Research In Multi-Level Issues. Vol 5

ABOUT THE EDITORS Francis J. Yammarino, Ph.D., is SUNY distinguished professor of Management and director and fellow of the Center for Leadership Studies at the State University of New York at Binghamton. He received his Ph.D. in Organizational Behavior (Management) from the State University of New York at Buffalo. Dr. Yammarino has extensive research experience in the areas of superior–subordinate relationships, leadership, self–other agreement processes, and multiple levels of analysis issues. He has served on the editorial review boards of seven scholarly journals, including the Academy of Management Journal, Journal of Applied Psychology, and the Leadership Quarterly. Dr. Yammarino is a fellow of the American Psychological Society and the Society for Industrial and Organizational Psychology. He is the author of 10 books and has published about 100 articles. Dr. Yammarino has served as a consultant to numerous organizations, including IBM, Textron, TRW, Lockheed Martin, Medtronic, United Way, Skills Net, and the US Army, Navy, Air Force, and Department of Education. Fred Dansereau, Ph.D., is professor of Organization and Human Resources and associate dean for Research in the School of Management at the State University of New York at Buffalo. He received his Ph.D. from the Labor and Industrial Relations Institute at the University of Illinois with a specialization in Organizational Behavior. Dr. Dansereau has extensive research experience in the areas of leadership and managing at the individual, dyad, group, and collective levels of analysis. Along with others, he has developed a theoretical and empirical approach to theorizing and testing at multiple levels of analysis. He has served on the editorial review boards of the Academy of Management Review, Group and Organization Management, and Leadership Quarterly. Dr. Dansereau is a fellow of the American Psychological Association and the American Psychological Society. He has authored nine books and about 80 articles and is a consultant to numerous organizations, including the Bank of Chicago, Occidental, St. Joe Corp., Sears, TRW, the United States Army and Navy, Worthington Industries, and various educational institutions. ix

LIST OF CONTRIBUTORS Kerstin A. Aumann

Columbia University, USA

Marietta L. Baba

Michigan State University, USA

Xiao-Ping Chen

University of Washington, USA

Fred Dansereau

State University of New York at Buffalo, USA

David V. Day

Singapore Management University, Singapore

Jaap Dronkers

European University Institute, Italy

Miriam Erez

Technion – Israeli Institute of Technology, Israel

Michael P. Farrell

State University of New York at Buffalo, USA

Maryann Feldman

University of Toronto, Canada

Richard L. Groesbeck

Virginia Polytechnic Institute and State University, USA

Joop Hox

Utrecht University, The Netherlands

Mariann Jelinek

The College of William and Mary, USA

Manuel London

State University of New York at Stony Brook, USA

Kenneth D. Mackenzie

University of Kansas, USA

Russ Marion

Clemson University, USA

Steven E. Markham

Virginia Polytechnic Institute and State University, USA

Cheri Ostroff

University of Maryland, USA

Valerie I. Sessa

Montclair State University, USA

Tom A.B. Snijders

University of Gro¨ningen, The Netherlands xi

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Bret R. Swan

Brigham Young University at Hawaii, USA

Brian W. Tate

Pennsylvania State University, USA

Anne S. Tsui

Arizona State University, USA

Mary Uhl-Bien

University of Central Florida, USA

Francis J. Yammarino

State University of New York at Binghamton, USA

OVERVIEW: MULTI-LEVEL ISSUES IN SOCIAL SYSTEMS Fred Dansereau and Francis J. Yammarino INTRODUCTION ‘‘Multi-Level Issues in Social Systems’’ is Volume 5 of Research in MultiLevel Issues, an annual series that provides an outlet for the discussion of multi-level problems and solutions across a variety of fields of study. Using a scientific debate format of a key scholarly essay followed by two commentaries and a rebuttal, we present, in this series, theoretical work, significant empirical studies, methodological developments, analytical techniques, and philosophical treatments to advance the field of multi-level studies, regardless of disciplinary perspective. Similar to Volumes 1–4 (Yammarino & Dansereau, 2002, 2004; Dansereau & Yammarino, 2003, 2005), this volume, Volume 5, contains five major essays with commentaries and rebuttals that cover a range of topics, but in the realm of social systems. In particular, the five ‘‘critical essays’’ offer extensive literature reviews, new model developments, methodological advancements, and some data for the study of human resources management in organizations, continuous learning in organizations, divorce in the family, industry–university dynamics involving intellectual property, and organizational leadership. While each of the major essays, and associated commentaries and rebuttals, is unique in orientation, they show a common bond in

Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 1–9 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05001-6

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raising and addressing multi-level issues or discussing problems and solutions that involve multiple levels of analysis in various types of social systems.

HUMAN RESOURCES MANAGEMENT In the first essay, Aumann and Ostroff focus on Human Resource Management (HRM) systems at the individual, organizational, and societal levels of analysis. They present a multi-level framework that elucidates the importance of the fit of human resource practices with individual, organizational, and societal values. Different variables are associated with different levels of analysis, and the interactions of these variables across levels are hypothesized to impact individual performance and the effectiveness of HRM practices. They also suggest that the strength of the societal culture within which an organization operates serves as a mechanism that affects the relationships between climate, value fit, and attitudes. The essay concludes with recommendations for future research as well as implications for practice. This represents, in our view, the first true multi-level view of HRM practices from a cross-cultural perspective. In their commentary, Chen and Tsui focus on the same levels of analysis, as did Aumann and Ostroff, but combine the pieces of the original model in different ways. The differences and similarities of the two approaches can be seen by comparing Figure 1 in the original essay with Figure 1 in the commentary. As Chen and Tsui point out, their multi-level model differs from the Aumann and Ostroff model in three ways: They (1) view organizational values as preceding HRM practices, (2) eliminate the concept of climate, and (3) shift the focus to the integration of organizational culture and individual values. In our view, these two different multi-level models suggest a need for more research to examine which insightful and plausible model seems better at predicting various outcomes. In her commentary, Erez takes a different approach and points out the many strengths of the original model presented by Aumann and Ostroff. At the same time, she shows how the multi-level model creates various types of challenges. For example, because of the focus on multiple levels and multiple interactions of variables at different levels of analysis, she raises the question of parsimony. A second particularly interesting question that she raises is: what are the boundary conditions on the model? In other words, Erez asks when will the complicated model apply and when will it not apply. Erez then points out and summarizes her own multi-level model. In combination with the other articles, Erez’s work provides an interesting agenda

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to try and sort out which models, levels, and boundary conditions help explain various outcomes. These three articles in combination create the basis for an extensive research agenda in the area of HRM and culture. In their reply, Aumann and Ostroff focus on three issues. First, they argue, appropriately in our opinion, that they have presented a multi-level model that can be used as a heuristic for developing theories that are focused more narrowly. Indeed, they suggest that the models offered by the commentators illustrate this point. Their discussion illustrates nicely the point that multilevel approaches tend to add more complexity because they focus on more than one level of analysis. As Aumann and Ostroff point out, however, beginning with complexity, of course, does not preclude future research from demonstrating that a more parsimonious model might be found to be more appropriate. Second, Aumann and Ostroff clarify their view of the relationship between societal and organizational values. Here we see some of the difficulties and controversies that arise in theorizing about the interface between multiple levels of analysis. The same multi-level controversy carries over to their third set of concerns about climate and culture, which we believe may be best represented as at different levels of analysis. The set of articles nicely illustrates many of the intellectual issues that arise in multi-level theorizing and at the same time suggests numerous ideas for future research.

CONTINUOUS LEARNING In the second essay, London and Sessa show how the living systems approach can apply to three levels of analysis: the individual, group, and organization. The main focus of the chapter is on continuous learning and how it plays out in similar and different ways at different levels of analysis. They provide a series of research questions that arise when one attempts to conceptualize continuous learning at the three levels of analysis. In addition to the questions, they provide different examples and conceptualizations at each of the three levels of analysis for: (1) adaptive, generative, and transformative learning, (2) system characteristics that affect readiness for learning between systems, and (3) the flow of learning within and between levels. They also provide three cases at the end of the essay that illustrate how various systems and learning issues at multiple levels play out in different situations. Essentially, London and Sessa provide a multi-level systems model of continuous learning that they suggest is similar across levels but which also plays out very differently at different levels. This work represents a new multi-level approach to continuous learning in organizations.

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In their commentary, Day and Tate raise a key question: Why does continuous learning sometimes not occur in organizations? They, then, organize the various obstacles to continuous learning by levels of analysis as follows: personal obstacles, relational obstacles, and structural obstacles. Thus, we see, at each of these three levels, that there are obstacles to continuous learning. In this way, Day and Tate provide ‘‘an emergent multi-level framework that presents a number of obstacles to continuous learning in organizations.’’ We believe that it would be very interesting to examine empirically whether and how these variables may serve as boundary conditions on the emergence of continuous learning in organizations. Markham, Groesbeck, and Swan, in their commentary, first review the key points raised by London and Sessa. They then raise some interesting questions about the meaning of continuous learning. After which, they show how a ‘‘parts’’ view at each level of analysis (individual, group, and organization) is useful in understanding continuous learning. They argue that the introduction of parts allows for an understanding of some of the key dynamics that are crucial to the processes of continuous learning. Additional implications of the introduction of parts are discussed. The commentary concludes with a discussion of the implications of their ideas for a variety of professionals who work in organizations, such as total quality management specialists, members of the Society for Organizational Leadership, Chief Learning Officers, and various teams, departments, and divisions. In their reply, Sessa and London view the two commentaries as raising a question about the conditions under which continuous learning takes place. They then articulate how various environmental triggers, aspects of readiness for learning, and different types of feedback provide leverage points that trigger learning within and across individual, group, and organizational systems. By beginning to specify the conditions that may be necessary for continuous learning to occur, Sessa and London further develop their original theory. The set of articles in this section provides a very rich set of research questions about continuous learning at multiple levels of analysis that can serve as a guide for research in this area for many years.

DIVORCE AND FAMILY In the third essay, Dronkers and Hox focus on the importance of the family level of analysis on children’s propensities to divorce. This study and actual data analysis illustrate, we believe, for the first time, ‘‘a multi-level, event– history analysis.’’ As such, this analysis combines two different analysis

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techniques. This type of analysis is useful in a variety of situations, such as where individuals in organizations are followed in a panel (overtime) design with outcome variables such as career changes, or events such as leaving the organization that are to be predicted by both individual- and organizational-level variables. The results of the actual data analyses allow for the plausibility of a potentially very controversial interpretation. The authors interpret the results as showing that there is a similarity in the divorce risks for the same family that does not seem explainable by the available child and family characteristics. Thus the authors argue that shared family genetics and social heritage play an important role in the intergenerational transmission of divorce risks. In his commentary, Farrell takes issue with the genetic interpretation of the results. He suggests that, in addition to siblings’ common family background and genetic heritage, the interactions of siblings over the course of their lives may influence their attitudes toward marriage and divorce. He proposes some variables that may help to identify these effects in future analyses. Snijders in his commentary raises some methodological issues about the analyses used by Dronkers and Hox. Based on his recommended additional analyses, he suggests that the use of his more elaborate approach will lead to new research about how best to distinguish between, on the one hand, correlations between siblings caused by the family of origin that are partially unobserved; and on the other hand, direct causal effects for the life course of one person on what happens later to a sibling of this person. In their reply, Dronkers and Hox, focus on the substantive and then statistical issues raised by the commentators. They point out where they disagree and agree with each commentator. In addition, they point out the type of data that might be useful for investigating this topic more effectively in future research. Nevertheless, even with such data, they indicate that multi-level analysis would still be appropriate to address questions of this sort. This set of articles illustrates, for the first time to our knowledge, a new approach to using multi-level analyses in combination with event-history analysis that we think has excellent potential for use in future research.

INDUSTRY–UNIVERSITY DYNAMICS In the fourth essay, Jelinek uses three levels of analysis: (1) the level of the deal between a university and company, (2) the industry and university level (called the sector level), and (3) the institutional level. Jelinek describes a

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multi-level model that attempts to explain how universities and organizations in different industries deal with intellectual properties (such as patents) and work together to develop various deals. The chapter describes the dynamics at each of the levels that Jelinek believes influences the relationships between universities and industry partners. The point in part is that universities and their industry partners develop linkages or deals that involve intellectual property depending on factors at multiple levels of analysis. Jelinek presents a truly new multi-level approach to this topic. In her commentary, Baba applies Jelinek’s ideas by illustrating many of the dynamics in Jelinek’s model with a case study about IBM. The case fleshes out how the dynamics described by Jelinek play out in the real world evolution of the linkage of IBM with universities. It also describes IBM’s current approach to developing such linkages. The case by Baba shows nicely the real world application of the more abstract theoretical model presented by Jelinek. Together the two articles provide a fascinating way to understand the multi-level factors that influence the deals between universities and other sectors (e.g., private companies). In her commentary, Feldman presents her model about the dynamics in universities that may influence whether an individual scientist gets involved in linkages with other organizations outside the university. Not only does Feldman focus on the individual scientist but also on how repeated transactions may influence the development of relationships between universities and industry partners. Jelinek in her reply to the two commentaries effectively illustrates how Baba’s commentary takes the view of the firm or industry partners, whereas, Feldman’s commentary takes the view of universities. She shows how the two approaches fit together within the overall model that she initially proposed. Finally, Jelinek, based on her new model, provides a number of additional suggestions for future multi-level research on this topic.

ORGANIZATIONAL LEADERSHIP In the fifth essay, Mackenzie focuses on processual agents that can include a number of ‘‘entities’’ such as individuals, groups, and organizations. He offers a model of five leadership effectiveness concepts that focuses on leadership, authority, management, power, and environment (thus, the term LAMPE). Of particular interest to us is the notion of leadership which he defines as ‘‘the processes of initiating, enabling, implementing, and sustaining changes for a group or organization.’’ He then focuses on the relationship

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among the five key leadership effectiveness concepts and provides a description of 29 leadership practices that follow from these five key concepts (LAMPE). Based on this approach, he presents 36 propositions, some of which focus on individuals, some on groups, and some on organizations. Despite what may appear to be similarities to other approaches to leadership, to many readers Mackenzie’s approach will seem very different from other approaches to leadership. His approach emphasizes the observable phenomenon of structures and processes. In this way, Mackenzie introduces multiple levels of analysis into the world of leadership processes. In their commentary, Uhl-Bien and Marion suggest that Mackenzie should pay greater attention to theories about complexity. They describe their own view of leadership as involving managerial leadership, adaptive leadership, and enabling leadership. They focus on, as do theories about complexity, the dynamics of complex adaptive systems or aggregations of interactive and interdependent agents. Based on this view, they draw out some of the implications of theories about complexity for Mackenzie’s work. In their conclusion section, Uhl-Bien and Marion suggest that Mackenzie’s theory may benefit from an infusion of the thinking that underlies complexity theories. In his reply, Mackenzie very strongly disagrees with the suggestion of Uhl-Bien and Marion that his LAMPE theory should include ideas from complexity theory. Mackenzie states instead that complexity theory should incorporate the theory, methods, and models already tested in the construction of his LAMPE theory. His argument is based on his view of the conditions under which a processual theory is testable. This argument is quite complex, as well as lengthy, so we asked Mackenzie to isolate the argument in an Appendix, while retaining his general ideas in the main text. Of particular interest to us is that a comparison of the position of UhlBien and Marion with that of Mackenzie illustrates very strongly held views about processes as they apply to multiple levels of analysis. We have not seen this type of disagreement about processes emerge previously in the area of multi-level theory. On one hand, we have the more quantitative-oriented approach to multi-level processes offered by Mackenzie, and on the other hand, the more qualitative approach to processes offered by Uhl-Bien and Marion. We believe by pursing both of these approaches jointly, even though they may be quite incompatible and perhaps require different individuals to pursue them, that over time researchers will gain additional insights about leadership in organizations–albeit perhaps from almost diametrically opposed, if not inherently conflicting, perspectives.

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CONCLUSIONS The essays, commentaries, and replies in this book illustrate the kind of issues that arise in dealing with multiple levels of analysis in social systems. The definitions of concepts (albeit, effective human resources management practices, continuous learning, understanding events, industry–university linkages, or leadership effectiveness) change depending on what combination of levels of analysis are involved and added to them. The nuances of analytical methods (albeit event analysis extended to multi-level modeling or case studies) change when one moves from one level of analysis to multiple levels of analysis. Moreover, although different paradigms may guide different scholars’ theories and research methods and techniques, levels of analysis issues must be resolved to have a viable paradigm (involving a more traditional view or process view). We believe that the demonstration of these issues in various social systems, ranging from the family to organizations to industries to institutions, shows that these insights, applications, and advances will apply to numerous areas of scholarly investigation. The authors in this volume have challenged theorists, researchers, and methodologists to raise and address multi-level issues in all their disciplinary and interdisciplinary work. If you would like to be a part of contributing ideas to this scholarly endeavor, please contact us directly or visit our website at: http://www.levelsofanalysis.com.

ACKNOWLEDGMENTS The publication of the Research in Multi-Level Issues annual series and this volume have been greatly facilitated by Mary Malin, Joanna Scott, and Julie Walker at Elsevier in the United Kingdom and the staff at Macmillan India Ltd. Closer to home, we thank our Schools of Management, the Center for Leadership Studies at Binghamton, the Jacobs Management Center at Buffalo, our secretaries, Marie Iobst, and Cheryl Tubisz, as well as our copy-editor, Jill Hobbs, for their help in preparing this book for publication. Finally and perhaps most importantly, we offer our sincere thanks to our contributors. The authors of the essays, commentaries, and rebuttals in this volume have provided new ideas and insights for unraveling the challenges of dealing with multiple levels of analysis and multi-level issues in a wide variety of areas. Thank you all.

