). Neural sensors record electrical potentials through the scalp and provide access to data on brain activity over time. Although not yet a widely used tool to understand teams, neural sensors can provide access to data on individual perspectives of social psychological phenomena through brain activity (Derks et al., 2008), and can shed light on constructs such as team identity, team affect, and other team attitudes. Scientists are also developing new IT-enabled platforms designed to enable data collection by providing a virtual space for participants to interact and engage. For example, Volunteer Science (Volunteer Science, n.d.) is an online data collection laboratory that allows researchers to bypass the cost and time involved in collecting social science data. Researchers conduct their experiments on the site and benefit from a large pool of study participants from around the world. For an experiment involving teams, participants select the research in which they wish to participate, and are placed in a waiting room while others join. The MyDreamTeam Builder (‘My Dream Team Assembler’, n.d.) is another example of an IT-enabled platform being used to understand organizing in teams. This recommender system helps individuals self-assemble into teams. Users begin by completing background measures (e.g., personality, expertise, values, social networks), before creating queries to identify their ‘Dream Team’. The builder provides recommendations as well as an interface for users to send and receive invitations to join teams. To date, The MyDreamTeam Builder has been used to build teams in university classrooms and real-world organizations. Initial findings from a sample of 95 university students shows that teams who exercised their agency by using the builder to choose teammates communicated more, had more confidence in their team, and expressed more balanced expectations of their team members, as compared to teams that were randomly assigned, or had less agency in team formation (DeChurch et al., 2013). Not only does this tool enable team self-assembly, researchers can use this tool to study team organizing processes in real-time.

Data analytic approaches

Teams research in general, particularly through the lens of organizing in teams, is benefiting from the increased uptake of methodological advances

including social network analysis, topic modeling and sentiment analysis, and computational modeling. In fact, many of the studies that have capitalized on the new sources of data just described are leveraging recent advances in network analysis, topic modeling, and sentiment analysis. We now consider how each of these analytic methods is currently advancing, and can continue to advance research on organizing in teams.

Social network analysis

Social network analysis is a useful tool for understanding the structural aspects of teams (Crawford & LePine, 2013; Katz, Lazer, Arrow, & Contractor, 2005). Many, if not most questions about teams, how they form and interact, what drives their performance, are essentially questions about structure. Social network analysis relies on dyadic (i.e., relational) data, evaluating the properties (e.g., type, magnitude, direction) of social ties that exist among a set of nodes (e.g., people, teams, organizations, concepts; Borgatti & Foster, 2003; Slaughter, Yu, & Koehly, 2009; Wasserman & Faust, 1994). Although network analysis has a long tradition in group research (e.g., Bavelas, 1950; Bavelas & Barrett, 1951); these methods have only recently gained wide recognition in the teams literature (Slaughter et  al., 2009). Social network methods are particularly well suited for revealing and predicting the many types of relationships that characterize organizing in teams. Social network descriptive indices describe relational patterning across multiple levels of analysis including the dyad (e.g., reciprocity), triad (e.g., transitivity), subgroup (e.g., clustering), and group level (e.g., centralization, density; Hanneman & Riddle, 2005; Wasserman & Faust, 1994). These indices can be used to depict compilational (i.e., patterned) emergent phenomena in teams (Kozlowski & Klein, 2000) such as team processes (Crawford & LePine, 2013), transactive memory systems (Mell, van Knippenberg, van Ginkel, & Heughens, 2014), or leadership (Carter, DeChurch, Braun, & Contractor, 2015; DeRue, 2011). Networks research on teams demonstrates that differences in teams’ relational patterns have meaningful implications for team performance (e.g., Drach-Zahavy, 2011; Granovetter, 2005; Henttonen, Johanson, & Janhonen, 2013; Oh, Labianca, & Chung, 2006; Sparrowe, Liden, Wayne, & Kraimer, 2001; Tröster, Mehra, & van Knippenberg, 2014). But beyond just characterizing structure, network methods can also be used to understand how these compilational emergent phenomena come about. Recent advances in network analysis, inferential models of network development, go beyond

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description. These include exponential random graph models (ERGM) or p* models (Lusher, Koskinen, & Robins, 2012), stochastic actor oriented models (Snijders, 2011; Snijders, van de Bunt, & Steglich, 2010), and relational event network methodologies (Butts, 2008; Leenders, DeChurch, & Contractor, 2015). These models allow teams researchers to understand the antecedent factors that give rise to patterns of relationships. For example Zhu, Huang, and Contractor (2013) used ERGM/p* models to investigate the team self-assembly mechanisms of ad hoc project teams. Their results showed that individuals tend to join ad hoc teams to complete short duration, difficult projects and tend to choose teammates with complementary skills as well as similar age, skill level, and/or organizational affiliation. Research using network approaches to teams generally explores three sets of questions, each of which positions networks at different places in the IMOI model. A first set of questions probe the ways in which social networks, such as friendship and advice networks, affect aspects of team functioning and effectiveness. These studies use social networks as team inputs examining either I→M or I→O relationships. For example, a recent study found that the density of team instrumental ties (i.e., relationships developed for exchanging work-related resources such as knowledge, expertise, advice) shapes members’ cognitive perceptions of the team’s procedural justice climate (Roberson & Williamson, 2012). A second set of questions explores how the social networks within teams come about. These studies use networks to represent team processes or emergent states, and examine I→M relationships. Liu, Hernandez, and Wang (2013) used this approach in their investigation of the antecedents of structural patterns (centralization and density) of procedural justice (PJ) relationships in teams. This study showed that differentiation in leader– member-exchange (LMX; Graen & Uhl-Bien, 1995) relations affected intrateam trust which significantly influenced the centralization and density of within-team procedural justice perceptions such that higher levels of LMX differentiation led to lower intrateam trust, more centralized and less dense PJ networks. A third set of questions considers the network structures that promote team outcomes. These questions use network approaches to represent mediators such as team processes or emergent states, and examine M→O relationships. Bavelas and colleagues (1950, 1951) began this work in the 1950s by manipulating communication patterns, such as centralization, in five-person teams and identifying the network structure(s) that were most optimal for team performance. In a more recent

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study, Balkundi, Kilduff, and Harrison (2011) showed that the centrality of a formal team leader in the team advice network drives followers’ perceptions of the leader’s charismatic leadership, which in turn, impacts team performance. Other recent studies show that a team’s task (workflow) network structure and cultural diversity impact team potency and performance (Tröster et  al., 2014), and certain structures (e.g., bonding ties within teams; bridging ties between teams) of social connections affect team identity and performance effectiveness (Henttonen et al., 2013). Although social network methods have always been useful for understanding team phenomena, they are becoming increasingly relevant and useful to understanding teams as we orient toward organizing in teams. Not only has the team phenomenon evolved to require such methodologies, but network methods have made significant recent advancements allowing them to go well beyond describing teams or providing basic metrics to characterize aspects of teams.

Lexical analysis

Groups and teams research, particularly within Communication and Social Psychology, has a long history of using lexical analysis to understand teams (e.g., Scholand, Tausczik, & Pennebaker, 2010). However, as described above in our examples of new sources of teams data, technology is increasing the extent to which collaborative interactions are more visible to teams researchers, making these methodologies increasingly valuable for teams research. Advancements in lexical-based techniques such as topic modeling and sentiment analysis can help researchers transition large tracts of digital trace communication data into analyzable representations of team phenomena. Whereas topic modeling is a machine learning approach used to uncover the themes (i.e., content) in collections of text (Blei, 2012), sentiment analysis characterizes emotions present in language (Pang & Lee, 2008). Lexical-based approaches are often used in combination with social network analysis to determine both the content and structure of relationships among actors. For example, Li et al. (2011) used topic modeling in communication data from a large online community in order to detect the thematic structure of communication within and across different subgroups. Their approach detected the emergence, spread, and disappearance of topics over time. Sentiment analysis, or opinion mining, is the computational treatment of subjective opinion and emotion in textual data (Pang & Lee, 2008). Pennebaker and colleagues’ linguistic inquiry and

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word count (LIWC) software (Pennebaker, Booth, & Francis, 2006; http://www.liwc.net/) is one example of a sentiment analysis program that can be harnessed to study organizing in teams. LIWC uses built-in dictionaries to classify words into dimensions, such as positive or negative emotion, and has been fruitfully applied in organizational research. For example, Huffaker (2010) used LIWC to analyze discussions in Google Groups in order to understand the language and social behaviors of leaders, finding that apart from their network centrality, leaders use affective, assertive, and diverse language to exert influence. In a second application of LIWC, Gilbert and Karahalios (2009) uncover intimacy words within Facebook exchanges in order to determine the tie strength between individuals. Although this work has intended implications in social media, gleaning the strength of relationships from textual information also has clear application to understanding relationships in teams.

Computational modeling

Another set of advanced analytic techniques that are being increasingly applied to team phenomena are computer simulations. At present, there are two common uses of the phrase ‘computer simulations’ in teams research. First, the phrase is commonly used by teams researchers to describe interactive tasks that human teams complete with the aid of computers (Marks, 2000). Many laboratory studies of teams have been conducted with the aid of these simulated tasks, such as DDD (e.g., Hollenbeck et al., 2002), ACES (e.g., Marks, DeChurch, Mathieu, Panzer, & Alonso, 2005), Longbow2 (e.g., Chen, Thomas, & Wallace, 2005), and SIMCity (e.g., Resick, Murase, Randall, & DeChurch, 2014). Here, the phrase ‘computer simulations’ is being used to describe a ‘human-in-the-loop’ simulation – a task where humans interact with realistic models of tools and tasks (Manning, 2000). For this first meaning of computer simulation, the ‘human-in-the-loop’ term is preferable because it avoids confusion with the second type of computer simulations used in teams research. The second use of the phrase ‘computer simulation’ refers to a class of computational methods in which computer-­ simulated agents, or in the case of teams, computer simulated team members, behave according to programmed interaction rules (Hastie & Stasser, 2000; Ilgen & Hulin, 2000; Kozlowski et  al., 2013; Vancouver, Putka, & Scherbaum, 2005). In particular, agent-based modeling (ABM) is a computer simulation approach that affords insight into emergent behavior resulting from actions and interactions that occur within complex systems

(Macy & Willer, 2002). In ABM, theories about human behavior and interaction are programmed into basic computational rules. ABM allows multiple, complex theories to be incorporated into a single model in order to understand the behavior that will result from multiple determinants. Moreover, this approach can be used to formally specify ‘a set of rules or goals that guide the behavior of entities or “agents” of interest, in dynamic interaction with other entities’ (Kozlowski et  al., 2013, p. 589, italics in original). Thus, scholars have advanced ABM approaches as particularly useful for understanding social context in the area of networks (Harrison, Lin, Carroll, & Carley, 2007; Monge & Contractor, 2003; Palazzolo, Serb, She, Su, & Contractor, 2006) and teams (Grand, Bruan, Kulijanin, Kozlowski, & Chao, 2016; Kennedy & McComb, 2014). ABM is particularly useful in the area of teams given the difficulty of obtaining rigorous, rich data on teams in context. Using models to first integrate theories, and then conduct ‘virtual experiments’, allows teams research to economize subsequent ‘human-in-the-loop’ team experiments. Virtual experiments are computer simulations where a computational/agent-based model is first used to generate observational data by ‘playing out’ some set of processes that are suggested by theory and translated into model equations. Then, initial conditions of focal variables in the model are varied, and resulting team variables measured. The virtual experiment approach was applied by Sullivan, Lungeanu, DeChurch, and Contractor (2015) to understand leadership emergence in multiteam systems. After building an agent-based model based on existing leadership, team, and MTS research, Sullivan and her colleagues investigated how manipulating two aspects of MTS structure, the geographic dispersion and organizational membership of component teams, would affect the emergence of different leadership network configurations. An important qualifier is that the effects identified in computational models are always tentative and require evaluation in human teams. Sullivan and colleagues’ virtual experiments resulted in a set of hypotheses about how the structure of component teams shapes the emergence of leadership; these hypotheses are ripe for evaluation in human-in-the-loop MTS studies. Two additional applications of computational modeling to understand teams were artfully conducted by Kuljanin (2011) and Zhou (2014). In his dissertation, Kuljanin modeled the effects of team member task and social skills, and the structure of collaboration, to predict the degree to which teams efficiently utilize members’ skills. A total of 216 experimental conditions were evaluated in 216,000 simulated teams. An interesting question

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posed by Kuljanin: how does the structure of collaboration (e.g., fully connected, three-star, twostar, one-star) affect the likelihood that the team will work interdependently? Findings suggest that although members have the most connections to one another in the fully connected collaboration network, the probability of them working interdependently is actually the highest in the one-star network. The framing of this research question clearly reflects the collaborative interactions aspect of the organizing in teams perspective. In her dissertation, Zhou (2014) built a model of team leadership in order to integrate and evaluate multiple theories related to leader and team regulation. Zhou’s model manipulates the team task structure, team composition, leader reactivity, and the magnitude and duration of environmental disturbances experienced by the team. She then ran the model to understand how different combinations of these factors would affect team leadership behavior. Her results showed that when teams experience either large or long disturbances, team leaders increase their task-contingent leader behavior, and these effects were stronger when the disturbance occurred closer to a team deadline. Given the complexity of social interaction within teams, and the large number of existing theories that explain team functioning, using computer simulation approaches such as ABM can be extremely useful to integrate theory. These models are best used alongside observational and experimental studies of teams, so that model results can be validated on human teams.

Future Research Directions Our review highlights a number of questions for future research that will be important to better understanding the organizing in teams phenomenon. First, just as research in teams shifted from a focus on how groups affected members’ thoughts, feelings, and actions to a focus on how patterns of interactions within teams affect performance, future research will need to shift further to consider how new contextual realities of the organizing in teams phenomenon affect team assembly, process, and performance. Given that teams are collaborating within and across organizational boundaries, what implications do such contextual dynamics have for team functioning (e.g., team identity)? Another important direction for future research is to explore the implications of greater selfagency in team formation, process, and goalorientation. The greater self-agency common to today’s technology-enabled organizations affects

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how best to conceptualize and study team composition, size, dynamics, and member motivations. Related questions include: (1) Which team and task characteristics determine the maximum effective team size? and (2) What constitutes a common team goal? With regard to this second question, we argue teams today are more purposive than they are goal-directed as members often collectively determine the team’s purpose and sub-goals rather than have these dictated by a manager. Future research might also explore how what we know about leadership in teams may differ in an organizing in teams perspective. For example, a traditional assumption within teams research has been that leadership is typically a function on hierarchical designation or authority. However, we have begun to realize that within today’s organizing in teams perspective, leadership is often a function of expertise and social capital rather than legitimate authority (Carter et al., 2015). Our conceptualization of interdependence within teams will also need to shift. For example, traditionally, interdependence has been a preset feature of the team’s task. However, we now see interdependence as a more fluid concept wherein team members determine interdependence through their patterns of interactions, and this interdependence may shift as work evolves or as membership changes over time. On a related point, since patterns of interactions within teams are likely to shift over time as a function of changing membership, member expertise, or task demands, future research should examine the role of dynamic role structures on team coordination effectiveness. New data sources and analytic strategies also give rise to new questions for research. For example, to what extent do these digitally derived metrics adequately capture the team behavioral, affective, and cognitive constructs they are intended to capture? Rigorous construct validation studies based on triangulated data streams will be critical to teams research.

Practical Implications We also identify a number of practical implications associated with a shift to an organizing in teams perspective. First, the shift from otherdetermined to self-determined team composition has practical implications for both human resource compliance as well as team functioning. For example, self-determined teams set both the membership of the team as well as its diversity. Given individuals’ natural preference for homophily, teams may be less diverse than would be desired for EEO compliance. Similarly, as we mention

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above, self-determined teams tend to be more cohesive and satisfied and face fewer logistical concerns than teams with less agency when making team selections, but these teams are also more prone to groupthink, conflict, and unmet performance expectations, which raises implications for management of these teams. Second, self-determined teams tend to have a shifting membership over time. Changing team membership will clearly have implications for the efficiency of team processes as well as the content of shared team cognition. This raises team management and training considerations as well as a need for future research to explore the extent to which changing membership can change and/or degrade team cognition and process. Finally, an important practical implication for organizational leaders is that the organization’s role within the organizing in teams phenomenon is significantly different from the role it played in the teams in organizations phenomenon. Leaders must appreciate the potential as well as the challenge of operating within and across, as well as without, organizational boundaries.

to traditional approaches to team theory and empirical research. These challenges invite teams researchers to ask new questions using new sources of observations combined with advances in network and computational approaches.

ACKNOWLEDGMENTS We thank our research collaborators, especially Noshir Contractor and Steve Zaccaro, who have generously contributed to many of the ideas in this chapter through insightful discussions. The preparation of this chapter was supported, in part, by the Army Research Office under contract W911NF-14-10686 and the Army Research Institute for the Social and Behavioral Sciences under contract W5J9CQ-12-R-0002. The views, opinions, and/or findings contained in this report are those of the authors, and should not be construed as an official Department of the Army position, policy, or decision, unless so designated by other documents. This material is also based upon work supported by the National Science Foundation under Grant Nos. SES-SBE 1219469, SMA-SBE 1262474, ACI-CSE 1244737.

CONCLUSION We have covered a lot of ground in our chapter. We began by considering some novel organizational contexts within which individuals are teaming (Edmondson, 2012). These new contexts challenge the field to reconsider some of the defining aspects of teams such as the upper limit on size, the nature of collaboration objectives and interdependencies, and the agency with which teams form and collaborate. We then elaborated a view of organizing in teams, and contrasted it with the dominant paradigm of teams in organizations. Next, we considered examples of recent work conducted in each of the thematic areas: team inputs, behavioral processes, affective emergent states, cognitive emergent states, and outcomes, with an eye toward the unfolding shift in perspective we term, organizing in teams. Lastly, we considered some exemplars of new data sources and analytic approaches used to understand teams that have been both inspired and enabled by new forms of organizing. We hope this chapter invites a broader perspective on teams, effectively marking the transition from explaining the functioning of teams in organizations to understanding organizing in teams. The knowledge base on teams has much to offer in the way of understanding the larger phenomenon of organizing in teams. Similarly, the phenomenon of organizing in teams poses many challenges

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15 Multiteam Systems: The Next Chapter John E. Mathieu, Margaret M. Luciano and Leslie A. DeChurch

INTRODUCTION In the inaugural version of this handbook, Mathieu, Marks, and Zaccaro (2001) advanced the concept of multiteam systems as a new unit of inquiry and analysis, in which teams-of-teams needed to coordinate their efforts to achieve one or more goals that were beyond the individual team goals. They suggested that these systems of teams sometimes came from a single organization, but in many other instances involved teams from different organizations; public and private, civilian and military, volunteer and paid, competitors and collaborators, foreign and nationals, and other nontraditional pairings. In some ways, these systems operated like ‘big teams’ – yet in other respects, they were more like traditional organizations. However, Mathieu et al. (2001) submitted that these systems were a bit different and defied conventional logic and labels. They coined the term multiteam systems (MTSs) to refer to these entities, distinguished them from other forms of organizations, and outlined their unique properties as well as potential levers for influencing effectiveness. It has been over fifteen years since Mathieu et al. (2001) advanced this notion of MTSs. In that time, numerous conference sessions and dedicated meetings have been held, empirical investigations

have been conducted, and edited volumes (e.g., Salas, Shuffler, & Rico, 2014; Zaccaro, Marks, & DeChurch, 2012) have been published on the topic. The MTS concept has taken root in a wide variety of disciplines including Industrial/Organizational Psychology (e.g., Marks, DeChurch, Mathieu, Panzer, & Alonso, 2005), Organizational Behavior (e.g., Gibson & Dibble, 2013), Communications (e.g., Keyton, Ford, & Smith, 2012), Network Science (e.g., Kratzer, Gemünden, & Lettl, 2008), Software Design (e.g., Scheerer, Hildenbrand, & Kude, 2014), Civil Emergencies (e.g., Healey, Hodgkinson, & Teo, 2009), Product Design (e.g., O’Sullivan, 2003), System Optimization (e.g., Liu & Simaan, 2004), Space Operations (e.g., Caldwell, 2005), and Healthcare (e.g., Taplin, Foster, & Shortell, 2013). The insights gained from this work are numerous and varied, but in desperate need of synthesis. Moreover, there have been few attempts to advance theoretical frameworks to unify the MTS domain (cf., Luciano, DeChurch, & Mathieu, 2015; and Zaccaro et al., 2012 for exceptions). Accordingly, the time is right to revisit some of the fundamental tenets of MTSs. Therefore, in this chapter we have two primary objectives: 1) to update the conceptual understanding of MTSs; and 2) to consider the most fruitful directions for future investigations. To address our first primary objective, we revisit

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the definition and nature of MTSs, discuss MTS effectiveness criteria from a multiple-constituencies perspective, and chronicle some recent organizing frameworks that have distilled important properties of MTSs. In so doing, we feature MTS structure, coordination, and leadership-related issues. Although we review some of the more prominent studies from the industrial/organizational psychology and management domains that have been conducted over the past 15 years, our review is selective and not intended to comprehensively review all of the work that has been done to date. In our final two sections we address our second primary objective and outline ‘the road forward’ for the MTS domain. We consider the advantages and disadvantages of some alternative research methodologies that may be used to study MTSs, and conclude with recommendations for future theory, research, and application in the domain of MTSs.

MTS CONSTRUCT REFINEMENTS AND ADVANCEMENTS Defining the nature and boundaries of MTSs has been a challenging task and the subject of some debate. Mathieu et  al. (2001) acknowledged that demarcating the system boundaries would be a difficult enterprise, similar to defining the boundaries of teams or organizations. In this section, we take inventory of what is known about MTSs and update the conceptual understanding of their properties.

The Definition and Nature of MTSs Mathieu et al. (2001) specified that the basic building blocks of MTSs were teams – which they referred to as component teams. They were not suggesting that individual members of those teams and their associated features – states, reaction, behaviors, and so forth – were unimportant, simply that the focal entities of MTSs were the teams that were included. Mathieu and colleagues went on to argue that, for an MTS to exist, those component teams need to be linked in some fashion. In other words, to be functionally ‘tightly coupled’ with one another. Specifically, they offered the following definition: 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 doing so exhibit input, process, and outcome interdependence with at least one other team in the system. (Mathieu et al., 2001, p. 290)

Their definition was an attempt to identify the nature of MTS boundaries, while not limiting the types of MTSs that might exist. The definition was functionally grounded in the sense that they emphasized the existence of a superordinate goal as the reason ‘why’ the teams were included in the system. Mathieu et al. (2001) noted that the component teams might perform quite similar activities or markedly different activities directed toward their proximal goals. However, the component teams’ actions toward their respective proximal goals need to be linked through a goal hierarchy and thereby to the realization of the overall system (superordinate) goal. The achievement of a superordinate goal(s) is the reason that MTSs exist and is a core defining feature of them. Superordinate goal achievement serves to align motives, provide a common identity, help to prioritize actions, and otherwise chart out common ground for the component teams and their members. In later work, Marks et al. (2005) and Zaccaro et  al. (2012) made the point that MTSs are not simply large teams. Their component teams are distinguishable entities capable of independent actions that may pursue different proximal goals. That is, the relative interdependence of team members is higher within component teams than between component teams comprising an MTS. Although the component teams are distinguishable entities, what defines the boundary for inclusion in an MTS is the fact that they share input, process, and outcome interdependence with at least another team in the MTS. It is the existence of the superordinate goal and the nature of team interdependencies that defines MTS membership, not organizational boundaries (Mathieu, 2012). Mathieu (2012) noted that the interdependence portion of the MTS definition was an attempt to operationalize a ‘tightly coupled’ network of teams. He went on to suggest that: it is debatable whether component teams must have all three forms of input, process, and outcome interdependence to be included in a MTS, or what the thresholds for those judgments might exactly be. I certainly believe that if all three forms of interdependence do exist to moderate or high degrees, a MTS exists. But there may be other ways to define ‘tightly coupled’ besides this operationalization. What is clear, however, is that component teams must be tightly coupled on some basis in order to constitute a MTS. (Mathieu, 2012, p. 514)

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In sum, the defining features of MTSs hinge on the existence of a superordinate goal, and teams that are highly interdependent – tightly coupled – in order to achieve that goal. Other subtleties of MTSs aside, those two features are key distinguishing characteristics of MTSs. Lanaj, Hollenbeck, Ilgen, Barnes, and Harmon (2013) offer an important insight regarding MTS characteristics, by highlighting that MTS size may be an important boundary condition or moderator variable. The original definition of MTSs advanced by Mathieu et al. (2001) sets the minimum threshold at two component teams such that there can be both within- and between-team processes. Naturally, however, cross-team coordination processes become qualitatively different when a third team is added to the system. The inclusion of the third component team creates the potential for alliances and other multi-group dynamics, as well as challenges component teams to determine which other component team(s) are appropriate to coordinate with at different points in time. Add even more teams into the MTS, and system coordination requirements become exceedingly more challenging (Lanaj et al., 2013). While recognizing this phenomenon, we maintain that MTSs are defined as two or more tightly coupled, interdependent teams in pursuit of a superordinate goal (Mathieu et  al., 2001) that necessitate both within- and between-team processes. However, we appreciate that the functioning of these MTSs may become qualitatively different under certain conditions, one of which is increased size.

MTS Effectiveness The concept of MTS effectiveness is alluded to by many authors, but there has been little detailed explanation as to what it is. In fact, the concept of MTS effectiveness was mentioned only twice in the entire Zaccaro et al. (2012) edited volume. As originally conceived, MTS component teams are linked through a goal hierarchy (Mathieu et  al., 2001). The notion was that individual component teams in an MTS pursue their own unique goals which, at some point, align with those of others to realize higher-level goals in the goal hierarchy. Ultimately, given the nature of the interdependencies between different teams in the systems at different times, the superordinate or highest level goal is reached (or not) with varying degrees of success. Mathieu et  al. (2001) argued that ‘Effectiveness of the MTS, then, is defined not only in terms of how well each team accomplishes its proximal goals, but more importantly on how well they collectively accomplish shared goals at

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the higher levels of the goal hierarchy’ (p. 291). Thus, embracing this hierarchical network of goals framework, the effectiveness of an MTS can be assessed on a macro level by superordinate goal accomplishment, as well as at a more micro level by evaluating the goal accomplishment of component teams and nestings of teams. Given the definition of MTS effectiveness hinges on goal accomplishment, the concept is necessarily context dependent. For example, goals may be in terms of points earned in a simulation (e.g., Marks et  al., 2005), the creativity of R&D MTSs (e.g., Kratzer et  al., 2008), saving lives and property (e.g., Healey et al., 2009), and successful space exploration and return to earth (e.g., Caldwell, 2005). Moreover, an MTS may be responsible for different superordinate goals under different circumstances, such as coalition forces shifting from combat to peacekeeping or humanitarian aid operations. Elsewhere, Resick, Burke, and Doty (2012) noted that the factors that underlie the effectiveness of MTSs in laboratory investigations hinge, in large part, on the effectiveness metrics that are built into the simulations. Regardless of the particular context, however, the ultimate criterion for MTS effectiveness is the extent to which the superordinate goal is realized. Naturally goals include several attributes including their level, quality standards, a temporal window, and resources expended to accomplish them – all of which factor into the equation of the extent to which they were successfully accomplished. But the raison d’être of MTSs is to accomplish the superordinate goal through collective efforts, so it is only appropriate that their effectiveness is gauged, first and foremost, in terms of the extent to which that is achieved. Whereas the accomplishment of the superordinate goal is the primary criterion for MTSs, it is valuable to adopt a multiple constituency framework. Multiple constituency frameworks suggest that numerous parties may have a valued stake in the operations of some organization, or in this case, MTS. Those parties would include, for instance, the members of various component teams as well as their host organizations. Achieving an MTS superordinate goal while burning out participating members or leaving ill-feelings between teams that need to coordinate their efforts in the future will, in the long run, undermine MTS effectiveness. A joint venture can facilitate relations between contentious competitors or nations, or serve to drive them apart. In short, anyone who has a vested interest in MTS function represents a measure of effectiveness to be considered. One can quickly see how this multiple constituency framework also foretells certain pressures on MTSs. For example, as Luciano et al. (2015) detail,

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when component teams have incompatible proximal goals (goal discordancy) or allegiances and obligations beyond the focal MTSs (diversion of attention), it can undermine MTS functioning. At issue is that another constituency, beyond the focal MTS, is vying for their attention. To the extent that maximizing the effectiveness of the focal MTS runs counter to the achievement of these other goals, component teams and their host organizations may conclude that the overall effectiveness of their efforts is less than optimal. Of course many other constituencies have vested interests in the operations of an MTS. For example, disaster relief MTSs have as their superordinate goals the preservation of life and property. However, how they go about their activities have both environmental consequences and costs to taxpayers which need to be considered. In short, whereas the superordinate goal achievement is the focus of an MTS, it is not the sole criterion upon which their effectiveness will be gauged. At some level, MTS effectiveness is akin to a balanced scorecard approach where different accomplishments are valued differently by different constituencies. On balance, to the extent that the MTS maximizes the overall value of its activities across its important constituencies, represents the ultimate effectiveness criterion.

MTS Frameworks Zaccaro and colleagues (2012) advanced a framework that featured three aspects of MTSs: 1) compositional; 2) linkage; and 3) developmental attributes. Compositional attributes include categories pertaining to number of component teams, size, boundary status, organizational diversity, proportional membership, functional diversity, geographic dispersion, cultural diversity, motive structure, and temporal orientation. Linkage attributes include interdependence, hierarchical arrangement, power distance, and communication structure. Developmental attributes include characteristics related to the genesis of the MTS, direction of development, tenure, stage, and transformation of system composition. They suggest that MTS compositional, linkage, and developmental attributes are antecedents of intrateam and interteam processes, which in turn influence system effectiveness (Zaccaro et al., 2012). In this section, we focus on the interplay of MTS attributes and integration mechanisms to facilitate system effectiveness. We will review new theory on MTS functioning, and consider how variations in structural elements affect functioning. We then review work on two key integration mechanisms, coordination and leadership, and consider how

they can mitigate the structural factors and forces that create rifts or divisions in the system. MTSs are a distinct organizational form. Similar to other entities (e.g., organizations, alliances, teams), in order to comprehensively understand how to build and manage effective MTSs it is critically important to understand their structures. Structure captures the totality of how an organizational form is: 1) divided; and 2) integrated. Numerous examples from both academic journals and popular press point to the interactions between teams as the weak point in MTSs during which errors and issues occur. MTSs seem to present a paradox; how does one simultaneously structure strong component teams that also function well as a part of a system. To help resolve this paradox we highlight the macro-level dynamics associated with the division and integration of multiteam systems. Drawing from the MTS literature, for division we focus on system differentiation and dynamism and for integration we focus on coordination and leadership. Our focus on more macro-level dynamics is consistent with the suggestion of DeChurch and Zaccaro (2010) who submitted: ‘when it comes to solving the problem of how complex sociotechnical systems tackle time-sensitive, multifaceted problems, the vast majority of organizational scientists have their microscopes set at the wrong magnification’ (p. 329). Essentially, they suggest that organizational scientists cannot see the forest for the trees, and argue that progress on understanding MTS functioning and leverage points for effectiveness requires a shift from an inward focus on intrateam dynamics, to an understanding of the macro-level dynamics governing the whole.

Division – The Rifts between Teams MTSs are complex forms that are difficult to study. The development of a science of MTSs has further been hindered by the absence of a unifying framework to characterize different forms. Luciano and her colleagues (2015) address the complexity of MTSs by answering the questions: 1) what about the system is complex; and 2) why does it matter? They develop a multi-dimensional framework of MTS structural features, describe the structural forces emanating from those features, then advance a meso-theory of MTS functioning linking relationships across levels of analysis. The structural features and forces describe various forms of divisions in the system. Luciano and colleagues (2015) highlight two overarching dimensions of MTS structural features, differentiation and dynamism. Differentiation characterizes the degree of difference and separation between MTS component teams at a particular point

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in time, whereas dynamism describes the variability and instability of the system over time. Luciano and colleagues identified five subdimensions of both dimensions. As detailed in Table 15.1, differentiation includes: 1) goal discordancy; 2) competency separation; 3) norm diversity; 4) work process dissonance; and 5) information opacity. Dynamism includes: 1) change in goal hierarchy; 2) uncertainty of task requirements; 3) fluidity of system structural configuration; 4) fluidity of system composition; and 5) diversion of attention. The overarching point of the framework is that MTSs of different forms will function differently. The patterns across subdimensions provides a useful schematic or template for understanding the critical fractures or rifts, as well as points of leverage, which may exist within the system. The different subdimensions may

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combine in complex fashions with some patterns being synergistic and others antagonistic. Generally speaking, however, MTSs that are high on different aspects of differentiation and dynamism will be more prone to fractionation from forces that drive teams apart and destabilize the system, respectively. Overall, Luciano and colleagues present a unified theoretical framework of entity structure by articulating both the shapes of the structure (i.e., differentiation) and how they change over time (i.e., dynamism; Ranson, Hinings, & Greenwood, 1980). Luciano et al. (2015) then go on to advance a meso-theory of MTS functioning. They posit the MTS structural features of differentiation and dynamism generate boundaryenhancing forces and disruptive forces in MTSs, respectively. Boundary-enhancing forces reinforce

Table 15.1 A multi-dimensional framework of multiteam system structural features Differentiation dimension Subdimension

Description

Goal discordancy

Captures differences in goals and goal priority across MTS component teams, ranging from similar goal priorities and compatible goals at lower levels to dissimilar goal priorities and incompatible goals at higher levels. Captures differences in knowledge and capabilities across MTS component teams, ranging from component teams containing very similar knowledge and parallel capabilities at lower levels to vastly different knowledge and disparate capabilities at higher levels. Captures differences in policies and expectations across MTS component teams, ranging from similar policies and compatible expectations at lower levels to dissimilar policies and incompatible expectations at higher levels. Captures differences in work processes across MTS component teams, ranging from congruent work processes conducted concurrently at lower levels to incongruent work processes conducted independently at higher levels. Captures differences in information about MTS component team activities, ranging from real-time information being available and interpretable at lower levels to information being generally unavailable or uninterpretable at higher levels.

Competency separation Norm diversity

Work process dissonance Information opacity

Dynamism dimension Subdimension

Description

Change in goal hierarchy Uncertainty of task requirements Fluidity of system structural configuration Fluidity of system composition

Captures modifications in the relative importance of system goals, ranging from a stable goal hierarchy at lower levels to drastic and frequent changes in the goal hierarchy at higher levels. Captures the uncertainty regarding component team activities required to fulfill system goals, ranging from requirements being well known at lower levels to being unknown at higher levels. Captures the changes in the linkages among component teams, ranging from stable linkages at lower levels to frequent shifts that substantially alter workflow at higher levels.

Diversion of attention

Captures the changes in system membership, ranging from system and team membership being stable at lower levels to frequent and substantial reconstitution of system and team membership at higher levels. Captures the extent to which component team members’ attention is focused on matters other than MTS-related tasks, ranging from members only being involved in one MTS at lower levels to members being concurrently involved in multiple non-overlapping systems at higher levels.

Source: Adapted from Luciano, DeChurch, & Mathieu (2015)

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the differences between component teams and increase the salience of team membership, whereas disruptive forces increase uncertainty and destabilize the system. In turn, these structural forces influence individuals’ belonging needs, affective motives, and cognitive motives, and thereby emergent collective states at the component team- and system-levels. Notably, these forces can direct MTS members’ needs and motives more toward their component teams than the MTS, which undermines collaborative interactions across teams.

Integration – Mending Rifts and Building Bridges In the premiere MTS chapter, Mathieu and colleagues (2001) recommended four critical levers for enhancing MTS coordination and effectiveness: 1) shared mental models; 2) leadership; 3) information technology (IT) systems; and 4) reward systems. The emphasis on coordination follows from the fact that between-team coordination is often the weak point of otherwise wellfunctioning systems. Unfortunately, empirical research in several of those areas has been limited. Accordingly, in this section, we lay the groundwork for future investigations to delve more deeply into the macro-dynamics of MTSs. Specifically, we discuss several features of coordination and leadership as well as offer insights regarding their alignment with system configurations (i.e., differentiation and dynamism) to enhance MTS effectiveness. Coordination and leadership mechanisms are both critically important to manage the MTS and promote effectiveness. These mechanisms can serve to offset structural elements of the system or compensate for the boundary-enhancing or disruptive forces creating rifts in the system (Luciano et  al., 2015). Stated differently, mechanisms are needed to mend the rifts or to build bridges over them. Individuals working in MTSs will react to differentiation and dynamism by focusing their attention inward toward the component teams. Interventions, then, will be effective to the extent that they direct emphasis toward the system (rather than the component team) or aid in aligning the efforts of component teams.

Coordination Early work on coordination in MTSs drew from the literature on (within) team processes (e.g., Marks, Mathieu, & Zaccaro, 2001). Examination

of team processes focused on members’ interdependent actions, rather than the mechanisms used to facilitate them. By way of extension, MTSs have to facilitate and enact processes at both the team- and system-levels. Empirical research on MTS coordination has examined the influence of coordination on MTS performance, detailing the role of different coordination functions, directed at different foci, exhibiting multiple forms, and being enacted during different phases. These aspects of MTS coordination can be defined as follows: • Coordination functions – coordination behaviors (e.g., action, understanding, interests) that enable component teams to attain proximal team goals and superordinate system goals; • Coordination foci – the collective (e.g., focal team, other team, system) whose needs are met by engaging in coordination functions; • Coordination forms – the structure (e.g., boundary-spanners, members, centralized, decentralized) of who in the MTS enacts the coordination functions; • Coordination phases – the timing of when coordination functions are enacted. The earliest empirical examination of MTS coordination explored coordination phases. In a study of eight MTSs working on subprojects associated with a three-year new product development project at a European automotive organization, Hoegl, Weinkauf, and Gemuenden (2004) illustrated the positive relationships between interteam coordination, project commitment, and teamwork quality. They found those three process variables were stronger positive predictors of component team performance (Time 3) when measured at the end of the initial concept phase (Time 1), than at the end of the later design phase (Time 2; Hoegl et al., 2004). These findings highlight the predictive power of collaborative processes and the importance of tending to these processes early in the project timeline. Later empirical examinations of coordination each featured more than one component of coordination (e.g., function and foci), however each offers insights regarding a particular coordination function. Definitions of coordination and its component functions abound (see Okhuysen & Bechky, 2009 for a review). Whereas many definitions focus on the action function of coordination (e.g., Marks et al., 2001), McGrath, Arrow, and Berdahl (1999) offered a more comprehensive definition identifying three dimensions of coordination – coordination of interests, understanding, and actions.

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Coordination of interests refers to the functional interconnections between member interests and goals and group interests and goals … Coordination of understanding refers to the development of shared perceptions and meanings among members, including an appreciation of the ways in which members reliably see and interpret events differently … Coordination of action is a synchronization and sequencing of member actions in time and place. (pp. 1–2)

Herein, we address the coordination functions of interests, understanding, and actions. In their study of undergraduates performing a flight simulation in a laboratory, Marks et  al. (2005) examined action processes (  function) both at the within-team and MTS (cross-team) levels ( foci). They found that cross-team action processes positively predicted MTS performance beyond that accounted for by within-team action processes. Further, they manipulated the level of goal hierarchy interdependence (i.e., the level of cross-team interdependence the situation calls for), which is conceptually akin to coordination of interests. They found cross-team action processes were more important for MTS effectiveness in situations with more interdependent goal hierarchies (Marks et al., 2005). Stated differently, the level of coordination of interests required by the situation can influence the relative importance of the coordination of action at different levels of foci. Hollenbeck, Ilgen, and their colleagues conducted a series of studies using US Air Force captains completing a remotely piloted aircraft simulation as a part of a leadership course. Each MTS was comprised of three specialized six-person teams, including a leadership team, a point team, and a support team. Notably, the leaders (i.e., the director and assistant director) of both the point and support component teams served in boundary-spanning roles and were also considered members of the leadership team (i.e., the leadership team contained two unique team members and four overlapping team members). This study design enabled important insights regarding coordination foci and form. Davison, Hollenbeck, Barnes, Sleesman, and Ilgen (2012) found cross-team coordinated action can be beneficial or detrimental to system performance, in part, depending on which team ( form) is enacting the coordination behaviors and the relative importance of the targeted teams ( foci). Specifically, coordinated action across teams only positively influenced system performance when directed at the component team most critical to addressing the demands of the task environment at that point in time (Davison et al., 2012). Additionally, Lanaj and colleagues (2013) demonstrated that the coordination form including the structure of linkages

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(i.e., being enacted in a centralized or decentralized manner), influences MTS performance. They found that decentralized planning can have both positive and negative effects; the positive effects were attributed to enhanced proactivity and aspiration levels, the (even stronger) negative effects were attributed to excessive risk seeking and coordination failures (Lanaj et al., 2013). The third article using the US Air Force captain simulation population was conducted by Firth, Hollenbeck, Miles, Ilgen, & Barnes (2015). This study offers important insights for the coordination function of understanding by examining the influence of frame-of-reference training. They posited that frame-of-reference training would reduce representational gaps and found that it enhanced between-team coordination and thereby MTS performance. Furthermore, they found that withinteam coordination improved the relationship between frame-of-reference training and betweenteam coordination, but the pattern varied based on team function (i.e., focal team, point team). This finding suggests that the quality of coordination of one foci can influence coordination of other foci. Likewise considering the coordination function of understanding, Healey and colleagues (2009) adopted a transactive memory system approach. They examined three MTSs, each involving 11–19 different organizations, which engaged in a civil emergency response training exercise. Their study found that knowledge of others’ expertise, at the component team and MTS levels ( foci), is critical for team and system performance. They also offer insights concerning the differential effects of different forms of training exercises. In sum, several empirical MTS studies provide important insights into the aspects of coordination that influence MTS performance. However, these studies often report differential effects of coordination on performance; some studies varied in strength of the effects (e.g., Marks et  al., 2005), whereas others demonstrated both positive and negative effects of coordination (e.g., Davison et al., 2012; Lanaj et  al., 2013). Accordingly, we anticipate complex contingency relationships between MTS structural configurations, their coordination mechanisms, and environmental factors, as related to MTS effectiveness. Discerning the precise nature of these contingencies remains a topic for the next generation of MTS research. However, below we offer some general guidance for such investigations. When considering the functions, foci, forms, and phases of MTS coordination, it is important to take into account both: 1) the mechanisms used to facilitate coordination; and 2) the enacted process of coordination. Coordination mechanisms can serve to offset the structural features of the system, compensate for the boundary-enhancing

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or disruptive forces creating rifts in the system, or both (Luciano et al., 2015). Additionally, unpacking the coordination process offers rich insight regarding how to best enact the process to facilitate system effectiveness. Stated differently, we submit that developing a better understanding of how MTS coordination occurs, and how it may be changed to better align system configurations, will offer insights regarding how to enhance MTS effectiveness. Herein, we provide suggestions for aligning coordination functions, foci, forms, and phases with increasing levels of differentiation and dynamism, respectively.

Coordination and differentiation

Differentiation, the degree of differences between component teams, fortifies the boundaries of MTS component teams and increases the salience of team membership. Conceptually, those distinctions build walls around the component teams, which create particularly intense challenges for between-team coordination processes that must permeate those walls. Differentiation can stem from multiple sources. The particular source, or subdimension, offers insight regarding the mechanisms that may be most effective. Notably, the differentiation subdimensions present more acute challenges for different coordination functions as they generate greater coordination needs. Higher levels of goal discordancy, dissimilar goal priorities and incompatible goals, directly challenges coordination of interests as within the system the goals are neither aligned nor interconnected. Additionally, higher levels of competency separation and information opacity are likely to increase challenges for coordination of understanding. High competency separation thwarts the development of shared perceptions and meaning because knowledge is siloed within component teams, which have vastly different capabilities. Coordination of understanding is likewise thwarted by high levels of information opacity as information regarding other component teams is unavailable or uninterpretable, which inhibits awareness and anticipation. Finally, higher levels norm diversity and work process dissonance, are likely to most directly influence coordination of actions. The synchronization of action will be hindered by incompatible expectations about ‘the way things work’ and the extent to which work processes are incongruent with and conducted separately from other teams, as they increase the potential for the component teams to be out of sync with other teams and the larger system. Stated differently, differences and incompatibilities in what component teams think they should be doing and how they go about doing it will

cause particular issues for coordination of actions. Although higher levels of each differentiation subdimension will generate a greater need for one coordination function in particular, the subdimensions do not necessarily influence only one coordination function (e.g., high levels of information opacity is also likely to undermine coordination of action by impeding dynamic adjustment). We chose to highlight the coordination functions potentially offering the greatest impact on MTS functioning. In their original MTS chapter, Mathieu and colleagues recommended four critical levels for MTS effectiveness: 1) shared mental models; 2) leadership; 3) IT system; and 4) reward systems. We suggest that overlaying these levers on the coordination functions could provide rich insight for intervention design (e.g., perhaps reward systems should be designed to coordinate interests between teams) and a fruitful area for future research (e.g., how do IT systems enhance coordination of understanding and/or action, do shared mental models indirectly influence coordination of action via enhancing coordination of understanding?). In addition to considering the coordination needs created by differentiation, it is also important to contemplate who, how, and when coordination should be enacted. The MTS literature highlights several contingencies to consider. Therefore, in order to more comprehensively discuss coordination and differentiation in MTSs, we discuss the elements of foci, forms, and phases. In other words, it is not just who is doing what to enhance coordination, it is also important to understand when it is being done. For instance, although coordination is important for system effectiveness across project phases, it may be particularly critical during initial phases. Hoegl and colleagues (2004) found support for the notion that interteam coordination at earlier stages of a project determines the team’s performance trajectory. Adopting a more nuanced approach to the implication of phases for coordination, we suggest integration of Marks and colleagues (2001) recurring phase model of team processes (i.e., transition and action processes; see Marks et al., 2005). Transition processes include activities such as goal specification and strategy formulation, which are followed by action processes which include activities such as monitoring progress toward goals and team monitoring (LePine, Piccolo, Jackson, Mathieu, & Saul, 2008; Marks et al., 2001). We suggest that within broader project phases (e.g., concept, design, development), transition and action processes will occur at both the team- and system-levels and not necessarily in parallel. For systems with higher levels of differentiation, we submit that during transition phases

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coordination should be more centralized ( form) and focused on between-team coordination processes ( foci), whereas during action phases coordination can be more decentralized and focused on focal teams. Providing some support for the importance of aligning coordination foci and phase in an MTS with higher levels of differentiation, Bruns (2013) conducted an ethnographic field study of biology cancer research involving teams of scientists from different domains (i.e., experiments, computational modeling). She advanced a process model which included phases of within-team expert practice (action) alternated with phases of between-team coordination practices used to communicate progress and establish the next objective (transition). In essence, she demonstrated the utility of working asynchronously, while routinely communicating across teams and aligning efforts in order to make diverse contributions compatible. Bruns’ (2013) study offers rich insight regarding how coordination occurs across multiple expert domains, however, she does not comment on which individuals or roles are enacting the coordination. As MTSs increase in level of differentiation (as well as dynamism and size), it becomes increasingly inefficient and impractical for everyone to be coordinating with everyone else. Some degree of centralized system-level planning is useful to keep all component teams working in the same direction and avoid coordination failure (Lanaj et  al., 2013), however boundary-spanners have a particularly critical role. Traditionally, the team boundary-spanning literature has focused on three type of activities; external representation, information search, and coordination of task activities with other groups (Ancona & Caldwell, 1992; Joshi, Pandey, & Han, 2009). In MTSs boundary-­ spanners are often members of both their own task-based component team and a leadership team (see Davison et al., 2012; Firth et al., 2015; Lanaj et al., 2013), which creates two sets of responsibilities – the boundary-spanners must represent, gain information for, and coordinate on behalf of both teams. For example, the boundaryspanner is tasked with: 1) getting the information for their task team to complete their task and maintain alignment with the system as well as; 2) providing information to the leadership team about the task team to enable making adjustments and overall system management. Davison and colleagues (2012) found that coordinated action across teams only positively influenced system performance when enacted by system leaders or boundary-spanners and directed at the component team most critical to addressing the demands of the task environment at that point in time (Davison et al., 2012).

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In sum, we have several overarching recommendations for systems with higher levels of differentiation. First, consider the sources of differentiation and their influence on coordination functions. Interventions are best targeted toward facilitating between-team interactions, either by offsetting the differentiation or compensating for the boundary-enhancing forces (see Luciano et al., 2015). Second, regarding coordination foci, forms, and phases; during transition phases, coordination at the system-level that is centralized and primarily enacted by boundary-spanners or system leaders will best align coordination processes to promote system effectiveness. Conversely, during action phases, more decentralized team-level coordination will best promote system effectiveness.

Coordination and dynamism

Dynamism, the variability and instability of the system over time, creates uncertainty and fractures the linkages in the system (Luciano et  al., 2015). Conceptually, dynamism captures the changes occurring in an MTS which destabilize the system and require adaptation. Interestingly, the ability to be adaptive and responsive to complex and challenging environments has been highlighted as one of the main benefits of MTSs (Mathieu et  al., 2001). For example, Uitdewilligen and Waller (2012) argued, ‘Particularly in emergency situations, characterized by high levels of ambiguity, uncertainty, and rapidly changing environmental dynamics, these flexible structures are crucial for rapid organization adaptation in an unknown and developing environment’ (p. 365). Whereas the basic configuration of MTSs may be well-suited to adapt, each instance of change destabilizes the system and creates coordination needs. Different subdimensions of dynamism present different coordination challenges. For example, higher levels of change in goal hierarchy, drastic and rapid changes to the relative importance of system goals, directly challenge the coordination of interests across component teams as the goal interconnections are broken and the goals may become misaligned. In contrast, fluidity of system composition and diversion of attention are likely to increase challenges for coordination of understanding. Fluidity of system composition impedes relationship formation and establishment of norms, thereby hindering anticipation of others’ behaviors, as well as generates the need for frequent on-boarding of new members. Diversion of attention reflects the level of members’ attention on non-MTS-related tasks. At higher levels, members frequently experience cognitive shifts when re-orienting to the task of the focal MTS, impeding development of shared understanding. Finally, higher levels of uncertainty

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of task requirements and fluidity of system structural configuration are likely to increase challenges for coordination of action. High uncertainty of task requirements inhibits the formation of routines and plans, which challenges appropriate sequencing of members’ actions. Fluidity of system structural configuration breaks the linkages between teams and alters workflow impeding the synchronization of actions. Similar to the influence of the differentiation subdimension, higher levels of each dynamism subdimension will generate a greater need for a specific coordination function, but not necessarily only one coordination function (e.g., higher levels of diversion of attention are also likely to thwart coordination of action by making it harder to call the system members to action). In sum, by undermining coordination it heightens the need for compensatory mechanisms to maintain or enhance MTS effectiveness. Okhuysen and Waller (2002) discussed coordination processes and mechanisms in terms of temporal semistructures which have rhythmic shifts in focus to either MTS component teams or the system as a whole. Brown and Eisenhardt (1997) argued that semistructures need to be ‘sufficiently rigid so that change can be organized to happen, but not so rigid that it cannot occur’ (p. 29). The optimum balance of flexibility and rigidity, will vary in MTSs at different levels of dynamism. Higher levels of dynamism put a premium on flexibility, whereas systems with lower levels of dynamism can be effective with a more permanent stable structure. However, it is important to appreciate that regardless of the levels of dynamism some amount of system-level coordination is required to effectively disperse information and cohere the system, whereas too much system-level coordination can impede communication between teams and create rigidity. Bigley and Roberts (2001) found that by combining traditional bureaucratic role-based structures with flexibility-enhancing processes, adaptive and improvisational capability could be attained. Continuing to build from those implications regarding form and foci of coordination, in systems with higher levels of dynamism the ability to shift system attention to different teams becomes increasingly critical. One coordination mechanism that may prove valuable in MTSs with higher dynamism is a transactive memory system (TMS). A TMS exists between individuals (or in the case of MTSs, between component teams) as a means to cooperatively store, retrieve, and leverage information (Lewis, 2003; Wegner, 1987). Behavioral manifestations of a TMS include members’ development of specialized knowledge (specialization), trust and reliance on others’ expertise (credibility), and efficient coordination of knowledge integration (coordination; Lewis, 2003). This collective awareness

of who knows what, should be supplemented with a collective understanding of who does what (i.e., role clarity) as well as a collective understanding of when certain actions should be taken (e.g., when contingency plans should be enacted). Notably, Faraj and Xiao (2006) present a coordinationfocused model of trauma patient treatment, which included both habitual and problematic trajectories and two major grouping of coordination practices: 1) expertise coordination practices (e.g., reliance on protocol, plug-and-play teaming); and 2) dialogic coordination practices (e.g., joint sensemaking, protocol breaking). As they discussed, the mastery of treatment protocols, such as the specification of care procedures often integrated with a flow chart for decision-making, can be critically important for high-quality performance. These protocols reduce task ambiguity, role ambiguity, and enhance decision-making in a dynamic environment; however, they do not apply to all situations (Faraj & Xiao, 2006) creating a need for other coordination practices and role-based structures. In addition to situation-related triggers that determine the most appropriate coordination practices, the phase may also serve as a trigger. Phases may be determined in terms of the development or maturity of an MTS, or in terms of periods of different type of activities. Earlier we discussed the recurring phase model of team processes advanced by Marks and her colleagues (2001). Here we suggest that MTSs with higher levels of dynamism will have shorter, partially overlapping transition and action phases. While these phases may partially co-occur in real-time, the shift to transition phases should trigger system-level coordination processes (form) in order to realign and stabilize the system before taking additional actions (potentially in varying or incorrect directions). In MTSs with high levels of dynamism it is nearly impossible and certainly impractical to anticipate all possible scenarios and eliminate uncertainty. Utilization of protocols and other expertise coordination practices combined with consensus on triggers that signal shifts to dialogic coordination practices will enhance coordination in MTSs, but will be particularly effective in MTSs with higher levels of dynamism. Notably, these coordination practices are examples of the flexibility-enhancing processes needed to supplement traditional role-based structures to promote system adaptive capability. These practices help manage uncertainty and stabilize the system. Finally, we would be remiss to conclude our discussion of coordination and dynamism in MTSs without commenting on the source of dynamism. The MTS literature assumes that the dynamism in MTSs is in response to environmental dynamism. Stated differently, the MTS needs to be dynamic in

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order to align with the task environment. Whereas this holds true in much of the existing MTS work, it is not necessarily the case. For example, the dynamism subdimensions fluidity of system composition reflects frequency and magnitude of membership change in the system. This change can certainly be the result of the team no longer being needed to pursue the system goal, however turnover can occur for dozens of other reasons (e.g., members may be voluntarily leaving the MTS system due to dissatisfaction with the system leadership). Regardless of the dynamism trigger, it still creates disruptive forces, however understanding why the system is experiencing dynamism should yield insights for the most appropriate coordination mechanisms.

Leadership Since the original observation concerning the premium on ‘between-team issues’ for MTS effectiveness (Mathieu et  al., 2001), researchers have concentrated on understanding the leadership implications of operating in such environments. Early work on MTS leadership built upon the functional approach which has been influential in understanding team leadership (Fleishman, Mumford, Zaccaro, Levin, Korotkin, & Hein, 1991; Zaccaro, Rittman, & Marks, 2002). The core idea being that the job of a team leader is to do what is not being adequately handled to meet the needs of the group (McGrath, 1964). An important concept here is that leadership is a process, a set of functions that need to be executed for a collective to be successful, whether that collective is a small team or a larger entity such as an MTS. As a process, leadership may fall to a single individual or to some collection or group of individuals (Contractor, DeChurch, Carson, Carter, & Keegan, 2012). By way of extension, the essential function of MTS leadership is to serve as a bridge, providing an overall vision for the MTS, communicating constraints and ideas from one team to another, and to provide coaching that enables the component teams to adjust their actions to be more in line with those of others (DeChurch & Marks, 2006). Empirical research on MTS leadership, to date, has examined the role of different leadership functions, directed at different foci, exhibiting multiple forms, and being enacted during different project phases. These aspects of MTS leadership can be defined as follows: • Leadership functions – leadership behaviors (e.g., strategy, coordinating) that enable component teams to attain proximal team goals and distal system goals;

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• Leadership foci – the collective (e.g., team, system, external boundary) whose needs are met by leadership; • Leadership forms – the structure (e.g., the number of leaders, reciprocity, density, centralization) of who in the MTS enacts needed leadership functions; • Leadership phases – the timing of when during MTS development leadership functions are enacted. The earliest examinations of MTS leadership examined leadership functions. DeChurch and colleagues examined the leadership functions of strategizing and coordinating in both a laboratory and an archival study. In the laboratory study, they found that the strategy and coordinating leadership functions needed to be directed toward the interface between teams. The laboratory task included a leadership team who was directing two component teams, each with different functions, performing a combat flight simulation (DeChurch & Marks, 2006). In later work, Murase, Carter, DeChurch, and Marks (2014) used the data from the DeChurch and Marks (2006) study to explore the psychological mechanisms by which leadership training facilitates between-team coordination processes. They suggested that the accuracy of the leader’s multiteam-interaction mental models (i.e., cognitive structures containing knowledge of appropriate between-team activities) is transferred to the team via strategic communication, which enhances the members’ mental model accuracy and in turn, between-team coordination (Murase et al., 2014). These same strategy and coordinating functions were supported in a historiometric study using archival accounts of MTSs operating in extreme conditions (i.e., provincial reconstruction teams and cross-regional hurricane emergency response teams; DeChurch, Burke, Shuffler, Lyons, Doty, & Salas, 2011). In addition to finding strategy and coordinating functions, their qualitative analysis found three foci of leadership in MTSs. Leadership behaviors that are aimed at directing individual component teams (within teams), aligning component teams (between teams), and bridging the system to its external environment (across the system; DeChurch et al., 2011). Millikin, Hom, and Manz (2010) considered the form of leadership and highlighted the importance of self-management strategies. They studied 21 MTSs in a US semiconductor plant and found that MTSs comprised of teams whose members widely practiced self-management strategies performed better (i.e., attain higher productivity gains). Furthermore, MTSs comprised of

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self-managing teams who were highly cohesive were the most productive. MTS studies examining the form of leadership have found both shared and redundant leadership can be differentially effective. For example, Johannessen, McArthur, and Jonassen (2012) advanced the importance of the availability of additional leadership resources (i.e., leadership redundancy), for system effectiveness in high-risk work in potentially extreme environments. In their case study of an MTS operating a petro-maritime vessel, they suggested that authority should be centralized into one role. However, Johannessen et  al. (2012) argued that when under stress and time constraints, the one leader may not be able to perform all leadership functions and other leaders are needed to fill the void. Along similar lines, Bienefeld and Grote (2014), in their study on airplane crews during a simulated in-flight emergency, found that shared leadership among the cabin crews predicted component team goal attainment, while shared leadership by the boundary-spanner (purser) predicted cross-team goal attainment. Conversely, in the associated cockpit crews, leadership was not shared and the captain’s vertical leadership predicted team goal attainment. Importantly, this study illustrates that the different regions of an MTS (i.e., foci) may benefit from different forms of leadership. Finally, prior MTS work also suggests that leadership from multiple sources can have differential benefits depending on project phases. For instance, in their study of new product development MTSs at a European automotive organization, Hoegl & Weinkauf (2005) differentiated between managerial functions (structuring and support) performed at the team and project (i.e., MTS) levels. They found that team-level managerial functions were particularly important for facilitating performance during the initial concept phase of the project. Conversely, project-level managerial functions hindered performance during the concept phase, but were the most important for facilitating performance during the later development phase. In sum, a steady stream of studies have begun to provide insight into the aspects of leadership that enable MTS effectiveness. However, these studies have largely focused on leadership as a direct antecedent of MTS effectiveness. Given the wide variation of MTS structural configurations and dynamism, we suggest that optimal leadership configurations are likely to be context-specific. Moreover, there may well need to be different types of leadership within different areas of an MTS and/or at different times. The complexity of MTSs precludes the one-size fits all approach to leadership.

Leadership and differentiation

Luciano et  al. discuss the structural shape variation in MTSs in terms of goal discordancy, competency separation, norm diversity, work process dissonance, and information opacity (see Table 15.1). Regardless of the source of differentiation in an MTS, the implications for leadership are clear– leadership needs to bring coherence and cooperation to the distinct teams in the system. This is a situation of intergroup leadership (Hogg, van Knippenberg, & Rast, 2012), where the leadership imperative is to create an intergroup relational identity. An intergroup relational identity allows members of distinct teams to define themselves both as members of the component team as well as the larger system of teams. The natural tendency in intergroup situations is for groups to draw strong comparisons, and use out-group comparisons to fuel a positive in-group identity (Tajfel, 1982). It has been said that in-groups require outgroups, and in MTSs, out-groups can often be other component teams. The five sources of differentiation in MTSs identified by Luciano et al. (2015) all create boundary-enhancing forces that require leadership integration. This integration can stem from each of the four aspects of leadership that have been examined in previous research: the functions, foci, forms, and phases. Leadership functions characterize the types of leadership behaviors that enable component teams to attain proximal team goals and distal system goals. Morgeson, DeRue, and Karam (2010) detailed specific team leader functions that align with important team processes outlined by Marks et  al. (2001). Leadership functions that facilitate transition or planning processes include composing the team, defining the mission, establishing expectations and goals, structuring and planning tasks, training and developing the team, sensemaking, and providing feedback (Morgeson et al., 2010). Leadership functions that facilitate action processes include monitoring the team, managing team boundaries, challenging the team, performing team tasks, solving problems, providing resources, encouraging self-management, and supporting the social climate. Each of these functions provides MTS leaders with the opportunity to build an intergroup relational identity. For example, in highly differentiated MTSs, defining the mission in terms of how each team’s efforts contribute to the efforts of other teams and to the attainment of the MTS goal is a clear way to build such an identity. The second aspect of MTS leadership with implications for mitigating boundary-enhancing forces is the leadership foci. Leadership foci were first introduced by DeChurch and Marks (2006), who found shifting the collective toward which

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leader actions were focused (i.e., from individual team members to collective teams) was associated with improved MTS interteam coordination. In later work, DeChurch et al. (2011) identified three levels or foci at which leader actions can be aimed: leading within component teams, leading between component teams, and leading across the MTS and its operating environment. MTSs require leadership at all three levels. Leading within component teams is team leadership. Team leadership sets the direction and aligns the efforts of individuals working within component teams. Team-focused leadership involves gathering information about the team’s performance environment and framing the team’s task, setting team goals, planning how team members will work together to accomplish their goals, managing the flow of information within, and coordinating the actions among team members. Leading between component teams involves setting the vision for the MTS, and building an intergroup relational identity so that teams can clearly see how their teams’ actions affect other teams, and how their team actions are instrumental in attaining MTS goals. Between-team-focused leadership gathers information about MTS functioning, sets MTS goals, and plans how component teams will integrate their separate plans and actions so that they are compatible with one another. Leading across the MTS and its environment is aimed at aligning the system with its operating environment. Behaviors in this foci involve gathering information about the MTS’s performance environment, framing the MTSs task to external constituents, and integrating MTS plans with those of outside constituents (DeChurch et  al., 2011). Behaviors in the ‘leading across’ foci can also be thought of from Ancona and Caldwell’s perspective (1992), consisting of mapping behaviors to determine who the key stakeholders are, molding behaviors which attempt to influence stakeholders beliefs about the MTS, coordinating behaviors to ensure the MTS is aligned with external parties as needed, and filtering behaviors that sift through external information relaying a smaller amount back to MTS teams as needed. Though MTS leadership is needed to attend to all three levels, differentiation within MTSs will increase the importance of the higher foci; the between-team and external boundary leadership. The third aspect of MTS leadership is its form. The form describes the pattern of influence within the MTS (Carter & DeChurch, 2013; Contractor et  al., 2012; Zaccaro & DeChurch, 2012). This aspect of leadership answers three basic questions: 1) how many leaders are there?; 2) how are these leaders related to the component teams within the

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MTS?; and 3) how are these leaders related to each other within the MTS? Some important aspects of leadership forms include the number and representativeness of leaders, the extent to which multiple leaders influence one another (Carter, DeChurch, & Zaccaro, 2014), and the degree of hierarchy among leaders. The form of leadership can offset the boundary-enhancing effects of differentiation. For instance, sharing leadership across multiple people requires constant negotiation of ideas to be adopted and implemented. In differentiated MTSs, leadership forms that involve multiple leaders representing the different teams may be preferable to those wherein the highly influential individuals are disproportionately drawn from any particular team. The fourth aspect of leadership that can offset the boundary-enhancing effects of differentiation is leadership phase – the timing of when during MTS development leadership functions are enacted. In line with Hoegl and Weinkauf (2005), leadership should shift over time from an early focus on the teams to a later focus on the system. However, with higher levels of differentiation, shifting the focus to the system may need to occur earlier to foster the needed intergroup relational identity. An additional caveat to this suggestion, there are circumstances (e.g., high levels of uncertainty of task requirements) under which leadership functions should first focus on the system-level in order to define the overall mission and determine the requisite structure of the MTS. Furthermore, these circumstances may generate the need for reciprocal processes across foci and phases. To further explore these circumstances, we turn to the second structural feature of MTSs: dynamism.

Leadership and dynamism

Luciano et al. discussed a second element of structural variation in MTSs in terms of dynamism. As shown in the lower portion of Table 15.1, dynamism can stem from changes in the goal hierarchy, from uncertainty in the task requirements, from fluidity of the structural configuration and composition of the MTS, and the diversion of members’ attention. In contrast to differentiation which creates boundary-enhancing forces, dynamism creates unpredictability and disruptive forces. The clear leadership imperative created by dynamism is to help teams to cope with the uncertainty in the system. Certain variations of leadership functions, foci, forms, and phases are better suited to deal with higher levels of dynamism. Returning to the functions of leadership detailed by Morgeson et  al. (2010), particular functions can be especially valuable in offsetting

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dynamism-created disruptive forces. For example, transition leadership functions that define the mission, establish expectations, and structure and plan work, will go a long way toward reducing the uncertainty facing each of the component teams. Additionally, leadership directed at multiple foci can also offset disruptive forces. The three foci of MTS leadership; leading within component teams, leading between component teams, and leading across the MTSs and its operating environment, each play an important role in allowing the system to cope with dynamism and stabilize post changes. MTSs require leadership at all three levels, and dynamism likely increases the importance of leadership at each level. Furthermore, leadership forms, especially those involving shared leadership arrangements where multiple people rotate or otherwise divide up the functions of leadership over time (Contractor et  al., 2012), can provide some needed flexibility, redundancy, and resilience to the system. Dynamism creates a clear need for built-in redundancy in leadership whereby multiple individuals have the capacity to direct and coordinate within, between, and across the MTS. As changes occur, leadership structures that involve greater decentralization may best enable a functional rotation regarding who guides the system. Leadership forms involving multiple leaders and decentralization or evenness in the status hierarchies of MTSs may be useful for coping with lower levels of dynamism or more predictable changes (e.g., shifts in system structural configuration are enacted in response to specific events). However, in circumstances that overlay temporal urgency and unpredictable changes, with higher levels of dynamism, more centralized forms of leadership focusing on the between-team and across system foci with redundant leadership focused on the within-team foci may be better suited to facilitate system functioning. Lastly, the timing (phase) of when leadership is enacted will play a role in setting up clear norms and expectations for how things are done within the MTSs. MTS leadership faces an inherent tradeoff between ideation and predictability. On one hand, to maximize ideation leadership activities are best introduced a bit later, after sufficient time has passed enabling multiple individuals and teams to have input. On the other hand, to maximize predictability leadership activities are best introduced as soon as possible, before confusion and inaction become normalized. Dynamism exacerbates this tradeoff, requiring leadership earlier on to provide needed predictability, but also gaining involvement early on to maximize ideation as well. In addition, after occurrences of change, in particular unpredicted changes of greater magnitude, MTS

leadership may be required to realign the component teams and stabilize the system. In sum, leadership in MTSs exhibits four factors that have been studied in relation to MTS effectiveness – function, form, foci, and phases. However, differently structured MTSs create different demands for leadership. Differentiation creates boundary-enhancing forces, which require leadership to build an intergroup relational identity that bridges the rifts between teams. Dynamism generates uncertainty creating disruptive forces, which require leadership arrangements that afford needed stability and predictability to component teams. Overall, leadership and coordination mechanisms can serve to integrate the teams in MTSs. In this section we offered numerous insights regarding the alignment of variations in aspects of coordination and leadership (i.e., function, foci, form, and phase) with different system configurations (i.e., levels of system differentiation and dynamism). We also highlighted the predictability of change and temporal urgency as potentially influential factors. However, we should note there are likely other factors that influence the suitability of different integration mechanisms. In particular, the nature of the proximal and distal goals may generate differences in performance requirements, and in turn, differences in integration requirements. For example, several studies have considered the leadership structure and behaviors best suited to support innovative goals or tasks (Hoch, 2013; Hunter & Cushenbery, 2011; Rosing, Frese, & Baush, 2011).

THE ROAD FORWARD Over the past 15 years, MTS researchers have journeyed down a long and winding road, to generate important advancements in understanding MTSs. While important progress has been made, there is still much farther to go. In this section we begin to map out the road forward for the MTS domain. This road includes both rigorous theoretical development, in particular native system-level theory, as well as substantial empirical research using multiple research methodologies.

Research Methodologies MTSs can be studied using a variety of research methods ranging from passive observational methods, to various types of simulations, to

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quasi- and experimental methods, to longitudinal designs. All approaches offer some unique advantages and disadvantages. Notably, outside of the laboratory, random assignment and manipulations/interventions will be difficult to come by, and research is likely to lean more heavily on correlational and longitudinal methods and analyses. Below, we briefly review four major approaches: 1) cross-sectional field studies; 2) laboratory simulations; 3) longitudinal field investigations; and 4) case studies that may be used to study MTS phenomena.

Cross-sectional field studies

Cross-sectional field studies are naturally limited by the inability to unpack developmental phenomena or to specify causal order. Yet, cross-sectional studies do provide some comparative insights. For instance, Kirkman and Rosen (1999), de Jong, Ruyter, and Lemmink (2005), and Mathieu, Gilson, and Ruddy (2006) all found MTS processes related positively to important team-level processes and outcomes. Using a cross-level design, Mathieu, Maynard, Taylor, Gilson, and Ruddy (2007) found that MTS processes interacted with team processes to influence team performance. Specifically, team processes evidenced a stronger positive relationship with team performance when MTS-level processes were less, in comparison to more, cooperative. These findings are consistent with those of Marks et al. (2005) which were found in an experimental laboratory investigation. Accordingly, correlation field studies provide some insights as to how current MTS states relate to other variables of interest. However, they are unable to inform us about how those MTS states evolved, nor can they permit strong cause–effect inferences.

Laboratory simulations

Laboratory simulations have developed in their sophistication from relatively simple four-person two-team combat flight simulations run for 4–5 hours with undergraduates (e.g., Mathieu, Cobb, Marks, Zaccaro, & Marsh, 2004), to complex 14-person three-team training platforms run over the course of five weeks with active duty Air Force Captains (e.g., Davison et  al., 2012). Modern-day simulations for studying MTSs come in a wide variety of configurations and can be played by a variety of sample populations. Notably, these simulations have common features such as clearly distinguishable teams, interdependencies between teams, and proximal team and superordinate system goals. Yet many of their surface features vary, such as the number of teams (cf., Mathieu et al., 2004; Lanaj et al., 2013), the nature of their interdependencies (see Marks

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et  al., 2005), and their duration of activities (cf., Carter et al., 2014; Davison et al., 2012; DeChurch & Marks, 2006). Resick and colleagues (2012) cautioned against the allure of these platforms and advised ‘(a) do not be enamored by the platform’s apparent capabilities, and (b) do not be constrained by the platform’s apparent capabilities’ p. 498). Their sage advice comes down to avoiding the creation of a science of the simulation, rather than a science of MTS phenomena. Resick et  al. (2012) also provided an analysis of the relative benefits and disadvantages of laboratory-based simulations in terms of threats to validity. In addition to Resick and colleagues’ (2012) observations, we should note that these simulations permit researchers to fully script scenarios, yet have computer controlled entities responding intelligently to evolving circumstances. A scripting feature ensures that all teams will confront identical performance conditions at the onset of an experimental trial or episode. This affords a high degree of experimental control and consistency. Given that the number and actions of various units are fully controllable, one can create anywhere from a very simple slowly evolving scenario to an exceptionally complex and demanding one to test the influence of factors such as workload, environmental complexity, stress, and so forth, on individual, team, and MTS performance outcomes and behaviors. In other words, in an experimental sense, MTSs may be placed in identical ‘initial states’ in different scenarios, but the exact nature of the engagement that is experienced will differ as circumstances evolve and artificial intelligence (AI) guided entities respond to MTS actions. In this sense, the participating MTS members’ actions actually create the nature of future environmental factors that they confront. The fact that computer controlled entities respond intelligently to evolving circumstances but in adherence to their programmed ‘strategies or rules or engagement’ yields a scenario that remains realistic even if different experimental teams execute actions that were not anticipated by research designers. This provides a far better balance of experimental control and fluid emergent phenomena than has been available in earlier laboratory tasks and environments. They also permit a blending of experimental and training platforms (e.g., Cromwell & Neigut, 2014; Davison et al., 2012), or what we refer to as hybrid settings (i.e., having features of both laboratory and field settings).

