Leader Thinking Skills: Capacities for Contemporary Leadership [1 ed.] 1138284335, 9781138284333

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LEADER THINKING SKILLS

This book examines the thinking skills that leaders may need to find success in organizations and institutions, covering a wide array of skills deemed important by key leadership scholars. Bridging theory and practice, chapters summarize major findings with respect to a particular ability, knowledge, or skill, providing theoretical frameworks for understanding how these contribute to leader emergence and performance, and considering implications for leader selection, assessment, and development. The text appraises the existing research on the critical cognitive capabilities that underlie leader problem solving and implications for the assessment and development of leadership potential in real-world settings. The role of creative thinking skills on leader performance is also addressed, bearing on the importance of processes such as problem definition and idea generation, but also using constraints to potentially stimulate creative thought. With contributions from some of the most eminent scholars working in the field of leadership, this book will be an invaluable resource to academics, researchers, graduate students, and professionals interested in leadership and leader skills, industrial and organizational psychology, and business management. Michael D. Mumford is the George Lynn Cross Distinguished Research Professor of Psychology at the University of Oklahoma and serves as the Director of the Center for Applied Social Research. Cory A. Higgs is a graduate of the University of Oklahoma’s Industrial and Organizational Psychology program. He has published peer-reviewed journal articles and book chapters in the areas of leadership, ethics, and creativity.

LEADER THINKING SKILLS Capacities for Contemporary Leadership

Edited by Michael D. Mumford and Cory A. Higgs

First edition published 2020 by Routledge 52 Vanderbilt Avenue, New York, NY 10017 and by Routledge 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2020 Taylor & Francis The right of the Editors Michael D. Mumford and Cory A. Higgs to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Names: Mumford, Michael D., editor. | Higgs, Cory A., 1989– editor. Title: Leader thinking skills : capacities for contemporary leadership / edited by Michael D. Mumford and Cory A. Higgs. Description: 1 Edition. | New York : Routledge, 2020. | Includes bibliographical references and index. Identifiers: LCCN 2019014160 (print) | LCCN 2019015509 (ebook) | ISBN 9781315269573 (ebook) | ISBN 9781138284319 (hardback) | ISBN 9781138284333 (pbk.) | ISBN 9781315269573 (ebk) Subjects: LCSH: Leadership. | Decision making. | Problem solving. Classification: LCC HD57.7 (ebook) | LCC HD57.7 .L4183 2020 (print) | DDC 658.4/092—dc23 LC record available at https://lccn.loc.gov/2019014160 ISBN: 978-1-138-28431-9 (hbk) ISBN: 978-1-138-28433-3 (pbk) ISBN: 978-1-315-26957-3 (ebk) Typeset in Bembo by Apex CoVantage, LLC

CONTENTS

Note on Contributors

Leader Thinking Skills Michael D. Mumford and Cory A. Higgs

  1 Intelligence and Leadership John Antonakis, Dean Keith Simonton, and Jonathan Wai

vii 1 14

  2 Leadership and Information Processing: A Dynamic System, Dual-Processing Perspective Robert G. Lord

46

  3 Uncertainty and Problem Solving: The Role of Leader Information-Gathering Strategies Jay J. Caughron, Teresa Ristow, and Alison L. Antes

71

  4 Are Satisfied Employees Productive or Productive Employees Satisfied? How Leaders Think About and Apply Causal Information David R. Peterson   5 Thinking About Causes: How Leaders Identify the Critical Variables to Act On Michael D. Mumford, Cory A. Higgs, Erin Michelle Todd, and Samantha Elliott

98

122

vi Contents

  6 Leaders’ Shifts in Attention During an Organizational Crisis: Longitudinal Evidence of Responses to a Crisis Within a Top Management Team Ian A. Combe and David J. Carrington   7 Creative Problem Solving: Processes, Strategies, and Considerations for Leaders Kelsey E. Medeiros, Belinda C. Williams, and Adam Damadzic   8 Seeing the Future Through the Past: Forecasting Skill as a Basis for Leader Performance Michael D. Mumford, Mark Fichtel, Tanner Newbold, Samantha England, and Cory A. Higgs   9 Leader Decision Making Capacity: An Information Processing Perspective Shing Kwan Tam, Dawn L. Eubanks, and Tamara L. Friedrich

148

176

205

227

10 Making Sense of Leaders Making Sense Peter Gronn

260

11 Leaders, Teams, and Their Mental Models Jensine Paoletti, Denise L. Reyes, and Eduardo Salas

277

12 Leader Social Acuity Stephen J. Zaccaro and Elisa M. Torres

307

13 Leadership and Monitoring Skills David V. Day, Ronald E. Riggio, and Rowan Y. Mulligan

340

14 Wisdom, Foolishness, and Toxicity in Leadership: How Does One Know Which Is Which? Robert J. Sternberg

362

Index382

CONTRIBUTORS

Alison L. Antes is an industrial-organizational psychologist. She is an assis-

tant professor of medicine and assistant director of the Center for Clinical and Research Ethics at Washington University School of Medicine in St. Louis, Missouri, where she conducts federally funded research on research ethics and the responsible conduct of research. Her research focuses on leadership and management in the scientific workplace, including leadership development for principal investigators. Alison is faculty in an evidence-based professional development program for investigators referred for lapses in research compliance or integrity. She also directs courses in the responsible conduct of research and ethics in biostatistics and data science. She has published on leadership and professionalism in science, ethical decision making, ethics training design and evaluation, and leader cognition. John Antonakis is professor of organizational behavior in the Faculty of Business and Economics of the University of Lausanne, Switzerland. He is a fellow of the Society of Industrial and Organizational Psychology and the Association for Psychological Science, as well as an elected member of the Society of Organizational Behavior. His research is mainly focused on leadership and research methods; he has published in prestigious journals such as Science, Psychological Science, Academy of Management Journal, Journal of Applied Psychology, Intelligence, and Organizational Research Methods, among others. He is editor in chief of The Leadership Quarterly. David J. Carrington is a teaching fellow in strategy and the program director

for BSc Business & Management at Aston Business School, Birmingham, UK. David completed his PhD at Aston University in 2017, focusing on managerial and organizational cognition during crises. This longitudinal study used a variety

viii Contributors

of cognitive research methods with particular focus on causal cognitive mapping through in-depth interviews. David’s research interests are in sensemaking, consensus, strategic cognition, and cognitive shifts. His work to date has been published in The Leadership Quarterly. Jay J. Caughron received his doctorate from the University of Oklahoma. He

is currently an associate professor of psychology at Radford University and has served as the program director of Radford’s Industrial and Organizational Psychology graduate program. Jay has authored articles on a variety of organizational topics, including job analysis, sensemaking, leadership, innovation, and ethical decision making. His research interests include leader cognition, worker motivations, and aggression in the workplace. Jay has also directed many projects with organizations, including leadership training, performance management, job analysis, selection techniques, validating measures, and entrepreneurship. Ian A. Combe is a senior lecturer in strategy, research convener, and research

director in the Marketing and Strategy Department at Aston Business School, Birmingham, UK. Ian has a range of research interests that focus on the strategic management-marketing interface. A major focus of his work is an exploration of different forms of strategic thinking used in the leadership and management of change. In addressing this research agenda he has investigated management cognition within different contexts, such as in retail store management and the health sector. More recently he has investigated leaders’ strategies used to exploit innovation in different sectors. He is experienced in conducting case study research and in the use of cognitive research methods such as sorting technique, laddering technique, and causal cognitive mapping. His work has been published, or is forthcoming, in journals such as The Leadership Quarterly, European Journal of Marketing, Journal of Strategic Marketing, and the Journal of Marketing Management and Managerial Auditing. Adam Damadzic is a doctoral student in the Industrial and Organizational Psychology program at the University of Nebraska at Omaha. His research interests include creativity, ethics, and leadership. David V. Day holds appointments as professor of psychology and academic direc-

tor of the Kravis Leadership Institute at Claremont McKenna College. He also is the Steven L. Eggert ’82 P’15 Professor of Leadership and a George R. Roberts Research Fellow at the college. He is a fellow of the American Psychological Association, Association for Psychological Science, International Association of Applied Psychology, and the Society for Industrial and Organizational Psychology. He has published more than 100 peer-reviewed journal articles, books, and book chapters, many pertaining to the core topics of leadership and leadership development. In 2010, Day received the Walter F. Ulmer Research Award from

Contributors  ix

the Center for Creative Leadership for outstanding, career-long contributions to applied leadership research. Samantha Elliott is a doctoral student in the University of Oklahoma’s program in Industrial and Organizational Psychology. Her research interests include creativity and leadership. Dawn L. Eubanks is an associate professor at Warwick Business School at Uni-

versity of Warwick (2011 to present). Before that she was an associate professor at University of Bath (2008 to 2011). Dawn received her MS and PhD in industrial organizational psychology from the University of Oklahoma (2008), MS in applied psychology and quantitative methods from University of Baltimore (1999), and BA in psychology from Illinois State University (1996). Dawn’s research interests are primarily in the areas of leadership and innovation, with particular interest in destructive leadership. Her current focus is on leader errors and follower reactions to errors. Innovation research includes a focus on how to foster this characteristic across a range of contexts. Dawn is a member is the editorial board for The Leadership Quarterly and served as associate editor for Journal of Occupational and Organizational Psychology. Dawn has been successful earning grants in excess of £100,000 from government funding bodies such as the Engineering and Physical Sciences Research Council (EPSRC). Her work has most recently appeared in journals such as The Leadership Quarterly, Current Directions in Psychological Science, and Computers in Human Behavior. Tamara L. Friedrich is an associate professor of entrepreneurship and innovation

at Warwick Business School. She received her MS (2007) and PhD (2010) in industrial and organizational psychology from the University of Oklahoma, and her BA (2005) in psychology and managerial studies from Rice University. She has worked on a number of research grants with several funding agencies in the US, including the NIH, Army Research Institute, and the Department of Defense. Her primary research interests fall into the broad categories of innovation and leadership, however much of her recent research falls into the intersection of these two areas. For instance, she has studied how organizational leaders may initiate, support, and sustain innovative efforts through hiring, performance management, and creating a climate for creativity. She has also conducted research on leadership in a collective context and how leaders are influenced by the social environment around them. Her work has appeared in several books and journals, including The Leadership Quarterly, Creativity Research Journal, and Human Resource Management Review. Peter Gronn is emeritus professor, University of Cambridge and Monash University. He is a leading scholar in the leadership field where he has contributed to recent developments in distributed leadership, and more recently to the idea

x Contributors

of leadership configurations. He has served on a number of editorial boards, including Leadership Quarterly, Leadership, Leadership and Organization Development Journal, and Educational Administration Quarterly. His most recent books are A University’s Challenge: Cambridge’s Primary School for the Nation (2016), co-edited with James Biddulph, and Just as I Am: A Life of J.R. Darling (2017). Peter is currently researching the life of the prominent Australian economist and educational policymaker Peter Karmel. Cory A. Higgs is a graduate of the University of Oklahoma’s Industrial and Organizational Psychology program. He has published peer-reviewed journal articles and book chapters in the areas of leadership, ethics, and creativity. Kelsey E. Medeiros is an assistant professor of management at the University of Nebraska at Omaha. She earned her doctorate in industrial and organizational psychology with a minor in quantitative psychology from the University of Oklahoma in 2016. She has published peer-reviewed journal articles and book chapters on the topics of creativity, leadership, and ethical decision making. Rowan Y. Mulligan graduated with a Bachelor of Arts in psychology and leadership studies sequence from Claremont McKenna College (Claremont, California, USA). After four years of working with the Kravis Leadership Institute as a research assistant, she wrote her honors senior thesis on the effects of mindfulness on organizational citizenship behavior, as mediated by authentic leadership. A firm believer in the ever-globalizing future, she now pursues an Erasmus Mundus Joint Master Degree in work, organizational, and personnel psychology at Universitat de València (Valencia, Spain) and Università di Bologna (Cesena, Italy). As an aspiring industrial-organizational psychologist, she envisions a career based on the fusion of theory and praxis with a specific emphasis on multicultural competencies. She plans to develop the field of mindfulness and facilitate its application into real-world settings. Michael D. Mumford is the George Lynn Cross Distinguished Research Profes-

sor of Psychology at the University of Oklahoma where he directs the Center for Applied Social Research. He received his doctoral degree from the University of Georgia in 1983 in the fields of industrial and organizational psychology and psychometrics. Mumford is a fellow of the American Psychological Association (Divisions 3, 5, 10, 14), the Society for Industrial and Organizational Psychology, and the American Psychological Society. He has published more than 400 peer-reviewed articles on leadership, creativity, planning, and ethics. Mumford has served as senior editor on The Leadership Quarterly and sits on the editorial boards of the Creativity Research Journal, the Psychology of Aesthetics, Creativity, and the Arts, the Journal of Creative Behavior, Group and Organization Management, and Ethics and Behavior, among other journals. He has served as principal investigator

Contributors  xi

on grants totaling more than $30 million from the National Science Foundation, the National Institutes of Health, the Department of Defense, the Department of Labor, the Department of State, and the Council of Graduate Schools. Mumford is a recipient of the Society for Industrial and Organizational Psychology’s M. Scott Myers award for Applied Research in the Work Place; the Academy of Management’s Eminent Leadership Scholar award; and the Society for the Psychology of Aesthetics, Creativity, and the Arts Arnheim award for lifetime contributions. Jensine Paoletti is a doctoral student in the Rice University’s Department of

Psychological Sciences studying industrial and organizational psychology. Her research interests include teamwork and leadership. David R. Peterson is an assistant professor of management in the James Madison

University College of Business. His research focuses on several cognitive elements of creativity and leadership, including how mental models and schemata affect the way individuals, including leaders, and teams solve complex ambiguous problems that require creative thinking. David received his PhD in industrial and organizational psychology from the University of Oklahoma. His work has been published in outlets such as the Journal of Applied Psychology, the Journal of Organizational Behavior, and Creativity Research Journal. Denise L. Reyes is a doctoral student in Rice University’s Industrial and Organi-

zational Psychology program. Her research interests include leadership, training, and diversity. Ronald E. Riggio is the Henry R. Kravis Professor of Leadership and Organizational Psychology and former director of the Kravis Leadership Institute at Claremont McKenna College and a visiting scholar at Churchill College, Cambridge University. Riggio is a leadership scholar with more than a dozen authored or edited books and more than 150 articles and book chapters. His research interests are in leadership, organizational communication, and social competence. He is part of the Fullerton Longitudinal Study, examining leadership development across the lifespan (from one year of age and through middle adulthood). Besides research on leadership development, he has been actively involved in training young (and not so young) leaders. Teresa Ristow, MA, completed two bachelors degrees at Florida State University,

one in psychology and one in human resource management. Upon the completion of a 2.5-year thesis, she graduated from Radford University with a master’s in industrial/organizational psychology. Teresa has worked mainly in topics including leadership, stress, leader cognition, and sensemaking. These topics span the work on her thesis, an SIOP poster, and work on several academic articles. Additionally, Teresa has worked on many consulting projects through her master’s

xii Contributors

program at Radford University as well as several human resource jobs. Most of the projects she has been involved with have been on the topics of training, performance management, recruitment, and job analysis. Eduardo Salas received his PhD degree (1984) in industrial and organizational

psychology from Old Dominion University. He is currently a professor and Allyn R. & Gladys M. Cline Chair in Psychology at Rice University. Previously, he was trustee chair and professor of psychology at the University of Central Florida and program director for the Human Systems Integration Research Department at the Institute for Simulation & Training. Salas was a senior research psychologist and head of the Training Technology Development Branch of NAVAIR-Orlando for 15 years. Dr. Salas has co-authored over 450 journal articles, has been on the editorial board of many psychology journals. Recently, he edited the special edition for the 100-year anniversary of the Journal of Applied Psychology. In 2016, he was honored with the American Psychological Association’s Lifetime Achievement Award. Dean Keith Simonton is distinguished professor emeritus of psychology at the

University of California, Davis. His more than 500 single-authored publications focus on genius, creativity, aesthetics, and leadership. Honors include the William James Book Award, the George A. Miller Outstanding Article Award, the Theoretical Innovation Prize in Personality and Social Psychology, the Sir Francis Galton Award for Outstanding Contributions to the Study of Creativity, the Rudolf Arnheim Award for Outstanding Contributions to Psychology and the Arts, the E. Paul Torrance Award for Creativity, and three Mensa Awards for Excellence in Research. In 2014, he edited The Wiley Handbook of Genius. Robert J. Sternberg is professor of human development at Cornell University.

He also is honorary professor of psychology at Heidelberg University in Germany. Previously, he was IBM Professor of Psychology and Education at Yale. Sternberg’s BA is from Yale summa cum laude and his PhD from Stanford. Sternberg also has 13 honorary doctorates from 12 countries around the world. Sternberg is a past president of the American Psychological Association and of the Federation of Associations in Behavioral and Brain Sciences, as well as past treasurer of the Association of American Colleges and Universities. Sternberg has been a university dean, provost, and president. Sternberg has won both the James McKeen Cattell and Williams James Awards of the Association for Psychological Science and the Grawemeyer Award in Psychology. Sternberg is a member of the National Academy of Education and the American Academy of Arts and Sciences. He is a fellow of the AAAS, APA, APS, and AERA. Shing Kwan Tam is a PhD student in management at the Warwick Business

School. Her area of research is primarily focused on cross-cultural leadership in

Contributors  xiii

organizations and how this subject is influenced by leader identity construction. Before beginning her doctoral program, she worked in the leadership development and talent management functions in different multinational corporations with the key responsibility of developing high potential leaders in different continents. In her last leadership role at the corporate level as the head of Asia Pacific Talent Management, she led a cross-cultural team with 40 members in Australia, China, Hong Kong, Malaysia, Singapore, and Taiwan. Her first book chapter, “Leadership Across Cultural Context”, included in the book International Human Resource Management: A Case Study Approach, was published in 2017. Erin Michelle Todd is a doctoral student in the University of Oklahoma’s pro-

gram in Industrial and Organizational Psychology. Her research interests include leadership, decision making, ethics, and creativity. Elisa M. Torres is a doctoral student studying industrial and organizational

psychology at George Mason University in Fairfax, Virginia, under Stephen J. Zaccaro. She has published in organizational and health services journals on leadership, organizational climate, and implementation outcomes. She is involved with a number of projects regarding leader and leadership development, multiteam systems, team and system leadership, and multidisciplinary teams. She has conducted research in contexts including emergency response, allied healthcare, and scientific collaboration. Prior to beginning her doctorate, Elisa worked on several National Institutes of Health grants focusing on developing psychometrically sound measurement tools and managing a leadership intervention that focused on leadership coaching and leader skill development for first-level leaders. Jonathan Wai is assistant professor of education policy and psychology, the twenty-first Century Endowed Chair in Education Policy at the University of Arkansas in the Department of Education Reform, and holds a joint (courtesy) appointment in the Department of Psychology. His research examines how individual and contextual factors collectively impact the development of educational and occupational expertise across a variety of domains. He has published in Perspectives on Psychological Science, Intelligence, Journal of Educational Psychology, Policy Insights from the Behavioral and Brain Sciences, among others, and has won multiple international Mensa Awards for Research Excellence. Belinda C. Williams graduated with her PhD from the University of Texas

at Arlington where she studied industrial and organizational psychology with research specializing in group creativity and applied work in selection assessment and training/development consulting. Williams currently works as an assessment consultant and maintains her licensure as a licensed professional counselor specializing in crisis evaluation and assessment.

xiv Contributors

Stephen J. Zaccaro is professor of psychology at George Mason University, Fair-

fax, Virginia. He is also an experienced leadership development consultant. He has written over 150 journal articles, book chapters, and technical reports on leadership, group dynamics, and team performance. He has authored a book titled The Nature of Executive Leadership: A Conceptual and Empirical Analysis of Success and has co-edited five other books on the topics of organizational leadership, leader development, multiteam systems, cybersecurity, and occupational stress. He has worked with executives and managers from private industry as well as from the educational, nonprofit, government, and military sectors. He serves on the editorial board of The Leadership Quarterly, and he is an associate editor for the Journal of Business and Psychology and for Military Psychology. He is a fellow of the Association for Psychological Science, and of the American Psychological Association, Divisions 14 (Society for Industrial and Organizational Psychology) and 19 (Military Psychology).

LEADER THINKING SKILLS Michael D. Mumford and Cory A. Higgs

People see leaders as romantic figures—people whom they attach themselves to at a deep emotional level (Meindl, 1995). People often acquire their identity from their association with a leader (Shamir, House, & Arthur, 1992). They seek positive feedback from their leaders, hoping that their service to a leader will result in attaining valued outcomes (Graen & Uhl-Bien, 1995). People want leaders to acknowledge their existence and consider their unique needs (Fleishman, 1953). Clearly, we seek much from those we consider leaders—perhaps too much. In keeping with these observations, much—or at least a substantial portion— of research on leadership has been based on models of the kinds of behavior leaders should exhibit to enthrall, motivate, and ensure the commitment of followers. Transformational leadership measures ask followers to indicate whether they believe their leaders evidence behaviors marking inspirational motivation, idealized influence, intellectual stimulation, and individualized consideration (Bass & Bass, 2009). Authentic leaders are held to evidence behaviors marking self-awareness, relational transparency, and balanced processing (Avolio & Gardner, 2005). Servant leaders are held to evidence behaviors that empower others, help others grow, and serve others first (Liden, Wayne, Zhao, & Henderson, 2008). Ethical leaders are held to evidence behaviors indicating fairness, power sharing, and a concern for sustainability (Kalshoven, Den Hartog, & De Hoogh, 2011). Although followers’ assessments of all these behaviors have been found to predict various outcomes in real-world settings, outcomes ranging from turnover to citizenship behaviors (Yukl, 2011), one is left with a queasy feeling about these varied behavioral models of leadership. It seems students of leadership are often simply proposing their ideal of leadership and finding that leaders who express their ideal have more impact on, or are evaluated more favorably, by their followers. As Mumford and Fried (2014) have pointed out, however, there is reason,

2  Michael D. Mumford and Cory A. Higgs

in fact many reasons, to be cautious with regard to these ideological models of idealized leader behavior. Consider just a few concerns. All these models assume the critical issue is how followers appraise leaders. What one must remember here, however, is that people’s appraisals of others are typically, almost solely, a function of their affective appraisal of others. Although people’s affective reactions to others in roles with significant control over their outcomes may influence follower behavior in many ways, a question of parsimony comes to fore. Are we really measuring anything beyond simple affect? In other words, agreement with the statement, “I like my boss”. The measures used in studies of leadership are one issue. At a substantive level, however, another issue comes to fore. Can all these behavioral theories really be true? Can a leader simultaneously be a servant, transformational, ethical, and authentic? If they can exhibit all these behaviors (i.e., behaviors exhibited in attempts to appeal to various followers), then the effective leader is a shapeshifting chameleon. Given the observations of Machiavelli (Paulhus & Williams, 2002), perhaps leaders are ultimately social chameleons. However, this observation seems to contradict the steadfastness of our greatest leaders—Roosevelt, Churchill, and Eisenhower, to mention a few. Perhaps more central, however, is the frame scholars employing a behavioral model apply in attempts to understand leadership. Implicit in all behavioral models is the assumption that leadership exists in the eyes of the follower. This frame, however, ignores the possibility that leadership may exist in the eye of the institution, in the eye of peers, in the eye of historians, and in the eyes of society.

Functional Leadership Recognition of the many different lenses through which we can understand, and seek to study, leadership presents the field with a core, critical problem: What exactly is leadership? At a global level, most agree with the proposition that leadership involves the exercise of social influence (Bass & Bass, 2009; Yukl, 2011). The question that arises here, however, is what is the effective exercise of social influence? One issue that arises is how influence should be exercised. In point of fact, influence can be, and often is, exercised in ways followers may not really see. A leader establishing budget priorities exercises exceptional influence, yet followers may know little about why these priorities were established. Thus, the issue that arises is exactly what is meant by the term “effective”. Did the budget priorities result in better or worse performance? These observations led Fleishman et al. (1991) to argue that the effective exercise of influence is best understood from a pragmatic, or functional, perspective (Mumford & Van Doorn, 2001). In their view, leaders occupy and are granted occupancy of a distinct role in a team, firm, or institution. Teams, firms, and institutions define these roles in an attempt to ensure viable, or adaptive, performance

Leader Thinking Skills  3

on the part of the team, firm, or institution. Thus, in this functional, or pragmatic, model leaders exercise influence to improve the survival and performance of the team, firm, or institution. If leaders exercise influence selectively, as the occupant of a role in a team, firm, or institution, to ensure survival and/or performance, it should be clear that the basis for leadership is discretion. Leaders must choose when, where, and how to exercise influence as indicated by their understanding of the needs of the team, firm, or institution. In keeping with this observation, Jacobs and Jaques (1991) argue that discretion (i.e., decision making) is the basis for leadership with the time frame over which discretionary decisions play out, increasing as one ascends a leadership hierarchy. This view of leadership is nicely summarized in George H. W. Bush’s comment, “I am the decider”. Teams, firms, and institutions, however, involve tens of thousands of decisions every day. This simple observation broaches another question: Exactly what types of decisions do teams, firms, and institutions expect leaders to make? Mumford, Zaccaro, Harding, Jacobs, and Fleishman (2000) argued the decisions leaders are asked to make are those that cannot be made by others. In other words, when people are presented with too complex a problem, they bring the problem to the leader and ask the leader to make a decision. Thus, leadership roles, in a functional or pragmatic model, are held to require a leader to solve complex, novel, illdefined problems—problems arising in a distinctly social context (e.g., the team, the firm, the institution). In technical terms, social problem solving is viewed as a form of complex cognition. Many variables may act to influence the nature and success of leaders problem-solving efforts. For example, Zaccaro, Green, Dubrow, and Kolze (2018) argue personality variables, such as extraversion and openness, along with mastery motives and learning goals, influence peoples’ success in solving the kinds of problems they are presented with in leadership roles. Ligon, Hunter, and Mumford (2008) have shown early life experiences, by shaping peoples’ understanding of their world, will influence the kinds of problems they view as significant and how they go about solving these problems. The kind of experience leaders have, whether it be case-based knowledge obtained from personal experience or stories (Vessey, Barrett, & Mumford, 2011) or case-based knowledge acquired from others (Watts, Steele, & Mumford, in press), will shape how they go about problem solving. Still, other work indicates that leader’s expertise in solving similar problems will influence their performance (Goodall, McDowell, & Singell, 2014). Although knowledge, experience, personality, and motivation all act to influence how people go about solving the complex, novel, ill-defined problems that will be presented to them as an occupant of a leadership role, both Mumford, Todd, Higgs, and McIntosh (2017) and Zaccaro et al. (2018) argued that the cognitive skills, or social cognitive skills, people have acquired as a function of experience will prove one of the most powerful predictors of effective leader performance. Our goal in the present volume is to examine the key skills people

4  Michael D. Mumford and Cory A. Higgs

need to solve the kinds of problems they will be presented with whenever they occupy a leadership role.

Early Studies An initial study examining the impact of cognitive skills on leader performance was conducted by Connelly et al. (2000). In this study, some 1,800 army officers were asked to complete a battery of cognitive skill measures where their performance in resulting critical incidents was assessed, along with rank attained and medals won. For example, creative thinking skills were assessed by asking leaders, second lieutenants to full colonels, to anticipate potential consequences to unlikely events. Social judgment skills (i.e., skills underlying wise problem solving) were assessed by asking officers to analyze the reasons an effort failed in a series of business social problem-solving scenarios. Expertise was assessed vis-à-vis the accuracy of task classifications. The findings emerging from this study indicated that performance in military leadership roles was very strongly related to the thinking skills evidenced by these officers. Rank attained, medals won, and performance in resulting critical incidents were positively correlated, in the .40s, with performance on these skill measures. In a noteworthy follow-up study, Zaccaro et al. (2015) examined how these skills predicted continuance in the army 20 years later. Bearing in mind continuance in an “up or out” system such as the United States Army marks good performance, it was found that these skill measures predicted, again at the .40 level, continuance over a 20-year period. Thus, skill measures are not just good predictors of leader performance, but the predictions hold up over long periods of time. The impact of skills on performance in leadership roles is not simply a matter of the army. For example, Marcy and Mumford (2010) asked undergraduate students to work on a university simulation exercise where exercise scores served as the outcome on performance variables. Prior to starting work on this simulation exercise, the goal of which was to improve institutional teaching effectiveness, participants were asked to work through a set of self-paced training modules where they were given more effective strategies for thinking about causes. Marcy and Mumford found training in causal analysis skills resulted in better performance on this simulation exercise. Indeed, Hester et al. (2012) have shown training in causal analysis skills also contributes to the quality of performance when undergraduates are asked to assume the role of principal (i.e., the leader) of an experimental secondary school and formulate a vision for leading this school. These skills, however, do not influence performance only on army or educational leadership tasks (Marcy & Mumford, 2007). For example, Marta, Leritz, and Mumford (2005) asked teams of undergraduates to formulate plans for turning around a failing firm. Turnaround plans were appraised by judges for quality and originality. After completing their plans, team members nominated their leader.

Leader Thinking Skills  5

Before starting work, the planning skills of all team members were assessed. It was found that those teams whose leaders had the strongest planning skills produced the strongest turnaround plans. In still another study, Byrne, Shipman, and Mumford (2010) examined another key leadership skill—forecasting. In this study, participants were asked to assume the role of a manager evaluating follower’s ideas for a marketing campaign. As they worked on the low-fidelity simulation exercise, they received “emails” asking them to forecast the implications of the ideas presented. Written responses to this email were content analyzed with respect to some 27 dimensions, and these ratings were factored. It was found that the extensiveness of forecasts was correlated in the mid .40s, with the quality, originality, and elegance of final campaigns provided by participants. Moreover, forecasting was more strongly related to performance than other traditional differential variables. Perhaps even more significant, however, is that these findings have been confirmed (i.e., replicated) in other studies by Shipman, Byrne, and Mumford (2010) and McIntosh, Mulhearn, and Mumford (in press). Taken as a whole, these studies lead to three key conclusions. First, cognitive skills are very strongly related to leadership performance. Second, these relationships extend across a variety of leadership tasks, being evident in military, marketing, and educational leadership tasks. Third, these strong predictive relationships replicate across studies while the predictive power of these skill measures are maintained over rather long periods of time. These observations are not just of interest in a theoretical sense, although they do provide some powerful evidence for the functional, or pragmatic, model of leadership, they are also of interest for three practical reasons. First, Mumford et al. (2017) have argued that given the strong, stable, predictive power of these cognitive skill measures, assessment of leader potential should be based on the skills evidenced by candidates for leadership roles. For example, it is not difficult to identify the key problems arising in leadership positions, and asking people to forecast the implications of potential problem solutions seems quite feasible if forecasts are appraised by experts. Similarly, one might give candidates relevant problems and ask them to identify the causes they would try to act on in resolving these problems. These examples, although somewhat speculative, clearly indicate that skill appraisal might contribute much to our attempts to assess people’s potential to perform well in leadership roles. Second, skills—including cognitive skills—can be developed by providing people with better strategies for working through problems where these skills are required. Indeed, the Marcy and Mumford (2007, 2010) studies have provided a clear demonstration of the value of developing leadership by seeking to provide people with better strategies for skill execution. The value of skill development in preparing people for leadership roles, however, is not limited to the laboratory. Mumford, Marks, Connelly, Zaccaro, and Reiter-Palmon (2000) examined the effectiveness of various army leadership development programs. They found that

6  Michael D. Mumford and Cory A. Higgs

the most effective programs were those focusing most “tightly” on development of requisite thinking skills. Moreover, the kind of career experiences that accelerated leader development were career experiences most likely to contribute to the development of cognitive skills. Third, the understanding of the cognitive skills required in leadership roles allows us to create conditions where skill execution, and the subsequent leader performance, is optimized. For example, management information systems might be developed in such a way as to tab or highlight critical causes. Identification of potential critical causes can then be expected to contribute to better leader performance. In formulating strategy plans, teams might be expressly asked to forecast the implications of plan execution, both good and bad outcomes, with these forecasts being used a basis for plan revision.

Leader Skills These practical implications of understanding how cognitive skills shape performance in leadership roles implies that we need a better understanding of the key cognitive skills that make performance in leadership roles possible. The present volume is intended to serve this need. In fact, many of the most eminent scholars working in the field of leadership have contributed to this volume. The chapters they have prepared establish some important new directions for leadership research. We will begin with the work of Antonakis, Simonton, and Wai (this volume) on the relationship between intelligence, speed and depth of information processing, and leader performance. For many years, intelligence has been considered a positive predictor of leader emergence and performance. However, the obtained relationships between measures of intelligence and leader emergence and performance were rather weak, and highly inconsistent, across samples. These weak, inconsistent relationships, although stronger and more stable after correcting for statistical artifacts (Lord, De Vader, & Alliger, 1986), led many to discount the importance of cognition (generally) and cognitive skills, specifically to performance in leadership roles. Antonakis, Simonton, and Wai (this volume) bring these assumptions to question. They argue that leaders must not only be able to solve problems but also communicate these solutions to followers. Thus, they argue that leaders need to be more intelligent, but not too much more intelligent, than their ­followers— roughly a quarter to a half standard deviation more intelligent. When one recognizes, however, that leadership roles in firms are hierarchically structured, these findings imply, a point noted by Antonakis et al. (this volume), that senior executives may need to be highly intelligent—in the top 5%. As a result, there is strong reason to suspect that intelligence is, in fact, a crucial determinant of performance in leadership roles. Antonakis, Simonton, and Wai (this volume), however, note that other capacities will be required beyond intelligence to ensure adequate performance in

Leader Thinking Skills  7

leadership roles. One key capacity required for applying intelligence is knowledge or expertise. Lord (this volume) examines how leaders use knowledge. He argues knowledge is critical to performance in leadership roles. He notes, however, that some information processing is automatic, where other information processing is conscious. Perhaps just as important, he argues that the information used by leaders (e.g., knowledge and expertise) is often layered, involving individuals, teams, and systems. The complex layering of information leaders must deal with in solving problems implies a new question. How do people working in leadership roles gather the information they need to identify and solve problems? Caughron, Ristow, and Antes (this volume) examine the impact of leader’s information gathering skills. They argue that certain information gathering strategies, such as pattern recognition or experimentation, contribute to leader’s success in acquiring requisite information. Moreover, they argue leaders will invest time and resources in information gathering only when uncertainty is evident. One implication of this observation is that identifying areas of uncertainty may prove central to leader information gathering and, subsequently, performance. Another implication, however, is that strong social consensus may undermine information gathering and, thus, inhibit effective problem solving by leaders. Underlying Caughron, Ristow, and Antes’ (this volume) observations is an assumption that information gathering is cognitively costly and information is gathered for a reason. Peterson (this volume) argues that information is gathered to identify the causes leaders need to act on. He shows causal analysis skills are a powerful predictor of leader performance. Perhaps just as important, he notes that a number of biases may also act to distort viable causal analysis. The criticality of causal analysis to leader performance and the potential operation of various biases in causal analysis led Peterson (this volume) to argue that leader development efforts may prove especially effective when they seek to minimize biased causal analysis, while simultaneously providing stronger strategies for better analysis of causes. Mumford, Higgs, Todd, and Elliott (this volume) make many of the same arguments as Peterson (this volume) with respect to the importance of causal analysis skill to leader performance. However, they note that causal analysis may prove especially difficult in complex organizational systems. Moreover, they argue that effective causal analysis may prove especially critical when leaders are asked to address rapidly emerging, high-stakes problems—or in other words, crises. This observation is noteworthy because it implies leaders may need substantial causal analysis skills to address various crises—as well as relevant knowledge to draw from in attempts to identify actions for crisis resolution. Crises, however, require leaders to focus on or attend to the crisis. This observation is noteworthy because it implies leaders will need substantial attentional management skills. Indeed, the many varied crises brought to leader’s attention suggests that both selective attention and flexibility in attention may be critical

8  Michael D. Mumford and Cory A. Higgs

determinants of leader performance. Combe and Carrington (this volume) examine how attentional management skills contribute to the performance of top management teams in addressing the crises broached by environmental change. They note that although leaders are often unsure what to attend to, attention is often directed by experience, or expertise, along with sustained attentional capacity contributing to identification of the key crises that define the need for problem solving. Crises, of course, often require novel problem solutions. The production of novel problem solutions is held to depend on creative thinking skills. Medeiros, Williams, and Damadzic (this volume) examine the impact of creative thinking skills on performance in leadership roles. They note leader’s creative problem solutions are often constrained, but by the same token, constraints may serve as a stimulus for creative thought. They then go on to show that key creative thinking skills, such as problem definition, conceptual combination, and idea generation, are critical determinants of performance in leadership roles. Not only are these conclusions consistent with the findings obtained in earlier studies (e.g., Zaccaro et al., 2015), but they point to a need to think about leaders differently—not just as judges or as social actors, but as creative problem solvers. Creative problem solutions, however, sometimes work out, and sometimes do not work out. Moreover, to execute creative problem solutions leaders must formulate plans. Planning has been defined as the mental simulation of future actions (Mumford, Schultz, & Van Doorn, 2001). As the mental simulation of the outcomes of future actions, one would expect that forecasting would prove crucial to both the evaluation of creative problem solutions, as well as planning potential courses of action. Mumford, Fichtel, Newbold, England, and Higgs (this volume) provide rather compelling evidence indicating that forecasting skills are critical to both planning and leader performance. Notably, it is not forecasting positive outcomes that contributes to leader performance. Rather, it is considering a range of potential outcomes of actions, both positive and negative, that contribute to leader performance. Forecasting both positive and negative outcomes of the actions being contemplated to solve a problem is useful, in part, because it provides a basis for decision making. Tam, Eubanks, and Friedrich (this volume) examine how decision-­ making skills contribute to leader performance. They argue leader decision making is not simply an evaluation of potential gains and losses. Rather, they argue the key skill leaders bring to decision-making scenarios is sensemaking—placing the decision and it’s outcomes in a broader organizational context. Although effective leaders sensemaking may require a number of other skills, such as emotional management, they argue leader’s sensemaking is often based on mental models, with leader sensemaking establishing a context in which followers can understand the significance of a decision for organizational action. Gronn (this volume) extends this argument. He examines how leaders go about sensemaking in the distinctly social, highly complex setting of institutions

Leader Thinking Skills  9

or firms. He notes effective leader sensemaking depends not only on expertise and experience but also on political skill and the effective use of power. These observations are noteworthy because they suggest political skill, a social-cognitive skill, may also provide some importance as we seek to understand the skills that make leader performance possible (Yammarino & Mumford, 2012). Like Tam, Eubanks, and Friedrich (this volume), Gronn (this volume) also notes that sensemaking will depend on how leaders understand their world. People typically understand complex events, or complex problems (Finke, Ward, & Smith, 1992), through mental models—models describing critical causes of key outcomes. Paoletti, Reyes, and Salas (this volume) examine how leaders employ mental models in problem solving. They argue that leaders acquire viable mental models as a function of experience and expertise, as well as reflection on this experience. More centrally, they note that leaders’ construction of a viable mental model provides the basis for formulating the vision needed to lead a team, firm, or institution (Strange & Mumford, 2005). With effective articulation of their model for understanding problems, leaders establish shared mental models within teams. Shared mental models have been found to be among our most powerful predictors of team performance. Thus, vision and team performance, ultimately, appear to be based on leader cognition and the thinking skills (e.g., causal analysis, active prediction of outcomes, reflection) that allow for the acquisition and effective articulation of leaders’ mental models for understanding problems. Of course, in order to induce a shared mental model leader’s must effectively articulate their vision to others—or in other words, followers. Followers are people interacting in a complex environment who may or may not “hear” what the leader has to say. Thus, leaders must gather, understand, and use information gathered about followers. Put differently, people are a noteworthy aspect of the leaders’ problem space. Accordingly, Zaccaro and Torres (this volume) argue a key skill needed by leaders is social acuity—or accurate perception and understanding of others. Zaccaro and Torres (this volume) note that social acuity requires social scanning, social construal skills, social forecasting, and activation of social resources. Thus, social acuity is a particularly complex skill that also involves the appraisal of affordances, potential opportunities and risks, and the specification of appropriate goals—goals attached to both the needs of the follower, and the needs of the tasks/problems at hand. We need to know more, far more, about how leaders analyze affordances and define goals with respect to followers (Strange & Mumford, 2005). Goals, shared mental models, and the leaders’ vision, however, provide a basis for monitoring both follower and leader performance. Although we commonly assume leaders must monitor followers, little research has examined more and less effective monitoring practices on the skills leaders need to monitor followers. Day, Riggio, and Mulligan (this volume) examine leader monitoring skills. They identify the skills leaders need to monitor individual and team task performance—for example, appraisal flaws, monitoring changes in context, and attending to individual and

10  Michael D. Mumford and Cory A. Higgs

team feedback information. They note, however, that leader monitoring may be a far more complex activity than simply monitoring production performance. They suggest that leaders must actively scan the organizational environment and appraise the implication of these observations. Moreover, they argue that skilled leaders must monitor their own behavior. The idea that self-monitoring is a critical leadership skill implies leader problem solving and leader actions must be balanced, and appropriate, within the context of a potential leadership role. These observations are noteworthy because they imply leader role performance will also require wisdom (McKenna, Rooney, & Boal, 2009). Sternberg (this volume) also argues that leader performance depends on wisdom. He asserts that wisdom ultimately involves taking a balanced perspective with respect to the problem at hand, bearing in mind the context in which this problem arose and the leaders tacit, case-based, practical experience in addressing similar problems. Perhaps just as important, Sternberg (this volume) holds that wise leadership typically results in ethical action. Thus, cognition, and leader thinking skills, are not seen as “dry”, emotionless, objective thinking skills. Instead, he views leader thinking skills as a powerful and evocative force shaping human life, where these thinking skills must involve wisdom and a concern for the people and the institutions in which they work.

Conclusions Our summary of the chapters presented in this volume is, in fact, far too simple. Each of the contributions examines these skills in far more detail than one can address in a general introduction. By the same token, a careful reading of all their contributions provides a clear, compelling argument as to why we need to attend to cognitive skills in the study of leadership. The cognitive skills people bring to leadership roles are a powerful, perhaps the most powerful, determinant of successful performance of those who occupy leadership roles. We may all want a nice leader, but for teams, firms, and institutions, it is more important we have leaders who have the skills needed to resolve significant problems. Additionally, the present volume points to a number of promising directions for future research. Consider a few examples: The Peterson (this volume) and Combe and Carrington (this volume) chapters point to the value of research examining what types of causes leaders focus on when confronted with crises. The Paoletti, Reyes, and Salas (this volume) and Medeiros, Williams, and Damadzic (this volume) chapters broach the question as to how leader mental models are used to identify and/or attempt to manipulate constraints. The Zaccaro and Torres (this volume) and Sternberg (this volume) chapters broach the question as to how leader social acuity contributes to the development of wisdom. Of course, many other research questions are posed by the various chapters presented in this volume. Thus, the present volume should not be viewed as the final statement of

Leader Thinking Skills  11

leader thinking skills. Instead, we hope it serves as an impetus for future research in an area we know to be critical in accounting for leader performance. Indeed, such research is not simply a matter of academic interest. All these chapters have some noteworthy practical implications. For example, the Antonakis, Simonton, and Wai (this volume) chapter suggests we might consider work group intelligence levels when setting standards for the intelligence we need among candidate leaders. The Lord (this volume) and Sternberg (this volume) chapters indicate a need to plan leader career development experiences and assignments in such a way as to ensure leaders have acquired the experience/expertise needed for effective execution of key leader thinking skills such as causal analysis and forecasting. The Tam, Eubanks, and Friedrich (this volume) chapter suggests we need new types of decision-making training for leaders that takes into account not only economic optimization but also how leader decisions contribute to follower sensemaking and institutional wisdom. These, as well as other, applications suggested by the chapters presented in this volume point to the practical value of understanding how leaders think through the complex social problems they are asked to solve. If the present volume encourages systematic, sustained, research on leader thinking skills, and we can develop this research in such a way as to build intervention systems that actually improve leader thinking skills, we may be able to develop the kind of thoughtful leaders that will improve the lives of those working in a host of different institutions.

Acknowledgments We would like to thank Tristan McIntosh, Tyler Mulhearn, Erin Michelle Todd, Robert Martin, and Samantha Elliott for their contributions to the present effort. Correspondence should be addressed to Dr. Michael D. Mumford, Department of Psychology, The University of Oklahoma, Norman, Oklahoma, 73019, or [email protected].

References Avolio, B. J., & Gardner, W. L. (2005). Authentic leadership development: Getting to the root of positive forms of leadership. Leadership Quarterly, 16, 315–338. Bass, B. M., & Bass, R. (2009). The Bass handbook of leadership: Theory, research, and managerial applications. New York, NY: Simon & Schuster. Byrne, C. L., Shipman, A. S., & Mumford, M. D. (2010). The effects of forecasting on creative problem-solving: An experimental study. Creativity Research Journal, 22, 119–138. Connelly, M. S., Gilbert, J. A., Zaccaro, S. J., Threlfall, K. V., Marks, M. A., & Mumford, M. D. (2000). Exploring the relationship of leadership skills and knowledge to leader performance. Leadership Quarterly, 11, 65–86. Finke, R. A., Ward, T. B., & Smith, S. M. (1992). Creative cognition: Theory, research, and applications. Cambridge, MA: MIT Press.

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Fleishman, E. A. (1953). The description of supervisory behavior. Journal of Applied Psychology, 37, 1–6. Fleishman, E. A., Mumford, M. D., Zaccaro, S. J., Levin, K. Y., Korotkin, A. L., & Hein, M. B. (1991). Taxonomic efforts in the description of leader behavior: A synthesis and functional interpretation. Leadership Quarterly, 2, 245–287. Goodall, A. H., McDowell, J. M., & Singell, L. D. (2014). Leadership and the research productivity of university departments. IZA Discussion Paper Series. Graen, G. B., & Uhl-Bien, M. (1995). Relationship-based approach to leadership: Development of leader-member exchange (LMX) theory of leadership over 25 years: Applying a multi-level multi-domain perspective. Leadership Quarterly, 6, 219–247. Hester, K. S., Robledo, I. C., Barrett, J. D., Peterson, D. R., Hougen, D. P., Day, E. A., . . . Mumford, M. D. (2012). Causal analysis to enhance creative problem-solving: Performance and effects on mental models. Creativity Research Journal, 24, 115–133. Jacobs, T. O., & Jaques, E. (1991). Executive leadership. In R. Gal & A. D. Manglesdorff (Eds.), Handbook of military psychology (pp. 431–447). Chichester, England: Wiley. Kalshoven, K., Den Hartog, D. N., & De Hoogh, A. H. (2011). Ethical leadership at work questionnaire (ELW): Development and validation of a multidimensional measure. Leadership Quarterly, 22, 51–69. Liden, R. C., Wayne, S. J., Zhao, H., & Henderson, D. (2008). Servant leadership: Development of a multidimensional measure and multi-level assessment. Leadership Quarterly, 19, 161–177. Ligon, G. S., Hunter, S. T., & Mumford, M. D. (2008). Development of outstanding leadership: A life narrative approach. Leadership Quarterly, 19, 312–334. Lord, R. G., De Vader, C. L., & Alliger, G. M. (1986). A meta-analysis of the relation between personality traits and leadership perceptions: An application of validity generalization procedures. Journal of Applied Psychology, 71, 402–410. Marcy, R. T., & Mumford, M. D. (2007). Social innovation: Enhancing creative performance through causal analysis. Creativity Research Journal, 19, 123–140. Marcy, R. T., & Mumford, M. D. (2010). Leader cognition: Improving leader performance through causal analysis. Leadership Quarterly, 21, 1–19. Marta, S., Leritz, L. E., & Mumford, M. D. (2005). Leadership skills and the group performance: Situational demands, behavioral requirements, and planning. Leadership Quarterly, 16, 97–120. McIntosh, T., Mulhearn, T., & Mumford, M. D. (in press). Taking the good with the bad: The impact of forecasting timing and valence on idea evaluation and creativity. Psychology of Aesthetics, Creativity, and the Arts. McKenna, B., Rooney, D., & Boal, K. B. (2009). Wisdom principles as a meta-theoretical basis for evaluating leadership. Leadership Quarterly, 20, 177–190. Meindl, J. R. (1995). The romance of leadership as a follower-centric theory: A social constructionist approach. Leadership Quarterly, 6, 329–341. Mumford, M. D., & Fried, Y. (2014). Give them what they want or give them what they need? Ideology in the study of leadership. Journal of Organizational Behavior, 35, 622–634. Mumford, M. D., & Van Doorn, J. R. (2001). The leadership of pragmatism: Reconsidering Franklin in the age of charisma. Leadership Quarterly, 12, 279–309. Mumford, M. D., Marks, M. A., Connelly, M. S., Zaccaro, S. J., & Reiter-Palmon, R. (2000). Development of leadership skills: Experience and timing. Leadership Quarterly, 11, 87–114. Mumford, M. D., Schultz, R. A., & Van Doorn, J. R. (2001). Performance in planning: Processes, requirements, and errors. Review of General Psychology, 5, 213.

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Mumford, M. D., Todd, E. M., Higgs, C., & McIntosh, T. (2017). Cognitive skills and leadership performance: The nine critical skills. Leadership Quarterly, 28, 24–39. Mumford, M. D., Zaccaro, S. J., Harding, F. D., Jacobs, T. O., & Fleishman, E. A. (2000). Leadership skills for a changing world: Solving complex social problems. Leadership Quarterly, 11, 11–35. Paulhus, D. L., & Williams, K. M. (2002). The dark triad of personality: Narcissism, Machiavellianism, and psychopathy. Journal of Research in Personality, 36, 556–563. Shamir, B., House, R. J., & Arthur, M. B. (1993). The motivational effects of charismatic leadership: A self-concept based theory. Organization Science, 4, 577–594. Shipman, A. S., Byrne, C. L., & Mumford, M. D. (2010). Leader vision formation and forecasting: The effects of forecasting extent, resources, and timeframe. Leadership Quarterly, 21, 439–456. Strange, J. M., & Mumford, M. D. (2005). The origins of vision: Effects of reflection, models, and analysis. Leadership Quarterly, 16, 121–148. Vessey, W. B., Barrett, J., & Mumford, M. D. (2011). Leader cognition under threat: “Just the Facts”. Leadership Quarterly, 22, 710–728. Watts, L. L., Steele, L. M., & Mumford, M. D. (in press). Making sense of pragmatic and charismatic leadership stories: Effects on vision formation. Leadership Quarterly. Yammarino, F. J., & Mumford, M. D. (2012). Leadership and organizational politics: A multilevel review and framework for pragmatic deals. In G. R. Gerris & D. C. Treadway (Eds.), Politics in organizations: Theory and research considerations (pp. 323–354). New York, NY: Routledge. Yukl, G. (2011). Contingency theories of effective leadership. In A. Bryman, D. Collinson, K. Grint, B. Jackson, & M. Uhl-Bien (Eds.), The SAGE handbook of leadership (pp. 286– 298). Thousand Oaks, CA: Sage. Zaccaro, S. J., Connelly, S., Repchick, K. M., Daza, A. I., Young, M. C., Kilcullen, R. N., . . . Gilrane, V. L. (2015). The influence of higher order cognitive capacities on leader organizational continuance and retention: The mediating role of developmental experiences. Leadership Quarterly, 26, 342–358. Zaccaro, S. J., Green, J. P., Dubrow, S., & Kolze, M. (2018). Leader individual differences, situational parameters, and leadership outcomes: A comprehensive review and integration. Leadership Quarterly, 29, 2–43.

1 INTELLIGENCE AND LEADERSHIP John Antonakis, Dean Keith Simonton, and Jonathan Wai

During the 2016 United States presidential campaign, the leading contender for the Republican nomination, Donald Trump, boasted of his superior intelligence, and after his election he more explicitly claimed to have a genius-level IQ. In contrast, the 43rd president of the United States, George W. Bush, was often viewed as exhibiting only an inferior intellect. Indeed, an internet hoax at the time estimated his IQ at a below-average 91, very different from the 156 score that supposedly belonged to Trump (Simonton, 2018). What these two events illustrate is that intelligence is widely deemed by the public to be relevant to presidential performance (see Cohen, 2018, for empirical evidence). In fact, it should be obvious to us why intelligence should matter in some capacity for success in any leadership position, political or otherwise. Leaders must be able to “join the dots”, that is, learn from information in their environment; they must be attuned to inferring from multimodal data signals—whether emotional, economic, behavioral—abstract from them, identify condition action links, and decide a course of action that increases the likelihood of success (Antonakis, 2011). They should be able to think fast, too, which is a characteristic of intelligent individuals (Baker, Vernon, & Ho, 1991; Sheppard & Vernon, 2008). The capacity to learn is key to modern notions of intelligence (Gottfredson, 1997a), and intelligence is vital for leadership success. Yet, has research discovered what seems to be so obvious? It turns out that the relation between intelligence and leadership is not particularly obvious. Instead, the association, though empirically present, varies and is rather more subtle than expected. This subtlety is demonstrated in the two main research traditions that have tackled this question: the psychometric and the historiometric. In this chapter we discuss these issues and others to make the case that intelligence is a key determinant of leader success. We bring to the fore issues concerning measurement, the criterion predicted, the importance of context, and

Intelligence and Leadership  15

methodological issues that should be considered by applied researchers interested in the topic of intelligence.

Psychometric Intelligence Psychometric research applies quantitative techniques to assess how individuals vary on abilities, traits, preferences, or other factors. These techniques date back to the very beginning of scientific psychology (e.g., Cattell, 1890; Galton, 1883). For our purposes, naturally, the most relevant methods are those associated with the measurement of intelligence as a trait. We adopt the definition that traits “are individual characteristics that (a) are measurable, (b) vary across individuals, (c) exhibit temporal and situational stability, and (d) predict attitudes, decisions, or behaviours and consequently outcomes” (Antonakis, 2011, p. 268). After discussing the general research literature on psychometric intelligence, we turn to the specific question about how the resulting scores are associated with leadership.

Research on General Intelligence The idea of intelligence is not by any means a new concept. Throughout history, philosophers have discussed human faculties. Aristotle, for example, discussed the “five wits”: imagination, fantasy, memory, reason, and the “common sense” (Ritchie, 2015). In The Republic, Plato wrote extensively about what qualities, including intelligence, would be required for effective leadership (Antonakis, 2011). The Chinese developed some of what might be considered the first criteria for measuring intelligence and hence assess the ability to “infer and predict from given information” (Higgins & Xiang, 2009, p. 258). The first true intelligence tests were developed by the psychologist Alfred Binet from the study of students who struggled intellectually. With Theodore Simon, Binet developed such tests as a way to identify students with learning disabilities. Lewis Terman further developed these tests to identify students with learning gifts, and during the First World War, Robert Yerkes designed tests that could be administered efficiently to large groups of potential military recruits. These were the first group intelligence tests; they formed the basis for many intelligence and standardized tests that we see in use today across various educational and occupational settings (for more comprehensive reviews of the history of intelligence and the development of intelligence tests, see Haier, 2016; Hunt, 2011; Ritchie, 2015). Some of the subsequent discussion surrounding intelligence has to do with how to best define or operationalize it. Though there are many verbal definitions of intelligence, Gottfredson’s (1997a) definition is often cited by intelligence researchers: Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly,

16  John Antonakis et al.

comprehend complex ideas, learn quickly, and learn from experience. It is not merely book-learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—“catching on”, “making sense” of things, or “figuring out” what to do. (p. 13) Thus, overall, intelligence has to do with the ability to learn; however, others have questioned the validity of such a definition and prefer the “rate with which learning occurs” (Carroll, 1997, p. 43). Important to note, too, is that speed of information processing is correlated with g (general intelligence) and in particular measures of fluid intelligence at r = .35 (Sheppard & Vernon, 2008); this correlation is probably explained by common genetic causes (Baker et al., 1991). We do not wish to belabor readers with definitional issues, especially because there is no consensus on how intelligence should be defined (Carroll, 1997). In any case, scientific advancement occurs largely through the quantification or measurement of key variables in order to determine genuine relationships between those variables. Therefore, researchers have proposed that quantitative definitions of intelligence can advance the field beyond verbal arguments ( Jensen, 1998; cf. Lubinski, 2004). Carroll (1993) synthesized the structure of human cognitive abilities through a comprehensive factor analysis of mental test data at the time, showing that general intelligence, or g, is important, in addition to specific abilities such as math and verbal abilities, along with a number of other abilities. Thus, though there are a number of cognitive abilities beyond a general factor, including specific abilities that have incremental prediction for outcomes (e.g., see Lubinski, 2004), g remains the largest source of predictive variance among a host of outcomes; therefore, g is the focus of the remainder of this brief review (later, we briefly discuss the utility of alternative notions of intelligence). In what ways can general intelligence be measured? Spearman (1927) noted that the specific content of a test may not be particularly important, because g is important for performance on mental tests generally. Nearly any challenging mental test that includes a wide variety of items will measure g to some extent (Chabris, 2007; Ree & Earles, 1991), and, in fact, g is measured to some degree in tests that were purposefully designed to measure a diverse array of achievements and abilities ( Johnson, te Nijenhuis, & Bouchard, 2008). Even measures commonly thought of by many researchers and practitioners as achievement or aptitude tests, such as the American College Test (ACT) or Scholastic Assessment Test (SAT), have been shown to actually measure g to a large degree (Frey & Detterman, 2004; Koenig, Frey, & Detterman, 2008), though of course such tests also reflect developed abilities. Researchers have also demonstrated that academic achievement g and cognitive g are essentially the same (Kaufman, Reynolds, Liu, Kaufman, & McGrew, 2012), which means that achievement and cognitive tests essentially both measure the same g. Some recent advances in the measurement of

Intelligence and Leadership  17

intelligence include chronometric research, the use of elementary cognitive tasks or reaction times to “clock the mind” (Beaujean, 2005; Jensen, 2006). Intelligence can be measured with better reliability than any other individual differences variable ( Jensen, 1998).

The Nature and Nurture of Intelligence Intelligence, like most other traits, is due in part both to nature and nurture. The nurture aspect regarding the development of intelligence, however, must account for the nature aspect. Thus, research on intelligence from the field of behavioral genetics is important because it may ultimately help us understand which aspects of the environment are important to bring out each individual’s intelligence and broader array of cognitive abilities to the fullest. There are two approaches being used: first, investigating the extent to which genes contribute to intelligence differences (known as quantitative genetics, often using twins and siblings), and, second, investigating which are the specific genes in the DNA that, when they differ among people, cause intelligence differences (known as molecular genetics). To date, an enormous amount of evidence has accumulated surrounding the heritability of intelligence using twin samples; however, there is now headway in the molecular genetics area when studying intelligence (for comprehensive reviews, see Plomin & Deary, 2014; Plomin & von Stumm, 2018). Suffice it to say, the evidence has accumulated such that intelligence is in part due to nature as well as nurture.

The Significant Correlates of Intelligence General intelligence, or g, has been called a “rosetta stone” or a crucial key given its broad links to many phenomena of interest in psychology, social science, and society ( Jensen, 2006). Large amounts of data spanning the last century have indicated links between intelligence and numerous outcomes of consequence, including educational achievement, occupational success, income, mortality, and others (e.g., for reviews, see Gottfredson, 2003b; Haier, 2016; Hunt, 2011; Jensen, 1998; Ritchie, 2015). To offer some illustrations, higher intelligence has been linked to positive health-related habits, such as exercising more, eating better, and smoking less (e.g., see Gottfredson, 2003b; Gottfredson & Deary, 2004). People with higher intelligence are less likely to have medical issues such as heart disease, obesity, or hypertension, and this is found for both physical and mental health (Wraw, Deary, Gale, & Der, 2015). Higher intelligence has also been linked to lower mortality or death risk (Batty, Gale, Tynelius, Deary, & Rasmussen, 2009). Higher intelligence has even been linked to higher creativity (Mosing, Pedersen, Madison, & Ullén, 2014; Nusbaum & Silvia, 2011; Wai, Lubinski, & Benbow, 2005). Intelligence has also been linked to political preferences and religion, among an array of other outcomes (for an extensive list of correlations between g and many outcomes, see Jensen, 1998; Strenze, 2015).

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However, for the purposes of this review, two sets of outcomes are most relevant. First, general intelligence has been linked to many educational outcomes. A very large and representative sample from the United Kingdom (Deary, Strand, Smith, & Fernandes, 2007) examined the scores of over 13,000 students at age 11 and correlated those scores with educational achievement at age 16. The researchers found that the correlation between g and the overall exam score was r = .81. This correlation is very high, especially in relation to what is typically found in social science, and indicates the importance of general intelligence for educational achievement. In the United States, the entire range of SAT scores (which, as noted earlier, also measure g) were discovered to be linearly related to college grade point average (GPA; Cullen, Hardison, & Sackett, 2004). In addition, SAT scores are associated with a wide variety of outcomes, including graduate school performance (Berry & Sackett, 2009). Kuncel, Hezlett, and Ones (2004) conducted a major meta-analysis illustrating that g as tapped by the Miller Analogies Test (MAT) predicted a range of graduate student academic criteria. General intelligence predicted GPA, comprehensive examination scores, time to degree completion and attainment, faculty ratings, and research productivity. Studies of intellectually precocious youths who have participated in a 7th-grade US talent search—where students complete the SAT before age 13—provide evidence for the predictive validity of SAT scores on long-term educational outcomes (e.g., Lubinski, Benbow, & Kell, 2014; Wai et al., 2005). Even by just looking within the top 1% of g—by comparing the top quartile to the bottom quartile of the top 1% on the SAT within the Study of Mathematically Precocious Youth (SMPY; Robertson, Smeets, Lubinski, & Benbow, 2010) and by cognitive ability tests in Project Talent (Wai, 2014)—shows that higher ability continues to predict higher educational achievement at the high end. Both SAT and cognitive ability scores predicted earning higher education degrees, specifically doctorates, with clear differences within the top 1% of scorers on these tests (Park, Lubinski, & Benbow, 2008). Additionally, in Wai (2013) and Wai and Rindermann (2015) who studied Fortune 500 CEOs, even within that highly restricted range sample, CEOs that attended more selective schools and hence had higher general intelligence tended to lead firms with higher gross revenues and that were ranked higher in the Fortune 500 correspondingly. Second, and likely most central, general intelligence has been linked to many occupational outcomes. Indeed, over 100 years of research has shown that g predicts both job training outcomes and job performance outcomes. A major meta-analysis of 85 years of research found that the core job performance predictor was g combined with another test, such as structured interviews or a measure of integrity (Schmidt & Hunter, 1998). General ability also predicts job training and job performance, and, among other things, the prediction of g on outcomes increased as the complexity of the job increased (Schmidt & Hunter, 1998). Other researchers

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have shown that g is positively related to everything from training success, leader effectiveness, occupational performance in low- and high-complexity jobs, and even creativity (Kuncel et al., 2004; Ones, Viswesvaran, & Dilchert, 2005). Within the top 1% of SAT scores, higher SAT scores predict a higher rate of long-term occupational outcomes, including the earning of publications, patents, income, and even university tenure (Robertson et al., 2010; Wai et al., 2005). Additionally, separate studies combining the prediction of SAT scores and cognitive ability scores indicates that g assessed in youth predicts cognitive occupational complexity later in life (e.g., Wai, 2014). Studies reviewed so far examine mental test performance in youth and the relationship between those scores and later life outcomes. Another approach to examine the impact of g on long-term occupational achievement includes starting with the occupational outcome of interest (e.g., people in elite occupations and positions of leadership across many areas of society) and identifying to what extent these people were in the top 1% in g when assessed earlier in life; thus, researchers must bear in mind, too, the context in which the effect of intelligence is being estimated because it will have differential effects on criteria. A series of studies have taken this approach by examining large samples of 30 millionaires, billionaires, CEOs, World Economic Forum in Davos attendees, House members, Senators, federal judges, journalists from the New York Times and the Wall Street Journal, and people listed by Forbes as the most powerful men and women. This research has indicated that about half (50%) of the people who are high achievers and leaders in the United States are likely in the top 1% in g relative to the population (Wai, 2013; Wai & Perina, 2018; Wai & Rindermann, 2015); studies by other teams have found similar results regarding CEOs being much higher than the average population on cognitive ability (Adams, Keloharju, & Knüpfer, 2018). By combining the prospective and retrospective approaches and databases, this indicates that g matters in the development of occupational expertise and leadership broadly. Hsu and Wai (2015) linked average cognitive ability level of undergraduate institutions with the per capita rate of production of scientific and technology prize winners by looking up where every member of the National Academy of Sciences, National Academy of Medicine, and National Academy of Engineering and where every Nobel Prize winner, Fields medal winner, and Turing award winner went for his or her undergraduate education. The undergraduate institution average g level based on SAT and ACT scores was correlated roughly r = .50 with how well the school produced prize winners per number of graduates in history. Thus, g measured at an aggregate level (e.g., at the level of institution) predicts long-term outcomes beyond education to include extraordinary performance at the very top of occupational performance. Given that leadership is a specific form of occupation, such findings are highly pertinent here, as we discuss next. First, we briefly discuss how correlates of intelligence may condition leader emergence.

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How Correlates of Intelligence May Condition Leader Emergence We know that intelligence is tightly connected to educational and occupational attainment and achievement as reviewed previously. Moreover, these correlates, in a way, do indeed condition access to leadership roles. Such a model would imply that IQEducational AchievementLeadership. This model would only be consistently estimated using an instrumental variable estimator (Antonakis, Bendahan, Jacquart, & Lalive, 2010; Shaver, 2005). An endogenous mediator (like “Achievement”) must be purged from endogeneity bias. As such, and whether or not IQ partially drives Leadership via Achievement, the “reduced form equation” (Kmenta, 1986) of IQLeadership is a consistent estimator of the effect of intelligence on the outcome. IQ is the distal outcome that drives the effect of the mediator on the outcome. It is important to note, too, that essentially a handful of “elite” schools contribute disproportionately to serving as a filter to US leadership roles (e.g., Wai, 2013; Wai, Brown, & Chabris, 2018). Still, there are huge selection effects in terms of elite schools taking the smartest students; carefully done econometrics studies show that once accounting for these selection effects, the effect of the school per se matters little (Dale & Krueger, 2002). Thus, it is largely the ability of the student that drives whether they get selected to the top school and also determines the outcome, though perhaps there are some school, network, or other effects that matter in conditioning leader emergence, and the reader should consider that lens seriously when reading the remainder of our chapter. Intelligence also conditions the acquisition of many complex skills required for leader emergence and performance, including accelerating skill acquisition, deliberate practice, and self-reflection among many other positive aspects (which we already demonstrate though the network of positive correlates reviewed in our chapter). Ackerman (1996) provides an important detailed look at how process, personality, interests, and knowledge combine in intellectual development. For a review of leader individual differences and leader emergence and outcomes, see Zaccaro, Green, Dubrow, and Kolze (2018).

Research on General Intelligence and Leadership Psychometric research on the effect of intelligence on leadership has a long, though tumultuous history. Although qualitative reviews did show that intelligence mattered for leadership (Mann, 1959; Stogdill, 1948) and managerial effectiveness, too (Campbell, Dunnette, Lawler, & Weick, 1970), the evidence was interpreted to be conflicting (for reviews see Antonakis, 2011; Antonakis, Day, & Schyns, 2012; Zaccaro, 2012; Zaccaro et al., 2018; Zaccaro, Kemp, & Bader, 2004). Of course, in the early to mid-twentieth century, measures were coarse and researchers did not have the tools to synthesize research findings. What characterized the zeitgeist were statements such as “Leadership resides not exclusively in the individual but

Intelligence and Leadership  21

in his functional relation with other members of his group” (Gibb, 1947, p. 283). These declarations signaled to researchers that individual differences did not matter and that situational aspects were key to understanding leadership effectiveness (Zaccaro et al., 2004). Because of the pessimism at the time, research on intelligence (and personality, too) by leadership scholars was essentially shut down by the late 1950s (Antonakis & Day, 2017). With the discovery of stronger statistical methods and metaanalysis in particular, Lord, De Vader, and Alliger (1986) showed that intelligence mattered, at least for perceptions of leadership. Yet, this work was largely ignored by mainstream organizational behavior textbooks (Zaccaro et al., 2004). Even in the late 1980s and 1990s influential textbook authors were stating that the trait paradigm for leadership was a “myth” (Muchinsky, 1987, p. 501), and that looking for which traits predicted leadership was “the wrong question to ask” (Sashkin & Morris, 1984, p. 272). Some scholars—Robert House in particular—encouraged researchers to continue looking for trait-leadership links (House & Aditya, 1997; House & Baetz, 1979; House, Shane, & Herold, 1996). Meanwhile another type of empirical work using rotation designs (i.e., leaders rotated to different tasks) showed that a large portion of stable variance in leader outcomes could be attributed to individuals though the exact nature of that variance was not wholly clear (Kenny & Zaccaro, 1983; Zaccaro, Foti, & Kenny, 1991). Others theorized that perhaps research on the relation between intelligence and leadership was misdirected, at least insofar as searching for linear relations were concerned; moreover the criterion, whether objective performance or perceptions should also be considered in order to model the correct functional form (Simonton, 1985). Today research on traits per se, including intelligence, is on the upswing. As mentioned, wide-scale studies have been conducted to examine the effect of intelligence on general and managerial performance (Salgado et al., 2003; Schmidt & Hunter, 1998; Schmidt, Shaffer, & Oh, 2008); the findings clearly show that as the complexity of the job increases—which characterizes the individuals who exert influence at the high echelons of the organization—the effect of intelligence on performance matters even more. Such has been the volume of research on leadership and intelligence that recent meta-analyses have been published on the topic as well (Hoffman, Woehr, Maldagen, & Lyons, 2011; Judge, Colbert, & Ilies, 2004).1 However, both sets of authors of the meta-analyses have lamented that the effects of intelligence on outcomes are, surprisingly, not as high as anticipated. That is, objectively measured intelligence on ratings of leadership effectiveness are low; both meta-analyses found an estimate of ρ = .17. Is this estimate accurate?

Joining the Dots, Correctly The Judge et al. (2004) meta-analysis provides us with some hints about the problems of modeling the effect of intelligence. For instance, the effect of objectively

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measured intelligence on objective performance is ρ =.33. Why is this correlation stronger—and more respectable, but still not very large—than when predicting perceptions of effectiveness? In our view, both estimates from this meta-analysis misrepresent the effect of intelligence on outcomes. Let us focus on perceptions of leadership for the time being. Refer to Figures 1.1 and 1.2. The data for Figures 1.1 and 1.2 are from Antonakis, House, and Simonton (2017) and depict the relation between scores on the Wonderlic Personnel Test (2002) on other ratings of leadership style, in this case idealized influence (attributes). The observed correlation in Figure 1.1 is r = .12, a small effect. Corrected for measurement error in the criterion and outcome suggests a correlation of r = .15, which is rather close to the meta-analytic results. However, the correct functional form is not linear. When modeling this functional form correctly, and when adjusting for measurement error and omitted fixed effects (of countries, firms, and time), and when also accounting for the effects of personality, gender, and age, we see that the adjusted predictions fall very nicely along an inverted U-shaped curve.2 The impetus for considering different functional forms stems from a theory written by Simonton (1985) decades ago. He suggested modeling the relation correctly depended on the criterion and that the relation between intelligence and leadership would never be linear. If the criterion is purely task oriented, the relation should follow one akin to the law of diminishing marginal utility; that is, the relation is positive initially and then at high levels of intelligence it tapers off.

3.5 3 2.5 2 1.5 1 .5

Idealized influence attributed

4

Observed

10

15

20

25 30 Wonderlic scores

35

40

45

FIGURE 1.1 Linear Relation (With Observed Scatterplot) Between Intelligence Scores

and Leadership

Intelligence and Leadership  23

3.5 3 2.5 2 1.5 1 .5

Idealized influence attributed

4

Adjusted

10

15

20

25

30

35

40

45

Wonderlic scores FIGURE 1.2  Quadratic

Relation (With Adjusted Scatterplot) Between Intelligence Scores and Leadership

However, the relation is always positive (i.e., refer to Simonton, 1985, Model 1 and Figure 2). His insights help explain why fitting a straight line to such a distribution will be a biased estimator. Still, fitting a straight line to a functional form that is monotonic and positive is less problematic than fitting a straight line to a functional form that is curvilinear (i.e., initially positive, flattens out, and then negative) as we show in our Figure 1.1. Thus, although the ρ = .33 that Judge et al. (2004) report is interesting, we think that it misrepresents the true effect of intelligence on ratings of leader performance insofar as objective (task performance) is concerned; the effect should be positive and monotonic with a diminishing slope at high levels of intelligence. Because the slope is constantly changing, it is more accurate to report the tangent slope (derivative) of the relation of intelligence to leadership at specific points of the curve. Such an elementary mathematical point has been overlooked by most researchers in the field (for recent exceptions, see Gignac & Starbuck, 2018; Reitan & Stenberg, 2018). We hope that this mistake will henceforth not be repeated. Simonton (1985) also considered how the intelligence–leadership relation would play out when incorporating others’ perceptions of the leader regarding two aspects, which bear in mind the context in which the leader exercises influence: (1) are others able to comprehend the leader’s message? and (2) can the leader can keep rivals at bay? In a more elaborate extension (i.e., see Model 4 in Simonton, 1985), he predicted that the functional form would be curvilinear as we show in our Figure 1.2. That is, the optimal intelligence level of a leader

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should be higher than the mean of the group led, but not too high. It must be higher so that leaders can have the needed insights and smarts to provide solutions to problems and to be taken seriously; but not too high or else they would not be understood and even be seen as unprototypical. Antonakis et al. (2017) report very strong relations of intelligence to perceptions of leadership (i.e., prototypical forms like transformational and instrumental) as intelligence scores increase. For the sample they studied, they found the peak to be around 120 IQ points (this peak will change depending on the mean level of intelligence of the group led). For instance they found, too, that at Wonderlic scores: 1. 2. 3. 4.

Of 23 (i.e., IQ = 106), the marginal effect of intelligence is β = .55. At the mean scores of managers (i.e., 25.31), the relation is β = .33. Of 32.5 (i.e., IQ = 124), the marginal effect of intelligence is β = -.37. Of 35 (i.e., IQ = 128), the marginal effect of intelligence is β = -.61.

We have aimed to show the reader thus far that an initial low correlation can mask the true relation of intelligence to leadership. Moreover, much depends on what the criterion is (Austin & Villanova, 1992); and, in particular, multivariate effects of known causes should be controlled for to minimize the effect of omitted variable bias (Antonakis et al., 2010). For instance, the corrected partial relation between the estimated intelligence scores of US presidents and expert ratings of historians regarding the presidents’ performance indicates a β = .54 (Antonakis et al., 2017). We know, too, that leaders of Fortune 500 companies are disproportionately in the top 1% of general intelligence (Wai & Rindermann, 2015), as are leaders across a wide array of leadership positions discussed earlier, including House members, Senators, top journalists, World Economic Forum attendees, the wealthy, and powerful politicians and other leaders (Wai & Rindermann, 2017), though the general intelligence level of these leaders exhibits significant variation. Simply put, intelligence matters. “But wait”, some may ask, “don’t other forms of intelligence matter too for leadership?” Simply put, the answer is a firm “no”. However, because authors supporting alternative notions of intelligence have made many unsubstantiated claims concerning alternative notions of intelligence, we provide some brief explanations in the Appendix for interested readers.

Intelligence as an “Instrumental Variable” for Causal Identification Apart from intelligence being useful as a predictor in models of leader outcomes, measures of intelligence have very useful properties and can help estimate other kinds of models. Recall, intelligence has high heritability (Bouchard & Loehlin, 2001; Bouchard & McGue, 2003; Polderman et al., 2015). As a result, it can help solve a major problem that we have in the field of leadership: identifying the

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causal effect of measured leadership style on outcomes. To estimate the causal effect of a predictor, IQ, on an outcome, y (subordinate performance), the predictor must be exogenous. If it is exogenous, the assumptions made by OLS or ML estimators for the following model, hold: y = b0 + b1IQ + e 

Eq. 1

That is, the assumption that the estimator makes is that IQ does not correlate with e, the disturbance term that reflects omitted causes of y. We avoid this problem in experimental research by randomizing subjects to treatment. Thus, any causes (whether observed or not) of y are balanced across treatment, and in this way b1 is consistently estimated (i.e., as the sample size increases the coefficient approaches the true value). Because intelligence has been randomized by nature and is reasonably stable across the lifespan (e.g., Deary, Pattie, & Starr, 2013), for most intents and purposes it can be considered as an exogenous variable in a regression model. It will not change if something in e affects y, because intelligence does not correlate with e (i.e., organizational resources, leader’s or follower’s personality, or what have you). It will also not change as a function of y (i.e., if a subordinate performs badly that will not change the leader’s intelligence). However, measures of leader style do not have these desirable properties. For instance (1) firm-level factors may affect leader behavior and also y, or (2) a leader behavior may be endogenous with respect to follower behavior (we discuss these points later in more detail). Oftentimes, these omitted variables are unknown or not modeled. Intelligence, however, is stable and is unaffected by e or y. Of course, intelligence is measured with error and this error must be accounted for because its estimate will be biased otherwise (Ree & Carretta, 2006); also, if there has been selection on intelligence in the sample then this selection must be modeled because it can result in censoring or range restriction (Schmidt et al., 2008); thus, at the minimum, fixed effects (e.g., firms in which leaders are nested) must be modeled (cf. Antonakis et al., 2017). To see the utility of intelligence for researchers, suppose one measures perceptions of leadership (x) in the following model: y = γ 0 + γ 1x + v 

Eq. 2

Suppose y is measured objectively or by also using a perceptual measure from a different source to avoid common method/source bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003); however, using a different source will not eliminate endogeneity problems and x could still correlate with the disturbance v if x is not exogenous (Antonakis et al., 2010). The predictor x may not be exogenous for many reasons. For instance, if those who rate the leader have knowledge of y, which is normal that they would, they may rate the leader higher or lower on x as

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a function of y, the well-known performance-cue effect (Lord, Binning, Rush, & Thomas, 1978). The leader’s gender may play a role; so, too, may the leader’s looks. The leader may adjust his or her behavior, treat subordinates differently, as a function of subordinate performance or personality; or the leader’s behavior may be determined by resources that are made available at the department level (these resources may also affect the subordinate performance). This problem of endogeneity (due to omitted variables, reverse causality, or simultaneity) renders the coefficient γ1 uninterpretable (Antonakis et al., 2010). The coefficient must be purged from endogeneity bias. One way to obtain a consistent estimate is to “instrument” x by first estimating the following: x = λ0 + λ1IQ + w 

Eq. 3

One then estimates Eq. 2 and Eq. 3 simultaneous and allows the cross-equation disturbances w and v to correlate; this correlation accounts for the omitted common causes of x and y. Estimating the system of equations with maximum likelihood (and correcting for measurement errors in x in Eq. 3), one can consistently estimate the effect of x on y. The formula for the instrumental variable estimate, Π, is (Angrist & Pischke, 2008; Bollen, 2012): � Πiv =

cov ( IQ , y )  cov ( IQ , x )

Eq. 4

Because IQ is exogenous, the portion of variance that it has overlapping with both y and x is used to derive the estimate, which will be isolated from the effects of v. This literature is technical and interested readers may refer to more basic introductions (Antonakis, Bendahan, Jacquart, & Lalive, 2014; Jacquart, Cole, Gabriel, Koopman, & Rosen, 2017). One more point to note is that the following model is also consistently estimated: y = α 0 + α 1IQ + u 

Eq. 5

This reduced-form model (Kmenta, 1986), which we introduced earlier is important to consider. That IQ is largely exogenous means that we can be agonistic as to how the effect of IQ on y is channeled if we care to estimate the effect of IQ per se on y. Of course, there are potentially many mechanisms (e.g., educational attainment) that are more proximal to outcomes, and knowing the mediation mechanism is very useful for designing efficient and targeted interventions (Fischer, Dietz, & Antonakis, 2017). As mentioned, intelligence is mostly heritable—between 60% and 77% across the lifespan (Polderman et al., 2015)—and we did say that intelligence can be considered for most intents and purposes exogenous. The preceding procedure should give researchers pretty good estimates and have useful policy implications. However, intelligence is not 100% exogenous because (1) intelligence tests are not

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perfect measures and corrections for this problem are approximations, (2) environmental factors can still have some impact on intelligence (e.g., Ritchie & Tucker-Drob, 2018), and (3) unknown selection or censoring could still bias results to some degree. To take instrumental variable estimation to the next level, genetic information and known markers of intelligence can be used to ensure that estimators are fully consistent. Known as Mendelian randomization (Smith, 2010), this procedure is now making inroads in applied research to identify the effects of phenotypes on a host of outcomes (e.g., see DiPrete, Burik, & Koellinger, 2018; von Hinke, Smith, Lawlor, Propper, & Windmeijer, 2016).

Historiometric Intelligence A long and roughly parallel research tradition on the relation between intelligence and leadership relied on historiometric techniques (Simonton, 2009). Historiometrics constitutes a set of methods for the extraction of objective and quantitative measures from historical data, particularly using biographical information about eminent individuals (Simonton, 2009). By definition, it constitutes a specific form of at-a-distance assessment of personal characteristics (Song & Simonton, 2007). Many of the early pioneers in psychometric methods also were in the forefront of historiometric methods. Notable examples include Quételet (1935/1968), who established the normal distribution for the description of individual differences; Galton (1869), who first explicitly applied the normal distribution to human variation in natural ability and advanced the concepts of correlation and regression; Cattell (1903), who introduced the term “mental tests” into psychology; Terman (1917), best known for devising the widely used Stanford-Binet Intelligence Scale; and Thorndike (1936, 1950), who made major contributions to measurement in educational psychology. Given the early publication dates seen for Quételet and Galton, it is clear that historiometrics might be considered the oldest method in the social sciences, and without a doubt older than psychometrics. Although a person may achieve eminence, a sociohistorical attribution, in a diversity of domains, it is obvious that a significant proportion of eminent individuals attain distinction as leaders. Hence, it comes as no surprise that historiometric methods have a special place in the empirical literature on leadership (Crayne & Hunter, 2018; Ligon, Harris, & Hunter, 2012; O’Connor, Mumford, Clifton, Gessner, & Connelly, 1995). Moreover, given the prima facie connection between intelligence and leadership, it would be expected that a portion of that literature should concern that empirical issue (Simonton, 2009). Next we will consider three illustrations that reveal different aspects of the subject.

Rated Intellect and the Evaluated Leadership and Eminence of European Monarchs The researcher who first coined the term “historiometry” was a geneticist rather than a psychologist, namely, Woods (1909, 1911). However, Woods was inspired

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by Galton’s (1869) classic Hereditary Genius, which promoted the family pedigree method for discerning genetic inheritance. But whereas Galton studied eminent leaders and creators, Woods (1906) had decided to focus on major European royal families, where the lineages are particularly well documented. He then exploited extensive biographical data to score each family member on two attributes, namely, intellect and morality (using a 7-point scale). His goal was to show not only that both characteristics demonstrate biological inheritance but also that the two should positively correlate. Interestingly, Woods (1913) later conducted a study of European monarchs in which he examined the correlation between the ruler’s overall leadership rating and an assessment of the general status of the nation ruled, but never thought to assess the relation between monarchal leadership and either intellect or virtue, a deficiency that was not rectified until many decades later, as will be seen shortly (Simonton, 1983, 1984). Because Woods (1906) conducted all of the assessments himself, in full awareness of the hypotheses he was testing, it would be wise to express skepticism about their reliability and validity. Yet two separate investigations validated his measures. First, Thorndike (1936) showed that the intellect scores correlated between .77 and .81 with independent ratings (and the virtue scores correlated between .64 and .69). He also replicated Woods’s positive correlation between intellect and virtue. Second, Simonton (1983) found that the original intellect ratings could be predicted by independent ratings of the descriptors “intelligent”, “able”, “shrewd”, and “educated” (whereas the original virtue ratings had the positive predictors “moral”, “prudent”, “well-meaning”, and “popular” and the negative predictors “licentious”, “tyrannical”, and “treacherous”). Therefore, the Woods’s measures were very reasonable. Simonton (1983) went a step further by combining Woods’s (1906) measures of intellect and virtue with his 1913 measures of monarchal leadership, which were also validated by a second set of independent assessments. In addition, Simonton quantified new measures, including a highly reliable 13-item composite indicator of eminence. For the 342 monarchs investigated, the intellect assessment was positively correlated not only with leadership (r = .67) but also with eminence (r = .32) and virtue (r = .23). This historiometric investigation also replicated two findings found in psychometric research, as reviewed earlier in this chapter: First, intellect correlated positively with life span, and second, intellect was subject to genetic inheritance, even within this range-restricted sample. Ultimately, even monarchs are still human beings. One year later, Simonton (1984) conducted a follow-up inquiry that added more variables, constructed a tentative recursive model, and tested for curvilinear functions. The details are too complex to review here. It suffices to say that the historiometric intellect measure continued its prominent place in the causal network, albeit largely as an exogenous variable with downstream effects among endogenous variables—much as argued earlier with respect to psychometric intelligence.

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Estimated IQ and the Achieved Eminence of Political, Military, and Religious Leaders As observed earlier, Terman (1916) developed the Stanford-Binet Scale, the first standardized measure of psychometric intelligence. Less well-known is the fact that Terman (1917), just one year later, came up with a method for the historiometric assessment of intelligence that operated according to very different principles from those seen in Woods (1906), Thorndike (1936), and Simonton (1983, 1986b). Rather than convert biographical descriptors into intelligence estimates, Terman illustrated how a biography’s implicit chronology of cognitive development could generate an actual IQ estimate. Remember that in the early years of intelligence testing, IQ was a literal quotient (aka IQ), meaning that it was defined as the child’s mental age divided by chronological age (and then multiplied by 100 to render 100 an average IQ). Terman just applied this operational definition to the information contained in a recent biography of Galton (written by Karl Pearson, of correlation coefficient fame). In general, young Galton was performing cognitive tasks that would normally only be seen in children nearly twice his age. On that basis, Terman estimated Galton’s IQ to be around 200. Some years later, Terman (1925) began his well-known longitudinal study that would follow more than 1,500 high-IQ children all the way into adulthood to determine if intellectually gifted boys and girls would grow up to become adult geniuses (this work was a precursor to the SMPY longitudinal study of high-IQ children discussed earlier in the chapter; Kell & Lubinski, 2014). Because the final wave of this inquiry was a long way off because longitudinal studies can take decades, Terman encouraged a graduate student of his to engage in an ambitious retrospective study that would apply the techniques used to estimate Galton’s IQ to provide IQ estimates for 301 geniuses, including both leaders and creators. The student (Cox) actually introduced important refinements, such as having the biographical data on intellectual development compiled separately from the rating process and using multiple independent raters. In any case, the results were reported in Cox (1926), one of the most monumental historiometric studies ever conducted. Although her findings are too rich to cover adequately here, she drew two main conclusions that directly concern the intelligence-leadership question. First, estimated IQ was positively correlated with achieved eminence, as gauged by Cattell’s (1903) calculations for 1000 eminent creators and leaders in Western civilization. The zero-order correlation was r = .25 (Cox, 1926); though given the range restriction of IQ, this correlation certainly understates the true effect. Moreover, although the exact value of the calculated association fluctuates, a significant relationship has been replicated multiple times in the historiometric literature using different samples or methods or both (Simonton, 1976; Simonton & Song, 2009; Walberg, Rasher, & Hase, 1978). Given that this correlation assumes a linear function, the result also falls in line with the psychometric literature discussed earlier.

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Second, the expected IQ varies systematically across the achievement domains (Cox, 1926). As a first cut, the predicted IQs of eminent leaders fall about a half standard deviation below those predicted for eminent creators (Simonton, 1976). But substantial contrasts are found among the various domains of leadership as well (Simonton & Song, 2009). These encompass politicians, revolutionaries, commanders, and religious leaders. The biggest contrast is between commanders—­ generals and admirals—and the overall mean. The difference is about 20 points, for a mean of around IQ 130 instead of 150. In line with Simonton’s (1985) model mentioned earlier, this gap might suggest that for these particular leaders, communication skills are far more important than problem-solving skills. Unfortunately, none of the studies just cited tested for curvilinear functions, nor did any determine whether the intelligence–eminence function was moderated by domain of achievement. Those questions are thus left for future researchers.