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REFERENCES Dansereau, F., & Yammarino, F. J. (Eds) (2003). Multi-level issues in organizational behavior and strategy. Research in multi-level issues (Vol. 2). Oxford, UK: Elsevier. Dansereau, F., & Yammarino, F. J. (Eds) (2005). Multi-level issues in strategy and methods. Research in multi-level issues (Vol. 4). Oxford, UK: Elsevier. Yammarino, F. J., & Dansereau, F. (Eds) (2002). The many faces of multi-level issues. Research in multi-level issues (Vol. 1). Oxford, UK: Elsevier. Yammarino, F. J., & Dansereau, F. (Eds) (2004). Multi-level issues in organizational behavior and processes. Research in multi-level issues (Vol. 3). Oxford, UK: Elsevier.

MULTI-LEVEL FIT: AN INTEGRATIVE FRAMEWORK FOR UNDERSTANDING HRM PRACTICES IN CROSS-CULTURAL CONTEXTS Kerstin A. Aumann and Cheri Ostroff ABSTRACT In recent years, theory and research have been increasingly devoted to understanding organizational behavior in cross-cultural and global contexts, with particular attention being paid to the appropriateness of various human resources management (HRM) practices because practices that may be effective within one cultural context may not be effective in other cultural contexts. This chapter argues that a multi-level perspective is needed to explain the interplay between HRM practices and employee responses across cultural contexts. Specifically, the multi-level framework developed in this chapter elucidates the importance of fit between HRM practices, individual values, organizational values, and societal values. Societal values play a key role in the adoption of HRM practices, and the effectiveness of these HRM practices will depend largely on ‘‘fit’’ or alignment with the values of the societal culture in which the organization is operating. HRM practices also shape the collective responses of Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 13–79 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05002-8

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employees through organizational climate at the organizational level and through psychological climate at the individual level. For positive employee attitudes and responses to emerge, the climate created by the HRM practices must be aligned with societal and individual values. Building on these notions, the strength of the societal culture in which the organization is operating serves as a mechanism that links relationships between climate, value fit, and attitudes across levels of analysis. The chapter concludes with some recommendations for future research and implications for practice.

INTRODUCTION Organizations have become increasingly global and multicultural over the past several decades. Worldwide business opportunities, cross-border mergers, and strategic alliances have created a plethora of organizations that connect people of myriad nationalities and cultures (House & Javidan, 2004). Beyond the dynamics of a global marketplace, increasingly diverse workforces are becoming the norm in the United States, Europe, and elsewhere (e.g., Fullerton & Toossii, 2001; Granrose, 1997; Tung, 1993). As more and more organizations face the vast opportunities and challenges associated with a global marketplace and culturally diverse workforces, the need for cross-cultural models of organizational behavior is greater than ever (e.g., Erez, 1994; Erez & Earley, 1993). Attention to human resources management (HRM) is critical for an organization to be successful in negotiating cultural differences and managing a multicultural workforce (Gelfand, Erez, & Aycan, in press). Although some debate has surfaced regarding the extent to which globalization will produce convergence in HRM practices across countries (e.g., Dunphy, 1987; Kelley, Whatley, & Worthley, 1987; Ralston, Holt, Terpstra, & Kai-Cheng, 1997), recent views emphasize that cultural differences are pervasive, and that they may amplify and present new challenges for organizations (e.g., Gelfand, Erez, & Aycan, in press; House, 2004). As such, an understanding of how to design culturally appropriate HRM systems and practices across different cultural contexts is sorely needed. The primary goal of this chapter is to integrate the literature on HRM practices and firm performance, HRM practices and comparative cultural values, and person–organization (P–O) fit so as to develop a multilevel framework for understanding linkages between culture, HRM, and organizational processes. Understanding culture and its relationship to

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organizational behavior requires an examination of cultural constructs at different levels of analysis as well relationships across multiple levels (Chao, 2000). Therefore, this chapter specifies constructs at the individual, organizational, and societal levels of analysis and explores how processes operate across these levels. We set the stage for the development of our model by providing a brief overview of the role of HRM in organizational contexts, then elucidate how cultural values operate at multiple levels of analysis. Next, we propose a multi-level model focusing on the relationships between societal values, organizational culture, HRM practices, organizational climate, employee attitudes and behaviors, and effectiveness. The theoretical linkages in the model are discussed in four successive sections of the chapter. First, we focus on the organizational level of analysis and delineate how organizational-level constructs such as organizational climate and collective employee attitudes emerge from individual-level phenomena. In doing so, we argue that the climate created by the HRM practices must be aligned with key societal values for positive, collective employee attitudes, and responses to emerge. That is, in cross-cultural situations, the effectiveness of an organization’s HRM practices will depend largely on their ‘‘fit’’ or alignment with societal values. Second, the notion that HRM practices send signals about cultural values is addressed. In this section of the chapter, we delineate the societal cultural values that are portrayed by various types of HRM practices. Third, we focus on the individual level of analysis and illustrate the importance of fit between societal values, personal values, and HRM practices. We explore how HRM practices shape the responses of employees through psychological climate at the individual level, arguing that the climate created by the HRM practices must be aligned with personal values for positive employee attitudes and responses to emerge. Fourth, we focus on processes that link the individual and organizational levels of analysis. This discussion takes into account the overall societal values as well as the degree of variability among personal values within a culture (culture strength) and elucidates how the strength of the societal culture in which the organization is operating serves as a ‘‘meso’’ construct (House, Rousseau, & Thomas-Hunt, 1995) that links relationships between climate, value fit, and attitudes across levels of analysis. The remainder of the chapter discusses future research directions and implications of this model for developing HRM practices that are congruent with the values in the culture context.

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THE ROLE OF HRM IN ORGANIZATIONS It has long been held that one of the keys to organizational effectiveness is the organization’s ability to effectively leverage its human resources (e.g., Gross & Etzioni, 1985; Likert, 1961; Mayo, 1933). Attention to people and adherence to technically superior HRM practices are believed to result in more productive, satisfied, motivated, and loyal employees who are willing to work toward achieving organizational goals. As Ostroff and Bowen (2000) point out, this premise is based on multi-level issues. First, there is an individual-level assumption that HRM practices influence individuals’ perceptions, behaviors, and attitudes. Second, at the organizational level, HRM practices are purported to relate to organizational effectiveness through processes such as organizational climate and collective attributes of employees. These practices are believed to contribute to firm performance because they ensure that employees have the skills and abilities to perform activities required to achieve the organization’s goals. In addition, HRM practices motivate employees to apply their skills, work harder, and improve the work process. As a result, HRM practices can increase the quality of the human capital pool and elicit valuable behaviors from employees. The firm can, therefore, develop a unique combination of human capital that is not easily imitated by other firms and is more difficult to substitute with other means (e.g., financial capital), thereby enhancing the competitive advantage of the firm (e.g., Nalbantian, Guzzo, Kieffer, & Doherty, 2004; Wright, McMahan, & McWilliams, 1994). Although a growing body of literature has supported these ideas (e.g., Ferris, Hochwarter, Buckley, Harrell-Cook, & Frink, 1999; Gelade & Ivery, 2003; Huselid, 1995; Kopelman, Brief, & Guzzo, 1990; Ryan, Schmit, & Johnson, 1996), theory and research that take into account how this process applies in cross-cultural situations or in organizations consisting of employees with diverse cultural backgrounds remain in their infancy. The lack of development in this area is surprising for two reasons. First, societal culture is purported to influence organizational culture and, in turn, the HRM practices that an organization chooses to adopt (Aycan, Kanungo, & Sinha, 1999; Kopelman et al., 1990). Second, attention to the appropriateness of various HRM practices is critical in global business because practices that may be effective within one cultural context may not be effective, or may even prove counterproductive, in another culture (e.g., Adler, 2002; Dorfman, 1996; Hofstede, 1980a, 2001). This raises the question of how an organization can ensure that its HRM strategies and practices will be effective for different cultural contexts and culturally diverse workforces.

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Presumably, one of the reasons why HRM practices do not travel well is because they are typically tailored to fit the primary cultural value orientation of the people in the country where the practices were created, rather than taking into account the cultural values of the employees who are experiencing these practices. Increasingly, scholars have implied that some degree of fit between societal culture, the organization and the individual is necessary for favorable results to occur in a cross-cultural situation (Aycan et al., 1999; Erez, 1994; Mendonca & Kanungo, 1994; Newman & Nollen, 1996). While the importance of considering fit and integrating variables such as societal cultural values, HRM practices, and employees’ values to understand both organizational effectiveness and individuals’ responses in organizations has been increasingly recognized, research and theory have lagged behind this thinking (e.g., Aycan & Kanungo, 2001; Erez & Earley, 1993; House & Javidan, 2004) and the processes that contribute to this phenomenon – particularly across levels of analysis – are not yet well understood. The large majority of research related to culture, HRM practices, organizational effectiveness, and employee responses has been fragmented along three lines. One stream of research has focused on the role of HRM practices in organizational effectiveness, with employee responses seen as a mediator in this relationship (e.g., Huselid, 1995; Huselid, Jackson, & Schuler, 1997; Ichniowski, Shaw, & Prennushi, 1997; MacDuffie, 1995). This work has paid little attention to the cultural context in which the organization and employees are operating. A second area of research has delineated differences in societal cultural values and their relationship to organizational practices and employee responses (e.g., Hofstede, 1980a, 2001; Morris & Pavett, 1992; Robert, Probst, Martocchio, Drasgow, & Lawler, 2000; Schuler & Rogovsky, 1998). A third area of research emphasis has been directed at understanding the fit between individuals’ values and organizational values and the influence of this fit on employees’ responses (e.g., Chatman, 1991; Kristof, 1996). Research and theory in these areas can be advanced by integrating these three streams of research – HRM practices and firm performance, HRM practices and comparative cultural values, and P–O fit – into a cohesive multi-level framework. As Earley and Mosakowski (2000) point out, ‘‘crosscultural management research must consider the relation between culture and other levels of analysis by developing theories that describe the mechanisms through which culture influences organizational phenomena’’ (p. 298). Before describing our integrative model and delving into the theoretical linkages between culture, HRM, and employee responses, it is necessary to define cultural values and to explain why cultural values provide a context for examining the influence of HRM across cultures.

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THE CULTURAL CONTEXT AND CULTURAL VALUES ACROSS LEVELS OF ANALYSIS Why Focus on Culture? A variety of environmental variables, such as the political, legal, educational and institutional environments (e.g., Gelfand, Erez, & Aycan, in press), as well as contextual features such as size, industry, technology, and labor market forces (e.g., Galbraith, 1974; Jackson & Schuler, 1995; Katz & Kahn, 1978; Lawrence & Lorsch, 1967), purportedly influence the strategy, structure, design, and processes in organizations, including the HRM practices adopted. Although we acknowledge the importance of these environmental and contextual factors, the focus here is on societal culture as the contextual variable of interest because our goal is to elucidate how organizational effectiveness can be influenced by the interplay between HRM practices and employee responses in different cultural contexts. Organizational effectiveness is at least partially dependent on organizational members responding and behaving in ways that support the organization’s goals and objectives (Angle & Perry, 1981; Kopelman et al., 1990). HRM practices are the means through which employee perceptions, attitudes, and behaviors are shaped; thus these practices play a critical role in motivating employees to work toward organizational objectives (Ostroff & Bowen, 2000). Thus, while contextual variables such as organizational size and technology are likely to be predominant influences on the formal characteristics of the organization (e.g., structural design features), culture has a strong influence on the informal processes (e.g., behavioral norms and communication patterns) that contribute to employee responses and influences the organization’s choice among different HRM practices (Faulkner, Pitkethly, & Child, 2002; Gelfand, Erez, & Aycan, in press; Tayeb, 1995). The focus on culture as a contextual variable is appropriate because culture shapes individuals’ cognitive processes and behavioral responses to situational stimuli (e.g., Kluckhohn, 1951; Rokeach, 1973), such as HRM practices experienced at work. Definitions of Culture The concept of societal culture has generated considerable controversy in terms of its sources, meaning, connotations, and indicators. Part of this inconsistency is due to the fact culture has been studied from numerous

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disciplines, including anthropology, sociology, psychology, and management. Despite this, Kopelman and his colleagues (1990) indicated that considerable agreement has emerged among the various schools of thought such that definitions of culture can be classified into two distinct, but related, categories. The first category, which is labeled the phenomenal perspective, focuses on observable artifacts and behaviors (e.g., language, myths, stories, rituals, ceremonies, visible behaviors). The second category, the ideational perspective, relies on the notion of shared meanings, assumptions, and values (e.g., concepts and beliefs that guide selection of or evaluation of events). The phenomenal perspective is related to the ideational perspective of societal culture in that artifacts (phenomenal perspective) purportedly represent more observable manifestations of the deeper assumptions, values, or ideologies (Schein, 1992; Trice & Beyer, 1993). Hofstede (1991) defines culture as the ‘‘collective mental programming’’ that controls individual responses in a given context, and proposes that culture manifests itself at several levels of depth. Values provide the core of culture at the deepest level and influence the higher-level manifestations of culture such as rituals, heroes, and symbols, which, taken together, constitute practices visible to outsiders, but are meaningful and interpretable only by insiders. Likewise, other scholars have emphasized the notion of culture as a set of systems that create shared meaning within a given situation (Shweder & LeVine, 1984). Culture shapes the cognitive schemas people use to process, interpret, and evaluate informational cues from their environment. People who live in the same cultural environment share similar values and cognitive schemas, which implies that they use similar criteria for evaluating the contributions of certain types of behaviors or situations in the development of a sense of self-worth (Triandis, 1989). A similar historical pattern is evident in the study of organizational culture. Early work on organizational culture took an anthropological approach by relying on qualitative methods, participant observation, interviews, and examination of historical information to understand the culture of an organization and its influence on employees’ behavior (Ostroff, Kinicki, & Tamkins, 2003). Although numerous definitions of organizational culture exist (Verbeke, Volgering, & Hessles, 1998), three commonalities are evident across these different interpretations (Hofstede, Neuijen, Ohayv, & Sanders, 1990) that are similar to those for societal culture. First, organizational culture includes multiple aspects, ranging from observable artifacts and behaviors to values and assumptions (Schein, 1992). Second, organizational culture is a socially constructed phenomenon that is

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influenced by historical and spatial boundaries (Schein, 2000). Third, the concept of shared meaning is central to all definitions of organizational culture (Schneider, 2000). Investigations of organizational culture are based largely on three fundamental layers: observable artifacts, espoused values, and underlying assumptions or ideologies (Hofstede et al., 1990; Schein, 1992). Artifacts represent surface-level manifestations of the underlying values and deeper assumptions. They include symbols (e.g., physical settings, objects), language and narratives (e.g., stories, myths), and practices (e.g., rituals, ceremonies) (Trice & Beyer, 1993). Espoused values are endorsed concepts and beliefs that pertain to desirable end states or behaviors, and that guide the evaluation of events (Schwartz, 1992). Finally, underlying assumptions are deeply held beliefs that often begin as values, but over time become so ingrained that violations of them are viewed as inconceivable (Schein, 1992). Some theorists argue that assumptions are not sufficient and that ideologies (i.e., shared beliefs, values, and norms that bind people together and enable them to make sense of their environment) are more fundamental to organizational culture (Trice & Beyer, 1993). Both societal- and organizational-level definitions of culture highlight that culture is a deep phenomenon based on symbolic meanings that reflect core values and their associated ideologies and assumptions. In this chapter, we focus on the notion of shared cultural values – that is, surface-level realizations of the assumptions and values in the organization or societal culture. This focus on cultural values allows for describing more than one environmental context at a time (as opposed to the idiosyncratic approach that occurs with anthropologically based approaches to the study of culture) as well as for examining process–outcome relationships that can enhance our understanding of the construct (Schneider, 2000). Further, shared values are purported to be the core or key element of both societal and organizational culture (e.g., Kluckhohn, 1951; Wiener, 1988).

Defining Values at Different Levels of Analysis Values are defined as enduring, global beliefs that guide human action (Rokeach, 1969; Schwartz, 1992, 1994). They pertain to beliefs about desirable end states (what ‘‘should’’ be), serve as the basis for making choices, and transcend specific situations to guide the selection and evaluation of behaviors and events (e.g., Schwartz & Bilsky, 1990). Values are held at a conscious level (within the person’s awareness) and are transmitted and

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acquired through socialization and social learning processes (Bandura, 1986; Erez, 1994). Some controversy exists about the level of analysis appropriate for conceptualizing and analyzing values. For example, Stackman, Pinder, and Connor (2000) argue that the construct of values is solely an individual-level one and suggest that by attributing human properties such as values to aggregations of individuals, we commit a fallacy of the wrong level. They argue that organizations (and, by extension, societies) do not possess values, other than metaphorically, and hence it is meaningless to speak of an organization’s values. Alternatively, some influential scholars hold that values do exist at higher levels of analysis. Kluckhohn (1951) writes: ‘‘A value is conception, explicit or implicit, distinctive of an individual or characteristic of a group y ’’ (p. 395). Further, the terms societal values and cultural values are pervasive in the literature and are used to characterize the fundamental beliefs of various aggregations of individuals (cf. Erez, 1994; Hofstede, 2001; O’Reilly, Chatman, & Caldwell, 1991; Triandis, 1994). In our view, this controversy can be resolved in a manner similar to earlier controversies about whether climate is an individual- or organizational-level attribute (cf. Glick, 1988; James & Jones, 1974; James, Joyce, & Slocum, 1988). In the climate literature, a distinction was drawn between psychological climate, whereby climate is conceptualized and measured at the individual level and represents individuals’ perceptions of the organizational context; an organizational climate exists when individuals’ perceptions are shared or when consensus among perceptions is formed. Relating this notion to values, it is meaningful to speak of personal values as an individual’s own system of beliefs (Rokeach, 1979; Stackman et al., 2000). Values reside within individuals and arise out of individuals’ physical and psychological needs, cognitive appraisals, social constructions, socialization, learning, and interactions with others. Because values are fundamentally an individual-level construct, measurement of values should begin at the individual level. When some degree of consensus or similarity among individuals in their values may be demonstrated, values can be meaningfully aggregated to represent some higher-level (e.g., group, organizational, or societal) construct and can viewed as unique characteristics across members of a particular culture (Erez, 1994). To the extent that personal values are shared across individuals within the same context (e.g., organization, society), we argue that higher-level conceptualizations of values emerge in the form of a composition theory (Chan, 1998; Kozlowski & Klein, 2000). Composition theories are based on the assumption of isomorphism and describe phenomena that are essentially the

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same across different levels of analysis. The convergence of personal values within a society, for example, yields a higher-level construct of shared values in the form of societal cultural values that is essentially the same as its constituent elements (personal values) in terms of its meaning and function. This theoretical aggregation (Roberts, Hulin, & Rousseau, 1978) rests on the assumption that organizational and societal values are conceptualized as distinct from, but related to, the individual characteristics from which they are drawn. At these higher levels of analysis, the social phenomena are not thought to result from the actions of isolated individuals, but rather from individuals operating within a social context (Roberts et al., 1978). Culture, along with the values it reflects, develops within a certain shared ecological environment (Berry, 1979). Culture and cultural values are transmitted through socialization and the social learning processes of modeling and observation beginning in early childhood (Bandura, 1986). Individuals are socialized in similar ways, are exposed to similar features within the settings, and share their interpretations of the setting with others. As a result, members of different cultures develop different ways of perceiving and evaluating the world around them, and individuals within a culture come to share similar values. Similarly, shared organizational cultural values are based on the sense-making process that individuals go through as they attempt to understand their organizational context. Individuals cognitively interpret stimuli in their environment and can develop the same schema or similar cognitive maps for important work-related events (Weick, 1995). Shared organizational values develop largely from social processes and interactions among employees as they interpret various artifacts, symbols, assumptions, and behavioral norms (Hatch, 1993; Peterson & Smith, 2000; Trice & Beyer, 1993). In essence, we agree with Rokeach’s (1979) argument that is just as meaningful to speak of personal values as it is to speak of societal and organizational values, provided that they are socially shared representations.