Longitudinal Field Studies Longitudinal field investigations are considered to be a powerful research design but are rarely

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conducted with MTSs. No doubt this stems from the difficulty of gaining access, the challenges of gathering data consistently over time, and the logistics of gathering data from comparable MTSs over time. However, many modern-day MTSs coordinate their actions primarily through digital means which may leave trace measures that can be used for research purposes (e.g., voice communications, texts, positional data). As with any archival trace measure, such data may not be suitable to examine certain research questions; however, they do reveal which component teams are interfacing with which others, in which patterns, and under which circumstances. Such interactions may also be amenable to content coding. That is to say, secondary data sources and archival records may yield unique insights into MTS operations that would not be feasible using more intensive approaches such as qualitative methods. Notably, detailed digital trace measures may be particularly suitable for multi-dimensional (i.e., multiplex) network analysis applications (cf., Borgatti & Foster, 2003; Contractor, 2009). Multiplex network analyses simultaneously model the impact of network properties along several dimensions (e.g., identity, cooperation, communication) and analyze their unique and combined influences (Xi & Tang, 2004). Modeled over time, multi-plex networks might reveal which MTS features, at what times, are most influential in yielding MTS effectiveness (Carley, 2003; Contractor, Wasserman & Faust, 2006).

Case Studies Given the evolving state of knowledge, there remains a need for well-conducted case studies and other forms of qualitative investigations to understand better the nature and development of MTSs. Yin (1989) defined the case study method as an ‘empirical inquiry that investigates a contemporary phenomenon within its real-life context, addresses a situation in which the boundaries between phenomenon and context are not clearly evident, and uses multiple sources of evidence’ (p. 23). Case study methodologies come in many varieties, and Yin (1993) outlined a 3 × 2 matrix of types; case studies can be exploratory, descriptive, or explanatory, each of which can be based on single or multiple samples. Yin (1993) further argued that ‘the exploratory case study has perhaps given all case study research its most notorious reputation … [as] in this type of case study, fieldwork and data collection are undertaken prior to the final definition of study questions and hypotheses’ (pp. 4–5). In other words, exploratory case studies are fine for generating

hypotheses, but not for testing them. Given the stage of our understanding of MTSs, we believe that there are many insights to be gained from exploratory case studies with rich descriptions of the phenomena. Descriptive case studies are efforts to categorize the nature and dimensions of a given phenomenon within its natural context. In this sense, descriptive case studies would be useful for illustrating similarities and differences in different aspects or dimensions of MTS frameworks such as the ones that Zaccaro et al. (2012) and Luciano et al. (2015) have advanced. In contrast, explanatory case studies seek to present data bearing on cause–effect relationships. Thus, while exploratory case studies can be atheoretical and designed to generate hypotheses, descriptive and explanatory case studies can leverage a priori theories to categorize and then tie certain dimensions to outcomes, respectively. Yin (1993) further noted that case studies could be one-shot deals or sample multiple situations offering the opportunity to replicate, and thereby to validate, researchers’ inferences. We suggest that if MTSs were selectively sampled from across theoretically identified dimensions (cf., Luciano et  al., 2015), then the resulting insights will be easier to interpret and more quickly advance the discipline. Finally, opportunities may exist to conduct mixed-methods investigations. For example, Xie, Wu, Lu, and Hu (2010) performed a multifaceted investigation of a (primarily) sequential construction MTS. They both gathered survey data from MTS members, and indexed their mail and other correspondence to conduct network analyses of their communication patterns. They then followed up with descriptive methods to reveal the underlying causes of the occurrence of problems. This is a vivid example of how different research methodologies can provide important insights into the functioning of an MTS.

Looking Toward the Next Chapter The past 15 or so years have witnessed an embracing of the MTS concept and a proliferation of work across a wide variety of domains. From individual and comparative case studies, to a range of relatively simple to exceedingly complex laboratory investigations, to longitudinal and multi-level investigations, work in the area of MTSs has been exciting. At the same time, however, it has been disjointed and difficult to synthesize. No doubt part of the problem stems from the grounded or contextualized nature of the work. In other words,

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efforts to simplify and control some of the myriad of effects operating in an MTS environment can lead to modeling of the simulation environment rather than generalizable relationships (Resick et  al., 2012). Alternatively, qualitatively rich, deep-dive investigations of MTSs in a particular context – while revealing – are no more generalizable than laboratory investigations. The multitude of idiosyncrasies present in any given MTS field study, too, limit the generalizability of their findings. Unifying frameworks and theories to guide the sampling and study of MTSs, as well as the integration of results, have been solely needed Zaccaro et al. (2012) and Luciano et al. (2015) have provided such frameworks, and we are encouraged that they will help to unify the field. These two works also make vivid the alternative approaches for studying MTSs. To date, we would characterize much of the MTS work as: 1) within- versus between-team processes; and 2) MTSs as context. The former style investigation typically considers the relative value of processes focused on component teams versus between-teams or system-wide phenomena (e.g., Marks et  al., 2005). The latter style of work either specifically indexes MTS features and uses them as contextual predictors of team-level phenomena (e.g., Mathieu et al. 2007), or uses the MTS generic environment as a contextual backdrop within which certain phenomena, such as shared leadership, should flourish (e.g., Millikin et al., 2010). We advocate two different directions for future research. First, there is a need for MTS level comparison studies. For example, considering the subdimensions of differentiation and dynamism (Luciano et al., 2015), why are certain MTSs more effective than others? What environmental forces influence that answer? Investigations of this sort require a sampling of MTSs ‘in kind’ – such as those in a laboratory environment (e.g., DeChurch & Marks, 2006) or hybrid situations (e.g., Davison et  al., 2012). Alternatively, creative approaches such as historiometric after-action analyses of events of a similar kind (e.g., fire-fighting engagements, disaster relief, military operations) might be leveraged (cf., DeChurch et al., 2011). Notably, this style of investigation would ostensibly treat MTSs as a collective whole – as unified systems of different types or indexed along different dimensions. The variance of interest would be that which differentiate MTSs from one another. A second approach for future investigations might be intra-MTS network style work. Here, we suggest that variations within MTSs, in terms of constituent parts, subsections, or over time, would be the variance of interest. Recall that Bienefeld and Grote (2014) found that different forms of leadership were more effective

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in- and between-different regions of MTSs working on emergency flight simulations. Davison et al. (2012) illustrated that there is a premium on the functioning of different teams at different times for optimal MTS effectiveness. What we are advocating here is along the lines of network indices of MTSs features and processes which can consider factors such as node (i.e., component teams) centrality, dyadic ties between pairs of teams, clusters, or sub-regions of MTSs (i.e., cliques), and other parameters. This recommendation parallels that of Crawford and LePine’s (2013) advocacy of network applications for the study of teamwork. Their argument was that teams are not likely to have a uniform structure or set of relationships that hold for all member pairs. That different individuals might, for example, work more closely, have better relations, etc. with some members than with others; and that ignoring such differences obscures important insights as to their functioning. Extended to the MTS context, between-team interfaces are not likely to be uniform throughout an MTS. A given component team may have drastically different interdependencies and working relationships with different teams within an MTS. Moreover, the configuration and intensity of that pattern of relationships may change as a function of time or circumstances. What is needed, therefore, are techniques that index MTS structure and processes as a set of evolving network relationships (cf., Borgatti & Foster, 2003; Contractor, 2009). The input for such networks could come from qualitative methods, surveys, or better still digital traces. This approach, which is conceptually, logistically, and analytically very intensive, may well hold the key to unlocking our understanding of MTSs and their effectiveness. For the MTS domain to advance theoretically, it will be critically important to develop native system-level theory that embraces MTSs as a unique form (cf., Luciano et al., 2015). Although other literatures offer important insights to draw upon, simply importing theories of team or organizational processes and performance, without considering the implications of system dynamics at the individual, team, and system-levels, will likely yield disappointing results. To be clear, we are not suggesting that existing theory will not apply to MTSs; rather we suggest existing theory is insufficient and additional system-level theorizing is required for a comprehensive understanding of MTSs. In the pursuit of MTS theory, we submit that focusing on factors that inherently influence functioning, rather than descriptive attributes that can vary in their influence, will more quickly build a deeper understanding of MTSs. Finally, we hope future MTS research, both theoretical and empirical, will continue to

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embrace and examine the tensions in MTSs (cf., Davison et  al., 2012; Lanaj et  al., 2013). MTSs seem to present a paradox; system effectiveness requires simultaneously structuring strong component teams that also function well as a part of a system. Resolving this paradox will require leveraging the polarities inherent in the system (Johnson, 1992, 2014). Leveraging polarities involves yielding the benefits of seemingly opposite interdependent pairs (e.g., decentralization and centralization; autonomous teams and a unified system; stability and change) while avoiding overemphasis on either pole. The shape and influence of these polarities is likely to be influenced by MTS configuration (e.g., perhaps a MTS with higher levels of dynamism will exhibit a different manifestation of the stability–change polarity than a system with lower levels) and presence of integration mechanisms (e.g., a system-level leadership team with members from each component team may help to leverage the decentralizationcentralization polarity). In sum, MTSs are tasked with addressing seemingly opposing needs and the integration of polarities thinking may assist in this endeavor. Assuming that we gain an increased understanding of the underlying drivers of MTSs, the best leverage points for influencing their effectiveness should become evident. As discussed here, interventions aimed at mitigating or compensating for differentiation and dynamismgenerated sources of complexity are natural starting points. So, too, is the multifaceted approach to leadership throughout the system that we advocated. Yet other potential sources of coordination exist including the role of IT systems, rewards, and other mechanisms (cf., Mathieu et  al., 2001). MTSs have proven to be difficult to investigate and to understand. Naturally, they are even more difficult to manage effectively. Hopefully, increased theoretical and empirical work will better enable the successful management of MTSs. In conclusion, the past decade and a half has produced a wealth of information and enthusiasm about the MTS concept. Yet far more work remains to be done. Progress will hinge on the advancement and application of unifying frameworks and theoretical models, on meso-style approaches that traverse two or three levels of analysis, and on innovative research methodologies and investigations. We believe that the confluence of those developments will occur in the next decade or so, and we anticipate that our understanding of, and ability to influence the effectiveness of, MTSs will accelerate rapidly. The journey thus far has been interesting, but the road ahead should be even more exciting.

ACKNOWLEDGMENTS We would like to thank Stephen Zaccaro and Michelle Marks for their continued support and encouragement, and in particular, for sharing their thoughts for this chapter.

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McGrath, J. E., Arrow, H., & Berdahl, J. L. (1999). Cooperation and conflict as manifestations of coordination in small groups. Polish Psychological Bulletin, 30(1), 1–14. Millikin, J. P., Hom, P. W., & Manz, C. C. (2010). Selfmanagement competencies in self-managing teams: Their impact on multi-team system productivity. The Leadership Quarterly, 21(5), 687–702. Morgeson, F. P., DeRue, D. S., & Karam, E. P. (2010). Leadership in teams: A functional approach to understanding leadership structures and processes. Journal of Management, 36, 5–39. Murase, T., Carter, D. R., DeChurch, L. A., & Marks, M. A. (2014). Mind the gap: The role of leadership in multiteam system collective cognition. The Leadership Quarterly, 25(5), 972–986. Okhuysen, G. A., & Bechky, B. A. (2009). Coordination in organizations: An integrative perspective. The Academy of Management Annals, 3(1), 463–502. Okhuysen, G. A., & Waller, M. J. (2002). Focusing on midpoint transitions: An analysis of boundary conditions. Academy of Management Journal, 45(5), 1056–1065. O’Sullivan, A. (2003). Dispersed collaboration in a multi-firm, multi-team product-development project. Journal of Engineering and Technology Management, 20(1), 93–116. Ranson, S., Hinings, B., & Greenwood, R. (1980). The structuring of organizational structures. Administrative Science Quarterly, 25(1), 1–17. Resick, C. J., Burke, C. S., & Doty, D. (2012). Multiteam System (MTS) research in laboratory settings: A look at the technical and practical challenges. In S. J. Zaccaro, M. A. Marks, & L. A. DeChurch (Eds.), Multiteam systems: An organization form for dynamic and complex environments (pp. 487–510). New York: Routledge. Rosing, K., Frese, M., & Baush, A. (2011). Explaining the heterogeneity of the leadership-innovation relationship: Ambidextrous leadership. The Leadership Quarterly, 22, 956–974. Salas, E., Shuffler, M. L., & Rico, R. (Eds.) (2014). Pushing the boundaries: Multiteam systems in research and practice (Vol. 16). Bingley, UK: Emerald Group Publishing. Scheerer, A., Hildenbrand, T., & Kude, T. (2014, January). Coordination in large-scale agile software development: A multiteam systems perspective. In System Sciences (HICSS), 2014 47th Hawaii International Conference on (pp. 4780–4788). IEEE. Tajfel, H. (1982). Social psychology of intergroup relations. Annual Review of Psychology, 33(1), 1–39. Taplin, S. H., Foster, M. K., & Shortell, S. M. (2013). Organizational leadership for building effective health care teams. The Annals of Family Medicine, 11(3), 279–281.

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Part vi

Work Design and Interventions

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16 Work Design for Performance: Expanding the Criterion Domain Daniela M. Andrei and Sharon K. Parker

INTRODUCTION The content of every job reflects a series of work design decisions made when the job is first set up or during its evolution over time. Example ‘work design’ decisions include: Which tasks should be grouped together and performed within a job? What (and how many) procedures guide the work of the job incumbent? How much control over work methods does the incumbent have? How does the job connect to tasks carried out by other individuals? Does the work have some degree of mental challenge? Sometimes these work design decisions are conscious and deliberate; other times they are unconscious or indirect. Sometimes these decisions are made by the job incumbents themselves, such as when a person crafts his/her work to reflect personal interests, whereas sometimes the work design arises out of management preferences and/or reflects broader strategic and technological choices. Either way, work design decisions create a configuration of ‘job characteristics’ for each job. Some of the most well-understood job characteristics include job autonomy, job variety, job demands, and social support. Job characteristics, in turn, affect multiple outcomes, including employees’ well-being at work and outside of work, their learning and development, and potentially their performance.

In this chapter, we focus on how work design affects performance. Although major theories of work design identify employee performance as a key outcome of job characteristics, this link has proved more inconsistent and elusive than evidence of the effect of work design on attitudes and well-being (Fried & Ferris, 1987; Griffin, Welsh, & Moorhead, 1981; Humphrey, Nahrgang, & Morgeson, 2007; Parker & Turner, 2002). Parker and Turner (2002) proposed that one source of these inconsistencies is that performance means different things. They therefore recommended that the criterion domain be extended and clarified to understand how work design might affect different types of individual performance. Research and theory on work performance has similarly evolved towards the same need for criterion expansion. Multidimensional, multilevel models provide new opportunities for reconsidering the link between work design and performance at work. Our goal in this chapter is to analyze the relationship between work design and performance by considering different dimensions of work performance that are proposed to be relevant for contemporary and future organizations. Drawing particularly on the performance framework proposed by Griffin, Neal, and Parker (2007), we consider the effect of work design on three types of performance: proficiency,

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adaptivity, and proactivity. We cover existing evidence that links work design with these three performance dimensions, as well as identify gaps in the literature and future research directions. To begin, however, we briefly outline the scope of work design, some history, and its major theories (for more details, see the first edition of this handbook, as well as more recent reviews: Grant & Parker, 2009; Parker, 2014). We then describe the work performance model. Having laid the foundation, we then go on to the main core of the chapter, which is whether and how work design affects multiple dimensions of performance.

SCOPE OF WORK DESIGN Work design refers to ‘the content and organization of one’s work tasks, activities, relationships, and responsibilities’ (Parker, 2014, p. 662). Traditionally this topic was referred to as ‘job design’, but it is increasingly being referred to as ‘work design’ (Morgeson & Campion, 2003; Parker & Wall, 1998) to signify interest not only in the content and organization of individuals’ prescribed and fixed tasks, but also in the more flexible, emergent, and sometimes self-initiated tasks and activities that characterize contemporary work (Ilgen & Hollenbeck, 1991). For example, when a school teacher carries out his/ her work, the teacher will not only educate students in accordance with the prescriptions of the curriculum, but the teacher will engage in any number of other activities (coaching students, dealing with difficult parents, running concerts, etc.) which make up the work role, and these broader activities are integral elements of work design. Work design is a component of organization design, which is more broadly concerned with structure, strategy, systems, processes, and practices of the organization. Work design is shaped by organizational design. For example, if the information system does not enable a flow of information to people at the lowest level of the organization, job autonomy will tend to be low because individuals do not have the necessary information to make sensible decisions. Likewise, work design is shaped by leadership, albeit distinct from it. For example, the level of support received from the management is an aspect of leadership, but the social support that arises from how roles are configured in teams is a core work design issue. Work design is also indirectly shaped by other organizational factors, such as technology and culture, as well as by nationallevel and occupational-level factors (Parker, Van den Broeck, & Holman, 2017).

Work ‘redesign’ refers to changes in the nature and organization of tasks, activities, relationships, and responsibilities. Work redesign can be introduced externally (through restructuring processes, for example) or it can be initiated in a more incremental way by the employees themselves (such as through job crafting). Sometimes work redesign is intended and a deliberate strategy; sometimes work redesign is an unintended consequence such as when new technology changes the nature of tasks.

History and Major Theories To understand work design, it is particularly useful to consider its historical roots. The design of work became a topic of interest during the Industrial Revolution when large numbers of people moved to cities to work in factories. The view emerged that overall effectiveness could be achieved by organizing work into simplified, narrow, and deskilled jobs. ‘Scientific Management’, proposed by Taylor (1911), aimed to systematically identify the most efficient (or best) way to carry out specific tasks by eliminating all unnecessary actions. The manager’s role was to ensure compliance with these best ways, as well as to make decisions and to carry out all thinking aspects of the work. A simplified job implied lower levels of employee skill; therefore employees could learn the job and perform the tasks faster. Also, by reducing all slow or unnecessary movements, employees could perform more tasks within the same amount of time. When moving assembly lines were introduced, employees not only had simplified tasks, but work was automatically moved between different stages resulting in jobs in which the pace was decided by the technology. For a long time, job simplification was the dominant paradigm for work design in manufacturing and beyond (Braverman, 1974). Even after a massive shift from production to services, and after much discussion about the value of enriched jobs, job simplification continued to be prevalent in many organizations. Campion and Stevens (1991) suggested job simplification seems to be the intuitive choice when people are faced with a job design task. Even today, more than twenty years after Campion and Stevens’ study, job simplification seems to be the preferred strategy that naïve participants use in work design simulations (Parker & Andrei, 2014). Despite the way job simplification took hold in industry, evidence surfaced to show that simplified work was associated with high psychological costs like dissatisfaction, alienation and even productivity losses (Braverman, 1984; Fraser, 1947). In the face of these observations, alternative ‘work redesign’ solutions were proposed, mainly job rotation

Work Design for Performance: Expanding the Criterion Domain

(employees regularly switching between equally simplified jobs) and job enlargement (expanding the range of activities that form a given job). Nevertheless, although they were performing more varied tasks, employees were still denied active involvement and decision-making in their jobs. The Two-Factor Theory (Herzberg, 1966) marked the beginning of theory building in the area of work design. This theory proposed that factors related to peoples’ intrinsic motivation (such as opportunity for achievement) are associated with satisfaction, while dissatisfaction arises mainly from extrinsic factors (such as financial rewards). Although the two-factor theory was not empirically supported by later research, from this theory emerged the concept and practice of work enrichment, which continues to be central to many contemporary notions of work design. An enriched job design has challenging and responsible work that offers opportunities for personal achievement, growth, and recognition of one’s efforts (Paul & Robertson, 1970). Importantly, an enriched work design is one in which employees are able to make decisions about, or control key aspects of their work. Therefore this theory challenges the previous focus on a vertical division of labor, and highlights the importance of increased work autonomy. This theory also sparked more attention to motivation as a key mechanism that carries the effect of work design on behaviors, attitudes, and well-being. The focus on motivation was further developed with the Job Characteristics Model (JCM; Hackman & Oldham, 1976). This now dominant work design model identified five core job characteristics: autonomy, skill variety, task identity, task significance, and feedback. These job characteristics were proposed to give rise to three critical psychological states (experienced meaningfulness, knowledge of results, and experienced responsibility) that account for the effects of job characteristics on different outcomes such as work satisfaction, internal work motivation, work performance, reduced absenteeism, and reduced turnover. The strength of these relationships was posited to depend on the level of individual growth need strength, with individuals higher in their desire for personal development benefiting more from an enriched work design. Later on, other moderators were added such as knowledge and skills, satisfaction with work context (Oldham, 1996). The JCM model specifically included performance as one of the outcomes explained by the motivating effects of work characteristics. Effort was the primary assumed mechanism. Basically, according to this model, individuals – especially those with high needs for growth – will exert more effort when they feel they are doing a meaningful job. Much evidence supports the main propositions of the JCM model. Meta-analyses (e.g., Humphrey

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et al., 2007) and longitudinal and quasi-experimental studies (e.g., Griffin, 1991) have shown that job characteristics predict a range of outcomes such as job satisfaction, commitment, and well-being, and that the critical psychological states (especially meaningfulness) are important mediators of these relationships. However, other aspects of the model have proved more problematic, such as a rather narrow range of job characteristics, mediators, and outcomes, and the somewhat inconsistent positive relationships between work design and performance outcomes (Parker & Turner, 2002). These deficits stimulated several extensions to the original JCM (Humphrey et  al., 2007; Morgeson & Campion, 2003; Parker, Wall, & Cordery, 2001). For example, scholars now often consider sets of job characteristics beyond the core motivational characteristics, such as knowledge characteristics, social characteristics (including interdependence), and characteristics of the work context (e.g., Grant, 2007; Kiggundu, 1983; Morgeson & Humphrey, 2006). Mechanisms beyond intrinsic motivation have also been advocated, such as prosocial motivation (Grant, 2007) and knowledge development (e.g., Parker, Wall, & Cordery, 2001). There has been some expansion to outcomes considered, such as the inclusion of creativity and safety (Parker, Wall, & Cordery, 2001). In addition to the JCM, a specific theory – the job demand-control model (Karasek, 1979) – was developed at around the same time to better understand stressful aspects of work. This model predicted that demands and control interact such that control can buffer the negative effects of demands, and that strain will be the greatest when demands are high and control is low. Later, Karasek and Theorell (1990) recognized social support as another antidote to job demands. The job demandresources model (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001) built on Karasek’s model to recognize that other work characteristics in addition to control and support (e.g., job security) might serve as job resources. Additionally, and relevant to this chapter, the demand-resources model considers not only strain processes, but also motivational processes, such as the idea that job resources lead to high levels of performance via engagement. A vast amount of work design research has tested the job demands-resources model in recent times (Parker, Morgeson, & Johns, 2017). Overall, however, relative to the effort put into expanding the job characteristics, or to the research unpacking strain effects of work design, performance as an outcome of work design had less systematic attention. Most studies have tended to focus on overall definitions/operationalizations of work performance (as seen in the Humphrey et  al., 2007 meta-analysis). When different performance outcomes have been considered in the

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work design literature (such as creativity, proactive behavior, extra-role performance, etc.), they tend to be considered independently, and as unrelated to one another, which means we have few insights into how work design might support different performance outcomes at the same time. Finally, although there are a few exceptions we discuss later, the focus is still on intrinsic motivation as the core mechanism by which work characteristics affect performance (Humphrey et al., 2007). Shortly, we propose an integrated approach for understanding the performance implications of work design, including a focus on differential mechanisms. To set the scene for this discussion, we briefly consider the concept of work performance.

WORK PERFORMANCE Work performance is about work behavior that contributes to organizational goals (Campbell, McCloy, Oppler, & Sager, 1993). For example, Viswesvaran and Ones (2000) defined job performance as scalable actions, behavior (and in some cases, outcomes) that employees engage in or bring about that are linked with and contribute to organizational goals (p. 216). Traditionally, work performance has focused on how well employees perform the core duties in their job description (Griffin et al., 2007). This focus was based on the idea that all individual behaviors that contributed to the overall organizational goals could be comprehensively prescribed within employees’ job descriptions (Murphy & Jackson, 1999). However, significant changes in society, organizations, and work itself have challenged this rather static view of organizational roles (Ilgen & Pulakos, 1999). The increased uncertainty, complexity, and interdependence of organizational life (Griffin et  al., 2007) means that tasks and duties circumscribed to a role can change quickly, that employees often have to go beyond their job descriptions in order to meet goals, that employees aren’t only responsible for themselves but also for the actions and outcomes of people they work with, and that employees often need to drive change in their work and their organization. Reflecting these changes and challenges, the approaches on work performance started to shift. A key development has been the identification of a range of performance behaviors considered critical for organizational success. New constructs such as contextual performance (Borman & Motowidlo, 1993), organizational citizenship behavior (Smith, Organ, & Near, 1983), adaptive performance (Pulakos, Arad, Donovan, & Plamondon, 2000), and

proactive behaviors (Parker, Williams, & Turner, 2006) have been proposed as necessary for ensuring a contemporary organization’s ability to attain its goals within a dynamic, unpredictable market. A further development involved recognizing that individuals can contribute to higher-level outcomes, beyond the goals of their immediate job. For example, with work becoming increasingly team-based, an individual’s performance as a team member was recognized as distinct from carrying out their individual tasks. A performance framework that synthesizes these two developments is that proposed by Griffin et al. (2007; see Table 16.1). Two characteristics of the context, uncertainty and interdependence, are placed at the core of this model as they are seen to shape or constrain the type of behaviors that will be expected and valued by organizations. The uncertainty dimension is used to classify the different existing performance constructs into three types of behaviors that reflect increasing capacity of dealing with uncertainty: proficiency, adaptivity, and proactivity. Proficiency refers to behavior that fulfills the requirements that are prescribed in one’s job role. Proficient behaviors can be specified and anticipated in advance. However, in uncertain and unpredictable contexts, it is not possible to pre-specify all required behaviors, and two forms of emergent behavior are also important. First, adaptivity refers to employees responding to, coping with, and supporting change. Second, proactivity refers to behaviors of initiating change in a self-starting, future-directed manner (Griffin et al., 2007). Adaptivity and proactivity are argued to be especially important in unpredictable operating environments characterized by customization, change, and innovation. These three types of behaviors are then crossclassified against a further dimension that considers the interdependence of the work context. That is, proficiency, adaptivity, and proactivity can all be executed in relation to one’s role as an individual, team-member, and organizational-member. For example, an individual can be adaptive in relation to their core individual job, such as coping well with a change in one’s core tasks (task adaptivity); in relation to their role as a team member, such as adjusting to new team members (team-member adaptivity); and in relation to their role as a member of the wider organization, such as adapting to a site-wide downsizing program (organizationmember adaptivity). The result of considering both uncertainty and interdependence dimensions is a model of nine types of positive work role behaviors that integrates constructs previously developed in the work performance literature, while at the same time bringing new aspects into focus. For example, Griffin et al. (2007) identified citizenship behaviors such as helping as an example of teammember proficiency: such behaviors are proficient

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Table 16.1  Model of positive work role behaviors Individual work role behaviors

Individual task behaviors Behavior contributes to individual effectiveness Team member behaviors Behavior contributes to team effectiveness rather than individual effectiveness Organization member behaviors Behavior contributes to organization effectiveness rather than individual and team effectiveness

Proficiency Fulfills the prescribed or predictable requirements of the role Individual task proficiency e.g., ensures core tasks are completed properly Team member proficiency e.g., co-ordinates work with team members Organization member proficiency e.g., talks about the organization in positive ways

Adaptivity Proactivity Copes with, responds to, and Initiates change, is selfsupports change starting and futuredirected Individual task adaptivity Individual task proactivity e.g., adjusts to new e.g., initiates better way of equipment, processes, or doing core tasks procedures in core tasks Team member adaptivity Team member proactivity e.g., responds constructively e.g., develops new methods to team changes (e.g., to help the team perform new members) better Organization member Organization member adaptivity proactivity e.g., copes with changes in e.g., makes suggestion to the way the organization improve the overall operates efficiency of the organization

Note: Reprinted from ‘A new model of work role performance: Positive behavior in uncertain and interdependent contexts’ by M. A. Griffin, A. Neal, and S.K. Parker, 2007, Academy of Management Journal., 50, p. 330. Copyright 2007 by Academy of Management. Reprinted with permission.

because they can be readily anticipated as necessary within the context; and they are team-member behaviors because they support the teams within the context. This framework helps to synthesize the increasing number of constructs (sometimes overlapping) proposed to study different aspects of work role performance (for example, see Carpini, Parker, & Griffin, in press). We adopt this framework to analyze the relationship between work design and work performance. Previous reviews on this relationship have focused on narrower classifications of performance (task and contextual – Parker & Turner, 2002). In the subsequent sections we analyze existing knowledge related to the relationship of work design with each type of work performance behavior: proficiency, adaptivity, and proactivity. For each dimension, we consider the evidence-base for each level of individual performance: individual, teammember, and organizational-member. Given the complexity of our endeavor, we will focus on individual level variables, although when appropriate, we draw on team-level studies to infer knowledge about the different levels of individual behavior.

WORK DESIGN EFFECTS ON TASK PROFICIENCY Proficiency refers to employee behaviors that are required within the role that can be anticipated a

priori and therefore prescribed, such as in a job description (Griffin et  al., 2007). Proficiency includes behaviors that are expected within the individual’s core job (individual task proficiency), their role as a team member (team-member proficiency, such as helping colleagues), and their role as a member of the organization (organizationmember proficiency). It is difficult to be precise about how work design affects task proficiency because work design research has not traditionally used a fine differentiation of performance outcomes. However, given the fact that proficiency behaviors map very well on the more classical concepts of job performance and contextual performance (Griffin et al., 2007), most of the empirical papers looking at the link between work design and performance outcomes operationalize performance in terms of some kind of proficiency, primarily individual task proficiency. As noted above, early meta-analyses and reviews (Fried & Ferris, 1987; Griffin et  al., 1981; Kelly, 1992; Parker & Turner, 2002) highlighted that work design can affect performance as theorized (e.g., self-managing teams tend to be associated with stronger performance: Wall, Corbett, Martin, Clegg, & Jackson, 1990). However, compared to outcomes like job satisfaction, the work design–performance association is more rarely investigated (as witnessed in the number of papers looking at this link in Humphrey et  al.’s (2007) meta-analysis), and the findings are more inconsistent. Humphrey

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et  al.’s (2007) meta-analysis of 259 studies sheds some light on the inconsistency: the effects of work design are weaker and more inconsistent when performance is operationalized using objective indicators (usually production figures or other distal indicators of performance that are not so easily controllable by employees) relative to subjectively measured performance (usually by assessments from supervisors or peers). Using the latter indicator, Humphrey et al. (2007) reported a medium positive association between an enriched work design and performance, with autonomy, task variety, and task significance having the strongest relationship (ρ = .23 for each), and with motivational job characteristics all together predicting 25% of the variance in performance. Social characteristics of work (such as interdependence) had incremental predictive power (ΔR2 = .09). Notably, autonomy was the only motivational job characteristic that was positively related to objectively measured performance (ρ = .17). Although we are not provided with much detail about how these studies measured performance, examining a sample of the papers suggests that typically they used assessments by supervisors on individual proficiency-oriented criteria (such as effort, quality of work, for example see Baird, 1976; Griffin, 1991). Even when assessments used overall evaluations of job performance, existing evidence shows that the factors contributing to them are mainly core task performance and citizenship behavior (Johnson, 2001; Rotundo & Sackett, 2002; Viswesvaran, Schmidt, & Ones, 2005), which constitute individual and team-member proficiency respectively. The main mediator of this link between work design and proficiency was meaningfulness. It seems that when jobs lack enriched work characteristics, individuals’ sense of meaning is impaired, and therefore individuals put lower levels of effort and care into their core work tasks and into their team responsibilities. Another outcome considered in team responsibilities meta-­ analysis was turnover. Turnover has some parallels with notions such as organizational loyalty and civic virtue (Griffin et al., 2007); therefore turnover might be considered a negative indicator of organization-member proficiency. The meta-­analysis showed that turnover intentions are predicted by social characteristics of work (ΔR2 = .24), rather than motivational characteristics, suggesting that the further we go with embedding the required behaviors within team and organizational social contexts, the more social characteristics might increase their explanatory power. Interestingly the effect of social characteristics on turnover was not mediated by meaningfulness or other motivating states, suggesting that the mechanisms explaining how work design affects more contextually embedded types of proficiency (that

is, organizational member proficiency) go beyond the traditional motivational considerations. One challenge with this meta-analysis is that it is based mostly on studies conducted with crosssectional research designs. In their review of more rigorous studies, Parker and Turner (2002) provided further evidence that work design affects proficient performance. Field experiments (e.g., studies conducted by Griffin, 1991; Jackson & Wall, 1991; Leach, Wall, & Jackson, 2003; Wall et  al., 1990) have looked mainly at effects of work redesign interventions on overall performance criteria (such as overall evaluations of performance, overall machine downtime, overall machine utilization), and have shown good support for a link between job design and performance. As an example, Griffin (1991) showed how a work redesign aimed at increasing some of the motivational job characteristics (job variety, authority over routine decisions, feedback, and customer interaction) increased job performance, measured in terms of quality, quantity, and overall performance. This performance effect, however, took time to materialize (it showed significant improvements only after 24 and 48 months), suggesting insufficient consideration of time lags might be another explanation for inconsistent findings for performance (see also Parker, Andrei, & Li, 2014, on this topic). However, the same intervention did not affect turnover, which might be because – as we suggested above – turnover is more strongly related to social characteristics of work which were not targeted in this intervention. More recent longitudinal studies also support our conclusion. For example, using a measure of work performance capturing mostly individual proficiency (although with some elements of adaptivity, as we discuss in the next section), Morgeson, Delaney-Klinger, and Hemingway (2005) showed that, even when more proximal antecedents of performance like cognitive ability are accounted for, autonomy predicted individual proficiency. In this study, the mechanism was role breath. Recent studies have emphasized the role of other job characteristics Humphrey et al.’s (2007) for performance. A series of well-crafted experiments by Grant and his colleagues (Grant, 2007; Grant, Campbell, Chen, Cottone, Lapedis, & Lee, 2007) showed that manipulations of task significance (via connecting employees with the end users of the work) affect proficiency-oriented types of performance (employee effort/work hours, sales). Moreover, prosocial motivational mechanisms rather than intrinsic motivation were highlighted as mediators for this relationship, such as perceived social impact and perceived social worth. This series of studies show that, even tasks characteristics that did not emerge as contributors to performance in the meta-analysis discussed above can have a strong effect on proficiency in

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some situations, albeit perhaps via different psychological processes. All these studies provide accumulating evidence that work design features are important for individual proficiency, or core task performance. In regard to proficiency from a team or organizational perspective, a meta-analytic study on citizenship behavior (Podsakoff, MacKenzie, Paine, & Bachrach, 2000) included both team-member proficiency dimensions of citizenship (altruism, courtesy) and organizational-member type of proficiency (civic virtue, generalized compliance, sportsmanship). Although relatively few work design variables were considered in this meta-analysis, routinization (an indicator of a simplified work design) was negatively associated with both categories of proficiency, whereas task feedback and intrinsically motivating tasks had positive associations with these outcomes. This data supports the importance of an enriched job design for team-member and organizational-member proficiency. The meta-analysis did not include social characteristics of work, although leader support predicted both types of proficiency, and team cohesiveness (which might imply a higher level of peer support) was positively associated to all forms of citizenship behavior, supporting our idea that social work characteristics can be important when considering socially embedded forms of proficiency. In a followup study, McAllister, Kamdar, Morisson, and Turban (2007) investigated different role perceptions related to helping (team-member proficiency) and taking charge (individual task proactivity). Although they did not focus on specific work design characteristics, they identified mechanisms that have potential relationships with work design. For helping behaviors, role breadth and perceived instrumentality played the most important role, whereas taking charge was influenced more by self-efficacy beliefs and discretion beliefs. Focusing on motivating task characteristics, Todd and Kent’s (2006) cross-sectional study showed direct and indirect (via job satisfaction) links between task characteristics and citizenship. Social job characteristics were not included in the design. Moving to the few studies that investigate the effects of work design variables on different types of proficiency simultaneously, Bakker, Demerouti, and Verbeke (2004) looked for different patterns of relationship between job characteristics (seen as resources and demands) with peer ratings of individual proficiency (or ‘in-role’ performance), team-member proficiency, and to some extent organization-member proficiency (referred to as ‘extrarole’ performance). Their results showed that job demands (such as workload and work–family interference) predicted lower individual proficiency via exhaustion as well as directly, whereas job resources (similar to motivational and social characteristics)

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had an impact on individual task proficiency only through reduced exhaustion. Therefore, individual task proficiency might be more directly influenced by negative aspects of work, while the positive effect of job resources might be indirect through different mechanisms such as (reducing) exhaustion. The same study showed that team-member and organizational-member proficiency outcomes (extra-role performance), were impacted only by job resources via an engagement mechanism. The job resources that were examined as predictors of engagement, and hence team/organizational member proficiency were autonomy, social support, and possibilities for development. However, even these authors admit that the variance in both individual and team/organization member proficiency explained by their model, although higher than previous studies, is still very low (8%). One reason they consider is inadequate measurement of behavior. We tend to agree, as we can see that in this case the behaviors targeted at the overall organizations are not well differentiated from those targeted at the team-members. Moreover, their individual proficiency measure is not well anchored in behaviors, which makes it more susceptible to bias, especially when peers act as assessors (Viswesvaran et  al., 2005) and they considered a reduced number of possible job characteristics. Overall, this study seems to suggest that there is value in differentiating between different types of work performance, as this differentiation allows us to better assess relationships and mechanisms, but even more differentiation and precision is needed if we want to gain more explanatory power over these types of outcomes. Piccolo, Greenbaum, Den Hartog, and Folger (2010) also distinguished between different dimensions of proficiency. These authors examined how leadership affects work design (autonomy and significance), and how, in turn, work design influences effort, task performance, and citizenship. Interestingly, their results showed that the path between autonomy and effort was not significant, although task significance was associated with greater effort. Also, effort was shown to influence both task performance and citizenship – although the effect was not large – suggesting the there are other mechanisms by which autonomy and job characteristics exert their influence, or that the effect of autonomy might be direct (as shown in previous research). However, only a limited set of motivating job characteristics was considered here, and social characteristics were not included. Another explanation for the above results is suggested by the study of Dodd and Ganster (1996) showing that the effects of the motivational work characteristics are not always additive, but sometimes interactive. Their results show that autonomy matters for performance only when task variety

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is high. When task variety is low, having autonomy does not increase performance. Similarly, task feedback contributed to performance only when autonomy was high. When autonomy was low, feedback did not influence performance. Performance here was operationalized in terms of quality and it is consistent with individual proficiency, but it is important to look at similar interactive effects for other dimensions of proficiency given that literature on teamwork provides empirical support for interactive effects between autonomy and other characteristics such as interdependence (Janz, Colquitt, & Noe, 1997) and task uncertainty (Cordery, Morrison, Wright, & Wall, 2010) on team-level outcomes. Indeed, most of the papers discussed so far assume an additive effect of work design characteristics on individual proficiency, but recent reviews and theoretical developments are starting to call for an increased attention towards interactive effects or configurations of work characteristics. Johns (2010), for example, drew attention to ‘deadly combinations’ of job characteristics such as low autonomy and high significance (see also Parker, 2014). An important issue we have not yet considered in depth is ‘why’ work design affects proficiency, or the mechanisms underpinning this relationship. Proficiency is about fulfilling expectations, or doing what is required. Proficiency thus requires that an individual knows the expectations (role clarity), and has the knowledge, skills/abilities, motivation, and opportunity to fulfill them. When considering individual task proficiency (task performance) it is typically assumed that work design affects proficiency because it enhances motivation, especially intrinsic motivation (such as believing that work is meaningful). Thus, more intrinsically motivated workers try harder, or put in greater effort and work to a higher standard, when completing their work requirements. The notion of psychological empowerment is similar to intrinsic motivation, encompassing feelings of meaning, impact, competence, and choice (Thomas & Velthouse, 1990). Psychological empowerment has been shown to predict proficiency-oriented outcomes, including employee effectiveness, productivity, and role performance (Chen & Klimoski, 2003; Koberg, Boss, Senjem, & Goodman, 1999), and studies have identified that work design is a key predictor of psychological empowerment (Wallach & Mueller, 2006). The relational approach to work design highlights the mechanism of prosocial motivation (Grant, 2007; Grant et  al., 2007) that is, work design stimulates individuals’ desire to make a difference for other people, which in turn prompts greater effort. These latter results are consistent with the proposed role of identified motivation, or

integrated regulation, for understanding effects of work design (Parker & Ohly, 2008). In terms of motivational mechanisms for teammember proficiency and organization-member proficiency, both are concerned with fulfilling expectations, but in this case the expectations incorporate going ‘beyond’ one’s individual focus. Reciprocity might be an important mechanism by which social characteristics affect team-member and organization-member proficiency (Podsakoff et  al., 2000). For example, employees who perceive support being offered to them by colleagues and supervisors are likely to want to reciprocate by investing more effort in performing tasks that will help the supervisor, their team, or the organization overall. This is consistent with the idea that work design generates organizational commitment, which has been shown to be especially important for team-member and organizationalmember proficiency (Griffin et  al., 2007). When looking specifically at team-member and organizational-member proficiency (circumscribed by extra-role performance or OCB) mechanisms such as engagement or organizational commitment (Bakker et  al., 2004) or affective commitment (Galletta, Portoghese, & Battistelli, 2011) emerge as important mediators of effects. Beyond motivation, many scholars have argued that work design can affect performance through promoting learning and the development of expertise. Job control and job complexity facilitate a deeper understanding of the task at hand (Frese & Zapf, 1994), as well as broader and more integrated understanding of one’s organizational system and the context. Interesting insights into this link can be provided by the work of Leach and colleagues indicating that work design interventions (targeted around increased autonomy, support, feedback) are definitely associated with gains in terms of job knowledge (Leach et  al., 2003). Moreover, this mechanism has further positive effects on both individual task proficiency (Leach, Jackson, & Wall, 2001) and team effectiveness (Leach, Wall, Rogelberg, & Jackson, 2005). The latter study suggests that enriched work, especially that which is team-based such as self-managing teams, is likely to help individuals learn teamwork knowledge, skills, and abilities such as helping that are critical for team-member proficiency. A final mechanism, beyond motivation and learning, is what has been referred to as the ‘quick response’ mechanism. This is likely to be especially relevant for individual proficiency and can be best understood in terms of knowledge application (Jackson & Wall, 1991; Wall et  al., 1990; Wall, Cordery, & Clegg, 2002). Essentially, this mechanism refers to the fact that when employees are empowered to deal with issues and problems

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without having to consult with or call for superiors or specialists, lost production time can be reduced and therefore proficiency increased (assuming that said employees have the adequate knowledge and skills to deal effectively with those issues). The mechanism seems to be again related to the facilitation of the execution of knowledge or expertise that has been explicitly or implicitly acquired (Wall, Jackson, & Davids, 1992). Overall, motivation is likely to underpin proficiency, with the type of proficiency potentially affecting the type of motivation (e.g., intrinsic motivation for individual proficiency, reciprocitybased motivation for team-member proficiency, organizational commitment for organizationmember proficiency). Similarly, different types of learning promoted by work design might be relevant for different types of proficiency. The quick-response mechanism most likely applies particularly to individual proficiency. Overall, however, relatively little attention has been given to understanding how work design affects teammember and organizational-member task proficiency. Parker (2014), for example, theorized how self-managing teams might, in the somewhat longer term, foster employees’ relational identity development, which in turn would be expected to affect proficiency directed towards the collective. In sum, our review supports the insights obtained from the Humphrey et  al. (2007) metaanalysis: that is, as Parker and Turner (2002) concluded more than 10 years ago, evidence suggest beneficial effects of an enriched work design on individual task proficiency. Our analysis also hints at the possibility that social work characteristics are especially important for the more interdependent forms of proficiency, or team-member and organization-member proficiency, but this needs further testing.

WORK DESIGN EFFECTS ON ADAPTIVITY Adaptivity refers to coping with, responding to, and supporting changes (Griffin et  al., 2007). Individual task adaptivity involves behaviors aimed at dealing with uncertain situations in one’s core role (such as changes in tasks caused by new technology). Team-member adaptivity includes behaviors by which people cope, respond to, or support changes in their roles as team members, and is similar to the notion of interpersonal adaptability (Pulakos et al., 2000) or co-ordinated interdependence (Kozlowski, Gully, Nason, & Smith, 1999). Organization-member adaptivity refers to employees’ adapting to changes that take place at

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the organizational level (e.g., as a result of restructuring, acquisitions and mergers, restructuring of business processes). Adaptivity requires knowledge (e.g., understanding the new requirements), skills (being able to change), motivation (willingness to do things differently), and opportunity (the chance to apply knowledge to cope with the change). Several of these aspects might be facilitated by work design, for example, the quick response mechanism (promoted by job autonomy) is a form of opportunity for adaptivity. Nevertheless, although adaptive processes have sometimes been used to explain why work design affects proficient or overall job performance (Cordery et  al., 2010; Wall et  al., 1990), empirically, adaptivity is the least explored performance outcome of work design. This point is consistent with a lack of attention to adaptivity as a type of performance in the wider literature (Carpini et al., 2017) as well as a reduced emphasis within the adaptive performance literature on work related and contextual predictors (Jundt, Shoss, & Huang, 2015). When elements of adaptivity have been investigated as an outcome of work design, they are usually merged with proficiency or proactive behaviors (such as the operationalization of performance used by Morgeson et al., 2005). The consequence is we don’t really know whether work design affects adaptivity, or whether it affects it in ways distinct from other outcomes. In this section we review the limited research that exists, and set out some future directions. Autonomy is theorized to play a central role in facilitating adaptive behavior. Sociotechnical systems theory highlights how autonomy enables individuals to deal with variances, or uncertainties, at the source (Cherns, 1987). In a study looking at managerial discretion in Chinese international joint ventures, Yan, Chong, and Mak (2010) showed that management autonomy predicted firm performance. These authors argued that autonomy supports quick adaptation to unanticipated changes in the markets in which the firms operate. Moreover, freedom in decision-making allows managers to deal with critical developments in the context without waiting for approval from the parent firm, which speeds up responsiveness to change (Takeuchi, Shay, & Li, 2008). In a similar vein, Cordery et al. (2010) showed that autonomy (increased by introducing a new work design based on self-managing teams in a water management corporation) facilitated team performance, especially when uncertainty was high. The focus of the study was on team performance, but these authors suggested that performance effects were obtained through supporting teams’ better adaptation to unplanned situations. As a further example, for people working in jobs that require

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frequent customer contact, structural empowerment seems to help employees to better cope with situations of customer aggression (Ben-Zur & Yagil, 2005). In situations that require adaptive performance, having adequate knowledge is insufficient: employees also need to be able to actually solve customer problems. The above line of thinking relates to the quick response mechanism referred to earlier, but additionally highlights that this process includes not only a faster response that is facilitated through autonomy, but often a better quality response because the individual making the decisions is doing so with the benefit of local expertise. In other words, one mechanism by which greater autonomy affects adaptive performance is that it allows the application of skills, knowledge, and expertise (Langfred & Moye, 2004; Wall et  al., 2002), including applying tacit knowledge (Wall et al., 1992). Showing findings consistent with this reasoning, Parker (2003) illustrated how autonomy is a key ingredient for skill utilization: reduced levels of autonomy associated with the introduction of lean production systems were associated with a decrease in self-reported skill utilization. As described earlier, work design can also promote learning and development. Autonomy, task and skill variety, feedback (both from peers and customers), interdependence, leader and peer support, specialization, task identity are job characteristics expected to facilitate learning and development (Frese & Zapf, 1994; Morgeson & Humphrey, 2008; Parker, 2014). For example, work design promotes self-efficacy (e.g., Parker, 1998), and individuals with higher self-efficacy are more likely to learn and develop skills at work (Morgeson & Humphrey, 2008), and more likely to engage in adaptive types of job crafting (Berg, Wrzesniewski, & Dutton, 2010). In the longer term, such learning might mean the development of enhanced cognitive, self, social, and affective complexity that is required by complex work environments (Parker, 2014), allowing employees to work and interact with others in adaptive ways, such as anticipate problems and challenges, and better cope/respond to them (Wall et al., 2002). A further perspective on the role of autonomy for adaptivity is provided by the job crafting literature. Although crafting tends to be seen as a form of proactive work behavior (e.g., Grant & Parker, 2009), a process perspective (Berg et  al., 2010) highlights important adaptive actions that employees engage in whilst trying to proactively alter their roles. In their qualitative study, Berg et al. (2010) described ‘adaptive moves’ involved in the job crafting process, and highlighted that it is the perceived freedom to alter the boundaries of one’s job that is important for adaptivity rather

than formal autonomy and power. Perceived freedom, and hence adaptivity, is likely to be reduced by higher levels of interdependence with other roles and by greater visibility of one’s role. For example, in conditions of high interdependence when one’s actions have the possibility of affecting the way other people do their work, adaptive and proactive behaviors might be inhibited. In contemporary organizations it is becoming increasingly important that adaptivity is achieved alongside proficiency and indeed proactivity, even when these different outcomes might not appear compatible (Parker, 2014). Insights for individual adaptive performance can be obtained from the ambidexterity literature (O’Reilly & Tushman, 2008; Raisch & Birkinshaw, 2008) looking at organizations that successfully promote a simultaneous focus on both control (exploitation) and flexibility (exploration). Parker (2014) highlighted the importance of an enriched work design for achieving required individual flexibility: having task variety, task identity, and task significance empower individuals to decide when they need to engage in task exploration (flexibility) or to just focus on execution. Parker further identified social characteristics (such as increased contact with customers, feedback, or support from leaders) as potentially promoting the flexibility required in ambidexterous organizations. In a similar vein, Gibson and Birkinshaw (2004) argued that effective performance in ambidextrous environments requires employees to be empowered so they can judge independently how to divide their efforts amongst competing demands. Going further than empowerment, Parker (2014) also argued that individuals working in ambidextrous contexts require a level of behavioral and cognitive complexity that matches the complexity of the environment; with the development of this complexity being facilitated by enriched work designs. For example, Leach et al. (2003) showed how autonomy coupled with enhanced feedback and support can result in enhanced cognitive complexity, defined in terms of the aquisition of knowledge, and more so amongst novice operators. A classic longitudinal study also supported the idea that task complexity (together with autonomy in the form of reduced supervision) predicted employees’ intellectual flexibility, even when controlling for the levels of these variables assessed 20 years earlier (Schooler, Mulatu, & Oates, 2004). Some research hints at the possible role of social characteristics of work in developing requisite complexity. Parker and Axtell (2001) revealed the role of job autonomy in enhancing employees’ tendency to adopt others’ perspectives; and Parker (2014) theorized such processes might in the long term promote enhanced epistemic cognition.

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Beyond learning and development, affective mechanisms have the potential to play an important role for individual adaptive performance. First, there is clear evidence that work design can promote positive affect at work, such as in the literature on engagement (Bakker et  al., 2004; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001). Second, there is extensive support for the role of positive affect for creative problems solving (Amabile, Barsade, Mueller, & Staw, 2005; Isen, Daubman, & Nowicki, 1987); a core element of adaptive performance (Pulakos et  al., 2000). Likewise, positive affect broadens thought–action repertoires (Fredrickson & Branigan, 2005) that are likely to help one to cope with change and uncertainty. Affect can also facilitate co-operation (Wright & Staw, 1999), which means that work design could promote both team-member and organization-member adaptivity; both forms of behavior that contain an inherent element of co-operation. These more interdependent behaviors might also be promoted by knowledge mechanisms. For example, autonomy has been related to the development of more integrated understanding or bigger picture perspective about the overall organizational system (Lawler, 1992; Parker, Wall, & Jackson, 1997), which arguably might support both team-member and organization-member adaptivity. A further mechanism by which work design might shape team-member and organization-member adaptivity is that individuals develop a broader sense of their responsibilities, which in turn leads them to assume ownership for higherlevel goals. When more aspects or broader goals are integrated in employees’ representation of their own role, higher flexibility is expected, not only at the task-level but also at the team and organizational-member level. These assertions are partially supported by preexisting data regarding role breath as a mechanism of work design. For example Fried, Hollenbeck, Slowik, Tiegs, and Ben-David (1999) highlighted how job autonomy leads to greater flexibility in the way employees define their own role. Moreover, Parker (Parker, 1998; Parker et al., 1997) highlighted that this link is particularly important in dynamic and uncertain environments when employees are called upon to increase their ownership of organizational problems but also to try and master different tasks or different ways to go about their tasks. Indeed, Morgeson et  al.’s (2005) study included some adaptive dimensions in their assessment of performance (e.g., problem solving, learning), and showed that job autonomy affected performance via role breadth, offering further support to the idea that one mechanism by which autonomy promotes team-member and organization-member

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adaptivity is that it facilitates individuals’ taking on a broader set of responsibilities. Beyond autonomy, other job characteristics are expected to play a role in facilitating adaptivity at multiple levels, although theory and empirical support for them is scarce. For example, Harrison and Humphrey (2010) argued that, within a team context, random assignment of employees to both teams and tasks might facilitate the development of an equal distribution of skills across roles that, in turn, will eventually build the capability to respond to changes in teams. Although proposed to act within a team context, there are indications that these characteristics are important for individual adaptivity. Task and skill variety create a deeper understanding of the task that allows employees to choose better strategies in dealing with new/unexpected situations (Frese & Zapf, 1994; Wall, Jackson, Mullarkey, & Parker, 1996). Having a wider repertoire of actions and expertise help people to better and more flexibly cope with changes in the way the overall organization operates. Interestingly, task specialization could have different effects at different levels. It might facilitate individual task adaptivity due to that deeper understanding of the tasks at hand, while for team and organizational member adaptivity, specialization might be seen as a possible inhibitor of adaptive actions due to reduced distribution of skills across roles (Harrison & Humphrey, 2010). At the team level, Harrison and Humphrey (2010) theorized a link between work design and shared mental models or shared cognition. They proposed that greater skill and task variety would contribute to more shared cognition and hence, improved performance. The way they discussed performance as including adaptive processes, we are tempted to agree that this mechanism might be especially relevant for one’s adaptivity as a team member. Having an accurate mental representation of the team task and its interdependencies will allow employees to react more quickly and accurately to any changes or challenges during teamwork. Their arguments seem to point to the idea that forms of adaptivity that are more embedded in the social context (team-member and organizational-member adaptivity) rely more on cognitive aspects such as mental models and transactive memory systems. Therefore a good way to start expanding our understanding of these relationships with adaptivity is to look at work designs that facilitate these aspects (such as work designs with task and skill variety, but also complexity, interdependence, feedback). An earlier similar argument has been made for the individual task adaptivity. Wall and Jackson (1995) showed that, when employees have the autonomy to deal with disruptive events at the source and to rectify

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problems by themselves, they develop anticipatory knowledge through repeated observations of causal relationships. This anticipatory knowledge plays a key role for more adaptive and proactive forms of work role behaviors. In sum, although specific studies linking work design and adaptivity are scarce, existing data and theoretical arguments converge on the idea that an enriched work design, especially autonomy, supports adaptive behaviors. Nevertheless, it appears that other work characteristics might be relevant, and these might even differ again when considering team- or organization-member adaptivity (for example, specialization that might facilitate individual task adaptivity but inhibit team-member adaptivity). All in all, more work is needed to understand how work design supports adaptivity within the work place (Harrison & Humphrey, 2010; Johns, 2010; Jundt et al., 2015; Parker, 2014). It appears that the role of work design in facilitating learning and development might be an especially important process for adaptivity, albeit supported by the motivation to actually apply that learning.

WORK DESIGN EFFECTS ON PROACTIVITY Calls to expand the criterion domain for work design research (e.g., Grant & Parker, 2009; Morgeson & Humphrey, 2008; Parker & Turner, 2002; Parker, Wall, & Cordery, 2001) have partly been answered with an increased interest in how work design shapes employee proactivity. In their framework of work role behavior, Griffin et  al. (2007) argued for the particular importance of proactive behavior, defined as self-starting and anticipatory change, for dealing with dynamic and unpredictable work environments. Individual task proactivity is engaging in proactive behavior to change one’s individual circumstances at work (their role, their work situations, or even themselves). Team-member proactivity refers to proactive behaviors that an individual undertakes in order to change circumstances within their work team (the way the team operates, or team situation). Last but not least, organization-member proactivity describes those self-starting, future and changeoriented behaviors directed at influencing/modifying the way the overall organization works or the situation an organization is confronted with. Existing literature highlights a consistent relationship between work design and individual task proactivity. Most attention has been given to the work design variable of job autonomy (Ohly & Schmitt, 2017). For example, perceived control positively predicts personal initiative (Frese, Fay,

Hilburger, Leng, & Tag, 1997), perceived discretion in one’s role predicts individual taking charge behavior (McAllister et al., 2007), job autonomy predicts proactive idea implementation and proactive problem solving (Parker et  al., 2006), and autonomy predicts suggestions for improvement (Axtell, Holman, Unsworth, Wall, Waterson, & Harrington, 2000). A recent meta-analysis including 163 independent samples (N = 36,079) found positive and significant relationships of job control with several proactivity concepts (Tornau & Frese, 2013). As to why job autonomy matters, research suggests autonomy can activate each of the three motivational states argued to be important for proactivity (Parker, Bindl, & Strauss, 2010). First, ‘can do’ motivation refers to individuals’ belief they can be proactive (self-efficacy) and/ or that their actions have agency (control orientations). There is substantial evidence to link self-efficacy to proactive behaviors. For example, self-­efficacy beliefs were related to higher taking charge measured by peer-report (Morrison & Phelps, 1999), and higher self-ratings of personal initiative (Frese, Garst, & Fay, 2007). Evidence is even more consistent when it comes to the role of role breadth self-efficacy, defined as individual beliefs about their capabilities to carry out a broader and more proactive role (Parker, 1998). Role breadth self-efficacy predicts many different types of proactive behaviors, such as suggesting improvements (Axtell et al., 2000); proactive problem solving and idea implementation (Parker et  al., 2006); and taking charge, individual innovation, problem prevention, voice, issue selling, and strategic scanning (Parker & Collins, 2010). It is not surprising that recent meta-analytical results support general self-efficacy beliefs and role breadth self-efficacy (as well as locus of control) as correlates of proactivity behaviors, with the coefficients for role breadth self-efficacy being higher than those for general self-efficacy (Tornau & Frese, 2013). These findings are in line with research on other complex organizational behaviors showing that specific forms of self-efficacy are more predictive than general forms. In turn, evidence suggests that enriched job designs, likely because they allow opportunities for enactive mastery, promote self-efficacy (e.g., Parker, 1998), and further research shows that the effects of job autonomy on proactive behaviors are mediated by self-efficacy (Parker et al., 2006). A second motivational state identified as facilitating proactivity is ‘reason to’ motivation (Parker et al., 2010), or individuals’ internalized willingness to behave proactively. One construct considered in this vein is a flexible role orientation. Flexible role orientation refers to the observation

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that some individuals construe their role more broadly and flexibly relative to others who possess a ‘that’s not my job’ mentality (Parker et al., 1997). Flexible role orientation reflects how some individuals actively redefine their roles in order to incorporate new tasks and goals (Frese & Fay, 2001). Parker et  al., (2006) showed that flexible role orientation is a significant predictor of proactive behavior even when role breadth self-efficacy is controlled for, and prior research (Parker et al., 1997) showed that having high levels of job autonomy (in the form of self-managing teams) can result in the development of more flexible role orientations. A third motivational state is ‘energized to’ motivation, or the experience of positive and activated affect. Bindl and Parker (2010) proposed a model in which affective mechanisms drive proactive behavior at the individual task, team, and organization level. They drew on theory that positive affect results in a broadening of thought–action repertoires (Fredrickson & Branigan, 2005), as well as studies showing a relationship between engagement and individual proactivity (Den Hartog & Belschak, 2007; Hakanen, Perhoniemi, & Toppinen-Tanner, 2008; Salanova & Schaufeli, 2008). Consistent with this, there is evidence linking positive affect to individual proactive behaviors such as personal initiative (Den Hartog & Belschak, 2007), taking charge (same day and peer-reported) (Fritz & Sonnentag, 2009), and proactivity (Bindl, Parker, Totterdell, & HaggerJohnson, 2012); as well as with organizationmember proactivity, such as strategic scanning (Parker & Collins, 2010). At the same time, there is much evidence that job design can promote enthusiasm and vigor, both activated positive states (Salanova & Schaufeli, 2008). Altogether, there appears to be growing evidence on the role of affective mechanisms for proactive performance, although there is still a need for further refinement, especially regarding specific effects for specific subdimensions or regarding interdependencies with other mechanisms. At this point it is relevant to comment on the role of affective commitment since this might be a mechanism expected to operate for proactive behaviors that are targeted at the organization. Whilst the effect of job autonomy on commitment is reasonably well-established (e.g., Galletta et al., 2011; Parker, Axtell, & Turner, 2001), existing data regarding the role of affective commitment in supporting proactive behaviors is inconsistent. Some scholars have shown commitment being potentially rather passive in orientation – does not predict proactive behavior (Parker et  al., 2006). Other studies show positive relationships between affective organizational commitment

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and proactive behaviors (Tornau & Frese, 2013). Griffin et  al. (2007) reported stronger effects of commitment on organization-member proficiency, but there were still small to medium positive associations with organization-member adaptivity and proactivity. Some scholars point to the fact that inconsistencies might be generated by the fact that specific forms of commitment relate to different types of proactive behaviors (Bindl & Parker, 2011), and other scholars suggests it will depend whether more powerful co-related predictors are included in the analysis (Parker et al., 2006). Altogether, there is quite solid evidence that job autonomy promotes proactivity via the can do, reason to, and energized to pathways. Fewer studies have considered job autonomy in relation to team-member proactivity or organization-member proactivity, although Hornung and Rousseau (2007) showed that job autonomy is positively related to commitment to structural change (operationalized in terms of proactive behaviors) through mechanisms such as role breadth self-efficacy and personal initiative. This latter study highlights that individual proactive motivation might also facilitate proactive contributions at team and organizational levels. Beyond autonomy, what other possible job design characteristics might promote or constrain proactivity? Job complexity has also been investigated in relation with proactivity, sometimes alongside autonomy, with positive evidence for this job characteristic (Ohly & Schmitt, 2017). For example, Frese et  al. (2007) showed that autonomy and job complexity were related to control orientations (a type of ‘can do’ motivation) that in turn predicted personal initiative, a form of proactive behavior. Moreover, they also showed a spiral effect, with more personal initiative leading to increased perceptions of autonomy and complexity. Ohly & Schmitt (2017) speculated that job complexity might even promote proactivity through ambiguity. They reasoned that complex jobs are ambiguous, which might stimulate proactive behaviors such as feedback seeking and jobrole negotiation. Social characteristics might also promote proactivity, such as by cultivating a psychologically safe environment in which individuals feel able to take the ‘risk’ to be proactive (‘can do’ motivation) or perhaps by creating a positive climate of shared responsibility. The meta-analysis by Tornau and Frese (2013) showed that support was also positively associated with proactive behaviors. In a similar vein, Parker et al. (2006) reported that coworker trust (but not supportive supervision) contributes to proactive behavior through supporting more flexible role orientations. Drawing on attachment theory to understand what types of support might be

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important for whom, Wu & Parker (2017) showed that supportive leadership in the form of securebase support (leader availability, encouragement, and noninterference) was especially important in stimulating proactivity for insecurely attached individuals. Also showing that supervisor support has contingent effects, Deichmann (2012) found that support predicted suggestion making but only when supervisors also had a favorable attitude towards the suggestion system. More recently, Parker, Johnson, Collins, and Nguyen (2013) showed that junior doctors increased their level of proactive care and proactive skill development when structural support was enhanced (via the presence of an advanced nurse on the shift), but this only applied to doctors who were not overly stressed. Stressed doctors responded to the intervention with lowered perceptions of role overload. The authors explained this finding in terms of conservation of resources theory: doctors not suffering from resource loss (non-stressed) responded to the expanded social support to accumulate resources for the future (via proactivity), whereas the distressed doctors used the social support to preserve their resources (via reducing their workload). We expect social support might be even more important for enabling and encouraging proactivity at the team and organizational level since such proactivity typically requires higher levels of cooperation with others. In a study looking mainly at organizationally-oriented organizationally oriented proactivity, Baer and Frese (2003) suggested that social support might be the mechanism by which innovation climate enhances innovation. However, as we argued in the previous section on adaptivity, social characteristics such as interdependence might also have a negative impact on proactivity, or constrain proactivity in various ways. Because change initiated by one individual might have knock-on effects for others, the high need for co-ordination might overwhelm an individual’s resources or motivation to be proactive. Frese and Fay (2001) observed that the proactive actions of one individual might not be always valued by colleagues or supervisors, especially when they are perceived as threatening. These observations suggest that the effect of the social job characteristics taken together, or even of all types of job characteristics, might not be always additive, but also interactive. Other elements outside work design (such as the organizational culture, or some individual differences) might also qualify these relationships. For example, a recent study showed that the effect of work design interacts with different selfconstruals to shape the type of proactive behavior that individuals engage in. Thus, Wu, Parker, Wu, and Lee (2017) showed that team-member

proactivity was fostered when individuals worked in interdependent jobs, and have interdependent (other-oriented) self-construals, whereas individually-oriented proactivity in the form of career oriented proactive behavior occurred more when individuals with independent self construals worked in autonomous jobs. In a similar vein, Vough, Bindl, and Parker (2017) have investigated team-member and organizational-member proactivity within a call center. They found that proactivity could be supported even in low autonomy settings if there are routinized processes that employees can follow to initiate change at the team or unit level. Thus, they highlight the importance of considering the larger social context that shapes proactive performance at higher organizational levels. Further job characteristics that have been considered in relation to proactivity are demands sometimes associated with job strain, that is, time pressure or situational constraints. Somewhat counter-intuitively, studies suggest that – for proactive and innovative/creative work behaviors – these job characteristics might have positive effects, provided that they are perceived as challenges and that the job also entails sufficient job resources such as control or social support (Fay & Sonnentag, 2002; Ohly & Fritz, 2010). Such findings tend to be explained in terms of control discrepancy theory: that is, contexts of pressure and difficulty signal suboptimal conditions that could trigger proactive behavior, especially when employees have adequate job control (and other resources) to be able to initiate change. Consideration of these work characteristics also highlights possible non-linearity of relationships. That is, perhaps time pressure and some constraints are positive influences on proactivity up to a certain level, but if they exceed that level their effect on behaviors becomes negative. A similar idea is also supported by research focus on ‘workday’ level job design. Scholars taking a workday design perspective have argued that some degree of ‘mindless’ work (work that is both simplified and devoid of performance pressures) might be important for creativity in high pressure jobs (Elsbach & Hargadon, 2006). These authors argued that bouts of simplified, non-challenging work can provide the chance for recovery in otherwise relentlessly demanding work. In sum, there is considerable research that supports links between work design and proactive behavior, with the evidence being clearest and most consistent for job autonomy, and evidence emerging for social characteristics and some types of demand. What is less clear is if the type of work characteristics that matter and the underpinning mechanisms are homogenous for all three types of proactive behavior (individual, team-member, organization-member). An important area of

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further enquiry is how to manage the growing intensity of demands in some jobs. Whilst time pressure and constraints might prompt proactivity in some circumstances, as work becomes more intense, it might be that engaging in at least a small amount of simplified/routine tasks will support proactivity in constant high pressure and complex contexts.

FUTURE RESEARCH DIRECTIONS We started this chapter by highlighting the types of performance that have become increasingly relevant for contemporary organizations, both in terms of forms of behavior (proficiency, adaptivity, proactivity) and in terms of the level they are targeted (individual, team, organization). Our review indicates that there is solid evidence for the role of work design in shaping individual task proficiency, but despite calls for work design theory to go beyond this focus, we are only part way there in understanding how to support more emergent forms of performance through work design. First, regarding emergent forms of performance, whilst there has been progress in terms of considering proactive behaviors as outcomes of work design, the understanding of how work design might impact adaptive types of behaviors is limited. Even when adaptivity is studied as an outcome of work design, adaptive behaviors are usually investigated as part of a more general criteria focusing either on proficiency (Morgeson et  al., 2005) or proactivity (Berg et al., 2010). Although there is some overlap amongst the different forms of performance, we believe that a more focused operationalization of adaptive behavior will enhance our understanding about which types of work design might better support this important outcome. In some organizations and industries in particular, such as high reliability industries and emergency response context, adaptive behaviors are especially important. Adaptivity is also important in light of the increased uncertainty and unpredictability within organizations, and with it the emergence of practices intended to support adaptivity, such as different work contracts (e.g., increased temporary, casual, part-time, contracts; greater outsourcing) and more flexible work arrangements (telework, flexible time management). Such practices raise questions such as how to balance flexibility and adaptivity at the individual level, with co-ordination requirements at a higher level of work role performance. One could easily see how performance as a team member might be impacted by

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co-ordination difficulties, especially when all members of the team have different flexible schedules; similarly, different types of performance as an organizational member might also be negatively impacted if flexible time arrangements lead to a reduced contact with the overall organization. Remote and virtual work are also contemporary types of work organization that shape work design, but how these practices help or hinder adaptivity, especially at higher levels, has had little attention. We recommend future work design research to give more attention to adaptive outcomes. Second, we can see that research on these different forms of behavior has evolved in different patterns. For proficiency, the tendency has been to use some general performance criteria that make it difficult to unpack how specific behaviors are affected, especially when we focus on proficiency behaviors embedded in the social context ((teammember proficiency and organization-member). For proactivity, the tendency has been to develop research on very specific proactive behaviors, with the main challenge residing in integrating these distinct behaviors. Adopting the performance framework focused on in this chapter (Carpini et al., 2017; Griffin et al., 2007) might help address both challenges. This framework can be used as a guide for a more careful operationalization of proficiency criteria, and, the use of the framework is helpful in integrating proactive behaviors around the level of their intended impact – the individual task/job, the team, or the organization. Third, our review highlights that our understanding is more limited as we consider more socially embedded behaviors. When the focus is on team-member performance, or organization-member performance, we found few studies and often have had to rely only on indirect data. We cannot deny the increasingly interdependent structure of contemporary workplaces, and therefore understanding how work design can affect performance directed towards improving the team, organization, or other collective is important. In essence, we are arguing for the need to consider context to a greater extent in work design theory development. To date, the way work design theory tends to study context is as a boundary condition for work design–outcome relationships. We suggest it also shapes the outcomes that should be considered. Our review highlighted that there are several work design features that can support each type of performance. Nevertheless, we observed that the tendency is to consider a limited set of job characteristics, usually focusing on job enrichment types of variables. However, we have highlighted how previously neglected job characteristics can be extremely relevant, such as task significance (Grant, 2007; Grant et  al., 2007). Also, we have

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observed that the relevance of certain work characteristics might change as we shift horizontally or vertically within Griffin et al.’s model (2007). For example, for individual proficiency, job characteristics that promote motivation might be the most relevant (Humphrey et  al., 2007), whereas when it comes to individual task adaptivity, work characteristics related to information processing become increasingly relevant. Moving vertically to consider individual contributions targeted towards the team and the organization, we highlighted the possibility that social characteristics like social support and interdependence might become important, with these elements potentially interacting in interesting ways. In essence, we echo long-made calls (e.g., Parker, Wall, & Cordery, 2001) for expanded work design characteristics to be considered in job design research. This point about expanding job design characteristics is also relevant for the mechanisms by which work design has an impact. Cognitive-motivational processes such as self-efficacy and intrinsic motivation appear to be predictive for all types of performance, although specific forms of self-efficacy are unsurprisingly most predictive when matched to the specific behaviors. When considering performance directed towards the team and organizational level, cognitive-motivational mechanisms concerned with a broader role orientation appear to be very important: it seems when individuals adopt a broader perspective on what their job is they are more likely to direct their efforts to collective outcomes. Affective processes have always had attention (such as early studies linking work design and well-being), but have had renewed emphasis in proactivity research where scholars have identified the particular importance of activated positive affect. Beyond cognitivemotivational and affective mechanisms, however, we have highlighted how learning is also an important but under-investigated mechanism, especially for adaptivity and proactivity. When it comes to team and organizationally oriented performance, mechanisms such as knowledge sharing, perspective taking, and distributed cognition might be important processes by which work design makes a difference. A further important issue is how the processes linking work design and outcomes play out over time, or the temporal issues (Parker et al., 2014). Reciprocal dynamics should also be considered. There are discussions in the literature around a possible positive spiral between autonomy and proactivity, with autonomy stimulating more proactivity that, in turn, leads supervisors to give (or employees to assume) more responsibilities and hence further increase their job autonomy (Berg et al., 2010; Crant, 2000; Frese et al., 2007). Overall, what is revealed by our review is the fact that work design is a potential antecedent of

all the different types of work role performance. There are several job design characteristics that were already related directly or indirectly with the different categories of behaviors that we considered here. There are also multiple mechanisms by which these characteristics potentially influence performance. In this sense, we agree with Johns’ (2010) observation that there might be several (or even numerous) patterns by which work design could support each of the three subdimensions of the three types of work behavior. What is more difficult is to identify those work design patterns that are capable of supporting several of these outcomes at the same time (see Parker, 2014). Therefore, we call for research that considers how patterns of work design characteristics impact multiple outcomes simultaneously. Finally, although a detailed discussion on the emerging workplace trends is not the focus of this paper (for more detailed discussion see the special issue on work design in Journal of Organizational Behavior, 2010, as well as Parker, Morgeson, & Johns, 2017), it is important to consider work design and performance in the context of wider changes. For example, increased complexity resulting from technological developments, globalization, and competition has the potential to intensify work demands, which re-focuses our attention on proficiency: what types of work design best enable proficiency when the demands are intense? Likewise the workforce itself is increasingly more diverse – in terms of an aging workforce, females having increased participation in work, and increased multiculturalism at work. For example, an aging workforce means we need to understand how to design work that will not only be able to retain the participation of older employees, but also maximize their skills and experience, and channel their performance to higher levels. Likewise, increased diversity gives rise to questions around the combinations of jobs and people in a team that best harness this diversity.