Assessed Intellectual Brilliance and the Leader Performance of US Presidents The previous two illustrations involved instances where the researcher decided to assess historic figures on intelligence—intellect or IQ—from the very onset of the investigation. Yet this final example entails a case where something like an intelligence measure emerged from an exploratory data analysis. The measure emerged out of continued attempts to understand the factors underlying the performance ratings that presidents of the United States receive at the hands of experts, mostly American historians and political scientists who specialize in the US presidency (e.g., Maranell, 1970; Murray & Blessing, 1983; Ridings & McIver, 1997). Although researchers were readily able to identify several situational factors underlying these “presidential greatness” ratings (e.g., Curry & Morris, 2010; Holmes & Elder, 1989; Kenney & Rice, 1988; Nice, 1984; Simonton, 1981, 1986b), the identification of individual factors was far more elusive (cf. Deluga, 1997, 1998; House, Spangler, & Woycke, 1991; Simon & Uscinski, 2012; Wendt & Light, 1976; Winter, 1987). In particular, many potential personality and motivational variables that enjoyed promising zero-order correlations with the presidential performance ratings turned out to exhibit no significant effects once established situational predictors were already accounted for (for a review, see Simonton, 2012). Hence, the correlations may represent spurious relations or, at best, indirect effects. Therefore, Simonton (1986b) attempted to start from scratch, building a set of potential personality predictors from a broad inventory of character traits. The inquiry began having research assistants extract personality descriptions from biographical materials, under explicit instructions to remove all identifying material. To avoid possible political biases as much as possible, the raw data sources were deliberately chosen to represent the consensus of historical scholarship on the presidents (e.g., Armbruster, 1982). The next step was to recruit a separate team

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of independent raters who would use these anonymous extracts to evaluate the presidents on the descriptors in the Gough Adjective Check List (ACL; Gough & Heilbrun, 1965). Given that many of the 300 ACL descriptors could not be reliably assessed (e.g., due to floor or ceiling effects), the adjectives were truncated to a subset of 110 evaluations that featured respectable reliabilities. These assessments were then factor analyzed to yield 14 orthogonal dimensions. One of these factors is especially important here, namely, a factor named Intellectual Brilliance, which had salient positive loadings on Wide Interests (.85), Artistic (.84), Inventive (.76), Curious (.74), Intelligent (.64), Sophisticated (.62), Complicated (.61), Insightful (.54), Wise (.46), and Idealistic (.43), but negative loadings on Dull (-.71) and Commonplace (-.41). The internal-consistency reliability (coefficient alpha) for the composite measure was .90, which compares favorably with intelligence tests (as noted earlier). Factor scores were thus calculated for all 39 US presidents between Washington and Ronald Reagan, inclusively. These scores were validated via their correlations with independent ratings of related constructs, such as IQ, creativity, charisma, idealism, book authorship, and birth order (all associations positive except the last, Simonton, 1986b, 1988), using data in Cox (1926), Maranell (1970), Simonton (1981), and Thorndike (1950); see also results in Emrich, Brower, Feldman, and Garland (2001). It deserves emphasis that Intellectual Brilliance does not correlate with the president’s party affiliation, thereby suggesting no bias in the biographical reference works (Simonton, 2006). Most significantly, Intellectual Brilliance emerged as an important predictor of presidential greatness even after controlling for relevant situational factors (Simonton, 1986a). Additionally, that result has been repeatedly replicated as new performance ratings emerged (e.g., Simonton, 1988, 1991, 2001, 2002, 2006), including replications by independent investigators (e.g., Cohen, 2003; Newman & Davis, 2016). The standardized partial regression coefficient typically ranges between .21 and .29, with the two largest coefficients coming in the most recent replications (viz. Simonton, 2002, 2006). Also worth noting is the fact that the relation between Intellectual Brilliance and presidential greatness is strictly linear. Admittedly, we still have to address what underlying construct is actually represented by the Intellectual Brilliance factor scores (Simonton, 2018). Although Brilliance correlates r = .80 with Thorndike’s (1950) intelligence evaluations for ten US presidents (also using biographical descriptors) and correlates r = .70 with Cox’s (1926) IQ estimates for eight US presidents (based on early intellectual development), it remains true that the ACL descriptor Intelligent displays only a middle-range factor loading (viz. .64). Furthermore, most of the ACL adjectives, and certainly the ones with the highest loadings, seem more strongly related to the Openness to Experience dimension of the Big Five Factor Model ( John, 1990). And, in fact, Intellectual Brilliance correlates r = .71 with the Openness scores that Rubenzer, Faschingbauer, and Ones (2000) assessed for the 41 US presidents prior to George W. Bush (using observer-based expert surveys; Simonton, 2001, 2006). Because several of the ACL descriptors defining the factor also define the

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Creative Personality Scale (Gough, 1979), it might be considered more creativity than intelligence, and yet it correlates only r = .47 with an independent assessment of presidential creative style (Simonton, 1988). For similar reasons, although a certain conceptual overlap seems to exist between Intellectual Brilliance and Sternberg’s (2018) concept of “Successful Intelligence”, they cannot possibly be equivalent because they correlate in contrary directions with traits like practical and idealistic (Simonton, 2018). Because Intellectual Brilliance appears to reliably integrate both cognitive abilities and personality traits, it seems analogous to the concept of the “intelligent personality” (Chamorro-Premuzic & Furhnam, 2006), but specifically tailored toward success as a chief executive in the White House.

Conclusions As our chapter has shown, literature on the measurement of general intelligence is long-standing and very broad; it has captured the interest of ancient philosophers to all the way to modern-day psychometricians, historiometricians, and beyond. Intelligence is a firmly established construct in the general and applied psychology literatures and, as far as we can surmise, probably more is known on intelligence than any other psychometric individual difference. Most of the research has focused on the measurement of intelligence, either directly or from a distance, and how intelligence predicts outcomes. This interest in intelligence is spreading beyond psychology to other fields. For instance, researchers in economics are now using intelligence measures to predict important economic and behavioral outcomes (e.g., Burks, Carpenter, Goette, & Rustichini, 2009; Dohmen, Falk, Huffman, & Sunde, 2010). Importantly, researchers from the basic sciences are studying intelligence, too, linking it to specific brain regions ( Jung & Haier, 2007) as well as microstructural architecture (Genç et al., 2018); even more impressive are efforts to identify single nucleotide polymorphisms (SNPs), that is, DNA markers of intelligence (Sniekers et al., 2017). Computer scientists will surely be the next group to be interested in measuring intelligence, particularly from a distance; using elements of deep (machine) learning is particularly useful to infer features from natural language (LeCun, Bengio, & Hinton, 2015). As more scientists join such efforts, we will be able to obtain a more complete picture of intelligence, and hence develop more advanced theories of its causes and consequences. Given what we know about the importance of intelligence and leadership success, there are important implications for practice. The most obvious one is that intelligence should be considered as a selection criterion, especially for positions where performance histories are not available. Smarter people are more effective, especially in cognitively complex domains (Schmidt & Hunter, 1998)—of course, as we suggested before, it is important to consider the criterion. For leadership outcomes that require technical skills, more seems to be better. If the criterion concerns interpersonal interaction, leaders must be smart but not too smart. The

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“sweet spot” is easily derived from the work of Simonton, who showed that the leader should have an IQ score of about 1.2 SDs higher than the group led (about 18 IQ points, assuming an SD in the population of about 15). Smarter people learn faster, too (Schmidt & Hunter, 1998), which suggest training interventions can be more efficiently done for higher IQ leaders. Nowadays, it is difficult for detractors or—for the want of a better term— “intelligence-test deniers” to ignore the impressive body of research showing the importance of intelligence in predicting educational and occupational outcomes, including leadership outcomes. Although many researchers and popular writers have attempted to displace general intelligence from the top spot on the individual-­ difference podium, their efforts have failed. Perhaps new forms of intelligence will be discovered; however, given what we know about g, and the multitude of factor analytic studies that have been done, it is unlikely that we will find another construct that predicts so much variance in cognitive-oriented performance outcomes. Of course, intelligence is not the alpha and the omega of individual differences; personality matters, too, especially for managerial and leadership success (Barrick & Mount, 1991; Judge, Bono, Ilies, & Gerhardt, 2002; Zaccaro et al., 2018). Still, even though much is known about intelligence, as our review has made clear, too, studying the impact of intelligence on leader outcomes is not as ­linear—that is, as straightforward—as one may have thought it was. Much of the research that has been done probably imposed an incorrect functional form on the relationship between intelligence and outcomes. Thus, there is still so much work to be done to conduct large-scale studies that model the relationship correctly. Only then will meta-analyses be fully informative for policy and practice. Of course, contextual (geographic, cultural, industry, etc.) effects must also be correctly accounted for (Liden & Antonakis, 2009) so that the boundary conditions of intelligence can be better understood, too, as is the criterion predicted. We are thus very excited about current research on intelligence and the research that will be conducted in the coming years. We leave readers with a quote from ancient Greece, which brings to light elements of the definition of intelligence, as well as some of the suggestions we have made about how intelligence should be studied. According to the playwright Menander (Xenophon, 1764): Δις ἐξαμαρτεῖν ταὐτόν, οὐκ ανδρός σοφοῦ3

Notes 1. The Judge et al. (2004) meta-analysis differentiated perceptual from objective ratings, the criterion for the Hoffman et al. (2011) meta-analysis mixed the two, noting their criterion consisted of “objective and rating-based measures of the leader’s overall effectiveness and performance” (p. 355). 2. Estimating the effect of leaders on outcomes is tricky. For instance, many studies have measures of leader outcomes that are at the team level—thus, to what extent is the team

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level outcome due to the leader or the team? Apart from omitted team effects, there could be other omitted variables, including at the firm, industry, or country level; time effects may matter, too (Antonakis et al., 2017). Thus, to partial out the effect of the leader on the team outcome one must account for the fixed-effect of the team by having a team constant and switching leaders, akin to what is done in experimental rotation designs (see Kenny & Zaccaro, 1983; Zaccaro et al., 1991); then individual differences that may account for the fixed effect can be measured to see how much they matter. Alternatively, one can use econometric techniques and estimate the fixed-effects of managers, while partialing out effects of firms, industry, country, and time and then estimate to the proportion of the leader fixed effect that is due to leader individual difference like intelligence (e.g., see Antonakis et al., 2016). Or better yet, one could “construct a manager-firm matched panel data set that allows us to track the same top managers across different firms over time” (Bertrand & Schoar, 2003, p. 1175), which would be akin to a rotation design. Still, that intelligence is largely exogenous means that estimates of the effect of intelligence on outcomes can be, to a large extent, consistently estimated (see section “Intelligence as an ‘Instrumental Variable’ for Causal Identification”). 3. Usually also written as “το δις εξαμαρτείν τ’αυτόν ουκ ανδρός σοφού”; translated loosely it means making the same mistake twice is not a mark of an intelligent (clever, wise, skilled) person.

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APPENDIX Do Other Forms of Intelligence Matter?

Although not directly relevant for what we discuss in this chapter regarding intelligence per se, we elaborate a bit on this topic because many researchers believe that alternative notions of intelligence matter much for outcomes. In particular, the farandinical claims by the popular writer Daniel Goleman and his colleagues (Goleman, 1995, 2000; Goleman, Boyatzis, & McKee, 2002) have created the impression that other forms of intelligence, like emotional intelligence, matter much more for leadership than does general intelligence. As concerns the link between emotional intelligence and leadership, there is much debate on the topic (Antonakis, Ashkanasy, & Dasborough, 2009). At this time, it is not clear to what extent constructs like emotional intelligence, or related measures, matter (e.g., see Gottfredson, 1997b, 2002; Gottfredson, 2003a), especially in predicting outcomes when controlling for the “usual suspects”, that is, personality and general intelligence (Antonakis, 2011). For example, there is stable variance in the leader due to traits—using analyses from panel data shows that about 40% of the variance in leadership ratings (i.e., a multiple R of about .63) can be attributed to leader individual difference and most of this variance is captured by personality and intelligence (Antonakis et al., 2017). There is not much variance left to predict. Narratives of the sort: “we all know people who are smart but do not have good social skills” seem to resonate with many individuals. However, well-done studies that account for the effect of personality and intelligence, as done by Cavazotte, Moreno, and Hickmann (2012), have found no effect for emotional intelligence on leadership outcomes. Importantly, results from two meta-analyses show that emotional intelligence, whether measured using self-reported trait-like questionnaires or ability (i.e., performance) tests, does nothing to predict leadership

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outcomes when controlling for personality and intelligence (Harms & Credé, 2010a, 2010b). We must note, too, that there have been some serious efforts to measure emotional intelligence as an ability (e.g., the MSCEIT measure of Mayer, Salovey, & Caruso, 2008), and we are sympathetic to such efforts. However, even the venerable MSCEIT has very serious deficiencies (Fiori & Antonakis, 2011, 2012; Fiori et al., 2014; Legree et al., 2014; Maul, 2012). Thus, the mostly null results shown for the predictive effects of emotional intelligence on leader outcomes come as no surprise to us (Antonakis, 2003, 2004, 2009; Antonakis et al., 2009; Antonakis & Dietz, 2010, 2011a, 2011b). These results parallel what is found in the general literature regarding the non-effect of emotional intelligence on work performance ( Joseph, Newman, & O’Boyle, 2015). What complicates these discussions further is the large number of varied constructs measured by researchers, which in fact may be measuring intelligence. This problem is usually called “the old wine in new bottles” problem and is also known as the “jangle fallacy” (e.g., Kelley, 1927).

2 LEADERSHIP AND INFORMATION PROCESSING A Dynamic System, Dual-Processing Perspective Robert G. Lord

As societies have evolved, leadership processes have changed and developed (Spisak, O’Brien, Nicholson, & Van Vugt, 2015), with modern executives managing decentralized, global organizations that exist as complex, international networks that compete for material and human resources. The effects of leadership processes in such networks are generally distributed over people, locations, and time, making it very hard to clearly assess the consequences of any particular leader or leadership act (Lord & Dinh, 2014; Meindl, 1995). Moreover, contemporary leadership research stresses that leaders and followers are both essential parts of leadership systems (Shamir, 2007; Uhl-Bien, Riggio, Lowe, & Carsten, 2014). In addition, leader–follower systems are embedded in groups (Grand, Braun, Kuljanin, Kozlowski, & Chao, 2016) and more extensive organizational systems (Hazy & Uhl-Bien, 2014). Hence, describing the information processing related to leadership processes is a daunting, albeit important, task. My strategy for understanding leadership and information processing is threefold. I begin by focusing on leadership and individual information processing, building on work that depicts individuals as dual-processing, adaptive systems (Chaiken, 1980; Dehaene, 2014; Smith & DeCoster, 2000). This approach helps us understand leadership topics such as the use of implicit leadership theories (ILTs) (Epitropaki, Sy, Martin, Tram-Quon, & Topakas, 2013) and implicit follower theories (IFTs) (Sy, 2010) as well as the development and use of leadership skills (Lord & Hall, 2005). A critical component of these processes, which is central to both leadership and followership, is the dependence of leadership on self-structures of both leaders and followers (Lord & Brown, 2004; Lord, Gatti, & Chui, 2016). These structures influence reactions to immediate task and social stimuli, but they also allow prospective thought, which attempts to anticipate the future and prepare for it in proactive ways that foster better future outcomes

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(Baumeister, Vohs, & Oettingen, 2016). Consequently, there are important ties among the self, virtual time travel, forecasting, vision development, and vision implementation (Shipman, Byrne, & Mumford, 2010; Stam, Lord, van Knippenberg, & Wisse, 2014). Next, I extend processes to look at information processing in groups, which are also dynamic, complex adaptive systems. I focus on the emergence and modification of structures that guide leadership processes. Complex adaptive systems (CAS) are defined as types of nonlinear dynamic systems that are composed of networks of interacting units such that the system is holistically meaningful. What makes such systems complex is that as they interact, new phenomena (e.g., meanings, insights, constructs, patterns) are created (Page, 2007). Importantly, CAS can selforganize as they adjust to and proactively create environments, and they are usually composed of multiple hierarchical levels (Simon, 1969). In groups, this may involve formal leaders, or informal group leadership structures may emerge over time through the interaction of members in CAS (Acton, Foti, Lord, & Gladfelter, 2019). Finally, I extend this line of thinking to understanding how leadership plays out in larger CAS (Hazy & Uhl-Bien, 2014; Uhl-Bien & Marion, 2008), which help structure leadership processes and are, in turn, structured by leadership processes as systems spontaneously adjust to and create changing environments.

The Individual as an Information Processing System The critical adaptive tasks for human beings is to acquire and use many types of skills that have been learned over time so as to effectively adjust to or anticipate their changing circumstances. In other words, they need to be adaptive systems. Evolution and extensive learning, as well as environments rich in task and social feedback, allow individuals to adapt remarkably well. Individuals are also motivated to grow and develop (Ryan & Deci, 2000), and they do this in a bottom-up, data-driven manner as they make sense of their actions and outcomes, but also in a top-down manner as they form and elaborate new identities (Braun & Lord, 2017; Strauss, Griffin, & Parker, 2012), develop more accurate mental models (DeChurch & Mesmer-Magnus, 2010), and build a richer causal understanding of environments (Mumford, Todd, Higgs, & McIntosh, 2017). When viewed from the outside, people seem to function as an integrated whole, but when analyzed from a cognitive or neurological perspective, there are many specific processing structures that are operating in parallel, but in a manner coordinated by one’s physical morphology (Pfiefer & Bongard, 2007), by conscious, integrative brain structures (Dehaene, 2014), and also by emotions (Cosmides & Tooby, 2000). Information processing, which occurs in a contextually embedded, embodied individual, also occurs in real time as people draw on a lifetime of learning to adjust to changing environments through both reactive and proactive strategies. Leadership processes both depend on and contribute to these adaptive processes.

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Dual-Processing Theories Extensive research on both local and brain-scale processing (Baars, 1997; Dehaene, 2014; Dehaene & Naccache, 2001) supports a dual-processing model that explains how the human mental system adjusts to context without central direction. Dehaene (2014) explains that processing in many local modules occurs in parallel, without conscious attention, and very quickly, but with limited flexibility. Processing in local modules can be very fast (because neural impulses do not have to travel very far), but it tends to be isolated from processing that occurs in other brain structures. Schema guided processes, such as the use of person schemas or scripts, are typical of such processing. To illustrate, managers may use implicit performance theories (IPTs) to understand follower behavior, while followers use ILTs to understand their supervisor’s behaviors (Engle & Lord, 1997). Such theories are learned over time but are based on stable cognitive categories. In contrast to local processing, Dehaene (2014) maintains that more global brain structures provide flexible, conscious integration of information from many areas of the brain. This conscious integration of meaning then serves as a premise for further processing and behavior. These global structures operate more slowly because they depend on neural impulses that need to travel throughout the brain, which involves much greater distances. But they are also able to contextualize the meaning they create to a vast array of input information. When a conscious meaning is constructed by these global systems, it is fed back to local modules, tuning them to this interpretation. In this way, the human dual-processing structure continually adjusts to context, but it does this in a way that draws on structures created by past learning as both a means to apply cumulated knowledge and as a system that fosters stability. However, because integrative, brain-scale processes can be creative, they often invent new meanings, which move CAS forward in time, often to new potentials that may not have been fully anticipated (Lord, Dinh, & Hoffman, 2015). Both local modular and brain-scale processing systems are structured to integrate information as addressed in the next section, but they do this on different timescales because of their physical organization.

Integration in Local, Modular Neural Networks Stability and Plasticity in Leadership Perceptions As Grossberg’s (2013) adaptive resonance theory stresses, “the brain autonomously learns to categorize, recognize, and predict objects and events in a changing world” (p. 2). Further, it must do this without catastrophic forgetting of old information when it encounters a new stimulus (e.g., a new type of leader). Thus, rather than basing our understanding of leadership on each new example we encounter, people develop ILTs based on their extensive experience (Epitropaki et al., 2013; Lord, Foti, & De Vader, 1984). But such theories aren’t rigidly applied.

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Instead, they are adjusted based on top-down constraints that allow perceptions and learning to be guided by a perceiver’s expectations, and such guidance is critical to having systems that maintain prior information when new stimuli are encountered. This perspective is consistent with extensive work on social perceptions processes (Freeman & Ambady, 2011), with research on leadership perceptions (Foti, Knee, & Backert, 2008; Sy et al., 2010) and with Grossberg’s (2013) theories of perception and learning. In short, cognitive and perceptual systems exhibit stability and plasticity, rather than exhibiting catastrophic forgetting when new stimuli are encountered.

Connectionist Neural Networks As described extensively in the following sections, stability and plasticity are inherent aspects of some types of neural networks, but they also accrue from the modular/hierarchical structure of the brain (Dehaene, 2014). For many tasks, information processing occurs primarily in modular systems that are composed of networks of neurons that pass activation and inhibition among connected nodes. For this reason, such networks are often referred to as connectionist networks (Bechtel & Abrahamsen, 1996; Dinh, Lord, & Hoffman, 2014). As these networks operate over time, they converge on aggregate patterns that involve the best combined interpretation of stimulus-driven inputs and top-down constraints from higher-level factors. These solutions or patterns could involve social perceptions, relevant goal states, affective interpretations, motor responses, etc. In general, activated patterns involve the local creation of meaning that, while not conscious, can nevertheless direct further thoughts and actions. For example, generally drivers can use perceptual schema and their connections to motor responses to automatically manage familiar tasks such as driving while conscious attention is focused elsewhere. Such networks satisfy the stability-plasticity requirement in very clever ways. As such networks learn over time, they revise the weights connecting units. In general, weights are strengthened when two units are simultaneously activated or when their activation is associated with favorable outcomes. However, the patterns that are created are a joint function of input activation and top-down constraints from higher-level systems. Thus, stability arises from weights that are slow to change, and flexibility occurs because the activation pattern reflects both contextual inputs and context-sensitive constraints. Because it is the pattern that is meaningful, and generally not the activation of a particular unit, these systems are contextualized, information integration devices. The resulting patterns are thus predictable in stable situations, but they can lead to novel interpretations when context or inputs change radically. For example, the face of a familiar colleague may be instantly recognized with little effort or attention, and this interpretation can then cue familiar knowledge, emotions, and behaviors. However, if stressed, tired, or highly emotional, the expression conveyed by the face may be quite

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different, and it may cue attention and conscious problem solving, or perhaps more automatic, but atypical responses (e.g., sympathy, support). In short, neural systems that normally produce efficient and predictable functioning can, under unusual circumstances, also foster creative, adaptive responses.

The Role of Person Schema Because stable patterns are associated with many relatively routine processes, their underlying structures and content are often studied in a particular domain such as leadership. To illustrate, extensive research has examined the elements of leadership categories, which are often referred to as ILTs (Epitropaki & Martin, 2004, 2005; Epitropaki et al., 2013; Lord, Foti, & DeVader, 1984; Offerman, Kennedy, & Wirtz, 1994). Scales to measure typical content of ILTs were developed by Offermann et al. (1994) and they were refined further by Epitropaki and Martin (2004), yielding six dimensions: sensitivity, intelligence, dedication, dynamism, tyranny, and masculinity. How these dimensions are used to classify leaders can vary with context because the leadership prototype is itself embedded in constraints from many other systems. In what are called recurrent networks, units are mutually activating, and the combined pattern defines a prototype for a category. Consequently, the degree of activation of recurrent networks created by the operation of a particular person’s input and constraints in a particular context can simultaneously define a relevant prototype and indicate the extent to which the person fits that prototype. People do not need to consciously think about the match of a person to a prototype when perceiving a leader, nor do they need to consciously adjust a leadership prototype to context. Rather, both processes are reflected in the activation of contextually constrained, recurrent networks. There is little that is unique to the leadership context in this basic perceptual process. In more general terms, what occurs is that a self-amplifying pattern results from the interactions among higher- and lower-level units. Grossberg (1999, 2013) terms this mutual amplification process resonance, and his overall theory adaptive resonance theory (ART). According to ART, when resonance is high enough and lasts long enough, stimuli are consciously noticed. This is how perceptions operate. This resonant pattern reflects a momentary stability in an ongoing process (i.e., an attactor in dynamic system terms), which is often maintained by the recurrent neuronal networks. This process can be a helpful guide to expectations (Grossberg, 2013), but it also may produce stereotypes and habits, and thus may limit flexibility. For example, individual social perceptions are guided by higher-level expectations based on gender, age, race, and ethnicity (Freeman & Ambady, 2011). Figure 2.1 abstractly represents such a network, which produces leadership perceptions as the recurrent hexagonal structure in the center (i.e., a leadership prototype) is constrained from many sources in a top-down manner and is

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Recent Cognitions Task

Self

Affect

Social Stimulus

FIGURE 2.1 A Schematic

Model Showing Constraints on Prototype (Central Hexagon) From Contextual Factors

activated from stimulus features. We have chosen to represent this as a network being surrounded by constraints because that literally is the case. The central prototype, in turn, could be activated by stimulus features as one perceives leaders (Hanges, Lord, & Dickson, 2000). To simplify this figure, we have not shown arrows from the perceptual stimulus or each type of constraint to each node in the hexagon showing a recurrent network. In this type of network, because each node in the prototype is connected with every other node, activation can reverberate around the prototype maintaining activation while encoding a stimulus. But this process can also activate features that were not part of the stimulus through what is called pattern completion or gap filling. Thus, while recurrent structures may be very good at using knowledge (contained in the weights linking units) to guide perceptions, they may not be very good at distinguishing prototype-consistent features that actually occurred from those that did not occur (Lord, 1985). Consequently, behavioral measures that rely on ILTs may reflect classification as a leader (from whatever means) as well as the effects of prior leadership behaviors. Our current research (Hansbrough, Lord, Schyns, Foti, & Liden, 2019) indicates that it may be possible to reduce the effects of ILTs by selecting measurement items that tend to elicit memories from episodic rather than semantic memory. Another problem for the leadership field is that in networks like that depicted in Figure 2.1, top-down expectations which tune a network prototype to context, may also limit potential and mobility of certain types of groups (see Hanges et al., 2000; Lord et al., 2001; Sy et al., 2010). To illustrate, as Sy et al. describe, the application of racial stereotypes (Asian American versus Caucasian American) inhibited some features of leadership prototypes in both Asian (e.g., dynamic) and Caucasian (e.g., intelligent, dedicated, sensitive) groups, while amplifying other features in Asian (intelligent, dedicated) and Caucasian (masculine, dynamic) groups. More­ over, through this mediational process of prototype contextualization, leadership

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expectations and perceptions are tuned to these racial constraints, creating “competent leader prototypes” for Asian Americans and “agentic leader prototypes” (Figure 3, p. 910) for Caucasian Americans. Research shows that category definitions can change with a number of factors such as a perceptual target’s race (Rosette, Leonardelli, & Phillips, 2008; Sy et al., 2010), the perceiver’s self-schemas (MacDonald, Sulsky, & Brown, 2008), culture (House, Hanges, Javidan, Dorfman, & Gupta, 2004), or the context of a particular group (Hogg, 2001; van Knippenberg & Hogg, 2018). In this way, perceptual processes in a specific domain show adaptability while still exhibiting stability in terms of the underlying units and their association. However, over longer periods of time, even the units that define leadership categories can change (Lord, 2017). For example, ILTs now include dimensions like creativity, which were not part of the content of earlier studies of ILTs (Offermann & Coats, 2018). Interestingly, schema for creativity can conflict with those of leadership (Epitropaki, Mueller, & Lord, 2018). However, this negative effect is eliminated if people have established themselves in leadership roles or if they are in contexts that call for creativity, such as filmmaking (Mainemelis & Epitropaki, 2015).

The Role of Event Schema Grossberg’s ART (2013) emphasizes that perception and action-related schemas are integrated in self-regulatory systems. The social-cognitive and motivational psychological literatures often conceptualize the action components as arising from script-based schemas that link goal structures related to events with actions aimed at attaining such goals (Foti & Lord, 1987; Goschke & Kuhl, 1993; Miller, Galanter, & Pribrim, 1960; Schank & Abelson, 1977). For example, familiar tasks, such as setting a table or going to a restaurant are linked to dining events, and they can trigger sequences of activities that can be executed with minimal conscious attention. In a more leader-relevant example, scripts for meetings may specify the order of relevant actions. Interestingly, person- and script-based encoding may provide alternative ways to understand actions (Foti & Lord, 1987). Scripts also can provide a structure for detailed task knowledge that is organized in a temporal sequence as in a story, and people understand stories by connecting them to a script-based logic involving goals and goal attainment. People also construct stories for their own lives as a way to organize self-relevant knowledge and foster self-development and self-creation (McLean, Pasupathi, & Pals, 2007) as well as authenticity (Shamir & Eilam, 2005). Such stories are also modified as people live their lives and think about what they would like to be. Often, this involves using the self as a vehicle for time travel into the future, and responding proactively as a way to change the future (Baumeister et al., 2016; Seligman, Railton, Baumeister, & Sripada, 2013). Because case-based knowledge follows a story structure, it may be particularly useful in helping individuals see how tasks can be accomplished (Mumford et al.,

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2017; Mumford, Medeiros, & Partlow, 2012). Such knowledge may incorporate event-related information from past experience, and it is most likely encoded in episodic memory; whereas person-based knowledge is more likely to involve semantic memory. The use of scripts and case-based knowledge often plays out over time and for this reason, it may interact with more conscious processes. For example, Mumford et al. (2017) emphasized that cognitive skills related to defining problems, planning, analyzing goals, and assessing constraints were essential to using case-based knowledge in leadership tasks, and these actions generally involve conscious processes. Conscious attention or “focus” in the terms of Ericsson and Pool (2016) is critical in effectively using feedback to improve task performance and learn from experience. Knowledge acquired from experience is critical in many leadership areas, and even in creative thinking, domain-specific knowledge, often in the form of case-based knowledge, is needed to support effective strategies and processes (Mumford et al., 2012). How one uses time is an important element in stories, in pragmatic prospection and time travel, and in forecasting future events (Lord, 2018). Although individuals time travel with respect to themselves, leaders follow similar practices in forecasting and in constructing visions of the future (Shipman et al., 2010). Interestingly, Partlow, Medeiros, and Mumford (2015) found that undergraduates constructing visions performed better when using simple mental models and emphasizing negative outcomes, which may prompt more careful information processing. Such processing likely uses brain-scale conscious processes to reason about events, understand causality, and, in combination with mental models, anticipate (often incorrectly) what the future will be. Forecasting and vision construction may work well when environments are very stable, or when the near future is the focus. But in CAS, many internal and external factors can change how systems function, so often forecasting involves developing and preparing for multiple scenarios. There are also many potentials in the future that cannot be foreseen until enacted by an individual, an organization, or its external environment (Lord et al., 2015). For this reason, even experts doing familiar tasks (e.g. surgeons) often have to revise their plans as tasks unfold (Ericsson & Pool, 2016).

Expertise and Limits to Conscious Processing There are other limits to the extent that conscious, rational processing actually describes leadership functioning, even though intuitively that seems to reflect how information processing works. Often, organizational systems are so complex that experimentation and complex network dynamics need to accompany conscious analysis for systems to be effective (Goldstein, Hazy, & Lichtenstein, 2010). At other times, people may short-circuit rational analysis (Simon, 1956), relying on satisfactory solutions that are readily available, or script-based knowledge to understand actions. For example, in assessing causality, people tend to stop at the first plausible explanation (Taylor & Fiske, 1978). Illustrating this effect, salient

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factors are overemphasized as causal sources. Applying this idea to the leadership area, a carefully designed experiment found that simply moving a person from the edge to the center of perceiver’s visual field increased casual attributions to that person as well as perceptions of the amount of leadership they exhibited, even though behaviors did not vary (Phillips & Lord, 1981). Another limitation to conscious processing is time. Often, in fast-paced situations, leaders must rely on knowledge rather than careful thought, and they can do this effectively in areas where they are experts (Ericsson & Charness, 1994). Experts in an area such as leadership, have many well-organized knowledge structures that can quickly interpret patterns that guide understanding and behavior (Ericsson & Pool, 2016; Lord & Hall, 2005). But the experience needed to become a true world-class expert may take many years to acquire (Ericsson & Charness, 1994). Rather than being conveyed in “leadership training” activities as is often attempted, expertise requires deliberate practice in very specific contexts or experience in those domains; and that practice needs to be challenging, to engage conscious focus, be tied to specific goals, and linked to feedback for it to increase skill. With respect to leadership, as expertise develops, leaders become less focused on themselves and more focused on external factors like followers (Lord & Hall, 2005), so goals may shift from self- to follower-development. Experts are also able to process information much more quickly because they can rely on recognizing learned patterns automatically, which activates knowledge and appropriate responses associated with such patterns (Ericsson & Charness, 1994). The capacity of expert leaders to quickly and accurately process information is illustrated in the actions of airline pilots Chesley Sullenberger and Jeffrey Skiles who guided U.S. Airways Flight 1549 to a safe water landing on the Hudson River after bird strikes had disabled both engines. Captain Sullenberger, a former fighter pilot, experienced glider pilot, and expert on aviation safety, drew on all this knowledge and experience to quickly recognize what was possible and what was not. Several critical choices occurred in the less than three minutes he had to communicate with air-traffic controllers, make decisions, and glide the plane to a safe water landing (Cooper, 2009). Such actions reflect the integration of meaning created by connectionist systems with higher-level conscious, cognitive processes skills. Precisely how this occurs is addressed in more detail in the following section.

Integration in Global Neuronal Workspace (GNW) Situating Leadership Processes Consciousness reflects a global sharing of information from local modules that are connected to a global neuronal workspace (Baars, 1997; Dehaene, 2014; Dehaene & Naccache, 2001). This can occur because local modules, which operate semi-autonomously, are also connected to a global network. Specifically, the

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neocortex consists of layers that process specific types of information (e.g., facial expressions) in columns through these layers. These are the modules that Dehaene (2014) discusses. But some layers also have connections to brain-scale networks (Grossberg, 2013; Trappenberg, 2002). For example, layer IV of the neocortex reflects many cortical inputs, and layer V reflects outputs of columnar modules, whereas layers II and III contain connections to broader brain structures. When a module resonates in response to a stimulus (e.g., a familiar face), the spike in neural activity can directly cue a rapid response, but information is also transmitted across the brain, affording a more global interpretation. Conscious processing occurs when activation from many different types of columnar modules creates a “wave of neuronal activity that tips the cortex over its ignition threshold” (Dehaene, 2014: 140) and creates an avalanche of information processing that produces a general interpretation of the pattern of inputs, which is a consciously recognized meaning. (Because the GNW is a dynamic system, this interpretation or contextualized meaning can also be viewed as a spontaneously emerging attractor.) This meaning is then broadcast back to local modules to tune them to this global meaning, creating contextualized perceptions, goals, and script-based actions. In other words, consciousness reflects an integration occurring in the GNW, and it is fundamentally a creative process that contextualizes interpretation of the aggregate pattern of activation in local modules. That context can emphasize either external situations or internal mental models, values, motivation, and desires. Because it is the entire pattern that is consciously meaningful, not specific elements of the pattern, this process also illustrates why such dynamic systems are only holistically meaningful. Note that although this avalanche of information processing occurs quickly, being triggered in as little as 300 milliseconds or literally the blink of an eye (which takes around 400 milliseconds), it links perceptual, cognitive/affective, evaluative, attentional, and decision-making networks. Over time, these systems also become dynamically linked with task and social environments, so that contextually sensitive responses allow self-regulation, even in novel situations, as was the case with the pilots’ response to the disabled engines in Flight 1549. Using script-like structures, these systems can draw on past experience and knowledge, yet they can apply this information in new contexts whether real or virtual. Thus, although leadership research has tended to ignore context (Oc, 2018), human information processing systems are structured to adjust to contextual variations, often automatically.

Simulated Context and Time Travel The GNW, in conjunction with local modules, is also a virtual simulator. This capacity allows one to visualize the consequences of actions on future outcomes as the GNW adjusts recall of episodic events to newly experienced or anticipated contexts. The hippocampus plays a particularly important role in such processes

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(Addis & Schacter, 2012) as it allows one to “virtually time travel” by retrieving elements from the past that are stored in episodic memory and combining them with new information regarding anticipated future situations. Such processes also allow one to “pre-adjust” or anticipate situations by forming expectations that create top-down constraints on perceptual processes (i.e., one tends to see what one expects to see). One can also regard the functioning of the GNW and the momentary creation of meaning as a quantum collapse (Dehaene, 2014; Stapp, 2009) in which a specific reality is selected from the many potential realities that could have been created. Why a specific reality is created from many possible potentials is a fundamental issue in understanding how leadership visions or strategies develop. This is neither a purely cognitive nor purely behavioral process, but rather one that grounds cognitions in the momentary contexts created by the continual behavioral enactment of reality (Lord et al., 2015). It is not the objective reality that creates meaning and behavior but rather that reality as represented in local modules and emergent GNW meaning structures. Moreover, behavioral streams are always creating new realities, as Hernes and Maitlis (2010) stress.

Stability and Plasticity in the Self Self-relevant knowledge is centrally involved in regulating within-person achievement behavior as well as between-person social perceptions and behaviors that are central to leadership (Ibarra, Wittman, Petriglieri, & Day, 2014; Lord & Brown, 2004; Lord, Diefendorff, Schmidt, & Hall, 2010; Markus & Wurf, 1987). Consequently, the self has a potentially profound effect on leadership processes and the development of social identities. Moreover, the development of the self is fundamental to the full attainment of human potential, and the development of a leadership identity is an important aspect of leadership development. For these reasons, we take a closer look at self-relevant information processing and also at how the self develops over time. The seeds of a leader’s vision may also lie in his or her understanding of oneself in a future context.

Information Processing and the Self The self is defined as an overarching knowledge structure (Kihlstrom & Klein, 1994) that contains cognitive or affective beliefs about the self (Fisk & Taylor, 2013). It is so fundamental to our understanding of how we relate to the world that humans have dedicated processing networks for keeping track of contexts and self-relevant outcomes and integrating this information into an autobiographical understanding (Gusnard, 2005). This understanding often is organized into a life story (McLean et al., 2007) that integrates the past, present, and future. In this story, new episodes are enacted in the present as they are composed, yet

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people also spend considerable time thinking about and planning for the future (Baumeister & Vohs, 2016).

An Application of Dual-Processing Theory Modular Self-Schema and Contextualized Identities Applying the dual-processing perspective already developed, the self can be viewed as a higher-level processing structure that constrains and motivates many other types of information processing. However, the use of this information occurs in a flexible manner, as various modular self-schemas are activated by context (Markus & Wurf, 1987) or self-identities are constructed based on task and social feedback (Ashforth & Schinoff, 2016). The self is critical in the leadership domain, as leaders can prime self-schema in followers (Lord & Brown, 2004; Lord, Brown, & Freiberg, 1999), active self-identities can elicit different types of leadership behavior ( Johnson, Venus, Lanaj, Mao, & Chang, 2012), self-structures may help one organize and deploy leadership skills (Lord & Hall, 2005), and the self is likely a critical element in vision construction (Stam et al., 2014) and personal development (Ryan & Deci, 2000). Further, self-identities are a central component of social identity theory (Hogg, 2001; van Knippenberg & Hogg, 2018), which is a highly influential leadership theory. For this reason, an extended example of how dual-processing theories can be applied to leadership is provided. Self-schemas, which are domain-specific, local, modular processing structures, are based on beliefs regarding one’s qualities or behaviors in a particular domain (e.g., I am intelligent, I am kind). They often operate automatically to guide thoughts and actions. Although they are activated in a context-sensitive manner, self-schemas are relatively inflexible processing structures, and they may constrain behavior in relatively rigid ways. For example, a belief that one is not an effective public speaker may constrain many social behaviors, keeping one from publicly offering many suggestions. In contrast, self-identities are a more flexible, global, brain-scale construct that reflects the integration of many factors in constructing an integrative, context-sensitive self-construal (Lord et al., 2016; Lord & Chui, 2018). They involve conscious assessments created by the GNW (Dehaene, 2014), which was described previously.

Mental Time Travel and the Self Because the GNW can act as a virtual simulator, it can locate the self-identity with respect to a specific contemporary task or social context, but it can also transport the self to the past, as when one recalls an event from the previous day or retrieves a childhood memory of a self-relevant event. Time travel to the future can also be simulated, and placing the self in an imagined future

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is even more common than thinking about past selves (Baumeister & Vohs, 2016). People do this for pragmatic reasons (Baumeister, Vohs, & Oettingen, 2016), bringing back information from the future that they use to change the present in ways they hope will impact future outcomes. Indeed, Seligman et al. (2013) maintain that the major function of consciousness is to facilitate prospection of the future. Leadership functions related to areas such planning, forecasting, goal setting, or vision creation also involve working within a simulated future. But those vision are implemented over time by multiple people and in contexts that change, so adaptability and sensitivity to feedback are also required (Lord, 2018). We know a lot about mental time travel (MTT) and prospective cognitions (see Addis & Schacter, 2012; Gilbert & Wilson, 2007; Schacter et al., 2012; Suddendorf & Corballis, 2007), but despite is potential relevance to vision creation, it has not been addressed by leadership researchers (Lord, 2017), with the exception of work on forecasting (Shipman et al., 2010). Briefly, in MTT individuals construct images of the future by using episodic memory to retrieve fragments of events from the past, which are combined in the hippocampus and associated self-relevant networks (Gusnard, 2005) with expectations for the future and selfrelevant structures. This process creates future scenes or frames in which the self (or larger entities like groups or organizations) could be located and scenarios could be developed. It is also likely that in doing this, the GNW draws on modular information related to person and script schemas, making schemas like ILTs or IFTs part of simulated futures. Explicit reasoning and beliefs or implicit theories about causality are also important to this process, as they are inherent in adjustments in the present as a way to influence the future.

Social Aspects of Self-Schemas and Identities It is also important to recognize that both self-schemas and self-identities have an essential social component. Indeed, social feedback is an important source of information for constructing context-relevant identities (Asforth & Schinoff, 2016). Asforth and Schinoff (2016) note that, “there is little that is more elemental and essential to organizational life than constructing a situated and socially validated sense of self ” (p. 131). This is a critical point in understanding leadership, because social structures like families, groups, or organizations often involve very specific self-identities, and individuals who want to maintain a socially validated self are often loth to deviate from social expectations. For example, individuals often do not want to move from technical positions to leadership positions in organizations because this change also requires changing identities, which are embedded and supported by social structures. On the other hand, occasionally social structures can prompt one to consider new identities. For example, one may not recognize their own leadership potential until feedback from others grant them this status.

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Skills and Emotions Learning and Using Leadership Skills Self-structures have two additional effects that bear on leadership processes. One is that because experiences are organized and interpreted within self-schemas and self-identities, skill learning can also be tied to such structures (Lord & Hall, 2005). Indeed, one critical factor in using skills is being able to access them at an appropriate time. Doing so may depend on the momentary salience of particular identities or identity-related goals. Being able to process leadership experiences in terms of self-structures may also be necessary for one to profit from leadership experience (Day & Sin, 2011).

Emotions and Cognition The second effect of importance is that active self-structures relate to emotions on a fundamental level. Indeed, primary appraisal of the potential of a situation to harm or benefit the self automatically evokes emotions (LeDoux, 1999). Perhaps more importantly, LeDoux and Brown (2017) recently theorized that conscious emotions are higher-order states occurring in a general cortical network of cognitions (e.g., a GNW), which are similar to higher order perceptual states, but they arise from subcortical systems more typically associated with emotions such as the fear circuit in the amygdala. Thus, as Damasio (1994) has long maintained, emotions and cognitions are intertwined in inseparable ways, and both are also closely related to self-structures. Indeed, LeDoux and Brown stress that the self must be included in this higher order representation; they maintain that without the self, there are no emotions. Emotions also bind context, actions, and outcomes in episodic memory (Allen, Kaut, & Lord, 2008), providing a micro-level basis for memory and skill development.

Emotions, Social Processes, and Leadership The importance of emotions to leadership processes has been widely recognized (Ashkanasy & Humphry, 2014). As LeDoux and Brown (2017) emphasize, emotions are grounded in self-systems, but they also reflect social processes, and thus emotions are a bridge to considering leadership and information processing in dyads or groups. Briefly, perceiver emotions influence within-person cognitive processes such as encoding and retrieving leadership behaviors (Brown & Keeping, 2005; Martinko, 2018; Naido & Lord, 2008) and also the perceptions of leader charisma (Sy, Horton, & Riggio, 2018). They also affect interpersonal behavior by structuring leader–member exchanges (Tse, Troth, Ashkanasy, & Colling, 2018); and they affect group creativity (Visser, van Knippenberg, van Kleef, & Wisse, 2013) and cooperation, conflict, and task performance (Barsade, 2002).

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To understand the information processing behind such effects, one needs to recognize that not all information processing involves brain structures, particularly with respect to emotions, with an important component being embodied (Dinh et al., 2014). For example, the human facial musculature, which is connected to soft tissues rather than the skeletal structure, provides a medium for conveying emotions to others and responding to the emotions that others express. Often labeled emotional contagion (Hatfield, Cacioppo, & Rapson, 1994), the social transmission of emotions involves multiple steps. First, emotions are expressed in terms of facial and other embodied structures; next, this structure is mimicked by others; then the facial mimicry by others conveys emotions to higher-level brain structures in perceivers that interpret the emotion and its relevance to the self, and finally this interpretation elicits more general emotional responses (e.g., arousal). There are many illustrations of this process in the leadership field. For example, Visser et al.’s (2013) study of creativity experimentally manipulated a leader’s emotional expression as he gave instructions to experimental participants, and it treated mimicked emotions by participants as a mediator that explained the effects of leader emotions on task performance. Two studies showed significant effects of leader emotions, with the effects of happiness facilitating creative performance and sadness facilitating analytical task performance. Barsade (2002) also examined mood contagion experimentally by using a confederate in groups to express emotions. Confederate emotions then affected facial expressions of group members (as indicated by trained coders rating videotapes of group member’s facial expressions of emotions) and ultimately their task processes such as cooperation and task performance outcomes. Similarly, Bono and Ilies (2006) showed that the effect of positive emotions expressed by leaders on charismatic leadership perceptions involved mood contagion from leaders to followers. Interestingly, charismatic leadership processes may also involve attempts by perceivers to suppress emotions, which then make perceivers more susceptible to influence from the leader (Menges, Kilduff, Kern, & Bruch, 2015). Finally, Sy et al. (2018) articulate a fivestage charismatic process pertaining to moral emotions in which leaders emotion elicitation occurs (Stage 1), followers have emotional responses (Stage 2), leader’s use this response to channel behavior (Stage 3), followers exhibit actions (Stage 4), and action-related emotional outcomes, such as trust and positive emotions, feed back on the leader, who can continue to elicit emotions (Stage 5). Collectively, this research illustrates an embodied and social information processing system that influences both the cognitions and the social behaviors of the people feeling emotions. Further this research demonstrates the capacity of leaders to elicit emotions in others through embodied processes and thereby influence their thoughts and behaviors. This embodied communication channel complements a more symbolic channel, which can create emotions in others through the use of language (Naidoo & Lord, 2008). Emotional communications may register locally in terms of facial mimicry, but their integration with other tasks or social processes likely reflects brain-scale and embodied processing. Thus, even with

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emotions, we have dual-processing systems that are dynamic, and they are able to flexibly tune social processes to a particular context, albeit an emotional one. In sum, we have seen that humans use cognitive structures as a basis for stability, but they can also adjust such structures to context both locally and with brainscale, conscious processes. Such structures allow one to flexibly use knowledge learned over time in adjusting to changing contexts, building new skills in the process (Ericsson & Pool, 2016). The leadership literature has used such dualprocessing models to guide theory and research in many areas that involve both leader and follower effects. Research on self-schemas and self-identities is particularly important because these structures are central components of social perceptions, self-regulation, and emotional responses. They are also essential to mental time travel and pragmatic prospecting, providing a proactive as well as a reactive component of information processing. We turn now to leadership information processing in groups, which follows many of the same principles found within individuals. To simplify exposition, we treat dyads and groups equivalently from an information-processing perspective, although they reflect different literatures.

Information-Processing Groups Information processing in groups also depends on a structural basis that is created in part by leadership processes (formal and informal), by the task environment, and by emergent processes that characterize dynamic systems. These structures allow the use of accumulated knowledge, and they facilitate coordination among group members. Typically, group processes also need to make sense of the past in a way that retains members’ group identification as they plan for and enact the future. Sensemaking is distributed among group members (Weick, 1995), and the meaning created at a particular moment can also be viewed as a quantum collapse of many collective meanings that could have been created. In this section, we will briefly address each of these issues, relating them to leadership and information processing. Paralleling the accumulation of structure from learning in neural networks, structure in groups emerges as activities cumulate over time. What is different though is that structure evolves from social behavior and interpersonal communications. Consistent leader activities can create structure like goal-orientation climate (Dragoni, 2005), and such structures then guide information processing and behavior in relatively automatic ways. Group identity is a particularly important structure (Hogg, 2001). It not only motivates members to contribute to groups, but it simplifies behavioral processes by specifying appropriate norms for behaviors. Dyadic relations also are a source of structure, and, interestingly, this may depend on how dyads are embedded in social networks (Sparrowe, & Liden, 2005). Another important source of structure is the task context itself, which recently has gained increased attention in leadership research (Oc, 2018). Task

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environments coupled with internal goals often cue scripts for behavior, which are then accomplished in a specific environment. Weick and Roberts (1993) provide a compelling example of this process. They used the term “heedful interrelating” to describe how group members coordinated actions using overall script structures when jets land or take off on aircraft carriers, a task that requires temporally and spatially coordinated interactions of many people and technical systems and is inherently dangerous. These authors emphasize that information processing and actions are interdependent, and in combination they create a “collective mind” through heedful interactions. Importantly, the collective mind in this case is distributed across many differentiated units (e.g., people, planes, space, catch wires), and it reflects an embodied, contextually embedded, complex, dynamic system. Collective minds have also been conceptualized in terms of team mental models (e.g., Lim & Klein, 2006), and, in general, the more similar and accurate mental models are, the better teams perform (DeChurch & Mesmer-Magnus, 2010). One role of leaders in terms of sensemaking or vision communication is to guide the development of mental models, which lets team members then function independently of central direction from leaders. However, when novel or extremely difficult events occur, leaders may need to intervene (Klein, Ziegert, Knight, & Xiao, 2006; Morgeson, 2005). As shown by their work observing emergency room teams in trauma centers, Klein et al. found that as well as carrying out routine processes guided by role structures, teams needed to improvise to achieve swift responses in difficult cases. Focusing on naval behavior, Hutchins (1995) describes naturally situated cognitions pertaining to navigating ships, which he calls Cognitions in the Wild. Navigation involves communications and actions among groups of individuals operating on abstractions from physical environments represented by tools, both physical and cognitive, and it is distributed across people, space, and time. Importantly, he emphasizes that social and cultural aspects are critical components of such navigation systems, and he argues that it is a mistake to try to understand this activity as being purely cognitive. This point pertains directly to leadership because leaders have important influences on such cultures, as they constrain the way culture and other structures emerge in dynamic systems. As all these examples show, information processing and leadership in real-world teams function as dynamic systems, coordinated by well-learned skills and structures, changing task environments, and moment-to-moment leadership activities, which can be proactive as well as reactive. Often, it is possible to anticipate how events will evolve, but unexpected twists and turns in events are also common and are typically addressed with a centralized structure involving leadership. Leadership structures themselves evolve, with informal leadership developing where no centralized leadership exists, or informal leadership developing in ways that supplements formal leadership. For example, in trauma centers, which have very clear and strong hierarchies, highly skilled and empowered nurses provided a redundant layer that reduced the likelihood of medical errors. They often stepped

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in to indicate to resident surgeons when additional expertise was needed (Klein et al., 2006). Leadership theory addresses such processes in groups, explaining that over time, clear interrelated patterns developed among team members, and these patterns then coalesced into team-level structures. Importantly, these patterns can be described in terms of the enactment and then social validation of leadership identities (Acton et al., 2019; DeRue, 2011). In this way, structures are tuned to the momentary requirements of dynamic systems, much as consciousness in the GNW operates for individuals.

Information Processing in Complex Adaptive Systems Information processing in larger CAS can be described by many of the same processes described heretofore, but because there are more hierarchical levels, these systems are more complex and are often slower to adapt. Further, because top-level leaders are separated from many environments, changes are often first noticed, and sometimes responded to, at lower organizational levels. That is, they are addressed by local modules in CAS, which have their own internal dynamics. The parallel with brain systems already described goes further in that CAS resemble neural networks in many ways (Marion & Uhl-Bien, 2001), with one important similarity being that institutional knowledge often resides in the connections among units, as well as within specific individuals or groups. Another key idea is that emergence is a central process in generating structures and behaviors, which creates momentary attractors among interacting units (Hogue & Lord, 2007; Uhl-Bien, Marion, & McKelvey, 2007). An important principle of leadership in CAS (Marion & Uhl-Bien, 2001) is that leaders cannot control how all the parts of complex systems operate, but they can catalyze or influence how processes unfold at various levels through a variety of indirect means. Leaders can create enduring, multilevel structures such as ethical climate (Schaubroeck et al., 2012) or orientations towards ideal versus ought selves ( Johnson et al., 2017). They can also create more specific ideals or “tags” that promote a unique identification in CAS and provide a premise for action. Action, in turn, leads to exploration, creation, and self-organization through processes that are neither highly predictable at a micro-level nor foreseeable in their specific outcome. Information processing by leaders in such organizations is often abstract and proactive, and it requires that leaders understand such organizing principles. Much like time travel and pragmatic prospection, it may require that leaders draw on fragments from the past using episodic memory and then creatively construct a more complete picture, which guides thinking, forecasting, and vision creation (Shipman et al., 2010). This picture then may provide a premise for a leader’s actions and the actions of others throughout a CAS (Lord, 2018). There are also many interesting aspects of information processing in CAS that go beyond insights drawn from individuals or small groups. For example, Goldstein et al. (2010) address the importance of novelty in creating new futures for

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CAS that are more adaptive. Novelty is often an inherent result of differences in such systems (Page, 2007), but it needs to be supported by leadership practices that in the terms of Goldstein et al. are “generative”. This may involve supporting experiments as a way to understand their consequences, communication through weak ties, and refinement by diverse groups that share a fundamental orientation. Innovation also creates tensions in CAS that must be accepted. In these ways, some CAS can be very proactive in creating new environments, products, and markets. Systems that foster novelty and learning (e.g., Apple) can outperform systems with more resources but a more regimented environment (e.g., the old AT&T), as noted by Goldstein et al. (p. 101).

Conclusions The perspective taken in this chapter is that leadership information processing, and leadership processes in general, are embedded in hierarchical structures that learn and adjust but are also proactive. These CAS are dual-processing systems in that processing often occurs in local modules in ways that are relatively independent from higher-level structures; however, higher-level structures also process information in ways that include activation from many modules, are conscious, and are symbolically based. Information processing in such systems can be viewed as a holistic event depending on the timescale involved and the level of focus or, alternatively, as a modular activity. CAS also involve local and more global processing, but this occurs through networks of individuals and leadership practices that can make innovation and novelty central or peripheral aspects of adaptation and growth.

Acknowledgments I would like to thank Rosalie Hall and Xiaotong ( Janey) Zheng for many helpful comments on an earlier version of this chapter.

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3 UNCERTAINTY AND PROBLEM SOLVING The Role of Leader Information-Gathering Strategies Jay J. Caughron, Teresa Ristow, and Alison L. Antes

Sensemaking is the process of working to reduce uncertainty and enhancing one’s understanding of an event (Bartunek, 1984; Benford & Snow, 2000; Cornelissen, 2005; Crosby & Bryson, 2005; Foldy, Goldman, & Ospina, 2008; Nicholson & Anderson, 2005; Sonnenwald & Pierce, 2000; Weick, 1995). It involves developing a coherent and plausible interpretation of emerging events in response to a state of uncertainty (Harris, 1994; Kikuit & Ende, 2007; Maitlis, 2005; Maitlis & Christianson, 2014; Mumford, Friedrich, Caughron, & Byrne, 2007; Sharma & Good, 2013; Thomas, Clark, & Gioia, 1993; Weick, 1993, 1995). Information gathering has been linked to sensemaking using experiments (Day & Lord, 1992), theory-driven rationales (Weick, 1995), and retrospective case analyses (Ford & Gioia, 2000; Weick, 1993). More specifically, information gathering is a critical process that helps leaders make sense of changing events (Anderson, 2008). However, even though leaders are widely believed to engage in information gathering when they experience uncertainty, relatively little research has examined the means by which they gather information. We define information gathering in response to uncertainty as the process by which leaders collect facts, data, opinions, observations, beliefs, and attitudes in order to make sense of their environment, reduce uncertainty, and increase the probability of achieving goals (Kikuit & Ende, 2007; Lubart, 2001; Mumford, Schultz, & Van Doorn, 2001; Mumford, Scott, Gaddis, & Strange, 2002; Scott, Lonergan, & Mumford, 2005). What is critical to understanding leader information gathering is the manner in which leaders engage in this process. Mental models are cognitive representations of environmental variables and how they interact with each other ( Johnson-Laird, 1983; Gentner, 1983; CannonBowers, Salas, & Converse, 1993). As such, they guide the process of information gathering by highlighting critical variables, contingencies to be navigated, and goals

FIGURE 3.1 Leader

Problem-solving Phases

Available Info Sources

Environmental Condions

Information Gathering Model

Info Gathering Strategy Selected

Leader Uncertainty Acvates Info Gathering

Informaon Collected

Sensemaking

Problem-solving Efforts

Uncertainty and Problem Solving  73

worth pursuing. More specifically, mental models leverage a leader’s past experience to guide information search processes as organizational problems emerge. Some information search processes are barely conscious, such as latent scanning; while others are overt and deliberative (Daft & Weick, 1984; Heath & Gay, 1997; Daft, Sormunen, & Parks, 1988; Thomas et al., 1993; Weick, 1998). It is these deliberate information search strategies that are the primary focus of this current work. Figure 3.1 proposes a model in which environmental conditions, information sources, and problem-solving processes impact a leader’s information-gathering strategy. We suggest that uncertainty is caused by discernable and quantifiable environmental conditions, that uncertainty motivates leaders to gather information, that information sources are a critical consideration, and that leaders shift their information-gathering strategy as they iterate through problem solving and solution crafting.

Uncertainty Uncertainty is frequently mentioned in discussions of leader decision making, problem solving, and sensemaking. It motivates sensemaking (Weick, 1995), causes leaders to be uncomfortable (Lachlan, Spence, & Nelson, 2010; Heath & Gay, 1997), and it must be reduced to develop solutions to problems ( Jameson, 2009). Experiencing uncertainty motivates leaders to initiate problem solving and is a prerequisite for a leader to begin seeking new pieces of information (Choo, Bergeron, Detlor, & Heaton, 2008). We argue that leader problem solving is multilevel, social, cognitively demanding, and linked to a leader’s ability to deal with uncertainty (Zaccaro et al., 2015). More specifically, differences seen in leader performance can be traced back to their effectiveness gathering information in order to reduce uncertainty and find new paths forward. In psychological terms, uncertainty has come to be defined as a lack of information (see Daft & Lengel, 1986; Downey & Slocum, 1975; Galbraith, 1973; Garner, 1962; Miller & Frick, 1949; Shannon & Weaver, 1949; Tushman & Nadler, 1978). While this definition highlights the fact that gaining information tends to reduce uncertainty, it is an extremely narrow definition. Uncertainty has also been described as existing when the details of situations are ambiguous, complex, unpredictable, or probabilistic; when information is unavailable or inconsistent; and when people feel insecure about their own state of knowledge (Brashers, 2001, p. 478). Similarly, it has been defined as “a cognitive state resulting from an individual’s assessment of the number of alternative predictions available” (Bradac, 2001, p. 464; Berger & Calabrese; 1975; Liu, Bartz, & Duke, 2016). Two issues become clear when examining these definitions of uncertainty. First, uncertainty is a cognitive state experienced by individuals. Second, information plays a critical role in defining or reducing uncertainty. While this first observation may seem trivial, it is important to recognize how leader uncertainty has typically been discussed. Uncertainty has been described in

74  Jay J. Caughron et al. TABLE 3.1 Summary of Environmental Variables Causing Uncertainty

Novelty Ambiguity Multiplicity Complexity Volatility Fragmentation

Environment presents a scenario the leader has not experienced previously Environment provides contradictory information Environment is characterized by a large number of variables Environmental variables interact with each other forming a network of cause and effect relationships Environment has a tendency to fluctuate quickly and dramatically Environmental information exists but is difficult to obtain

a variety of ways, such as a challenge to be overcome, an obstacle to be removed, a factor leaders are confronted with, an issue followers must contend with, conditions under which leaders operate, or as a means to quantify a chaotic environment (see Daft & Lengel, 1986; Jameson, 2009; Mumford, Antes, Caughron, & Friedrich, 2008; Mumford & Hunter, 2005; O’Driscoll & Beehr, 1994; Thau, Aquino, & Wittek, 2007). In all of these cases, uncertainty is not recognized as an internal state experienced by the leader; rather it is discussed as a characteristic of the environment. It may seem like a purely semantic issue. However, when addressing how leaders gather information to respond to “uncertainty”, it is important to clarify that uncertainty is an internal state experienced by a particular leader and there are conditions in the environment that cause leaders to experience it. The term “uncertainty” has been used alongside or synonymously with concepts like novelty, ambiguity, complexity, and volatility (Artinger, Petersen, Gigerenzer, & Weibler, 2015; Liu et al., 2016; Maitlis & Christianson, 2014). Additionally, uncertainty may result when a leader faces a scenario with a large number of variables or possible outcomes that must be managed (Daft & Lengel, 1986). Thus, we describe uncertainty as an internal state leaders experience when confronted with an environment marked by variables that are novel, ambiguous, complex, volatile, or numerous (multiplicity), each of which makes the leader’s decision-making process and alternatives unclear. We later describe how these environmental characteristics impact leader information gathering and summarize them in Table 3.1.

Specific Causes of Uncertainty New experiences for a leader create the perception of novelty. A novel environment is perceived as unfamiliar (Herrmann & Felfe, 2013), either because the leader lacks relevant experience, has failed to recognize the relevance of past experiences, or relevant information does not exist because the event is truly novel. In contrast, ambiguity is an environmental characteristic that causes a leader to experience uncertainty but for completely different reasons. Ambiguity arises

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from experiencing conflicting variables within an environment (Hill & Levenhagen, 1995). When sources of information contradict each other, it makes it challenging for the leader to identify what information to pay attention to and utilize. Therefore, the leader is tasked with seeking additional information to clarify or qualify what they already know. Similarly, a high number of environmental factors creates what we label multiplicity. Multiplicity occurs when information is bountiful or the scope of leader’s responsibilities has become overwhelming—essentially information overload. This is increasingly more common with the use of technology and the ease with which information can be provided to leaders (Hill, Kang, & Seo, 2014). Thus, a leader is forced to gather and process information quickly, filter irrelevant information, and consider a high number of variables simultaneously. Leaders also experience uncertainty as they gather information in a complex environment. Complex environments are those in which variables have high levels of interdependency (Halbesleben, Novicevic, Harvey, & Buckley, 2003). As a leader interacts with a complex environment, they may be altering the environment in the process. Thus, rendering the information they gather obsolete as it is gathered. A volatile environment can be similarly problematic (Dulewicz & Higgs, 2005; Eisenhardt, 1989). Volatility, occurs when there is rapid change that is outside the leader’s control. The critical challenge in a volatile environment is time and timing. Information will become outdated rapidly in a volatile environment. Thus, leaders may require subordinate assistance in the information gathering process or meetings among stakeholders to access information simultaneously. At the individual level, they may aim to identify information about patterns that govern volatile aspects of the environment. Additionally, seeking information about gaining control over volatile variables will also be highly valuable. Finally, an environment is fragmented when information exists but is inaccessible. Fragmentation is a particularly vexing problem for organizational leaders (Belderbos, Carree, & Lokshin, 2006; Bensaou, 1997; Hedman & Valo, 2015). Typically, fragmentation occurs when some members of the organization possess the information but the leaders that actually need it do not know where it is. It can also occur when a leader’s access to information is blocked for legal, political, or pragmatic reasons. Organizational constraints, including poor communication and leader distance—social, psychological, or physical—often cause or exacerbate fragmentation (Choo et al., 2008; Friedrich, Vessey, Schuelke, & Mumford, 2009). Given the volume, velocity, and variety of information today, facing the challenges outlined in Table 3.1 will be an increasingly common experience for leaders (Chen, Chiang, & Storey, 2012; Gunther, Mehrizi, Huysman, & Feldberg, 2017). Leaders no longer lack information, rather they tend to have too much, poorly organized information that must be processed. Essentially, the problem for leaders has shifted from information finding to information filtering. Leaders are tasked with identifying small troves of relevant information amidst a deluge of irrelevant information (Allard, Levine, & Tenopir, 2009; Bates, 2006). They will

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need strategies to effectively execute this increasingly important responsibility. Ideally, filtering will occur as a non-conscious or low-resource process stemming from sensemaking. However, a leader’s existing sensemaking structures and strategies are likely to align poorly with emerging challenges such as those described in Table 3.1.

Information-Gathering Strategies Gick (1986) suggested a set of specific information-gathering strategies in her review of approaches to problem solving. They include information search directed toward pattern recognition (Greeno, 1976), searching based on compartmentalizing the problem into subunits (Greeno, 1980b), and searching for analogies that shed light on problem features (Hershey, Walsh, Read, & Chulef, 1990; Polya, 1957). In addition to these three strategies, Weick (1995, 1998) proposed an experimentation approach to gathering information. He suggested that as leaders interact with their environment they create hypotheses, engage in an action, read the result, and use that information to direct problem-solving efforts (Mumford & Hunter, 2005; Weick, 1995). Weick (1995, 1998) also proposed that leaders will engage in an ad hoc, open-ended, or improvisational information search process when they believe the current processes for gathering information are breaking down—specifically, when they have difficulty identifying critical cause–effect relationships in the environment (Daft & Weick, 1984; Weick, 1998). Lastly, observations from organizational communication literature suggest that leaders will often use more idiosyncratic information-gathering strategies (Choo et al., 2008; Kikuit & Ende, 2007; Mohr & Nevin, 1990). This occurs when leaders make use of well-trod information channels that are fairly static and reliable even if they are not directly tied to the problem at hand. These strategies are summarized in Table 3.2. For leaders, pattern recognition usually entails identifying relationships between variables or noting recurring events in time, often in a social context (Nijstad & Stroebe, 2006). Literature on the development and application of mental models provides a strong background for how leaders seek and structure information using pattern recognition. When a leader is building a mental model they are identifying patterns that link variables in terms of causes, effects, and contingencies (Mumford & Connelly, 1992). Pattern recognition is likely to be a time-consuming and laborious way to collect information given that the leader must consider many variables, establish linkages between some, and discount the relevance of others, until a structure of interrelated concepts has been developed—­that is, until they have developed a mental model that represents their environment (Gick, 1986; Huang & Hutchinson, 2013). Compartmentalization is the process of simplifying the information-gathering task by breaking it into subunits or structuring it such that information flows can

Uncertainty and Problem Solving  77 TABLE 3.2 Summary of Information-Gathering Strategies With Selected References

Pattern Recognition

Compartmentalization

Analogizing

Experimentation

Ad Hoc

Idiosyncratic

Seeking information to describe relationships between variables and/ or recognize patterns over time Simplifying the information-gathering task by breaking the process into subunits or creating distinct information flow channels Seeking information that will enable the leader to map current problem features to previously experienced problem scenarios Gathering information by using test actions to see how the environment reacts

Improvised information gathering, usually performed in an open-ended fashion, because the leader lacks enough information to select more well-defined strategies Using existing communication channels within an organization that have developed over time

Greeno, 1976

Greeno, 1980b

Polya, 1957

Brown, Bransford, Ferrara, & Campione, 1983; Mumford & Hunter, 2005 Daft & Weick, 1984; Weick, 1998

Mohr & Nevin, 1990

be monitored in distinct channels (Greeno, 1980b; Maitlis & Christianson, 2014; Mohr & Nevin, 1990). One line of research that supports the concept of compartmentalized information gathering stems from comparing expert and novice problem solvers. When given a complex task, novices tend to compartmentalize information gathering, likely because they do not possess a set of case-based experiences to draw from, nor do they have a well-developed mental model (Greening & Johnson, 1996; Swanson, O’Connor, & Cooney, 1990). An additional line of research that supports compartmentalization is that relating to communication channels. Information flow between organizational units tends to follow a set of fairly well-defined norms. These norms specify timing, content, modality, and tone as information is exchanged. Leaders can channel information flow in order to fast-track priority information, relegate trivial issues to subordinates, or simplify follower tasks by only exposing them to certain pieces of information most relevant to their assigned task (Hedman & Valo, 2015; Mohr & Nevin, 1990). In contrast to compartmentalizing and pattern recognition, analogizing is a strategy that is likely to be used by experts rather than novices (Hershey et al., 1990). Here expert leaders gather information that allows them to map the features

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of a current problem state to a preexisting case or mental model. In this way the leader identifies the most relevant concepts and ideas and reapplies them, shortening the information-gathering process and leading them to implement a strategy that has proven successful in the past (Hill et al., 2014; Mumford et al., 2002). Leaders use an experimentation approach to gather information when they interact with the environment in order to elicit a response and then use that response as a source of information (Mumford & Hunter, 2005; Weick, 1993). Typically, these test actions are small-scale, low-consequence trials that can help the leader fill in gaps in their knowledge. Work on leader scanning suggests that the purpose of latent scanning is to determine if the situation is normal or abnormal. If cues in the environment indicate that the situation is normal, then the leader can enact routine scripts and direct their time and cognitive resources to other issues. However, when latent scanning uncovers environmental cues suggesting the situation is abnormal, the leader must pay attention to the situation and become more purposeful in their information gathering (Scott et al., 2005). Thus, ad hoc information gathering is more likely when the leader is in the early stages of dealing with a previously unexamined abnormal situation. That is, situations in which the leader has not identified critical causal variables (Daft & Weick, 1984; Weick, 1998). When leaders do not know what kind of environment they are embedded within nor the variables they need to be attending to, they are likely to actively engage in an open-ended, undirected (or ad hoc) search for information. This serves two functions; first, it minimizes the impact of leader actions that could cause a cascade of unintended outcomes, which is likely to occur in an environment the leader does not understand well. Second, it allows the leader to focus attention on observing rather than acting. By emphasizing an active environmental scanning approach, the leader can be less biased in their observation of variables that they might otherwise have ignored if they were enacting a strategy, case, or mental model they have used before (Daft, Sormunen, & Parks, 1988). Additionally, when a leader is repeatedly resolving problems within a fairly fixed environment, they are likely to create information-gathering strategies that are idiosyncratic to that setting. In this scenario, the type of problem may shift but the organizational setting remains fairly static. That is, the leader works with the same people and organizational units but the challenges they are assigned vary, sometimes widely. In this case, leaders can be opportunistic in their use of existing communication channels. They may craft information gathering strategies that allow them to direct followers’ information gathering, highlight particularly important sources of information, and respect organizational norms regarding information flow. This will rely heavily on the leader’s use of contextual expertise and should result in a more efficient information-gathering process (Choo et al., 2008; Mohr & Nevin, 1990). Table 3.3 is provided to reflect, but also extend, these arguments and provides propositions that researchers could test to confirm the use of these strategies and the contexts in which leaders are likely to be successful implementing them.

Uncertainty and Problem Solving  79 TABLE 3.3 Propositions About Leader Information Gathering Strategies

Proposition 1: Pattern recognition information-gathering strategies • Will help leaders identify critical cause-effect relationships in novel circumstances • Will help leaders learn to predict environmental factors in volatile circumstances • Will be time consuming until the leader has developed a viable, reusable mental model Proposition 2: Compartmentalization of the information search process • Is more likely to be used by novice leaders than expert leaders • Will help leaders collect information by creating distinct communication channels in volatile situations • Will help leaders collect information by creating distinct communication channels in multiplicitous situations • Will help leaders prioritize information in ambiguous circumstances Proposition 3: Experimental information gathering strategies • Will by adopted by leaders when a pattern recognition search strategy fails to identify critical cause–effect relationships in novel situations • Will be adopted by leaders to identify cause–effect relationships in complex environments Proposition 4: Ad hoc information search strategies • Will be used when pattern recognition and compartmentalization strategies fail in novel circumstances • Will be used when the leader has not been able to identify causal variables Proposition 5: Analogizing information search strategies • Will be used when a leader has a well-formed mental model or set of experiences for comparison • Will significantly shorten information-gathering processes for leaders with applicable past experiences Proposition 6: Idiosyncratic information search strategies • Will be used when the leader’s problem space changes but the organizational context remains static • Will be used when the leader needs a communication channel that does not currently exist

Information Sources The source of the information may have a dramatic impact on the type of information strategy a leader uses. Ultimately, leaders gather information from two sources: communication with others and directly observing their environment. Communication is an interpersonal form of information gathering that involves receiving information after it has been processed by another person (Boies, Fiset, & Gill, 2015). As such, it includes implementing the information-gathering strategies described earlier in conversations with colleagues, while reviewing documents prepared by others, and through social networking efforts.

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Communication Tactics and Organizational Structure Communication between individuals is a complex process in which messages must often be tailored in order to be effective (Mohr & Nevin, 1990). Leaders can use a predefined strategy, such as pattern recognition or compartmentalization, for simpler information-gathering tasks. Thus, in some circumstances, leaders will create an idiosyncratic strategy to accommodate the organization’s distinctive norms and processes for handling information. Mohr and Nevin (1990) described tactics for communicating in terms of manipulating the frequency, direction, modality, and content of communication. They described frequency as a ratio that compares the actual amount of communication to the level of communication that is needed to accomplish a set of tasks. Thus, the absolute number of information exchanges is less important than whether or not two correspondents communicate too much, too little, or just the right amount. Direction refers to the flow of communication in an upward, downward, or lateral direction based on the positions occupied by those sending and receiving information. The modality of communication is the format used to transmit the information. This can be seen in multiple ways within an organization, such as face-to-face, electronic, telephone, and written documents. Leaders can also manipulate their communication strategy by selecting message content; that is, what information is being communicated. Leaders can manipulated these messaging variables to select information-gathering strategy in response to characteristics of their organization (e.g., hierarchical or flat), the nature of emerging environmental changes (e.g., causes of uncertainty), or their relationship to an information source (e.g., social networking). In a factor analytic study of more than 1,000 senior managers, Marchand, Kettinger, and Rollins (2001) identified six critical components of organizations that facilitate the sharing and use of information (Choo et al., 2008). They are information integrity, information sourcing, information control, transparency, information communication, and proactive information management. First, organizations that emphasize information-driven processes promote a value of information integrity. Second, these organizations encourage their leaders to be mindful of information sources favoring formal information sources over informal ones. Third, information control is prioritized. Processes and technologies that give leaders access to information in a timely manner are present and well maintained. Fourth, transparency is valued and individuals are encouraged to communicate about failure events so that information can be gained by other organizational members about what is and is not working. Fifth, organizational leaders emphasize appropriate communication regarding information. Not only is refusing to share information discouraged, dumping information on others without communicating its meaning or structuring it for the consumer is also discouraged. Sixth, and finally, information-oriented organizations encourage a proactive stance when it comes to the use of information. Leaders that encourage

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a proactive stance with regard to information use will discuss the use of information, evaluate information sources, and encourage the use of new information when coaching subordinates on problem-solving and decision-making tasks. Clearly, research on communication tactics and organizational information structures presents a complex (and perhaps overwhelming) picture of a leader’s potential information-gathering activities. For information gathering to be effective, leaders at multiple levels and across multiple units must manipulate the frequency, direction, modality, and content of their communications, while bearing in mind the culture, climate, power structures, and technical capacity of the organization.

Social Networking Leaders’ relationships to sources of information can be described as being strong or weak ties (Granovetter, 1973; Kikuit & Ende, 2007). Strong ties are described as relationships between individuals that are characterized by frequent contact, trust, information exchange, and mutual understanding (Gilsing & Nooteboom, 2005; Handley, 2006; Hansen, 1999; Reagans & McEvily, 2003; Roberts, 2006; Uzzi, 1999). Additionally, strong ties are more difficult and costly to maintain but provide greater levels of social support than weak ties. They also allow for more complex information exchange than weak ties and for greater recognition of minority viewpoints (Handley, 2006; Nemeth & Staw, 1989; Roberts, 2006). Weak ties are characterized by less frequent contact and lower levels of trust, and they take on a more transactional tone (Granovetter, 1973; Hansen, 1999; Perry-Smith & Shalley, 2003). Weak ties exist with lower cost in terms of time and effort, provide less social support, and are easier to dissolve. However, weak ties provide an advantage over strong ties in some instances. When individuals are bonded by a strong tie, the likelihood that one member of the dyad has unique information is low. In contrast, those bonded by weak ties, frequent different social circles and the probability that one member of the dyad has information that the other member does not have is much higher. Additionally, weak ties allow the dyad members to maintain a greater level of autonomy than do strong ties (Granovetter, 1973; Kikuit & Ende, 2007). Given the relational nature of strong ties, it is likely that leaders will use different information search strategies with strong-tie colleagues compared with weak-tie colleagues. Gathering information on complex issues is more likely to be conducted with strong-tie colleagues. They will use a strategy that communicates that complexity quickly and with mutual understanding. This suggests an analogizing strategy. Additionally, given the apparent closeness of strong-tie colleagues, more open-ended, ad hoc, and idiosyncratic information-gathering strategies are likely to be developed as the dyad iterates through multiple rounds of information exchange.

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Leaders are more likely to seek out weak-tie colleagues when a situation demands information that is not available to their core social network. Thus the types of information needed should be well-known and can be solicited fairly directly from a weak-tie colleague. This suggests a compartmentalized information search strategy. Similarly, experimental information-gathering approaches are also more likely with weak-tie colleagues than strong-tie colleagues. Individuals bonded by strong ties have greater levels of mutual understanding and thus will already know the result of most information-gathering experiments. However, when seeking an outside opinion on a topic, a leader can suggest a possible course of action to a weak-tie colleague and then gauge their response or discuss concerns regarding likely responses of stakeholder groups. The different costs and benefits associated with strong and weak ties suggests that leaders need to be given the opportunity and must take advantage of opportunities to develop, expand, and maintain their social ties (Anderson, 2008; Balkundi & Kilduff, 2005; Kikuit & Ende, 2007; Zaheer & Bell, 2005). This suggests that ad hoc information strategies will help leaders when interacting with weak-tie colleagues, in a manner different than the use of ad hoc strategies with strong-tie colleagues. With strong-tie colleagues, ad hoc information gathering is likely to represent casual exchanges about emerging events and the scenarios that may arise from them, especially with regard to shared or interdependent tasks and roles. In the case of weak-tie colleagues, ad hoc strategies are more likely to occur during a structured social context when performance is not being evaluated. The topics are more likely to emphasize specific information sources and content in an attempt for both individuals to identify new pieces of information or avenues for future collection efforts.

Direct Observation In some cases, leaders may elect to use direct observational techniques rather than communication. Most of the research on managers’ directly observing employees has focused on performance appraisal and the validity of manager ratings of performance. Research on the popular management technique called “management by walking around” has been sparse and the findings are mixed as to whether it promotes better performance by organizational units (Luria & Morag, 2012). Bass (1998) saw management by walking around through the lens of transformational leadership. He suggested that it was more effective as a means to communicate individualized consideration rather than as a technique for information gathering. In short, the literature on direct observation by leaders is sparse and focused narrowly on the observation of employees. While observing employees is often an important form of information gathering, it does not address the central issue of focus here, which is how leaders use direct observational techniques to gather information to reduce uncertainty. In his examination of the Mann Gulch disaster, Weick (1993) discussed direct observation by leaders. Among the firefighting crew attending to the impending

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disaster, the information-gathering activities of two individuals stood out, one a local ranger and one a long-tenured member of the forest service. Upon arriving at the scene of the forest fire, these two individuals instructed other members of the team to set up camp while they elected to scout the area. Interestingly, these two individuals brought unique expertise to the situation, one was an expert in the local area and one was an expert firefighter. This suggests that one criteria a leader is likely to use when considering how to collect information is that of follower expertise. Collective leadership theory supports this assertion, suggesting that the leadership of any particular team task should be directed by the individual team members with the most relevant expertise (Friedrich et al., 2009). When combined with research from Fiedler’s work on leader contingency theory and Hershey and Blanchard’s work on situational leadership theory, a sharp focus develops on follower competency, follower motivation, leader power, and task structure (Blanchard, Zigarmi, & Nelson, 1993; Fiedler, 1971). While these theories do not explicitly address the issue of leader information gathering, they are a useful starting point for proposing how a leader might engage in direct observation. In situations where the followers lack competence and motivation, a leader cannot trust them to attend to the correct variables or to report on them even if they did happen to notice relevant information. Alternatively, when follower competency and motivation are both high, the leader will likely rely on follower information, provided the reporting structure is clear. By relying on follower information gathering, the leader can save considerable cognitive resources and time. When follower motivation and competency are not aligned, a leader’s ­informationgathering strategy is more complex. In cases in which the follower competency for information gathering is high, but their motivation to report is low, the leader is likely to mandate reporting functions to the followers so that relevant information is communicated in an appropriate and timely manner. When competency is low but motivation is high, the leader will likely resort to direct observation but also focus on training the follower on environmental cues that are critical to monitor. More research on leaders engaging in direct observation is needed. Work on collective leadership and followership may provide useful clues for describing how leaders gather information by directly observing the environment. Table 3.4 is provided as a reference. These propositions should be examined by researchers using a variety of techniques that will capture the multilevel and time-sensitive nature of leader information gathering.

Problem Solving Once a leader has made sense of their environment by collecting enough information to sufficiently reduce their uncertainty, they can decide on whether or not a problem state exists. This decision is likely to be determined largely by examining the emerging situation in light of goals and values (Mumford et al., 2007).

84  Jay J. Caughron et al. TABLE 3.4 Propositions About Information Sources in Information Gathering

Communication Tactics and Organizational Structures Proposition 5: Information integrity values cause leaders to have greater confidence in information accuracy and validity. Proposition 6: Formal information sourcing values will cause leaders to be cautious about using informal, non-vetted information sources. Proposition 7: Formal information sourcing values will cause leaders to be cautious about using ad hoc information-gathering strategies. Proposition 8: Information control values will cause leaders to be more likely to use well-defined, idiosyncratic information-gathering strategies. Proposition 9: Information control values will cause leaders to use compartmentalized information-gathering strategies. Proposition 10: Transparency norms will facilitate leader information gathering from weak-tie colleagues or loosely coupled organizational units. Proposition 11: Proactive information-sharing values will cause leaders to develop skills associated with collecting and analyzing information. Proposition 12: Proactive information-sharing values will cause leaders to be more accepting of using information to change organizational processes. Social Networking Proposition 13: Leaders will develop idiosyncratic modes of communication with their strong-tie colleagues. Proposition 14: Leaders will use analogizing information search strategies with strong-tie colleagues, especially when communicating about complex issues. Proposition 15: Leaders will use a compartmentalized approach to gathering information from their weak-tie colleagues. Proposition 16: Leaders are more likely to use experimental information search strategies with weak-tie colleagues than with strong-tie colleagues. Proposition 17: Leaders who use ad hoc information-gathering strategies during informal social gatherings will develop more weak-tie colleague information sources. Direct Observation Proposition 18: Leaders will rely on direct observation when • • • • •

Follower competency and motivation are both low The leader possesses unique expertise pertaining to emerging change events Reporting structures are unclear Environmental cues are poorly understood by followers Follower motivation to report is low and the leader cannot structure reporting mechanisms

If the emerging event threatens goals or could result in the violation of values, the leader is likely to consider the situation a problem and then move to a concerted problem-solving effort. A great deal of research has been conducted on creative problem solving, much of it focused on leaders (Mumford & Connelly, 1992). Problem solving has been identified as a critical predictor of leader continuance—one measure of leader success—in an examination of US Army officers (Zaccaro et al., 2015). This

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recent evidence bolsters the arguments for problem solving as a critical contributor to leader success. Models of leader cognition, problem solving, and decision making suggest that leaders must engage in a number of cognitive and social processes to perform well when experiencing uncertainty. Information gathering functions differently across leader problem-solving functions. Gathering information during the implementation of a solution takes a different form than information gathering while generating ideas. Different stakeholders are relevant in earlier stages than later stages. The leader is looking for more specific details later in the process than early in the process. Thus, we have grouped these processes into three overarching problem-solving functions: problem construction, ideation, and execution. When constructing the problem space, leaders need to understand the causes of environmental changes, identify contingencies that must be navigated, and consider the goals operating within the organization (Vessey, Barrett, & Mumford, 2011). Thus, problem construction involves identifying contingencies, causes, and goals. Ideation consists of selecting concepts, generating and evaluating ideas, visioning, coalition building, and forecasting. Idea generation is the process of combining relevant concepts to create hypotheses, decision alternatives and potential solutions, and to forecast likely outcomes of idea implementation (MacCrimmon & Wagner, 1994; Nijstad & Stroebe, 2006; Osborn, 1953; Smith, 1998). Lastly, execution involves planning, contingency planning, solution implementation, plan revision, and implementation monitoring (Fleishman et al., 1991; Mumford et al., 2001; Mumford, Steele, McIntosh, & Mulhearn, 2015; Mumford, Todd, Higgs, & McIntosh, 2017; Mumford, Zaccaro, Harding, Jacobs, & Fleishman, 2000; Murase, Carter, DeChurch, & Marks, 2014). Table 3.5 is provided as a summary of different leader problem-solving processes. It is important to note that information gathering is often discussed as its own early stage of problem solving. In some cases, researchers discuss it as something that occurs before a problem has been constructed (Kikuit & Ende, 2007; Lubart, 2001), and at other times it occurs after a problem is confronted (Amabile, 1996; Reese, Parnes, Treffinger, & Kaltsounis, 1976; Scott, Leritz, & Mumford, 2005). We argue that it is more accurately described as a critical activity that occurs throughout a leader’s response to their changing environment (Caughron, Shipman, Beeler, & Mumford, 2009; Weick, 1995; Weick, Sutcliff, & Obstfeld, 2005). That is, while information gathering is particularly important early in the TABLE 3.5 Problem-Solving Processes

Problem Construction Ideation Execution

Contingency identification, casual analysis, goal analysis Concept selection, concept combination, idea generation, idea evaluation, visioning, coalition building, forecasting Planning, contingency planning, plan implementation, plan revision, solution monitoring, inter-team communication

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process of responding to uncertainty, it cannot be isolated to the early stages of sensemaking and problem construction. Leader problem solving is enacted in an environment that is changing over time, and the process is inherently iterative. Thus, information gathering must be accounted for across a wide range of leader problem-solving, decision-making, and cognitive processes (Mumford, Baughman, & Sager, 2003; Mumford & Gustafson, 1988; Mumford, Supinksi, Baughman, Costanza, & Threlfall, 1997; Scott et al., 2005; Mumford, Antes, Caughron, Connelly, & Beeler, 2010). Given the number of different conditions that cause leaders to experience uncertainty, the number of information gathering strategies they can use, and the fact that they will need to use them in light of different problem-solving challenges, a set of particularly complex interactions emerge. This makes it difficult to predict how leaders will respond when they try to solve problems in novel, complex, ambiguous, volatile, multiplicitous, and fragmented environments. However, it is possible to suggest some likely trends and explore how leaders could use these information-gathering strategies to increase their problem-solving effectiveness. In fact, we argue that while some information-gathering strategies are more likely to increase leader effectiveness during certain problem-solving phases or under certain environmental conditions, it is more likely the case that each strategy can be used in each type of environment or during each problem-solving process. Thus, the issue is not to match each strategy to a particular problem-solving process or a particular environmental situation, but to explore how leaders can use these strategies to increase their effectiveness.

Information Gathering Strategies Throughout Problem Solving Problem Construction During problem construction, leaders are likely to use an ad hoc strategy to remain open-minded when they are facing a truly novel circumstance. As they gain an understanding of causal variables in this type of problem space, they are likely to move toward pattern recognition and experimentation as they seek to describe the impact different causes are having on their environment. Alternatively, under conditions of ambiguity, the leader will be less concerned with identifying causes and more focused on conflicting information about which causes (and goals) are important. Essentially, different stakeholder groups are pushing the leader to prioritize the variables they feel are most important (Bundy & Buchholtz, 2013; Maitlis, 2005; Meer, Verhoeven, Beentjes, & Vliegenhart, 2017). A leader may still use an ad hoc strategy, as they did in a novel situation, but the motive and purpose for using an ad hoc strategy is different. Under conditions of ambiguity, the leader is likely to use an ad hoc strategy as a means of managing stakeholder concerns, entering discussions with different groups with an open mind and with

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a posture of listening rather than of asserting a set of priorities they have already decided upon. Other information-gathering strategies are also likely to be useful in disambiguating the environment. Experimentation can be used to send up “trial balloons” in the form of project proposal pitches to stakeholder groups or as small-scale implementation of a new process (Bundy, Shropshire, & Buchholtz, 2013). Seeking analogies can help the leader identify similar concerns and develop a language that speaks across organizational units. Compartmentalizing the search for information may be critical so that rival stakeholder groups do not come into conflict, making the development of a unified plan to solve the problem more difficult. Leaders are also likely to benefit from compartmentalizing information-­ gathering activities when dealing with complexity, volatility, and multiplicity. In each of these cases the leader can simplify information-gathering tasks by looking for blocks of information that can be collected and managed as chunks. Compartmentalizing the search for information in this way can help leaders keep different sources of information clear, prevent information leaking from one group to another, align information collection at different points in a problem cycle, or minimize the possibility of collecting information that leads to cascading events that further disrupt the organization (Mohr & Nevin, 1990). Taking advantage of idiosyncratic channels of information flow is likely to be of particular import when the leader needs to gather information quickly or when it is difficult to obtain. An adaptive response may be required in the case of a volatile or complex environment. Here leaders need to be responsive to the environment as it changes in order to shift their information-gathering strategies or mitigate an event cascade stemming from leader information-gathering actions (Zheng, Yang, & McLean, 2010).