Variability within Cultures and Culture Strength Individuals within a culture are likely to have values that are more similar to one another’s values than to the values held by individuals across different cultures. Nevertheless, although individuals within a given culture are likely to share a common system of core values, significant and meaningful variations may exist at the individual level and should be considered when explaining cultural influences on individual behavior (Earley

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& Mosakowski, 2002). Several mechanisms can produce variability in values among individuals within a culture. Mental programs and meaning systems are imperfectly shared by members of a particular society, which can lead to idiosyncratic interpretations or the development of different subcultures (e.g., Hofstede, 1998; Trice & Beyer, 1993). Thus a person’s value system can be taken as an individual difference attribute in that it ‘‘makes each person a unique individual’’ (Locke, 1991, p. 297) and influences that person’s choices, actions, and responses to events in the environmental context. Individuals within the same context, while displaying some degree of consensus, are also likely to vary to some degree in their personal values. Further, the cross-generational transmission of culture is by no means perfect, such that, over time, individuals may acquire variations on the cultural meanings that were held by previous generations (Rohner, 1984). In addition, the degree of tightness or looseness of the culture has implications for the degree of variability in values displayed among individuals. In tight cultures, norms are expressed unambiguously, and sanctions are imposed when individuals deviate from these norms (Triandis, 1989). Such cultures are often rigid and strict in requiring that members of their in-group adhere to these cultural norms, with little tolerance for aberrant behavior. As a result, homogeneity in values is likely to result. In a homogenous culture, individuals tend to share similar values as well as beliefs about what constitutes appropriate normative behavior (Triandis, 1989). In contrast, loose cultures either have less clearly defined norms, or they have a higher tolerance for deviant behavior (Triandis, 1989). As a consequence, members of loose cultures tend to have more flexibility and greater choice with respect to their behavioral options in a given social situation, and idiosyncratic values are more likely to emerge in a loose culture than in a tight culture (Gelfand, Nishii, & Raver, in press). In summary, significant and meaningful variability within a culture with respect to values exists at the individual level. For this reason, it is important to differentiate between the values per se (i.e., content) and the degree to which consensus exists among individuals on these values. The content of the values refers to the degree to which the culture is defined as possessing some level of a value (e.g., the degree to which the culture is high, medium, or low on the value dimension of collectivism). The strength of the culture refers to the degree to which these values are shared or are held uniformly across individuals within the culture. The content of cultural values can be thought of, for simplicity’s sake, as the mean ‘‘score’’ or level of a particular cultural value, while culture strength can be viewed as the degree of variability or dispersion around this mean score. When culture strength is

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high, greater convergence or homogeneity is present among individuals in their values regardless of the content (e.g., collectivism) of the focal value.

Societal Values as an Overarching Context Societal culture can be viewed as an overarching context in which organizational values and personal value systems are embedded. As noted earlier, two forms of societal culture can be differentiated: (1) the cultural value orientation shared by its members (i.e., content of values) and (2) the tightness of the culture expressed as the degree to which order is imposed and deviation from cultural norms are not tolerated (Gelfand, Erez, & Aycan, in press). In our framework, we operationalize tightness as the degree of variability among individuals in their values. Based on the cultural values and their strength, each societal culture produces a unique set of influences on its individual members and the organizations to which they belong (Aycan et al., 1999; House & Javidan, 2004; Kopelman et al., 1990). At the individual level, shared values within a societal culture influence personal value systems at the individual level (Erez & Earley, 1993). For example, individuals socialized in a societal culture that places a high value on individual achievement are likely to have personal value systems that include a high value on individual achievement. Further, Markus and Kitayama’s (1991) work shows that cultural differences in individual values lead to differences in cognition, emotion, and motivation. This relationship suggests that individuals’ value systems influence how they cognitively process and interpret their organizational context and the HRM practices within that context (Erez & Earley, 1993). At the organizational level, while organizational culture is determined by macro-level contextual factors, such as the political, legal, industry, and business environment, as well as the organization’s goals, size, and age, the broad values of the societal culture in which the organization operates affect the development of more specific and narrowly defined organizational values (Aycan et al., 1999; Gelfand, Erez, & Aycan, in press). To illustrate, a societal value that strongly emphasizes collectivism should influence the range of the more specific organizational cultural values, such that these latter values are consistent with a higher-order societal value of collectivism. In a strong collectivist culture, for example, one organization may emphasize the organizational values of teamwork and cooperation, while another may emphasize consensus in decision making as a value. In both cases, these more specific organizational values are consistent with the higher-order

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societal value of collectivism. In contrast, in looser or weaker societal cultures, the appropriate organizational values are likely to be broader in range because greater variability in values exists among individuals in the society, and there is greater tolerance for differences. As such, in a weaker collectivist societal culture, appropriate organizational values might display some characteristics of collectivism, such as teamwork and commitment, but also value more individualistic performance-based rewards. In summary, societal culture represents an overarching contextual influence on variables and processes at both the individual level and the organizational level of analysis. Further, both the content and the strength of cultural values have implications for fit between HRM practices and values at different levels of analysis and the resulting responses of individuals.

A GENERAL FRAMEWORK We now turn to the development of a model for understanding the processes involved in shaping individual and collective attitudes and behaviors as a result of the application of HRM practices in a cross-cultural context. Starting on the left of Fig. 1, individual and organizational values are embedded within the context of their societal culture (Aycan & Kanungo, 2001). Individual value systems are shaped by the cultural context in which the person was raised and socialized (Bandura, 1986; Erez, 1994). Similarly, societal values influence organizational values, which in turn affect an organization’s selection and use of HRM practices (Aycan et al., 1999; Kopelman et al., 1990). These HRM practices reflect a certain cultural value orientation (Hofstede, 1980b) and signal to employees which behaviors and attitudes are valued and rewarded within the organization (Guzzo & Noonan, 1994). At the organizational level, HRM practices shape the organizational climate (i.e., shared perceptions of what the organization is like in terms of its practices, policies, and procedures), which in turn influences collective behaviors and attitudes (e.g., satisfaction, commitment, skill development, performance, turnover) and ultimately organizational effectiveness (Kopelman et al., 1990; Ostroff et al., 2003). Further, as indicated by ‘‘Fit Between Societal Cultural Values and HRM Practices’’ at the top of Fig. 1, congruence or fit between the societal cultural values and the values signaled by the HRM practices moderates the relationship between organizational climate and collective responses. Organizational climate will not lead to appropriate and positive collective responses among employees unless the

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Societal Values

Organizational Values

Organizational Climate

HRM Practices

Effectiveness

Collective Fit

Culture Strength

Fit Mechanisms

Psychological Climate (Including Appraisal of Values Conveyed)

Individual Values

Fig. 1.

Fit Between Individual Values and Values Conveyed

Responses: Attitudes Behaviors

Multi-Level Model of Values, Human Resources Management, and Fit.

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KERSTIN A. AUMANN AND CHERI OSTROFF

Emergent Processes

Collective Responses

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values conveyed by the HRM practices are consistent with the societal values in which employees are embedded. These organizational-level processes emerge from individual-level processes, as depicted by the arrows connecting the individual- and organizational-level variables in Fig. 1. At the individual level, HRM practices shape individuals’ psychological climate perceptions about what the organization is like and what is valued and expected in the organization, which in turn influence individuals’ attitudes, behaviors, and performance (Carr, Schmidt, Ford, & DeShon, 2003). Personal values play a role in determining which cues individuals attend to and how they cognitively process the signals conveyed by the HRM practices (Erez, 1994). However, it is important to note that HRM practices influence the relationship between climate and individuals’ responses because they affect P–O fit. Much research has indicated that a match between an individual’s characteristics and an organization’s characteristics results in more positive individual responses than a mismatch between individual and organizational attributes (e.g., Kristof, 1996). We extend this notion of fit to focus on fit between the values held by individuals (which are assessed in appraisals of psychological climate) and the values conveyed by the HRM practices (which are shaped by societal values) by including this type of fit as a moderator between individuals’ psychological climate perceptions and their behavioral and attitudinal responses (see Fig. 1). When the HRM practices signal values that are consistent with individuals’ personal values, individuals will react more favorably (Erez & Earley, 1993) and the relationship between climate and employee responses will be positive. Fig. 1 also indicates the multi-level nature of the constructs of climate, fit, and employee attitudes and responses. When psychological climate perceptions are widely shared across individuals in the organization, organizational climate emerges from the individual-level psychological climate to affect collective responses and outcomes at the organizational level (Ostroff et al., 2003). Similarly, to the extent that fit between an individual’s values and the values conveyed by the HRM practices occurs for most employees within the organization, collective fit emerges. We propose that the emergence of shared climate perceptions (organizational climate) and collective fit depends on the degree of societal culture strength (center of Fig. 1). When the societal culture is strong, individuals are more likely to have similar personal values and to share similar modes of cognitive processing, thereby making shared perceptions of the climate more likely to emerge. Further, when individuals’ values are more similar, as in a strong or tight culture, the degree to which individuals collectively fit the values portrayed by the HRM practices will be high. In contrast, when culture strength is low, the greater

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variability in individuals’ values will result in more variability in fit. Thus a weaker culture implies more idiosyncratic psychological climate perceptions, values, and fit. This lack of cohesion will subsequently result in wider variability in individuals’ responses within the organization, diminishing relationships between organizational climate, collective responses, and organizational effectiveness. As such, the degree of culture strength has implications for whether these processes operate primarily at the individual level of analysis or whether these processes at the individual level translate into analogous processes at the organizational level. As shown by the bidirectional arrows connecting individual- and organizational-level variables in Fig. 1, reciprocal relationships exist between levels for values, climate, and employee attitudes and responses. In developing the linkages in this model, we focus on the top-down effect of societal values on organizational and individual values. Of course, individuals’ values may change over time in response to different societal or environmental conditions (Rokeach, 1973) and hence may have an emergent effect in changing societal values. Further, while we focus on the emergence of organizational climate and collective fit and attitudes in explaining the linkages in our model, we note that individuals’ climate perceptions are subject to contextual and social influences and are influenced in part by the existing organizational climate (Kozlowksi & Klein, 2000). Similarly, individual attitudes, behaviors, and responses are influenced in part by collective attitudes and behaviors (Ostroff & Bowen, 2000). We also point out that we did not include a number of other contextual variables, nor did we include all possible linkages between constructs, based on theory and research, in developing Fig. 1. Instead, we sought to highlight the key relationships that are most critical for understanding relationships between values, HRM practices, and outcomes across levels of analysis. We now take a closer look at the mechanisms through which the processes proposed in the model occur. The next section focuses on the organizational level of analysis and provides a brief review of the general theoretical assumptions regarding the relationships between societal values, organizational culture, HRM practices, organizational climate, employee attitudes and behaviors, and effectiveness.

ORGANIZATIONAL-LEVEL RELATIONSHIPS The model (top left of Fig. 1) depicts societal cultural values as the overarching context in which organizations and individuals operate. All

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organizations operate within a societal culture, and different societal cultures produce unique sets of influences (Aycan et al., 1999; Kopelman et al., 1990). In particular, organizational values are embedded in societal values (Aycan et al., 1999), both of which influence an organization’s choice of HRM practices (Hofstede, 2001; House & Javidan, 2004; Kanungo & Jaeger, 1990). Organizations embedded in the same societal cultural context are likely to develop organizational cultures that share certain cultural attributes derived from that culture. Further, these organizations are likely to use similar HRM practices as a result of their shared cultural influences. Kopelman and his colleagues (1990) suggest that organizations within the same culture tend to be more similar than originally thought and that these organizations tend to adopt similar practices. They further suggest that societal differences in culture have a greater effect than organizational culture in determining the nature of a particular organization’s HRM practices. Societal values and normative beliefs drive similarities across organizations in how they manage their human resources. As a result, organizations that differ with respect to their organizational cultures (e.g., due to differences in their business environment, organizational goals, or size) are still likely to use somewhat similar HRM practices, particularly with respect to practices that are related to employees’ behaviors and motivations (Gelfand, Erez, & Aycan, in press). Thus societal culture may be a more powerful explanation for differences in HRM practices than organizational culture. In essence, these arguments suggest that while differences in HRM practices certainly exist within countries, there are likely to be larger differences in organizational culture and resultant HRM practices between countries than within countries. Organizations use HRM practices to influence the collective attitudes and behaviors of their workforce and ultimately to facilitate organizational effectiveness and performance (Bowen & Ostroff, 2004; Kopelman et al., 1990). However, as depicted in the organizational-level relationships in Fig. 1, the effect of HRM practices on collective attitudes and behaviors is not a direct one; rather, it is mediated by organizational climate (Ostroff et al., 2003). To explain these linkages, it is helpful to first clarify the distinction between organizational culture and organizational climate, as these are two overlapping, yet distinguishable, constructs. Both culture and climate focus essentially on how organizational members make sense of their organization (Schneider, 2000). Organizational climate represents the collective, shared perceptions of organizational members about what the organization is like in terms of its procedures, policies, and practices (Ostroff et al., 2003). In this sense, climate is a collective

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perception and interpretation of ‘‘what’’ happens in an organization. Organizational culture helps members of an organization explain ‘‘why’’ these things happen (Schneider, 2000). In other words, the underlying societal and organizational values and assumptions lead to the adoption and implementation of certain organizational structures, systems, and practices, which in turn shape the day-to-day experiences of the organization’s members. The perceptions and interpretations of their day-to-day experiences in the organization affect the collective attitudes and behaviors of organizational members. Thus organizational climate can be envisioned as a mediating variable between organizational values and HRM practices on the one hand, and collective attitudes and behaviors on the other.

Emergence from the Individual Level In Fig. 1, organizational climate is viewed as an emergent phenomenon that arises from the individual level of analysis (Kozlowksi & Klein, 2000). Individuals’ own perceptions and interpretations of their day-to-day experiences and interactions within the organization are conceptualized as psychological climate and are influenced by their personal values and social cognitive processes (James & Jones, 1974; Ostroff et al., 2003). When individuals within an organization share the same or a similar psychological climate (i.e., when they perceive and make sense of their experiences and the organization in a similar way), organizational climate emerges in a bottomup compositional process (Kozlowksi & Klein, 2000). A lack of shared psychological climate implies idiosyncratic perceptions and behaviors. When such idiosyncratic psychological perceptions within an organization dominate, they impede the facilitation of the consistent collective attitudes and behaviors required for effective organizational performance (Bowen & Ostroff, 2004). Shared climate perceptions can be facilitated by a number of emergent processes that influence whether organizational climate emerges from psychological climate (see Fig. 1). For example, according to the attraction– similarity–attrition (ASA) process (e.g., Schneider, 1987; Schneider & Reichers, 1983), individuals who are attracted to an organization tend to have personal values that are similar to those held by current organizational members. In addition, individuals with similar values are more likely to be selected by the organization, and thus to be and to remain in the organization. As a result of these processes, over time members in the organization come to perceive the organization similarly, which implies similar climate

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perceptions. In addition, overlapping schemas or mental maps can be facilitated through the socialization process (e.g., Ostroff et al., 2003), social exchanges, and transactions among employees (e.g., Morgeson & Hofmann, 1999), which in turn can produce similar interpretations of the environment. Further, shared perceptions may result through leadership processes, as leaders play a large role in creating, communicating, and engineering the climate in an organization (e.g., Naumann & Bennett, 2000; Rentsch, 1990).