CONCLUSION Although the field of work design has partly expanded the criteria of work performance in light of a contemporary context dominated by greater uncertainty and increased interdependence, the picture is still far from being clear. We have proposed how research in this field can be advanced, especially in regards to considering adaptivity as a crucial performance outcome and in terms of considering contributions to co-ordinated performance. Beyond stimulating research and theoretical

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development, our paper highlights important practical considerations. The inconsistency apparent in the research regarding how work design affects performance might have obscured the use of work design as an important tool for generating competitive advantage. If scholars are able to demonstrate the value of work design across multiple contemporary outcomes, then this might enhance managerial take-up of work design as a vehicle for change. Our observation, supported by research (Campion & Stevens, 1991; Parker & Andrei, 2014), is that the natural tendency of most naïve job designers is to design simplified, low variety/discretion jobs. Such work designs, as we have argued here, diminish proficiency because individuals are less committed and engaged, but they also are likely to be especially inhibiting of adaptivity and proactivity because they lower self-efficacy and potentially suppress learning. The gap between work design strategies executed in practice, and the work design strategies that theory and research suggest will maximize performance in contemporary work, could well grow. We hope that this chapter and the research it might spur will help to close this gap.

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performance: A handbook in the psychology of management in organizations (pp. 69–93). Chichester, UK: Wiley. Parker, S. K., & Wall, T. D. (1998). Job and work design: Organizing work to promote well-being and effectiveness. San Francisco, CA: Sage. Parker, S. K., Andrei, D. M., & Li, W. D. (2014). An overdue overhaul: Revamping work design. In A. Shipp & Y. Fried (Eds.), Time and work (Vol. 1, pp. 191–228). New York: Psychology Press. Parker, S. K., Axtell, C. M., & Turner, N. (2001). Designing a safer workplace: Importance of job autonomy communication quality, and supportive supervisors. Journal of Occupational Health Psychology, 6, 211–228. Parker, S. K., Bindl, U. K., & Strauss, K. (2010). Making things happen: A model of proactive motivation. Journal of Management, 36, 827–856. Parker, S. K., Johnson, A., Collins, C. G., & Nguyen, H. (2013). Making the most of structural support: Moderating influence of employees’ clarity and negative affect. Academy of Management Journal, 56, 867–892. Parker, S. K., Morgeson, F., & Johns, G. (2017). Work design theory and research: Looking back over 100 years and looking forward to the future. Journal of Applied Psychology, 102, 403–420. Parker, S. K., Van den Broeck, A., & Holman, D. (2017). Work Design Influences: A Synthesis of Multilevel Factors that Affect the Design of Jobs. Academy of Management Annals, 11(1), 267–308. Parker, S. K., Wall, T. D., & Cordery, J. L. (2001). Future work design research and practice: Towards an elaborated model of work design. Journal of Occupational and Organizational Psychology, 74, 413–440. Parker, S. K., Wall, T. D., & Jackson, P. R. (1997). ‘That’s not my job’: Developing flexible employee work orientations. Academy of Management Journal, 40, 899–929. Parker, S. K., Williams, H. M., & Turner, N. (2006). Modelling the antecedents of proactive behavior at work. Journal of Applied Psychology, 91, 636–652. Paul, W. P., & Robertson, K. B. (1970). Job enrichment and employee motivation. London: Gower. Piccolo, R. F., Greenbaum, R., Den Hartog, D. N., & Folger, R. (2010). The relationship between ethical leadership and core job characteristics. Journal of Organizational Behavior, 31, 259–278. Podsakoff, P. M., MacKenzie, S. B., Paine, J. B., & Bachrach, D. (2000). Organizational citizenship behaviors: Critical review of the theoretical and empirical literature and suggestions for future research. Journal of Management, 26, 513–563. Pulakos, E. D., Arad, S., Donovan, M. A., & Plamondon, K. (2000). Adaptability in the workplace: Development of a taxonomy of adaptive

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17 Management Interventions R o b e r t C a r d y a n d T. T. S e l v a r a j a n

INTRODUCTION In the previous Handbook of Industrial, Work and Organizational Psychology, we defined management interventions as planned changes in the work setting designed to change the behavior of organizational members and lead to desirable organizational outcomes (Porras & Silvers, 1991). We maintain this definition for this update of the chapter on management interventions. For purposes of this chapter, we also maintain our previous limits of exploring interventions since the Industrial Revolution and focusing on those that are meant to work through people. We also restrict coverage here to interventions that we view as widely practiced or having had a major influence on management thinking and practice. We hope that the boundaries we have set on this broad topic result in coverage and balance sufficient to spark additional research on management interventions. Before considering specific management interventions, we first consider some of the characteristics of interventions that can influence their effectiveness in organizations. The content of an intervention can, of course, influence its effectiveness. Aside from the obvious issue of a proposed program containing sound content, whether the breadth of an intervention fits with an

organizational situation can be an important influence on effectiveness. By breadth, we refer here to the scope of an intervention being a very narrow or broad focus. In addition to breadth, some additional characteristics associated with intervention effectiveness include the source of the program, the signals sent by the program, and how the intervention is implemented. We begin by considering the source of an intervention and then turn to the factors of content, signals, and implementation.

THE SOURCE OF INTERVENTIONS The topic of management interventions can be the bridge between theory and practice, with interventions being the practices that are derived from sound theory. For example, management-byobjectives is a practice that is based on goal setting theory. However, the practical reality is that interventions can emerge and gain popularity, but not be based on theory or research regarding the effective management of people. An intervention may be based, for example, on the beliefs of top management, the practice of a persuasive consultant, or the assertions of a popular business book.

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The source of an intervention does not necessarily indicate whether an intervention is right or wrong: an intervention based on sound theory may not work in all organizational environments and top management may have an understanding of what will work in their organization. Nevertheless, the variety of sources can add an important source of variance in interventions that may play a role in determining effectiveness. The source of an intervention can influence effectiveness in at least a couple of ways. One path of influence is the perceived validity of the intervention. Does the planned program seem based on theory and research or could it reflect biases and preferences of a proponent? Does the program come from within the company or industry or does it come from outside? These types of issues about the source of an intervention may not be explicit considerations but can influence how the steps and activities involved in the intervention are perceived. Another possible path of influence for source has to do with the perceived importance of an intervention. Is the boss sold on the program and is s/he involved in its rollout, or is the program an initiative taken on by the staff function? If top or line management is not involved and openly supportive of a program, attendance at programs and hitting deadlines involved in the intervention may not be given priority. Figure 17.1 is a simple graphical representation of the possible influence of source. As presented

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in the figure the influence of source is mediated by perceived validity and perceived importance.

Intervention Breadth The content of interventions can vary from specifying how jobs are carried out to broad strategiclevel initiatives. The breadth of interventions can be considered a bandwidth issue in which the scope of the intervention needs to fit with the organizational situation that the intervention is designed to improve or solve. An intervention designed to improve the efficiency with which tasks are carried out is not likely to lead to improvement when performance issues in the organization are due to poor communication. Management interventions are programs usually meant to improve the efficiency or effectiveness of the organization or to remedy a performance problem. If an improvement or remedy is going to work, it needs to address an area of weakness or the cause for a deficit. The need for fit between an intervention and the organizational issue is, perhaps, obvious. However, misfits can occur. For example, an intervention may be adopted because of its popularity or because competitors have adopted the program. Interventions may also be instituted based on incorrect assumptions or analysis of the cause for a performance deficit or of the needs in an organization. In these and other similar cases, a

Source of intervention

Perceived validity

Perceived importance

Intervention effectiveness

Figure 17.1 Influence of source on intervention effectiveness

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Narrow

Focus

Broad

Task-oriented

Skill-based

Process

Strategic

Task conduct

Person

Group

Organization

Routine jobs

Dynamic situations

Integrative or cooperative operation

Organizational direction and alignment

Fit

Figure 17.2 Intervention breadth

mismatch between an intervention and the organizational situation can occur. Of course, a mismatch will likely lead to unsatisfactory results and a failed intervention. Figure 17.2 presents a framework that categorizes interventions based on their breadth. The framework can be useful for considering the type of intervention that should be selected, given the organizational situation. The framework could also be useful for researchers as a means to categorize and contrast various interventions and to study their effectiveness. The breadth of interventions can conceptually be seen as a continuum, but is arbitrarily divided into four categories in the framework. As presented in Figure 17.2, breadth at the narrow end of the continuum consists of task-oriented interventions. These interventions have a focus on the performance of specific tasks. Scientific management (Taylor, 1911) for example, focused on tasks and specified how they should be carried out. Interventions at the task level would be expected to be a fit and be potentially effective in organizational situations in which jobs are routine and repetitive. The next breadth category is skill-based interventions. These interventions focus on person characteristics, rather than tasks. Skill-based interventions would fit when the organizational situation is more dynamic so that tasks are not a stable and repetitive basis for an intervention. Programs directed at identifying and increasing competency levels of employees (Cardy & Selvarajan, 2006) provide examples of interventions at the skill level. At the next level of breadth, process interventions shift from a focus on individuals to a focus at a group level. Process interventions would fit and potentially be effective when the organizational process involves integration and cooperation. Interventions aimed at improving communication and teamwork are examples of interventions in this category. Strategic interventions are at the broad end of the breadth continuum. Interventions at the

strategic level are focused at a function or crossfunctional level. Strategic interventions would fit and potentially be effective when the organization is adopting a new initiative and alignment of components in the organization is needed. Process reengineering provides an example of an intervention at the strategic level. Categorizing interventions according to their breadth provides an overall view of the possible range of interventions and the possible range of organizational needs. When misfits occur between these two ranges, it should be no surprise that interventions that are ineffective or failures are the result.

Signals Interventions can convey information beyond their content. Interventions can provide signals to employees and others about issues, such as what is important, the conclusion about the underlying cause for a problem, or what direction the organization should take. These signals can be unintended or can be purposeful. For example, employees might interpret a task-focused intervention as an organizational diagnosis that employee lack of execution is the source of performance shortfalls. This message may not have been intended and the interpretation by employees may be incorrect. Nonetheless, an intervention may convey such unintentional messages. On the other hand, an intervention may be chosen in order to send a signal to stakeholders (Freeman, 1984). For example, a corporate social responsibility program may be put into place in order to send the signal to a group of constituents that the organization is seriously embracing its role as a socially responsible citizen. Whether unintentional or by design, interventions bring with them additional information. How an

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intervention is interpreted depends on the individual. A manager might view an intervention as a welcome improvement while an employee may view that same intervention as overly controlling and restrictive. The perceived signals associated with an intervention may be secondary to the actual intervention, but these interpretations can influence the effectiveness of an intervention.

Implementation Another key factor that can influence the effectiveness of an intervention is implementation. How a program is implemented can have a great deal to do with how well the program works. A top-down directive could get some quick movement, but also lead to negativity that has longer-term impact on the program. Individuals who are internal or external to the organization can also drive implementation. In addition to the degree of participation and who is associated with putting a program in place, training, timelines, and incentives, among other factors, can be important characteristics involved in implementation. All of these factors could have important influence on the effectiveness of an intervention. Figure 17.3 provides a graphical summary of the characteristics discussed here that can influence

Source

Breadth

the effectiveness of interventions. The importance of these factors is probably commonsensical, but some characteristics can be overlooked when an intervention is considered. Also, there is need for research on these, and potentially other, factors and how they may influence an intervention. Which aspects of source are important and how these source characteristics might influence an intervention could be interesting and useful directions for research. For example, what aspects of source are important and are there contingency factors involved in determining when source characteristics are important? In regard to breadth, are there differences in effectiveness across the bandwidth with, perhaps, narrower interventions yielding greater measured effectiveness? Further, what types of fit are most important for interventions to be successful? Research on signaling when putting in place an intervention offers interesting issues. For example if an organization puts an intervention in place as a means to signal a stakeholder group, does this influence the effectiveness of the intervention to achieve its stated purpose? And does the planned signaling work and have the intended effect, or is the attempt to manipulate perceptions perceived negatively by the stakeholders? What are the conditions that result in positive or negative shareholder perceptions of signaling? Research on implementation and its role in determining the effectiveness

Signals

Intervention effectiveness

Figure 17.3 Summary of intervention characteristics

Implementation

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of interventions has the potential to provide useful guidelines for practice. The relative importance of implementation facets is also an interesting issue. Assessing the overall importance of implementation versus the content of an intervention could be instructive. Implementation could be just as important as a sound intervention. Finally, how might the intervention characteristics configure and what configurations might be most effective? For example, if the source is external, is an intervention more effective when the breadth is narrower or broader? The preceding brief consideration of intervention factors and how they might influence effectiveness highlights the number of factors that can be involved in determining whether an intervention is effective. When considering the array of influences on effectiveness, there should be little surprise that many management interventions are now classified as fads (Carson, Lanier, Carson, & Birkenmeier, 1999; Gibson & Tesone, 2001). An intervention may gain popularity but failures or poor effectiveness can lead to the approach falling into disfavor. It is possible that the intervention itself, being the focal issue, is at fault. However, as our discussion here highlights, there may be many factors that are not part of the content of the intervention that determine its effectiveness. A consideration of these other factors could provide useful contextual information in studies of fads in management interventions. We next turn to consideration of the following selected management interventions: 1) Quality management; 2) Talent management; 3) Knowledge management; and 4) HR analytics. For purposes of this updated chapter, we focused on management interventions popular in the academic and/or practitioner literature during the past decade. Consistent with our definition of management intervention, we restricted our focus to management interventions that are planned changes in the work setting designed to change the behavior of organizational members that can lead to desired organizational outcomes. Starting with a literature search on the interventions covered in the earlier version of this chapter, we found that only the quality management intervention continued to be popular during the past decade. Thus, we include quality management in this review. Other interventions, such as knowledge management, talent management, and HR analytics, were found in the academic and popular literature and are also reviewed here.

QUALITY MANAGEMENT In our previous chapter on management interventions, we noted that organizational and

management scholars had paid scant attention to Total Quality Management (TQM). Our review of the organizational and management literature over the past decade indicates that the situation has not changed. We did a search on ‘quality management’ and ‘Total Quality Management’ in leading outlets in organizational behavior and management and found that quality management continues to be neglected, barring a few notable exceptions (e.g., Douglas & Judge, 2001). In contrast, operations management scholars have embraced quality management and it is regarded as one of the most important interventions in that field. Further, operations management scholars have utilized theories from management and organizational research and integrated these concepts into the quality management framework (e.g., Sila, 2007). From the perspective of operations management, the focus of quality management has gone beyond technical aspects of quality management and includes organizational and human resource components. Thus, although our quality management literature review is heavily drawn on research conducted by operations management scholars, these scholars often used integrated models combining technical and human perspectives. Quality management has been identified as a fad (e.g., Gibson & Tesone, 2001). Fads typically run a course (Carson et al., 1999) in which, after falling off in popularity, the approach is promoted only by a few hard-core proponents. The arc of quality management seems to differ from this typical scenario. While quality management has fallen off in popularity in the management literature, the approach remains a central framework for practitioners and scholars in operations management. Further, the operations management scholars have adopted management theories and models into their work. As management researchers and practitioners, it would be useful to have awareness of the status of an approach that remains popular in a core business area and has implications for how people are managed. At the least, our students may end up working with colleagues, either practitioners or scholars, who adhere to quality principles and they should have awareness of this approach and understand its basic principles. Thus, we have included quality management in our review of management interventions. The following review focuses on four areas: a) recent developments in the conceptualization and operationalization of quality; b) the relationship between the quality management intervention and outcomes including operational and innovation performance; c) contextual factors that may influence the relationship between TQM and outcomes; and d) issues involved in successful TQM implementation.

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Definition of Quality Management We first consider how quality management scholars in recent years have defined quality. Garvin (1984) defined product quality as a multidimensional construct consisting of eight dimensions: performance, features, reliability, conformance, durability, serviceability, aesthetics, and perceived quality. Drawing on the works of organizational researchers (e.g., March, 1991; Sitkin, Sutcliffe, & Schroeder, 1994), quality management scholars have expanded the notion of quality to include ‘quality exploitation’ and ‘quality exploration’ (e.g., Zhang, Linderman, & Schroeder, 2012). The notion of quality exploitation deals with control and conformance to existing standards. The focus of the quality exploitation concept is consistency and efficiency. Thus the exploitation is consistent with Garvin’s definition of quality. The concept of quality exploration is related to the pursuit of novel and creative products or solutions (Garvin, 1993). Given competitive and uncertain environments, organizations are often required to discover new solutions or pursue unknowns in order to remain viable (Zhang et al., 2012). The exploration concept is an important extension of the definition of quality. The exploration concept allows practitioners to apply quality management practices, such as a customer focus and process improvement to the areas of creativity and innovation. Empirical research conducted by Zhang et al. (2012), provides support for the idea of quality as an exploration mechanism for achieving competitive advantage for organizations operating in a dynamic environment. Innovative companies such as 3M have found limited success in their quality management efforts since quality was defined using the exploitation paradigm (e.g., Canato, Ravasi, & Phillips, 2013) and such companies may be far more successful in their quality management efforts if they shift their emphasis to quality as an exploration paradigm. Even within the traditional definition of quality as exploitation, research indicates that organizations have not fully leveraged the multidimensional nature of quality (Sousa & Voss, 2002). Defining quality as a multidimensional construct as proposed by Garvin (1984) will help organizations to better relate their quality management efforts to a variety of organizational contexts and outcomes. This will prevent organizations from falsely rejecting their quality management efforts as ineffective when quality is defined as a unidimensional construct. Quality management scholars during the past decade, following Dean and Bowen (1994), have broadly defined quality management as an intervention consisting of principles, practices, and techniques to enhance the quality of products

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or services offered by the organization (e.g., Rungtusanatham et al., 2005). The principles are at a high level of abstraction and provide general guidelines for implementing quality management. In contrast, practices are observable facets of quality management at an empirical level (Sousa and Voss, 2002). Since a specific practice (e.g., process management) can be implemented by a variety of techniques (e.g., statistical process control or process data collection), the practices are often too detailed for researchers to obtain reliable measures of quality management (Sousa and Voss, 2002). Quality management scholars during the past decade have increasingly classified TQM into two categories: 1) Core TQM principles or hard TQM; 2) Infrastructure TQM or soft TQM (Rahman & Bullock, 2005). Core TQM consists of technical aspects of TQM such as statistical process control, continuous improvement, zero defects, and advanced manufacturing systems and just in time (JIT). The technical factors are related to the design, implementation, and improvement of quality management systems (Rahman & Bullock, 2005; Calvo-Moro et al., 2014). The soft or social dimension of TQM deals with social support mechanisms such as top management support, employee empowerment, training, and teamwork. The social aspect of TQM focuses on providing the environment necessary for the hard or technical aspects of TQM to perform effectively. Thus, the social or infrastructure aspect of TQM works through the core of technical performance to improve quality (Sousa & Voss, 2002).

Relationship between TQM and Performance The relationship between TQM and performance has been extensively examined during the past decade. Although previous research for the most part focused on operational performance, a few studies have examined the relationship between TQM and innovation performance. We review literature on the two types of performance below.

TQM and operational performance

Research in this area has focused on the relationship between hard and soft aspects of TQM and outcomes. Research on the relationship between TQM and outcomes has focused on operational and/or business performance. In a review of TQM research, Sousa and Voss (2002) found that social aspects of TQM were positively related to technical TQM which in turn resulted in higher levels of

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operational performance. However, the relationship between TQM and business performance was weak and not always significant. Recent research using a sample of Spanish firms corroborates earlier findings that the technical aspects of TQM were directly related to performance and the social aspects of TQM were indirectly related to performance via technical TQM (Calvo-Mora et  al., 2014). Similarly, research by Rahman and Bullock (2005) suggests that technical TQM aspects were related to performance and social TQM was indirectly related to performance via technical TQM. In a comprehensive meta-analytic review of the relationship between quality management and performance, Nair (2006) found that social and technical TQM were related to operational and financial performance. While most of the above-mentioned research was in a manufacturing setting, recent research in service settings indicates a different pattern of results. In research on quality management practices in 372 US hospitals, Gowen et al. (2006) found that both technical and social aspects of TQM were related to operational performance. However, the relationship between social aspects of TQM and operational performance was much stronger. The pattern of results is interesting since it indicates that, at least in a service setting, the social aspects of TQM have a much stronger influence on operational performance when people, rather than machines, are the primary mechanisms for delivering quality.

TQM and innovation performance

One of the criticisms of TQM is that it is internally focused with a heavy emphasis on maximizing efficiency of internal processes rather than focusing on external entities such as customers (Zatzick, Moliterno, & Fang, 2012). In addition, the focus on efficiency and efficiency may also stymie innovativeness and customer orientation. Zatzick et al. (2012), in their research on manufacturing organizations in Canada found that TQM was positively related to firm performance for organizations pursuing cost leadership strategy and this relationship was negative for organizations pursuing differentiation strategy. That is, their research indicates that there is a fit between TQM principles and a cost reduction focus since TQM is oriented toward improving internal processes to achieve cost efficiencies. In contrast, for firms pursuing a differentiation strategy where the focus is on innovation and responsiveness to customer preferences, TQM, with its focus on efficiency of internal processes, seems to be a misfit. In empirical research on Spanish industrial firms, PedromoOrtiz, González-Benito, and Galende (2009) found that TQM processes were negatively related

to technological innovation and the authors reasoned that emphasis on the control and improvement of processes may negatively influence the relationship between TQM and development of new knowledge and skills required for innovation. Prajogo and Sohal (2006) studied the relationship between TQM and innovation performance of Australian manufacturing firms and their results indicated that TQM was positively related to operational performance but was not related to innovation performance. Thus, it seems that TQM is related to operational performance but is not related to outcomes related to customer orientation and innovation. The pattern of results is surprising given that customer focus and teamwork are important components of TQM and these dimensions are also important for customer satisfaction and innovation. For example, innovation requires effective teamwork of employees across functions. Thus, it is possible that although TQM may not have a direct relationship to innovation and customer orientation, the relationship is mediated by other organizational processes. For example, Prajago and Sohal’s (2006) research indicated that TQM was positively related to research and development capabilities which in turn was positively related to innovation performance. Findings by Pedromo-Ortiz et al. (2009) suggest that TQM was positively related to business innovation capability which in turn was positively related to technological innovation. Thus, available evidence indicates that TQM and innovation may be related although not directly. However, it is also possible that the soft aspects of TQM which focus on leadership, human resource management, teamwork, etc. may have a positive influence on any organizational outcomes regardless of the efficacy of hard aspects of TQM. Future researchers should focus on the relationship between TQM and innovation performance to better understand the nature of relationship between TQM and innovation performance.

TQM and Performance Relationship: The Role of Contextual Factors The context in which a quality program occurs can be an important determinant of the effectiveness of the program. Researchers have examined the potential influence of a variety of contextual factors, including the external business environment, organizational structure, organizational size, and organizational culture. Previous research on the relationship between TQM and performance has examined the moderating role of organizational structure. In a study of

Management Interventions

hospitals in the USA, Douglas and Judge (2001) found that TQM was positively related to financial performance when the organization structure exhibited both control and learning properties. That is, the successful organizations emphasized both structural control of procedures and activities but also provided an open and conducive environment for learning. The authors argued that both control and learning can operate synergistically by standardizing routine operations and at the same time allowing the organization to be open and flexible for new ideas. Company size was another contextual variable examined in previous research. In a study of American companies, Sila (2007) found that company size did not moderate the relationship between TQM and performance. Thus, it appears that larger companies, although having advantages in terms of resources available for establishing TQM systems, do not have any edge over smaller companies in terms of the effectiveness of a TQM intervention. In the study of USA hospitals mentioned previously, the researchers examined the role of environmental uncertainty and organizational structure as contingency factors in the relationship between quality management and performance (Zhang et  al., 2012). This research found that the relationship between quality exploitation and performance was stronger for hospitals that operate in more stable environments and for organizations that are more mechanistic. In contrast, the relationship between quality exploration and performance was stronger for hospitals operating in a more unstable environment and where the organizational structure was more organic. Strategy is another moderating variable that has been found to influence TQM effectiveness. In a study of 780 manufacturing plants in the USA, Zatzick et  al. (2012) found that TQM was effective when organizations use cost leadership strategy but ineffective when organizations use a product differentiation strategy. It appears from this research that, for quality management efforts to be effective, there needs to be an appropriate fit between organizational structure, strategy, the external environment, and the nature of quality management intervention (exploitation or exploration). Previous research has examined the role of organizational culture in the adoption of TQM practices. In a study of US manufacturing plans, Zu, Robbins, and Fredendall (2010) examined if organizational culture (operationalized as developmental culture, rational culture, and group culture) was related to the adoption of TQM practices. This research revealed that both rational culture and group culture were related to the adoption of TQM practices, but a developmental culture was not related to the adoption of TQM

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practices. Although this research did not address the relationship between TQM and performance, it is possible that the relationship between TQM and performance is stronger for group and rational cultures. It would be useful for future empirical work to explore the contextual effect of organizational culture on the relationship between TQM and performance.

TQM Implementation Scholars in quality management have recognized that implementation can pose difficulties for organizations and have addressed the topic. In a review of TQM programs, Beer (2003) identified several issues that are important for effective implementation of quality management programs. The author argues that it is not the seed but the soil that matters. That is, the failure of TQM is that of TQM implementation and not TQM theory and method. First, TQM is a long-term process and previous research indicates that the relationship between TQM and performance is apparent in the five-year post implementation period and not in the first five-year implementation period (Beer, 2003). Managers are often impatient and shortsighted when implementing TQM and often conclude that the intervention is not effective based on evaluation of the results during the short term without realizing that positive results are often seen in the long term. Thus, they may prematurely abandon TQM and move on to the next management intervention or fad on the horizon. Second, managers often adopt an intervention for political purposes to attain legitimacy in the eyes of stakeholders. For example, an organization may adopt TQM or ISO certification because a competitor has adopted the intervention and thus may view the intervention as a means to attract new customers or to retain customers. Since, in this example, the purpose of the intervention is to attain legitimacy in the eyes of stakeholders and there is not genuine interest in improving quality, the implementation of TQM is often superficial with a shallow understanding of the principles of TQM. Moreover, these organizations do not realize that, although the nature of TQM practices is broadly similar across organizations, TQM intervention has to be customized because each organizational situation is unique. Empirical research in this area suggests that TQM interventions are more effective when the implementation is customized for a specific organization rather than adopting the intervention without customization (Beer, 2003). Third, TQM implementation often fails because there is a gap between TQM rhetoric and reality.

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Top management often uses a top-down approach without soliciting feedback from lower-level employees about potential gaps between TQM rhetoric and the reality of implementation (Beer, 2003). Fourth, interventions like quality management suffer when employees are coerced and where there is lack of cultural fit between the nature of intervention and organizational culture. In a study of 3M corporation, Canato et al. (2013) found that a Six Sigma intervention (a quality management program) at 3M by an outsider CEO (who was a top manager at GE where Six Sigma was extensively adopted), created resistance among employees at 3M since there was a mismatch between the highly structured intervention and the nature of the innovative organizational culture at 3M. To overcome implementation issues for quality management, organizations should genuinely believe in the intervention as an effective tool for organizational effectiveness rather than rhetoric to satisfy an external stakeholder. Beer (2003) offered some specific guidelines for successful TQM implementation. First, management should determine if the TQM intervention is appropriate and can benefit the organization. Second, employees should be involved as partners in a TQM implementation. Third, data should be collected on TQM implementation so that gaps or deficiencies in successful implementation can be identified and rectified.

review of talent management, Gallardo-Gallardo, Dries, and González-Cruz (2013) proposed two broad approaches to defining talent in the world of work. The talent-as-object approach views talent as characteristics of people. The characteristics include individual difference factors such as ability, knowledge, experience, skills, potential, motivation, commitment, and attitudes. That is, the talent-as-object perspective includes traditional knowledge, skills, and abilities as well as motivation, personality, affect, and attitudes. The second approach, talent-as-subject, views talent as people. Further, talent as people can be considered as inclusive when the approach includes all the people in the organization or exclusive when the talent is applied a specific group of highly talented individuals. In the inclusive approach, organizations treat all the employees as talented with their own strengths and potential to contribute to the organization. With the inclusive approach there is no need to exclude any individuals from the purview of talent management. However, such an inclusive definition for talent makes differentiation between talent and traditional human resources difficult. The exclusive approach includes a small set of individuals identified as high performers or people with extraordinary potential who can contribute to the organization in unique ways.

Talent Management TALENT MANAGEMENT One of the important management interventions that was prevalent during the past decade is talent management. The intervention is popular among practitioners and recently has garnered the attention of academics. The idea for talent management was first promoted by a group of McKinsey consultants who used the phrase ‘war on talent’ for organizations trying to achieve competitive advantage by leveraging human talent that is scarce and difficult to replace (Michaels, Handfield-Jones, & Axelrod, 2001). In this section, we review the following talent management topics: 1) The concept of talent; 2) Talent management and activities involved in talent management; and 3) Implementing talent management.

Defining Talent Before we discuss the concept of talent management, we review the concept of talent. In a recent

The concept of talent management lacks a definitive conceptualization and some researchers have defined talent management as including a broad range of all of the traditional human resource management activities that makes it indistinguishable from traditional HRM (Collings & Mellahi, 2009). For example, talent management is often defined as identifying, recruiting, and retaining top performers for all positions. This broad definition seems to be synonymous with human resource management. In contrast, Collings and Mellahi (2009) define talent management as activities and processes that involve the systematic identification of key positions that contribute to an organization’s sustainable competitive advantage, development of a talent pool of individuals to fill these positions, and development of differentiated human resource architecture to facilitate effective management of these talented individuals. Thus, talent management includes three activities/processes. First, organizations need to identify pivotal positions that can have the greatest impact on organizational effectiveness and that can provide sustainable competitive advantage to the organization. The idea

Management Interventions

is that certain positions can be disproportionately important for formulating and executing a company’s strategy relative to other positions. The second component of this talent management definition consists of the identification and development of a talent pool to fill the positions. This step requires recruitment ahead of the curve, consisting of proactive identification of incumbents with potential to fill the pivotal positions (Collings & Mellahi, 2009). The third step is to create a differentiated HR structure for the talent pool identified in the previous step. The approach contrasts traditional HR where a unitary HR policy exists across the entire organization. The approach for managing the talent pool is similar to that of high performance HR systems (Lepak & Snell, 1998) where organizations create commitment-oriented HR systems that can lead to higher performance. The emphasis in high performance HR is on building the motivation, commitment, and development of the talent pool (Collings & Mellahi, 2009).

Implementing Talent Management Although the practitioner literature is full of ‘best practices’ for implementing talent management in organizations, academics have proposed a more contingency-based approach that can be customized to the individual organization (Dries, 2013). Using a contingency approach, Dries (2013) proposed that organizations should implement an approach that is consistent with the strategy, culture, structure, and existing HR activities for the successful implementation of talent management. Organizations should define the goals of talent management in terms of strategic, operational, and financial targets so that the intervention can be linked to important outcomes (Thunnissen, Boselie, & Fruytier, 2013). As with other management interventions, top management commitment and leadership are important factors for successful implementation of talent management as these factors can send a message across employees that the intervention is not just a passing fad (Schuler, Jackson, & Tarique, 2011).

KNOWLEDGE MANAGEMENT During the past decade, knowledge management has emerged as an important management intervention since in today’s economy, knowledge is considered a key resource. Effective management of knowledge can be a source of sustainable

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competitive advantage (Arikan, 2009; Carillo & Gaimon, 2004; Connell & Voola, 2013; Tallman et  al., 2004). Knowledge management has theoretical underpinnings in the knowledge-based view of the firm where knowledge is viewed as a valuable resource for competitive advantage (Argote, McEvily, & Reagans, 2003; Kogut and Zander, 1992). Our review of this intervention includes: 1) Definition of knowledge and knowledge management; 2) Knowledge management practices; and 3) Relationship between knowledge management and outcomes.

Definitions of Knowledge and Knowledge Management Before we define knowledge management, we consider the literature on the definition of knowledge. Scholars in knowledge management have defined knowledge as information processed by individuals or group of individuals and includes expertise, facts, ideas, and judgments (Wang & Noe, 2010). Thus knowledge in an organizational context can exist among individuals, teams, and other organizational units. Knowledge management scholars have also categorized knowledge in terms of whether knowledge is tacit or explicit (Nonaka, 1991; Polanyi, 1966). The essence of the concept of tacit knowledge is captured by Polanyi’s (1966) quote: ‘we know more than we can tell’ (p. 4). Tacit knowledge is knowledge that is difficult to be codified and articulated and thus this type of knowledge typically resides with an individual or group of individuals in an organization (Nonaka and Von Krogh, 2009). This type of knowledge is also subjective and difficult to share in an organization. In contrast, explicit knowledge can be codified and documented so that this knowledge can be easily shared in an organization. Although this dichotomy of explicit/tacit knowledge can be helpful in our theoretical understanding of the nature of knowledge, in practice, all knowledge can have both tacit and explicit components (Brown & Duguid, 2001; McIver, Lengthni-Hall, & Ramachandran, 2012). For example, individuals, based upon their own experiences and beliefs, can have different interpretations of knowledge that is explicit and thoroughly documented. This perceptual influence can inject a subjective or tacit component to explicit knowledge. McIver et al. (2012) define knowledge from three alternative perspectives: knowledge as commodity, knowledge through communities of practice, and knowledge in practice. Knowledge viewed as a commodity is objective knowledge that can be analyzed in terms of tacit or explicit

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knowledge. Moreover, with the commodity approach, knowledge can be transferred from one form to another using codified text or documentation. From the perspective of communities-ofpractice, knowledge is socially constructed based on the subjective experience of participants. In addition, this perspective views tacit knowledge as unspecifiable and such knowledge can be transferred through participation and learning by doing (McIver et al., 2012). The knowledge-in-practice perspective focuses on the ways in which knowledge is used. The authors define knowledge in practice as ‘information and know-how involved in the sequences, routines, capabilities, or activity systems in an organization’ (McIver et al., 2012, p. 91). Based on the knowledge-in-practice perspective, knowledge is captured through participation in activities of knowing. From this perspective, organizational context is an important factor since knowledge and the process of knowing can vary across different domains and settings. Knowledge management is considered a process that consists of four components: creating, storing, distributing, and applying knowledge that can help organizations to compete effectively (Alavi & Leidner, 2001). Hult and Tomas (2003) defined knowledge management as a systematic process of selecting, distilling, and deploying knowledge to create unique value that can be used by organizations to achieve competitive advantage. Knowledge management has also been defined as a business process in which organizations create, distribute, and use their collective knowledge (Savary, 1999). According to Powell and Ambrosini (2012), knowledge management can be considered as a systematic tool for enhancing organizational knowledge which can lead to organizational effectiveness. Darroch (2003) proposed that knowledge management can be represented as a three-stage process consisting of knowledge acquisition, dissemination, and use to enhance better organizational decision making and performance. The above review of the definition of knowledge management suggests several key elements. First, knowledge management is regarded as a process and thus the content of knowledge, whether it is tacit or explicit is less of a concern for defining knowledge management. Second, the knowledge management process is closely intertwined with creating organizational knowledge that can be leveraged to provide sustainable competitive advantage for the organization. While some scholars (e.g., Filius, de Jong, & Roelofs, 2000), have viewed knowledge management as a tactical tool by which employees use the knowledge to solve specific problems, knowledge management scholars have defined knowledge management

from a strategic perspective. Third, knowledge management is essentially a management process that goes beyond technical aspects of managing knowledge, such as creating and maintaining electronic databases for creating and disseminating knowledge.

Knowledge Management Practices Knowledge management practices involve creating, storing, and disseminating/sharing knowledge. Below we review each of the three components of the knowledge management practice process.

Creating knowledge

Informally, organizations have traditionally used social networks for creating knowledge (Powell & Ambrosini, 2012). For example, individuals in organizations may approach someone in their social network who is knowledgeable about a topic or a problem. According to these researchers, such an informal approach can be problematic for at least three reasons: First, social networks have limited reach and often knowledge can be found outside the social network. Second, even if individuals with needed knowledge are located, the person may not be responsive. Third, social networks are subject to attrition and loss of knowledge. Thus, Powell and Ambrosini are supportive of organizations using formal approaches for knowledge creation. They identify personalization and codification as two formal approaches for creating knowledge in organizations. Personalization involves the creation of a database of experts, or knowledge map that allows organizations to map expert individuals for different knowledge domains (Powell & Ambrosini, 2012). In the codification strategy, organizations strive to create an external database that documents the knowledge of its members. A codification strategy requires more effort to create knowledge but has the benefit of knowledge not being dependent on the responsiveness of the person possessing knowledge and, further, such knowledge is not affected by attrition. Thus, while each of the two strategies has their own advantages and disadvantages, the actual strategy used by an organization to create knowledge may be based on the organizational context. For example, if the knowledge is more explicit and if it can be easily learned, the codification strategy may be more appropriate (McIver et al., 2012). In contrast, a personalization strategy may be more appropriate for knowledge that is more tacit and difficult to learn. To understand how knowledge creation can occur across different organizational contexts,

Management Interventions

Anand, Ward, and Tatikonda (2010) provide a framework which suggest, that knowledge can be created by means of four mechanisms: socialization, externalization, internalization, and combination. Socialization practices enable individuals to interact with other members of the organization and capture tacit knowledge which is not codified and documented in organizations. Such practices are important in enhancing the collective organizational knowledge. Externalization practices encourage and enable individuals to codify and document their tacit knowledge. In contrast to socialization practices where distance and time can be a limiting factor for creating knowledge (because it requires physical proximity and joint action), externalization practices can transcend both time and distance (Anand, Ward, & Tatikonda, 2010). Internalization practices translate explicit knowledge to tacit knowledge. Individuals, while capturing explicit knowledge, often make improvements to the process based on their own experiences and context. For example, a quality expert may create tacit knowledge by converting the knowledge from explicit organizational documentation on quality management by making improvements based on their own experiences and unique requirements for the specific project. Combination practices combine knowledge from various external sources to create new explicit knowledge. For example, a quality expert can combine knowledge from previous best practices documented to create a unique set of practices that can be applicable to the specific organization.

Storing/retrieving knowledge

Knowledge storage practices often consist of electronic databases (Alavi & Leidner, 2001). Such databases act as a central knowledge repository which enables easy and fast access to knowledge and includes sophisticated search and retrieval mechanisms (Nevo & Chan, 2007). External databases may require information systems that support storage and retrieval of knowledge and constant updating may be involved, especially given that knowledge quickly becomes obsolete due to the fast pace of technological change. Knowledge created by socialization and internalization practices reside with the individual and thus can be subject to attrition, unlike external databases. However, expert individuals are more likely to hold current knowledge compared to external databases.

Knowledge sharing/dissemination

Knowledge dissemination practices include knowledge sharing/transfer through formal and informal channels (Villar, Alegre, & Pla-Barber, 2014). Formal channels may include systems to

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codify/document tacit knowledge into explicit knowledge. Formal human resource management systems such as socialization, training, and job rotation may also help in knowledge sharing and dissemination. Information systems are an important component of knowledge dissemination and sharing. However, effective knowledge sharing may be more dependent on a variety of individual and organizational factors (Wang & Noe, 2010). Wang and Noe provide a framework for better understanding knowledge sharing. Their framework identifies five areas that may impact effective knowledge sharing: organizational context, individual characteristics, interpersonal and team characteristics, cultural characteristics, and motivational factors. Organizational context includes management support, incentive/reward systems for employees to share knowledge, and organizational structure. For example, knowledge may be more effectively shared in organizations with an organic structure than in those with a traditional hierarchical structure. Individual characteristics may include factors such as education, work experience, personality, self-efficacy, impression management, and perceptions. Previous research suggests that more educated and more experienced individuals may be more willing to share their knowledge (Wang & Noe, 2010). This may be because such individuals may have high levels of self-efficacy and confidence in sharing their knowledge. Further, individuals with openness to experience and those who have impression management skills are more likely to share their knowledge. In terms of interpersonal and team characteristics, research has shown that less knowledge sharing occurs in diverse groups than in more homogenous groups (Wang & Noe, 2010). Team cohesiveness is another important factor that may influence knowledge sharing and members of a cohesive team may be more inclined to share knowledge. On the other hand, interpersonal conflict may impede effective knowledge sharing. Cultural characteristics include factors such as collectivism. Individuals in collectivistic cultures may be more willing to share knowledge among in-group members (Wang & Noe, 2010). Motivational factors include trust and fairness. Past empirical research has supported the notion that climate of trust and fairness promotes knowledge sharing (Wang & Noe, 2010).

Relationship between Knowledge Management and Outcomes Previous research suggests that knowledge management practices are linked to a variety of

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organizational outcomes. In a study of team knowledge management at a large Korean insurance company, Sung and Choi (2012) found that effective team knowledge management practices were positively related to financial performance by fostering greater levels of creativity among team members. Powell and Ambrosini (2012), in a qualitative study of management consultancies in the UK, found that knowledge management practices were positively related to organizational effectiveness by boosting employee productivity. In a study of service firms in Bahrain, Birasnav (2014) found that knowledge management practices were positively related to organizational performance. Lai et  al. (2014) studied the relationship between knowledge management practices and innovation performance in a sample of Taiwanese firms and found that knowledge management practices related to knowledge creation, storage, and sharing were positively related to both product and market innovation performance. Gowen et al. (2006) in their research on US hospitals, found that knowledge management practices, such as knowledge dissemination, positively impacted both quality management efforts and the company’s sustainable competitive advantage. From the above review, it seems that, although empirical research on the relationship between knowledge management and organizational outcomes is not extensive, available evidence indicates that knowledge management practices are positively related to a broad range of organizational outcomes across industries and cultures.

HUMAN RESOURCE ANALYTICS Human Resource (HR) analytics is another intervention that has attained popularity during the past decade (Lawler, Levenson, & Boudreau, 2004). HR analytics is a data driven approach to managing human resources in organizations. Rather than relying on intuition, guesswork, or rule of thumb for managing human resources, HR analytics focuses on analysis of data about various aspects of human resources to gain insights on effective management of human resources. Although this intervention is very popular among practitioners, academic research on HR analytics is sparse. Thus, our review for the most part relies on articles from the practitioner literature. Our review of HR analytics includes: 1) Definition of HR analytics; 2) Using Analytics to effectively manage human resources; and 3) Implementing HR analytics.

Definition of HR Analytics Mondore, Douthitt, and Carson (2011) defined HR analytics as a data driven approach to manage human resources that can impact important business outcomes. According to the authors, a key function of using HR analytics is to ensure that human resource effectiveness is linked to business outcomes that are considered important by top management. Davenport, Harris, and Shapiro (2010) similarly defined analytics as a sophisticated set of methods for analyzing employee data to enhance organization’s competitive advantage. The definition of HR analytics, with its emphasis on taking a data driven approach is consistent with Pfeffer and Sutton’s (2006) evidence-based management. Evidencebased management is based on hard facts about what is effective rather than managing people based on conventional wisdom, since conventional wisdom can be based on half-truths and fads. For example, based on conventional wisdom, many organizations recruit employees based on grade point average from prestigious schools. However, Google used HR analytics to establish that ability to take initiative is a better predictor of employee performance (Davenport et al., 2010). Mondore et al. (2011) also emphasize that analyzing people’s data in organizations should be linked to organizational outcomes valued by the management. Thus, metrics such as employee retention rate or time-to-hire should not be considered part of HR analytics if these metrics are not related to business outcomes since these metrics have little meaning outside the HR function (Mondore et al., 2011). A high retention rate is meaningless if it is not linked to some type of desirable business outcome. Similarly, benchmarking is not necessarily part of HR analytics unless the benchmarking metrics are related to business outcomes. As Gardner, McGranahan, and Wolf (2011) observed, managers are often tired of hearing information about best practices and what they are interested in knowing is how the information is related to success specific to their businesses. In addition to linking HR management to business outcomes, HR analytics is also viewed as a model of predictive management (CAHRS, 2010). That is, HR analytics is viewed as a management tool to model and predict future capabilities and competencies based on future business needs. This information can be useful for HR staff for proactive planning. In sum, HR analytics deals with analyzing people’s data with the objective of linking human resource management practices to specific business outcomes in the present or in the future.

Management Interventions

Using Analytics to Manage Human Resources Business managers have effectively used data analytics in the areas such as finance and marketing. For example, banks have used data analytics to build a risk profile of customers seeking loans before making a lending decision. Likewise, marketing managers have effectively used data analytics to identify high value customers for target marketing. Data analytics can be used to effectively manage human resources as well. Organizations typically collect employee data about metrics such as demographics, performance, attrition, compensation, training, skills, and competencies. HR analytics can be effectively used to link these metrics to specific HR practices and business outcomes. Below we review how HR analytics can be applied to specific areas of managing human resources. While HR analytics can be applied to any of the human resource areas, our review below focuses on some of the important areas of human resource management that are linked to organizational outcomes.

Leadership

In a recent study of Fortune 500 companies, the Center for Advanced Human Resource Studies at Cornell University (CAHRS, 2010) found that an organization used HR analytics to identify managers who are competent even under adverse economic conditions. Such data is valuable for proactively deploying these managers to business units that are likely to face an adverse business environment. Google analyzed the data on leadership characteristics, leader performance, and business outcomes and identified leadership qualities that are important for the organization (Davenport et al., 2010). The company uses this data to train current and future managers so that they attain the competencies of an effective leader.

Performance appraisal and management

Performance appraisal is one of the most contentious areas of management, prompting Deming to call performance appraisal one of the seven deadly diseases of management (Cardy & Dobbins, 1994). Organizations, based on conventional wisdom, often blame individuals for poor performance. An effective performance appraisal system requires identification of person and system factors for employee performance (Cardy & Dobbins, 1994). HR analytics can be helpful in delineating person and system factors for employee performance. Google, using HR analytics, found that the performance levels of poor performers (bottom 5%) were related to poor placement and poor management

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(Davenport et al., 2010). Thus, rather than blaming the employees for their poor performance, which is a typical reaction for most companies, Google was able to identify systems factors, such as poor person–job fit and poor leadership, as factors responsible for poor employee performance. In one of the organizations surveyed by the Cornell Center for Advanced Human Resource Studies (CAHRS), the researchers found that the organization used HR analytics to design managerial performance appraisal based on person factors such as individual characteristics and systems factors such as span of control and time in role (CAHRS, 2010). Such an approach enables organizations to control for system factors when appraising individual performance. In sum, HR analytics can enable organizations to design more effective performance management systems by analyzing data on person and system factors affecting performance.

Diversity

In the study conducted by CAHRS, one organization analyzed the data relating to gender diversity and plant performance (CAHRS, 2010). The data showed that an increased presence of female employees was positively related to outcomes in terms of fewer customer complaints and greater efficiency. The organization is currently collecting further data on what the female employees are doing in the plant that has led to positive outcomes for the organization. Another organization that employs teams from various national cultures found that diversity was positively related to outcomes related to operational performance and safety but only to a certain extent. This analysis will help the organization to design cross-functional teams with an optimum level of diversity.

Workforce planning

One of the organizations surveyed by CAHRS combined data on the size of workforce, nature of competencies with the workforce, and the projected financial growth data for the next five years to make predictions about the size and nature of talent required for future projected growth (CAHRS, 2010). This analysis enabled the company to identify talent that is available within the organization for future projected growth and additional talent that needs to be hired externally or trained internally to support future growth.

Employee engagement and satisfaction

Organizations can collect data based on surveys about employee engagement and satisfaction and relate the data to business outcomes. Sysco, a food

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services company, used HR analytics to analyze data on employee satisfaction and its relationship to revenues, customer satisfaction, and cost (Davenport et al., 2010). The company found that higher satisfaction levels were related to higher revenues, lower costs, and greater customer satisfaction. The company also collected data on factors related to job satisfaction such as supervision, quality of life, and diversity so that the company can take action to improve employee satisfaction levels.

Compensation system

The consulting firm, PriceWaterhouseCoopers, analyzed data on different compensation systems such as deferred compensation and work/life benefits and its relationship to employee retention and a company’s operational and strategic goals (Levenson, 2010). The consulting firm found that deferred compensation had a much smaller impact on retention than work/life balance programs. Thus, HR analytics enabled the organization to focus on strengthening the work/life programs which were more strongly related to employee retention and to the company’s operational and strategic goals.

Recruitment

Organizations can analyze data on current employees’ characteristics and their performance to identify predictors of high level of performance. For example, Frito-Lay found that sales performance was strongly related to prior sales experience. The company used this information in future recruitment models to give a stronger weight to prior sales experience when recruiting new hires.

Integrated HR

Analyzing HR practices in isolation sometimes may lead organizations to erroneous conclusions. Since several of the HR practices are complementary, taking a holistic view of HR functions may provide a more accurate picture of the relationship between HR practices and organizational outcomes (Aral, Brynjolfsson, & Wu, 2012). A complement of HR practices such as incentives, teamwork, job design, leadership, performance management may be a better predictor of organizational outcomes than HR practices considered in isolation. HR analytics enables organizations to combine HR practices that complement each other in terms of the impact of HR on outcomes.

Implementing HR Analytics Mondore et al. (2011) proposed a model for implementing HR analytics consisting of six steps: First,

organizations should determine critical business outcomes. This step requires active collaboration with the top management, functional managers, and managers of various organizational units. The second step is to create a cross-functional data team consisting of managers/employees who own important data relevant for HR analytics. Third, assess the measures of critical outcomes identified in step 1. The measurement should take into account characteristics such as frequency, level of analysis, and owners of outcome measures. Fourth, conduct data analysis using the appropriate statistical tools. Five, build the HR analytic system using appropriate software tools. Six, make adjustments to the systems if required to improve the system. The research conducted by CAHRS identified factors that may impact successful implementation of HR analytics (CAHRS, 2010). Data quality may be inconsistent across various sources. For example, organizational units may have different standards for data definition and measurement. There is a need to standardize data definition and collection practices so that it is easier to integrate and interpret data for analysis. Members of organizational units may also not be willing to share the data and an incentive mechanism may be needed that encourages employees to share data for analysis. An additional concern is that companies should be transparent and open in defining their objectives for data collection for HR analytics, since some of the information such as performance and attitudes are sensitive in nature. Employees may be apprehensive about providing such sensitive information.

MANAGEMENT INTERVENTIONS IN A CROSS-CULTURAL CONTEXT The management interventions discussed above were mostly developed and implemented in American and Western cultural contexts. It is important to understand if these interventions can be successfully adapted to other cultural contexts. In this section, we review research on the interventions in non-Western cultural contexts.

Quality Management Quality management has a long history and previous research suggests that quality management has been successfully implemented in virtually all parts of the globe although there were important crosscultural differences based on cultural factors. An interesting question to examine is if national culture can influence successful implementation of quality

Management Interventions

management programs. In a cross-cultural study of TQM among employees in Mexican and Canadian cultures, Galperin & Lituchy (1999) found that TQM implementation was more successful in a Mexican culture, which is collectivistic, than in the individualistic Canadian culture. The research suggests that TQM beliefs and values are more consistent with collectivistic cultures. Using global manufacturing and cultural data from organizations in Asian and non-Asian cultures, Kull and Wacker (2010), found that specific country-level cultural variables influenced the effectiveness of quality management programs. Specifically, they found that quality management programs were more effective in cultures characterized by high uncertainty avoidance. The theoretical rationale is that employees in high uncertainty avoidance cultures are more comfortable with rules and process controls and desire predictability and conformance. The research also found that assertiveness was negatively related to quality management effectiveness. Individuals in cultures that are assertive tend to be less conforming to systems and engage in competitive behaviors which are counterproductive in creating an effective quality management system. However, the Spanish study mentioned earlier (Calvo-Mora et al., 2014) supports the effectiveness of TQM in a Spanish culture. Further, the research on TQM in Mexican maquiladora (Jun, Cai, & Shin, 2006) indicates that social aspects of TQM are related to positive employee outcomes. In addition, several studies from Nair’s (2006) meta-analytic research mentioned above were drawn from samples across several cultures and all of these studies reported significant relationships between TQM and performance. It is to be noted that these studies are from a single culture and were not crosscultural, as in Kull and Wacker’s (2010) research Thus, from this limited set of studies it appears that TQM can be effective across cultures but there could be important cross-cultural variation in terms of relative effectiveness of TQM interventions. In particular, cultural factors such as uncertainty avoidance and assertiveness may play important roles in determining the effectiveness of a quality intervention. Cross-cultural research in TQM indicates that there may not be a one size fits all approach to effective implementation of TQM and managers need to consider cultural variables into account when implementing TQM across cultures.

Talent Management This intervention is relatively recent and this intervention is relatively less diffuse across the cultures. This practice, originated in the West, has

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been mainly brought to other cultures by multinationals operating in countries in Asia and elsewhere. In a cross-cultural comparison of talent management practices in China and India, Cooke, Saini, and Wang (2014) found important crosscultural differences. The researchers found that Chinese managers were influenced by Confucian values in their orientation toward adopting talent management practices. Specifically, managers in Chinese cultures viewed talent as highly competent individuals and this definition is consistent with high value placed on education and excellence in the Confucian value system. Thus, talent in the Chinese context is viewed as more selective and elitist. In contrast, Indians viewed talent as encompassing all employees which is consistent with the Universalist and inclusive perspective shared by Western managers. This may be because the Indian sample was from the high technology and knowledge intensive sector which has embraced Western values. The researchers also found that Chinese managers emphasized a strong desire for learning and growth advancement within the individual as a key outcome for talent management. This is consistent with the Confucian value of lifelong learning. In contrast, for Indian managers, the criterion for talent management is more strategic and linked to organizational goals. Consistent with the findings of the above research, Iles, Chuai, and Preece (2010), in a study of talent management in China, found that talent management focused on either exclusive individuals who showed extraordinary talent or potential or exclusive positions that are of strategic importance to the organization. Motivating talented individuals and reducing turnover of talented individuals is a key function of talent management. Tymon, Stumpf, and Doh (2010) examined if intrinsic rewards can be related to motivating and retaining talented employees in an Indian cultural context. Consistent with the notion that intrinsic motivation is universal across cultures (e.g., Ryan & Deci, 2000), the researchers found that intrinsic rewards were positively related to employee motivation and intention to stay. In research on talent management in the Arab region, Sidani and Ariss (2014) found that organizations in the Arab region used a two-tiered approach to talent management. This approach is necessitated by the fact that organizations in the gulf region employ a large number of expatriates. For the expatriates, the focus on talent management is to attract and retain people with necessary competencies and thus there is little need to develop talent of the expatriate employees. For the Arab citizens, the focus of talent management was nurturing and developing talent of individuals who show potential. In summary, talent management in global cross-cultural

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context indicates that organizations customize talent management based on cultural contexts.

Knowledge Management This intervention in the Western cultural context is mostly oriented toward creation, distribution, and reuse of existing codified knowledge (Cohen, 1998). In contrast, knowledge management in a Japanese cultural context is oriented toward tacit knowledge (Nonaka, 1991). The knowledge management process including creating and disseminating knowledge is part of a broader socialization process that exists in the Japanese organizations. The unique cultural context of Japan also facilitates extensive knowledge sharing among organizations and their suppliers and customers. Researchers have questioned generalizability of Nonaka’s (1991) conceptualization of knowledge management that seems to be successful in the Japanese cultural context. For example, Glisby and Holden (2003), contended that knowledge management in a Japanese context relies on unique characteristics such as personal commitment to the organization, the use of informal networks inside and outside the organization, communitarianism and group orientation, and a holistic approach to participative management. It is difficult for organizations in other cultures to embed such values that are unique to the Japanese culture and thus the authors caution against adoption of Japanese knowledge management techniques to other cultures. Research also indicates that knowledge management is becoming increasingly important for China (Burrows, Drummond, & Martinsons, 2005). The researchers found that just as in the Japanese context, knowledge management in China is largely tacit and contextual. However, the goal of knowledge management intervention in China is both profits and people while in Japan the criteria is social harmony and consensus. Thus, in terms of criteria, Chinese knowledge management shares the characteristics of both US and Japanese cultures. The Chinese approach to knowledge management also differs in terms of knowledge management roles. While knowledge management is the responsibility of everyone in Japanese culture, in the Chinese culture it seems that senior management and trusted supervisory staff are repositories of knowledge. Thus, knowledge management is more constrained in China due to the one-way flow of knowledge. Research by Ye, Tweed, and Toulson (2011) in a Chinese manufacturing facility corroborates the finding that knowledge management seems to be centralized in China with no apparent sharing among employees at lower levels of the organization.

HR Analytics This intervention is relatively new and outside US and Europe the adoption of this intervention elsewhere seems to be limited. HR analytics is popular in India due to its strong information technology industry coupled with business education modeled after Western universities. Thus, this intervention is readily embraced by HR managers in India (Srikanth, 2014). Research on Chinese HRM systems suggests that China seems to be lagging behind in implementing HR analytics (Shi, 2010). The human resource information system in China seems to be more limited in function and less automated and integrated. These limitations are more pronounced in medium and small enterprises while large state owned enterprises with better resources have better capabilities for building analytics systems. In addition, Chinese managers are also seen to be more apprehensive about adopting Western management without incorporating Chinese characteristics.

DIRECTIONS FOR FUTURE RESEARCH AND PRACTICE The preceding consideration of the cultural context of interventions suggests that practitioners and researchers should consider the potential impact of cultural factors. There are findings that indicate that the cultural context can be a significant factor in management interventions, but more work is needed. How interventions are perceived and implicit criteria for assessing the effectiveness of interventions may differ across cultures. Managers and researchers would benefit from identifying how interventions might best fit and operate in different cultural contexts. In addition to fit, there is also the possibility that steps could be taken to reduce the impact of cultural factors that might work against the effectiveness of an intervention. In a diverse or cross-cultural setting, it may be important to first carefully define key constructs and explicitly link intervention characteristics to desired outcomes. This initial step could help to bring a common understanding across people regarding the intervention. If people differ in terms of values, assumptions, and other characteristics, it is important that the organization recognizes and takes steps to reduce the variance. The effectiveness of this approach to implementing interventions in a cross-cultural context needs to be empirically examined. This chapter began with a consideration of factors that may influence the effectiveness of

Management Interventions

management interventions. We now turn to a consideration of possible directions for research and practice suggested by the framework and the review of interventions.

Source As discussed earlier, the source of intervention can include origins ranging from sound theory, management beliefs, a popular book, or the influence of successful practice at a competitor. Research focusing on the role of source on intervention effectiveness could be interesting and useful. In the shorter term, it may be that sources of popular influence drive interventions, but longer-term effectiveness may be more associated with theoretically sound interventions. Management interventions based on sound theory should be more likely to have an enduring influence on organizational effectiveness, but enthusiastic adoption of popular interventions may mask the importance of a sound theoretical basis, at least in the shorter term. The quality management intervention could be an interesting focus for research on the role of source on intervention effectiveness. While the quality management intervention started by being championed by quality gurus, there is theory and scholarly work that forms a basis for the approach, particularly in the operations management field. Perhaps the continued popularity of the quality management intervention is due to the theoretical basis for the approach. Certainly, other factors such as management support and the extent to which a practice was diffused in the organization (e.g., Beer, 2003) are important for the success of an intervention. In addition, individual organizational practices of quality management may vary based on social context (Ansari, Fiss, & Zajac, 2010). However, the source of a management intervention could be revealed in research examining quality management programs. For instance, quality management interventions in organizations could be classified as being initiated based on faddish reasons of popularity versus concern for sound theory and practice. The difference in source across organizations could then be investigated as a possible influence on the long-term viability and effectiveness of the intervention. Knowledge management has a theoretical basis (Nonaka, 1991), but adoption of the intervention can have a faddish character. Drawing upon the models of management fads developed by Abrahamson and colleagues (Abrahamson, 1991; Abrahamson & Fairchild, 1999), Ponzi and Koenig (2002) analyzed if knowledge management is

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another management fad. Their research concluded that knowledge management outlived the typical life of a management fad and is in the process of establishing itself as a new aspect of management. Thus, as with quality management, research could examine whether the relatively enduring presence of knowledge management can be attributed to its source being popularity or theoretical foundation. In contrast, the theoretical basis for talent management is still developing (e.g., Gallardo-Gallardo et al., 2013). Likewise, the HR Analytics approach seems to be mainly driven by practitioners. The arc of these interventions could also provide interesting data for research on the role of source in interventions. The source factor also has applied implications. Practitioners might be mindful of the role of source when considering an intervention. A strong leadership style may prompt managers to take ownership for an intervention. However, management as the source could result in employees questioning the validity of the approach. A risk-averse management approach could be to stress the external source of an intervention. On the one hand, with an external and highly regarded source, the intervention may enjoy the benefit of having broad positive assessment of its validity. On the other hand, a less-than-effective or failed intervention can be blamed on the external source. Realistically, there may be political advantage if the source of an intervention is perceived to be external. While a risk averse and calculative management may see real benefit from promoting the perception that the source of an intervention is external, an internally developed intervention may have great merit. A skilled management team, for example, could have been understanding of what is needed in the organization. If the management team also enjoys the respect of employees, an internal source may, in this type of circumstance, have the best potential for having a positive outcome for the organization. Of course, management in this circumstance would have difficulty distancing itself from the intervention if it failed. The two by two matrix presented in Figure 17.4 summarizes the preceding discussion concerning potential costs and benefits associated with an internal versus external source of an intervention. As depicted in the figure, perceptions of an external source can have the benefit of having external validity. If the intervention does not go well, the perceived external source can also reduce costs to management by reducing the amount of direct responsibility management had for the design of the intervention. An intervention with an internal source has the potential benefit of being carefully designed based on understanding of the

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Lowered due to reduced responsibility

Internal

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Internal information

Higher due to management responsibility

Figure 17.4 Potential benefits and costs associated with sources of intervention organization. The potential cost associated with an intervention having an internal source is that the designers, likely management, would have direct responsibility if the intervention did not go well. As mentioned previously, a calculative and politically oriented management would seem likely to choose an external source for an intervention, or at least to take steps to engender the perception that the source of an intervention was external. In contrast, management more focused on performance improvement might be more likely to pursue internal development of an intervention. From a practitioner perspective, it can be instructive to consider the costs and benefits that can be associated with an internally or externally developed intervention. It may also be instructive to consider why a particular source is being chosen and what this might indicate about the nature of management in the organization. If, for example, steps are being taken to distance management from an intervention, why isn’t the intervention being embraced and ‘owned’ by management? Perhaps top management doesn’t really believe in the efficacy of the intervention? Perhaps the management team has political motives and wants to include the latest fad but wants to reduce any responsibility for it? Raising these types of issues can be difficult but the process has the potential to clarify and possibly change motives. From a research perspective, the perceived costs and benefits associated with interventions having an internal or external source could be a fruitful area of study. Whether the extent of the political orientation of management influences these perceived costs and benefits is a question

that could help to explain the decisions and actions of management in regard to interventions and other choices. Further, contrasting the perceived costs and benefits of management and employees and with estimates of actual costs and benefits could reveal differences and biases in assessment that might exist between the management and employee perspectives.

Signal Signal was also discussed as a dimension that can influence the effectiveness of interventions. Signals that the purpose for an intervention is something other than improving organizational effectiveness can have important effects on the motivation and dedication of employees to the intervention. For example, interventions such as Six Sigma and talent management have suffered from the ‘hopping on the bandwagon’ effect which can lower the effectiveness of the intervention. We encourage future research on the possible role of signals in management interventions. Whether the intent of an intervention is perceived to be organizational effectiveness or a political purpose could be an important variable in predicting intervention effectiveness. It is possible that perceptions of political motives could lower the effectiveness of even theoretically sound interventions. The signals factor underscores the possibility that the perception of interventions can be an active rather than passive process (e.g., Balcetis

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& Lassiter, 2010). That is, employees and other stakeholders likely don’t just passively receive information regarding an intervention. Rather, given their perspectives, experiences, values, and so on, employees and others interact with incoming information and actively interpret the meaning of an intervention. Given that employees are active perceivers, an intervention may bring with it a number of unintended messages. For example, the announcement of a diversity program could convey a variety of unanticipated and unintended messages. A diversity program could, unintentionally, convey to workers that discrimination and conflict among various subgroups is such an issue that the intervention has to be rolled out. Or, the general perception could be that diversity problems are nonissues in the organization and the intervention is being carried out because other organizations are implementing such programs. In either case, these perceptions may be inaccurate and unintended. However, those unintended signals could have important influences on the effectiveness of the intervention. In the case of diversity interventions, some negative effects of diversity programs, such as increased discomfort (Bieschke, Gehlert, Wilson, Matthews, & Wade, 2003), has led to the suggestion that diversity programs should be presented as opportunities (Curtis & Dreachslin, 2008; Jayne & Dipboye, 2004). As graphically presented in Figure 17.5, an intervention can have both intended and unintended signals. The intended signals consist of the direct messages associated with an intervention. For example, the major content and purpose for an intervention could be part of the direct

and intended message associated with an intervention. These intended messages should be related to the effectiveness of the intervention. For example, a clear presentation regarding why a program is needed and what it will consist of would be expected to lead to positive outcomes, such as increased acceptability of the program and smoother implementation and operation of the intervention. In addition to direct messages, an intervention will likely include unintended signals. These signals may, for instance, be inferences regarding the ‘real’ cause or reason for the program and the unstated goals behind the program. These unintended signals can, in turn, influence both the direct messages and the effectiveness of the intervention, as depicted in Figure 17.5. Unintended signals can influence the interpretation of the direct messages concerning an intervention. If, for example, employees perceive the reason for a program to be political in nature, this perception will likely impact how the direct message regarding the reason for the intervention is interpreted. It is likely that the unintended signals in this type of situation can lead people to discount the direct message and to reinterpret it as rhetoric. To the extent this happens, there would be a negative influence on the effectiveness of the intervention. In addition to altering the direct messages, unintended signals can have a direct effect on intervention effectiveness. This link in the model illustrates that unintended signals can influence, for example, the degree to which a program is acceptable and might be undermined or embraced by organizational members. Perceptions regarding the purpose of a program despite the stated

Intended Consequences

Effectiveness

Intervention

Unintended Messages

Figure 17.5 Intended and unintended intervention signals as influences on effectiveness

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Intended

Unintended

Strong

intervention. The rationale for an intervention needs to be made clear so that people have an understanding of the purpose for the intervention. Presented in cell 1 of the matrix, other actions to amplify the signal include a consistent message and a systematic approach to implementation. A top management announcement of a planned intervention can be seen as a signal that can be boosted by consistent messages being provided by leaders at lower levels throughout the organization. Opportunities to provide consistent messages could include meetings, workshops, and the use of social media to disseminate accurate information. The intended signals regarding an intervention can also be maintained and boosted by a systematic approach to implementation. For example, the steps involved in a planned intervention and a reasonable timetable can make the operational plan clear to workers. If the intended signals regarding an intervention are weak, the message regarding the planned intervention would be deficient. In the absence of a strong signal, people would be expected to be unsure about the genesis and intent of the program. Uncertainty can be a state that motivates people to seek out other information and reduce their uncertainty (Afifi & Weiner, 2004). If the information can’t be found in the intended message because it is too weak or inconsistent, people can be expected to turn to other sources to understand the program. Unfortunately, unintended signals could be available and, by default, provide reduction in uncertainty. The matrix conceptualization in Figure 17.6 also presents factors that can strengthen or weaken the noise of unintended signals. An organizational

Clear statement Consistent message Systematic implementation

Low trust Negative culture

Weak

purposes, can have an important influence on the implementation and ultimate success or failure of an intervention. Future research on interventions could include examination of the direct and indirect effects of signals. Surveys or interviews of employees could provide assessment of espoused and perceived purposes for an intervention. Contrasting these perceptions between management and employees could reveal interesting discrepancies between the management and employee perspectives regarding an intervention. Measurement of the direct and indirect signals regarding an intervention will allow testing of the model proposed in Figure 17.5. Practitioners also need to be cognizant of signals regarding an intervention. Launching an intervention can be thought of as a message that involves signal and noise. This analogy has been applied to performance evaluation in which steps can be taken to increase the signal and other actions can be taken to reduce noise (Cardy & Leonard, 2011). Applying the signal and noise framework to an intervention is presented in Figure 17.6. The matrix conceptualization in Figure 17.6 presents signals as intended and unintended, with intended messages being the signal and unintended being noise. As a rough analogy, the successful launch of an intervention is akin to tuning in a radio: you want to maximize the signal and limit the noise. As presented in the matrix there are steps that can be taken to increase the signal and to decrease the noise. Steps that can be taken to increase the intended signals of an intervention include, perhaps first and foremost, a clear statement regarding the planned

Deficient

Positive culture Transparency

Figure 17.6  Conceptual framework for increasing intended signal strength and reducing noise

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environment characterized by low trust and a negative culture would be expected to heighten the noise surrounding an intended intervention. This type of environment would likely lead to workers looking for an alternative explanation and viewing a planned intervention through a political lens. In contrast, a positive culture and a track record of management transparency would weaken the unintended signals. Overall, an applied implication of the conceptualization portrayed in Figure 17.6 is that managers should take steps to boost intended signals and also take steps to reduce the noise of unintended signals. Both sets of actions could help assure a smooth launch of an intervention.