Ideation There are two dominant schools of thought regarding the process by which ideas are generated: rational idea generation and intuitive idea generation. The rational approach emphasizes purposeful, deliberative, convergent thought involving stepby-step processes that consider key elements of the problem space (Dane, Baer, Pratt, & Oldham, 2011; Janis & Mann, 1977). Intuitive approaches emphasize cognitive associations, divergent thinking, and holistic consideration of the problem space (Dane et al., 2011; Dane & Pratt, 2007). Rather than being opposing approaches to idea generation as often presented, it is likely that both models are applicable depending on the leader’s skill set and situational demands. When using a deliberative idea generation approach, leaders will focus on critical elements of the problem gathered in earlier stages of problem solving and may seek to gather more information about these variables if needed (Caughron et al., 2009; Mumford et al., 2007). Thus, when using a deliberative approach, leaders are more likely to rely on compartmentalizing strategies and readily available

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idiosyncratic channels for information gathering. However, when leaders use an intuitive approach to ideation, information gathering is more likely to rely on serendipity and be guided by non-conscious processes that are difficult to verbalize in a step-by-step, logic-based narrative (Dane et al., 2011; Garfield, Taylor, Dennis, & Satzinger, 2001; Miller & Ireland, 2005). As such, information gathering supporting intuitive ideation will focus more on analogizing, pattern recognition, and ad hoc search processes. Concept selection and combination are particularly critical for problem solving as processes that support idea generation. These processes occur when a leader maps the features of a problem to the features of a past case, mental model, or desired future state (Scott et al., 2005). Mapping these features from one domain to another and assessing the degree of match will rely heavily on the leader’s ability to engage in analogical reasoning and to gather information that can be used to recognize the most relevant matching case, mental model, or desired end state. Idea evaluation is the process of considering the merits of an idea by comparing its features to the features of the problem (Lubart, 2001). Information gathering, as it relates to idea evaluation, will largely take on an experimentation approach. After ideas are developed, they are processed through mentally simulating the likely outcomes if the idea was enacted or by vetting the idea through other trusted colleagues familiar with the problem space (Kikuit & Ende, 2007). Thus, the timing of information gathering as it relates to idea evaluation is likely a critical consideration. Prematurely collecting evaluative information regarding an idea can undermine motivation to continue developing an idea and can result in the rejection of the idea before it is fully formed (Liedtka, 2014; Lockwood, 2009; Osborn, 1953; Van Dijk & Van den Ende, 2002). Leaders engaged in idea evaluation are also likely to implement an analogizing information-gathering process by which they seek to map the features of the problem and proposed solution to similar cases from their own past or from the experiences of other trusted colleagues (Kikuit & Ende, 2007). Criteria used to evaluate an idea include market prospects, feasibility, absorptive capacity, and organizational alignment (Cohen & Levinthal, 1990; Cooper, Edgett, & Kleinschmidt, 1997; Kikuit & Ende, 2007; Roussel, Saad, & Erickson, 1991). As such, information regarding these items will be of special import when collecting information in connection with idea evaluation. Information evaluation often includes the process of developing support for the idea (especially after initial, substantive objections have been overcome). This makes social information gathering more important in later stages of idea evaluation where the process begins to shift toward idea promotion in organizational settings (Kikuit & Ende, 2007).

Execution Once a viable idea has been developed and vetted, a leader can begin to enact the idea. Leader implementation planning involves a detailed alignment of problem features, steps of solution implementation, and organizational characteristics.

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Information gathering associated with implementing an idea will focus around details regarding processes. This includes practical considerations such as manpower, expertise of workers, timetables for deliverables, and available resources (Dulewicz & Higgs, 2005; Mumford & Hunter, 2005; Slater, Mohr, & Sengupta, 2013). When engaged in implementation planning, leaders are likely to use a compartmentalization approach to information gathering. They are likely to compartmentalize the problem into phases or segregate the plan into more manageable components. Once they have divided up the plan in this way, information regarding each subunit of the implementation plan can be collected, catalogued, and reviewed such that coordinated action can be facilitated. Leaders are also likely to use experimentation when planning. This will allow them to test critical organizational functions that are undergoing change during the roll-out of a new process. Knowing if specific subunits are able to meet time, capacity, and quality demands is critical to successful plan implementation. Monitoring the implementation of the solution plan will focus on identifying key bottleneck points and viable measurement points for the plan. Bottlenecks are areas in which the plan could get derailed due to a lack of resources, whether time, manpower, expertise, money, or materials. Measurement points are places in which the progress of the plan can be assessed that are indicative of the process progressing smoothly, potential derailment, or quality of workmanship. Bottlenecks are often discussed in terms of constraints on plan implementation. In fact, constraints analysis has been identified by Mumford and colleagues as a critical skill contributing to leader performance (Mumford et al., 2017). Gathering information regarding constraints is likely to involve recognizing patterns to identify key failure points and making use of idiosyncratic information channels to gather information from frontline workers in an unbiased fashion. Compartmentalizing is also likely as leaders develop well-defined reporting mechanisms so that information can be transferred across groups and to frontline managers. Experimentation may occasionally be used as a means to “stress test” production systems for speed, quality, and quantity restrictions (Belderbos et al., 2006; Mumford et al., 2017). Table 3.6 is provided to suggest examples of how each information-­gathering strategy may play out given different problem-solving demands.

TABLE 3.6 The Use of Information Gathering Strategies as a Function of Problem-Solving

Phase Problem-Solving Phase Info Gathering Strategy Functional Example Problem Construction

Pattern Recognition Compartmentalizing Analogizing

Identifying similar values and goals across organizational units Keeping rival units separate until information gathering is complete Developing a unified language across units (Continued)

90  Jay J. Caughron et al. TABLE 3.6 (Continued)

Problem-Solving Phase Info Gathering Strategy Functional Example Experimentation Ad Hoc Idiosyncratic Ideation

Pattern Recognition Compartmentalizing Analogizing Experimentation Ad Hoc Idiosyncratic

Execution

Pattern Recognition Compartmentalizing

Analogizing Experimentation Ad Hoc

Idiosyncratic

Testing solution ideas across units with differing goals and constraints Gathering information from stakeholders with an open mind Making use of existing communication channels to communicate quickly Selecting causal variables to manipulate for maximum effect Keeping evaluative information private until ideas can be fully developed Mapping problem features to past problem cases Proposing solution options with stakeholders for evaluation Supporting intuitive or serendipitous idea-generation activities Making use of informal communication channels to vet early stage ideas Identifying production cycles or repetitive failure points Keeping units focused on their assigned task so as not to interfere with other units Drawing comparisons between old and new methods of working Stress testing functional units to gauge production capacity Receiving upward feedback regarding plan implementation from frontline workers Creating communication channels between units to facilitate coordination

Conclusions It is clear that leaders engage in information-gathering activities, yet little research has examined these critical processes. Information-gathering processes are ­complex—they are influenced by the environment, the state of the problem space/solution development, and the sources of information accessible to leaders. We explored information gathering as an activity leaders engage as they iterate through problem-solving phases from a multilevel perspective. We focused in particular on the information-gathering strategies successful leaders use when

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they need to solve problems. We also outlined caveats and contingencies associated with this process. Leaders experience uncertainty because of the tasks they are expected to complete in their high-stakes positions. Being uncertain when they need to enact action and solve problems puts leaders in an uncomfortable situation and motivates them to gather information. Typically, uncertainty is described as a factor of the leader’s environment. However, we argue that novelty, ambiguity, complexity, volatility, multiplicity, and fragmentation in the environment cause the leader to experience uncertainty and provide an important backdrop for a ­leader’s ­information-gathering options. Should these environmental circumstances threaten important organizational goals or values, the leader will begin the ­problem-solving process. In executing these information-gathering strategies, a leader also engages different sources of information. Key multilevel sources we explore include communication at the group or organization level, and direct observation on the part of leaders at the individual level. Problem construction is relevant for a leader to examine the nature of the problem at hand. Stakeholders across the organization are likely to have loosely aligned or even competing goals. Leaders have to consider goals operating at multiple levels as they navigate contingencies and manipulate causes to effect a solution. They then will begin the process of ideation, which is their attempt to generate problem solutions. A critical component of this process is gathering information from multiple sources, including their own past experiences, their immediate group, the organization as a whole, or a mix of these. Finally, they will need to move into the execution phase of problem solving in order to implement a solution and work to ultimately reduce the experienced uncertainty and solve the problem identified within the situation. The interplay between the environmental characteristics and available sources of information places a great deal of pressure on the leader to effectively gather information as they engage in the problem-solving processes. This suggests that leader information gathering is inherently iterative and multilevel. In conclusion, effective problem solving is necessary for a leader to be successful. While this problem-solving process is rooted in information, we have limited knowledge of precisely how leaders obtain and work with information and suggest this topic be further explored in future research. Information and how it is manipulated is integral to how effective the leader is at executing all phases of their problem-solving tasks. Therefore, leaders who have become effective at using, collecting, and organizing information tend to be those who have higher performance outcomes (Zaccaro et al., 2015). We hope our propositions stimulate research to verify that the environmental conditions and information search strategies as described here serve as a useful taxonomy for describing leader information search processes and the environments that prompt them.

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4 ARE SATISFIED EMPLOYEES PRODUCTIVE OR PRODUCTIVE EMPLOYEES SATISFIED? How Leaders Think About and Apply Causal Information David R. Peterson Leaders seem to be constantly looking for ways to motivate their followers. One belief is that increasing employees’ satisfaction at work will cause higher motivation, which, in turn, will lead to higher performance. For example, a report from the Society of Human Resource Management recently tied problems in employee recruitment and retention to low job satisfaction, concluding that “effective employee engagement can perhaps alleviate these issues” and that employee engagement is “the most pressing human capital challenge in today’s economic environment” (Society for Human Resources Management, 2016, p. 43). This belief is sometimes supported by correlational data interpreted causally. A report of a recent Gallup study noted that companies with higher employee engagement experienced “17% higher productivity, 20% higher sales, and 21% higher profitability among many other positive metrics resulting from higher engagement levels” (Corbin, 2017). The problem with this perspective is that it incorrectly assumes that employee satisfaction causes higher job performance. More rigorous evidence depicts a different causal relationship between satisfaction and productivity. As summarized by Latham (2008): The widespread belief that improving employee motivation requires improving job satisfaction is not borne out by the research. Instead, research points to the ability to be productive in one’s job as the real heart of motivation . . . [and] job satisfaction, in turn, typically results from being productive. Thus, if you want motivated employees, you should focus on ways your employees can be high performers, rather than focusing on ways to increase their job satisfaction per se. (p. 85)

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The confusion over the causal link between productivity and job satisfaction highlights the role of causal information in leaders’ attempts to interpret and influence their environments. Leaders’ naturally seek information about causeand-effect relationships and then leverage certain causes to obtain desired outcomes (Ahn, Kalish, Medin, & Gelman, 1995; Perales & Catena, 2006). Dissatisfied employees are believed to cost US businesses between $450 and $550 billion per year (Corbin, 2017). Leaders who believe job satisfaction causes productivity will understandably look for ways to move the satisfaction lever in order to improve productivity. Leaders who believe satisfaction is a result of productivity will instead make it easier for employees to do their jobs. The analysis of causal information is one of several leader cognitive skills seen as critical for leader performance, yet has been historically undervalued in both research and practice (Mumford, Todd, Higgs, & McIntosh, 2017). This is concerning given the presence of causal information in nearly all aspects of leader performance. For example, effective execution of Morgeson, DeRue, and Karam’s (2010) 15 team leadership functions likely requires that leaders understand, at least intuitively, causal mechanisms underlying training, performance monitoring, and giving encouragement, to name a few. Causal information is considered integral to leader sensemaking (Balogun & Johnson, 2004; Weick, 1979), mental models (Gary, Wood, & Pillinger, 2012; Hodgkinson, Maule, & Bown, 2004), forecasting and vision formation (Mumford, Steele, McIntosh, & Mulhearn, 2015; Shipman, Byrne, & Mumford, 2010), and planning and goal setting (Marta, Leritz, & Mumford, 2005; Smith, Locke, & Barry, 1990). To date, however, no synthesis of several research streams bearing on leader causal analysis has been produced. An integrated view of leader causal analysis could be of great value. Leader causal analysis may function as a core capacity enabling and enhancing the execution of many facets of leader performance (Mumford, Watts, & Partlow, 2015), similar to how general mental ability underlies specific abilities (Schmidt & Hunter, 2004) or how motivation facilitates many aspects of human performance (Deci & Ryan, 2008). The ability of leaders to mentally break down the social and technical environments in which they operate to discover core causal relationships may enable leaders to be more effective in their attempts to influence the human and nonhuman resources within those systems (Mumford et al., 2017). In this chapter I argue that leader causal analysis is such a core competency for leaders. First, I explain how leaders use mental models to understand their environments and to formulate plans for manipulating causal elements in those environments to achieve desired outcomes. I then provide an integrated view of leader causal analysis by reviewing research from two distinct literatures: causal analysis and causal reasoning. Here I distinguish between retrospective causal attributions, which leaders make to explain causes of past events, and prospective causal analysis, which refers to leaders’ application of causal information to guide

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goal-directed actions. I conclude by noting both directions for future research on leader causal analysis and implications for leadership practice and development.

Mental Models and Complex Systems Leaders operate in profoundly complex systems—organizations (Katz & Kahn, 1966). From a general systems perspective, the entire world is a system composed of subsystems of subsystems, etc. (Boulding, 1956). While there are many different types of systems in the world—social, biological, mechanical, conceptual, and more—all systems consist of certain components and the relations between those components which make them interdependent (Scott, 2016). Other important characteristics of organizations as systems are that they are goal directed and dynamic, they transform inputs into outputs, and they change in response to feedback mechanisms or information produced from cause-and-effect events (Kast & Rosenzweig, 1972). As open systems, the complexity in organizations stems from the number and variety of components as well as the vast interdependence of those components. For example, organizational performance can be traced to at least political, economic, social, and technological subsystems (Aguilar, 1967), each of which is in turn composed of many layers of subsystems. Leaders operate in complex sociotechnical systems, composed of various human and technical (nonhuman) subsystems (Cooper & Foster, 1971). Leadership from a systems perspective refers to the influence a leader exerts on the components or component relationships within the system in the pursuit of some desired outcome (Katz & Kahn, 1966). The ability to purposefully manipulate system components to achieve one’s goals depends in part on one’s understanding of how components are interrelated. Thus, the way leaders make sense of the complexity in their systems is an important determinant of leader performance (Weick, Sutcliffe, & Obstfeld, 2005). A particularly useful approach for understanding how leaders make sense of complex sociotechnical systems is to examine the mental models of leaders. Mental models refer to the way people store and structure information about a system, and they are typically composed of concepts, representing people, places, objects, abstract constructs, etc., and links, representing how concepts are related (e.g., this person has authority over that person; people with firm, vigorous handshakes are perceived as more extraverted). Theoretically, mental models underlie most, if not all, human conscious and unconscious behavior ( Johnson-Laird, 1980). Individuals may possess mental models for each of the systems in which they ­operate— work, home, hobby, or sport. These mental models, moreover, influence the way people think about and behave within each system. For example, that people from similar cultures possess similar mental models of common social environments, such as eating in restaurants or visiting the dentist, partially explains why they tend to exhibit similar behaviors in these scripted situations (Bower, Black, & Turner, 1979).

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A particularly important feature of mental models is the way individuals represent information about cause-and-effect ( Johnson-Laird, 1980). Studies have provided evidence that causal information in mental models influences creative problem solving (Marcy & Mumford, 2007; Mumford et al., 2012), skill acquisition (Frese et al., 1988; Kieras & Bovair, 1984), and teamwork (Mohammed, Ferzandi, & Hamilton, 2010). Numerous studies have provided evidence that leaders use causal information to interpret subordinate performance (see Martinko, Harvey, & Douglas, 2007, for a review). Other studies have demonstrated how leaders use causal information to make strategic decisions (Bateman & Zeithaml, 1989; Gary et al., 2012) and manage innovation (Drazin, Glynn, & Kazanjian, 1999). Leaders obtain and apply causal information through their efforts to make sense of and to manipulate the sociotechnical systems in which they operate (Cheng, Park, Yarlas, & Holyoak, 1996; Mumford, Friedrich, Caughron, & Byrne, 2007; Mumford, Steele, et al., 2015; Strange & Mumford, 2005). More specifically, leaders form descriptive mental models to understand the way a sociotechnical system operates currently. Descriptive mental models are formed as leaders abstract information from their experiences in a system, experiences which are interpreted vis-à-vis their personal life histories and social feedback (Strange & Mumford, 2013). When confronted with a situation requiring action, leaders rely on these descriptive mental models to identify goals to pursue, causes to manipulate in pursuit of those goals, and additional information relevant to the issue at hand (Mumford et al., 2007). As leaders analyze system-relevant information they formulate prescriptive mental models. Prescriptive mental models can be seen as a form of template plan through which leaders can develop, simulate, and refine specific action plans (Mumford et al., 2007). Notably, prescriptive mental models contain subjective information, such as which goals should be pursued, and objective information, such as what casual variables can be manipulated to achieve those goals. Thus, the basis for leaders’ attempts to influence the complex sociotechnical systems in which they operate is the causal information embedded in their descriptive and prescriptive mental models.

Causal Analysis Causal information includes “identifiable events, activities, or actions that result in or cause downstream events or outcomes, be they immediate or distant” ( Johnson et al., 2012, p. 64). Bettman and Weitz (1983) distinguished between prospective and retrospective reasoning about causal information. Prospective causal reasoning refers to leaders’ attempts to “process information in such a way as to maximize future benefits relative to costs” and is focused on “providing accurate explanations of events to enhance control of future outcomes” (Bettman & Weitz, 1983, pp. 165–166). Causal analysis, a form of prospective causal reasoning (Harvey, Madison, Martinko, Crook, & Crook, 2014), is the process whereby leaders make

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sense of causal information and use it to guide goal-oriented actions (Mccormick & Martinko, 2004; Stenmark et al., 2010). Retrospective causal reasoning “consists of rationalizing prior behavior in an attempt to make it appear rational” and is focused on “providing [causal] justifications for prior actions” (Bettman & Weitz, 1983, pp. 165–166). Far more research has been conducted on retrospective causal reasoning in leadership. While a full review of this research is beyond the scope of this chapter, such causal attributions play an important role in the way leaders encode causal information into their descriptive mental models. Less research has been conducted on the prospective analysis of causal information by leaders.

Prospective Causal Analysis Both retrospective and prospective causal analysis are important leadership cognitive processes because although people rely on causal information when solving problems (Hershey, Walsh, Read, & Chulef, 1990), they tend to make many cognitive errors when doing so (Hogarth & Makridakis, 1981; Mumford et al., 2007; Xiao, Milgram, & Doyle, 1997). For example, in one study participants preferred simpler but inaccurate causal explanations and only favored the accurate, more complex explanations when they were provided with unambiguous probability information supporting a more complex explanation (Lombrozo, 2007). In the real world, leaders rarely, if ever, operate with unambiguous information. Such findings present an uphill cognitive battle for leaders working in highly complex sociotechnical environments. With its focus on predicting and controlling future events, prospective causal analysis is best understood in terms of the input and processes leaders use to formulate prescriptive mental models.

Causal Content With regard to the causal information that serves as input into leader causal analysis, the adage “garbage in, garbage out” seems appropriate. Indeed, evidence suggests that not all causal information is equally useful. Johnson and Keil (2014) described two criteria for causal information that would be more useful “as control variables for bringing about desired effects” (p. 2224). First, more useful causes are insensitive (invariant, or robust) to background conditions. For example, intelligence has been found to be a very robust predictor (cause) of job performance in jobs requiring problem solving (Ree & Earles, 1992), making it a desirable selection tool in the repertoire of leaders in charge of selection and development. Second, useful causal information is specific—effects can be dissected into causeand-effect relationships with specific causal variables. For example, the romanticized perspective of creative performance, which holds that creativity is mostly an unknowable, innate talent, is far less useful than scientific perspectives, which trace creative performance to an individual’s motivation, creative thinking skills,

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and domain-specific knowledge (Amabile, 1983; Mumford & Gustafson, 1988), the impact of which is moderated by the context in which an individual is meant to be creative (Amabile, Conti, Coon, Lazenby, & Herron, 1996; Oldham & Cummings, 1996). Some support for this conceptualization of more and less useful causal information can be found in a study by Shipman et al. (2010). They asked 252 undergraduates to assume the role of leader (principle) of a new high school and design a plan for running the school and to write a vision to communicate their plan to students, parents, and teachers. Prior to producing their plans and vision statements participants were provided with three successful case studies of similar schools. The focus of information to be extracted from these examples was manipulated, with some participants being induced to focus on isolated facts from the cases and other participants being induced to think about specific implications, or consequences, of case material. In a second manipulation, participants were then induced to focus their vision statements either on their goals for the school or on the causes of school performance. A medium size effect was found indicating that better vision statements were produced when participants focused their analysis on specific cause-and-effect relationships (causes + implications) compared to when they focused on causes and isolated facts. It appears that causal information was more useful when it focused on specific cause-and-effect relationships. In a similar study, Strange and Mumford (2005) asked 212 undergraduates to engage in the same educational leadership task. This time, participants were exposed to either three strong or three weak case studies before creating their plans. Strong versus weak case studies were identified by asking secondary school teachers to rate a sample of cases on several dimensions that, together, reflect the robustness of the causal information contained in them (e.g., How pedagogically sound was the program? How much difference would the program make in school achievement?). Like the Shipman et al. (2010) study, participants were also induced to focus on either their goals for the school or on specific causes of performance. The large effects found in this study indicated that when participants focused on specific causes and had access to robust causal information, they created vision statements that produced stronger affective reactions and were perceived as more useful by student, teacher, and parent judges. The same focus on robust causal information also led to higher ratings of plan effectiveness. One implication of asserting that specific and invariant causal information is more useful is that leaders must understand the scientific principles driving the social and technical causes operating in their systems. This includes the science of human behavior at individual, group, and organizational levels and the scientific principles underlying the technical work in which they are engaged, be it insurance sales, battery production, or hospitality. Scientific information tends to be more specific and more focused on revealing cause-and-effect relationships than non-scientific information such as industry publications or expert analysis (Rousseau, 2006; Rousseau, Manning, & Denyer, 2008). Moreover, it is unlikely

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that leaders will be able to reach the same level of effectiveness on their own that they might otherwise achieve with the use of scientific evidence to inform their mental models (Rousseau & McCarthy, 2007). Another, more practical implication of identifying more and less useful causal information is that leader development programs may greatly benefit from assessing leaders’ mental models of their sociotechnical systems. By identifying specific knowledge gaps, and then designing instruction or experiences that will provide targeted high-quality information, development programs may better prepare leaders to reach exceptionally high levels of performance (Ericsson & Moxley, 2012). Such an approach may prove both more useful and more cost effective than providing a general principles of leadership intervention to leaders with potentially different causal knowledge gaps. It is important to consider the temporal nature of leaders’ mental models and, specifically, how causal information may be acquired over time. Mental models are built incrementally over time and with experience (Albrecht & O’Brien, 1993; O’Brien & Albrecht, 1992), in part because causal information is complex and can easily overwhelm people’s simplicity-preferring processing capacities (Lombrozo, 2007). Complexity, however, is relative to an individual’s current mental model of a system. Compared to leaders with years of varied experience, new leaders are likely to be more overwhelmed by longer lists of unfamiliar social and technical causes pertaining to their new organization. Johnson et al. (2012) studied the relationship between causal analysis and ethical decision making in a sample (graduate students from various scientific fields) more closely representing novice rather than experienced leaders. They manipulated the complexity of causal information embedded in the experimental case material. Participants were asked to make decisions and forecast outcomes of those decisions, both critical leadership tasks. The authors found a medium effect for the cause complexity manipulation where better forecasts were produced when participants were given less versus more complex causal information. Participants in the high causal complexity condition produced significantly more favorable forecasts than participants in either the low or control conditions (medium effects), reflecting a common problem-solving error where individuals ignore negative information to predict overly optimistic outcomes of their own decisions (Xiao et al., 1997). The preceding evidence suggests that when presenting causal information to leaders, it is better to focus on certain, key causes at first and then build on this foundation to increasingly represent the complexity of the leaders’ sociotechnical systems. This approach may also help leaders make sense of their environments, especially unfamiliar environments, while minimizing cognitive fatigue. For example, in an experimental study Marcy and Mumford (2010) examined the effects of causal analysis training on performance in a leadership simulation. After receiving varying levels of training, 160 undergraduates completed the computerbased simulation and were scored on game performance (weighted to reflect realistic leadership outcomes such as organizational performance), sensemaking

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activities, and adaptation in response to environmental changes. Not only did they find that leadership scores were lower in more complex simulations (medium effect) but they also found that causal analysis training increased scores (medium effect), and did so regardless of complexity, the quality of prior information, and the quality of their mental models of the problem scenario. Interestingly, they also found that causal analysis training eased participants’ sensemaking load (small effect). In a similar study, Marcy and Mumford (2007) examined the impact of the same causal analysis training on performance on several leader social innovation tasks. Causal analysis led to more original solutions (small/medium effects) when participants were induced to reflect on relatively unfamiliar, versus familiar, problems. It may be that the causal analysis training provides leaders with a useful starting point—the key causes in the system—for developing creative solutions when solving unfamiliar problems.

Causal Analysis Strategies It is not enough for leaders to simply possess high-quality (invariant and specific) causal information in their descriptive and prescriptive mental models. They must also employ effective strategies for applying that information in the development of visions and action plans. Johnson and Keil (2014) noted that because “insensitive and specific causal relationships lead to improved abilities for inference and intervention, heuristics that rule out causal relationships lacking these features would potentially be able to narrow the space of candidate causes without much risk to ruling out useful causal generalizations” (p. 2224). As noted previously, people often make many errors both when working with causal information (Anderson, Glassman, McAfee, & Pinelli, 2001; Hogarth & Makridakis, 1981; Lombrozo, 2007; Mumford et al., 2007; Xiao et al., 1997) and when thinking about people (Macrae & Bodenhausen, 2000). This indicates that leaders’ reasoning about causality in sociotechnical systems is a unique form of cognition, especially because the causality involves social/human causes or outcomes. Causal analysis strategies are effective, in part, to the extent that they direct one’s attention towards useful causal information ( Johnson & Keil, 2014), help avoid biases and cognitive errors (Dörner & Schaub, 1994; Mumford, Schultz, & Van Doorn, 2001), result in more accurate mental models (Hodgkinson et al., 2004; Strange & Mumford, 2013), and increase leader performance (Marcy & Mumford, 2010). Based on a review of relevant literature, Marcy and Mumford (2007) identified seven heuristics leaders might use to identify more useful causal information. The studies by Marcy and Mumford (2007, 2010) provide evidence for the utility of these strategies. The seven strategies encourage leaders to identify causes that 1. Can be manipulated, 2. Can be controlled (by the leader), 3. Have direct effects,

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4. 5. 6. 7.

Have large effects, Influence multiple outcomes, Are functionally similar to other causes, and Interact with other causes.

Further support for some of these strategies can be found in two studies examining the impact of causal analysis on creative problem solving (Hester et al., 2012) and ethical decision making (Stenmark et al., 2010). In the Hester et al. (2012) study, 232 undergraduates drew link-and-node representations of their mental models for, and provided solutions to, two marketing leadership problems. Prior to working on the second problem, participants were provided varying types and levels of causal analysis training. Participants’ mental models were assessed for several objective and subjective quality indicators (e.g., number of critical causes included, perceived coherence) and a single index score assigned (Mumford et al., 2012). Solutions were scored for creativity. They found that causal analysis strategies that encouraged a focus on identifying causes affecting multiple outcomes and on identifying causes that might have unanticipated negative effects contributed to the development of effective mental models of the problem at hand. Strategies that encouraged a focus on critical causes, causes affecting multiple outcomes, and on contingencies (causes that interact with other causes) led to the development of more original solutions, and strategies that focused on causes affecting multiple outcomes also led to higher quality solutions. Stenmark et al. (2010) asked 87 undergraduates to work on ethical decision-making tasks embedded in a leadership context. For each problem, participants were asked to list the causes of the problem, generate possible actions, forecast outcomes of those actions, and select their preferred actions from a preidentified list of lower, moderate, and highly ethical decisions. The findings indicated that the extent to which participants identified critical causes (those with large effects) was significantly positively related to the quality of forecasts and the ethicality of participants’ ultimate decisions (both medium effects). A study by Vessey, Barrett, and Mumford (2011) highlights the importance of perceived control for effective causal analysis. They asked 170 undergraduates to complete several marketing leadership problems. Solutions were scored for both creative performance (e.g., quality, originality of solutions) and business performance (e.g., feasibility, cost effectiveness of plan, brand reputation maintenance). The researchers manipulated participants’ perceived control over the sociotechnical system at hand by listing all variables that participants were or were not allowed to modify. For example, in the low-control condition, participants could only modify finances and distribution of the marketing channel. A second manipulation provided participants with information analysis strategies with some participants being encouraged to focus on objective performance information, some on subjective social information, and some on both. It is important to note that both objective and subjective strategies were framed in terms of

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causal analysis. For example, participants might have been encouraged to identify causes and contingencies (objective) or affective causes and causes embedded in social systems (subjective). For both the creative and business criteria, medium size effects were observed indicating higher performance resulted when objective, or both objective and subjective, strategies were given to participants in the high control condition, or when subjective social strategies were given to participants in the low control condition. This pattern suggests that when leaders have the option to utilize social or technical causal information they prefer to work with objective information, but when they have less control, they look for manipulable causes within the social system to achieve better solutions. While the aforementioned studies provide evidence for the utility of the seven strategies identified by Marcy and Mumford (2007), further research is needed to expand our understanding of cognitive processes that might influence the application of causal information. For example, one notable causal pattern not found among the Marcy and Mumford (2007, 2010) strategies is the analysis of indirect causal effects or, similarly, causal sequences. Indirect effects are sequential cause-and-effect relationships where at least one effect is also a cause of a subsequent effect. These are, of course, widely present in scientific research (Preacher & Hayes, 2008; Zhao, Lynch, & Chen, 2010). Indirect causal effects also satisfy the criteria for useful causal information noted previously in that they are specific and invariant ( Johnson & Keil, 2014). Yet very little research has been conducted to understand how leaders analyze both indirect and interacting causes. With the exception of interacting causes, the analysis of indirect causes is more complex than the other causal strategies noted previously (Busemeyer, McDaniel, & Byun, 1996). To begin, thinking of indirect and interacting causes inherently requires simultaneous simulation of more variables than, say, identifying causes with large or direct effects. This suggests that working memory may facilitate online processing of such effects ( Just & Carpenter, 1992). Similarly, more complete mental models may ease the cognitive demands of analyzing indirect effects (Anderson, Spiro, & Anderson, 1978; Mayer & Moreno, 2003). Finally, cognitive flexibility is likely to facilitate leaders’ analysis of indirect and interacting causes because doing so will require the mental simulation of multiple alternative causal relationship (Hayes-Roth & Hayes-Roth, 1979; Spellman, 1996a). Another causal strategy not well understood is how leaders might evaluate competing causes. Some research suggests that individuals tend to prefer both simpler and more salient, or obvious, causal information (Lombrozo, 2007). Spellman (1996b) provided evidence that “in cases of multiple potential causes humans do what scientists do, and that is evaluate the efficacy of a target cause conditional on the constant presence and/or absence of alternative causes” (p. 168). Together, these findings indicate that while leaders may evaluate multiple causes, they may unnecessarily discount more complex or more unfamiliar causal variables. Leaders with exposure to multiple and varied experiences, including negative experiences (Williams, 1996), with specific causal factors may be able to more accurately assess

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the relative causal contributions of those variables (Spellman, 1996b; Wasserman, Kao, Hamme, Katagiri, & Young, 1996).

Retrospective Causal Attribution Prospective causal reasoning is focused on predicting and controlling future events through identification and analysis of causal information contained in sociotechnical systems. Prospective causal analysis is affected by the content of leaders’ descriptive mental models (Cheng et al., 1996) and occurs primarily when leaders are applying prescriptive mental models to plan and execute goal-directed behavior (Strange & Mumford, 2013). In contrast, retrospective causal attributions affect the formation of descriptive mental models. Research indicates that people naturally seek causal explanations of observed events (Ahn et al., 1995; Perales & Catena, 2006). Assumptions about cause-and-effect within a sociotechnical system are influenced by a leader’s life history and the experiences and feedback obtained within that system (Strange & Mumford, 2013). The primary construct in this research is causal attribution—the explanations leaders ascribe to specific events as they make sense of the system in which they operate (Martinko et al., 2007). Over time, leaders generalize to form assumptions about causality for specific types of events. Decades of research on causal reasoning processes in organizations have provided a substantial body of evidence supporting several theoretical models. For more substantive reviews of these theories and the empirical evidence supporting them, the reader is directed to Harvey et al. (2014), Martinko et al. (2007), and Martinko and Thomson (1998). Two findings from causal attribution research are particularly important for understanding leader causal analysis. First, the retrospective causal attributions people make both have identifiable characteristics (Abramson, Seligman, & Teasdale, 1978; Weiner et al., 1971) and are influenced by certain characteristics of the information used to make those attributions (Kelley, 1973; Kelley & Michela, 1980). Second, a number of biases arise during the attribution process that can cause inaccurate information to become stored in leader’s mental models (Martinko et al., 2007).

Causal Attributions The information people use to make causal attributions can be described in terms of consensus, consistency, and distinctiveness (Kelley, 1973; Kelley & Michela, 1980). Consensus refers to whether the observation at hand is characteristic of other entities within the same context. For example, if a coworker speaks loudly in a meeting in which everyone else is also speaking loudly there is said to be high consensus. If the coworker was the only one speaking loudly there would be low consensus for that individual’s behavior in that situation. Consistency considers the temporal consistency of the observation for a given entity within a given

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context. If the loud coworker is always (rarely) loud in meetings there is said to be high (low) consistency. Finally, distinctiveness compares the observation to observations of the same entity in other contexts. For example, is the loud coworker also loud outside of meetings or outside of work? If so, there would be high consistency. If the coworker is not consistently loud in multiple situations then consistency would be low. Causal attributions are judgments wherein individuals ascribe a cause to a specific effect. For example, if a car salesperson sets a sales record in April, he may determine it was because of his particularly good persuasive skills. If a coworker beats his record in the following month the salesperson may conclude that her success was due to the better weather in May. Causal attributions can be characterized on at least three dimensions (Abramson et al., 1978; Weiner et al., 1971). The locus of causality dimension describes attributions that are internal or external to the entity involved in the cause-and-effect event. The aforementioned salesperson made an internal attribution about his sales performance and an external attribution about his coworker’s success. The stability dimension refers to whether the individual making the attribution determines that the cause is stable or changes over time. Finally, the globality dimension refers to the attributor’s determination of whether the cause can be generalized to other contexts. The salesperson may make a global attribution and conclude that his persuasion skills also apply at home, when buying cable television, and even at the grocery store. His coworker may make a specific attribution about his performance if she determines he just worked extra hard in April. Martinko and Thomson (1998) combined the information types and attribution dimensions aspects of causal attributions to explain how different types of causal information will likely lead to certain types of attributions (see Table 1 and Figures 6.2 and 6.3 in their article). For example, they would argue that the car salesperson would be most likely to tie his coworker’s success to her sales skills if other salespeople did not perform as well in May (low consensus), she was consistently a high-performing salesperson at work (high consistency), and she was not a very persuasive person in other situations (high distinctiveness). Martinko and Thomson (1998) identified eight potential combinations of attribution dimensions (e.g., external, stable, and specific vs. external, stable, and global) that may arise from different combinations of types of causal information (e.g., high consensus, high consistency, and high distinctiveness vs. high consensus, high consistency, and low distinctiveness). It is important to note that the attribution research described earlier has dealt almost exclusively with causal attributions made about people, and mostly individual people (Harvey et al., 2014; Martinko & Gardner, 1987; Martinko & Thomson, 1998). In the case of leaders, causal analysis occurs within sociotechnical systems. Causal variables in sociotechnical systems can be human or nonhuman, single entities or subnetworks of interacting concepts (Cooper & Foster, 1971). While none of these attribution models purports to be comprehensive, the lack of

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coverage of group, organizational, and nonhuman attributional targets (outcomes) is problematic when considering that leaders must make decisions intended to influence not just individuals but research and development processes, resources, operational and manufacturing procedures, financial accounts, groups of stakeholders, departments, boards, etc. Thus, to be effective, leaders must analyze the causes of both social (human) and technical (nonhuman) targets. An attributional target represents the effect in a cause-and-effect relationship. Social attributional targets can be any number of outcomes at the individual, group, organizational, or other collective level. For example, the same leader may need to understand how to increase the creativity of one of her direct reports, how to manage the expectations of an entire research and development department, and how to foster a collaborative team environment in her top management team. Technical attributional targets are tangible and intangible nonhuman outcomes. They refer to the technical work for which the leader is responsible. For example, a civil engineering project manager may seek ways to improve the structural reliability of local bridges when repairs are being made. A recruiting manager may be interested in advances in psychometric testing that could increase the reliability of a company’s selection and placement process. A CFO will likely be interested in new investment opportunities that can enhance the firm’s position against risk. While some technical targets may often operate vis-à-vis social targets— one selection system metric used may be the performance of recent hires—the cause-and-effect relationships are fundamentally nonhuman—the reliability of a predictive psychometric measure is largely a statistical matter even though human variables are used. The original three attributional dimensions—locus of causality, stability, and globality—do not seem to adequately address the complexity of this expanded range of attributional targets and their causes that leaders will encounter in complex sociotechnical systems. When analyzing the social and technical cause-andeffect relationships embedded in a system, leaders should consider more than the type of causal information or the dimensions of attributions. Rather, extant research suggests that leaders should focus their analysis on certain types of causes (Barrett, Vessey, & Mumford, 2011; Hester et al., 2012; J. F. Johnson et al., 2012; Marcy & Mumford, 2010; Shipman et al., 2010; Stenmark et al., 2010; Strange & Mumford, 2005; Vessey et al., 2011). More specifically, leaders should focus their attention on causes that can be manipulated and controlled, have direct and large effects, influence multiple outcomes, are functionally similar to other causes, and interact with other causes (Marcy & Mumford, 2007).

Attributional Biases Individuals, including leaders, are prone to several well-documented biases when they make causal attributions (Brown & Mitchell, 1986; Harvey, Martinko, & Douglas, 2006; Martinko et al., 2007). These biases are likely to create inaccuracies

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in leaders’ descriptive mental models of their sociotechnical systems (Strange & Mumford, 2013). Given the role of descriptive mental models in prospective causal analysis (White, 1995) and the formation of prescriptive mental models, such errors are a likely source for a variety of leader performance failures. A study of 491 leaders, from first-line supervisors to executives, from a wide range of industries conducted by Halbesleben, Bowler, Bolino, and Turnley (2010) demonstrated how leaders’ attributions can substantially influence important decisions. Leaders were asked about the organizational citizenship behavior (OCB) of their subordinates. Results indicated that leaders’ attributions of subordinates’ behavior were significantly related to their causal explanations of the subordinates’ motives, which, in turn, were related to the leaders’ emotional responses to the subordinates’ behavior. The performance ratings leaders gave to subordinates were significantly related to leaders’ emotional responses, such as anger (negatively related) and happiness (positively related). These findings support the arguments that leaders’ retrospective causal attributions of even typically positive events (OCBs) can substantially influence outcomes (performance ratings) via the leaders’ interpretation of what caused the event to occur (subordinates’ motives for engaging in OCBs). These findings are reflective of various other studies demonstrating errors in leader attributions (Eberlin & Tatum, 2005; Furst & Cable, 2008; Knowlton & Mitchell, 1980; Notz, Boschman, & Bruning, 2001).

Suggestions for Leader Development With such potentially harmful effects from such seemingly pervasive errors it would be useful to consider how attribution errors might be addressed. More generally, there are several ways to improve leader performance through causal analysis. One approach is to focus on improving leaders’ use of prospective causal analysis strategies. Other interventions may be based on helping leaders to obtain higher quality causal information.

Improve Prospective Causal Analysis Improving leaders’ prospective causal analysis may mitigate the negative effects of retrospective attributional biases in at least three ways. First, adopting the causal analysis strategies described previously should help leaders increase both the depth and breadth of their search for useful causal information. Moreover, because these strategies focus on distinguishing more and less useful causal information, using them is unlikely to require substantially more time or effort on the part of the leaders in the long term. Second, as leaders adopt more effective prospective causal analysis strategies, the quality of information contained in their descriptive mental models and the completeness of those models should increase. As noted previously, descriptive mental models are formed in part through leaders’ interactions within their sociotechnical

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systems as they obtain feedback from the system about actions they take. Explicitly focusing influence attempts on specific types of causes should allow for clearer system feedback. This is in part due to a focus on more specific causal variables. For example, it is better to understand how specific elements of interpersonal communication affect followers’ reception of messages rather than simply knowing that a certain piece of information should versus should not be communicated. Similarly, as leaders engage in broader and deeper targeted searching for causal information, gaps in their descriptive mental models should diminish. A third way prospective causal analysis may minimize the effects of retrospective attributional errors is by directly undermining the bias processes. The available evidence indicates that individuals can reason about multiple competing causes (Spellman, 1996a, 1996b) but that in order to do so they must be aware of those alternative causes. Spellman, Price, and Logan (2001) demonstrated this by asking participants in an experiment to judge the strength of two potential causes of plants’ blooming—either being treated with a fertilizer or being planted in a pot marked with a star-shaped emblem. Notably, participants had no prior knowledge of how a star on the pot might help the plants grow. Some participants were told, in varying degrees, that the star emblem was part of a mechanism inside the pot that would inject a growth agent while other participants were given no information about the emblems. All participants were shown a series of plants and told whether the plant received the fertilizer and whether it was planted in a star pot. Participants were asked to indicate whether the plant would bloom before receiving feedback about whether it did bloom. After some trials participants were asked to assess the strength of the fertilizer and the star-shaped pots as causes of plant growth. They found that participants adjusted their causal judgments of each cause only when they possessed information about the causal mechanism for the star-marked pots, and that adjustments were made in accordance with how much causal information they were given about the pots.

Obtain High-Quality Causal Information Leaders may face a particularly challenging development environment. Mental models develop as one receives feedback bearing on the actions taken (Albrecht & O’Brien, 1993; Strange & Mumford, 2005). For leaders, perhaps the most useful form of feedback might be the reactions—mental, emotional, and behavioral—that their actions cause to occur within sociotechnical systems. However, useful causal information may be at times unavailable or even intentionally hidden from leaders. Subordinates are motivated to manage their leaders’ perceptions of them (Bolino, 1999), and studies have shown that subordinates sometimes withhold information from or distort information given to leaders (Glauser, 1984; Roberts & O’Reilly, 1974). Other organizational forces also act to obscure useful causal information from leaders in teams (Mesmer-Magnus & DeChurch, 2009), new product development ( Jespersen, 2012), and creative work (Alge, Ballinger,

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Tangirala, & Oakley, 2006; Gong, Kim, Lee, & Zhu, 2013), to name a few. This makes it particularly difficult for leaders to obtain the useful causal information ( Johnson & Keil, 2014) that would help them determine whether or not their influence tactics are effective. Complicating things further is the likelihood that seemingly similar outward follower behaviors might actually reflect very different motives. And it is in these deeper motives where leaders have a more lasting impact—where leadership really happens. A follower who engages in extra-role citizenship behaviors, for example, may do so for the good of others. Another may do so because he or she believes it will create a good impression that will help advance his or her career (Bolino, 1999). Both of these followers, moreover, may increase these behaviors in response to a particularly inspiring leader (Wang, Oh, Courtright, & Colbert, 2011). On the surface, there may not be much to distinguish the outward behaviors of these followers, especially early on when little other information is available to the leader. In some cases—highly structured manufacturing for example—there may never be much difference in what each of these followers produce. However, in other scenarios—such as work requiring that followers engage in creative problem solving—the different reactions caused by the leader’s influence efforts may only become apparent after those differences have in turn led to eventual performance discrepancies. In other words, feedback indicating the ineffectiveness of a leader’s attempts to influence others may at times only appear after it is too late to adjust those tactics. This presents a bit of a guessing game on the part of leaders. Effective experienced leaders likely will have responded to many episodes of repeated too-late feedback. Inexperienced leaders, on the other hand, may have to simply accept that they will fail many times but that these failures will be important learning experiences. But what if inexperienced leaders could develop their skills faster? What if they could do so without the varied casualties of fired subordinates, ethical missteps, or public relations fires? There is no need to dispute whether anyone developing a skill as complicated as leadership skills will inevitably experience failure as they learn. Nor am I arguing that we seek to eliminate failures. I am, however, proposing that the nature of those failures need not be catastrophic. In the same way that new product developers attempt to identify product failures early on so that the repercussions reverberate more in the development lab than in the market, it should be possible for leaders to identify failures to influence others before those failures lead to unalterable products. One way to do this would obviously be to train leaders to be more effective in obtaining relevant feedback from those they lead. Communication skills, data literacy, diplomacy, and emotional intelligence may be useful skills in this regard. Another, perhaps more fundamental approach here is for leaders to develop a deeper understanding of human (and nonhuman) systems. Such efforts should involve leaders learning more about the science of human cognition and behavior.

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As noted previously, scientific information tends to be more useful for causal analysis ( Johnson & Keil, 2014; Rousseau et al., 2008). This means that leaders and organizations should set high standards for their sources of leadership or managerial information. One way to do this would be to rely less on popular press sources and on so-called experts and instead become more informed consumers of scientific evidence (Abrahamson, 1996; Lewis, Schmisseur, Stephens, & Weir, 2006; Rousseau & McCarthy, 2007).

Conclusions In this chapter I have attempted to integrate research on how leaders work with causal information. People in general naturally seek causal information to better understand and control their environments (Perales & Catena, 2006). To be useful, this information should be both specific (refer to specific causes and effects) and invariant to modest situational changes ( Johnson & Keil, 2014). The beliefs leaders possess about cause-and-effect relationships heavily influence the actions leaders take to achieve their goals. Indeed, leader causal analysis may prove to be a central influence on leader performance. Through retrospective causal attributions, leaders form descriptive mental models to explain cause and effect mechanisms in their environments. These beliefs in turn serve as filters for the subsequent search and interpretation of causal information (Pearl, 1996; Strange & Mumford, 2013). However, the encoding of causal information into descriptive mental models is subject to numerous biases and errors (Lombrozo, 2007; Martinko et al., 2007; Martinko & Thomson, 1998; Notz et al., 2001), some of which appear to be particularly difficult to avoid (Stanovich & West, 2008). When leaders plan goal-directed behaviors they engage in prospective causal analysis. They search for causal information that can be leveraged to achieve their goals. This search results in the formation of prescriptive mental models containing leaders’ beliefs about which causes to manipulate and the likely outcomes of their actions. Prescriptive mental models have been shown to exert a direct influence on leader creative problem solving, planning, vision formation, and performance (Marcy & Mumford, 2007, 2010; Mumford et al., 2012; Shipman et al., 2010; Strange & Mumford, 2005). Both the content of leaders’ causal analysis and the strategies they use to apply causal information influence the effectiveness of leaders’ prospective causal analysis (Hester et al., 2012; Marcy & Mumford, 2007, 2010; Stenmark et al., 2010).

Directions for Future Research There are several promising avenues for additional research on leader causal analysis. For example, future research should examine indirect effects of attributional

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biases on leader performance via the formation of prescriptive mental models, action plans, and vision statements. In addition to linking attribution research with a broader range of outcomes (Harvey et al., 2014), such research could highlight additional leadership developmental activities, focused on, for example, source monitoring of assumptions contained in sensegiving efforts (e.g., prescriptive mental models, action plans, vision statements) ( Johnson, Hashtroudi, & Lindsay, 1993; Mitchell & Johnson, 2000). Another avenue for future research would be to examine the priming effects leaders’ descriptive mental models on prospective causal analysis. For example, researchers could assess leaders’ attributions, attributional styles, or specific causal assumptions about a given system. It is unclear exactly how these characteristics related to descriptive mental models may influence the extent to which leaders engage in prospective causal analysis, the content of the causal information they consider, and the strategies they adopt. Similarly, additional research is needed both to provide stronger support for the distinct roles of retrospective and prospective causal reasoning in the formation of descriptive and prescriptive mental models. While the evidence reviewed in this chapter lends support to this conceptualization, a number of questions remain unanswered. For example, how, if at all, do retrospective causal attributions interfere with prospective causal analysis? Which, if any, prospective causal analysis strategies may influence subsequent causal attributions, especially erroneous attributions? Do individuals evidence prospective causal analysis styles similar to the way individuals possess different retrospective attributional styles (Harvey et al., 2014)? Finally, more research is needed to better understand boundary conditions of leader causal analysis. Organizations as systems are comprised of subsystems existing at multiple levels (Scott, 2016). Leaders’ causal analysis may vary depending on the level of the causal components being considered. For example, only a limited set of system components will ever be under a leader’s control. The study conducted by Vessey et al. (2011) showed that leaders favored different causal content depending on the level of control they believed they had over the system. Future research could investigate how leaders’ causal analysis changes depending on whether the leader is attempting to influence upward, laterally, or downward effects (Dutton, Ashford, O’Neill, Hayes, & Wierba, 1997; Schriesheim, Castro, & Yammarino, 2000), or individuals or groups of individuals (Higgins & Bargh, 1987). Whether employee engagement is the most pressing human capital challenge today is probably less important than whether leaders believe satisfied employees are more productive or productive employees are more satisfied. Additional empirical evidence bearing on how leaders obtain and use causal information will allow for more effective interventions to help leaders identify high-quality causal information. This research will also help to determine whether leader causal analysis is, in fact, a critical leader competency underlying leader performance.

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5 THINKING ABOUT CAUSES How Leaders Identify the Critical Variables to Act On Michael D. Mumford, Cory A. Higgs, Erin Michelle Todd, and Samantha Elliott

Consider some of the things leaders must do on the job. They must appraise the reasons for drops in the quality of production. They must appraise how budget reductions will influence morale in customer service operations. They must appraise teams reactions to the selection of a particular person for promotion to a key directors role. They must appraise how competitor actions will affect the successful fielding of a new product. They must appraise how changes in technological production processes will effect workforce recruitment strategies. Clearly, all these actions required of those asked to assume leadership roles differ from each other in many ways (Fleishman et al., 1991). Different accountabilities require different forms of expertise and different experiences (Day, Harrison, & Halpin, 2012). Some of these activities require substantial intelligence ( Judge, Colbert, & Ilies, 2004), yet others require more emotional intelligence (Caruso, Mayer, & Salovey, 2002). Some of these activities require interpersonal appraisal and decision-making skills (Klein, 2008; Scandura, Graen, & Novak, 1986). Other activities, however, stress the importance of traditional cost/benefit analysis (Yukl, 2011). Although these varied actions impose very different requirements on those asked to assume leadership roles, we would argue all these actions require a key critical skill—leaders must be able to analyze the causes of events in organizations— and, analysis of event causes will dictate the nature of the actions taken by leaders. This observation is noteworthy because it implies leaders, to perform effectively, must possess causal analysis skill. Indeed, the very complexity of organizations (Uhl-Bien, Marion, & McKelvey, 2007) suggests that leaders may require truly exceptional causal analysis skill (Marcy & Mumford, 2010). In the present effort we examine what we know about the impact of causal analysis skills on leader performance. Subsequently, we examine how leaders should work with

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causes as they seek to formulate solutions to the many varied problems that influence individual, team, and firm performance (Mumford et al., 2007).

Causes and Leadership People, including many scholars, assume that leaders simply make decisions (Snowden & Boone, 2007), typically decisions about when, where, and how to exercise influence (Bass & Bass, 2008; Yukl, 2011). What must be recognized here is that the issues presented to leaders that call for the exercise of influence are highly complex—complexity which arises from the multiple sociotechnical interactions required to exercise influence on system operations. Not only are the issues presented to leaders inherently complex, they are often ill defined or poorly structured (Mumford & Connelly, 1991). To complicate matters even further, if the problem or issue was routine, it would not need to be brought to the attention of the leaders. Thus, the issues, or events, leaders must grapple with when exercising influence to resolve events to the benefit of multiple stakeholders are often novel (Mumford, Connelly, & Gaddis, 2003). What should be recognized here, however, is that complex, ill-defined, novel events—the events leaders are expected to deal with (DeChurch, Burke, Shuffler, Lyons, Doty, & Salas, 2011)—can be structured or understood in a number of different ways (Baran & Scott, 2010; Humphreys, Ucbasaran, & Lockett, 2012). One way leaders might make sense of complex, novel, ill-defined events is by appraising events with respect to goals (Berson & Avolio, 2004)—goals for followers, themselves, the team, or the firm. Another way leaders might make sense of complex, novel, ill-defined events is by understanding the causal operatives giving rise to this event. People, however, and especially those who have expertise (Ericsson, 2009), prefer to understand complex, novel, ill-defined events with respect to presumed causes of the event because assumptions made about causation help people to identify the actions likely to bring about goal attainment (Weick, 1995). Thus, peoples’ tendency to understand events based on presumed causes is, ultimately, an adaptive activity.

Causation and Leadership Styles Our foregoing observations suggest that leaders, as they attempt to make sense of events, will impose assumptions about the likely causes of events. In fact, it can be argued that the nature of the assumptions people make about the causes of events in sensemaking reflect not just the objective attributes of the event, but also cumulative life experience (Ligon, Hunter, & Mumford, 2008). The preferences people have to apply to different causes, and/or different types of causes, in explaining events represent styles of thinking (Tyler, 1964). Mumford (2006) has, in fact, argued the major styles of leadership we talk about, charismatic, ideological,

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and pragmatic leadership (Weber, 1947), are based, in part, on assumptions leaders make about the nature of causes and how they operate to shape events. Mumford (2006) has argued charismatic leaders see people as the causes of events because they, and other people, can control causes and how they operate to give rise to certain events. In contrast, ideological leaders see situational variables as the key causes of events, believing neither they, nor others, have much control, or at least direct control, over the operation of these situational causes. Pragmatic leaders see events as caused by both people and situational variables because some causes, but not others, are subject to control. Although Mumford’s (2006) model describing the charismatic, ideological, and pragmatic leadership styles is more complex, including propositions about goals sought and type of experience used (Antes & Mumford, 2012), the available evidence does indicate that the assumptions leaders make about causation influence both behavior and performance in addressing certain events. Mumford and colleagues (Bedell-Avers, Hunter, Angie, Eubanks, & Mumford, 2009; Bedell-Avers, Hunter, & Mumford, 2008; Hunter, Bedell-Avers, & Mumford, 2009; Mumford, 2006; Mumford et al., 2007) examined how these styles gave rise to different forms of leader behavior. For example, it was found that stylistic differences, arising from differing assumptions about causes, gave rise to different ways of interacting with followers and different forms of leader behavior. Along somewhat different lines, it was found that these stylistic differences were related to differential proclivities for inciting violence in organizations being led. Still, other research has shown that expression of those styles is related to differing levels of success in solving different types of leadership problems—problems involving consideration, structuring, participation, and change management. Thus, the assumptions leaders make about causation apparently have a wide-ranging impact not only on their appraisal of events, but also leader behavior with respect to these events. The work of Mumford (2006) and colleagues examined the behavior of charismatic, ideological, and pragmatic leaders in general. However, in another study, Hunter, Cushenbery, Thoroughgood, Johnson, and Ligon (2011) provided direct evidence indicating that the differences observed among charismatic, ideological, and pragmatic leaders are due, in part, to differences among leaders in the assumptions made about causation. They obtained 103 biographies for 66 football coaches who had won national championships, either professional or collegiate. Judges content analyzed the chapter in each biography that best described the coaches philosophy and characteristics interacting with followers. Three other judges classified these coaches as charismatic, ideological, or pragmatic. Subsequently, charismatic, ideological, and pragmatic coaches were contrasted with respect to their status on these two key types of causes—the locus of causation (people, situation, or both) and the controllability of causes (controllable, uncontrollable, or mixed). They found that charismatic leaders saw people as causes with those causes being controllable. Ideological leaders, in contrast, saw situational

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variables as the key causes operating. Pragmatic leaders, however, were more likely to see both people and situational variables as causes with some, but not all, causes being subject to control by the leader.

Causation and Mental Models The findings obtained by Mumford, Hunter, and colleagues (e.g., Hunter et al., 2011; Mumford, 2006) clearly indicate the assumptions leaders make about causation have a wide-ranging impact on leader behavior. Mumford (2006) and Strange and Mumford (2002) have argued that assumptions leaders make about the nature of causation are noteworthy because they provide a basis, or foundation, for the nature and structure of the mental models leaders use to understand problems or events. In its simplest form, a mental model involves an assumption that the status of a certain causal operative or variable results in a certain level of an outcome—either a desirable or undesirable outcome (Goldvarg & JohnsonLaird, 2001). Thus, one might have a mental model which holds that high levels of job satisfaction cause high levels of job performance, or an alternative mental model which holds that high levels of job performance cause high levels of job satisfaction. Of course, mental models may be far more complex than this simple illustration. People may see many causes operating, they may see the operation of causes as contingent on certain situational variables, or they may see the effects of certain causes as being mediated through certain other variables. Mental models appear to be acquired as a function of instruction, experience, and self-reflection on deliberation as people work on solving a certain class of problems (Ericsson, 2004). With instruction, and reflection of actual experiential events or cases (Kolodner, 1997), people abstract variables describing attributes of the situation and actions in the situation which they believe to have resulted in outcomes of one sort or another. In other words, people define a set of variables and interrelationships, or causal interrelationships, among these variables. More­ over, these variables are associated with, and are used to organize and recall, actual incidents of performance—incidents, or cases, which can be used to identify the actions needed to induce change in a cause. As Rouse and Morris (1986) and Weick (1995) have pointed out, the availability of these mental models permits adaptive problem solving and provides a basis for solutions to novel, ill-defined problems, as well as complex problems where multiple interactive causes could give rise to certain outcomes. In fact, Day, Gronn, and Salas (2004) have argued it is for this reason that leaders rely on mental models in problem solving. Mental models, however, have a number of other advantageous characteristics. First they provide leaders with a global strategy for addressing problems—­ specifying causes to be investigated and, vis-à-vis associated cases and experience, thereby suggesting actions to be taken. Second, mental models permit rapid directed and adaptive responses to emergent crises—crises which leaders are typically held to account (Mumford et al., 2007). Third, they provide a structure for

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follower actions which allow followers to maintain autonomy when actions occur in the context of the model being articulated by the leader (Mumford, 2006). Fourth, induction of shared mental models by leaders will typically result in far stronger team performance (Day et al., 2004). Fifth, mental models allow forecasting and planning on the part of both leaders and teams (Mumford & Frese, 2015)—notably, planning does not constrain but instead encourages opportunistic explanation and formation of adaptive backup plans (Giorgini & Mumford, 2013). Sixth, mental models, when simplified, provide a basis for leaders’ communication of vision to followers (Partlow, Medeiros, & Mumford, 2015). Mumford et al. (2012) asked undergraduates, some 230 in all, to assume the role of principal (the leader) of a new experimental secondary school. They were to prepare plans for leading this school—plans which judges appraised for quality, originality, and elegance. Prior to preparing these plans, however, participants were asked to work through a set of self-paced instructional modules where they were to show how to illustrate their mental models in structural equation modeling terms. Following instruction, participants were asked to illustrate their mental models for understanding this leadership problem. Judges appraised both objective (e.g., number of causal connections indicated, number of moderators, number of outcomes) and subjective (e.g., coherence, novelty, complexity) features of participants mental models. It was found that the highest quality, most original, and most elegant leadership plans were produced by people who evidenced stronger mental models for understanding the problem. Notably, the strongest plans, and best mental models, were provided by people who based their mental models on coherent sets of core critical causes, where potential mediators and feedback loops were taken into account for addressing the leadership problem. Thus, the causal systems embedded in peoples’ mental models, in fact, appear to provide the basis for solving leadership problems. Hester et al. (2012) have provided somewhat more direct evidence bearing on the importance of causal analysis on mental model formation. In this study, participants, some 230 undergraduates, were asked to assume the role of a senior manager (the leader) responsible for providing marketing plans for a new line of extreme sports athletic shoes. Judges appraised the resulting leadership plans for quality, originality, and elegance. Again, prior to proposing these plans, participants were provided with training demonstrating how they could illustrate their mental models. Both before and after participants worked on this problem they were asked to provide illustrations of their mental models. Moreover, participants were asked to complete another set of self-paced instructional modules where training in the analysis of causes was provided: (1) think about causes that have big effects, (2) think about causes that effect multiple outcomes, (3) think about causes that have negative effects, (4) think about causal interdependence. Four key findings emerged from this study. First, those participants who evidenced stronger mental models prior to starting work on the problem produced solutions of better quality, originality, and elegance. Second, instruction in causal

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analysis strategies also resulted in the production of higher quality, more original, and more elegant problem solutions. Third, the value of causal analysis instruction interacted with participants’ mental models with causal analysis training proving more effective for people evidencing stronger mental models. Fourth, training in causal analysis strategies appeared to result in acquisition of stronger post problem-solving mental models. Thus, analysis of causes appears to give rise to acquisition of stronger mental models—mental models that provide a basis for leaders’ formation of a vision (Partlow et al., 2015).

Causal Analysis The Hester et al. (2012) study, however, brings to fore a new question—how do people work with causes embedded in their mental models? An initial answer to this question has been provided in a study by Strange and Mumford (2005). They asked undergraduates, some 200, to assume the role of principal of an experimental secondary school and formulate a plan for leading this school along with a speech to be read to students, parents, and teachers describing their vision for leading this school. Doctoral students appraised plans with respect to their quality and originality, while teachers, students, and parents appraised speeches with respect to perceived utility and affective impact. Prior to preparing these plans and speeches, participants were presented with good or poor case exemplars drawn from the educational literature. They were instructed to use causes, goals, both, or neither relevant to formulating their plan and speech, and they were, or were not, asked to reflect on their prior experience in secondary school. It was found that the strongest plans, and visionary speeches of the greatest perceived utility and affective impact, emerged when people thought about goals evident in poor case examples or causes in good case examples, with self-reflection serving to strengthen these effects. Thus, analysis of viable case-based knowledge with respect to causes appears to contribute to the production of better plans for addressing leadership problems and, more centrally, the production of more workable and more compelling visions for leading institutions. Somewhat more specific information about the kind of causal analysis strategies contributing to leader performance has been provided in a study by Marcy and Mumford (2007). They asked some 180 undergraduates to solve three social innovation problems drawn from the business domain and three problems drawn from the public policy domain. It is of note all six problems, for example reorganization of an R&D department, reflected the kind of problems presented to leaders working in these two fields. Problem solutions were appraised by judges for quality and originality. Prior to starting work on those problems, participants either were, or were not, asked to work through self-paced instructional modules. These modules defined, provided practice applying, and gave feedback concerning seven potentially viable strategies for analyzing causes: (1) work with causes that can be manipulated, (2) work with causes that influence multiple outcomes, (3) work

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with causes that have large effects, (4) work with causes that can be controlled, (5) work with causes that have synergistic effects, (6) work with causes that work together and, (7) work with causes that have direct effects. In addition, manipulations were made which either did, or did not, encourage forecasting and did, or did not, make the problem situation directly relevant to close personal contacts. It was found that causal analysis resulted in production of higher quality and more original solutions to these problems, proving especially effective when people were led to believe that problem solutions would be directly relevant to close contacts or significant others. Thus, causal analysis seems critical to performance in solving leadership problems, especially when people are directly (e.g., personally) engaged in the problem solution. One question that might be broached by the foregoing studies is that the evidence provided for the relevance of causal analysis is all based on performance (problem-solving performance) when people are asked to adopt a leadership role. However, acting in a leadership role also seems to be influenced by causal analysis skills. Thus, Marcy and Mumford (2010) asked some 150 undergraduates to assume the role of president of a university where they were asked to improve educational quality on campus—as opposed to other potential objectives such as improving research or improving finances. Participants worked through this simulation exercise and objective indices produced by the simulation provided the measures of performance in this leadership role. Prior to working on this role simulation exercise, participants were asked to complete, or not complete, the seven self-paced instructional modules developed by Marcy and Mumford (2007) to provide viable strategies for causal analysis. It was found that providing participants with these causal analysis skills, as opposed to not training them, resulted in substantially better performance on the simulation exercise. Thus, causal analysis skills apparently contribute not only to leader problem solving but also to actual incidents of leader performance. In another, perhaps under-cited, study along these lines, Isenberg (1986) presented experienced participants (e.g., leaders) and business students with business problems where both groups were asked to think aloud as they worked through these problems. Verbalizations were coded with respect to variables such as causal reasoning, conditional reasoning, and evaluation. It was found that use of causal reasoning was a key feature of these protocols distinguishing, and favoring, experienced managers, but not students. Thus, causal analysis skill also seems to underlie the real-world performance of effective institutional leaders. The findings emerging from the studies described earlier indicate that causal analysis skills contribute both to real-world leader performance and people’s ability to solve the type of problems they are likely to confront in leadership roles. Moreover, causal analysis, and reflection and deliberation on key causes, contributes to the acquisition of stronger, better developed, better organized, mental models for understanding leadership issues. Notably, these mental models provide a basis not only for leader planning and forecasting (Mumford, Mecca, & Watts, 2015), but provide a basis for the construction of viable vision statements

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which have long been held to be a critical feature of effective leadership. Thus, the available evidence, in fact, indicates analysis of causes, and the causal analysis skills contributing to effective analysis, are likely a critical skill underlying both movement into leadership roles (e.g., emergence) and viable performance in these roles.

Applying Causal Analysis Skills Our foregoing observations, taken as a whole, indicate that causal analysis skills are a critical capacity underlying leader performance. Indeed, causal analysis skills are not only likely critical to performance, they may become an ever more important influence on leader performance as leaders advance to higher level positions ( Jacobs & Jaques, 1987; Mumford, Campion, & Morgeson, 2007). The impact of causal analysis skills on leader performance, however, broaches a number of questions about how leaders should seek to understand and attempt to act on causes. In the following discussion, we will attempt to address seven key questions leaders must ask themselves as they analyze and act on causes: (1) What mental model should I apply? (2) What causes should I try to work with? (3) How do I know these are the “right” causes to try to work with? (4) How do I deal with differing interpretations of causes? (5) How do I try to act on certain causes? (6) How do I know when to act on these causes? (7) How do I know my actions worked? Although many other questions might be asked about leaders’ causal analysis, for example when do leaders see people, as opposed work contingencies, as key causes, potential answers to those seven basic questions may lay a foundation for future attempts to understanding leaders’ causal analysis and the skills leaders need for effective causal analysis.

What Model Should I Apply? Causes, and thus causal analysis, are embedded in leaders’ mental models. What should be recognized here however, is people, and leaders, do not possess just one mental model. Thus, a leader may have a mental model of follower performance and they may have a mental model of unit profitability. The availability of multiple mental models, in turn, poses a key question. How do leaders decide what mental model to apply in framing their causal analysis efforts? One answer to this question lies in the fundamental nature of mental models. As noted earlier, mental models entail a set of cause-goal linkages (Goldvarg & Johnson-Laird, 2001). Thus, it seems reasonable to assume that the goals bearing on the performance situation at hand will serve to activate a particular mental model (Rouse & Morris, 1986). By the same token, most issues leaders must grapple with entail multiple goals. Thus, different stakeholders, including the leader, have different goals in any situation. As a result, it seems reasonable to expect that the goals activating application of a particular mental model in any situation will be those of high value stakeholders. What is of note here, however, is that high-value stakeholders are defined, in part, as a function of experience and, in

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part, as a function of institutional enculturation, personal values, and professional experience. In many, perhaps most, leadership situations, however, multiple “valued” stakeholders and the goals sought by these stakeholders will be relevant to leaders’ understanding of the situation. This observation implies leaders, in selecting mental models to provide a framework for causal analysis, must identify and prioritize the goals of key stakeholders. At times, goal priorities may be obvious—consider a fire department captain directing units responding to a fire (DeChurch et al., 2011). At other times, however, relevant goals may not be apparent or may be in conflict. Under these conditions more extensive analysis (e.g., active cognitive analysis) may be required to identify the goal set used to identify a relevant mental model (Strange & Mumford, 2005). Our foregoing observations imply that under certain conditions leaders must define the nature of the problem to be addressed when selecting an applicable mental model. Problem definitions, however, need not always be simply based on activated goals. Mumford, Baughman, Threlfall, Supinski, and Costanza (1996) asked undergraduates, some 120 in all, to formulate advertising campaigns for a new product—the 3D holographic television. The resulting advertising campaigns were appraised by judges for quality, originality, and elegance. Prior to preparing these campaigns, however, participants were asked to complete a measure of problem definition skill. Here participants were asked to identify alternative ways of defining other problems where a problem situation was presented along with 16 alternative definitions of the problem at hand. Participants were asked to select their four preferred redefinitions. The redefinitions as presented, however, reflected (1) goals, (2) diagnostics, (3) procedures, and (4) constraints. It was found that those producing the highest quality and most original advertising campaigns did not define problems based on goals and diagnostics, but instead employed procedures for working through the problem and constraints. Thus, selection of relevant mental models need not always be solely based on goals and goal priorities. Leaders may at times prefer to allow goals to unfold or emerge as a function of subsequent action (Bass & Bass, 2008). Instead, problem definition, and presumably selection of applicable mental models, may be based on other considerations. For example, leaders may select mental models that they believe to be free of key constraints operating in the situation. Alternatively, they may expressly select a mental model that directly takes into account critical constraints which might limit leaders’ actions. At times leaders may extend diagnostic search to identify potentially viable mental models. Clearly, far more research is needed to describe the specific strategies leaders might use in defining the problem confronting them vis-à-vis relevant mental models. Such research is especially likely to prove of value when evidence is provided as to when, where, and why leaders shift from a goal-based framework for mental model selection to another alternative framework for the selection of a mental model. In this regard, however, it is important to bear in mind the observations of Weick (1995).

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More specifically, his observations suggest that selection of an inappropriate mental model for understanding problem situations may have disastrous consequences for both the leader and his or her followers.

What Causes Should I Work With? With selection of a mental model for understanding, or making sense of, the problem at hand, people will have identified a set of causes that they can work with in problem solving. The Marcy and Mumford (2007, 2010) studies, as well as the Hester et al. (2012) study, point to some of the strategies leaders might work with as they attempt to analyze causes: (1) work with causes that can be manipulated, (2) work with causes that influence multiple outcomes, (3) work with causes that have large effects, (4) work with causes that can be controlled, (5) work with causes that have synergistic effects, (6) work with causes that work together, and (7) work with causes that have direct effects. Although all these strategies might, at times, prove of value as leaders seek to analyze causes, the question remains as to what strategies, at what times, should leaders choose to work with in causal analysis. In fact, given what we know about human cognition, it seems reasonable to expect that leaders, like people in general, will prefer to work with causes that have large direct effects. In part, this preference arises from peoples’ belief that acting on causes which have large direct effects will result in attainment of the desired outcomes (Hogarth, 1980). Here, however, certain caveats must be borne in mind. First, even causes that are perceived to have large direct effects may not, in reality, have especially large total effects on outcomes. The reason for this phenomenon is straightforward—firms are complex entities where many causes, causes often exerting relatively weak absolute effects, operate. As a result, the bias to work with causes having large direct effects may not always have the expected effects. Second, not all causes are under the control of the leader. Thus, in seeking to identify causes that have large direct effects, leaders will prefer to work with causes over which they personally have control ( Jaques & Clement, 1994). Although this preference is adaptive on the part of leaders, it should also be recognized that leaders may be subject to an illusion of control (Weber, Camerer, Rottenstreich, & Knez, 2001). Illusions of control, given a preference to work with controllable causes, may in fact result in ineffective causal analysis on the part of leaders. The potential negative impact of illusions of control on causal analysis may not simply be a result of the leaders’ beliefs. Situations can emerge which create a belief that causes are controllable when, in fact, they are not. This observation is noteworthy because it suggests that successful leaders in causal analysis will actively analyze the controllability of causes and will do so in an objective fashion where causal controllability can be empirically established vis-à-vis methods such as role modeling, benchmarking, and reflection on personal past

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experience. What should also be recognized here, however, is that leaders will seek to establish control over key causes to ensure their analysis is meaningful and that they have requisite flexibility of action. Thus, at one level causal analysis by leaders will always be somewhat Machiavellian as leaders appraise and seek to increase their control over key causes (Den Hartog & Belschak, 2012; Deluga, 2001). Here, however, it should also be recognized that leader’s Machiavellianism with respect to establishing control over causes does not necessarily imply Machiavellian behavior—rather here the focus is on causal analysis per se. Although it may be attractive for leaders to focus on causes having large direct controllable effects on outcomes in causal analysis, large direct controllable causes are most likely to be identified with respect to one or two outcomes sought. The problems presented to leaders, however, typically involve multiple outcomes for multiple different stakeholders. Thus, if leaders focus causal analysis on causes having sizable direct effects on one or two outcomes, other outcomes may suffer. A case in point may be found in Wells Fargo where a focus on cross-selling as a cause of profitability led client trust to be sacrificed. As a result, in causal analysis leaders, specifically high performing leaders, are more likely to seek causes that have positive effects on multiple desired outcomes, and are likely to search for and analyze the potential for synergistic operation among these causes. The search for and active analysis of causes contributing to multiple outcomes which operate synergistically allows leaders to avoid potential tradeoffs (e.g., attaining one outcome at the expense of multiple other outcomes). For some issues at some times, for example in crises, it may be appropriate for leaders to focus or analyze a limited set of controllable causes having strong effects on a single outcome. Generally, however, leaders will seek to work with causes operating synergistically with respect to attaining multiple outcomes. One implication of this statement is that leaders will seek to identify and work with fundamental causes which contribute to multiple outcomes. Another implication of this observation is that in analyzing causes leaders, at least high performing leaders, will seek to identify conditions that would give rise to synergies among causal operatives. Still another implication of this observation is that leaders may forego or delay actions on certain causes of certain other outcomes because such actions might interfere with the causal synergies being sought. Finally, by analyzing and taking action on multiple causes that operate synergistically, it becomes possible for leaders to allow new outcomes, and potentially new causes, to emerge without disrupting current operations. Another conclusion implied by our foregoing observations is that in causal analysis leaders will seek to identify causes whose effects build over time. To help simplify discussions of causal analysis, we often assume causes are fixed and that the strength of a cause’s effects on outcomes are fixed. In firms or in teams, however, causes are not a fixed set. Thus, leaders will think about causes that can be introduced to a system that will amplify the effects of other causes with which they are working. For example, a new hire with a unique skill set might

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be introduced to a team. The introduction of new cause, of course, implies leaders using causal analysis do not just respond to the system as it is, but they may actively seek to recreate a system of causal operatives (Mumford & Connelly, 1991). In other words, leaders are active forces reshaping firms through analysis and induction of new causes (Mumford et al., 2003). Of course, the induction of new causes into a complex system always entails risk and instability. As a result, leaders must analyze both the value and risk associated with the introduction of a new cause. Moreover, they must allow the effects of the cause being introduced to unfold overtime. Allowing causation to unfold over time, accompanied by close monitoring of the cause, implies that leaders in thinking about causation in complex systems must take an active approach where both gains and loss are taken into account. For example, induction of a new team member with unique skills may disrupt communication in the team. Thus, if team communication is critical to outcome attainment then the cost of placing the new team member in the group may not be tolerable. The need for control of and patience with regard to induction of effects as new causes are introduced points to another critical set of features with regard to leaders’ causal analysis. More specifically, in complex systems causes operate only under certain conditions. Thus, leaders in causal analysis must take into account extant conditions permitting the operations of causes. Leaders must also analyze causes to identify conditions that might amplify and/or diminish the operation of certain causes. Of course this observation implies that leaders causal analysis skills will depend on both expertise and an understanding of the context in which they are working (Hedlund et al., 2003). It also implies, however, that in causal analysis leaders must appraise the likely operation of causes in context—an observation that implies the effectiveness of causal analysis by leaders may also require wisdom (McKenna, Rooney, & Boal, 2009). Not only must leaders in causal analysis think about the conditions that permit causal operation, the impact of causes, especially fundamental causes, on key outcomes is mediated through the effects of these causes on other more immediate causes of outcomes. This point, although stated rather abstractly, has an important albeit often overlooked implication for leaders’ causal analysis. Leaders must appraise the status of these mediators and ensure that mediators are operating at a level that will allow other causes to operate. Of course, this observation also points to the need for expertise and wisdom in leaders’ causal analysis. It also points to the need for leaders in causal analysis to be able to appraise how causes operate as an integrated system.

How Do I Know That I Have the Right Causes? Our foregoing observations suggest that although leaders may at times analyze causes in a simplistic fashion, for certain problems effective leaders must employ a more complex, subtle approach to causal analysis considering multiple causes

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operating synergistically where the causes impact multiple outcomes (albeit under certain conditions). The complex nature of leaders’ causal analysis, however, broaches another question. How can leaders be sure their analysis of causes works? Put differently, how do leaders test their causal analysis? Broadly speaking, leaders in testing causal analysis may employ either a cognitive or observational approach. Leaders who employ neither of these approaches may be viewed as decisive, but the effectiveness of their causal analysis will be open to question (Shipman & Mumford, 2011). By the term cognitive appraisal, we have in mind a unique characteristic of mental models and causes. More specifically, mental models, and the causes embedded in them, permit people to forecast the implications of making changes in these causes (Mumford, Steele, McIntosh, & Mulhearn, 2015). The impact of forecasting on performance in solving leadership problems has been demonstrated in a series of studies by Byrne, Shipman, and Mumford (2010) and Shipman, Byrne, and Mumford (2010). In the Byrne et al. (2010) study, participants were asked to assume the role of a manager formulating an advertising campaign for a new high energy root beer. In the Shipman et al. (2010) study participants were asked to formulate a plan for leading an experimental secondary school. Judges appraised the resulting advertising campaigns and school plans for quality, originality, and elegance. As participants worked on their advertising campaigns and plans they received “emails” where they were asked to forecast the outcomes of their campaign or plans. Another panel of judges appraised these forecasts for relevant attributes (e.g., forecasting positive outcomes, forecasting negative outcomes, forecasting contingencies). Subsequent factorings yielded four dimensions: (1) extensiveness of forecasting, (2) time frame of forecasting, (3) forecasting constraints, and (4) forecasting negative outcomes. It was found that more extensive forecasting, and forecasting over longer time frames, contributed to production of stronger, higher quality, more original, and more elegant plans and advertising campaigns. Apparently, forecasting provides people with a basis for appraising the effects of acting on causes. As mental simulations of the effects of causes, and actions on causes, forecasting provides a low-cost mechanism which allows leaders to assess whether they have selected the right causes to work with. In this regard, however, it should be recognized that the cost to the leader (e.g., psychological costs) may not be low. Thus, extensiveness of forecasts and forecasting over longer time frames, proved to be the two variables must strongly related to leader p­ erformance—both psychologically costly, resource-intensive activities. The demands imposed on leaders in forecasting suggest that both expertise and implementation intentions (e.g., motivation) may be critical to encouraging effective forecasting by leaders. In fact, Dailey and Mumford (2006) have provided evidence indicating forecasting improves when people have expertise in the domain and believe that actions will result from their forecasts.

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Although forecasts provide leaders with a cognitive approach for appraising causes, leaders may also employ an observational approach in appraising the relevance of the causes they have chosen to work with. One approach that might be used here is to appraise causes with respect to benchmark cases (e.g., cases drawn from other firms or other people [Leseure & Brookes, 2004]) or, alternatively, to reflect on cases drawn from past personal experience (Vessey, Barrett, & Mumford, 2011). Of course, the effectiveness of reflection on prior cases largely depends on the relevance of the cases selected to the problem at hand. This point is of some importance because people often select cases to be employed in causal appraisal based on two attributes: superficial similarity of the case selected to the problem at hand, and the success, including social visibility of this success, of the case in resolving the problem at hand. Of course, superficial similarity, as opposed to similarity in case causal structure, is a suboptimal strategy for causal analysis. Moreover, use of successful cases may result in people missing critical contingencies and ignoring causes that might inhibit or block the effects of a cause. Thus, case based evaluations of causes may require a more objective and thorough analysis than is commonly assumed (Barrett, Vessey, & Mumford, 2011; Vessey, Barrett, & Mumford, 2011). One might work with cases in appraising causes, however, one might also attempt to gather information on actual effects of acting on causes. Put differently, causes might be appraised based on the actual effects of changing the cause in real-world settings. Although the costs of acting on causes as an evaluation strategy will often limit application of this approach, this need not always be the case. Leaders can conduct such studies by making small changes on a cause, often in an isolated setting or isolated team, and appraising the effects of these small changes. Although this quasi-experimental approach to evaluation of causes may have some value, it should also be recognized that all the various biases relevant to experimental efforts apply, ranging from inappropriate control and failure to assess long-term effects to the leaders personal involvement in the experiment and the potential for self-confirmation.

How Do I Work With Differing Interpretations? Clearly, both techniques for the evaluation of causes have their limitations. As a result, one would expect leader performance would improve when both these techniques, forecasting and observation, are used in appraising causes. Another way leaders might appraise causes, however, is to seek feedback from others concerning their analysis of causes. And, in fact, one might argue that leaders conduct meetings with peers, superiors, and followers as a means of seeking feedback bearing on their appraisal of causes (Yukl, 2011). Bearing in mind the many variables that influence adoption of social feedback, processing time, feedback clarity, normative framing, social consensus framing, and the status of the people providing

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the feedback, there may be some value in leaders seeking social feedback in the appraisal of causes. Some support for this argument has been provided by Gibson and Mumford (2013). In this study participants were asked to assume the role of a marketing manager asked to formulate a campaign for a new product where judges appraised the resulting campaigns for quality, originality, and elegance. Prior to preparing these campaigns participants were presented with a set of candidate ideas formulated by teams they managed. Participants were asked, prior to preparing their campaign, to critique these ideas. These critiques were appraised with respect to various attributes such as number, depth, complexity, and usefulness. It was found that the highest quality, most original, and most elegant problem solutions were provided by those who provided a limited number of deep criticisms of others’ ideas. Thus, social feedback as a vehicle for appraising causal assumptions is most likely to prove of value for leaders when it is obtained from experts who have given the issue adequate thought. This observation is perhaps not especially surprising. However, another question which arises in this regard is how much change in leaders’ appraisal of causes can be expected based on social feedback. This issue has been addressed in a study by McIntosh, Mulhearn, and Mumford (in press). In this study undergraduates were asked to formulate a vision statement for leading an experimental secondary school. Judges appraised the resulting vision statements for quality, originality, elegance, perceived utility, and affective impact. Prior to preparing these visions statements, however, participants were presented with an alternative mental model and manipulations were made to encourage, or not encourage, deliberation on the alternative mental model and to think about, or not think about, acting on this alternative model. It was found that deliberation on, but not thinking about acting on, alternative mental models contributed to the production of the strongest vision statements. This pattern of findings is noteworthy because it suggests leaders do not change mental models or causes they have identified based on the presentation of alternatives. In other words, leaders are invested in, and seek to maintain, their mental models and will not act on alternative mental models. Rather leaders in seeking social feedback may be doing one of two things. First, they may be seeking delimited, isolated, feedback with respect to a particular cause which they are uncertain about. And, based on the feedback, they may make small adjustments in their mental model to incorporate, or eliminate, a potential cause. Thus, preservation of alternatives through social feedback is used as a refinement technique. Second, in seeking social feedback leaders may be seeking to resolve ambiguity in their evaluations of a specific or particular causes significance. Although these efforts may vary as a function of leadership styles (Mumford & Van Doorn, 2001), they do have some noteworthy implications for understanding leader behavior. To begin, leaders’ visions are based on their mental models and the causal relationships embedded in these mental models. As a result, leaders,

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to protect their vision, may be unwilling to consider alternate mental models. Perhaps more centrally, leaders may be inclined to discount counterfactuals or alternative causal explanations. One implication of this observation is that leaders, in general, may be unwilling to explicitly search for disconfirmatory evidence. Another implication of this observation, however, given the importance of taking into account counterfactuals and disconfirmatory evidence in causal analysis (Andersen, Barker, & Chen, 2006), is that leaders evidencing exceptionally high performance may be those who expressly seek to incorporate counterfactuals and disconfirmatory evidence in their analysis of causes.

How Do I Act on Causes? Of course, another reason exists for why leaders may seek social feedback in causal analysis. They may be seeking information about the approaches that should be used to alter a given cause, or set of causes, to impact certain outcomes. Action by leaders to change the status of causes implies three key activities. First, leaders must assess the status of the causal variable, or variables, as they operate at the moment. Second, leaders must formulate a plan for acting on these causes. Third, leaders must execute this plan in an adaptive, active, fashion where adjustments are made to actions taken on causes based on observation of the effects of actions on the status of causes and relevant outcomes. Each of these three key activities, in fact, places real demands on those who occupy leadership positions. In firms, many causes are operating and the status of causes, or level at which these causes operate, is subject to change. As a result, leaders must monitor and assess the status of key causes. Indeed, one might argue that routine functional reporting provides leaders with a method for monitoring the status of one set of key causes. By the same token, many other causes operate to influence various goals in firms and in the case of many of these causes, the status of the cause is not systematically or routinely assessed. As a result, leaders must actively monitor multiple causes. Studies examining how leaders monitor causal status are not available. It seems plausible to argue, however, that in monitoring the status of causes leaders will guide monitoring based on key diagnostics held to mark the status of causes (Ericsson, 2009). Any given diagnostic, however, will vary with respect to both its reliability and validity as a marker of the causes of concern. Thus, leaders must monitor multiple diagnostics to ensure a reliable assessment of the status of a cause. Furthermore, they must work with information bearing on these diagnostics based on their validity. These observations are noteworthy because they point to multiple potential problems in leaders monitoring of diagnostics. To begin, leaders may monitor a single, salient diagnostic in assessing the status of a cause. As a result, leaders’ assessment of the status of causes may be unreliable. To complicate matters further, the diagnostics leaders employ will often be based on personal experience. When experience is relevant such diagnostics may result in

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the selection of valid appraisals. In many cases, however, the diagnostics employed by leaders may have little basis in fact. One way leaders may address these problems, however, is to guide monitoring in a search for patterns of diagnostics with patterns being employed more extensively in assessment of causes as ambiguity increases. Of course, these observations imply leaders may invest substantial resources in diagnostic monitoring (Yukl, 2011). When leaders see, or identify, suboptimal or unacceptable patterning of diagnostic markers, action is likely to ensue. What should be recognized here, however, is the actions leaders take to effect causes are not random but instead will be based on systematic plans. In fact, Marta, Leritz, and Mumford (2005) have shown planning skills are a critical influence on both one’s emergence as a leader and the subsequent performance of the teams for which they are responsible. Mumford et al. (2015) have also examined the knowledge and skills leaders need in planning. With respect to knowledge Mumford et al. (2015), in keeping with Mumford, Schultz, and Van Doorn (2001), they argued that plans are not abstract. Rather, plans appear to arise from case-based or experiential knowledge. When cases are used to formulate plans, however, their complexity makes it difficult for people to work with multiple cases in planning (Scott, Lonergan, & Mumford, 2005). As a result, leaders must carefully search for appropriate cases to be employed in planning action on various causes. The problem here, however, arises from peoples bias to employ cases of superficial content similarity in formulating plans as opposed to cases reflecting deep or structural similarity to the situation at hand (Mumford, Schultz, & Osburn, 2002). Accordingly, leader performance in acting on causes is likely to be based on deep analysis of case models and their relevance to the situation at hand (Antes & Mumford, 2012; Gibson & Mumford, 2013). Once leaders have selected a case (or a limited number of cases) to work with in planning actions intended to effect causes, a number of processes come into play as they seek to work with this knowledge in plan formation (Mumford et al., 2015). Of these processing activities, however, two key processes appear to be especially important: (1) forecasting the effects of actions (Shipman et al., 2010) and (2) backup planning (Giorgini & Mumford, 2013). Leaders in selecting actions to be taken with respect to causes must forecast the large effects of each action to be taken with respect to the cause or causes of concern. These forecasts will be used to identify viable actions and the actions to be taken to affect the status of causes. In firms, however, causation is complex and certain actions taken to impact any given cause may or may not work out. As a result, leaders must formulate backup plans to cope with situations where the leaders’ plans do not work out as intended. Not only must leaders formulate backup plans but they must actively monitor the environment in which actions are taken to allow assessment of whether backup plans need to be executed for actions being taken to affect a cause. The need for backup plans and monitoring when leaders act on causes, of course, implies that leaders, in acting, must be adaptive and flexible. This adaptive

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flexibility, in turn, implies that leaders when acting on causes must be engaged, in real-time, in actions taken with respect to the cause or causes of concern. Such engagement, however, comes at a risk. When people act, and act in an environment, deliberation, and thus depth processing, are inhibited (Gollwitzer, 1999). This observation implies that leaders must, when acting on causes, be able to stand back and reflect on actions taken and their effects. Put somewhat differently, in deciding when and how to act on causes, leaders must leave time for reflection and after-action review (Strange & Mumford, 2005).