The Moderating Role of Fit between Societal Values and HRM Practices An organization’s choice of HRM practices has important implications for organizational climate (i.e., the shared, collective psychological climate perceptions and interpretations of the organization’s attributes and practices), which in turn affects employees’ collective responses. The societal values of the organization’s cultural context influence the organizational values and the HRM practices the organization selects to facilitate accomplishment of its objectives and goals. The predominant view among scholars who examine relationships between organizational culture, HRM practices, employee responses, and effectiveness is that societal culture is primarily a contextual determinant of organizational values, structure, and practices (e.g., Ferris et al., 1999; Kopelman et al., 1990; Ostroff et al., 2003). However, this perspective ignores the notion that societal culture has implications for the values that employees hold. Hence, societal values may interact with other organizational features in explaining their effect on employees’ responses (Earley & Mosakowski, 2002). In recent years, a growing body of literature in the area of cross-cultural HRM has emphasized that it is the fit between societal culture and HRM practices that influences employee responses, not societal values or HRM practices working independently (e.g., Aycan et al., 1999; Earley, 1994; Newman & Nollen, 1996; Robert et al., 2000). That is, while non-cultural societal factors (e.g., legislative policies, industrialization, educational system), societal cultural factors and values, and macro-level contextual factors (e.g., industry, size, strategy, resource availability) are likely to affect the range and types of HRM practices considered suitable in a particular country (e.g., Gelfand, Erez, & Aycan, in press), the impact on employee attitudes and responses in organizations occurs through more proximal events such as HRM practices and the degree of fit between cultural values and these HRM practices (Robert et al., 2000). In essence, cultural differences in values may enhance or diminish the effects of HRM practices on employee attitudes and

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behaviors (e.g., Erez, 1994; Hofstede, 1991); further, the success of these practices may depend on the fit between the values inherent in the HRM practices and the culturally based values of those parties affected by these practices (e.g., Mendonca & Kanungo, 1996; Robert et al., 2000). These arguments imply that the degree to which HRM practices fit with societal cultural values moderates the relationship between organizational climate and collective employee attitudes (depicted by the ‘‘Fit Between Societal Cultural Values and HRM Practices’’ box in Fig. 1). Different HRM practices may reflect different types of cultural values (Hofstede, 1980b) and, when adopted by an organization, serve as a symbolic or signaling function to employees about what is valued, expected, and rewarded in the organizational climate (Bowen & Ostroff, 2004). Nevertheless, to promote desired and positive work attitudes and behaviors, HRM practices must be congruent with or fit the environmental context (Schuler & Jackson, 1987). To promote effective organizational functioning, the organizational culture, the actual HRM practices, and the resulting organizational climate should be in alignment (Ostroff et al., 2003). In other words, ‘‘alignment-based strength’’ is achieved when the HRM practices and organizational climate consistently reflect the values of the organization’s culture. Extending this line of reasoning, it follows that societal culture, as a contextual influence, should be aligned with HRM practices. The resulting climate should then have a more positive effect on collective attitudes and behaviors of employees, and ultimately on organizational effectiveness. This effect arises because alignment and fit on values allow for employees’ needs to be met or fulfilled, thereby producing more positive responses (Kristof, 1996). When alignment-based strength between culture and practices exists, the degree of fit between societal cultural values and HRM practices is high. Societal values will be consistent with employees’ shared interpretations of what the organization is like (organizational climate) and employee attitudes and behaviors will be influenced positively (which, in turn, should yield greater organizational effectiveness). To the degree that the signals conveyed by the HRM practices result in a climate that is inconsistent with the societal values, a mismatch will occur and will result in more negative collective employee attitudes and responses than will a better fit (Robert et al., 2000). Misalignment between societal culture and HRM practices is particularly likely in multinational corporations that operate divisions or offices in various countries. In such cases, the organization’s values (as determined by its home country culture), its customary HRM practices (which tend to be consistent with the organization’s

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values and the societal values of its home country), and the resulting organizational climate often do not fit well with the societal culture of the country in which a division of the organization operates (e.g., Schuler & Rogovsky, 1998). The relationship between climate and collective employee reactions will then be negative due to the mismatch with values. This process is depicted through the organizational-level relationships in Fig. 1. The relationship between HRM practices and collective employee attitudes and behaviors differs across societal and cultural contexts (Hofstede, 1980a,b). For example, individually based incentive pay plans have been popular and successful in U.S. companies, but have been less well received by non-U.S. workforces (Schuler & Rogovsky, 1998). Similarly, quality circles – a Japanese management technique credited with helping manufacturers in Japan achieve a competitive advantage over many of their foreign competitors – have been far less successful when implemented by companies based in the United States (Lawler, 1986). In addition, the few studies that have specifically addressed congruence between HRM practices and cultural values have tended to show that congruence between management practices and local cultural values is related to more positive work-unit financial performance. Newman and Nollen (1996) found that work units that were managed in a manner that was consistent with the value orientation of the national cultural context outperformed work units in which the fit between practices and cultures was poorer. Similarly, Morris and Pavett (1992) found participative management to be related positively to performance in the United States, where employee participation tends to be valued, but not in Mexico, where more authoritarian practices are consistent with societal values. In reality, organizations sometimes use HRM practices that are not consistent with the prevalent societal norms (Erez, 1994; Hofstede, 2001), which is likely to produce undesirable consequences. For example, when employers in collectivistic countries use HRM practices that run counter to the societal norm of treating one’s employees as in-group members, employees are likely to respond by no longer viewing and respecting the organization as their in-group. The result may be a decrease in loyalty to the organization. When organizations in some parts of India (a collectivistic society) violated cultural norms and treated their employees in a way that was not consistent with how in-group members should be treated, employees’ loyalty to labor unions as their in-group increased, contributing to escalating conflicts between labor unions and management (Hofstede, 2001). Taken together, the preceding arguments suggest that the degree of fit between the values of the societal culture in which the organization operates

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on the one hand, and the underlying values of the HRM practices and organizational climate on the other, should moderate the relationship between organizational climate and collective employee attitudes and behaviors. As yet, the underlying mechanisms that explain how and why these relationships operate at the organizational level have not been well developed. A number of assumptions are presumed in these higher-level relationships, which begin to suggest that these processes occur through people in the organization. A multi-level model depicting how these organizational-level relationships emerge from individual-level processes is needed to more fully explain the mechanisms through which fit between HRM practices and societal values operate on employee responses. One assumption contained in the organizational-level relationships depicted in Fig. 1 is that HRM practices include built-in value assumptions, and their adoption and use signal employees about what is valued in the organization (Hofstede, 2001; Kirkman & Shapiro, 1997; Robert et al., 2000). Employees interpret these signals in their formation of psychological climate perceptions, which, when shared, produce the organizational climate. Second, some type of fit must be achieved between individuals’ own values and those conveyed by the HRM practices (and interpreted as climate) to promote positive employee attitudes and responses. This requirement for congruence implies that it is not just fit at a macro level between HRM practices and societal values that must be considered, but also fit among individuals within the organizational context. Third, there is an assumption that fit between the values inherent in HRM practices and the culturally based values in the society is sufficient for explaining the relationship at the organizational level. That is, the aggregate content of the societal value needs to be considered and matched with HRM practices, not the strength of those values. Indeed, much of the crosscultural HRM theory and research assumes that the aggregated societal value applies to all individuals in that culture, and in many cases researchers simply use the country as a proxy for these values (e.g., Erez & Earley, 1987; Jaeger, 1996; Schuler & Rogovsky, 1998). Yet, as noted earlier, both the content of the values (aggregate societal value) and the degree of variability among individuals in the society on those values (culture strength) are important considerations for understanding the impact of HRM practices from a cross-cultural perspective. Thus, to understand the effect of macrolevel fit between HRM practices and societal values, it is necessary to show how individuals’ values, fit at the individual level, and cultural strength interact to produce emergent properties for explaining these organizationallevel relationships.

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The next section first discusses how HRM practices serve as a mechanism through which cultural values are signaled, and contribute to producing the ‘‘Fit’’ box at the organizational level as depicted in Fig. 1. A preliminary taxonomy is created for understanding the types of values that different HRM practices convey to employees. We then explain how value fit at the individual level operates and discuss meso-mechanisms that link the individual- and organizational-level relationships.

SENDING SIGNALS: HRM PRACTICES AND CULTURAL VALUE DIMENSIONS Signaling Values through HRM Practices HRM practices reflect the cultural values of the country in which they were created (Hofstede, 1980b) as well as the cultural values of the organization (Ostroff et al., 2003). To produce positive employee responses and to foster effective employee behaviors, HRM practices need to be culturally appropriate for the context in which they are applied (e.g., Adler, 1997; Hofstede, 1980b; Newman & Nollen, 1996; Robert et al., 2000). HRM practices that reinforce national cultural values are more likely to yield predictable behavior (Wright & Mischel, 1987) because congruent management practices are consistent with individuals’ behavioral expectations and normative beliefs (Erez, 1994). Conversely, employees are likely to become distracted from work performance when HRM practices run counter to their cultural values (Newman & Nollen, 1996). HRM practices serve as a signaling function in that they send messages to employees about what is expected, valued, supported, and rewarded in the organization (Rousseau & Greller, 1994; Schneider, 1990). If different theories about how to manage employees reflect different types of cultural values (Erez, 1994; Hofstede, 2001), then the HRM practices developed as applications of these theories also reflect different underlying cultural values. Employees use the messages sent by HRM practices to make sense of and define the psychological meaning of their work situation (Rousseau, 1995). As part of that process, the climate perceptions that employees develop as they make sense of the HRM practices will include the cultural values conveyed by the HRM practices. For example, an HRM practice of individually based rewards should send a signal to employees that individual achievement is valued. In turn, employees should interpret this signal and describe the organization as one in which individual contributions are

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emphasized. To date, little research has systematically addressed the types of values conveyed by various HRM practices.

HRM Practices and their Associated Values In the model depicted in Fig. 1, responses to HRM practices depend on the extent to which the values inherent in these practices fit with employees’ cultural values at both the macro- and the individual level. To portray the different cultural values that are reflected in various HRM practices and can be used to achieve a fit (see Table 1), we rely on Hofstede’s (1980a, 2001) dimensions of societal values. We chose Hofstede’s framework because data from numerous studies over the past decades have validated one or more of its cultural dimensions (Hofstede, 2001), and because it is one of the more popular classification schemes utilized in cross-cultural research studies. Hofstede (2001) identified five dimensions of work-related values along which national cultures differ: individualism–collectivism, power distance, masculinity–femininity, uncertainty avoidance, and long-term versus shortterm orientation. For each HRM practice in Table 1, we identify those cultural values that we believe are most salient or most likely to be reflected by that practice. In discussing these relationships, a few caveats should be noted. First, although the set of HRM practices included in Table 1 is consistent with much prior work on types of HRM practices (e.g., Butler, Ferris, & Napier, 1991; Nalbantian et al., 2004; Ostroff & Bowen, 2000), this list is not exhaustive. Second, cultural typologies can be complex and multifaceted. Hofstede’s typology is only one of several that could be have been used (alternatives include the GLOBE studies by House, Hanges, Javidan, Dorfman, & Gupta, 2004; Schwartz & Bilsky, 1990; Trompenaars & Hampden-Turner, 1998). Finally, it is beyond the scope of this chapter to delve into the details of how and why each practice reflects each cultural value. Rather, the goal here is simply to highlight the notion that different practices reflect different cultural values and to provide a general heuristic for deeper considerations of these linkages and the congruence between HRM and values (as proposed in Fig. 1) in future theory and research. Individualism–Collectivism The individualism–collectivism dimension describes the relationship between the individual and the group in a given cultural context. In cultures characterized by strong individualism, such as the United States, Australia, and most Western European countries, people tend to think of themselves

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Table 1. Cultural Value Dimensions Inherent in Human Resources Management Practices. Individualism

Power Distance

Human capital (selection, training) Formal, valid + ‘‘testing’’ procedures Training for skill development Team-building – training Socialization of new employees Collective (vs. – individual) Sequential (vs. random) Serial (vs. disjunctive) – Divestiture (vs. – investiture) Rewards and incentives Merit pay Unit-based pay Organization-based pay Skill-based pay Contingent or variable pay Promotion from within Job security Employee relations Survey-feedback Employee assistance Due process Promotion of diversity Work–family policies

Masculinity

Uncertainty Long-Term Avoidance Orientation

+

+

+

+

–

–

+ +

+ +

+

+ –

+ –

–

+ – –

+ +

+ +

– +

+

+

+ – +

Involvement and decision making Participation in + decision making Quality circles Consensus-based decision making

–

–

+

+

+

+

+

– – – –

+ + +

– –

– – –

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Table 1. (Continued ) Individualism

Work processes Teams Job enrichment Narrowly defined jobs

Power Distance

Uncertainty Long-Term Avoidance Orientation

– +

+

Managerial structure and direction Control/direction of employees Narrow span of control Supervisory appraisals Multisource feedback Management by objectives Participative goal setting

Masculinity

+ +

+

+

+

+

+

– –

– +

+

–

–

+

+ Indicates that the practice reflects the cultural value positively; – Indicates that the practice reflects the opposite end of the continuum for the cultural value.

primarily as individuals – that is, as independent entities separate from the group. In individualistic cultures, major themes include an emphasis on personal goals and freedom, individual achievement, and relatively little concern about emotional attachment to groups. Employees are expected to act rationally according to their own interest. In contrast, in collectivistic societies, such as Japan and China, personal goals are considered subordinate to group goals, and the well-being of the community is given priority over personal freedom and choice. Individuals define themselves primarily as members of collective entities, such as the family, community, or work organization. Whereas individualistic cultures are characterized by loose social networks in which each individual is supposed to take care of himself or herself and the immediate family, collectivistic cultures are characterized by tightly knit social frameworks in which people distinguish between in-groups and out-groups, expecting that their in-group (e.g., extended family, community, organization) will look after them in return for loyalty to the group. The extent to which societal values and social norms in a given cultural context favor individualism or collectivism influences how people relate to and expect to be treated by the organizations in which they work (Hofstede,

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2001). For example, employees who have internalized individualistic values expect to be hired, promoted, and rewarded based on their individual attributes and performance. Some HRM practices signal these individualistic values to a greater extent than others (see Table 1). Practices such as valid testing procedures that identify the skills and attributes of individuals, self-focused training, merit-based pay, and contingent reward systems that reward employees based on their individual contributions encourage individualistic values due to their strong focus on individual achievement (e.g., Earley, 1989, 1993; LeBlanc & Mulvey, 1998). Similarly, practices such as survey feedback, participation in decision making or empowerment, and job enrichment reflect individualistic values due to their emphasis on providing employees with opportunities to voice their individual opinions and to make decisions about their work situation (e.g., Robert et al., 2000). In collectivistic cultures, the hiring process focuses on the in-group, rather than on individual differences, and preference is often given to relatives, those with strong sociopolitical connections, or those with appropriate socioeconomic and family backgrounds (Gelfand, Bhawuk, Nishii, & Bechthold, 2004). Further, the manner in which employees are socialized is one signal an organization can send that reflects collectivistic values. For example, collective socialization tactics whereby a group of new employees goes through a similar set of experiences together, serial socialization tactics in which the newcomer is provided with significant help and guidance from experienced employees and mentors, and divestiture tactics in which the organization ignores or denies the value of the newcomer’s personal characteristics foster the belief that those group goals and group identity are paramount (e.g., Bauer & Taylor, 2001; Jones, 1986). Group-based training signals the importance of developing group capabilities (Earley, 1994). Likewise, unit-based pay (pay based on performance of the group as a whole) and organizational-based pay systems (e.g., profit sharing) reward individuals on the basis of collective achievement. For example, employees in Sweden, a relatively collectivistic culture, ranked the equality principle over the equity principle (rewards based on individual contribution) as the most desirable rule for allocating rewards (To¨rnblom, Jonsson, & Foa, 1985). A study comparing reward allocation norms in India and the United States revealed that employees in India generally preferred the need principle (employees are rewarded based on who needs the reward the most) over the merit or equity principle, possibly because need is a much more salient part of life in India (Berman & Singh, 1985) and individuals from a collectivistic culture such as India tend to be more sensitive toward taking care of members of their in-group.

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Power Distance Power distance reflects the degree to which status differences and inequalities in terms of power and resources are accepted in a given culture. In cultures that are high in power distance, hierarchies and differences in status and power are generally accepted as the norm; in contrast, cultures that are low in power distance tend to have more egalitarian views of how power should be distributed. Implications of power distance include the extent to which employees are comfortable with hierarchical structures and authoritarian supervision. In high-power-distance cultures, employees expect to be told what to do by their superiors and tend to be reluctant to share ideas or voice criticism openly. In low-power-distance cultures, people expect to be treated by their superiors as equals rather than as subordinates. Different HRM practices send messages to employees about the degree of power distance that is expected (see Table 1). For example, practices related to managerial structure reflect how organizations direct the work of employees, ranging from managerial direction with high control to individual discretion with low control (Nalbantian et al., 2004). High power distance can be signaled when managers are very directive of employees’ behaviors, when they set goals and standards for employee performance and monitor adherence to these standards, and when they evaluate employee performance regularly. In contrast, participation in decision-making signals a lesser degree of power distance because this practice reflects more egalitarian relationships and less emphasis on authority (e.g., Pavett & Morris, 1995; Robert et al., 2000). Similarly, a more egalitarian value is portrayed by practices such as management by objectives (MBO) (e.g., Drucker, 1954), multi-source feedback (e.g., Atwater & Yammarino, 1997), and participatively set goals (e.g., Locke & Latham, 1990). MBO aims to spread a pragmatic results orientation throughout the organization and reflects low power distance because subordinates need to be able to work independently, with sufficient autonomy and discretion to accomplish their objectives (Hofstede, 2001). Organizations that adopt multi-source feedback practices send a message of low power distance to employees because feedback from subordinates and peers is valued in assessing a manager’s performance (e.g., Atwater, Ostroff, Waldman, Robie, & Johnson, 2005). In terms of goal setting, Erez and Early (1987) found that in the United States, a culture characterized by medium power distance, there was no significant difference in the effect on performance for participatively set versus assigned group goals. In contrast, in Israel, which is relatively low in power distance, the effect on performance was significantly higher for participatively set group goals than for assigned

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group goals. These findings suggest that participative practices convey lower levels of power distance and are therefore more congruent with lowerpower-distance cultures. Masculinity–Femininity Masculinity–femininity represents the degree to which gender roles are clearly distinct within a societal culture. In masculine cultures such as in the United States, Latin America, and parts of Western Europe, men are expected to be assertive, aggressive, and focused on achievement of material success, while women are expected to be modest, tender, nurturing, and concerned with the quality of life and interpersonal relationships. Masculine values imply a high valence for status, achievement, and financial rewards based on equity norms. Further, masculine cultures tend to emphasize task and results over process. In feminine cultures, such as Scandinavia, gender roles tend to overlap; that is, both men and women are expected to be similarly concerned with assertiveness, achievement, relationships, and quality of life. Employees in more feminine cultures tend to have a higher valence for work life balance as well as a higher concern for process and relationships. Some controversy over the inherent meaning of the masculinity–femininity cultural value exists, which has led some scholars to reframe this value as two distinct values: gender egalitarianism and assertiveness (Emrich, Denmark, & Den Hartog, 2004; House & Javidan, 2004). Gender egalitarianism refers to the extent to which societies seek to minimize or maximize gender role differences (House, 1999). Assertiveness concerns the extent to which people in a given society are encouraged to be assertive, aggressive, and tough in their social relationships as opposed to tender, nurturing, and caring (Den Hartog, 2004). Masculine or assertiveness values are signaled through practices that emphasize goals such as personal achievement, status, and recognition (see Table 1). For example, rewards systems that focus on material rewards and providing status symbols (e.g., a larger office, a company car) based on individual achievement convey masculine or assertive values. Feminine values or low assertiveness, by contrast, are reflected by practices that focus on ‘‘social’’ goals, such as relationships, helping others, or the welfare of others. Thus practices that emphasize the promotion of positive employee relations, such as due process grievance procedures, team-building training, consensus-based decision making, employee assistance programs, work– family balance programs, and attention to diversity, indicate to employees that interpersonal relationships and the caring, nurturing, and well-being of employees are considered important (e.g., Hofstede, 2001; Jaeger, 1996).