Breadth The framework for management interventions at the beginning of this chapter also identified the breadth of interventions as a potentially important variable. The breadth factor refers to the content of an intervention. The content of an intervention can vary from task-oriented and narrow to strategic and broad, as depicted in Figure 17.2. Interventions can be viewed as offering potential solutions, but those solutions range in content and can be narrow or broad. Taking medical interventions as an analogy, the job of management is akin to a doctor selecting the treatment that will be successful. A key to rationally selecting the best treatment or intervention is an accurate diagnosis. Unfortunately, a number of factors can lead to

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misdiagnosis. Even in the medical setting, where diagnosis is an obvious key step in determining treatment, it is estimated that there is at least a 5 percent error rate (Singh, 2014). This rate translates into 12 million or more adults in the United States being misdiagnosed each year. The rate of misdiagnosis in management would not be expected to be better. The process of selecting an intervention is presented in Figure 17.7. Diagnosis of the organization is a path that determines the need for an intervention. Careful consideration of, for example, strengths, weaknesses, and causes of performance problems in the organization should be part of the diagnosis step. Once the diagnosis is complete, the needs of the organization can be identified. The result of this internal and rational analysis should be the selection of an intervention that best addresses the need in the organization. This process may, however, involve errors. The diagnosis step, for example, may be incomplete and be given short shrift. The cause of problems and what needs to be done to improve could be based on assumption, rather than analysis. Diagnosis is not the only path that determines the need for an intervention. As depicted in Figure 17.7, factors external to the organization may also lead to a perceived need and the adoption of an intervention. This path reflects the possibility that an intervention is adopted due to the popularity of the program or its adoption by key competitors in the market. This external path reflects the criticisms of faddishness that are often raised regarding management interventions. Selecting and implementing an intervention based on external factors separates the

External factors

Organization • Strengths • Weaknesses

Diagnosis

Figure 17.7 Intervention selection process

Needs

Intervention

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intervention from diagnosis of issues in the organization. In other words, the external path in the model results in a treatment that may not address deficiencies in the organization. The needs addressed in this path may reflect, for instance, the desire to avoid stakeholder perceptions that the organization is not keeping pace with competitors. The external path of selecting an intervention may address needs, but it would not be expected to result in alignment between the breadth of the intervention and the internal needs of the organization. Since the internal needs of the organization would not drive the selection of an intervention based on the external path, there can be misalignment between the breadth of the intervention and internal organizational needs. This misalignment would be expected to result in a difficult or failed intervention in that it may not fit with and address internal issues. However, the adoption of the program may still be considered a success based on the needs driven by the external factors. Researchers examining the effectiveness of management interventions could examine effectiveness from the internal and external paths. The criteria would be markedly different. There is the provocative possibility that an internal failure could still be considered by management to have been necessary and to have served its purpose. More broadly, the process of selecting interventions depicted in Figure 17.7 could be tested. The extent of diagnosis and the degree of fit between intervention breadth and the needs of the organization could be examined as determinants of the effectiveness of interventions. Research regarding the selection process could illuminate how misfits occur and when organizations might be susceptible to fads. Practitioners need to carefully consider whether external factors should drive the adoption of interventions. Are there circumstances when externally driven needs supersede internal needs? In terms of the internal path, diagnosis is critical to selecting an appropriate intervention. An understanding of performance issues in the organization and their causes is needed. An understanding of the intervention options is also needed so that an intervention with a breadth that fits the organization’s needs can be selected.

Implementation Implementation was also identified in the opening framework as an important characteristic relating to organizational effectiveness. For example, the importance of top management support and involvement is commonly emphasized in the implementation of management interventions. However, successful implementation may need more than endorsement from top management.

For example, providing incentives has been suggested as an important component in implementing an intervention (Ajmal, Helo, & Kekäle, 2010; Raineri, 2011). An intervention may be a great plan to those who design it and decide to put it in place. However, it is the employees who experience the program and need to make it a reality. The employees may need an incentive to help them make whatever changes might be needed and to adopt the new program. Implementation efforts may also benefit from an approach that emphasizes multiple means of communication and involvement. For example, an examination of interventions to improve the professional practices of health professionals found little or no effectiveness for efforts that relied on one means of influence (such as mailing of materials or holding of conferences). In contrast interventions that combined multiple means of disseminating information were found to reduce the incidence of inappropriate performance at levels ranging from 20 to 50 percent (Oxman, Thomson, Davis, & Haynes, 1995). Another factor that may play a role in implementation is the degree to which employees have familiarity with the intervention (Ajmal et  al., 2010). Simply stated, employees need an understanding of an intervention if it is to be successfully adopted into the organization. Implementation factors such as leadership and top management support, incentives, multiple means for communication, and employee familiarity may be important to the successful adoption of an intervention. Future research on these and other implementation tactics could provide useful findings for scholars and practitioners. The components of implementation identified here can be broadly categorized as reflecting employee ability or employee motivation regarding an intervention. For example, employee familiarity would reflect the knowledge and ability people have regarding an intervention. Incentives would influence employee motivation regarding the intervention. The simple human resource management equation of performance is a multiplicative function of ability and motivation may also generically apply to the implementation of an intervention. To be successful, management needs to take steps to ensure that employees have the ability and motivation to make the intervention a reality in the organization. Future research could identify implementation components and score them in terms of their influence on ability and/or motivation. Such research could identify implementation factors that most effectively address ability and motivation. Further, the circumstances under which ability or motivation may be more influential in implementation could be identified.

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Configuration Finally, research examining the configuration of the influences of source, signal, breadth, and implementation is also recommended. It may be that there is no one best approach to any of these characteristics, but that the package of how these issues are addressed may be important. For example, it may be that a particular source works best with a certain set of implementation characteristics. Research addressing the configuration of characteristics involved in interventions is needed in order to address this issue. Considering the configuration of factors is a complex task for practitioners and researchers. For practitioners, determining the most workable combination of factors may be difficult and involve some intuition and guesswork. For future research, considering the configuration of factors may be difficult to empirically capture. However, configural effects may prove important and useful determinants of intervention effectiveness. Overall, we have discussed a broad array of factors for consideration in future research. Management interventions are a rich and applied domain in which numerous models and linkages can be tested. We hope we have piqued interest in pursuing some of the many possibilities.

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18 Employee Participation Melissa Chamberlin, Jeffery A. LePine, D a n i e l W. N e w t o n a n d L i n n Va n D y n e

INTRODUCTION Shortly after being asked to write this chapter on participation, we spent some time brainstorming about which specific aspects of participation to cover. Each of us was aware of the literatures on participative decision-making (e.g. Black & Gregersen, 1997; Dachler & Wilpert, 1978; Locke & Schweiger, 1979) and participative goal setting (e.g. Latham, Erez, & Locke, 1988; Locke, Latham, & Erez, 1988), however, we also recognized that research on these specific topics peaked in the mid1990s after reviews indicated weak effects for participation when methodological artifacts were taken into account (Wagner & Gooding, 1987; Wagner, 1994). At that point, we wondered what we could accomplish given the existence of these previous reviews and the findings they report. After some reflection, we realized there is research on employee participation centered on concepts that do not reference the word ‘participation’, or derivative words such as ‘participative’, ‘participating’, or ‘participate’, explicitly in the concept’s label or definition. As an example, quality circles (e.g. Banker, Field, Schroeder, & Sinha, 1996; Brockner & Hess, 1986; Griffin, 1988; Marks, Mirvis, Hackett, & Grady, 1986; Munchus, 1983) are formal structures established by organizations to involve employees in matters affecting production

and operations. Again, however, research on quality circles peaked at least two decades ago, and so we asked ourselves where the research literature on employee participation might live today. It soon became clear to us that scholars continue to study employee participation, but the focus has shifted away from organizational practices and policies that are intended to encourage participation towards activities that reflect participation itself; voice (e.g. Van Dyne & LePine, 1998), issue selling (e.g. Dutton & Ashford, 1993), and upward influence (e.g. Athanassiades, 1973; Foste & Botero, 2012) are prime examples of this shift. Additionally, there is research on psychological states, such as psychological empowerment, that reflect perceptions of participation in terms of influence and involvement (e.g. Conger & Kanungo, 1988; Kirkman & Rosen, 1999; Spreitzer, 1995; Thomas & Velthouse, 1990). Unfortunately, research on these participationrelated concepts has not been linked in a way that provides a coherent theoretical overview of employee participation. Thus, our goal in this chapter is to provide a review of the research literature on topics related to employee participation and to connect these topics in a way that provides an understanding of what participation involves and how the different elements of participation are connected. More specifically, we offer a unifying

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definition of employee participation, an integrative framework that binds together the various elements of participation-related research, and a set of recommendations for future research based on important gaps in our understanding of participation policies and practices, participating performances, and psychological participation.

A BRIEF HISTORY OF EMPLOYEE PARTICIPATION The roots of employee participation appeared during the early 1900s in a management philosophy called industrial democracy. Though the term was initially introduced by English social economists Sidney and Beatrice Webb (1898) as a part of their work on trade unions, Fritz Naphtali and colleagues of the German Trades Union Congress in 1928 broadened industrial democracy, or ‘Wirtschaftsdemokratie’, to the idea of co-­ determination (Müller-Jentsch, 2008), which described more general employee involvement in organizational decision-making and ownership of work responsibilities. During the 1920s and 1930s, the Columbia Conserve Company was one of the first organizations to introduce concepts of industrial democracy into the workplace (Bussel, 1997; Hapgood, 1975). In contrast to the goal of management practices that aimed to achieve efficiency in employee activities related to core job tasks, company owner William Hapgood believed it was important to treat employees as valuable, almost familial, members of the company. Hapgood provided his employees with opportunities to hold a position on the company’s council, where they could take part in the company’s primary governing body and make decisions that influenced employees. With the ultimate goal of shifting company control to the workers themselves, Hapgood offered employees education and on-the-job training. Notwithstanding Hapgood’s efforts, and to his surprise, employees were reluctant to accept management roles and instead preferred to have their leaders make decisions that impacted the course of the company (Bussel, 1997; Hapgood, 1975). This reluctance, together with external market pressures, ultimately resulted in the company abandoning its practice of direct employee participation. Though the Columbia Conserve Company’s ideals of industrial democracy fell short in achieving its goals, the seeds of employee participation were being sown elsewhere. Specifically, the human relations movement began to emphasize the notion that managing organizational effectiveness required an

understanding of a broader array of employee capabilities and needs than was previously considered by scientific management principles (Roethlisberger & Dickson, 1939). In a series of experiments in the 1920s and 1930s at Western Electric’s Hawthorne plant, researchers demonstrated that changes in social settings – work conditions, rest periods, social support, and supervision – had important implications for employee morale, satisfaction, and productivity (Mayo, 1946; Roethlisberger, 1959; Roethlisberger & Dickson, 1939). The findings of the Hawthorne studies hinted more broadly at what Dewey (1917) had earlier suggested: that individuals respond to cues in their physical environment and in social interactions, particularly those that satisfy the need for social control. As the ideas of industrial democracy and the human relations movement spread, additional research sought to explore the importance of contextual influences and social interactions to employees, small groups, and organizations. In particular, Kurt Lewin and colleagues conducted numerous studies at the Harwood plant in Marion, Virginia during the 1940s. These studies extended their earlier research on groups of children that suggested democratic groups had less conflict and tension between leaders and followers than autocratic groups (Lewin, Lippitt, & White, 1939; Marrow, 1969). Situating this finding more firmly in the workplace, Lewin focused the Harwood studies on the idea that democratic participation was important to individuals because involvement gave them new perspectives on how to understand and structure their environment (see Burnes, 2007 for a review of this work). That is, active participation allowed employees to explore problems and issues associated with their work, in addition to offering them opportunities to make decisions that influenced how they managed and executed their work responsibilities (Burnes, 2007; Lewin, 1951; Marrow, 1957, 1969). From this perspective, the benefits of participation were two-fold: employees would be less likely to resist change if they were involved in making decisions, and involvement would motivate them to follow through and execute those decisions (Lewin, 1951; Marrow, 1969). Especially in the early years at Harwood, Lewin was the driving force behind participation studies (Burnes, 2007; Marrow, 1969) on topics such as overcoming resistance to change (Coch & French, 1948), the importance of leadership training vis-à-vis role-playing (French, 1945), and altering stereotypes (Marrow & French, 1945). The work of Lewin and his colleagues established the idea that the ability of employees to make meaningful contributions to work-related decisions through participation is important because it promotes their satisfaction and performance.

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While Lewin’s work focused on the psychological processes of participation, other researchers began to consider how the interaction between workers and environmental conditions of the work – particularly technology used to complete tasks and duties – could influence employee involvement. In a series of studies of British coal miners, Trist and colleagues (Trist & Bamforth, 1951; Trist, Higgin, Murray, & Pollock, 1963) determined that implementing new technologies was insufficient for improving production; instead, alignment between workers and the technological systems was essential. More specifically, production was highest when employees formed and managed their own work teams (Trist et al., 1963) which could better respond and adapt to new technology and changes in the work environment. The formation of these so-called composite work groups had important consequences. For example, the composite work groups reduced the extent to which miners suffered from psychosomatic disorders and decreased interpersonal and intergroup conflict (Herbst, 1974). Stated differently, by implementing their own methods for completing work tasks, employees were not only more productive, but also healthier. Trist and colleagues (Trist & Bamforth, 1951; Trist et al., 1963) referred to this interaction between workers and technology as the socio-technical system, and suggested that organizations will only function optimally when social and technological systems are designed to fit the demands of one another and the environment (Pasmore, Francis, Haldeman, & Shani, 1982). Building on this socio-technical school of thought, Swedish auto manufacturers introduced a variation on this idea, which was epitomized in the ‘reflective production’ that developed at Volvo during the late 1980s. Moving away from traditional assembly line manufacturing, Swedish work design focused on reducing vertical divisions of labor and creating work groups with greater autonomy, independent decision-making, and emergent leaders (Berggren, 1992). At Volvo, this meant workers were divided by function and given responsibility for producing a sub-system or functionally coherent part of the vehicle (Ellegard, 1995; Pil & Fujimoto, 2007). Employees were provided opportunities to think about the vehicle holistically (e.g. Pil & Fujimoto, 2007) and logically anticipate what parts would be necessary for construction (Freyssenet, 1998). By creating greater individual accountability for the final product and allowing for greater flexibility and participation in production-related decisions, the quality of cars at Volvo improved (Karlsson, 1996). The experiments and foundational research described in the preceding paragraphs suggest that employees are influenced by factors beyond the demands of the work itself and direct instruction

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from supervisors, and that opportunities to participate in decisions may play a particularly important role in this regard. Indeed, these perspectives led scholars to consider specific means by which managers could provide employees with explicit and direct opportunities to get involved (e.g. Locke, 1968; Lowin, 1968), and participation came to be understood in terms of decision-making, problemsolving, action-planning, and related activities (Wagner, 2000) that reflect intentional efforts of individuals at higher organizational levels to provide individuals or groups at lower levels with opportunities to exercise influence (Glew, O’Leary-Kelly, Griffin, & Van Fleet, 1995) or share expertise (Mitchell, 1973). Subsequent research focusing on specific intentional efforts and practices continued for the next several decades with an emphasis on the practical question of how participative structures influenced the effectiveness of employees (e.g. Brockner & Hess, 1986; Dossett, Latham, & Mitchell, 1979; Erez & Arad, 1986; Locke & Schweiger, 1979). However, not unlike the original conclusions of Hapgood regarding the Columbia Conserve Company’s experiment with industrial democracy, reviews of this research have indicated weak and inconsistent effects (e.g. Pereira & Osburn, 2007; Wagner, 1994; Wagner & Gooding, 1987, Wagner, Leana, Locke, & Schweiger, 1997). An apparent consequence of the lack of consistent effects produced or reported in the reviews of participation was a diminishing amount of research on participation in the early 1990s that slowed to a trickle by the middle of the decade. At about the same time that interest in practices encouraging participation started to diminish, scholars began devoting more attention to understanding employee behaviors that were outside of core job responsibilities (e.g. Smith, Organ, & Near, 1983; Borman & Motowidlo, 1993). Rather than focusing on the motivational ramifications of these behaviors, scholarly interest was driven more by the realization that such behaviors had been ignored in research and practice, despite their prevalence in organizations and importance for organizational functioning and effectiveness (Katz, 1964; Katz & Kahn, 1978). Importantly, although a wide range of affiliative behaviors have been studied as examples of organizational citizenship behavior (Organ, 1997) and contextual performance (Motowidlo, Borman, & Schmit, 1997), researchers have also focused on behaviors that are more directly relevant to the earlier research on participation. Perhaps the most obvious example is voice behavior, which is defined as extra-role expressions of constructive ideas and suggestions that challenge the status quo or initiate change (Van Dyne, Cummings, & McLean Parks, 1995).

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To date, however, scholars have yet to connect participation-related behaviors, such as voice, to the practices and structures that grew out of Lewin, Trist, and their colleagues’ influential work on participation. Accordingly, our understanding of participation is fractured. Although the research on

participation spans several related phenomena as shown in Table 18.1, these domains have not been connected, and it is difficult to understand relationships across the various concepts that have been studied over the last century. This lack of a conceptual scaffolding has made it difficult to accumulate

Table 18.1 Summary of the history of participation and participation-related concepts Participation lens

Citations

Definition

Industrial democracy Human resource perspective

Webb & Webb, 1898; Naphtali, 1928 Hawthorne studies: Roethlisberger & Dickson, 1939; Mayo, 1946; Roethlisberger, 1959 Trist & Bamforth, 1951; Trist, Higgin, Murray, & Pollock, 1963

Encompasses employee involvement in organizational decisionmaking and greater ownership of work responsibilities.

Socio-technical perspective

Introduced the idea that treatment of employees has a direct impact on morale, satisfaction, and productivity.

Workers interact with their technology and their environment and can only be fully productive when these elements (social and technical) are aligned. Employees are most productive when given flexibility to manage themselves and adapt to work tasks as needed. Participation Current review The encouragement of employee involvement and influence in matters related to work and the management of work, as well as the psychological and physical influence- and involvement-related activities that manifest from this encouragement. Participation policies Current review Initiatives implemented by management that provide employees with and practices opportunities to be involved in and/or influence the course of work-related issues.  Participative Locke & Schweiger, 1979 Process for joint decision-making wherein supervisors and decision-making subordinates make decisions together.   Participative goal Locke, 1968; Locke & Latham, Collective activity where group members set meaningful goals setting 1990 together and cooperate to attain the goals. As a derivation from goal setting theory, it suggests that employee involvement in goal setting has motivational benefits to the employee.   Quality circles Brockner & Hess, 1986; Groups of employees who meet regularly and voluntarily to solve Munchus, 1983 problems relevant to the work and make suggestions to leaders on how to improve the work environment. Psychological Current review An internal motivational state that is activated, at least in part, by participation the policies and practices management puts in place, and which manifest in behavioral activities to influence work.  Psychological Spreitzer, 1995; Thomas & Considered a motivational construct that reflects the psychological empowerment Velthouse, 1990 connections employees have to their work and work performances. Includes four dimensions (meaning, competence, self-determination, and impact). Participating Current review Employee activities that reflect involvement and influence in their performances work and the management of work, beyond what is established as part of the employee’s work role or job itself.  Voice Van Dyne, Cummings, & Constructive challenges to the status quo and change-oriented McLean Parks, 1995; Van suggestions for improvements to unit and organizational Dyne & LePine, 1998 processes, policies, and standard operating procedures.   Issue selling Ashford et al., 1998; Dutton & Discretionary behavior of employees that attempts to shape strategic Ashford, 1993 agendas by emphasizing organizational trends or drawing attention to specific issues.   Upward influence Athanassiades, 1973; Roberts Employee attempts to influence or transfer information to supervisors & O’Reilly, 1974 or leaders higher in the organizational hierarchy.

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knowledge across the various topic areas into a more cohesive literature, and has created challenges in identifying critical participation-related questions that need to be studied. Consequently, the field could use a fresh perspective on participation that integrates and clarifies definitional boundaries, reconciles mixed empirical findings, and provides a foundation for future inquiry. The framework we propose next is offered with this need in mind.

A MULTIDIMENSIONAL MODEL OF EMPLOYEE PARTICIPATION We begin by conceptualizing employee participation as reflecting the encouragement of employee

involvement and influence in matters related to work and the management of work, as well as the psychological and physical influence- and involvement-related activities that manifest from this encouragement. To this end, and as depicted in Panel 1 of Figure 18.1, we view employee participation as a broad overarching concept that embeds many narrower participation-related constructs. As we noted earlier, some of these constructs explicitly reference ‘participation’ (e.g. participative decision-making or participative goal setting), although most do not. Considering the nature and functioning of these participation-related concepts, and as depicted in Panel 2 of Figure 18.1, several distinct types of participation emerge. First we have participation policies and practices, which refer to the techniques and methods of encouraging employee input and

Participation-related concepts

Participative decision-making Participative goal setting

Empowerment

Voice

Self-managing teams

Issue selling

Shared leadership

Upward influence

Quality circles

Panel 1 Participation policies and practices

Participative decision-making Participative goal setting Quality circles

Participating performances

Empowerment Self-managing teams Shared leadership

Voice Issue selling Upward influence

Panel 2

Figure 18.1  Conceptualization and grouping of participation-related concepts

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influence in matters related to the management of work and work responsibilities. As depicted in the left circle of Panel 2, these policies and practices are often reflected in organizational structures and specific management practices and policies that directly support employee involvement. This type of participation, therefore, includes participative decision-making, participative goal setting, and quality circles. The second aspect of participation, participating performances, appears in the right circle of Panel 2 and refers to the activities and work behaviors through which employees enact their involvement and influence at work. To clarify the differences in these first two types of participation, participation policies and practices are nouns – things the organization puts into place to encourage participation. In contrast, participating performances are verbs – actions that the employees take as they participate. These participating performances include voice, issue selling, and upward influence. Although this perspective on employee participation accounts for a relatively full domain of participation-related concepts, it also brings new insight to concepts that are ‘fuzzy’ because they include elements from multiple types of participation. For example, empowerment, selfmanaging teams, and shared leadership include elements of participation policies and practices as well as participating performances. Thus, we depict them in the overlapping section of Panel 2. As we will explain later in this chapter, the field can make progress in understanding these ‘fuzzy’ concepts by explicitly disentangling the underlying elements of participation in future research. As an example, we argue that splitting empowerment into structural empowerment and psychological empowerment (e.g. Spreitzer, 1995; Thomas & Velthouse, 1990) illuminates a vital element of the participation process. Indeed, there are specific psychological mechanisms, exemplified by psychological empowerment, which reflect activation of egoinvolvement with participation, and therefore serve to transmit the effects of participation policies and practices into the behaviors of participating performances. This idea is not new. As Allport (1945) noted, true participation is distinguished from mere activity to the extent that the activity reflects investments of the self. Curiously, this third and final type of employee participation, psychological participation, has been virtually overlooked as a focal element of the participation process. We next review the concepts in the three general areas of our framework, and then discuss ‘fuzzy’ concepts that span more than one type.

Participation Policies and Practices Participation policies and practices refer to initiatives implemented by management that provide employees with opportunities to be involved in and/or influence the course of work-related issues. We sample the breadth of participation policies and practices by examining three concepts that have received significant research attention: participative decision-making, participative goal setting, and quality circles. Though not exhaustive, each of these concepts provides insight into how participation has been understood and enacted by organizations. Participative decision-making and participative goal setting focus primarily on interactions between supervisors and employees, while quality circles focus primarily on interactions among employees, followed by upward communication to supervisors. By reviewing these concepts, we aim to clarify our conceptualization of participation policies and practices.

Participative decision-making

Participative decision-making (PDM) describes a process for joint decision-making (Locke & Schweiger, 1979) wherein supervisors and subordinates make decisions together even though the supervisor maintains the ultimate authority for the decision (Abdel-Halim, 1983). There is, however, great variability in how PDM is enacted. For example, PDM may be required by law or contract (Dachler & Wilpert, 1978; Locke & Schweiger, 1979) or it can result from a volitional agreement between management and employees. This required or volitional distinction, in particular, was a relevant distinguisher between how PDM was manifest in Europe and the United States. In Europe, PDM was imposed, whereas in the United States, it was voluntarily encouraged. PDM can also arise formally (e.g. boards or committees) or informally in the course of day-to-day work interactions (Black & Gregersen, 1997; Cotton, Vollrath, Froggatt, Lengnick-Hall, & Jennings, 1988; Dachler & Wilpert, 1978; Locke & Schweiger, 1979). It can be direct (e.g. employees personally contribute) or indirect (e.g. employees elect representatives to make decisions on their behalf; Cotton et  al., 1988; Dachler & Wilpert, 1978; Locke & Schweiger, 1979). The length of PDM can be extensive (e.g. continuous involvement in work decisions) or more limited (e.g. onetime training sessions; Cotton et  al., 1988). Likewise, PDM can vary in scope and deal with issues ranging from job design to more general work conditions or personnel functions (Black & Gregersen, 1997; Dachler & Wilpert, 1978; Locke & Schweiger, 1979). Finally, the nature of the participation varies depending on when employees

Employee Participation

are invited to participate in the decision-making process. They can contribute to discovering problems, generating proposed solutions, or helping choose solutions (Black & Gregersen, 1997; Locke & Schweiger, 1979). Even though the context, type, length, content, and scope may vary, PDM represents an organizational practice whereby employees are encouraged to contribute to the decisions they may ultimately execute (Lowin, 1968) and to take greater responsibility for work-related issues that impact them directly. PDM can benefit employees and organizations. PDM is satisfying to employees and enhances their creativity and motivation to perform (Lawler, 1986; Locke, Alavi, & Wagner, 1997; Locke & Schweiger, 1979; Schweiger & Leana, 1986). PDM is positively associated with perceived influence (Jackson, 1983) as well as affective and normative commitment (Elele & Fields, 2010; Liu, Wang, Hui, & Lee, 2012), and is negatively associated with role conflict and role ambiguity (Jackson, 1983). Finally, PDM can strengthen connections between supervisors and employees by providing opportunities to develop responses to complex challenges (Ashmos, Duchon, McDaniel, & Huonker, 2002). Despite these findings, PDM studies have not demonstrated consistent or meaningful relationships with outcomes such as job performance, satisfaction, or motivation. Although variation in empirical results may be caused by poor measurement or lack of conceptual clarity, Locke et  al. (1997) concluded that the effects of participation on satisfaction and performance, reported in the reviews of Wagner and Gooding (1987) and Wagner (1994), may be too small to provide useful insights. Yet even though Locke and colleagues (1997) de-emphasized PDM as a technique for enhancing motivation or performance, they argued that the right to participate in decisions at work is a ‘moral imperative’. This perspective presumes that there is inherent value in self-determination and that employee participation in decisions is simply the right thing to do (Dachler & Wilpert, 1978).

Participative goal setting

Participative goal setting is a second major form of participation policies and practices that describes a collective activity where group members set meaningful goals together and cooperate to attain the goals (e.g. Anderson, Dekker, & Sedatole, 2010; Erez & Arad, 1986). Participative goal setting is grounded in goal setting theory, which suggests that specific and difficult goals have greater motivational benefits than easy, vague, or do-your-best goals (Locke, 1968; Locke & Latham, 1990). Locke (1968) drew from the idea that supportive opportunities to get involved

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at work are important, and applied it to goal setting. Consequently, research in the early 1970s sought to determine if outcomes are superior when employees are involved in setting goals compared to when goals are assigned (e.g. Latham & Yukl, 1975). Overall, this research showed that participative goal setting may be associated with higher goals (e.g. Dossett et  al., 1979; Latham, Mitchell, & Dossett, 1978; Latham & Saari, 1979a), improved learning (e.g. Erez & Arad, 1986), greater effort to reach the goal (e.g. Dossett et al., 1979), and individual job satisfaction (e.g. Erez & Arad, 1986; Pearson, 1987). As the concept of participative goal setting developed, two competing camps emerged. In one group, Erez and colleagues demonstrated that participative goal setting is superior to assigned goals in enhancing goal acceptance (e.g. Erez & Arad, 1986; Erez, Earley, & Hulin, 1985). In the other group, Latham and colleagues – with one exception (Latham & Saari, 1979b) – found no significant difference between participative goal setting and assigned goals as predictors of goal acceptance or commitment (e.g. Dossett et  al., 1979; Latham et  al., 1978; Latham & Saari, 1979a). To shed light on whether participative goal setting is important to employee effectiveness, Erez and Latham collaborated on multiple studies (Latham et al., 1988; Locke et al., 1988) whereby they ultimately determined that their conflicting results could be explained by differences in methodological procedures. Latham used a warm and supportive approach to assigning goals that motivated participants to perform at similar levels as participatively set goals, while Erez used a direct approach that was less motivating (Latham et al., 1988). Thus, an assigned goal can be just as motivational as a participative goal depending on how it is explained to participating individuals and subsequently implemented (Locke & Latham, 2002). After Latham and Erez’s resolution, research on participative goal setting began to taper off and currently lacks a strong scholarly following.

Quality circles

Quality circles (QCs) are the last example of participation policies and practices that we review. QCs occur when groups of employees meet regularly and voluntarily to solve work problems and make suggestions to leaders on how to improve their work setting (Banker et al., 1996; Brockner & Hess, 1986; Griffin, 1988; Marks et  al., 1986, Munchus, 1983). Influenced by the quality control ideas of Edwards Deming and Joseph Juran, QCs began in post-war Japan and were put into practice on a large-scale basis between 1955 and 1960; initial success of the technique, as well as a shortage in the labor force, facilitated the proliferation of

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QCs in Japan through the 1970s (Munchus, 1983). Encouraged by the potential for higher quality production of goods, QCs spread globally, later emerging in the United States ‘as the most popular participative technique of the eighties’ (Buch & Spangler, 1990, p. 573). Research on QC includes the following assumptions: involved employees feel more valued and have higher self-efficacy (Buch & Spangler, 1990; Elvins, 1985; Ferris & Wagner, 1985; Steel & Lloyd, 1988), employees who work together can better identify organizational problems (Elvins, 1985; Ferris & Wagner, 1985), and employees who focus on continuous improvements should feel responsible for performance outcomes (Munchus, 1983). Thus, although rooted in quality improvement and control, QCs did more than focus on continuous improvement of manufactured product quality (Goldstein, 1985) because they also focused on enhancing the affective and motivational states of employees. Research on QCs has produced mixed results. On the one hand, research has reported positive relationships between QCs and performance (Banker et  al., 1996; Brockner & Hess, 1986; Marks et al., 1986), quality (Banker et al., 1996), goal alignment (Dean, 1985; Steel & Lloyd, 1988), social support (Marks et  al., 1986), perceived employee influence (Rafaeli, 1985), trust (Steel & Lloyd, 1988), and potential cost savings (Griffin, 1988). On the other hand, other research shows no relationships with job satisfaction (Rafaeli, 1985, Steel & Lloyd, 1988), organizational commitment (Steel & Lloyd, 1988), autonomy, or motivation (Head, Molleston, Sorensen, & Gargano, 1986). Pereira and Osburn’s (2007) meta-analysis reported small, non-statistically significant effect sizes for QCs with satisfaction, participation, and commitment. Most telling is Griffin’s (1988) longitudinal study, which demonstrated that satisfaction, commitment, and performance increased initially after a QC intervention, but then dropped off and eventually returned to prior levels after two years. The lack of consistent results for QCs have led some to conclude that ‘the majority of studies constituting the quality circle evaluation literature are, at best, seriously flawed and, at worst, potentially misleading’ (Steel & Shane, 1986, p. 450). Although some have noted that the formalized hierarchy of QC leaders, coordinators, and steering committees may actually be a bureaucratic impediment to making participative suggestions actionable (Goldstein, 1985; Munchus, 1983), others have suggested that QCs presented the perfect management fad: ‘how the coming together of a charismatic guru, a faddish management technique, and gullible managers deinstitutionalized normal approaches to running organizations, triggering euphoric bandwagon imitation of the

guru’s management technique’ (Abrahamson & Eisenman, 2008, p. 721). Not surprisingly, like other formal mechanisms intended to enhance employee participation (Griffin, 1988; Munchus, 1983), research and practical interest in QCs also eventually diminished.

Participation policies and practices summary

The research reviewed above on participative decision-making, participative goal setting, and quality circles exemplifies the participation policies and practices literature. These facets of participation illustrate three different ways that management policies and practices can provide individuals with opportunities to participate in their work by getting involved in joint decisionmaking, setting collective goals, or being part of quality circles. These structural and procedural aspects of participation are important, but, as we argue, are inadequate for generating positive, long-term effects if management or employees fail to internalize the principles espoused by participation (e.g. Lee & Wei, 2011). In the next section, we discuss participating performances as a more contemporary approach to research on employee participation. In discussing this research, we argue that the effects of participating performances are stronger and longer lasting than the effects of participation policies and procedures because they operate from within the individual employee and the behaviors are more proximal to meaningful organizational outcomes.

Participating Performances We define participating performances as employee activities that reflect involvement and influence in their work and the management of work, beyond what is established as part of the employee’s work role or job itself. Participating performances, therefore, do not involve activities that are prescribed by job descriptions or established in-role expectations. Research on participating performances is more recent, but has yet to be connected to the earlier research on participation policies and practices, and tends to be either epiphenomenal or focused on direct implications for job performance. In this section, we discuss three examples of participating performances: voice, issue selling, and upward influence.

Voice

One of the earliest references to employee voice comes from Hirschman (1970), who conceptualized voice as efforts to change the current state of

Employee Participation

organizational affairs in response to dissatisfying conditions. This conceptualization of voice is explicitly positioned as an alternative to quitting or putting up with the dissatisfying state of affairs out of loyalty or apathy (Rusbult, Farrell, Rogers, & Mainous, 1988; Rusbult, Zembrodt, & Gunn, 1982; Withey & Cooper, 1989). Due in part to mixed empirical support for this definition of voice (e.g. Withey & Cooper, 1989), research shifted to a conceptualization that does not directly imply dissatisfaction as the root cause of the behavior. Specifically, Van Dyne and colleagues (Van Dyne et  al., 1995; Van Dyne & LePine, 1998) conceptualized voice as constructive challenges to the status quo and change-oriented suggestions for improvements to unit and organizational processes, policies, and standard operating procedures. Importantly, Van Dyne and her colleagues viewed voice as an extra-role behavior, and thus not part of the formal job description or established role responsibilities. Relative to the Hirschman view, this perspective is more consistent with the idea of participation as involvement and influence in work and the management of work. Scholars have built on this original conceptualization and introduced additional types of voice (e.g. promotive/prohibitive: Liang, Farh, & Farh, 2012; defensive/acquiescent/prosocial: Van Dyne, Ang, & Botero, 2003; supportive/ challenging: Burris, 2012; supportive/constructive/defensive/destructive: Maynes & Podsakoff, 2014; speaking up to superiors/speaking out to peers: Ashford, Sutcliffe, & Christianson, 2009; and speaking up to leaders/skip-level leaders: Detert & Treviño, 2010; Liu, Tangirala, & Ramanujam, 2013). Overall, most scholars view voice as a relatively discretionary behavior that is constructive and improvement-oriented (e.g. Detert and Burris, 2007; LePine & Van Dyne, 1998; Morrison, 2011). Scholars have identified many antecedents of voice. Sample antecedents include individual differences such as the elements of core self-evaluation (Axtell, Holman, Unsworth, Wall, Waterson, & Harrington, 2000; Graham & Van Dyne, 2006; Liang et  al., 2012; Tangirala & Ramanujam, 2012), felt obligation, felt responsibility, and perceived influence (Fuller, Marler, & Hester, 2006; Liang et al., 2012; Tangirala & Ramanujam, 2012; Venkataramani & Tangirala, 2010), employee motives (Grant & Mayer, 2009; Kim, Van Dyne, Kamdar, & Johnson, 2013), positive and negative affect (Detert & Edmondson, 2011; Tangirala & Ramanujam, 2012; Venkataramani & Tangirala, 2010), and the Big-5 personality traits (Grant, 2013; LePine & Van Dyne, 2001; Tangirala, Kamdar, Venkataramani, & Parke, 2013). Other predictors of voice include employee perceptions

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of context, such as autonomy, social support, informational access (Axtell, Holman, & Wall, 2006; Axtell et  al., 2000; Chiaburu, Lorinkova, & Van Dyne, 2013; Fuller et  al., 2006), transformational leadership (Detert & Burris, 2007), leader–member exchange (Van Dyne, Kamdar, & Joireman, 2008), abusive leadership (Burris, Detert, & Chiaburu, 2008), psychological safety (Edmondson, 1999), and justice (Zhang, LePine, Buckman, & Wei, 2014). In contrast to the research on antecedents of voice, there is less research on outcomes of voice. Nevertheless, even though voice can be interpersonally risky and can have negative implications for interpersonal relationships (Kish-Gephart, Detert, Treviño, & Edmondson, 2009; Van Dyne et al., 1995), supervisors tend to view voice positively when making assessments of overall job performance (Van Dyne & LePine, 1998; Whiting, Podsakoff, & Pierce, 2008). Additionally, experimental evidence shows that job candidates who engaged in more voice were rated as more competent, received higher overall evaluations, and higher salary recommendations than those who engaged in less voice (Podsakoff, Whiting, Podsakoff, & Mishra, 2011). Clearly, the sharing of creative ideas is important to organizational functioning and is reflected in these relationships with performance outcomes.

Issue selling

Similar to the concept of voice is issue selling (Morrison, 2011), which refers to discretionary behavior that attempts to shape strategic agendas by emphasizing organizational trends or drawing attention to specific issues (Ashford, Rothbard, Piderit, & Dutton, 1998; Dutton & Ashford, 1993). Issue selling occurs when employees pitch ideas to top management with the intent of enhancing awareness and gaining support for a specific issue or idea. It can focus on broad trends and ideas related to the external environment or on specific individual issues related to employee dissatisfaction (Ashford et al., 1998). As Ashford and colleagues (1998) noted, participation involves formal programs created and managed by company leadership, while issue selling occurs when proactive individuals (Crant, 2000; Grant, Parker, & Collins, 2009) champion ideas to top management outside formal participation structures. To date, the issue selling literature has emphasized the decision-making processes that employees experience when they consider whether or not to engage in the behavior. As with voice, employees may weigh the potential benefits and drawbacks of proposing their ideas (Detert &

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Burris, 2007; Edmondson, 1999). Adopted issues can result in career benefits, while controversial ideas can tarnish employee reputations and hinder career progress (Ashford et al., 1998; Dutton, Ashford, O’Neill, Hayes, & Wierba, 1997). The issue selling process also includes consideration of different channels of communication, how and when to present ideas, the number of issues to bundle, dynamics of organizational power, and relational, normative, and strategic considerations (Dutton & Ashford, 1993; Dutton, Ashford, O’Neill, & Lawrence, 2001). Issue selling therefore requires employees to evaluate organizational cues and anticipate consequences of participative involvement. They ‘read the wind’ before deciding to engage in issue selling (Dutton et al., 1997). In sum, issue selling is another participating performance and it is influenced by perceptions of the context and anticipated consequences.

Upward influence

We include upward influence as a final example of participating performance. Upward influence is defined as employee attempts to influence or transfer information to someone higher in the organizational hierarchy (Athanassiades, 1973; Morrison, 2011; Porter, Allen, & Angle, 1983; Roberts & O’Reilly, 1974). In one sense, upward influence is broader than voice and issue selling because it can include any form of communication targeted at superiors. Yet some aspects of upward influence are more specific because the exchange is exclusively between an employee and supervisor (Morrison, 2011). Upward influence includes upward dissent (Kassing, 2002), principled organizational dissent (Graham, 1986), and general upward influence. Given hierarchical processes and power differences, upward influence may involve distortion of information (Athanassiades, 1973) or incomplete communication of information, and employees may feel pressured to agree or remain silent because they fear their suggestions may fall on deaf ears. Overall, upward influence differs from voice and issue selling because it is broader and it may or may not be positively intended. Proactive individuals engage in upward influence when they communicate with their superiors with the goal of affecting their attitudes, perceptions, and behaviors (Wayne, Liden, Graf, & Ferris, 1997). As a result, upward influence depends not only on individual proclivities, but also depends on employee career aspirations, supervisor power preferences, and related traits (Glauser, 1984). Similar to voice, issue selling, and participating performances more generally, employees who attempt upward influence consider contextual

factors and the dispositions of organizational targets (Foste & Botero, 2012). When employees view superiors as having the potential to help them reach their goals, upward influence aims to tap into the power and control of superiors. Research has demonstrated that the supervisor’s potential access to organizational resources and institutional knowledge is key to employee upward influence: the greater the supervisor’s influence, the more likely that employees will engage in upward influence (Cohen, 1958; Read, 1962; Roberts & O’Reilly, 1974). Because employee’s participating behavior represents efforts that might guide their work or work situations, other relevant factors of upward influence include trust (Liu et  al., 2013; Read, 1962; Roberts & O’Reilly, 1974), quality of relationship with leaders (Van Dyne et al., 2008), relevance and importance of the message, and the general structural characteristics of the situation such as proximity of the actors and organizational hierarchy.

Participating performances summary

Although participation policies and practices provide systematic and planned opportunities for employees to contribute to issues regarding their work, participating performances reflect proactive involvement and attempts to influence. Participating performances allow employees to express their needs, offer suggestions, and recommend improvements that may benefit the organization. Voice, issue selling, and upward influence, as reflections of participating performances, consist of specific proactive behaviors employees use to participate outside of their formal roles. Importantly, these activities may be inspired or encouraged by features of the work context. In sum, it is clear that although research on participation policies and practices has floundered, research on employee participation has continued based on the large literature on participating performances.

Psychological Participation Thus far, we have defined and contrasted participation policies and practices with participating performances. As we have described, initial conceptualizations of employee participation focused on structures and practices designed to encourage employee involvement and influence, while current conceptualizations focus on the nature of the participating behavior itself. Additionally, however, researchers have focused their attention on psychological participation, which captures an internal motivational state that is activated, at least in part, by the policies and practices management puts in

Employee Participation

place, and which manifest in behavioral activities to influence work. Here we consider a specific construct that reflects psychological participation, and in a subsequent section, we explore relationships of this construct with participation policies and practices versus participating performances. As we will explain, consideration of psychological participation allows for integration of different aspects of the broader employee participation, and a more cohesive and comprehensive understanding of the participation phenomenon.

Psychological empowerment

Psychological empowerment is a motivational construct that reflects the psychological connections employees have to their work and work performances, and includes four dimensions: meaning, competence, self-determination, and impact (Spreitzer, 1995; Thomas & Velthouse, 1990). Meaning is the value placed on work goals and is often compared to one’s standards or expectations (Spreitzer, 1995). Other scholars have labeled this meaningfulness, defined as the belief that task work is important and worthwhile (Kirkman & Rosen, 1999; Thomas & Velthouse, 1990). Competence refers to an employee’s perception that he/she can effectively perform a task (Spreitzer, 1995; Thomas & Velthouse, 1990). This is similar to Gist’s (1987) description of selfefficacy. Competence has also been described as potency (Kirkman & Rosen, 1999). Selfdetermination is an employee’s ability to control his/her actions (Spreitzer, 1995). Thomas and Velthouse (1990) described this as choice, such that employees assume responsibility for their actions, while Kirkman and Rosen (1999) refer to this as autonomy, defined as the sense of freedom and discretion in making work decisions. Finally, impact is the influence a person has relative to work and work outcomes (Spreitzer, 1995; Thomas & Velthouse, 1990). In our view, each dimension of psychological empowerment reflects a state of mind that is relevant to employee participation. The impact dimension reflects a sense of influence, and thus might be most obviously related the participation phenomenon. However, beliefs related to meaningfulness (meaning), efficacy (competence), and control (self-determination) also seem quite germane. In fact, a significant amount of research has focused on predictors of empowerment and many of these reflect aspects of the participating practices that we described earlier. Sample predictors of empowerment include: autonomy (Chen, Kirkman, Kanfer, Allen, & Rosen, 2007; Spreitzer, 1995, 1996), participative unit climate characterized by HR policies that foster

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appropriate rewards (Kirkman & Rosen, 1999; Mathieu, Gilson, & Ruddy, 2006; Seibert, Wang, & Courtright, 2011; Spreitzer, 1995), leader support (Arnold, Arad, Rhoades, & Drasgow, 2000; Chen et  al., 2007; Kirkman & Rosen, 1999; Maynard, Gilson, & Mathieu, 2012; Seibert et al., 2011; Zhang & Bartol, 2010), organization support (Maynard et  al., 2012; Seibert et  al., 2011), access to information and resources (Seibert, Silver, & Randolph, 2004; Spreitzer, 1995, 1996), and individual differences such as locus of control and self-esteem (Spreitzer, 1995, 1996). In addition to antecedents, researchers have examined outcomes of psychological empowerment that are similar to those related to other participation-related constructs. For example, scholars have demonstrated that empowerment is positively associated with the task performance of employees (Seibert et  al., 2011), managers (Spreitzer, 1995), and teams (Chen et  al., 2007; Kirkman & Rosen, 1999; Kirkman, Rosen, Tesluk, & Gibson, 2004; Mathieu et al., 2006; Srivastava, Bartol, & Locke, 2006) as well as individual job satisfaction (Kirkman & Rosen, 1999; Maynard et  al., 2012; Seibert et  al., 2011), organizational commitment (Kirkman & Rosen, 1999; Maynard et  al., 2012; Seibert et  al., 2011), motivation (Zhang & Bartol, 2010), and proactivity (Kirkman & Rosen, 1999). Interestingly, there is evidence that empowerment may operate differently in diverse geographies. For instance, meta-analytic examination shows significantly stronger relationships between empowerment and task performance in Asia than in North America or Latin America (Seibert et  al., 2011). Further, empowerment has the strongest relationship with job satisfaction in North America when compared to Asia and Latin America. Finally, psychological empowerment predicts outcomes that are closely associated with participating performances, such as innovation and creativity (Hon & Chan, 2013; Seibert et al., 2011; Spreitzer, 1995; Zhang & Bartol, 2010), and voice (Frazier & Fainshmidt, 2012).

Linkages among Types of Employee Participation Our review of the employee participation literature is organized into three elements: participation-related policies and practices, participating performances, and psychological participation. As we have described, each of these types of participation plays a different role in the process of employee participation, and each type of participation includes specific concepts that have been

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studied by different groups of scholars. For example, participation-related policies and practices includes participative decision-making, participative goal setting, and quality circles. Each of these describes structural actions initiated by management with the goal of encouraging active employee participation. Participating performances of employees includes behaviors that reflect employee participation, such as voice, issue selling, and upward influence. The final type is psychological empowerment, which we position as a motivational state that is activated by participation policies and practices and influences intentions and behaviors, leading to participating performances. We now overview linkages among these three elements in more detail, and in doing so, explicitly delineate an integrative employee participation framework. Our synthesis explicitly accounts for a broad array of participation-related constructs as well as their interrelationships. As summarized in Figure 18.2, we propose a positive relationship for participation policies and practices with participating performances. On the surface, this seems an obvious and straightforward idea. After all, who would doubt the intuitive suggestion that policies aimed at encouraging participation will lead to participative behaviors. There is also some research that has substantiated this notion, drawing connections between human resource practices and voice behaviors (e.g. Harley, 2014). However, empirical evidence examining this relationship has been surprisingly limited. Instead, research in this area has been driven by the idea that participation policies and practices satisfy human needs, enhance motivation, and result in greater productivity. As a consequence, empirical research has focused almost exclusively on the effects of participation policies and practices on satisfaction, motivation, and performance (e.g. Liao, Toya, Lepak, & Hong, 2009; Takeuchi, Chen, & Lepak, 2009). In our view, research which focuses explicitly on the link between participation policies and practices with participation performances is absolutely necessary for gaining insight into the mediating and moderating factors that explain and influence the relationship. Our second proposal is that participation policies and practices will enhance psychological

Participation policies and practices • Participative decision-making • Participative goal setting • Quality circles

participation. This proposition is supported by social information processing theory, which posits that features of organizations are translated into cognitions and attitudes through a socially influenced sense-making process (Salancik & Pfeffer, 1978). Consistent with this view, participation policies and practices should influence social interactions and result in perceptions of opportunities for self-determination and influence. Empirical evidence has shown the importance of this relationship. For example, among bank branches in Japan, Liao and colleagues (2009) have shown the positive relationship between human resource practices and empowerment. More recent metaanalytic work has further reinforced the strength of this relationship (ρ = .48, 95% CI = .39, .56; Seibert et al., 2011). In other words, policies and practices should predict employees’ sense of psychological empowerment. Finally, we propose that the effect of participation policies and practices on participating performance is mediated, at least in part, through psychological empowerment. Indeed, participation policies and practices, such as PDM, participative goal setting, and QCs, should influence participating performances, such as voice, issue selling, and upward influence, because they allow employees to develop a sense of control, self-determination, competence, and impact. Stated differently, participation policies and practices should facilitate all four dimensions of psychological empowerment, and each of these has implications for participating performances of employees. Believing that their work will impact others should trigger idea sharing and suggestions for ways to improve work processes. Having a sense of competence and self-determination should signal knowledge, expertise, and control of the work, and lead to employees’ active involvement in work issues. Finally, seeing the value and meaning of their work should facilitate employees’ upward influence. Thus, the participating practices inherent in structural empowerment should influence the extent to which employees feel psychologically empowered, and this should inspire participating actions and behaviors. Although this overall framework has not been examined directly, recent research has explored some of the linkages (Frazier & Fainshmidt, 2012;

Psychological participation • Psychological empowerment

Participating performances

• Voice • Issue selling • Upward influence

Figure 18.2 Employee participation as a process of employee involvement and influence

Employee Participation

Fuller et  al., 2006; Lee & Wei, 2011; Raub & Robert, 2013). In a sample of workers in China, for example, Lee and Wei (2011) demonstrated that psychological empowerment mediated the relationship of participative goal setting with team identity and team performance. As another example, Frazier and Fainshmidt (2012) showed that psychological empowerment partially mediated the relationship between group-level climate perceptions and individual-level voice. This finding connects psychological empowerment with a specific participating performance, voice, and also suggests that organizational climate, which could be a reflection of participation policies and practices, leads to empowerment. Although there are some differences in the relationships examined in these studies and those proposed in our integrative model, prior results provide initial indirect evidence of the validity of the relationships in our proposed model.

THEORETICAL IMPLICATIONS In this chapter we reviewed the literature on employee participation and offered a theoretical framework that accounts for a wide variety of participation-related concepts. Our goal was to integrate the research and provide a cohesive view of

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the broader literature on participation so that accumulation of research is more efficient and future studies can target critical gaps in our understanding of participation. In this section, we use our framework to highlight issues that warrant additional attention, and a summary of these potential future directions is included in Table 18.2.

Clarifying Inconsistency in Participation-Related Concepts Our integrative framework relies on existing constructs that reflect participation in very specific and meaningful ways. We grouped these constructs into three broad categories that vary with respect to their functioning in the participation process. Although this approach resulted in a parsimonious and yet integrative framework, it also highlights inconsistencies in prior construct conceptualizations that require additional clarification. Most obviously perhaps, some of the examples we considered are ‘fuzzy’. In some instances, different research teams use the same word to denote very different constructs. In other instances, a variety of different terms are used to represent basically the same construct. Finally, some terms are used to refer to elements of structural policies and procedures, internal psychological states, and participating performance behaviors. This can cause

Table 18.2  Theoretical implications for future research Future research direction

Research questions

Which organizational policies and practices lead to employee voice behavior? What is the relationship between implicit voice opportunities and subsequent voice behavior? Conceptualizations of How may structural and psychological empowerment interact to encourage participation empowerment behaviors? Which empowerment perspective is more influential in advancing actual employee participation? Self-managed teams What is the nature of the dimensionality of SMTs that spans multiple facets of participation? Which of these aspects of participation is most salient? Shared leadership Which component of participation best predicts the positive relationship between shared leadership and performance? Moderators and boundary conditions Leadership What is the impact of different kinds of formal leaders (e.g. autocratic, abusive, directive, or transformational) on participation? Individual differences What is the role of personality in participation? Which traits are more likely to seek out opportunities to get involved as opposed to merely responding to offered invitations to participate? Cross-cultural influences Do differences in participation exist between collectivist and individualist cultures? What is the role of cultural paradigms of power in influencing employee participation? Narrow vs. broad approaches How does self-determination mediate the relationship between participative goal setting and upward influence? How do high performance work practices influence constructive-promotive behaviors? Conceptualizations of voice

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confusion if the same term is used with different meanings or multiple meanings. As we argue below, our framework can be applied to help clarify how alternative conceptualizations of terms could fit together to provide a more precise and cohesive understanding of employee participation.

Accounting for alternative views of voice

An attribute of previous research on employee participation is the lack of attention paid to similarly labeled, but fundamentally distinct, constructs. A vivid example is the general lack of integration of conceptualizations of voice as used in the organizational justice (e.g. Lind, Kanfer, & Earley, 1990; Tyler & Lind, 1992) and the organizational citizenship behavior literatures (e.g. LePine & Van Dyne, 1998, 2001; Van Dyne et al., 1995; Van Dyne & LePine, 1998). Although researchers from both of these camps employ the term ‘voice’, the concepts are quite different, and yet, both relate directly to employee participation. Organizational justice conceptualizations of voice focus on structural mechanisms that give employees opportunities to make suggestions (e.g. Lind et  al., 1990; Price, Lavelle, Henley, Cocchiara, & Buchanan, 2006; Tyler & Lind, 1992). In contrast, organizational citizenship conceptualizations of voice focus on employee behavior, acts of speaking up with change-oriented suggestions (e.g. Van Dyne & LePine, 1998). Using terms from our model, the justice conceptualization of voice represents a participation policy and practice: it provides the structure for employees to share their opinions (i.e. suggestion box). In contrast, the organizational citizenship conceptualization of voice represents a participating performance. Although scholars have acknowledged both views of voice (e.g. Morrison, 2011; Van Dyne & LePine, 1998) we are not aware of any published attempts to reconcile these two perspectives in a comprehensive way. Viewing these two types of voice through the lens of our conceptual model provides a solution. Future research could more fully reconcile these differences by explicitly considering voice-related policies and practices. In other words, it would be useful to use a justice literature perspective to identify specific policies and procedures that should facilitate employee voice behavior. This would provide a structure for moving beyond prior voice behavior research that has considered employee perceptions of the quality of social exchange relationships (Kim et  al., 2013; Van Dyne et al., 2008). It would also justify a different set of contextual predictors that influence whether, when, and how employees engage in voice in response to structural opportunities

provided by justice-related policies and procedures that give voice to employees.

Accounting for multiple conceptualizations of empowerment

As we argued above, psychological empowerment is a key mechanism that connects participation policies and practices with participating behavior. However, empowerment is a ‘fuzzy’ construct that sometimes refers to structural characteristics of the workplace and is also used to refer to internal psychological states of employees. Specifically, structural empowerment refers to top-down organizational systems that transfer authority or responsibility from managers to employees (Arnold et  al., 2000; Conger & Kanungo, 1988; Maynard et al., 2012; Thomas & Velthouse, 1990). Situated in the job design and job characteristics literatures, structural empowerment represents decentralization of authority with decision power flowing from a formal structure to employees (Mills & Ungson, 2003). It is relational and grounded in power sharing (e.g. supervisors share power with subordinates). Accordingly, structural empowerment is parallel to the participation policies and practices we have described in this chapter, such as participative decision-making, participative goal setting, and quality circles. In contrast, psychological empowerment refers to the employee’s internal motivation and drive to create meaning and positively impact the organization (Spreitzer, 1995). It reflects the psychological state of ego-involvement as well as a sense of influence and engagement. Thus, psychological empowerment differs significantly from structural empowerment. The distinctions between structural empowerment and psychological empowerment are similar to the distinctions we highlighted for voice in the justice and organizational citizenship literatures – and would similarly be categorized into different parts of our integrative model. Importantly, research has demonstrated links between the two types of empowerment. For example, Maynard et al. (2012) observed that structural empowerment is an antecedent to psychological empowerment, and this perspective aligns with our proposal that participation policies and practices predict psychological participation. Given the field’s current emphasis on psychological empowerment, we note the value of additional research that integrates both structural empowerment and psychological empowerment as an attractive way to advance our understanding of the overall process of employee participation. It also would be worthwhile to investigate a wider range of behavioral outcomes of structural and psychological

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empowerment – such as voice, issue selling, and upward influence. More generally, we suggest that researchers adopt greater precision in the use of terms for voice, empowerment, and other participationrelated constructs so that structural policies and practices are not confounded with psychological states related to participation or with participating performance behaviors. Additionally, future research should test our framework of participation policies and procedures as predictors of participation performances via participation-related psychological states. Applied specifically to empowerment, it would be useful to consider psychological empowerment as a mediator that links structural empowerment with participation performance behaviors.

Accounting for other participationrelated constructs

Our review is incomplete in so far as it does not include a complete array of participation-related constructs. In particular, we have not discussed constructs such as self-managed teams and shared leadership that imply a mix of structural, psychological, and behavioral content, and thus, do not fit into our integrative framework. Although one could argue that our framework is incomplete because it does not account for the full range of participation-related constructs, we suggest that the framework can be applied to provide conceptual clarity to these additional constructs. Specifically, we next consider ways that the framework might help to enhance our understanding of self-managing teams and shared leadership. A self-managing team (SMT) refers to an interdependent group of employees that have some discretion and control in managing work tasks, making production- or service-related decisions, and taking responsibility for team performance (Cohen, Ledford, & Spreitzer, 1996; Kirkman & Rosen, 1999; Kirkman & Shapiro, 1997; Manz & Sims, 1987). The value of SMTs was a key finding that emerged from reflective production in Sweden (e.g. Pava, 1986) as well as in many auto manufacturing plants in the United States (Manz & Sims, 1987). Although these teams are largely autonomous, they typically have an external team leader or coordinator who oversees the team and provides support and encouragement (Cohen, Chang, & Ledford, 1997; Druskat & Wheeler, 2003; Manz & Sims, 1987). SMTs reflect participation policies and practices in so far as they are instituted as a work structure intended to encourage employee involvement and influence in decision-making processes (de Leede, Nijhof, & Fisscher, 1999). SMTs also

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include elements of psychological participation because they reduce the role of hierarchy (Roberson & Williamson, 2012), and like the selfdetermination aspect of psychological empowerment, have been conceptualized as individual team members’ sense of autonomy and control in the work environment (Manz & Sims, 1987). Finally, SMTs are often described in terms of the team member efforts to enhance performance through input, influence, organizational learning, and sense-making (Ashmos et al., 2002). Thus, SMTs involve elements of participating performances as well as participation policies and practices and psychological participation. Researchers have demonstrated that SMTs can elicit effective performance (Alper, Tjosvold, & Law, 1998; Cohen & Ledford, 1994; Pearson, 1992; Rousseau & Aubé, 2010; Stewart & Barrick, 2000) but can also result in low trust and low performance (Langfred, 2004). Research findings are similarly mixed regarding SMTs and satisfaction (Cohen & Ledford, 1994; Cohen et  al., 1997; Cordery, Mueller, & Smith, 1991; Mueller & Cordery, 1992; Wall, Kemp, Jackson, & Clegg, 1986). Based on these inconsistent results, Stewart, Courtright, and Manz (2011) concluded that SMTs are not universally beneficial, and Spreitzer, Cohen, and Ledford (1999) observed that the benefits of SMTs were ‘oversold in the literature’ (p. 359). It is possible, however, that the mixed empirical results for SMTs can be attributed to the ‘fuzzy’ conceptualization of SMT that includes multiple aspects of the employee participation process. Perhaps the inconsistent results can be reconciled by focusing more precisely on relationships between SMT policies and practices, psychological empowerment resulting from SMTs, and specific participating behaviors. In addition, some of these relationships are potentially stronger than others. Future research that distinguishes structural, psychological, and behavioral aspects of SMT may illuminate prior inconsistencies and reinvigorate this particular stream of participation research. Shared leadership is a dynamic process of leadership that occurs when influence is distributed across multiple team members (Carson, Tesluk, & Marrone, 2007; D’Innocenzo, Mathieu, & Kukenberger, 2014; Mehra, Smith, Dixon, & Robertson, 2006; Pearce, 2004; Pearce & Conger, 2003; Pearce & Sims, 2002; Sivasubramaniam, Murry, Avolio, & Jung, 2002). In shared leadership, the entire team assumes responsibility and does not rely on one centralized individual (Ensley, Hmieleski, & Pearce, 2006; Pearce & Conger, 2003; Yammarino, Salas, Serban, Shirreffs, & Shuffler, 2012). The shared leadership process assumes that leadership is a set of role functions

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that can be accomplished by multiple individuals in multiple ways (Yammarino et al., 2012) and that individuals can act as both leaders and followers. Shared leadership has both deliberative and emergent properties (Denis, Langley, & Sergi, 2012), such that leading and following occur simultaneously across time and actors (DeRue, 2011). The shared leadership conceptualization includes participation policies and practices as provided by the hierarchical leader in a formal participation structure that facilitates team design (e.g. clarifies task specifications, secures resources, and aligns task demands), manages the boundaries of the team, coaches and encourages team members, and engenders shared team commitment (Carson et al., 2007; Hackman & Wageman, 2005; Pearce, 2004). The shared leadership concept also involves activities of members, who simultaneously engage in leadership behavior and exert influence on other team members (DeRue, 2011). Thus, elements of shared leadership represent participating performances of individuals (e.g. Houghton, Neck, & Manz, 2003; Neck, Neck, Manz, & Godwin, 1999) that aim to promote team trust, potency, and commitment (Bligh, Pearce, & Kohles, 2006; Denis et al., 2012). Researchers have suggested many positive outcomes of shared leadership. Shared leadership is positively related to consensus, cohesion, and higher intra-team trust, and also negatively related to team conflict (Bergman, Rentsch, Small, Davenport, & Bergman, 2012). More importantly, shared leadership is positively related to team performance (Carson et  al., 2007; D’Innocenzo et al., 2014; Mehra et al., 2006; Wang, Waldman, & Zhang, 2013) and explains more variance in team effectiveness than vertical leadership (Ensley et  al., 2006; Pearce & Sims, 2002). Although the research on shared leadership has promising results, it is difficult to discern whether the participation policies and procedures aspects of shared leadership or the participating performance aspects of shared leadership explain performance outcomes. Future research on shared leadership should be more precise in specifying and differentiating participation policies and procedures, psychological states, and participating performances to refine the shared leadership findings. SMTs and shared leadership include a mixture of participation-related constructs that we have differentiated in our integrative participation framework. Both SMTs and shared leadership include management involvement in structuring work duties (e.g. participation policies and practices). In these situations, opportunities to participate should increase interdependent and autonomous work in teams, which, in turn, should cause individuals to engage in participating performance

behaviors. They also include specific psychological states (i.e. the sense of accountability that accompanies SMTs and shared leadership) which lead individuals to engage in participating performances such as taking initiative and speaking up with suggestions for change (voice), championing ideas to management (issue selling), and working to influence managers’ opinions and behaviors (upward influence). Even though our integrated framework does not neatly accommodate SMTs and shared leadership, future research should consider how these constructs that include a mixture of different aspects of participation (policies and practices, psychological states, and performance behaviors) can be more precisely delineated to avoid confusion and confounds. As suggested above, future theoretical and empirical research should clarify the multidimensionality of SMTs and shared leadership. This would help highlight which aspects of participation predict positive individual and organizational outcomes. In other words, we do not know if the benefits of participation are more strongly predicted by structural opportunities, individual psychological states of employees, or participating performance behaviors. Thus, there are important theoretical reasons for thinking about these constructs and teasing apart their dimensionality.

Moderators and Boundary Conditions Organizations undoubtedly vary in how they implement participation policies and employees differ in their responsiveness to these policies. It is therefore unlikely that main effect relationships will adequately explain the connections among the concepts in our framework. Instead, we need additional theorizing and research that considers more complex models that include boundary conditions. We specifically recommend special attention to moderating conditions that weaken or reverse the expected positive relationship between participation policies and participating behavior. For example, as noted in a recent meta-analysis on empowerment (Seibert et  al., 2011), leadership may be one such contextual moderating condition. Applied to our participation framework, it seems likely that different leadership styles change the nature of the relationship between participation policies and participating performances. Empowering leadership may strengthen the relationship, while autocratic leadership may weaken it or, in extreme instances, cause a negative relationship between participation policies and participating performances based on employee reactance. If organizations or groups have the

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structure of participation policies in place but are managed by bad or abusive leaders (e.g. Tepper, 2000), employees might pull back or withdraw, causing a negative relationship between participation policies and participating performances. Likewise a directive leader (e.g. Pearce & Sims, 2002) could issue commands and instructions (i.e. participation policies and procedures), which trigger resentment and result in weak, inconsistent, or negative relationships between policies and behaviors. In contrast, transformational leaders (e.g. Bass, 1985, Bass & Avolio, 1995) may strengthen the relationship between participation policies and participating behaviors. As these examples suggest, developing and testing theoretical predictions that account for contextual influences provided by leadership would shed additional light on when policies and practices positively predict participation performances. Personality characteristics of individual employees may also serve as important boundary conditions because individuals react differently to participation policies and practices. For instance, extraverted individuals may respond to opportunities to participate more positively than introverted individuals who may hesitate to act on the opportunities, or neurotic employees who may respond in opposite ways because they are uncertain and fearful of extending themselves beyond defined work roles. Furthermore, those who are conscientious, particularly those who are high in achievement-striving, are more likely to respond to opportunities to engage and get involved in their organizations (Hon & Rensvold, 2006). Thus, the relationship between participation policies and participating performances may be stronger for those who are conscientious and extraverted, weaker for those who are introverted, and negative for those who are neurotic. In sum, we recommend future research on the moderating role of personality and other individual difference characteristics as factors that most likely change the nature of the relationship of participation policies and practices with participating performances. Third and finally, culture may serve as an important moderating influence. The model we propose is based largely on the assumption that individuals have a desire to contribute their ideas to the organization and that organizations, in turn, value this input. Although these propositions may apply in individualistic cultures which stress the individual as an active agent (Hofstede, 1983), the paths among these variables may be less straightforward when considering more collectivistic cultures. It seems to reason that employees in collectivistic cultures may not value the autonomy and responsibility associated with participating in work, and cultural norms may discourage

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behaviors which disrupt the status quo. However, some evidence suggests that individuals in a collectivistic culture may be more in tune with the interests of others, and could better align their personal goals with those of the group or the organization (e.g. Gelfand & Christakopoulou, 1999). Consequently, their participating performances may be more valuable to the organization as opposed to employees in an individualistic culture who may have greater self-interest embedded in their participating. Additionally, the way in which different cultures perceive power distance could also influence the nature of our proposed relationships. As Brockner and colleagues suggest (2001), the lack of opportunity to participate is likely to have more detrimental effects on employees’ commitment, work attitude, and job performance in countries with a low power distance than in countries with high power distance. Similarly, scholars have indicated that the process of empowerment and subsequent outcomes may differ for individuals in low power distance cultures versus those in high power distance cultures (Eylon & Au, 1999). In sum, there are reasons to believe that characteristics of culture could influence our proposed model such that the relationships we suggest are exacerbated or mitigated, and future research could help delineate how culture influences the process of participation at work.

Narrow Versus Broad Model Distinctions Research on our framework and the extensions mentioned above would be particularly insightful if researchers examined the proposed relationships in a comprehensive way, and we suggest two distinct paths through which this could be accomplished. First, future studies could isolate important characteristics or variables within each participation category we have suggested. As an example, a researcher could examine the role of self-determination (a dimension of psychological empowerment) as a transmitter of the effects of participative goal setting (a narrow participation practice) on upward influence (a narrow participating performance). The narrow focus of such studies could help us understand specific policyrelated constructs that impact specific forms of participative behavior that might be relevant in a given context. The second path for future research could consider operationalizations that measure broader constructs suggested by our framework. For example, policies and practices could be reflected in high performance managerial practices (a combination of PDM, participative goal

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setting, training, and rewards; Seibert et al., 2011) while participating performances may be represented through constructive-promotive workplace behaviors (a combination of voice, issue selling, and creative performance; Chamberlin, Newton, & LePine, 2014). Importantly, however, research that examines comprehensive models of participation should be conducted with the compatibility principle in mind (e.g. Ajzen & Fishbein, 1977; Epstein, 1980; Harrison, Newman, & Roth, 2006). That is, researchers should consider relationships among constructs that are compatible in their breadth. Although examining relationships among both narrow and broad participation-related constructs would enhance our understanding, research efforts will be most fruitful when the breadth of the constructs within each study are consistent.

PRACTICAL IMPLICATIONS Our model introduces a number of considerations for organizations and managers. Even though policies and practices should align with business strategy (Miles & Snow, 1984) and human capital can be a source of competitive advantage through successful management (Pfeffer, 1995), it is often difficult to understand the precise mechanisms through which these human resource policies create value (Becker & Gerhart, 1996). Our study provides some indication of the way in which policies can bring about desired participation behavior among employees in ways that are beneficial and add value to the organization. First, our model suggests that simply implementing policy is not sufficient to produce the desired results of participating at work. Instead, individuals are more likely to become engaged when they feel psychologically empowered to do so. Consequently, policies and practices need to be designed with this element in mind. Targeted HR policies should increase employee control and input foster employee beliefs that their involvement will have a positive impact within their work group or organization (Seibert et al., 2011). To this end, managers may also play a key role in facilitating an employee’s sense of empowerment. For instance, employees who feel their managers are supportive are also more likely to feel empowered (Logan & Ganster, 2007). Empowering leader behaviors can also supplement low levels of knowledge and skills such that employees feel a greater sense of self-efficacy (Ahearne, Mathieu, & Rapp, 2005). Perhaps more importantly, managers are often responsible for interpreting human

resource practices which are then experienced by the employees (e.g. Liao et  al., 2009), and the way in which managers implement new policies can influence employee perceptions of human resource practices and subsequent feelings of empowerment. Consequently, beyond creating policies and practices which provide the necessary conditions to encourage empowerment, organizations need to provide specific training to the managers and leaders responsible for executing these practices among employees. Training could not only influence leadership styles which encourage employee empowerment, but could also provide managers with resources for effectively communicating intentions and objectives of practices designed to stimulate employee participating behaviors. In addition to considering the design of policies and leader training, human resource managers may also want to be more intentional in their selection process. In particular, our model hints at the importance of individual characteristics that may influence the process of participation. For example, individuals who are conscientious, extraverted, and agreeable are more likely to engage in voice behaviors (LePine & Van Dyne, 2001). Similarly, individuals with a proactive personality are also likely to engage in voice (Crant, Kim, & Wang, 2011; Detert & Burris, 2007; Parker & Collins, 2010), as are those with high self-efficacy (Tangirala et  al., 2013). These qualities suggest that individuals differ in the extent to which they are likely to engage in participating behaviors at work, and evaluations of job candidates should include not only traditional measures of personality, but also assessments of characteristics likely to predict participating behavior. Finally, many organizations compete in a global market, and technological advances have made it more common to have work groups or teams comprised of multinational individuals. Individuals with different cultural perspectives could respond to participation policies and enact participating behaviors very differently. For instance, dimensions of individualism versus collectivism and power distance could be particularly influential in the extent to which individuals are receptive to practices which encourage participation and subsequently choose to involve themselves beyond their prescribed work role. Managers need to be aware of these differences and encourage multiple different facets of participating behaviors (e.g. Landau, 2009). With multinational teams, an external leader may need to help set group norms of participating behavior in order to achieve maximum effectiveness with these types of teams.

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CONCLUSION Employee participation has a lengthy history in the applied psychology and organizational behavior literatures. Although some may contend that the idea of participation has lost its usefulness, we argue that the employee participation literature is alive and well, but operates under construct labels that emphasize different aspects of the employee participation phenomenon. In this chapter we argued that employee participation is best conceived as an umbrella term that encompasses participation policies and practices, psychological participation, and participating performances. Participation policies and practices represent the formal techniques and methods used to encourage employee participation. They are linked to the activities and behaviors of participating performances through psychological participation. Increased connection to the work through psychological participation further enhances more proximal participation behaviors, such as employee behaviors of voice, issue-selling, and upward influence. By explicating and linking these different types of participation, our general framework provides a new and more integrated conceptualization of involvement in the workplace. We hope this chapter stimulates additional theory building and empirical research on participation policies and practices, psychological participation, and participating performances.