How Do I Know When to Act? It is one thing to know how to act on a cause, it is another thing to know when to act on a cause. Timing of actions is commonly considered critical by leaders, and actions that work at one time to effect a cause may not work at another time. Implicit in this observation, however, is a key consideration leaders must take into account when timing actions to causes. Leaders must be aware of the context in which action is to be taken to determine when, and if, the actions being contemplated are, in fact, feasible (McKenna et al., 2009). This observation, although important, is at one level too superficial. Actions taken to effect causes are only likely to prove effective when there is a readiness by followers in the firm to accept the actions to be initiated. Put somewhat differently, there must be readiness to accept the actions being taken to affect the cause (Holt, Armenakis, Feild, & Harris, 2007; Weiner, 2009). Actions taken to affect a cause induce a change and, thus, readiness to accept change on the part of both followers and the sociotechnical system at hand, will influence the timing of the leaders’ actions on causes. Typically, people and systems are more willing to accept change when the change actions are normatively consistent, of low cost to those involved, and have demonstrated benefits (Rogers & Adhikarya, 1979). What should be recognized here, however, is that leaders, through communication and use of an active and open change process where multiple stakeholders are involved, can create conditions where acceptance of, and readiness for change, is present. The presence of a readiness for change is a key condition determining the timing of actions. Leaders timing of actions to induce change in causes, however, is not simply a matter of creating conditions of readiness. To begin, causes operate in systems. Thus, a key contingency needed for a cause to operate may not be in place. Similarly, the impact of a cause to be acted on may be mediated through another variable. Thus, in timing actions leaders must ensure mediators and moderators of the effects of a cause are operating in such a way that the causal action is likely to prove successful. What should be recognized here, however, is that leaders may need to take preparatory actions—actions intended to ensure appropriate status of relevant mediators and moderators. Clearly, these preparatory actions will, in part, determine the time at which action on a key cause is likely to prove workable.

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Not only must leaders establish conditions where their actions on causes are likely to prove effective, they must also bear in mind another issue. Rarely will leaders take a single action to impact a given cause of concern. Instead, a chain of actions will often be required. Actions taken at one point in this chain may often inhibit, or accentuate, the effects of actions taken at a later point in this chain. Thus, leaders in acting on causes must establish an appropriate, sequential chain of actions such that early actions do not inhibit the effects of later actions intended to effect a given cause or set of causes. The need for leaders to time sequences of causal actions intended to effect outcomes points to another issue bearing on the timing of actions with respect to causes. Causes in firms, and causes in the leaders’ mind (e.g., their mental models), do not operate in isolation—sometimes these causes operate together. At other times, they act in opposition. Thus, in selecting the time to act on a cause, leaders must search for and take into account synergies. One implication of this statement is that leaders will time actions taken on causes to maximize synergies. Another implication of this statement is that leaders, in timing their actions on causes, may display substantial patience as they wait for “the right conditions” to emerge.

How Do I Know It Worked? Leaders take well-timed actions to influence certain outcomes or goals—typically outcomes embedded in their mental models. Accordingly, leaders are likely to assess the effectiveness of their actions with respect to causes based on the outcomes observed. As a result, one can expect leaders to actively monitor outcomes and, perhaps, define the specific levels of the outcomes sought as a result of the actions taken on causes. In this regard, however, it is important to bear in mind a key point about actions on causes. The effects of changes in a cause do not necessarily show up immediately in an outcome. Thus, leaders cannot appraise an outcome just once in evaluating the effects of acting on a cause. Not only must leaders appraise the status of outcomes at multiple points in time, ideally leaders should anticipate the time at which effects of actions on causes are likely to appear in outcomes, recognizing that the effects of actions are likely to vary and may often be lagged. In appraising the effects of causal actions on outcomes, however, effective evaluation is not necessarily with regard to the absolute level of the outcome. Rather, the key in outcome evaluation is changes in outcomes as a result of the actions leaders have taken with respect to causes. This point is of some importance because it implies that leaders, in appraising the effects of their actions on causes, must establish baseline conditions for appraising the amount of change evident in outcomes. The key problem here, of course, is that leaders will tend to appraise any change in baseline conditions as evidence success although the change may reflect nothing more than random or seasonal variation (Cook, Campbell, & Day, 1979). In fact, in firms where outcomes vary naturally, failure to establish the significance of a change vis-à-vis other fluctuations may not only

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result in inappropriate appraisals of success, it may be used by leaders to justify their success based on a non-event. Our foregoing observations, of course, point to another concerning appraising the effects of actions on causes with respect to outcomes. Actions in firms are not isolated events which effect one, and only one, cause. Rather, actions taken to causes occur in a setting where the actions taken may, or may not, actually effect the intended cause. Instead, actions might effect any one of a number of other causes. In other words, control is required when leaders seek to assess the effects of their actions on causes. On the other hand, it is clear that in firms, leaders are unlikely to conduct the tightly controlled studies apparent in much academic work. By the same token, quasi-experimental work by leaders may, from time to time, occur. And leaders, especially effective ones, may in their mind attempt to appraise how changes in other causes influenced sought after outcomes. Unfortunately, the focus of leaders, and the need for outcome attainment, may undermine such cognitive gaming, however necessary it may be, to attain a realistic appraisal of the effects of their actions on outcomes. Although these problems are endemic to action in the real-world, the other problems in appraising outcomes may act to undermine leaders’ effective appraisal of the outcomes of their actions on causes. First, outcomes are attached to causes in leaders’ mental models. Thus, leaders’ mental models delimit the number and range of the outcomes that leaders consider in appraising the effectiveness of their actions. In firms, moreover, actions taken to influence one outcome may have unintended side effects with respect to other outcomes—including the outcomes valued by other stakeholders. Thus, leaders in appraising outcomes should take multiple alternate outcomes (e.g., outcomes salient in other mental models) into account. Put somewhat differently, leaders must understand and evaluate actions with respect to the mental models employed by key stakeholders. Simply put, leaders must understand and evaluate the outcomes sought by others, especially key stakeholders. Second, leaders have a bias to action—specifically actions contributing to attainment of select outcomes. Actions taken in firms, however, often effect not just one outcome but potentially several other outcomes. By focusing on action to effect one specific outcome, leaders may lose sight of multiple other outcomes relevant to the actions they have taken. At one level, the observation points to the need for cognitive complexity, and caution, among those asked to lead in firms (Shipman & Mumford, 2011). At another level, however, it provides an explanation for the surprise leaders often exhibit after their actions have unintended consequences.

Conclusions Before turning to broader conclusions flowing from the present effort, certain limitations should be noted. To begin, we have in the present piece focused on how leaders act on and analyze causes. Of course, causal analysis is not the only,

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nor necessarily the most important, cognitive skill leaders must employ. Mumford, Todd, Higgs, and McIntosh (2017) have noted leaders must also be able to define problems, analyze constraints, and evaluate ideas. No evidence has been provided indicating that these skills are any more, or any less, important than the leaders causal analysis skills in shaping leader performance. Along related lines, we have in the present effort focused on leaders’ analysis of causes. As a result, nothing has been said about the impact of more basic abilities, such as intelligence, on leader performance ( Judge et al., 2004). Moreover, we have not, in the current effort, examined how these more basic abilities might contribute to more effective causal analysis on the part of leaders. Although prior research has made it clear that causal analysis skills make a unique contribution to leader problem solving and performance in leadership roles (Marcy & Mumford, 2007, 2010), we do not know how causal analysis skills develop from and/or interact with other more basic abilities. It should also be recognized that little has been said in the present chapter about how causal analysis skills impact specific forms of leadership behavior. Leadership scholars focus much of their research on different models of leader behavior—behaviors such as transformational leadership (Bass & Avolio, 1990) or consideration and initiating structure (Fleishman, 1953). Although it seems likely leader causal analysis skills will influence subsequent leader behavior, a point implied by our discussion of causal actions, more work is needed on this topic. Finally, it should be recognized that many of the studies examining leader causal analysis skills, virtually all of these studies, have focused on casual analysis skills as they influence leader problem solving in laboratory settings. Thus, research is needed examining how leaders apply causal analysis skill in real-world, real-firm settings. Although prior research on naturalistic decision making (Klein, 2008; Zsambok & Klein, 2014) and leadership crises (Weick, 1995) have provided some important clues about how leaders might analyze causes in the real-world, a more explicit focus on how leaders perceive, analyze, and act on causes in grappling with the many varied types of problems they confront does seem indicated. With this said, we do believe the observations made in the course of the present effort point to the value of further work along these lines. We have provided evidence indicating that the mental models leaders use to formulate leadership vision and based in large part on the assumptions leaders make about c­ ausation— assumptions that give rise to the charismatic, ideological, and pragmatic leadership styles (Mumford, 2006). We have shown that leaders mental models arise from and delineate the key causes leaders think about in solving problems (Hester et al., 2012). We have also shown that interventions intended to provide those acting in leadership roles with better causal analysis skills result in improved performance in both solving leadership problems and acting in leadership roles (Marcy & Mumford, 2007, 2010). Thus, at this juncture, it seems clear that causal analysis skills represent a key skill making a real contribution to performance in leadership roles.

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Not only have we in the present effort demonstrated the impact of causal analysis skills on leader performance, we have addressed two other issues of real importance. First, we have examined the kind of knowledge leaders are working with when they analyze causes. More specifically, it appears leaders’ causal analysis skills are applied to case-based or experiential knowledge and mental models leaders have constructed to organize their experience (Vessey, Barrett, & Mumford, 2011). In this regard, however, it is important to bear in mind an observation emerging from the present effort. Causal analysis skills contribute to the acquisition of stronger knowledge structures just as leaders’ causal analysis skills are limited by the knowledge structures they have available. Clearly, far more work is needed to understand how knowledge used in different domains of leadership activities condition how leaders require, and apply, causal analysis skills. Second, in the present chapter we have examined how these causal analysis skills might be applied by leaders. We have examined questions such as what causes should leaders work with? How do leaders appraise the importance of causes? How should leaders attempt to act on causes? And, how should leaders appraise the effectiveness of their actions with respect to causes? Although much of our discussion in this regard was speculative, it does point to some noteworthy issues that need to be addressed in future work. For example, we need to know more about how leaders employ diagnostics to identify relevant causes. We need to know what kinds of experiences encourage leaders to think about, and work with, more complex causal analysis strategies. We also need to know when complex versus simple causal analysis strategies are especially beneficial for leaders. These and a number of other basic questions posed by the present piece are of interest not only for academic reasons. Findings along these lines might be of real importance for those who seek to develop leadership potential. Moreover, the findings emerging from such studies might provide a new set of tools for assessing peoples’ potential for filling key leadership roles, allowing for the development of new, potentially highly valid measures for appraising leadership potential. We hope the present effort provides an impetus for future work along these lines.

Acknowledgments We would like to thank Rich Marcy, Sam Hunter, Katrina Bedell-Avers, Tamara Friedrich, and Dawn Eubanks for their contributions in the present effort. Correspondence should be addressed to Dr. Michael D. Mumford, Department of Psychology, The University of Oklahoma, Norman, Oklahoma, 73019, or [email protected].

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Rogers, E. M., & Adhikarya, R. (1979). Diffusion of innovations: An up-to-date review and commentary. Communication Yearbook, 3, 67–81. Rouse, W. B., & Morris, N. M. (1986). On looking into the black box: Prospects and limits in the search for mental models. Psychological Bulletin, 100, 349. Scandura, T. A., Graen, G. B., & Novak, M. A. (1986). When managers decide not to decide autocratically: An investigation of leader-member exchange and decision influence. Journal of Applied Psychology, 71, 579. Scott, G. M., Lonergan, D. C., & Mumford, M. D. (2005). Conceptual combination: Alternative knowledge structures, alternative heuristics. Creativity Research Journal, 17, 79–98. Shipman, A. S., Byrne, C. L., & Mumford, M. D. (2010). Leader vision formation and forecasting: The effects of forecasting extent, resources, and timeframe. Leadership Quarterly, 21, 439–456. Shipman, A. S., & Mumford, M. D. (2011). When confidence is detrimental: Influence of overconfidence on leadership effectiveness. Leadership Quarterly, 22, 649–665. Snowden, D. J., & Boone, M. E. (2007). A leader’s framework for decision making. Harvard Business Review, 85, 68–76. Strange, J. M., & Mumford, M. D. (2002). The origins of vision: Charismatic versus ideological leadership. Leadership Quarterly, 13, 343–377. Strange, J. M., & Mumford, M. D. (2005). The origins of vision: Effects of reflection, models, and analysis. Leadership Quarterly, 16, 121–148. Tyler, L. E. (1964). The psychology of human differences. Englewood Cliffs, NJ: Prentice-Hall. Uhl-Bien, M., Marion, R., & McKelvey, B. (2007). Complexity leadership theory: Shifting leadership from the industrial age to the knowledge era. Leadership Quarterly, 18, 298–318. Vessey, W. B., Barrett, J., & Mumford, M. D. (2011). Leader cognition under threat: “Just the Facts”. Leadership Quarterly, 22, 710–728. Weber, M. (1947). The theory of social and economic organization. New York, NY: The Free Press. Weber, R., Camerer, C., Rottenstreich, Y., & Knez, M. (2001). The illusion of leadership: Misattribution of cause in coordination games. Organization Science, 12, 582–598. Weick, K. E. (1995). Sensemaking in organizations. Thousand Oaks, CA: Sage. Weiner, B. J. (2009). A theory of organizational readiness for change. Implementation Science, 4, 67. Yukl, G. (2011). Contingency theories of effective leadership. In A. Bryman, D. Collinson, K. Grint, B. Jackson, & M. Uhl-Bien (Eds.), The SAGE handbook of leadership (pp. 286– 298). Thousand Oaks, CA: Sage. Zsambok, C. E., & Klein, G. (2014). Naturalistic decision making. New York, NY: Psychology Press.

6 LEADERS’ SHIFTS IN ATTENTION DURING AN ORGANIZATIONAL CRISIS Longitudinal Evidence of Responses to a Crisis Within a Top Management Team Ian A. Combe and David J. Carrington Attempting to explain how some firms successfully manage environmental change and overcome any subsequent organizational crises by solving complex and ambiguous problems has long been an important goal in management research. The importance of this research is starkly highlighted by the quest to understand how individuals can cause high profile disasters and subsequent organizational crises, such as the industrial accident in Bhopal, India (see Shrivastava & Mitroff, 1987; Weick, 1988). More recently, research has emphasized the challenges of dealing with continuous change (Teece, 2007). The complexity of sensemaking when leading or responding to change and undertaking goal-directed behavior requires a high degree of selectivity (Lachter, Forster, & Ruthruff, 2004) and shifts of attention (Broadbent, 1958). The selective attention leaders give to different issues, therefore, has been investigated as a major reason for their performance (Dearborn & Simon, 1958; Li, Maggitti, Smith, Tesluk, & Katila, 2013). Selective attention refers to a filter mechanism “that once set can block the processing of some stimuli and allow the subject to further process other stimuli easily” (Cowan, 1988, p. 172). Kahneman (1973) points out that there is more to attention than mere selection, because the intensity of attention is also important. Leaders’ attention is critical when dealing with organizational crises in a number of areas. In normal conditions, expertise reduces attentional demands so that leaders can engage in less demanding searches for information and problem solving (see Mumford, Todd, Higgs, & McIntosh, 2017). However, under complex ambiguous crisis conditions, leaders face a serious situation as it is their key responsibility to resolve matters. They will have to engage in analytical conscious deliberations under time pressure, stress, and fatigue, which will invariably reduce attention capacity (Mumford, Friedrich, Caughron, & Byrne, 2007). Negative emotions may also reduce attention capacity (Collins & Jackson, 2015). Therefore,

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the novelty and complexity faced during a crisis may overwhelm their limited attention capacity (Kahneman, 1973). The focus of leaders’ attention is also critical at an early stage of crisis resolution as they will first need to engage in additional evaluation as a precursor to making sense of the new conditions (Weick, 1995). Leaders’ attention may be directed to investigate particular causes of a crisis, rather than others. This selective attention will influence followers who are also trying to make sense of the crisis. Leaders’ mental models are key to this sensemaking, because their content in the form of causal beliefs, define individuals’ domains of attention and direct information gathering when attempting to resolve a crisis (Mumford et al., 2007; Salas, Rosen, & DiazGranados, 2010; Walsh, 1988; Weick, 1995). Mental models are based on the idea that thought predicts events and models reality (Craik, 1943). They are representations in the mind (Gentner & Stevens, 1983; Johnson-Laird, 1983), built over time, based on experiential knowledge and learning (Kiesler & Sproull, 1982; Mumford et al., 2007). After making sense of the crisis conditions, leaders’ then need to generate and evaluate alternative solutions through mental simulation (Dane & Pratt, 2007; Klein, 1993, 2008; Salas et al., 2010). Leaders’ mental models are also thought to be critical to decision making when undertaking this task (Salas et al., 2010). This mental simulation is required to imagine the future (Taylor, Pham, Rivkin, & Armor, 1998) and construct a vision to overcome the crisis (Mumford et al., 2007). The construction of an agreed vision to overcome the crisis requires consensus within the leadership, and this consensus enables them to direct followers so that there is a unified response to the crisis within the organization (Akrivou & Bradbury-Huang, 2011; Mumford et al., 2007). However, problems are likely to occur. Due to their slow development through experiential learning, leaders’ mental models are difficult to change to fit new crisis conditions. Prior empirical research highlights the presence of cognitive inertia (Hodgkinson, 1997; Reger & Palmer, 1996), so initially during a crisis leaders’ mental models are based on historical environments (Mumford et al., 2007). This is a major problem for the leadership when attempting to resolve a crisis, because there is a time delay for leaders’ mental models to catch up to accurately model new crisis conditions so inducing errors of judgment. Another major problem can occur when leading the resolution to an organizational crisis. Different leaders within a top management team can direct their attention in alternative ways, so they identify different problems thrown up by the crisis and suggest alternative resolutions. This diversity in thinking and attention allocation seems partially inevitable when organizations face complex and ambiguous situations. As mental models have been developed through different experiential learning (Salas et al., 2010), it is very likely that diversity in attention allocation will be a feature of leadership teams grappling with a crisis. There is limited longitudinal empirical research to help understand how leaders shift their attention during organizational crises. Combe and Carrington

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(2015) and Carrington, Combe, and Mumford (2019) provide rare examples of changes to leaders’ mental models as they resolve an organizational crisis over time. In this chapter we build on this work and report on shifts in attention within a top management team when attempting to resolve a crisis. If individual leaders within a top management team have a different focus of attention, such as a focus on different issues as possible causes of the crisis and focus their attention on alternative solutions, ignoring others, then consensus in how to deal with a crisis will be lacking. This may delay organizational change and exacerbate the organizational crisis, and in a worse-case scenario, lead to company failure.

Individual Differences in Selective Attention In an early study, Dearborn and Simon (1958) provided evidence of differences in managers’ selective attention based on the functional departmental background within a single firm. When standardizing for a competitive situation in an experimental research design, managers within the same firm were found to perceive different problems that needed to be addressed in a case study. Further experimental research followed, which questioned the importance of functional background as a driver of selective attention to different problems in a management setting (Walsh, 1988). However, this research confirmed the importance of managers’ beliefs or mental representations in underpinning attention to different issues important for organizational change. More studies followed which confirmed the importance of managers’ mental representations, which both facilitate and limit attention to information about changes in organizational environments (Barr, 1998; Barr, Stimpert, & Huff, 1992). These studies used a different research design. The researchers used longitudinal data, which can be welcomed to more fully understand responses to environmental change and organizational crises as they evolve over time. However, both studies used secondary evidence, in the form of letters to shareholders, to arrive at conclusions. This evidence inevitably introduced errors into the research studies, because conclusions were made on the basis of letters placed in the public domain, which may have only included beliefs deemed appropriate for the public, and assumed total consensus within the leadership. A major theoretical contribution occurred around the same time. Ocasio (1997) highlighted three premises of the attention-based view of the firm. One, decision makers’ behavior is based on their focus of attention. Two, their focus is based on their situation (situated attention). Three, their situation is based on the firm’s rules, resources, and relationships so that there is a structural distribution of attention. In taking an attention-based perspective we contribute to understanding of how individuals within a top management team can focus their attention on different issues in their responses to resolve a crisis.

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In attempting to make sense of an environmental change, which often precedes organizational crises, the process starts with scanning, monitoring, and information gathering (Mumford et al., 2007). However, this process is not explained merely by rational decision making because leaders are prone to errors and biases (see Eubanks & Mumford, 2010). Leaders are known to focus their attention on events that they interpret to be a threat to their organizations ( Jackson & Dutton, 1988). This focused attention is likely due to their attempts to minimize negative outcomes (Partlow, Medeiros, & Mumford, 2015). It seems that when scanning the environment leaders pay much more attention to potential threats, which may give rise to a crisis in future. In contrast, researchers have also found selective attention and attention intensity in top management teams when they are searching for new knowledge and information when evaluating opportunities (Li et al., 2013). Therefore, it is possible to conclude that leaders’ attention, when directed to both threats and opportunities, is critical to leaders’ performance. Directing attention to an environmental change that is likely to produce a crisis, to be forewarned, is only one significant challenge for leaders. Another, even more important challenge, is directing attention to a particular response to overcome an organizational crisis when it occurs.

Errors in Leaders’ Decision Making Decision-making errors within leaders and their influence on leaders’ performance has been discussed by scholars such as Mumford et al. (2007) and investigated by Eubanks and Mumford (2010). These authors point to a series of errors that can influence leaders’ performance, especially when facing complex and ambiguous problems involving time pressure and stress associated with resolving organizational crises. Prior research suggests that errors can occur when leaders think in similar ways so cognitive diversity may help eliminate bias and errors of judgment. One way firms can help ensure cognitive diversity, for example, is to incorporate individuals from different cultural backgrounds into top management teams (Hitt, Dacin, Tyler, & Park, 1997). This may help ensure different perspectives, which can enrich interpretations (Huber & Lewis, 2010). Cognitive diversity in teams is also likely to increase creativity (Kilduff, Angelmar, & Mehra, 2000; Shin, Kim, Lee, & Bian, 2012) and is said to lead to more extensive discussions about strategic options to enable firms to change (Lant, Milliken, & Batra, 1992; Miller, Burke, & Glick, 1998). Differences in thinking are likely to increase the range of vision so that promising options are not overlooked (Eden & Ackermann, 2010). However, a considerable dilemma is set up for firms facing a crisis. Research suggests that there are advantages when leader’s attention within a top management team is directed in different ways, but this diversity is likely to reduce the chance of consensus required to resolve the crisis. To address this dilemma leaders need to direct their attention in different ways to make sense of a crisis. After

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sensemaking, leaders need to suggest alternative strategic options, but then form consensus and collectively direct their attention to implementing the best option to resolve the crisis. Problems can occur when leaders focus on outcomes too early as this may undermine a thorough analysis thereby giving rise to errors (Mumford et al., 2007). When resolving organizational crises, which unlike some emergency and military contexts, can evolve incrementally, forming consensus around a vision for the future can take some time.

Mental Models, Sensemaking, and Vision Formation To address the complexity faced when attempting to resolve a crisis, individuals construct mental models as simplified representations of their world so that they are not overwhelmed by data (Daft & Weick, 1984; Walsh, 1988). Mental models are useful for describing a system’s purpose, explaining its functioning, and predicting future states (Rouse & Morris, 1986). Cognitive vision formation theory, developed by Mumford and Strange (2002), has been fruitful to help understand how individual leaders and teams resolve crises over time. In developing this theory, Mumford et al. (2007) discuss two types of mental model critical when resolving crises. Initially leaders activate descriptive mental models based on case based prior knowledge and experience as they confront ambiguous and novel problems (Eubanks & Mumford, 2010; Mumford et al., 2007). In other words, leaders would initially use a mental model based on how their world is currently configured to try to make sense of what is happening. As these descriptive mental models are idiosyncratic, because they are based on prior knowledge and experience, individuals will likely focus their attention to different aspects of any external change and its impact on the firm (Mumford et al., 2007). The notion of enacted environments arises at the start of a crisis when leaders try to make sense of the new conditions they face as leaders can produce part of the environment they face (Daft & Weick, 1984; Weick, 1995). In other words, leaders’ subjective perceptions and their selective attention to specific issues associated with the crisis form an important driver of decision making, rather than only the objective reality they face. Consequently, sensemaking at the initial stages of dealing with crises highlights that the environments that leaders deal with are, at least in part, socially constructed (Weick, 1995). After making sense of the new situation, leaders would then need to develop a prescriptive mental model over time as the basis of a vision formation process to resolve any crisis (Mumford et al., 2007; Mumford & Strange, 2002; Strange & Mumford, 2005). The development of a new prescriptive mental model is critical, because the stability of leaders’ cognition in the face of a changing external environment has been found to be a contributor to inertia and organizational failure (Barr et al., 1992; Hodgkinson, 1997).

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Attentional Demands and Information Processing in Decision Making Judgments under uncertain conditions, such as those thrown up by an organizational crisis, have long been an important topic for research (see Tversky & Kahneman, 1980). Experimental evidence suggests that individuals usually think in terms of causes and effects and have more confidence in causal predictions (Tversky & Kahneman, 1980). Causal predictions are invariably important when analyzing the reasons behind an organizational crisis, so leaders are likely to pay much more attention to those factors deemed to be the cause of a crisis when suggesting a solution. For example, the availability heuristic (Tversky & Kahneman, 1973) may be responsible for the attention of leaders to different causal explanations for an organizational crisis. Certainly, an organizational crisis is an uncertain environment and different leaders within a top management team can focus their attention in alternative ways and suggest alternative solutions to overcome a crisis (Combe & Carrington, 2015). The work on naturalistic decision making highlights several models of decision making, but they all reach similar conclusions (Klein, 2008). There is consensus that individuals use knowledge based on prior experience to categorize situations and rely on a synthesis of this experience, such as in a mental model, to form judgments and suggest courses of action (see Klein, 2008). Prior research confirms that expertise, usually assumed to be based on past experience, has a positive effect on performance in leaders (see Mumford et al., 2017). Experts are known to possess deep, well-organized, more comprehensive knowledge structures than novices, and can use this knowledge in mental shortcuts to categorize ill-defined problems faster (Day & Lord, 1992). This expertise reduces attentional demands on leaders (Mumford et al., 2017) so they can focus their attention on what they think really matters. While prior research points to the importance of experiential knowledge as the basis of expert judgments in leaders, an organizational crisis is likely to throw up unique problems where past experience may be of little help. In these situations, decision making during an organizational crisis may be a blend of fast intuitive and slow analytical thinking (Kahneman & Klein, 2009). Furthermore, critical thinking skills, or the ability to evaluate evidence and arguments independently of prior beliefs, may be required (West, Toplak, & Stanovich, 2008).

Leaders’ Shifts in Attention During an Organizational Crisis Currently, there is limited research on how the attention of leaders within a top management team is directed to similar or different issues when resolving an organizational crisis within a real-life setting. There is an expectation, based on

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prior research, that the different levels of past experience and different experiential knowledge will influence the way attention is directed within a top management team when resolving a crisis. In this chapter we provide an overview of our longitudinal data to explore this issue.

Method We investigated attention in the leaders when responding to a cumulative crisis using a single case study method (see Yin, 2013), which would provide rich data on the complexities in cognition at an individual level (see Bougon, Weick, & Binkhorst, 1977; Combe & Carrington, 2015; Hodgkinson & Johnson, 1994; Markóczy, 1997; Wacker, 1981; Walsh, 1988; Weick, 1979).

Case Study/Context The chosen case study firm was a not-for-profit organization that for the purposes of this research was anonymously named Health Change UK. The organization operates in the United Kingdom’s health sector, which over recent years has undergone dramatic changes. These changes were due to major political reforms to the healthcare system which transformed the commissioning of contracts (the TABLE 6.1 Timeline of Key Events, 2008–2015

Year

Event

2008 2010

Global Financial Crisis and UK Economic Recession New UK Minority/Coalition Government Government Funding Cuts/Efficiency Savings Health White Paper Funding Cuts to XXX Contract (Service A—55% of turnover*) Lost XXX Contract (Service B—21% of turnover*) Data Collection—Phase 1 Lost XXX Contract (Service C—6% of turnover*) Won XXX Contract (Service D—19% of turnover*) Won XXX Contract (Service E—5% of turnover*) Retained XXX Contract (Service F—6% of turnover*) Won XXX Contract (Service G—2% of turnover*) Data Collection—Phase 2 Lost XXX Contract (Service H—8% of turnover*) Lost XXX Core Contract (Service A—40% of turnover*) Formed partnership with Large National Service Provider New UK Majority Government Lost XXX Contract (Service D—15% of turnover*) Data Collection—Phase 3

2011

2012

2013 2014

2015

* Approximate % of turnover at the time contract was won or lost

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sector’s principle funding source). There was deregulation allowing for increased competition from the private sector and this radical change eventually led to a crisis within the firm due to the loss of contracts worth 27.2% of turnover over a three-month period of time. To examine attention during a cumulative crisis a longitudinal research design is required. Therefore, the research investigates leaders’ cognition at three different phases over a four year period from 2011 to 2015. The sample consisted of the entire top management team of Health Change UK which comprised of four members. As the composition of the top management team did not alter over the three phases of data collection there were no issues with sample attrition. A timeline of events surrounding the cumulative crisis and the dates of data collection are outlined in Table 6.1.

Data Collection To capture top managers’ attention, we established an interview protocol with multiple stages of different data collection techniques. Due to the need to investigate individuals’ cognition, sorting technique which is a common psychological research method (Rosenberg, 1982) was adopted. This formed the basis of the standardized procedure for developing cognitive maps outlined by Markóczy and Goldberg (1995). Each top manager was subjected to three face-to-face interviews by the same researcher (the co-author); once in 2011, again 18 months later in 2013, and finally 24 months later in 2015. This time frame was chosen due to the slowly evolving nature of the change events, initiated by the UK government, which eventually led to the crisis. The crisis itself, was not predicted at the outset of the research, but the dramatic changes in the funding process were known and were a cause of concern to the leadership during early discussions when planning the research. These interviews included multiple data collection techniques across four stages. These follow the four stages presented by Combe and Carrington (2015) and Carrington et al. (2019). Stage 1 consists of the standardized sorting technique (Markóczy & Goldberg, 1995) to identify each participant’s beliefs about important factors for success. The essence of the technique is that a large identical pool of factors is consistently presented to a variety of respondents for them to sort out which are the ten most important. The factors were developed from prior research (see the factors listed in the appendices of Carrington et al., 2019; Combe & Carrington, 2015; Markóczy & Goldberg, 1995; Walsh, 1988). Consequently, we adopted the 54 factors that can be found in the study by Combe and Carrington (2015) and Carrington et al. (2019). This technique is used to standardize the production of cognitive maps which is vital when they are to be compared and contrasted. In stage 2, these ten factors are used to generate cognitive maps in real time by asking each participant to

156  Ian A. Combe and David J. Carrington

Employee relationships

Internal efficiency

2

3 3 Developing staff

3 Motivation of staff

2 3 3

Helping clients achieve recovery

3 Service quality

Targeting new funders FIGURE 6.1 Cognitive

1

2

Relationships with partner agencies

3 3

3

2 Innovative services 1

Knowledge of competitors

Map of TM03 in Phase 2

place the ten cards on a blank sheet of A3 paper and to draw lines to indicate relationships between the factors. Participants were also asked to rate the strength of the relationships between factors. Sorting technique is also used to reduce interview bias because there is no communication between researcher and respondent during the sorting process (Walsh, 1988). Figure 6.1 illustrates an example of a cognitive map taken from one of the top managers in this study. Stage 3 follows the cognitive mapping procedure with an in-depth interview to develop a more detailed understanding. Finally, stage 4 consisted of a short questionnaire to capture information on age, gender, job role, tenure, stakeholder focus, and objectives for the company.

Data Analysis To aid in the detailed analysis, each hand-drawn map was transferred to Cognizer, which is a statistical software package (Clarkson & Hodgkinson, 2005). This allows for various calculations of standardized causal cognitive maps that are first presented by Markóczy and Goldberg (1995). To investigate attention within the top management team individual differences between pairs of maps were analyzed (see Markóczy & Goldberg, 1995). The calculation for the distance ratios given by Markóczy and Goldberg (1995) is a development of formula 12 proposed by Langfield-Smith and Wirth (1992). This provides a statistical value between individual maps so each participant’s cognitive map was individually compared to the other participants. Therefore, the dataset of four top managers across three phases

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resulted in 66 pairs of distances calculated. Following Markóczy and Goldberg (1995), if a value of 0 is present then the maps are exactly identical whereas a value of 1 represents a completely different cognitive map (maximum difference). Table 6.2 shows the distance ratios between each individual in each phase. These distance ratios can be illustrated visually through multidimensional scaling (MDS). MDS was applied to the dataset to provide an overview of the top management team across the three phases of the cumulative crisis. The MDS settings were modified using PROXSCAL, with Proximities = Dissimilarities, Proximity Transformations = Interval, and Initial Configuration = Torgerson. The stress values in this study are found to be quite high (S-Stress = 0.184) but are expected due to the complexities around dimensionality when analyzing this type of dataset (Markóczy & Goldberg, 1995, p. 317). This technique allowed the data to be presented in a two-dimensional space so that the positioning of top managers with respect to each other could be evaluated indicating similarities and differences in their cognitive maps. Figure 6.2 illustrates the distances between top managers at each phase of the crisis. Figure 6.2 demonstrates several key points regarding the variation in attention within the top management team during the cumulative crisis. At each phase of data collection during the crisis, attention continues to shift. The diagram depicts a general movement from left to right. This demonstrates that attention continuously evolves as the crisis perpetuates. Therefore, what the top management team pay attention to near the onset of the crisis largely differs to that at the end of data collection. Figure 6.2 also demonstrates that the level of consensus within the top management team on what to attend to changes within each phase. Attention is substantially diverse within the top management team in the initial response (Phase 1). In other words, near the onset of the crisis, each top manager pays attention to different facets of the crisis and what is important to success through that time. Subsequently, 18 months later (Phase 2) the level of agreement on what to pay attention to begins to converge within the top management team. Therefore, as the crisis matures top managers are more consistent to one another in what they attend to. However, two years later (Phase 3), divergent perspectives within the top management team begin to materialize again. The data as a whole start to demonstrate the dynamic nature of consensus in attention within a top management team during a cumulative crisis. The convergence from Phase 1 to Phase 2 of data collection not only demonstrates the building of consensus, but also that the content of attention becomes more focused between the two phases. In other words, the top management team in Phase 2 is a more unified version of Phase 1 with little shift outside of the boundaries of the first phase. Therefore, what top managers attend to doesn’t necessarily shift; it just becomes more focused. However, attention shifts greatly between Phases 2 and 3, with three members of the top management team moving further away from both of the initial phases.

0.000 0.971 0.686 0.784 0.889 0.660 0.597 0.822 0.898 0.727 0.797 0.971

0.000 1.000 0.809 0.670 0.659 0.889 0.971 0.903 0.792 0.674 0.590

ATM02

0.000 0.889 0.771 0.814 0.686 0.720 0.889 0.693 0.912 0.971

ATM03

0.000 0.809 0.814 0.839 0.553 0.784 0.805 0.971 0.597

ATM04

A = Phase 1; B = Phase 2; C = Phase 3; TM = Top Manager

ATM01 ATM02 ATM03 ATM04 BTM01 BTM02 BTM03 BTM04 CTM01 CTM02 CTM03 CTM04

ATM01

TABLE 6.2 Distance Ratios Between Top Managers

0.000 0.809 0.550 0.903 0.682 0.784 0.805 0.693

BTM01

0.000 0.541 0.731 0.971 0.389 0.809 0.686

BTM02

0.000 0.593 0.792 0.570 0.663 0.809

BTM03

0.000 0.788 0.705 0.971 0.903

BTM04

0.000 0.797 0.805 0.889

CTM01

0.000 0.809 0.792

CTM02

0.000 0.917

CTM03

0.000

CTM04

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Object Points Common Space

1.0

CTM03

ATM01

Dimension 2

0.5

BTM03

Phase 1 0.0

M03 M ATM03

–0.5

M02 M BTM02 ATM02

CTM TM M02 CTM02

BTM M0 M BTM01

Phase 2

CTM04 C

BTM04

Phase 3

ATM04 –1.0 –1.0

–0.5

0.0

CTM01

0.5

1.0

Dimension 1 FIGURE 6.2 MDS

of the Top Management Team Over Three Phases

This demonstrates that not only does the team become more diverse in their attention, but also shift the content of their attention. Figure 6.2 also demonstrates some important findings in regard to individual top managers throughout this cumulative crisis. We observe that the positioning of TM02 largely differs from the other top managers throughout the crisis. Firstly, in the beginning TM02 clearly had a strong view of the future direction of the organization. This is reflected in being positioned on the extreme right of the diagram which reflects the general trajectory of the top management team over the three phases from left to right of the diagram. Secondly, TM02 by the final phase is positioned centrally to the team in all three phases. This shows that this top manager towards the end was directing his/her attention to a combination of factors shared by other top managers across the three phases. This central position reflects the convergence towards the middle ground of attention within the top management team across the three phases. This is also demonstrated by TM02’s shift in attention from right to left. This is the only top manager who makes this

160  Ian A. Combe and David J. Carrington

continuous movement over the three phases. Therefore, the data indicates that this top manager has moved towards a more neutral and collective position. However, one of the other top managers (TM03) shifts his/her attention in the complete opposite way, which does reflect the general movement of the top management team over the three phases.

Content of Attention What is clear, based on our findings is that attention within the top management team evolves and shifts over time throughout the cumulative crisis. However, this analysis only considers the distances between individuals derived from multidimensional scaling. The analysis does not reveal the content of individual similarities and differences. Therefore, it is essential to consider how the content changes in individuals over time. Consequently, the next mode of analysis is to investigate the content of the attention within the top management team at the different phases. While our previous analysis focuses on the similarities and differences between the cognitive maps of top managers, we can now start to analyze what these similarities and differences relate to. Consequently, we first consider the beliefs (factors) that top managers have considered important for responding to the crisis within each given phase. Within the phases of data collection during the crisis, each top manager shares certain beliefs with other members of the team, as well as having their own individual beliefs. Aggregated cognitive maps were formed to ascertain these different and shared beliefs. Any different (non-shared) beliefs were removed from the aggregated cognitive map resulting in a cognitive map based on shared beliefs. For inclusion into the aggregated cognitive map a factor had to be shared by at least two or more top managers (see Table 6.3). The strength of the relationships between factors were also aggregated. Figure 6.3 is the shared cognitive map of the top management team in Phase 1. Key to the first cognitive map (see Figure 6.3) is that the top management team couldn’t universally agree on a belief most important to dealing with the onset of the crisis. This meant that there wasn’t one factor deemed important for the resolution of the crisis and success that grabbed all leaders’ attention. The TABLE 6.3 Shared and Different Beliefs in Phase 1

Shared

Different

TM01 TM02 TM03 TM04

7 3 6 7

3 7 4 3

Total

23

17

3

3

3

3

3

3

Target focused

3 3

3

5

5

5

3 3

2

3

3

2 3 3

Control of service costs

3 3

4

6

2

Planning ahead

3 3

3 3

6 3

6 6

2

2

2 2

3

1

3

Internal efficiency

6

3

3

3

Employee relationships

2

Motivation of staff

2

Relationships with partner agencies

Boxes (N/A); Majority Shared Beliefs (3 out of 4) = Grey Boxes; Partially Shared Beliefs (2 out of 4) = White Boxes

FIGURE 6.3 Aggregated Cognitive Map of Shared Beliefs Within the Top Management Team. Unanimously Shared Beliefs (4 out of 4) = Black

Competitor analysis

Service quality

3

Measuring customer achievements

–2 3

Helping clients achieve recovery

162  Ian A. Combe and David J. Carrington

analysis starts to demonstrate that from the onset of the crisis the top management team were divided on how to respond to the crisis. Despite the majority (i.e., three out of four) agreeing that planning ahead was vital in responding to the crisis, what this entailed remained a source of differences. With the radical changes in the external environment, top managers began to attend to different facets of that environment. Issues relating to the need to plan ahead stemmed from government reforms, funding cuts, deregulation, increased competition, new market conditions, and an emerging sense of a loss of organizational identity in a transformed sector. Due to these complex issues surrounding the organization during the onset of the crisis, the top management team varied in what they attended to. For example, TM02 selectively attended to helping clients achieve recovery, while TM01 attended to government policy. Figure 6.3 demonstrates beyond this need to plan ahead and think strategically, the top management team were divided into what to focus on when dealing with the crisis. There appears to be multiple important beliefs that are either partially shared or only important to one individual top manager. These beliefs are divided between factors relating to customers, competitors, employees, partners, finance, efficiencies, and targets. The analysis begins to show a focus on largely external factors as well as some internal issues to deal with the external pressures. Consequently, the top management team’s principal objective in responding to the initial crisis was to plan ahead through the development and implementation of an agreed clear and coherent strategy. However, the means to achieving this objective were varied. Furthermore, it is problematic to have planning ahead as an objective, as this is a strategy process rather than the end. Figure 6.3 also demonstrates the importance of one other factor beyond planning ahead. This is “helping clients achieve recovery”. This factor has aggregated causal relationships with at least eight other factors. Many of these provide in-degrees to “helping clients achieve recovery”. This also indicates an important objective within the top management team. Therefore, to half of the team this driving principal (philosophy) of the organization is still heralded as the key objective. However, it is clear that during the onset of the crisis, despite this being vital to the organization, other top managers have turned their attention away from this core belief. We also find further support as to why TM02 was positioned to the extreme right of the common space map within the first phase. When responding to the initial crisis, this top manager focused on seven different factors important for success, which were all different to the other top managers within the team. However, TM02 did share beliefs with other top managers around the importance of planning ahead as well as control of service costs and service quality. An identical approach was taken with the top management team in the second phase of data collection as the crisis evolved further. Table 6.4 shows the shared and different beliefs for each top manager while Figure 6.4 is the shared cognitive map of the top management team in Phase 2.

An Organizational Crisis  163 TABLE 6.4 Shared and Different Beliefs in Phase 2

Shared TM01 TM02 TM03 TM04 Total

Different

6 6 9 6

4 4 1 4

27

13

Earlier analysis demonstrated that the top management team by Phase 2 had built consensus and became more unified around some of the shared beliefs from Phase 1. First, this second aggregated map demonstrates the universal importance within the top management team of service quality. As with the importance of planning ahead in Phase 1, in Phase 2 service quality becomes an important objective for the organization to respond to the evolving situation. Again, despite acknowledging its importance, this belief is more prescriptive (vision, aspirational) rather than descriptive (actually happening). This attention on the future may be due to either not being aware of the level of service quality or awareness of inconsistencies of service quality levels across the organization. Second, we also see a strong agreement on staff motivation and development, as well as helping clients recover, relationships with partners, and innovative services. The attention on these factors is demonstrated through the majority (three out of four) top managers agreeing to the importance of these for the resolution of the crisis. Here, not only do we observe the building of consensus and a more unified approach, but we also begin to see a focus on the internal operations of the business as a means of improving service quality. Service quality is thought to be improved by the core relationships between staff, clients, and partners. Furthermore, it is seen as important that efficiencies and metrics are implemented into these relationships in order to measure service quality and satisfy funders. At the heart of this focus is the relationship with staff, in viewing staff as a core asset to the organization in order to deliver high quality services. As with Phase 1, “helping clients achieve recovery” is also another key objective as this factor has indegrees from six other factors. Therefore, in this phase of the crisis the philosophy of the organization begins to return to the majority of the top management team. Following this strong unified aggregated cognitive map of the second phase, we begin to see where the divergence begins in the third phase of data collection. Table 6.5 shows the shared and different factors for each top manager while Figure 6.5 is the shared cognitive map of the top management team in Phase 3. In the third phase of data collection we observe a universal belief within the top management team, of the need to target new funders to respond to the cumulative crisis. This was not surprising following a further significant loss of contracts

3

6

Motivation of staff

3 5

3 3

3

6

6

6

3

3 3

5 6 3 3

6 2

2

2

2 2

Measuring customer achievements

2

6

Service quality

1

3

5

3 3

3 3

3

Innovative services

Relationships with partner agencies

3 6

Helping clients achieve recovery 1

3

Targeting new funders

Boxes; Majority Shared Beliefs (3 out of 4) = Grey Boxes; Partially Shared Beliefs (2 out of 4) = White Boxes

FIGURE 6.4 Aggregated Cognitive Map of Shared Beliefs Within the Top Management Team. Unanimously Shared Beliefs (4 out of 4) = Black

Employee relationships

Developing staff

2

3

Internal efficiency

An Organizational Crisis  165 TABLE 6.5 Shared and Different Beliefs in Phase 3

Shared TM01 TM02 TM03 TM04 Total

Different

6 6 5 4

4 4 5 6

21

19

and revenue immediately prior to this phase of data collection. The loss of these contracts nearly halved the amount financial income generated by the company. Consequently, in such a short period of time a radical decline in revenue of this proportion inevitably threatens the organization’s survival. This results in the top management team focusing their immediate attention to the critical targeting of new funders. In this third phase of data collection, planning ahead, which is shared by the majority of the top management team, is focused on the objective of targeting new funders. However, although the objective is to bring in new income streams, the means or strategies used to do so remains mixed. The attention is divided between the importance of staff (motivation and development), competition (service differentiation and price differentiation), innovation, promotion, and the building resources for the future.

Discussion of Findings This empirical research has made the following contributions to understanding shifts in attention in the leaders within a top management team during a response to a crisis over time. First, the study investigated individuals who all experience exactly the same environmental conditions. The findings point to evidence confirming the socially constructed nature of crisis environments (Weick, 1995). The findings here, which found a cycle of “diversity, consensus and again diversity” in beliefs in how to respond to a crisis over time, can be contrasted with earlier empirical research. The findings question evidence of consensus in beliefs in a cross sectional studies within a specific industries (e.g., Porac, Thomas, & Baden-Fuller, 1989), as well as earlier longitudinal studies that observed the development of cognitive consensus over time (Combe & Carrington, 2015; Kilduff et al., 2000; Markóczy, 2001). These latter studies were hindered by only capturing data from two points in time; beginning and end. The inclusion of a midpoint in the data collection has allowed for a more detailed understanding of longitudinal analysis of convergence ( Jehn & Mannix, 2001). However, these observed fluctuations between

Innovative services

3 2 3

3

Price differentiation from competitors

3

6

3

3

3

3

Promoting the service

3 3

Planning ahead

3

Differentiation of services from competitors

3

3

2 2

6

Motivation of staff

3 6

2

3 3

Developing staff

Boxes; Majority Shared Beliefs (3 out of 4) = Grey Boxes; Partially Shared Beliefs (2 out of 4) = White Boxes

FIGURE 6.5 Aggregated Cognitive Map of Shared Beliefs Within the Top Management Team. Unanimously Shared Beliefs (4 out of 4) = Black

3

Building resources for the future

3 3

6

6 6

Targeting new funders

An Organizational Crisis  167

three phases over four years must be met with some caution, because this was an ongoing crisis so identifying its beginning and end is problematic. The study demonstrates that consensus is not just about aligning strategic objectives and priorities (Kellermanns, Walter, Lechner, & Floyd, 2005; Knight et al., 1999) but also other supporting beliefs with key causal relationships relevant to the situation in hand (Markóczy, 2001). The need for studying longer time-scales has also been highlighted in relation to cognitive convergence (Dionne, Sayama, Hao, & Bush, 2010) and leader cognition (Marcy & Mumford, 2010). The additional time frame for data collection has contributed to a more comprehensive understanding of what happens when individuals respond to crises. Additionally, this study has contributed further by investigating cognitive convergence of executives in their real-life organizational setting rather than through business simulations or student samples (Harrison, Price, Gavin, & Florey, 2002; Jehn & Mannix, 2001; Kilduff et al., 2000). The findings raise serious concerns about the influence of cognitive diversity in change situations. “These are not mere differences of opinions on simple and insignificant matters but are divergent views on highly important matters that would have substantial ramifications for the organization” (Olson, Parayitam, & Bao, 2007, p. 200). Previous studies have found that diversity is beneficial at the early stages of planning (Tegarden, Tegarden, & Sheetz, 2009) and is favorable for high performing teams (Kilduff et al., 2000). Decisions need to me made quickly during crises, even cumulative ones in organizations, which requires both commitment and consensus (Dooley, Fryxell, & Judge, 2000; Eisenhardt, 1989; Eisenhardt & Bourgeois, 1988). Two potential interrelated explanations for the fluctuations in diversity and consensus need to be considered. First, the levels of higher and lower diversity may be indicative of the context surrounding each phase of the crisis. As documented in the research, the first phase is shrouded by a chaotic external environment that is both highly complex and highly dynamic. At this early stage, the crisis and its effects were felt across the leadership of the organization. This may result in diverse perspectives and possible responses to the unfolding crisis. Second, as well as the environmental uncertainty at this time, the financial performance of the organization was declining and the viability of the organization placed in threat. In other words, this finding may start to indicate that the turbulence of the environment and the fall in revenue through the loss of contracts and reduction in funding is closely aligned with the initial cognitive diversity. Prior research has demonstrated how antecedents to cognitive diversity can be generated externally, by complex and dynamic environments (Dess & Origer, 1987; Homburg, Krohmer, & Workman, 1999; Hrebiniak & Snow, 1982; Olson et al., 2007) or internally, through recent poor performance (Kilduff et al., 2000). However, it is difficult to differentiate which of the two are effecting the levels of diversity in this case, or whether it is combination of both, as they are not mutually exclusive. The stabilizing of the industry and better understanding of the crisis

168  Ian A. Combe and David J. Carrington

by the second phase may have resulted in a possible movement towards building consensus. This is coupled with stronger financial performance through the winning of additional contracts. However, after the midpoint in data collection, the external environment destabilizes further and also financial revenues fall more sharply. At the same time the data highlights cognitive divergence as different perspectives emerge of how to deal with the subsequent fallout from the crisis. Therefore, although this organization may have reached a situation of stability and heightened consensus by the midpoint, it becomes clear that this was only temporary. It is likely that not all of the aftershocks of the crisis were fully understood within the organization. Additionally, the research provides further evidence that organizational crises are not static one-off events, but perpetuating cascading situations that have lasting implications.

Limitations The case study is based on a cumulative type of crisis and its implications over a four year period. In contrast, sudden and abrupt crises can emerge within hours and require an immediate response (Hwang & Lichtenthal, 2000; James & Wooten, 2005). However, the causes of these crises are often less ambiguous in comparison to cumulative crises (Hwang & Lichtenthal, 2000, p. 134). While cumulative crises are likely to be common in organizations, the complexity of dealing with them suggests that the findings are limited to this context. The findings are based on a single in-depth case study when a top management team confront a cumulative crisis, so the study only offers analytical generalization (generalization to theory) rather than statistical generalization. However, single case study method is justified by the need for a rich understanding of complex issues related to shifts in attention within an organization’s top management team. A focus on cognition has meant that other effects have not been studied. Antecedent effects such as the influence of power dynamics on cognition during the crisis were not studied (Dess & Priem, 1995). Also the performance implications of similarities and differences in mental models were not investigated in this research. Leader performance is key during crises (Barrett, Vessey, & Mumford, 2011; Mumford et al., 2007), but tracing the links between mental models and performance at different organizational levels was beyond the scope of this study. Consequently, the purpose of this research wasn’t to replicate other studies that have tested the link between consensus and performance.

Managerial Implications The main managerial implications of our research are that leaders need to be aware of individual differences in the focus of attention that can occur within a management team when sensemaking and putting forward resolutions to crises over time. We contend that a major implication of our findings is that leaders need to reflect on their individual differences as a first step to discussions for

An Organizational Crisis  169

consensus building around a vision for the future. The standardized cognitive mapping technique we have employed here can be used to highlight both similarities and differences in individuals’ focus of attention. Understanding where the differences lie helps consensus building in action-oriented beliefs linked to vision formation required to resolve crises. Currently, a major technique to help organizations develop consensus when responding to change involves aggregating idiographic cognitive maps. The idiographic method used to elicit cognitive maps (Ackermann & Eden, 2011; Eden, 1992; Eden & Ackermann, 2004; Eden, Ackermann, & Cropper, 1992) adopts an open and unstructured method to interviewing. This gives complete acknowledgment to the idiosyncrasies and richness of an individual’s subjective world. Consequently, a cognitive map elicited from this technique is personalized and should be more accurate as valuable information is not lost. This technique developed and used by Eden and Ackermann (2004) involves combining individuals’ beliefs to be displayed on an aggregate or strategy map to act as a vehicle for negotiation in consensus discussions. Typically, these discussions occur in workshop sessions using Decision Explorer software, which allows a very large number of beliefs and the relationships between them to be combined and visualized (see Eden & Ackermann, 2004, ch. P1, pp. 284–302). Eden and Ackermann (2004) discuss the advantages of using individual interviews as the basis of eliciting strategy maps, but also point to a disadvantage in that it is a very time consuming and costly process. Eden and Ackermann (2004) suggest that is takes between 60 to 90 minutes to conduct the interviews using their method, plus a further 60 minutes post interviews to merge each map into an aggregated strategy map. To overcome this time consuming and costly procedure, group workshop method using Oval Mapping Technique, is advocated (see Eden & Ackermann, 2004, ch. P2, pp. 303–320). While aggregating cognitive maps has some advantages for developing negotiated beliefs by seeing the big picture, one major problem is the considerable complexity presented. Depending on the number of participants included, a very large number of ideas are included in strategy maps, plus the relationships between them (see Eden & Ackermann, 2004, ch. P2, pp. 303–320). The magnitude of complexity created by combining individual causal beliefs into one huge map may detract from consensus development. We advocate a more structured less time-consuming method, which incurs less costs, as an alternative technique. Developing cognitive maps through a more nomothetic approach using a standardized elicitation procedure is useful for longitudinal research so that similarities and differences can be investigated over time. The ideographic approach to eliciting causal cognitive maps involves building maps based on the language used by participants. There is no limit set as to the number of beliefs included in a cognitive map. The standardized more nomothetic approach used in this current study involves presenting the same large pool of a priori beliefs so that participants chose the ten most important to them. The approach, using sorting technique as a basis for producing hand drawn causal cognitive maps in real

170  Ian A. Combe and David J. Carrington

time during face-to-face interviews, takes approximately 20 minutes to produce a cognitive map. There is less potential for interviewer bias as no direct communication occurs between the interviewer and participants. The input into Cognizer software is also quick, because the cognitive maps are less complex focusing only on the essence of causal beliefs by the inclusion of the ten most important factors. Analysis using multidimensional scaling (MDS) in SPSS is also relatively straightforward and not time consuming. The analysis is synthesized into a visual display which is useful for presentations to top management teams. This approach also has the advantage of highlighting differences in individuals’ focus of attention much more, which aids reflexivity. We have found subsequently, that when leaders are presented with a common space map (see Figure 6.2) and the details showing differences in individuals’ thinking within the top management team, they are very surprised, even shocked. They reflect on the differences of opinion and this reflexivity is a starting point in forming consensus around a vision for the future. The two contrasting approaches to develop management consensus are outlined in Table 6.6.

TABLE 6.6 Comparison of Two Alternative Approaches to Building Organizational Consensus

Aggregated Strategy Map Approach

Individual Standardized Cognitive Map Approach

Involves interviews to produce individual cognitive maps and then aggregating beliefs Open and unstructured method of interviewing and elicitation of cognitive maps More accurate by using ideographic methods: more personalized including the language used by participants, idiosyncrasies and richness of thinking to construct cognitive maps A very complex aggregate map to help see the big picture; individual similarities and differences in thinking are mostly hidden Slower form of data collection and aggregation and more costly; problematic for comparisons over time More potential for researcher bias due to high levels of interaction

Involves interviews to produce individual cognitive maps and then analysis using multidimensional scaling A structured, systematic and consistent method of interviewing and elicitation of cognitive maps Less accurate by standardizing a pool of factors a priori and by focusing only on the essence of beliefs (ten most important factors only)

Consensus building by combining individual differences

Smaller, simpler cognitive maps which highlight individual similarities and differences in thinking Faster form of data collection, less costly and better for comparisons over time Less potential for researcher bias; no direct verbal communication between researcher and participant Consensus building by highlighting and reflecting on individual similarities and differences

An Organizational Crisis  171

Future Research Future research could investigate leaders’ attention in multiple case studies to cross reference or embark on a large-scale quantitative research design exploring different industries, sectors, and contexts such as stable and turbulent environments exploring different types of crises (abrupt versus cumulative). Additional studies into sensemaking could also examine power and politics in relation to strategy discourse in more detail (Balogun, Jacobs, Jarzabkowski, Mantere, & Vaara, 2014; Weick, Sutcliffe, & Obstfeld, 2005). Social and political pressures mediate individual cognitive diversity (Hodgkinson & Johnson, 1994, p. 546). Particularly, future research could focus on the role and influence of the CEO (Bromiley & Rau, 2016). As a result, additional empirical research could help understand the power dynamics that play a part in vision formation and action to resolve crises within top management teams.

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7 CREATIVE PROBLEM SOLVING Processes, Strategies, and Considerations for Leaders Kelsey E. Medeiros, Belinda C. Williams, and Adam Damadzic

Creativity, once ascribed primarily to those with great artistic or inventive talent such as that of Pablo Picasso or Maya Angelou, has since been recognized by both researchers and practitioners as a critical skill for effective leadership (e.g., Mumford & Connelly, 1991). Indeed, a 2010 study conducted by IBM surveyed 1,500 CEOs, finding that creativity was consistently recognized as the number one competency for successful leadership (IBM, 2010). Although popularly labeled as creativity, the skill in this context is more aptly referred to as creative problem solving, or the development of high-quality and novel solutions to a new, illdefined, and complex problem (Besemer & O’Quin, 1999; Mumford & Gustafson, 1988). Given the highly dynamic nature of the world today, it likely comes as no surprise that creative problem solving is essential for leader success. Creative problem solving is a complex phenomenon involving multiple interrelated processes that leaders must engage in on their own and with their followers (Mumford et al., 1991). When execution of one of these processes goes awry, so may the implementation of the solution. This is perhaps best observed in cases of traditional innovative efforts. Major companies famous, in part, for innovating in their field, have experienced failure with new product launches for a variety of reasons including poor market analysis, inappropriate timing, poor product or technological performance, and leadership (e.g., Crawford, 1977; Loewe & Dominiquini, 2006; Schilling, 2002). For example, in the 1990s, Pepsi launched a new beverage, Crystal Pepsi, which ultimately failed due to leadership’s lack of attention to evaluative information provided by those working on the project. The CEO at the time, David Novak, recalled: The bottlers told me, “David, it’s a great idea, and we think we can make it great, but it needs to taste more like Pepsi,” Novak said. And I didn’t

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want to hear it. I was rolling the thing out nationally and I didn’t listen to them. (Pollack, 2007) Other major companies have experienced innovation failures including Kodak with Advantix, Neflix with Qwickster, Nintendo with Virtual Boy, and Cosmopolitan with Cosmopolitan Yogurt. There are many explanations for why these products fail, but often, issues in innovation arise due to a failure of effective ­problem-solving skills within the team and the leadership (Loewe & Dominiquini, 2006). Specifically, as demonstrated by the previous example, issues arise due to a failure of leadership to execute key cognitive processes associated with effective creative problem solving. In the case of Crystal Pepsi, David Novak failed to attend to evaluative information regarding the product and make the necessary adjustments based on this evaluation. The taste of Crystal Pepsi, which had been identified as potentially problematic, ultimately became one of the reasons for the product’s demise. Given the importance of creative problem solving to effective leadership, it then becomes vital to understand how leaders may successfully execute the cognitive processes, such as idea evaluation, and the underlying skills necessary to do so. Thus, the present chapter focuses on these processes as well as the individual differences and cross-process cognitive strategies necessary for facilitating creative problem solving and, more broadly, effective leadership.

Leader Creative Problem Solving Even outside of innovative efforts, leaders are often faced with novel, rapidly evolving problems that require creative problem-solving skills in order to generate viable solutions (Mumford & Connelly, 1991; Mumford et al., 2000; ReiterPalmon & Illes, 2004; Mumford, Todd, Higgs, & McIntosh, 2017). It is during these situations, compared to more routine problems, where leadership becomes especially critical (Hackman & Walton, 1986; Tushman & Anderson, 1986). Given the desire for organizations to be more flexible and adaptable in a modern economy characterized by rapid change and increased competition, the importance of creative problem solving to leadership becomes even more apparent and necessary for the survival of the organization (Shalley & Gilson, 2004). Thus, leader creative problem-solving skills are generalizable to effective leadership, a notion supported by previous work (e.g., Connelly et al., 2000; Bray, Campbell, & Grant, 1974; Chusmir & Koberg, 1986; Sinetar, 1985; Covington, 1987; Zaccaro et al., 2000, 2015). Indeed, previous research in this area has explored the relationship between creative problem solving and leadership across a range of performance criteria. For instance, Simonton (1988) examined the characteristics of US presidents and performance outcomes. Presidential biographies, in conjunction with

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previous data, were analyzed to identify themes in presidential leadership. Simonton identified five dimensions related to presidential style, including creativity. Although scores on all dimensions were uniquely related to performance and style, pertinent to the present effort, scores on the creativity dimension were significantly and positively related to a number of key presidential outcomes, including number of acts passed and legislative victories. Thus, creativity is argued to be an important component of leadership at one of the highest, and certainly, most visible, levels of leadership. In the years following, scholars began examining this relationship within a military context, a sample particularly useful for studying leadership given its hierarchical structure. For example, Connelly et al. (2000) examined the skills associated with success in a large sample of US Army officers. Nearly 2,000 army officers completed measures of creative problem-solving skills, motivation, personality, and intelligence. Results from the creative problem-solving measures were positively related to career achievements as well as the quality of solutions to leadership problems. Further, it was found that these creative problem-solving skills predicted leadership criteria above and beyond motivational, cognitive, and personality variables. Mumford et al. (2000) found further support for this relationship when examining differences in creative problem-solving capacity across levels of army leadership. Specifically, the sample collected by Connelly et al. (2000) was re-examined, differentiating between three leadership levels—(1) first and second lieutenants and junior captains, (2) senior captains and majors, and (3) lieutenant colonels and colonels. Each group completed a series of measures expected to relate to effective leadership, including measures of creative problem solving. Notably, as leaders moved up the ranks, higher scores in creative problem solving were observed. Although creative problem solving is generally important for leader performance, it appears to become increasingly important as leadership level increases. Along these lines, Zaccaro et al. (2015) examined a series of cognitive capacities, including complex problem solving and divergent thinking, in US Army officers. The researchers found that these assessments, both typical measures of creativity, predicted unique outcomes including self-reported development. Further, developmental experiences partially mediated the relationship between divergent thinking and continuance with the army, suggesting that divergent thinking may, in part, predict the opportunity to participate in development experiences, which in turn lengthens one’s stay with the organization. It is also important to note that the effects of divergent thinking and complex problem solving on continuance were stronger than general mental ability. This aligns with findings by Mumford et al. (2000), supporting the notion that creative problem solving becomes increasingly important at higher levels of leadership. Lastly, a critical study by Vincent, Decker, and Mumford (2002), examined the influence of divergent thinking, intelligence, and expertise on leader creative problem solving, and performance. Using a sample of 110 military leaders in the

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US Army, the researchers found that divergent thinking, intelligence, and expertise did not directly predict leader performance. However, a mediating relationship was identified, such that these attributes predicted problem-solving activities (i.e., idea generation, implementation planning), which, in turn, predicted creative problem solving and performance. This finding highlights the importance of both underlying characteristics and processes to leader performance. Thus, there is strong evidence supporting the notion that creative problem solving is critical for effective leadership. It then becomes important to consider how leaders successfully navigate this process. When considering this, the complexity of leader creative problem-solving skills should not be underestimated. Mumford and Gustafson (1988) argued that creativity should be considered an “interactional syndrome” in which at least five factors influence an effort’s success: (1) individual capacity to engage in processes related to the generation of novel ideas, (2) characteristics of the leader facilitating the execution of creative problem-solving processes, (3) characteristics of the leader managing the transition from abstract idea to concrete implementation, (4) situational characteristics influencing one’s willingness to pursue a particular solution, and (5) situational characteristics influencing the evaluation process. Thus, leaders must possess the capabilities required to effectively execute creative processing while navigating a complex landscape of organizational and team dynamics influencing these processes. Undoubtedly, creative problem solving is a highly complex, dynamic, and difficult endeavor, requiring multiple different types of cognitive abilities and skills. The present chapter aims to illuminate the key processes involved in creative problem solving to aid leaders in developing creative solutions to the complex and novel problems they face. We begin with a discussion of creative problem solving generally to provide an overview of the processes in which leaders must engage. This is followed by individual differences related to creative problem solving, including expertise, intelligence, and convergent and divergent thinking. Following, is a discussion of cross-process cognitive strategies necessary to effectively execute the creative problem-solving process. Lastly, methods for intervening in leader creative problem solving are discussed.

Creative Problem-Solving Processes Creative problem solving, by nature, is complex, requiring the execution of a dynamic set of processes and skills. Similar to other complex problems, it can be best understood through the use of process models (Mumford, Medeiros, & Partlow, 2012). Process models assume knowledge as a foundation, from which a new idea, product, process, or solution may be created and subsequently evaluated. Each process relies on the previous process and often cycles back at the conclusion (Mumford et al., 2012). In creative problem solving, later processes depend on the execution of earlier processes, necessitating that each process be carried out with thought and care.

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Multiple models of creative problem solving have been presented and discussed over the years (e.g., Amabile, 1996; Lubart, 2001; Mumford et al., 1991; Osborn, 1952; Treffinger & Isaksen, 1992). Generally, these models all contain four similar, key processes: problem identification, information gathering, idea generation, and idea evaluation. However, Mumford et al. (1991) discussed an additional four processes which prove important to creative problem solving. Thus, it is valuable for leaders to understand how to navigate eight key processes: problem identification, information gathering, concept selection, conceptual combination, idea generation, idea evaluation, implementation planning, and solution monitoring.

Problem Identification A key feature of creative problem solving is that the problem is inherently illdefined. Thus, leaders often face novel problems that require active processing to identify and structure. Reiter-Palmon and Robinson (2009) reviewed the literature in this area and strongly argued for the importance of problem identification for creativity. Indeed, several studies have argued for, and established the link between, active engagement and training, in problem identification and creative performance (e.g., Basadur, Graen, & Green, 1982; Ellspermann, Evans, & Basadur, 2007; Mumford et al., 1994, 1997; Reiter-Palmon et al., 1997; Reiter-Palmon & Robinson, 2009; Scott, Leritz, & Mumford, 2004). A clear problem definition should be set to identify the problem’s scope and boundaries in order to direct future solution search strategies. To set these boundaries, definitions must include key pieces of information such as broad goals, including relevant constraints (Reiter-Palmon & Robinson, 2009). However, it should be noted that defining a problem too tightly to a particular goal may prove detrimental (Mumford et al., 1997). Thus, leaders must carefully balance how they define the problem, providing a specific focus to direct cognitive and motivational resources while leaving room for exploration and new connections. Of additional importance is understanding that in creative problem solving, the problem may shift or change, and the leader is responsible for redefining the problem. Thus, leaders must constantly search the environment for new information to incorporate into their problem definition. This leads to the second key process: information gathering.

Information Gathering At times, information gathering is discussed without definition, as the phrase tends to speak for itself. Generally, however, information gathering refers to the process by which individuals collect information either on their own or collaboratively (Mumford, Baughman, Supinski, & Maher, 1996). This is accomplished by searching both the external and internal environment for information and cues bearing on the problem at hand. During this process, it is important that leaders

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identify key facts and look for anomalies in the information gathered (Mumford et al., 1996). In other words, leaders must search for patterns in the information and identify those pieces that stand out. Both patterns and anomalies are important as, when combined during conceptual combination, a process described later, may form the bases for novel solutions (Baughman & Mumford, 1995). Each piece of information should be assigned a level of importance to be organized in a coherent manner in order to direct future problem-solving activities (Caughron et al., 2011). Although the problem should be borne in mind during this process, information gathered may illuminate factors not considered during the initial problem identification process. Thus, there is a complex interplay between problem identification and information gathering such that information may lead to problem re-definition as well as the linear relationship in which problem definition influences the information search. Further, the order and importance of this information may change as the problem definition and other problem characteristics change. Thus, it is important that leaders remain flexible in their thinking when working through this process (and others).

Concept Selection Concept selection refers to the extraction of pertinent concepts from the information gathered (Mumford et al., 1991, 1996). Thus, leaders must be able to identify key concepts from the information presented. Another manner of explaining concept selection is through categorization of knowledge or information. Leaders may create categories and group together those that are similar while eliminating those that are not appropriate such as concepts that do not fit with the problem at hand (Mumford et al., 1991). The concept selection process is rooted in the literature on knowledge and expertise. More specifically, knowledge is recognized as the meaningful organization or categorization of information (Mumford et al., 1996). Thus, once leaders have gathered information, they must structure it in a logical manner. In a study examining the relationship between concept selection and creativity, Mumford et al. (1996) found that abstract concepts that were organized around long-term, practical goals proved especially beneficial to creativity. In other words, information gathered can be structured according to abstract principles, but should also be structured in a practical manner that allows for the application of these principles to the problem at hand. Once a structure has been imposed, these concepts can be combined to begin the generation of new knowledge, ideas, and solutions.

Conceptual Combination Referring to the combination or reorganization of two or more existing concepts, conceptual combination is the process by which individuals reshape and reorganize existing information to create new cognitive structures (Mumford,

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Connelly, & Gaddis, 2003). By combining existing concepts, leaders lay the groundwork for generating new and novel solutions. Indeed, early studies by Mumford and colleagues found that when people were asked to combine existing concepts, they generated more novel ideas (Mobley, Doares, & Mumford, 1992), and that performance on conceptual combination tasks predicted creative performance (Mumford et al., 1997). Leaders may access principle-based (analogical, abstract knowledge) or casebased (incidents of prior performance) knowledge to form conceptual combinations. Although previous research has shown that principle-based knowledge proved more useful than case-based knowledge for conceptual combination due to the ability of leader’s to think abstractly and analogically map concepts together, case-based knowledge also has some advantages. Specifically, accessing case-based knowledge is less cognitively demanding, often allowing leaders to access their experiential cases and rapidly participate in conceptual combination (Mumford et al., 2003). Thus, leaders may draw on multiple, previous, related experiences to form new knowledge structures. The concepts previously selected as critical to the problem at hand should activate a search for relevant cases and prompt the recombination of these experiences, in a novel way. This prompts a novel combination of information that may not have occurred unless otherwise activated. This process provides a platform for idea generation.

Idea Generation Likely the most researched and familiar process of creative problem solving is idea generation. Referring to the creation of unique ideas, idea generation was previously seen as the heart of creativity. More recent research, however, has discussed that idea generation, although important, may be no more important than other processes in creative problem solving (Mumford et al., 2012). In order to generate viable solutions, this process inherently relies on the effective execution of previous strategies. Idea generation may be accomplished be looking across the surface of a wide set of information, or, alternatively, may be accomplished by looking deeply within a small set of information (Nijstad, Dreu, Rietzschel, Baas, 2010). Both processes may be effective, but their success likely depends on the characteristics of the problem at hand. For instance, a more tightly defined problem may prompt a deeper idea search, whereas a broadly defined problem may allow for a wider idea search. Regardless, the tie between idea generation and problem characteristics should be noted. Leaders should engage in idea generation in a focused manner, with attention paid to the problem at hand. Unbridled idea generation where the problem is ignored will likely prove fruitless. Thus, the manner in which the problem is defined will influence idea generation activities and also provides a framework by which to evaluate the potential viability of solutions generated.

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Idea Evaluation Although previous conceptions of creativity held idea evaluation as an unnecessary, negative component of creative thinking which inhibited idea generation (Runco, 2003), research has continuously challenged this notion, finding idea evaluation to be essential for creative problem solving, especially for leaders (Mumford et al., 2003). Indeed, Mumford et al. (2003) discussed that leadership is an inherently evaluative process. Further, Lubart (1994) demonstrated that earlier idea evaluation can lead to the generation of more creative works, likely a direct result of increased creative thought through evaluative thinking. Thus, idea evaluation is a critical component of effective leadership and, more specifically, the creative problem-solving process necessary to execute effective leadership. Of specific importance to idea evaluation is a leader’s skill in projecting the potential outcomes of an idea if implemented, as well as considering how the idea meets expected and future standards and requirements. Specifically, leaders must consider how ideas generated meet the needs of the identified problem, how it will be received by those it impacts, and how its potential success may vary depending on external variations (e.g., new information or technology). This process is highly iterative and may often force leaders to reconsider their initial solutions, potentially generating new alternatives (e.g., Mumford et al., 2003). This process may also reveal new information that may influence the conceptualization of the problem or a new set of information that should be considered prior to moving forward. Thus, like other processes, idea evaluation requires flexible thinking, considering both existing information and circumstances, as well as imagining potential future considerations that may impact the success of a solution (Reiter-Palmon & Illes, 2004). After several iterations, however, a leader will select their solution and subsequently begin planning its implementation.

Implementation Planning An important predictor of creative performance is implementation planning (Lonergan, Scott, & Mumford, 2004), which has been defined as the “mental simulations of future actions” (Anzai, 1984; Mumford, Schultz, & Osburn, 2002; Mumford & Van Doorn, 2001; Noice, 1991). In other words, implementation planning requires that leaders consider how to execute their idea or solution. As an idea or solution develops, it is typically poorly structured. Planning, however, adds necessary structure that allows for implementation (Sharma, 1999). Similar to idea evaluation, during planning, ideas are reshaped and refined, which may stimulate new ideas to overcome potential problems (Osburn & Mumford, 2006). Planning begins with a search for information related to goals of the problem at hand (Earley & Perry, 1987; Thomas & McDaniel, 1990). A key piece of information gathered during this process is information bearing on constraints relevant to the implementation process such as production capabilities and resources. In

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other words, when planning, a leader must consider the practicalities of implementing their idea and address how he or she plans to see it through given their current environment (Mumford, Hunter, & Bedell-Avers, 2008). Although this information may be collected earlier, it is important to revisit the capabilities and constraints as the idea becomes more refined as circumstances or the idea may have changed since their initial identification. From there, the information collected can be used to create an initial template plan, which must be refined and revised by considering plan implementation under various circumstances (Dörner & Schaub, 1994). When planning, a leader should consider how the plan may unfold if the current environment or expectations change. Thus, it is important that a leader generate multiple, viable plans and back-up plans that may be called upon and executed when appropriate. Of particular importance during planning appears to the be the consideration of constraints, specifically, resource requirements (Giorgini & Mumford, 2013). A leader may then construct a final plan, including formulating contingency plans, and create markers to monitor progress during plan execution.

Solution Monitoring The last process in creative problem solving, solution monitoring, focuses on systematically examining solutions created to the problem and the potential issues and successes of implementation. Although monitoring may lead to unclear or ambiguous feedback (Hogarth, 1980), this process proves especially useful for identifying potential improvement or revisions (Mumford et al., 1991). Specifically, once a solution has been implemented, a leader must observe how the solution unfolds in real time and compare the reality of implementation to the expectations developed during the idea evaluation and planning processes. If the solution meets expectations, then the leader may continue to monitor its implementation over time. However, if solution implementation does not meet expectations, contingency plans, or changes to the extent plan, may be called upon to tailor the implementation process to the new circumstances (Byrne, Mumford, Barrett, & Vessey, 2009). Although solution monitoring is related to planning and implementation, it is also closely tied to problem identification (Mumford et al., 1991). The relationship between these processes is evident in that solution monitoring may reveal new problems that need to be addressed. Specifically, the solution itself may introduce new problems to the environment that did not exit prior to its implementation. Alternatively, if implementation does not unfold according to plan, leaders may need to identify the problem that occurred and how to address it. In essence, solution monitoring provides a leader with feedback regarding the performance of the solution after implementation. It is critical that leaders continuously monitor their solutions as new information and changes may emerge, which may impact the effectiveness of the solution.

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Summary The creative problem-solving process described is quite complex, warranting advanced leader cognitive strategies for successful implementation. Additionally, as highlighted in the previous discussion, leaders should be aware that this process is not necessarily linear and does not often lead clearly from one process to another. Instead, the relationships between the processes may require that a nonsequential pattern is followed to solve a creative problem. Further, the successful execution of these processes relies on a number of individual differences, as well as cross-process cognitive strategies that exert influence throughout the problemsolving process. Each are discussed in detail in the following sections.

Individual Differences Related to Creative Problem Solving There are three key individual differences related to effective creative problem solving: (1) divergent and convergent thinking, (2) intelligence, and (3) expertise and knowledge. As discussed in the following sections, each is necessary for creative problem solving and drive the execution of cross-process strategies, and subsequently, creative problem-solving processes.

Divergent and Convergent Thinking At a fundamental level, creative thinking relies on the interplay of divergent and convergent thinking—two opposing processes focused on the generation of multiple alternative solutions (divergent; Guilford, 1950) and the identification of a single correct solution (convergent; Cropley, 2006). Although Guilford (1950) emphasized the importance of both processes to creative problem solving, the literature and popular interest has largely remained focused on divergent thinking. Indeed, divergent thinking is perhaps one of the most studied predictors of creativity (Silvia et al., 2008; Runco & Acar, 2012). Divergent thinking refers to the process of generating multiple different ideas or solutions (Guilford, 1967; Kalargiros & Manning, 2015) and has consistently been shown to be influential in creative problem solving (e.g., Basadur & Hausdorf, 1996; Harvey & Kou, 2013; Kalargiros & Manning, 2015; Howell & Higgins, 1990; Vincent et al., 2002). The original concept, however, as formulated by Guilford (1950) and re-emphasized by Mumford (2001), argued that divergent thinking is a combination of several cognitive capacities, including fluency, flexibility, and originality. Fluency refers to the speed at which one generates ideas. Research (e.g., Diehl & Stroebe, 1987; Mullen, Johnson, & Salas, 1991; Paulus, Kohn, & Arditti, 2011) continuously supports the relationship between generating more ideas with generating a novel solution. Thus, if one can generate a large number of ideas in a short amount of time, they may be more likely to generate

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more potential novel solutions compared to someone who takes a longer amount of time to generate ideas. Flexibility refers to the switch between categories or approaches when generating ideas. Wilson et al (1954) identified two types of switches—spontaneous and adaptive. Adaptive switching refers to changes that are motivated by the problem demands whereas spontaneous switching was a change for the sake of change. Previous research suggests that adaptive shifts are especially beneficial to creative problem solving (Mumford, Costanza, Threlfall, Baughman, & Reiter-Palmon, 1993). However, little is known about how to effectively make these shifts (Mumford, 2001). Lastly, originality refers to the identification of “uncommon lines of thought on problems where there was no right answer” (Mumford, 2001, p. 270). Whereas divergent thinking refers to the generation of multiple different ideas, convergent thinking focuses on the identification of a single correct solution, playing a large role in the evaluation of ideas (Cropley, 2006). Thus, convergent thinking is more aligned with traditional problem solving, where problems are more well-defined. Scholars (e.g., Guilford, 1950; Mumford, 2001; Lonergan et al., 2004) have emphasized the importance of both types of thinking to creativity, suggesting that creativity requires the generation of novel ideas as well as the exploration, or evaluation, of the novelty. Thus, both divergent and convergent thinking are critical components of creative problem solving and both are related, in part, to leader intelligence.

Intelligence Scholars have been intrigued by the intelligence and creativity relationship since the early years of creativity research. Many scholars have argued that intelligence is a necessary requirement for creativity (e.g., Silvia, 2008; Schubert, 1973). Some have suggested a threshold theory of intelligence, such that a certain level of intelligence is necessary for creative performance. After reaching that tipping point, however, significant gains in creativity are no longer observed (Barron & Harrington, 1981). Generally, however, research appears to support a positive relationship between intelligence and creativity (Barron & Harrington, 1981; Furnham & Bachtiar, 2008; Silvia, 2008). It is important to note that some have argued that divergent thinking was potentially a subset of intelligence or vice versa. However, previous research and the magnitude of the correlation implies that the two are distinct (Vincent et al., 2002; Cropley & Maslany, 1969). The relationship between intelligence and creativity appears to vary as a function of measurement and operationalization. For instance, Silvia (2008) found that the relationship differed when lower-order cognitive ability factors (i.e., verbal fluency) were assessed compared to a higher-order cognitive ability factor. Further, when creativity is operationalized differently, unique relationships are observed. Batey, Chamorro-Premuzic, and Furnham (2010), for example, assessed creativity vis-à-vis ideational behavior and found a small relationship with intelligence.

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Adding to the complexity, Vincent et al. (2002) observed a strong positive relationship with divergent thinking but a small relationship between intelligence and idea generation. Thus, they argued that intelligence may interact with divergent thinking and expertise to influence creative problem solving. Taken together, the literature tends to support the notion that intelligence is a necessary, but not sufficient, contributor to leader creative performance. Intelligence, in turn, is related to the acquisition of knowledge necessary for creative problem solving (Vincent et al., 2002).

Expertise and Knowledge The importance of knowledge and expertise to creative thinking was first stressed by Weisberg (1995, 1999) who argued against traditional models of creativity that primarily focused on the influence of divergent thinking and intelligence. Vincent et al. (2002) found empirical evidence of the unique importance of expertise in creative problem solving in an experimental study comparing the influence of intelligence, divergent thinking, and expertise. Given the complexity of information collected and manipulated by leaders during the creative problem-solving process, it likely comes as no surprise that expertise is a necessity. Indeed, in order to successfully collect and manipulate information, leaders must possess the requisite expertise to know where to search, how to separate relevant from irrelevant information, and how to appropriately integrate the information gathered (Mumford, Scott, Gaddis, & Strange, 2002). Building this expertise, however, occurs over a long period of time (e.g., Ericsson & Charness, 1994). Further, the complex problems faced by many leaders often require expertise in multiple different areas, expertise that the leader may not possess (Mumford et al., 2002). Thus, leaders may need to “outsource” or collaborate with others on portions of the creative problem-solving processes in order to acquire the necessary levels of expertise in all pertinent domains to arrive at a viable and novel solution.

Summary These individual differences suggest that leaders must possess the ability to think flexibly while rigidly applying standards to evaluate solutions. Still, these processes interact with a leader’s expertise and knowledge base, such that the effectiveness of a leader’s divergent and convergent thinking processes will depend on their knowledge and expertise level. It is also valuable to take into account the unique role intelligence may play in the interaction of these individual differences such that intelligence, at some level, likely influences the acquisition of expertise as well as a leader’s ability to engage in divergent and convergent thinking. These individual differences will subsequently contribute to several key cognitive strategies necessary to effectively engage in the creative problem-solving processes.

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A Model of Creative Processes and Strategies These underlying individual differences form the foundation of creative problem solving. Creative problem solving, however, also relies on a number of cognitive strategies, the execution of which is influenced by these individual differences. These strategies, in turn, each influence multiple processes involved in creative problem solving, and, when applied appropriately can improve creative solution development (Mumford et al., 2012). Thus, convergent and divergent thinking, intelligence, and expertise may directly influence problem-solving activities, but may also do so indirectly through an influence on cross-process strategies. ­Figure 7.1 provides an overview of these relationships, suggesting that individual differences influence both creative problem-solving processes as well as crossprocess strategies. These strategies, in turn, influence the success of the creative problem-solving process. The curved arrows connecting the individual differences represent the correlational relationships, whereas the straight arrows in the cross-process strategies and creative problem-solving processes represent the progression from one strategy or process to the next. As highlighted in the figure, it should be noted that although a leader may progress linearly from one strategy to another, it is also likely that a leader may return to a prior strategy, resulting in a nonlinear execution of these strategies. The following section aims to provide an overview of the cross-process strategies, as well as link these strategies to the individual differences described in the preceding sections.

Cross-Process Cognitive Strategies The complex nature of the creative problem-solving process necessitates a dynamic thinking approach that moves between multiple cognitive strategies including scanning, constraint management, ideation, forecasting, and sensemaking. As described in the following sections, each of these strategies is complex in their own right. When executed to achieve a creative solution, however, the interchange between strategies adds to the complexity and increases the importance of understanding how they operate and their applicability to creative problem solving. The following discussion highlights their uniquely important role during specific processes. However, it is important to bear in mind the cross-process nature of these strategies. Thus, although they may be linked to early- or latecycle processes, these strategies may also be called upon when executing other processes as well.

Scanning During the early processes of creative problem solving, leaders must identify the problem as well as gather information relevant to the problem at hand. To do so, leaders scan, or search the environment for information bearing on events, trends,

FIGURE 7.1 Leader

Creative Problem-Solving Model

Knowledge & Expertise

Intelligence

Divergent Thinking

Convergent Thinking

Individual Differences

Sensemaking

Forecasting

Ideation

Constraint ID & Mgmt

Scanning

Cross-Process Strategies

Solution Monitoring

Implementation Planning

Idea Evaluation

Idea Generation

Concept Selection

Conceptual Combination

Information Gathering

Problem ID

Creative Problem-Solving Processes

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and relationships both within and outside of an organization, allowing leaders to assess the current environment to incorporate relevant information to identify problems as well as direct problem-solving activities (Choo, 1999; Verhaeghe & Kfir, 2002). Scanning involves both looking for, and looking at information. Meaning, leaders scan by viewing their environment as well as searching their environment for relevant information. This suggests that leaders may both absorb information as it becomes available, as well as actively direct their focus to identify specific information. Both are critical components of creative problem solving. Further, effective scanning requires the search for requisite expertise both within and outside of the leader (Choo, 1999). Thus, expertise regarding the problem at hand is especially important for scanning—directing an internal search, as well as guiding the leader’s external search for additional relevant sources of information. The importance of scanning to creative problem solving generally, and specifically to early-cycle creative processes, has been highlighted by several scholars (e.g., Rokeach & Rothman, 1965; Friedman, Raymond, & Feldhusen, 1978; Mumford et al., 1996; Shalley, 1991). For example, Koberg, Uhlenbruck, and Sarason (1996) found that leader scanning intensity was positively related to firm innovation. Along these lines, Souitaris (2001) examined leader scanning behaviors in 105 manufacturing firms, finding that scanning the external environment for information such as market research, customer feedback, and competitor activity, was positively related to innovative activity. Thus, more broadly, leaders who scan the environment, both internal and external, may produce more creative solutions compared to leaders who do not or do so with less intensity. By scanning the environment, leaders may identify emerging trends, which may give way to new problems that need to be addressed. For instance, searching an organization’s internal environment may reveal emerging behaviors, practices, or technologies that may influence future performance. Similarly, scanning the external environment may allow leaders to identify emerging trends in the market, such as new technologies and customer preferences (Cowan, 1986). Once these emerging themes, both internal and external, have been identified, leaders may use this information to formulate a problem definition and direct subsequent information-gathering activities to this problem. In addition to playing a key role in early-cycle creative problem solving, scanning also emerges as an important late-cycle creative thinking strategy. Specifically, once a solution has been implemented, leaders must monitor the environment to determine the effectiveness of the solution (Mumford et al., 1991, 2012). To do so, leaders scan the relevant environment to identify reactions as well as implementation progress. This process may reveal new problems needing creative solutions, and thus, prompt a return to early-cycle creative processes (Hayes & Flower, 1986). Further, although this section highlights the important role of scanning in problem identification, information gathering, and solution monitoring, it should not be considered exclusive to these processes. Indeed, leaders should be continuously scanning their environment to identify emergent information bearing

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on the problem at hand. This information may inform the specific process or potentially lead back to earlier processes. Key information that should be identified during the scanning process are the constraints operating in the environment.