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Furthermore, who gets promoted to higher-level positions sends a message to members of the organization about what is valued and rewarded. For example, an organization that promotes primarily men or that promotes women who act with a high level of assertiveness and aggressiveness sends the message that masculine attitudes and behaviors (e.g., assertiveness, competitiveness, focus on task achievement) are valued more highly in the organization (e.g., Kanter, 1977; Tharenou, 2001). Uncertainty Avoidance Uncertainty avoidance reflects the extent to which people feel threatened by ambiguity, uncertainty, and novel situations. In cultures that are high in uncertainty avoidance, such as in most Western European countries and Japan, there is a tendency to create formal rules and regulations to increase control and predictability. Thus employees in high-uncertainty-avoidance cultures expect a plethora of formal policies, structures, and standardized rules that aim to make organizational life more predictable. In lowuncertainty-avoidance cultures, employees are more likely to feel uncomfortable with and stifled by an abundance of formal rules and policies. Uncertainty avoidance can be signaled to employees through the socialization tactics adopted (see Table 1). For example, in sequential socialization, employees proceed through a formal sequence of discrete and identifiable steps leading to the target role. In contrast, random socialization and disjunctive socialization tactics send a message that uncertainty is expected: With random socialization, the steps are unknown, ambiguous, and continually changing, while with disjunctive socialization, the employee must learn by himself or herself how to handle a new role (Jones, 1986). In addition, a greater emphasis on formal education requirements, structured training programs, and qualifications signals high uncertainty avoidance. Less-formal training requirements and programs, as well as acceptance of a broader range of educational backgrounds, signals a higher tolerance for uncertainty. Practices such as contingent or variable pay increase both responsibility and risk on the part of the employees, thereby communicating to employees that their roles and rewards are less predictable and more uncertain. In contrast, practices such as formal testing procedures in selection, provision of job security, strong career paths with promotion from within, short-term feedback, supervisory monitoring through appraisals, narrow spans of control, clear reporting relationships, and narrow job definitions serve to indicate that tasks, jobs, and careers are orderly, predictable, and more certain (e.g., Hofstede, 2001; Horovitz, 1980; Nalbantian et al., 2004). Practices that signal confidence in the ability of ordinary employees to participate in

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leadership activities (e.g., participative management) are consistent with low uncertainty avoidance, whereas practices that signal a belief in experts are consistent with higher levels of uncertainty avoidance (Hofstede, 2001). Long-Term Versus Short-Term Orientation Originally termed ‘‘Confucian dynamism’’ (Hofstede & Bond, 1988), this fifth dimension was developed from findings on the Chinese Value Survey and was added to Hofstede’s typology to make it more relevant for East Asian societies. Long-term orientation refers to the fostering of virtues (such as thrift and perseverance) that are oriented toward future rewards. Short-term orientation emphasizes the past and present, in particular with respect to traditions, preservation of ‘‘face,’’ and social obligations. Most East Asian countries score high on the long-term end of the dimension, while most Western societies score on the lower on this dimension. The GLOBE study identified a similar value dimension termed ‘‘future orientation’’ (Ashkanasy, Gupta, Mayfield, & Trevor-Roberts, 2004) that differentiates cultures based on the extent to which future-oriented behaviors, such as planning and delayed gratification, are valued and rewarded. In organizations, long-term or future orientation is signaled by practices that foster long-term stability, security, and retention. For example, longterm employee development plans convey the message that the organization values a long-term orientation by planning for future staffing needs. Flexibility in practices also signals a long-term orientation because it implies that the organization is prepared to adapt to changing needs and environmental conditions in the future. Similarly, reward systems can signal a long-term orientation by rewarding employees for anticipating future needs, problems, or changes; alternatively, they can signal a short-term orientation by rewarding employees for meeting current demands and solving immediate problems. Pay-for-performance plans such as profit sharing or gain sharing encourage a future orientation and long-term planning, whereas incentive programs (such as commissions or piece-work) reward employees for present levels of performance. In addition, hiring practices can reflect a long-term or short-term orientation. For example, an organization that hires aggressively to meet immediate short-term staffing needs, only to let a significant number of employees go once these needs have been fulfilled, conveys a short-term orientation to its workforce. An organization that is more conservative in its hiring practices, considering long-term developments in staffing needs and emphasizing long-term job security of those hired, signals a long-term orientation through its hiring practices.

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Summary Clearly, different values are inherent in, and reflected by, different HRM practices. As such, different types of HRM practices will have different degrees of fit with societal cultural values and the values of the employees who will be subject to these practices. Both the framework presented in Fig. 1 and the values–practices taxonomy proposed in Table 1 illustrate this idea. Naturally, the cultural dimensions proposed by Hofstede by no means capture all of the variability and nuances of cultures that exist, and the cultural dimensions described in the preceding section should not be taken as absolutes. Consequently, it is important to understand that the dimensions discussed in this chapter merely provide a theoretical framework and a common language that enables researchers and practitioners to talk about the cultural values that are reflected in different practices so that fit between HRM practices and societal values can be achieved. We also emphasize that these five societal values are very broad based. Certainly, among societal cultures that share similar broad-based values (e.g., Japan and China are both high on collectivism), there are likely to be more refined and specific values within the broader cultural dimension. For example, Gelfand, Erez, and Aycan (in press) highlight that, in Japan, the emphasis on seniority followed by performance in defining promotion criteria reflects the collectivism value of honoring one’s commitment to the in-group or organization (Evans, 1993). In China, functional experience and excellent performance records are important for promotions (Easterby-Smith, Malina, & Lu, 1995) based on the notion of giving oneself whole-heartedly to the group. This distinction serves to illustrate that the broad-based cultural values are merely a starting place. Cultural nuances between seemingly similar cultures must always be taken into account when attempting to achieve fit between societal values and HRM practices. While these dimensions describe cultural differences at a national level, it is important to recognize that cultural differences do not always conform to national boundaries in the real world, nor do all members of a nation necessarily share the same levels of these value dimensions. In fact, the increasing mobility of people across national borders and the more frequent interactions among people from different cultural backgrounds suggests that cultures are becoming more mixed (Chao, 2000). This trend implies that it is important to consider cultural orientation at the individual level, as an organization’s workforce may, on average, differ quite a bit from their national cultural orientation. The signals about values that are communicated

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to employees by the HRM practices can be used to help employees achieve fit in values at an individual level. We now turn to the individual-level relationships depicted in the bottom portion of Fig. 1. In elaborating these relationships, we rely on two overarching theoretical perspectives: (1) cultural self-representation theory (Erez & Earley, 1993), which proposes that a person’s self-concept acts as the mediating link through which cultural value dimensions exert influence on an individual’s behavior, and (2) person–environment (P–E) fit, which proposes that a fit or alignment between individuals’ characteristics and those of the context result in more positive outcomes (Kristof, 1996).

INDIVIDUAL-LEVEL RELATIONSHIPS At the individual level of analysis, societal culture provides the context for the development of employees’ personal values, and these personal values in turn influence employees’ cognitive, affective, and motivational processes (Markus & Kitayama, 1991) as well as their interpretations of the work context (Erez & Earley, 1993). Further, individuals’ perceptions of HRM practices, policies, and procedures (psychological climate) will be partially interpreted through the lens of their own personal values (see Fig. 1). The process through which this perception occurs can be explained, in part, through cultural self-representation theory (Erez, 1994; Erez & Earley, 1993). Cultural self-representation theory suggests that HRM practices are evaluated by the ‘‘self’’ according to a set of criteria grounded in societal cultural norms and values. The self is essentially a composite view of oneself consisting of the mind and the individual’s basic values. The self’s attributes are shaped largely by the shared understanding within a particular culture (e.g., societal values). Based on these cultural influences, individuals develop cognitive schemas that they use to interpret organizational situations. While cultural self-representation theory does not explicitly include the construct of psychological climate, the interpretative process is representative of definitions of climate. Psychological climate is based on individuals’ perceptions of the organizational context and is purported to arise out of individuals’ personal values, cognitive appraisals, social constructions, and sense-making processes (James & James, 1989; James et al., 1988). Thus societal culture is a central factor influencing employees’ understanding of work, their approach to it, and the way they expect to be treated (e.g., Erez, 1994; Newman & Nollen, 1996). Cultural values are assumed to

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influence the differential interpretation and perceived meaning attributed to the HRM practices (i.e., psychological climate) by individuals from different cultures (Erez & Early, 1993). At the same time, when HRM practices are delivered in a salient manner (e.g., practices are visible, administered consistently, and communicated clearly), they send unambiguous messages to employees about what is expected and valued, reducing the extent of idiosyncrasies in definitions of the psychological climate within a given work context (Bowen & Ostroff, 2004). In this way, personal values and the cognitive processing modes they influence, as well as the nature of the administration’s HRM practices, influence psychological climate perceptions. In turn, psychological climate has been consistently related to employee attitudes and behaviors (e.g., satisfaction, commitment, turnover) and performance (Carr et al., 2003; Ostroff et al., 2003). Interestingly, while both cultural self-representation theory and psychological climate theories emphasize that interpretations of the work context are largely driven by personal values, cognitions, and valuations, the psychological mechanisms used to explain employee responses and behaviors in reaction to these interpretations of the work context differ somewhat between the two. In the climate literature, climate serves as a basis for interpretation and, therefore, as a guide to action (e.g., Pritchard & Karasick, 1973). Because psychological climate is based on employees’ perceptions of what the important goals in the organization are and what is supported, expected, and rewarded in this context, it serves as a motivational tool by providing a ‘‘sense of imperative’’ or directive for how to behave (Kopelman et al., 1990; Schneider & Rentsch, 1988). Further, individuals impute desirable or undesirable attributes to the work environment. When the evaluation of the work context is undesirable, the climate is viewed as detrimental to one’s well-being, thereby producing negative attitudes and affect (James, James, & Ashe, 1990). Cultural self-representation theory addresses similar ideas, but focuses to a greater extent on the evaluative process. The ‘‘self’’ evaluates the HRM practices based on cultural values, which serve as the criteria by which an individual determines to what extent a particular practice or experience contributes to the person’s well-being and need fulfillment. The meanings ascribed to HRM practices and their subsequent evaluation, then, vary by culture or society in line with differences in cultural values. In addition, employees’ attitudes and work behaviors are guided by the self in response to certain HRM practices. Thus this theory implies that no direct relationship exists between interpretations of the work context (psychological climate) and employee responses, but rather that the work context is evaluated

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in terms of how well it allows for personal fulfillment and well-being, a determination that depends on the degree to which the practices align with cultural values. This suggests that fit between an individual’s cultural values and the values conveyed by the HRM practices plays a moderating role, and that theories of P–O fit can be used to explain the dynamics of the relationships at the individual level.

Value Fit as a Moderator We propose that the degree to which employees’ psychological climate perceptions result in positive attitudes and behaviors will depend on the extent to which the HRM practices and their subsequent interpretations are consistent with individuals’ personal cultural values (see Fig. 1). P–O fit is a form of P–E fit, and it concerns the antecedents and consequences of the compatibility between characteristics of people and characteristics of the organizations in which they work (Kristof, 1996). P–E fit theorists suggest that both person and environment factors influence behavior but, more importantly, that the two factors should be congruent or aligned (Schneider, 1987). From a social psychology perspective, the person factor is an individual’s personality, values, and beliefs, while the environment factor is the psychological meaning of a given situation (Magnusson & Endler, 1977). Similar to cultural self-representation theory, the meaning that is ascribed to the situation (i.e., psychological climate) depends to a large extent on the individual’s self-construal (Hallowell, 1955), which in turn is based on internalized values and norms and influences how people perceive, evaluate, and respond to the world around them (Markus & Kitayama, 1991). This perspective implies that it is the psychological meaning employees give to the HRM practices they see and experience at work that determines how they will respond to these practices (James et al., 1990). If the psychological meaning is positive, employees are likely to respond positively (e.g., with greater satisfaction, higher commitment, lower turnover, and increased productivity). If the psychological meaning of the HRM practices is perceived as negative, employees will either respond neutrally (i.e., with little effect on their attitudes and behaviors) or negatively (e.g., lower satisfaction, commitment, and productivity, and increased turnover and absenteeism). Whether the psychological climate of a situation is positive depends on the extent to which it conforms to an individual’s cultural orientation. Thus, P–O fit is conceptualized here as a form of value congruence (O’Reilly et al., 1991) reflecting the degree to which the value orientations

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of the person and those of the environment (e.g., as signaled through the organization’s HRM practices) match or are similar to each other. A number of studies have indicated that employees whose values fit with or are consistent with those of the organization are more satisfied, committed, and adjusted, and exhibit higher levels of performance and lower turnover (e.g., Chatman, 1991; Judge & Cable, 1997; Kristof, 1996; O’Reilly et al., 1991; Verquer, Beehr, & Wagner, 2003). Similarly, in an examination of employee–organization relationships, employees’ performance, commitment, citizenship behaviors, and trust were higher when there was a better fit between employee expectations and HRM practices (Tsui, Pearce, Porter, & Tripoli, 1997). Theoretical explanations for why value congruence is related to more positive outcomes rest largely on notions related to need–supply theories. Rooted in need–press theory (Murray, 1938), which proposes that environmental ‘‘presses’’ facilitate or hinder the fulfillment of a person’s physical and psychological needs, the underlying premise of this type of fit is that preferences, desires, needs, or other personal characteristics are fulfilled by being in a context that is similar in nature to that personal characteristic, or that affords opportunities for personal needs, desires, and or preferences to be met (Dawis & Lofquist, 1984). This type of match is viewed as rewarding, satisfying, and instrumental in obtaining highly valued outcomes (Schneider, Smith, & Goldstein, 2000). In essence, the environment is seen as affording individuals the chance to gratify their needs (Schneider, KristofBrown, Goldstein, & Smith, 1997), allows people to utilize their skills and abilities to their fullest potential (e.g., Hollenbeck et al., 2002), or provides employees with a sense of self-esteem or self-worth due to their ability to operate effectively in an environment that is conducive to their own attributes (Brief & Aldag, 1981; James et al., 1990). Emphasis should be placed on satisfying employees’ physical and emotional needs, because only then will employees be willing give their services wholeheartedly to the organization and perform up to their potential (Agyris, 1964; Likert, 1961; McGregor, 1960). From a cross-cultural perspective, individuals’ needs and preferences are embedded in and influenced by their cultural orientation (Erez, 1994; Locke, 1991). Because internalized cultural values shape a person’s self-construal, cultural orientation also influences the self-regulatory processes – such as goal setting, self-monitoring, self-evaluation, and self-reaction – that help individuals attain their goals and meet their physical and psychological needs (Bandura, 1986). HRM practices that are congruent with an employee’s cultural orientation should facilitate need fulfillment, which in turn should produce more positive work attitudes and behaviors.

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Studies that have directly or indirectly assessed fit between cultural values and HRM practices have consistently demonstrated the importance of fit for employee responses. For example, Earley (1989) found that Chinese participants (members of a collectivistic societal culture) performed best when they were told their performance would be measured for groups of 10, and that their names would not be associated with the documents they handled; they performed worst when they were told they were operating individually and their names would be on the products of their work. For American participants, the data yielded the opposite pattern. Similar results were obtained in a follow-up study among Chinese, American, and Israeli participants (Earley, 1993). Participants performed best when the performance assessment fit the value orientation of their societal culture. In another study on training, self-focused training had a stronger effect on self-efficacy (i.e., a person’s estimate of his or her ability to do a task) than did group-based training among employees in individualistic cultures; for people from collectivistic cultures, group-based training had a stronger effect on self-efficacy than individual-based training (Earley, 1994). Societal culture is the context in which both organizational values and individual values and beliefs are embedded. This implies that societal culture plays a major role in shaping both organizational culture (Kopelman et al., 1990) and individual values and beliefs (Erez & Earley, 1993). As a result, organizational- and individual-level values are likely to display some degree of similarity or congruence with societal culture. Furthermore, HRM practices are value-laden. Different practices inherently contain and convey different values to employees. At the organizational level of analysis, fit between societal values and the values inherent in the HRM practices fosters effectiveness (depicted in the organizational-level relationships in Fig. 1). At the individual level of analysis, individuals judge the HRM practices in accordance with their cultural values to determine their contribution to the fulfillment of their needs (Erez, 1994). In this way, fit between the personal values of individuals and the values signaled through HRM practices fosters more positive employee responses and performance (as depicted in the individual-level relationships in Fig. 1). The relationships depicted in Fig. 1 and discussed in the chapter so far have addressed only the content of the cultural values. Significant and meaningful variation in values exists at both levels of analysis, and it is important to consider this variability in understanding the multi-level relationships proposed in the model. Culture strength or the degree of variability among individuals in the society on their cultural values is purported

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to be the meso-mechanism that links the individual- and organizational-level relationships (see center of Fig. 1).