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Tyler, T. R., & Lind, E. A. (1992). A relational model of authority in groups. Advances in Experimental Social Psychology, 25, 115–191. doi: 10.1016/ s0065-2601(08)60283-x Van Dyne, L., & LePine, J. A. (1998). Helping and voice extra-role behaviors: Evidence of construct and predictive validity. Academy of Management Journal, 41(1), 108–119. doi: 10.2307/256902 Van Dyne, L., Ang, S., & Botero, I. C. (2003). Conceptualizing employee silence and employee voice as multidimensional constructs. Journal of Management Studies, 40(6), 1359–1392. doi: 10.1111/1467-6486.00384 Van Dyne, L., Cummings, L. L., & McLean Parks, J. (1995). Extra-role behaviors: In pursuit of construct and definitional clarity (a bridge over muddied waters). Research in Organizational Behavior, 17, 215–285. Van Dyne, L., Kamdar, D., & Joireman, J. (2008). In-role perceptions buffer the negative impact of low LMX on helping and enhance the positive impact of high LMX on voice. Journal of Applied Psychology, 93(6), 1195–1207. doi: 10.1037/ 0021-9010.93.6.1195 Venkataramani, V., & Tangirala, S. (2010). When and why do central employees speak up? An examination of mediating and moderating variables. Journal of Applied Psychology, 95(3), 582–591. doi: 10.1037/a0018315 Wagner III, J. A. (1994). Participation’s effects on performance and satisfaction: A reconsideration of research evidence. Academy of Management Review, 19(2), 312–330. Wagner III, J. A. (2000). Use participation to share information and distribute knowledge. In E. Locke (Ed.), The Blackwell handbook of principles of organizational behavior (pp. 304–315). Oxford: Blackwell. Wagner III, J. A., & Gooding, R. Z. (1987). Shared influence and organizational behavior: A metaanalysis of situational variables expected to moderate participation–outcome relationships. Academy of Management Journal, 30(3), 524–541. Wagner III, J. A., Leana, C. R., Locke, E. A., & Schweiger, D. M. (1997). Cognitive and motivational frameworks in US research on participation: A meta-analysis of primary effects. Journal of Organizational Behavior, 18(1), 49–65. Wall, T. D., Kemp, N. J., Jackson, P. R., & Clegg, C. W. (1986). Outcomes of autonomous workgroups: A long-term field experiment. Academy of Management Journal, 29(2), 280–304. Wang, D., Waldman, D. A., & Zhang, Z. (2013). A meta-analysis of shared leadership and team effectiveness. Journal of Applied Psychology, 99(2), 181–198. Wayne, S. J., Liden, R. C., Graf, I. K., & Ferris, G. R. (1997). The role of upward influence tactics in

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human resource decisions. Personnel Psychology, 50(4), 979–1006. Webb, S., & Webb, B. (1898). Industrial democracy. London. Whiting, S. W., Podsakoff, P. M., & Pierce, J. R. (2008). Effects of task performance, helping, voice, and organizational loyalty on performance appraisal ratings. Journal of Applied Psychology, 93(1), 125–139. doi: 10.1037/0021-9010.93.1.125 Withey, M. J., & Cooper, W. H. (1989). Predicting exit, voice, loyalty, and neglect. Administrative Science Quarterly, 34(4), 521–539. doi: 10.2307/2393565 Yammarino, F. J., Salas, E., Serban, A., Shirreffs, K., & Shuffler, M. L. (2012). Collectivistic leadership

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Part viI

Psychology of Work Relationships

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19 Trust at Work Ana Cristina Costa, Donald L. Ferrin and C. Ashley Fulmer

INTRODUCTION Trust is a core concept in understanding and explaining individual, team, and organizational phenomena. In organizations the study of trust has become increasingly important over recent years as the nature of work has become ever more interdependent, and the traditional compacts between employers and employees have given way to more flexible and unpredictable work arrangements. Trust is now considered a key success factor in modern-day work environments where teamwork, decentralized structures, requirements for flexibility, innovation, and high levels of cooperation predominate (Costa & Anderson, 2012). Over the last two decades, an extensive literature on trust has developed within the area of Industrial, Work, and Organizational (IWO) Psychology. From a series of challenging debates about the definition and measurement of trust, considerable consensus has emerged in recent years about how trust should be conceptualized and measured, including important advances in deconstructing trust as a generic term and identifying and operationalizing its constituent components (see in particular Mayer, Davis, & Schoorman, 1995; Rousseau, Sitkin, Burt, & Camerer, 1998). Scholars have developed extensive literatures

comprising consistent findings about the importance of trust in work relationships, and the conditions that promote the emergence of trust at work (e.g., Dirks & Ferrin, 2001; 2002 for reviews). More recently, scholars have also recognized that trust operates at different levels of analysis including the individual, team, and organizational levels, and it is applicable to multiple referents such as trust in colleagues, trust in leaders, and trust in organizations (Fulmer & Gelfand, 2012; Kramer, 1999; Rousseau et  al., 1998). This, of course, highlights the existence of numerous overlaps and interfaces between these levels and referents. In a given organization an individual may have divergent levels of trust toward different referents at different levels, such as having a high level of trust toward their supervisor but a lower level of trust toward their team colleagues; or having a high level of trust toward the immediate work group or team but a low level of trust toward the organization. In this chapter we provide an overview of the literature on trust in work settings. We start with a brief historical description of the advances in research on trust. Next, we discuss different conceptual and operational definitions of trust along with the dominant perspectives and measures of trust. We also distinguish trust from other

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related constructs. Thereafter, we describe the contributions of studying trust at different levels including interpersonal trust, team trust, and organizational trust. At each level, we discuss key findings, determinants, and consequences of trust. We conclude this chapter by reviewing and proposing a number of directions for future research and practice.

HISTORICAL BACKGROUND OF TRUST RESEARCH IN ORGANIZATIONS Although trust has long been recognized as an important element of organizational functioning and effectiveness (e.g., Argyris, 1962; Likert, 1967), it is primarily in the last two decades that trust scholars have made substantial progress in defining trust as a scientific construct, recognizing the levels at which trust operates, developing psychometrically validated measures, and mapping out the nomological network of trust (Ferrin, 2013). The trajectory of trust research within IWO Psychology has been shaped by numerous influences from a range of literatures. Two areas were particularly influential: personality and behavioral psychology. Personality theorists approached trust essentially as a ‘trait’ that individuals carry from one situation to another and that develops in varied degrees depending on their personal experiences and prior socialization (e.g., Rotter 1967, 1980). This work was instrumental in recognizing the role played by individual differences in determining one’s inherent propensity to trust others and how much trust is available at the start of a relationship. Behavioral psychology research examined trusting behavior in the context of experimental cooperation and competition (e.g., Deutsch, 1960). Here, the focus of trust was on the interaction between individuals and on identifying behaviors, relationship, and contextual factors that provide the foundation for drawing inferences regarding a counterpart’s trustworthiness and making predictions about their future behavior. This work has enlarged our understanding of the vulnerability and interdependence conditions associated with trust. Other relevant literatures contributing to the advance of trust research included sociological approaches to trust as a social reality (e.g., Lewis & Weigert, 1985a) which furthered the understanding of trust at different levels of analysis including the institutional level (e.g., Shapiro, 1987; Zucker, 1986); political science approaches which explored the function of trust in reducing complexity by generating expectations about behavior (e.g., Luhmann, 1988); embedded approaches

in which trust was understood as emerging from networks of social relationships within which organizational activity occurs (e.g., Granovetter, 1985); and research on close relationships (e.g., Johnson-George & Swap, 1982; Rempel, Holmes, & Zanna, 1985) which identified the nature and determinants of trust between romantic partners. While all these different approaches contributed to uplift trust in the organizational research agenda, they also caused confusion and disagreement about the conceptualization, operationalization, and measurement of trust. A number of seminal articles and reviews in the 1990s (e.g., Mayer et al., 1995; Kramer, 1999; Kramer & Tyler, 1996; McAllister, 1995; Rousseau et al., 1998) were instrumental in helping the field consolidate around a set of conceptual and operational definitions of trust which will be detailed below. In particular, researchers began to recognize the complex nature of the trust concept and argue for a multidimensional conceptualization and measurement (e.g., Costa & Anderson, 2011; Cummings & Bromiley, 1996; Mayer et al., 1995; McAllister, 1995). The last two decades also witnessed a striking increase in trust research (see Colquitt, Scott, & LePine, 2007; Dirks & Ferrin, 2002; Ferrin, 2013; Fulmer & Gelfand, 2012 for reviews). While the initial foci were to demonstrate the importance of trust through numerous positive outcomes and to identify the major determinants of trust, more recently the field expanded to address a range of other topics such as trust at multiple levels (e.g., Fulmer & Gelfand, 2012), violations of trust and repair (e.g., Kramer & Lewicki, 2010), trust development processes (e.g., Lewicki, Tomlinson, & Gillespie, 2006), and an increasing emphasis on rigor and methods (e.g., Lyon, Mollering, & Saunders, 2011). To further understand the development of trust research, we next turn our attention to the theoretical foundations of the trust concept including its definition, measurement, and distinctiveness from other constructs.

CONCEPTUAL AND OPERATIONAL DEFINITION OF TRUST The field of trust has witnessed much debate on the definition of trust. Over the years several definitions of trust have been put forward, some receiving greater acceptance than others, some containing overlapping features, but all promising to offer a valid construct for studying trust. Table 19.1 ­summarizes chronologically the most cited definitions of trust in organizational research. There are

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Table 19.1 Definitions of trust in organizational research: chronological developments over time Authors

Primary focus/Level of analysis Definition

Deutsch (1958: 268)

Interpersonal level

Zand (1972: 230)

Interpersonal level

Golembiewski & Interpersonal level McConkie (1975: 133) Rotter (1980: 1) Interpersonal level

Cook & Wall (1980: 39)

Interpersonal level

Barber (1983: 164–165)

Cross-level

Lewis & Weigert (1985a: 456)

Cross-level

Zucker (1986: 54)

Cross-level

Gambetta (1988: 217)

Interpersonal level

Boon & Holmes Interpersonal level (1991: 194) Sitkin & Roth (1993: 368) Cross-level

Mayer, Davis, & Schoorman (1995: 712)

Interpersonal level

McAllister (1995: 25)

Interpersonal level

Cummings & Bromiley (1996: 303)

Interpersonal and intergroup levels

‘An individual may be said to have trust in the occurrence of an event if he expects its occurrence and his expectation leads to behavior which he perceives to have greater negative motivational consequences if the expectation is not confirmed than positive motivational consequences if it is confirmed.’ ‘… the conscious regulation of one’s dependence on another that will vary with the task, the situation, and the other person.’ ‘… implies reliance on, or confidence in, some event, process or person.’ ‘… a generalized expectancy held by an individual that the word, promise, oral or written statement of another individual or group can be relied on.’ ‘… the extent to which one is willing to ascribe good intentions to and have confidence in the words and actions of other people. This willingness will in turn affect the way in which one behaves towards others.’ ‘… a set of socially learned and socially confirmed expectations that people have of each other, of the organizations and institutions in which they live, and of the natural and moral social orders that set the fundamental understandings for their lives.’ ‘… a reciprocal orientation and interpretive assumption that is shared, has the social relationship itself as the object, and is symbolized through intentional action’. ‘… a set of expectations shared by all those involved in an exchange.’ ‘… the probability that a person with whom we are in contact will perform an action that is beneficial or at least not detrimental is high enough for us to consider engaging in some form of cooperation with him.’ ‘A state involving confident positive expectations about another’s motives with respect to oneself in situations entailing risk.’ ‘… a belief, attitude, or expectation concerning the likelihood that the actions or outcomes of another individual, group or organization will be acceptable or will serve the actor’s interests.’ ‘The willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party.’ ‘… the extent to which a person is confident in, and willing to act on the basis of, the words, actions, and decisions of another.’ ‘… an individual’s belief or a common belief among a group of individuals that another individual or group (1) makes good-faith efforts to behave in accordance with any commitments implicit or explicit, (2) is honest in whatever negotiation proceeded such commitments and (3) does not take excessive advantage of another even when the opportunity is available.’ (Continued)

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Table 19.1 Definitions of trust in organizational research: chronological developments over time (continued) Authors

Primary focus/Level of analysis Definition

Creed & Miles (1996: 17)

Cross-level

Rousseau, Sitkin, Burt, & Camerer (1998: 395)

Interpersonal level

Shockley-Zalabak, Ellis, & Winograd (2000: 37)

Cross-level

Langfred (2004: 386)

Team level

Nooteboom (2002: 11)

Cross-level

De Jong & Elfring (2010: 536) Fulmer & Gelfand (2012: 1174)

Team level Cross-level

particularly large differences the earlier definitions of trust, as scholars proposed definitions mainly according to their own disciplines and perspectives. For example, some definitions stress the importance of conditions of dependence, ­reliance, or confidence in other individuals (e.g., Cook & Wall, 1980; Golembiewski & McConkie, 1975; Zand, 1972). Other definitions emphasize the role of expectations, either about others in general (e.g., Rotter, 1980), or about specific others that are acquired through social interaction and interpersonal relationships (e.g., Barber, 1983; Boon & Holmes, 1991). Still other definitions highlight conditions of vulnerability and focus on risk-taking behaviors such as cooperation (e.g., Deutsch 1958; Gambetta, 1988) or nonopportunistic actions (e.g., Sitkin & Roth, 1993). Thanks in large part to seminal articles by McAllister (1995) and Kramer (1999), the integrative definitions by Mayer et  al. (1995) and Rousseau et  al. (1998), and meta-analytic work by Colquitt et al. (2007), the field of trust in organizations is now coalescing around a set of conceptual definitions of trust and related concepts. Specifically, Mayer et  al. (1995) and Rousseau et  al. (1998) defined trust essentially as a ‘psychological state’ consisting of two interrelated components, (i) the willingness to accept vulnerability, based on (ii) positive expectations of

‘The specific expectation that another’s actions will be beneficial rather than detrimental and the generalized ability to take for granted, to take under trust, a vast array of features of the social order.’ ‘ … a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behavior of another.’ ‘Organizational trust is a positive expectation individuals have about the intent and behaviors of multiple organizational members based on organizational roles, relationships, experiences and interdependencies.’ ‘Intrateam trust is the aggregate perception of trustworthiness that team members have about one another’. ‘The expectation that a partner will not engage in opportunistic behavior, even in the face of short-term opportunities and incentives, in the absence of control.’ ‘… shared generalized perceptions of trust that team members have in their fellow teammates.’ ‘A psychological state that can be shared comprising willingness to accept vulnerability based on positive expectations of a specific other or others.’

trustworthiness (Fulmer & Gelfand, 2012). This conceptualization has been adopted to a great extent by researchers studying trust across multiple referents and levels of analysis, suggesting the view of trust as a quasi-isomorphic construct (e.g., Fulmer & Gelfand, 2012). The now widespread agreement about this definition has also helped the field move toward integration and consolidation in two major ways. First, it has helped the field to recognize what trust is and to structure the literature in terms of antecedents and consequences of trust. This is critically important for theory building, measurement, and alignment of measures of trust with conceptual definitions. Second, it helped the field distinguish trust from other related variables such as trust propensity, perceived trustworthiness, and risk-taking behaviors, all of which have been studied as manifestations of trust in prior research. These distinctions will be discussed in more detail later in this section. Despite the rising consensus around the definition and conceptualization of trust, many studies continue to measure trust using a variety of measures based on different operationalizations of trust. Table 19.2 summarizes the measures of trust that have been mostly used in organizational research. Although this proliferation of measures has expanded trust research to different contexts and levels of analysis, it also constitutes

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an impairment to further advances in our understanding about the meaning, causes, and consequences of trust. Using different trust measures not only makes consistencies and inconsistencies between findings across studies more difficult to compare and to determine, but also hinders the effort to accumulate research findings effectively in narrative or meta-analytic reviews (McEvily & Tortoriello, 2011). The consensus toward trust as a two core component, quasi-isomorphic construct, also raises some challenges for research. One challenge concerns the matter of construct variance or invariance across levels and referents, which to date has not been empirically tested. Although definitions of trust at different levels include to a certain extent the core trust components as defined by Mayer et al. (1995) and Rousseau et al. (1998), future research is needed to actually test construct invariance across levels and referents (Fulmer & Gelfand, 2012). A second challenge concerns the fact that most psychometrically validated measures of trust tend to cover only one of the core elements, either positive expectations of trustworthiness (e.g., Butler, 1991) or the willingness to accept vulnerability in the form of risk-taking behaviors (e.g., Currall & Judge, 1995; Gillespie & Mann, 2004). Measures that assess both components of trust are rare (e.g., Costa & Anderson, 2011; Cummings & Bromiley, 1996; Mayer & Davis, 1999). A third challenge refers to the variation of factors measured on each core component (see in Table 19.2) which is due, in large part, to the context/referent-­specific nature of trust. While Mayer and Davis’s (1999) measure was designed to measure trust in ‘top management’, the OTI was designed to measure interpersonal or collective trust between negotiation counterparts (Cummings & Bromiley, 1996), and Costa and Anderson’s (2011) measure was developed for intra-team contexts. Thus, although agreement on the core trust components is a good start, scholars also need to work toward measures that use the two core component conceptualization consistently across a range of contexts and referents. Among the existing measures of trust, these in particular have been successfully adopted for use across a range of contexts and referents, Mayer and Davis’s (1999) trustworthiness scales, Mayer & Gavin’s (2005) trust scale and McAllister’s (1995) cognition- and affect-based trust scales.

Trust and Other Related Constructs Trust has often been considered to be closely related to, but conceptually and empirically

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distinct from, a number of other constructs. In this section, we highlight the differences between trust and these other constructs including perceived trustworthiness, trust propensity, risk-taking behavior, and distrust.

Perceived trustworthiness

Despite being separate constructs trust and perceived trustworthiness are often studied interchangeably. While trust concerns the willingness to accept vulnerability based on positive expectations of trustworthiness, perceived trustworthiness refers to the characteristics or attributes that lead to positive expectations. According to Mayer et  al. (1995), trustworthiness perceptions are formed across three dimensions: ability, benevolence, and integrity. Ability concerns issues related to the competence, skills, efficiency, and dedication that lead to expectations about whether the trustee(s) can successfully complete an expected action. Benevolence reflects a positive orientation of the trustee(s) toward the trustor, i.e., a sense that the trustee wants to ‘do good’ to the trustor, which leads to expectations about the trustee(s)’s motives and intentions. 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. Each of these trustworthiness dimensions has been found to have a direct and unique relation with trust as demonstrated by the Colquitt et al. (2007) metaanalysis. In addition, each dimension has been found to impact other outcomes both directly and indirectly via trust (Colquitt et  al., 2007). This suggests that perceived trustworthiness dimensions are distinct constructs in their own right, and therefore researchers should clearly specify whether they are studying trust or perceived trustworthiness, as the distinction will have important implications for modeling and testing of hypotheses.

Trust propensity

Trust propensity is often viewed as a relatively stable disposition that will affect the likelihood that one party will trust another. According to Mayer et al. (1995), trust propensity has a direct effect on trust and a moderating effect on the relation between trustworthiness perceptions and trust, such that trust propensity will become more relevant to trust when there is little information to form expectations about others. Meta-analytic evidence from Colquitt et al. (2007) indicates that trust propensity exerts a direct effect on trust and also influences a number of other outcomes both directly and indirectly via trust. The importance of trust propensity has also been acknowledged in

Organizational

Management

Specific trustee

Specific trustee

Specific boundary spanner

Specific trustee Specific team

Employer

CTI Butler (1991)

McAllister (1995)

Currall & Judge (1995)

OTI Cummings & Bromiley (1996)

Robinson (1996)

Organization

Interpersonal Intergroup

Inter-organizational

Dyad

Dyad

Individual

Others in general

ITS Rotter (1967) Cook & Wall (1980)

Level of analysis

Referent (who is being trusted)

Instruments Authors

Table 19.2  Measures of trust frequently used in IWO psychology

Perceived trustworthiness

Affect (A) Cognition (C) Behavior intentions (BI)

Confident positive expectations Trusting behaviors

Perceived trustworthiness

Perceived trustworthiness

Propensity to trust

Measures/Focus

Trust in peers; Trust in institutions (25 items; split-half reliability = .76, .77 for male and .75 for female respondents) Faith in intentions (6 items; α =.77); Confidence in actions (6 items; α =.78) Integrity (4 items, α = .92); Competence (4 items, α = .91); Fairness (4 items, α = .93); Consistency (4 items, α = .87); Loyalty (4 items, α = .92); Discreetness (4 items, α = .93); Openness (4 items, α = .92); Receptivity (4 items, α = .94); Availability (4 items, α = .85); Fulfillment (4 items, α = .96) Cognition-based (5 items, α = .91); Affect-based (6 items, α = .89) Communication openness (5 items, α = .72/α = .71); Informal agreement (5 items, α = .71/α = .68); Task coordination (5 items, α = .70/α = .74); Surveillance(r) (5 items, α = .79/α = .82)* Long-version: Keep commitments (21 items; cr = .98 (A); .95 (C) and .83 (BI)) Honest in negotiations (21 items; cr = .92(A); .94 (C) and .77 (BI)) Does not take advantage (21 items; cr = .89(A); .91 (C) and .88(BI)) Short version (12 items: Keep commitments, cr =.94; Honest in negotiations, cr = .94; Does not take advantage, cr = .90) Integrity, Predictability, and Benevolence (7 items; α = .82/α = .87)

Factors/Content /Number of items/Cronbach alphas

Top management

Employees in general

Top management Immediate supervisor Top management team (TMT) Specific supervisor Team members

Team

Immediate supervisor Team members

Others in general

Mayer & Davis (1999)

Spreitzer & Mishra (1999)

Ellis & Shockley –Zalabak (2001) Mayer & Gavin (2005)

Costa & Anderson (2011)

BTI Gillespie (2012)

Frazier, Johnson, & Fainshmidt (2013)

Interpersonal

Interpersonal Team

Team

Team

Organization Interpersonal Organization

Interpersonal

Organization

Interpersonal Inter-organizational

Propensity to trust

Trust behaviors

Trust behaviors

Propensity to trust Perceived trustworthiness

Intra-team trust

Trust

Perceived trustworthiness

Perceived trustworthiness

Propensity to trust Trust Perceived trustworthiness

Perceived trustworthiness

Confidence, Integrity, and Benevolence (5 items; α = .88- Interpersonal/ α = .76- Inter-organizational) Propensity to trust (8 items α = .55/ α = .65) Trust (4 items α = .55/ α = .60); Ability (6 items α = .85/ α = .88); Benevolence (5 items α = .87/ α = .89); Integrity (6 items α = .82/ α = .88)** Competence, Reliability, Openness Concern (r) (16 items; α = .93) Top management (6 items; α = .94); Immediate supervisor (14 items; α = .92) Willingness to be vulnerable (8 items; α = .74 – TMT / α = .81-supervisor) Confident positive expectations Risk (5 items; α = .91) Propensity to trust (7 items; α = .95); Perceived Trustworthiness (6 items; α = .94); Cooperation (8 items; α = .93); Monitoring(r) (3 items; α = .92); Reliance (5 items; α= .95-supervisor / α = .93-team members); Disclosure (5 items; α = .95-supervisor/ α = .85-team members) Propensity to trust (4 items; α = .89)

TS – Interpersonal Trust Inventory; CTI – Conditions of Trust Inventory; OTI – Organizational Trust Inventory; BTI – Behavioral Trust Inventory (r)- reverse scale; α is Cronbach alpha * Cronbach alphas reported for two samples: superintendent sample and president sample ** Cronbach alphas reported for two samples: top management sample and supervisors sample

De Jong & Elfring (2010)

Contact person Supplier

Zaheer, McEvily, & Perrone (1998)

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forming new organizational relationships (e.g., McKnight, Cummings, & Chervany, 1998) and to be directly relevant to the formation of intra-team trust under conditions of low social capital between team members (Costa, BijlsmaFrankema, & DeJong, 2009). Thus, trust propensity plays a valuable role in trust development in new interpersonal relationships, which should in turn sustain the interactions that provide further opportunity to gather information about another’s trustworthiness (Mayer et al., 1995).

Risk-taking behavior

Risk-taking is seen as an outcome of trust. Risktaking behaviors can differ depending on the context and levels of the trust relationship being studied. At the individual and team levels, trust has been associated with behaviors of cooperation between individuals, information sharing, organizational citizenship behaviors, reliance on another’s work and expertise, and 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). At the organizational level, trust can be demonstrated through increases in knowledge exchange between units, fewer organizational controls, and organizational learning (Howorth, Westhead, & Wright, 2004; Pablo, Reay, Dewald, & Casebeer, 2007; Tsai & Ghoshal, 1998). Research on teams found that risk-taking from one team sends a trust signal to the partner team, which prompts the partner team to reciprocate the risk-taking in return (Serva, Fuller, & Mayer, 2005). Thus, if the outcome of the risk-taking behavior is favorable, it can strengthen the perceptions of trustworthiness of a given party and contribute to increase trust (Mayer et  al., 1995). Conversely, when the outcome resulting from risk-taking is negative it may hinder trust. 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 risktaking is to confound the focal construct with its logical consequence. Several examples underline why this is inadvisable. First, risk-taking can be influenced by the trustor’s orientation toward interpersonal risk (Bohnet, Greig, Herrmann, & Zeckhauser, 2008). Thus, a trustor may have a high level of trust toward a trustee but decide not to cooperate due to a high risk aversion. Second, trust may not necessarily lead to risk-taking behavior if the trustor considers the potential risks of that behavior to be too great, such as when the

environment is uncertain (Mayer et  al., 1995). Third, risk-taking behaviors can be influenced by factors other than trust, such as norms, the presence or absence of satisfactory alternative behaviors, etc. Thus, risk-taking may take place in the absence of trust, and risk-taking may not occur even if trust is present (Mayer et al., 1995).

Distrust

While trust is often regarded as ‘… positive expectations regarding another’s conduct’, distrust refers to ‘… negative expectations about another’s conduct’ (Lewicki, McAllister, & Bies, 1998, p. 439). Traditional perspectives view trust and distrust as one bipolar construct, i.e., as two opposite ends of a continuum (e.g., Rotter, 1971) such that low trust is equivalent to high distrust and vice versa. However, more recent approaches conceptualize and operationalize trust and distrust as two distinct constructs (e.g., Hosmer, 1995; Lewicki et  al., 1998). Although both constructs refer to expectations about future behavior, trust focuses on the possible occurrence of a desirable behavior, whereas distrust focuses on the probable (or even certain) occurrence of undesirable behavior, thereby enabling preventive and defensive actions to be taken (Lewicki et  al., 2006; Luhmann 1988). This suggests that low trust is not the same as high distrust. While low trust can be characterized by lack of hope, absence of faith, and hesitance, high distrust can manifest itself in fear, vigilance, or suspicion (Lewicki et al., 1998; Ross, Mirowsky, & Pribesh, 2001; Sitkin & Roth, 1993). The distinction between trust and distrust has also been supported by a number of findings and there is evidence that trust and distrust coexist in a given relationship (e.g., Cho, 2005; Vlaar, Van den Bosch, & Volberda, 2007). Since trust in a particular relationship tends to be domain specific, i.e., ‘in what aspect’ and ‘under what circumstances’ (Lewis & Weigert, 1985b, p. 970), it is possible that partners might trust each other in some aspects and distrust each other in other respects (Lewicki et  al., 1998). Moreover, trust and distrust can be cultivated at the same time in order to secure the benefits from both and to compensate for the weaknesses associated with each of them individually. For example, trust alone can be naïve in certain circumstances, with monitoring and vigilance providing complementary safeguards (e.g., Langfred, 2004). In sum, this section suggests that trust, trust propensity, perceived trustworthiness, risk-taking behaviors, and distrust are distinct constructs. Under different circumstances, some constructs

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Trustor factors: • •

Propensity to trust Emotions Outcomes: Trustee factors:

• • • •

Ability Integrity Benevolence Leadership behaviors

Interpersonal trust

Trustor–trustee relationship: • • •

• • • • • •

• • • • • •

Past history Similarity characteristics Developmental processes

• • • • •

Contextual factors:

•

Positive job attitudes Risk-taking behaviors Cooperation Information sharing Individual performance Communication quality and quantity OCBs Goal acceptance Decision commitments Proactive work behavior Counterproductive behaviors(-) Turnover intentions

Culture Monitoring and control Communication medium HR practices Institutional factors Goals and rewards

Figure 19.1 Interpersonal trust: selected determinants and consequences

can be more critical to a particular situation or more vital for addressing a research question than the others. Therefore, it becomes imperative that researchers select the appropriate construct, and define and measure it accordingly. While this is important for any construct, it is particularly important in the study of trust where multiple, closely related constructs coexist.

INTERPERSONAL TRUST Interpersonal trust refers to the trust that one person (the ‘trustor’) has in another person (the ‘trustee’). Within organizations, trustors and trustees may be at the same or different hierarchical levels. Research on interpersonal trust typically studies the trust of employees toward their direct supervisors or a top leader, or trust toward coworkers. For the most part, the definitions and measures of trust described above were developed for the purpose of studying interpersonal trust. Accordingly, in research to date, interpersonal trust has been operationalized as perceptions of trustworthiness (ability, benevolence, and/or integrity), positive confident expectations (cognitive and

affective), willingness to accept vulnerability, and variations on these. Research on interpersonal trust is extensive and has been the subject of a number of narrative and meta-analytic reviews (Burke, Sims, Lazzara, & Salas, 2007; Colquitt et al., 2007; Dirks & Ferrin, 2001, 2002; Fulmer & Gelfand, 2012; Johnson & Mislin, 2011; Lewicki, Tomlinson, & Gillespie, 2006; Searle, Weibel, & Den Hartog, 2011). Thus, much is known about determinants and consequences of interpersonal trust in work settings. Below, we briefly review these findings, and also some interesting, new areas of research emerging particularly in the last decade. Figure 19.1 summarizes both the determinants and consequences of interpersonal trust.

Determinants of Interpersonal Trust Logically, given that a dyad comprises a trustor, a trustee, the relationship between them, and the context within which they are situated, the determinants of interpersonal trust can be categorized as trustee factors, trustor factors, relationship factors, and contextual factors. We will discuss each in turn.

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Trustee factors

Trustee factors are among the most studied determinants of trust. Dirks and Ferrin’s (2002) meta-analytic review of trust toward leaders reported that transformational and transactional leadership behaviors, and distributive, procedural, and interactive justice behaviors, all had strong effects on employees’ trust in their leaders. Participative decision-making and breach of expectations had moderate positive and negative effects, respectively. More recent research has explored a range of other behaviors predictive of interpersonal trust, including ethical leadership and authentic leadership behaviors (e.g., Brown, Treviño, & Harrison, 2005; Mayer, Bobko, Davis, & Gavin, 2011), helping behaviors, and information sharing and other communication behaviors (see also Fulmer & Gelfand, 2012). Researchers have also focused considerable effort on developing a deeper understanding of the relationship between justice and trust. Some studies have focused on contingent effects. For instance, Choi (2008) found that employees’ perceptions of supervisor fairness moderated the effect of the perceived justice of a particular event on their trust toward managers. Likewise, Van Dijke, De Cremer, and Mayer (2010) found that unfairly enacted procedures decrease followers’ trust in authority particularly when leaders have high power over their followers. Other research has focused on the cognitive processing of fairness-related information. In a laboratory setting, Johnson and Lord (2010) observed that experimentally-manipulated fairness and unfairness activated interdependent and independent self-identities, respectively, which in turn influenced trust. Also focusing on cognitive processing, Holtz (2013) argues that because trust exists at the very earliest stage of a relationship, before justice events occur, justice evaluations are always preceded and influenced by trust. This possibility that trust may be a determinant of justice perceptions is further extended in two longitudinal field studies in which trustworthiness perceptions and justice were found to have a reciprocal relationship over time (Colquitt & Rodell, 2011), and in fact trust toward supervisors predicted withinand between-person variance of subsequent supervisor justice perceptions (Holtz & Harold, 2009). Interestingly for our review, these research findings highlight that justice should no longer be considered simply a ‘trustee’ factor, but instead can also be considered a trustor factor, a relationship factor, and also a consequence of trust.

Trustor factors

Factors associated with the trustor have received relatively less research attention than factors associated with the trustee. Meta-analytic review evidence suggests that propensity to trust has a significant but modest effect on interpersonal trust (Colquitt et al., 2007; Dirks & Ferrin, 2002). Propensity to trust or the ‘trait of trusting’ has often been treated as a subscale of agreeableness in the Big-Five personality framework (e.g., Digman, 1990; Goldberg, 1990; McCrae & Costa, 1987). In relation to the traits in the Big-Five, propensity to trust is found to be positively associated with consciousness, agreeableness, openness, and extraversion, and negatively related to neuroticism (Colquitt, Scott, Judge, & Shaw, 2006). More recently, several authors have viewed propensity to trust as a more central personality variable that can have both a direct and indirect effect on individual behaviors and performance (e.g., Colquitt et  al., 2006; Evans & Revelle, 2008). As described earlier, propensity to trust is particularly important in explaining variations in initial trust levels since other potential sources of information about trustworthiness are limited in such situations. One recent study by Yakovleva, Reilly, and Werko (2010) utilized the Kenny, Kashy, and Cook (2006) Actor–Partner Interdependence Model (APIM) to trace the potential within- and between-person effects of propensity, and found that an individual’s propensity influenced his or her own trust toward a fellow team member via trustworthiness perceptions, and also influenced the partner’s trust via the partner’s trustworthiness perceptions. This finding suggests that an individual’s propensity may not only influence his or her own trust judgments, but also attract more favorable trust judgments from others. In a recent review, Ferrin and Gillespie (2010) also noted that mean country-levels of trust propensity vary greatly from country to country. High propensity to trust countries included Nordic countries plus China, Vietnam, New Zealand, Switzerland, and Saudi Arabia, and low propensity scoring countries included Malaysia, Indonesia, Turkey, Brazil, and a number of African countries (Delhey & Newton, 2005). Some research indicates that other trustor factors, such as longterm time perspective and secure attachment, are also predictive of propensity to trust (Fulmer & Gelfand, 2012). A number of studies have turned their attention toward understanding the effects of other trustor characteristics on trust formation. In a series of lab studies, Lount and Pettit (2012) found that people with high status tended to attribute more

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benevolence to others, and as a consequence placed more trust in them. Ballinger, Schoorman, and Lehman (2009) demonstrated the relevance of trustor’s emotions in workplace trust judgments, as the affective reaction toward the departure of an existing leader had a significant effect on trust toward the successive leader, with positive affective reactions increasing the trust toward the future leader.

Trustor–trustee relationship

Considerable research has focused on factors associated with the relationship between the trustor and the trustee. This ‘relationship’ factor includes several facets. First, a central assumption of the Mayer et al. (1995) model is that trust and trustworthiness perceptions are influenced by the trustor’s and trustee’s past history. This past history comprises not only trustee behaviors, but also trustors’ past experiences of placing trust and then seeing that trust honored or violated. Supporting this idea, studies of repeated trust games in an investment setting typically show that honoring of trust by the trustee encourages future placement of trust (‘risk-taking in the relationship’) by the trustor (Berg, Dickhaut, & McCabe, 1995). Implicit in this consideration of the effect of past history on trust is an assumption – or more precisely two competing assumptions – about trust baselines. Most interpersonal trust models implicitly assume that interpersonal trust begins at a baseline of zero and then develops over time. 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 (see Lewicki et  al., 2006, for a review). A recent meta-analytic review of trust games research (Johnson & Mislin, 2011) provides strong evidence of trusting behavior at zero acquaintance and provides a range of insights into the factors that increase or decrease such trusting behavior. Interestingly, this trust at zero acquaintance finding seems robust at the interpersonal level, but not necessarily at other levels as will be discussed in the ‘teams’ section below. A second facet of the ‘relationship’ factor is that the degree of similarity between the trustor and trustee, for instance in terms of cultural-ethnic similarity, shared values, and identity, is also predictive of trust (see Fulmer & Gelfand, 2012 for a review). Third, aspects of the relationship itself, such as the communication medium and smoothness of communication, are predictive of trust (Fulmer & Gelfand, 2012). Recent research on relationship factors has demonstrated a strong focus on the process of

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relationship development, tackling this issue in two different ways. First, scholars have explored how trustors may attend to different trustee factors as the relationship develops over time. Consistent with Lewicki and Bunker’s (1996) stages model of trust development, Levin, Whitener, and Cross (2006) found that trustworthiness perceptions were predicted by demographic similarity in newer relationships, by trustworthy behaviors in more established relationships, and by shared perspective in more mature relationships. Taking a similar approach, van der Werff and Buckley (2017) found that trust in coworkers was predicted by propensity at the initial stage of relationships, but in later stages trust was predicted by perceived competence and benevolence, and in even later stages by group identification. Although social exchange theory is frequently used as a foundational theory for the study of trust, and scholars recognize that trust development is likely to spiral between trustor and trustee, only a very small number of studies have specifically modeled such spiraling. For example, Ferrin, Bligh, and Kohles (2008) in a lab setting used the APIM to test competing models of how trust and cooperation might spiral between individuals and groups. They found strong support for a trust–cooperation spiral in which a partner’s cooperative behavior influenced actor trust perceptions which in turn influenced actor cooperative behaviors. Similarly, in a field setting Halbesleben and Wheeler (2015) found support for a reciprocal resource gain spiral in which an employee’s OCBIs toward a coworker influenced the coworker’s perceived social support, which in turn influenced the coworker’s trust and then the coworker’s OCBI toward the employee. Finally, Cameron and Webster (2011) developed a spiral model of incivility behaviors in people’s use of electronics to manage multiple conversations at a single time. In an analysis of participants’ recalled multi communications experiences, the researchers found that both parties’ behaviors in the spiral can influence an individual’s incivility perceptions, which in turn influence affect-based trust.

Contextual factors

Contextual factors reflect a broad set of potential determinants. First, monitoring and control mechanisms designed to foster or ensure trustworthy behavior have complex effects on trust (Ferrin, et al., 2007). Existing evidence suggests that when controls are in place to promote trustworthy behavior, trustors attribute the behavior to the safeguards rather than the person, and therefore

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attributions seem to hinder trust development (Korsgaard, Brodt, & Whitener, 2002; Malhotra & Murnighan, 2002). Thus, controls may foster trustworthy behavior while simultaneously suppressing the formation of positive trust perceptions. Further, the perceived intent of the control may also matter: Controls that signal a relationship motivation (such as monitoring used for constructive feedback) tend to have more positive effects on trustworthiness perceptions than controls signaling an instrumental relationship (Searle et  al., 2011). Second, a range of other organizational characteristics can influence interpersonal trust at work, including the mix of cooperative vs. competitive elements in reward structures or the organizational context, HR practices such as employment security, selective hiring, and profit sharing, and perceived organizational support (see Fulmer & Gelfand, 2012). Finally, even elements of the societal context, such as laws and regulations, and political systems, may influence levels of interpersonal trust at work (Fulmer & Gelfand, 2012). Much of the recent research on contextual impacts has focused on how national culture may influence the manifestation and development of trust. Gunia, Brett, Nandkeolyar, and Kamdar (2011) found that in negotiation settings, Indian negotiators reported a lower willingness to trust their counterpart than American negotiators did. In a field study, Jiang, Chua, Kotabe, and Murray (2011) found that Chinese executives reported higher levels of affect-based trust in overseas partners of the same ethnicity than overseas partners of a different ethnicity. In a scenario design aimed at comparing trust formation in collectivist vs. individualist societies, Branzei, Vertinsky, and Camp (2007) found that Japanese participants tended to base their trust more on perceived benevolence whereas Canadian participants based their trust more on perceived ability and integrity. Chua, Morris, and Mor (2012) found that individuals with higher levels of metacognitive cultural intelligence – the tendency to be mindful of one’s own and others’ assumptions when interacting with individuals from different cultures – attracted higher levels of affect-based trust. In combination, these studies illustrate differences in raw levels of trust across cultures, tendencies to develop higher trust in same-culture relations than cross-cultural relations, attention to different predictors of trust, and individual differences in people’s ability to earn trust in intercultural interactions. Addressing even more fundamental questions about culture and trust, Wasti, Tan, Brower, and Önder (2007) took a quantitative approach toward assessing the cross-cultural generalizability of perceived trustworthiness and trust

constructs. Using data from participants from the US, Turkey, and Singapore, Wasti et  al. (2007) found that perceived integrity was invariant across cultures, indicating that it is manifested similarly across cultures. Perceived ability and benevolence were partially invariant, meaning that the concepts generalized across cultures, but some of the items were understood differently across cultures. Other research on the effects of context on trust development has focused on the level of unpredictability, danger, and vulnerability in the situation, and also the level of media richness in communication. Colquitt, LePine, Zapata, and Wild (2011) found that in ‘high-reliability’ contexts such as firefighting characterized by high levels of unpredictability and danger, trust was based on coworkers’ integrity, whereas in more typical work contexts trust was more likely to be based on coworkers’ benevolence. Similarly, in a critical incidents analysis, Lapidot, Kark, and Shamir (2007) found that for officers in cadet training courses describing incidents of leader trust building and erosion, benevolence was mentioned most frequently in trust-building incidents whereas integrity was mentioned most frequently in trust-eroding incidents. Finally, Rockmann & Northcraft (2008) studied the effects of media richness (i.e., computer-mediated vs. video-mediated vs. face-to-face communications), finding that communications occurring in leaner media produced lower levels of trust, and consequently higher rates of deception. In sum, it is clear that context does impact the processes of trust development, yet research seems to have only just scratched the surface of how context may influence trust development. What is needed is a systematic way of conceptualizing contexts, and then using such conceptual frameworks to drive the study of context, so that researchers will be able to validate some broader frameworks for understanding context and trust.

Consequences of Interpersonal Trust Interpersonal trust has been found to have generally positive effects on a wide range of employee outcomes including communication accuracy, openness, and quantity of information shared, organizational citizenship behaviors, individual performance, job satisfaction, risk-taking behaviors, acceptance of goals, belief in information, decision commitment, and organizational commitment, and negative effects on conflict, counterproductive behaviors, and turnover intentions (Colquitt et  al., 2007; Dirks & Ferrin, 2001, 2002). More recently,

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interpersonal trust has been associated with a range of other positive outcomes such as reduced uncertainty at work, negotiation partner selection, mentor support, feedback and help seeking, future cooperation, expression of opinions, implementation of negotiated agreements, virtual work adjustment, reduced work–nonwork conflict, and creativity (see Fulmer & Gelfand, 2012, for a review). Recent research on the consequences of interpersonal trust in leaders can be categorized into two groups. In the first group, a number of studies have focused on ‘new’ outcomes that have received little or no prior research attention. For instance, trust in leaders has been associated with employees’ ability to focus on value-producing activities (Mayer & Gavin, 2005), coworker trust has been associated with proactive work behavior (Parker, Williams, & Turner, 2006), and mentors’ trust toward protégés has been found to predict mentoring behaviors such as career-related support, psycho-social support, and role modeling (Wang, Tomlinson, & Noe, 2010). The second group reveals numerous studies finding a moderating effect of trust on downstream variables. For example, De Cremer and Tyler (2007) reported that, across lab and field settings, procedural fairness has a stronger effect on cooperation with an authority when trust in the authority figure is high. In a fund-raising setting, the effect of prosocial motivation on performance – number of calls made and dollars raised – was increased when employees trusted their managers (Grant & Sumanth, 2009). In China, leaders’ participative decision-making interacted with employees’ trust in leaders in predicting employee voice (Gao, Janssen, & Shi, 2011). In a study of nurses in a multiservice hospital, Hofmann, Lei, and Grant (2009) found that coworker trust interacted with coworker expertise and coworker accessibility in predicting nurses’ seeking of help from coworkers. And in a study of employees and supervisors in a government agency, Kacmar, Bachrach, Harris, and Noble (2012) found that the negative effect of coworker relationship conflict on task-focused OCBs was attenuated by employees’ trust in the supervisor. Researchers have also recently begun to study the effects of ‘felt trust’: an individual’s belief about how much he or she is trusted by others. Dirks and Skarlicki (2009) provided evidence, based on data from a sample of bank financial services employees, that employees who were perceived as trustworthy by their coworkers demonstrated higher performance. Dirks and Skarlicki also found that the trustworthiness effect reflected an interaction of perceived capability and integrity, thus providing some support for previous authors’ (e.g., Mayer et al., 1995) speculation that the factors of perceived trustworthiness may interact.

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Overall, interpersonal-level trust research has tackled a number of core questions in trust research including what are the major benefits of trust and what are the behaviors in particular from leadership that predict trust. This literature is extensive and includes a number of narrative and meta-analytic reviews which have helped consolidate past findings and provide a foundation for future research. Although at this stage much is already known about interpersonal trust, many streams are relatively new and underdeveloped such as interpersonal trust in the context of teams, organizational networks, trust development processes, and trust violations and repair, thus many interesting and important questions remain. In the next section we turn to review team-level trust research and discuss its relevant determinants and consequences.

TEAM TRUST Trust research at the team level has grown considerably over the last two decades (Costa, Fulmer, & Anderson, 2017). Scholars have started paying more systematic attention to trust as a team-level phenomenon and to understand how team trust differs from interpersonal trust. Although several theories and guiding perspectives at this level have been adopted from the interpersonal trust research, a distinction between interpersonal-level trust and team-level trust is critical to further our understanding of this construct (Fulmer & Gelfand, 2012). Team trust refers to the collective trust that is shared among team members (Costa & Anderson, 2011; De Jong & Elfring, 2010; Kramer, Hanna, Su, & Wei, 2001; Langfred, 2004). Many conceptualizations of team trust include the same underlying dimensions as interpersonal trust, 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. This is consistent with Chan’s (1998) consensus composition models in which the basic content of the construct remains unchanged, the only change is the level of the construct, i.e., from an individual psychological state to a team-level collective state. Within-group agreement is then necessary to justify the aggregation of individuals’ collective trust perceptions to the higher level (i.e., team-level) construct (Chan, 1998), as constructs at a team level are only meaningful when shared among members (Chan, 1998; Kozlowski & Klein, 2000). Team trust is believed to develop from ongoing interaction and team membership (Williams,

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Team composition and relationship structure:

• •

Diversity/similarity between members Tie structure and strength Outcomes

Reciprocity and developmental process:

• • • •

Self-reinforcing loops Cooperation Monitoring Team development

Team trust

Team leadership:

• •

Trust in the leader Transformational and consultative leadership

• • • • • • • • • • • •

Positive job attitudes Risk-taking behaviors Cooperation Information sharing Knowledge creation Team climate Psychological safety Team OCBs Team learning Team creativity Conflict resolution Team performance

Contextual factors:

• •

Functional diversity Communication medium

Figure 19.2  Team trust: selected determinants and consequences

2001) in which collective sense-making about shared experiences emerges (Shamir & Lapidot, 2003). This suggests that team trust is essentially a collective phenomenon that entails generalized expectations of trustworthiness and the willingness to accept vulnerability to all members. However, and according to Chan’s (1998) consensus models, team members may share a high or low collective trust and yet individually maintain different levels of interpersonal trust between them. For instance De Jong and Dirks (2012) found that 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 from the team trust construct. Team trust has been investigated across a variety of teams ranging from ongoing (longcycled) to project (short-term) teams, including cross-functional and virtual teams, and also across a range of different contexts from R&D to health-care teams, to customer service and student teams. The diversity in the types and contexts of teams studied is in part responsible for some of the fragmentation that is seen in terms of the determinants and consequences identified. The team trust literature is mostly centered either on intra-team trust, i.e., the collective trust that members have about their team colleagues, or on inter-team trust, i.e., the collective trust one team

has toward another team. Empirical evidence of the relation between team trust and selected determinants and outcomes is summarized in Figure 19.2 and reviewed below.

Determinants of Team Trust Teams consist of social systems of interdependent individuals working together for a common goal, and thus they are inherently multilevel (Kozlowski & Bell, 2003). Consequently, team trust is affected by both top-down and bottom-up influences.

Team composition and relationship structure

Team composition variables are among the most studied bottom-up predictors of team trust, with diversity and identity receiving particularly strong attention. Trust is believed to develop more naturally in teams where members have more commonalities, are more similar affectively, and are more comfortable with each other’s interpersonal interaction than in teams where members are more diverse (e.g., Blatt, 2009). In diverse teams members can have divergent vocabularies and priorities due to different backgrounds or professions, which may increase uncertainty about future

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behavior and lead to low trust (Webber, 2002). In particular, research studying the effects of demographic diversity on team 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 of student teams working in one location. In a field study of entrepreneurial teams, Chowdhury (2005) found that diversity in age and functional background was negatively related to perceived trustworthiness between team members. However, research to date has not found any effect of gender diversity on team-level trust (e.g., Curşeu & Schruijer, 2010; Chowdhury, 2005). Team trust is also dependent upon the existing relationships and their configuration within the structure of the team. 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). Meta-analytic evidence from 37 team studies shows that teams with strong and dense ties not only have higher levels of trust, but also attain their goals more effectively, and members are more likely to stay together, as compared to teams with weak and sparse configurations (Balkundi & Harrison, 2006). Similarly, Levin and Cross (2004) 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, Chen, Chang and Hung (2008) found that interpersonal ties and mutual trust within teams were positively related.

Reciprocity and developmental process

A number of studies have focused on reciprocity between team members to explain the development of trust through self-reinforcing paths. In an experimental setting with groups of managers, Zand (1972) found that initial experiences of high trust vs. low trust determined the level of trust developed within groups. Initial experiences of high trust within the groups led members to increase their vulnerability by exchanging more

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relevant information, accept each other’s influence, and decrease efforts to control other members’ behavior, which then supported and reinforced the initial high trust; in contrast, initial low-trust experiences made members hesitate in accepting vulnerability, less open to exchange information, and less willing to accept the influence of others, which then reinforced the initial low trust (Zand, 1972). Further empirical evidence by Munns (1995) in the context of construction projects also supported the self-reinforcing feedback loops in trust development, showing that cooperation was anticipated by trust between members, which then was reciprocated with behavior validating that trust. Studying trust between teams in an experimental setting, Serva et  al. (2005) also found that risk-taking behavior on the part of one team had consequences for the other team’s perceptions of trustworthiness and subsequent reciprocity of trust. Researchers have also paid close attention to the reciprocal effects of 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. De Jong and Elfring (2010), for example, found that monitoring was positively related to team trust in a study involving ongoing tax teams. Likewise, Bijlsma-Frankema, De Jong, and Van den Bunt (2008), with a study sample of 57 research intensive teams, found 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 monitoring between team members communicates low trust within teams (e.g., Costa, Roe, and Taillieu, 2001; Langfred, 2004) which suggests a negative relationship. 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. Equally important is the finding that the relation between trust and monitoring is not static. In a longitudinal setting, Costa et  al. (2009) found that monitoring and trust relate differently at different stages of the project, i.e., negatively at the start and positively both in the middle and at the end of the project. 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. 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,

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particularly in terms of assisting team members to perform their tasks (Marks, Mathieu, & Zaccaro, 2001). This is consistent with McAllister’s (1995) evidence that individuals expressing high affectbased 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. While team trust creates willingness to cooperate and contribute to a common goal, monitoring enables fine-tuning and redirection of behaviors of team members toward that common goal (BijlsmaFrankema et al., 2008).

Team leadership

Trust in the team leader/supervisor is considered to be one of the most important top-down determinants of team trust, as leaders often play a primary role in establishing and developing trust in teams (Dirks, 2000; Gillespie & Mann, 2004). Several leadership theories view trust as an essential component of team leadership. For example, the Leader–Member Exchange (LMX) theory (Graen & Uhl-Bien, 1995) includes trust as a major component of the quality of the dyadic relationships between the leader and team members. Similarly, theories of charismatic leadership by House (1977) and Conger and Kanungo (1998) include trust in the leader as an essential component of leadership, and transformational theories of leadership place trust at the center of the leader–followers relationship (Yukl, 1998). Empirical evidence on transformational 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 of R&D teams, Gillespie and Mann (2004) found that transformational leadership practices including consultative leadership, instilling 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 Mayer and Gavin (2005) supports the theory that subordinates’ trust in their leader depends on the perceived levels of ability, benevolence, and integrity of the leader. When teams trust their leader, they become motivated to perform beyond expectations. This is because team leaders will make team members more aware of the importance and value of team goals, inducing them to transcend self-interest for the good of the team/organization, and appealing to members’ higher order needs (Bass, 1985). 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).

Contextual factors

Functional diversity in teams is one relevant contextual factor in the formation of team-level trust. The research has followed two different directions. One research direction is consistent with the theories of social identity (Tajfel & Turner, 1986) and attraction-similarity (Byrne, 1971) and has focused on the challenges associated with functionally diverse teams. For example, Newell, David, and Chand (2007) reported that an ‘us vs. them’ attitude often prevails and is detrimental to trust in cross-functional teams. Cross-functional teams, in particular, are believed to have more difficulty in developing trust between members due to unshared and sometimes conflicting goals, reporting relationships, and perceived differences in professional allegiance (Jasswalla & Sashittal, 1999). 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). A careful selection of the team members designed to increase perceptions of similarity can therefore be an important strategy to mitigate some of the barriers to trust development (Triandis, 1995; Webber, 2002). For example, organizations might select members who occupy similar hierarchical-level positions in the organization, or who have had successful previous experiences of working together (McAllister, 1995; Watson, Kumar, & Michalesen, 1993). In addition, organizations can also create opportunities for interpersonal trust relationship building between functionally diverse members so that knowledge will be transferred more successfully (Newell et al., 2007). Another research direction emphasizes the different bases for trust development and its effects on team performance. These studies are often rooted in information processing perspectives and argue that functionally diverse teams are more likely to develop trust based on team member reliability, consistency, and responsiveness, or what is known as cognition-based or competence-based trust, rather than similarity or affect-based trust (e.g., Kirkman, Rosen, Gibson, Tesluk, & McPherson, 2002). While studying antecedents of team creativity, Barczak, Lassk, and Mulki (2010) found that cognition-based trust together with a collaborative culture were significant predictors of team creativity while affect-based trust was not. Similarly, in

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a study surveying 85 top-management hospital teams, Olson, Parayitam, and Bao (2007) found that functional diversity was positively related to competence-based trust. These results suggest that while similarity may lead to a quick development of trust earlier on in the team formation, to have a positive and significant impact on team performance cognition-based trust is more important. The medium of communication within or between teams is another important contextual determinant of team trust. Computer-mediated teams have been found to be lower in trust than 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).

Consequences of Team Trust Trust at the team level has been associated with several positive outcomes. Intra-team trust has been found to increase team effectiveness outcomes including team satisfaction and commitment (Costa, 2003), information sharing and exchange (Howorth et  al., 2004; Nahapiet & Ghoshal, 1998), team learning (Bogenrieder & Nooteboom, 2004), and team OCB (Hempel, Zhang, & Tjosvold, 2009; Joshi, Lazarova, & Liao, 2009). Edmondson (1999) found trust in teams to be important to develop 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. Farrell, Flood, MacCurtain, Hannigan, Dawson, and West (2005) found that intra-team trust was related to knowledge sharing within the team and beyond. Intra-team 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

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innovation (Szulanski, Cappetta, & Jensen, 2004; Tsai & Ghoshal, 1998). Recent research has demonstrated that the relationship between team trust and team performance is positive and significant (Breuer, Hüffmeier, & Hertel, 2016; De Jong, Dirks, & Gillespie, 2016). In addition, researchers have also explored indirect effects between team trust and team performance. For example, De Jong and Elfring (2010) found that intra-team trust influences performance through mediation effects by team effort and team monitoring. Porter and Lilly (1996) also found an indirect relation between team trust and performance through conflict and task processes. Examining team trust as a moderator, Langfred (2004) found that high team trust was only associated with higher team performance when individual autonomy was low. De Jong and Dirks (2012) found that team trust positively influenced team performance only under conditions of low trust asymmetry between team members. Cognitionbased 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. In a meta-analysis, DeDreu and Weingnart (2003) concluded that at relatively high levels of intrateam trust, openness, and psychological safety, task conflict can have positive effects on team performance. In virtual teams Jarvenpaa, Shaw, and Staples (2004) found that team trust affected virtual teams differently depending on the level of team structure such that trust is likely to have a direct, positive impact on a team member’s attitudes and perceived outcomes when the team structure was weak rather than strong. In sum, although team trust is still a relatively new research area, empirical evidence suggests that trust is an important driver for effective functioning and performance of teams is emerging. Having considered trust at the team level of analysis, in the next section we move on to review the organizational trust literature and cross-level effects.

ORGANIZATIONAL TRUST In this section, we review the research on organizational trust, including some topics such as trust

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Organizational structure: • Centralization • Formalization • Employee participation Outcomes Organizational culture and climate: • Values/beliefs Organizational trust HRM policies and practices: • Perceived HR practices • Organizational justice • Employee–organization relation Organizational leadership:

• Positive impact on employee job attitudes • Organizational citizenship behaviors • Information sharing and knowledge exchange between individual and teams • Inter-organizational collaboration • Organizational effectiveness • Successful performance

• Credibility and consistency • Participative and servant leadership behavior Cross-level effects: • Social networks • Third-parties reputation • Trust violation and repair

Figure 19.3 Organizational trust: selected determinants and consequences repair and networks that have been studied in cross-level ways or at multiple levels including the organizational level. The study of organizational trust has its origin in the notions of ‘system trust’ (e.g., Luhmann, 1988) or institution-based trust (e.g., Zucker, 1986). This literature has been predominantly developed within disciplines such as strategic management, sociology and economics where trust is seen as an ‘organizing principle’ (e.g., McEvily, Perrone, & Zaheer, 2003) or as an efficient means to coordinate expectations and interaction (Bachmann & Inkpen, 2011). Within IWO psychology, however, organizational trust is viewed as the global evaluation of an organization’s trustworthiness as perceived by its employees, and their willingness to become vulnerable to the organization’s actions (Shockley-Zalabak et  al., 2000). Similar to the conceptualization of trust at the team level, organizational trust refers to the collective trust that is ‘shared among its members comprising willingness to accept vulnerability based on positive expectations’ (Fulmer & Gelfand, 2012, p. 1174). Organizational trust research is starting to flourish within the field of IWO psychology. One sub-set of the organizational trust literature has received particular attention, i.e., trust between organizations or inter-organizational trust. Given

the growth of alliances, outsourcing relationships, and other forms of inter-organizational exchange and the significance attributed to the management issues associated with these relationships, scholars are placing greater attention on the work environment where trust occurs. Organizational level variables that have been shown to influence collective trust in the organization include HRM policies and practices, structures and level of employee participation, organizational climate and leadership. Figure 19.3 summarizes selected determinants and consequences of organizational trust.

Determinants of Organizational Trust Organization structure

The structure of the organization determines the degree of centralized or decentralized decisionmaking and therefore impacts the level of trust within and between groups and organizations (Hardin, 1996). Creed and Miles (1996) argue that highly controlled, highly centralized, and highly formalized organizations with a primary focus on efficiencies will constrain the interactions necessary for a high trust environment. Less formalized structures, on the other hand, provide employees

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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 organizational trust, while bureaucratic structures negatively affected trust (Moorman et al., 1993). Within inter-organizational collaborations, although structure provides an essential foundation for the creation of the relationship, it is not sufficient for its continuation (Madhok, 1995). The success of inter-organizational collaborations implies a shift from control-based structures to interaction-based structures that influence behavior through the nurturing of relationships (Beamish, 1988). Indeed, Yan and Gray (1994) found that trust between organizations could have important mutual benefits, even when one partner is dominant in the inter-organizational collaboration. A study by Wong and Cheung (2005) suggests that structures that facilitate permeability between partners, i.e., openness to share and receive information and knowledge and dealing with others in a straightforward manner, lead to increases in trust in inter-organizational collaboration.

Organizational culture and climate

Organizational trust can result from the values that an organization embraces. For example, the cultural norms of ethical values (Li, Bai, & Xi, 2012) and the initiatives of corporate social responsibility (Bhattacharya, Korschun, & Sen, 2009) have been found to facilitate trust in organizations. 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). Employees continually observe the organizational environment when they consider whether or not to trust their organization. Consistent with this view, Barney and Hansen (1994) note that only when the organization’s values and beliefs are supported by organizational practices and decision-making systems, is it possible that a particular climate emerges. These shared perceptions evolve over time and are important to the creation of trust (Hosmer, 1995). When the positive values are expressed but not enacted, organization trust can fall (Jahdi & Acikdilli, 2009). Chatman and Barsade (1995) suggest that organizational climates emphasizing individualistic values focus on individual achievement and

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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. Similarly, Elangovan and Shapiro (1998) suggest that a lower level of benevolence and honesty perceptions of the organization can increase betrayals of trust, the most common type of which is opportunistic betrayal among employees. Likewise, Gillespie and Dietz (2009) and Palanski and Yammarino (2009) suggest that high collective perceptions of organizational integrity through fair and transparent policies lead to high shared trust among employees in the organization. Other findings also indicate that climates characterized by politics and manipulation can lower the levels of trust in the organization (e.g., Chen & Indartono, 2011).

HRM policies and practices

HRM policies and practices are relevant to the development of organizational trust by the employees. While HRM policies can be seen as statements of intent by the organization, HRM practices relate to how such policies are perceived to be delivered or implemented, which then influence perceptions of how the organization can be trusted by its employees (Skinner, Saunders, & Duckett, 2004; Six & Sorge, 2008). To the extent that an organization invests in the employees and engages in supportive employment practices, trust in the organization is higher (Whitener, 1997; Zhang, Tsui, Song, Li, & Jia, 2008). A study by Hom, Tsui, Wu, Lee, Zhang, Fu, and Li (2009) suggests that mutual investments in the employee– organization relationship (EOR) lead to higher employee trust in the organization, as the corporate benevolence is associated with an expression of concern for employees’ welfare. Early research on specific HRM practices such as benefits, rewards, and promotion suggest that the extent to which these are perceived as being adequate and fair by the employees is a determinant of the level of organizational trust (Daley & Vasu, 1998). Similarly, high job security practices can signal benevolence and care for the employees, whereas transparency in promotion and selection practices can signal honesty from the organization (Iles, Mabey, & Robertson, 1990). In addition, insecure future of the organization, inadequate working conditions, and poor treatment (Kiefer, 2005) or job insecurity (Wong, Wong, Ngo, & Lui, 2005) could lead to employees’ distrust in the organization. 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

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consistency of organizational policies and practices can contribute to trust in the organization (Gillespie & Dietz, 2009; Pučėtaitė & Lämsä, 2008; Schnackenberg & Tomlinson, 2016). In contrast, a legalistic approach and formalization such as keeping a paper trail and disclosing only the necessary information can lower trust in the organization (Li et al., 2012; Sitkin & Roth, 1993). Other practices, such as ongoing changes and the use of temporary employees, have also been found to negatively relate to trust in the organization (George, 2003; Kiefer, 2005).

Organizational leadership

Organizational leaders play a pivotal role in determining the overall level of trust in the organization. Leaders initiate most vertical exchanges, have the power to reward and allocate resources, and control flows of information and opportunities to share key information in ways that influence levels of trust across organizational levels or units (Cangemi, Rice, & Kowalski, 1989; Cassel, 1993; Creed & Miles, 1996). Leaders also initiate and formalize most inter-organizational collaborations which range from full mergers to loose alliances to serve different purposes, leading to different degrees of openness as well as rationales for reciprocity (Powell, 1996; Sydow, 1998). Creed and Miles (1996) suggest that organizational trust is a function of management philosophies and structures, characteristic (dis) similarities, and experiences of reciprocity between the employees and organization. Gill, Levine, and Pitt (1999) identified two important dimensions of leadership behavior that enhance organizational-level trust: credibility (when leaders do what they say they are going to do) and consistency (behave in a predictable manner). Research by Korthuis-Smith (2002) found that participative leadership, organizational support, performance feedback, and improvement opportunities were highly related to trust in the organization. Using data from the State Employee Survey, Daley and Vasu (1998) found that leadership was positively associated with levels of organizational trust. In particular, when organizational leaders are perceived as putting other people’s needs and interests above their own (e.g., servant leadership) it leads to higher trust in the organization (e.g., Joseph & Winston, 2005). Taking a different perspective, Salamon and Robinson (2008) examined the impact of the employees’ collective perceptions of being trusted by management. In a longitudinal study involving 88 retail stores, their findings suggest that employees’ perception of being trusted by management enhances organizational performance through responsibility norms,

which suggests that existing trust models may be overlooking an important factor that needs to be considered in the trusting decision (Salamon & Robinson, 2008). Within inter-organizational collaborations, Nikandrou, Papalexandris, and Bourantas (2000) found that frequent and consistent communication both before and after an acquisition increased the acquired employees’ perceptions of the new management’s trustworthiness.

Cross Level Effects Social networks

A number of studies have explored the development and role of trust in the context of individual and organizational networks. For example, Chua, Ingram, and Morris (2008) explored managers’ professional networks to identify what types of ties were associated with cognition- vs. affect-based trust. Friendship ties were positively associated with affect-based trust, task advice ties were positively associated with cognition-based trust, career guidance ties were positively associated with both types of trust, and economic resource ties were positively related to cognition-based trust but negatively related to affect-based trust. Other research has focused on the information benefits of trust networks. Levin and Cross (2004) found that the effect of strong ties on the receipt of useful knowledge was mediated by competence- and benevolence-based trust. And in a study of research scientist teams, Chung and Jackson (2011) found that scientists’ knowledge creation was predicted by the level of in-degree trust centrality of team members toward the scientist. Another stream of research has examined the potential effects of past network relationships. In a multi-industry sample of ethnic Chinese work teams, Chou, Cheng, Huang, and Cheng (2006) examined the effect of guanxi on group members’ trust centrality and member performance in the group. They found that guanxi (being fellow members of a common group such as family, kindred, school, geographical setting, job relationships, fellow soldiers, etc.) positively affected cognition trust, and cognition-based trust positively mediated the effects of guanxi on group members’ satisfaction, commitment, and performance. In a similar vein, Luo and Yeh (2012) note that Chinese guanxi involves at least three rings of successively weaker relations within a guanxi circle, each ring has its own set of norms and assumptions governing relationships, and each ring therefore has specific types of trust that are appropriate. Taking a different approach, Levin, Walter, and Murnighan (2011) argued that networks include both active ties (whether strong, moderate, or weak) and inactive, ‘dormant’ ties, defined

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as relationships that have been inactive for three years or more. They found that dormant ties, when reactivated, displayed moderately high levels of trust and led to the receipt of useful knowledge and novel insights.

Third parties

Third parties play an important role in the development and diffusion of trust within and between organizations. First, third parties can shape the reputation of an individual, team, or organization based upon their ability to tell stories that corroborate one’s trustworthiness (or lack of it) (Burt & Knez, 1996). For example, Ferrin, Dirks, and Shah (2006) demonstrated that interpersonal trust ties were positively affected by the number of mutually trusted third parties (trust transferability), and by similar communication ties with all other potential third parties (structural equivalence). Likewise, Lau and Liden (2008) found that within groups, individuals developed higher trust in fellow group members who were trusted by the group leader than fellow group members not trusted by the group leader. Between organizations, the longevity of inter-organizational collaborations was found to be associated with trust and interpersonal relationships between boundary spanners (Currall & Judge, 1995). Second, third parties can act as important go-betweens in new relationships, enabling individuals to roll over their expectations from well-established relationships to others about whom adequate knowledge or history is not yet available (Uzzi, 1997). Wong and Boh (2010) focused on the role of advocates as third parties in determining managers’ reputation for trustworthiness among peers. They found that the structure of advocates’ professional networks, specifically the extent to which the advocate’s network was non-overlapping with the focal manager’s network and the extent to which the advocate’s network was heterogeneous (tapping into multiple units of the organization), positively impacted focal managers’ reputation for trustworthiness, even after controlling for the focal manager’s raw number of advocates. In inter-organizational collaborations, interpersonal trust between boundary spanners can shape the structure of the collaboration and affect the extent of sharing that can take place between organizations (Janowicz-Panjaitan & Noorderhaven, 2009).

Trust violation and repair

With trust levels declining worldwide and government, corporate, and organizational leaders committing trust violations with discouraging regularity, scholars have focused increasing

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attention on the study of trust violations and repair. A trust violation can be defined as a violation of a positive expectation (cognition- or affectbased) and/or the exploitation of another’s vulnerability (e.g., Kim, Ferrin, Cooper, & Dirks, 2004). The term ‘trust repair’ is used in research to refer to two distinct phenomena: the actions that an accused party might take to increase trust following an alleged violation, and the extent to which trust actually does increase after a violation (Dirks, Lewicki, & Zaheer, 2009; Kramer & Lewicki, 2010). Researchers have studied a range of responses to repair trust violations that can be broadly grouped under categories of (i) verbal responses (e.g., apologies, denials, excuses, explanations, reticence), (ii) substantive responses (e.g., penance), and (iii) structural strategies (e.g., regulation of future behavior, hostage posting) (Kramer & Lewicki, 2010). Voice, in particular has been shown to be a critical factor for organizationallevel trust. In dispute resolution, a lack of opportunities for voice and a mandate of binding arbitration have been found to lower trust in the organization (Bernardin, Richey, & Castro, 2011). In delivering bad news, opportunities for voice, as well as truth telling, are important for trust in the organization (Bies, 2013). In the case of an organizational violation, practices such as timely management and voluntary provisions of monitoring, sanctions, and external regulations can prevent further trust loss and facilitate future trust development (De Cremer, Tenbrunsel, & Van Dijke, 2010; Gillespie & Dietz, 2009; Nakayachi & Watabe, 2005). Research on trust repair tactics has focused on assessing or demonstrating the effectiveness of the tactics, identifying mediating mechanisms (e.g., perceived repentance), and identifying moderating factors (e.g., violation type). Perhaps the most frequently reported finding in the trust repair literature to date is that apologies are more effective than denials in repairing competence-based violations, whereas denials are more effective than apologies in repairing integrity-based violations (Dirks, Kim, Ferrin, & Cooper, 2011; Ferrin, Kim, Cooper, & Dirks, 2007; Kim, Dirks, Cooper, & Ferrin, 2006).

Consequences of Organizational Trust The overall positive effect of organizational trust is shown across several referents. For the individual employee, shared trust in the organization has been found to have a positive impact on job satisfaction and perceived organizational effectiveness

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(Shockley-Zalabak et al., 2000), and can promote OCBs (Hodson, 2004) and better communication and knowledge exchange between individuals and groups (Collins & Smith, 2006; Trapp, 2011). From a managerial point of view, shared organizational trust can ease the introduction of organizational change (Sonpar, Handelman, & Dastmalchian, 2009), reduce vulnerability in selection practices (Beatty, Ewing, & Tharp, 2003), and increase organizational ambidexterity (Gibson & Birkinshaw, 2004). Inter-organizational trust is found to be an important lubricant for cooperation between organizations (Stahl, Larsson, Kremershof, & Sitkin, 2011). Positive effects of inter-­ organizational trust include transaction and negotiation costs reduction (Dyer & Chu, 2003; Gulati & Nickerson, 2008; Zaheer & Harris, 2006), contract flexibility (Faems, Janssens, Madhok, & Van Looy, 2008; Vlaar et al., 2007), contract compliance (Davies, Lassar, Manolis, Prince, & Winsor, 2011), positive interaction patterns (Lado, Dant, & Tekleab, 2008; Lui & Ngo, 2005), and continued and increased collaboration (Malhotra & Lumineau, 2011). Inter-organizational trust has also been found to enable successful performance of inter-organizational joint ventures (Lane, Salk, & Lyles, 2001) and lead to higher satisfaction with the partner and relationship (Gainey & Klaas, 2003; Gulati & Sytch, 2007) and willingness to support the partner (Wu, Wang, Chen, & Pan, 2008). While research at this level has revealed many benefits of trust, some evidence also has emerged suggesting that high trust alone can be detrimental. The negative outcomes of too much inter-­organizational trust could include failing to respond to signs of partner failure (Blois, 2003) and stunting growth (Morck & Yeung, 2004).

FUTURE RESEARCH DIRECTIONS This review highlights the considerable progress that has been made over recent years in the study of trust at work within IWO psychology. From an earlier fragmented research field, an extensive and rich literature on trust at work has now developed around a consensual conceptualization of trust, allowing the field to expand to different levels of analysis. Although the majority of trust research has focused on interpersonal trust, more recent efforts toward understanding collective trust at both the team and organizational levels, and identifying cross-level determinants of trust, are becoming quite substantial. Thus, we see a field in

which some areas such as interpersonal trust are becoming mature but still identifying important and interesting research questions, and other areas such as collective trust in which research efforts are still gathering momentum and many important questions remain virtually untouched. Therefore, in concluding this chapter, we highlight four directions for future research that we hope can help scholars better identify and tackle important research questions, and also conduct their research in ways that better allow for the accumulation of knowledge.