Constraint Identification and Management A constraint may be defined as a limitation or restriction in a given problem (Medeiros, Partlow, & Mumford, 2014). For example, the time frame in which a solution must be generated and implemented is a constraint on the creative problem-solving process as it limits the possibilities to those feasible within the allotted time. Although previous research suggests that constraints may negatively impact creativity (e.g., Amabile, 1983; Amabile & Gryskiewicz, 1989; Friedman, 2009), more recent empirical research and case studies suggest that constraints may have a positive influence on creative problem solving (e.g., Medeiros et al., 2014; Medeiros, Steele, Watts, & Mumford, 2018; Onarheim, 2012; Stokes, 2007). Further, Medeiros, Watts, and Mumford (2017) argued that all creative problemsolving efforts are inherently constrained in some manner. For instance, generating solutions to a specific problem implies that the process is constrained by the bounds of the problem. Thus, it is critical that leaders appropriately identify and manage constraints throughout the creative problem-solving process. Indeed, Onarheim (2012) found that leaders would self impose constraints when the creative problem appeared too rigid or open. It is important to note that constraint identification is heavily tied to scanning processes. For instance, existing technologies, team capabilities, and organizational resources may all influence the direction of a creative effort. Thus, scanning is also a critical skill needed to identify constraints. Beyond identification, however, constraints must also be managed. Although the literature on how to manage constraints during creative problem solving remains a nascent, early work points to key considerations in executing this strategy. For example, there are multiple different types of constraints (e.g., Medeiros et al., 2017; Onarheim, 2012) that may be introduced or operating during creative problem solving. Early results suggest that the type of constraint, and the number, introduced matter (Medeiros et al., 2014; Onarheim, 2012). For example, Medeiros et al. (2014) found that introducing malleable constraints did not hinder creative problem solving when individuals were motivated to work with these constraints. Additionally, Onarheim (2012) argued that there is likely a “sweet spot” in the number of constraints introduced. Further, constraints may not be static. Instead, new constraints may emerge, and old constraints may be removed or adjusted during creative problem solving. To examine the effects of introducing new constraints, Medeiros et al. (2018) conducted an experimental study introducing constraints during various creative processes. Findings suggest that introducing constraints prior to problem identification did not hinder creative problem solving, whereas introducing constraints during mid- and late-cycle processes negatively impacted creative outcomes.

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These findings should be interpreted in conjunction with Onarheim’s (2012) findings that when new constraints were introduced later in a project, those working on the creative tasks would adjust their existing ideas to align with the new requirements. The complexity with which constraints operate, suggests that to successfully navigate the creative process, leaders must actively attend to, and manage, constraints. To do so, leaders must scan the environment early on in the creative problem-solving effort and subsequently incorporate this information into their problem definition to direct subsequent processing activities. However, it is critical to note that constraint identification is an ongoing process. As highlighted by Medeiros et al (2018) and Onarheim (2012), leaders must pay careful attention to constraints emerging during later processes and consider how their presence influences previously executed processes as well as processes to come. The identification and management of constraints likely requires a high level of expertise regarding the topic and problem at hand. Leaders must be familiar enough with the area to know where to look for constraints, filter relevant and irrelevant constraints, understand how these constraints may impact the problem-solving effort, and how to introduce constraints given the problem at hand to maximize subsequent processes, including ideation.

Ideation Traditionally, ideation has been associated with brainstorming (Brown & Paulus, 2002; Kohn, Paulus, & Choi, 2011; Paulus & Brown, 2007) which refers to the spontaneous generation of ideas to solve a specific problem (Osborn, 1979). Through the years, researchers have attempted to implement strategies to enhance brainstorming. For instance, Osborn (1952) introduced a series of “rules” to help individuals generate ideas in a more effective manner. These rules include generating as many ideas as possible, reserving evaluation during the ideation process, “freewheeling”, or stating all ideas that came to mind, and to combine and improve on ideas as presented. Osborn’s (1952) goal was to increase organizational creativity and was a proponent of group brainstorming. His claim, however, that group brainstorming was more effective than solitary brainstorming has since been unfounded through multiple studies exhibiting that individuals typically outperform face-to-face group brainstorming (Diehl & Stroebe, 1987; Mullen et al., 1991). Thus, leaders may successfully ideate on their own when generating solutions. This literature suggests that brainstorming, a form of ideation, inherently relies on divergent thinking. However, the emerging literature on constraints and creative performance, as well as the original work by Guilford (1950), suggests that ideation may, in fact, be a dynamic process between divergent and convergent thinking. Indeed, inherent in the creative problem-solving process is the need to solve a particular problem. Ideation then is, by definition, constrained to the

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problem at hand (Medeiros et al., 2017). This necessitates that potential solutions be considered within the boundaries set forth by constraints and other information gathered during the scanning and constraint management processes. Thus, in order to generate viable alternative solutions that address the problem, a certain level of convergent thinking is required. Further, the ideation process may introduce new constraints and how these constraints are managed must be revisited, suggesting that leaders may potentially revisit the previous strategy, constraint identification and management. Additionally, although ideation is typically associated with idea generation, it is also a key strategy for successful idea evaluation. In order to account for the short- and long-term implications of implementing a solution, one must ideate about future circumstances and implications. In other words, while evaluating a solution to a specific set of standards and constraints, leaders must also ideate in order to evaluate multiple potential future outcomes. Thus, ideation is a critical component of idea evaluation and, more specifically, forecasting.

Forecasting Once leaders have generated ideas, it is critical to evaluate them to the standards, goals, and constraints of the project at hand as well as plan for their implementation (e.g., Lonergan et al., 2004; Merrifield, Guilford, Christensen, & Frick, 1962; Mumford et al., 1991). A key cognitive strategy to successfully evaluate creative solutions is forecasting—the identification of downstream consequences, or the generation of multiple alternative actions (Byrne, Shipman, & Mumford, 2010). Guilford and colleagues’ early work on creative thinking emphasized the importance of forecasting to creative efforts (e.g., Berger, Guilford, & Christensen, 1957; Wilson, Guilford, Christensen, & Lewis, 1954; Mumford, 2001). Additionally, Mumford (2001) argued for the importance of penetration during which leaders identify key causes and restrictions. In other words, leaders identify constraints and forecast according to these constraints, emphasizing the importance of constraint identification and management to the forecasting process. Forecasting is especially important as it links the generation of creative ideas with successful innovation (e.g., Mumford, Lonergan, & Scott, 2002; Byrne et al., 2010). However, forecasting the effects of creative solutions proves uniquely difficult. Creative ideas are inherently new and thus, differ from existing ideas, making it difficult to determine their potential impact, which highlights the importance of prototyping and testing (Fleming, 2002). Additionally, it appears that those who are traditionally in managerial positions charged with selecting which ideas to move to market may be particularly ineffective at forecasting due to an overreliance on convergent thinking and therefore, conventional models of success (Berg, 2016). This issue is further compounded by a tendency to overestimate the potential success of one’s one ideas (Runco & Smith, 1992; Berg, 2016), suggesting that those who generate solutions may not forecast markedly well either.

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Thus, forecasting the potential success of a creative solution is a particularly difficult exercise. Despite difficulty in successful execution, the importance of forecasting to creative problem solving has been bolstered by recent empirical work. For instance, Byrne et al. (2010) asked undergraduates to generate ten marketing campaign ideas for a new product. Following, participants were asked to predict, or forecast, how these ideas would unfold if implemented. They were subsequently asked to develop a plan for three ideas and forecast the implications of these plans. Lastly, participants proposed a final marketing plan. Results suggest that the extensiveness of forecasts, including mental simulations during planning and identification of constraints, positively influenced the quality, originality, and elegance of the final marketing plans. Interestingly, multivariate analysis revealed that forecasting negative outcomes was not significantly related to creative outcomes. Thus, it appears important for leaders to not just focus on potential negative outcomes, but to explore a wide range of implications (McIntosh, Mulhearn, & Mumford, in press). Although more research is needed in this area, the evidence suggests that leaders must actively engage in forecasting processes to effectively evaluate and plan for solution implementation. When forecasting, leaders should consider a wide breadth of extent factors, or constraints, and their impact on implementation. Further, leaders must consider those constraints that have yet to emerge but may arise after implementation. Although it is particularly difficult for individuals to make accurate long-term predictions (e.g., Pant & Starbuck, 1990), these forecasts may be improved when leaders do not rely solely on existing causal understandings. Instead, leaders must think divergently about how new constraints, technologies, or markets may dramatically shift the landscape and manipulate their extant knowledge, especially the information and constraints gathered through the scanning and constraint identification processes, to manipulate and forecast future predictions. Leaders should then incorporate these forecasts into their schema, or mental models of the problem solution by engaging in sensemaking.

Sensemaking If leaders must not rely entirely on existing causal interpretations, then they must generate new models to which they will plan and implement their solution. This is accomplished through a complex sensemaking process, which refers to one’s collection and interpretation of existing circumstances in order to develop a framework for understanding, describing, and acting upon a situation (Drazin, Glynn, & Kazanjian, 1999; Weick, Sutcliffe, & Obstfeld, 2005). Sensemaking forms the basis for mental model development. Mental models, or schema, offer a means by which one creates a shared understanding of a situation, problem, or event (Lyles & Mitroff, 1980; Morgan, 1980) that are formed based on conceptual knowledge of underlying causal relationships (Bradley, Paul, & Seeman, 2006).

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However, in the case of creative problem solving, leaders must incorporate both existing information as well as forecasted circumstances to generate a viable mental model that depicts the situation in which the solution implementation will unfold. Mumford et al (2017) recognized sensemaking as a critical leadership skill demonstrating particular relevance to vision formation. Regarding creative problem solving, leaders must form a vision of how to execute their proposed solution. This vision is formed based on an underlying mental model generated by making sense of the problem, information, solutions, and forecasts identified during the creative problem-solving process. However, previous research suggests that strong visions are those in which leaders have a simple, compared to complex, focus, with an emphasis on reducing potential negative outcomes (Partlow, Medeiros, & Mumford, 2015). Thus, a key goal of the sensemaking process is to condense the information into a clear, navigable framework that will, in turn, influence planning and implementation processes. Although noted here as especially relevant to late-cycle creative problem-­ solving activities, leaders are likely engaged in sensemaking throughout the creative problem-solving process. As new information is acquired and new solutions are generated, leaders must incorporate it into their mental model vis-à-vis sensemaking, forcing adjustments in their vision. Thus, despite being an ongoing process, sensemaking must become less abstract and more concrete toward the end of the creative problem-solving process to form a coherent vision for solution implementation. Lastly, when introducing a new solution, leaders must communicate their vision to followers. Thus, the vision must be clear enough to share with others and foster support (e.g., Hill & Levenhagen, 1995). Indeed, Drazin et al. (1999) argued that each individual brings with them a unique lens through which they view the problem at hand. Thus, it is critical that a leader creates a coherent vision to share with their followers in order to align their mental models. As this vision unfolds, it is critical that the leader continuously reassess his or her vision with reality, which requires active scanning of the environment.

Summary When faced with a problem requiring a creative solution, leaders must employ several complex cognitive strategies including scanning, constraint identification, ideation, forecasting, and sensemaking. Although the preceding discussion highlighted the unique role of these strategies in specific processes, it is critical for leaders to recognize the highly dynamic nature of this process and the need to possibly execute multiple strategies simultaneously. As noted in the discussion, as well as Figure 7.1, the execution of these cross-process strategies depends on several individual differences. Together, these strategies and individual differences interact to influence the processes necessary for leader creative problem solving.

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Application The importance of creative problem solving in leadership highlights the need to understand how organizations may best select for, develop, and encourage leader creative problem solving. Further, there are opportunities for leaders to develop and promote creative problem solving in their daily lives through their own interventions. Both are addressed in the following sections to provide practical suggestions for leaders and organizations.

Selection The preceding discussion provides several opportunities for assessing potential leaders. In their discussion of hiring an innovative workforce, for instance, Hunter, Cushenberry, and Friedrich (2012) highlighted opportunities to assess knowledge (e.g., domain relevant expertise), skills (e.g., idea generation, forecasting), and abilities (e.g., intelligence). Further, a meta-analysis by Ma (2009) suggests that selecting for specific early-cycle creative processing skills may prove especially beneficial. These knowledge, skills, and abilities (KSAs) may be assessed through a number of different approaches including self-report, other-reports, biodata, interviews, situational judgment tests, and assessment centers. Although a full review of these methods is outside of the scope of this chapter, it is important to note that each have their strengths and weaknesses for identifying leader creative problem solving (for a full review see Hunter et al., 2012). However, assessment centers may prove particularly useful in this context. Assessment centers use simulations to assess leaders on a number of job-relevant dimensions (Thornton & Rupp, 2006). In the case of leader creative problem solving, assessment centers allow for the presentation of ambiguous situations in which leaders must develop creative solutions, and, subsequently, demonstrate the underlying creative processing skills or cross-process strategies.

Training In some instances, organizations may be interested in improving the creativity of their leaders, or, leaders may be interested in improving their own creative problem-­solving skills. Thus, training interventions may be of interest. Indeed, several previous studies have found that creative processes, divergent and convergent thinking, and cross-process strategies can be trained and this training may subsequently influence creative performance (e.g., Osburn & Mumford, 2006; Scott et al., 2004; Ellspermann et al., 2007; Peterson et al., 2013; Basadur et al., 1982). In a meta-analytic review of creativity training, Scott et al. (2004) found training on cognitive processes to be beneficial across multiple outcomes and populations.

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Further, previous research found support for the benefit of training cross-process strategies as well (e.g., Osburn & Mumford, 2006; Peterson et al., 2013). Findings that creative thinking may be improved by training efforts brings to the fore a key question—how can leaders most effectively improve their creative thinking skills? There are several key components beyond content to consider, including activities and practice. Regarding activities, results from Scott et al. (2004) should be borne in mind. Specifically, this review found that although cognitive-focused programs were highly influential in improving creativity, focusing too narrowly on a specific strategy such as analogy training, may limit the training’s impact. A similar pattern of results may be observed in Byrne et al. (2010) study of forecasting training. Results from this study further suggest that training individuals to focus on a specific strategy may prove less beneficial than a more holistic approach.

Other Interventions Creative problem solving may also be improved through job-focused activities such as job design, as well as organizational factors such as climate. Regarding job design, ensuring that leaders have room to engage in creative problem solving is critical. For instance, Zhou and Shalley (2003) emphasized the importance of a challenging role, creativity goals, and developmental feedback to encourage creative problem solving. Along these lines, Hackman and Oldham’s (1976) job characteristics theory provides several intervention points for designing jobs that encourage creative problem solving, including allowing leaders the freedom to make their own decisions, emphasizing task significance, and providing the resources necessary to accomplish their goals. Further, Elsbach and Hargadon (2006) emphasize the importance of structuring one’s day carefully to balance cognitively demanding and less demanding work—an activity that leaders themselves may have control over and may not need to rely on the organization to implement. Lastly, if organizations want to encourage creativity in their leaders, they should consider the organization’s climate. In a meta-analysis of creative climate, Hunter, Bedell-Avers, and Mumford (2007) found positive interpersonal exchange, intellectual stimulation, and challenge to be particularly important for creative performance. In other words, organizations must ensure that leaders feel challenged and that they are tackling tough problems in a supportive environment. More broadly, however, it is important that organizations emphasize that creativity is valued and encouraged (Gilson & Shalley, 2004).

Conclusions Leaders are often faced with unstructured and ill-defined problems that require a novel solution. Thus, leaders must be prepared to engage in creative problem

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solving if they are to lead effectively. As the foregoing discussion highlights, this is no easy task, as leaders must possess a sufficient amount of expertise, think both flexibly and rigidly, and engage in multiple complex cognitive strategies in order to successfully execute the requisite creative processes. However, with careful thought and execution, leaders may successfully navigate this process to develop effective solutions to an organization’s or society’s most pressing problems.

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8 SEEING THE FUTURE THROUGH THE PAST Forecasting Skill as a Basis for Leader Performance Michael D. Mumford, Mark Fichtel, Tanner Newbold, Samantha England, and Cory A. Higgs

Over the years many models of the key activities to be executed by those adopting leadership roles have been proposed (Bass & Bass, 2008; Yukl, 2011). These activities, however, are often held to depend on the level, institutionally, in which the leader must act (Mumford, Campion, & Morgeson, 2007). And, in executive or senior level positions some of the capacities we have found to shape leader performance include creative problem solving (e.g., Zaccaro et al., 2015), crisis management (e.g., DeChurch et al., 2011), planning (e.g., Hemlin & Olsson, 2011, 2017), and articulation of a vision which proposes a viable future for followers (e.g., Mumford, 2006). One question broached by this list, a somewhat truncated list, of executive activities is what, exactly, ties all these activities together? One potential answer to this question may be found in Jacobs and Jaques (1990). They argued that as people move into more senior leadership positions, the activities they engage in require them to think ever further downstream. Put differently, they must think about the effects of their actions on the institution 5, 10, or 20 years downstream. Given the time it takes to develop and deploy new technologies, or new processes, or a new line of business, this argument does not at all seem unreasonable. Implicit in this argument, however, is an unstated assumption with respect to the skills required for leadership. This argument implies leaders must be able to forecast. In fact, virtually all of the key executive activities noted earlier appear to involve, in one way or another, forecasting. For example, forecasting has been held to reflect a key skill involved in planning (Marta, Leritz, & Mumford, 2005). Forecasting appears critical to effective crisis resolution (Drazin, Glynn, & Kazanjian, 1999). Forecasting has been shown to contribute to creative problem solving (Osburn & Mumford, 2006). And, forecasting has been shown to be a key capacity contributing to the construction of viable visions by institutional leaders (Mumford & Strange, 2013).

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Given these observations, our intent in the present effort is to examine the impact of forecasting skill on leader performance. We begin by examining what we know about how effective people are at forecasting and the conditions giving rise to viable forecasts. Subsequently, we examine how forecasting contributes to leader performance. We then present a model of forecasting skill and examine the implications of this model for both forecasting performance by leaders and how leaders might go about creating conditions that will allow more effective forecasting.

Forecasting and Leadership Forecasting The term forecasting refers to the attempts of people, individuals, teams, or collectives to envision how events will unfold in the future (Einhorn & Hogarth, 1981). Thus, forecasting can be viewed as a form of mental time travel—albeit time travel into the future (Schacter, Addis, & Buckner, 2008). This definition of course implies that forecasting represents a form of prediction—and, at least at times— formal, quantitative, predictive systems may be involved in forecasting (Clemen, 1989). Forecasting, however, is ultimately based on a human, judgmental, prediction as to how events will unfold over time (Lawrence, Goodwin, O’Connor, & Önkal, 2006) where forecasts serve to reveal affordances, opportunities, risks, and actions, that may be exploited by the person, team, or collective. Thus, forecasting is an activity that serves to promote human adaptability in a world subject to ongoing change. Indeed, it has been found that forecasting may be the critical function that makes human life and human growth possible (Klein, Moon, & Hoffman, 2006). Given its potential adaptive significance, it is not surprising that forecasting comes in many potential forms. People might make short-term forecasts or longterm forecasts. People might forecast technology, or they might forecast others’ emotional reaction to the actions they are thinking about taking. People might forecast positive future events or negative future events. People might forecast the imposition or the removal of constraints on their actions. Thus, forecasts are unbounded with respect to content. And, given the complex nature of our world, the fact that forecasts are unbound with respect to content is adaptive. These observations, however, pose two key questions. How accurate are people’s forecasts? And, how accurate are people’s forecasts with respect to certain types of content? If people cannot forecast accurately, then the adaptive value of forecasting is inherently limited. Moreover, leaders forecasting will be of little practical significance representing nothing more than an oddity of the human mind. In fact, initial research examining the accuracy of people’s forecasts was not at all promising. Armstrong (1986) conducted a review of various studies of forecasting accuracy with respect to the future status of specific outcomes. Broadly

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speaking, his findings indicated that people, including experts, were poor at forecasting outcomes, especially long-term outcomes. He attributed this poor performance in forecasting to various cognitive biases operating to undermine accuracy in forecasts. Indeed, many studies (e.g., Bovi, 2009; Buehler & McFarland, 2001) have indicated that the operation of certain cognitive biases can disrupt the accuracy of outcome forecasting. To make matters even worse, when asked to forecast specific downstream outcomes—point estimates of end of year stock prices (Pant & Starbuck, 1990)—people appear especially ineffective in forecasting, perhaps due to biases, perhaps because they simply cannot do it. What must be recognized here, however, is most of these early studies focused on people’s accuracy in forecasting specific point outcomes. So one question which arises is what happens if people are asked to forecast something other than specific downstream outcomes? An initial answer to this question may be found in Mellers et al. (2014). In this effort some 2,000 people working on jobs requiring forecasting were asked to forecast the occurrence of geopolitical events over a two-year period. Participants worked in teams of up to 25 members in formulating forecasts, or alone, and training, either a scenario or probability training, was, or was not, provided. It was found teams outperform individuals, especially when given probability training in over a year’s time. Notably, however, the best forecasts in year one were identified, and the accuracy of their event forecasts in year two was assessed. Referred to as “superforecasters”, these people were found to produce exceptionally accurate event forecasts in year two. These findings point to two key concessions. First, there may be strong individual differences among people in their skill in forecasting. Second, skilled, knowledgeable, forecasters can be very accurate in forecasting the occurrence of events, if not the specific levels of event outcomes. In another study along these lines, Dailey and Mumford (2006) asked undergraduates, 158 in all, to adopt the role of members of a review panel appraising proposals for a non-profit foundation. They were asked to review six proposals, three drawn from the educational domain and three drawn from the public policy domain, where proposals described the content of the idea, the idea itself, and the plan for implementing this idea—each in three to four paragraphs. Participants were asked to rate, on a 5-point scale, ten resource requirements (e.g., how long will it take to implement the idea, how much financial support will be needed, how much personnel time will be needed) and ten potential outcome events (e.g., how likely is the idea to be accepted by others, how many positive outcomes will flow from the idea, how much disruption will result from implementation of this idea). What is of note is all proposals were drawn from historic cases where available case material was used to establish actual resource requirements and outcome events. In addition, an instructional manipulation was made to encourage people to believe, or not believe, their recommendations would be followed by the foundation. The resulting data indicated peoples’ forecasts were not, in general, especially accurate. In forecasting, people tended to overestimate outcomes and

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underestimate resources required for obtaining these outcomes. However, when participants had expertise in the domain, the educational proposals, as opposed to public policy proposals, and they believed their forecasts, or evaluations, would result in action by the foundation (i.e., implementation intentions), their forecasts became quite accurate—to within a fifth of a standard deviation with respect to actual resource requirements and outcomes. Thus, when people have expertise and believe their forecasts will be acted on, they are in the zone, even if their point forecasts are not exactly deadly accurate. Being in the zone, or being close, however, implies their forecasts are sufficiently accurate to permit action. In still another study along these lines, Dunn, Brackett, Ashton-James, Schneiderman, and Salovey (2007) asked participants to complete Mayer, Salovey, and Caruso’s (2002) problem solving based (i.e., performance based) measure of emotional intelligence. These 84 undergraduate participants were asked to forecast how they would feel following three events in three different domains—politics, academics, and sports (all predictable events with respect to occurrences). They then reported their feelings after the event occurred. It was found people high in emotional intelligence exhibited greater accuracy in forecasting their affect with respect to future events. Thus, it is not only objective events (e.g., resources and outcomes) that can be forecasted—people, at least people high in emotional intelligence, can also forecast their own and other’s emotional reactions (e.g., Hoerger, Quirk, Lucas, & Carr, 2010) to events. Taken as a whole, the findings obtained in these studies indicate that at least some people can produce reasonably accurate forecasts. It is true that they seem to be more “in the zone” than “spot on”, but being in the range is sufficient to permit action. Those who produce forecasts “in the zone” seem to have domain expertise and possess requisite abilities. Moreover, although they may be more accurate in predicting some material (e.g., events, resources, products, and affect as opposed to specific outcomes), note the content of the material people can accurately forecast is a topic of some importance for future research, it does appear the material leaders should consider in forecasting (e.g., events, resources, products, and affect) can be accurately forecasted.

Forecasting and Leadership Performance We do not have evidence indicating that peoples’ skill in forecasting influences their emergence as a leader. However, we do have a growing body of evidence indicating that forecasting skill results in better performance among those who occupy leadership roles. In one study along these lines, Shipman, Byrne, and Mumford (2010) asked participants, some 250, to work through Strange and Mumford’s (2005) educational leadership task. On this task, participants are asked to assume the role of the principal (i.e., the leader) of a new experimental secondary school, and prepare a written plan for leading this school and formulate a speech to be given to students, parents, and teachers describing their plan. Plans

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were appraised by judges, who displayed adequate agreement, for quality, originality, and elegance, while speeches were appraised by judges for perceived utility and emotional impact. In the Byrne, Shipman, and Mumford (2010) study, participants, some 140 undergraduates, were asked to assume the role of a senior manager of leading the development of an advertiser’s campaign for a new soft drink—a high energy root beer called IBC Impact. Participants provided written advertising campaigns. These advertising campaigns were evaluated by judges, again judges who displayed adequate agreement, for quality, originality, and elegance. As participants worked through these two low fidelity simulation tasks, they received “emails”, from a consulting firm in the case of the Shipman et al. (2010) study, or from their supervisor, the vice president for sales, in the Byrne et al. (2010) study. Written forecasts were appraised with respect to 27 potential attributes of the content of these forecasts, such as the number of positive outcomes forecasted, the number of negative outcomes forecasted, obstacles forecasted, side effects forecasted, resource changes forecasted, and potential errors forecasted. These ratings were subsequently factored with four dimensions emerging: (1) the extensiveness of forecasting, (2) the time frame of the forecasting, (3) forecasting resource requirements, and (4) forecasting negative outcomes. In both studies it was found that the extensiveness of people’s forecasting activities was strongly positively related (r¯ =.40) to the quality, originality, and elegance of both the marketing plans and the school educational plans, along with the perceived utility and affective impact of the speech to be given by those adopting the role of school principal. In addition, use of longer time frames in forecasting was found to be positively related, albeit less powerfully (r¯ =.25), to these outcomes of performance in leadership roles. Here, two points should be noted. First, forecasting influenced performance on two different leadership tasks drawn from different domains. Second, forecasting skill was very strongly related to effective performance in these leadership roles. In a more recent study by McIntosh, Mulhearn, and Mumford (in press), some 270 participants were asked to assume the role of a product development manager working for a restaurant consulting firm. Participants were asked to formulate ideas for a new restaurant. After formulating their ideas, participants were asked to select their best idea and then provide a written plan for implementing their best idea. Judges appraised these final restaurant plans for quality, originality, and elegance—again, displaying adequate agreement. After participants had generated ideas, and after they selected their best idea, they received “emails” asking them to forecast potential downstream outcomes of these ideas or the idea they selected for formulating their plan. A panel of judges were asked to appraise the overall quality of these forecasts and the extensiveness of the participant’s forecasting activities. It was found that the extensiveness of participant’s forecasting activities was positively related to plan quality, originality, and elegance in the low .40s—a finding in keeping with earlier studies (Byrne, Shipman, and Mumford 2010;

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Shipman et al., 2010). Moreover, appraisals of overall forecast quality were also strongly positively related (r¯ =.38) to plan quality, originality, and elegance. In yet another study, Mulhearn, McIntosh, and Mumford (in press) asked 260 undergraduates to assume the role of marketing director for a clothing firm and develop a marketing campaign intended to help a specialty clothing firm enter the southern market. These written marketing plans were appraised by judges, who displayed adequate agreement, for quality, originality, and elegance. Prior to preparing these marketing plans, however, participants were asked to review historic cases of movement into this market, outline their plan, and forecast the implications of their plan. Judges appraised the time frame and extensiveness of these written forecasts. And, it was found that extensiveness was correlated in the mid-twenties (r¯ =.28) with appraisals of plan quality, originality, and elegance, while time frame was correlated in the high-teens (r¯ =.18) with appraisals of plan quality, originality, and elegance. These studies point to a single clear conclusion—forecasting is strongly positively related to performance on a variety of leadership exercises drawn from a number of domains—education, product development, marketing. In fact, the strongest of these relationships, those in the .40s, is such that forecasting may be one of the most important determinants of leadership performance. Indeed, the Byrne, Shipman, and Mumford (2010) and Shipman et al. (2010) studies indicated that forecasting skill is more strongly related to performance in leadership roles than capacities such as intelligence, divergent thinking, and motivation.

Forecasting Model Given the relationship between forecasting and leader performance, a new question comes to fore. How do people go about forecasting? An initial answer to this question has been provided by Mumford, Steele, McIntosh, and Mulhearn (2015). This model, describing the processes required for effective forecasting, is presented in Figure 8.1. The basis for execution of these processes is knowledge. Of course, people possess many types of knowledge (Hunter, Bedell-Avers, Hunsicker, Mumford, & Ligon, 2008). Typically, knowledge structures are held to be either schematic (i.e., conceptual), case-based (i.e., experiential), or associational. Prior studies, however, by Dörner and Schaub (1994), Meyvis, Ratner, and Levav (2010) and Schacter et al. (2008) all indicated that people’s forecasts are based on past experience, or case-based knowledge. Put differently, people use the past to predict the future. What should be recognized here, however, is case-based knowledge structures are complex entities (Scott, Lonergan, & Mumford, 2005). Cases include knowledge about causes, resources, restrictions, contingencies, actors, affect, goals, and systems (Hammond, 1990). Attached to these cases are diagnostics, which, if encountered, serve to activate a set of cases (Hershey, Walsh, Read, & Chulef, 1990; Xiao, Milgram, & Doyle, 1997). People can act on a single case. However,

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Scanning

Situational Cues

Prescriptive Mental Model

Vision

Mental Model

Key Causes / Key Outcomes Case Activation

Case Prototypes

Case Exceptions

Case Analysis

Situational Monitoring

Forecasting Attributes

Situational Contingencies

Forecasts

Action Selection FIGURE 8.1 Forecasting

Model

people typically have available multiple cases relevant to any action. These cases are held to be stored in a library system (Kolodner, 1997). In this library structure, cases are organized in terms of case prototypes which summarize commonly encountered events, along with a limited number of case exceptions where activation of exceptions is tied to certain diagnostics. These cases are also indexed with regard to significant elements guiding action. Thus, cases might be indexed with respect to causes (Marcy & Mumford, 2007) or constraints (Medeiros, Partlow, & Mumford, 2014). Indexing of cases with respect to key action attributes provides one basis for organizing experience. However, it appears people abstract from experience general mental models describing how events unfold in certain domains (Goldvarg & Johnson-Laird, 2001; Rouse & Morris, 1986). These mental models provide a basis for organizing both indexing systems and the cases unto themselves, allowing people to adapt experiences to anticipate the future or forecast.

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In fact, it appears that case-based knowledge is the key type of knowledge used as people attempt to address the issues brought to them in leadership roles (Berger & Jordan, 1992). For example, Strange and Mumford (2005), in a study of secondary school leadership, found that the cases presented to participants influences the quality and impact of the visions they formulated for leading an experimental secondary school. Other work by Barrett, Vessey, and Mumford (2011) and Vessey, Barrett, and Mumford (2011) has shown the way people work under case-based knowledge influences their performance in leadership roles. Still other work by Watts, Ness, Steele, and Mumford (2018) has shown that cases acquired through stories provided by others also act to influence leader performance—­ specifically, leader ethical performance. If there is reason to believe cases provide a basis for leader thinking, then a new question comes to fore. How do people work with experience to forecast the future? Mumford et al. (2015) argued people scan their environment to identify potential affordances—opportunities and risks. This environmental scanning serves to identify key situational cues or diagnostics. Identification of these situational cues, in turn, activates certain mental models—a mental model which in the case of leaders is influenced by leader’s prescriptive mental model, their image of idealized system operations and their vision for leading others built from this prescriptive mental model (Mumford & Strange, 2013). The activated mental model provides a basis for searching through cases based on the case indexing attributes, although people typically search through cases with respect to causes and goals at times other attributes of cases used in indexing may be employed—for example, constraints or actors. This search activates relevant case prototypes along with commonly encountered exceptions to this case prototype. The activated cases are then analyzed with respect to the situation at hand and a new case, a transitory case, is formulated. The perceived attributes of the situation at hand are then used to specify evaluative attributes for the forecast. And, the new transitory case is used to provide forecasts with the outcomes of anticipated action being appraised with respect to desirable or undesirable attributes of the forecast. Of course, multiple cases may be used in executing these processes, and how these forecasts unfold over varying periods of time may be appraised.

Forecasting Content Causes and Goals The model presented earlier holds that forecasts are typically based on identification of key causes and goals. In fact, Lawrence et al. (2006) have argued that forecasts will prove more effective when they are based on an accurate appraisal of key causes. Moreover, a series of studies by Stenmark et al. (2010) and Stenmark et al. (2011) has provided some rather compelling support for this proposition.

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In both of these studies, some 100 participants, all undergraduates, were asked to assume the role of a middle manager working in an electronics firm. After describing the firm and their managers (e.g., their leaders) role, participants were presented with a set of eight “emails” sent to them by their followers. Participants were to provide written responses to each email which described an issue being encountered by the follower. In writing these emails, participants were instructed to indicate (1) potential actions that might be taken to resolve the issue, (2) forecast the potential outcomes of these actions, and (3) indicate critical concerns indicated by these forecasts. In both studies, judges, who all evidenced adequate agreement, were asked to evaluate the overall quality of the forecasts provided. In the Stenmark et al. (2010) study, participants were asked to describe the critical causes giving rise to the issue anticipated in each email. Judges, again evidencing adequate agreement, were asked to evaluate the number of cause articulated and the criticality of the causes articulated by participants. In addition, manipulations were made to induce time pressure and a deliberative mindset. Although this time pressure and deliberative manipulation had little effect on forecast quality, it was found that identification of critical causes (r = .43) was strongly positively related to the quality of the forecasts provided. The Stenmark et al. (2011) study provided support for this conclusion, pointing to the importance of causal identification in forecasting. In this study, it was again found that the identification of critical causes was strongly positively related (r = .60) to the quality of the forecasts provided. In fact, in both studies identification of critical causes had a stronger impact on forecast quality then either intelligence or planning skills. This identification of critical causes does, in fact, seem to contribute to forecasting performance. It also appears that identification of viable goals may also contribute to forecasting performance. Thus, Strange and Mumford (2005) asked undergraduates to assume the role of principal of an experimental secondary school and presented cases which might help them formulate leadership plans. An instructional manipulation was used to encourage analysis of causes, goals, both, or neither. They found that both analysis of causes and analysis of goals evident in these cases resulted in the production of stronger plans for leading the school. The importance of cause/goal analysis to effective forecasting is noteworthy not only with respect to the model of forecasting at hand, but also because cause/ goal analysis skills can be developed among those who occupy leadership roles. For example, Marcy and Mumford (2010) asked undergraduates, 150 in all, to work on a university simulation exercise where they were to adopt the role of a president and make a variety of policy decisions involving faculty hiring, finances, athletics, enrollment management, etc. Prior to starting work on this simulation exercise, they were provided with training in various causal analysis strategies: Specifically they were instructed to think about (1) causes that can be manipulated, (2) causes that influence multiple outcomes, (3) causes that have large effects, (4) causes that have direct effects, (5) causes that they could control, (6) causes that work together, and (7) causes that work synergistically. It was found that training

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in viable strategies for causal analysis resulted in better performance on the simulation exercise—in part, perhaps, due to better leader forecasting.

Case Content The model of forecasting at hand, however, doesn’t assume analysis of causes/goals is abstract. Instead, it holds that cause/goal analysis occurs with respect to cases or case-based knowledge. A series of studies by Bagdasarov et al. (2013), Harkrider et al. (2013), Johnson et al. (2012), Peacock et al. (2013) and Harkrider et al. (2012) supports this assertion. In all these studies, participants, some 100 to 120 doctoral students, enrolled in a professional ethics education course and were recruited to participate in an experimental study conducted in one module of instruction. Participants were presented with a one- to two-page case. Subsequently, participants were presented with a transfer case—for example, assume the role of a city council member (e.g., a leader)—involved in a faulty bid and contract process. Participants were then presented with a set of probe questions to identify critical issues involved in the transfer case, key challenges involved in the case, and likely outcomes of the case. They then prepared a plan for addressing these issues where judges appraised plans provided with respect to the overall quality of the forecasts, as well as the complexity, specificity, and criticality of the forecasts provided. All manipulations occurred in the context of the initial instructional case presented. The findings obtained in these studies indicated that when the cases clearly described the social context involved, as well as the goals of the key actors involved, stronger forecasts are obtained. When the case was well structured (e.g., a case providing a clear, viable summary of the relevant material), better forecasts were obtained. When the case articulated affective, emotional, outcomes for key actors, better forecasts were obtained. When the case articulated potential negative outcomes, better forecasts were obtained. And, when outcomes linked to action in the case were more clearly articulated, better forecasts were obtained. Thus, the context of the cases people are asked to think about in forecasting clearly impacts the production of viable forecasts.

Mental Models The model of forecasting presented earlier, however, suggests that forecasts depend not only on the cases used, but also the mental model abstracted from these cases. Bagdasarov et al. (2016) examined the impact of mental models on forecasting. In this study, 218 undergraduates were asked to complete an instructional program. In this instructional program, participants were taught how to illustrate their mental models in terms of structural equations models. Prior studies by Mumford et al. (2012) have shown not only that this instruction is effective, but that those who illustrate stronger mental models also evidence better performance

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on leadership tasks such as formulating a visionary plan for leading a new experimental secondary school. In the Bagdasarov et al. (2016) study, following training participants were presented with a complex two- or three-page case and were asked to illustrate their mental model for understanding this case. Subsequently, participants were presented with an ethical leadership problem involving dealing with fraudulent bid and contract practices as one member, a leader, on the city council. Judges were asked to rate the quality of the forecasting evident in their responses to a series of probe questions bearing on this problem. In addition, judges were asked to evaluate the complexity of mental models illustrated in the initial case, as well as the number and criticality of the causes and constraints noted in response to the probe questions bearing on the city council problem. It was found participants who articulated stronger, more complex, mental models produced forecasts of higher quality (r = .26). Moreover, articulation of stronger mental models was positively related (r¯ = .35) to identification of critical causes and critical constraints conditioning potential actions. Finally, identification of critical causes and constraints were found to be strongly positively related (r¯ = .45) to forecast quality. Thus, mental models, by shaping people’s analysis of causes and constraints, contributes to their ability to produce viable forecasts when working through leadership problems.

Forecasting Performance Our foregoing observations not only provide support for the model of leaders’ forecasting processes presented earlier, they also indicate that the viability of forecasts produced depends on leaders’ mental models, the nature of the cases considered, and analysis of causes, goals, and constraints. The issue not addressed, however, is what actions can leaders take in forecasting that will improve their performance.

Extensiveness and Time Frame The Byrne, Shipman, and Mumford (2010) and Shipman et al. (2010) studies cited earlier indicated that two key variables shape forecasting performance—the extensiveness of the forecasting activity and the time frame over which forecasting occurs. In fact, a number of studies have provided evidence indicating that the extensiveness of people’s forecasting activities (e.g., the number, range, and depth of the situations in which the effects of actions are anticipated) contributes to performance. For example, Martin et al. (2011) asked judges to code the amount of elaboration in written plans as they worked on a leadership task. They found that more extensive elaboration (r¯ = .40) was strongly positively related to forecasting effectiveness. In another study along these lines, Antes et al. (2012) also found elaboration to contribute (r¯ = .20) to the effectiveness of forecasting.

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Somewhat more direct evidence bearing on the importance of extensiveness to forecasting performance has been provided in a study by Beeler et al. (2010). In this study, undergraduates were asked to solve a series of four ethical leadership problems. More extensive forecasting was encouraged through a series of prompt questions which requested forecasting more. Judges appraised forecast quality as well as leader decision quality. It was found that forecast quality in response to these prompts was positively related to leader performance. In another study along these lines, Antes et al. (2012) asked participants (e.g., undergraduates) to assume the role of a manager (e.g., the leader) of a small business and make a series of decisions. Participants were, or were not, asked to reflect on processes, outcomes, or both processes and outcomes in relevant prior experiences. It was found reflection on process, a manipulation intended to encourage more extensive forecasting, contributed to better leader performance, at least when reflection was with respect to positive past experience. In addition to the impact of more extensive forecasting on leader performance, forecasting over a longer time frame also appears to contribute to leader performance. Martin, et al. (2011) asked undergraduates, some 120, to assume the role of leader of a small chain of electronic stores. They were asked, following a description of the store and their role, to provide written answers to four leadership problems. Judges coded solutions for effectiveness. Temporal generation was manipulated by asking participants to think about a similar problem from their past or to think about a similar problem that might occur five years in the future. They found that a future-oriented time frame contributed to both higher quality forecasts and better decisions in this leadership role.

Outcomes Although these findings confirm the importance of more extensive forecast over a longer period as key variables contributing to both forecasting quality and leader performance, they leave open a key question. Exactly what should be forecasted? In an initial study intended to address this issue, Stenmark et al. (2011) asked participants, some 100 undergraduates, to assume the role of a manager in a hypothetical electronics organization. Participants were asked, after reading a description of the firm and their role, to respond to eight “emails” from different characters. They were to write a reply email describing their actions, forecasting the potential outcomes of their actions, and describing their final decision. Judges appraised both forecast quality and the viability of leader decisions. It was found that forecast quality positively contributed (r¯ = .45) to the viability of leader decisions. More centrally, in this study, judges were also asked to appraise the content of the forecasts provided. Specifically, judges, all displaying adequate agreement, were asked to appraise the number of positive consequences forecasted, the number of negative consequences forecasted, and the number of critical consequences

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forecasted. It was found forecast quality, and the viability of leader decisions, were most strongly related to the number of positive consequences forecasted (β = .22) and the number of critical consequences (β = .64) forecasted. Of course, thinking about positive consequences encourages people to invest resources in forecasting, resulting in more extensive forecasting and, thus, better performance. However, it appears the key in forecasting is not positive forecasts but rather forecasts that take critical consequences, consequences for good or ill, into account. Some support for this conclusion has been provided in a recent study by ­McIntosh, Mulhearn, and Mumford (in press). In this study, participants, some 270 undergraduates, were asked to adopt the role of a restaurant consultant and formulate a written plan for a new restaurant concept. After participants formulated their initial ideas for this restaurant, and as participants evaluated these ideas, an instructional manipulation asked them to forecast positive outcomes, forecast negative outcomes, or forecast both positive and negative outcomes. Judges appraised final plans for quality, originality, and elegance. Not only was it found that forecast quality was positively related to plan quality, originality, and elegance (r¯ = .40), but that the highest quality, most original, and most elegant plans were produced by those who were asked to forecast both positive and negative outcomes during idea generation and idea evaluation. In another key study along these lines, Mulhearn, McIntosh, and Mumford (in press) asked undergraduates, some 260, to assume the role of marketing director for a clothing firm. They were asked to prepare a written plan for expanding the firms clothing line into the southern market. Plans were appraised for quality, originality, and elegance, as well as forecast quality. Manipulations were made to encourage participants to analyze firm strengths and weaknesses and to analyze the strengths and weaknesses of the plan. It was found the strongest forecasts, and thus the best plans, emerged when participants analyzed both firm weaknesses and potential positive outcomes of their plans.

Objectivity Apparently, forecasting performance requires people to think about critical downstream outcomes—both positive and negative outcomes. People, of course, prefer to think about positive outcomes (e.g., Martin et al., 2011). Thus, another key question comes to fore: What conditions allow people to contemplate the negative as well as the potential positive outcomes of their actions? A potential answer to this question has been provided in a set of studies by Caughron et al. (2011) and Caughron et al. (2013). In the Caughron et al. (2011) study, some 160 undergraduates were asked to assume the role of a manager (e.g., a leader) working on producing a new drug in a pharmaceutical firm. After reading a description of the firm, and their role in the firm, they were presented with a set of six “emails” requiring a decision in their role as a leader. Written responses to these emails were

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coded by judges who appraised forecast quality. The description of the firm and their role as a leader focused on locus of control, personal versus situational context of events, and outcomes sought, either personal or organizational. It was found the highest quality forecasts emerged when participants forecasted with respect to organizational as opposed to personal outcomes. Moreover, those who forecasted with respect to organizational outcomes identified more critical potential outcomes of their actions and organized information bearing on these leadership problems more coherently. These findings are noteworthy because they suggest that personally disinterested objectivity may be crucial to effective forecasting. Of course, in firms many forces operate to undermine leader objectivity. In the Caughron et al. (2013) study, the impact of variables acting to undermine objectivity in forecasting performance was examined. In this study, some 200 undergraduates were asked to assume the role of a manager (e.g., a leader) in a firm maintaining social websites. Participants received a series of “emails” requesting a leadership decision and written responses to these emails were appraised by judges for (1) forecasting consequences of self and others, (2) considering the best and worst case scenarios, (3) thinking about short and long-term outcomes, and (4) thinking about potential costs and benefits. Manipulations occurred through the description of the organization where competitors were (1) described as close or distant, (2) collaborative or selfish, and (3) information about whether competitors had, or had not, been corroborated. It was found that the best forecasts emerged when competitors were close, information was confirmed, and the competitor was motivated to maintain the relationship. The worst forecasts emerged when competitors were distant, acted in their own interest, and information was not corroborated. These findings suggest that intense competition and high turbulence act to undermine leader forecasting by limiting the leader’s ability to maintain an objective stance when forecasting.

Expertise Our image of expertise and our assumptions about the nature of expertise hold that experts are more objective in their appraisals of the future than novices working in the same domain. Thus, one would expect that expertise would contribute to forecasting skill. Indeed, given the fact that experts have more cases, better mental models, and are better able to identify key causes of outcomes (Ericsson, 2009), there are several substantive reasons for expecting experts would prove more effective at forecasting. Unfortunately, conclusions in this regard have proved ambiguous—sometimes experts do better in forecasting, but sometimes not (e.g., Armstrong, 1986). Of course, many factors such as expert’s assumptions (e.g., Hershey et al., 1990), expert overconfidence (e.g., Lawrence et al., 2006), and failing to fully analyze the issue across a range of considerations (e.g., Hoerger et al., 2010) all may act to undermine expert forecasting.

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By the same token, the findings obtained in the Marcy and Mumford (2007) study cited earlier should be borne in mind. In this study, participants were asked to anticipate the outcomes of and resource required for executing public policy and educational proposals. And, it was found that when participants had expertise and believed their recommendations would be acted on, their forecasts were for more accurate. Thus, expertise, at least when experts are vested in the task, does appear to contribute to forecasting skill. Some support for this conclusion is provided in another study by Brock et al. (2008). In this study, 15 expert faculty and research scientists in electrical engineering, computer science, and meteorology were compared to 17 first-year doctoral students in working through a series of complex, novel, ill-defined technological problems. Think aloud protocols were obtained as both experts and novices worked through these problems. The resulting think aloud data was content coded with respect to variables relevant to forecasting: (1) anticipation of goals and expectations of others, (2) anticipation of potential threats and likely opportunities, and (3) forecasting alternative solutions that might be needed. What was found is experts outperformed novices on all three of these markers of forecasting quality. Thus, when the problem lies in the expert’s field, they appear to be more effective in forecasting. In this regard, however, it is important to bear in mind another point. Each expert will bring their mental model to the issue at hand, their beliefs about key causes and contingencies, and their personal experience. As a result, different experts may produce radically different forecasts. Thus, the best forecasts typically are obtained when teams of forecasters work with objective data and systematically analyze the future events that might unfold with respect to causes, contingencies, and outcomes (Armstrong, Green, & Graefe, 2015). As a result, it seems reasonable to expect that expertise in leading collectives of experts in forecasting may be as important as individual level expertise when firm, and firm leaders, are attempting to forecast the impact of their actions (Friedrich, Vessey, Schuelke, Ruark, & Mumford, 2009). Thus, leaders may need to call attention to anomalies (Mumford, Baughman, Supinski, & Maher, 1996), call attention to non-prototypic cases (Mumford, Peterson, Robledo, & Hester, 2012), and ask experts to appraise the impacts of changing contingencies (Peterson et al., 2013).

Biases The kind of leadership actions described earlier, along with expertise, objectivity, viable models and cases, and through analysis of causes, all contribute to the production of better forecasts. Nonetheless, there is substantial reason to suspect people evidence their biases in forecasting. Broadly speaking, two sets of biases seem to act to undermine peoples’ effectiveness in forecasting. One of these biases is an optimistic bias (Buehler, Griffin, & Ross, 1994). This optimistic bias leads people to underestimate potential negative events, such as the resources required

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on emergent contingencies, and overestimate potential outcomes of actions. The second key bias is derived from the focus of forecasting (Wilson & Gilbert, 2005). This focal bias leads people to overestimate the effects of the event being forecasted while discounting other events that might arise. Put differently, focal bias is an attentional bias to a single key event. Focal and optimistic biases clearly can operate to undermine the accuracy of peoples’ forecasts. By the same token, however, one must remember these biases unto themselves may not necessarily totally undermine peoples’ forecasting effectiveness. For example, optimism may encourage people to invest more resources in forecasting, resulting in richer, more extensive, forecasts—bearing in mind forecasting extensiveness is a key contributor to forecasting performance. Similarly, focal bias may encourage more extensive forecasting with respect to the key issue at hand and, potentially, forecasting over a longer time frame. Given the ambiguity of these effects, and the failure of prior studies to find robust techniques for reducing bias (Mumford, Schultz, & Van Doorn, 2001), a more pragmatic approach might involve management of work conditions in such a way that inappropriate operation of these biases might be minimized. For example, after formulating initial forecasts, people might be asked to forecast contingencies likely to disrupt their planned actions (Xiao et al., 1997). Alternatively, one might be required to present forecasts to others—others expected to provide deep, timely, criticisms of the forecast (Gibson & Mumford, 2013). Yet another approach that might be used is to explicitly ask people to forecast side effects of contemplated actions which might emerge under different conditions or different assumptions (Dörner & Schaub, 1994). These observations, although of some interest in their own right, point to a broader conclusion. By formulating a better understanding of how people go about forecasting, it might be possible to manage work processes in such a way as to improve the accuracy and effectiveness of peoples’ forecasting activities.

Conclusions Before turning to the broader conclusions flowing from the present effort, certain limitations should be noted. To begin, for some time scholars have discounted the possibility of people consistently producing accurate forecasts (Pant & Starbuck, 1990). By discounting the possibility that people can forecast their future, few saw studying forecasting as of much value. As a result, we lack a strong, deep, research literature on forecasting skill. Along somewhat different lines, it should be recognized that the few studies conducted on forecasting skill to date have primarily been experimental in nature. Experiments, of course, have real value in identifying causation under controlled conditions. Nonetheless, with a few notable exceptions, for example, Mellers et al. (2014), we do not know much about how forecasting occurs in real-world settings.

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Even bearing these limitations in mind, however, we believe the present effort has some noteworthy implications for understanding forecasting and the importance of forecasting skill for those who occupy leadership roles. Leaders must plan (Hemlin & Olsson, 2011), and planning requires forecasting (Mumford, Schultz, & Van Doorn, 2001). Leaders must formulate a vision for the future— a future vision which requires forecasting (Mumford & Strange, 2013). Leaders must think about how they will interact with followers—interactions whose impact on followers must be forecasted (Gaddis, Connelly, & Mumford, 2004). Leaders must anticipate (e.g., forecast) how others will react to their speeches (House, Spangler, & Woycke, 1991). The point here should be clear. Many of the key critical factors required of those who occupy leadership roles rely on forecasting. Perhaps not all people can forecast accurately and use these forecasts appropriately. However, some people (Mellers et al., 2014), apparently, can produce viable forecasts, at least under certain conditions (Dailey & Mumford, 2006). This observation is noteworthy because it suggests there might be substantively significant differences among people in their forecasting. The results examined in the present effort not only suggest that people differ in their forecasting skill, but that differences among people in their forecasting skill have a very strong impact on their performance in leadership roles (e.g., Shipman et al., 2010); McIntosh, Mulhearn, & Mumford [in press]). It is rare in studies of leadership that we find any capacity consistency correlated with performance in the low to mid .40s. The simple strength of the relationship suggests we need more, far more, research on how leaders acquire forecasting skill and when they forecast more, or less, effectively. In fact, research along these lines is especially important because forecasting appears to be a particularly complex cognitive ability (Mumford, Steele, McIntosh, & Mulhearn, 2015). To forecast, one needs a viable mental model. Forecasting requires a library of relevant cases. Forecasting requires careful analysis of key causes and relevant goals. But we need to know why attainment of a particular goal should be or should not be forecast. We need to know more about why people focus on one cause as opposed to others in constructing forecasts. Studies of the variables shaping leader performance in forecasting do provide some clues in this regard. For example, we now know that forecasting more extensively and forecasting over a longer, future-focused, time frame contributes to leader performance. We know leader forecasting improves when leaders consider both positive and negative outcomes, along with contingencies shaping the feasibility of attaining those outcomes. We know leader forecasting improves when they stay objective and focus on key causes. Each of these findings, however, broaches a number of new questions which needs to be investigated. For example, when and what types of negative outcomes should leaders think about in forecasting? What is a viable time frame for forecasting with respect to different types of leader activities? How do leaders determine when forecasting is sufficiently

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extensive? Although we cannot, at this point, provide concrete answers to these questions, we hope the present effort, by demonstrating the powerful impact of forecasting skill on leader performance, will provide an impetus for further work along these lines.

References Antes, A. L., Thiel, C. E., Martin, L. E., Stenmark, C. K., Connelly, S., Devenport, L. D., . . . Mumford, M. D. (2012). Applying cases to solve ethical problems: The significance of positive and process-oriented reflection. Ethics & Behavior, 22, 113–130. Armstrong, J. S. (1986). The ombudsman: Research on forecasting: A quarter-century review, 1960–1984. Interfaces, 16, 89–109. Armstrong, J. S., Green, K. C., & Graefe, A. (2015). Golden rule of forecasting: Be conservative. Journal of Business Research, 68, 1717–1731. Bagdasarov, Z., Johnson, J. F., MacDougall, A. E., Steele, L. M., Connelly, S., & Mumford, M. D. (2016). Mental models and ethical decision making: The mediating role of sensemaking. Journal of Business Ethics, 138, 133–144. Bagdasarov, Z., Thiel, C. E., Johnson, J. F., Connelly, S., Harkrider, L. N., Devenport, L. D., . . . Mumford, M. D. (2013). Case-based ethics instruction: The influence of contextual and individual factors in case content on ethical decision-making. Science and Engineering Ethics, 19, 1305–1322. Barrett, J. D., Vessey, W. B., & Mumford, M. D. (2011). Getting leaders to think: Effects of training, threat, and pressure on performance. Leadership Quarterly, 22, 729–750. Bass, B. M., & Bass, R. (2008). The Bass handbook of leadership: Theory, research, and application. New York, NY: Free Press. Beeler, C. K., Antes, A. L., Wang, X., Caughron, J. J., Thiel, C. L., & Mumford, M. D. (2010). Strategies in forecasting outcomes in ethical decision-making: Identifying and analyzing the causes of the problem. Ethics & Behavior, 20, 110–127. Berger, C. R., & Jordan, J. M. (1992). Planning sources, planning difficulty and verbal fluency. Communications Monographs, 59, 130–149. Bovi, M. (2009). Economic versus psychological forecasting. Evidence from consumer confidence surveys. Journal of Economic Psychology, 30, 563–574. Brock, M. E., Vert, A., Kligyte, V., Waples, E. P., Sevier, S. T., & Mumford, M. D. (2008). Mental models: An alternative evaluation of a sensemaking approach to ethics instruction. Science and Engineering Ethics, 14, 449–472. Buehler, R., Griffin, D., & Ross, M. (1994). Exploring the “planning fallacy”: Why people underestimate their task completion times. Journal of Personality and Social Psychology, 67, 366. Buehler, R., & McFarland, C. (2001). Intensity bias in affective forecasting: The role of temporal focus. Personality and Social Psychology Bulletin, 27, 1480–1493. Byrne, C. L., Shipman, A. S., & Mumford, M. D. (2010). The effects of forecasting on creative problem-solving: An experimental study. Creativity Research Journal, 22, 119–138. Caughron, J. J., Antes, A. L., Stenmark, C. K., Thiel, C. E., Wang, X., & Mumford, M. D. (2011). Sensemaking strategies for ethical decision making. Ethics & Behavior, 21, 351–366. Caughron, J. J., Antes, A. L., Stenmark, C. K., Thiel, C. E., Wang, X., & Mumford, M. D. (2013). Competition and sensemaking in ethical situations. Journal of Applied Social Psychology, 43, 1491–1507.

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Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5, 559–583. Dailey, L., & Mumford, M. D. (2006). Evaluative aspects of creative thought: Errors in appraising the implications of new ideas. Creativity Research Journal, 18, 385–390. DeChurch, L. A., Burke, C. S., Shuffler, M. L., Lyons, R., Doty, D., & Salas, E. (2011). A historiometric analysis of leadership in mission critical multiteam environments. Leadership Quarterly, 22, 152–169. Dörner, D., & Schaub, H. (1994). Errors in planning and decision-making and the nature of human information processing. Applied Psychology, 43, 433–453. Drazin, R., Glynn, M. A., & Kazanjian, R. K. (1999). Multilevel theorizing about creativity in organizations: A sensemaking perspective. Academy of Management Review, 24, 286–307. Dunn, E. W., Brackett, M. A., Ashton-James, C., Schneiderman, E., & Salovey, P. (2007). On emotionally intelligent time travel: Individual differences in affective forecasting ability. Personality and Social Psychology Bulletin, 33, 85–93. Einhorn, H. J., & Hogarth, R. M. (1981). Behavioral decision theory: Processes of judgement and choice. Annual Review of Psychology, 32, 53–88. Ericsson, K. A. (2009). Development of professional expertise: Toward measurement of expert performance and design of optimal learning environments. Cambridge, UK: Cambridge University Press. Friedrich, T. L., Vessey, W. B., Schuelke, M. J., Ruark, G. A., & Mumford, M. D. (2009). A framework for understanding collective leadership: The selective utilization of leader and team expertise within networks. Leadership Quarterly, 20, 933–958. Gaddis, B., Connelly, S., & Mumford, M. D. (2004). Failure feedback as an affective event: Influences of leader affect on subordinate attitudes and performance. Leadership Quarterly, 15, 663–686. Gibson, C., & Mumford, M. D. (2013). Evaluation, criticism, and creativity: Criticism content and effects on creative problem solving. Psychology of Aesthetics, Creativity, and the Arts, 7, 314. Goldvarg, E., & Johnson-Laird, P. N. (2001). Naive causality: A mental model theory of causal meaning and reasoning. Cognitive Science, 25, 565–610. Hammond, K. J. (1990). Case-based planning: A framework for planning from experience. Cognitive Science, 14, 385–443. Harkrider, L. N., MacDougall, A. E., Bagdasarov, Z., Johnson, J. F., Thiel, C. E., Mumford, M. D., . . . Connelly, S. (2013). Structuring case-based ethics training: How comparing cases and structured prompts influence training effectiveness. Ethics & Behavior, 23, 179–198. Harkrider, L. N., Thiel, C. E., Bagdasarov, Z., Mumford, M. D., Johnson, J. F., Connelly, S., . . . Devenport, L. D. (2012). Improving case-based ethics training with codes of conduct and forecasting content. Ethics & Behavior, 22, 258–280. Hemlin, S., & Olsson, C. L. K. (2017). Creativity-stimulating leadership in R&D groups. In M. D. Mumford & S. Hemlin (Eds.), Handbook of research on leadership and creativity (pp. 458–473). Northampton, MA: Edward Elgar. Hemlin, S., & Olsson, L. (2011). Creativity-stimulating leadership: A critical incident study of leaders’ influence on creativity in research groups. Creativity and Innovation Management, 20, 49–58. Hershey, D. A., Walsh, D. A., Read, S. J., & Chulef, A. S. (1990). The effects of expertise on financial problem solving: Evidence for goal-directed, problem-solving scripts. Organizational Behavior and Human Decision Processes, 46, 77–101.

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Mumford, M. D. (2006). Pathways to outstanding leadership: A comparative analysis of charismatic, ideological, and pragmatic leaders. Mahwah, NJ: Erlbaum Press. Mumford, M. D., Baughman, W. A., Supinski, E. P., & Maher, M. A. (1996). Processbased measures of creative problem-solving skills: II. Information encoding. Creativity Research Journal, 9, 77–88. Mumford, M. D., Hester, K. S., Robledo, I. C., Peterson, D. R., Day, E. A., Hougen, D. F., . . . Barrett, J. D. (2012). Mental models and creative problem-solving: The relationship of objective and subjective model attributes. Creativity Research Journal, 24, 311–330. Mumford, M. D., Schultz, R. A., & Van Doorn, J. R. (2001). Performance in planning: Processes, requirements, and errors. Review of General Psychology, 5, 213. Mumford, M. D., Steele, L., McIntosh, T., & Mulhearn, T. (2015). Forecasting and leader performance: Objective cognition in a socio-organizational context. Leadership Quarterly, 26, 359–369. Mumford, M. D., & Strange, J. M. (2013). Vision and mental models: The case of charismatic and ideological leadership. In B. J. Avolio & F. J. Yammarino (Eds.), Transformational and charismatic leadership: The road ahead (10th anniv. ed., pp. 125–158). Bingley, UK: Emerald Group Publishing. Mumford, M. D., Peterson, D. R., Robledo, I. C., & Hester, K. S. (2012). Cases in leadership education: Implications of human cognition. In S. Snook, N. Nohria, & R. Khurana (Eds.), The handbook for teaching leadership: Knowing, doing, and being (pp. 21–33). Thousand Oaks, CA: Sage. Mumford, T. V., Campion, M. A., & Morgeson, F. P. (2007). The leadership skills strataplex: Leadership skill requirements across organizational levels. Leadership Quarterly, 18, 154–166. Osburn, H. K., & Mumford, M. D. (2006). Creativity and planning: Training interventions to develop creative problem-solving skills. Creativity Research Journal, 18, 173–190. Pant, P. N., & Starbuck, W. H. (1990). Innocents in the forest: Forecasting and research methods. Journal of Management, 16, 433–460. Peacock, J., Harkrider, L. N., Bagdasarov, Z., Connelly, S., Johnson, J. F., Thiel, C. E., . . . MacDougall, A. E. (2013). Effects of alternative outcome scenarios and structured outcome evaluation on case-based ethics instruction. Science and Engineering Ethics, 19, 1283–1303. Peterson, D. R., Barrett, J. D., Hester, K. S., Robledo, I. C., Hougen, D. F., Day, E. A., . . . Mumford, M. D. (2013). Teaching people to manage constraints: Effects on creative problem-solving. Creativity Research Journal, 25, 335–347. Rouse, W. B., & Morris, N. M. (1986). On looking into the black box: Prospects and limits in the search for mental models. Psychological Bulletin, 100, 349. Schacter, D. L., Addis, D. R., & Buckner, R. L. (2008). Episodic simulation of future events: Concepts, data, and applications. Annals of the New York Academy of Sciences, 1124, 39–60. Scott, G. M., Lonergan, D. C., & Mumford, M. D. (2005). Conceptual combination: Alternative knowledge structures, alternative heuristics. Creativity Research Journal, 17, 79–98. Shipman, A. S., Byrne, C. L., & Mumford, M. D. (2010). Leader vision formation and forecasting: The effects of forecasting extent, resources, and timeframe. Leadership Quarterly, 21, 439–456. Stenmark, C. K., Antes, A. L., Thiel, C. E., Caughron, J. J., Wang, X., & Mumford, M. D. (2011). Consequences identification in forecasting and ethical decision-making. Journal of Empirical Research on Human Research Ethics, 6, 25–32. Stenmark, C. K., Antes, A. L., Wang, X., Caughron, J. J., Thiel, C. E., & Mumford, M. D. (2010). Strategies in forecasting outcomes in ethical decision-making: Identifying and analyzing the causes of the problem. Ethics & Behavior, 20, 110–127.

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9 LEADER DECISION MAKING CAPACITY An Information Processing Perspective Shing Kwan Tam, Dawn L. Eubanks, and Tamara L. Friedrich

Decision making is regarded as a crucial activity for leaders because their decisions have tremendous impact on organizations and their followers (Westaby, Probst, & Lee, 2010). A decision is defined as a commitment to actions with the objectives of serving people’s values and interests (Yates & Oliveira, 2016). In this sense, decision making capacity helps leaders to address complex organizational issues by collecting information, framing the problem, assessing options, and ultimately formulating solutions (Mumford, Zaccaro, Harding, Jacobs, & Fleishman, 2000). Decision making is not just a capacity but also a highly situational and complex cognitive process (Weick, 1995). According to prospect theory, decision making is a process that concerns how decision makers utilize the information available to form their perception of a problem and evaluate the options and outcomes to make a decision (Tversky & Kahneman, 1985). In this chapter, the combined view of decision making as a capacity and how the process needs to be managed will be discussed. The relationship between leader skills and decision making capacity, as depicted in Figure 9.1, presents the key underpinning logic. Two points are particularly noteworthy here. First, decision making is not just about information processing, problem framing, and option evaluation. It is also a capacity that is dependent on problem solving, social judgment, and emotion management. Second, because decision making is sensitive to the situation and environmental changes, contextual factors such as time pressure and the wider environment will cause variations in leader decision making capacity. A key contribution of this chapter is that it addresses the complicated nature of decision making capacity with a comprehensive view from skills and contextual perspectives. Decision making research has been a dominating subject in economic and later psychology research. Decision making research has been

Social Judgment

Sensemaking Ideas evaluation

Problem Solving

Environmental Context

Time

Contextual Factors

Model of Decision Making

Awareness of emotion in self Awareness of emotion in others Emotion Regulation

FIGURE 9.1 Capacity

•

•

•

Emotion Management

• •

Skills

Decision

include further info collection and interpretation)

Option evaluation (may

Problem framing

Information collection and interpretation

Decision Making Process

Decision Making Capacity

Decision Making Effectiveness

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departing from such a mathematical approach in the past decades, and establishing, through experimental studies, how the phenomenon often violates the expectations of rational behavior (Oppenheimer & Kelso, 2015). We now must move a step further by realizing that the complexity involved in decision making cannot be understood through experiments alone. According to the model suggested in this chapter, the nature of decision making is complicated because it involves people and is sensitive to the context. From the methodology perspective, there is a need to study this subject with a more comprehensive method that suits the dynamic nature of decision making. Notably, no research method is without drawbacks. In view of the complex nature of decision making, more diversified methods are urged to be adapted such as qualitative, inductive, or research designs that can study human-environment interactions better. Some examples of alternate methods are simulations based on the individuals and their relationship and environment, use of technology to study the connections between neurological activity and leader behavior, and the use of an event-based approach to reveal how varied events affect leader behaviors and further trigger subsequent decisions, etc. (Conger, 1998; McHugh et al., 2016; Morgeson, Mitchell, & Liu, 2015; Waldman, Balthazard, & Peterson, 2011).

Decision Making Foundations A key focus of decision making research is on the question of how people choose actions rationally, or how they make decisions under ambiguous situations or with conflicting goals (Newell, Lagnado, & Shanks, 2015). The principle of rationality is the underlying logic of decision making where the criteria of consistency and coherence in how people make decisions are assumed to be fulfilled (Tversky & Kahneman, 1985). The origin of decision making research can be traced to the late 1940s where von Neumann and Morgenstern (1944) developed Expected Utility Theory (EUT) to evaluate decision making in relation to the principle of maximizing expected utility. It was developed within the economic discipline but also gained attention from psychologists due to the irrational nature of human decision making behaviors and their impact on the maximization of utility (Savage, 1954). There has been increasing evidence that individuals systematically violate the rational principle of decision making (Kahneman & Tversky, 2000). In order to understand the limitations of human cognition in processing information and environmental limitations on the information available during decision making, a prevalent approach to understanding decision making is to look for domainspecific heuristics (Tversky & Kahneman, 1974). These heuristics are suggested to reduce the complexity of assessing task probabilities and value prediction to ­simpler decision making rules (Tversky & Kahneman, 1974). These simple ­decision making rules include the choice of an option that comes to mind most

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easily, or an option that has highest priority on the most important dimension (Oppenheimer & Kelso, 2015). This approach concerns the problem of decision making from an information processing perspective and emphasizes the use of available information to achieve the desired outcome in a rational way (Oppenheimer & Kelso, 2015). In addition to the approach of understanding decision making as a test of rationality of people’s choices used in economic studies, there have been a number of attempts trying to investigate leader capacities and how they make decisions from the perspective of leader cognition development (e.g., Lord & Hall, 2005; Mumford, Connelly, & Gaddis, 2003). Mumford, Friedrich, Caughron, and Byrne (2007) developed a model of leader cognition that focused on how leaders formulate solutions to problems through the generation of sensemaking systems. Throughout the decision making process, a leader’s use of experiential knowledge and management of multiple processes (e.g., scanning of the environment for information gathering, case analysis, and forecasting) are needed in order to make a better decision (Mumford et al., 2007). Although there have been different approaches to studies on leader decision making, there is a common focus on a leader’s need to gather and make sense of available information for their understanding of the problems and further formulation of solutions (Tversky & Kahneman, 1985; Mumford et al., 2007). In this sense, there are three key factors that are to be considered in decision making: information processing, problem framing, and option evaluation. These will be discussed through the lens of decision making capacity.

Decision Making Capacity In view of the complexity of decision making, the improvement of leader decision making in this area is essential for enhancing leader performance (Santos, Caetano, & Tavares, 2015). Development of capacities and acquisition of knowledge are argued to be dependent on a complex set of abilities, motives, and personal characteristics (Mumford, Zaccaro, Connelly, & Marks, 2000). While some research emphasizes the importance of abilities such as general cognition and intelligence to leadership performance, these abilities are unlikely to change drastically (e.g., House & Aditya, 1997; Lord, De Vader, & Alliger, 1986; Lord, Foti, & De Vader, 1984; Schmidt & Hunter, 2000). However, it is suggested that decision making, emotional intelligence, problem solving, and social judgment can be developed ( Judge, Colbert, & Ilies, 2004; Mumford, Todd, Higgs, & McIntosh, 2017). In terms of the relationship between capacities and skills acquisition, it is argued that general cognitive ability, or intelligence, is related to biology rather than experience, yet some skills such as problem solving and coordination that are related to skill acquisition, change with practice (Fleischman & Mumford, 1989; Mumford et al., 2000). In this sense, although general cognitive capabilities are less likely to be changed drastically, certain capacities such as problem solving and

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decision making can be enhanced with practice and career experience through the acquisition of skills (Connelly et al., 2000; Yukl, 2013). Mumford et al. (2017) suggest enhancement of certain leader skills can in turn improve leadership performance including decision quality. For example, leaders need to identify problems and generate solutions objectively while managing the emotions of themselves and also their followers. Therefore, the skills of problem solving, social judgment, and emotion management are deemed to be critical for enhancing decision making capacity and facilitating a smoother decision making process (Yukl, 2013). In this chapter, a conceptual model of decision making is developed as depicted in Figure 9.1. Two points are particularly noteworthy regarding the conceptual model presented. First, decision making capacity can be reflected through the decision making processes and outcomes, thus the understanding of the process and the criteria contributing to a better decision outcome will explain why problem solving, social judgment, and emotion management skills matter. Second, decision making capacity is not just influenced by skill development, because it is also a process where the decision making outcome is dependent on the context, and the consideration of contextual factors (i.e., time and environmental context) will facilitate our understanding of the outcome variations.

Decision Making Processes Decision making as a process is highly sensitive to available information (Kahneman & Tversky, 1979; Weick, 1995). Kahneman and Tversky (1979) further enhanced the Expected Utility Theory described earlier with Prospect Theory, to explain decision making with framing and evaluation. A decision frame is the decision maker’s conception of outcomes and contingencies associated with a particular option, and this frame is influenced partly by the problem formulation and partly by the norms, habits, and personal characteristics of the decision maker (Tversky & Kahneman, 1985). It has been found that people frame problems according to the order and manner that the problems are presented, and, with the consideration of a reference point, they evaluate the options and possible outcomes in relation to gains and losses (Tversky & Kahneman, 1985). That is, how the problem is framed may make people include or omit certain options that would alter the final decision. This theory claims that the value of an outcome is evaluated either as a gain or loss. In behavioral terms, it means people seek risk for loss avoidance and are risk averse for gains. The effect is that people tend to avoid risk to ensure a certain gain and to seek risk to avoid the incurrence of a certain loss (Kahneman & Frederick, 2007). Next, this theory asserts that people over weight unlikely events (small probabilities) and under weight highly likely events (moderate and high probabilities) (Tversky & Kahneman, 1985). It means when people are under the condition of risk, they may irrationally give too much attention to low probability events when they weigh the options, but higher

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probability events are not given enough weight during decision making. Framing influences how people perceive a problem and in turn make judgments about choice preferences, therefore, leaders, particularly when they face a risky situation, need to maintain rationality and objectivity in order to make consistent and coherent choices for optimal outcomes. Information is critical for decision making not just because decision making is a process that is sensitive to information (Zeni, Buckley, Mumford, & Griffith, 2016), it is also because the perception of problems and evaluation of options may cause violations to rationality that make the leader unable to make rational decisions in the end (Tversky & Kahneman, 1985). The importance of information is reflected in the information collection and framing process in particular. During the process of decision making, the information collected and how it is interpreted are deemed to be pivotal because leaders need these reference points to make sense of the event (Weick, Sutcliffe, & Obstfeld, 2005). The information collected will affect leader problem framing and subsequent option evaluation steps. Tversky and Kahneman (1985) claim that people adopt a decision frame to define problems in the initial decision making stage. Tversky and Kahneman (1986) further emphasize the importance of framing and that the framing of options (e.g., gains or losses) would cause variations that yield varied preferences in a systematic way. Decision preferences are influenced by the framing of a problem, thus framing also has an impact on the outcome due to formulation effects. That is, changes in framing are suggested to cause shifts of preferences from risk aversion to risk seeking or vice versa, and this effect is found to influence the ultimate decision because the decision maker may favor the preferred outcome associated with the frame while ignoring the bigger picture (Kahneman & Tversky, 1986; Zeni et al., 2016). This leads to the topic of how rational or irrational we are as we make decisions.

Irrationality and Decision Making Although it is assumed that people would make decisions rationally, the subjective nature of human behavior does impact problem framing and option preferences (Tversky & Kahneman, 1982). Along related lines, Oppenheimer and Kelso (2015) suggest that the integration of diversified evidence is necessary in decision making to help offset irrational decisions. Information has an impact on how a decision is made, yet irrational factors such as emotions may also influence how the problems are framed (Kahneman, 2011). That is, choices are made according to initial emotional evaluations and people interpret risk with the “risk as feeling” approach, meaning emotional reactions drive behaviors and decisions (Kahneman & Fredrick, 2007; Loewenstein, Weber, Hsee, & Welch, 2001). De Martino, Kumaran, Seymour, and Dolan (2006) demonstrated this emotional effect suggested by Loewenstein et al. (2001) by framing a prospect in one of two ways—“keep $20 of the $50” (a gain frame) or “lose $30 of the $50” (a loss frame). Although the equivalence of

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the alternative formulations is transparent, the option that was framed positively (with the use of the word “keep”) was selected more frequently than the option that was framed negatively (with the use of the word “lose”). In this vein, the words “keep” and “lose” evoked emotional evaluations and showed the subjects’ tendency of avoiding risk to ensure a certain gain and seeking risk to avoid a certain loss. In the context of problem framing, while the use of information cannot be ignored during this process, it is noteworthy that the emotion experienced by decision makers also plays a role at the moment of decision making (Loewenstein et al., 2001). Emotion management becomes important because leaders are required to regulate their emotions for better decision making outcomes, and their selection of actions are dependent on strategies they use as they experience emotions ( Jordan & Lindebaum, 2015). The awareness of emotions in themselves and others and their regulation of their emotions enable leaders to maintain emotional stability under stressful situations and influence the followers positively during the decision making process. Taking all the previous discussions into consideration, leaders’ skills to rationally gather multiple source of information for problem framing and solution generation with minimal emotional distractions are undoubtedly what the leaders need to be equipped with (Mumford et al., 2017). A recent example of the application of the problem framing, information collection, and option evaluation in decision making is the case of Apple CEO Tim Cook’s refusal to unlock the terrorist Syed Rizwan Farook’s iPhone for the FBI’s investigation of a terrorist attack that took place in San Bernardino, California, in December 2015 (Lichtblau & Benner, 2016). Cook refused to develop software to disrupt the encryption system of Farook’s iPhone to unlock the data for the FBI’s investigation. As we will show later, Cook’s response demonstrated the elements of Prospect Theory. The essence of Prospect Theory is that subsequent choices are made by the framing of problems and choices (Tversky & Kahneman, 1985). Cook framed the whole event as a “dangerous precedent” (A dangerous precedent section, para. 5) and “an unprecedented step which threatens the security of our customers” (A letter to our customers section, para. 1). In this sense, he adopted a loss frame under this risky situation. By framing the compromise and request to “build a backdoor to the iPhone” (The San Bernardino section, para. 6) as a tremendous threat (Cook, 2016), the consequence of this action is interpreted as a bigger risk. During the decision making process, he evaluated the outcome according to a few reference points. First, the losses that were associated with the compromise, in particular the new software would “make it easier to unlock an iPhone by ‘brute force’ trying thousands or millions of combinations with the speed of a modern computer” (Cook, 2016, A dangerous precedent section, para. 5) that could cause uncontrollable consequences. Second, the protection of Americans’ civil liberties from the breach of privacy and less safe situations was framed as the favorable alternative. Third, by using his experience and knowledge, he pointed out the

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government downplayed the impact of building a “backdoor” software because once it is created, it could be used by other devices, through hacking or carelessness, to crack open other iPhones, and that would “put millions of people at risk” (Grossman, 2016). In brief, the reactions to Cook’s controversial decision of refusing to aid the US government were mixed. However, as the leader of Apple, he defended the interests of his customers and his position, and used the protection of data security and civil liberties as frames to make the best possible decision in a situation characterized by risk.

Leader Skills: Problem Solving, Social Judgment, and Emotion Management Capacities are argued to be associated with leader skills and knowledge (Yukl, 2013). Certain skills such as verbal comprehension are found to be related to performance in earlier stages of skill acquisition, whereas other types of skills such as reaction time and simultaneous coordination are more strongly related to performance in later stages of skill acquisition (Fleischman & Mumford, 1989). In a similar vein, Mumford et al. (2000) suggest that some people will learn certain types of skills faster than others. They further discuss in their Skills Model that decision making and problem solving are crystallized cognitive capacities that can grow continuously because they are a type of intellectual capacity that can be developed over time through experience (Mumford et al., 2000). The types of skills that impact leader thought processes and subsequent decision making outcomes are of a key concern for leaders (Mumford et al., 2017), because decision making concerns the method of thinking in order to achieve the best possible results (Baron, 2008). In view of the importance of information processing, problem framing, and option evaluation in decision making, together with the influence of subjectivity in human decision making behaviors, leaders need to have skills such as problem solving, social judgment, and emotion management to navigate the environment, understand the needs and root causes of problems, and judge the situation for a good quality decision (Yukl, 2013). We now review these three sets of skills and how they facilitate decision making.

Problem Solving Problem solving helps leaders to make sense of situations for problem framing and facilitates the process of option evaluation (Mumford et al., 2000). More specifically, leaders can solve problems by making sense of the situation through the continuous process of evidence accumulation (Weick, 1995). The information collected is then used for framing the problems and leads to further analysis of the available options for a decision (Hogarth & Makridakis, 1981). It is noteworthy

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that information embedded in the social network is not to be neglected because the information collection process plays a critical role in decision making, and leaders need diversified information for more objective judgments (Mumford et al., 2000). Leaders are required to make decisions about complex problems in organizations, and they need to identify problems, gather information, formulate ideas and options, and develop plans to solve the problems (Mumford et al., 2000). Sensemaking can help leaders make sense of the information and situation, and idea evaluation facilitates the solution generation. These are regarded as key skills that leaders need during problem solving (Mumford et al., 2017). We will now elaborate on these two skill subsets in detail.

Leader Sensemaking Sensemaking enables individuals to organize and bring meaning to their experiences. It involves actors’ cognitive work to label and connect meanings, and make sense of the world (Whiteman & Cooper, 2011). In order to generate solutions for problems, Hogarth and Makridakis (1981) assert that sensemaking is guided by mental models as a framework for managing the basis of making decisions— information scanning, evaluation, and appraisal of appropriate actions. Schön (1983) further points out that sensemaking varies among individuals. He argues that those with more experience are able to reflect on their experiences and previous knowledge while formulating actions more effectively and are more sensitive to changes in the situation. Sensemaking allows leaders to interpret and make sense of uncertain and complex situations during problem solving (Hahn, Preuss, Pinkse, & Figge, 2014), and this articulation of sensemaking helps to reduce leader stress levels and clarify the root causes and goals for the formulation of further actions (Weick, 1995). In light of problem solving, sensemaking is based on the capacity of an individual to accurately construct a problem frame, narrow information collection, evaluate the information collected, and ultimately make a decision (Thiel, Bagdasarov, Harkrider, Johnson, & Mumford, 2012). In the context of information processing and narrowing during problem solving, leader sensemaking skills help them to make sense of the environment, and it creates rational accounts of the world that lead to further actions (Maitlis, 2005; Weick, 1995). Decision makers are assumed to follow rational and comprehensive steps with the application of rules to information in order to develop and implement plans (Vessey, Barrett, & Mumford, 2011). In particular, Thomas, Clark, and Gioia (1993), argue that sensemaking involves environmental scanning, interpretation, and related responses. Similarly, Mumford et al. (2007) assert that leader sensemaking starts with internal and external environmental scanning. After mental models are developed as a framework, the information gathering process is initiated to define the nature and consequences of the event (Weick, 1995). This will, in turn, activate the descriptive mental models that include the causes and goals of the event.

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During the information interpretation process, decision makers develop or apply ways of comprehending the meaning of information. It facilitates the fitting of information into frameworks for understanding and further actions (Gioia, 1986). Along similar lines, Mumford et al. (2007) assert in their leader cognition model that information interpretation facilitates the information gathering process because it affects leader understanding of an event. Together with the cues obtained via both external and internal environmental scanning, information gathering will be impacted by the descriptive mental models that are used to understand the event (Weick, 1995). Because sensemaking entails a continuous redrafting process of an emerging event, during the problem framing phase, sensemaking facilitates the incorporation of additional observed data, generating a more comprehensive story. Thomas et al. (1993) remark that leaders frame events as threats or opportunities by sensemaking. Weick (1995) also states that leaders make decisions by creating a cognitive structure for understanding and responding to the situation. That is, sensemaking is helpful to clarify root causes and goals operating in the situation in which it is helpful to provide a basis of actions for both leaders and followers (Weick, 1995). Descriptive mental models are then activated by the information gathered for further case analysis (Mumford et al., 2007). As such, the use of information actually has a long-term impact on decision quality because decision making errors may occur if the solution stems from bad information and bad sources, as the subsequent actions taken are dependent on the interpretation of information (Maitlis & Christianson, 2014; Zeni et al., 2016).

Idea Evaluation Being able to construct solutions is regarded as one of the effective problem solving behaviors for leaders (Zaccaro, Rittman, & Marks, 2001). With the information gathered and analyzed, it forms a basis for planning and forecasting that help the leaders to generate ideas and actions (Mumford et al., 2017). However, as argued by Mumford et al (2000), selecting and implementing the best possible actions for goal achievement is a form of problem solving, and it denotes the importance of generation, evaluation, and execution of solutions for leader effectiveness. It is not feasible for leaders to act on all generated solutions. They instead need to appraise and select the most appropriate solutions for further execution of the plan (Mumford et al., 2017). Thus, leaders need to be able to evaluate ideas in order to perform well in problem solving (Zaccaro et al., 2001). The key function of idea evaluation is to recognize original ideas (Runco & Basadur, 1993). What is relevant about idea evaluation to problem solving is the appraisal of a projected outcome of adopting an idea according to applied performance standards (Kuipers, Moskowitz, & Kassirer, 1988). Runco, Okuda, and Thurston (1987) assert that ideas are evaluated initially in regards to appropriateness and relevance, and, subsequently, on originality. That is, the appropriateness of

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criteria involves two elements, first, the practical benefit, such as low implementation cost and the fit of the idea with the current system and environment, and, second, the originality of the idea that provides a new solution to the problem (Bink & Marsh, 2000). What complicates the idea evaluation outcome is that the application of evaluation standards is found to be influenced by the context (Blair & Mumford, 2007). For example, research conducted by De Dreu (2003) and Suri and Monroe (2003) has shown that contextual factors such as time pressure and stress negatively influence information processing capacity, causing superficial analysis and a preference for rapid closure. This will be discussed further in the section on time restriction later in the chapter. In view of the complexity of idea evaluation, scholars have suggested ways to enhance this skill of leaders. Runco and Basadur (1993) suggest that training has a positive impact on improving leader evaluative accuracy. Leaders were found to be able to provide more original solutions to problems and to judge original ideas more accurately after training, both in their skills to identify original ideas and to recognize unoriginal ideas. In addition to training, Lonergan, Scott, and Mumford (2004) remark that the acquisition of experience is beneficial to leaders for having more comprehensive standards for idea evaluation. With such standards, leaders are then able to improve the problem solving and decision making outcomes through more appropriate idea evaluation (Liu, Eubanks, & Chater, 2015). Along similar lines, as leader idea evaluation can be improved by experience (Mumford et al., 2000), Mumford et al. (2017) advocate for the use of case-based knowledge, because it is an experience-based knowledge where leaders are able to reflect and learn. This type of knowledge typically includes both performance information (i.e., causes, resources, restrictions and contingencies) and social information (i.e., actors involved, affect, goals, and social system) (Vessey et al., 2011). With the acquisition of more experience and expertise, leaders are more capable of organizing and utilizing their case-based knowledge. Consequently the complexity of the problem decreases and leads to better idea evaluation (Mumford et al., 2017). The use of problem solving in decision making can be illustrated by how Hillary Clinton made decisions and worked with her team as a leader. Although there is controversy surrounding her decision making (such as the vote for the Iraq War), those who have worked with her closely consistently compliment her as someone who “really listens to you” and has excelled in her governance period (Klein, 2016). As a good listener, Clinton clearly understands the importance of information collection and she heavily relies on this information during the ­decision making process. As her fellow senator has commented, “She always comes with the memo and the binders. . . . When we had issues, she studied. She was always well-prepared, almost without exception” (Davis, 2016). As recalled by her followers, during her time as senator, she had regular “cardtable” sessions every few months where she and her team came together and worked with two tables of newspaper clippings, position papers and random scraps

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of papers (Klein, 2016). It is a categorization exercise where they discussed and prioritized issues, and, most importantly, Clinton requested her team to follow up on these issues. From the decision making theory perspective, Clinton collects multiple sources of information (including factual reports and opinions collected by her team) to make sense of the situations comprehensively. This is argued to be helpful for securing the solution quality (Tversky & Kahneman, 1985; Mumford et al., 2007). However, as Maitlis and Christianson (2014) argue, solution quality is impacted by how information is interpreted. Some argue that one of the biggest mistakes Clinton made was the vote for the Iraq War and it is reported by some that it was caused by listening to the wrong intelligence assessments (Klein, 2016). The implication of this example is that leaders need diversified information to understand the problem without doubt, yet decision errors may occur if the information is of bad quality and is gathered from questionable sources (Zeni et al., 2016). As such, leader awareness of problem framing and how to deal with information properly is deemed necessary for decision making (Tversky & Kahneman, 1985; Mumford et al., 2007). In brief, problem solving has a fundamental influence on decision making because it requires the leader to make sense of the event, and take further actions to make a decision (Weick, 1995). Because the problem solving outcomes are also dependent on the quality of idea evaluation, leaders are advised to receive training about how to better manage performance and social information during the information gathering and interpretation stage in order to improve their ultimate idea evaluation performance (Mumford et al., 2017; Vessey et al., 2011).