LINKING THE LEVELS: THE ROLE OF CULTURE STRENGTH Collective Fit The process described in the preceding section suggests the importance of fit at the individual level of analysis, such that consistency between individuals’ personal values and the values individuals perceive to be signaled by the HRM practices influences individuals’ responses in organizations. Fit can also influence responses and effectiveness at higher levels of analysis (Ostroff & Schulte, in press; Schneider, Kristof-Brown, Goldstein, & Smith, 1997). When higher levels of analysis are considered, such as in an examination of the impact of P–O fit on organizational effectiveness, both the person (P) and organization (O) components of fit need to be considered at the organizational level of analysis. For example, some global or aggregate measure of P (e.g., values) can be compared to an organizational-level O attribute (e.g., climate) and then related to a group or organizational effectiveness index (cf., Ostroff, 1993b; Vancouver, Millsap, & Peters, 1994). Alternatively, some overall assessment across individuals of the degree to which individuals’ values fit the organization could be obtained and related to organizational-level outcomes. The underlying assumption is that people within the organization ultimately determine organizational processes; hence, the effectiveness of the organization may depend on the kind of people behaving in it (Schneider & Reichers, 1983). When people overall fit the organization, they should be more likely to understand the context and work toward achieving the organization’s goals, thereby enhancing organizational effectiveness (Ostroff, 1993b). Thus organizational climate (accumulated from psychological climate perceptions of the HRM practices) may not directly link to collective responses and effectiveness outcomes. Rather, employees’ values must also collectively fit the climate that is driven by the HRM practices to promote positive outcomes. As can be seen in Fig. 1, collective fit operates as a moderator between organizational climate and collective responses. Much of the rhetoric in the cross-cultural HRM literature – especially that focusing on organizations operating abroad – implies similar notions. HRM practices can lose their effectiveness when they are transported to cultural

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environments that differ from the culture in which they were conceived (e.g., Hofstede, 1980a; Schuler & Rogovsky, 1998). The underlying assumption here is that a mismatch between societal values and the values inherent in the HRM practices produces poor collective fit, undermining their effectiveness. In Fig. 1, the process through which this phenomenon occurs emerges from the individual-level processes. As individuals make their assessments of the HRM practices in forming psychological climate perceptions and evaluating the contribution of the practices to their need fulfillment, some degree of fit or misfit results (Erez & Earley, 1993; Erez, 1994). To the extent that individuals within the organization are homogenous on their cultural values and that these cultural values are consistent with the values conveyed through the HRM practices, collective fit will result, enhancing organizational-level responses. In contrast, when the personal values of employees are largely inconsistent with the values signaled by the HRM practices, collective misfit will result, creating more negative organizational-level responses. At least two underlying assumptions are present in the explanation of this collective fit process. First, the fit between HRM practices and societal cultural values at the macro level is analogous to the collective fit that emerges from individuals; hence the value fit at the societal level and the organizational level have the same effect on organizational-level processes. Second, for these two types of fit (societal-level and collective fit) to be analogous, homogeneity of values across individuals in the society must be present. Yet, the degree of homogeneity in cultural values varies across societies and is related to the degree of tightness or looseness in the society (Triandis, 1989; Gelfand, Nishii, & Raver, in press). In homogenous societies, values and norms are shared widely within groups, resulting in tight cultures (Gelfand, Nishii, & Raver, in press). In looser and more heterogeneous societies, subgroups exist, such that the values of different subgroups are partly similar and partly dissimilar (Erez, 1994; Triandis, 1989). We argue that societal culture strength – that is, the degree of homogeneity or variability in personal values across individuals within a society – determines whether the fit between the values signaled by HRM practices and individual values will translate into an analogous construct and process at the organizational level.

Culture Strength as a Moderator Much research has demonstrated significant mean differences between countries in their overarching dimensions of cultural values (e.g., Hofstede,

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1980a; Schwartz & Bilsky, 1990; Trompenaars & Hampden-Turner, 1998; House et al., 2004). As noted by Schwartz (1992), Hofstede’s research takes an approach whereby the national sample, rather than the individual, is the unit of analysis. Responses across individuals within each country are aggregated or averaged to represent the cultural values of the country. While this approach can be valuable in examining societal-level or organizationallevel relationships, it treats individual variation within a culture as if it were error variance. One argument suggests that aggregate (or mean) responses are more accurate representations of higher-level characteristics or constructs (Glick, 1985). Intraorganizational – or in this case, intracultural – variance, contributions to the error term, and individual variation are viewed as a source of inaccuracy. Here, individual deviations are equated with measurement error and individual deviations are not of substantive interest. Others have argued that individual variation around an aggregate or mean score often reflects real systematic variance or real individual differences that may result from substantive factors (e.g., Klein, Dansereau, & Hall, 1994; Ostroff, 1993a). In such cases, this variance should not be ignored, but rather can be important in explaining how variables and processes operate across levels of analysis. The degree of dispersion or variability in responses within a specific context can represent an important construct in its own right (Chan, 1998; Kozlowksi & Klein, 2000). In recent years, the degree of variability has often been termed ‘‘strength,’’ such that lower variability across employees in their values or perceptions reflects a ‘‘stronger’’ culture or climate (Lindell & Brandt, 2000; Ostroff et al., 2003; Schneider, Salvaggio, & Subirats, 2002). Thus a culture can be high or low on the value itself (e.g., individualism–collectivism) and high or low on the strength of that value (Gelfand, Nishii, & Raver, in press). In terms of cultural values, we propose that two sources of dispersion or individual variability can lead to differences in culture strength. The first is based on the societal cultural dimensions. In addition to mean differences between countries in cultural values (e.g., Hofstede, 1980a; House, 2004), countries differ in the extent of variability in cultural values among individuals in the society (cf. Earley, 1989; Robert et al., 2000). Tight cultures tend to be more homogeneous than weak cultures, because clear norms, expectations, and social pressures to conform dictate what is considered acceptable within the former cultures’ boundaries (Triandis, 1989), resulting in less individual-level variation in values (high culture strength). The less stringent norms and expectations in looser cultures make deviation more readily acceptable, resulting in tolerance for variability in cultural values (weak culture strength). Significant and meaningful individual-level variance

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with respect to cultural values and beliefs is more likely to occur in looser cultures. A second source of variation relates to the diversity of the organization’s workforce. Some organizations include employees from a wide variety of different cultural backgrounds, whereas others consist of employees with more similar cultural backgrounds. As a consequence, organizations will differ in the degree to which employees’ personal cultural values are similar to one another, thereby creating stronger or weaker cultural strength within the organization. We propose that the emergence of both organizational climate and collective fit are moderated by culture strength, which is itself based on the extent of variability in values within a society or variability within an organization due to diversity in cultural values among employees. Low culture strength implies greater variability in personal values, more idiosyncratic perceptions of the climate, and greater variability across individuals in the organization in their degree of fit between values and HRM practices. Shared meanings are the foundation of organization climate. Four emergent processes (see Fig. 1) have been proposed to explain how organizational climate emerges from individual perceptions of psychological climate (e.g., Ostroff et al., 2003):  Climate purportedly arises out of structural characteristics of the organization, such as the size, structure, practices, and degree to which HRM practices are delivered in such a way as to enhance clarity and interpretation (Bowen & Ostroff, 2004). These structural and practice variables establish a common reality that provides the basis for shared perceptions to emerge.  Social interaction and communication processes foster shared perceptions because group members construct the meaning of organizational events from repeated social interactions (Ashforth, 1985; Morgeson & Hofmann, 1999).  The attraction–selection–attrition (ASA) process (Schneider & Reichers, 1983) naturally facilitates homogeneity in organizations, and this homogeneity makes it more likely that individuals will perceive the organization more similarly.  Leaders can help foster shared perceptions of the climate by serving as interpretive filters of relevant organizational processes and practices and by exposing employees to a set of practices and procedures (Kozlowski & Doherty, 1989; Naumann & Bennett, 2000).

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In addition to these four processes that help foster the emergence of organizational climate, societal culture strength has a similar impact in linking the individual and organizational levels (see Fig. 1). More specifically, when culture strength is high, values of employees tend to be more similar. This similarity in values is likely to produce overlapping schemas for interpreting the environment, which in turn facilitates agreement on which aspects of the environment to attend to and how to interpret them appropriately (Weick, 1995). In contrast, when societal culture is weak, greater variation among individuals’ values is present, making idiosyncratic interpretations of the work context are more likely. Thus, when culture strength is weak, relationships will be stronger at the individual level than at the organizational level, because shared perceptions of the work context (organizational climate) will not emerge. The degree of culture strength also has implications for the development of collective fit (see Fig. 1). In strong cultures, the greater homogeneity in values implies that individuals will view the HRM practices similarly, will form similar climate perceptions, and will evaluate the degree to which the HRM practices are in accordance with the cultural norms and allow for fulfillment of needs similarly. Hence, individuals in strong cultures will be more similar in the degree to which they fit the HRM practices. This similarity in values and psychological climate perceptions will produce a situation whereby most individuals either fit or do not fit the values signaled by the HRM practices, producing a high level of collective fit (or misfit). In contrast, in a weaker culture, the variability in values may produce idiosyncratic interpretations in climate perceptions or idiosyncratic degrees of fit. Some individuals will have values that are consistent with those conveyed through the HRM practices, whereas others will not (Erez, 1994). Therefore, in a weak culture, collective fit across individuals is unlikely. As a result of their differing degrees of fit, employees will respond differently. Those with adequate fit will react positively, while those with poor fit may be negatively affected in terms of their attitudes and behaviors. Consequently, collective responses (attitudes and behaviors) will not emerge due to the variability in individuals’ responses. Taken together, this outcome implies that in weaker cultures, relationships among these variables operate more strongly at the individual level than the organizational level of analysis. Of course, even in a weaker culture, emergent processes such as leadership, strong administration of HRM processes, and interactions among employees (Ostroff et al., 2003) can result in the emergence of shared perceptions of climate (see Fig. 1). However, even in this case, relationships at

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the organizational level will not operate unless collective fit is also achieved. That is, even if similar climate perceptions do emerge, the variability in values will lead to idiosyncratic fit, not collective fit.

Within-Society Variability in HRM Practices The degree of culture strength is also related to within-society variability in HRM practices. In some countries, multiple types of practices may be appropriate and in use in organizations because different subgroups of employees with different values are present (Erez, 1994; Hofstede, 1980b). In a tight culture (i.e., high culture strength), we would expect less withincountry variability in HRM practices. Cultural tightness implies strong social norms and little tolerance for deviation (Triandis, 1989), attributes that are also likely to apply to the social norms governing the HRM practices of work organizations. Here, the organization’s choice of practices is constrained by the attributes of the broader societal context (Kopelman et al., 1990). As a result, organizations face a significant amount of social pressure to select and implement the HRM practices expected by their country’s social norms. In tight cultures, fit at the macro level can be achieved because values are strong and little variability in the range of appropriate HRM practices is evident. Decision makers primarily need to attend to societal values and choose HRM practices that are consistent with those values. This selection will lead to both individual and macro fit and strong relationships at both levels of analysis. However, when variability among individuals in their values is high and culture strength is weak (as would occur in a loose culture), the influence of societal culture on organizational values is less uniform and organizations have a broader range of choice in HRM practices that fit with the culture. That is, the variability in values among individuals produces a situation whereby different types of HRM practices may be equally appropriate depending on the subgroup of employees under consideration. In this case, the notion of equifinality (Delery & Doty, 1996) applies. In looser cultures with lower culture strength, multiple types of HRM systems might be equally appropriate and effective. Here, decision makers must attend to both the mean or aggregate value and the degree of variability in values in choosing appropriate HRM practices. In such cultures, it is possible to achieve macro-level fit between the overarching mean or aggregate societal value and HRM practices, yet still not achieve collective fit. For example, reliance on the aggregate societal value in determining the appropriate

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HRM practices suggests that only individuals who are close to this aggregate value in their personal values will fit the HRM practices; individuals who do not match this description will not fit. Likewise, because multiple types of HRM practices may be equally appropriate, some subgroups of individuals will fit a particular practice while others will not. The notion of culture strength and extent of individual-level variability in values has two types of implications for cross-cultural HRM. First, corporations must consider the set of HRM practices that would be most appropriate for the local culture, given its predominant value orientation. Second, the organization must consider the degree of tightness or looseness in the society, because it produces varying degrees of culture strength. In both tight and loose cultures, organizations must first ensure some degree of ‘‘macro’’ fit between their HRM practices and the societal values of the local workforce. However, when culture strength is low, achieving ‘‘fit’’ also depends to a significant extent on fit at the individual level. As a consequence, organizations must pay careful attention to mechanisms that can facilitate individual fit and that may ultimately help produce collective fit. The next section delineates some of the processes that facilitate fit and fall into the ‘‘fit mechanisms’’ box in Fig. 1.

Facilitating Fit through Organizational Processes and Practices Individual attributes or elements emerge in higher-level phenomena through various interactions and social exchanges (Kozlowski & Klein, 2000). Further, collective fit at higher levels of analysis (e.g., group or organization) can emerge from fit at the individual level (Ostroff & Schulte, in press). Organizations can use a number of mechanisms to facilitate fit among individuals with the goal of producing a greater degree of collective fit. The attraction–selection–attrition (ASA) process, discussed earlier, is purported to facilitate the emergence of shared perceptions of organizational climate because attraction–similarity processes lead individuals to be attracted to, and seek employment in, organizations that have attributes similar or congruent with their own individual attributes (e.g., needs, values, personality). In other words, individuals are naturally attracted to organizations that they perceive will result in a ‘‘good fit.’’ Similarly, organizations select or hire individuals who are perceived to be a good fit with the organization’s characteristics. The ASA framework also proposes that individuals who do not fit will leave the organization, either voluntarily or involuntarily. As a result, ASA is a naturally occurring process that

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increases similarity and homogeneity among organizational members, thereby increasing collective fit in organizations (Schneider & Reichers, 1983). Fit between cultural values and the values inherent in the HRM practices employed by the organization can be facilitated by making the values salient during recruitment so that individuals whose cultural values are consistent with the HRM practices will be attracted to the organization (Rynes & Cable, 2003). Likewise, specific attention to these particular values in selection procedures will also help in the achievement of value fit. In addition, socialization processes can increase fit (Fisher, 1986). Through socialization practices such as orientation programs, training, mentoring, and informal discussions, new employees learn the predominant values, attitudes, goals, social knowledge, and expected behaviors in the organization. As a result, new employees may change or adjust their values or attitudes in the direction of the organization’s values, or they may leave the organization if the organizational characteristics continue to be incompatible with their personal attributes (Chatman, 1991). Similarly, mentors can help newcomers understand the work context and acquire important information necessary to facilitate adaptation to the organization’s attributes (e.g., Ostroff & Kozlowski, 1993). These practices and interventions (ASA, targeted recruiting, socialization, mentoring) are aimed at the individual level to increase fit between an individual and the organization. When they are applied consistently across individuals, most employees will achieve fit with the organization, and hence these fit mechanisms can have an emergent effect by facilitating collective fit and ultimately influencing organizational-level relationships (as depicted in Fig. 1).

IMPLICATIONS AND FUTURE DIRECTIONS Is Fit Always Desirable? At the individual level of analysis, with an emphasis on individual responses and outcomes, P–O fit between an individual’s characteristics and those of the organization (in this case, personal cultural values and the values conveyed through HRM practices) usually results in more positive individual attitudes and consequences (Schneider et al., 1997; Verquer et al., 2003). However, at the organizational level of analysis, some controversy exists as to whether fit produces desirable outcomes.

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For example, Bowen, Ledford, and Nathan (1991) proposed that above and beyond individuals being capable of performing the job, organizational effectiveness can be enhanced when employees are selected whose values, goals, and personalities fit with the culture, values, and goals of the organization. Likewise, the assumption in cross-cultural HRM is that fit between the values inherent in HRM practices and the predominant societal values of those individuals who are subjected to those practices will lead to greater employee acceptance of the practices and ultimately to more positive organizational functioning (Hofstede, 1993). In contrast, Schneider and his colleagues (Schneider, Goldstein, & Smith, 1995; Schneider et al., 1997) argue that a strong emphasis on P–O fit may jeopardize the long-term viability of the organization because the homogeneity that results from fit can lead to stultification, lack of creativity, and inability to adapt to a changing environment. Hence, it is important to strike a balance between fit for individuals to yield individual satisfaction and positive individual responses, while at the same time ensuring the breadth of perspectives that is necessary for organizational success. Schneider et al. (1997) suggest that ‘‘good fit’’ must be considered in terms of ‘‘for whom’’ and ‘‘on what.’’ More specifically, in achieving an appropriate balance, fit for lower-level employees may be more important than fit for top managers. Top managers should possess a diverse set of values or attributes that can be used to serve the longer-term interests of organization (e.g., Richard, Barnett, Dwyer, & Chadwick, 2004). At the same time, employee fit on all factors or across all dimensions is not desirable because too much homogeneity among lower-level employees will emerge, which in turn will lessen their ability to adapt, change, and remain flexible in achieving the organization’s goals (Schneider et al., 1995). For example, a greater degree of fit on a limited number of climate dimensions – but not all dimensions – has been shown to be related to organizational effectiveness (Ostroff, 1993b). This finding suggests that an organization might focus on achieving individual-level fit for core values, but allow for more diversity in values and perspectives on other dimensions so that there will be enough variability in perspectives to ensure the organization’s longer-term effectiveness. Cultural values are fundamental core values, but are also broad based. As such, an appropriate balance might be struck by focusing on fit on the core cultural values, while allowing for variability on other dimensions (e.g., personality, goals, skills) and values (e.g., specific personal values). Future research that explores these fit notions from a cross-cultural and multi-level perspective over longer periods of time would be worthwhile.