1. Advancing Theory Building of Cross-Level and Multilevel Trust Research Despite an increasing scholarship in broadening overarching perspectives on trust at work, few researchers have attempted to model trust at multiple levels of analyses. Basically, scholars continue to maintain separate micro- and macrostreams of research, tending to not acknowledge the broader and inescapable aggregates in which individuals, teams, and organizations are embedded. Current research has demonstrated that levels of trust in teams are affected by levels of trust in specific individuals (e.g., leaders) (Gillespie & Mann, 2004). Similarly, organizational-level values have been found to affect trust at the individual level (e.g., Shamir & Lapidot, 2003). Hence, how trust at one level may impact trust at another level is a promising area for future research, and developing cross-level models will be important to guide future research to gain further understanding of cross-level trust effects. In addition, mixed-effect models in which trust can affect outcomes across levels (e.g., Klein, Dansereau, & Hall, 1994) should also be considered (Fulmer & Gelfand, 2012). For example, collective team trust has been found to influence outcomes at the team and the individual levels (e.g., Costa et al, 2001; Costa, 2003). In addition, trust may amplify and suppress factors at other levels. Dirks and Ferrin (2001) proposed that trust tends to moderate the effects of other variables. Among the determinants discussed in this chapter, leadership has been identified across the three levels of trust. Future studies may also benefit from researching how leadership influences trust at different stages of development. Thus, while considering different cross-level and multilevel relationships, future research needs to be explicit about the models tested and use terminology and methodology recommended by the

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levels of analysis literature to identify and examine the appropriate constructs. Further knowledge about how determinants and consequences of trust across levels relate at a given level will provide a more comprehensive view of trust at work from an IWO psychology perspective (Fulmer & Gelfand, 2012).

2. Advancing and Integrating Theory on Trust Dynamics The 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). In reality, however, work relationships have very complex interactive histories with concomitantly complex effects that lead to particular trust dynamics. However, these have yet to be studied systematically. It seems reasonable to expect that work relationships may vary in how they develop trust depending on the referent in question and the contextual pressures associated with their level of analysis. For example, at the team level the dynamics of trust between members have been shown not to be linear (e.g., Costa et  al., 2009). Therefore, it is important that future research focuses on trust as a process which should also provide insight into how trust behaviors and perceptions of trustworthiness might spiral between the trustor(s) and trustee(s) (Ferrin et  al., 2008), how trust becomes mutual, how asymmetries come into existence and are resolved, etc. These dynamics reflect the reality of trust in organizational settings, but they also challenge existing methods for studying, and even existing assumptions for understanding, the development of trust scientifically. Therefore, a better integration of theory and empirical research is necessary. In particular, longitudinal studies are highly needed. They afford examination of trust trajectories such that, depending on individual differences and social-contextual factors, trustors may exhibit a fast or slow trust building or trust repair (Fulmer & Gelfand, 2013). Such studies should have a more systematic approach and also include different time frames – probably longer time frames rather than the typical short cycled projects – 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

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contribution to further understand the dynamics of trust.

3. Adopting Consistent Practices in Operationalizing and Measuring Trust While consensus around the definition and agreement on the core components of trust has been a crucial step forward in trust research over the last two decades, it is important that future research also adopts consistent practices regarding the operationalization and measurement of trust at different levels. One important way that future trust research can improve is to use contemporary, reliable, and valid instruments to generate a cumulative body of work and facilitate comparisons across studies to build on further knowledge. Therefore, scholars should first follow the best practice of using established scales rather than creating their own. In particular, studies using the conceptual definitions of trust consistent with Mayer et al. (1995) and Rousseau et al. (1998) as we have discussed here should use the appropriate operational definitions and measures. Although measuring trust in different contexts is likely to reflect specific features of that context, the choice between instruments should be guided by theoretical considerations and the underlying characteristics of the trust relationship at study. Second, research studying collective trust in teams or organizations should reflect the appropriate consensus (i.e., agreement) among members, as conceptualizations at team or organizational level are only meaningful when the attributes (e.g., perceptions, attitudes, etc.) are shared between members (Kozlowski & Klein, 2000). Finally, studies focused on related constructs such as trust propensity, perceived trustworthiness, or risk-taking behaviors, should use accurate measures that clarify the nature of the construct so that they are not confused with trust.

4. Moving from ‘Pro-Trust Bias’ The great majority of trust research has emphasized the multiple benefits of trust rather than its shortcomings. Researchers and practitioners alike tend to assume that the higher the level of trust, the better, despite the inconsistent and weak findings on the trust–performance relationship (e.g., Dirks & Ferrin, 2001, 2002). Although being perceived as trustworthy by coworkers has been found to relate positively to the trustee’s performance, the mechanism through which this

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relationship occurs is not straightforward (Dirks & Skarlicki, 2009). In fact, some research suggests that only when perceptions of trustworthiness reflect an interaction between the capability (i.e., ability) and integrity of a coworker can these affect performance by facilitating the willingness to share resources with the same coworker. Such results suggest that the causal relation of trust in predicting performance can be better understood in the context of indirect rather than main effects (e.g, Colquitt et al., 2007; Dirks & Ferrin, 2001; Dirks & Skarlicki, 2009). In addition, considering that in reality some trustors and situations may warrant lower levels of trust than other trustors and situations (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 indiscriminate trust enhancement. The main risk of trust is associated with the vulnerability of the trustor, with the danger that too much trust may lead to overly relaxed attitudes and insufficient vigilance toward trustees. Several studies in the judgment and decision-making literatures have indicated that high trust may result in less critical discussion of issues or failing to make corrections in a timely manner to a set course of action (e.g., Das & Teng, 2004; Tsai, Chi, Grandey, & Fung, 2012). For example, Langfred (2004) showed that the more team members trust one another, the less they chose to monitor one another, which under conditions of high levels of individual autonomy, led to lower team performance. Teams characterized by high cohesiveness and trust can exert a powerful influence on individual members to conform (Baron, Vandello, & Brunsman, 1996), which can make teams and their members susceptible to group decision biases such as ‘groupthink’ (Janis, 1982). This question of trust calibration is obviously important in upward trust relationships in which employees are particularly susceptible to persons of higher power and authority. However, the question is also important in downward trust relationships, i.e., the trust of leaders toward their subordinates. One of the most important tasks of a leader is to delegate and empower subordinates. To do this successfully, leaders must be able to assess the likelihood of subordinates being able to make decisions and perform tasks autonomously. In such a situation, insufficient trust is likely to result in insufficient delegation and empowerment, whereas excess trust is likely to result in poor decisions and unsuccessful task completion. Similarly, very high levels of trust can also result in failure

to catch unethical acts in a timely manner. One means of advancing understanding in this area is to focus on what leaders see in followers that informs them about followers’ trustworthiness. A recent study by Sy (2010) revealed that when leaders’ perceptions of their followers were consistent with the implicit fellowships prototype (i.e., hard working, being productive, extra-role enthusiasm, and good citizenship), leaders’ trust in the followers was higher. Thus, arriving at the optimal level of trust is critical for successful empowerment, yet research on trust calibration and downward trust is very much lacking.

FUTURE DIRECTIONS FOR PRACTICE In completing this review and reflecting on the state of research, we are struck by the stark disparity between the impressive advances that scholars have made in understanding and operationalizing trust at multiple levels of analysis and documenting its nomological net, versus the trust deficits and violations that pervade organizational contexts worldwide. There is a clear and pressing need for trust scholars to shift some of their efforts toward translating trust science for practice. Trust scholars can look to other, more institutionalized literatures, such as leadership and negotiation, to identify ways of talking to and influencing practice (Ferrin, 2013). And equally, there is a clear and pressing need for organizational leaders to look to the science of trust to address the often deplorable state of trust within their organizations. Some scholars have devoted efforts to translating trust research into practice (e.g., Hurley, Gillespie, Ferrin, & Dietz, 2013) and applying trust theory to a variety of important organizational problems and issues such as conflict management (e.g., Lewicki & Bunker, 1996), problem solving (e.g., Zand, 1997), innovation (e.g., Ruppel & Harrington, 2000), etc. However, much remains to be done in terms of identifying ways for organizations to diagnose their levels of trust, prevent trust violations, and repair trust violations and restore trustworthiness. The increases in the volume and rigor of trust research have, unfortunately, been accompanied by a seeming increase in the quantity and severity of trust violations committed by businesses, governments, and leaders worldwide. This correlation of trust research and trust violations highlights both the importance of trust research, but also the limited extent to which trust research is being adopted into practice.

Trust at Work

Limitations of Our Review Considering the breadth and depth of the trust literature, any present-day review of trust is necessarily incomplete. In this paper we aimed to review the areas that are most important and dominant, most developed theoretically, and/or that have received the most empirical attention. As a consequence, for every area reviewed, we were required to provide a high level view and could not afford the luxury of reviewing any area in detail. In addition, there were areas we judged to be relatively underdeveloped or prominent that we had to omit from our review altogether, such as for example national culture. We hope that readers will understand that this paper is intended as an initial introduction to trust, with signposts provided to guide the reader to areas where they are likely to benefit from a deeper reading.

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20 Politics in Organizations Gerald R. Ferris, John N. Harris, Z a c h a r y A . R u s s e l l a n d L i a m P. M a h e r

INTRODUCTION In recent years, it has been widely accepted that politics are ubiquitous to all social entities, including work organizations. In response to this pervasiveness, scholarly examinations have increased dramatically, thus prompting politics research to assume status comparable to other well-­established constructs in the organizational sciences. At this point in its evolution, acknowledging where the scholarly research has been and recommending where it needs to go is necessary to advance the field. In pursuit of these objectives, we first provide a brief overview of organizational politics, paying particular attention to prominent issues that have been examined in the scholarly literature. In so doing, we address the foundations and outcomes of politics in organizations, as well as the role of individual and contextual differences in political behavior, the role of leaders in organizational politics, and how others in organizations perceive political behavior. We also offer thoughts on emerging and future directions in organizational politics research. Organizational politics has been studied in many different disciplines and perspectives, thus amassing a literature that is voluminous in nature, making it difficult to cover all of this work within

the page constraints of this chapter. Thus, we do not make the claim that our review is comprehensive or exhaustive in nature. Instead, we examine a set of issues we believe are important and perhaps have not been fully investigated in past work. Consequently, we hope this review is regarded as supplementing the several more comprehensive reviews of the organizational politics literature that have been published in recent years (see Ferris, Adams, Kolodinsky, Hochwarter, & Ammeter, 2002; Ferris, Hochwarter, Douglas, Blass, Kolodinsky, & Treadway, 2002; Kacmar & Baron, 1999; Lux, Ferris, Brouer, Laird, & Summers, 2008; Silvester, 2008; Vigoda-Gadot & Drory, 2006).

FOUNDATIONS OF ORGANIZATIONAL POLITICS RESEARCH Lasswell (1936), the classic political scientist, described politics as ‘who gets what, when, and how’. This is a very succinct description of what organizational researchers are looking to examine in organizational politics research. Which individuals or groups in the organization (i.e., who) achieve which goals or outcomes (i.e., what) in

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what time or context (i.e., when) and through what means (i.e., how)? Broadly, all scholarly work on politics in organizations has endeavored to better describe one or more parts of this question. Although there was some work in the area earlier (e.g., Burns, 1961), research in organizational politics began in earnest in the 1970s and 1980s, with several scholars authoring pieces that are still considered foundational works in the area (Mintzberg, 1983; Mintzberg, 1985; Pfeffer, 1981a; Porter, 1976). At this point, research on politics in organizations, which had not been on the radar of many organizational scholars for many years (Porter, Allen, & Angle, 1981), began in earnest. The field of organizational politics is broad, incorporating theories and research on power, influence, and politics, and how each of these appear and are executed in organizations (Ferris & Treadway, 2012). Politics has been described as a means by which an individual can use influence tactics and his or her political skill in order to gain power in organizations (e.g., Mintzberg, 1983). Politics as a means to wield power or influence over others has been investigated or articulated in some form since antiquity (e.g., Aristotle, Plato), and in a variety of disciplines (e.g., micro- and macro-organizational research, social psychology, political science). Although, as mentioned above, serious scholarship on politics in the organizational sciences is a relatively recent occurrence, the concept and importance of politics has been as ubiquitous throughout human history as it is today in organizations. Essentially, organizational politics represent the means by which individuals in organizations are able to influence one another. These political resources, and the influence they allow, provide individuals who wield them with power in organizations (Pfeffer, 1981a). This political power then can be turned around and used to develop further political power and resources which influence valued outcomes (Salancik & Pfeffer, 1974). Political behavior can take on many forms, all of which are designed and targeted at enhancing the desired outcomes of political actors. These behaviors have been classified under two broad umbrellas in the organizational politics literature: Impression management tactics (Jones & Pittman, 1982; Leary & Kowalski, 1990) and influence tactics (Kipnis, Schmidt, & Wilkinson, 1980). The former (i.e., impression management) has been more or less subsumed by the latter (i.e., influence tactics) as the more passive forms of political behavior (e.g., ingratiation). This is in comparison to the tactics at the other end of the range of available influence tactics, which typically are more aggressive and may involve conflict or confrontation.

Although all organizations contain some degree of political behavior (Burns, 1961), it is important to note that the level of organizational politics is different in each organization. Further, the degree to which organizational politics may or may not be present in a particular organization also depends on the individuals perceiving the organization and the goings on of the individuals within it. Because politics, and the power and influence associated with them, are subjective in nature, their existence, importance, and magnitude are a function of the perceptions of those involved in them (Gandz & Murray, 1980). The concept of perceptions of politics (discussed in greater detail later) indicates that there are a variety of factors, specific to each context or individual, which determine both the extent of the politics and political behavior in the organization as well as how those individuals function within that political environment.

INDIVIDUAL DIFFERENCES IN ORGANIZATIONAL POLITICS Personality Personality undoubtedly affects the perception of, and participation in, organizational politics. Research has investigated the ways different facets of personality affect these aspects of organizational politics, and the ways personality and politics interact to affect organizational outcomes. The literature shows how intertwined these topics can be, and the impact the interaction has on both individuals and the organization. Very little research has investigated personality variables as antecedents of perceptions of politics (POPS). In one of the few empirical studies, Adams, Treadway, and Stepina (2008) found personality may account for much more variance in politics than demographic variables. They found that negative dispositions (i.e., negative affectivity and equity sensitivity) may act as antecedents of POPS. Additionally, they found that positive dispositions (i.e., positive affectivity and self-­ efficacy) may attenuate or help as coping mechanisms to POPS. Although just a single study, it provides support for individuals’ positive or negative disposition greatly impacting their perceptions of politics. Research has investigated the relationship between the Big Five personality dimensions and politics. Witt, Kacmar, Carlson, and Zivnuska (2002) found those who were higher in agreeableness, extraversion, and conscientiousness were less likely to experience the negative implications

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that can occur between perceived organizational politics and contextual performance. Additionally, political skill, which has been shown to greatly increase the success of an individual’s political acts (Ferris, Treadway, Brouer, & Munyon, 2012), interacted with individuals’ extraversion to improve job performance (Blickle, Wendel, & Ferris, 2010). The literature appears to indicate that personality traits generally perceived as positive result in positive outcomes for individuals in political situations. Ferris and Kacmar (1992) proposed that individuals high in Machiavellianism and self-­ monitoring were more likely to perceive organizations as political. These personality traits, along with locus of control, social identity, and internal versus external motivation, were significantly related to different influence tactics (Barbuto & Moss, 2006). Research indicates that certain personality differences and the way individuals view the world will impact the types of political influence tactics they use. Additionally, personality differences’ interaction with political skill could predict the success of the different tactics. Further investigation into what contextual factors might interact with personality factors is a potential avenue for future research.

Demographics Demographic differences have received a fair amount of attention in the organizational politics literature. Among the demographic variables investigated are gender (which has received a great deal of attention) and race (which has received very little attention). Unfortunately, more often than not, demographic differences are relegated to control variables, and are not the primary focus of the study. Gender-appropriate behaviors are socially created and learned through individuals’ interactions with society (e.g., Geis, 1993). Because of these learned gender roles, individuals tend to behave within and react to situations in a way that is in accordance with their identified gender roles, and thus may undertake political behavior for different end goals. Specifically, men tend to act in ways to improve personal status, while women tend to act in ways that will improve their relationships with others in their organization (Gardner & Gabriel, 2004). Research has shown that men utilizing strong influence tactics were more likely to receive mentoring functions from their supervisor. In contrast, females were more likely to receive mentoring functions when utilizing weak influence tactics (Tepper, Brown, & Hunt, 1993).

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A study by Kacmar, Bachrach, Harris, and Zivnuska (2011) found that perceptions of politics moderated the relationship between ethical leadership and organizational citizenship behaviors. They found when males had high perceptions of politics, the relationship between ethical leadership and OCBs was strong. However, when the perceptions of politics was low, the relationship was muted. On the other hand, when females perceive politics to be high, the relationship between ethical leadership and OCBs was small, but it increased when perceptions of politics were high. These examples demonstrate how gender can be a moderating factor in organizational politics. Although the impact of race in organizations has received some research attention, very little has focused on the role of race in organizational politics (Charles & Nkomo, 2012). Of the limited research that does exist, Ibarra (1995) found that minorities in managerial positions had fewer intimate networks. It is reasonable to believe that less intimate networks could reduce the opportunity for minorities to participate in, and take advantage of, political opportunities. Of the few empirical studies involving race and politics, Brouer, Duke, Treadway, and Ferris (2009) found that political skill of subordinates can moderate the way race affects leader–subordinate dyads. Previous research has shown race-dissimilarity can negatively impact leader–subordinate relationships; the study by Brouer et  al. found that those high in political skill were less likely to experience negative effects of race-dissimilarity. Although the limited studies showed race to moderate politics relationships, the paucity of literature provides an excellent opportunity for further investigation.

Political Skill Political skill is a key individual difference in terms of understanding the effectiveness of individuals in organizational politics. Political skill is defined as ‘the ability to effectively understand others at work, and to use such knowledge to influence others to act in ways that enhance one’s personal and/or organizational objectives’ (Ferris, Treadway et al., 2005, p. 127). Individuals high in political skill are able to effectively read their environments and the people in them, acting and influencing others in ways that allow the politically skilled to achieve their goals. The construct consists of four dimensions: social astuteness, interpersonal influence, networking ability, and apparent sincerity (Ferris et  al., 2007, 2012).

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These four components work together to allow individuals who possess them to effectively navigate their environments.

Political Will Though little attention has been paid to it thus far in the organizational literature at the individual level, political will is emerging as an increasingly important construct and complement to political skill (Treadway, 2012). Mintzberg (1983) described an individual’s political success as a product of both their political skill and the willingness to employ those skills, expending or risking their personal capital or resources in the pursuit of their desired goals and outcomes. Political skill is a far less useful characteristic if one is not willing to engage in political behavior. What little research has considered political will at the individual level of analysis has described it in a basic form as an ‘actor’s willingness to expend energy in pursuit of political goals’ (Treadway, Hochwarter, Kacmar, & Ferris, 2005, p. 231). More recently, however, political skill has become more clearly articulated as a more involved, multidimensional construct, and defined as ‘the motivation to engage in strategic, goal directed behavior that advances the personal agenda and objectives of the actor that inherently involves the risk of relational or reputational capital’ (Treadway, 2012, p. 533). Political will can either be self-serving or benevolent in its motivation, and it must always be considered against the contextual factor of risk to one’s personal capital due to engaging in political behavior. Political will operates as the driving force behind individuals’ willingness to engage in political behaviors, while political skill operates more as a resource that individuals may deploy. Risk is an element of the context in which the political behavior may occur, and political skill and political will may interact to understand and successfully navigate the balance of risk and reward for engaging in political behavior (Treadway, 2012).

Citizenship Behaviors Organ, Podsakoff, and MacKenzie (2006, p. 3) defined organizational citizenship behavior (OCB) as ‘individual behavior that is discretionary, not directly or explicitly reorganized by the formal reward system, and in the aggregate promotes the efficient and effective functioning of the organization’. Since the construct was first introduced in 1983 (Smith, Organ, & Near, 1983), the majority of scholarship has focused on the positive aspects of the behavior, and it is widely treated as altruistically motivated. However, the motivation for such behavior is not considered in the definition, and starting predominantly in the late 1980s and early 1990s, a body of research began to form that considered the possibility that OCBs can also be inspired by less commendable agendas, and used for impression management purposes (Fandt & Ferris, 1990; Ferris, Russ, & Fandt, 1989). The importance of OCB motivation is an unsettled debate among organizational researchers. Some scholars have acknowledged the importance of OCB motivation as a predictor of the construct, but downplay the means when considering the importance of the positive consequences with which OCBs largely have been associated. Others have contended that the perception of OCB motivation plays an integral part in forming these desirable outcomes (Bolino & Turnley, 2003). Empirical studies have shown that instrumental intentions do predict OCBs, but more variance is explained by pro-social or organizational motives (Finkelstein & Penner, 2004; Rioux & Penner, 2001). Additionally, the attribution of OCB motivation has been shown to impact reward decisions (Allen & Rush, 1998), demonstrating that motivational attribution does impact certain OCB-related outcomes. Bolino and Turnley (2003) suggested that future research on OCBs through a political lens should focus on the interplay between altruistic and political motivation, factors that influence the motivational attributions of other organizational actors, and distinguish OCBs from other impression management constructs.

Organizational Justice Perceptions OUTCOMES OF ORGANIZATIONAL POLITICS Politics in organizations leads to a number of outcomes for both the individuals involved and the organization as a whole. A few of these are discussed here.

Perceptions of justice are synonymous with perceptions of fairness. An organization’s ability to justly distribute costs and resources amongst its members is encapsulated by distributive justice, while procedural justice attends to matters of fair and transparent decision-making through the use of systematic procedures. By definition, instances

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of political behavior seek to bend, break, or circumvent the mechanisms that are designed to preserve perceptions of justice. Despite the conceptually evident link between politics and justice, the academic literature that theoretically and empirically links the two constructs is less than twenty years old and needs further development. Ferris, Frink, Beehr, and Gilmore (1995) were among the first to provide a theoretical discussion of organizational politics and organizational justice, and this work laid the foundation for future pieces. Notably, Andrews and Kacmar (2001) used confirmatory factor analysis to empirically support the notion that organizational politics and organizational justice were indeed separate constructs. Research that post-dates these seminal pieces has fallen into four general categories (Ambrose, 2012). First, organizational justice is seen as an antecedent to organizational politics. Othman (2008) found that job ambiguity led to greater justice and trust, which predicted perceptions of politics. Second, justice and perceptions of politics have been conceptualized as independent predictors. Rosen, Chang, Johnson, and Levy (2009) found support for two different models that incorporate justice and perceptions of politics as predictors of work attitudes and contextual performance. Third, justice has been explored as a consequence of perceptions of politics. This category is perhaps the most robust, and the underlying theme of the literature suggests a negative relationship between perceptions of politics and perceptions of justice (Ambrose, 2012). Finally, justice has been proposed as a moderator of the politics–outcomes relationship. Harris, Andrews, and Kacmar (2007) investigated the moderating effects of procedural and distributive justice on the relationship between perceived politics and both turnover intentions and job satisfaction. Their results suggest that politics has a minimal impact on turnover and satisfaction under conditions of both procedural and distributive injustice. Furthermore, perceived politics has its greatest impact on these outcomes when there is disagreement between the two justice constructs. In all, three of the four categories in this area are represented by fewer than a dozen combined studies. Hopefully, future research in these areas will address these voids in the literature. Additionally, Ambrose (2012) identified a nonexhaustive list of five additional areas for future researchers to explore. First and foremost, many fundamental issues relating to the ontology of organizational justice and politics constructs still need to be addressed. Second, Ambrose concurs with Hochwarter’s (2012) call to explore potential instances where politics can have a positive

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impact on organizational justice. Third, to date, few studies have looked at these constructs in tandem at multiple levels of the organization. Fourth, although studies have hypothesized and provided support for why people behave politically, it is unclear if these situational and dispositional determinants apply to acting unfairly. Finally, the relationship between perceptions of politics and justice has not been explored using political skill as a moderator. Perhaps the social astuteness dimension of political skill allows a skilled organizational actor to operate politically without alarming others to instances of injustice. Clearly, a plethora of opportunities exist in this area of political research.

Stress Many researchers have examined the role of politics on stress (Cropanzano, Howes, Grandey, & Toth, 1997; Ferris, Frink, Galang, Zhou, Kacmar, & Howard, 1996; Ferris et al., 1989), and the clear consensus among these scholars is that politics has the potential to act as a workplace stressor. Stress is defined by Schuler (1980) as opportunities, constraints, or demands perceived by an actor. A critical component of this definition is the notion that stress is caused by perception. Indeed, perceptions of both stress and organizational politics are subjective assessments of an actor’s own reality, and it is through this self-reported lens that most stress-politics-related scholarship has been conducted. Perceptions of threats and opportunities are suggested to have a moderating role in the political behavior–stress relationship. Building on the work of McGrath (1976), Ferris et al. (1996) suggested that political behavior can be a vehicle to create and take advantage of opportunities for status gain, as well as risky behaviors that can threaten an actor’s status or social capital (Ferris et al., 1996). In either case, stress plays a central role. Demands related to opportunities for status advancement still can be a stressor. However, these types of situations are normally associated with positive outcomes, such as higher job satisfaction and reduced anxiety (Schuler, 1980). It follows that threat of loss also causes stress for organizational actors, and these stressors are often associated with lower job satisfaction and other negative psychological outcomes. Another contextual variable that causes stress is an actor’s perception of an uncertain or ambiguous organizational environment, as identified by Schuler and Jackson (1986). Ferris et al. (1989) also identified these same settings as fertile milieu for

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political behavior, which in turn impacts the politics perceptions of other organizational actors. Although Ferris et al. suggested that uncertainty breeds political behavior, Cropanzano et al. (1997) argued that the muddled reward allocation system caused by political behavior actually is the cause of the uncertainty, which in turn causes the stress. In either case, it is reasonable to believe that stress, political behavior, and uncertainty share meaningful relationships. If the above conceptualizations of the political behavior–stress relationship hold, then it stands to reason that an actor who is particularly talented at navigating such situations would have the means to reduce stress caused by political behavior. This was the proposition put forth by Perrewé, Ferris, Frink, and Anthony (2000), who specifically argued that not only do actors who are high in political skill perceive their environment as less stressful, but their belief in their ability to navigate even the most stressful political situations acted as a moderating coping mechanism.

Abusive Supervision Abusive supervision deals with hostility in the workplace, excluding acts of physical violence or aggression, which is repeated over time (Tepper, Duffy, & Breaux-Soignet, 2012). The vast majority of the literature explores the predictive antecedents of abusive supervision, with the intention of learning how to eliminate this seemingly dysfunctional organizational phenomenon. Scholars often characterize incidents of abusive supervision as an involuntary response to a given stimuli, or ‘hot’ aggression. However, this brief review focuses on research that has categorized abusive supervision as a ‘cold’, or voluntary and strategic behavior. Scholars have theorized that abusive supervision can be used by supervisors as a mechanism to control their subordinates. Practical examples could include hostile behavior aimed at altering behavior in the wake of a subordinate’s refusal to perform a task, poor workplace performance, or acts of workplace deviance (Tepper et al., 2012). Jones and Pittman (1982) argued that intimidation could be viewed as an effective impression management behavior that sculpts a ruthless, competitive, uncompromising reputation. Additionally, abusive supervision can be used as a form of negative feedback by managers to address instances of injustice in the organization (Tepper et al., 2012). Anecdotally, this could appear as a supervisor berating unprepared employees for their poor performance in a meeting, resulting in embarrassment and damaged reputation for both the subordinates and the supervisor.

Despite the impactful literature on interpersonal influence tactics, few scholars have ever explored abusive supervision in this light, and future research examining situational, dispositional, and tactical issues would add new knowledge in this underexplored area. Furthermore, little research has been done on organizational mechanisms that may explicitly or implicitly encourage abusive supervision as a managerial tactic. Lastly, few researchers have examined perceptions of hot or cold abusive supervision as a moderator of the abuse and response relationship. Research in this area would evaluate the practicality of this practice, and add to theory by helping to distinguish the difference between hot and cold abusive supervision (Tepper et al., 2012).

PERCEPTIONS OF POLITICS Perceptions of organizational politics (POPs) have received a great deal of attention over the past 25 years. Though perceptions like POPs are subjective in nature, they are important to understand as they are typically the driver of political behavior, as individuals act upon their perceived, subjective reality rather than objective reality (Gandz & Murray, 1980). Although the study of politics in organization has been around for many years, the methodical study of individuals’ perceptions of politics began to develop with the introduction (Kacmar & Ferris, 1991) and further refinement (Ferris & Kacmar, 1992) of a measure of POPs, a self-report assessment of degree to which individuals perceive politics in the workplace. Other authors have since developed politics perception scales of their own (e.g., Hochwarter, Kacmar, Treadway, & Watson, 2003), and a variety of scales are still in use today, though there has not yet been a comprehensive comparison of these scales, which may suggest this to be an avenue for future research. Since the initial development of the POPs scale, research investigating the antecedents, consequences, and moderators of POPs has blossomed. Earlier in this chapter, some personality variables were discussed in regards to their role in predicting perceptions of politics. Adams et al. (2008) found personality might account for greater variance than demographic variables. Specifically, they found negative affectivity and equity sensitivity may act as antecedents to POPs, while positive affectivity and self-efficacy may attenuate or help as coping mechanisms to POPs. Barbuto and Moss (2006) also found that several personality traits, in particular Machiavellianism and self-monitoring, may lead to perceptions of politics.

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Self-monitoring is a construct within the nomological network of social-intelligence constructs (e.g., emotional intelligence). To date, few studies (Vigoda-Gadot & Meisler, 2010) have answered the call by Poon (2003) to examine which of these constructs predict perceptions of politics. Further inquiry into dispositional antecedents of perception of politics could help determine whether perceptions of politics are generally accurate personal assessments of how the organization operates, or if certain individuals are pre-disposed to perceive their environment as political regardless of the actual behaviors performed around them. Parker, Dipboye, and Jackson (1995) investigated several individual and job characteristics that could influence POPs. They found that age, gender, education level, and occupational grouping were not significantly related to POPs. In contrast, race was found to be an important factor. Specifically, they found minorities were more likely to perceive politics than were nonminorities. Job and organization influences also were significant. Employees perceived fewer politics when they believed they had access to career development opportunities and were recognized for good performance. In addition, the authors found cooperation between different work groups helped to reduce employees’ perceptions of politics. The perception of politics has several consequences for individuals and the organization. Valle and Perrewé (2000) found POPs to be negatively related to job satisfaction and positively related to turnover intentions. A proposed mediator of these relationships is an increase in job stress. However, results for this relationship are mixed. Some authors have found there to be no relationship (e.g., Hochwarter, Perrewé, Ferris, & Guercio, 1999), while others have found support for the predicted relationship (e.g., Valle & Perrewé, 2000). Much of the research regarding POPs and organizational commitment and job performance has mixed results. While some studies have reported the hypothesized negative relationships, many have reported no relationship at all. A meta-analysis by Miller, Rutherford, and Kolodinsky (2008) found that there is a negative relationship between POPs and organizational commitment, but it is likely affected by moderators. In addition, they found that the proposed relationship between POPS and job performance had no clear support. In general, POPs results in negative consequences for both the individual and the organization. However, some research has shown that moderators (e.g., political skill) can impact this relationship in such a way that POPs can lead to various organizational consequences (Brouer, Harris, & Kacmar, 2011).

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The relationships between POPS and its consequences often are equivocal, suggesting that the relationships are moderated by other variables (Miller et  al., 2008). One interesting moderating relationship involves whether the employees work in the public or private sector and their intention to turnover. In particular, private sector employees are less likely to turnover when facing politics than are public sector employees. Interestingly, the majority of moderators tested for by Miller et  al. were not significant. This fact, combined with the fact that a large amount of the variance in several relationships was not accounted for by statistical artifacts, suggests that other moderating variables must be present. Future research should seek to identify these variables and determine the ways they affect POPs and its consequences.

POLITICS AND LEADERS Leadership and organizational politics are fundamentally intertwined. Leaders can be characterized as those individuals able to wield their influence or power in order to achieve certain goals (Pfeffer, 1981a), and this power and influence often is gained through the leader engaging in organizational politics (Mintzberg, 1985). Success in these informal, political channels allows for leaders to more successfully develop their own power and influence. Playing the political game can be, and often is, characterized in a negative way, typically as selfserving to the leader. Whereas this result is possible, with leaders acting politically simply to gain power to benefit their own personal interests, it is not the only possible outcome. Leaders also can act in the interests of their subordinates, providing what is known as leader political support (Ellen, Ferris, & Buckley, 2013). This is the process of leaders behaving politically toward their own superiors so that they may provide their followers with resources and advance the interests of the group rather than just the interests of the leader. Regardless of the leader’s desired outcomes, there are a number of components that can go into how a leader can effectively operate in a political environment. The research in several of these areas is discussed in greater detail below.

Leader Influence Tactics Social influence and its many applications is a rich enough literature to merit its own chapter in

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this volume, but some of the research specific to how leaders use social influence to effectively engage in organizational politics is discussed here. Much of the work examining leader influence tactics in organizations deals with ingratiatory behavior, those behaviors by which individuals try to appear more likeable or appealing to a target. In many of these cases, it is leaders attempting to ingratiate their superiors, particularly the CEO of the organization, in order to gain rewards, particularly from the board of directors (e.g., Westphal & Stern, 2006, 2007). Effective application of social influence techniques, such as ingratiation, has been demonstrated to serve as a substitute for more ‘legitimate’ characteristics, such as education credentials, when a leader is seeking to advance through the organizational hierarchy and board appointments (Westphal & Stern, 2006). However, careful selection of the tactic to be used is critical because some behaviors are more likely to be construed as manipulative, and less likely to arrive at the goal of effective social influence (Stern & Westphal, 2010). Subtle flattery, for example, can be more effective because it does not draw excessive attention to the attempted ingratiation. All this flattery is not without risk, though. Leaders who are frequent targets of these ingratiation attempts from those reporting to them are at risk of having their confidence in their own abilities overinflated, which can lead to detrimental effects for their team or organization (Park, Westphal, & Stern, 2011). The targets of leaders’ influence, of course, also can be downward toward subordinates and other members of the organization or laterally to peers. A leader may adopt an entirely different set of techniques and strategies for these sorts of attempts at social influence. For instance, leaders may attempt to provide favors to raters or reviewers observing the leader’s organization or team in an attempt to garner more favorable reviews, thus increasing the reputation or perceived quality of the unit (Westphal & Deephouse, 2011). Leaders also may retaliate against those who evaluate them poorly in an attempt to deter future negative assessments (Westphal & Clement, 2008). Social distancing is a specific form of retaliation that leaders could use against their peers in an attempt to discourage particular negative behaviors (Westphal & Khanna, 2003).

Leader Reputation Researchers have found that organizations can be greatly impacted by the leader’s reputation.

Petrick, Scherer, Brodzinski, Quinn, and Ainina (1999) proposed that leader reputation, and the ability to use leader reputation as capital, is a way for an organization to gain and maintain a competitive advantage. Leaders with positive reputations give credibility to an organization, and often have greater access to important contacts, which can provide resources that will benefit the organization. The social network to which leaders belong can affect their reputations, and as leaders’ networks grow and their social capital increases, their reputations also will increase (Mehra, Dixon, Brass, & Robertson, 2006). Further, as leaders gain social capital they can gain access to informational brokers, who may benefit the leaders and their organizations (Galunic, Ertug, & Gargiulo, 2012). The reputation that leaders bring to an organization can both benefit the organization as well as offer access to new networks that further build the leaders’ reputation. Blass and Ferris (2007) proposed that leaders who have developed political skill and are able to adapt to new organizational situations are likely to receive positive evaluations from others, thus improving their reputation. As reputation continues to improve, leaders gain trust from their followers and are less likely to be held accountable to strict organizational rules and guidelines. Less stringent accountability can have both positive and negative consequences (Ranft, Ferris, & Perryman, 2007). Leaders with great celebrity can inspire followers, and are often given the freedom to direct the organization to their vision. They are able to reshape the organization and the leader and organization can become almost one and the same, which sometimes can cause share prices to increase. Undesirably for organizations, leaders with positive reputations often have the latitude to take greater risks that can potentially harm organizations’ financial performance. However, because of the positive reputation, followers often will attribute the negative performance to external factors, allowing for the leader to benefit from dysfunctional behavior. The ways leaders are able to use their reputation and the associated social capital and trust of others to influence and gain access to information and resources potentially can greatly impact an organization. Leaders with greater reputations can influence others in ways that leaders with lesser reputations cannot. With this ability lies the possibility for both positive and negative organizational outcomes. The use of reputation as an influence mechanism is a growing area in the study of organizational politics that can expect additional investigation in the future.

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Leadership, Celebrity, and Influence In recent years, as media has become involved in all aspects of society, the concept of celebrity is no longer exclusive to the entertainment industry and has entered the organizational context. Rindova, Pollock, and Hayward (2006) defined celebrity as the degree to which a social actor attracts a high level of public attention and generates positive emotional responses from stakeholder audiences. Thus far, the organizational research on celebrities has focused primarily on how mass media creates and drives the celebrity status, and how media strives to associate CEO actions and firm success (e.g., Ketchen, Adams, & Shook, 2008; Rindova et  al., 2006). Wade, Porac, Pollock, and Graffin (2006) found that CEOs with greater celebrity often are rewarded with larger increases in salary than are those CEOs with lesser celebrity. Hayward, Rindova, and Pollock (2004) found also that as CEOs gain greater levels of celebrity, they often gain increased control over their organization’s operations. Predominantly, research has found CEOs who achieve celebrity often receive positive benefits. The increased control over the firm reported by Hayward et  al. (2004) has many potential influences on organizations. Finkelstein and Boyd (1998) found CEOs with increased discretion are better able to make important contributions to their firms, and are more likely to influence decisions that can impact firm success. Further, when celebrity CEOs influence decisions that impact firm success, the perception of their actions is likely to be positive. Pfarrer, Pollock, and Rindova (2010) found that firms who achieve celebrity are more likely to receive benefits for positive surprises and less likely to receive negative feedback for negative surprises than are those with lesser celebrity. It is likely that this ‘benefit of the doubt’ treatment also applies to CEOs, as stakeholders want to maintain consistency and maintain their elevated thoughts of the celebrity. The positive affect celebrity CEOs receive, and the ability to always get their way, is not always positive for the organization. Hayward et  al. (2004) proposed that CEOs may gain inflated views of themselves, and begin to believe the hype the media bestows on them. This narcissism may lead to greater risk taking and actions that can hurt the organization. Consequently, although celebrity may allow CEOs to influence others, gain access to greater resources, and to have greater control over the organization, it could, due to greater autonomy, result in negative consequences for the firm. The study of celebrity and its impact on individual influence tactics is in its infancy, and the future holds great potential for this area of study,

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as media reaches all aspects of organizations and provides greater potential for celebrity and its consequences.

Leader Language, Rhetoric, and Symbolism More than three and a half decades ago, Pondy (1978) described leadership as a ‘language game’. As such, he suggested that leadership is a form of social influence that is employed by social agents who vary in position from religious prophets to factory foreman, and explicitly named executives to informal leaders within work groups. Leadership, he argued, was an ability to create shared meaning with others through the use of language. Pfeffer (1981b) built upon this notion of shared meaning and argued that leadership should be conceptualized as ‘the use of political language and symbolic action’ (p. 1). Specifically, the study of charismatic leadership employs rhetoric as a specific type of language, in a strategic attempt to manage impressions, motivate, and persuade others through shared meaning (Gardner & Avolio, 1998). Thus, many scholars (e.g., Baur et al., under review; Ferris, Davidson, & Perrewé, 2005) described leadership as the skillful use of communication, which includes language, rhetoric, and symbols. The strategic use of communication often defines events for others in an attempt to create meaning, but not necessarily shared meaning, for a follower. The most common medium of communication in organizations is verbal language, and thus debate, conversation, and other verbal exchanges represent the most common and productive method of social influence (Pfeffer, 1992; Ferris et al., 2005). Rhetoric, the often bombastic use of persuasive compositional communication techniques, becomes of central importance as mastery of this type of communication can shape the realities of others. The ability to successfully traverse followers’ perceptions of employed rhetoric relies on the leader’s political skill (Blickle, Frieder, & Ferris, 2018). It is the role of managers to optimize their employees, and one avenue for this is to help them navigate ambiguity in the organizational environment through meaning creation. The skillful use of rhetoric can help achieve this end, but can also help create ambiguity and vagueness when strategically desired. It is important to note that not all leadership activities are reciprocal in nature, as many leaders lack direct interaction with their followers (House, Spangler, & Woycke, 1991). Surely, the vast majority of those who have heard Martin Luther King’s I Have a Dream

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speech engaged in discourse afterwards, although it helped create meaning for millions of followers. Countless other leader–follower relationships have occurred with little or no interaction, and thus the persuasion element required by definition can take other forms. Symbolic behavior is the creation or enactment of environments through shared meaning (Russ, 1991), and it represents another powerful tool that leaders employ to shape the realities of others. Pfeffer (1992) argued that symbols are illusionary devices that when properly created, maintained, or manipulated elicit powerful emotions that shape reality and cloud rational judgment in others. Symbols also are one of the foundational apparatuses in reputation building, and thus enable and enhance the perception of power in leaders (Ferris et al., 2005).

FUTURE DIRECTIONS Positive Politics In the organizational realm, the term ‘politics’ often elicits a negative reaction and conjures images of inequitable treatment for any number of organizational decision-making processes. However, politics in organizations are not inherently bad, and do not necessarily imply evil intentions, or even undesirable outcomes (Hochwarter, 2012). Despite this, the vast majority of the politics literature shades political behavior in the prevailing negative viewpoint, defining politics as inherently self-interested and unsanctioned (Lepisto & Pratt, 2012), and largely ignores the notion that politics is not inherently bad, and in fact can be used for good. One of the few exceptions is Hochwarter’s (2012) chapter on positive politics, and this review informs our discussion of the potentially bright side of organizational politics. The Greek philosopher Aristotle once opined that a man who does not use political activity at some level to survive is either astonishingly selfsufficient, or below the standards of society. His observation suggests that, negating outlier levels of behavior, humans rely on social and political interaction to survive, and some evolutionary scientists agree that political behavior may provide a survival advantage (Hochwarter, 2012). However, if political behavior is truly engrained in our DNA, it does not necessarily imply that it is positive. However, Hochwarter identified numerous situations that could be interpreted as positive. Actors often experience stress caused by unjust or ambiguous situations within the organizational

context. There are myriad different antecedents of organizational stress, but it has been suggested that the ability to skillfully manage and exchange information and other resources through political means may be able to reduce ambiguity, rebalance unjust situations, and thus reduce individual stress (Hochwarter, 2012). Political behavior also has the potential to help others in the organization. Organizational actors who leverage social capital are able to promote a climate of sharing information and resources, which synergistically lubricates exchange between co-workers, suppliers, and clients (Cheung & Chan, 2010). Thus, members of work teams and groups are able to exchange resources that aid each other’s task-specific performance through collectively strengthening the supporting social organizational context. Recently Ellen et al. (2013) proposed that leaders can act on behalf of and in benefit to their teams or subordinates though a construct called leader political support. Leaders may behave politically in order to secure resources or provide opportunities to those under them, and, though this process can be mutually beneficial for both the leader and the subordinates, it is clearly no longer a purely self-interested activity as other definitions of politics have previously described. The previous section is not meant to be an exhaustive review of all possible positive political outcomes. There is much definitional, theoretical, and empirical work to be done in this venue, and hopefully, future research can be conducted that balances our understanding of political behavior.

Politics through Different Mediums of Communication As globalization of business continues and organizations spread their geographical reach, the daily face-to-face interaction typical of traditional business operations may be replaced with different mediums of communication. Instant messaging, email, and phone and video calls are now commonplace in most organizations. The ways an individual can influence others will likely be affected by a change in medium, and future researchers should investigate what differences exist. Although very little organizational research currently exists, researchers from other areas have investigated how influence operates over new mediums of communication. Online dating is now a billion dollar industry where millions of people attempt to influence others daily. In a recent study, Hall, Park, Song, and Cody (2009) analyzed 5,020 online dating website users. They found that individuals routinely

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misrepresent themselves with the intention of influencing others’ perceptions. This strategic misrepresentation, which they defined as the conscious and intentional misrepresentation of characteristics about oneself, was prevalent among online daters, with self-monitoring directed toward others as the most significant predictor of this behavior. The findings were interesting as people were willing to mislead others about personal attributes (e.g., skills, physical characteristics, financial stability), even when the goal was to meet them in person at a later date. Similarly, O’Sullivan (2000) found that there is often a much greater concern of deception in computermediated communication than face-to-face communication, as people can create a self-description and put forth an image that is strategically developed with the intent to influence. Organizational researchers should investigate how individuals utilize the vagueness that exists in online communication to their benefit. The role of technology on influence has received attention in some organizational settings as well. Thompson and Nadler (2002) reviewed the many ways negotiations and the individuals participating in negotiations are affected by the medium of communication. Typically, negotiators are able to succeed via four key social behaviors (i.e., rapport building, social contagion, coordination, and information exchange). As the ability to directly interact, read, and connect with the audience is reduced due to communication medium, they found individuals may need to alter their techniques to maintain the same level of influence. Thompson and Nadler reported that often the target of influence will develop certain biases that arise simply due to miscommunication issues that occur due to technology-based communication. Future researchers could investigate how individuals can overcome these biases, and maintain the ability to build a rapport and exchange information allowing them to succeed. In sum, the techniques used and their effectiveness are likely to be influenced by the medium of communication. Investigation into how social influence techniques and political skill are affected by the utilization of email, instant messaging, and similar mediums is an exciting avenue for future research.

Nonlinear Effects of Organizational Politics As hinted at in the earlier discussion of positive politics, the outcomes of organizational politics are not always as straightforward as they may

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seem, both in terms of their valence (i.e., positive or negative) or their linearity (Hochwarter, 2012). It has also been posited that all seemingly linear effects reach a contextually specific inflection point at which point the linear effect levels off or inverts. This principle has been termed the ‘toomuch-of-a-good-thing effect’ (Pierce & Aguinis, 2013), and it serves as a potential basis from which curvilinear findings for presumed linear relationships can be understood. There has been little empirical work examining the nonlinear effects of organizational politics. In one of the few examples, Hochwarter, Ferris, Laird, Treadway, and Gallagher (2010) demonstrated compelling evidence for the effect of organizational politics on work outcomes to be considered nonlinear. The authors hypothesized that perceptions of politics would have a U-shaped relationship with job tension and an inverted U-shaped relationship with job satisfaction. Both hypotheses were supported and the results were replicated in a second sample, providing strong support for the nonlinear nature of politics perceptions on two valuable outcomes. Hochwarter et al. also investigated the possibility of a moderator of these relationships. The politics perceptions–job tension relationship was tested again with the perceived resources of the individual acting as a moderator. A form of restricted nonlinearity was found, with only those individuals reporting few perceived resources demonstrating the nonlinear, U-shaped relationship between politics perceptions and job tension. The scarce, though, compelling findings related to the nonlinear effects of organizational politics, along with growing interest in the nature of nonlinear relationships in general, suggest that this is a likely fruitful avenue for future organizational politics research (Grant & Schwartz, 2011). Future work should more thoroughly examine the nature of the relationships between organizational politics and the various work outcomes to which politics has been demonstrated to be related (e.g., citizenship behaviors, justice perceptions). Other moderators also could be explored in order to better understand the potential restrictions of the nonlinear effects of organizational politics.

Reputation and Organizational Politics Research has shown that reputation can influence audience’s responses to individuals and gain individuals’ resources, but little research has directly investigated the intentional political use of reputation in organizations. Future research has much to

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gain from studying how individuals can signal certain aspects of their reputations to distinct audiences. The perceptual nature of reputation (Ferris, Blass, Douglas, Kolodinsky, & Treadway, 2003) allows for the attempted use and resulting response of reputation to vary dramatically from audience to audience. The development of and selection and signaling of aspects of one’s reputation for political influence is an area of organizational politics research in need of attention. Ferris et al. (2014) suggested that despite reputation’s presence as a multi-level construct, it is essentially the same thing at each level of organizational analysis (i.e., individual, unit/team, organization). This proposal opens many new doors for the investigation of reputation and organizational politics. As reputation is essentially the same construct at different levels, it is reasonable that audiences would have difficulty separating individual reputation from the reputation of a team or organization they are associated with. Future research should seek to see how individuals can use this associated reputation to their benefit. The study of reputation’s effect on politics requires the development of several current issues within the reputation literature. The review by Ferris et  al. (2014) found that the current measures of reputation may be somewhat lacking. Some authors have captured reputation through objective measures (e.g., Deephouse & Carter, 2005; Shamsie, 2003), while others have captured reputation through subjective measures (e.g., Hochwarter, Ferris, Zinko, Arnell, & James, 2007; Rindova, Williamson, Petkova, & Sever, 2005). However, Ferris et  al. (2014) noted none of the prominently used measures capture all three dimensions of reputation (i.e., performance/ results, character/integrity, and prominence). Future research investigating reputation should attempt to develop new measures of the construct in attempts to capture all three of the dimensions associated with reputation. Improved measurement also will help other areas of concern in the reputation literature. Many authors have proposed that reputation has value (e.g., Ferris et  al., 2003, 2014), but few have attempted to determine how value is measured, and what the value truly means. In regards to organizational politics, it is reasonable that higher value reputations would be more influential politically. Placing an economic value on individuals’ reputations would influence compensation (Bok, 1993) and the power individuals have relative to others. Thus, in addition to a measure of reputation, a measure of reputation’s value also is an area in need of future development. In sum, the role of reputation in organizational politics is inescapable. Despite this, relatively

little research exists investigating how individuals can intentionally utilize reputation and its value. There is great potential for investigation into measurement of reputation, its value, and the way it can be used in organizational politics.

Politics in Social Networks Politics likely play a large role in the way individuals can access and maintain social networks. Influence can help individuals gain access to networks and build alliances within the networks. Politics within networks also can determine who controls and gains access to information, and how the network can help those within it. Kelley (1976) proposed that those within an organizational network must know how to influence and gain the support of the elite members of the network in order to successfully implement new innovations. This goes hand-in-hand with the finding that it is important to manage relationships with different members of the network, and gain the favor of the important ‘brokers’ within networks (Galunic et al., 2012) who can access resources. It appears that simply being a member of a network is not enough; one must know who the important members of a network are, and how to influence them in order to see benefit. Individual characteristics may influence success in networks. Mehra, Kilduff, and Brass (2001) found high self-monitors – those who actively construct social perceptions of themselves – are more likely to hold central and strategically advantageous positions within social networks. These positions can then lead to greater workplace performance which will, in turn, allow for the individual to be perceived highly and have greater access to elite network ties. Mehra et al. (2006) found leaders with greater network ties were more likely to have higher or greater reputations, and that high reputation can lead to even greater network ties. It appears that those who can influence others and create ties that allow them to gain access to information and resources are able to gain the most benefit. The social relations between actors is one of the most important streams of research within network analysis (Kilduff & Brass, 2010), and those who are successful at political action are likely to maintain good social relations. In general, despite the evident relationship between politics and networks, the research addressing how they influence each other is somewhat sparse. Social interaction and the role of relationships often is mentioned as a key factor in network analysis, but the role of political influence in these relationships is not often the

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direct focus of research. Investigating which influence tactics work best in different network types, the way political influence tactics are perceived in networks, and the role of political skill are research areas in need of attention. The increase of electronically based and/or social-media-based networks is another area that offers great potential. As technology provides geographically divided, electronically connected networks, the influence and political techniques that are most successful may change. Future research needs to address these changes, and how they will impact politics and social networks.

Organizational Politics and Entrepreneurship The entrepreneurship literature recently has begun to investigate the role of organizational politics on new venture success. New ventures must navigate the environment within which they exist in order to survive (Stinchcombe, 1965). This can include navigating social networks, acquiring resources, and selling ideas and credibility. These activities all involve political maneuvers, and thus necessitate the study of the interaction between organizational politics and entrepreneurship. New ventures often require angel funds or startups funds in order to survive. In order to obtain such funding, entrepreneurs generally need to pitch their ideas to investors. Huang, Frideger, and Pearce (2013) found individuals with nonnative speaking accents were less likely to receive new venture start-up funding because they were perceived to have less political skill (i.e., political skill fully mediated the effects of nonnative accent on funding decisions). Using an experimental design, they found that those who were perceived to have greater political skill were seen as more likely to succeed and were in turn, provided with the start-up money. Political skill has been shown to lead to many positive outcomes for individuals within organizations. The use of political skill by entrepreneurs to navigate the environment of a new venture likely will lead to success for the new venture, as new ventures are ‘small by definition, consisting initially, solely of the founders of the venture’ (Baron, Lux, Adams, & Lamont, 2012, p. 384). When founders of new ventures are better able to present themselves in a positive manner, they are able to gain access to resources that help both the individuals and the new venture. When a new venture attempts to gain access to resources and establish itself within an industry, it can suffer from a lack of reputation (Rindova et al., 2005). As the founders

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often are viewed synonymously with the new venture, the reputation and credibility of the founders may act as a proxy for the organization. Investors and customers may assume a certain level of product or service from the founders, and thus the founders can use their reputation to gain access to resources that those without such reputations could not (Ferris et al, 2014).

CONCLUSION Politics is a fact of life in organizations, and has considerable and widespread effects on the attitudes and behavior of people at work. Fortunately, scholars today have embraced this reality, and increased research activity in this most important area of scientific inquiry. By representatively reviewing key areas in the politics literature, addressing some important challenges, and identifying key issues and areas for future research to investigate, we hope to have contributed to the organizational politics knowledge base in an important way. We hope some of the issues we raise will serve to stimulate even further interest on this important topic.

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21 Toxic Emotions at Work M i c h e l l e K . D u f f y a n d L i n g t a o Yu

INTRODUCTION Organizational researchers are increasingly interested in the role of emotions at work (Barsade & Gibson, 2007; Elfenbein, 2007). Several recent reviews demonstrate that emotions play a critical part in a range of phenomena in organizational settings, including performance, turnover, prosocial behaviors, group dynamics, leadership, harming behaviors, and even those areas that have been traditionally thought of as the exclusive field of cognitive behaviors, such as decision making (Barsade & Gibson, 2007; Brief & Weiss, 2002; Elfenbein, 2007). Emotions are described as feeling states that ‘are elicited by a particular target or cause, often including physiological reactions and action sequences, and are relatively intense and shortlived’ (Barsade & Gibson, 2007: 37). Our chapter is not designed to be exhaustive; in other words, our chapter does not aim to provide a comprehensive review of theories and research on emotions in the workplace (for thorough reviews, see Barsade & Gibson, 2007; Brief & Weiss, 2002; Elfenbein, 2007). Rather, our goal is to present work from a discrete emotion lens, in which we: a) introduce a critical theoretical perspective from which to view discrete toxic emotions – social comparison; b) review key empirical studies; and c) suggest important, yet unexplored questions. Our chapter

specifically focuses on discrete toxic emotions in organizations (i.e., envy, guilt, and shame), and calls attention to new directions for theories and research on discrete emotions in the workplace. We start by discussing our core theoretical perspective – social comparison, which we argue is a key source of discrete toxic emotions in the workplace. Under this social comparison framework, we then turn our attention to three specific discrete emotions which are traditionally considered to be toxic: envy, guilt, and shame. Acknowledging their potential positive outcomes, we don’t claim that these discrete toxic emotions exclusively lead to toxic effects, rather, in this chapter; we focus on the mechanisms and boundary conditions through which these discrete toxic emotions result in functional or dysfunctional interpersonal relationships and behaviors in the workplace. We conclude with suggestions on some underexplored topics and new directions for discrete emotions research in organizational settings.

SOCIAL COMPARISON – A CORE THEORETICAL PERSPECTIVE With dramatically increasing competition in today’s economy, organizations are facing a paradox. On the one hand, organizations are attracting

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and retaining high-performers by executing a variety of competition-oriented policies and practices (i.e., tournament-based compensation system and reward system) and offering a variety of resources (i.e., pay raise, family support, and career development opportunities) (Ployhart, Van Iddekinge, & MacKenzie, 2011); on the other hand, those competition-eliciting organizational policies, practices, and resources create situations where employees must compete with each other to strive for those benefits, which are likely to lead to unexpected and often undesirable consequences, for example, social comparisons in the workplace (Buunk & Gibbons, 2007; Duffy, Shaw, & Schaubroeck, 2008; Greenberg, Ashton-James, & Ashkanasy, 2007; Smith, 2000). Classic and recent theoretical development and research in social comparisons suggest that the social comparison process, by nature, is a process of self-evaluation (e.g., Buunk & Gibbons, 2007; Festinger, 1954; Greenberg et  al., 2007; Smith, 2000). Social comparisons occur when the comparison other is similar in characteristics related to the comparison and in the domain that is central to the self (Smith, 2000; Tesser, 1991). Tesser’s characterization of social comparison-based emotions and self-evaluation maintenance model (1991) suggest that a psychologically close comparison other is more likely to elicit social comparisons in the domain of high self-relevance. In fact, it is such similarity and self-relevance on those comparisonrelated characteristics that determine the occurrence of social comparisons and the consequences for the self and the comparison other (Lockwood & Kunda, 1997; Smith, 2000). Because of the emphasis on self-relevance and self-evaluation, which are criteria often assumed necessary for discrete emotions to arise, social comparisons offer a theoretical perspective to explain the emergence and implications of a variety of discrete emotions among individuals (Lazarus, 1991; Smith, 2000). Depending on whether the comparison other is superior or inferior to the self, scholars have distinguished between two types of social comparisons: upward social comparison (with someone superior) and downward social comparison (with someone inferior). Recent theoretical development in social comparisons suggests that both upward and downward comparisons have their own ‘ups and downs’ (Buunk & Gibbons, 2007: 11), and can generate both contrastive and assimilative processes (for reviews see Smith, 2000). Contrastive processes occur when the social comparison process highlights what one lacks (Smith, 2000), while assimilative processes highlight what one has or can easily have (Smith, 2000). It is the direction of the comparison combined with whether contrastive or assimilative processes are

generated, that determine the nature of the discrete emotions triggered. While both upward and downward contrastive and assimilative processes are encountered in organizational settings, our focus is on upward contrastive processes, as they are what tend to generate discrete toxic emotions. Consistent with the dominant view that negative rather than positive things (e.g., negative emotions) often carry larger and more significant effects (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001), those upward contrastive social comparison-based emotions (e.g., envy, guilt, and shame) are viewed as complex interpersonal emotions, in that they often involve either desirable or undesirable consequences of not only the self, but also the comparison other, as well as the interpersonal relationships and behaviors (Frijda, 1986; Frijda, Kuipers, & ter Schure, 1989; Lazarus, 1991; Smith, 2000). In upward contrastive social comparison processes, the major focus is on the inferiority of self vs. the superiority of the comparison other. Contrastive comparisons are likely to occur when the discrepancy seems unchangeable or immutable, in that people believe they are unable to close the gap between self and the comparison other. Such upward contrastive social comparisons generate counterfactual thoughts regarding how ‘things could be much better’ and ‘what might have been’ (Coricelli & Rustichini, 2010; Duffy, Dineen, Henle, & Lee, 2011; Duffy, Ganster, Shaw, Johnson, & Pagon, 2006; Lewis, 1973; McMullen, Markman, & Gavinski, 1995; Smith, 2000). For example, during upward contrastive social comparison processes, people tend to ask themselves: ‘what might have been if I had similar characteristics (or did similar things)?’ All in all, when the comparison occurs in a domain that is important to self, the comparison other is similar on comparison-related characteristics, and the discrepancy seems unchangeable, a variety of discrete toxic emotions will arise. Extant literature suggests that while envy arises from uncomfortable social comparisons with others who are better-off (Smith & Kim, 2007), guilt and shame are often derived from social comparisons in which people fail to achieve self- or other’s expectations (Leary, 2007; Tangney, Stuewig, & Mashek, 2007). Despite their relevance to a variety of outcomes (Leary, 2007; Smith & Kim, 2007; Tangney et al., 2007), studies of these social emotions in the workplace are scant (Greenberg et al., 2007). Therefore, the goal of this chapter is to explore desirable and undesirable implications of these three upward contrastive social comparisonsbased discrete toxic emotions – envy, guilt, and shame, in the workplace. In the following sections, we discuss their nature and effects on interpersonal

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Table 21.1 A summary of definitions of discrete emotions Terms used in this chapter

Definition

Discrete emotions

Feeling states that are elicited by a particular target or cause, often including physiological reactions and action sequences, and are relatively intense and short-lived (Barsade & Gibson, 2007: 37) An unpleasant and painful blend of feelings characterized by inferiority, hostility, and resentment caused by a comparison with a person or group of persons who possess something we desire (Smith & Kim, 2007: 46) Pain at another individual’s good fortune (Tai, Narayanan, & McAllister, 2012: 107) An emotional state with possible objections to one’s own actions, inactions, circumstances, or intentions (Baumeister, Stillwell, & Heatherton, 1994: 245) A person’s experience of negative self-evaluations based on anticipated or actual depreciation by others due to a failure to meet standards of behavior (Tracy & Robins, 2004; Turner & Stets, 2005)

  a) Envy

  b) Guilt   c) Shame

relationships and behaviors in organizational settings. We then provide suggestions regarding unexplored questions and new directions in future research. Table 21.1 summarizes definitions of discrete emotions described in this chapter.

ENVY Envy is defined as ‘an unpleasant and painful blend of feelings characterized by inferiority, hostility, and resentment caused by a comparison with a person or group of persons who possess something we desire’ (Smith & Kim, 2007: 46), and is experienced as ‘pain at another individual’s good fortune’ (Tai, Narayanan, & McAllister, 2012: 107). Despite organizational and societal policies and norms discouraging employees’ feeling of envy and inhibiting employees from negative behavioral reactions (e.g., social undermining, unethical behaviors, and aggression), envy is prevalent in today’s organizations (Menon & Thompson, 2010). Envy is evoked via upward contrastive comparisons, and as such, is often viewed as a complex social emotion because of its dual focus on both what the self lacks and what the comparison other possesses (Parrott, 1991; Smith, 2000; Yu & Duffy, 2014, 2016). The focus on one’s disadvantage elicits a sense of inferiority (Smith, Parrott, Diener, Hoyle, & Kim, 1999), thus producing depressive and negative feelings toward self, whereas the focus on the other’s advantage is likely to trigger a sense that the envied other’s advantage is undeserved, thus producing anger, hostility, and resentment toward the envied other (Smith et al., 1999). Of the three emotions covered in this chapter, envy is the most widely studied. The majority of

envy scholarship suggests that envy is an unpleasant emotion, frequently associated with negative individual, interpersonal, and organizational consequences (for a thorough review see Duffy et al., 2008). For example, current research has shown that envy is positively related to lower levels of individual self-esteem and decreased group performance (Duffy & Shaw, 2000), increased moral hypocrisy (Polman & Ruttan, 2011), reduced helping behaviors (Gino & Pierce, 2010), reluctance for openness to sharing information with the envied person (Dunn & Schweitzer, 2004), the occurrence of unethical behaviors (CohenCharash & Mueller, 2007; Gino & Pierce, 2009; Vecchio, 2005, 2007), résumé fraud (Duffy et  al., 2011), abusive supervision (Yu & Duffy, 2014), and social undermining behaviors toward the envied target (Duffy, Scott, Shaw, Tepper, & Aquino, 2012). In general, the traditional view in envy research relates envy with negative relational and behavioral tendencies. Why does envy lead to negative interpersonal relationships and behavioral consequences, especially in the workplace? One key reason is that the relative good fortune or advantage of the comparison other (i.e., the envied target) affects the envier’s perceptions of injustice. For instance, recent research suggests that upward contrastive comparisons will often create a subjective evaluation that the envied target does not deserve those advantages (i.e., unfairness), which in turn, motivates negative interpersonal relations and behaviors toward them to avenge the injustice and balance the score (e.g., Cohen-Charash & Mueller, 2007; Duffy et al., 2008; Smith & Kim, 2007). Second, envious individuals often believe that the envied targets deserve ‘bad treatment’ and do not deserve the moral and ethical considerations afforded others. Through a process of moral disengagement, in

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which a set of cognitive strategies (e.g., blaming the victim, minimizing the consequences of one’s actions) allow envious employees to overcome the normal self- and public-sanctions against harmdoing, negative behavioral reactions (i.e., social undermining) are facilitated for envious individuals (e.g., Duffy et al., 2012). Third, envious individuals are likely to perceive self-esteem threat from the advantaged envied targets, thus motivating negative behavioral reactions toward them to restore wounded self-esteem. For example, Yu and Duffy (2014) conducted two field studies among supervisor–subordinate dyads, and the results demonstrated that a supervisor’s envy toward a specific subordinate is more likely to lead to abusive supervision through supervisor perceived self-esteem threat. What factors can serve as possible antidotes or poisons that can either mitigate or exaggerate the negative action tendencies to the feeling of envy? Recent reviews suggest a number of individual and social contextual factors may play a critical role in altering people’s reactions to envy in social life in general (Exline & Zell, 2008), and in organizational settings in particular (Yu & Duffy, 2016). For example, Cohen-Charash and Mueller (2007) explored the interrelationships among envy, perceived injustice, self-esteem, and counterproductive work behaviors (CWBs). According to two studies, they found that the envy–CWBs link is strengthened by a higher level of perceived injustice, particularly among people who are high in self-esteem. In addition, Yu and Duffy’s (2014a) study suggests supervisors’ bottom-line mentality (i.e., secure bottom-line outcomes while neglecting competing priorities) and perceived subordinates’ warmth and competence further mitigate (or exaggerate) the likelihood of abusive supervision in response to the experience of envy. Specifically, a supervisor’s envy toward a specific subordinate is more likely to lead to abusive supervision when the supervisor holds a higher level of bottom-line mentality and perceives the envied subordinate to be cold but competent. Moreover, Duffy and her colleagues (2012) found that social identification (i.e., perceived similarity) and team undermining norms may shape employees’ behavioral response to envy in the form of social undermining behaviors, such that employees’ envy is more likely to lead to social undermining behaviors when the perceived co-worker’s similarity is low, and the team undermining norm is high. Because of the dual focus on the envious self and the envied other, whether the envious person perceives the envied target to be similar or not has garnered considerable attention, but has returned seemingly paradoxical results. Some studies have shown that people are more likely to envy others

who are perceived as highly similar (Schaubroeck & Lam, 2004; Smith & Kim, 2007; Vogel, Duffy, Mitchell, & Yu, 2014), whereas others have shown that perceived similarity may mitigate the negative consequences of envy as well (e.g., Duffy et  al., 2012; Vogel et  al., 2014). For example, Schaubroeck and Lam (2004) conducted a study to examine promotion rejectees’ invidious reactions to promotees, and results have shown that promotion rejectees are likely to experience promotion envy, especially when they perceive the promotee as being more similar to themselves. In addition, Vogel and colleagues (2014) found that while negative upward social comparisons are more likely to lead to the feeling of envy when the envied co-worker is perceived as highly similar, the envious employees are less likely to undermine the envied co-worker who is perceived as highly similar. Indeed, Duffy and her colleagues (2012) suggest that despite the fact that perceived similarity may motivate one to find reasons to envy the advantaged person so as to justify the subsequent behavioral reactions, it is probably true that it is also difficult to behave badly toward an envied other who is perceived as highly similar. In other words, perceived similarity may play distinct roles in eliciting and responding to the feeling of envy.

Future Research Directions Moving beyond the dominant emphasis on the negative emotional, relational, and behavioral reactions of envious employees in the workplace, scholars have paid increasing attention to the potential positive effects of envy, considering it as benign rather than malicious (e.g., van de Ven, Zeelenberg, & Pieters, 2009). Some scholars have challenged the dominant view and pointed out that envy could be benign. For example, recent studies have suggested that envy can motivate people to put more effort on self-improvement (van de Ven et  al., 2009), to increase job performance (Schaubroeck & Lam, 2004), and to activate a systematic learning process (Lee, 2014). That is to say, people can reduce and even close the gap between them and targets of envy, not only by bringing others down, but also by enhancing themselves. Indeed, Tai and colleagues (2012) recently proposed a new conceptualization of envy, and suggest that the substance of envy should be distinct from the consequence of envy. By defining envy as just a painful experience, envy can be considered an adaptive emotion in which the motivation of addressing the painful experience can drive people toward diverse behavioral tendencies, as what they refer to:

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challenge-based action tendency and threat-based action tendency. Some recent work provides evidence for this distinction. For example, Vogel and colleagues (2014) found that a co-worker’s envy could lead to increased impression management (i.e., exemplification) through a desire to rebuild diminished self-esteem. Lee’s (2014) study also suggested that envy may lead to a higher level of task performance through active learning processes. A recent study conducted by Duffy and her colleagues (2011) demonstrated that job search envy could lead to both functional and dysfunctional job search behaviors. Specifically, their study found that envious job seekers utilize more effort rather than résumé fraud earlier in their searches, especially when employment opportunities are plentiful, but are more likely to engage in résumé fraud in the later stage of the job search process. Indeed, employees are often required to show ‘smile at the fortune of another’ (Heider, 1958: 289) and put more effort on self-­ improvement, rather than simply envy what coworkers possess. We hope to see scholars build and test theories about the potential positive effects of envy, and the boundary conditions that help to differentiate the positive effects from negative effects of envy. Another exciting pathway for extending the dominant view of envy involves examining the experience of being envied. Based on the integration of two classic theories – Foster’s (1972) ethnographic analysis of envy, and Exline and Lobel’s (1999) analysis of sensitivity about being the target of a threatening upward comparison (STTUC), the extant research has demonstrated the ambivalent experiences that are associated with being envied (e.g., Exline & Lobel, 1999; Foster, 1972). On the one hand, people may have a feeling of pleasure as being the target of envy and are thus likely to provide assistance to others including the envious person; on the other hand, being the target of envy can also be a source of interpersonal strain, making people feel unpleasant, anxious, and stressed due to the potential threat of being reacted to negatively by the envious party (Exline & Lobel, 1999; Rodriguez Mosquera, Parrott, & Hurtado de Mendoza, 2010; van de Ven, Zeelenberg, & Pieters, 2010; Zell & Exline, 2010). For example, a recent study conducted by Lee, Duffy, Scott, and Michaela (2014) suggests that reactions to being envied vary depending on whether the envied target is higher in independent (i.e., personal) or interdependent (i.e., social) self-concept. An independent self-concept activates a challenge-based axis, increasing work engagement and subsequent performance. In contrast, higher levels of interdependent self-concept activate a fear-based response, lowering performance via engagement.

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Although the examination of the experience of being envied is still a relatively new research area, a closer look at the envied target’s emotional, relational, and behavioral responses would be a benefit to the envy literature.

GUILT Guilt is ‘an emotional state with possible objections to one’s own actions, inactions, circumstances, or intentions’ (Baumeister, Stillwell, & Heatherton, 1994: 245). In the work domain, guilt is another discrete emotion that often results from upward contrastive social comparisons (i.e., failure in achieving self or other’s expectations; Greenberg et al., 2007; Tangney & Fischer, 1995). Although the experience of guilt can be coupled with the experience of shame, guilt and shame are distinct discrete emotions. Recent research suggests that the major difference in ‘ego-oriented’ versus ‘other-oriented’ concerns stems from the distinction that while shame involves a focus on the self or the entire person, guilt is thought to be only related to a specific behavior (Lewis, 1971; Tangney et  al., 2007). Therefore, although both are negative and painful emotions, shame is considered the more painful emotion because other’s negative evaluations center on one’s entire self, not simply one’s specific behavior. Guilt, on the other hand, is often a less painful emotional experience because the object of guilt is a specific behavior, not the entire self. Guilt is likely to elicit a variety of positive consequences for interpersonal behaviors at the workplace. Because people experiencing guilt are concerned about the potential effect of their specific behavior on others, research has demonstrated that guilt consistently correlates with other-oriented empathy (e.g., perspective-taking or future-oriented thinking of their behaviors; Cohen, Panter, & Turan, 2012, 2013; Joireman, 2004; Leith & Baumeister, 1998; Tangney 1991, 1995; Tangney & Dearing, 2002). This sense of empathy is likely to motivate reparative actions such as confessing, apologizing, non-hostile discussion, direct corrective action, and repairing the damage done (de Hooge, Zeelenberg, & Breugelmans, 2007; Ketelaar & Au, 2003; Sheikh & JanoffBulman, 2010). In addition, it has been found that the experience of guilt is likely to increase a sense of personal responsibility, compliance, and forgiveness, and thus less likely to generate harming behaviors (e.g., Jordan, Flynn, & Cohen, 2011; Strelan, 2007; Tangney et  al., 2007). A series of studies conducted by Cohen and her colleagues

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(2012, 2013) has shown that high guilt-proneness individuals are less likely to make unethical decisions such as lying for monetary gain, dishonesty in negotiation, CWBs, delinquency, and criminal behaviors. Furthermore, recent cross-cultural studies have shown that the experience of and behavioral responses to guilt are very similar across different cultures (Breugelmans & Poortinga, 2006; de Hooge et al., 2007), which supports the argument that guilt is a universal social emotion.