Social Judgment and Using Multilevel Information Sources Decision making takes place at different levels in organizations, and people’s perspectives and opinions are found to have an impact on the decision making outcome (Mumford et al., 2007). As stated earlier, decision making needs both performance information (such as causes, resources available, restrictions, and contingencies) and social information (such as actors involved, affect, goals, and the social system) (Vessey et al., 2011). In this sense, social judgment becomes essential for leaders to understand the needs of others in the organization. By building a closer relationship with others (particularly followers) and cultivating a team climate that promotes open-mindedness, leaders are able to more easily gather information from different sources in the social network. It is noteworthy to point out that decision making is not necessarily a top down but also a bottom up process (Sonenshein, 2010), thus the involvement of multilevel sources of information is deemed to be pivotal for the ultimate decision quality (Murase, Carter, DeChurch & Marks, 2014). Along similar lines, leaders need to have a multilevel understanding of decision making because decision making is regarded as a process involving all team members. Their behaviors and activities that happen across different organizational levels are argued to influence

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the overall group decision making outcomes (Hollenbeck et al. 1995). As such, in order to have a comprehensive view in the context of decision making, it is essential for leaders to consider both performance and social information from different levels of the organization (Mumford et al., 2007). Information gathering and interpretation have a fundamental influence on decision making at different organizational levels, and actors (both followers and leaders) from any organizational level may participate in the decisionmaking process (Mumford et al., 2007). As a result, leaders need to develop solutions interactively or with the assistance of their subordinates, peers, and superiors (House, 1996). Thus, social judgment is necessary to understand people’s needs and social systems (Mumford et al., 2000). Precisely speaking, it enables leaders to work with others during the decision making process and to marshal support from the social network for executing changes in an organization (Connelly, et al., 2000). The importance of social judgment can be revealed by how information collection and processing takes place in organizations. A top down approach happens when leaders, by using their actions and communication with their teams, actively influence and change members’ existing mental models and develop team knowledge (Marks, Zaccaro, & Mathieu, 2000; Murase et al., 2014). Conversely, followers can also develop their own team shared knowledge through bottom up processes, meaning continuous communication and interaction with one another over time (Pearsall, Ellis, & Bell, 2010; Murase et al., 2014). Taking all these processes into consideration, it is clear that information gathering and interpretation in the decision making process is dynamic among leaders and followers. In particular, leaders need to be aware that this dynamic process involves different actors and knowledge in the social network, and the follower interpretation of the situation will have an impact on the information they will collect for the leaders (Day, Gronn, & Salas, 2004; Zaccaro & Klimoski, 2002). In other words, followers’ understanding and interpretation of information can also affect leaders’ judgments because leaders’ decisions are dependent on the information collected. Leaders also need to have social judgment to consider the more subjective elements of decisions for generating more pragmatic and feasible decisions that serve the interest of both followers and the organization (McKenna, Rooney, & Boal, 2009). Social judgment involves perspective taking, social perceptiveness, behavioral flexibility, and motivating others during the decision making process (Mumford et al., 2000). That is, leaders are expected to be sensitive to how their ideas fit in with others (Connelly et al., 2000). It is about how well leaders understand the perspectives and needs of others, the flexibility of them adapting their ideas to others, the collaboration with others in the face of resistance and conflicts, and the people skills necessary to foster changes in an organization (Mumford et al., 2000). In other words, this skill is applicable when leaders need to collect the information from the followers during decision making.

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In regards to the information collected from followers, it is suggested that one of the most direct ways to influence follower understanding about the problem and situation is to show them clear directions about how to perform the task (Marks et al., 2000; van Ginkel & van Knippenberg, 2008). Yet, by considering the bi-directional nature of leaders and followers in decision making, follower motivation and openness about information gathering is a factor that needs to be considered (Park & Nawakitphaitoon, 2017). From the emotional point of view, followers are found to have challenges and difficulties in terms of sharing information and suggestions with their leaders when they have a fear of expressing their opinions openly (Lebel, 2016). It is argued that employee openness fosters their contributions in decision making through actively expressing their opinions and suggestions, and the sharing of their viewpoints can help leaders make decisions (Pyman, Cooper, Teicher, & Holland, 2006). However, it has been found that the fear of speaking up, in general, lowers employee willingness to share their ideas when they have a negative perception of their leaders’ openness about accepting their ideas (Lebel, 2016). Studies show that followers withhold their opinions and input with the fear of negative consequences such as punishment from supervisors, causing harm to their work relationship, being labeled negative (e.g., troublemaker or whiner), being unsupportive, or ruining one’s image (Detert & Edmondson, 2011; KishGephart, Detert, Treviño, & Edmondson, 2009; Milliken, Morrison, & Hewlin, 2003). The impact of followers’ fear is particularly obvious at times of uncertainty that is marked by the emotion of feeling unsettled (Gino, Brooks, & Schweitzer, 2012; Kish-Gephart et al., 2009). Leaders need to properly manage situations where followers feel fearful to share views, because they need to count on the information collected by others for decision making (Mumford et al., 2017). A perceived high level of leader openness can increase the likelihood that followers will express their opinions to make changes in the decision making process, and it can also reverse follower fear tendencies towards withdrawal and avoidance (Lebel, 2016). From the emotional viewpoint, higher perceived leader openness is also found to foster follower positive feelings that their suggestions and opinions can change the situation, and their pessimistic feeling of being helpless can be minimized (Tangirala & Ramanujam, 2012). As a result, what leaders should pay attention to with regard to information gathering at the team level is that they need the information and feedback from their followers in order to make favorable decisions, and the flow of information from the followers can actually improve their decision quality. Thus, it is recommended to consider their role in terms of creating an open-minded and fear free atmosphere, and encourage active participation from their followers (Lebel, 2016; Morrison & Milliken, 2000). The importance of social judgment can be illustrated by how the former Proctor & Gamble CEO A. G. Lafley led the turnaround of P&G. Lafley was named as the CEO of the Year in 2006 by Chief Executive magazine (Hashemipour, 2016).

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Lafley took over the position of CEO in 2000 while P&G was in the midst of a crisis with a loss of US$85 billion in the market capitalization (Lafley, 2009). He realized it was “a crisis of confidence”—the employees (including P&G leaders), customers, and investors lost confidence in P&G. In order to turn around the adverse situation, he realized the importance of collaboration with his team for generating the future transformation decisions. The active participation of his employees was crucial, and as a result he prioritized the promotion of the company’s core values (i.e., trust, integrity, ownership, leadership, and a passion for winning) as key initiatives during the first year of the transformation (Lafley, 2009). To realize the long-term goal of creating better customer values, perspective taking, and internal open culture were deemed to be pivotal to Lafley. He selected his leadership team with one very specific criterion: Instead of having yes-people in the team, he wanted people who had good judgment and could challenge every decision (Starling, 2011). This attitude of Lafley helped him to build an open culture where his followers felt comfortable expressing themselves and sharing their opinions. This type of social information is argued to help leaders understand the full picture of the situation and make a more favorable decision with diversified information at hand (Mumford et al., 2000; Pyman et al., 2006). He was also sensitive about the value of social information and put much effort into collecting feedback from the external customers. All of these examples show Lafley’s social judgment and his sensitivity to the importance of social information during decision making.

Emotion Management Related to social judgment are emotions. Affect and emotions are closely intertwined with the process of leading and leader outcomes such as decision making (Gooty et al., 2010). Affect as a broader concept is defined as longer lasting emotional experience where emotion is short-term and context specific (Gooty, et al., 2010; Wang & Seibert, 2015). Individuals’ affective responses to events has implications for behaviors (Gaddis, Connelly, & Mumford, 2004). Leader affect is suggested to impact followers’ behaviors because followers would use and share leaders’ affect via emotional contagion (Hatfield, Cacioppo, & Rapson, 1993). Specifically, the display of leaders’ emotions act as a signal and valuable information to the followers regarding their behaviors of decision making in response to the leaders’ feelings (Fisher, 2000; Griffith, Connelly, Thiel, & Johnson, 2015; Lazarus, 2000; Schwarz & Clore, 1983). Therefore, emotions are one of the factors that wise and effective leaders need to manage during decision making, because leaders’ displays of emotions serve as a constraint of the interaction and information sharing within the team (Griffith et al., 2015; McKenna et al., 2009; Savage, 1954). Emotion management is regarded as a key part of effective leadership because it is not just about how leaders manage their own emotions, but also how they manage the emotions of their followers (Connelly et al., 2013). Previous studies have

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demonstrated the link between emotional management, such as emotional awareness of oneself and others and emotion regulation, to decision making processes such as problem framing, information processing, divergent thinking, and risk assessment of options (Amabile, Barsade, Mueller, & Staw, 2005; Connelly et al., 2013; Gooty et al., 2010; Isen, 2001; Madjar, Oldham, & Pratt, 2002; Tversky & Kahneman, 1982; Vosburg, 1998; Yukl, 2013). Awareness of one’s own emotions and the emotions of others are both fundamental to decision making. In regards to emotional self-awareness, as Mumford et al. (2007) remark, emotion is one of the information sources that leaders use during decision making. It is suggested that subjective interpretation of the available information has an impact on problem framing and subsequently determines preference of option evaluation (Tversky & Kahneman, 1982). Leaders recognize and decode emotional information to appraise threats and opportunities in situations (Lopes, Cote, & Salovey, 2006), which in turn affects how they frame the situation through sensemaking (Thomas et al., 1993). Following the same logic, emotional self-awareness is about how much the leaders understand their own emotions, how these emotions change over time, and the impact on leader performance, including decision quality and interpersonal relationships (Yukl, 2013). It allows leaders to accurately identify the emotions they are experiencing. With higher awareness of ones own emotions, leaders would find it easier to understand their needs and likely reactions under different situations, thereby facilitating evaluation of alternative choices in decision making (Yukl, 2013). This capacity becomes salient during situations with high stress and strict time limitations such as a crisis, because the formulation of plans for addressing the crisis is impacted by the problem frame developed (Vessey et al., 2011). Effective decision making requires leaders to remain calm, stay focused on the problem, and provide decisive direction to their followers rather than panicking, denying the existence of an issue, or shifting responsibilities to others in a crisis (Yukl, 2013). The second skill is the awareness of emotions in others. Recognizing others’ emotions is also crucial for developing emotion management, because decision making is a dynamic process that takes place between leaders and others (Connelly et al., 2013; van Ginkel & van Knippenberg, 2012). It facilitates the recognition of others’ emotions, differentiation of genuine and false expression of emotions, and understanding of others’ possible reactions to the leaders’ emotions and behaviors (Yukl, 2013). Being sensitive to others’ perspectives helps leaders understand different groups’ needs, goals, and demands (Zaccaro, Gilbert, Thor, & Mumford, 1991). People’s views that are embedded in the social network affect framing of the problems and envisioning of solutions. The social network is also a platform for accessing resources and marshaling people’s active support in policy decision making (Hoppe & Reinelt, 2010). As such, if the opinions in the social networks are managed and utilized effectively, the leaders will find it more convenient to seek

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a discussion about the issues of concern, mobilize support, influence policy, and allocate resources during the decision making process (Hoppe & Reinelt, 2010). Without taking social information into consideration during decision making, leaders would encounter problems of only focusing on limited sources of information such as those more predictable and controllable aspects of the situation, and it, in turn, may decrease the decision quality because unpredictable factors would be neglected (Vessey et al., 2011). This impact is particularly prominent in crises due to the unpredictable nature of the reactions of actors (Hunt, Boal, & Dodge, 1999; Weick, 1995). The third skill involved in emotion management is emotion regulation. It involves leaders’ attempts to influence what emotions they experience, when, and how they are experienced and expressed (Gooty et al., 2010). That is, leaders adopt different strategies to manage their experienced emotions in response to specific circumstances, workplace stressors, and during interactions with others ( Jordan & Lindebaum, 2015). The range of emotion regulation strategies suggested (such as cognitive reappraisal and suppression) are all with the key aim of facilitating leader emotional stability in order to stay calm and provide direction in decision making (Lawrence, Troth, Jordan, & Collins, 2011; Yukl, 2013). The effectiveness of emotion regulation strategies depends on the situation (Connelly et al., 2013). For example, suppression of emotion is found to cause a less favorable result in interpersonal functioning that can limit close social relationships with others (Gross & John, 2003). That has an impact on the information collection process in decision making, because both leaders and followers are involved in the process, and leaders need to count on followers to provide information and resources for making decisions (Hunter, Tate, Dzieweczynski, & Bedell-Avers, 2011). Followers’ perceptions of leader openness will affect their willingness to share information openly, which in turn affects the information communicated to the leaders (Lebel, 2016). However, because unpredictable situations lead to overly optimistic or pessimistic risk assessments, suppression of optimism may be preferred in a high-risk situation where severe consequences of failing are expected because suppression of optimistic feelings would help leaders to assess the level of risk more accurately (Lerner & Keltner, 2000). In addition to the suppression strategy, the reappraisal strategy, a form of cognitive change that alters the emotional impact of a situation, is found to change the views and framing of an individual about an emotionally charged situation (Connelly et al., 2013; Gross & John, 2003). The impact of perspective taking is also examined in emotion regulation where it has been shown to help people see the bigger picture that in turn can reduce the negative affective reactions to distressing stimuli (Schartau, Dalgleish, & Dunn, 2009). In sum, there will be less emotional influence during decision making if the leaders have collected more diversified information and perspectives from different sources to frame problems and make judgments (Mumford et al., 2007). It is clear that emotions impact leader performance of cognitive tasks including information processing and decision making (Thiel, Connelly, & Griffith, 2012).

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Thus, it is necessary for leaders to enhance their skills to manage emotions by being sensitive to emotions in themselves and others, and, most importantly, they need to adopt appropriate emotion regulation strategies to minimize the negative impact of emotions on decision making, and maximize their usefulness as an additional source of information (Yukl, 2013).

Contextual Factors Decision making is sensitive to the changing environment and the context affects how leaders frame problems and evaluate options (Lord & Shondrick, 2011; Tversky & Simonson, 1993; VanLehn & Ball, 1991). One key element of the context is the time available for working through the decision. Leaders need to manage the impact and intensity of time pressure because it can benefit or disadvantage their decision quality. That is, it may lead to biases and irrational choices or the generation of creative ideas (Blair & Mumford, 2007; Hunter et al., 2011). What we need to pay attention to is that leadership is suggested as a “function of the leader, the follower and the situation” (Burke, 1965, p. 60) and leaders’ decision making as a response to the context varies according to the external environment and the organization (Vroom & Jago, 2007). At the macro level, national culture, market forces, and organizational context are the most studied contextual dimensions (Oc, 2018), and these factors are found to shape leaders’ decision making behaviors and respective outcomes (Vroom & Jago, 2007). Leaders are required to consider the preferences of decision making styles due to cultural differences as they are argued to influence information sharing (Westaby et al., 2010). At the organizational level, the context of the organizations such as strategic complexity, organizational design, and contingencies serve as an input that influences leaders’ decisions (Vroom & Jago, 2007). Therefore, the impact of the contextual factors of time and environmental context will be discussed in this section.

Time as a Contextual Factor As Tversky and Kahneman (1985) suggest, leaders are unaware that their preferences are changed by framing, and their perspectives do change over time with more information and evidence collected along the way. Thus, time as a contextual factor needs to be considered when it comes to decision making. Busemeyer and Townsend (1993) assert that preferences change during deliberation and final choices are impacted by the amount of time spent on decision making. As information accumulates during the deliberation process, it has an influence on the outcome of a decision because a repeated sampling of relevant information is collected over time. Moreover, the amount of attention distributed to the varied outcomes also changes over time during the deliberation process. In other words, during the process of decision making, many different consequences may be considered, preference for an action is formed according

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to gradual accumulation of evidence, and that will eventually lead to a decision (Oppenheimer & Kelso, 2015). The aforementioned decision making process takes place under situations where sufficient time is allowed for collection of information and decision making, yet in reality leaders likely need to cope with situations where they have strict time restrictions in terms of information processing and choice evaluation. Research on decision making shows how the variations in choice preference can be explained by the contextual factor of time pressure (Oppenheimer & Kelso, 2015). For example, some studies (e.g., Busemeyer & Diederich, 2002; Diederich, 1997; Svenson & Edland, 1987) have found that variations in choice preference occur under situations with time restriction where the most important factor (e.g., cost) has a weak impact on one option, and the less important factor (e.g., quality) strongly favors the alternative choice. Moreover, Zhao and Olivera (2006) explain that people tend to adopt information processing strategies that require fewer cognitive resources when time pressure increases. It is found that, under the condition where time constraints are introduced, instead of evaluating all attributes of each alternative option, people quickly make a decision to reject alternatives that do not meet a minimum acceptable level on any attribute (Ford, Schmitt, Schechtman, Hults & Doherty, 1989). Hence, it is expected that individuals tend to shorten and simplify decision making processes when they are under time pressure by taking into account fewer elements in the assessment (Zhao & Olivera, 2006), and this approach will in turn lead to mistakes or omitting original ideas that could disadvantage the decision quality (Zhao & Olivera, 2006; Blair & Mumford, 2007). As a result, time could influence decision quality because it can lead to an overly simplistic decision making process. Time restriction is argued to lead to the occurrence of errors under certain working conditions such as when there is irrelevant information and situations where there is processing overload (Eubanks & Mumford, 2010). That is, when individuals work in conditions where there is a time restriction and they are exposed to information that is irrelevant, they may commit more errors because they do not have sufficient time to properly frame the problem by considering the contingencies and restrictions present in a situation. This may be a result of information overload with irrelevant information making it difficult for leaders to make a favorable decision. As a result, decision quality is negatively affected (Eubanks & Mumford, 2010). Another impact of time pressure is that more errors are caused by error avoidance behavior (Edland & Svenson, 1993). It is found that people who tend to make less risky choices are more selective when they search for information, and they focus more heavily on negative attributes when they face time pressure. This approach can cause more errors (i.e., decrease of decision accuracy) due to the lack of considerations of viable decisions (Hunter et al., 2011). Emotion regulation, a concept discussed earlier, is particularly impactful when leaders are under stressful circumstances such as organizational change or crisis where leaders need to make a decision with limited time and inadequate

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information (Lawrence et al., 2011). It is found that stress or anxiety experienced as a result of restrictions such as time pressure leads to a decrease in information processing capacity, and under such stressful situations, people show a tendency of opting for superficial analysis and quick decisions, and rejecting ideas that are difficult to understand (Blair & Mumford, 2007; De Dreu, 2003). To illustrate this point, Judge et al. (2004) have found that leader cognitive resources are decreased when they are under stress and effective emotion regulation may free up cognitive resources that can improve leader performance in decision making, planning, and judgment of options. Nevertheless, time as a contextual factor does not necessarily cause a negative impact on decision making (such as making errors). It is also argued to contribute to the generation of original ideas under certain conditions (Hunter et al., 2011). It has been found that when there is less time pressure, people tend to choose options that are aligned with the current social norms and reject original and risky ones, yet when making decisions where evaluation criteria are less strict, they prefer original and risky options even when the time pressure is greater (Runco & Acar, 2012). Although leaders who are under time pressure show a tendency of underestimating the originality of novel ideas and it may lead to premature rejection of new approaches, original and risky ideas would still be preferred if creative solutions are required and the evaluation criteria are less stringent (Blair & Mumford, 2007).

Environmental Context National Culture: Power Distance and Collectivism/Individualism In addition to the time factor, the cultural background of leaders and the culture they are operating in, is also suggested to impact the framing of problems (Westaby et al., 2010). This is because culture is defined as a shared belief and sensemaking system, and people solve problems using culture as a reference point (Yukl, 2013). Differences in how people make decisions can be caused by the social and cognitive differences embedded in the culture (Yates & Oliveira, 2016). Furthermore, leaders need the contribution of information from their followers for decision making, yet the cultural background of the followers can influence their preference of opinion sharing with the leaders. In the case of lack of willingness for sharing, it will actually cause a negative impact on leader decision making due to the availability of limited information (Hahn et al., 2014; Hofstede, Hofstede, & Minkov, 2010; Kirkman, Chen, Farh, Chen, & Lowe, 2009). In particular, power distance and individualism/collectivism have a more direct impact on follower attitudes about information sharing and their relationships with their leaders, because these factors affect their decision making and communication styles (Hofstede et al., 2010).

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Power distance is one aspect of culture frequently used to explain the variations in leader decision making (Lee, Scandura, & Sharif, 2014). Power distance means the degree that followers show a willingness to disagree with the leaders (Hofstede et al., 2010). Individuals from high power distance cultures show a tendency of having unquestioning respect for authority (leaders) (Chen, Friedman, Yu, Fang, & Lu, 2009) and have an accepting attitude of the extended social distance between leaders and followers (Kirkman et al., 2009). In light of such dynamics between leaders and followers, studies have found that employees from high power distance cultures value their participation in decision making less, regardless of their positions, and have a lower desire for empowerment (e.g., Hui, Au, & Fock, 2004; Kirkman, Lowe, & Gibson, 2006; Kirkman et al., 2009; Robert, Probst, Martocchio, Drasgow, & Lawler, 2000). Thus, even if leaders desire active participation and opinions from their followers regarding decision making, these followers, in general, may not have a high motivation to express their opinions (Kirkman et al., 2009). In this sense, the low participation of followers during decision making would lead to limited information that the leaders can collect from them. Due to the limited understanding of the situation, leaders could eventually make a decision that ignores important information (Hahn et al., 2014). Individualism/collectivism, another way of classifying cultures, is suggested to impact the degree of willingness individuals have to express opinions and beliefs, as it relates to conflict avoidance (Hofstede et al., 2010). Leaders need to pay attention to this because they need diversified information from others for decision making (Park & Nawakitphaitoon, 2017). Specifically, people from individualistic cultures (e.g., Americans) tend to adopt more assertive and confrontational styles for conflict resolution, whereas those from collectivistic cultures (e.g., South Koreans) do not like to engage in social disagreements and show a tendency for using more passive, collaborative, and avoidance strategies to deal with conflict (Park & Nawakitphaitoon, 2017). In other words, conflict avoidance is regarded as a style of avoiding the expression of differences of opinions and beliefs among the group members. Following this logic, conflict avoidance is actually a style that avoids explicit and open discussion (Thomas & Dunnette, 1992). That is, during decision making, leaders may encounter difficulties in collecting diversified information from their followers with a collectivist background as they tend to avoid conflict and expressing differing opinions.

Market Forces and Organizational Context The study of the external environment where organizations are embedded is important because they do not exist in isolation. They need to constantly interact with other institutions such as markets and communities in regard to business development (Whetten, Felin, & King, 2009). It is asserted that organizations earn support and legitimacy through conforming to the rules, requirements, and norms of the external environment (DiMaggio & Powell, 1983; Scott, 1987). In

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the context of decision making, leaders are found to adopt different decision rules in response to diverse situations, and the market elements may in turn influence how the leader’s perception of the situation and decision framing is formed ( Jago, 1978; Oc, 2018). This impact can be revealed under situations such as making ethical and forecasting decisions and through crisis scenarios. Desmet, Hoogervorst, and Van Dijke (2015) report that when the leaders are faced with increased market competition, they are less likely to make a decision of taking disciplinary actions against transgressors. It is mainly because the competitive situation makes the leaders frame their decisions based on instrumental (as opposed to ethical) perception of others’ unethical behaviors. Yet it is not that easy for leaders to make a high quality forecasting decision when they are in the face of uncertainty and external competitions, because intense competition and high turbulence are suggested to undermine leaders’ objectivity and harm their forecasting decision’s accuracy (Mumford, Steele, McIntosh, & Mulhearn, 2015). Going further, when organizations are in a crisis caused by an external threat, there is less time for sufficient sensemaking, and it is argued that a leader’s decision making style would become more directive with detailed step by step guidance to followers in order to keep the situation under control (Hannah, Uhl-Bien, Avolio, & Cavarretta, 2009; Weick, 1988). Further, the impact of environmental context is not limited to cultural differences and market forces. The leader’s actions that are selected and implemented can be seen as a form of decision making for the sake of goal attainment purposes (Mumford et al., 2000). In this sense, the type of organization (such as bureaucratic, entrepreneurial, voluntary, or professional) is crucial because it can influence leaders’ decision making styles due to the differences in the business direction and goal attainment (Mumford et al., 2000; Oc, 2018). For example, in entrepreneurial organizations, the decisions are likely driven by the need for achievement as opposed to other needs, whereas in bureaucratic organizations, the leaders’ decisions are based on the need for power (Oc, 2018; Spangler, Tikhomirov, Sotak, & Palrecha, 2014). As such, it is apparent that the complexity of organizations’ social contexts create tensions for leaders because decisions need to be made according to the contradictory yet interrelated strategies, goals and stakeholders’ needs (Smith, 2014). Notably, the complex nature of the organization also makes decision implementation more difficult and collaboration of multiple individuals and internal systems are required. As a result, leaders’ decisions about planning, such as contingency identification, prioritization of actions, and coordination, are deemed essential (Mumford & Connelly, 1991). As suggested by Vroom and Jago (2007), effective leaders’ decisions are to be made in response to the situation because we should not assume all decisions, which are in general thought to be desirable, would be equally effective in each situation. In order to tackle the restrictions and uncertainties that exist in the environment that were discussed earlier, leaders play a critical role in responding to the aforementioned tensions. Their decisions and actions in response to

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limitations can create conditions to influence their followers and performance (Floyd & Lane, 2000; He & Wong, 2004; Jarzabkowski, 2008; Smith, 2014). Precisely speaking, facilitating a multilevel and flexible context for joint decision making can help leaders under stressful situations (Smith, 2014). It is because the involvement of people in the dynamic decision making process is inevitable, and uncertainty and ambiguity could also pose threats to leaders emotionally and cognitively (Day et al., 2004; Lewis, 2000; Zaccaro & Klimoski, 2002). In practical terms, leaders can create a more favorable and flexible situation for decision making such as reducing constraints (e.g., bureaucratic limitations) and removing obstacles that limit decision quality (e.g., avoidable errors, quality defects, etc.) (Yukl, 2011). Most importantly, by creating a more open and collaborative team environment, and managing group processes and groupthink effectively, leaders will be able to provide better decision quality particularly when the situations are more complex (Carmeli & Schaubroeck, 2006; Kerr & Tindale, 2004). In summary, leaders need to consider how time and environmental context impact information gathering, option evaluation, and analysis, and their followers’ willingness to participate during the decision making process.

Conclusions In the past several decades, scholars have been continuously studying the behavioral pattern for which decision making theories cannot easily account (Oppenheimer & Kelso, 2015). In the context of decision making, Kahneman and Tversky (1986) point out that both rationality and people’s beliefs and preferences that are influenced by framing are factors that need to be considered, and the tension between these two types of considerations have been the key subjects studied in decision making research. An important contribution made in this chapter is about the integrated view of decision making capacity from skills and contextual viewpoints. Decision making research has been dominated by an economic and later psychological approach that has focused on the calculation of optimal ­utility through experimental studies to address the irrationality influence in decision making (Oppenheimer & Kelso, 2015). From the methodological standpoint, since decision making is such a dynamic process that involves people and is sensitive to the context, more diversified methods, such as neurological, event-based, qualitative and inductive methodologies, are suggested for studying the decision formation process (Heyler, Armenakis, Walker, & Collier, 2016; McHugh et al., 2016; Waldman et al., 2011). Information processing, framing, and option evaluation processes have a fundamental influence on decision making. Framing is argued to cause variance in preferences and beliefs of decision makers due to the impact of people’s emotions and experiences (Kahneman & Tversky, 2000; Tversky & Kahneman, 1985). That is, the capacity of decision makers to resist irrelevant information during decision making is affected by the problem and option framing. For example, emotionally

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loaded words can impact the selection or avoidance of choices, which may eventually impact the outcome (De Martino et al., 2006; Kahneman & Frederick, 2007). Following the same logic, because subjective interpretation of outcomes could cause biases and errors, and lead to variations in preferences and judgments (Tversky & Kahneman, 1974), leaders need to enhance their cognitive capacities to handle the performance and social information appropriately for the sake of decision quality (Mumford et al., 2017). This chapter emphasizes the importance of leader problem solving, social judgment, and emotion management to provide a broader view of decision making. Leaders are expected to make sense of a situation, frame the problem with comprehensive performance and social information they have collected, and evaluate the ideas for generating the best possible solutions while managing their stress levels (Connelly et al., 2013; Mumford et al., 2007; Yukl, 2013). However, one has to note that decision making is highly contextual in which the decision making outcome is dependent on factors such as time and environmental context. Time pressure can cause variations in choice evaluation (Oppenheimer & Kelso, 2015), and a more simplified information processing strategy may also be adopted for quicker decisions (Zhao & Olivera, 2006). The decision quality may then deteriorate due to the occurrence of errors (Eubanks & Mumford, 2010; Hunter et al., 2011). Furthermore, the cultural background of people may influence their views and preferences about the situation and in turn influence problem framing (Westaby et al., 2010). Moreover, the environmental context outside and inside the organization are also important factors to be considered because leaders’ decisions are made based on the situations at hand (Vroom & Jago, 2007). This chapter has discussed the importance of problem solving, social judgment, and emotion management on leader decision making outcomes. While the dominant view in management practice is that emotions and feelings are potential risk factors that are unfavorable to decision quality, and are to be suppressed or constrained (Ashforth & Humphrey, 1995; Putnam & Mumby, 1993), this notion is to be challenged because emotional information can be a factor to facilitate or hinder the decision making outcome (Seo & Barrett, 2007). Because decision making is a contextual subject (Tversky & Kahneman, 1985), and by nature is impacted by subjective behavior, a further understanding about emotional and social knowledge structures, and how this information impacts decision making outcomes is needed (Connelly & Gooty, 2015; Zaccaro et al., 1991). Moreover, different types of skills and strategies are needed for making various kinds of decisions since different problems do arise across performance domains (Mumford et al., 2017). As such, it is necessary to have a more systematic and comprehensive understanding of how specific leader skills requirements can help to manage certain problems that confront leader roles (Mumford et al., 2017). Furthermore, the influence of followers needs to be considered more carefully. The follower situation is one of the factors that would shape the degree to which a leader would involve followers during decision making, because leadership has

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been conceptualized as a mutual influencing process among leaders and followers (Day et al., 2004; Oc, 2018; Pearce & Conger, 2003; Morgeson, DeRue, & Karam, 2010). In essence, a more comprehensive view that integrates follower impact in terms of leader decision making is recommended.

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10 MAKING SENSE OF LEADERS MAKING SENSE Peter Gronn

During the resurgence of leadership in the late 1970s and the succeeding two decades or so, the idea of sensemaking claimed space as a focus of theory and research. Its emergence was due principally to the efforts of Karl Weick, an organizational psychologist who asserted that “organizations are in the business of making sense”, so much so that “if they attend to anything with consistency and regularity, it is to their sense-making activities” (Weick, 1979, p. 250). To substantiate this claim, Weick sought to divert scholars away from orthodox views of organizations as predictable, fixed, structurally immutable, and rationally functioning to an understanding of them as loosely coupled, in continual fluidity and flux, and as constantly re-accomplished in everyday negotiations between their members and associated actors. Weick emphasized process rather than product, organizing instead of organization, a shift in keeping with the tenor of the times, when the idea of socially constructed reality was attracting attention. Sensemaking’s distinctive phenomenological appeal was seductive for scholars seeking to better understand the experiences of managers and leaders. According to Weick, the working day for organization members comprised being in flow, immersed in frequently contingent, ambiguous or equivocal sets of experiences, in which there were few self-evident solutions to problems, as they sought to resolve issues and bring order out of indeterminacy. Weick offered a (playfully expressed) sensemaking recipe for besieged managers: “How can I know what I think until I see what I say?” To answer this question, organizations were presumed “to talk to themselves over and over to find out what they’re thinking” (Weick, 1979, pp. 133–134). From this starting point, Weick articulated a theory of sensemaking as enactment. This chapter analyzes sensemaking and its legacy for the field of leadership. Chapter-length space allocation precludes a full-scale literature review, but in the

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early discussion sections the key features of leader sensemaking are summarized, as initially outlined and then later developed by Weick himself (1979, 1995), and then by others (e.g., Maitlis & Christianson, 2014). Since Weick wrote, however, there have been significant advances in understanding leader cognition, including sensemaking’s place in this rapidly accumulating research corpus. Mumford, Friedrich, Caughron, and Byrne (2007), for example, have modeled leader cognition processes and, after elucidating Weick’s claims, their work frames discussion of the domain-specific nature of leader cognition along with the potential for leader sensemaking skill acquisition in leader development. The final part of the chapter discusses two research-related implications of sensemaking: First, whether in light of recent refinements of Weick’s original idea, aspects of leader and organizational sensemaking might be more accurately captured by an analytical discourse of power; second, whether, as a result of recent cognitive and neuro science research, the ontological assumptions on which leader sensemaking and its model of cognition rest might benefit from a rethink.

Enacting Enactment Weick’s style of expression (especially in his earlier writings) does not always guarantee ready accessibility to the substantive import of his discussion. This is because he relies very heavily on metaphor and the license that this affords, he resorts to a highly liberal (even permissive) view of human agency, and at times his claims border on the obscurantist (as in “Departments are arbitrary collections of double interacts” [Weick, 1979, p. 260]). Just how literally, for example, should a text subheading be taken when it is an injunction to “Complicate yourself!” (Weick, 1979, p. 261, original capitalized)? Weick uses confounding modes of reasoning (such as claims that action precedes thought, effects precede causes, believing is seeing, and an environment is an output rather than an input) and his presumption that organizational processes are analogous to natural evolutionary mechanisms (i.e., variation, selection, and retention) is by no means uncontested or self-evident. The essence of his enactment claim is that “[organizational] meaning is retrospective” and that “only elapsed experience is available for meaningful interpretation” (Weick, 1979, p. 245). To validate that assertion, Weick focused on the experience flows in which managers and others find themselves embedded. It is the encountering of discontinuities in these flows that “provide the enactable environment, the raw material for sense-making” (Weick, 1979, p. 130, original emphasis). In his initial (brief ) exposition of environmental enaction, Weick (1969, pp. 63–71) drew on the writings of the Austrian-born philosopher, Alfred Schutz, and he viewed enactment as the equivalent of natural variation. It expressed the “more active role that we presume organizational members play in creating the environments which then impose on them” (Weick, 1979, p. 130). This environmental creation was said to be facilitated by the participants’ (mental)

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construction of cause maps, which in Weick’s illustrative case of performing musicians are superimposed on the making of music. The imposition of one of a range of potential cause maps is evidence of selection and retention having taken place. But the proclaimed environmental creation (which implies the exercise of agency by organization members) brings with it a sting in the tail. This is because successful cause map imposition has consequences: Observance of a cause map’s dictates as part of member enaction henceforward becomes constraining in its framing of organizational actions (i.e., the previously implied sense of agency suddenly morphs into structuring and potential structural obduracy). Weick is imprecise about the environments in which his organization members operate, although given “the richness and multiplicity of meanings that can be superimposed on a situation that organizations must manage” (Weick, 1979, p. 174), the clear inference is that he means cognitive and/or decision-making environments. This definitional slipperiness facilitates as loose or as tight an understanding of such environments as he requires for his explanatory purposes, except that his elasticity leaves questions begging. Thus, significant queries remain about the properties of environments, in particular their differentiation (a war zone, for example, differs in magnitude and impact from an orchestra pit) and their duration (momentary musical instrument improvization is vastly different from prolonged peace treaty negotiations). Likewise, the conditions which govern the agencystructure interplay, let alone the degrees of freedom for choice-making that may be enabled and the qualitative differences between various structural constraints, are nowhere systematically specified. Nonetheless, Weick (1979, pp. 141–142) is unequivocal about what equivocality reduction or elimination entails. While the processes that he articulates are claimed to occur “in the minds of solitary actors”, the interpretational process at the heart of environmental enactment has to involve “at least two members interlocking some behaviors to accomplish this removal”. Overall, however, the key point of his modeling of interlocked sensemaking is that it is always retrospective, which is a big call to make in respect of some important organizational shibboleths and orthodoxies. Strategy is a typically misconceived illustration of his point (Weick, 1979, p. 188, original emphasis): Organizations persistently spend time formulating strategy, an activity that literally makes little sense given the arguments advanced [about sense-­ making through enactment]. Organizations formulate strategy after they implement it, not before. Having implemented something—anything— people can then look back over it and can conclude that what they have implemented is a strategy. In effect, the conventionally understand sequencing is reversed, because “meaning is always imposed after the fact and only after elapsed actions are available for review” (Weick, 1979, p. 188).

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Amplification and Illustrations About 15 years after his initial monograph-length exposition of enactment, Weick’s (1995) follow-up analysis shored up further the intellectual underpinnings of the how-do-I-know-what-I-think-until-I-see-what-I-say recipe. Some previously missing properties of sensemaking were spelled out: He dealt with the identity of the I who was claimed to do the knowing; the retrospective nature of that I’s seeing; enactment by that knowing I; the socialization of that knowing I and the audience’s receptivity to its enacting; the ongoing nature of sensemaking; the cues that trigger enactment; and, the plausibility (although not necessarily the accuracy) of what gets to be sensed (Weick, 1995, pp. 17–62). Moreover, he articulated the types of occasions in which sensemaking is triggered, the characteristics of the circumstances in which triggering occurred, and the properties of those occasions (quintessentially interruptions to the ongoing flow of experience). Words were still taken to be the prime vehicles for conveying the sense that might be made (Weick, 1995, pp. 106–107), with actions and beliefs shown to play a stronger role in enactment than previously. Expectations also assumed more prominence, because when “perceivers act on their expectations, they may enact what they predict will be there” (Weick, 1995, p. 152). A useful illustrative avenue—to take an instance of Weick’s solitary actor— into knowing what one thinks after having seen what one has had to say might be thinking aloud in a diary entry or a letter. In this biographical example (from 1928), following the visit to Australia by James Russell, the recently retired dean of Teachers’ College, Columbia, the possibility of an independent educational research agency, funded by the Carnegie Corporation, was being mooted. The man appointed as that agency’s inaugural director, a young Australian educationalist, K.S. Cunningham—from 1930 until 1954, chief executive officer (and from 1939 director) of the Australian Council for Educational Research—pondered in a letter to a fellow educationalist how (Williams, 1994, pp. 150–151): Since hearing of the proposal for the establishment of some sort of research institute in educational work I have from time to time turned the matter over in my mind and, based partly on my visits to the [Federal] Bureau [of Education] at Washington and to the headquarters of the National Education Association, have come to certain more or less clearly formed ideas as to the possible directions of usefulness of such an institute in Australia. At face value, this thinking appears to be sensemaking pure and simple, but Weick (1995, p. 40) insists that sensemaking is “never solitary because what a person does internally is contingent upon others”. Better still, then, than such a straightforward reflection might be the public reporting of one’s words (in, say, a newspaper) followed by a later diary reflection, because this interplay between the two sources introduces the public expectations to which one has been required to respond.

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Eastward across the Tasman Sea, in early 1929, another educationalist, a youthful Englishman, James Darling—headmaster of Geelong Grammar School, Australia (1930–1961)—was leading a party of English schoolboys on a tour of New Zealand. At one point he reflected on the ordeal experienced in having to make numerous speeches to impress his dominion hearers about the strength of imperial ties. One day the tour party visited a New Plymouth war memorial and, in a “graceful tribute”, a newspaper quoted Darling as having said that (Gronn, 2017, pp. 100–101): [I]t was right that [the boys] should stop at that spot and that he should speak to them in order that at least once while in New Zealand they should recognize the sacrifice New Zealanders had made for the Empire in [World War 1]. Self-deprecatingly, Darling later recorded that the tour group “seemed fairly good and I myself was, I think, the only person whom it struck as odd that I, Jim Darling, should be standing in so pompous a fashion, uttering a funeral oration” (Gronn, 2017, pp. 100–101).

Elaborations and Modifications In Sensemaking in Organizations, Weick (1995, p. 169) cited the surprise of a reader of the book’s draft that there was a paucity of empirical (in this instance quantitative) research in his historical overview of sensemaking. Weick (1995, pp. 65–69) himself summarized briefly 55 studies spanning 1890–1994, although a decade later he noted only a “modest amount” of empirical research (Weick, Sutcliffe, & Obstfeld, 2005, p. 41). Later still, by contrast, Maitlis and Christianson’s (2014, pp. 107, 108) extensive review of recent work in the sensemaking field drew attention to “considerable advances” in research, particularly over the previous decade, so much so that one database search yielded 4,000 scholarly articles that included the word “sensemaking”. The other noteworthy development mentioned was the appearance of six sensemaking-related terms in the literature (Maitlis & Christianson, 2014, p. 69): sense-breaking, sense-demanding, senseexchanging, sense-giving, sense-hiding, and sense-specification. Of these, sensebreaking and sense-giving have become especially prominent. In a discussion of strategy formulation—at odds, incidentally, with Weicks’s earlier view—Gioia and Chittipeddi (1991, p. 433) drew on their findings of an ethnography of a new university president, and defined sense-giving as the effort of a CEO who, having himself made sense of an altered vision of an organization’s strategy, sought “to influence stakeholders and constituents to accept that vision”. Sense-giving, in short, was the “attempt to influence the sensemaking and meaning construction of others towards a preferred redefinition of organizational reality” (Gioia & Chittipeddi, 1991, p. 442)—Maitlis and Christianson (2014, p. 78) refer to “influence tactics”. But there is a problem here. While the labeling of the CEO’s or president’s new self-understanding of future directions as making sense seems sensible enough, why should his subsequent action be characterized as

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sense-giving? Given that the infinitive “to influence” is used twice in the quoted extracts, sense-giving is either superfluous or even redundant. That is, what does the infinitive “to give sense” add to understanding that “to influence” cannot? The authors don’t say. (Maitlis & Sonenshein, 2010, p. 572, call for a “tighter integration between sensemaking and influence research”.) Perhaps Gioia and Chittipeddi’s intention is to signal benign action by their attribution of sensegiving to a CEO; if so, then what was benign about the university president’s initiatives, some of which “called for restructuring, others called for growth, and still others called for retrenchment” (Gioia & Chittipeddi, 1991, p. 440)? After all, determined as the president was to make the university in question one of the nation’s top ten public universities, the vision for that university was his vision at the end of the day, as the authors note, and prior to its finalization he did not shy away from deciding by executive fiat. Similar questions can be asked about the sense-giving leadership of the Ford Foundation awardees discussed by Foldy, Goldman, and Ospina (2008, p. 516), who introduce as the outcome of joint collective sense-giving a “building block concept” that they label a cognitive shift. This idea is virtuous because it “operationalizes or breaks down the slippery, hard-to-define area of sensemaking into discrete events or units” for further comparison and analysis (Foldy et al., 2008, p. 517). On-site interviews with the awardees and their organizations revealed considerable evidence of their attempted re-framing of issues, so as to bring about cognitive shifts in their constituencies’ perceptions. But intended sense-giving is one thing and its accomplishment is altogether different, because, the authors admit, “we can’t establish whether a shift actually took place” (Foldy et al., 2008, p. 527). Similar points can be made about sense-breaking. Marcy (2015) built on the cognitive shift idea of Foldy et al. (2008) and Gioia and Chittipeddi (1991) to articulate a notion of sense-breaking and creative destruction by radical social innovators. This leader category applies to people who contest elite mental models—“breaking the sense that [these] normally make” (Marcy, 2015, p. 380, original emphasis)—to provide audiences with new models of preferred cultural and social relations and values. But once again there are limitations. Marcy reports that his leader cognitions were “inferred from [the] behaviour” of a Frenchman, Guy Debord, during the years 1952–1978, while he concedes that alternative explanations of Debord’s behavior are possible. Moreover, Marcy’s (2015, p. 381) historical study “cannot provide conclusive causal evidence” of cognitive shifts. The only conclusion to be drawn by his readers, therefore, is that the sole mental model accessed is Debord’s and that, without evidence of its existence, sense-breaking, so called, is conjectural.

Probabilities and Distributions In relation to such proliferating peripheral sensemaking-related concepts, Maitlis and Christianson (2014, p. 107) caution that, because these are “not always clearly defined”, researchers should concentrate on the core process of sensemaking.

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For Weick, that core process shifted in two overlapping directions: analysis of empirical studies of crises (low probability, although high consequence, events) and instances of distributed or collective mind (in what he termed high reliability systems). These two developments overlapped in Weick’s analyses because, if actions are the key to understanding and “actions devoted to sensemaking play a crucial role in the genesis of crises”, then “the number and quality of actors available to do that acting and interpretation become crucial variables” (Weick, 1988, pp. 308, 312). Weick’s focus on collective mind paralleled the emergence in leadership of distributed leadership and distributed cognition in psychology. Such a distributional (or plural) turn, so to speak, probably reflects a changed view of complex professional work practices, examples of which include the role of nursing in healthcare systems, in which (Weick et al., 2005, p. 412): knowledge of this unfolding sense [about the correctness of treatment] is not located just inside the head of nurse or physician. Instead, the locus is systemwide and is realized in stronger or weaker coordination or information distribution among interdependent healthcare workers. Another example is defense force supercarriers (Weick & Roberts, 1993). Collective mind on such vessels is evident in the coordinated and interdependent actions of many individuals. Thus, when in Weick and Roberts’s example a pilot lands a fighter aircraft on a flight deck there may be up to 25 or more people stationed simultaneously on a landing platform, a tower, and a bridge to facilitate the landing (or recovery). Accident-free completion of such activity—as also in the example of the scheduled ordnance loading of aircraft—requires numerous trained dispositions that are heedful or mindful, because heedfulness is claimed to enact a collective mind (although not a group mind; see following sections). If a collective mind is to manifest the requisite understanding of unknown and unforeseen events then a totality of individual know-how sufficient to deal with unexpected events has to be connected up. An even better possibility will occur if such a mind experiences sustained density of interrelations across time and activities. Despite the heedfulness of collective minds, this achieved heedfulness may break down due to system overload (e.g., unforeseen demands and/or too much information)—in one instance documented, overload resulted in the loss of a fighter plane valued at $38 million (Weick & Roberts, 1993, pp. 363, 366, 372). A graphic illustration of the failure of collective mind (albeit of a temporary system) was the disastrous fire at Mann Gulch, Montana (in 1949), caused by a lightning strike, in which 13 men burned to death. The Mann Gulch Fire Service was minimal in size and much less sophisticated in comparison to a supercarrier, for it comprised merely a leader, a second-in-command, and crew members held together by a structure of interlocking routines. The simultaneous collapse of interrelated sensemaking and structure produced the disaster at Mann Gulch. Disintegration in a situation of potential death caused the firefighters to panic. In an improvised response to being trapped, the fire service leader ordered

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his men to drop their tools and he ignited a fire in advance of fast-moving flames so as to create (by way of elimination of a flammable fuel source) a safe zone and possible exit route, and ordered the crew to lie down in it with him. They refused, chose flight, and most of them perished. He survived (Weick, 1993, pp. 633, 634, 637, 638).

The Status of Sensemaking Knowledge These were high stress circumstances. Is there any way that sensemaking knowledge might be utilized to forestall the occurrence of such extremes and to facilitate decision making in more mundane situations of low risk? Weick et al. (2005, p. 419) are hopeful, because they claim that sensemaking analyses suggest that there are “important skills and capabilities that warrant attention and development”. If so, then what would it take to devise a portfolio of such sensemaking skills to be utilized in leader preparation and the monitoring of leaders subsequent practice? To answer this question is to run up against a logical conundrum inherent in the presumptions of sensemaking, because if actions precede event cognition, as Weick and sensemaking proponents claim, and if actions are likely to trigger the very outcomes that they might be intended to prevent (as in crises), then it is near to impossible to prescribe preventative measures in advance. This observation applies to low probability, high consequence circumstances, and to high probability, low consequence events. One way to utilize research knowledge, particularly professional occupational knowledge, would be to embody it in regimes of standards consisting of a range of capabilities, each specifying a level of accomplished performance in a sphere of practice. Standards-setting elevates the performance bar high, especially if an occupation’s practitioners are required to be accredited against such standards and also if career progression is dependent on the provision of practitioner data to assessors that they have attained the standards. Nonetheless, in school education, for example, this possibility of assessment of evidence-informed high quality teaching practice is attracting increasing interest ( James, Pollard, & Gronn, 2016). Support for standards among professional groups of teachers varies globally, but professional groups tackling the issues include the National Board of Professional Teaching Standards, in the United States; the new Chartered College of Teaching, in the United Kingdom; and, the Australian Institute for Teaching and School Leadership. Attempts in school education to formalize the knowledge base are coalescing around the idea of effective leadership of student learning, as in item 2.2 of the professional knowledge section of the recently published draft professional principles of the Chartered College of Teaching (undated [2018]), in which a chartered teacher: Identifies and draws on relevant education research and combines this with their knowledge of subject or specialism to develop a subject- or specialismspecific teaching repertoire.

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Such expertise might be displayed by a classroom teacher or a schoolwide teacher leader (in, say, curriculum). In respect of school principals’ leadership in assisting teachers to improve their teaching practice, Stein and Nelson (2003, p. 426) suggest that the classroom level expertise required of principals includes such leadership content knowledge as: subject matter, what is known about how to teach the subject matter, and how students learn the subject matter. The knowledge required to do this . . . will also include knowing something about teachers-as-learners and about effective ways of teaching teachers. Schoolwide leaders also impact on teacher learning and the learning of their students. Robinson (2011, pp. 9, 104) analyzed nearly 200 survey items related to teacher learning and development from about 15 studies (that asked teachers about their schools’ leadership) and found an average effect size of 0.84 for these items, which is strong evidence of school leaders making a difference. What about sensemaking knowledge?

Leader Cognition and Sensemaking In the absence of a cognitive model with which to frame his discussion, Weick (1993, pp. 641, 643) randomly listed as sensemaking skills curiosity, openness, complex sensing and wisdom to foster adaptability, speech training, and (Weick & Roberts, 1993, p. 367) socialization of newcomers and re-socialization for high reliability system work, and heedfulness. Mumford et al.’s (2007, p. 526) model of leader cognition, by contrast, when taken in conjunction with the findings of recent experiments in leader cognitive skill training (Barrett, Vessey, & Mumford, 2011; Marcy & Mumford, 2010; Vessey, Barrett, & Mumford, 2011), offers both a systematic rationale for, and approach to, skill itemization, and removes the potential interventionist hurdle arising from sensemaking’s inherent retrospectivity. The first point here is that, despite the continued search for a grand (and presumably generic skill-based) theory of leadership (e.g., Wren, 2006, p. 34), leader cognition and knowledge are domain-specific. This feature is graphically evident in the school education example and has also been affirmed by Mumford et al. (2007, pp. 519–520; 2015, p. 303) as applying to military officers. The second point is that, within various domains, leaders try to solve performance-related problems. In respect of such problems, cognition encompasses the variables pertinent to problem resolution, with a solution taking the form of a “cognitive model for understanding and responding” to a change event within the particular “time frame and conditions at hand” (Mumford et al., 2007, pp. 518, 522). While Weick (1995, p. 51) accorded significance to context (i.e., conditions at hand) as generating cues for making sense, he said little about the concept. Moreover, because he modeled retrospectively determined enactment as an isolated mental act, he said

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little about time. In reality, however, cognitive modeling for problem solving, be the problems straightforward or protracted, entails constant leader or leadership team scanning, attention to feedback, and reworking of mental models. That is, the cognitive process and the sensemaking are iterative.

Acquiring Sensemaking Skills Mental models underlie the skills of envisioning and sensemaking, and when devising such models, leaders draw on deep reserves of case-derived experiential learning (Mumford et al., 2007, pp. 523–525). In respect of the skill of vision formulation, for example, Partlow, Medeiros, and Mumford (2015, p. 451) suggest that “leader performance will improve when a leader focuses on a limited number of critical concepts or schemas”. Thus, when (during 1951–52) he originally promoted the idea of his world-famous mountain adventure-style school, Timbertop, James Darling was careful to win the hearts and minds of the school community by emphasizing just four core elements: enhanced emotional development accruing to adolescent boys in a remote setting, the production of future moral leaders for the nation, the simultaneous rectification of a severe accommodation shortage at the existing school site, and the ways in which his innovation built on the school’s outdoor education traditions (Gronn, 2017, pp. 352–355). With a reserve on which to draw of over two decades of case-based experiential knowledge as headmaster, as Mumford et al. refer to it, Darling sensed that the time was right for Timbertop. The narrative that he devised to justify the experiment proved to be highly imaginatively appealing. Droves of (local and international) visitors flocked to the new venture; their praise and the international media’s reception were glowing. By 1961 over 1,000 boys had spent time at Timbertop (Gronn, 2017, pp. 356–373). In addition to vision formulation, Mumford, Watts, and Partlow (2015, p. 303) have identified other cognitive leader skills that facilitate effective leadership, including divergent thinking, idea evaluation, causal analysis, forecasting, planning, and wisdom. Moreover, with the identification and development of potentially emergent leaders in mind, as well as those with varying levels of experience, benefit from training in evidence-based and targeted training programs has been shown to augment the acquisition and refinement of sensemaking capabilities. Causal analysis, for example, is one prerequisite for improved sensemaking. Darling’s realization that Timbertop might help solve his school’s accommodation crisis, for example, emerged as the outcome of his prudent analysis of the interrelationship between parental capacity to pay (tuition fees), quality of supply (facilities and programs), and market demand. Among a suite of findings, Marcy and Mumford (2010, p. 17) found, after a computer simulation exercise with 160 undergraduate students, that “leaders’ performance can benefit from training in causal analysis, particularly those working in complex environments where strategic interventions are often required”. Apposite strategic interventions might

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require variation, however, according to the degree of environmentally induced threat or stress experienced by leaders. In a closely related study, therefore, Barrett et al. (2011) distinguished between informational strategy training (i.e., a focus on causes and resources) and social strategy training (i.e., a concern with actors, affect, and goals). After working with 193 undergraduate participants in a study of leader problem solving, which required them to assume the role of the principal of an experimental school, Barrett et al. (2011, p. 747) showed that improved leader cognition might be facilitated by providing leaders with “strategies for working with, or thinking about, the knowledge they have acquired with experience”, provided that when leaders are under threat the strategies are social, and informational when they are not. Finally, in an experiment, this time with 170 undergraduates solving leadership problems in the realm of marketing, using self-paced instruction kits, the performance information-social information distinction was refined further to increase range of the strategic possibilities to 16 (Vessey et al., 2011). Perhaps the stand-out conclusion here related to intervention tailoring; that is, provision to leaders of better strategies tailored to “working with their knowledge under specific conditions” promises to do “much to enhance leader cognition and problem-solving, especially when leaders must solve problems under crisis conditions” (Vessey et al., 2011, pp. 725–726).

Power? Despite the initially expressed qualms with Weick’s discussion of sensemaking, the argument in the preceding two sections has demonstrated the utility of its application to instances of personal reflection by leaders, its significance for a range of demonstrated leader skills such as mental modeling and envisioning, along with the possibilities for the further refinement of such skill acquisition through targeted sensemaking training interventions. That said, the earlier caveat about some of the more recent refinements of sensemaking (sense-giving and sense-breaking) remains. Such conceptual stretching is problematic. The question here is: What, if anything, might be gained by introducing new terminology when, arguably, existing concepts will do? Consider power. When (as happened in 1944) about 15 years into his 32-year incumbency as headmaster of Geelong Grammar School (predominantly a boarding school, along English public school lines), James Darling struggled to contain an issue arising from alleged breaches of wartime travel restrictions by the school’s boarders heading home interstate, he, the media, and the federal politicians who became embroiled in this matter were certainly making sense of a sticky situation, except that breaking or giving sense are under-whelming descriptions of Darling’s problem. For about a fortnight (much to the embarrassment of the headmaster and the school) the Sydney press—the boys had been apprehended in Sydney by transport officials—covered the story, and it was debated in the federal parliament, until in the end the newspapers and (it was thought) public opinion

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had turned in the school’s favor. In their reporting, the newspapers may have been trying (at one level) to make sense of the rights and wrongs of the boys’, politicians’, and officials’ actions, but if this problem (for all the parties) is interpreted in power terms then they were also prejudicing the reputation of the wartime government, in their graphic depiction of an internal battle within the cabinet, as ministerial hairy-chestedness between two senior members of that government became increasingly bellicose in respect of possible prosecutions of the boys and who might launch them (Gronn, 2017, pp. 247–249). The incident not only provides evidence of a power struggle between ministers, therefore, but also of a hearts-and-minds battle to influence or persuade the other interests involved in respect of the legalities, and risks to institutional brand and reputation. The school that Darling headed was an elite school at the high end of the tuition fee-paying independent school market in Victoria, Australia, and had considerable social cachet. The inner workings of another elite institution, this time policymaking with global impact in banking, the US Federal Reserve, analyzed in detail by Abolafia (2010, p. 363), also highlights the issue of appropriate terminology. Transcripts of confidential meetings (in 1992) of the Federal Reserve’s Open Market Committee provide evidence of collective narrative construction in response to external events and the key role played by the meeting chairman, Alan Greenspan, who “was able to capture a majority” of the committee. While Greenspan is shown to not entirely get his way, such was his significance in the proceedings that the designation “sense-maker-in-chief ”—the term utilized by Denis et al. (2009, p. 238) to refer to key people officially mandated to devise new strategies for major reform of the Quebec health sector—seems appropriate. Debate in the meeting concerned whether to continue easing interest rates or to increase the current rate. The committee’s “operating model”, for which it was attempting to build a narrative that perpetuated its commitment to rate easing, acted as “a dominant perceptual filter that shapes and biases sense-making” (Abolafia, 2010, p. 363). At face value, here is a good illustration of Weick’s point about the subsequent constraining effects of actions that eventually solidify into structures. It is certainly that, but it is more, because the case portrays a high status authority figure exercising his chairman’s prerogative. Thus, dexterously, Greenspan steered a middle course between potentially progressive and retrogressive narratives of unfolding economic events, to achieve a short time delay before interest rates might be eased further. As Abolafia (2010, p. 362) comments, Greenspan used his prerogative sparingly and with a soft touch. Apropos these points about power and authority, Weick et al. (2005, p. 418) acknowledge that sensemaking “strikes some people as naïve with regard to the red meat of power, politics and critical theory”, and they concede that power shapes the cues on which people rely to make sense, who talks to whom, the plausibility criteria for assessing what may be happening, and so on. If so, then where does that leave sensemaking? In their review, Maitlis and Christianson (2014, p. 98) also concede that attention to power had been inadequate until

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publication of the preceding acknowledgment and concession by Weick et al. (2005), and that over the succeeding decade sensemaking research has become “less politically naïve”: Much more common now are analyses that recognize the multiple competing accounts present in organizations, and explore the political process through which some interpretations become legitimate while others “evaporate”. If, indeed, “the power struggles inherent in collective processes of meaning construction” (Maitlis & Christianson, 2014, p. 98) are becoming more visible, and formal authority is acknowledged as a sensemaking resource in organizations, then these are welcome developments. But there is an even bigger concession made about the influence of macro-social structures on sensemaking in organizations (Maitlis & Christianson, 2014, p. 99): Quite overlooked, or certainly underplayed, are the social, cultural, economic, and political forces that shape what groups will notice, how they can act, with whom they interact, and the kinds of environments that can be collectively enacted. These reflections are a belated recognition of analytical gaps and omissions in the literature. If sensemaking is the appropriate conceptual vehicle for getting to grips with the internal conversations going on in the minds of leaders, then the discourse of power and influence trumps it as explanatory machinery to analyze meetings or clashes of those minds. In such realms, it is difficult to see how typifying actions as sense—giving or sense—breaking really cuts the mustard.

Minds and Extended Minds The final consideration is about future directions for research. Some recent developments in cognitive and neuro science (an evidence source drawn on by Mumford et al., 2007, p. 536, and Marcy & Mumford, 2010, pp. 3, 16), may help move the field of sensemaking knowledge forward, because they have significant implications for the model of cognition on which sensemaking theorists and researchers tend to rely. When individual sensemaking is the focus, the received or orthodox notion of cognition (referred to euphemistically as the Cartesian view) is that a mind is located within a person, bounded by skull and skin, and is the sole locus of understanding. Here, the mind, when considered in isolation is both part and whole. With collective sensemaking, however, the locus of understanding is broadened by the presence of 2+ individuals and the conception of mind is modified. In

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one modification, the skull-bound mind view is retained but augmented. Thus, an aggregation of individuals may interact to create a cognitive collection (or system) of individual minds that possesses shared understanding. In this instance, individual minds aggregate to form a distributed whole. This seems to be what Weick and Roberts (1993, p. 360) had in mind with: individuals who act as if they are a group. People who act as if they are a group interrelate their actions with more or less care, and focusing on the way this interrelating is done reveals collective mental processes that differ in their degree of development. Our focus is at once on individuals and the collective, since only individuals can contribute to a collective mind, but a collective mind is distinct from an individual mind because it inheres in the pattern of interrelated activities among many people. Weick and Roberts’s choice of the words “as if ” signals that they were eschewing any reification of interrelating behavior as an entity, such as a group mind. In a second modification, individual minds, singly or as members of a collective mind, may out-source, delegate, or extend their capabilities (e.g., memory, computation) by incorporating technical artifacts into the cognitive system (individual or collective), such as calculators, computers, files, and records. Collectively, then, parts (individual minds) plus tools are aggregated and, with some degree of integration achieved, a functioning distributed cognitive whole (minds plus tools) emerges. This second modification is known as extended mind. But there is a third modification. In an influential article, Clark and Chambers (1998, p. 8) distinguished an active external mind. They proposed a two-way system comprising an individual organism linked with an external entity, in which: All the components of the system play an active causal role, and they jointly govern behaviour in the same sort of way that cognition usually does. If we remove the external component the system’s behavioural competence will drop, just as it would if we removed part of its brain. Our thesis is that this sort of coupled process counts equally well as a cognitive system, whether or not it is wholly in the head. Later, Clark argued for a new entity (although not a group mind). Informed by research into brain-machine interfaces, robotics, sensory substitution technologies, advances in understanding brain functioning, and the like, Clark (2007, p. 274, original emphasis) proposed newly configured systemic wholes “that are themselves the determiners of what is and what is not intelligible”. By this he means that non-biological tools and structures “can become sufficiently well integrated into our problem-solving activity to count as parts of new wholes in just this way”. While Giere (2007, p. 319) has queried the resort to what he regards as a “super, or collective, agent” in order to understand “how members of groups

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collectively make the system work”, Gallagher (2013, p. 10) defends the view that engagement with worldly externalities transforms humans’ cognitive processes, both neural and non-neural. This active extended idea of mind brings changed understandings of bodies and embodiment, and sensing and sensory agents. While at face value the pay-offs for sensemaking leaders may not be readily apparent, with Gallagher’s (2013, p. 11) view of a leadership capability such as decision making, as a matter of “embodied, emotion-rich, environmentally modulated processes”, in contrast with the conventional image of solitary reflection in an individual head, the potential for enhanced research understanding becomes evident. Alert to these possibilities, Maitlis and Sonenshein (2010, p. 573) have proposed the theme of embodiment as a focus of future sensemaking research, principally on the grounds that human bodily states play a role in making sense (e.g., through emotional sensitivity during interaction). The full import of the active extended mind claim, however, is that biology and the material world come together to constitute (and not merely contextualize) the mind, in which case appropriate engagement with Clark’s challenging claims—cognition as “embodied, embedded and extended” (Wilson, 2014, p. 21)—entails more than mere bodily co-presence and the cues that bodies may transmit for the purposes of sensemaking.

Conclusions Sensemaking is an important component of leader cognition and for that reason has warranted detailed review. The discussion of it provided a summary of developments, along with an appraisal of thinking and research in this area of knowledge, which as noted has been mostly shaped by Weick. Since its emergence as a scholarly focus, sensemaking has been, and continues to be, grounded primarily in the ontological assumptions of social constructionism (Maitlis & Christianson, 2014, pp. 94–95). Space limitations have precluded discussion of more than a handful of the key studies, in what after a life span of nearly 40 years has ballooned into a vast literature. Also due to the constraints of space, there has been only a nod in the direction of some of the field of sensemaking’s more recent emphases, notably identity and emotions, and the role that they play in sensing. Here, the identity of one authority figure discussed, the Federal Reserve’s Alan Greenspan, was clearly significant, and in the realm of emotions, there was the panic and all that that word implied in the highly condensed summary provided of the horror of Mann Gulch. The discussion also queried the currency and value of some new terminological extensions of sensemaking, and whether the idea might be productively restricted to the self-reflections of individuals and sets of aggregated individual minds. Given the importance of sensemaking as a skill for leaders, the discussion also documented some highly productive possibilities for training uptake in leader development. The chapter ended by pointing to some

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challenging developments in the wider domains of cognitive and neuro science that, in the coming decades, might yield some new and potentially exciting perspectives on sensemaking.

References Abolafia, M. Y. (2010). Narrative construction as sensemaking: How a central bank thinks. Organization Studies, 31, 349–367. Barrett, J. D., Vessey, W. B., & Mumford, M. D. (2011). Getting leaders to think: Effects of training, threat, and pressure on performance. Leadership Quarterly, 22, 729–750. Chartered College of Teaching. (undated [2018]). Chartered Teacher Programme. London: Chartered College of Teaching. Clark, A. (2007). Re-inventing ourselves: The plasticity of embodiment, sensing, and mind. Journal of Medicine and Philosophy, 32, 263–282. Clark, A., & Chambers, D. (1998). The extended mind. Analysis, 58, 7–19. Denis, J.-L., Lamothe, L., Langley, A., Breton, M., Gervais, A., Trottier, L.-H., Contandriopoulos, D., & Dubois, C-A. (2009). The reciprocal dynamics of organizing and sense-making in the implementation of major public-sector reforms. Canadian Public Administration, 52, 225–248. Foldy, E., Goldman, L., & Ospina, S. (2008). Sensegiving and the role of cognitive shifts in the work of leadership. Leadership Quarterly, 19, 514–529. Gallagher, S. (2013). The socially extended mind. Cognitive Systems Research, 25–26, 4–12. Giere, R. N. (2007). Distributed cognition without distributed knowing. Social Epistemology, 21, 313–320. Gioia, D. A., & Chittipeddi, K. (1991). Sensemaking and sensegiving in strategic change initiation. Strategic Management Journal, 12, 443–448. Gronn, P. (2017). Just as I am: A life of J.R. Darling. Melbourne: Hardie Grant Publishing. James, M., Pollard, A., & Gronn, P. (2016). Research and a standards-based teaching profession. In P. Gronn & J. Biddulph (Eds.), A university’s challenge: Cambridge’s primary school for the nation (pp. 197–218). Cambridge: Cambridge University Press. Maitlis, S., & Christianson, M. (2014). Sensemaking in organizations: Taking stock and moving forward. Academy of Management Annals, 8, 57–125. Maitlis, S., & Sonenshein, S. (2010). Sensemaking in crisis and change: Inspiration and insights from Weick (1988), Journal of Management Studies, 47, 551–580. Maitlis, S., Vogus, T. J., & Lawrence, T. B. (2013). Sensemaking and emotion in organizations. Organizational Psychology Review, 3, 222–247. Marcy, R. T. (2015). Breaking mental models as a form of creative destruction: The role of leader cognition in radical social innovations. Leadership Quarterly, 26, 370–385. Marcy, R. T., & Mumford, M. D. (2010). Leader cognition: Improving leader performance through causal analysis. Leadership Quarterly, 21, 1–19. Mumford, M. D., Friedrich, T. L., Caughron, J. J., & Byrne, C. L. (2007). Leader cognition in real-world settings: How do leaders think about crises? Leadership Quarterly, 18, 515–543. Mumford, M. D., Watts, L. L., & Partlow, P. J. (2015). Leader cognition: Approaches and findings. Leadership Quarterly, 26, 301–306.

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Partlow, P. J., Medeiros, K. E., & Mumford, M. D. (2015). Leader cognition in vision formation: Simplicity and negativity. Leadership Quarterly, 26, 448–469. Robinson, V. (2011). Student-centred leadership. San Francisco, CA: Jossey-Bass. Stein, M. K., & Nelson, B. S. (2003). Leadership content knowledge. Educational Evaluation and Policy Analysis, 25, 423–448. Vessey, W. B., Barrett, J., & Mumford, M. D. (2011). Leader cognition under threat: “Just the facts”. Leadership Quarterly, 22, 710–728. Weick, K. E. (1969). The social psychology of organizing. Reading, MA: Addison-Wesley. Weick, K. E. (1979). The social psychology of organizing (2nd ed.). Reading, MA: AddisonWesley. Weick, K. E. (1988). Enacted sensemaking in crisis situations. Journal of Management Studies, 25, 305–317. Weick, K. E. (1993). The collapse of sensemaking in organizations: The Mann Gulch disaster. Administrative Science Quarterly, 38, 628–652. Weick, K. E. (1995). Sensemaking in organizations. Thousand Oaks, CA: Sage. Weick, K. E., & Roberts, K. H. (1993). Collective mind in organizations: Heedful interrelating on flight decks. Administrative Science Quarterly, 38, 357–381. Weick, K. E., Sutcliffe, K. M., & Obstfeld, D. (2005). Organizing and the process of sensemaking. Organization Science, 16, 409–421. Williams, B. (1994). Education with its eyes open: A biography of Dr K.S. Cunningham. Melbourne: Australian Council for Educational Research. Wilson, R. A. (2014). Ten questions concerning extended cognition. Philosophical Psychology, 27, 19–33. Wren, J. T. (2006). A quest for a grand theory of leadership. In G. Goethals & G. L. J. Sorenson (Eds.), The Quest for a General Theory of Leadership (pp. 1–38). Cheltenham, UK: Edward Elgar.

11 LEADERS, TEAMS, AND THEIR MENTAL MODELS Jensine Paoletti, Denise L. Reyes, and Eduardo Salas

As we go through our daily lives, work, learn, and perform, we have an unseen mechanism guiding our actions and thoughts. Mental models are an internal representation of our views of the world and include the information we know. As our knowledge grows, we update our internal mental model. These models are useful ways to understand the world, and metacognitively to represent thinking. Mental models are powerful depictions of our place in life and our perspective on society. Essentially, we experience life through mental models, as they provide a framework for new and old experiences, our conversations with others, and our information that drives our choices and outlook on our inhabited domain (Goldvarg & Johnson-Laird, 2001). Mental models are the building blocks to interactions. They are an integral component to everyday decisions and actions that serve to maintain the effectiveness of teams and organizations (Forrester, 1971). Change-related functions are considered one of the main components of leadership behaviors (see Yukl, Gordon, & Taber, 2002), and leaders’ mental models play an important role in these functions. For example, the survival of liberal arts colleges in America in the 1970s and 1980s is partially credited to changes in the leadership’s mental models. During this era and the preceding decade, American college attendance sharply increased, but students began to prefer degree areas such as the sciences and professional fields (i.e., business, nursing, and journalism). The liberal arts colleges that refused to adopt professional programs were failing at a greater rate than previous years, while many of the schools that brought in new presidents were able to create professional degree tracks and faced better odds of staying in operation. This change was not always welcome, as it declined to follow the philosophy of liberal arts education, but changes in college leadership sometimes helped to change the universities’ program offerings, as the incoming college presidents

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brought their previous organization’s mental model of professional degrees within liberal arts education. Some of these new leaders came from state universities, some migrated from liberal arts colleges that had adopted professional programs, while others moved from non-selective universities. The college presidents from liberal arts colleges with professional programs and from the non-selective universities tended to adopt professional programs at their new liberal arts colleges, thus making their new colleges more competitive for the changing consumer demands (Kraatz & Moore, 2002). While the strategies of liberal arts colleges are not applicable to every organization, one lesson remains: leaders’ mental models are important. A divergent example is the Egyptian revolution, which occurred in 2011. Mohga Badran, management professor at the American University in Cairo credits shared mental models with the success of the Egyptian Revolution; “this was a leaderless revolution. The vision was the leader. Leadership was not a person. It was a feeling, a mental model, and a vision” (Youssef, 2011, p. 226). The article goes on to describe how the people shared a mental model desiring change in their country after seeing a similar regime change in Tunisia. Throughout the course of the revolution, there was a shifting vision and a shared mental model among citizens guiding them through the spontaneous organization and shifts throughout the course of the revolution that made it successful (Youssef, 2011). Change was achieved through a shared mental model, where leadership was shared among a group of citizens. This same process occurs, though much less dramatically, in organizational teams that share leadership. Thus, shared leadership’s mental models are not to be overlooked. Individuals and teams possess mental models, including those that serve as designated and informal leaders. The focus of this chapter is on leaders’ mental models, how they form, how they affect the leaders themselves, how they affect the leaders’ teams, and how leaders can develop a team’s shared mental model.

What Are Mental Models? Theory of Mental Model Before continuing with our probe into leaders’ mental models, it is first important to define mental models, so that we can all have the same understanding as to their meaning and implications. Mental models are “the end result of perception, imagination, and the comprehension of discourse” (Goldvarg & Johnson-Laird, 2001, p. 566). Essentially, they are defined as cause-goal linkages within an action system applying in some domain. The contemporary and generally accepted definition of mental models comes from a theoretical paper about reasoning, as metal models are a foundational aspect of reasoning (Goldvarg & Johnson-Laird, 2001). Notably, metal models do not apply to any information “represented in the mind”, as some earlier articles suggest.

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Importantly, mental models are not the only way to consider cognition. Transactive memory states are also used to understand cognition, particularly in teams where they are a way to consider who holds what knowledge (DeChurch & Mesmer-Magnus, 2010). They will not be discussed further in this chapter, but the reader should understand there are other ways of considering the cognition of leaders and teams.

Mental Models Across Fields Mental models find their origins in cognitive science. They are used to comprehend the world and are particularly applicable for drawing inferences ( JohnsonLaird, 1983). In the domain of human factors, mental models are descriptors of current states of a system and are used to predict states in the future of the system (Rouse, Cannon-Bowers, & Salas, 1992). In organizational science, where the authors find their academic roots, mental models usually refer to the representation of the employee’s knowledge and how it is related to their environment (Klimoski & Mohammed, 1994). These varying definitions are quite similar in nature; their biggest differences are the subjects of the mental model. In human factors, the mental models of interest center on the system that interacts with human users, while in organizational psychology, the mental models refer to the work-related knowledge that a member of an organization possesses and stores for performance. This domain will be the continuing focus of the chapter. Again, mental models are cause-goal linkages that are applied to some system within a domain (Goldvarg & Johnson-Laird, 2001). Here, our domain of interest lies within team leaders. In organizational psychology, we generally evaluate an employees’ mental model based on its accuracy or similarity to a subject matter expert’s (SME) mental model of the same topic (DeChurch & Mesmer-Magnus, 2010). Likewise, in team settings, shared mental models are appraised based on the similarity of one member’s mental model to the other members’ mental models in the team. Ideally, the cognitive content of the individuals’ (either the employee/expert or the teammates in question) should be the same (DeChurch & Mesmer-Magnus, 2010).

Leader’s Mental Models By relying leadership as an influence process, the follower must have cognitive change due to effective leadership. According to Lord and Maher, leadership “involves behaviors, traits, characteristics, and outcomes produced by leaders as these elements are interpreted by followers” (Lord & Maher, 1993, p. 11). Therefore, effective leadership must occur within the context of the followers’ interpretations and perceptions. It necessarily includes a cognitive component of a leader’s influence on the followers’ mental models. This is what differentiates mental models of a lay individual from leaders’ mental models. Followers’ models must

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be modified through the leadership process, through the leaders’ mental models (Benson, 2016). Leadership is defined in this chapter as an influence process ( Jacobs, 1971), which can be accomplished by one formal leader or shared among the team members, therefore shared mental models will also be considered as an important component. Leader’s mental models are important for their performance and the performance of their teams. Their leadership style goes together with their prescriptive mental models, which translates to sensemaking of the environment and then to visions that are disseminated to the followers. For example, charismatic leaders’ mental models focus on the future, while ideological leaders’ mental models are about failures and pragmatic leaders’ mental models center on pragmatics embedded within a complex system. The focus of these prescriptive mental models affects which population that the leader can most effectively influence, resulting in more distal effects on the leader’s performance (Bedell-Avers, Hunter, & Mumford, 2008). Thus, leader’s metal models guide information search, indicate causes to act on and goals to be sought. Readers should keep in mind that this is only one path for the leader’s mental models to result in performance.

How Are Leader Mental Models Acquired? Mental models are dynamic entities that need to be acquired and consistently updated with new information ( Johnson, 2008). This is especially true for leaders operating in the modern world. Today, forces such as globalization, swift technological developments, and shifts from manufacturing to a service-based economy have combined to create a world of work in which leaders and organizations must constantly adapt (Chell, 2001; Ilgen, 1994; Jarvenpaa & Ives, 1994). For this reason, the following section contains information about the acquisition and updating of mental models, as these updates are necessary for models to remain viable.

Theory For years the prevailing wisdom told organizations that knowledge creates leaders and that leaders would be more effective if they have more information in their relevant mental models. A more popular recent idea is of transformative learning, the process of editing, pruning, and enhancing existing mental models with new information and knowledge rather than creating new mental models. By integrating the new information into existing models, proponents argue that leaders will be more effective (Kegan, 2000; Mezirow, 1991). A study by McCall, Lombardo, and Morrison (1988) asked leaders to rate the most formative experiences for their mental models as effective leaders. The authors found that leaders reported the most important experiences were challenges and hardships experienced on the job, rather than graduate school, conferences, and workshops. This suggests that job rotation and job enlargement may be effective ways to enhance leader

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mental models within the transformative learning framework. However, this is not conclusive evidence, as there is also evidence that training can be effective for updating leaders’ mental models.

Biological Basis Thanks to the marriage of neuroscience and cognition, the modern leader can understand that the right hemisphere is largely at play for creating and updating mental models (Filipowicz, Anderson, & Danckert, 2016). Through neuroimaging and legion overlay analysis, researchers have been able to find evidence that certain brain regions are used for different components of mental models. The anterior insula preserve the individual’s current model, the inferior parietal lobe identifies salient information at odds with the model, while the medial prefrontal cortex decides when to examine new or updated models (Filipowicz et al., 2016). According to researchers at the University of Waterloo, there is a simple threestep process for updating mental models. Three basic components are required to accurately update mental models: (a) current predictions of a model need to be established in some way, (b) new information must be compared against those predictions to determine model efficacy, and (c) some form of hypothesis generation is required when predictions from a current model no longer lead to optimal outcomes. (Filipowicz et al., 2016, p. 207) This process happens within each person when updating their mental models, something that must happen continuously to ensure that our predictions according to our mental models are consistent with the information given to us through our environment ( Johnson-Laird, 1983). For leaders, this is an especially important process, as their predictions and actions have organizational impacts.

Training Training leaders is one useful way for them to acquire mental models for their jobs. The construction and articulation of mental models is considered an essential process for leader performance (Marcy & Mumford, 2010). Gaining the appropriate mental model can lead to task performance. One common metric for evaluating and training mental models is to use an expert’s model as the standard. Research verifies that a more expert-like mental model results in higher performance (Cuevas, Fiore, & Oser, 2002). Examples of an expert-like and a non-expert-like model can be seen in Figure 11.1. Components of training help to build leader mental models. For example, diagrams within training helped to build accurate participant mental models (Cuevas et al., 2002). The use of diagrams in training also helped participants to make connections across parts

Course Availability

Teaching/ Student Ratio

Priority for enrolling student athletes

Faculty course development time

Number of Undergrads Admitted

IMPROVED TEACHING ON CAMPUS

Course Availability

Source: Marcy & Mumford (2010)

Expert Mental Model (Left) Compared to a More Novice Mental Model (Right); Both on the Subject of Improved Teaching on Campus

FIGURE 11.1 A More

Faculty morale

Libraries & Technology Budget

IMPROVED TEACHING ON CAMPUS

Faculty course development time

Faculty out of class student contact

Hiring of teaching faculty

Faculty class prep time

Faculty salary linked to teaching performance

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of a training program in one study, as evidenced by integrative but not declarative knowledge (Fiore, Cuevas, & Oser, 2003). This suggests that individuals, particularly leaders, may unknowingly build their mental models with the help of diagrams within the context of training programs that transfers to other aspects of their work life. This may be particularly important considering that researchers regularly worry that only a small portion of what is trained is applied to the job (e.g., Baldwin & Ford, 1988; Grossman & Salas, 2011; Salas, Tannenbaum, Kraiger, & Smith-Jentsch, 2012). However, the literature is nuanced. In one study, the results indicate that training directly affects performance measures, actually accounting for the differences between high and low quality models between leaders (Marcy & Mumford, 2010).

Coaching Similar to double-loop learning, double-loop coaching is used to improve the mental models of leaders (Witherspoon, 2014). It is argued to be better suited for building and altering mental models because of its metacognitive nature. Born from executive coaching, there is some support for this type of leadership development (Witherspoon, 2014; Witherspoon & White, 1997), but it is still in its infancy and needs to be studied more (Gosling & Mintzberg, 2006). The three components to this style of coaching are reflection, reframing, and redesigning. When leaders are asked to reflect during a coaching session, they think about their behavior as leaders and their automatic reactions. This method is based on the reflection-in-action model (Schon, 1984). Coaches ask their clients questions like “What did you or others learn from the situation (e.g., about each other’s perspectives and challenges, their impact on others or the issue itself )?” or “What did you say or do that was particularly important in determining the results?” (Witherspoon, 2014, pp. 4–5). These questions probe into the leader’s thought process, allowing them to consider what happens throughout the course of their leadership and why. Reframing, the next element of this coaching framework, asks leaders to examine their schemas and thoughtfully modify or keep existing ones. This can be intrapersonal, interpersonal, or task-related in nature. Questions like “How do you see yourself/others in this situation—your/their roles and responsibilities, your/their intentions and actions to date, the impact others have on your skills/their skills, what you/they are up against, etc.?” and “How do you see the task at hand—your goals, needs, aspirations, and expectations in the situation you face—simply, what are you trying to accomplish?” help the coach and the leader to understand the leader’s mental models of themselves, others, and the tasks (Witherspoon, 2014, p. 5). Redesigning is the last step in the double-loop coaching process. Here, leaders take the thoughts and behaviors that they identified in the first two steps and implement any needed changes. This culminates to result in modifications to the leader’s mental models and attitudes at work across a potentially wide variety of topics and situations.

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Leadership Development Academics and scholars define leadership based on a variety of theories that have become standards for approaching leadership. Implicit leadership theories (ILT), the models of leadership unconsciously within individuals, are thought to develop throughout childhood (Antonakis & Dalgas, 2009; Ayman-Nolley & Ayman, 2005). By considering these perceptions of one’s prototypical “leader”, leaders can make their own mental models more explicit. This allows them to know themselves better and develop as leaders in a self-directed manner (Hall, 2004). One of the key components to this process is the use of metaphors as the leader is describing their style. According to scholars, metaphors are useful because they are a distilled version of conceptual understanding, although there is still debate about how they work within the context of cognition (Cairns-Lee, 2015; Lakoff & Johnson, 1980). Metaphors work together with modeling and clean language for the leader to understand their own mental models, and therefore develop those models more thoroughly. Modeling is the actual behavior that is trying to be uncovered, referring to the subconscious following of experiences, lessons or other leaders in a leader’s own leadership behavior. Through metaphors, the leader at hand will pay attention to their own perspective and make sense of their view (Lawley & Tompkins, 2000). Then by explaining the metaphors of leadership with clean (non-metaphor) language, the leader discovers their own mental model of leading (Cairns-Lee, 2015). Clean language helps “To acknowledge clients’ experience exactly as they describe it, to orientate clients’ attention to an aspect of their perception, and to send them on a quest for self-knowledge” (Lawley & Tompkins, 2000, p. 52). When leaders develop from a novice to an expert, they rely less on working memory, ILT, and heuristics. By practicing their leadership skills, leaders develop domain-specific knowledge and contextualize problem solving. Leadership skill develops as leaders practice, experience, and reflect on their leadership role, thus building their mental models. Both the actions and reflections are important for developing leader’s mental models. This results in less time needed for searching for solutions to future problems as leaders become experts; however, expert leaders spend more time than novices on interpreting situations and planning actions. Leaders’ mental models contain their problem-solving knowledge, guide interpretation of an environment, and prescribe the skills associated with leadership including task, emotional, social, identity level, meta-monitoring, and value orientation (Lord & Hall, 2005).

Leader Mental Models’ Impacts on Leader Performance Individuals’ mental models act as a perceptual filter through which information is passed. The same is true for leaders, however they are in a unique position of power within their organizations. This allows for their mental models to have

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widespread effects throughout the organization and interpersonally (RitchieDunham & Puente, 2008). Leaders’ mental models are important for vision formation, a step towards planning, goal achievement, and performance. Leaders’ mental models affect their performance on all ends of the task spectrum, from guiding information searching to facilitating effective task monitoring (Partlow, Medeiros, & Mumford, 2015).

Leader Level Vision and Sensemaking Leaders, particularly top management, are responsible for creating a vision for the organization. This vision serves to set a unified outlook on the future that provides meaning to the organization’s work for the employees (Klein & House, 1995; Meindl, 1990; Shamir, House, & Arthur, 1993). Besides guiding the future, a leader’s vision also creates a present culture within the organization and helps members face contemporary challenges (Hunt, Boal, & Dodge, 1999; Jacobsen & House, 2001). This is true as related to sensemaking, the process of reducing complexity to understandable mental models (Daft & Weick, 1984; Walsh, 1988). Especially in times of crisis or challenge, sensemaking is vital for organizations (Combe & Carrington, 2015). Leaders first rely on descriptive mental models and then evolve toward prescriptive mental models, which is the foundation of vision formation (Mumford, Friedrich, Caughron, & Byrne, 2007; Mumford & Strange, 2002). This way mental models affect sensemaking during crisis via vision. One study found that leaders’ visions were actually more impactful when their mental models were simple, not when they were complex. The authors explain that too much information can be distracting rather than useful (Partlow et al., 2015). They also touch on the cognitive limits of both the leader and followers, which can be challenged by a complex, rather than straightforward, vision (Ericsson, 2009).

Forecasting Forecasting, an often overlooked leadership skill, is essentially prediction of future events for individuals, groups, or organizations that is specifically not tied to a goal (Mumford, Schultz, & Osburn, 2002; Mumford, Schultz, & Van Doorn, 2001). Forecasts have their roots in leaders’ mental models because they are based on information about cause and effect within the leaders’ cognition (Goldvarg & Johnson-Laird, 2001). They are also related to leader performance because understanding the current and future state prepares the leader for action. Forecasts are also related to vision, mentioned before, through prescriptive mental models. Figure 11.2 shows the model of forecasting developed by Mumford and colleagues in context with other cognitive processes (Mumford, Steele, McIntosh, & Mulhearn, 2015).

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Scanning

Situational Cues

Prescriptive Mental Model

Vision

Mental Model

Key Causes / Key Outcomes Case Activation

Case Prototypes

Case Exceptions

Case Analysis

Situational Monitoring

Forecasting Attributes

Situational Contingencies

Forecasts

Action Selection FIGURE 11.2 Model

of Forecasting

Source: Mumford et al., 2015, p. 5

Leader–Leader Interactions Leadership literature established that leaders and their followers tend to have different types of interactions depending on the leaders’ style (Dansereau, Graen, & Haga, 1975). For example, charismatic leaders tend to be more interpersonally driven while ideological leaders tend to be more firm with their values and standards (Strange & Mumford, 2002). Interactions between leaders is largely related to leadership style, which is based on mental models (Bedell-Avers, Hunter, Angie, Eubanks, & Mumford, 2009). These mental models have five distinguishing components based on the style of leadership crisis: condition, sensemaking, type of experience, targets of influence, and locus of causation (Bedell-Avers et al., 2009; Mumford, 2006). One historiometric study of civil rights leaders found that

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charismatic, ideological, and pragmatic leaders interact with leaders outside of their mental model of leadership differently compared to their followers. Charismatic and pragmatic leaders, for example, appear to capitalize on the strengths and weaknesses of other leaders in a manner that better serves their goals. Ideological leaders, in contrast, remain loyal to their beliefs and values and appear to be unfaltering in their vision commitment—despite the best efforts of both charismatic and pragmatic leaders. (Bedell-Avers et al., 2009, p. 313) Therefore, leader mental models are the basis not only for the leader’s interaction with their followers, but for their interactions with other leaders.

Organizational Level Organizational Learning Culture Leaders’ mental models shape organizational learning culture. In turn, this can change the direction and mental models of an organization. There are three types of organizational learning cultures, which combine to create or modify mental models at the organizational level (Tran, 2008). Reflexive learning is primarily used by companies in stable markets and by governments, which do not have much need for development or change (Salancik & Meindl, 1984; Starbuck, 1983). Rather, reflexive learning focuses on sustainment through guarding traditions, values, and existing infrastructure. Leaders are imperative to the creation, change, and sustainment of culture, so they therefore also have an impact on the models developed through the context of learning culture (Tran, 2008). Singleloop learning, or bounded learning, refers to the impression of a static organization and context with direct causal arrows between phenomena (Slater & Narver, 1995). Organizations that know their customer base well, follow established rules, and guide innovation with values may fall into this category (Tran, 2008). The last type of organizational learning culture is called second-loop or critical learning; it is distinguished by its willingness to “unlearn” bias from tradition and values of the organization (Hedberg, 1981; Weick & Westley, 1996). This type of learning culture is the most radical and is most useful to organizations who need improvements. Critical learning can be exemplified by IBM’s transition from computer manufacturing to consulting for businesses due to the realization that technology service was a growing industry. Their focus on customer service allowed for a successful adaptation of organizational mental models due to leader-directed culture change in a knowledge-based economy, driven by globalization and technological advances (David & Foray, 2003; McGregor, Arndt, Berner, Rowley, & Hall, 2006; Von Krogh, Ichijo, & Nonaka, 2000). In this environment, leaders’ mental models

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can impede or promote innovation. Simply by living in the past and not understanding the nuances of the global market, leaders can hamper innovation and progress for their organization. By aligning their mental models of the economic and cultural landscape of the contemporary world, leaders can reduce the effects of this potential barrier to innovation ( Johannessen & Olsen, 2010).