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Is Fit Always Relevant? The concept of P–E fit, as well as its related theories and research, has been developed within the context of U.S. culture. Therefore, the theory is grounded in U.S. societal values. Just as we have come to question whether U.S.-based HRM practices are transferable to other cultural contexts, we need to question whether organizational behavior theories and concepts, such as P–O fit, are applicable in other cultural contexts. For example, in tight cultures, little variability in individual values is apparent: Everyone shares more or less the same personal value system, consistent with the country’s societal values. Similarly, in highly collectivistic cultures, less emphasis is placed on individual differences. As a result, everyone is expected to ‘‘fit in’’ by definition. Thus, in some cultures (perhaps those very high on collectivism), the notion of individual fit may simply be irrelevant because the concept of an individual ‘‘fitting’’ to an organization so as to be happy simply is not part of the system of work-related values in these countries. Likewise, some have questioned the relevance of HRM in societal contexts that are economically underdeveloped, that rely heavily on agriculture, or in which attention to people is not viewed as a means for enhancing organizational effectiveness (Gelfand, Erez, & Aycan, in press). In such cases, fit to HRM practices may be irrelevant. A related question is whether the ASA framework (Schneider & Reichers, 1983), as an explanation of how fit naturally occurs in organizations, is relevant in cultural contexts other than the United States, where the framework was created. The ASA model assumes the existence of variability in values and HRM practices across organizations in the same culture (Aycan et al., 1999), such that both individuals and organizations can make choices about who best fits in the context. In some cases, non-cultural factors, such as the legal system, degree of institutionalization, or level of industrialization, may severely constrain the choice of HRM practices in a society (Gelfand, Erez, & Aycan, in press). In such a scenario, the ASA process as a fit mechanism becomes irrelevant. In some countries, social norms, economic considerations, or political realities may limit individuals’ choices with respect to employment, thereby limiting the extent to which attraction–similarity processes affect individuals’ choice of employer. For example, in cultures where parental values and expectations are strongly conveyed and expected to be adhered to (Leung, 2002; Super & Harkness, 1997), young adults may be driven to enter certain careers or to join certain organizations based on these pressures and not on their own attraction to organizations that are the best ‘‘fit.’’ This outcome is

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particularly likely in tight cultures such as China and Korea, where hiring is often based on nepotism or family background (Gelfand, Erez, & Aycan, in press). Family and social pressures are also likely to influence mobility and movement across organizations in some cultures. For example, in highly collectivistic societies such as Hong Kong, the strong emphasis on family and social loyalties implies that individuals have few socially acceptable options of making changes in their careers (Leung, 2002) in pursuit of selfactualization or a ‘‘better fit.’’ In Hong Kong, a collectivistic and tight culture, individuals’ freedom to choose and make changes in their careers is much more limited than that of individuals in the United States (Leung, 2002), where an emphasis on self-actualization goals, freedom of choice, and mobility movement are the norm of the ‘‘new career.’’ In some countries, such as Germany, a strong institutionalized school-towork link provides young adults with occupation-specific standardized job skills (Buchmann, 2002). This practice is consistent with Germany’s strong value on uncertainty avoidance. As a result, the education system and employment are tightly coupled, such that schools prepare students to meet the well-defined standardized requirements of a specific job (in contrast to the education systems of most Anglo-Saxon countries, which focus on providing general skills and leave training of organization-specific skills and competencies to employers’ on-the-job training programs). The institutionalized work-to-school link in Germany means that young adults are trained for very specific jobs and employment in a narrow range of organizations (Buchmann, 2002), which reduces opportunities for both attraction and attrition based on the individual’s fit with the organization. In summary, while the ASA process may operate as a driving force to help achieve fit in cultures that emphasize individual choice and mobility and that afford individuals more autonomy in making decisions about their careers, it may not be as strong a force in tighter cultures or cultures that emphasize adherence to social and institutionalized traditions, norms, and expectations. Clearly, more research is needed to explore these nuances and to delineate how the contextual conditions in the culture, culture values, and their strength may differentially affect the degree to which fit between HRM and values is an appropriate avenue for organizations to pursue as part of their objectives.

Methodological and Measurement Issues A number of interrelated methodological issues are raised by our propositions. In particular, methodological concerns arise in dealing with multi-level

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data, aggregation issues, and moving from individual-level constructs to collective constructs. In addition, both between-culture and within-culture variability in values are of concern. First, the model proposed in this chapter is a multi-level one. Therefore, to adequately test the propositions developed here, data must be collected from multiple individuals across multiple situations and organizations, and across multiple cultural contexts. Second, relationships in our model operate at the individual level (e.g., relationships between individual values, psychological climate, fit between personal values and the values conveyed through HRM practices, and individual attitudes and behaviors), the organizational level (e.g., relationships between HRM, organizational climate, fit between HRM and societal values, and collective responses and effectiveness), and the cross-level of analysis (e.g., relationships between HRM practices, psychological climate, individual fit, and responses). Different issues arise depending on which (or all) of the levels are of interest in a given study. At the individual level of analysis, if the researcher seeks to examine the relationships specified in the model, the ‘‘level’’ of individuals’ responses on the variables is most useful, and the researcher can proceed with a traditional ‘‘individual differences’’ type of study. One caveat here is that multiple individuals within the same group, organization, or culture are likely to provide data. When individuals are nested within higher-level groupings, the nested structure of the data must be taken into account to produce unbiased results. Random coefficient modeling (e.g., Cohen, Cohen, West, & Aiken, 2003), cross-level operator techniques (e.g., James & Williams, 2000), or hierarchical linear modeling (HLM) procedures should be utilized with nested data (e.g., Hofmann, Griffin, & Gavin, 2000). Similarly, cross-level relationships – for example, the influence of HRM practices on psychological climate after accounting for individual values, or the effect of higherlevel moderators (i.e., emergent properties, culture strength, collective fit) on individual-level relationships – can also be appropriately tested with HLM procedures. When moving to higher levels of analysis, additional methodological issues emerge. Several of the constructs we specify (i.e., values, climate, collective responses, fit) are purported to be compositional in nature and to be emergent from the attitudes, cognitions, perceptions, or responses of individuals. That is, these phenomena reside within individuals and hence should be measured at the individual level of analysis; nevertheless, through interactions, communications, and interdependencies among individuals, a degree of ‘‘sharedness’’ can occur, producing an emergent property at the

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organizational level (Kozlowksi & Klein, 2000). In such cases, some degree of within-unit or within-organization agreement in these constructs must be demonstrated to justify aggregation to the higher level of analysis. Techniques and indices such as rwg (James, Demaree, & Wolf, 1984), intraclass correlations (e.g., Bliese, 2000), or within and between analysis (WABA) (Dansereau, Allutto, & Yammarino, 1984) can be useful in this respect. Once such justification has been provided, the mean score across individuals could be used to represent the ‘‘level’’ of the variables in an organizationallevel analysis. Researchers studying such phenomena at the organizational level should also be cognizant of the fact that response bias problems (when data are collected from the same individuals at the same time on the same instrument) tend to become exacerbated at aggregate levels of analysis. To minimize these effects, alternative procedures, such as a split-sample design or time delays, should be used (Ostroff, Kinicki, & Clark, 2002). Obtaining data from multiple sources can also prove useful. For example, while constructs such as organizational climate, collective fit, and collective responses should be assessed through individuals, the aggregate level of societal values could be assessed based on available research (e.g., Hofstede, 2001), HRM practices could be measured from the perspective of HR directors and key managers, fit indices between societal values and HRM practices could be created through procedures such as polynomial regressions, and objective indicators of organizational effectiveness such as financial performance or productivity could be incorporated. Finally, the extent of within-unit agreement is a phenomenon worthy of study in and of itself, focusing on the determinants and consequences of the extent of agreement (Kozlowski & Klein, 2000). In assessing values, it is important to examine both the level (e.g., the overall level of collectivism) and the variability in responses or variability in the degree to which the value is consistently held across individuals in the society. A key construct in the model presented in this chapter is culture strength, which we represent as being a within-societal measure (or within-organizational variability on cultural values). As argued earlier, based on the degree of tightness or looseness in the culture, there is likely to be within-societal agreement on values for some societies and organizations but not for others in the sample. As a consequence, measures of variability in responses or dispersion (Chan, 1998) become useful as constructs in and of themselves, and can be captured through indices such as a standard deviation (SD) or agreement-based measure (rwg). These indices can then be used as moderator variables in testing the notion that organizational-level relationships will be evident in

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the presence of high strength (e.g., low variability) whereas individual-level relationships will dominate with weak culture strength.

Relationships over Time Our model represents relationships at a single point in time. To this point in the chapter, culture has been represented as a societal- or macro-level construct that influences organizational values, the values and beliefs held by individuals within the cultural domain, and the HRM practices that are used by organizations within the contextual culture domain. However, these processes are dynamic over time. Individuals not only react to environmental contexts, but also create or enact them (Schneider, 1987; Weick, 1995). As noted by Schneider (1987), the values and personalities of individuals within the organization can serve to ‘‘make the place’’ or determine the culture and climate of the organization. Many multi-level relationships are bidirectional or reciprocal, whereby particular phenomena may appear to originate at the higher or lower level depending on the point in time at which the phenomena are studied (Dansereau, Yammarino, & Kohles, 1999; Kozlowski & Klein, 2000). For example, organizational-level processes such as culture and climate are more likely to emerge from the individual level during periods of dramatic change; in contrast, during periods of stability, when the culture is stabilized, there is likely to be a top–down effect from the higher level to the lower level (Kozlowski & Klein, 2000). To illustrate, an environmental-level ‘‘shock,’’ such as a war, may cause individuals in the society to reconceptualize some of their values. Here, individual-level cognitive processes and social constructions of the environment (Weick, 1995) would be predominant. Through event cycles, interactions, and exchanges among people, changes in cultural values may emerge over time (Morgeson & Hofmann, 1999), which may in turn produce emergent changes in organizational culture and climate, thereby requiring different HRM practices to achieve fit. Further, relationships among constructs may shift levels over time (Dansereau et al., 1999). For example, changing the HRM practices may initially cause discord and disagreement among individuals. A previously homogenous group with shared values, shared perceptions of climate, and collective fit to the HRM practices may lose their agreement and fit in the ensuing chaos, thereby making relationships operate only at the individual level rather than at the organizational level.

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To illustrate, some theory and research suggests that convergence across cultures in organizational values and HRM practices may be increasing (Boyacigiller, Kleinberg, Philips, & Sackman, 1996; Dorfman, 1996). In other words, organizations around the world are becoming more Western (McGuire, O’Donnell, Garavan, Saha, & Murphy, 2002; Ralston et al., 1997). At the same time, the personal value systems of individuals in the workforce are purported to remain largely unaffected by the influx of practices, ideas, and economic ideologies from other societal cultures (Hofstede, 2001; Ogbor & Williams, 2003). Recent evidence suggests that the cultural value dimensions identified by Hofstede (2001) are highly resistant to change, and that they change very slowly, if at all. Thus, while some convergence on HRM practices may be taking place, the more fundamental aspects of cultural values are likely to show a great deal of stability over time (Dorfman & House, 2004). This implies that a change to more Western HRM practices in a non-Western societal context may produce poor fit for many individuals in the organization. Whereas fit may have once been achieved at both the individual and organizational levels, the change in practices may lead to variability in the degree of fit among individuals, resulting in strong relationships at the individual level and not at the organizational level. This hypothesis implies that the relationships proposed in our model – particularly the notion of fit between values and practices on both the individual and collective levels – will be increasingly important to understand over time if convergence of HRM practices continues.

Culture as a Multidimensional Construct Culture is a complex and faceted construct that should be viewed as a multidimensional structure wherein differences in the strength of cultures on multiple dimensions are used in combination to explain differences in work behavior (Clark, 1987; Dorfman, 1996). The configuration of different value dimensions, combined with the interactions among these dimensions, makes up the unique character of a national culture and shapes its normative beliefs and behaviors. While this chapter has treated cultural values independently in portraying the values inherent in various HRM practices (Table 1), we acknowledge the importance of simultaneously considering all five (or more) cultural dimensions in determining which HRM practices are most likely to fit the cultural values. Future work might, for example, view cultural values and strength from a configural approach (e.g., Delery

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& Doty, 1996) to capture the patterns across values (e.g., high on collectivism, low on uncertainty avoidance, and high on power distance). A more comprehensive taxonomy of culture–practices–people fit that depicts different configurations of values coupled with HRM practices would be beneficial in anticipating employees’ collective responses and subsequent behaviors in two ways: (1) informing future research on cross-cultural management and culture–practices fit, and (2) informing practice, such as by providing assistance in selecting the culturally most appropriate and effective combination of management practices. Future work should also investigate the relative weight of certain cultural dimensions for the effectiveness of a particular set of HRM practices.

CONCLUSIONS HRM practices reflect certain cultural values associated with the culture in which they were developed and facilitate the creation of a specific organizational climate. Employees, at home and abroad, evaluate the HRM practices they experience at work based on their own systems of internalized values and norms with respect to their contributions to their well-being. In other words, employees have certain needs, preferences, and expectations, all of which are grounded in the cultural values and norms that are represented within their collective self-concept, regarding how they want to be treated at work and what the organizational climate should be like. If the values represented by an organization’s HRM practices and those of the employees are not congruent, outcomes will be less than desirable for both organizations and individuals. For a number of years, scholars in cross-cultural management have explicitly or implicitly espoused the importance of fit between societal values and HRM practices (e.g., Aycan et al., 1999; Earley & Erez, 1993; Hofstede, 1980b; Robert et al., 2000; Turban, Lau, Ngo, Chow, & Si, 2001). Much of this work has implied that the HRM practices adopted by organizations within a country are driven by the cultural values in that country (e.g., Kopelman et al., 1990). Problems arise when HRM practices from one country are transported to organizations in another country, as in the case of some multinational firms, thereby creating a mismatch between HRM practices and cultural values. At the macro level, the assumption is that HRM practices that are consistent with one culture, such as performance appraisals and rewards based on individual achievement in the United States, may not be consistent with another culture, such as Japan, where the

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group philosophy dominates (Erez, 1994). Within-country gaps between societal values and HRM practices are also likely. For example, in a survey of Japanese managers, assertiveness was ranked as the most important cultural value but was reflected very little in the HRM practices actually in use (Den Hartog, 2004). The acceptance of HRM practices by employees depends largely on the degree to which they are congruent with the overarching societal values of their home country (Hofstede, 2001). Explanations for why this relationship occurs have often been based on interpretation processes that occur at the individual level of analysis. One prevalent theory in this respect is cultural self-representation theory, which states that cultural values serve as criteria for interpreting the meaning of HRM practices (Erez & Earley, 1993). Practices that will allow for personal fulfillment of needs or that contribute to a person’s well-being are purported to be more acceptable to individuals and, therefore, are evaluated more positively. Presumably, an element of individual-level fit is incorporated into cultural self-representation theory, which we have made more explicit in our model. That is, cultural values serve as criteria for evaluating the practices, and it is fit between these cultural values and the values inherent in the HRM practices that allows for individuals to be fulfilled and feel a sense of well-being. Nevertheless, both the macro-level view of fit between HRM practices and societal values and the micro-level explanations of why consistency between HRM practices and societal values results in acceptance or positive responses from employees often neglect to explicitly consider individual variations within a society on cultural values. By considering both societal values and culture strength (the degree of variability within societies in values), we show that the processes for achieving fit and for understanding HRM from a crosscultural perspective may be more complex than originally thought. In tight cultures, where conformity is expected, strong norms exist, and deviations for cultural norms are not tolerated, variability among individuals in their cultural values will be small (i.e., high culture strength). In this case, individuals are likely to perceive and evaluate the HRM practices similarly (both psychological climate and organizational climate will be similar). Individuals’ reactions to these practices are likely to be similar (i.e., most employees will have values that fit the values inherent in the practices and will react positively, or they will find the practices to be inconsistent with their values and will react negatively). The fact that individual employees are fairly homogeneous with respect to their personal values and beliefs further implies that poor fit may prove more debilitating to organizational effectiveness in a tight societal culture, because negative attitudinal

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and behavioral responses to negative evaluations of psychological climate are shared across individuals, which then produces an emergent effect at the organizational level. Thus, from a practical standpoint, in very tight cultures, the same process likely operates at the individual and organizational levels of analysis, and the primary concern is achieving fit between societal values and HRM practices. In contrast, when cultures are more loosely defined and culture strength is lower (i.e., more variability among individuals within the society in their values), the situation becomes substantially more complex. It is not sufficient for an organization to achieve an overall fit between the aggregate societal values and HRM practices, because there is a greater degree of variability in values among individuals who are managed by these practices. In this case, relationships among psychological climate (interpretations of the HRM practices), fit, and employee responses are likely to operate strongly at the individual level. Consistent with cultural self-representation theory (Erez & Earley, 1993), individuals’ cultural values are used to interpret the meaning inherent in the HRM practices, and then are evaluated to determine whether the practices of the organization will allow for need fulfillment, personal well-being, and a sense of self-worth. With variability in cultural values among individuals, some will find the HRM practices to be consistent with their values (high fit) and will respond positively, whereas others will experience inconsistencies between their values and the HRM practices and will respond negatively. Thus weak culture strength makes it more difficult to achieve collective fit among employees, and relationships at the organizational level are likely to be attenuated. To mitigate this problem and to allow for organizational-level relationships to develop in weak cultures, organizations can focus on attracting, selecting, and retaining those individuals within the society whose cultural values are closer to the values conveyed through the HRM practices. Alternatively, they can rely on practices such as socialization, mentoring, and leadership to help foster fit across individuals. A related point is that organizations operating in situations with low culture strength can choose from a wider range of HRM practices that might be congruent with employees’ cultural values. That is, equifinality (Delerey & Doty, 1996) is likely to exist in that several different HRM systems may be equally effective and appropriate for the cultural values of societal members. Values are the ‘‘fundamental and enduring aspects’’ of people and organizations (Katz & Kahn, 1978). As a result, any attempt to create value congruence by manipulating or trying to change an individual’s cultural value orientation should prove difficult, although not impossible (James