Future Research Directions Beyond these exciting theoretical developments and increasing number of empirical studies in guilt, we see several contemporary issues and unanswered questions for emotion research to address. First, while guilt has long been viewed as an adaptive moral emotion that produces beneficial consequences, recent research suggests otherwise (de Hooge, Nelissen, Breugelmans, & Zeelenberg, 2011). The summary of guilt literature above, albeit non-exclusive, shows that a damaged interpersonal relationship that needs to be addressed is the key theme in the experience of and response to guilt. People who feel guilt are concerned about what they have done wrong and the potential negative effects of their behaviors on others (Lewis, 1971; Tangney et  al., 2007). Therefore, it is not surprising that guilt often motivates prosocial behaviors aimed at rebuilding that hurt interpersonal relationship. However, de Hooge and colleagues (2011) proposed that guilt may motivate unethical or antisocial behaviors as well. Specifically, they argued that while most guilt research focuses on the interpersonal interactions at dyad level, people have to interact with multiple others in their daily social and/or organizational life. As a result, when focusing on repairing the damaged relationship with the specific other at dyad level, people may concurrently pay less or even no attention to others. In other words, while a variety of prosocial behaviors aimed at repairing damaged relationships toward a specific other are viewed as positive consequences of the experience of guilt, it is also possible that those benefits are achieved at the cost of others (i.e., less resources to others, or even doing harm toward others). These recent theoretical development and empirical studies in guilt research suggest that guilt may motivate both prosocial and antisocial behaviors toward different targets (de Hooge et al., 2011). Future research would benefit from understanding the conditions under which guilt motivates prosocial or antisocial behaviors toward others in the workplace.

Second, the existing guilt literature has almost exclusively examined and measured guilt as a proneness, which is defined as ‘a personality trait indicative of a predisposition to experience negative feelings about personal wrongdoing, even when the wrongdoing is private’ (Cohen et  al., 2012: 355). Yet, studies of guilt at state level in organizational contexts are still scant (Tangney et  al., 2007). A recent study conducted by Lee, Duffy, Dineen, Henle, and Trzebiatowski (2012) suggests that a closer look at the functions of state guilt in the workplace may benefit and advance the current literature. Drawing insights from theories of bounded ethicality (Tenbrunsel, Diekmann, Wade-Benzoni, & Bazerman, 2010) and selfregulation (Bandura, 1977; Bandura, Caprara, Barbaranelli, Pastorelli, & Regalia, 2001), Lee and his colleagues examined the complex relationships among unethical behavior, state of guilt, and success. Specifically, they found that individuals are less likely to experience the state of guilt about behaving unethically when they achieve success, and that state of guilt has limited power to inhibit subsequent unethical behaviors for those individuals with low self-efficacy. All in all, the state guilt research is at its infant stage, but the current studies open a bright future, and suggest that a more systematic investigation of state guilt is required to paint out a complete and accurate picture regarding the role of guilt in people’s relational and behavioral reactions, especially in the work context.

SHAME Shame is defined as a person’s experience of negative self-evaluations based on anticipated or actual depreciation by others due to a failure to meet standards of behavior (Tracy & Robins, 2004; Turner & Stets, 2005). Like guilt, shame is another discrete emotion linked to a self-focus triggered by an upward contrastive social comparison (Lewis, 1992), and is viewed as an emotion with relatively high toxicity (Tomkins, 1963). Specifically, shame is often triggered by perceived inferences regarding personal failures and others’ negative evaluations of this failure. Importantly, feelings of shame flourish when individuals contrast their own behaviors with that of others, and are exacerbated by perceptions that others share this negative evaluation (Leary, 2007; Tangney et al., 2007). Consistent with the characteristics of upward contrastive social comparisons, the question frequently asked in shame is ‘why am I that kind of person?’ (Smith, 2000). Indeed,

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researchers have argued that the experience of shame is more likely to arise when the sense of inferiority can be blamed to the self, the entire person (Lazarus, 1991; Smith, 2000). Unlike guilt, whose focus lies on a specific disappointing behavior, shame’s focus is on the disappointment of the entire self. For example, Felblinger’s (2008) study has shown that when receiving mistreatment (i.e., incivility and bullying) in the workplace, nurses are likely to perceive a negative evaluation from the instigator, blame themselves, and thus are likely to experience shame as a response. The experience of shame is likely to arise in a variety of circumstances involving evaluations from others, as long as the other makes unfavorable evaluations during the social comparison processes. In short, when a person feels ashamed, it is likely that a self-relevant, upward contrastive social comparison is at work. As a member of upward contrastive social comparison-based emotions, shame has important implications for interpersonal interactions: the experience of shame not only arises from evaluations of others, but also carries a capability to influence interpersonal relationships and behaviors (Barrett, 1995; de Hooge et al., 2011; Tangney et al., 2007). Due to its emphasis on self’s inferiority, shame has immediate effects in the following relational and behavioral reactions toward others, especially in organizational settings which are saturated with triggers of upward contrastive social comparisons. In the history of shame research in social psychology, scholars have proposed different theories or models to explain the experience of and response to shame (e.g., Lewis, 1992; Nathanson, 1994; Poulson, 2000). A consensus view is that shame often has negative influences on interpersonal relationships and behaviors. When people experience shame about the self, they are likely to feel a sense of diminishment and worthlessness, which in turn, leads to a range of defensive responses, including submission, social avoidance, withdrawal, and rejection from others (e.g., Dickerson, Gruenewald, & Kemeny, 2004; Tangney, 1991; Tangney et  al., 2007). Besides those defensive responses, the experience of shame is also likely to make people who are ashamed blame others for their own failures (e.g., Bear, Uribe-Zarain, Manning, & Shiomi, 2009; Luyten, Fontaine, & Corveleyn, 2002; Tangney, 1991; Tangney, Wagner, Fletcher, & Gramzow, 1992), which then has been directly linked to interpersonal aggression (Stuewig, Tangney, Heigel, Harty, & McCloskey, 2010). Thus, unlike guilt which can lead to restorative behaviors, shame leads to problematic responses. Negative relational and behavioral consequences of shame may also be explained by the

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mediating role of anger or hostility in the direct shame–aggression link. Recent research provides support for the relationship between shame and anger (or hostility) across diverse populations (e.g., Andrews, Brewin, Rose, & Kirk, 2000; Bennett, Sullivan, & Lewis, 2005; Harper & Arias, 2004; Paulhus, Robins, Trzesniewski, & Tracy, 2004; Tangney & Dearing, 2002). Compared with those who are not experiencing shame, individuals who are ashamed are more likely to engage in externalization of blame, experience higher levels of anger or hostility, and thus express or displace such anger in destructive ways, including both direct and indirect aggression (e.g., actively harm something important to the target, talking bad behind the target’s back). These empirical evidences align with the spiral of shame–rage proposed by Lewis (1971). Specifically, the negative shame–rage spiral begins with an experience of shame, which then causes a feeling of anger or hostility toward others, as well as the externalization of blame which in turn leads to more aggression, which is then further likely to lead to increased experience of shame; as a result, a negative spiral of shame– anger–aggression arises.

Future Research Directions The traditional view in shame research considers shame as a painful moral emotion, which motivates antisocial or unethical behaviors to reduce or even remove the painful experience. However, recently, scholars have questioned whether negative behavioral reaction is the only response to the experience of shame. In other words, scholars have argued that it is critical to investigate under what conditions shame is likely to lead to those antisocial or unethical behaviors. For example, recent studies have shown that the norms or values of the group to which people want to belong may influence the way people choose to respond to the feeling of shame (e.g., De Hooge et  al., 2011; Tangney et  al., 2007). Specifically, when the group norms include competition, outperforming others, reaching a senior position, and placing ingroup above other groups, shame often motivates unethical or antisocial behaviors such as bullying, discrimination, and abuse against others and outgroup members. However, group norms may also include cooperation and unit cohesion, in which case shame may motivate prosocial behaviors, rather than unethical behaviors. In addition, by moving up to a higher level of analysis, Creed, Hudson, Okhuysen, and Smith-Crowe (2014) theorized the role of shame in institutional processes. Specifically, they proposed that the feeling

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of shame may serve as a signal to people that a social bond is at risk, and the value associated with such social bonds may be damaged, which thus is likely to catalyze a fundamental motivation to preserve valued bonds in institutional processes. To date, little research has explored the potential positive responses to the feeling of shame across levels in organizations. Therefore, we hope to see further research building on this evidence to examine the boundary conditions under which the experience of shame will lead to antisocial or prosocial behaviors. Another key direction involves identifying cultural influences in the antecedents and consequences of shame. Most current studies of shame are rooted in the Western culture, in which shame exclusively leads to negative interpersonal relations and behaviors, with no positive consequences. In fact, shame is often considered maladaptive in Western social organizational settings (Tangney & Fischer, 1995; Tangney et al., 2007). However, cross-cultural psychology research has shown that individuals from different cultures differ in their understanding of and response to emotions (Kitayama, Markus, & Matsumoto, 1995). This raises a specific issue with respect to shame: whether and how does culture play a critical role in the experience of and response to shame? Bagozzi, Verbeke, and Gavino (2003) have conducted a cross-cultural study in which they examined whether Filipino and Dutch employees have different responses to the feeling of shame. They found that while shame leads to enhanced customer relationship building, civic virtue and helping among Filipino employees, shame leads to diminished sales volume, communication effectiveness, and relationship building among Dutch employees. We hope to see further research build on this evidence to examine why, how, and when cultural factors influence people’s reaction to the experience of shame. Such investigations will further enhance our understanding of the functions of shame, especially in today’s global workplace.

OVERALL FUTURE DIRECTIONS Although current literature on discrete emotions in organizations provides useful insights, there are many exciting questions about discrete emotions that have yet to be explored. In this section, we call attention to three key future directions: examining potential positive and negative effects of discrete emotions and boundary conditions under which positive or negative effects are likely to

arise, studying the nature and implications of discrete emotions in a multilevel framework, and considering novel social and cultural influences on the functions of discrete emotions at work.

Two Sides of Discrete Emotions In our view, one of the most important new directions for inquiry involves gaining a deeper understanding of both sides of discrete emotions. Historically, either a positive or a negative valence is often assigned to each discrete emotion. As noted earlier, envy has been framed as a green-eye monster which links to a variety of deleterious outcomes for individuals, team or workgroups, and organizations (Duffy et  al., 2008; Smith & Kim, 2007); guilt is viewed as a self-conscious emotion that relates to various positive interpersonal interactions and behavioral consequences (Cohen et al., 2012, 2013; Tangney & Dearing, 2002); and shame, characterized by a negative self-­evaluation based on a failure of self, is associated with negative, unethical, aggression, or even crime in the social and organizational life (Leary, 2007; Tangney et al., 2007). However, researchers have recently noted the possible other side of these discrete emotions. For example, envy could be benign and trigger more effort on self-improvement (Van de Ven et al., 2009, 2010); guilt could be harmful to the third parties when people exclusively focus on the repair of a damaged relationship with a specific target (de Hooge et al., 2011); and shame might stimulate positive interpersonal interactions when the group norms highlight cooperation and group benefit (de Hooge et  al., 2011; Tangney et  al., 2007). Although relatively little research has explicitly explored these ideas, we believe that these discrete emotions are double-edged swords. We encourage researchers to begin studying when, why, and how discrete emotions can lead to positive or negative influences on a variety of work-related outcomes. This new direction also benefits the managerial practice. Take envy as an example, gaining a deeper understanding of both positive and negative effects of envy offers useful insights to organizations on how to maximize the benefits of a competitionoriented compensation system while minimizing possible costs (e.g., elicited envy and subsequent negative consequences). In short, to the extent that organizations and managers are aware of both sides of discrete emotions and their implications, they are better informed to make more effective decisions to help employees manage their emotions, which ultimately contribute to the organizational effectiveness.

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Positioning Discrete Emotions into a Multilevel Framework Existing research has primarily examined discrete emotions at the level of the individual employee. However, it is noteworthy that these discrete emotions are often triggered from social comparisons with groups of employees. For example, envy comes from upward contrastive social comparisons with superior others (i.e., superior co-workers at work; Duffy et al., 2008), and both guilt and shame are generated from anticipated or actual negative evaluations from others regarding either the specific behavior or the self (Leary, 2007; Tangney et al., 2007). This raises important questions about whether these discrete emotions are contagious and exists at the group or organizational level. Do groups of employees who experience the feeling of envy together develop shared emotions that reinforce and enhance their collective envy toward other groups? Is guilt and shame more potent when activated and experienced in groups than among isolated individual employees? A recent theoretical work by Creed and colleagues (2014) offers useful insights in this direction. Examining the discrete emotion of shame at the organizational level, authors theorized the role of a set of shame-related constructs (e.g., felt shame, systemic shame, sense of shame, and episodic shaming) into the explanations of institutional reproduction and change. Given that these discrete emotions often occur in social comparisons with others (Smith, 2000) and the influences of individual emotions on group and organizational functioning (Ashkanasy, 2003), it will be both theoretically interesting and practically important to explore the phenomenon of discrete emotions in a multilevel framework.

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insights in this direction, where they delineate a variety of social and contextual factors, including social norms, group identity, cultural characteristics, that might be viewed as either poison or antidote to the experience of envy. Bagozzi and colleagues (2003) conducted a cross-cultural study on the feeling of shame, and results supported that culture shapes the way people interpret and respond to the discrete emotion of shame. Both researchers and practitioners may wish to continually explore how social and cultural factors influence the understanding of and response to other discrete emotions in the workplace.

CONCLUSION The goal of this chapter was to summarize key trends in the study of discrete toxic emotions in the extant literature, and to direct and energize future research. Based on classic and more recent theoretical development and empirical work on social comparisons, we began with the assumption that by serving as a key source, social comparisons in the workplace are likely to elicit a variety of discrete toxic emotions, which in turn, affect interpersonal relationships and behaviors at work. We then focused on three discrete toxic emotions: envy, guilt, and shame, which have received the most attention over the past decades in organizational psychology literature. Under each of them, we reviewed key empirical studies, controversies, and unanswered questions, and suggested new directions for future research. We hope this chapter serves as an important step for further investigation of discrete emotions and their roles in the workplace.

Influences of Social and Cultural Factors on Discrete Emotions

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Finally, we hope to see more research on how a variety of social and cultural factors initiate, maintain, facilitate, and mitigate discrete emotions and their implications at work. This new future direction is also related to the other two directions we discussed above, because a deeper understanding of both positive and negative effects of discrete emotions needs a comprehensive consideration of various organizational, social, and cultural factors, and because these factors are likely to significantly and uniquely influence the implications of discrete emotions at and across levels. For example, Yu and Duffy’s review (2016) on a socialcontextual view of envy in organizations offers

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Tangney, J. P., Wagner, P. E., Fletcher, C., & Gramzow, R. (1992). Shamed into anger? The relation of shame and guilt to anger and self-reported aggression. Journal of Personality and Social Psychology, 62, 669–675. Tenbrunsel, A. E., Diekmann, K. A., Wade-Benzoni, K. A., & Bazerman, M. A. (2010). The ethical mirage: A temporal explanation as to why we are not as ethical as we think we are. Research in Organizational Behavior, 30, 153–173. Tesser, A. (1991). Emotion in social comparison and reflection processes. In J. Sules & T. A. Wills (Eds.), Social comparison: Contemporary theory and research (pp. 49–61). Hillsdale, NJ: Lawrence Erlbaum. Tomkins, S. (1963). Affect-imagery-consciousness. New York: Springer. Tracy, J. L., & Robins, R. W. (2004). Putting the self into self-conscious emotions: A theoretical model. Psychological Inquiry, 15, 103–125. Turner, J. H., & Stets, J. E. (2005). The sociology of emotions. New York: Cambridge University Press. Van de Ven, N., Zeelenberg, M., & Pieters, R. (2009). Leveling up and down: the experiences of benign and malicious envy. Emotion, 9, 419–429. Van de Ven, N., Zeelenberg, M., & Pieters, R. (2010). Warding off the evil eye: When the fear of being envied increases prosocial behavior. Psychological Science, 21, 1671–1677. Vecchio, R. P. (2005). Explorations in employee envy: Feeling envious and feeling envied. Cognition and Emotion, 19, 69–81. Vecchio, R. P. (2007). Cinderella and Salieri in the workplace: The envied and the envier. In S. Gilliland, D. D. Steiner, & D. Skarlicki (Eds.), Managing social and ethical issues in organizations (pp. 109–134). Charlotte, NC: Information Age Publishing. Vogel, R., Duffy, M. K., Mitchell, M., & Yu, L. (2014). Two faces of coworker envy: A process model of antecedents and consequences of envy. Symposium presentation at the 2014 Annual Meeting of Academy of Management, Philadelphia, PA. Yu, L., & Duffy, M. K. (2014). I abuse you, because I envy you! A self-esteem threat model of abusive supervision. Paper presented at Annual Meeting of Academy of Management, Philadelphia, PA. Yu, L., & Duffy, M. K. (2016). A social-contextual view of envy in organizations: From both enviers and envied perspectives. In Smith, R., Merlone, U., & Duffy, M. K. (Eds.), Envy at work and in organizations (pp. 39–56). Oxford: Oxford University Press. Zell, A. L., & Exline, J. J. (2010). How does it feel to be outperformed by a ‘good winner’? Prize sharing and self-deprecating as appeasement strategies. Basic and Applied Psychology, 32, 69–85.

22 Exchange in the Employee– Organization Relationship Lynn M. Shore, Jacqueline A.-M. Coyle-Shapiro and Chiachi Chang

INTRODUCTION Extraordinary progress has been made in the last 40 years in the study of the employee–­organization relationship (EOR). The EOR is ‘an overarching term to describe the relationship between the employee and the organization’ (Shore et al., 2004, p. 292) and encompasses psychological contracts (Rousseau, 1995), social and economic exchange (Shore, Tetrick, Lynch, & Barksdale, 2006), perceived organizational support (POS; Eisenberger, Huntington, Hutchison, & Sowa, 1986), leader–member exchange (LMX; Scandura & Graen, 1984), and the employment relationship (ER; Tsui, Pearce, Porter, & Tripoli, 1997). Social exchange theory (Blau, 1964) and the inducements–contributions model (March & Simon, 1958) have provided the dominant theoretical foundation to understanding the employee– employer exchange relationship. Each of these EOR constructs has developed distinct lines of research, but they all have in common a focus on the relationship between the employee or groups of employees and the organization or the leader. Likewise, all EOR constructs are based on theories of exchange as noted above. However, there are some important distinctions between the EOR constructs (see Table 22.1). Psychological

contracts involve employee perceptions of what compensation, resources, and/or support can be expected from an exchange partner (usually the organization) in return for meeting perceived jobrelated duties and obligations. Both the fulfillment of promises or obligations and the breach and violation of promises and obligations are studied. Similarly, social and economic exchange involve exchange between employee and employer, but unlike psychological contracts which describe the content of the exchange (what each party provides), social and economic exchange focus on two types of exchange processes (a high quality social exchange involving mutual obligation and socio-emotional elements and a low quality economic exchange with formalized and impersonal agreements). In contrast, LMX focuses on perceptions of the quality of the relationship formed with the immediate leader, and POS involves perceptions of resources and support received from the organization. LMX and POS reflect perceptions of the actual state of the relationships with the immediate leader or the organization, respectively. Thus, these constructs characterize the tenor of the social exchange relationships with these two parties without the explication of mutual obligations between the employee and either the manager or the organization. Furthermore, these two

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Table 22.1 Employee–organization relationship constructs EOR Constructs Attributes of exchange constructs

Leader–member exchange

Perceived organizational support

Psychological contract

Focus of exchange Relationship quality

Organizational support

Fulfillment/breach of promises

Exchange focus – singular or mutual

Leader contributions to the exchange – singular

Organizational contributions to the exchange – singular

Level of exchange Theoretical basis

micro Social exchange

micro Social exchange

Social and economic Employment exchange relationships

Relationship quality High and low levels of inducements and contributions Separate Integrated Integrated organizational organizational organizational and employee and employee and employee contributions to contributions to contributions to the exchange – the exchange – the exchange – mutual mutual mutual micro micro meso, macro Social exchange Social exchange Inducements– contributions

constructs have a singular focus in that only the contributions of one party to the exchange relationship is described. Note that all four of these constructs described in this paragraph involve individual perceptions of the exchange relationship. The employment relationship was first articulated in the conceptual article by Tsui, Pearce, Porter, and Hite (1995) in which they discussed the employment relationship as consisting of clusters of human resource practices offered to groups of employees, and employee contributions as a result of such varying practices. Subsequent empirical research has focused on meso (group) and macro (organizational) exchange between groups of employees at either the job or organizational level. Contributions of each party to the exchange are integrated into four types of relationship with an emphasis on balance or imbalance in the exchange. Like social and economic exchange, employment relationships thus reflect mutual, integrated exchanges between parties. Like psychological contracts, the content of the exchange for each party is specified. However, a dissimilarity is that the employment relationship also reflects the quality of the relationship by involving balanced and unbalanced exchanges and by combining contributions of the two parties into relationship types. The typology is similar to the process model of social and economic exchange in that the contributions of each party to the exchange are integrated so that the focus is on the relationship, and not separate contributions to the exchange. In deciding which EOR concepts to include or exclude, we were guided by the criteria of a relational focus, incorporating an exchange between the employee and employer explicitly,

and capturing high and low quality exchanges. Psychological contracts, social and economic exchange, and the employment relationship albeit in different ways are relational frameworks that capture the employee–employer exchange and also low quality (psychological contract breach, economic exchange, quasi spot contracts and underinvestment) and high quality exchanges (psychological contract fulfillment, social exchange, mutual and overinvestment). We excluded reviews of perceived organizational support (POS) and leader–member exchange (LMX) literatures in this chapter as they exclusively focus on employees’ perceptions of support from the organization or leader and high quality relationships. Our chapter is structured as follows. We first take a broad perspective to reviewing the theoretical underpinnings of the EOR and highlighting the major developments in the EOR domain. Specifically, we summarize books, chapters, and articles that have sought to review and integrate the EOR literature. Then, we review research on psychological contracts, the employment relationship, and social and economic exchange literatures. We end with a discussion of emerging and developing issues in the EOR prior to outlining future research directions and practical implications.

THEORETICAL UNDERPINNINGS AND DEVELOPMENTS IN THE EOR The EOR literature has relied primarily on two theoretical frameworks, social exchange theory

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(Blau, 1964; Gouldner, 1960) and the inducement– contribution model (March & Simon, 1958). Both frameworks focus on exchange between parties, though the level of analysis is somewhat different. Specifically, the inducements–contributions model has been applied primarily to group- and organizational-level EOR frameworks (c.f., the employment relationship (ER), Tsui et  al., 1997) and social exchange theory chiefly to individual-level EOR frameworks (c.f., psychological contracts, Rousseau, 1989; social and economic exchange, Shore et al., 2006). The inducements–contributions model (March & Simon, 1958) has its roots in Barnard’s (1938) idea of ‘exchange of utilities’ and focused on the resources exchanged in the employee–­organization relationship. March and Simon (1958) in their inducement–contribution model viewed the employment relationship as an exchange of organizational inducements for employee contributions. A key component in their model is the idea of balance between inducements provided and contributions given. This was further developed in subsequent work by Blau (1964) who took a broader focus on the type of exchange relationship that develops between employees and their organization. Social exchange theory owes much to the work of Blau (1964) who distinguished between economic and social exchange relationships. Blau (1964) describes economic exchange, as one where the exchange is specified and the method used to assure that each party fulfills its specific obligations is the formal agreement upon which the exchange is based. In contrast, social exchange involves unspecified obligations – ‘favors that create diffuse future obligations, not precisely specified ones, and the nature of the return cannot be bargained about but must be left to the discretion of the one who makes it’ (p. 93). Therefore, social and economic exchanges differ in the extent to which each party’s obligations are specified as part of the exchange. Trust plays an important role in the process of developing social exchange since these relationships involve unspecified obligations. That is, one party needs to trust the other to reciprocate in the initial stages of the exchange without a formalized agreement as to when and how the obligation will be discharged. Nevertheless, Blau (1964) claims that the timing of reciprocation is important since remaining obligated for a period of time to another party and the trust that the obligations will be discharged serves to strengthen the social exchange. Likewise, the social exchange process takes time to develop, beginning with minor dealings in which little trust is required. If the recipient returns the minor benefits received, this serves

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as evidence of their trustworthiness and facilitates the ongoing provision of benefits and discharging of obligations. The norm of reciprocity (Gouldner, 1960) in which recipients of beneficial treatment feel obligated to provide favorable treatment in return, and the role of trust distinguishes social exchange from economic exchange. Thus, social exchange is characterized by investment in the relationship with the potential risk that the investment will not be recompensed. Another difference between economic and social exchange is the duration of the relationship. Economic exchanges are time limited whereas in social exchange, there is a long-term orientation where the exchange is ongoing and indefinite. The long-term orientation is essential for the development of trust and for a pattern of predictability to develop between giving and receiving of benefits between the exchange partners. Thus, trust and unspecified obligation with a long-term time horizon differentiates social exchange from economic exchange where there are more formalized and specified obligations without socio-emotional elements such as trust and commitment. Starting in 2004, several publications have sought to summarize and synthesize the EOR literature broadly, to suggest new avenues of research, and to link the EOR literature with other literatures in which there may be overlaps. These publications sought to clarify the EOR literature as a whole, as well as broaden the depth and breadth of the literature, and create bridges across literatures. Below we briefly summarize each of these publications in chronological order to reflect the evolution of the EOR literature. Coyle-Shapiro, Shore, Taylor, and Tetrick (2004), in an edited book on the EOR designed to provide clarity in the literature, first discussed the many theories that had been applied to exchange relationships between employees and organizations, including social exchange theory, justice theory, industrial relations, legal theory, and economics, and how these theories had contributed to the EOR literature. They concluded that in spite of the different disciplinary lenses, all identified exchange as a core element of the relationship. Further, the editors suggested the need for research to examine the many levels of exchange (individual, group, organization) as well as the potential focus of exchange (work team, department, union local, job category, top management, the organization) in order to understand the EOR more fully. In a comprehensive chapter focused on reviewing the EOR literature, Shore and her colleagues (2004) sought to create clarity in the literature by (a) defining each construct within the EOR domain and comparing it with other EOR constructs and (b) by examining the theories underlying the

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varied constructs. They described and discussed the EOR literature from the perspective of how it evolved, and the distinctiveness and overlap of the various research strands within this domain. The authors particularly highlighted the importance of context, and the need for researchers to develop models in which the context is specified and studied in relation to the EOR. Another point raised by the authors was the value in linking individuallevel concepts such as psychological contracts and group-level concepts such as the employment relationship. Subsequently, Coyle-Shapiro and Shore (2007) critiqued the EOR literature in a more fine-grained manner, and suggested areas that needed greater conceptual development in EOR models. First, they raised questions about the assumption that the employee has a relationship with the organization. Specifically, who is the party representing the organization? Is it the immediate supervisor, top management, or even past supervisors? Likewise, the manager is assumed to be representing the organization as an agent when interacting with employees. However, the manager may not always act in the best interest of the organization, and may in fact be self-interested or more concerned about the employee than about the organization itself. Second, they pointed out that the norm of reciprocity and exchange may not be the only basis for the EOR. Individual differences can impact employee interpretations of treatment they receive from the organization and from their manager. Third, an assumption in EOR research is that the inducements offered by organizations are valued by employees, leading to reciprocation of beneficial treatment. The authors argue that this assumption needs more evaluation, especially given that employees may differentially value inducements such as job security and career development (Smola & Sutton, 2002). Shore, Coyle-Shapiro, Chen, and Tetrick (2009) developed a typology of EOR concepts in which they defined content, mixed, and process models. The goal was to differentiate among the plethora of EOR concepts that were evolving in the literature. They pointed out that most empirical literature has focused on content models, that is, what is exchanged between employee and employer. In content models the contributions of employee and employer are separate, such as in the psychological contract, in which employees reciprocate the obligations fulfilled by the employer (e.g., job security) with the fulfillment of their obligations in return (e.g., job performance). The quantity and type of resources exchanged are emphasized in content models. In contrast ‘process models assume an ongoing and interdependent exchange in which both parties make contributions in a

non-sequential manner that reflects the tenor of the relationship’ (p. 292). Social and economic exchange (Shore et al., 2006) is an example of a process model, reflecting the relationship itself in which the type of exchange between employee and employer is embedded. Thus, the quality of the exchange relationship is emphasized in process models. Finally, mixed models combine the contributions of employee and employer to reflect the type of relationship. An example of a mixed model is the employment relationship (ER; Tsui et al., 1997), in which the inducements of the employer and contributions of the employee are combined into four ER types. In the present review we include a content model (psychological contract), a mixed model (ER), and a process model (social and economic exchange). In light of the increasingly greater clarity and distinctiveness of constructs within the EOR literature, and growth in studies of the EOR worldwide, Shore, Coyle-Shapiro, and Tetrick’s edited book (2012) sought to expand the EOR literature through greater integration with other literatures. A number of themes emerged from the contributing chapters that suggest areas of needed development and empirical examination in the EOR literature going forward. First, even though social exchange theory and the norm of reciprocity are broadly supported by research evidence and are seen as indispensable to EOR research, many authors highlighted the need to expand the conceptual foundation to explain the EOR. In particular, social identity theory could provide a basis for both positive and negative EORs. Another theme was the prominence of cross-level and trickledown models of the EOR. The view that the EOR is occurring at multiple linked levels is becoming increasingly common. Finally, attention to context and the recognition of ways that the workforce and the world of work are changing with a need to examine the EOR in view of these changes. In sum, the development and evolution of the EOR literature underlines the importance of relational models for understanding employee participation in organizations. Human relationships in their simplest forms involve exchange between two individual, human partners. In the more complex environment of organizations, the relational model of exchange is still used by the human beings who are employees, and many of the same expectations for inducements and contributions are applied whether the parties involved are individuals, groups, or organizations. The study of what makes a high quality relationship has been applied within and even across organizations via the lens of exchange. As noted by Shore et al. (2009), content, process, and mixed models of exchange all have enhanced our understanding of relationships

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in organizations. Each type of model has evolved in its own way and provides a lens through which to examine exchange relationships. In recent years, increasing numbers of studies have sought also to integrate concepts across varying models in the EOR (e.g., the employment relationship and psychological contracts; Tsui & Wang, 2002) as will become evident in subsequent sections.

EOR: PSYCHOLOGICAL CONTRACTS, EMPLOYMENT RELATIONSHIP, AND SOCIAL AND ECONOMIC EXCHANGE We begin our review of our three EOR frameworks with psychological contracts. Unlike the employment relationship and social and economic exchange, there are existing reviews and metaanalyses of psychological contract research that we use as the primary basis of our review. For the employment relationship and social and economic exchange, we review the conceptual and empirical studies in these domains. These latter two reviews provide a synthesis of existing literature.

Psychological Contracts Rousseau’s (1989) seminal paper reinvigorated interest in the employee–employer exchange relationship through the lens of psychological contracts. Therefore, it is not surprising that the first review of this domain was completed in 1998 with subsequent reviews following in 2004 and 2009. Table 22.2 outlines the issues and research priorities identified by these reviews. Anderson and Schalk (1998) identify a number of issues with the psychological contract: (a) lack of consensus on the definition, (b) distinction between transactional and relational dimensions, and (c) how the changing nature of the psychological contract needs to be assessed. The authors highlight several future research directions. First, they suggest that there doesn’t need to be one single definition of the psychological contract so researchers need to be clear on the definition that they are adopting; diversity in definitions could lead to more fruitful research efforts. Second, greater development is needed in the methodologies employed to investigate psychological contracts. Third, does the psychological contract add value beyond existing theories? Finally, how to capture the dynamic nature of psychological contracts and the impact of changes in psychological contracts merits examination.

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Taylor and Tekleab’s (2004) review of psychological contract research focuses on changes in the conceptualization of the psychological contract (pre- and post-1989); identifies troublesome issues and sets priorities for future research. Changes in the conceptualization of the psychological contract from pre-Rousseau to Rousseau’s (1989) seminal work include a shift from the employee–organization exchanges to the perceptual perspective of the employee and; a shift from expectations to promissory-based obligations. The authors outline several troublesome issues with psychological contracts: (a) common theoretical foundation in social exchange raises concerns about the discriminant validity of psychological contracts and perceived organizational support and to a lesser extent leader−member exchange; (b) the distinction between transactional and relational contracts and the instability and crossover of items capturing both; (c) the interconnectedness between contract breach and violation and which has greater predictive power; (d) psychological contracts as mental models and the role of schema in contract formation and change. Based on their review, Taylor and Tekleab (2004) outline a number of research priorities: first and foremost, the inclusion of the employer’s perspective into psychological contract research in order to capture the interdependent interactions and reactions of both parties; second, the development of comprehensive models that go beyond contract breach to provide a process view of antecedents, mediators and consequences; third, the consequences of contract violation and over-­ fulfillment; fourth, greater attention to the process of contract development; fifth, improvement in measurement of psychological contracts and the use of varying methodologies. Subsequently, Conway and Briner (2009) conducted a comprehensive critical review of psychological contract research and highlight challenges facing this strand of research. Their review highlights a number of issues organized around conceptual ambiguities and confusion surrounding the definition of the psychological contract, lack of empirical studies on the antecedents of contract breach, methodological concerns with existing empirical work, and unaddressed challenges. In the interests of brevity, we will outline some of the key issues rather than providing a comprehensive review of their comprehensive review. First, the definition of the psychological contract comes under scrutiny. The authors argue that there is disagreement in terms of what beliefs constitute the psychological contract with researchers using expectations, obligations, and promises. Second, Conway and Briner (2009) point to the ambiguity surrounding the notion of

How to capture changing nature of PC

Contract formation and change

Development and change in PC

Measurement and methodologies

Research priorities Added value/discriminant validity

Conceptualization

Methodological issues

Not addressed

Contract breach and violation

Shift from focus on exchange to perceptual perspective; from expectations to promissory-based obligations Relationship between PC and other constructs such as POS and LMX that draw upon social exchange theory Number and distinctiveness of PC dimensions; features of PC and balance in contract terms Distinctiveness of contract breach and violation

Taylor & Tekleab (2004)

Dynamics of PC change: revision or abandonment

The extent to which PC adds value beyond existing constructs Diversity of methodologies needed

Improvement in measurement and adoption of varying methodologies Greater attention to the process of contract development

Not addressed

Role of schema in psychological contract formation Not addressed ‘Methodological rut’ of single source cross sectional surveys Researchers’ clarity of meaning of PC and Inclusion of the employer’s perspective different definitions acceptable

Distinction between transactional and relational contracts

Lack of consensus on definition: perceptions, expectations, beliefs, promises, and obligations Not addressed

Anderson & Schalk (1998)

Dimensions of PC

Theoretical basis

Research issues identified Conceptualization

Issues and research priorities identified

Table 22.2 Issues and research priorities identified by prior reviews of psychological contracts

Greater robustness needed in qualitative and quantitative approaches Discusses how PCs are implicitly negotiated, how psychological contracting is captured, and how the unfolding nature and ongoingness is captured

Differentiation of social exchange and PC

Differentiation of breach and related constructs. Different ways of breaching psychological contracts. Whether breach and fulfillment are on a single continuum Disagreement in the extent to which organizations can influence PCs Cross sectional studies are limited in advancing understanding Distinction between promises, obligations, needs, and expectations; and who the other party is

Discusses beliefs that constitute the PC. Mutuality in the exchange and explicating who is the other party to the exchange Under theorizing of explanatory mechanisms linking breach to outcomes beyond social exchange and reciprocity Problematic nature of the measurement of relational and transactional dimensions

Conway & Briner (2009)

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exchange agreement on two levels: the extent to which there is specificity in the exchange (i.e. employee performance in exchange for promotion); and what agreement means which could range from strong mutuality in that both parties have agreement as to the terms and specific exchange linkages to weak mutuality that captures agreement that a contract exists. Third, who is the other party to the exchange? The authors argue that the assumption made by researchers appears contradictory wherein employees anthropomorphize the organization and treat it as a single entity yet at the same time, the organization cannot hold a psychological contract with its employees though managers, as organizational representatives can. Finally, Conway and Briner (2009) highlight disagreement in the extent to which psychological contracts are shaped by organizations or are influenced by a range of factors. The latter is manifested in earlier work on psychological contracts (e.g., Schein, 1980) whereas the former is epitomized in the work of Rousseau (2005). Regarding existing empirical work, Conway and Briner (2009) highlight the problematic nature of measurement of transactional and relational contracts; under theorizing of the mechanisms linking content of psychological contract to outcomes and a lack of exploration of additional mechanisms that explain the relationship between psychological contract breach and outcomes such as trust, rebalancing obligations and inequity, deprivation of rewards, thwarting or impeding goals or signaling a lack of value of employee contributions. The authors outline challenges to the concept of breach and ask, whether breach differs from related constructs? Are there different ways of breaking a promise? And imprecision in how breach is measured. Overall, they conclude that ‘breach has much weaker links with work behavior than work attitudes’ (p. 105) and ‘the common approach of using surveys is fundamentally inappropriate to examining psychological contracts’ (p. 107). Conway and Briner (2009) highlight five pressing challenges facing the concept of psychological contracts that should be research priorities: (a) the distinction between promises, obligations, needs, and expectations; (b) the lack of precision relating to links between what employers offer and what employees contribute – consistent with social exchange theory which operates at a general and unspecific level which raises a question as to what differentiates social exchange from psychological contracts particularly if one views the latter as mental models; (c) who is the other party? Can an employee have a psychological contract with an organization if the organization itself cannot hold a psychological contract with employees?; (d) how are psychological contracts implicitly negotiated? – how does contract drift occur while remaining within

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the spirit of the psychological contract?; (e) how is the process of psychological contracting captured? How is the unfolding nature and the ongoingness of psychological contracts captured? In concluding, the authors argue that the concept of the psychological contract is ill defined, the methodologies employed (particularly cross sectional studies) do little to enhance understanding and finally, with the exception of breach, there is little theory underpinning the construct.

Meta-analyses of psychological contracts

A meta-analysis conducted by Zhao, Wayne, Glibkowski, and Bravo (2007) of 51 empirical studies on psychological contract breach found that breach was positively related to feelings of violation and mistrust, negatively related to job satisfaction, turnover intentions, organizational commitment, organizational citizenship behavior (OCB), and in-role performance but not actual turnover. The type of breach measure was important with the global measure (versus a composite measure) having higher correlations with violation, job satisfaction, organizational commitment, and OCB. The authors argued that Affective Event Theory (AET) provides a more comprehensive theoretical basis to understanding the effects of contract breach on outcomes through affective reactions (violation and mistrust). An interesting finding of their meta-analysis was that the effect of mistrust is greater than perceived violation suggesting that breach triggers self-oriented emotions such as anger as well as other oriented emotional reactions in terms of distrust. Subsequent empirical studies have corroborated the findings of the meta-analysis in terms of the relationship between the psychological contract and individual performance. In a sample of sales employees in the Philippines, Restubog, Bordia, Tang, and Krebs (2010) found that the negative relationship between perceived contract breach and supervisory rated in-role performance was stronger when the employee has a high quality leader–member exchange. When employees had a high quality relationship with their supervisor, they decreased their performance as their experienced level of contract breach increased. Chen, Tsui, and Zhong (2008) found that contract breach was negatively related to objective performance (measured by salary) in a sample of Chinese employees and this relationship was weaker for employees who were high on traditionality. Conway and Coyle-Shapiro (2012) found support for the relationship between psychological contract fulfillment and objective performance measures amongst sales advisors in the United

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Kingdom. Employee performance at time 1 was positively related to psychological contract fulfillment at time 2; psychological contract fulfillment at time 2 was positively related to employee performance at time 3; and employee performance at time 3 was positively related to psychological contract fulfillment at time 4. Overall, these studies provide additional evidence linking employee perceptions of their psychological contract (breach or fulfillment) to important organizationally desired outcomes such as performance. A complementary meta-analysis by Robbins, Ford, and Tetrick (2012) examined the relationship between psychological contract breach, organizational justice, and employee health. The authors used the following indicators to reflect psychological contract breach: violation, breach, psychological contract, broken promises, feeling betrayed, organizational fulfillment of obligations, breach history, and multiplicative violation. The findings suggest that although distributive justice was most strongly associated with mental health problems, psychological contract breach was the second strongest; psychological contract breach was most predictive of burnout and negative emotional state. Psychological contract breach explained additional variance beyond distributive, procedural, and interactional justice in burnout, stress, and negative emotions. However, none of the three justices explained additional variance in burnout, stress, and negative emotions beyond psychological contract breach. The meta-analysis also revealed that although the psychological contract did not explain additional variance in health problems and mental health beyond the effects of justice, when the psychological contract breach was entered in the first step, it retained its significant effect on health problems (in addition to procedural justice) and mental health (in addition to distributive justice). The authors concluded that ‘this study provides evidence that psychological contract breach is not redundant with measures of organizational justice in the association with health outcomes’ (p. 247). In sum, the three reviews of psychological contracts share some common insights and priorities. All three highlight the lack of clarity of the definition of the psychological contract and take issue with what in essence is being captured by researchers. Second, given the emphasis of the conceptualization of the psychological contract as a reciprocal exchange, two reviews highlight the absence of the employer’s perspective in psychological contracts research. The reviews echo similar messages in terms of research priorities – definitional issues, development and change in psychological contracts, and finally, the need for greater variety and sophistication in

the use of qualitative and quantitative methodologies. Likewise, the two meta-analyses support the contention that psychological contract breach carries with it negative implications for employee emotions, attitudes, behavior, and well-being. Although studies linking psychological contract breach/fulfillment to individual performance are in the minority, even when performance is measured objectively, the findings support the view that psychological contract breach (fulfillment) have negative (positive) consequences for individual performance. However, neither meta-analysis found support for the relationship between psychological contract breach and turnover or psychological contract breach and absences. Overall, evidence across both conceptual and empirical summaries of the psychological contract literature suggest despite the plethora of increasingly thoughtful and complex research studies, that many questions and issues remain unanswered and unresolved.

Emerging and developing themes

A number of emerging and developing themes in psychological contract research deserve attention as these highlight how research has deepened our understanding of the conceptualization of psychological contracts, the effects of psychological contracts, influences on psychological contracts, and the applicability of psychological contracts.

Role of promises and ideology

The reviews of the psychological contract literature (Anderson & Schalk, 1998; Taylor & Tekleab, 2004; Conway & Briner, 2009) highlighted a number of conceptual issues that researchers have begun to address. These are: (a) a lack of clarity as to what the psychological contract is capturing; (b) the consequences of over-fulfillment; and (c) the dimensionality of psychological contracts. A significant body of empirical research has examined the consequences of contract breach that captures a discrepancy between what the employer promised to provide and what the employer actually delivered. However, because the effects of promises and delivered inducements are confounded in the measurement of contract breach, this raises a question as to whether the centrality of promises as is portrayed in the conceptualization of the psychological contract holds promise empirically. Montes and Zweig (2009) explored whether promises matter and found that the role of promises explained a negligible amount of variance in breach perceptions suggesting that what really matters is what employees receive. In a follow-up study, Montes and Zweig (2014, SIOP) examined when promises matter and specifically for whom do they matter. They found

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that individuals who are neurotic, disagreeable, and weak on conscientiousness are more likely to pay attention to promises because they are salient. For these individuals promises matter because they serve to reduce anxiety, promote reciprocity, and minimize the expression of disagreeableness. Another way of interpreting these findings is that promises may matter most when the promise is salient either due to individual dispositions or the promise itself. A second issue relating to contract breach is the idea that contract breach encapsulates both underfulfillment (which the overwhelming majority of empirical research focuses on) as well as overfulfillment (Lambert, Edwards, & Cable, 2003; Montes & Irving, 2008). The former deficiency leads to negative outcomes and the latter fulfillment leads to positive outcomes. Lamberet et al. (2003) present an expanded view of psychological contract breach that takes into account the combined effect of promised and delivered inducements while capturing them separately. Their findings suggest that the effects of breach and fulfillment vary contingent upon whether breach signified deficiency or excess and; whether the level of fulfillment was low or high. For example, excess levels of pay, recognition, and relationships were associated with increased job satisfaction whereas excess levels of variety, skill development, and career training was associated with decreased satisfaction. Further, satisfaction is greater at higher levels of delivered inducements and the authors challenge the idea that promises and inducements contribute equally to breach and fulfillment. Montes and Irving’s (2008) findings support the traditional conceptualization of breach, employees responded negatively to under-fulfillment and positively to over-fulfillment when the promises are transactional in nature. However, for relational promises, the picture is more complex. Feelings of violation increase when inducements diverge in either direction from promised inducements and violation was most felt when there was under-fulfillment such that employees were promised a lot of relational inducements but received very little. These findings suggest that transactional and relational dimensions do not trigger the same reactions to contract breach. Do transactional and relational dimensions encapsulate all of the promises that might be made in the employee–organization relationship? Although these two dimensions have received a lot of attention, recent developments suggest that ideological promises may also be important – as Thompson and Bunderson (2003) note ‘the espousal of a cause can present a distinct inducement to elicit employee contributions and commitment’ (p. 571). Bingham (2005) developed and validated a measure of ideological fulfillment of

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psychological contracts and explained additional variance in OCB beyond relational fulfillment. Bal and Vink (2011) found that ideological fulfillment explained additional variance beyond other facets of psychological contract fulfillment in explaining employee obligations to the employer. In a sample of nonprofit employees, Vantilborgh, Bidee, Pepermans, Willems, Huybrechts, and Jegers (2012) found that under-fulfillment of ideological psychological contracts led to an increase in work effort whereas under-­ fulfillment of relational psychological contracts led to a decrease in work effort suggesting that ideological currency operates differently by not adhering to the ‘tit for tat’ underpinning the norm of reciprocity. These developments signify progress that undoubtedly will inform future research. Promises are integral to psychological contract theory and examining when they matter and for whom they matter deserves greater attention. The idea that not all over-fulfillment of promises will lead to positive outcomes is an intriguing one and carries with it important practical implications for organizations. Finally, the idea that employees develop psychological contracts that include ideological currency is consistent with Blau’s (1964) idea that ideological rewards are effective inducements because ‘helping to advance cherished ideals is intrinsically rewarding’ (p. 239) and may increase the explanatory power of psychological contracts in predicting employee attitudes and behaviors.

Trickle-down and cross-level effects

A recent emerging strand of research examines interdependency in psychological contracts and in particular, the role of the supervisor’s psychological contract in affecting employees’ psychological contracts. Bordia, Restubog, Bordia, and Tang (2010) examine the trickle-down effect of psychological contract breach in the Philippines. Using three independent samples, the authors find support for a trickle-down effect of supervisor and subordinate perceptions of psychological contract breach on customer service. When supervisors perceive that the organization has breached their psychological contract, this is positively related to their subordinates’ perception of contract breach by their supervisor. The explanation for this relationship is that when supervisors experience psychological contract breach by their organization, this may render the supervisor unable to provide mentoring or support to his/her subordinates and hence triggering subordinates’ perception of contract breach by their supervisor. In response to contract breach by the supervisor, subordinates reduce their citizenship toward customers with a consequential effect on customer satisfaction.

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Subordinate citizenship behavior toward customers mediated the effects of perceptions of supervisor contract beach on customer satisfaction. This empirical study demonstrates the interdependency in the operation of psychological contracts where a breach occurring higher in the organizational hierarchy has a knock-on effect on employees’ perception of supervisor contract breach that in turn affects customer satisfaction. A subsequent study of federal government teams and their team leaders in Belgium (De Vos, Tekleab, de Jong, & Coyle-Shapiro, 2015) replicates the findings of Bordia et  al. (2010). The authors find that a team leader’s evaluation of their psychological contract fulfillment is positively related to their commitment to their team which is positively related to individual team members’ psychological contract fulfillment. While Bordia et al. (2010) found a trickle-down negative effect of contract breach, De Vos et al. found a positive trickle-down effect of psychological contract fulfillment – both studies support the contention that supervisors’ psychological contracts matter in understanding how employees’ evaluate their psychological contract. De Vos et al. also examine how psychological contract fulfillment climate (i.e. shared team evaluation of psychological contract fulfillment) was related to individual team members’ psychological contract contributions. They found that team psychological contract fulfillment level (but not strength – low variability in team members’ perceptions of psychological contract fulfillment by the organization) was positively related to team members’ psychological contract contributions. Furthermore, psychological contract climate level moderated the relationship between employees’ perception of psychological contract fulfillment and their contributions to the organization such that the relationship is stronger when there is a high level of contract fulfillment climate. Similarly, employees’ perception of psychological contract fulfillment is strongly related to their contributions when there is high climate strength. These two studies highlight the interdependency and influence of psychological contracts at different levels in organizations. Not only is the supervisor’s psychological contract influential in shaping employees’ perceptions of their psychological contract but as Tekleab and De Vos demonstrate, the idea that team members’ shared evaluations of contract fulfillment moderates the relationship between an individual’s psychological contract fulfillment and their contributions to the organization. This suggests that the team context is important in understanding how psychological contracts work at an individual level.

Diversity

Another developing theme in psychological contracts recently is the role of diversity and in particular the role of age, tenure, and gender on the functioning and impact of psychological contracts. The role of age in psychological contract research is usually addressed by socio-emotional selectivity theory, a motivational theory that claims that the selection and pursuit of social goals depends on how open-ended or limited time is perceived to be in certain lifespan stages (Carstensen, Isaacowitz, & Charles, 1999). The impact of age on the psychological contract can be understood from a range of perspectives. First, there is disparity in the interpretation and perception of psychological contracts between younger and older employees. For older employees, as they are approaching the end of their working life they will have a tendency to see their current relationship with the organization as the final one, whereas younger employees are more likely to think there is always a chance to form a new relationship with another organization (Hedge, Borman, & Lammlein, 2006). As a result, older employees have more positive interpretations toward their current organization (Carstensen et  al., 1999). Second, with accumulated working experience, the content of psychological contracts of older employees would be more accurate in terms of their expectations (Carstensen et al., 1999). Therefore, the perceived intensity of psychological contract breach of older employees tends to be lower than that of younger employees (Bal, De Lange, Jansen, & Van Der Velde, 2008). Finally, how employees react to psychological contract breach depends on age. Most of the scholars investigate this using the lens of emotion regulation suggesting that older employees can regulate psychological contract violation better than younger employees (Gross, 2001). Ng and Feldman (2009) note that older workers are more likely to process positive emotional information more deeply than negative emotional information and hence be more tolerant of deviations from expectations. Furthermore, Kafetsios (2004) demonstrated that emotional intelligence can gradually increase throughout life. Therefore, the negative reactions to psychological contract breach in terms of attitudes and behavior is mitigated by age (Bal & Smit, 2012). A recent meta-analysis on age and psychological contracts (Bal et al., 2008) showed that age moderates the relationship between contract breach and job attitudes. Compared to younger employees, older employees respond less negatively to contract breach as evidenced by significantly lower correlations between breach and job attitudes, including trust and organizational commitment (Bal et al., 2008). Similarly, there is evidence also

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that affective reactions to contract breach are also attenuated by age. Bal and Smit (2012) found that the negative relationship between contract breach and negative affect is stronger for younger employees than for older employees. Age-related factors, such as future time perspective and regulatory focus, were also found to significantly moderate the relationship between contract breach and work motivation. Researchers found that open-time perspective amplifies, and prevention focus mitigates the negative impact of breach on intrinsic motivation (de Lange, Bal, Van der Heijden, de Jong, & Schaufeli, 2011). Research has demonstrated that age and tenure are independent; although they moderate the relationship between the psychological contract and job satisfaction, they do so in opposite directions (Bal et  al., 2008). Tenure has not been as extensively studied as age and when it is researched, tenure is treated as a control variable with a few exceptions (e.g., Bal, De Cooman, & Mol, 2013; Conway & Coyle-Shapiro, 2012). These studies postulated that longer tenure would dampen the strength of the relationship between psychological fulfillment and work outcomes but no significant moderation was found in the relationship between psychological contracts and employee performance (Bal et al., 2013; Conway & CoyleShapiro, 2012). Turning to gender differences, the main focus of the research has been on exploring whether gender affects the content of the psychological contract. From the perspective of social cognition, men and women confront different expectations and stereotypes, so may face unconscious biased challenges at the workplace (Hoobler, Wayne, & Lemmon, 2009). Therefore, men and women could develop different perceptions of the psychological contract. Tallman and Bruning (2008) found that women have higher levels of perceived obligations of both the employee and the organization than men; women hold stronger psychological contracts regarding the organization’s obligation to provide good pay, growth opportunity, support in the job, and support in employees’ personal needs and finally, women hold stronger psychological contracts regarding employee obligations to the organization in terms of job and organizational commitment (Tallman & Bruning, 2008). Further, women place greater emphasis on the importance and fulfillment of the psychological contract. One study with nearly 5,000 employees revealed a significant gender difference – woman reported higher importance in all the dimensions in psychological contracts and lower contract fulfillment from the organization (Willem, De Vos, & Buelens, 2010). The moderating role of gender in the contract breach–outcome relationship

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is mixed. For women, contract breach relating to promotion opportunities and work–family balance were significant predictors of turnover intentions, whereas contract breach relating to the clarity of the job description is the better predictor of turnover for men (Blomme, van Rheede, & Tromp, 2010). However, the moderation effect of gender on the breach–turnover relationship was not supported in another study conducted in Taiwan (Chin & Hung, 2013), suggesting that other complex sociocultural factors may be important to consider.

Psychological contracts in non-EOR relationships

Although the psychological contract was introduced and developed in the context of the employee–organization relationship, it has subsequently been applied to a variety of relationships such as students and their professor (Bordia, Hobman, Restubog, & Bordia, 2010; Taras & Steel, 2007), mentoring relationships (Haggard & Turban, 2012), supplier–distributor relationships (Kingshott & Pecotich, 2007) and customer–­ supplier relationships (Koh, Ang, & Straub, 2004). Taras and Steel (2007) employed a deception simulation to breach students’ psychological contract and gave students the opportunity to engage in collective action. Two classes contained a confederate who acted in the role of ‘union organizer’. Mobilization to engage in collective action took 6–8 minutes and 10 minutes without the presence of a confederate. Bordia et  al. (2010) found that students who experienced psychological contract breach by their advisor reported lower levels of satisfaction with the thesis project and lower psychological well-being. The negative effect of psychological contract breach on psychological well-being was stronger for highly conscientious students. Haggard and Turban (2012) adopted a psychological contract lens to examining obligations in protégé–mentor relationships. Both protégés and mentors reported mutual obligations – obligations they had toward the other and obligations from the other to them. When mentors were informal, they reported greater relational obligations to the protégé and similarly when protégés were informal, they reported greater relational obligations to their mentor. In contrast, when the mentoring relationship was also a supervisory one, mentors and protégés reported greater transactional obligations to and from each other. Although the authors demonstrated the applicability of mutual obligations to understanding the protégé–mentoring relationship, the study did not explore how dysfunctional mentoring relationships can develop through psychological contract breach and violation.

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These aforementioned studies have examined the psychological contract in relationships outside the employee–organization relationship and Koh, Ang, and Straub (2004) continue in this vein in their study of psychological contracts in IT outsourcing. Their findings demonstrate that perceived psychological contract fulfillment by the supplier explained 46% variance in customer perception of outsourcing success; perceived psychological contract fulfillment by the customer explained 38% variance in supplier perceptions of outsourcing success. Kingshott and Pecotich (2007) adopted a psychological contract perspective to examining supplier–distributor relationships and the findings are supportive of psychological contract theory – psychological contracts facilitate the development of trust which in turn enhances distributor commitment to the supplier relationship. Perceived psychological contract violations reduced distributor trust in the supplier. Taken together, these studies demonstrate the applicability of the psychological contract lens to understanding a variety of relationships outside the employee–organization relationship. Although the relational contexts are different, the findings are consistent in that contract breach carries with it negative consequences and contract fulfillment is associated with positive outcomes.

Concluding remarks on psychological contracts

The amassing evidence supports the negative effects of psychological contract breach on employee attitudes, behavior, and well-being across a variety of cultural contexts. The popularity of the psychological contract is evidenced in its application to non-employee–organization contexts and provides additional empirical support for the negative consequences of contract breach. Research continues to evolve that will undoubtedly address some of the conceptual thorny issues raised by the three psychological contract reviews. In particular, empirical evidence on the role of promises in psychological contract breach highlight nuances in the conceptualization of contract breach – excess or deficiency and their differential effects on outcomes. Furthermore, the inclusion of an ideological dimension in psychological contract research may increase our understanding of the consequences of psychological contracts for employees. The recent attention to trickle-down and cross- level effects in psychological contracts demonstrates recognition that psychological contracts are embedded in a number of intra-organizational contexts that serve to influence how individuals’ interpret and respond to the fulfillment or breach of their

own ‘deal’. This may help explain why there is a non-significant relationship between psychological contract breach and turnover, for example. The impact of diversity on psychological contracts merits additional research that moves beyond examining the impact of age and gender.

The Employment Relationship Building on the inducement–contribution model (March & Simon, 1958), Tsui et al. (1997) describe the Employment Relationship (ER) model as involving human resource practices indicating the employer’s investment in the employee (e.g., training that prepares employees for future jobs and career development) as inducements, and human resource practices focusing employee attention to the unit (e.g., rewards employees based on unit performance) as employer-expected employee contributions. By combining high and low levels of inducements and contributions, the authors developed four types of employment relationships that differ on two dimensions: the degree of balance/ imbalance in each party’s contributions and whether the focus of these contributions is economic or social. Specifically, the quasi-spot contract (also called job-focused) is a balanced economic exchange that occurs when the employer offers short-term purely economic inducements in return for highly specified outcomes. Mutual investment (also called organization-focused) is a balanced social exchange that arises when both parties offer an open-ended and long-term investment to each other. The underinvestment approach occurs when the employer expects open-ended commitment and long-term investment from employees in return for short-term economic inducements. The overinvestment approach is provided when the employer offers long-term investment in return for highly specified employee outcomes. An important distinguishing feature of the ER model is that it reflects the employer’s perspective, specifically how the manager views the exchange of inducements and contributions for employees either in particular jobs within the organization or for employees in the organization as a whole. In their original empirical study of US employees, Tsui et  al. (1997) found that a mutual investment employment relationship relative to the other three ERs was most positively associated with employee attitudes (affective commitment, perceived fairness, trust in coworkers) and performance (task performance, OCB, dependable continuance, frequency of absences) in a managerial sample. Tsui and her colleagues have written two conceptual papers since her 1997 study. First, Tsui

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and Wang (2002) compare the ER model developed by Tsui et  al. (1997) with psychological contracts in which the employee has beliefs about promises involving expected obligations between themselves and the employer. Specifically (pp. 105–6) ‘we define the employment relationship as the employer’s expectations of contributions desired from the employees and inducements the employer actually offers …. The relationship is usually defined at the job level.’ Unlike psychological contracts where the employee is typically the informant on their own perceptions of mutual obligations, the ER informant is typically a manager or human resource manager who describes the inducements and contributions that are expected for employees in particular jobs or in the organization as a whole. Tsui and Wang (2002) further argue that the ER should provide the chief input into the psychological contract, and that agreement between the employee and employer should lead to beneficial outcomes such as satisfaction, commitment, trust, performance, innovation, and cohesion. Second, Tsui and Wu (2005) argue persuasively that organizations would be best served by employing a mutual investment employment relationship, rather than the new employment relationship akin to underinvestment and quasi-spot contract ERs. They suggest several HR practices that would create the mutual investment ER with employees, including emphasizing the value of long-term commitment and loyalty with new employees, training and development preparing employees for future job responsibilities as a signal of long-term investment, performance criteria focused on broad contributions to the organization, and compensation and benefits that emphasize long-term contributions rather than short-term economic gain.

Empirical studies since 1997

The literature has continued to evolve since Tsui et al.’s (1997) original empirical work in the following ways: an examination of effects of the type of ER on firm performance, mediators and moderators between ERs and both individual and organizational outcomes, and studies of the employment relationship in China, Spain, Belgium, and the United States. Below, we first review papers conducted in each of the countries, and following, we provide a summation of what has been found in the literature and needed future research.

Studies of the ER in China

Tsui, Wang, and Zhang (2002) examined the ER between middle managers and their organizations in China, and compared state-owned and nonstate-owned firms. In this study, the authors

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expanded upon Tsui et  al.’s (1997) original inducements and contributions. Specifically, they studied two forms of expected contributions including professional managerial duties and loyalty, and two forms of inducement including developmental rewards and materialistic rewards. In comparing the firms based on ownership structure, the authors found that non-state-owned firms (foreign firms and domestic firms) expected more managerial professional duties from middle managers, but only the foreign firms offered more developmental rewards. For state-owned firms, more loyalty was expected, but fewer developmental rewards were offered. This study suggests that in China, ownership structure is related to the type of ER provided to employees. Wang, Tsui, Zhang, and Ma’s (2003) study replicated the 4 types of ERs found in Tsui et al.’s original study via cluster analysis. They found that the mutual investment employment relationship generally was most strongly and positively related to firm performance. Two contingency factors were found to affect this relationship, including business strategy and ownership structure (state-owned versus privately owned). Generally, the mutual investment and overinvestment approaches were found to be superior to the underinvestment or quasispot contract ERs in enhancing firm performance. However, evidence from the moderator effects showed a somewhat more complex set of relationships. Specifically, they found the relationship between the mutual investment ER and firm performance was weaker for firms using the prospector strategy (the firm monitors external environments and continually changes products and services to fit customer needs). The defender strategy (the firm emphasizes specialization and use of standardized technical processes) did not moderate relations between the ER and firm performance. Ownership structure also moderated the relationship between the type of ER and firm performance. Both stateowned enterprises and foreign-owned firms performed best with the mutual investment ER. However, unlike the other ownership structures, the results suggested the underinvestment ER showed higher firm performance for the domestic privately owned firm. In sum, while the mutual investment ER showed the consistently most positive effect on firm performance, strategy and ownership structure moderated these effects. Zhang, Tsui, Song, Li, and Jia (2008) examined the impact of the ER and supervisory support on trust in the organization in a sample of Chinese middle managers. They replicated Tsui et al.’s original four-type ER structure via cluster analysis, and found a reinforcing effect such that when supervisor support was high, a mutual investment ER enhanced trust in the organization. However, when supervisor

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support was low, the ER did not encourage trust. This suggests that both the organization and the supervisor need to show that they are trustworthy, as indicated via a mutual investment ER and high supervisory support, in order to create trust in the organization. Zhang et al. also suggested that norms focusing on the collective in China may help to explain these results and that they may not replicate in more individualistic cultures like the United States. Tsui and her colleagues next conducted studies using cross-level models to determine the underlying psychological processes that determine how the ER influences employee contributions. First, Song, Tsui, and Law (2009) examined two types of ERs (mutual investment and quasi-spot contract) and two other firm-level antecedents (executive leadership style and the organization’s culture) on employee task performance and affective commitment. They also examined individual-level social and economic exchange perceptions as mediators of these relationships. They found that the relationship between the firm-level antecedents of the CEO’s transformational leadership, the mutual investment employment relationship, and an integrative culture and individual-level task performance and affective commitment were partially and positively mediated by the employee’s social exchange. In contrast, economic exchange partially mediated the effects of a hierarchical culture on affective commitment, task performance, and OCB, and fully mediated the effects of a quasi-spot contract ER on affective commitment and OCB. The effects of economic exchange on all outcomes were damaging, specifically contributing to lower levels of commitment, performance, and OCB. The mutual investment ER was positively associated with social exchange and the quasi-spot contract positively related to economic exchange. This study suggests that the ER sets the stage for perceptions of social and economic exchanges, which in turn influence individual-level outcomes. A second cross-level study by Hom and his colleagues (Hom, Tsui, Wu, Lee, Zhang, Fu, & Li, 2009) replicated Tsui et al.’s four ER types, and then compared the mutual investment and overinvestment ERs with the quasi-spot contract and underinvestment ERs in two samples of Chinese managers. They argued that even though social exchange processes provide a basis for examining the effects of ERs on employee attitudes and intentions that another process, job embeddedness, may also serve to explain the effects of the ER on employees. Job embeddedness includes sacrifice (firm-specific skills involving sunk costs), fit (firm-specific selection and training), and links (connections to people in the firm and community). They found the mutual investment ER was associated with

both social exchange and job embeddedness, but the overinvestment ER was only related to social exchange. Interestingly, both social exchange and job embeddedness served as mediators in cross sectional designs with social exchange showing stronger effects on affective commitment and turnover intentions, but job embeddedness had a more enduring mediating effect on employees in a longitudinal design. Overall, these studies suggest that the ER has unique effects on employee attitudes and intentions relative to other firm-level antecedents (culture and executive leadership) and that several psychological processes (social exchange, economic exchange, and job embeddedness) explain the effects of firm-level antecedents on individual-level outcomes. Jia, Shaw, Tsui, and Park (2013) examined the impact of ERs on team creativity in high technology organizations. In their study, they treated inducements and contributions as separate components (rather than combining them into four types as in Tsui et  al.’s (1997) original study). They applied a social-structural model to incorporate the role of team member work-related communication density as a mediator and task complexity as a moderator of the relationship that the ER has with team creativity. Specifically, they argue that high expected contributions motivate team members to develop dense communication networks, while high levels of inducements provide the proficiencies and opportunities to do so. The authors found support for their prediction that the mutual investment ER would be the most effective ER in soliciting team creativity and that this relationship was mediated by communication density in the team. Furthermore, the highest creativity was shown when the task complexity was also high. They concluded that the benefits of mutual investment relationships consisting of high inducement levels and broad and open-ended contributions are more likely when team tasks are highly complex. Jia et al. also argued that their study provides additional evidence that social exchange is not the only mechanism through which the ER contributes to team creativity, in light of the mediating effects for communication density. Zhang, Song, Tsui, and Fu (2014) examined firm-level ERs of middle managers in China. They determined whether traditionality, a valueorientation regarding submission to authority and endorsement of hierarchical role relationships, moderates relations that the ER has on psychological empowerment, performance, and commitment. The authors did not combine inducements and contributions into the four types of ERs that Tsui et  al. (1997) first developed, but left these two components of the ER separate to determine if they were influenced differently by traditionality,

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which is an element of Chinese culture. Consistent with separating the two components, the authors argue that (p. 811) ‘our theoretical model presents dual pathways. One is social exchange and extrinsic motivation for offered inducements; the other is social learning and intrinsic motivation through psychological empowerment for expected contributions.’ They found that the relationship between offered inducements and affective commitment was highest when traditionality was high whereas the relationship between expected contributions and affective commitment was highest when traditionality was low. Expected contribution and job performance was also moderated by traditionality such that when contributions were expected to be low, that job performance was highest. Traditionality also negatively moderated empowerment effects on performance and commitment. These results suggest that traditionality operates differently in relation to inducements and contributions such that those with high traditionality appear to respond positively when offered inducements are high but respond negatively when expected contributions are high. This pattern of results suggests that the mutual investment model may not be universally preferred, in light of the results pertaining to traditionality.

Studies of the ER in Spain

Two studies were conducted by teams of researchers studying the ER in Spanish firms that showed the effects of the ER on learning and innovation. First, Lopez-Cabrales, Valle, and Galan (2011) found they were only able to replicate the mutual investment and quasi-spot contract ERs in their sample using cluster analysis, but not the overinvestment or underinvestment ERs. They showed that mutual investment ERs were more effective than quasi-spot contract ERs in facilitating functional flexibility, and exploitive and exploratory learning. This suggests the importance of the ER for creating an environment in which employees are flexible and able to learn, both key elements of organizational success in a quickly changing environment. Second, in a study of innovation, BornayBarrachina, De la Rosa-Navarro, Lopez-Cabrales, and Valle-Cabrera (2012) examined the influence of the ER on innovation. They were able to replicate only three of the four ER types shown in Tsui et  al.’s original study since their cluster analysis showed no evidence of the overinvestment ER. They found that human capital fully mediated the relationship between the mutual investment ER and innovation. A different set of relationships were found for the underinvestment ER (low inducements, high contributions) where human

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capital served to moderate the relationship with firm innovation. Specifically, when underinvestment was high and human capital was high, the greatest levels of innovation occurred. The authors suggest that this likely is effective when it is a temporary ER, but over the long term mutual investment ERs are most effective.

Studies of the ER in Belgium

Audenaert’s (2014) dissertation focused on joblevel ERs among government employees in Belgium. In a departure from previous studies, employees worked in the same organization, but in different jobs. ERs were created via aggregation among employees within each job that were then cluster analyzed. She found support for three clusters, specifically, mutual investment, underinvestment, and quasi-spot contract. Audenaert compared the mediating effects of social exchange and psychological empowerment on the relationship that mutual investment, underinvestment, and quasispot contract ERs had on affective commitment and job performance. She found that social exchange mediated relations that mutual investment ER had with affective commitment and job performance. However, psychological empowerment mediated relations that both mutual investment and underinvestment had with affective commitment and job performance. Audenaert also found that social exchange had stronger mediating effects on affective commitment and empowerment had stronger mediating effects on job performance. Audenaert (2014) conducted a second study on ERs for her dissertation involving multi-level mediated moderation. Specifically, she examined the mediating effects of psychological empowerment on relations between LMX and affective well-being (job satisfaction and emotional exhaustion), and the moderating effects of the ER on relations that psychological empowerment had with LMX. She argued that consistency in the expectations and rewards of the organization and leader would have the most positive effects. Results supported the mediating effect of psychological empowerment, and showed that the underinvestment ER moderated relations that empowerment had with LMX. Specifically, a high quality relationship between an employee and manager (LMX) was not as effective when the job-level ER was one of underinvestment.

Studies of the ER in the US

Shaw, Dineen, Fang, and Vellella (2009) examined the ER in two US organizations. In their study, they treated inducements and contributions as separate components (rather than combining them into four types as in Tsui et  al.’s (1997)

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original study) to study quit rates of good performers and poor performers. They posed that high levels of expectation-enhancing practices (contributions) attenuate the negative relationship between low levels of HRM investments and inducements and good performer quit rates but exacerbate the relationship for poor performer quit rates. Results for the two samples in their study were not consistent. They found support for attenuation only in their first study, that is, quit rates for good performers were low for all levels of HRM investments and inducements as long as expected contributions were high but were high when expected contributions were low. In contrast, in the second sample exacerbation occurred for poor performers whereby quit rates for poor performers were low for all levels of HRM investments and inducements when expected contributions were low, but quit rates were high when expected contributions were high. In comparing their results with Tsui et al., 1997, Shaw et al. argue that for good performers, both mutual investment and underinvestment ERs lower quit rates.

Concluding remarks on the ER

In sum, the literature generally suggests the superiority of the mutual investment ER relative to the other three ER types for enhancing employee commitment, job satisfaction, learning, performance, innovation and creativity, and lowering emotional exhaustion and turnover. The positive effect of the mutual investment ER has been established cross-culturally, including the US, China, Spain, and Belgium. In addition, these studies found that the ER operates separately from other contextual effects on employee attitudes and behavior, including leadership, human capital, and organizational culture. This is supportive of the importance of the ER as a framework for understanding relationships in organizations. Lastly, these studies showed that social exchange, economic exchange, human capital, psychological empowerment, job-embeddedness, communication density, and task complexity all served to mediate or moderate ER effects on outcomes. This last set of findings suggest that ER effects are only partly explained by social and economic exchange, and that there are other important mechanisms for explaining the impact of the ER on a variety of individual, group, and organizational outcomes. While generally the effects of the mutual investment ER appear to be positive in all four national contexts in which the ER has been studied, it is notable that the four ER types have not been consistently replicated. Of particular note is that the balanced ER types appear to be much more common than the unbalanced types, especially

overinvestment ERs. Perhaps the infrequency of the overinvestment ER reflects the changing nature of employment relationships in a global economy in which some organizations are less able to be generous in their inducements while still having high employee expectations for contributions that serve to enhance firm performance. Going forward, there is still much more significant research to be done on the ER. While it is clear that the ER is meaningful at the job, occupational, team, and organizational levels, there are other groups to which individuals belong in organizations that may be examined with an ER lens. The varying group memberships that employees have (union, job category, profession, team, division, etc.) within an organization may each be critical to defining the ER provided, and have not yet been evaluated empirically. Nor has there been conceptual work to help explain these potential effects. Moreover, additional studies are needed that are conducted in other cultural contexts to more fully determine the role of culture in forming ERs and influencing their effects on employees. Zhang et al.’s study of traditionality suggests that more careful examination of cultural elements may lead to some revision of existing EOR models. Finally, an exploration of industry effects on the ER and more in-depth study of employee expectations of what is a suitable exchange in their role would greatly enhance the literature. For example, is there consistency in expectations for managers in the inducements and contributions making up the ER regardless of the industry, or does industry trump occupation? Many interesting avenues of research remain for better understanding the ER, and its influence on employee attitudes and contributions to the organization.