Ethics Mental models are not necessarily accurate depictions of the world, as they are subjective in nature. Therefore there is also an element of social construction to these models (Werhane, 2008). The potential incompleteness of these models means that individuals tasked with decision making (especially leaders) may have “blind spots” related to information, particularly ethics (Bazerman & Chugh, 2006). These ethics blind spots within leaders’ mental models can affect those within and outside of the organization. According to one argument, leaders in middle or lower management are particularly vulnerable to these oversights because they are so concerned with looking incompetent that they never question the ethics or morality of their actions at risk of a reduction in performance (Moberg, 2006). One author uses Walmart as an organizational example. The typical stakeholder map of an organization includes suppliers and employees but does not delve further to examine supplier’s sweatshop workers, a relevant ethical concern for consumers. Moral imagination, “the ability to discover, evaluate and act upon possibilities not merely determined by a particular circumstance, or limited by a set of operating mental models, or merely framed by a set of rules” allows leaders to question and expand their mental models to address ethical issues (Werhane, 1999, p. 93). Therefore, mental models can take a systematic approach by including previously forgotten components (e.g., sweatshop workers), and leaders may revise and build their mental models according to moral imagination to reconsider their organization’s role within the broader global society (Werhane, 2008). Leaders are in a unique position to redirect their organization’s path to avoid or amend overlooked ethical considerations. Relatedly, there has been a cultural shift in organizational expectations in Australia with a push towards corporate social responsibility (Lindorff & Peck, 2010). Leaders of the financial structure had their mental models examined through qualitative interviews with researchers. They discovered that the sample of leaders’ mental models were more closely aligned with the shareholder model rather than the stakeholder model. However, other research suggests that leaders whose mental models support their organization’s social responsibility and fairness have greater engagement, commitment, and satisfaction from their employees (Lindorff & Peck, 2010). This combines “to create an organizational climate for CSR which contributes to a firm’s overall social reputation” (Aguilera, Rupp, Williams, & Ganapathi, 2007, p. 840). Therefore, leaders’ models can hinder and support ethics in an organization.

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Leader Mental Models’ Impacts on Team Performance Leaders’ mental models impact their teams’ performance (e.g., Dionne, Sayama, Hao, & Bush, 2010; Zaccaro, Rittman, & Marks, 2001). Although not specified, topics discussed previously have explicit (vision, sensemaking, innovation, etc.) implications for those working under the leader. So far, the focus has been primarily on designated leaders, but the focus will begin to include information about leadership that is shared rather than given to one member exclusively. This type of team is becoming more common. Self-managed teams, common in fields where innovation is key, sometimes share leadership among members by dividing responsibilities based on expertise (Moe, Dingsøyr, & Dybå, 2010). However, shared leadership is a spectrum for which leader mental models refer to the shared, team mental model, not to one individual’s model. Therefore, some teams with shared leadership have high levels of performance while others have lower performance (e.g., McIntyre & Foti, 2013). This was found to be true in a sample of church paid and volunteer leaders, for whom shared task mental models of goals and decision processes were predictors of the church’s financial standing, accounting for about 30% of the variance in the church financial well-being (Solansky, Duchon, Plowman, & Martínez, 2008). This study demonstrates the useful, objective organizational outcomes from the team’s shared mental models. One of the pathways for leaders’ mental models affecting their teams’ performance is through the capabilities allotted to the team. Specifically, leaders who have the power to choose their team members and allocate resources to their team hold the potential to impact their team’s performance (Ritchie-Dunham & Puente, 2008). These choices are all based on the leader’s model of the team’s needs and ideal composition, which may be accurate or less than accurate. Additionally, participative leadership styles, in which the leader and the followers make decisions together, has been shown to increase the team’s mental model convergence. This people-focused leadership, which contributes to team mental model convergence, then leads to team performance when combined with members’ diversity in expertise and the team’s collective confidence (Dionne et al., 2010). One article argues that transformational leadership is a type of participative leadership style, which should increase the team’s mental model convergence per Dionne et al. (2010). This study found that followers’ perception of the leader’s transformation leadership was predictive of the teams’ performance (Braun, Peus, Weisweiler, & Frey, 2013). Therefore, it seems there is an effect of leadership style on team performance through shared mental models; in particular, transformational leadership demonstrates this effect in work teams. Another pathway between leader mental model and team performance is through information exchange. Marks, Zaccaro, and Mathieu (2000) found that the more information given to leaders during a briefing resulted in greater mental model similarity within the team. Their study compared adaptive performance between teams whose leaders had been briefed and teams whose leaders had

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not been briefed. In their study, they found that leaders’ knowledge was communicated to their teams, which was thought to positively influence team mental model development (Marks, Zaccaro, & Mathieu, 2000). Thus, there is evidence that leaders mental models may promote adaptive team performance.

Decision Making Leaders, just like all people, are suspect to cognitive biases that can result in poor decisions. Given a leader’s inherent informal or formal power in the organization, a leader’s decision making may be more consequential to the team’s performance relative to a follower’s decision making. In this way, leaders’ mental models have a pathway to affect their team’s performance. According to research on expert decision makers, mental models allow for a cognitive simulation of possible outcomes. The quality of the mental model is the key to effective decision making, rather than a trait of the decision maker (DiBello, Lehmann, & Missildine, 2011). This is key information for those looking to improve their leadership abilities. By reducing gaps in information, a leader can bolster their own mental model to make more informed and, presumably, better decisions. One guide to decision making says that (1) self-awareness, (2) development orientation, (3) systems perspective, (4) emotional orientation, (5) complexity, and (6) generative conversation together produce decisions characterized by discovery and collaboration (Benson & Dvesdow, 2003). Self-awareness helps the leader to understand their own strengths and gaps in knowledge and understand. Development orientation is also referred to as “learning orientation”, where the preferred outcome is gaining skills rather than immediate performance. Systems perspective recognizes the world as a “thousand-way interaction” of organizational, cultural, and societal factors (King, 2017). Emotional orientation overlaps with emotional intelligence, or understanding and managing one’s and others’ emotions (Davies, Stankov, & Roberts, 1998). Complexity considers the roles of a decisions’ shareholders. Generative conversation stresses a lack of judgment paired with listening and asking questions to understand solutions to the decision at hand. According to theory, these six facets are important for decision making that is holistic and sensitive to multiple perspectives (Benson & Dvesdow, 2003). According to a qualitative, thematic analysis on collaborative information seeking during team decision making, leadership is an integral part of developing a team’s mental model at different parts of the process. One participant discussed the benefits of a leader at the start of a project. The leader encourages the team to come to a shared understanding of the overall goals rather than each member attempting their own part without knowledge of others’ roles (McNeese, Reddy, & Friedenberg, 2014). The leader’s behavior sets the team up for successful coordination throughout the project. The team is able to get a sense that “everything is laid out, everyone’s on the same page, you don’t really, like you don’t waste any time, it’s more efficient”, per one participant in McNeese et al. (2014).

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This efficiency translates to more productive teamwork. This higher performance quality is spurred by the team’s leader via the team’s mental model. Additionally, the leader also helps manage conflict within the team (McNeese et al., 2014). From meta-analytic evidence we know that conflict management is important for success as a team, as interpersonal conflict and conflict on how the task is completed (process conflict) are negatively related to team performance (O’Neill, Allen, & Hastings, 2013). However, when the team has high psychological safety, or the members feel comfortable for interpersonal risk-taking, then conflict on the task itself can benefit the team’s performance (O’Neill et al., 2013). The team leader should harness this knowledge and protect their team’s performance from relationship and process conflict while building psychological safety so that task conflict can allow their team’s performance to thrive.

Shared Team Mental Models Leaders are not the only ones on a team that must develop a high-quality mental model; every member of the team should develop a mental model that aligns with one another’s, which is known as a shared mental model (SMM), team mental model, or team cognition. Specifically, SMMs are defined as “knowledge structures held by members of a team that enable them to form accurate explanations and expectations for the task, and in turn, to coordinate their actions and adapt their behavior to demands of the task and other team members” (CannonBowers & Salas, 2001, p. 228). In other words, it means that everyone on the team is on the same page, reducing ambiguity and making it easier to anticipate each other’s actions. There are four types of mental models identified by Cannon-Bowers and Salas (2001): (1) task-specific knowledge (i.e., grasping the specific procedures and actions required to perform a task), (2) task-related knowledge (i.e., awareness of team member roles, interdependence, and responsibilities for the task) (3) knowledge of teammates (i.e., understanding information about the other team members such as their skills and preferences), and (4) knowledge of attitudes or beliefs (i.e., familiarity with each other’s values and motives related to work). Zaccaro, Rittman, and Marks (2001) explain that the combination of these types are the building blocks to form a team interaction model: The prescribed roles of team members need to emerge from a consideration of (a) the equipment or other materials that team members will use in completing subsequent collective tasks, (b) the specific task requirements that must be addressed through collective action, and (c) the task-relevant characteristics of team members that help define the contributions each can make to successful collective action. The strategies and tactics that emerge from a consideration of these factors, their moderating contingencies, and specific roles of each task member in particular action plans become

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incorporated into the team interaction model. The quality and elaboration of this model is associated with how well team members will be able to coordinate their subsequent activities. (p. 460) Cannon-Bowers, Salas, and Converse (1993) identify SMM as one of the main coordinating mechanisms for effective teamwork due to the reduction in overt communication required for performance. That is, a shared understanding of the topic or problem at hand reduces the need for blatant discussion. In addition, extensive research has linked it to improved team performance (DeChurch & Mesmer-Magnus, 2010; Marks, Sabella, Burke, & Zaccaro, 2002; Marks et al., 2000; Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000; Smith-Jentsch, Mathieu, & Kraiger, 2005). DeChurch and Mesmer-Magnus (2010) demonstrated meta-analytical evidence that across a variety of team types, team cognition positively predicts team task-related processes (e.g., planning, goal setting, and coordinating), motivational states (i.e., shared reactions of the interpersonal aspects of the team), and performance. According to McComb (2007), the process of developing a shared mental model requires that an individual shift from an individual perspective about the team to a team perspective. This involves three phases: (1) the orientation phase, (2) the differentiation phase, and finally, (3) the integration phase. The orientation phase consists of becoming familiar and aware of the team situation and individual ideas and opinions. During the differentiation phase, team members form their ideas of the team and respond to each other’s opinions, and then in the integration phase they form a collective focus from their individual perspectives.

The Leader’s Role in Developing a Shared Mental Model The leader plays a crucial role in developing a strong SMM (Dionne et al., 2010). In this section, we describe how a leader is involved in the process of shifting individual mental models to the team level. The leader is involved in influencing, developing, and sustaining an SMM through (1) encouraging effective communication patterns, (2) implementing specific leader behaviors, and (3) providing shared experiences.

Communication Communication, in general, influences the creation of an SMM a great deal. Specifically, the quality is more important than the frequency (Marlow, Lacerenza, Paoletti, Burke, & Salas, 2018). Team members, including the leader, should share unique information as opposed to common knowledge. Although this seems intuitive, oftentimes team members may feel discomfort sharing information that they think could deviate from what the team already has established, resulting

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in groupthink rather than having an accurate understanding of the team’s taskrelated problem and solution ( Janis, 1982). The studies described later elaborate on methods of how to communicate effectively. DeChurch and Mesmer-Magnus (2010) suggested that leadership is probably one of the most instrumental factors in molding a team’s cognition. Leaders can use communication to enhance the team’s SMM. Marks et al. (2000) empirically demonstrated how leader briefings and team interaction training can do this. Their study on team adaptation to novel environments involved 79 three-person undergraduate teams playing a computer-based tank war-game simulation. Before each round of performance, the teams either participated in an enriched briefing with the leader (i.e., the leader communicated knowledge about aspects of the task environment that were important) or a standard informational leader briefing. This manipulation was induced to compare the quality of a leader’s communication on the team members’ similarity and accuracy of their mental models about the team task. The teams also received either team-interaction training to teach participants how to effectively interact on a team, or a control video that only addressed the task information. Both interventions (i.e., enriched briefing and the team-interaction training) had a positive impact on the similarity and accuracy of the team’s cognition. Visibly, these findings suggest that a leader should conduct enriched leader briefings that provide contextual information about a new task. But it also implies that the leader should support the practice of training for their teams because leader buy-in and support bolsters the impact of team training on the team (Goldstein & Ford, 2002). The study acknowledges that it only focused on premission communication, but that leaders can continue to be involved throughout the lifespan of a team’s task. Therefore, the influence of a leader on SMMs may not stop at briefing prior to a performance episode. A leader can communicate the team’s purpose and specific tasks through oneon-one communication to individual team members or through plenary meetings with the entire team. Sætrevik (2015) found that in emergency response teams, their shared beliefs were associated with their specific team, rather than their specific role. He believes this could have been due to leader behaviors and their communication patterns because the leaders had the role to task the team and share information on their priorities during one-on-one meetings and brief meetings. However, he could not discern whether the one-on-one meetings or status meetings had more of an impact on the team’s shared beliefs, and suggests that future research should analyze whether the communication structure makes a difference. Another example of a leader’s indirect impact on establishing an SMM is a longitudinal study on 51 database design teams, by He, Butler, and King (2007), which found that frequency of meetings and phone calls were positively related to the development of SMMs. However, email exchanges did not show any effect. Similarly, Sætrevik and Eid (2014) note from their field studies on emergency

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management teams, that even if a leader is well-informed, the team’s similarity index score can be negatively affected if they do not effectively communicate their knowledge about the team’s work, goals, and priorities, in turn, lowering their SMM. Leaders can draw from the conditions that positively influence the team’s cognition and implement it in their own teams. Given the findings from He and colleagues’ study, leaders should facilitate face-to-face meetings and phone calls more than emails to convey valuable information. Zaccaro et al. (2001) emphasize the importance of a leader’s ability to effectively communicate their mental model to influence the team’s SMM. They point out that first, the leader must execute sensemaking (i.e., comprehend and make meaning of the team’s situation; Weick, 1995) and then they need to complete sense-giving processes, which includes determining crucial environmental cues and relating them to the team’s context and forming a coherent framework. Then, quite possibly the most critical step is to communicate this knowledge to the team. Providing the teammates with the environmental cues, explaining how they are critical to the team’s performance can positively guide the development of the team members’ SMM (Burke, 1999; Marks et al., 2000).

After Action Review Communication must be carried out effectively. If a leader does not fully comprehend his or her own mental model, then the leader cannot properly induce a shared mental model for the team. A simple communication strategy that is highly effective, affordable, and underutilized is conducting a debrief, or afteraction, review. When conducted correctly, it can improve team performance by 25% (Tannenbaum & Cerasoli, 2013). Leaders play a large role in the debriefing process. Before the debrief, leaders need to be aware of how employees are performing during the job. They should make note of serious failures or preventable errors so that the most critical areas of improvement are discussed, and constructive feedback can be provided. This is also an opportunity for leaders to point out any successes and express gratitude for hard work. Positive feedback can help employees feel appreciated and recognized by upper management. During the debrief, they should start by covering the team’s mission and objectives and then discuss previous performance. Leaders must help guide team members through self-discovery by asking questions rather than talking at them. They should also structure the debrief to focus on issues that are relevant and uncover solutions (Reyes, Tannenbaum, & Salas, 2018). A conditional element that must precede a debrief is having a psychologically safe environment. Psychological safety is the shared belief that it is safe to take interpersonal risks and speak up, even if the idea is unpopular (Edmondson, 1999). Leaders can foster psychological safety by admitting their own faults. These practices can help ensure that the team is all on the same page.

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Using strategic communication (e.g., debriefing, planning, and sense-giving) can even foster the development of multiteam system collective cognition. Multiteam systems are two or more teams that work interdependently to accomplish shared goals (Mathieu, Marks, & Zaccaro, 2002). Murase, Carter, DeChurch, and Marks (2014) study on multiteam systems found that leader strategic communication enhanced coordination between teams by helping followers establish accurate mental models. Therefore, a leader’s approach to communication can have much larger impacts than just the SMM development of a single team.

Leader Behaviors Leadership style can also influence the convergence of mental models from the individual to the team level (Dionne et al., 2010). Dionne and colleagues compared leader-member exchange (LMX) theory to participative leadership. LMX suggests that leaders can have either high- or low-quality exchanges with their followers (Graen & Uhl-Bien, 1995). The more effort and loyalty a follower displays, the more valued they are by the leader, in which case the leader provides them with more freedom and influence on the project (illustrating high-quality exchange). Those who do not show the same productivity and efforts have lowquality exchange. Followers with low-quality exchange need more guidance and consequently do not have as much to contribute of their own. The participative leadership approach, on the other hand, is when the leader exhibits the same behaviors toward all team members—treating them equally (Koopman & Wierdsma, 1998). Using an agent-based simulation model of team development processes, Dionne and colleagues (2010) found that the participative leadership approach did a better job of promoting SMM development. Participative leaders encouraged connections for all team members to create a fully connected network, rather than having an “inner circle” with outsiders. Sætrevik (2015) also suggests that leadership style can influence how teams find motivation, form relationships, and share information to form shared beliefs. Particularly, he noted that transformational leadership (i.e., leaders who inspire followers and lead by example; Eid et al., 2004) styles may have this effect. In a sports context, Filho, Gershgoren, Basevitch, Schinke, and Tenenbaum (2014) explained that a team leader needs to have open communication about the team’s performance, and that she needs to provide her information on her field observations, as well as messages from outsiders of the team (e.g., coaching staff ). It is also the leader’s responsibility to demonstrate social support and relay efficacy information to motivate the team and elicit shared affective states (e.g., mutual support). Other team contexts can also benefit from a leader exhibiting this kind of behavior. The leader is in a unique position to have a motivational role on the team, which can influence how they motivate each other and find a shared meaning in their team’s work.

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Shared Experiences Experience over time can help team members have more similar cognitions about the team and their task. Kivlighan, Markin, Stahl, and Salahuddin (2007) found that in a group leader training program, the trainees’ cognitive models become more like the experienced group leaders’ over time. Socialization, or interpersonal interactions between team members, can provide experiences for the team to communicate information verbally and nonverbally. This allows team members to become familiar with one another, form the team climate, and facilitate knowledge sharing for the task (Brown & Duguid, 1991). A survey of research and development teams from high-tech companies in India concluded that internal group communication and mutual cooperation were key factors for developing SMMs and discussed how collaborators’ experience with each other over time can help them become familiar with one another and the interdependent task (Misra, 2011). This shared knowledge can facilitate their task and teamwork. Geographically distributed teams can have more difficulty partaking in these developmental experiences because they are not usually in the same place at the same time. Diverse team members from different backgrounds might also have fewer shared experiences when starting out in a team, reducing their common understanding of a task. Although this lack of similarity could initially lead to conflict, once the team has formed, they can participate in shared experiences to resolve any issues or misunderstandings, while maintaining any creative strategies that are cultivated from having diverse perspectives (Skilton & Dooley, 2010).

Coleadership, Shared Leadership, and Shared Mental Models Organizations are increasingly using teams to solve complex problems that cannot otherwise be solved by a single individual. Teams are made up of two or more people who work interdependently to accomplish a common goal (Salas, Dickinson, Converse, & Tannenbaum, 1992) A common practice in organizations is for teams to have more than one leader (Miles & Kivlighan, 2008). When a team has coleaders, the leaders can be conceptualized as their own pair or team. In which case, the coleaders, themselves, must form a shared mental model. Miles and Kivlighan examined undergraduate intergroup dialogue groups with graduate students, faculty members, and affiliates of the university as coleaders to understand the coleaders’ mental models on influencing the overall group’s productive group climate (i.e., high member engagement, low avoidance, and unresolved conflict; MacKenzie, 1983). They found that over time, the coleaders had more similar mental models about the group, and that the more similar their mental models were, the more productive group climate was. Also, the similarity of mental models was positively related to immediate increases in group engagement in

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following group sessions. They suggest that the required pre-group preparation and discussions between the coleaders may have possibly contributed to the convergence of their mental models. Other researchers also share this idea that allotting time for discussion helps facilitate the development of a shared mental model (Fiore & Schooler, 2004; Yalom, 1995). Similar to coleadership is the idea of shared leadership. Rather than leadership existing solely as a hierarchy with one person in charge, it can also exist laterally, distributing leadership throughout members (e.g., shared leadership). Shared leadership is a form of team leadership, which is defined as the skill of motivating and developing a team, as well as assessing team performance and guiding the team through their lifespan (Salas, Sims, & Burke, 2005). Yammarino, Salas, Serban, Shirreffs, and Shuffler (2012) identify shared leadership as all members equally contributing to decision and actions. Shared leadership provides members with equally distributed influence on the team (Ensley, Hmieleski, & Pearce, 2006; Mehra, Smith, Dixon, & Robertson, 2006; Pearce & Sims, 2002). In order for a self-managed team to effectively promote shared leadership, there are a couple of mental model conditions that need to be met. First, the team members must have a shared expectation of what occurs during specific situations and who is supposed to lead at that point (Burke, Fiore, & Salas, 2003; Klimoski & Mohammed, 1994). Then, they must maintain flexibility in their mental model to ensure that they are using the most efficient and effective strategy, rather than just sticking to a norm that might not be the best approach (Burke et al., 2003). Facilitating metacognition, or adapting and monitoring previous interpretations of a team’s leadership responsibilities (Garner, 1994), allows the team to modify the current mental models in order to fit the context of their current situation (Burke et al., 2003). Establishing shared leadership can also have implications for strengthening an SMM among team members. Particularly, shared leadership could possibly lead to more team satisfaction and team effectiveness through the mediation of an SMM. Implementing shared leadership provides an atmosphere that allows all team members to speak up and share unique and useful information. When team members are given leadership responsibilities, they are more likely to express their ideas to contribute to the team’s goals (D’Innocenzo, Mathieu, & Kukenberger, 2016). In turn, having more leaders can improve team knowledge distribution (McIntyre & Foti, 2013). In military ad-hoc teams without an appointed leader, it was found that simply including a brief ten-minute team strategy discussion positively influenced the development of an SMM, team processes, and team performance (Dalenberg, Vogelaar, & Beersma, 2009). The members of these teams, compared to the control teams who did not receive instructions to hold brief team strategy discussions, demonstrated more initiative and leadership. These examples demonstrate how giving team members the freedom to speak up and step up as leaders can foster a strong SMM.

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Future Directions From our exploration into leaders’ mental models, we can see that much progress has been made in recent decades to advance our understanding of how leaders, non-leaders, and teams hold and process information in the form of mental models. However, there are still gaps in the literature that future research should address. Specifically, there is room for advancements in methods of studying mental models, replication studies, and empirical advancements. One method that can be used for future studies is heart rate variability. It was shown to be a good metric of executive function when determining a team’s shared beliefs, a highly related construct to SMM (Sætrevik, 2015). Future researchers can make strides by understanding how the brain is able to update mental models with new information. Tools such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) may be useful in this pursuit (Filipowicz et al., 2016). There were also calls for replications of studies. One such request came from a study that used a mostly male sample of Israeli research and development teams to find that high transformational leadership attenuated the relationship between skill heterogeneity on the team and the team’s shared mental model. The authors would like the external validity of their finding to be tested in more gender-diverse contexts, particularly those in other cultures (Reuveni & Vashdi, 2015). Another study found that the relationship between learning organization and quality commitment was moderated by LMX, but advises future researchers to investigate this effect in an exclusively manufacturing sample (Choi, Kim, & Yoo, 2016). We would like to add the possibility of replicating this study in other industries, such as the service industry. Updating mental models can be better understood from an organizational lens, too. There is an opportunity for understanding how knowledge transfer, a precursor to mental model updating, differs according to type and complexity, particularly as it applies to tacit knowledge (Krylova, Vera, & Crossan, 2016). Additional room for literature advancement lies in the team goal mental model domain, where teams with aligned goals should be compared to teams with asymmetrical goals on team learning orientation and team identification. Research will test team learning orientation and team identification’s theoretically predicted role in conflicting goals within a team (Pearsall & Venkataramani, 2015). There are important gaps in research to address why leaders choose one model over another. Perhaps organizational culture affects the leader’s mental model choice. Lastly, we need to understand why leader mental models might result in team failure. For instance, does a lack of psychological safety affect the follower’s willingness or ability to challenge a supervisor’s mental model with new information?

Conclusions Leaders are key components to their workplaces, whether they are operating in a team or group setting, whether they are experts or novices, and whether

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they work in a hierarchical or distributed leadership environment. Their mental models, or cognitive lenses to view the world, are drivers of their behaviors and attitudes. These mental models are evaluated via their similarity or accuracy compared to an expert’s model. In such examples, an educator with low organizational tenure was compared to an educator with high organizational tenure. Their mental models reflected differences in locus of control, perception of hierarchy, and outcome goals (Ruff & Shoho, 2005). Leaders’ mental models are developed through processes such as training, coaching, and leader development (see Marcy & Mumford, 2010). As individuals strengthen their leadership skills, they build and revise the mental models connected to leading. Leaders’ mental models have a direct effect on their own performance as leaders. At the organizational level, this performance effect occurs within the context of organizational climate, innovation, and ethic, while the leader’s mental model can affect performance via vision, sensemaking, forecasting, and interleader interactions. Leaders’ mental models affect their team’s performance through the leadership style, decision making, and information exchange. We also discussed the role of SMM in the team context. The leader can develop SMM in their team with the extent they participate in information exchange, build relationships with followers, and share experiences with the team. In cases where leadership is shared among two or more teammates, SMM can be fostered through cognitive flexibility and shared expectations. We wrapped up the chapter by noting what areas of the literature could be further augmented by future research endeavors. Leaders’ mental models are important for leaders, followers, teams, and organizations.

Acknowledgments This work was supported in part by contracts NNX16AP96G and NNX16AB08G with National Aeronautics and Space (NASA) to Rice University. This work was also supported, in part, by research grants from the Ann and John Doerr Institute for New Leaders at Rice University.

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12 LEADER SOCIAL ACUITY Stephen J. Zaccaro and Elisa M. Torres

Leaders contribute to organization effectiveness by identifying emerging problems, generating solutions, and implementing the best-fitting ones (Mumford, Zaccaro, Harding, Jacobs, & Fleishman, 2000). This view reflects a functional approach to leadership in which leaders are responsible for helping teams and organizations achieve their goals (Hackman & Walton, 1986; Morgeson, DeRue, & Karam, 2010). The leader problem-solving process involves a series of sub-processes pertaining to problem definition, solution generation and evaluation, and solution implementation (Mumford, Baughman, Supinski, & Maher, 1996; Mumford, Uhlman, Mobley, Reiter-Palmon, & Doare, 1991). The leader’s cognitive skills and capacities drive the success of these activities (Mumford, Todd, Higgs, & McIntosh, 2017). Of particular importance are skills that foster perceptual accuracy of elements in the problem space and solution domains. By its fundamental nature, leader problem solving occurs within complex social domains (Fleishman, Zaccaro, & Mumford, 1991; Mumford et al., 2000). Organizations are multifaceted social systems in which problem spaces contain a variety of integrated social elements (Katz & Kahn, 1978). Moreover, leader actions and their implications can reverberate through multiple stakeholders and social units. Accordingly, when solving complex organizational problems, leaders need to perceive, interpret, and factor social dynamics into their problem meaning-making and solution generation/evaluation processes. Thus, their skill in employing social cognitive processes complements other cognitive skills in driving leadership effectiveness. Social acuity refers to skill in perceptual accuracy and understanding of social domains (Funder & Harris, 1986). It reflects the detection of determinative cues and patterns in such domains and the correct interpretation of their meaning. Leaders with high social acuity skills are consistently effective at maintaining

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social awareness and engaging in sensemaking. They possess more complex social schemas that aid in such detection and interpretation (Cantor & Kihlstrom, 1987; Zaccaro, Gilbert, Thor, & Mumford, 1991). Several skills and personal attributes that have been linked to leader effectiveness reflect social acuity skill, including social intelligence (Zaccaro et al., 1991), nonverbal sensitivity (Rosenthal, Hall, DiMatteo, Rogers, & Archer, 1979, 2013), emotional intelligence (Salovey & Mayer, 1990), cultural intelligence (Earley & Ang, 2003), political savvy (Ferris et al., 2005), empathy (Hogan, 1969), perspective-taking (Galinsky, & Moskowitz, 2000), and self-monitoring (Snyder, 1974). This chapter explores the role of social perception and evaluation processes in leader complex problem solving. We specify where in leader problem solving these processes have dominant roles. We also define the key elements in the organizational space that are the primary foci of leader social perception. We suggest that leaders’ perceptions of these elements contribute to problem definition and sensemaking. We also propose that social perceptual processes of leaders include the detection of performance requirements and action potentialities (Zaccaro, Green, Dubrow, & Kolze, 2018). Performance requirements refer to those actions that leaders must accomplish for their followers and units to be successful. Action potentialities refer to social affordances (Baron & Boudreau, 1987; McArthur & Baron, 1983) that help leaders determine what is possible in terms of leadership activities in particular organizational contexts. In this chapter, we also provide a summary of leader attributes that incorporate elements of social acuity in their construct definitions. We describe what parts of the social space they target in their perceptual focus, and we summarize research linking these attributes to leadership processes and success. We conclude by describing several key theoretical and practical implications of this work. The goal of the chapter is to provide deeper insight into leader social perceptual processes and their connections to leadership success. Because such success is grounded in effective problem solving, we begin by summarizing models of leadership as organizational problem solving.

Leadership as Organizational Problem Solving Mumford and his colleagues offered a definition of organizational leadership as “discretionary problem solving in ill-defined social domains” (Fleishman et al., 1991, p. 240; see also Fleishman, Mumford et al., 1991; Mumford, 1986; Mumford & Connelly, 1991; Mumford et al., 2000). The lack of clear definition in social domains results from the sociotechnical systems nature of organizations (Katz & Kahn, 1978; Mumford & Connelly, 1991; Zaccaro et al., 1991). Mumford and Connelly (1991) noted that integrated and embedded organizational systems mean that problem solutions can entail multiple connected stakeholders. Accordingly, the first, second, and third order effects of such solutions can reverberate across numerous social sectors within the organizational space. Strong social

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acuity skills allow leaders to perceive these social influences and patterns when defining problem scope and solution parameters. Leadership occurs in problem contexts in which leaders have discretion in their solution and decision choices. Katz and Kahn (1978) defined leadership as entailing “influential increment over and above mechanical compliance with the routine directives of the organization” (p. 302), meaning that leaders are confronting nontypical problems for which routine and automatic solutions are not available. Fleishman, Mumford et al. (1991) noted that “influence attempts completely specified by environmental demands or normative role requirements represent a management function. Leadership, on the other hand, requires some degree of personal discretion or choice concerning exactly when, where, how, and why action will be taken to influence subsystem goal attainment” (p. 259). Thus, leadership occurs when the organizational context offers leaders considerable choice in the range of problem solutions that can be considered, selected, and implemented. The organizationally embedded property of leadership means that solution and decision discretion is often socially constructed. While the nonroutine nature of the problem will afford discretion, social factors may also determine how much discretion leaders actually have in considering possible solutions. For example, decision autonomy is often a function of the relationship between a leader and his or her supervisor. Leader member exchange theory describes high-quality relationships as including greater autonomy granted by a supervisor to a subordinate; low quality relationships are characterized by closer supervision and greater constraints on subordinate actions (Graen & Uhl-Bien, 1995). Moreover, leaders who adopt some leadership styles, such as high structuring and transactional orientations, are more directive in their interactions with subordinates (Bass, 1985; Fleishman, 1953, 1995), reducing the degree of discretion they may allow leaders who are subordinate to them. Thus, leadership discretion is not solely a property of the problem or of task elements, but also of the leader’s social context. This argument combines the problem-based functional leadership approaches with recent relational perspectives to leadership (DuRue, 2011; DuRue & Ashford, 2010; Uhl-Bien, 2006). While the former defines leadership as actions taken to facilitate goal achievement by subordinates, the latter approaches define leadership as a social constructed relationship between potential leaders and followers. DeRue (2011; see also DeRue & Ashford, 2010) noted that “leadership involves a social interaction process whereby people engage in influence acts that are then socially constructed as leadership or followership” (p. 130). Thus, the designation of someone as a leader, with the attendant “permission” to have decision discretion emerges from the endorsements by followers, or from followers granting the claim of leadership by a potential leader (DeRue & Ashford, 2010). Uhl-Bien (2006) notes that “a relational orientation . . . [focuses] on the social construction processes by which certain understandings of leadership come about and are given privileged ontology” (p. 655). This suggests that how leadership is to be practiced derives from social interactions and agreements with multiple

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stakeholders within a leadership network. Thus, leadership granting occurs not only “upward” from followers to potential leaders but also in contexts where middle managers and senior executives grant leadership roles “downward” to their subordinate managers. In these instances, the granting of leadership autonomy by higher level supervisors reflects an understanding of the kinds of problems subordinate leaders have discretion to solve, and what the boundary conditions are on such discretion. These arguments focus on the social construction processes of how leadership in the form of discretionary problem solving is offered or granted to others. However, the potential leader is not simply a reactive agent in these interactions. Leaders can choose their level of engagement in organizational problems, with such engagement ranging from full involvement to none at all. Zaccaro et al. (2018) argued that leaders “are not always passive recipients of contextual influences to which they need to respond; instead they choose situations in which to take on the leader role, or they can shape the situation to better suit their dispositions and capacities (Dalal et al., 2015; Schneider, 1987)” (p. 30). They noted that leaders make choices not only whether to engage in leadership at all but also about which social settings and problems they would exert leadership. Prior research has suggested that such choices are governed by an individual’s dominance motives or motivation to lead (Chan & Drasgow, 2001). However, the choice to engage in leadership is also determined by social factors that (1) legitimize leadership status and (2) afford the leadership practices intended by the leader. Individuals are not likely to engage in leadership problem-solving activities unless they perceive that the leadership role, with appropriate degrees of discretion, is being granted to them. Even when leadership status is granted, the leader’s level of engagement is also influenced by whether they evaluate the situation as either corresponding to their preferred approach to the organizational problem, or pliable so that it can be reshaped to match their preferred style (Zaccaro et al., 2018). This line of reasoning suggests that individual social acuity skills will matter greatly in the relational process of taking and granting leadership. Individuals are more likely to engage in organizational problem solving as a leader if (1) they perceive followers (and supervisors) as granting the legitimacy for such engagement, and (2) they evaluate the situation, including senior organizational managers, as offering a sufficient degree of discretion. Those capable of displaying high accuracy in social perceptions are more likely to make correct judgments of follower endorsements of leader role taking and of the amount of solution discretion offered by their supervisors and the organizational context. Indeed, Holland (2015) found support for a link between social acuity skills and leadership roletaking agreements. She examined the degree of agreement between individuals who were seeking a leadership role and those who granted that role. She found that social skills of the leader, which included social perceptiveness skills, were positively correlated with such agreements. Individuals without such skills were

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more likely to have disconnected leadership relationships (i.e., rejected leadership taking, or unrequited leadership granting). These arguments offer a foundation for the importance of social acuity skills in leader problem solving. However, we hasten to add that these skills are not equally necessary across all leader problem situations. Such situations can vary in terms of the social load they exert on leaders and therefore the attendant requirement for social acuity skills. Zaccaro, Weis, Chen, and Matthews (2014) defined social load as the amount of social cognitive resources required of individuals in particular social settings. Situations of higher social complexity will exert a heavier social load and require greater amounts of social resources. Zaccaro et al. (2014), defined social complexity as: the number and variety of individuals, teams, and organizations that are actors within performance episodes (Zaccaro, 2001). Such variety can be reflected in surface features, such as gender, race, cultural background, functional expertise, and deep features such as personality, attitudes, and beliefs (Harrison, Price, & Bell, 1998; Harrison, Price, Gavin, & Florey, 2002). (p. 99) In situations of higher social load and complexity, leaders will need to make greater use of social acuity skills. For example, as individuals ascend levels of leadership within organizations, they encounter greater numbers and variety of stakeholder types (Zaccaro, 2001). System dynamics become more complex as greater numbers of social actors are implicated in the solutions to organizational problems. Accordingly, at higher organizational levels, skills in detecting and accurately interpreting these social elements and their integrated patterns become even more crucial to leadership success (Gilbert, 1995; Zaccaro, 2001). Thus far, our discussion has focused on the importance of leader social perception processes and corresponding social acuity skills. This importance is derived from the definition of leadership as discretionary problem solving and from its relational properties. In the next section of this chapter, we will define particular leader problem-solving processes, adopting the model offered by Mumford and his colleagues (Mumford et al., 1991; Mumford et al., 1996; Mumford et al., 2017), and integrating Endsley’s situational awareness framework (Endsley, 1995, 2015). We will also define the social percepts that are the focal elements in each of these processes.

Leader Problem-Solving Processes Researchers have identified a number of leader problem-solving transition and action processes that are drivers of effective leadership (Maitlis, 2005; Morgeson et al., 2010; Mumford et al., 2000). Table 12.1 identifies a number of these processes drawn from multiple sources. The first two columns present a problem-solving

Perception of situational status and detection of changes that indicate possible problems. (Endsley, 1995) “The process by which people first become concerned that events may be taking an unexpected and undesirable direction that potentially requires action”. (Klein, Pliske, Crandall, & Woods, 2005, p. 14) The process of “identifying the nature of the problem at hand and the kind of goals, procedures, and information that should be considered in later problemsolving activities”. (Mumford et al., 1996, p. 64). Gathering information about the problem to define its important elements. “The discursive process of constructing and interpreting the social world”. (Gephart, 1993, p. 1485) “A process, prompted by violated expectations, that involves attending to and bracketing cues in the environment, creating intersubjective meaning through cycles of interpretation and action, and thereby enacting a more ordered environment from which further cues can be drawn”. (Maitlis & Christianson, 2014, p. 67) “[Projecting] the future actions of the elements in the environment”. (Endsley, 1995, p. 37) “Forecasting involves making predictions based on observations of the situation at hand”. (Stenmark et al., 2011, p. 25)

Problem Detection

Problem Forecasting

Information Organization and Sensemaking

Problem Construction and Information Gathering

Definition

Process

• Identification of the likely outcomes of a particular problem trajectory for social actors • Prediction of whether social actors are still able to meet individual and collective goals given a particular problem trajectory • Projecting whether social actors will leave or disengage from the problem context

• Interpretation of the hidden agenda, motives, and values contributing to the problematic behaviors and choices of social actors • Understanding the connections among social actors and events that contribute to an emergent problem

• Identification of all stakeholders connected to the problem (Smith, 1989) • Identification of the social drivers of the problem, including stakeholders’ capacities, levels of engagement, and interactions with other stakeholders

• Unexpected changes in social actors, units, processes, and system dynamics that indicate possible emergence of social problems

Social Dynamics and Elements

TABLE 12.1 Definition of Leader Problem-Solving Processes With Corresponding Social Dynamics and Elements

Solution Implementation and Monitoring

Solution Forecasting

Solution Generation, Evaluation, and Planning

Solution generation involves “formulating a solution framework or set of ideas that might be used to understand the problem and develop initial solution strategies”. (Mumford et al., 2000, p. 15) “Planning involves the active, conscious construction or mental simulation of future action sequences intended to direct action and optimize the attainment of certain outcomes”. (Mumford, Schultz, & Van Doorn, 2001, p. 214) “Forecasting involves envisioning multiple different outcomes of alternative actions”. (Byrne et al., 2010, p. 120) Mentally simulating possible solution trajectories and projecting likely consequences. Tracking the implementation of solutions to assess desired goal attainment. • Mentally simulating the roles social actors and units • Predicting how social actors and units will respond to proposed solutions • Determining likely effects of possible solutions on social actors and units • Monitoring social actors and unit engagement in solution implementation

• Analysis of the KSAs (capacities) and likely engagement of social actors • Analysis of collective and unit capacities • Combination of social information to derive best fitting solutions

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process with corresponding definitions. The third column identifies particular social dynamics that can imbue that process, and therefore need to be perceived and interpreted by the leader. Organizational problem solving begins with problem detection, defined as “the process by which people first become concerned that events may be taking an unexpected and undesirable direction that potentially requires action” (Klein et al., 2005, p. 14). Cowan (1986) defined this as the “gestation/latency stage of problem recognition”, stating that “gestation refers to situations where conditions in the environment are changing and building toward recognition” (p. 766). Latency refers to the temporal process of individuals perceiving the existence of problem conditions. Cowan defined perceptual scanning as the key cognitive process activated in this stage. Leaders detect problems by scanning the organization’s internal and external environments and perceiving changes from anticipated patterns; in essence, they detect discrepancies between expected and actual actions and outcomes until accumulated observed discrepancies signal a potential problem (Cowan, 1986; Smith, 1989). Established events have an expected trajectory (Klein et al., 2005). Effective leaders are actively attuned to this trajectory and note deviations from it. They evaluate the degree to which any deviations are sufficient to signal an emerging problem that requires a response (Klein et al., 2005; Smith, 1989). The focus of the leader’s problem detection is not limited to problem task elements, but also includes the social actors and the patterned interactions among them. For example, detection of discrepant financial data (e.g., sales are lower than expected; Smith, 1989) may entail only indirect social cues and patterns. However, a slow decrease in collective morale may be detected more directly from increases in displays of negative affect, missed deadlines, absences, and reductions in organizational citizenship behaviors (Podsakoff, Mackenzie, Paine, & Bachrach, 2000). Each of these cues may not individually signal a problem, but their accumulation will trigger problem detection—that something is amiss—by the socially astute leader who is attuned to and scanning the social context. Problem detection is distinct from problem construction (Cowan, 1986; Klein et al., 2005). Cowan (1986) followed the gestation/latency phase of problem recognition with stages in situation analysis “to achieve greater certainty about a problem description” (p. 766). Mumford and his colleagues defined problem construction as the process of “identifying the nature of the problem at hand and the kind of goals, procedures, and information that should be considered in later problem-solving activities” (p. 64). This process entails information seeking and gathering to identify the key problem elements. Leaders use problem schemas derived from their prior experiences in different problem spaces to either identify the nature of the problem or to help them “recognize inconsistencies and appraise inconsistencies with respect to known facts” (Mumford et al., 2017, p. 29). In social domains, problem construction entails gathering information in order to identify the stakeholders connected to the problem, including how each contributes to the problem. For example, Smith (1989) defines the initial stage in the

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development of a problem definition as the “identification of relevant stakeholders. Who owns the problem, is directly concerned with its resolution? What other agents are involved in the situation?” (p. 973). He also notes: One can consider the situation in terms of relevant systems (Checkland, 1981), identifying agents concerned with inputs, outputs, and transformation processes. Or one might assess the problem’s causes and effects, keying on agents implicated in either way (Moore, 1976). Stakeholder identification enables identification of the goals and values to be considered in the problem’s solution. (p. 974) This identification includes a perceptual focus on how the behaviors of social actors and stakeholders are contributing to the problem. Such information gathering includes the psychological states of the actors as well as the values and perspectives of different stakeholders (Smith, 1989). Leaders often myopically approach the difficult problems they typically face, reducing their attentional capacities to focus only on the social actors in their immediate space, that is, their team or their direct subordinates and superiors (Collins & Jackson, 2015). However, leaders with high social acuity are adept at identifying all of the relevant actors, including those who may be more indirectly involved in the problem. They possess a high degree of network acuity (Balkundi & Kilduff, 2006). According to Balkundi and Kilduff, “people perceive the same network differently, with some individuals achieving a high degree of accurate perception, whereas other individuals lead their organizational lives in relative ignorance of the actual network of relationships within which work is accomplished (Kilduff & Krackhardt, 1994)” (p. 925). Thus, leaders with high network acuity can accurately perceive the existence and nature of all ties in a social network. In problem solving, then, such leaders are likely to be more accurate in identifying the network of people most connected to an emerging problem, the nature of the ties among them (e.g., influence, friendship, functional or taskrelated), and the more peripheral networks of social actors that can touch on or be influenced by the problem. The discrepancies that lead to problem detection and the information gathered in the problem construction process becomes the fodder for the problem sensemaking process. In this process, individuals develop an integrated ­understanding—a mental model—of the emerging problem (Endsley, 1995). The detection of a problem, in the form of numbers of discrepancies that break the problem-detection threshold, galvanizes a need for explanation (Cowan, 1986; Louis, 1980) and the process of developing problem meaning. This meaningmaking process includes cause analysis and establishes the basis for subsequent goals and constraint analyses that in turn provide a foundation for planning and solution generation (Mumford et al., 2017).

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The process driving problem sensemaking entails development of a situation model and a comparison of this model to existing problem schemas in long-term memory (Endsley, 1995, 2015; Mumford et al., 1996). Endsley (2004) describes sensemaking “as effortful processes of gathering and synthesizing information, using story building and mental models to find some representation that accounts for and explains the disparate data” (p. 324). When the situation model exhibits a higher degree of approximation to existing mental schemas and prototypes, then recognition-primed processes provide a framework to interpret the nature and causes of a problem (Endsley, 1995, Maitlis & Christianson, 2014; Klein, 1989). The narrative that is formed as a part of problem sensemaking in organizations is one that integrates both the task and social elements of the problem. Thus, an understanding of the problem includes an evaluation of actors’ and stakeholders’ motives, goals, and agendas. It also specifies the patterns among actors and stakeholders that may be contributing to an emergent problem. For example, declining team performance may be rooted in a combination of insufficient material resources, lower motivation of employees stemming from new compensation models, interpersonal conflict among team members, interdepartmental process loss due to inefficient coordination mechanisms and from the contagion of lower morale across ties in employee social networks. While the first element is a task one, the remaining elements denote social factors. In developing a problem model, leaders combine these elements into an integrated coherent frame that provides a comprehensible explanation for the problem of declining performance. The role of enriched and expert-level social schemas in social acuity is a critical one, particularly at this stage of leader problem solving. Regarding the role of such schemas in social intelligence, an attribute that incorporates social acuity, Zaccaro et al. (1991) noted: Of critical importance to the accurate perception and understanding of others is the richness of information that can be directed toward the interpretation of social events and their participants (Cantor & Kihlstrom, 1987; Fiske & Taylor, 1991). A basic premise, then, is that individuals high in social intelligence have a more sophisticated and better organized store of social information than those low in social intelligence (Cantor & Kihlstrom, 1987). This enriched information store creates quicker and more accurate perceptions of social events and of the needs, desires, motives, and intentions of social participants. (p. 326) Leaders with higher social acuity, then, are able to interpret social cues and derive problem models more quickly than those lower in social acuity. Their problem models are also likely to be more complex, meaning that they “make more fine-grained distinctions among types of persons, situations, and social episodes”

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(Zaccaro et al., 1991, p. 327), and integrate these distinctions into a more comprehensive narrative (Zaccaro, 2001). Problem meaning-making is not restricted to the lone leader. Several perspectives of organizational sensemaking define it as “a social process that occurs between people, as meaning is negotiated, contested, and mutually co-constructed” (Maitlis & Christianson, 2014, p. 66). Leaders may develop a tentative problem model and then engage in social comparison processes to evaluate that model against those of other selected peers, subordinates, and superiors. Or, leaders may engage with other peer leaders, managers, and other social actors to collectively interpret the meaning of an event (Morgeson et al., 2010). High social acuity leaders are likely to have more developed and diverse “operational networks” (Ibarra & Hunter, 2007, p. 41) that enable leaders to select more appropriate social comparison targets to evaluate nascent problem models, or partners in constructing problem meaning. Conjar (2014) suggested support for this premise in her focus on developmental social networks, or networks used to foster leadership growth. These reflect a type of social network used in part to define and make sense of new information (albeit about leadership concepts in general). She found that individuals who scored higher on a measure of social intelligence had networks of greater size and tie strength (meaning the intensity or frequency of their interactions with individuals in their network). This suggests that leaders with high social acuity are more effective in utilizing their social networks—or at least use them more often—in meaning-making processes. A constructed problem model includes the basis for problem forecasting and for specifying the parameters of appropriate solutions. For example, Endsley (1995) noted that perception of situational elements and interpretation of their meaning led to “the ability to project the future actions of the elements in their environment” (p. 37). Problem forecasting entails a mental simulation or playing out of a problem trajectory to identify likely consequences (Hegarty, 2004; Trickett & Trafton, 2007; Stenmark et al., 2011). In social problem solving, forecasting is used to specify what the likely outcomes are for social actors in the problem context, and particularly whether they can still meet individual and team goals. Such forecasts are used to shape the magnitude of necessary solutions (Lonergan, Scott, & Mumford, 2004). Forecasts that indicate likely and significant performance decrements will suggest solutions that represent major changes from existing action pathways. Slight projected decrements would mean more minor solutions to such pathways. Higher social acuity should result in more accurate problem forecasting. Research suggests that expert knowledge structures result in more accurate forecasts (Endlsey, 2015; Hershey, Walsh, Read, & Chulef, 1990; Klein & Crandall, 1995). Thus, leaders with more elaborated social schemas and knowledge systems are likely to consider a greater range of actors and social outcomes in their schema-based problem forecasting (Cantor & Kihlstrom, 1987; Zaccaro et al., 1991).

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Problem representations and forecasting provide a basis for goals and constraint analyses that in turn contribute to solution generation and planning (Mumford et al., 2017). Mumford and colleagues (2017, p. 29) defined goals analysis as “the leader’s identification of legitimate, and potentially viable goals to be pursued from a larger set of potential objectives”. This analysis entails a balancing and prioritization of different goals relative to the capacity of the social systems that will implement goal actions (Mumford et al., 2017). Constraints analysis refers to an evaluation of boundaries and limitations on solutions pathways. According to Mumford and colleagues, these analyses help define the parameters of potential solutions and establish the basis for planning activities, starting with the generation of solutions. Solution selection processes entail the identification of possible solutions that can best resolve emerging problems, their vetting, evaluation, and choice of the best-fitting options (cf. Scandura, 1977). Social acuity skills influence these processes in several ways. Solution generation and planning needs to occur with a high degree of cognizance about the social capacities in the extant problem context. Leaders need be aware of the skills, priorities, and motivation levels of the social actors who will be instrumental in solution implementation. When managing multiple units, leaders will often be confronted with different unit priorities and goals. Accordingly, an effective goals analysis will likely require (1) accurate perception of these different goals, (2) the best integration of these goals, (3) the prioritizing of different goals that can be integrated, and (4) an awareness how social actors will react to such prioritizing (also part of a constraints analysis and solution forecasting). The power of different stakeholders and the influence they hold over potential solutions will act as a possible brake on their selection (Mitchell, Agle, & Wood, 1997). Socially acute leaders are likely to be more adept at identifying these stakeholders and interpreting the constraints they may exert on possible solutions. The generation of solutions needs to consider the skills and capacities of social actors that will be necessary to implement the selected one. The evaluation of potential solutions is also a social process in which stakeholders help vet offered ideas. While an individual may come up with ideas and potential solutions, their vetting is typically an externalized cognitive process whereby members of the leader’s social context exchange information about the idea viability and limitations (Carmeli, Gelbard, & Reiter-Palmon, 2013; Hunter & Cushenbery, 2011). High social acuity likely contributes to this externalized cognitive process in several ways. First, socially astute leaders will have more extensive social networks that will help in identifying the right people to evaluate different ideas (Balkundi & Kilduff, 2006). However, different stakeholders will provide their evaluations of ideas within the contexts of their own needs, values, and agendas. Leaders with higher social acuity would be more adept at discerning these stakeholder motive structures and incorporating their perceptions into their own process of solution selection and implementation.

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Earlier we noted forecasting as a key process determining how an emerging problem is likely to play out if left unresolved. Forecasting is also an important aspect of leaders’ problem solving in terms of predicting “the outcomes of executing their plans” (Mumford et al., 2017, p. 31). Leaders mentally simulate the possible scenario suggested by the implementation of particular solutions (Christensen & Schunn, 2009), factoring into their simulation the social actors, units, processes, and dynamics activated by the solution. Thus, leaders would project the roles different actors and units play in the solution and the likely interaction dynamics among these actors and units. They also forecast the likely effects of particular solutions on social actors and units, including on their interaction dynamics, and use this information to predict how they will likely respond to potential solutions. Shipman, Byrne, and Mumford (2010) found support for the significant contribution of forecasting to leader problem solving. They examined the effects of forecasting skill on the quality of leader visioning. Participants in their study were asked to develop vision statements for an organization. These statements were rating on several dimensions of forecasting skill (i.e., extensiveness, time frame, projection of negative outcomes, and resources). They were also rated on their overall quality, utility, and affective impact. The results of this study yielded significant associations between the skills of forecasting extensiveness and time frame, respectively, and the measures of vision performance. Byrne, Shipman, and Mumford (2010) reported similar findings in which the extensiveness of idea and plan forecasting were each associated with the quality of proposed advertising campaigns. While these studies did not examine the full range of social elements that we suggest would be an aspect of such forecasting, the researchers did include in their ratings assessments of the proposal impact on people and systems. We have presented this framework as a linear one, suggesting that planning precedes forecasting. However, the results of forecasting are used to inform and reposition solution planning (Byrne et al., 2010; Dailey & Mumford, 2006). The projections leader form of the effects of possible solutions on social actors and units are used to calibrate possible solutions to maximize social gains and/or minimize social impairments. Leaders may adjust resource requirements, plans for social engagement, and the utilization of particular social actors and units in light of conclusions from their social forecasting. Indeed, models of leading change place great weight on the malleability of plans to respond to social factors that emerge in change implementation (Kotter, 1996; Zaccaro, 2001). Skilled forecasting processes can allow leaders to be proactive in this aspect of leading change. The social expertise of leaders facilitates this iterative process. Along this line, Dailey and Mumford (2006) found that forecasters with higher experience and familiarity with a domain made more accurate evaluations of solution resource requirements and the social outcomes of problem solutions. We have noted that social expertise and the complexity of social knowledge stores comprise a major element of social acuity (Cantor & Kihlstrom, 1987; Zaccaro et al., 1991).

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The results of the study by Daily and Mumford suggest that these knowledge representations play a critical role in forecasting the social implications of problem solutions, and the subsequent revisions of these solutions in the projected face of undesirable social outcomes. The remaining phases of leader problem solving focus on the implementation and monitoring of solutions. While solution implementation entails a variety of task-focused activities, it also includes actions taken to inform and engage social actors and units who are instrumental in the solution. Here, leaders with high social acuity are likely to be able to shape presentation of solutions and the actions they require of social actors in ways that maximize their likely engagement. This aspect of leadership is a central element of charismatic/transformational leadership (House, 1977). Shamir, House, and Arthur (1993) argued that leaders accomplish heightened engagement by connecting their visions for change to the self-concepts of their followers. Along this line, Shamir, Arthur, and House (1994) argued that compared to non-charismatic leaders, the communications of charismatic leaders were likely to include, among other factors, (1) “more references to the collective and to collective identity, and fewer references to individual self-interest”; (2) “more positive references to followers’ worth and efficacy as individuals and as a collective”; and (3) “more references to the leader’s similarity to followers and identification with followers” (p. 29). We would argue that leaders with higher social acuity are more able to make the kinds of social inferences about their followers that afford these kinds of communications.

Social Percepts in Leader Problem Solving In the previous section we alluded to a number of social factors that leaders should be attuned to during their organizational problem solving. We have argued that skills associated with high social acuity foster greater detection, interpretation, and utilization of these factors when solving social problems. In this section we elucidate more clearly the kinds of social percepts that socially astute leaders are attending to throughout the different stages of problem solving. We organize these factors into social capacities and action potentialities. Social capacities reflect the range of actor knowledge, skills, abilities, and motive states that are present in a problem context. Action potentialities refer to cues in the social context that point to both required and possible leadership actions.

Social Capacities At different stages of problem solving, leaders are attuned to the knowledge, skills, and abilities (KSAs) and motive states of their followers, peers, and supervisors. For example, in problem detection, construction, and sensemaking, an observed negative delta between expected and actual performance may signal a disconnect between the KSAs of an actor and the performance requirements of a particular

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task. The meaning-making process may center on whether the task requirements have changed in ways beyond the capacity of the performer. An observed delta may also signal a decrease in motivation and engagement of the actor for the task. Leaders would use their social perception and interpretation skills to ascertain the meaning of possible reduced motivation. These social perceptual processes have been affirmed in several leadership research paradigms. For example, Leader-Member Exchange (LMX) theory posits that leaders will bifurcate followers into sets with which they have varying degrees of relationship quality (Graen & Uhl-Bien, 1995). In high LMX relationships, followers will allow greater autonomy in follower decision making; in low LMX relationships, followers generally receive closer management, more structuring, and less flexibility in performance activities (Graen & Uhl-Bien, 1995). LMX differentiation is a function in part of the leader’s perceptions of followers’ KSAs, motives and values, and trustworthiness (Bauer & Green, 1996; Graen & Scandura, 1987; Linden, Wayne, & Stilwell, 1993). Thus, the quality of the leadership relationship depends upon the leader’s and follower’s perception of the attributes of the other. Social percepts of the leader are not limited to the attributes of individuals in the social context. Leaders possessing higher levels of social acuity are also more attuned to collective capacities and social states of units in the organization and of the organization as a whole. Zaccaro et al. (1991) noted that social perceptiveness refers to a capacity to be particularly aware of and sensitive to needs, goals, demands, and problems at multiple system levels, including individual members, relations among members, relations among organizational subsystems, and interactions among a leader’s constituent organization and other systems in the embedding environment. (p. 321) This suggests that socially astute leaders are attuned to the relationships and dynamics among individuals within a team and among teams within the organization. They can detect the emergent states that characterize different teams (Marks, Mathieu, & Zaccaro, 2001), information that can be pertinent in problem sensemaking, solution generation, and implementation planning. Finally, socially astute leaders are aware of the interaction patterns, dynamics, and climate that exists across an organization.

Action Potentialities The social acuity of leaders extends to their perceptions of leadership demand characteristics and social affordances in their extant organizational context (Zaccaro et al., 2018). These perceptions refer to the behavioral requirements and potential action choices in the environment relative to the leaders’ goal intentions.

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According to Zaccaro et al. (2018) “leadership demand characteristics refer to contextual information that signals or cues which leadership strategies and activities are necessary for performance success in a particular situation” (p. 30). Problem contexts provide information that portend the nature and elements of successful solutions. Such information then is both the fodder for—and product of—goals and constraints analyses described as critical parts of leader problem solving by Mumford et al. (2017). The importance of leadership demand characteristics is that, when perceived accurately, they foster an alignment between performance requirements and corresponding possible responses in a leader’s behavioral repertoire (Hooijberg, 1996; Zaccaro et al., 1991). Situational signals regarding leadership demands activate corresponding leader traits, attributes and behavioral tendencies (Tett & Burnett, 2003). Tett and Burnett organized these signals into sets of tasks and social and organizational cues. The latter two sets are particularly relevant to social acuity skills. According to Tett and Burnett (2003), social cues refer to the “needs and expectations of peers, subordinates, supervisors, and clients regarding an individual’s effort, communication, and related socially prescribed behaviors, as well as team functions (e.g., production vs. support service)” (p. 504). Organizational cues refer to such attributes as company culture, climate policies, and structure (Tett & Burnett, 2003). Earlier, we defined social capacities as the social percepts which leaders detect to make sense of problem contexts and determine the range of possible solution paths. Here, we extend this idea to also include the role of these social percepts in sharpening the leader’s solution generation processes, the criteria for solution effectiveness, and their personal characteristics necessary to achieve success. Accordingly, these social perceptions establish the foundation for the leader behavioral flexibility across different problem contexts (Dinh & Lord, 2012; Zaccaro et al., 1991). This perspective defines leaders as somewhat reactive to the social context, in that they perceive and respond to extant cues that specified desired actions. However, Zaccaro et al. (2018) argued that leaders often have considerable agency over their situations in terms of being able to choose and/or shape them in accordance with their leadership preferences (see also Dalal et al., 2015). Such proactivity extends to their perception of problem situations in terms of what embedded social elements afford to them relative to their “social affordances”. Affordances refer to environmental features that signal “opportunities for actions” (Stoffregen, 2003, p. 124; see also Gibson, 1979); however, these perceptions are directed a priori by both the actor’s personal performance capacities and their intentional goals (Baron & Boudreau, 1987; McArthur & Baron, 1983). Thus, according to Zaccaro et al. (2018), “what the person desires or intends to accomplish in a situation attunes their perception of affordances in that situation; that is, situational characteristics offer information about the person’s ‘intentional aspects’, or those purposes for which contextual elements can serve (Fiebich, 2014, p. 152)” (p. 31).

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These arguments suggest that characteristics of the leader will influence their proactive perception of the social context, as they attend to social elements that correspond to their intended or preferred actions. When their perceptual scanning identifies matching social elements, the consequence should be greater leader effectiveness. Support for this argument is suggested by research on regulatory focus and fit (Higgins, 2000; Lanaj, Chang, & Johnson, 2012). Individuals differ in terms of whether they adopt a promotion or prevention focus toward their goals; individuals can adopt an orientation toward the achievement of positive outcomes or avoidance of negative outcomes (Higgins, 2000). Johnson and colleagues (2017) noted: Promotion focus (i.e., a growth orientation that is sensitive to gains) and prevention focus (i.e., a security orientation that is sensitive to losses) are fundamental orientations that impact how people interpret environmental information, regulate their emotions and behaviour, and respond to goal achievement (Higgins, 1997). These effects are particularly evident when people’s regulatory focus is congruent with the orientation of the immediate context, a phenomenon known as regulatory fit (Higgins, 2000). (pp. 379–380) Thus, regulatory focus influences how individual perceive and make sense of their social environments, and regulatory fit occurs when the focus of the leader corresponds to the foci of other actors in the social context. Regulatory fit has been positively associated with several leadership outcomes (Gorman et al., 2012; Lanaj et al., 2012). We suggest that regulatory focus may influence how leaders perceive affordances for action in their social contexts. They attune to those social elements, particularly followers’ regulatory focus, that invite (or repel) actions corresponding to their own orientation. A match between a leader’s promotion focus and that of followers suggest that followers are predisposed toward growth strategies that may be advanced by the leader, and more likely to engage in the enactment of such strategies. Thus, we suggest that processes of perceptual scanning and interpretation of the social context precede the observation of regulatory fit (or misfit) and subsequent actions such fit may afford the leader. There is little research that has integrated concepts of social acuity with those of social affordances. Indeed, perceptual accuracy with respect to social affordances appears on the surface to be an anomaly, as the perception of affordances is idiosyncratic to the leader’s performance capacities and goal intentions. However, we suggest that skills in social acuity would influence such perceptions in several ways. First, leaders need accurate self-awareness of their personal KSAs in order to align them with perceived action possibilities in the environment. Second, they need clear awareness of their goals and intentions, and particularly if they are calibrated accurately to their problem sensemaking and solution parameters.

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The quality of their goal analysis would contribute to this awareness. Third, higher social acuity is associated with more complex knowledge stores and schemas about the social context (Cantor & Kihlstrom, 1987; Zaccaro et al., 1991). These complex schemas could extend the perceptual array that leaders apply to their physical contexts and could highlight environmental features that not clearly discernable by leaders with more simple social schemas. Thus, for example, leaders with complex social representations of their organizational contexts should be more aware of subtle differences in the social elements present in these contexts, and more attuned to distinctions among followers in terms of such attributes as abilities, personality, and regulatory fit. Accordingly, social acuity may correspond to an ability to detect a wider array of social affordances.

Summary: Core Social Acuity Skills in Leader Problem Solving In this chapter, we have denoted a number of processes in leader social problem solving, identified the social elements that are considered in such problem solving, and highlighted the role of social acuity in facilitating the identification and consideration of these elements. Leaders with high social acuity skills have more multifaceted schemas and stored social knowledge structures that are used to make more complex inferences about the social contexts of leadership problems and derive solutions that are better fitting to those contexts and more likely to be effectively implemented. Thus, socially astute leaders will be more adept at detecting key changes in the organization’s social context, identifying key stakeholders, and knowing where and how to get information needed for problem construction and sensemaking. They are better able to build more complex mental models of socially embedded problems and forecast the social implications of these problems. Finally, they will be more skilled in developing solutions that account for the social complexity of the problem space.

Core Social Acuity Skills Our premises suggest a set of core skills that compose social acuity. Figure 12.1 presents six of these skills. Social scanning and social detection skills refer to the capacities to attend to and detect critical elements of a social context, including the proactive search and selection of social information (e.g., Smith & Collins, 2009; Waggoner, Smith, & Collins, 2009). Social construal skill refers to the leader’s ability to interpret and extract accurate meaning from social events (e.g., Griffin & Ross, 1991; Zaccaro et al., 1991). Utilization of social resources indicates skills in using social knowledge to determine the best-fitting use of social resources and appropriate behavioral responses in solution planning and implementation (e.g., Ferris, Witt, & Hochwarter, 2001). Social forecasting reflects the skill to accurately project the consequences of potential solutions in a social context (e.g., Shipman et al., 2010). Activation of social capacities refers to skill in using social knowledge stores

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Social Scanning Activation of Social Capacities

Social Detection

Social Acuity Skills Social Forecasting

Social Construal Utilization of Social Resources

FIGURE 12.1 Social

Acuity Skills

to ensure higher levels of engagement and use of social actors in solution implementation (e.g., Riggio, 1986). While some of these skills have appeared in several social and problem-solving skills inventories (e.g., Morgeson, Reider, & Campion, 2005; Mumford et al., 2017; Riggio, 1986), future research is needed to elucidate them in more detail, including delineating their cognitive and behavioral markers in ways that will facilitate their accurate assessment.

Constructs Related to Social Acuity A range of personal attributes related to social acuity have appeared in the leadership literature. Table 12.2 presents several of these constructs, including their definitions. A full review of these constructs is beyond the scope of this chapter; we refer interested readers to multiple published reviews of them, including some that are cited here. Instead, we will briefly describe how each construct is related to social acuity and note a few studies linking the construct to leadership outcomes. We placed at the top of Table 12.2 definitions of social acuity skill and social perceptiveness as anchors for the related attributes. Social perceptiveness skill is one of the core attributes of social intelligence (Zaccaro et al., 1991). Social intelligence refers to both the ability to interpret and understand social contexts and select appropriate behaviors reflecting that understanding (Marlow, 1986).

TABLE 12.2 Social Acuity and Related Constructs

Concept

Definition

Social Acuity

“The ability and inclination to perceive the psychological state of others and to guide one’s behavior in accordance with the perception”. (Funder & Harris, 1986, p. 530) “Social perceptiveness refers to a capacity to be particularly aware of and sensitive to needs, goals, demands, and problems at multiple system levels, including individual members, relations among members, relations among organizational subsystems, and interactions among a leader’s constituent organization and other systems in the embedding environment”. (Zaccaro et al., 1991, p. 321) “Social intelligence is the ability to understand the feelings, thoughts, and behaviors of persons, including oneself, in interpersonal situations and to act appropriately upon that understanding”. (Marlowe, 1986, p. 52) “Can be construed as declarative and procedural expertise for working on the tasks of social life in which social goals are especially salient”. (Cantor & Kihlstrom, 1987, p. 71) “Ability to infer, acquire, integrate, and recall information about persons, social situations (including operative norms), and social episodes; and reason with and adapt that information to attain social goals to which one is committed”. (Schneider & Johnson, 2005, p. 6) “The prototypic high self-monitoring individual is one who, out of concern for the situational and interpersonal appropriateness of his or her social behavior, is particularly sensitive to the expression and self-presentation of relevant others in social situations and uses these cues as guidelines for self-monitoring (that is, regulating and controlling) his or her own verbal and nonverbal self-presentation”. (Snyder, 1979, p. 6) “Includes three characteristics of a high self-monitor: a concern for social appropriateness, a sensitivity to social cues, and an ability to control one’s behavior in response to those cues”. (Briggs, Cheek, & Buss, 1980, p. 679) “The intent is to perceive another person’s beliefs, emotions, and perspectives, particularly when they are different from the observer’s own beliefs, emotions, and perspectives”. (Wolff, Pescosolido, & Druskat, 2002, p. 513) “The process of imagining the world from another’s vantage point or imagining oneself in another’s shoes”. (Galinsky, Ku, & Wang, 2005, p. 110) “Perspective-taking hinge on the same set of abilities: (a) ascertaining that other social agents actually possess mental states, (b) recognizing that these mental states are not necessarily identical to our own, and (c) overcoming our innate egocentrism in favor of such a different literal (visuospatial) or metaphorical (psychological) point of view”. (Erle & Topolinski, 2017, p. 683)

Social Perceptiveness

Social Intelligence

Self-Monitoring

Perspective Taking

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Concept

Definition

Empathy

“Empathy is defined as an affective state that stems from the apprehension of another’s emotional state or condition, and that is congruent with it”. (Eisenberg & Miller, 1987, p. 91) “The capacity to (a) be affected by and share the emotional state of another, (b) assess the reasons for the other’s state, and (c) identify with the other, adopting his or her perspective”. (de Waal, 2008, p. 281) “Emotional intelligence is the ability to perceive and express emotions, assimilate emotion and thought, understand and reason with emotion, and regulate emotion in the self and others”. (Mayer, Salovey, & Caruso, 2000, in Salovey, Brackett, & Mayer, 2007, p. 82) “The ability to purposively adapt, shape, and select environments through the use of emotionally relevant processes”. (Gignac, 2010, p. 131) “A person’s capability for successful adaptation to new cultural settings, that is, for unfamiliar settings attributable to cultural context”. (Earley & Ang, 2003, p. 9) “Comprises four factors: (1) metacognitive CQ, or one’s mental capability to acquire and understand cultural knowledge; (2) cognitive CQ, or one’s knowledge about cultures and cultural differences; (3) motivational CQ, or one’s capability to direct and sustain effort toward functioning in intercultural situations; and (4) behavioral CQ, or one’s capacity for behavioral flexibility in cross-cultural interactions (Ang & Van Dyne, 2008; Earley & Ang, 2003)”. (Ang, Van Dyne, & Rockstuhl, 2015, p. 279)

Emotional Intelligence

Cultural Intelligence

Socially intelligent individuals hold more elaborated social knowledge stores that aid in the interpretation of social cues and defining responses that are appropriate to those cues. Zaccaro et al. (1991) offered a conceptual framework describing social intelligence as a core leadership skill. In support of this framework, studies have linked social intelligence to several leadership outcomes, including leader and executive performance (Boyatzis, Good, & Massa, 2012; Sosik, Gentry, & Chun, 2012), leadership potential (Guerin et al., 2011), and leadership emergence (Ferentinos, 1996). Self-monitoring involves perception and situational awareness of social cues in order to (1) manage one’s self-presentation in different social contexts, and (2) determine what behaviors are appropriate in different contexts (Snyder, 1974). As with social intelligence, social perceptiveness skills provide the basis for behavior flexibility and situational appropriateness (Briggs et al., 1980). For leaders, the analysis and evaluation of social situations produces judgments of best-fitting leadership responses. Along this line, Zaccaro, Foti, and Kenny (1991) found that self-monitoring skills were positively associated with leader emergence across

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different situations composed of varying task requirements and group composition. Each leadership context required different sets of leadership responses. Presumably, individuals who consistently emerged as leaders across the situations use their social acuity skills in detecting the shifts in leadership requirements and adjusting to those shifts. Other studies have demonstrated similar positive associations between self-monitoring and leader emergence (Foti & Hauenstein, 2007; Kilduff, Mehra, Gioia, & Borgatti, 2017) and leadership effectiveness (Foti & Hauenstein, 2007; Day, Schleicher, Unckless, & Hiller, 2002). Perspective taking offers a narrower perspective of social acuity. The accent in this construct is on detecting and being aware of the psychological states of other social actors, and in particular being able to ascertain their perspectives of the social context (Galinsky et al., 2005; Wolff et al., 2002). Erle and Topolinski (2017) emphasized that perspective taking capacities entail the difficult task of switching from one’s own egocentrism to consider the context from the viewpoint of the other. Such skills are likely to foster leader problem-solving processes in several places, particularly in determining the agendas of social actors during problem construction and sensemaking, and in forecasting the consequences of solution implementation on these actors (e.g., their likely receptivity to leadership actions). Accordingly, perspective taking skills have been shown to predict leadership emergence (Wolff et al., 2002) and effectiveness (Mansen, 1993). The concepts of empathy and emotional intelligence narrow the range of social acuity skills to focus specifically on the detection and awareness of emotional states. Empathy refers to an awareness of the emotional states of others, to the point of sharing those states or adopting the emotional perspectives of others (de Waal, 2008). Emotional intelligence reflects a broader focus to include an awareness of one’s own emotions as well as skills in managing the emotional responses of self and others (Salovey & Mayer, 1990). In leader problem solving, changes in the emotional states of social actors may be the first clue to an impending problem. Thus, awareness of such changes would be instrumental in problem detection, while their interpretation would contribute to problem construction and sensemaking. Anticipated emotional reactions would factor into solution generation and evaluation. Past studies have linked both empathy and emotional intelligence to a variety of leadership outcomes (Côté, Lopes, Salovey, & Miners, 2010; Skinner & Spurgeon, 2005; Wolff et al., 2002). Cultural intelligence refers to the application of acuity skills across diverse cultural contexts (Earley & Ang, 2003; Lisak & Erez, 2015). According to Earley and Ang (2003), this construct includes cognitive, motivational, and behavioral facets; the cognitive facet includes cultural knowledge representations, and the processes of pattern recognition, external scanning, and self-awareness. Thus, this construct incorporates a number of skills and processes we have defined as reflecting social acuity but applies them specifically to multicultural or cross-cultural contexts. Cultural acuity skills would facilitate leadership in such diverse contexts. Accordingly, Lisak and Erez (2015) reported that cultural intelligence was associated with

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leader emergence in multicultural teams, but only when such acuity skills were paired with a global identity and an openness to cultural diversity. Rockstuhl and colleagues (2011) found that cultural intelligence was more strongly related to leader effectiveness in international assignments than in domestic ones. Both political skill and network acuity refer to perceptions of social dynamics within the organizational space. The emphasis, here, is in on interactions, ties, and influence patterns among social actors. Political skill has been 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” (Ahearn, Ferris, Hochwarter, Douglas, & Ammeter, 2004, p. 311). It includes four dimensions: social astuteness, networking ability, interpersonal influence, and apparent sincerity (Ferris et al., 2005). The factor of particular relevance to this chapter, social astuteness, was defined by Kimura (2015) as “the ability to accurately understand social interactions and interpret one’s own behaviour as well as that of others, and to be keenly attuned to diverse social situations” (p. 314). Thus, leaders with high political savvy construe not only the capacities, motives, and mental states of individual social actors, but also the influence patterns among them. Along this line, DeLuca (1999) provides an assessment tool— the Organizational Politics Mapping Technique—that involves a mapping of all social actors and stakeholders around an issue regarding (1) organizational influence, (2) applied influence for or against an idea or project; (3) changeability of applied influence, (4) level of relationships among actors, and (5) the quality of the relationships (positive or negative). People high in political savvy, particularly its social astuteness factor, should be able to produce organizational influence maps of greater breadth, depth, and accuracy. Network acuity also reflects a perceptual sensitivity to the existence and nature of relationships among social actors connected in an organizational social network (Balkundi & Kilduff, 2006). Most organizational problems, especially those confronting leaders as they rise through organizational levels, exhibit a degree of complexity, not only in terms of their informational properties, but also in the range and diversity of stakeholders and different social units (Zaccaro, 2001). Therefore, effective leader problem solving requires accurate assessment of attributes of social actors and units, as well as the shifting patterns of influence among them. Accordingly, several studies have linked political skills and network acuity to positive leadership outcomes (e.g., Brouer, Douglas, Treadway, & Ferris, 2013; Ferris, Perrewé, Daniels, Lawong, & Holmes, 2017; Morris, 1997; Tocher, Oswald, Shook, & Adams, 2012).

Conclusions This brief summary of concepts in the leadership literature that include elements of social acuity in their definitions indicates that they have become widespread in models of leadership effectiveness. In their review of leader individual differences

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and the leadership context, Zaccaro et al. (2018) argued that social acuity and behavioral flexibility help foster a greater congruence between leader attributes and situational elements. Social acuity increases the probability that leaders will be able to perceive situational elements that define performance requirements and indicate social affordance. Behavioral flexibility, in turn, facilitates the ability of the leader to respond appropriately to perceived situational demands. Zaccaro et al. (2018), reported the results of several studies that show how the concepts described earlier as related to social acuity indeed moderated the relationships between leader attributes and various leadership outcomes. The perspective of this chapter is one that integrates cognitive and social models of leader performance. The cognitive models offered by researchers such as Mumford and his colleagues (Mumford et al., 2017) and Sternberg (2007) rest on the premise that leadership entails complex problem solving. Social models of leadership focus on the interpersonal influence that leaders exert to facilitate follower, team, and organizational performance (Bass, 1985; Graen, & Uhl-Bien, 1995; House, 1977). The framework presented in this chapter suggests that these two models are inextricably entwined as cognitive capacities are placed in the service of social influence. The social information processing models of leadership offered by Lord and his colleagues (Lord, 1985; Lord & Mayer, 1993; Shondrick, & Lord, 2010) reflect this integration. However, in the past, studies of leader individual differences tended to focus either on cognitive or social attributes to the exclusion of the other or treat them in terms of additive influence (Zaccaro et al., 2018). Recently, there has been an increase of profile or pattern approaches to leadership that take a “whole person” approach (Foti & Hauenstein, 2007), integrating different sets of attributes (Zaccaro et al., 2018). However, very few of these studies included both cognitive and social capacities in their profiles (see exceptions by Bray, Foti, Thompson, & Wills, 2014; Foti, Bray, Thompson, & Allgood, 2012; Foti & Hauenstein, 2007). Future research on leader attributes should focus more specifically on integrated models of leader performance, that combine cognitive and social individual differences. Our perspective also suggests a number of implications for leader selection and development. Leader assessments for selection and promotion should include the social acuity skills enumerated here. Assessment centers already assess cognitive and social attributes. Eurich, Krause, Cigularov, and Thornton (2009, p. 397) found that the four most common leadership skills included in assessment centers were “communication” (91%), “problem solving” (91%), “organizing and planning” (77%), and “influencing others” (63%). Eurich et al. also found a dimension that reflects some social acuity, “consideration and awareness of others” that has been included in 49% of assessment centers (p. 390). Herd, Alagaraja, and Cumberland (2016) described assessment centers for evaluating global leadership competence. Based on Conger and O’Neil (2012), they noted that the competencies to be included in such assessment centers were “skills in reading and responding appropriately to cross-cultural cues and norms, and working in a

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variety of cultural contexts to influence others in order to achieve organizational objectives” (p. 29). This recommendation would include several of the social acuity skills presented in Figure 12.1. However, they also observed that “very little is known empirically about global leadership competency assessment using AC methodology” (p. 32). This suggests that (1) social acuity needs to be included in more assessment centers, and (2) assessment centers need to provide a more nuanced evaluation of the subcomponents of social acuity skills. Practitioners who develop and/or manage assessment centers can use the concepts laid out in this chapter as a guide for choosing the social capacities that can be integrated into the assessment center evaluations. Exercises in such centers would need to be carefully tailored to activate each of these skills, with assessors also carefully trained to evaluate these skills (Lievens, Schollaert, & Keen, 2015). We believe the complex simulations that typically compose many assessment centers would offer the potential and a rich enough venue to evaluate social acuity skills. We encourage future research and assessment practices that yield more strategies to actualize this potential. Leader development experiences should also be tailored to activate and facilitate growth in social acuity skills. The fact that most leadership assignments will occur within complex social domains suggests that they will likely foster some level of improvement in such skills. However, assignments will likely vary in how much they activate social acuity skills. For example, Yip and Wilson (2010) described five clusters of challenging assignments. Two of them, “Stakeholder engagement” and “Work in a different culture”, are likely to carry an even higher social load than the other assignments (i.e., “Increases in scope”, “Creating change”, and “Job rotation or transition”; pp. 66–67). Yip and Wilson also describe “boundarycrossing assignments” where “managers work across organizational and cultural boundaries with groups that have different sets of beliefs, practices, or goals” (p. 89). The social diversity of these types of challenging assignments can provide abundant learning opportunities to nurture social acuity skills. However, the effectiveness of these challenging assignments will also depend on the feedback leaders are provided by learning partners (DeRue & Wellman, 2009). Thus, those providing developmental feedback, such as coaches, mentors, and supervisors, will also need to possess the skills to detect and evaluate the social acuity skills targeted by these leader developmental assignments. While the contribution of social acuity to leadership is not a new idea, our contribution in this chapter has been to (1) delineate more clearly its precise role in leader problem solving, and (2) to articulate specific core skills that define social acuity. We hope that future research will focus on constructing more elaborated conceptual frames of these skills. We also hope such research includes development of assessment tools that can measure the levels of these skills in leaders across the organization. Progress in these areas can further our understanding of a construct that is at once ubiquitous in the leadership domain but still obscured in terms of all the precise ways it advances the effective practice of leadership.

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13 LEADERSHIP AND MONITORING SKILLS David V. Day, Ronald E. Riggio, and Rowan Y. Mulligan

Leaders have many and varied responsibilities. Included among these myriad responsibilities is ensuring that work goals are being met, that ethical standards are upheld, and that relevant financial and fiduciary duties are fulfilled. An overarching orientation across these and other leadership responsibilities is monitoring. To monitor involves activities such as to watch, observe, or track different aspects related to a leader’s role responsibilities. This chapter reviews different monitoring orientations related to leadership responsibilities. In particular, we will organize the review around three central features: (a) monitoring self, (b) monitoring others, and (c) monitoring the organization. From the earliest days of social science research on leadership, scholars explored the role of leadership skills involving the monitoring of behavior by self and others as critical for leadership success. For example, Bass (1990) reviewed studies suggesting that leaders need to control and regulate (i.e., monitor) their moods and emotions to be effective. Monitoring self in the form of one’s emotional states is a prerequisite for effectively regulating emotions, which is an important leadership skill (for a review see Gooty, Connelly, Griffith, & Gupta, 2010). From a social psychological perspective, leadership is a complex process that involves one individual having power and influence over another, even if only temporary or transitory (e.g., social influence as a dynamic interpersonal process). In order to fulfill a leadership role effectively, a leader must engage in ongoing impression management among other things. Two specific subskills of impression management, exemplification and ingratiation, have been positively related to perceived transformational leadership, leader effectiveness, and follower satisfaction (Gardner & Cleavenger, 1998). Exemplification refers to taking personal risks and making personal sacrifices, while ingratiation encompasses charming helping behavior, work praise, and non-job-related compliments to elicit a desired image

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in the minds of others. That is, leaders who have portrayed themselves as worthy role models and communicated a genuine interest and prioritization of the individual follower are more likely to be perceived as an effective transformational leader (Gardner & Cleavenger, 1998). Building on Goffman’s (1959) dramaturgical approach, Gardner and Avolio (1998), suggested that leaders in general, and charismatic leaders in particular, must carefully manage impressions in order to have influence over followers. One necessary skill for impression management is the ability to monitor the behavioral and emotional state of the self, others, and the organization. This chapter reviews research on monitoring skills and applies them to the process of leadership, especially in the face of complex task accomplishment (Thiel, Connelly, & Griffith, 2012). The first section reviews the notion of monitoring self, which is the area with the most available empirical evidence. Subsequent sections review what we know—and need to know—about monitoring others and the organization.

Monitoring Self Another way to frame the leadership activities related to monitoring self is selfmonitoring. Care must be taken when using the latter term, because it can refer to a dispositional orientation as well as various behavioral activities. Probably the best-known work on the dispositional, or personality-based, perspective on selfmonitoring comes from the research of psychologist Mark Snyder, especially in terms of developing and validating a measure to assess individual self-monitoring propensities (Snyder, 1974, 1987). Snyder maintains that high self-monitors are acutely sensitive to situational cues regarding social behavior. These individuals have a dispositional propensity to actively monitor the interpersonal environment for such cues and for regulating and controlling their expressive behavior and self-presentations. In contrast, the low self-monitor is less attentive to social cues about what is situationally appropriate and does not have a highly developed set of self-presentation skills. Following on Snyder’s impressive body of conceptual and empirical work (also see Snyder, 1979), researchers have labeled high self-monitoring individuals as social chameleons who actively change their attitudes and behaviors to fit with the prevailing social expectations. Conversely, low self-monitoring individuals act more accordingly with the credo of “to thine own self be true”. A relevant question flowing from these different dispositional perspectives is whether or not so-called chameleons can lead, and lead effectively (Bedeian & Day, 2004). Research involving the intersection of self-monitoring and leadership indicates that high self-monitors are more likely to emerge as leaders than low selfmonitors (Dobbins, Long, Dedrick, & Clemons, 1990). Meta-analytic estimates across 23 samples demonstrated a positive, non-zero relationship (corrected r = .21) between self-monitoring and various measures of leadership behavior (Day, Schleicher, Unckless, & Hiller, 2002). Subsequent research has demonstrated

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that self-monitoring moderates the relationship between leadership style and vision theme. Specifically, the form of the relationship is such that high self-­ monitoring strengthened the relationship between charismatic leadership and inspirational vision themes, whereas low self-monitoring strengthened the relationship between contingent reward leadership and instrumental vision themes (Sosik & Dinger, 2007). That is, self-monitoring could be an important orientation for a leader to establish her or his vision on which the group constructs their respective values, behaviors, and goals. Moving from a dispositional or personality-based perspective on self-monitoring to one that is focused more on behavioral skills suggests several categories of behaviors related to monitoring self. Such skills-based perspectives on monitoring self as a leader include self-awareness, self-presentation, strategic self-disclosure, and emotional regulation and control.

Self-Awareness Research suggests that a foundational skill for a leader to be effective is self-­ awareness. Even the ancient Chinese philosopher Lao Tzu hinted at this idea with his observation that knowing others is wisdom, while knowing self is enlightenment (Lau, 1972). An effective leader should be aware of how he or she behaves toward followers, and perhaps more importantly, how that behavior is perceived by others. Atwater and Yammarino (1997) defined self-aware leaders as those whose self-ratings of their own behaviors agreed with others’ ratings of that same behavior (i.e., high self-other agreement). Furthermore, they proposed that more highly self-aware leaders are more effective. Subsequent research supports that notion (Atwater, Ostroff, Yammarino, & Fleenor, 1998; Atwater, Waldman, Ostroff, Robie, & Johnson, 2005; Church, 1997). Furthermore, Sosik and Megerian (1999) provided evidence suggesting that self-awareness in the form of congruence between self and others’ perceptions and ratings of leadership establishes the base from which other so-called emotional intelligence skills (e.g., empathy) grow and flourish. Self-awareness has also been gaining attention through the lens of authentic leadership, which is also based on perceived valuebehavior alignment (Gardner, Avolio, Luthans, May, & Walumbwa, 2005). This focus on leader self-awareness and self-actualization has been proposed as a critical component for self-development and leader development. That is, the leader reveals his or her “true self ” while simultaneously cultivating the true group identity, via “leaderly choices” (Ladkin & Taylor, 2010, p. 64).

Self-Presentation Self-awareness also contributes to an area that has received a great deal of attention: leader self-presentation, which is the notion that leaders monitor their own performance in a leadership role. Self-presentation is an omnipresent ingratiatory

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behavioral strategy in that most individuals and leaders seek to present themselves in the best light in order to gain respectable and positive impressions among others (Liden & Mitchell, 1988; Barrick, Shaffer, & DeGrassi, 2009). This is supported by the finding that individuals are more prone to behavior that controls others’ perceptions of them if they expect to encounter those individuals in the future (Gergen & Wishnov, 1965). Cheng and Chartrand (2003) supported a very similar concept of altered behavior upon future expectations of encounter (i.e., attempts to get along with potential future classmates) among high self-monitors. Leaders tend to project the image of themselves that they believe their followers want to see (Leary, Robertson, Barnes, & Miller, 1986). Without an intentional emphasis on creating a promising social impression among the followers, the individual may experience a disconnect with his or her own progress as a leader. This might compromise followers perceived social image of their leader. In line with Mintzberg’s (1973) managerial role theory, Gardner and Martinko (1988) found that educational managers (i.e., principals of primary and secondary schools), as their institution’s main organizational spokesperson, managed the school’s impression that it made in the community. The principals provided more organizational descriptors to their targets of external and high-status audiences (Gardner & Martinko, 1988). Sosik, Avolio, and Jung (2002) tested supervisors at an information technology-consulting firm, using other source ratings from the managers’ superiors and subordinates. The findings indicated that more complex desired charismatic identities were positively associated with self-monitoring, and that self-monitoring was positively related to self-serving impression management and negatively related to pro-social impression management (Sosik et al., 2002). However, pro-social impression management was positively correlated with charismatic leadership, which predicted performance as a manager and as a unit. Additionally, the propensity to self-present has been shown to moderate the positive relationship between transformational leadership and task performance (Rank, Nelson, Allen, & Xu, 2009). Although some evidence supports the positive relationship between leader self-presentation and performance, Barrick et al. (2009) reported meta-analytic findings suggesting that self-presentation tactics (e.g., impression management, appearance, verbal and nonverbal behavioral strategies) in the context of job interviews shared stronger positive relationships with interviewer ratings than with future job performance measures. These findings reflect the same proposed research needs of the 1980s that would further develop the self-presentation subfield: the degree to which ingratiation (i.e., self-presentation) is an unconscious, nonconscious, and/or conscious process (Liden & Mitchell, 1988). Other researchers found that positive and negative self-presentations were not mediated by conscious awareness, thus supporting the assertion that self-presentation is mainly an unconscious process (Gergen & Wishnov, 1965). Future research offers an opportunity to delve further into the interplay between leader self-presentation, follower impressions, as well as objective and

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subjective measures of job and task performance of both the leader and the group. Perhaps, to a certain extent, job performance and output quality ratings are moderated by the overall impressions that the leader has unconsciously made on the followers. However, these impressions and subsequent implicit biases may not necessarily be commensurate to important organizational and leadership outcomes, such as task and leader performance.