Social and Economic Exchange Shore et al.’s (2006) process model distinguished between two types of exchange relationships based on Blau’s (1964) theorizing about social and economic exchange. They used a number of criteria for making this distinction and for developing measures of social and economic exchange. These criteria included trust, investment, time orientation (long-term versus short-term), and socio-emotional versus financial focus of exchange. They defined these criteria and their relevance to social and economic exchange as follows. Trust is needed for building a social exchange relationship since it is unclear when and how beneficial treatment will be reciprocated as compared with economic exchange which is impersonal and with the return for benefits received in the exchange specified. Investment is essential for building social exchange

Exchange in the Employee–Organization Relationship

but is not an element of economic exchange relations. Social exchange has a long-term orientation since the timing of returns for beneficial treatment is unspecified and diffuse, whereas neither is the case for economic exchange. Last is the emphasis on socio-emotional elements (e.g., trust, commitment, give and take) for social exchange and financial and tangible elements (e.g., pay and benefits) for economic exchange. Shore et  al. found their measures of social and economic exchange distinct, but negatively correlated, and differentially related to both antecedents and outcomes. Additional empirical research supports this (O’Donnell, Jayawardana, & Jayakody, 2012).

Social and economic exchange and the EOR

Many of the studies of social and economic exchange have embedded these process constructs into existing models of the EOR. In particular, these forms of exchange are often treated as mediators reflecting underlying psychological mechanisms that explain relations that other EOR concepts have with outcomes. Below we summarize individual-level models of the EOR (POS, LMX, psychological contract breach, and i-deals), and then cross-level (ER) models of the EOR in which social and economic exchange have been embedded. Following the section on the EOR, we summarize studies of leadership and the organizational context that have focused on social exchange and economic exchange. Tables 22.3 to 22.6 summarize the literature by listing antecedents and outcomes of social and economic exchange.

POS

Shore et al.’s (2006) original study argued that high levels of POS provided a signal of investment and caring that was conducive to social exchange perceptions whereas low levels signaled to the employee that an economic exchange was present. They found that social exchange mediated relations that POS had with job performance, OCB, absence, and tardiness, consistent with their arguments (Tables 22.3 and 22.4). While economic exchange was negatively related to POS (Table 22.5), it did not mediate the effects of POS on outcomes. Another study by O’Donnell et al. (2012) was conducted in Sri Lanka and examined the mediating effects of social and economic exchange on the relationship between POS and affective and continuance commitment. POS was negatively related to economic exchange and positively to social exchange. For both social and economic exchange, support for partial mediation was found (Tables 22.3, 22.4 and 22.5). In a third study, Byrne, Pitts, Chiaburu, and Steiner (2011) examined the mediating role of social

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exchange between (a) two antecedents, managerial trustworthiness and POS, and (b) two outcomes, job performance and affective commitment. They found that social exchange fully mediated relations that POS and managerial trustworthiness had with affective commitment, and also fully mediated relations that managerial trustworthiness had with job performance (Tables 22.3 and 22.4). Finally, Gakovic and Tetrick (2003) examined relations that POS had with social and economic exchange for US students working part-time and full-time, and found that POS was strongly and positively related to social exchange (Table 22.3), but more so for those working full-time. Interestingly, for economic exchange, relations with POS were significant, similar in magnitude, and negative for both full-time and part-time groups (Tables 22.5). Overall, these studies suggest that POS is antecedent to both social and economic exchange, that work status (part-time, full-time) affects social exchange more so than economic exchange, and that exchange mediates effects of POS on attitudinal and behavioral outcomes (Tables 22.4 and 22.6).

LMX

Loi, Mao, and Ngo (2009) examined whether social and economic exchange mediated relations between leader–member exchange (LMX) and both affective commitment and intention to leave in a Chinese sample. LMX was negatively associated with economic exchange, and positively with social exchange (Tables 22.3 and 22.5). They also found that social exchange was positively related and economic exchange negatively related to affective commitment, while the opposite set of relations was found for intention to leave (Tables 22.4 and 22.6). These results suggest that the immediate supervisor’s relationship with the employee serves as a signal of the extent of social and economic exchange perceived with the organization, and that these perceptions fully mediated effects of LMX on the outcomes. Buch (2014) built on research by Kuvaas, Buch, Dysvik, and Haerem (2012) in which they showed that the exchange relationship between the leader and follower can be examined as separate components of social and economic exchange (SLMX and ELMX). While SLMX is comparable to LMX as a measure of social exchange, ELMX reflects economic exchange with the leader and is distinct from SLMX. In Buch’s study, he examined the interactive effects of social and economic exchange with the organization and SLMX and ELMX in relation to affective commitment. As in prior research, social exchange was positively and economic exchange negatively related to affective commitment (Tables 22.4 and 22.6). In addition, SLMX moderated relations that economic exchange had with affective commitment such that when

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Table 22.3 Antecedents of social exchange Antecedents

K

Direction of relationship

Types of sample

Countries

Author and year

EOR studies overinvestment

1

+

China

Hom et al., 2009

mutual-investment

3

+ +

managers attending part-time MBA programs and middle managers in firms government employees managers attending part-time MBA programs and middle managers in firms middle managers along with top managers in 31 companies telecommunications employees

Belgium China

Audenaert, 2014 Hom et al., 2009

China

Song et al., 2009

Sri Lanka

O’Donnell et al., 2012

employees in single organization MBA students and aerospace employees and their managers students working part-time and full-time technical and administrative employees service sector employees

USA USA

Byrne et al., 2011 Shore et al., 2006

USA China

Gakovic & Tetrick, 2003 Loi et al., 2009

USA

Bal et al., 2008

government employees hospital employees hospital employees employees and managers from an IT company employees working on MBAs, employed MBA students and undergraduates employees in single organization

Norway USA USA South Korea

Buch et al., 2014 Rousseau et al., 2009 Rousseau et al., 2009 Shin et al., 2012

USA, China

Hu et al., 2011

USA

Byrne et al., 2011

+ perceived org’l support

4

+ + + +

LMX

1

+

psychological contract breach

2

–

development i-deals work hours i-deals organizational inducements reciprocity

1 1 1

– + – +

1

+

trustworthiness of manager P/O fit

1

+

1

+

exchange ideology

1

–

Leadership ethical leadership 1 authentic leadership 1

+ +

transactional 1 leadership transformational 1 leadership Organizational context procedural justice 1

+ +

+

subordinate–supervisor dyads of a South Korea large manufacturing company a not-for-profit professional USA association

Kim et al., 2013

waste management corporation a not-for-profit professional association a not-for-profit professional association middle managers along with top managers in 31 companies

USA USA

Hansen et al., 2013 Chiaburu et al., 2011

USA

Chiaburu et al., 2011

China

Song et al., 2009

China

Jiang & Law, 2013

teachers and their corresponding peers in a middle school

Chiaburu et al., 2011

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Exchange in the Employee–Organization Relationship

Table 22.3 Antecedents of social exchange (continued) Antecedents

K

Direction of relationship

Types of sample

Countries

Author and year

climate for wellbeing integrative org’l culture climate of cooperation HR practices competency model fairness and relevance

1

+

nurses

Netherlands

1

+

1

+

middle managers along with top China managers in 31 companies temporary employees in the client Norway organization

Veld & Van de Voorde, 2014 Song et al., 2009

1

+

training intensity

1

+

perceived investment

2

+ +

accumulated fixed pay Employee attributes psychological 1 resilience workplace 1 loneliness job satisfaction 1

+

+ – +

employees and supervisors from four companies (two consulting, one banking, and one property management) employees working in three organizations temporary employees in the client organization temporary employees in the client organization employees working in an insurance company employee and managers from an IT company schoolteachers a not-for-profit professional association

Kuvaas & Dysvik, 2011

Norway

Redmond, 2013

Norway

Kuvaas et al., 2012

Norway

Norway

Kuvaas & Dysvik, 2011 Kuvaas & Dysvik, 2009 Kuvaas et al., in press

South Korea

Shin et al., 2012

China

Lam & Lau, 2012

USA

Chiaburu et al., 2011

Norway

Table 22.4 Outcomes of social exchange Outcomes

K

Direction of relationship

Types of sample

Countries

Author and year

Work attitude turnover intention

4

– –

telecommunications employees managers attending part-time MBA programs and middle managers in firms technical and administrative employees employees of a large Korean electronics organization nurses

Sri Lanka China

O’Donnell et al., 2012 Hom et al., 2009

China

Loi et al., 2009

South Korea

Shore et al., 2009

Netherlands

government employees subordinate–supervisor dyads of a large manufacturing company

Belgium South Korea

Veld & Van de Voorde, 2014 Audenaert, 2014 Kim et al., 2013

– – affective commitment

10

+ + +

(Continued)

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Table 22.4 Outcomes of social exchange (continued) Outcomes

K

Direction of relationship

Types of sample

Countries

Author and year

+ + +

waste management corporation employees in single organization managers attending part-time MBA programs and middle managers in firms middle managers along with top managers in 31 companies technical and administrative employees employees of a large Korean electronics organization undergraduate and graduate students undergraduate and graduate students undergraduate and graduate students employee and managers from an IT company

USA USA China

Hansen et al., 2013 Byrne et al., 2011 Hom et al., 2009

China

Song et al., 2009

China

Loi et al., 2009

South Korea

Shore et al., 2009

USA

South Korea

Gakovic & Tetrick, 2003 Gakovic & Tetrick, 2003 Gakovic & Tetrick, 2003 Shin et al., 2012

+ + + + normative commitment continuance commitment normative commitment to change affective commitment to change job satisfaction

1

+

1

+

1

+

1

+

employee and managers from an IT company

South Korea

Shin et al., 2012

2

+

South Korea

Kim et al., 2013

trust in employer

1

+ +

Sri Lanka South Korea

O’Donnell et al., 2012 Shore et al., 2009

comparable future opportunity Work behavior absence

1

+

subordinate–supervisor dyads of a large manufacturing company telecommunications employee employees of a large Korean electronics organization employees in 20 research and development groups

USA

Lai et al., 2009

1

–

USA

Shore et al., 2006

tardiness

1

–

USA

Shore et al., 2006

turnover

1

–

South Korea

Shin et al., 2012

OCB

6

+

Norway

Redmond, 2013

China Norway

Lam & Lau, 2012 Kuvaas & Dysvik, 2011

Norway

Kuvaas & Dysvik, 2009

USA USA

Bal et al., 2008 Shore et al., 2006

+ + + + +

MBA students and aerospace employees and their managers MBA students and aerospace employees and their managers employee and managers from an IT company employees and supervisors from four companies (two consulting, one banking, and one property management) schoolteachers temporary employees in the client organization temporary employees in the client organization service sector employees MBA students and aerospace employees and their managers

USA USA

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Exchange in the Employee–Organization Relationship

Table 22.4 Outcomes of social exchange (continued) Outcomes

K

Direction of relationship

Types of sample

Countries

Author and year

OCB-O

1

+

South Korea

Kim et al., 2013

OCB-I

1

+

China

Jiang & Law, 2013

work effort

2

+

subordinate–supervisor dyads of a large manufacturing company teachers and their corresponding peers in a middle school employees and supervisors from four companies (two consulting, one banking, and one property management) temporary employees in the client organization undergraduate and graduate students employees and supervisors from four companies (two consulting, one banking, and one property management) employees of a large Korean electronics organization

Norway

Redmond, 2013

Norway

Kuvaas & Dysvik, 2011

USA Norway

Gakovic & Tetrick, 2003 Redmond, 2013

South Korea

Shore et al., 2009

schoolteachers temporary employees in the client organization middle managers along with top managers in 31 companies government employees employees and supervisors from four companies (two consulting, one banking, and one property management) subordinate–supervisor dyads of a large manufacturing company employees in single organization service sector employees 76 business establishments from 56 different companies MBA students and aerospace employees and their managers temporary employees in the client organization

China Norway

Lam & Lau, 2012 Kuvaas & Dysvik, 2009

China

Song et al., 2009

Belgium Norway

Audenaert, 2014 Redmond, 2013

South Korea

Kim et al., 2013

USA USA Japan

Byrne et al., 2011 Bal et al., 2008 Takeuchi et al., 2007

USA

Shore et al., 2006

Norway

Kuvaas & Dysvik, 2011

nurses

Netherlands

Veld and Van de Voorde, 2014

+ employee fulfillment 1 of obligation employability 1 activities

+ +

altruism

1

+

Performance task performance

3

+ + +

job performance

7

+ +

+ + + + + work quality

1

+

1

–

Well-being strain

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Table 22.5 Antecedents of economic exchange Antecedents

K

Direction of relationship

Types of sample

Countries

Author and year

EOR studies quasi-spot

1

+

China

Song et al., 2009

3

–

middle managers along with top managers in 31 companies telecommunications employees

Sri Lanka

O’Donnell et al., 2012 Shore et al., 2006

perceived org’lsupport

– – LMX

1

–

psychological contract breach development i-deals work hours i-deals Reciprocity

1

+

1 1 1

– + –

exchange ideology

1

+

Organizational context climate for well1 being climate of 1 cooperation hierarchical org’l 1 culture HR practices competency model 1 fairness and relevance

+

training intensity

1

–

perceived investment variable pay

1

–

1

+

1

–

1

+

Leadership transactional leadership directive leadership

MBA students and aerospace employees and their managers students working part-time and full-time technical and administrative employees government employees hospital employees hospital employees employees working on MBAs, employed MBA students and undergraduates a not-for-profit professional association

USA USA China

Gakovic & Tetrick, 2003 Loi et al., 2009

Norway

Buch et al., 2014

USA USA USA, China

Rousseau et al., 2009 Rousseau et al., 2009 Hu et al., 2011

USA

Chiaburu et al., 2011

Netherlands

Veld and Van de Voorde, 2014 Kuvaas & Dysvik, 2011 Song et al., 2009

–

nurses

–

temporary employees in the client Norway organization middle managers along with top China managers in 31 companies

–

employees and supervisors from four companies (two consulting, one banking, and one property management) employees working in three organizations temporary employees in the client organization employees working in an insurance company a not-for-profit professional association a not-for-profit professional association

Norway

Redmond, 2013

Norway

Kuvaas et al., 2012

Norway

Kuvaas & Dysvik, 2009 Kuvaas et al., in press

Norway

USA

Chiaburu et al., 2011

USA

Chiaburu et al., 2011

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Exchange in the Employee–Organization Relationship

Table 22.6 Outcomes of economic exchange Outcomes

K

Direction of relationship

Types of sample

Countries

Author and year

Work attitude turnover intention

3

+ +

Sri Lanka China

O’Donnell et al., 2012 Loi et al., 2009

South Korea

Shore et al., 2009

Sri Lanka

O’Donnell et al., 2012

1

+

telecommunications employees technical and administrative employees employees of a large Korean electronics organization telecommunications employees

5

–

nurses

Netherlands

–

middle managers along with top managers in 31 companies technical and administrative employees employees of a large Korean electronics organization undergraduate and graduate students undergraduate and graduate students

China

Veld and Van de Voorde 2014 Song et al., 2009

China South Korea

Loi et al., 2009 Shore et al., 2009

USA USA South Korea

Gakovic & Tetrick, 2003 Gakovic & Tetrick, 2003 Shore et al., 2009

USA

Lai et al., 2009

Norway

Redmond, 2013

Norway

Kuvaas & Dysvik, 2011

China

Song et al., 2009

Norway

Redmond, 2013

Norway

Kuvaas & Dysvik, 2011

Norway

Kuvaas & Dysvik, 2009

USA

Gakovic & Tetrick, 2003

Norway

Kuvaas & Dysvik, 2009

China

Song et al., 2009

Norway

Redmond, 2013

+ continuance commitment affective commitment

– –

normative commitment trust in employer comparable future opportunity Work behavior OCB

1

– –

1

–

1

–

3

–

– – work effort

3

–

– – employee fulfillment 1 of obligation Performance task performance

2

–

– –

job performance

1

–

employees of a large Korean electronics organization employees in 20 research and development groups employees and supervisors from four companies (two consulting, one banking, and one property management) temporary employees in the client organization middle managers along with top managers in 31 companies employees and supervisors from four companies (two consulting, one banking, and one property management) temporary employees in the client organization temporary employees in the client organization undergraduate and graduate students

temporary employees in the client organization middle managers along with top managers in 31 companies employees and supervisors from four companies (two consulting, one banking, and one property management)

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SLMX was high the relationship was not as negative but when low, the relationship was more strongly negative. This suggests that high social exchange with the manager can buffer the negative effects of economic exchange with the organization on employee affective commitment.

Psychological contract breach

Bal, Chiaburu, and Jansen (2010) examined the moderating effects of social exchange on the relationship between psychological contract breach and both performance and OCB for a sample of US service sector employees. They posed two possibilities about the effects of social exchange when breach occurs. One possibility is that social exchange serves as a buffer, such that breach does not undermine performance and OCB as much when social exchange is high. They also posed the opposite influence in which social exchange may intensify the effects of breach since this reflects a betrayal of the high quality relationship that the employee perceives they have with the organization. The authors found support for the intensification explanation, since high social exchange led to greater negative effects on performance and OCB in the face of breach than did low social exchange. The performance and OCB of employees with low social exchange were unaffected by breach. Buch, Kuvaas, Shore, and Dysvik (2014) studied the effects of psychological contract breach with a prior employer (past breach) on exchange relations with the current employer among Norwegian employees. They wanted to know if there were carryover effects from the prior employer to the current EOR. Results showed that employees’ experience of breach with a previous employer was related to higher levels of economic exchange and lower levels of social exchange with the current employer. In addition, they found that POS with the current employer attenuated the relationship between past breach and economic exchange such that employees low in support with the current employer had higher economic exchange. On the contrary, for employees with high support from their current employer, there was no association between breach with the prior employer and economic exchange with the current employer. Overall, breach and support are both reflective of organizational treatment, and employees respond differently to such treatment depending on the levels of social or economic exchange they perceive. Contract breach may signal a lowering of the quality of the relationship, undermining social exchange and increasing economic exchange.

i-deals

Rousseau, Hornung, and Kim (2009) examined the impact of i-deals on social and economic

exchange among US hospital employees. Specifically, they examined whether employees had asked for, and successfully negotiated arrangements pertaining to work hours and development. Results showed that social exchange was positively related and economic exchange negatively related to development i-deals, whereas the opposite pattern was found for work hours i-deals. Development i-deals are consistent with the investment element of social exchange by focusing on skill development, career development, and performance goals, supporting the argument that there is a link between HR practices that promote a long-term relationship and career development and the EOR element of social exchange (Shore & Shore, 1995). The work hours i-deals had direct effects on the financial element of work (pay and salary), and hence, not surprisingly, increased the emphasis on economic exchange in the EOR. In a second study, Lai, Rousseau, and Chang (2009) examined the impact of social and economic exchange on US employee perceptions of the likelihood of receiving future opportunities for i-deals that were comparable to coworkers, and found social exchange increased and economic exchange decreased such perceptions. Comparable future opportunities in turn led to support for coworker i-deals. This study suggests the importance of the nature and magnitude of exchange relationships for determining employee responses to the i-deals of coworkers. Both studies as a set suggest that managers who make individualized arrangements (i-deals) with particular employees may be wellintended, but not be aware of potential undermining effects on the quality of relations with the organization (increased economic exchange) and with coworkers who may have lower social exchange and higher economic exchange.

ER

Hom and his colleagues (Hom, Tsui, Wu, Lee, Zhang, Fu, & Li, 2009) compared the mutual investment and overinvestment ERs with the quasi-spot contract and underinvestment ERs in two samples of Chinese managers. Specifically, this study found that social exchange served as a mediator between these ERs and both affective commitment and turnover intentions. In a second cross-level study in China, Song, Tsui, and Law (2009) found that social exchange partially mediated the relationships that the mutual investment ER had with task performance and affective commitment. In addition, economic exchange fully mediated the effects of a quasi-spot contract ER on affective commitment and OCB. These effects were found even though the ER was assessed at the firm level, and social and economic exchange

Exchange in the Employee–Organization Relationship

at the individual level. Audenaert’s (2014) study showed that social exchange mediates relations that mutual investment ERs at the job level have with affective commitment and job performance. Overall, these studies suggest that firm-level and job-level ERs are translated by employees into exchange perceptions which in turn influence several important outcomes including affective commitment, turnover intentions, task performance, and OCB. As a set, both the individual-level and cross-level studies show that social and economic exchange serve to mediate relations that other EOR concepts have with a variety of outcomes. Interestingly, the EOR concepts studied in relation to social and economic exchange were either content (POS and breach) or mixed models of the EOR (ER and LMX), whereas social and economic exchange is a process model. This supports the importance of social and economic exchange as reflective of the quality of the relationship the employee perceives with the organization, and the role that these exchange perceptions play in linking other content and mixed EOR concepts with a variety of important organizational outcomes.

Social and economic exchange and leadership

In addition to the Loi et al. (2009) study on LMX and the Buch (2014) study on SLMX and ELMX already discussed above, two other studies examined leadership and exchange. Chiaburu, Diaz, and Pitts (2011) examined the impact of three types of leadership behavior (authentic, directive, and transactional) on social and economic exchange in a US sample. They found that authentic leadership was positively related to social exchange, but unrelated to economic exchange. The types of behaviors that authentic leaders engage in promote trust, consistent with theorizing about the role of trust in facilitating social exchange with the organization (Wayne, Shore, & Liden, 1997). Directive leadership was positively related to economic exchange, which the authors viewed as consistent with the position that this form of leadership promotes distrust leading employees to view the relationship with the organization as more financially focused with specified obligations. They also showed that transactional leadership was positively related to social and negatively to economic exchange. While somewhat surprising, the authors concluded that this is likely a result of the contingent reward component of transactional leadership, which leads to higher quality relationships by providing assurance to employees that accomplishments lead to rewards and recognition. Hansen, Alge,

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Brown, Jackson, and Dunford (2013) found that social exchange partially mediated relations that ethical leadership had with organizational commitment. Since ethical leaders are viewed as caring for their employees and as trustworthy and fair (Walumba & Schaubroeck, 2009), the strong association with social exchange is not surprising. However, the role of social exchange as a mediator suggests that managerial treatment is viewed as indicative of the quality of the relationship which in turn can affect commitment to the organization. In sum, these results suggest that when managers treat employees in a manner that suggests that they are cared about, trustworthy, and deserving of rewards and recognition, this results in perceptions of social exchange with the organization whereas managerial treatment suggestive of distrust and lack of rewards and recognition for accomplishments leads to higher levels of economic exchange perceptions.

Social and economic exchange and the organizational context

A number of studies examine the effect of various organizational context variables on social and economic exchange including (a) organizational change, (b) HR practices, fairness, reciprocity, and P/O fit, and (c) factors facilitating or harming employee well-being.

Support for change

Two studies examined the role of social exchange during organizational change efforts. First, Kim, Hornung, and Rousseau (2011) studied the effects of several antecedents on change supportive behavior over time, including social exchange, formal involvement in the change, and benefits of the change to employees. The high quality relationship with the organization reflected in social exchange was expected to enhance support for organizational change efforts. Interestingly, formal involvement in the change was a significant predictor of change supportive behavior during early and later time periods, whereas benefits of change was only significant during the earlier time period and social exchange only significant during the later time period. According to the authors (p. 1686) ‘cognitive assessment of anticipated benefits becomes less important and the socioemotional quality of employment relationship more important for predicting active behavioral support as the change progresses’. Second, Shin, Taylor, and Seo (2012) examined the mediating effects of social exchange on the relationships between organizational inducements (developmental rewards and materialistic rewards) and both normative and affective commitment to change in a South Korean organization. These effects were studied in an

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organizational setting in which there was a major restructuring effort underway. They found that social exchange significantly mediated organizational inducements and both normative and affective commitment to change. These two studies showed the facilitative role of social exchange in supporting organizational change efforts.

HR practices, fairness, and P/O fit

Several studies point to the importance of HR influences on social and economic exchange. First, Kuvaas, Buch, and Dysvik (2012) examined the relationships among training intensity, knowledge sharing, and social and economic exchange for Norwegian employees. Training intensity was positively related to social exchange and negatively to economic exchange, as would be expected since training is a form of investment in employees. In addition, knowledge sharing was higher when social exchange was high and lower when economic exchange was high. Finally the relationship between training intensity and knowledge sharing was moderated by both exchanges, with social exchange enhancing and economic exchange undermining the relationship. In another study, Kuvaas and Dysvik (2009) examined the mediating effects of social and economic exchange of temporary Norwegian workers in the relationship between perceived investment in permanent employee development and OCB and task performance of temporary employees. Results showed that social and economic exchange mediated perceived investment and the outcomes. Specifically, social exchange was positively related to task performance and OCB, and economic exchange negatively related to task performance only. In a subsequent study, Kuvaas and Dysvik (2011) examined the basis for perceived investment in permanent employees for influencing social and economic exchange of temporary employees toward the client organization, and found that climate of cooperation in the client organization mediated these relations. The authors (p. 275) stated ‘it seems that organizations that invest in the development of their permanent employees obtain a better cooperative climate, which in turn affects the psychological climate as perceived by temporary employees and the relationship temporary employees develop with their client organization’. Social exchange of temporary employees was positively related to work quality, OCB, and work effort, and economic exchange negatively related to work effort and OCB. These two studies by Kuvaas and Dysvik are an extension of prior research since the target of organizational investment was to permanent employees, and yet, this investment affected temporary employee perceptions of their own social and economic exchanges

with the client organization and served to enhance their contributions to the organization. Fourth, Kuuvas, Shore, Buch, and Dysvik, (in press) examined the effects of pay on social and economic exchange in a sample of Norwegian employees working in an insurance company. They argued that pay contingency (whether pay is contingent on performance) would differentially affect social and economic pay perceptions. The results showed that higher accumulated fixed pay, which is based on a person’s history of merit pay over their employment with the organization, was positively related to social exchange perceptions. In contrast, variable pay, which is tied to recent performance and is short term and specified, was related to economic exchange such that the more variable pay received the higher the economic exchange perceptions. This study shows that different components of pay (fixed and variable) are ascribed different meaning by employees. Fifth, Redmond (2013) examined the mediating effects of social and economic exchange on the relationship between competency model fairness and relevance and work effort, work quality, OCB, employability orientation, and employability activities in four Norwegian companies. In this study, perceptions of competency models were defined as ‘the degree to which employees perceive the organization’s competency model to be both strategically and personally relevant and that they are fairly rewarded for displaying behaviors outlined in the competency model’ (p. 782). She found full mediation of both social (positive effect) and economic exchange (negative effect) for work effort and OCB, and social exchange positively mediated employability activities. Additionally, economic exchange had negative effects on work quality but did not serve as a mediator. The results of these studies suggest the effects of HR practices on work performance are primarily indirect, via employee interpretations of their meaning through the lens of social and economic exchange. The last three studies in this section are not HR practices per se, but do signal a positive organizational context, including procedural justice, organizational reciprocity, and P/O fit, that are often reflective of supportive HR practices. A study conducted by Jiang and Law (2013) examined social exchange and organizational identification as mediators between procedural justice and OCB. Interestingly, social exchange served to mediate relations with OCB-I and organizational identification with OCB-O. This suggests that procedural justice is a mechanism for building both high quality relationships and strong organizational identification, resulting in two different foci of OCB. Hu, Tetrick, and Shore (2011) applied Sahlins’ (1972) theory of reciprocity involving three types of exchange between the employee and organization (equivalence, immediacy, and

Exchange in the Employee–Organization Relationship

interest) and examined their relations to social and economic exchange. They found that all three types of exchange were positively related to social exchange, and that only the exchange of interest was associated (negatively) with economic exchange. Finally, Kim, Aryee, Loi, and Kim (2013) were interested in the underlying motivational mechanisms linking P/O fit and both attitudes and behaviors. They chose to examine two EOR concepts as mechanisms, including social exchange as a mediator and LMX as a moderator of this mediated relationship. Social exchange was found to mediate relations that P/O fit had with job satisfaction, affective commitment, job performance, and OCBO. Furthermore, moderation was shown for LMX. When LMX was high the relationship between P/O fit and social exchange was stronger than when LMX was low. The authors suggested the importance of high quality relations with the organization and the manager for enhancing the effects of P/O fit. Overall, the studies in this section suggest support for the important mediating effects of social and economic exchange between several aspects of the organizational context and important organizational outcomes.

Well-being

Three studies focused on mediating effects of social or economic exchange in relation to health and wellbeing. Veld and Van De Voorde (2014) examined the mediating effects of social and economic exchange on the relationship between climate for well-being and affective commitment and job strain among nurses in the Netherlands. Social exchange partially mediated the climate for well-being on both commitment (positive effect) and strain (negative effect), and economic exchange partially and negatively mediated affective commitment only. Another study found support for the mediating effects of social exchange on the relationship between workplace loneliness and task performance, and LMX on the relationship between workplace loneliness and OCB-I (Lam & Lau, 2012). In both cases, the negative effects of workplace loneliness on the outcomes became less when either social exchange or LMX was high. Third, Zhang, Zhu, Dowling, and Bartram (2013) examined the effects of high performance work systems and social and economic exchange on the engagement, emotional exhaustion, and job satisfaction of nurses, medical practitioners, and administrative staff in China. They found that economic exchange increased emotional exhaustion and social exchange increased engagement. Social and economic exchange also moderated the relationship between high performance work systems and emotional exhaustion, such that high economic exchange and low social exchange increased the emotional exhaustion of employees working in an

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environment with a high performance work system. The pattern of results in these three studies suggests that social exchange may have a buffering effect whereas economic exchange may have a stressing effect on employees when faced with challenging situations and negative experiences.

Social and economic exchange and individual differences

Only one study was found which examines the moderating effects of an individual difference variable on social and economic exchange. In a study of Korean employees, Shore, Bommer, Rao, and Seo (2009) considered the moderating effects of reciprocation wariness (fear of exploitation in exchange relationships) on the relationships that social and economic exchange had with affective commitment, turnover intentions, employer trust, and altruism. As expected, social exchange was positively related to affective commitment, trust, and altruism, and negatively to turnover intentions. As predicted, the highly wary did not respond as positively to social exchange compared with the low wary, as shown by the lower effect of social exchange on commitment and the non-­significant effect on trust or turnover intentions. The low wary responded to social exchange as expected, with strong and positive effects on commitment, trust, and a strong and negative effect on turnover intentions. When perceiving higher levels of economic exchange, the highly wary employees were more likely to have higher turnover intentions whereas the low wary were not. Even though social exchange is generally responded to favorably as expected, for wary employees who fear exploitation if they reciprocate beneficial treatment, it may be more difficult for organizations to reap the benefits of positive treatment. In this study, economic exchange lowered affective commitment and trust and increased turnover intentions, but was especially likely to encourage turnover intentions among the wary. In sum, this study suggests the importance of an individual difference variable, reciprocation wariness, and thus implies that more of these variables should be incorporated into studies of social and economic exchange.

Concluding remarks on social and economic exchange

In conclusion, the set of studies on social and economic exchange suggest strong support for their importance. Specifically social and economic exchanges have been incorporated into studies with a wide variety of both content (POS, breach, and i-deals) and mixed models (ER, LMX) of the EOR and have consistently been

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shown to meaningfully capture employee perceptions of relational quality. In addition, the pattern of results for mediators suggest that, employees interpret organizational and leadership actions and treatment as signals of the quality of the relationship they have with the organization. And, these two forms of exchange in turn directly affect a variety of important organizational outcomes, including commitment, satisfaction, performance, commitment to organizational change, and OCB. In terms of moderation of social and economic exchange, there have been fewer studies published. Social exchange seems to intensify the effects of breach on performance and OCB, suggesting that breach is more of a betrayal when the employee has a high quality relationship with the organization. Furthermore, low social exchange and high economic exchange increase the emotional exhaustion of employees when there is a high performance work system in their organization. These studies underline the need for research that more fully examines moderation effects of social and economic exchange, as these few studies suggest that employees reflect on the quality of their relationship with the organization when considering their treatment and have fewer (economic exchange) and greater (social exchange) resources when faced with stress in their working lives.

REFLECTIONS ON THE CURRENT EOR LITERATURE Overall, what can be gleaned from this review of EOR concepts in terms of consequences, potential redundancy, and generalizability?

Consequences First, it is clear that the three relational exchange paradigms reviewed here have significant effects on individual employees, groups, and organizations. Psychological contract breach has negative implications for employees’ emotions, attitudes, in-role and extra-role performance and well-being but does not appear to lead to absences or quitting the organization. While the majority of empirical evidence focuses on contract breach, the evidence that does exist points to psychological contract fulfillment having positive consequences for employee performance. Recall that the ER captures managers’ description of the inducements and expected contributions for employees in a particular job or the organization as whole. A mutual

investment ER (a balanced open-ended and longterm commitment) is positively associated with social exchange relationships, job embeddedness, job performance, creativity, innovation, learning, and firm performance. Social exchange and economic exchange are both important mediators and moderators of managerial and organizational treatment and outcomes. Social exchange was found to mediate the effects of perceived organizational support, leader–member exchange on for example, job performance and OCB. Social exchange also mediated the relationship between mutual investment ER and task performance. The studies reviewed here provide evidence that the three EOR constructs independently have an effect on organizationally desired outcomes.

Potential Redundancy In light of the above conclusion, one could reasonably ask whether there is redundancy between the three constructs. Based on this review, we would argue that EOR concepts are not redundant with one another as each adds a piece of the jigsaw puzzle to create a more complete understanding of the effects of the EOR on employees and organizations. For example, social exchange can influence how employees respond to psychological contract breach, social exchange also provides one explanation for the positive relationship between a mutual investment ER and individual task performance. In particular, social and economic exchange seem to be important mediating and moderating processes that link the ER and psychological contracts, with critical organizational outcomes. Going forward, it would greatly enhance the EOR literature if integration of constructs in this domain continued to allow for a more complete understanding of the multi-level and multi-focal EOR domain.

Generalizability On the basis of the studies reviewed here, it would appear that the main findings seem to hold true across cultural contexts. Psychological contract breach carries with it negative implications across cultural boundaries. Perhaps, where culture becomes important is in understanding the strength of employees’ negative response. Similarly, those studies conducted on the ER in China, Spain, Belgium, and the US paint a broadly similar picture in terms of the positive consequences of a mutual investment ER on a variety of outcomes. Social exchange and

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economic exchange provide explanations for the relationship between managerial and organizational treatment and critical organizational outcomes and also highlight the boundary conditions between those relationships. These effects have been shown in many different world regions, including North America, Europe, and Asia. In the absence of evidence to the contrary, it seems that psychological contract fulfillment, mutual investment ER, and social exchange provide the cornerstone to positive employee–organization relationships irrespective of locality.

EMERGING AND DEVELOPING ISSUES Having outlined current research on the EOR, we now turn to look at some themes including international and cross-cultural issues, the negative EOR domain, and the EOR and health that are beginning to receive attention in the literature.

International and Cross-Cultural Issues While it is clear that the EOR literature has successfully been conducted in a variety of national and world region contexts, cultural effects have rarely been examined in a systematic manner theoretically or empirically. Shore et  al. (2009) edited a special issue on social exchange for Management and Organization Review, and made several points pertaining to cross-cultural issues in their introductory article. They pointed out that while social exchange theory appears to be relevant cross-culturally, that there may be important differences that emerge across cultures and nations. Social exchange may be especially relevant to Chinese cultural settings where long-term orientation, collectivism, and human relationships are highly valued. Economic exchange may be more applicable to individualistic societies with focus on self-interest and social exchange may be more relevant to collectivistic societies with focus on collective interest. Finally, Shore et al. (2009) noted a pattern which may have implications for social exchange research in which process models focused on relational quality were more common in Chinese studies whereas content models of the EOR were more common in the Western studies in their special issue. They speculated that process models with a relational focus may fit with the Chinese culture while the content model focusing on what is exchanged (e.g., from employer to

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employee) may fit with the stronger instrumental focus of Western cultures. Two articles point to cultural elements that may be important to consider in studies of the EOR. First, based on social exchange theory, Brockner, Chen, Mannix, Leung, and Skarlicki (2000) expected self-construal to influence relations between procedural justice and favorable outcomes. Self-construal is associated with differences between Eastern and Western cultures such that in the former, individual’s identities are more interdependent (connected) with others whereas in the latter, individual’s identities are more independent (distinct) from others. They argued that people with interdependent self-construals should view their social exchanges as more important than those with more independent self-construals due to the greater sense of connectedness. They found support for this prediction as shown by the stronger effects of procedural fairness on outcomes favorability for Chinese participants as opposed to US participants. Second, in a study of the effects of culture on relational and transactional contracts, Ravlin, Liao, Morrell, Au, and Thomas (2012) focused on collectivist orientation, ‘the tendency to view the self as interdependent with selected others, to be concerned about consequences of behavior for in-group goals, and to be willing to sacrifice personal interests for group welfare’ (p. 773). This orientation is stronger among individuals in Eastern than in Western nations. As expected, collectivistic orientation was associated with relational contracts but not with transactional contracts, suggesting that organizations working with Eastern employees need to put greater emphasis on relational contracts, that is, socio-emotional relationships of longer duration emphasizing social exchange. Both of these studies suggest the importance of culture for influencing the EOR, especially elements of culture that affect how people think about relationships such as self-construal and collective orientation. Clearly, much more research is needed that directly links the concepts in the EOR literature with cultural elements in order to begin the process of understanding their relevance to varied cultural contexts.

Negative EOR Domain The EOR literature has focused much more theoretical and empirical attention on positive relationships (psychological contract fulfillment, POS, LMX, social exchange, and mutual investment and overinvestment ERs) than on negative relationships in which employees perceive that

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their relationship with the organization is harmful. Three exceptions are psychological contract breach and violation (Robinson, 1996; Rousseau, 1995), consisting of employee perceptions of and emotional reactions to broken promises, underinvestment and quasi-spot contract ERs (Tsui, Pearce, Porter, & Tripoli, 1997), and economic exchange (Shore et  al., 2006). In the case of breach and violation, the emphasis is on lack of organizational fulfillment of promises, a reflection of the damage that can occur when the norm of reciprocity and expectations of exchange of favors are not honored. In the case of the ER, the focus is on imbalance of inducements by the organization and contributions by the employee that favors the organization (underinvestment ER) or low levels of inducements and contributions (quasi-spot contract ER). For economic exchange, the emphasis is on the degree to which the relationship is perceived to be formalized and focused on economic and impersonal elements of the EOR. These literatures provide a starting point for developing a model of negative EORs by highlighting the key roles of reciprocation, balance, and relational quality in establishing and maintaining positive EORs. One developing stream of research has sought to spotlight the development of negative EORs. Although there is a significant body of empirical evidence that examines the antecedents and consequences of negative employee–supervisor relationships (e.g., abusive supervision), there has been little theorizing into how employees develop negative relationships with their organization and even less empirical evidence. To our knowledge, three recent constructs attempt to broaden the conceptualization of the EOR and in doing so counterbalance the existing emphasis placed on positive EORs. These include: Perceived Organizational Obstruction, Perceived Organizational Cruelty, and Exploitative Employee–Organization Relationships. Perceived Organizational Obstruction is a defined as ‘an employee’s belief that the organization obstructs, hinders or interferes with the accomplishment of his or her goals and is a detriment to his or her wellbeing’ (Gibney, Zagenczyk, & Masters, 2009, p. 667). The authors demonstrate that perceived organizational obstruction explains additional variance in exit, voice, loyalty and neglect beyond psychological contract breach, perceived organizational support, organizational politics, procedural justice, and organizational frustration and argue that employees differentiate between positive, negative, and neutral treatment. Shore and Coyle-Shapiro (2012) introduce a more negative EOR relationship and define perceived organizational cruelty as ‘the employee’s

perception that the organization holds him or her in contempt, has no respect for him or her personally, and treats him or her in a manner that is intentionally inhumane’ (p. 141). While acknowledging the role of harmful supervisory treatment, their conceptual model accounts for organizational harm enablers in employees developing a perception that their organization is a cruel one and hypothesize that there will be negative consequences for employees’ health and well-being as a result. Specifically, perceived organizational cruelty is thought to have a deleterious effect on metabolic syndrome, anxiety, depression, fear, and anger. Livne-Ofer, Coyle-Shapiro, and Shore (working paper) developed a conceptual model that explores how organizational underinvestment and supervisory malevolency contributes to the development of perceived exploitative employee–organization relationships which in turn is thought to lead to alienative involvement. Perceived exploitative EOR is defined as ‘an employee’s perception that they have been purposefully and repeatedly taken advantage of by the organization for the benefit of the organization itself, with anticipation of continued harm in the future’ (p. 2). These recent works have opened the door to exploring more negative EORs (as opposed to exploring negative events in a neutral or positive relationship as is the case with psychological contract breach). However, empirical studies are needed to explore how and when these types of relationships develop. In addition, research is needed to determine when such negative EORs can be repaired, and when relationships between employees and organizations are beyond repair.

EOR and Employee Health Thus far, there has been little attention placed on the implications of the EOR for employee health (Robbins et  al., 2012) though there is some evidence from the organizational justice field that establishes a relationship between injustice and health outcomes. Hietapakka et  al. (2013) found that psychological distress partially mediated the effects of distributive justice and procedural justice and fully mediated the effects of interactional justice on sleep problems. De Vogli, Ferrie, Chandola, Kivimäki, and Marmot (2007) found that unfairness predicted metabolic syndrome and its components (waist circumference, triglycerides, lipo-protein, fasting glucose, and blood pressure). Chang, Wu, and Coyle-Shapiro (2015) argue that the ‘health’ of the exchange relationship is likely to affect the health of the employee.

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Specifically, the nature of contract breach is likely to trigger different emotions that in turn have different health consequences – the accumulative breach process would have a damaging effect on employee’s healthy immune system while eventbased psychological contract breach could harm the cardiovascular system and sleep quality of employees. Shore and Coyle-Shapiro (2012) posit that being in a negative EOR is likely to have deleterious consequences on employees’ health. Therefore, conceptually there seems to be grounds for linking the EOR to employee health and given that health care costs are rising (Feldstein, 2012), there is an organizational imperative to do so. Future research could more directly focus on empirically examining the relationship between EOR and health. For example, do different employment relationships (overinvestment, mutual investment, underinvestment, and quasi-spot contract) differentially impact employee physical and psychological health? Existing empirical evidence has demonstrated that social exchange may have a buffering effect on employee well-being while economic exchange has an accentuating effect (Lam & Lau, 2012; Zhang et  al., 2013). Future research could begin to examine if social and economic exchange affect employees’ physical health. If so, what explains this and what are the boundary conditions? Heaphy and Dutton (2008) conclude that some but not all work relationships are associated with physiological outcomes so the question becomes which relationships matter and why. The authors argue that positive experience of work relationships result in (a) lowered blood pressure and heart rate, (b) strengthened immune system, and (c) healthier cortisol levels. The question therefore is whether the EOR matters in explaining employee physical health or whether the important relationships for employees are the people they work with such as coworkers.

FUTURE RESEARCH DIRECTIONS There are many possible research directions for the EOR literature. One clear direction is to increase the amount of integration across EOR concepts. Our review shows that integration is occurring and informative, but not being conducted as systematically as would be needed to enhance the understanding of relationships within organizations. At present, it appears that social and economic exchange serve to mediate links that organizational and managerial treatment (as shown in studies of POS, LMX, PC breach and

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fulfillment, and the ER), have with critical organizational outcomes (performance, OCB, affective commitment, and turnover). It is thus important to understand the psychological processes, such as social and economic exchange, that link treatment at work with critical organizational outcomes. There are likely other explanatory variables, as suggested in this literature (e.g., empowerment and job embeddedness) that need fuller exploration as well. Another area that needs additional research is the role of existing EORs during major organizational change processes or when there are signals that the relationship between the employee and organization is changing for the worse. High quality social exchange relations create expectations for continued good treatment and signals that this is no longer the case (e.g., when breach occurs or the employee has an abusive manager) may be especially detrimental to employee perceptions of the EOR and associated attitudes and behavior. Therefore, more research is needed that is longitudinal and examines change in the EOR over time. Added research on the ER is clearly needed, as this is the least studied EOR concept. To date, the research is very supportive of this model and its effects on employees. In addition, the ER has been shown to be effective at both the group and organizational level. Because of its focus on the group and that the ER is not an employee perception; it appears to be the most distinctly different representation of the EOR among those we reviewed in the chapter. There is also a recent trend in this literature to move away from the original four ER types to treat inducements and contributions as separate variables. This new measurement approach in recent studies also needs to be compared with the original typology to determine how differently these approaches affect important outcomes. Negative EORs is an area of the literature that needs additional development conceptually and empirically (Shore & Coyle-Shapiro, 2012). Akin to abusive supervision, the notion that the relationship between the employee and organization can be destructive has not been developed to any great extent. Similarly, studies of the link between the EOR and health, including physical and mental health, would greatly add to the literature. Destructive EORs have the potential, like other abusive relationships, to be very harmful to health. New conceptualizations of the types of EORs most likely to be associated with health, and the exploration of already existing EOR concepts that are health promoting or undermining need careful examination. As has been described above, PC breach and fulfillment, the ER, and social and economic exchange have all been studied successfully in a

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variety of cultures and nations. However, most of the research has speculated, rather than empirically studied, the effects of culture on the EOR. It is possible that some EOR concepts are more relevant and meaningful in some cultural contexts than in others. Likewise, some EOR concepts may have more powerful effects in some cultures. Such ideas need much greater development than has been done thus far, both conceptually and empirically.

IMPLICATIONS FOR PRACTICE Evidence continues to mount showing the positive effects of PC fulfillment, mutual investment ERs, and social exchange, on performance, OCBs, innovation, creativity, commitment, and trust. Likewise, an increasing number of studies have confirmed that breach, underinvestment ERs, and economic exchange have negative effects on the same set of variables. However, it is also the case that employees within the same work group may have widely varying views of their treatment by the organization and its agents, raising questions as to who contributes to employee perceptions of the relationship and how individual differences may impact the varying perceptions that arise. The direct manager certainly has an influence on employee perceptions of, and reactions to the EOR. Organizations need to help managers appreciate the criticality of their role in relationship management, and provide managers with training and development opportunities that can build positive relationships with employees. In addition, organizational leaders need to understand how quickly positive EORs can erode when there are signals of declining or broken relationships in the organization, and consider how best to work with employees in ways that communicate a desire to maintain positive and fair relationships. While the tasks that employees are responsible for may well be done by others when individuals are fired, laid off, or quit due to a poor relationship with the organization, the negative fallout from mistreatment has the potential to undermine future EORs by contributing to a climate of mistrust and even adversarial relations between employees and managers. Evidence continues to grow about the importance of maintaining positive EORs, and the benefits to both individuals and organizations for doing so. We hope this current chapter contributes to the betterment of EORs through increasing the dissemination of this literature, and encouraging the efforts of managers and employees to create and sustain such positive relationships.

CONCLUSION While a large body of literature has evolved on the EOR, it is clear from our review that this literature is still developing and inspiring a great deal of research attention. One of the most interesting trends is the increasing interest globally in the EOR. These studies show that social exchange and the norm of reciprocity are relevant across national and cultural boundaries. However, there are also studies that signal cultural differences in responses to the EOR. Cultural elements such as collective orientation (Ravlin et  al., 2012), traditionality (Zhang et  al., 2014) and self-construal (Brockner et  al., 2000) all point to the importance of a cultural lens in understanding the EOR. Triandis and Gelfand’s (1998) typology in which the cultural dimensions of individualism–collectivism and power distance are combined also have implications for the EOR. For example, vertical (high power distance) collectivism nations such as China emphasize communalism in which sharing and favor doing is done without an expectation of equal matching by recipients of favorable treatment whereas in horizontal (low power distance) individualistic nations such as the United States there is an emphasis on market pricing (economic value) and equality in exchange. These studies suggest the criticality of research that more fully develops theories of the EOR that integrate cultural elements. In light of the extensive interest in the EOR worldwide, this type of theoretical development seems especially important. Another trend that is quite apparent from this review is the generally negative employee responses to breach, economic exchange, and quasi-spot contracts. Each of these constructs is distinct, but in some way reflects a relationship that is of low quality. Breach involves broken promises, whereas economic exchange and quasi-spot contract contain limited exchange terms that focus on shortterm and financially oriented elements. The limited contributions provided by employees who report these EORs (breach and economic exchange) or are working in jobs or organizations offering these limited and narrow terms (quasi-spot contract ER) are strikingly consistent. It is equally obvious that much more research attention has been given to positive than to more negative EOR arrangements. We encourage authors to more fully explore the less favorable forms of exchange reviewed here but also to develop models of the EOR that are similar to the evolution in the leadership literature with abusive supervision a prime example (Tepper, 2007). We were very pleased at the recent research reflecting an integration of EOR concepts. Crosslevel (cf. Hom et  al., 2009; Song et  al., 2009) and trickle-down models (De Vos et al., 2015)

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highlight the value of such integration by showing the unique and overlapping effects of various EOR concepts. In addition, there are studies showing that aggregated perceptions of psychological contracts (De Vos et  al., 2015), job-level ERs (Audenaert, 2014), and organizational-level social exchange (Takeuchi, Lepak, Wang, & Takeuchi, 2007) all are meaningful and can influence organizational effectiveness and individual employee experiences and contributions. These studies suggest that the relational lens used in the EOR literature is meaningful for multiple referents (job, occupation, organization) and multiple levels, as suggested by Shore et  al. (2004). Thus, the relational lenses used in the EOR literature are quite robust, and suggests the criticality of relationships in work organizations. We expect the growth in these studies to continue as scholars seek to understand the multi-level, multi-concept, and multifocal elements of the EOR. Even though there are many empirical studies in the EOR domain, the literature is still evolving and this is likely to continue. Cross-cultural influences, the negative EOR, and employee health and the EOR are all emerging areas of the literature. Other areas that are underway that we expect to continue are further exploration of diversity effects, the role of individual difference in responses to the EOR, and mechanisms aside from social exchange and the norm of reciprocity (e.g., identification and job embeddedness) that serve to link the EOR with organizational outcomes. In sum, there are many potential avenues for developing the EOR domain and we encourage scholars around the world to develop the literature both theoretically and empirically for greater understanding of the relationships between employees and organizations.

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Subject Index abusive supervision, 474 acceleration of employee development, 254–5 achievement theory, 153 action learning, 252–4 components of, 252 ‘schools’ of, 252–3 action-oriented mental models, 127 action regulation theory, 122–38 applied to entrepreneurship, 132 basic concepts of, 123–7 differentiation from other cognitive-behavioral theories, 127–9 foci of, 126–7 and the functions of emotion, 133 hierarchical structure of, 125–6 research areas based on, 129–33 and work-related learning, 130 active learners, 255–6 adaptation–coordination–CRM training, 220–1 adaptivity, 365–8 affect, 51–2, 367–9 affective events theory (AET), 8, 505 affective self-regulatory system, 84 agency theory, 103, 181, 189 agent-based modeling (ABM), 322 aging workforce, 137 agreeableness, 135, 204 ambidexterity, 132, 135 American Society for Training and Development (ASTD), 215, 217 antecedents: group-referenced, dyad-referenced and self-referenced, 35 anticipatory justice, 55 ‘Apple paradox’, 279 applied behavioral analysis (ABA), 81 appraisal effectiveness, 196–7, 208 appraisal theory, 52 approach–avoidance dichotomy, 74 assessment centers see developmental assessment centers assignments see developmental assignments attentional control, 87 attitudinal constructs, 3, 13–14 automatization, 134–5, 137 autonomous motivation, 111 autonomy, 98–9, 102, 106–7 and adaptive behaviors, 365–9 see also team autonomy

avoidance goals, 81 banking, social networks in, 178 basic psychological needs theory (BPNT), 97–100, 102, 111 Belgium, 513 bonuses, 103, 189 brain function, 73–4, 79 call centers, 158 career development, 136 case studies, 348 Catholic Church, 277 causality orientations theory, 106–7 celebrity, 477 chaos theory, 70 chief executive officers (CEOs), 60, 279, 477 China, 150, 189–90, 511–13, 522, 530 choice architecture, 85 citizenship behaviors, 472; see also organizational citizenship behaviors classical test theory, 197 coaching, 246–7 cognitive ability, 133 cognitive diversity, 293 cognitive evaluation theory (CET), 100–3, 180 cognitive therapy, 87 Columbia Conserve Company, 406–7 commitment to goals, 147, 241 normative and continuance types of, 11–13 organizational, 3, 5, 8–13, 50 communication, mediums of, 478–9 compensation see pay composite work groups, 407 computational modeling, 83–4, 322–3 conscienciousness, 204 construal theory, 87 contrastive processes, 488 control theory, 127–8, 154 controlled orientation, 106 core evaluations theory, 149 core self-evaluations, 177–8 corporate social responsibility (CSR), 60 counterproductive managerial behaviors, 275 counterproductive work behaviors (CWBs), 51–3, 58, 490–2 creativity, 53

This Subject Index was independently and professionally prepared by the Publisher.

subject Index

cross-cultural issues, 527 cross-training, 220 ‘crowding out’ effects, 103 cultural influences, 421 cybernetic hypothesis, 127 decision-making, structured, 86 demographics, 471 deontic model of justice, 50–1, 55, 60 derailment, managerial, 275 development antecedents and moderators, of 239–41 valuing of, 239 development needs, 239 development opportunities, 255 development practices, 241–56 changing landscape of, 254–6 developmental assessment centers, 243–6 developmental assignments, 249–52 characteristics of assignments and those involved, 250–1 developmental experiences and relationships, 238–9 discounting, temporal, 70–1 dispositional approaches to job attitudes, 7–8 distributive justice, 47–8, 52–4, 56, 205 distrust, 442–3 dynamic approach to organizational behavior, 82 dysfunctional work behaviors, 34 ecological perspective on rater assessment, 202 economic exchange antecedents of, 520 outcomes of, 521 see also social and economic exchange emergent states for teams, 316–17 emotional exhaustion, 84, 525 emotional intelligence, 85, 130, 272–3 emotional labor, 109–10, 126 emotions definition of, 487 discrete, 494–5 toxic, 487–95 employee development, 235–57 acceleration of, 254–5 outcomes of, 238 employee–organization relationship (EOR), 499–503, 515, 525–31 definition of, 499 and employee health, 528–9 literature on, 526–7 negative domain of, 527–8 and social/economic exchange, 515 theoretical underpinnings of and developments in, 500–3 employee participation, 405–23 history of, 406–9 linkages between types of, 415–17 multidimensional model of, 409–17

538

theoretical implications of, 417–21 employment relationship, 510–14, 522–3 in particular countries, 511–14 empowerment, 297–9, 422 multiple conceptions of, 418–19 psychological, 410, 415 endowment effect, 80 engagement of employees, 105, 276–7, 391–2 ‘entity’ paradigm in organizational justice theory, 55 entrepreneurship, 132–3, 481 envy, 488–91 definition of, 489 equity theory, 47, 51, 189 error management, 131–2 errors, function of, 128 ethical leadership theory, 59 ethics and justice, 59–60 European Organization for Nuclear Research (CERN), 309–10 evaluation, formative and summative, 225–7 evaluative processes, 488 ‘event’ paradigm in organizational justice theory, 55 exchange relationships, 34, 299–300, 499–531 executive coaching, 246–7 expectancy theory, 71–2, 153–4 ‘expectancy × value’ models, 76–80, 85–6 experience and leadership, 274 expertise, distributed, 295–6 fairness, 33, 46–51, 56–7, 50, 60–1, 205–6 fairness heuristic theory, 50, 55 ‘false hope’ syndrome, 80, 86 feedback, 125–6, 241 on attainment of goals, 147–51 see also multisource feedback; 360-degree feedback field studies, 347–8 financial incentives for managers, 189–91 flexible action patterns, 126 flexible role orientation, 368–9 followers’ view of leadership, 271–2 frame of reference (FOR) training, 200 further research possibilities, 16–17, 36–7, 57–61, 84, 110–11, 130–7, 187–90, 207–8, 218–21, 224, 227–8, 256–7, 323, 349–50, 371–2, 394–5, 417, 421–2, 456–8, 490–5, 529–30 gainsharing, 184 gender-appropriate behaviors, 471 gender differences, 391, 509 gender pay gap, 170, 177, 187 reversal of, 177 general mental ability (GMA), 125, 178 generalized self-efficacy, 29–31 antecedents of, 29–30 consequences of, 30 as a moderator, 30–1

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The SAGE Handbook of Industrial, Work and Organizational Psychology

GitHub community, 309–11 goal conflict, 86 goal content theory (GCT), 107 goal limitations, 155–6 goal orientation, 122, 149 goal phase system (GPS), 74–6, 82, 85, 88 goal setting, 80, 86–7, 145–57, 241 applications of, 156–7 controversies over, 150–3 core tenets of, 146–7 early research on, 145–6 longitudinal studies of, 153 participative or by assignment, 151, 411 theory of, 123, 128, 145–9, 153–5 goals commonly-accepted, 310 conscious and unconscious, 123 development and selection of, 123 and personality, 148–50 primed in the subconscious, 157–9 self-set, 151–2 short-term and long-term, 124 types of, 147–8 Group Engagement Model and Group Value Model, 49–50 group-level justice, 56 guided reflection, 136 guilt, 488–92 definition of, 491 halo, 198–9 ‘HARKing’, 159 Harwood studies, 406 Hawthorne studies, 146, 406 Heineken (company) 254 heuristics, 50, 126 Hewlett-Packard (company), 271–2, 279 hiring process, 55 human relations movement, 406 human resource analytics, 390–4 definition of, 390 implementation of, 392 used to manage resources, 391 human resource integration, 392 human resource management creation of value by, 422 and organizational trust, 453–4 i-deals, 522 identity and identification, 32 impersonal orientation, 106 implicit leadership theory, 271–2 impulsivity, 73–4, 82, 86–7 incentive pay systems, 102–3, 178, 187 individual development, 237–8 definition of, 237 lenses on, 236–7 inducement contribution model, 500–1

industrial democracy, 406–7 informational justice, 48 innovation, definition of, 135 innovation performance and total quality management, 384 input–process–output (IPO) framework, 289 intellectual level, 126 intelligence and leadership, 273–4 interactional justice, 48–51, 53, 56, 205 interdependence, definition of, 310 internalization, 103–4 international comparisons, 14–16 international issues, 527 interpersonal justice, 48 interpersonal trust, 443–7 consequences of, 446–7 contextual factors in, 445–6 interruptions, 134 intervention breadth, 379–80, 399–400 intrinsic values, 108 issue selling, 413–14 job attitudes as constructs, 11 definition of, 3 international research on, 14–16 in relation to task performance, 9 and self-construal, 34 job characteristics model (JCM), 5–6, 359 job demand–control model, 359 Job Descriptive Index (JDI), 9–10 job design, 100 ‘workday’ level of, 370 Job in General (JIG) scale, 9–10 job involvement, 3–5, 8–10 consequences of, 8–9 measurement of, 9–10 job satisfaction, 3–10, 53 consequences of, 8–9 measurement of, 9–10 journal editors’ preferences, 159 ‘just noticeable differences’ in pay, 188 justice concept of, 47 and ethics, 59–60 and leadership, 59 see also distributive justice; interactional justice; procedural justice; organizational justice justice events, 55 knowledge creation of, 388–9 definitions of, 387–8 sharing and dissemination of, 389 storage and retrieval of, 389 knowledge management, 387–90, 394 related to outcomes, 389–90

subject Index

laboratory simulations, 347 layoffs, 57 leader behavior, 33 leader–member exchange (LMX), 53, 200, 299, 450, 499–500, 515–22, 525 leadership and celebrity, 477 charismatic, 450 contrasting views of, 282 definition of, 269–71, 282 directive, 523 empirical literature on, 271–5 and employee participation, 420–1 empowering, 297–9 and justice, 59 language, rhetoric and symbolism associated with, 477–8 organizational, 454 and organizational politics, 475–8 and reputation, 476–7 and social/economic exchange, 523 research on, 283 shared, 300 of teams, 277, 297–301, 450 transactional, 300–1 transformational, 59, 277, 297, 450 leadership competency models, 279–80 leadership development, 281–2 spending on, 281 leadership in organizations, 269–84, 391 assessment of, 280–1 and employee engagement, 276–7 and experience, 274 financial consequences of, 275 impact on organizational effectiveness, 276–9 and intelligence, 273–4 and organizational culture, 277–8 and organizational strategy, 278–9 and personality, 272–3 role of psychology in, 279–82 soft skills of, 272–3 and team functioning, 277 learner-centered training design, 216–17 learning and action regulation theory, 130 climate for, 255 self-efficacy for, 239 learning, training and development in organizations see training and development learning goals, 148 learning strategies, 135, 240–1 leniency on the part of raters, 199–200 lexical analysis, 321–2 locus of control, 149 longitudinal studies, 347–8 management by objectives (MBO), 147 management failure, 275

540

management interventions, 378–82, 392–401 in a cross-cultural context, 392–4 definition of, 378 implementation of, 381–2, 400 signals given by, 380–1, 396–9 sources of, 378–82, 395–6 mapping the environment, 124 materialistic values, 107–8 Mayo Clinic, 277 mental effort, 82 mental models, 127, 136 mentoring, 247–9 definition of, 247 Microsoft, 60 military service, 135 Minnesota Satisfaction Questionnaire (MSQ), 9–10 molar theory, 122 money, use of, 108, 188 monitoring of employees, 109 of the execution of actions, 125 mood, 53 motivation, 108–9 during goal choice, 76–8, 85–6 during goal planning, 78–80, 86–7 during goal striving, 80–2, 87–8 extrinsic and intrinsic, 72, 97, 102–4, 111, 187 and proactivity, 368–9 of the unemployed, 105 for workplace behavior, 53, 57 motivational crowding theory, 80–1 motivational interventions, 84–8 motivational states, 239 multilevel modeling, 83 multilevel trust, 456–7 multinational work groups, 422 multisource feedback (MSF), 200, 204, 208, 241–3 multiteam systems (MTSs), 333–50 coordination and differentiation functions in, 340–1, 344–5 definition and nature of, 334–5 dynamism in, 341–3, 345–6 effectiveness of, 335–6 frameworks for, 336 leadership of, 343 research methods for, 346–7 rifts between teams and bridge-building in, 336–8 structural features of, 337 MyDreamTime Builder, 320 National Basketball Association (NBA), US, 178 national differences and impact on job attitudes, 14–16 National Longitudinal Surveys of Youth (NLSY79), 170, 177–8 neuroeconomics, 74 neuroscience see brain function

541

The SAGE Handbook of Industrial, Work and Organizational Psychology

online dating, 478–9 optimistic judgments, 76–7 organismic integration theory (OIT), 103–6 organization-based self-esteem (OBSE), 180, 184 organizational citizenship behaviors (OCB), 51–8, 505–7, 515, 523–6 Organizational Community Questionnaire (OCQ), 10 organizational culture and climate, 277–8, 453 organizational effectiveness, 279 organizational justice, 46–61, 472–3 consequences of, 52–5 definition of, 46, 48 history of, 47–9 implications for practice, 60 at the organization level, 60 research on, 48–9, 55–7 theories of, 49–52 organizational leadership, 454 organizational strategy, 278–9 organizational trust, 451–6 consequences of, 455–6 determinants of, 452–5 organization-based self-esteem (OBSE), 27–9 antecedents of, 27–8 consequences of, 28–9 as a moderator, 29 organizing in teams, 311–12, 318–24 leveraging of new data sources and analytics for, 318–23 participating performances, 410–14 definition of, 412 participation, psychological, 414–15 participation policies and practices, 410–12 participation-related concepts, 409–10, 417–20 inconsistency in, 417–20 participative decision-making (PDM), 410–11 participative goal-setting, 411 pay, 102–3, 169–91, 392 availability of data on, 186–7 cross-cultural differences, 190 empirical studies and findings on, 170–6 and ethnicity, 177 paucity of research on, 186–7 performance-related, 178, 187 secrecy about, 189 pay changes, 181–6 and antisocial behavior, 184–5 and creativity/innovation, 182 and job attitudes, 182–4 and performance, 181–2 pay levels, 170–80 and compensation attitudes, 178–80 and general job attitudes, 180 and individual characteristics, 177–8 and physical weight, 177 peer justice, 56 perceived organizational support (POS), 53, 499–500, 515

perceptions of politics (POPS), 470, 475 performance appraisal (PA), 196–208, 391 early research on, 197–8 social context of, 196, 204 performance management (PM), 196, 207–8 person–environment fit, 137 personal initiative, 129–30, 134 personality and characteristics of individual employees, 421 and goals, 148–50 and leadership, 272–3 and organizational politics, 470–1 in relation to work, 134–5 and trust, 436 planning, 124–5, 132–3 ‘planning fallacies’, 78, 124 political skill, 471–2 political will, 472 politics in organizations, 469–81 diversity of, 470–2 and entrepreneurship, 481 foundations of research on, 469–70 future directions for, 478–81 and leadership, 475–8 nonlinear effects of, 479 outcomes of, 472–4 perceptions of, 474–5 and reputation, 479–80 ‘positive politics’, 478 ‘positive psychology’, 273 proactivity, 129–30, 368–71 concept of, 105–6 and motivation, 368–9 social characteristics of, 369–70 and work design, 370–1 procedural justice, 47–51, 54, 205, 524–5 procrastination, 71 prospect theory, 74 pro-trust bias, 457–8 psychological contracts, 499, 503–10 breach of, 522 in non-EOR relationships, 509–10 psychological needs, 97–102, 109–11 quality circles (QCs), 405, 411–12 quality management, 382–6, 392–3 definition of, 383 quasi-rationality, 76 race, impact of, 471 rating accuracy of appraisals, 200–6 definition of, 201 rating and rater error, 197–200, 204 reactive strategy, 132 reciprocity between team members, 449 norm of, 9

subject Index

recruitment processes, 392 referent conditions theory (RCT), 50 reflection, concept of, 241 regulation obstacles and regulation uncertainty, 134 regulatory fit, 79 regulatory mode theory, 75 relationships motivation theory (RMT), 108 reputation of leaders, 476–7 and organizational politics, 479–80 resource allocation theory, 81 rewards extrinsic and intrinsic, 80–1 and punishers, 81 see also motivation rhetoric, 477 ‘ripple’ effects of organizational justice, 54–5 risk aversion, 74 risk-taking behavior, 442 Robert Wood Johnson Foundation, 254 Rocket Model, 277 Rubicon Model, 75, 124–5 sabotage, 54 scientific management, 358 Sears (company), 274–5 selection, optimization and compensation (SOC), 137 self-concept, 25–6, 34–7 deconstruction of, 26 definition of, 25 self-construal, 25–6, 31–5 antecedents of, 32–3 consequences of, 33–4 as a moderator, 34 within organizational contexts, 31–4 self-correction training for teams, 221 self-determination theory (SDT), 80–1, 97–110, 129 as a help in understanding other phenomena, 108–10 research on, 109–10 use in building occupational health and safety practices, 109 self-efficacy, 36, 71, 78, 128, 149, 154, 239, 422 definition of, 29 see also generalized self-efficacy self-esteem, 26–7, 149, 180, 184 relational, 36 see also organization-based self-esteem self-evaluation maintenance model, 488 self-evaluations, 25–6, 34–5 within organizations, 26 see also core self-evaluations self-regulation theory, 78–9, 126–7 sentiment analysis, 321–2 sequence of action regulation, 123–5 shame, 488–9, 492–4 definition of, 492 signal detection theory, 83

542

signal sensitivity theory, 183 ‘skill traps’ (Frese and Stewart), 137 small group research, 307 social and economic exchange, 514–15, 523–6 difference between, 501 and the employee–organization relationship, 515 and individual differences, 525 and leadership, 523 social categorization theory, 177 social cognitive theory, 128 social comparison, 487–91 level model, 7 upward and downward, 488 social exchange antecedents of, 516–17 outcomes of, 517–19 role of, 523 social exchange theory, 9, 51, 500–1 social influences theory, 7 social network analysis, 320–1 social networks, 454–5 politics in, 480–1 societal culture, 149–50 Society for Industrial and Organizational Psychology, 160 sociometric badges, 319 soft skills, 272–3 source effects, 51 Spain, 513 spillover effects, 33, 54 sport, 157 strain, occupational, 133–4 ‘strategic core’ concept, 295 stress and stressors, 133–4, 275, 473–4 subconscious processes, 36, 157–9 supervisory justice, 51 ‘superworkers’, 125, 130 System 1 and System 2, 73–4 talent, definition of, 386 talent management, 386–9, 393–4 implementation of, 387 practices of, 388–9 task design, 290–2 future directions for, 292 relationship with team performance, 291–2 task proficiency, 361–5 team adaptation and coordination training (TACT), 220–1 team autonomy, 290–1 team building, 221 team cognition, 317–18 team composition, 292–7, 448–9 future directions for, 296–7 relationship with performance, 294–5 team design, 289–303 future directions for, 302–3

543

The SAGE Handbook of Industrial, Work and Organizational Psychology

team efficacy, 36 team independence, 291–3 team inputs, 313–14 team leadership, 277, 297–301, 450 relationship with performance, 298–9 team mediators, 314–17 team outputs, 317–18 team performance, operationalization of, 319 team processes, 315–16 team size, 296, 310 team theory, extensions to, 312–24 team training and development, 219–22 team trust, 447–51 consequences of, 451 contextual factors in, 450–1 determinants of, 448–51 team work, 136 teams research data analytic approaches to, 320 development of, 307–8 teams, definitions of, 308–11, 324; see also multiteam systems; organizing in teams technology-based training, 217–19 temporal motivation theory, 70–1, 74, 77 theory-building, deductive and inductive, 159–60 360-degree feedback, 241–3 threshold effects on pay, 188–9 thriving, 105 ‘tools-to-theories’ heuristic (Gigerenzer), 82 topic modeling, 321 total quality management (TMQ), 382–6 implementation of, 385–6 and innovation, 384 relationship with performance, 383–5 training design, learner-centered, 216–17 training and development, 215–28 of teams, 219–22 training evaluation, 225–8 types of, 225–6 training transfer, 222–5 evolution of, 222–3 multilevel perspective on, 224 predictors of, 223–4 social network perspective on, 224–5 trait affectivity, 7–8 trickle-down effects, 507, 510 trust at work, 52–3, 435–59 definitions of, 436–43 future directions for practice, 458–9 history of research on, 436

operationalization and measurement of, 440–1, 457 propensity for, 439–42 third parties’ role in development and diffusion of, 455 violation and repair of, 455 see also interpersonal trust; organizational trust; team trust trust dynamics, 457 trustee factors, trustor factors and the trustor-trustee relationship, 444–5 trustworthiness, perceptions of, 439 two-factor theory of work design, 359 uncertainty management theory, 50, 60 United States, 513–14, 530 United States Army, 277 upward influence, 414 ‘use it or lose it’ hypothesis, 137 validity, external (of goal-setting), 150 value, definition of, 71–2 value–precept model, 6 value theory (Locke), 10 VIE theory, 76, 153–4 voice alternative views of, 418 concept of, 407, 412–14 Volvo (company), 407 well-being, 525 Wikipedia, 310 willpower, 81 work adjustment theory, 6–7 work design, 357–73 effects on adaptivity, 365–8 effects on proactivity, 368–71 effects on proficiency, 361–5 historical roots of, 358 and proactive behavior, 370–1 ‘quick response’ mechanism in, 364–6 relational approach to, 364 scope for, 358–60 team-level, 367 work performance, 360–1 work role behaviors, 361 workforce planning, 391 workplace development, process model of, 236–7 workplace events, role of, 8 World Values Survey, 190