Strategic Self-Disclosure A sub-skill of leader self-presentation includes strategic self-disclosure, or the sharing of personal information to better know and understand one another (Collins & Miller, 1994; Pettigrew & Tropp, 2006). The practice of leadership is often considered to be a form of performance art as it dances along the line between portrayed modesty and transparency (i.e., revealing past obstacles that were overcome). The decision to remain modest or to disclose hardships can affect the credit bestowed upon a leader, the perceived difficulty of his or her accomplishments, and the suggested recognition for his or her success as a leader (Giacalone & Riordan, 1990). Phillips, Rothbard, and Dumas (2009) proposed a theory that the effectiveness of self-disclosure may be contingent on various factors, such as status characteristics and identification with personal characteristics. This may carry important implications with respect to outcomes in further increasing or decreasing status distance between the leader and followers (Phillips et al., 2009). For instance, based on group status, politicians, entertainers, and experts have been shown to use modesty or humble self-descriptions to create likable impressions of themselves among their respective followers (Schütz, 1997). Self-disclosure has also been suggested to be a reciprocal process. Gergen and Wishnov (1965) found that individuals who were presented with modest self-disclosures responded with modest self-presentations. Furthermore, Jones, Gergen, and Jones (1963) addressed the self-presentation patterns across the status hierarchy of leaders and subordinates in a Naval ROTC program. High status individuals defaulted to modest self-presentations while under pressure to place themselves in an attractive light, rating themselves especially more modestly on the traits that they identified as unimportant. Lower status individuals exhibited the same modest ingratiation strategies; however, they tended to be more modest on their unimportant traits and more self-enhancing on their important traits. Paulhus, Graf, and Van Selst (1989) proposed that individuals act according to their “automatic egotism” by habitually portraying themselves in a positive light initially, which supports the observed tendency for leaders to positively portray themselves to their followers (Liden & Mitchell, 1988; Barrick et al., 2009). It is not until later that individuals present more modest self-depictions that require more effort to enact (Paulhus et al., 1989). Effortful self-presentations (i.e., those involving unfamiliar or atypical presentations) have been conceptualized as

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a generalized resource that can deplete overall self-regulatory resources (Vohs, Baumeister, & Ciarocco, 2005). For instance, counter-normative self-presentation (e.g., violations of gender norms) has led to impaired self-regulation in future interpersonal interactions (Vohs et al., 2005). Additionally, these researchers found that when self-regulatory resources had already been expended by previous self-control behavior, less-effective self-presentation strategies were used (e.g., unsatisfactory self-disclosures). That is, when the individuals had already enacted self-control, they exhibited inappropriately intimate or distant self-­ disclosures. This foundational research suggests that leaders need to balance the exhaustion of self-regulatory resources in order to maintain reserves of effective self-­presentation and self-disclosure strategies. However, this subfield could benefit from studies that specifically address strategic self-disclosure practices in the distinct context of leadership.

Emotional Regulation and Control There are many instances where leaders need to regulate and control their emotional displays. A comprehensive, state of science review of the literature on leadership, affect, and emotions indicates that there are robust links between leader affect and various workplace outcomes, including follower moods (Gooty et al., 2010). In addition, regulation of emotion is considered to be a key competence associated with effective leadership (Haver, Akerjordet, & Furunes, 2013). For example, in times of crisis, leaders must intrapersonally control their arousal and felt emotions (e.g., fear, anger) and maintain a calm and confident demeanor. Considerable research suggests that leaders who display proportionally more positive affect/emotions, compared to negative emotional displays, are rated more positively and as more effective (although these relationships are complex; Gaddis, Connelly, & Mumford, 2004; van Knippenberg & van Kleef, 2016). Moreover, George and Bettenhausen (1990) found that groups with leaders in a positive mood experienced less turnover and demonstrated more pro-social behaviors. This can be explained by Sy, Côté, and Saavedra’s (2005) findings that leader affect, both positive and negative, influences follower affect via mood contagion. Individuals tended to report positive moods when the leader reported a positive mood or negative moods when the leader was in a negative mood (Sy et al., 2005). This supports Riggio and Reichard’s (2008) proposed leadership and emotions model of the dyadic exchange between leaders and followers in that current states of being can be interpersonally transmitted to both parties. Thus, the leader’s ability to regulate her or his own emotions in front of the followers not only contributes to the leader’s presence, but also to followers’ general emotional state (Gooty et al., 2010). In sum, the leader’s mood can influence the group’s affective tone (i.e., collective mood; Sy et al., 2005). Related to this line of research, Barsade (2002) found that the valence of leader affect was key, while the intensity and arousal of leader moods had no effect on

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the work group’s task cooperation or conflict. Displays of positive affect have also been suggested to lead to higher ratings of charismatic leadership (Damen, van Knippenberg, & van Knippenberg-Wisse, 2008). Caza, Zhang, Wang, and Bai (2015) filled a gap in leader affect literature by going beyond leader emotional valence and follower affective reaction to the more nuanced sphere of leader portrayed emotion and follower cognitive reaction. Perceived emotional sincerity influenced follower trust in both American and Chinese worker cohorts, and trust positively influenced worker in-role and extra-role performance (Caza et al., 2015). The notion that a leader’s ability to regulate emotional displays—­producing felt emotions to followers and engaging in a great deal of positive affect—is an important skill that has been demonstrated in studies to show that trained leaders engage in emotional regulation in producing so-called authentic emotions (Edelman & van Knippenberg, 2017). Leaders can also interpersonally employ emotional regulation among their followers in order to help them strategically manage discrete emotions (Thiel et al., 2012; Thiel, Griffith, & Connelly, 2015). Thiel et al. (2012) used two strategies in a controlled experiment to study the effect of a leader’s proposal of regulation techniques on either recently angered or discouraged followers and their subsequent planning ability. The emotionally manipulated subordinates (i.e., anger and pessimism conditions) were provided with two different emotional regulation tactics: downward social comparison and reappraisal (i.e., deep acting). The “group leader” assured the aggravated or discouraged study participants that their group was still performing better than the other teams (i.e., downward social comparison) or encouraged them to take a more objective approach by factoring in the other “group members’ ” perspective (e.g., what provoked them to say what they did) (i.e., reappraisal). Participants in both emotional conditions who received reappraisal as the suggested strategy reported decreased levels of their respective previously heightened emotions of anger or pessimism. Those in the anger-reappraisal conditions also demonstrated higher scores on plan quality and originality. Thiel et al. (2015) found that suppression (i.e., surface acting)—the masking of post-emotional experience emotions—coupled with leader empathy had a greater effect on decreasing work-related stress. That is, emotional regulation could be an advantageous technique for leaders to use with their followers not only for improved emotional states, but also for important organizational outcomes.

Monitoring Others Leaders have an important responsibility in monitoring others in their work groups and teams. The reasons why this is an important function vary from simply understanding others in the workplace, including being able to understand both verbal and nonverbal/emotional messages, to more complex forms of monitoring that include the more managerial aspects of performance management and

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appraisal, to the inherent responsibility all leaders have to develop others (Pulakos, Mueller-Hanson, & Arad, 2019). Appraising performance and helping others to develop rests on a foundation of monitoring others, their individual work performance and development, and processes and performance in group or team contexts. Each of these concerns is addressed in this section.

Emotional Sensitivity and Empathy Emotional sensitivity (aka emotional decoding skill) is the ability to monitor and read the emotional messages sent by others—typically done through nonverbal cues of facial expressions and tone of voice. This is a critical skill that underlies the construct of empathy and is frequently associated with charismatic and transformational leaders and their ability to read followers’ emotions and respond to them (Riggio, 2014; Rubin, Munz, & Bommer, 2005). There is some evidence, however, that leaders should not be overly sensitive to followers’ emotions to the point that they feel that the leader is “eavesdropping” or inappropriately monitoring their feelings (Elfenbein & Ambady, 2002). An important stream of research has been devoted to investigating the role that empathy (i.e., taking perspective and being sensitive to the feelings of others) plays in effective leadership (e.g., Kellett, Humphrey, & Sleeth, 2006). Empathy can be defined as the ability to strategically and advantageously manage self and others’ emotions, identify distinct emotional states, and integrate this information into action planning and problem solving (Mayer & Salovey, 1993). Kellett, Humphrey, and Sleeth (2002) found empathy to be the strongest emotional ability to predict perceived leadership (i.e., leader emergence). Empathy’s effect on leader–member exchange quality has also been shown to be fully mediated by relations-oriented behavior that is intended to support and develop followers (Mahsud, Yukl, & Prussia, 2010). That is, empathy can enable leaders to identify and exhibit the proper interpersonal behaviors for nuanced contexts and unique individuals. This aligns with transformational leadership’s individualized consideration dimension, in which the leader acts according to the follower’s specific needs (Bass & Riggio, 2006). Skinner and Spurgeon (2005) found that three of the four individual dispositions of empathy (i.e., empathic concern, perspective taking, and empathic matching) were associated with follower-rated levels of transformational leadership behavior. With respect to follower empathy, Lewis’s (2000) findings suggested that followers are more likely to empathize with leaders who exhibit emotions that they deemed as appropriate to the situation. Both as a leader and a follower, empathy requires the monitoring of others’ emotional states and messages. A construct that is closely related to emotional sensitivity and empathy is empathic accuracy. Empathic accuracy is the ability to accurately infer the feelings and thoughts of another (Ickes, 2003). It is a critical element of relationship development and maintenance, and as such, is important for leader-follower relationships. Successful empathic accuracy requires carefully monitoring the behavior

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of another, searching for cues of emotion, attitude, and dominance/submissiveness (Ickes & Tooke, 1988). A practical question to consider is the extent that empathic accuracy can be trained. This seems to be a likely possibility; however, it should be considered as an important domain on its own without invoking scientifically dubious constructs such as emotional intelligence.

Performance Monitoring Perhaps one of the most important tasks of supervisors and leaders is monitoring the performance of direct reports and followers, providing constructive feedback for performance improvement (Aguinis, 2019; London, 2015). For the most part, workplace performance monitoring focuses on assessments of worker productivity, but it could also include the observation and assessment of employees’ abilities to work well with others (i.e., team skills). Much of the research on performance monitoring on the part of supervisors and leaders has either looked at the impact of performance monitoring on improving performance, or has examined the impact that performance monitoring has on the worker (Larson & Callahan, 1990). Employee reactions to performance monitoring range from changes and corrections in employee performance to employee dissatisfaction, employees’ perceptions of fairness, and stress reactions (see Stanton, 2000, for a comprehensive overview of employee reactions to performance monitoring). The very act of having one’s performance monitored can influence the perceived importance of the task and performance on the task (Larson & Callahan, 1990). Performance monitoring is part of a broader performance management process involving leaders’ skills in scanning the workplace environment, recognizing an employee’s level of performance, and making decisions about if and when to intervene and provide either encouragement or suggestions for improvement. In this manner, performance monitoring is inherently tied to the development of others (Pulakos et al., 2019; Smither, 2012). In addition to ongoing monitoring of performance, organizational supervisors and leaders must provide performance feedback on a regular basis, and conduct formal performance evaluations (London, 2015). When leaders provide performance feedback to employees, monitoring and decoding skills are essential for understanding how the feedback is being processed and understood by the individual, in order to successfully continue to motivate follower performance (Riggio, 2005). The strong emphasis on monitoring performance/productivity has led to the development of technology for electronic performance monitoring to assist leaders/ managers with this essential function (see Alge & Hansen, 2014 for a review). The idea is that for many jobs in production, sales, and customer service, computers can aid leaders in performance monitoring when productivity can be easily quantified. Leader performance monitoring is an essential skill for coaching employees. In order to be an effective coach a leader must monitor the employee’s work

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performance, paying attention to attainment of goals, the employee’s development of skills and competencies, and what the employee needs to better perform his or her job, as well as diagnose performance problems (Aguinis, 2019). Performance monitoring is critical for helping others meet performance expectations while also developing their respective knowledge, skills, and abilities as part of a broader performance management function.

Monitoring the Team Teams have become increasingly common in organizations, especially as a means of accomplishing complex and interdependent work. Experimental research on team monitoring has demonstrated its relationship to team feedback and coordination processes, which mediated effects on team performance (Marks & Panzer, 2004). A related concern is the role of leadership and leader monitoring behavior in facilitating effective performance and team success in organizational field settings as well. Although team leadership can originate from within the team through the development of a collective capacity for leadership (Day, Gronn, & Salas, 2004), it is more often enacted by an external leader (Hackman & Walton, 1986). In either case, functional leadership theory suggests that the role of a leader is essentially “to do or to get done, whatever is not being handled for group needs” (McGrath, 1962, p. 5). In this manner, anyone who serves as a completer of essential team functions is exercising leadership. From this perspective, the source of team leadership can vary but the overarching goal is to enhance team effectiveness by satisfying unmet team needs. Morgeson, DeRue, and Karam (2010) elaborated on team leadership by identifying 15 team leadership function categories, the most relevant of these to this chapter is the category of monitoring the team. This category of action phase leadership functions is composed of five separate but interrelated functions: (1) monitors changes in the, team’s external environment, (2) monitors team and team member performance, (3) gives information about what other teams are doing, (4) requests task relevant information from team members, and (5) notices flaws in task procedures or team outputs. These functions involve examining the team’s processes and performance, as well as the external team context. It also involves tracking and evaluating the teams progress toward task completion, availability of resources to the team, the external environment, and team member performance. In essence, monitoring team performance provides important data that informs many of the other team leadership functions. In a qualitative study of extreme action teams, researchers identified a process of what they called dynamic delegation of which monitoring played a key role (Klein, Ziegert, Knight, & Xiao, 2006). The focus was on trauma resuscitation units in a teaching hospital where the senior leader (i.e., attending surgeon) engage in “rapid and repeated delegation of the active leadership role to and withdrawal of the active leadership role for more junior leaders of the team” (p. 590).

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In this model, team leaders monitor the performance of the team by watching and questioning performance to ensure that the team does not make any serious errors in treatment. The observational data suggested that monitoring can be close and highly active (i.e., watching another closely and actively questioning their actions) or more distant and passive (i.e., periodically observing a resident in their work but not closely monitoring their actions). These findings provide an interesting perspective on leader monitoring skills, in that the key behaviors of observation and questioning can vary along a continuum of highly active and intense to highly passive and removed. This suggests that leader monitoring skills and behaviors can vacillate in terms of their intensity depending on the criticality of performance and possible outcomes. The focus of another field study, this time involving maintenance line workers and their supervisors, was on examining the effects of a leadership intervention designed to modify monitoring and rewarding of subordinates’ safety performance (Zohar, 2002). Related to the focal topic of leader monitoring is whether the leader monitored all aspects of subordinate performance (facet-free perspective) or monitored only certain performance aspects and adjusted their monitoring depending on relative priorities (facet-specific perspective). This study was designed to test the effect of enhanced facet-specific monitoring and supervision on safety records in various organizational subunits. Results of the interrupted time-series analysis of supervisory safety practices on employee safety outcomes demonstrated that modifying facet-specific monitoring behavior on the part of leaders brought about positive changes on the shop floor in terms of employee safety. Overall, the results of research investigating supervisory monitoring behavior suggests that it is an important contributor to team processes and team performance. The conceptual work of Morgeson et al. (2010) further suggests that monitoring the team is an important category of team leadership functions.

Monitoring the Organization Leaders occupying positions of strategic importance in organizations are involved in what has been termed executive leadership (Day & Lord, 1988; Zaccaro, 2001). These leaders have greater responsibilities than merely managing and monitoring self and others. At top levels in organizations, effective leadership also requires the expanded attentional bounds and awareness limits to encompass the overarching context in which the self and others are situated (Ladkin & Taylor, 2010). In particular, these top-level executive leaders also have responsibility for monitoring the organization and positioning it in a competitive landscape that best serves both internal and external stakeholders. This type of executive monitoring has also been called environmental scanning, in which information about events, trends, and relationships at the organization or institutional level is acquired and used for strategic action (Choo, 2001; Choo & Auster, 1993). Given that one of the fundamental requirements of top-level leaders is to shape the strategic direction of their

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respective organizations through sound decision making (Donaldson & Lorsch, 1983), this requires ongoing monitoring of the organization and its internal and external environments. The following subsections examine in more detail what has been suggested in terms of monitoring the internal environment (i.e., climate) for citizenship behavior, developing a theory of executive leadership, as well as the role of executive monitoring in shaping identity and image at the organizational level.

Organizational Citizenship Behavior and Strategic Assessments Organizational citizenship behavior (OCB) can be considered a critical organizational monitoring sub-skill. It has been defined at the individual level as discretionary pro-social behavior that goes above and beyond formal job responsibilities (i.e., doing good), and it has been suggested to be a reaction to the way in which one perceives characteristics of the organization (Organ, 1988; Borman & Motowidlo, 1993; Blakely, Andrews, & Fuller, 2003). Consistent with this point, Chang, Rosen, Siemieniec, and Johnson (2012) found that in high self-monitors, who were also highly conscientious, their self-monitoring lessened the negative effects of politics perceptions on their exhibited OCB. However, coming from a functional approach, Bolino (1999) has suggested that OCB can be used as a form of impression management, in that personal aims and potential benefits motivate individuals to perform citizenship behaviors in their organization (i.e., looking good). Thus, these types of voluntary helping behaviors might play a role in organizational leadership and monitoring, not only as helping behavior but also as strategic advancement catalyzers. Blakely et al. (2003) conducted a longitudinal study in which supervisors rated their subordinates’ OCBs. Their results suggested that high self-monitors were more likely to partake in OCBs. Moreover, Kilduff and Day (1994) followed a cohort of 139 MBA graduates in a five-year longitudinal study. The results revealed a significant main effect of self-monitoring on career mobility. That is, high self-monitors were more likely than low self-monitors to change companies or locations and to receive cross-company promotions. Kilduff and Day’s (1994) results suggest OCB’s and impression management’s success in realizing desired outcomes through organizational monitoring. Thus, the research supports OCB’s place in the metaphorical monitoring tool belt. It is worth noting that the line between “do good” behavior and “look good” behavior is blurred. Nonetheless, either type of behavior that manipulates and cultivates one’s desired image among others requires a certain degree of prowess in organizational awareness in order to know and understand the organization in its entirety (via monitoring) and to manipulate the organizational situation accordingly, which will in turn achieve desired outcomes. For instance, monitoring comes into play in the organizational context with respect to social networks.

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Mehra, Kilduff, and Brass (2001) found that high self-monitors were more likely to have achieved central positions in social networks in the organization. High selfmonitors also exhibited a unique tendency, not present in the low self-monitors, for their tenure to correlate with occupancy of beneficial network positions (Mehra et al., 2001).

Executive Leadership There is strong evidence that executive leadership is important for effective organizational performance (Day & Lord, 1988; Thomas, 1988). A more pertinent question might be exactly how top-level leaders shape the performance of their respective organizations. A fundamental tenet of this perspective is that organizations are open systems that must—to varying degrees—interact with their external environments (Katz & Kahn, 1978). Relatedly, leaders can influence organizational performance by actions that target the internal or external environment and can do so through either direct or indirect means (Day & Lord, 1988). Such targeted actions are in large part a function of environmental monitoring that serve as a foundation for strategic decision making on the part of executive leaders (Donaldson & Lorsch, 1983). In terms of strategic planning, decisions must be made regarding the choice of markets and environments in which to compete or engage. Doing so effectively depends on actively and accurately monitoring the external environment and taking direct actions to adapt to important features of the environment. Relatedly, executive leaders might directly take action to acquire resources and maintain strong boundaries by promoting horizontal and/or vertical integration through mergers and acquisitions. There might also be indirect means taken to influence or adapt to the external environment, such as creating a favorable public image of the organization or its core products or using indirect political influence to shape the environment in favorable ways. Regardless of the overall strategy or specific tactics involved in influencing or adapting to environments, there is the need for active monitoring of the organization and its environments. An important component of leadership at the strategic apex in organizations involves positioning the overall organization in a competitively advantageous position. As noted, this inherently involves active monitoring of environments, especially the external environment. Whereas leaders at lower organizational levels have mainly an internal monitoring focus, those at the top need to be concerned about both and would likely spend more of their efforts understanding what is going on externally. The types of skills involved in this type of monitoring would likely be quite diverse, including an understanding of the relevant economic or financial presses, the ongoing political climate, as well as information gathering about potential new opportunities whether they be in emerging markets or through mergers and acquisitions to enhance horizontal or vertical integration. These involve high-level monitoring skills on the part of leaders, as well as being

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able to consult with specialists across various technical domains (e.g., finance, strategy, legal). An area of potential future interest is in better understanding the link between developing strategic thinking competency (Dragoni et al., 2014) and the skills needed to effectively monitor organizational environments.

Monitoring Organizational Identity and Image Building on the assertion that those organizations best attuned to their environment are most likely to have the most adaptive capacity and thus be able to deliver the most value to various stakeholders, the notion of a self-monitoring organization has been proposed (Price & Gioia, 2008). According to these authors, “selfmonitoring is a multilevel concept that can be applied not only to individual but also to the organizational level and can contribute to an organization’s ability to monitor the ways in which its many images are interpreted” (p. 208). From this perspective, the monitoring involved at top leadership levels is focused on the image portrayed and interpreted by various stakeholder groups. A relevant question concerns the similarities and distinction between organizational identity and image. In general, organizational identity is who members believe themselves to be as an organization (Albert & Whetten, 1985), whereas an intended (as compared with unintended) organizational image pertains to the question of who do organizational members want others to think they are as an organization (Brown, Dacin, Pratt, & Whetten, 2006). The argument forwarded by Price and Gioia (2008) is that organizations—as represented by their toplevel leaders—that monitor how they are perceived in the external competitive environment (i.e., marketplace) enhance their capacity to align with stakeholder perceptions. This alignment is considered to be critical because when internal and external images are consistent, organizations are “usually seen as more consistent, stable, and trustworthy than those with differing or potentially contradictory images” (p. 219). Thus, the question of how executive leaders might best monitor images in the marketplace is a potential feature for creating a competitive organizational advantage. In addition to monitoring organizational identity and image, top-level leaders also need to be concerned about their organization’s reputation. Instead of questions regarding who we are as an organization (identity), or what does the organization want others to think about that organization (intended image), reputation deals with what stakeholders actually think of the organization (Brown et al., 2006). In terms of organizational value, in addition to human capital and social capital of an organization, there is also the somewhat intangible value associated with reputational capital. The conventional wisdom is that reputations are built up over long periods of time what can be lost in an instant, and there certainly are examples of how transitory reputations—including organizational reputations—can be (Volkswagen as a recent example). In addition to monitoring organizational identity and image, top leaders need to monitor organizational

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reputation as they are the key stewards of that important organizational asset. This would likely involve marketing and advertising functions within an organization, which are beyond the scope of the present chapter. Nonetheless, it is important to emphasize that the environmental monitoring function of top level, executive leaders is critically important for the success – and ultimately the survival – of their organizations.

Next Steps: Research and Practice With all of the previously stated monitoring sub-competencies, leaders are now tasked with applying them in real-world settings and leadership situations. In addition, researchers have the responsibility to extend an evidence-based understanding of how leadership and monitoring behavior add value and build organizational capability. From the perspective of a practicing manager, a first step is awareness of one’s own monitoring skills, which can be greatly catalyzed by the practice of mindfulness. Mindfulness is present-moment nonjudgmental awareness and acceptance of one’s own internal and external surroundings (Kabat-Zinn, 1994). Mindfulness has been considered to imply a type of self-regulated awareness that is theoretically comparable to situational self-awareness—­awareness of internal (i.e., the self ) and external (i.e., others) conditions (Lau et al., 2006; Buss, 1980). Thus, by being more mindful, leaders can empower themselves to become more conscious of the monitoring skills that they have, or that they may lack, and they can turn to others who model the appropriate monitoring skill behaviors in order to gain the skills that they lack. There is also a need to advance research and practice around assessing leader monitoring skills. Two of the most likely assessment approaches would involve third-party assessments of a target leader’s monitoring behavior. One approach might be to have independent assessors evaluate videotaped segments of performance to rate the quality and quantity of leaders’ monitoring behavior. Another relevant and somewhat independent source of ratings would come from subordinates. As the most likely target of monitoring on the part of leaders, subordinates would have a good sense as to the monitoring behavior of leaders. Something to consider regarding monitoring behavior is that too much of it could be as bad (or possibly worse) than too little of it (i.e., too-much-of-a-good-thing effect; Pierce & Aguinis, 2013). In particular, excessive performance monitoring might fall into the category of micromanagement, which is associated with negative attitudes and feelings of mistrust on the part of subordinates. Another potentially important issue is how to develop effective monitoring skills. It could be the case that the skills development aspect of monitoring is only one aspect of the domain. Understanding the timing and amount of monitoring to be used is also very relevant in being able to deploy self-, other-, and organization-monitoring skills to their maximum effects. At present, there does not appear to be much evidence-based guidance on the scope or timing of leader

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monitoring behavior. This is an area in need of further research before concrete recommendations can be made with some of the more nuanced aspects associated with the monitoring behavior of leaders. There is also a future research need to evaluate the return on leader monitoring behavior in organizations. As suggested, there may be an inflection point or level of diminishing returns when it comes to leadership and monitoring. This would involve hypothesizing and testing for possible curvilinear effects of monitoring, which would necessitate sophisticated methods and analyses to best assess whether the relationships between leader monitoring behavior and various outcomes is mainly linear or contains some degree of nonlinearity. In summary, there appears to be a large number of research-related questions to be answered before any conclusive practical recommendations can be made.

Conclusions The purpose of this chapter was to review the various forms of monitoring skills enacted by organizational leaders. The review focused on aspects of monitoring self, monitoring others, and monitoring the organization. At the level of monitoring self, a distinction was drawn between dispositional approaches to understand self-monitoring personality in organizations (e.g., Day et al., 2002) and specific behavior and skills devoted to monitoring oneself as part of one’s organizational role (e.g., leader self-awareness, self-presentation, strategic self-disclosure, and emotional regulation and control). In terms of monitoring others, the point was made that a key function of every leader is to track the performance of subordinates (i.e., followers) and provide feedback in contributing to meeting organizational performance standards as well as developing the capabilities of others. Given that much of the work in contemporary organizations is being conducted in team contexts, leaders are also responsible for monitoring the performance and development of teams. At the level of monitoring the organization, executive leaders take on the major responsibility of monitoring both internal and external environments. This can take the form of adopting a strategic perspective on the citizenship climate within an organization, as well as monitoring the external competitive environment in which an organization is situated. Related to this function, executive leaders maintain a high level of responsibility for protecting the reputation of the organization, which includes monitoring the various images associated with the organization. In conclusion, leaders are responsible for monitoring behavior across multiple levels, potentially including self, others, and the organization. This is not a stand-alone set of skills, but is linked to actions associated with behavioral regulation, development, and enhanced competitive positioning at the most macro (i.e., organization, industry) levels. Framing this in somewhat different terms, leadership monitoring is part of a broader set of skills that leaders need to ensure effective functioning at the individual, team, and organizational levels. More fully

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unpacking these related skills can offer a potential template for purposes of leader development. Monitoring behavior is an inherent component of functions associated with developing self, developing others, and developing the organization. As such, they deserve greater theoretical and empirical attention in terms of ongoing development of leaders and leadership (Day & Dragoni, 2015).

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14 WISDOM, FOOLISHNESS, AND TOXICITY IN LEADERSHIP How Does One Know Which Is Which? Robert J. Sternberg

One would expect, in the twenty-first century, that people would have learned enough from history to select wise leaders. So much for expectations. Are you impressed with the wisdom of many of our elected leaders? Well, let’s try a lower standard: Are you impressed with the wisdom of any of them? If so, why? If not, why not? If one looks at leaders, not only elected ones, but ones in other fields, it often is hard to find even a handful (or any) who appear to be wise. Why? In this chapter, I present a new theory of not only the production but also of the reception of wisdom. I further discuss the relationships between wise, foolish, and toxic leadership. I discuss what wisdom is, but also why people often tend more to be attracted toward leaders who appeal to the fool within each of us and their ideas, rather than to leaders and ideas that are wise. Wise leaders never have had an easy time. If one thinks of the two presidents of the United States whom almost anyone would view as wise, George Washington and Abraham Lincoln, both had many detractors and both fought wars that easily could have come out the opposite of the way they came out, with profound consequences for the future history of the country. It also probably says something that, beyond Washington and Lincoln, it would be harder to gain consensus on which presidents would be labeled as “wise”. I first describe the balance theory of wisdom (Sternberg, 1998a). In the first part of the chapter, I describe the theory and its application to the production of wise ideas and actions. Then I describe why it is that people often reject wise people and wise ideas—why they often prefer foolish to wise ideas (see also Aczel, Palfi, & Kekecs, 2015; Sternberg, 2004). That is, I will propose a theory not only of the production of wisdom but also of its reception (and why it often is not well received). The augmented balance theory represents an integration of work

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I have done on the psychology of cognitive skills and wisdom (e.g., Sternberg, 2003b, 2003c, 2018) and work I have done on the psychology of loving and liking (e.g., Sternberg, 1998a, 1998b, 2006).

Production of Wisdom: The Original Balance Theory of Wisdom Some leaders, like Mohandas Gandhi or Nelson Mandela, radiate wisdom. Such leaders are hard to find. Other people, like Joseph Stalin or Adolf Hitler, radiate toxicity. What is the difference?

The Nature of Wisdom Various scholars have attempted to understand wisdom in different ways. Many of these approaches have been reviewed elsewhere (Staudinger & Glück, 2011; Sternberg, 1986, 1990a, 1990b; Sternberg & Glück, 2019; Sternberg & Jordan, 2005). I discuss here primarily a balance approach. In the balance theory, wisdom is understood in part as an outgrowth of practical intelligence (Sternberg, 1997a; Sternberg & Hedlund, 2002), and in particular, of tacit knowledge (Polanyi, 1976). Tacit knowledge, however, in itself is not necessarily wise. Rather, tacit knowledge is procedural and thus oriented toward action. Further, tacit knowledge usually is acquired informally and without purposeful or otherwise direct help from other people. It permits people to work toward outcomes they value for achieving their goals in life (Sternberg et al., 2000; Sternberg, Wagner, Williams, & Horvath, 1995). For example, suppose one needs to deliver bad news to one’s employer. One might want to wait until the employer is not stressed out with other matters. Here, and in all cases, tacit knowledge is linked to particular situations or kinds of situations. Tacit knowledge also is practically useful in one’s work and one’s life. It is instrumental and often essential to the attainment of goals people value. As a result, people use tacit knowledge to achieve success in life, according to their own definition of success. Academic knowledge with no relevance to solving problems in people’s lives would not be viewed, from this standpoint, as tacit knowledge. Finally, people acquire tacit knowledge in the absence of direct and, often, even indirect assistance from others. Teachers, mentors, or colleagues can assist people in acquiring tacit knowledge. But in the end, tacit knowledge comes about through one’s own experiences, including successes but especially failures. Because tacit knowledge is wedded to contexts, the tacit knowledge that would apply in one specific context would likely not precisely apply in a different context. To help a learner develop tacit knowledge, a mentor should furnish mediated learning experiences rather than direct instruction as to what to do, and when to do it. Leaders need to learn how contexts apply constraints on tacit knowledge: Tacit

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knowledge that works in one context may fail miserably in another, as the recent spate of scandals regarding sexual abuse has illustrated. Behavior that may have once been marginally acceptable today often is not. University presidents are resigning because responses to sexual harassment that may once have been viewed as adequate no longer are. Similarly, sweeping athletic scandals under the rug may once have merited a wink of the eye; today, such imagined sweeping away of scandals may result in an athletic director losing his or her job. Tacit knowledge is not what is measured by tasks on conventional ability tests. Why would tacit knowledge be psychologically and statistically largely distinct from the kinds of academic abilities measured by tests of intelligence and related abilities? There are a number of reasons (see also Neisser, 1979). First, academic test problems are created by teachers or testing companies; in contrast, on the job, people have to figure out what the problems are—no one typically is there to tell them exactly what the problems are or what they mean. Second, academic test problems are often uninteresting or even boring and have little relevance to people’s everyday lives; in contrast, tacit-knowledge problems are relevant and often engage people’s interest because they are essential to accomplishing one’s life goals. Third, academic test problems contain the complete information one needs to be able to solve the problems, assuming one brings sufficient knowledge to the problems; in contrast, real-world problems almost always contain incomplete information, and often require solution when important facts or ideas are unattainable. Fourth, academic test problems are embedded in the context of school-based tasks; tacit-knowledge problems are embedded in the context of life success. Fifth, academic test problems are well defined—the steps needed to reach solution should, in theory, be known by a competent test taker; in contrast, tacit-knowledge problems often have no clear set of steps for reaching solution. (How, for example, would one reduce carbon emissions with all the competing pressures on a government to accomplish a multiplicity of goals?). Sixth, academic test problems have a correct or at least clearly a “best” answer; tacit-knowledge problems may have no meaningfully “correct” answer and different answers may optimize on different criteria so that there is no one clear best answer. Finally, academic-test problems often only have one way, or at most, a few ways to reach an answer; tacit-knowledge problems often have many, sometimes uncountable ways of reaching an answer (Sternberg et al., 1995). Problems of leadership exhibit all the characteristics of ill-defined problems and none of the characteristics of well-defined academic problems. It is an irony of our society that we value most students who display skills as measured by standardized tests that are largely irrelevant to leadership (Sternberg, 1997b, 1997c; Zhang & Sternberg, 1998). I would argue that the poor quality of leadership to be found in the United States (and other countries) at this time is in part a result of our use of such tests. The people who get into power sometimes have the intelligence to figure out how to get what they want, but not the wisdom to apply their skills more broadly to society nor even the desire even to want to

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apply wisdom. The result can be a government that is self-serving for people in it and for those like the people in it (e.g., large contributors to campaigns). If you disagree, try to compile a list of wise politicians in 2019. I suspect the list will be short—very short. The upshot is that we are selecting as leaders in our society people who have the intelligence to serve themselves but not the wisdom much to serve others. Yet, successful leadership almost always involves serving others (Greenleaf, 1977; Liden, Wayne, Zhao, & Henderson, 2008; Liden, Panaccio, Hu, & Meuser, 2014). Practical intelligence and the role of tacit knowledge in practical intelligence provide an entrance point for understanding wisdom, but they do not provide a complete and holistic basis for understanding wisdom. Practical intelligence does not always involve precisely the same particular kinds of balances that are involved in wisdom. In particular, although practical intelligence always involves a balancing of different possible responses to the environment (adaptation to the environment, shaping of the environment, selection of a new environment), it does not always involve a balance among various competing interests. And practical intelligence and the tacit knowledge underlying it, unlike wisdom, can be used for good or even evil ends. Therefore, consider the balance theory of wisdom in further detail.

The Balance Theory of Wisdom The most important construct in the balance theory of wisdom is, as its name implies, balance (Sternberg, 1998a, 2008a). Other theories also have proposed that balance is important to wisdom, but they generally used the term differently. For example, Labouvie-Vief (1990) suggested that wisdom involves balance among different kinds of thinking. Kramer (1990) suggested the importance of balance among various self-systems, including the cognitive system, the conative (motivational) system, and the affective (emotional) system. Other theorists have spoken of other kinds of balance (e.g., Kitchener & Brenner, 1990; see also Birren & Svensson, 2005; Osbeck & Robinson, 2005; Staudinger & Glück, 2011). Takahashi and Overton (2005) have reviewed some accounts of wisdom that could be viewed as involving balance. The balance view presented in this chapter expands on but also differs in significant ways from these other kinds of notions in providing for particular kinds of balance in wisdom.

The Role of Balance In the balance theory, wisdom inheres in the interaction between an individual (or group) and one or more situational contexts, much as is the case for intelligence, broadly defined (see also Grossman, 2017; Sternberg, 1997a; Valsiner & Leung, 1994), as well as for creativity (see also Csikszentmihalyi, 1996; Mumford, Connelly, & Gaddis, 2003; Mumford & Gustafson, 2007; Sternberg, 2004, 2005;

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Sternberg & Lubart, 1995). Thus, the kinds of balance incorporated into the balance theory of wisdom are different from those in many other theories of wisdom. They are interactional among people, tasks, and situational contexts. The definition emphasizes the importance of context because behavior that is viewed as wise in one context (e.g., raising interest rates on loans) may be viewed as foolish in another. The definition of wisdom proposed in this chapter draws both upon the notion of tacit knowledge, as described earlier, and on the notion of balance. In particular, wisdom is defined as the application of tacit knowledge as mediated by positive ethical values toward the attainment of a common good, through a balance among multiple 1. Interests: (a) intrapersonal, (b) interpersonal, and (c) extrapersonal so as to achieve a balance among 2. Responses to environmental contexts: (a) adaptation to existing environmental contexts, (b) shaping of existing environmental contexts to create new contexts, and (c) selection of new and different environmental contexts. Thus wisdom is like all practical intelligence (Sternberg, 1985a) in requiring a balancing of responses to diverse environmental contexts (Grossmann, Gerlach, & Denissen, 2016), but it involves only that subset of practical intelligence that requires balancing of interests, something that is not a necessary aspect of all practical intelligence. Thus, wisdom and practical intelligence are related in that they both draw on tacit knowledge. This tacit knowledge can be with respect to oneself, or others, or the situational contexts in which one finds oneself. But there is one crucial difference. Practical intelligence often is used to maximize only one’s own personal gains, or the gains of oneself plus one’s family and friends (Wagner, 1987; Wagner & Sternberg, 1985). Wisdom, in contrast, must involve more than personal self-interest, something some leaders seem not to have learned. It must involve a balance among self-interest, the interests of others, and higher-level interests (such as organizational or national interests). Wisdom often arises in the context of advice—either advice that one gives to another or even advice one gives to oneself! This viewpoint implies that a person, and especially a leader, may apply practical intelligence in seeking results that are good for him or her but bad for achieving any kind of common good. Tyrants and would-be tyrants, for example, typically are high in practical intelligence in managing to control a country (or other entity) exclusively for the benefit of themselves and perhaps their family and friends. They also may be practically intelligent in disguising their intentions, usually by stating publicly that what they are doing is for the good of the common man or woman. But they are not wise because their ends are all related to their own interests, not the interests of others or the interests of their organizations or

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nations. When speaking of a “common good”, it must truly be a common good for all, not just those whom one views as like oneself, in the case of Hitler, the imaginary “Aryans” and in the case of other leaders, particular groups they decide to favor.

The Role of Ethical Values One cannot discuss the concept of wisdom without at the same time considering the ethical values in a given cultural context that give rise to its expression (see also Ambrose & Cross, 2009; Kohlberg, 1983; Kupperman, 2005; Tirri, 2010). Similarly what is practically intelligent also occurs within a cultural context (Weststrate, Ferrari, & Ardelt, 2016). Ethical values determine, to a large extent, how one decides to balance various interests with responses that are intended to serve these interests. Psychology cannot really determine what constitutes “ethical” behavior. Thus, for wisdom problems involving ethical decisions, there probably cannot be any one answer that is “psychologically correct”. Rather, various responses may be more or less appropriate, depending on the cultural and temporal context (Kohlberg, 1969; Sternberg & Glück, 2019). I have argued that ethical decision making involves a series of steps that may be executed sequentially or iteratively, with the order of the steps varying somewhat from one iteration to another (Sternberg, 2009, 2016). The steps as indicated in these previous works are: 1. Recognize that there is an event to which to react; 2. Define the event as having an ethical dimension; 3. Decide that the ethical dimension is of sufficient significance to merit an ethics-guided response; 4. Take personal responsibility for generating an ethical solution to the problem; 5. Figure out what abstract ethical rule(s) might apply to the problem; 6. Decide how these abstract ethical rules actually apply to the problem so as to suggest a concrete solution; 7. Prepare for later possible repercussions of having acted in what one considers an ethical manner; 8. Enact the ethical solution. The most difficult steps for a leader almost certainly are the last two. Acting ethically often results not in accolades and praise but rather in criticism and even derailment (Sternberg, 2009, 2016). When leaders realize that acting ethically may be costly (Step 7), they may become reluctant to act as they know they should, resulting in their failing to enact the ethical solution (Step 8). It might seem that ethical values would differ greatly from one culture to another. There seem to be some ethical values, however, that cross cultures, such as honesty, integrity, sincerity, and reciprocality (Golden Rule) (Sternberg, 2012).

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Thus, I believe core ethical values are universal. At the same time, these values may be warped, intentionally or otherwise, by toxic leaders seeking to impose on people a system of false ethical values that actually do little more than to serve the leader’s cynical ends.

Mental Processes Underlying Wisdom Tacit knowledge is acquired by a combination of three mental processes. Selective encoding is involved in taking in new information that has some kind of relevance for one’s goals in learning in a given context. Selective combination is involved by putting together pieces of information that are relevant for these goals. And selective comparison is involved in comparing information newly received to information that is already stored in one’s long-term memory (Sternberg, Wagner, & Okagaki, 1993). General intelligence, or so-called g, also makes use of such processes (Sternberg & Grigorenko, 2002). The question for wisdom, however, is not just adeptness at processing information, but what one does with new information, if indeed one acquires it in the first place. Unfortunately, many leaders are resistant to new information, and some, simply make stuff up and leave it to their echo chambers in the media and elsewhere to make sure that the fictions are presented as fact.

Relation of Wisdom to Other Related Constructs Wisdom is related to other constructs relevant to how people deal with everyday problems. For example, wisdom is related to social intelligence, which is one’s ability to deal with other people sensitively and effectively (see Cantor & Kihlstrom, 1987; Kihlstrom & Cantor, in press; Sternberg & Smith, 1985). It also is related to emotional intelligence, or the ability to be sensitive to, understand, and manage one’s own emotions as well as those of others (Goleman, 1995; Mayer & Salovey, 1993; Rivers, Handley-Miner, Mayer, & Caruso, in press; Salovey & Mayer, 1990; see also Csikszentmihalyi & Nakamura, 2005). Finally, it is related to what Gardner (1983) refers to as intrapersonal and interpersonal intelligences (see also Kornhaber, in press). At the same time, there are key differences between these constructs, on the one hand, and wisdom, on the other. A leader may be socially intelligent and yet be unwise. For example, a glad-hander may know how to get people to do his or her bidding but not have ideas that will help achieve any good beyond his or her own. Similarly, an emotionally intelligent leader may prey on, not just understand the emotions of his or her followers. Finally, a leader might be intrapersonally intelligent, in understanding him or herself, and be totally fine with using this understanding to exploit other people. In each case, a leader may be intelligent in some way without being wise.

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Measuring Wisdom In the balance theory, the ideal problems for measuring wisdom are likely to be complex conflict-resolution problems involving the formation of wise judgments, given multiple competing interests and a lack of a clear resolution regarding how these interests can be reconciled. Such situations are similar to the ones leaders encounter on a regular basis. My colleagues and I measured conflict resolution at the interpersonal, interorganizational, and international levels (see, e.g., Sternberg & Dobson, 1987; Sternberg & Soriano, 1984). For example, one might present a problem of two nations that share a common river-based water supply. The country upriver is taking so much water that the country downriver is being deprived of adequate water resources for its people. Such problems measure an aspect of practical intelligence. Sternberg and Dobson (1987) discovered that a strategy of mitigating conflicts is related to higher levels of academic intelligence (practical intelligence was not assessed in the study). These findings complement those of Smith, Staudinger, and Baltes (1994), who found that clinical psychologists, as a group, are particularly adept at wisdom-related tasks. Similarly, Grossmann et al. (2010) found that foreign-service officers and other kinds of skilled negotiators do particularly well on wisdom-related tasks.

Reception of Wisdom: Why Wisdom Is Not So Attractive to Many People How can one recognize a wise leader? I believe that, in a way, it is simple: A wise leader brings out the best in others; an unwise leader does not; and a toxic individual brings out the worst in people. Toxic leaders—Stalin, Hitler, Maduro, Orban—prey on people’s weaknesses and work against a common good. Rather, they seek a common good for those in their particular (usually small) ingroup. Anyone in the outgroup is prey, even if the individual is one the leader pretends to favor. That “favoritism” is simply a way of exploiting the individual for the leader’s own ends. One can see this in populist leaders’ frequent courting of the working class while systematically adopting policies that destroy their well-being. If wisdom is such a good thing, are people always eager to display and experience it? Perhaps not. Consider why. If there is a fundamental principle of attraction, it is that we are attracted to others like ourselves (Sternberg, 1995, 1998a, 1998b). How many wise people do you know? How many people who strike you, in one way or another, as fools or even as toxic? Toxic individuals, and especially toxic leaders, tend to be high in a kind of practical intelligence, Machiavellian intelligence (Byrne & Whiten, 1988). Toxic leaders are ones who are destructive of rather than constructive for the units they are supposed to lead. They appeal to people’s susceptibility to being manipulated and even fooled, and harm the very people who help them.

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One could choose any toxic individual or leader to illustrate why toxic leaders are so engaging (Lipman-Blumen, 2006). I have chosen primarily (although not exclusively) one with whom everyone is familiar in some degree: Adolf Hitler. He was about the worst, but there are plenty of toxic leaders all around the world today, including (and especially) in the United States. How do toxic leaders (and other toxic individuals) insinuate themselves into our lives, actually leading us to prefer them and their ideas to wiser individuals with better ideas? If people all were wise, perhaps it would not be a problem. But they are not wise, so the problem remains today as it did in the early twentieth century when Hitler and his cronies came into power. For this part of the augmented balance theory, I will show how a duplex theory of liking and loving (Sternberg, 2006) can be applied to understanding the intense attraction some people feel toward toxic leaders and their ideas. What is truly impressive is that the same playbook seems to work for toxic leaders in the twenty-first century as it has throughout all of history. This fact is a good illustration of George Santayana’s principle that those who do not learn from history are doomed to repeat it. First consider three components of love and interpersonal attraction: intimacy, passion, and commitment. Note for each component how, as the leader uses the three components to inspire love for him, he simultaneously uses those components to inspire hatred toward other groups (Sternberg, 2003a; Sternberg & Sternberg, 2008).

Triangulating Wisdom: Intimacy, Passion, and Commitment Intimacy One characteristic of leaders to whom we are attracted is that we feel an intimate connection to them and their ideas, often, no matter how bad the ideas are. The leaders prey on our gullibility to instill in us feelings of intimacy. The leaders usually are sociopathic in some degree, so are not bothered at all by what they are doing. On the contrary, they view what they are doing as completely respectable. •

Trust. Some people come to believe that the toxic leaders are worthy of their trust, and indeed, the leaders work hard to deceive people in order to gain their trust, even though it is all for show. Oddly, some of them lie continually and yet their followers believe in them as though their remarks were trustworthy. Predictably, the toxic leaders accuse almost everyone, except themselves, of lying. Hitler (like Vladimir Putin, Fidel Castro, and Victor Orban, among so many other toxic leaders) emphasized nationalism and national pride. The ploy remains the same—only the particular country changes. Their line is that past leaders have betrayed the country. You can trust Hitler (or Stalin or Orban or whoever) to put the country and its people first, not

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•

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like other people who have designs upon the country and its resources. Usually, the toxic leaders define the “people” narrowly, setting preferred groups against vilified groups. Understanding. Many people come to believe that they understand the toxic leader. They are wrong, but of course, the leader obfuscates who he is and what his goals are, making it easier falsely to believe one understands him. Preferred groups are led to understand that they have been disregarded or even swindled by past leaders; now they will have a leader who has their back and will get them their just desserts. Then the leader turns out to be a hate-monger, or genocidal, or supportive only of his own pocketbook, but by then, it is too late. The leader is in power, often with no plans to leave power (as in Russia, Venezuela, likely China, and any number of other places). Connection/attachment. The leader is typically charismatic and establishes a sense of connection with his target group, often if not usually at the expense of other groups that then are disenfranchised (and likely portrayed as “enemies of the real people”). Charismatic leadership can be positive (Conger & Kanungo, 1998; House, 1976; Howell & Avolio, 1993; Hunt & Conger, 1999), but it also can be a gateway to toxic leadership. Today, people in many locations seem so caught up in charisma and “made for TV” leadership that they may prefer reality-show leadership to wise leadership. Charisma, even toxic charisma, may seem preferably to wise but boring leadership. In such cases, the target group feels a connection with, or attachment to the leader and his ideas. The leader’s ideas are somehow personally meaningful to members of the target group. The group members are drawn to him. Others not in the target group may be horrified. For example, if the United States were taken over by a ruthless despot with no conscience, roughly two-thirds of the population might do pretty much anything he or she asked (Milgram, 1963, 1974). Familiarity/Comfort. The leader and his program somehow become a part of the target audience—that audience is comfortable with it. It is readily accessible to them to use to interpret phenomena in their life and the lives of others. Those the leader curries favor with like the ideas that they are the ingroup and that others (especially those against whom they always have held implicit and probably explicit prejudice) are on the outs. They are comfortable with once again being recognized as the true, important stakeholders. What they do not realize is that this comfort is false—they are being used to help achieve the leader’s self-aggrandizing ends.

Passion A second characteristic of toxic leaders some people come to love is that they may feel passionate about them. (Passion also can arise out of ideas or people we hate—see Sternberg & Sternberg, 2008). Observe rallies for Hitler (available online) or for Donald Trump, for that matter, with mobs screaming “Lock her up”

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against Hillary Clinton. People differ widely in the extent to which they become passionate (Tennov, 1979) but all of us feel passion under some circumstances to at least some extent. The leader incites passion and simultaneously the abnegation of reason and of wisdom. •

•

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•

Excitement. The leader and his ideas excite the favored group. They arouse enthusiasm not just for the leader and his ideas, but for what is possible in the future. If anyone questions whether democracy is fragile, just think about the Milgram experiments (Milgram, 1963, 1974) and one will understand how easily an authoritarian leader could destroy democracy, here or anywhere. People can become excited about the worst leaders and their ideas, just as they once became excited about individuals engaged in gladiatorial combat. As it is sometimes said, the more things change, the more they stay the same. Intense focus. Charismatic leaders become the subject of intense focus. Charismatic leaders, such as the late Hugo Chavez, also constantly drew attention to themselves. They seemed to be everywhere and the news coverage was never enough. They act in unpredictable ways, so as to keep their presence and actions in the news. Their very unpredictability as leaders led one to wonder, “What next?” In the triangular theory of love (Sternberg, 1998b), passion persists as long as a relationship is largely unpredictable. Charismatic leaders, many of whom are toxic, in the same way maintain an air of unpredictability. In the times of Stalin, one could be a loyal Communist one day and a target of assassination the next. People had to be focused on Stalin, because no one never knew what the next day would bring. Transformation. Passion can transform a person. The person may come to feel like a different person because of it. They may feel an intense attraction to whatever arouses their passion. Hitler was transformational for Germany, albeit in an extremely negative way. Chavez was transformational for Venezuela. Between him and his successor Maduro, the richest country in South America has become one of the poorest, if not the poorest. Often, the electoral system is tampered with to make sure the leadership stays in the hands of those who initially have taken it over. Exclusivity. Passion often is experienced together with a sense of exclusivity. That is, the feelings of passion are directed toward a single person (or cause, or object, or whatever). The target of the feelings of passion sometimes occupies what amounts to a unique place in both people’s minds and hearts. Many people cried when Stalin or Hitler died. They were more than a leader; they were in some respect deified. Although toxic leaders work by the same playbook, charismatic and usually toxic leaders each appear unique. Germany did not have anyone like Hitler before he came onto the scene. The moral and ethical sense of followers comes to be severely degraded, often without the followers realizing it.

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Commitment •

•

•

•

Enduringness. Commitment carries with it enduringness—a relationship with a person or an idea that lasts over a long period of time. Some leaders continue to have an impact year after year, even decade after decade. Stalin was in power for roughly 25 years. Chavez presumably would have stayed in power had he not died of cancer; his tradition is continued through his hand-picked successor, Maduro. Putin became president of Russia in 2000, and except for a few years of the shadow presidency of his sidekick Medvedev (when Putin was prime minister), Putin has been president and has now been elected for yet another term. Xi in China has defied tradition by not naming a successor and it seems likely that there will not be one while Xi is alive. Lasting engagement. When we are committed to a person or an idea, we engage with the person or idea over a long period of time. Hitler was generally popular in Germany. Despite his toxicity, the German people might have kept Hitler in power indefinitely had they not lost World War II. Meaningfulness. A truly committed relationship has a unique meaning. It has a distinctive purpose in our life. Toxic leaders have a doctrine, often some version of a populist theme that benefits some groups and harms other groups. There is no sense at all of a common good—their doctrine is the antithesis of wisdom. Hitler had a peculiar doctrine. Mao had and now Xi has a particular doctrine. These doctrines have been written into the Chinese Constitution. Chavez proposed a doctrine of “Bolivarianism”. With toxic leaders, one gets not only a dictator or would-be dictator but also at least the semblance of a doctrine to go with him. Resilience. A committed relationship is one that people stick with, both in better and in worse times. People who are in committed relationships hope that their relationship, instead of being torn apart by challenges, will instead grow stronger. Nicolas Maduro is still in power despite his destruction of Venezuela as a country. Robert Mugabe, one of the most toxic leaders of all time, lasted from 1987 to 2017, when he finally was forced to step down at age 93. Similarly, Joseph Kabila refused to give up power, even when his term in the so-called Democratic Republic of the Congo expired. Jacob Zuma and his cronies managed to stay in power from 2009 to 2018.

To summarize, toxic leaders and ideas represent in many respects the opposite of wisdom. They do not seek the common good. They do not seek to balance interests; rather, they put their interests first and create conflict among groups in order to further their own personal interests; and their values are anything but ethical. One might wonder why people would let themselves be fooled, or in some cases, convinced with eyes open. One goes back to the fundamental principle of attraction: We are attracted to others like ourselves. As the great Biblical

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and other prophets recognized, we all have toxic elements within ourselves. Jesus seemed perhaps unique in history for not having such elements, and it has been understood in part because he was, for Christians, part of a Holy Trinity comprising God. But Jesus aside, many of us, with greater or lesser success, fight those toxic elements in ourselves (and others either are indifferent to these elements or succumb to them or actually welcome them.) Toxic leaders turn things around by glorifying rather than abhorring those toxic elements. Sad to say, religious leaders are often among the first to fall under the sway of toxic leaders, showing that they have the same elements in themselves as the toxic leaders. Indeed, as church-abuse scandals have shown, some of them are toxic leaders. Some leaders are not wise and not toxic but merely foolish. Any number of leaders show themselves to be smart but foolish. They may have garnered great standardized test scores and impressive degrees but lack the wisdom to make a positive difference. Enron, Arthur Andersen, Tyco, and numerous other scandals reminded us of how easy it is for leaders to be intelligent but unwise. The failed leaders of these companies showed certain common characteristics of foolishness (Sternberg, 2004): (1) unrealistic optimism, by which they believe that merely their having an idea will guarantee its success; (2) egocentrism, whereby they believe that what matters is their own gain rather than that of their stakeholders collectively; (3) feelings of omniscience, through which they come to believe they know everything, and thus cannot learn from mistakes because they do not perceive themselves as making any; (4) feelings of omnipotence, by which they feel that they are all-powerful and can do whatever they want; (5) feelings of invulnerability, by which they come to believe that they can get away with anything, no matter how egregious it may be; and (6) a sense of moral disengagement, through which they increasingly see problems and their solutions purely in utilitarian terms, without considering the moral antecedents and consequences of what they are doing.

Stories of Wise and Unwise Leadership I previously have proposed that leadership often is based in terms of certain stories (Sternberg, 2008b; see Table 1; see also Sternberg, 2006). The stories are metaphorical ways of understanding leaders’ conceptual systems (see also Sternberg, 1985b, 1990a). Leaders tend to rally their followers around them through these stories that they create. There is no story that, in and of itself, is wise, independent of its execution. Rather, much depends on how the story is implemented. Will the deep thinker really be a deep thinker? Will the communicator truly be a communicator, or the ethicist an ethicist? But there are certain stories that are very likely to be or to turn toxic, such as the conqueror, deity, lifesaver, warrior chieftain, for example. Toxic leaders present themselves as saviors (deity story) and one can be pretty confident that any potential leader who advertises himself as a savior will be anything but, just as anyone who advertises himself as wise almost certainly isn’t.

Wisdom, Foolishness, Toxicity in Leadership  375 TABLE 14.1 Stories of Leadership

• The carpenter—The leader who can construct a new organization or society • The CEO—The leader who is able to “get things done” • The communicator—The leader who communicates well with diverse followers • The conqueror—The leader who plans to conquer all enemies • The conserver—The leader who plans to keep things the wonderful way they are • The cook—The leader who offers the recipe to improve the life of his or her followers • The deep thinker—The leader who claims to make sense out of what is going on • The defender—The leader who claims he saves all followers from harm • The deity—The leader who presents him or herself as savior of the people • The diplomat—The leader who is able to get everyone to work together • The doctor—The leader who can fix what is wrong with the organization • The ethicist—The leader who pledges to clean up the organization • The lifesaver—The leader who plans to rescue followers from otherwise certain death • The organizer—The leader who can create order out of chaos • The plumber—The leader who plans to fix all the leaks • The politician—The leader who claims to understand how “the system” works • The replicator—The leader who is going to be like some past leader • The scout—The leader who plans to lead followers to new and uncharted territory • The ship captain—The captain of a ship trying to navigate through turbulent times • The turn-around specialist—The leader who plans to turn around a failing organization • The warrior chieftain—The leader who will lead followers to fight, defensively or offensively, alleged enemies, seen or unseen Based on Sternberg (2008b)

The stories are the leaders’ way of inspiring intimacy, passion, and commitment. Believers who come to deify their leaders understandably feel intimacy, passion, and commitment toward those leaders. Toxic leaders who are effective communicators prey on people’s often a rational thinking (Stanovich & West, 2016) to sway people toward belief in what, rationally, is unbelievable. Lifesavers promise to save people from the destruction wrought by their predecessors, only to be even more toxic than those predecessors. And warriors convince people, as George W. Bush did, that there is a need for a serious war, in the absence of serious evidence. Often, as in Bush’s case, the evidence is manufactured. The existence of toxic leaders in so many countries today, in Europe, Africa, Asia, North America, and South America, should give any reader pause. Anyone who thought the Milgram (1963, 1974) experiments are of historical interest only should reconsider. The more things change, the more they stay the same. People do sometimes choose wise leaders. Toxic leaders are aware of this, and their technique to gain power appears always to be the same—manufacture a threat, real or much more likely imagined, that supposedly only they can deal with. The threat can be people who are of a certain nationality, or religion, or ethnicity, ideology, or whatever. There are any number of stories of hate that the

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cynical, toxic leader can call upon—those targeted groups as manipulators, subhuman, evil, enemies of God, power-crazy, unworthy outsiders, thieves trying to steal land or other resources, and so on (Sternberg, 2003a; Sternberg & Sternberg, 2008). It is sad that, even in the twenty-first century, these crude manipulative techniques often work like a charm, just as they always have.

Conclusions IQs increased, quite substantially, in the twentieth century (Flynn, 1987, 2012, in press; Neisser, 1998). The gains, roughly 30 points of IQ over 100 years, were primarily in fluid abilities and only secondarily in crystallized abilities. But it is difficult to discern a contemporaneous increase in wisdom (Sternberg, 2017a). Intelligence is not enough (Grossmann, Na, Varnum, Kitayama, & Nisbett, 2013). Wisdom is critical to people’s well-being and their ability to contribute to others and to the world (Glück & Bluck, 2013). The time has come for psychologists and other behavioral scientists to take more seriously the importance of wisdom not only for science but also for the world (Sternberg, 2013a, 2013b), and especially the role of wisdom in leadership. Indeed, what is usually called transformational leadership requires wisdom (Bass & Avolio, 1993). The world is not suffering from a lack of intelligence, at least in a narrow sense; it is suffering greatly from a lack of wisdom (Sternberg, 2017b). Experiences to develop wisdom are discussed at length in Sternberg (1999b) as well as by Sternberg and Hagen (2019). They include instructional techniques such as the following: encouraging students (1) in making decisions, to consider not only what decision might be good for them as individuals, but what decision might help toward the achievement of a common good; (2) to do (1) by explicitly considering not only what is good for them as individuals, but what is good for others and for institutions as well; (3) to question what values are guiding their decisions, and whether these values are the ones that benefit themselves, others, and institutions; (4) to consider whether their actions represent for them an appropriate balance of adaptation to, selection, and shaping of environments (5) to think in a dialectical manner, placing their decision making in the context of the time and place in which they live. Teaching students to think wisely does not involve giving them answers, but rather helping them devise ways of coming to wiser answers through a process of reflection. But given the toxicity of leadership around the world today, and the extent to which it has been welcomed by peoples in many different countries, students must learn to think creatively and insightfully (Niu & Sternberg, 2003; Sternberg & Davidson, 1982), critically (Sternberg, 1985c), and wisely (Sternberg, 2016) in order to right a world that is, in many respects, on the wrong track. In schools, we tend to view students as in trouble if they do not read or do mathematics well (see, e.g., Spear-Swerling & Sternberg, 1994). But the greatest problem today is not reading or math—it is the failure to teach students to think wisely and well.

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Given the turmoil in the world today, educators need to pay much more attention than they have to the development of wisdom, not just to the development of knowledge or to academic skills. Global climate change for example, is close to being out of control, if it has not already reached that point. Can we afford to be oblivious? If schools do not teach for wisdom, who will (Maxwell, 2013; Sternberg, 2017a)? We ignore wisdom at our peril. Note: My ideas about leadership, wisdom, toxicity, and related constructs are also discussed in sources such as Sternberg (2003a, 2003b, 2017a, 2017b, 2018).

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INDEX

Note: Page numbers in italics indicate figures, and page numbers in bold indicate tables on the corresponding pages. accuracy: bias and 20, 25 – 26, 111 – 112, 219 – 220; in decision making 245, 248; empathic 347 – 348; in forecasting 206 – 208; in idea evaluation 237; mental models and 279, 293, 299; social acuity and 307 – 308, 310, 323, 329 achievement tests 16, 18 – 19 ACL see Gough Adjective Check List (ACL) ACT see American College Test (ACT) action potentialities 308, 320, 321 – 324 ad hoc information gathering 76 – 82, 77, 79, 84, 86 – 88, 90 adaptive resonance theory (ART) 48 – 50 adaptive switching 186 ambiguity 74, 74 – 75, 86 – 87, 91, 136, 138 American College Test (ACT) 16 analogizing 76 – 78, 77, 79, 84, 87 – 88, 89, 90 ART see adaptive resonance theory (ART) authenticity 1 – 2, 342, 346 Badran, Mohga 278 balance theory of wisdom 363 – 363, 365 – 367; augmented 362 – 363, 369 – 374 bias 20, 25 – 26, 111 – 112, 219 – 220 Binet, Alfred 15

Bolivarinism 373 bounded learning 287 brainstorming 192 Bush, George W., 3, 14, 375 CAS see complex adaptive systems (CAS) case study: causal analysis in educational contexts 103; constraints and creative problem solving 191; content of attention during crisis 160 – 168, 160, 161, 163, 164, 165; leaders’ attention and organizational crisis 153 – 160, 154, 156, 158, 159, 161 Castro, Fidel 370 causal analysis 101 – 114, 127 – 129; acting on 137 – 140; attributional bias in 111 – 112; causal attributions 108 – 110; causal reasoning vs. 99; evaluating action based on 140 – 142; evaluating competing causes 107 – 108, 131 – 135; forecasting and 134 – 135, 213 – 214; future research in 100, 114 – 115; indirect effects 30, 107, 114 – 115; information gathering in 102 – 105, 112 – 114; interpreting causes 133 – 137; leadership styles and 123 – 124; as a leader skill 122 – 123; mental models and 125 – 127, 129 – 131; presumed causes 123;

Index  383

prospective 102 – 108; retrospective 102, 108 – 111; skills 129; strategies 105 – 108, 127, 128, 131 charisma: charismatic leadership 59 – 60, 123 – 125, 142; communication and 320; emotions and 345 – 346, 347; in leader– leader interactions 286 – 287; mental models and 280; monitoring skills and 341 – 343; in US Presidents 31; wisdom and 371 – 372 Chavez, Hugo 373 – 373 Clinton, Hillary 237 – 238, 371 – 372 cognition 3, 6, 9 – 10; Cartesian view of 272; conscious processing 53 – 55; during crisis 152, 154 – 154, 167 – 168; decisionmaking capacity 230 – 231; emotion and 59; leader development and understanding 114 – 115, 284; mental models and 278 – 279, 281; navigation example 62; sensemaking and 235 – 236, 261, 266 – 275; of teams 291 – 294, 295 Cognitions in the Wild 62 cognitive appraisal 134 cognitive diversity 151, 167, 171 cognitive maps 155 – 170, 156, 158, 159, 164, 166, 170 cognitive skills 3 – 6, 9 – 11; causal analysis as a kind of 105, 131; cross-process cognitive strategies 188 – 195, 189; sensemaking and 268 – 269; testing 16, 18 – 19; see also political skills; problem solving cognitive vision formation theory 152 coleadership 296 – 297 collective leadership theory 83 collective minds 62, 266 – 267, 273 communication: after action review 139, 294 – 295; charisma and 320; direction 80; future research in 293; information gathering tactics 80 – 81, 84; mental models and 292 – 295; pattern recognition and 80; shared mental models (SMM) 292 – 294 compartmentalization 76 – 80, 77, 79, 82, 84, 87 – 89, 89 – 90 complex adaptive systems (CAS) 47 – 48, 53, 63 – 64 complexity: causal analysis and 122 – 123; in creative problem solving 186 – 188; in decision making 229 – 230; of mental models 104 – 105, 110, 152, 168 – 169, 215, 298; in occupational contexts 18 – 19, 21; organizational 100; sensemaking during complex

crises 148 – 149; social 311, 319, 324; in solution evaluation 126, 138; uncertainty and 74,74, 81, 89, 91 concept selection 85, 88, 180 – 181, 189 conceptual combination: creative problem solving and 181 – 182, 189 Congo 373 conscious processing 53 – 55 consensus 108; in causal analysis 108 – 109, 135 – 136; as limiting problem solving 7; during organizational crisis 149 – 153, 157, 163, 165 – 170, 170 constraints 48 – 51, 51, 56; analysis 318, 322; in forecasting 134, 211 – 212; identification and management of 191 – 192, 215; in implementation planning 183 – 184; organizational 75; in problem solving 89, 90, 130, 180, 189, 191 – 194; recurrent networks 50 – 52, 51; reducing 249; solution implementation 89; on subordinates 309; on tacit knowledge 363 – 364; time 245; top-down 48 – 51, 51, 56 Cook, Tim 233 – 234 corporate contexts: career achievement 178; causal analysis in 106 – 107, 126 – 127, 136; forecasting in 193 – 194, 207 – 210, 217; intelligence and outcomes 18 – 19, 21; job characteristics theory 197; job design and creative problem solving 197; job satisfaction 98 – 100, 125; managerial role theory 343; marketing campaigns 5, 126 – 127, 193 – 194, 207 – 210, 217, 270; middle management and 213, 216 – 218, 310; product development 112 – 113, 209 – 210; sensemaking in 270 corporate social responsibility (CSR) 288 Creative Personality Scale 31 – 32 creative problem solving see problem solving creativity 17, 102; in groups 59 – 60, 110, 151; as “interactional syndrome” 179; leadership performance and 19, 52, 176 – 178; problem-solving and 176 – 187, 191 – 192; training in 196 – 197; in US presidents 30 – 32 critical learning 287 Crystal Pepsi 176 – 177 CSR see corporate social responsibility (CSR) cultural intelligence 308, 327, 328 – 329 Cunningham, K.S. 263

384 Index

Darling, James 263 – 264, 269 – 271 decision making: accuracy in 245, 248; attentional demands 153; capacity 227 – 229, 228, 230 – 231; complexity in 229 – 230; emotion management and 227, 228, 231 – 234, 241 – 244, 250; errors in 151 – 152; idea evaluation and 236 – 237; irrationality in 229, 231 – 234, 244, 249; leader skills 234 – 244; market forces and 244, 247 – 249; organizational structures and 247 – 249; processes 232 – 234; sensemaking and 235 – 236; social judgment and 239 – 241; timing and 244 – 246 direct observation 82 – 83, 84, 91 divergent and convergent thinking 185 – 186, 189 dual-processing theories 48, 57 – 58, 60 – 61, 64 dynamic delegation 349 – 350 early research 4 – 6; in forecasting 206 – 207; in intelligence 15 – 17, 20 – 21 educational contexts: achievement in 4, 16 – 20; causal analysis in 103 – 106, 126 – 130, 134, 136, 269 – 270; coleadership in 296 – 297; elite schools 20; forecasting in 208 – 209, 212 – 215; mental models in 213 – 215, 277 – 278, 281 – 283, 282, 296 – 297; monitoring in 343; sensemaking 263 – 265, 267 – 271; standardizing testing 16, 18 – 19; tacit knowledge in 364; training 281 – 283, 282; wisdom in 376 – 377 Egyptian Revolution 278 emotion management: decision making and 227, 228, 231 – 234, 241 – 244, 250 emotional contagion 60, 241 emotional intelligence 44 – 45, 326, 328; forecasting and 208 emotions: authentic 346; cognition and 59 empathy 308 327, 328; empathic accuracy 347 – 348; self-awareness and 342, 346 encoding: in causal analysis 114; in recurrent neuronal networks 50 – 51, 59; selective 368 endogeneity bias 20, 25 – 26 environment: changing 148; decision making and the 246 – 247; environmental enaction and sensemaking 261 – 262; mental models for complex systems 100 – 101; uncertainty and 71 – 76, 74, 97; variables 74 – 75, 74, 87, 91, 262 environmental scanning 78, 235 – 236, 350

ethics 1 – 2, 63; case-based knowledge and 212, 214 – 216; causal analysis and 104, 106; mental models and 288, 299; toxic leadership and 372 – 374, 375; values 366, 367 – 368; wise leadership and 367 – 368 European leaders 27 – 28 EUT see Expected Utility Theory (EUT) event schema 52 – 53 executive leadership 350 – 351, 352 – 353 exemplification 340 – 342 Expected Utility Theory (EUT) 229 – 231 experimentation 7, 53, 76 – 78, 77, 87 – 89, 90 expertise see knowledge extended mind 272 – 274 Farook, Syed Rizwan 233 – 234 Federal Bureau of Investigation (FBI) 233 – 234 Federal Reserve Board 271, 274 flexibility: in connectionist neural networks 49 – 50; during crisis 7 – 8; cultural intelligence and 327, 327; defined 186; in divergent thinking 185; in dual processing 48; in LeaderMember Exchange (LMX) theory 321; in shared mental models (SMM) 297, 299; social acuity and 322, 327, 327, 330; in social judgment 239 fluency 185 – 186 fluid intelligence 16 followers: follower trust 321, 346, 353; implicit follower theories (IFTs) 46 – 47, 58 foolishness see wisdom Ford Foundation awardees 265 forecasting: accuracy in 206 – 208; causal analysis and 134 – 135, 213 – 214; cause/goal analysis 213 – 214; constraints in 134, 211 – 212; content 212 – 215; emotional intelligence and 208; future research in 208; idea evaluation/generation 217; knowledge and 210 – 212, 218 – 219; leader performance and 205 – 210; mental models 214 – 215; mental time travel (MTT) and 206; models of 210 – 212, 211; objectivity 217 – 219, 323, 248; outcomes and performance 215 – 220; problem solving and 189, 193 – 194; social dynamics and 312, 313; social forecasting 9, 319, 324, 325; timing and 206, 215 – 216

Index  385

Fortune 500 companies 18 – 19, 24 fragmentation 74, 75, 91 functional leadership 2 – 4, 309, 349 future research 10 – 11; causal analysis 100, 114 – 115; communication 293; forecasting 208; intelligence 30; leader attributes 325, 330 – 331; leaders’ attention 171; mental models 293, 298 – 299; monitoring skills 343 – 344, 354 – 355; problem solving and information 91; social acuity and 325 Galton, Francis 27 – 29 Gandhi, Mohandas 363 gap filling 50 general intelligence see intelligence genetics 16, 17, 27 gestation/latency stage of problem identification 314 global neuronal workspace (GNW) 54 – 59, 63 GNW see global neuronal workspace (GNW) goals analysis 318 Gough Adjective Check List (ACL) 31 – 32 Greenspan, Alan 271, 274 groups: creativity in 59 – 60, 110, 151; monitoring team performance 340, 346 – 350; team cognition 291 – 294, 295; transformational leadership and team participation 289, 295, 298 healthcare contexts: case study in organizational crisis 153 – 160, 154, 156, 158, 159; dynamic delegation in 349 – 350; intelligence and health 17; intelligence and outcomes in 17; leadership structures 62 – 63; sensemaking in 266, 271 high reliability systems (collective minds) 62, 266 – 267, 273 Hitler, Adolf 363, 369 – 373 idea evaluation 85, 180, 183, 184, 189; accuracy in 237; decision making and 235 – 238; forecasting and 217; ideation in 193; trust and 88 idea generation 8, 85, 87 – 88, 90; forecasting and 217; ideation and 193; intelligence and 187; problem solving and 179 – 180, 182, 189 ideation 85, 85, 87 – 88, 90, 192; intelligence and 186 – 187; problem

solving and 186 – 187, 189, 192 – 193; rational vs. intuitive 87 – 88 idiosyncratic information gathering 76 – 79, 77, 79, 81, 84, 87 – 89, 90 IFTs see implicit follower theories (IFTs) ILTs see implicit leadership theories (ILTs) implementation planning 88 – 89, 179 – 180, 183 – 184, 189 implicit follower theories (IFTs) 46 – 47, 58 implicit leadership theories (ILTs) 46 – 52, 58, 284 impression management 340 – 343, 351 indirect effects 30, 107, 114 – 115 informal leadership 62 – 63 information gathering 7; ad hoc 76 – 82, 77, 79, 84, 86 – 88, 90; analogizing 76 – 78, 77, 79, 84, 87 – 88, 89, 90; causal analysis and 102 – 105, 112 – 114; communication tactics 80 – 81, 84; compartmentalization 76 – 80, 77, 79, 82, 84, 87 – 89, 89 – 90; decision-making and 231 – 232; direct observation 82 – 83, 84, 91; event schema 52 – 53; experimentation 7, 53, 76 – 78, 77, 87 – 89, 90; idiosyncratic 76 – 79, 77, 79, 81, 84, 87 – 89, 90; leader scanning 78, 190; limits to conscious processing 53 – 54; mental models and 71 – 73, 72; multilevel information sources 238 – 239; pattern recognition 7, 76 – 80, 77, 79; problem-solving processes 86 – 89, 84, 85, 89 – 90, 180 – 181; sensemaking and 71; social dynamics and 307, 311 – 314, 312, 329; social networking 81 – 82, 84; sources 79 – 84; strategies 76 – 79, 77, 79; uncertainty and 71 – 76, 74, 97 information processing: by groups 47 – 48, 53, 63 – 64; by individuals 46 – 47, 47 – 61; self and 56 – 61; speed of 16 ingratiation 340 – 344 innovation failures 176 – 177 intelligence 6 – 7; alternative notions of 24, 44 – 45; chronometric research 16 – 17; correlates of 17; creative problem solving and 186 – 187, 189; definitions of 14, 15 – 16; DNA markers of 32; early research in 15 – 17, 20 – 21; future research in 30; historiometric research 27 – 32; as an “instrumental variable” 24 – 27; IQ 14, 20, 24 – 27, 29 – 33, 376; leadership and 14 – 15; Machiavellian 369; modeling 21 – 22; morality and 28; nature vs. nurture 17; practical 364 – 365; psychometric research 15 – 27; threshold

386 Index

theory of 186; see also cognition; IQ (intelligence quotient) intimacy 370 – 371, 375 intrapersonal and interpersonal intelligences 368 iPhone 233 – 234 IQ (intelligence quotient) 14, 20, 24 – 27, 29 – 33, 376; estimated IQ and eminent leaders 29 – 33 irrationality: in decision making 229, 231 – 234, 244, 249 “jangle fallacy” 45 job characteristics theory 197 job satisfaction 98 – 100, 125 Kabila, Joseph 373 knowledge: case-based knowledge 3, 10, 52 – 53, 210 – 212; forecasting and 210 – 212, 218 – 219; knowledge, skills, and abilities (KSAs) 196, 313, 320 – 321, 323, 349; principle-based knowledge 182; problem solving and 187, 189; tacit 298, 363 – 365, 366, 368 Lafley, A. G. 240 – 241 leader contingency theory 83 leader development: career experiences and 6; causal analysis 7, 104, 111 – 114; mental models and 299; monitoring skills 355; problem solving 196 – 197; self-awareness and 342; sensemaking skills 269 – 270; social acuity skills 331; understanding human cognition 114 – 115, 284 leader performance 4, 5 – 6, 33n1, 33n2; forecasting skills and 205 – 210; leader mental models and 284 – 289, 286, 299; monitoring team performance 340, 346 – 350 leader scanning 78, 190 Leader-Member Exchange (LMX) theory 59, 295, 309, 321, 347 leaders: attention of 147 – 148, 150 – 151; decision-making capacity and 227 – 229, 228, 234 – 244; errors by 151 – 152; individual differences in problem solving 185 – 187, 189; leader–leader interactions 286 – 287; mental models of 148 – 149, 279 – 289, 282, 286, 295 – 296, 299; selfawareness in 342; self-monitoring 340, 341 – 346; self-presentation 342 – 344; skills 6 – 10; social acuity in 307 – 308;

strategic self-disclosure 344 – 345; traits of 15, 325, 330 – 331; wise 362; see also charisma; personality leadership: behavioral models of 1 – 2; charismatic 59 – 60, 123 – 125, 142; coleadership 296 – 297; definitions of 280, 309; effectiveness 2 – 3; emotions and 59 – 61, 345 – 348; executive leadership 350 – 351, 352 – 353; as exercise of social influence 2; functional 2 – 4, 309, 349; future research in 10 – 11; implicit leadership theories (ILTs) 46 – 52, 58, 284; informal 62 – 63; intelligence scores and 21 – 27, 22, 23; national culture and 246 – 247; as organizational problem solving 308 – 311; problem solving and 177 – 179; race and 50 – 52, 311; servant 1, 2; stories of wise/unwise 374 – 376, 375; structures of 62 – 63; styles 123 – 124; from a systems perspective 100; toxic 372 – 376, 375; understanding causality 98 – 100; see also transformational leadership LMX see Leader-Member Exchange (LMX) theory local brain networks 55 – 56 Machiavelli 2 Machiavellianism 132, 369 Maduro, Nicolas 372 – 373 “management by walking around” 82 managerial role theory 343 Mandela, Nelson 363 Mann Gulch fire 82, 266 – 267, 274 market forces: decision making and 244, 247 – 249 MAT see Miller Analogies Test (MAT) MDS see multidimensional scaling (MDS) Mendelian randomization 27 mental models 277 – 280; accuracy in 279, 293, 299; in causal analysis 125 – 127, 129 – 131; cognition and 278 – 279, 281; communication and 292 – 295; complexity of 104 – 105, 110, 152, 168 – 169, 215, 298; in educational contexts 277 – 278; ethics and 288, 299; forecasting 214 – 215; future research in 293, 298 – 299; in information gathering and 71 – 73, 72; leader mental models 148 – 149, 279 – 289, 282, 286, 295 – 296, 299; organizational climate and 288, 296 – 297; power and 284 – 285; sensemaking and 152; shared

Index  387

experiences 296; shared leadership 278, 289, 297; shared mental models (SMMs) 291 – 296, 297, 299 mental time travel (MTT) 47, 52 – 53, 55 – 58, 61; forecasting as 206 microstructural architecture 32 military contexts: cognitive skills and leadership in 4 – 6, 84 – 85; creativity in 178 – 179; hierarchical structures in 178; intelligence in 15, 29 – 30; problem solving in 178 – 179; self-managed teams in 297; sensemaking in 266; shared mental models (SMMs) in 297; wargame simulation 293 Miller Analogies Test (MAT) 18 models: of behavior 1; of creative processes and strategies 188 – 195, 189; of decision-making capacity 227 – 229, 228; of forecasting 210 – 212, 211; of leader problem-solving 72, 73 185 – 187, 189 molecular genetics 17 monitoring skills 340 – 355; charisma and 341 – 343; evaluating solutions 184, 194 – 195, 313; future research in 343 – 344, 354 – 355; organizational 340 – 345; team performance 340, 346 – 350 MTT see mental time travel (MTT) multidimensional scaling (MDS) 157, 160, 170, 170 multiplicity 74, 74 – 75, 87, 91, 262 network acuity 315, 329 neural networks 48 – 54, 51; collective minds 62, 266 – 267, 273; connectionist 49 – 50; global neuronal workspace (GNW) 54 – 59, 63; local brain networks 55 – 56 Nobel Prize 19 Novak, David 176 – 177 novelty 62 – 64, 74, 74, 91; in idea generation 126, 186; leader attention and 148 – 149 objectivity: in forecasting 217 – 219, 323, 248 OCB see organizational citizenship behavior (OCB) occupational contexts see corporate contexts “old wine in new bottles” problem 45 Open Market Committee 271, 274 Orban,Victor 369 – 370 organizational citizenship behavior (OCB) 111, 314, 351 – 352

organizational climate: creativity in 197; goal orientation in 61; mental models and 288, 296 – 297; multilevel structures 63, 238, 288, 355 organizational crisis: content of attention during 160 – 168, 160, 161, 163, 164, 165; leaders’ attention during 153 – 160, 154, 156, 158, 159 organizational cues 322 organizations: attention of 149; decision making and 247 – 249; early definitions of 260; leadership level and creativity 178; monitoring of 340 – 345 Oval Mapping Technique 169 passion 371 – 372, 375 pattern completion 50; in communication 80; in information gathering 7, 76 – 80, 77, 79; in problem solving 86 – 88, 89 – 90 perceptual scanning 314, 323 performance monitoring 348 – 349, 354 performance-cue effect 25 – 26 personality: in intellectual development 20 – 22, 25 – 26, 44 – 45; in problem solving 3; self-monitoring and 10, 326, 327 – 328, 341 – 346; of US presidents 30 – 33 perspective taking 239, 241, 243, 326, 328, 347 planning 4 – 5, 8, 138; implementation planning 88 – 89, 179 – 180, 183 – 184, 189 Plato 15 political contexts: assessing political climate 352; Egyptian Revolution 278; European leaders 27 – 28; mental models in 278; see also US presidents; wisdom political skill 9, 329 power: intelligence and 19; leaders’ need for 248; mental models and 284 – 285; sensemaking and 270 – 272; toxic leadership and 375 – 376 power distance 246 – 247 problem solving 176, 234 – 235; application of 196 – 197; concept selection 85, 88, 180 – 181, 189; decision making and 234 – 235; execution 85, 88 – 89; future research in 91; ideation 85, 87 – 88; implementation planning and 183 – 184, 189; information gathering and 86 – 89, 84, 85, 89 – 90; model of 72, 73; “outsourcing” of 187; pattern recognition 86 – 88, 89 – 90; problem

388 Index

construction 85, 85 – 87, 314, 312; problem detection 180, 312, 314 – 315, 321 – 321, 328; processes 84 – 86, 84, 85, 179 – 185, 311 – 313, 312 – 313; social 3 – 4, 317, 324; social percepts in 320 – 324 process models: creative problem solving 179 – 180 Proctor & Gamble (P&G) 240 – 241 Project Talent 18 promotion focus 323 prospect theory 227, 231, 233 Putin,Vladimir 370, 373 quantitative genetics 17 Quételet 27 race 50 – 52, 311 recurrent networks 50 – 52, 51 regulatory fit 323 – 324 rotation designs 21, 34n2 Russell, James 263 SAT see Scholastic Assessment Test (SAT) scanning: constraint identification and 191; creative problem solving and 188 – 191, 189; environmental 78, 235 – 236, 350; leader 78, 190; perceptual 314, 323 Scholastic Assessment Test (SAT) 16, 18 – 19 Schutz, Alfred 261 second-loop learning 287 selective attention 7 – 8, 148 – 152 selective combination 368 selective comparison 368 selective encoding 368 self: defined 56 – 57; dependence on selfstructures 46 – 47; mental time travel (MTT) and 57 – 58; self-identities 57 – 59, 61; self-schemas 52, 57 – 59, 61 self-monitoring 10, 326, 327 – 328, 341 – 346; organizational citizenship behavior (OCB) and 351 – 353 sense-breaking 264 – 265 sense-giving 264 – 265 sensemaking; causal analysis and 269; cognition and 235 – 236, 261, 266 – 275; cognitive skills 268 – 269; collective 272 – 274; in corporate contexts 270; creative problem solving and 189, 194 – 195; decision making and 235 – 236; defined 194; during complex

crises 148 – 149; educational contexts 263 – 265, 267 – 271; as enactment 260 – 261; as environmental enaction 261 – 262; in an environment of change 148; knowledge in 267 – 268; leader development 269 – 270; mental models and 152; power and 270 – 272; social dynamics and 312; vision formation 195, 285 servant leadership 1, 2 shared leadership 278, 289, 297 shared mental models (SMMs) 291 – 296, 297, 299 single nucleotide polymorphisms (SNPs) 32 single-loop learning 287 Skiles, Jeffrey 54 Skills Model 234 SMM see shared mental models (SMMs) SMPY see Study of Mathematically Precocious Youth (SMPY) SNPs see single nucleotide polymorphisms social acuity 307 – 308; accuracy in 307 – 308, 310, 323, 329; flexibility 322, 327, 327, 330; future research in 325; leader development 331; perspective taking 239, 241, 243, 326, 328, 347; skills 324 – 329, 325, 326 – 327; solution implementation 318, 320, 324 – 325, 328; utilization of social resources 324 social astuteness 329 social capacity 318, 320 – 322, 330 – 331; activation of 9, 324 – 325, 325 social chameleons 2, 341 social complexity 311 social construal skill 9, 324, 325 social dynamics 307, 311 – 314, 312 – 313, 329 social feedback 47, 57 – 58, 101, 135 – 137 social forecasting 9, 319, 324, 325 social identity theory 57 social intelligence 308, 325 – 327, 326, 368 social judgment: decision making and 239 – 241 social load 311, 331 social networking 81 – 82, 84 social perceptiveness 239, 310, 321, 325 – 327, 326 social problem solving 3 – 4, 317, 324 solitary actor 262 – 263

Index  389

solution generation 233, 235; social dynamics and 307, 313, 318, 321 – 322 solution implementation 85, 88; monitoring and evaluating solutions 184, 194 – 195, 313; social acuity skills and 318, 320, 324 – 325, 328 spontaneous switching vs. adaptive switching 186 Stalin, Joseph 363, 369 – 373 Stanford-Binet Intelligence Scale 27 Study of Mathematically Precocious Youth (SMPY) 18, 29 Sullenberger, Chesley 54 “superforecasters” 207 teams see groups threshold theory of intelligence 186 Timbertop 264 timing: of actions 139 – 140; as decision making context 244 – 246; forecasting and 206, 215 – 216 toxic leadership 372 – 376, 375 training see leader development transactive memory states 279 transformational leadership 1 – 2, 24, 82, 142, 376; empathy and 347; leader self-concept and 320, 340 – 341; team participation and 289, 295, 298; toxic 372, 376 transparency 1, 80, 84, 232 – 233, 244 Trump, Donald 14, 371 – 372

trust 60, 81 – 83; follower trust 321, 346, 353; idea evaluation and 88; toxic leadership and 370 – 371 uncertainty: complexity and 74,74, 81, 89, 91; defined 73 – 74; information gathering and 71 – 76, 74, 97 US presidents: evaluating 177 – 178; intelligence of 14, 24, 30 – 32; wisdom of 362 variables: endogenous vs. exogenous 20, 25 – 26, 28, 34n2; environmental 74 – 75, 74, 87, 91, 262 vision formation 99, 114, 269 – 270; cognitive vision formation theory 152; consensus and 169; sensemaking and 195, 285 volatility 74 – 75, 74, 87, 91 Weick, Karl 260 – 264, 266 wisdom: defined 363 – 366; emotional intelligence and 368; measuring 369; mental processes 368; reception of 369 – 370; social intelligence and 368 Wonderlic Personnel Test 22 – 24, 22, 23 World Economic Forum 19, 24 Xi Jinping 373 Yerkes, Robert 15 Zuma, Jacob 373