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et al., 1990). Similarly, it would be difficult, if not impossible, to manipulate value congruence at the societal/macro level. Instead, a more promising point of intervention is at the organizational level – more specifically, targeting the HRM practices an organization uses to manage its workforce. Organizations should carefully consider the societal/macro-level cultural values of their workforce, as well as the underlying values a given set of HRM practices represents, before making an informed decision as to which practices would be most appropriate given the cultural value orientation of their employees (Hofstede, 2001). Equally important, organizations need to consider the degree of culture strength to determine whether equifinal sets of practices might be appropriate given the variability among society members in their cultural values. In addition, the organization needs to pay attention to climate, both at the individual (i.e., psychological) and the organizational levels, because climate represents the psychological meaning of the situation created by the organization’s HRM practices, which in turn determines individual and collective responses. In other words, organizations need to carefully monitor and evaluate the psychological and organizational climate emerging as a result of their HRM practices so that they can detect potential incongruencies and address any problems that may arise. The thinking outlined in this chapter implies that organizations must understand the values and norms, management philosophy, and practices of their own national culture as well as those of their overseas workforce when applicable. The proposed model of value congruent practices (see Table 1) aims to spur additional thinking and research in this area. More research is sorely needed to develop tools to assist organizations in assessing culture– practices–people fit and selecting the most appropriate and effective HRM practices for a specific cultural context. Work is also needed to gain a deeper understanding of the relevance and effects of incongruence in different cultural contexts. For example, other societal factors – such as the educational system, degree of institutionalization between school and work, level of industrial development, or legal system – have important implications for HRM practices in organizations (Gelfand, Erez, & Aycan, in press), and the effects of some of these factors may render issues of fit less salient or important. A related point is that cross-cultural research and theory often treat organizational phenomena as being unitary within national cultures, when there may actually be organizational variation within cultures due to institutional forces (e.g., industry, ownership) and organizational variables such as size, structure, and technology (Aycan et al., 1999). A future challenge for cross-cultural management researchers is to develop theories that consider additional dimensions of culture and their relevance for

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organizational phenomena, yet specify how such phenomena vary within cultures as a function of the contexts within which organizations are invariably embedded. The model introduced in this chapter focused on the individual and organizational levels of analysis. Of course, the issues of societal culture, cultural values, HRM practices, and their influences apply to the group level as well (e.g., Gelfand, Erez, & Aycan, in press). Subcultures and subclimates meaningfully exist in organizations (e.g., Martin, 1992). Within-unit social interactions, communications, interdependencies in tasks and work activities, and different leadership processes can lead to the formation of a culture or climate within a group that may differs from that espoused by other groups in the same organization (Hofstede, 1998). Likewise, different divisions or units in the organization may utilize different sets of HRM practices (e.g., Becker & Gerhart, 1996), particularly when they are housed in different locations. Thus, not only are the processes and issues addressed through our model applicable at the organizational level, but attention to these issues at the group or unit level is important as well. While multinational corporations and other types of international organizations certainly need to concern themselves with fit between culture, the organization, its practices, and its workforce, domestic organizations with demographically diverse employees need to consider cross-cultural fit as well. Having a highly diverse workforce implies a high degree of individual-level variability in values, which may lead to low cultural strength within the organization. While some research is beginning to address the implications of hiring diverse sets of employees (presumably with diverse values) for performance-related outcomes at the group or organizational level (e.g., Richard et al., 2004), much more attention in this area is needed. Similarly, additional research is needed that continues to address mechanisms that mitigate the potential negative consequences of dissimilarity or incongruence among employees in diverse or transnational teams (e.g., Earley & Mosakowski, 2002; Harrison, Price, Gavin, & Florey, 2002). In cases where culture strength is low or when achieving congruence in values across employees is difficult or unattainable, a greater understanding of the means and mechanisms that can be used to facilitate positive responses and produce individual- and higher-level effectiveness is critical. The question of selecting culturally sensitive HRM practices, the issues of fit between values and HRM practices, and the relationships between culture, the individual, and the organization in cross-cultural situations offer a myriad of opportunities for future study. The intensifying environmental pressures on organizations to adopt an increasingly global scope in their

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work and manage people across cultures create a strong sense of urgency for developing cross-cultural models of organizational behavior and methodologies for assessing and increasing fit between culture, practices, and people.

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AN ORGANIZATIONAL PERSPECTIVE ON MULTI-LEVEL CULTURAL INTEGRATION: HUMAN RESOURCE MANAGEMENT PRACTICES IN CROSS-CULTURAL CONTEXTS Xiao-Ping Chen and Anne S. Tsui ABSTRACT Aumann and Ostroff proposed a very comprehensive framework that attempts to identify the antecedents, mediators, moderators, and consequences of human resource management (HRM) in cross-cultural contexts. It is an ambitious framework that spans three levels of analysis (society, organization, and individual) with mechanisms of fit occurring at both the macro- and microlevels, focuses on both structure and process, and identifies cross-level interactions. The authors considered organizational and psychological climate as the key integration between culture and employee responses, and in this process inadvertently dismissed the function of organizational culture. We propose an organizational perspective on multi-level cultural integration and discuss its

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implication for cross-cultural HRM, highlighting the role of organizational culture as the major focus for integration with a host country’s societal culture and its local employees’ values. The analysis is enriched by considering the strength of both organizational and societal culture and the cultural distance between the home and host country of the multinational firm. We identify how our approach has both augmented and simplified Aumann and Ostroff’s framework to facilitate future research.

INTRODUCTION The notion of person–environment (P–E) fit, defined as the degree of congruence or match between a person and the environment, has long been prevalent in the psychological and management literatures (e.g., Holland, 1997; Kristof, 1996; Pervin, 1968; Schneider, 1987). Among the various forms of P–E fit, management researchers have most extensively studied person–organization (P–O) fit and person–job (P–J) fit. P–O fit refers to the compatibility between a person and the organization, emphasizing the extent to which a person and the organization share similar characteristics and/or meet each other’s needs (Kristof, 1996). P–J fit refers to the match between the abilities of a person and the demands of a job or the desires of a person and the attributes of a job (Edwards, 1991). Accumulated research findings suggest that a high level of P–O fit or P–J fit leads to a number of positive outcomes such as job satisfaction, organizational commitment, and high job performance (e.g., Edwards, 1991; Kristof, 1996). At the macrolevel, the idea of fit has also received much attention. The foundation of the wellaccepted contingency theory is fit. To achieve organizational effectiveness, the firm’s structure should fit the nature of the external environment (Lawrence & Lorsch, 1967) or the human resource (HR) system should fit the nature of the business strategy of the firm (Delery & Doty, 1996; Youndt, Snell, Dean, & Lepak, 1996). Along the theme of fit, Aumann and Ostroff proposed a very comprehensive framework that attempts to identify the antecedents, mediators, moderators, and consequences of human resource management (HRM) in cross-cultural contexts. In this commentary, we will adopt a middle-range theoretical approach that offers an extension and potential simplification of their work. We will first consider the ideas in their framework and highlight a major omission in their framework. To remedy this omission, we will then present an organizational perspective on multi-level cultural integration and discuss its implications for cross-cultural HRM.

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THE THEME OF FIT IN AUMANN AND OSTROFF’S CHAPTER Aumann and Ostroff integrated the literature on (a) HRM and firm performance, (b) HRM and comparative cultural values, and (c) P–O fit to develop a multi-level framework for understanding the linkages between culture, HRM, and organizational processes. Their paper provides a comprehensive framework for understanding the process by which multiple levels of fit occur in transporting HR practices across borders and to discern the importance of considering external and internal fit between different domains and levels. Their paper also offers a very extensive literature review in the areas of strategic HR, cross-cultural management, and organizational behavior, and expends great effort to bridge different streams of literature and develop an overarching framework. This multi-level model of values, HRM and fit as captured in their Fig. 1 has both breadth and depth, focuses on both structure and process, and identifies interactions among constructs at different levels. The key integrating mechanism between HR practices and employee responses is the concept of climate, which can be observed at both the organizational and the psychological levels. It is an ambitious framework that is intended to guide research at both the firm and the individual levels of analysis. It is an extremely informative paper that is rich in interesting ideas. Underlying the complexity of their model is the basic premise of fit at two levels. The effectiveness of an organization’s HR system will depend on its fit or alignment with societal values (macro-level fit) and its fit with individual values (micro-level fit). The macrofit at the organizational level ensures positive collective responses that are important for organizational effectiveness. The microfit at the employee level ensures positive attitudes and high performance of individual employees. Curiously, this focus on societal values and individual values leaves out a most important reality in organizations, the role of organizational culture–defined as a consensual set of values that guide organizational priorities, actions and decisions, and shape individuals’ cognitive processes and behavioral responses (Kluckhohn, 1951; Schein, 1992).

ORGANIZATIONAL CULTURE IN AUMANN AND OSTROFF’S CHAPTER As Aumann and Ostroff implied, an organizational culture and the set of values that it embodies play a role in the HR practices of a firm. Aumann

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and Ostroff devoted considerable discussion to the idea of organizational culture and how it differs from climate. The latter is treated as the primary mediator linking HRM practices to employee responses. The former received little attention after an introductory definition that distinguished it from the concept of climate. This is an interesting and unfortunate omission in Aumann and Ostroff ‘s framework. To remedy this omission, our modest goal in this commentary is to highlight the role of organizational culture in bridging societal culture and the firm’s HRM practices. By doing so, we hope to augment (and, ironically, also to simplify) Aumann and Ostroff’s framework. Before proceeding to a discussion on how and why organizational culture is important in a cross-cultural analysis of HRM, it is necessary to clarify a few points. First, when we discuss HR practices across cultures here, we consider only multinational or global companies operating in societies outside of their own national boundaries. Domestic companies of these societies are thus excluded from this analysis. Second, the HR practices of these companies have been developed over the years and should be consistent with their organizational culture (Ostroff, Kinicki, & Tamkins, 2003), which also should be consistent with the dominant cultural values of the (multinational) company’s home country culture (Hofstede, 1980). Thus, we assume that the organizational culture of the multinational subsidiary is many times at odds with the host country’s national culture to various degrees. Consequently and thirdly, when the companies operate in these foreign contexts, conflicts may occur between the subsidiary’s organizational culture, the parent company’s set of HR practices, and the local culture. Moreover, while the issue of ‘‘fit’’ arises at the point of entry, few if any companies achieve fit instantaneously. The process of alignment is ongoing for most multinational companies. We assume that the national culture is relatively stable and ingrained in the social fabric even though some changes have been observed in some contexts (Egri & Ralston, 2004) due to evolution in the institutional environment, economic growth, and increased interaction with other societies. However, most foreign firms seek to understand and adapt to the local culture rather than change it. Finally, we maintain that, in many societies, there is a great deal of individual difference in value orientation (e.g., Triandis, 1989; Trompenarrs & Hampden-Turner, 1998). These individual differences may be especially prevalent in developing economies where changes are occurring at an unprecedented pace. Therefore, a global/multinational company should distinguish the fundamental societal cultural values from values held by specific individuals in the developing economy context.

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Three levels of culture are the basis of this discussion: societal culture, organizational culture, and employees’ individual cultural value orientations. We adopt the same definition of each term as Aumann and Ostroff do. To strive for theoretical parsimony and to develop what scholars have advocated as a middle-range theory (Bourgeois, 1979), we propose an organizational perspective to discuss the notion of ‘‘fit,’’ or more accurately, the notion of ‘‘cultural integration.’’ HRM is an intrafirm phenomenon, and the most proximal influence of HRM is the firm’s internal culture–its organizational culture. Moreover, while both ‘‘fit’’ and ‘‘integration’’ denote changing one thing or another to make them congruent or consistent, ‘‘fit’’ has a more passive connotation while ‘‘integration’’ has a more active connotation. We therefore choose the word ‘‘integration’’ to discuss value conflicts across individual, organizational, and national cultures. This middle-range theory is also pragmatic as it offers a concrete starting point from which firms can take action. This action starts at the strategic level. In other words, the firm’s leaders must first determine whether and how much to integrate the subsidiary’s organizational culture with the host country’s societal culture. Once this decision is made, culturally relevant or culturally sensitive HRM practices will follow.

THE ORGANIZATIONAL PERSPECTIVE ON MULTI-LEVEL CULTURAL INTEGRATION We focus on two levels of integration: the integration of both organizational culture and societal culture and the integration of organizational culture and the values of individual employees. A graphical summary of this multi-level cultural integration model is presented in Fig. 1. We propose that the integration between organizational and local societal values will moderate the relationship between organizational culture and its manifested HRM practices. Moreover, the integration between employee values and organizational values will moderate the relationship between HRM practices and organizational outcomes at both the organization and the individual employee levels. We further posit that the cultural distance between the home and host countries as well as the strength of the organizational and societal cultures will play critical roles in the integration process. The underlying theory of this organizational perspective is the contingency approach (Donaldson, 2001), which maintains that firms should adopt systems that fit the special conditions of their environments or other

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Cultural distance between home and host country

Strength of organizational culture

Org. Culture Org. culture (Core values, (core values: e.g. equality, e.g., equality, diversity, or diversity) teamwork) The integration of organizational culture and societal culture

Fig. 1.

Strength of host country’s national culture

HRM Practices Recruiting Training Reward system

Outcomes Organizational Effectiveness Positive employee attitudes High employee performance

The integration of organizational culture and employee values

An Organizational Perspective of Multi-Level Cultural Integration.

contingencies. In our current context, this implies that firms should develop HRM practices that fit the firm’s cultural values and that the performance effect of such practices are realized through their application in appropriate cultural settings. The contingency approach has been successfully applied to the study of strategic HRM. Many authors have found that correspondence or fit between HR practices and the firm’s business strategy produces greater firm performance than when there is a lack of such fit (e.g., Arthur, 1994; Huselid, 1995; McDuffie, 1995; Youndt et al., 1996). Integration of Organizational Culture and Societal Culture Kanter and Dretler (1998) argued convincingly that integration is the key to a global strategy and a necessary component in the definition of a global company. They wrote: ‘‘Global connotes holistic, integrated activity. Global strategy involves thinking in an integrated way about all aspects of a business–its suppliers, production sites, markets, and competition y It means meeting world standards even before seeking world markets and being world class even in local markets. It means deepening the company’s understanding of local and cultural differences in order to become truly global’’ (p. 62). The integration between organizational culture and national culture will be more demanding with increasing degrees of cultural distance between the company’s home country and the host country (Shenkar, 2001). In the international management literature, cultural distance is an important concept to account for differences in choice of foreign investment locations (Davidson, 1980), entry mode (Boyacigiller, 1990; Pan, 1996), survival (Li &

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Guisinger, 1991), and longevity of the foreign venture (Barkema, Shenkar, Vermuelen, & Bell, 1996). An important mechanism for bridging the cultural distance between the organizational culture of the foreign company and that of the host culture is the HRM system. However, the HRM function usually requires guidance and direction from the top leadership of the parent company in its practices such as the determination of how expatriate employees should behave in the local culture and how local employees are to be managed in the subsidiary. Therefore, the first level of integration is not HRM with local culture but rather the organizational culture with the local culture. If the values embedded in the company’s organizational culture are not consistent with that of the local culture, leaders of the multinational firm must decide what to change in the company’s own cultural values and how much change is needed. A study by Weber, Shenkar, and Raveh (1996) showed the importance of national and corporate cultural fit in both international and domestic mergers. Some companies adopt a global strategy by maintaining the same cultural values throughout their foreign subsidiaries but achieve local responsiveness by adapting to local methods of managing their human resources. Using Gillette Singapore as an example, Kanter and Dretler illustrated the meaning of integration as reflecting ‘‘a global strategy, the ability to pull it off requires a local sensitivity and orientation’’ (p. 63). In the HRM arena, integration means that global companies show great respect for local culture and integrate the local way of recruiting, training, and rewarding employees, while at the same time maintaining the core values of the organization. It is a balancing act between standardization (in values) and localization (in practice). Integrating Organizational Culture and Societal Culture at Starbucks Starbucks’ HR practices provide an example to illustrate the link between standardization and localization–reflecting a process of moving toward integration rather than seeking fit. Starbucks emphasizes teamwork, equal participation, and diversity as the core values of the company, and these values are the product of the American culture (e.g., low power distance, Hofstede, 1980, 1991). There is a natural fit of the company values with the societal values in the United States and the company has been successful in terms of organizational performance and employee satisfaction, commitment, and job performance. However, when it opened stores in different parts of the world, e.g., South Korea, where the national culture values power distance, challenges arose. The company had to decide if it should change its organizational structure to be more hierarchical to ‘‘fit’’ South Korea’s national culture, or remain the same to maintain its core values.

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This is where integration differs from ‘‘fit.’’ Starbucks decided to keep the organizational structure the same but to use other means of integration that are more acceptable to Koreans in maintaining the Starbucks values. For example, in the U.S., employees (Starbucks call them ‘‘partners’’ to emphasize equality) call each other by their first names both among workers at the same level and across the hierarchy, but it is almost impossible for the Koreans to do this. It is cultural etiquette to use a person’s title and his/her last name. To solve this conflict, Starbucks came up with a solution– managers give every Korean employee an English name so that they can call each other by their English names and everyone feels comfortable (because the English name is a substitute for the title with the last name) and, at the same time, the organization’s cultural value of equality between employees is retained. Another example is that Korean men usually do not do housework. Washing dishes or cleaning toilets are simply not men’s jobs. But in the store where teamwork is emphasized, everyone needs to share in the work. To help the Korean male employees overcome the psychological barrier and to help with washing the dishes and restrooms, Starbucks adopted the role modeling approach that is effective in high power distance cultures. Lower level employees accept the directives, including imitating the behaviors, of top leaders. Starbucks had the International Director from the headquarters do all these activities and even hung a picture of him cleaning the toilet! The organizational perspective on cultural integration puts organizational values in focus, and, based on that, determines what HR practices (those indicating peripheral values of the organization) might be changed to fit the local culture, and what HR practices (those representing the core values of the organization) should stay the same but should be practiced in ways that are acceptable to local employees and with sensitivity to the local culture. We offer another example in the area of recruiting. When Starbucks recruits in the U.S., no personal photos are allowed on application forms as required by U.S. nondiscrimination laws, but in many other countries such as Chile, photos are required. To be responsive to local customs, Starbucks allows job seekers in Chile to send in their photos. However, the management trainers from headquarters explain to the local managers that using appearance in making recruiting decisions (e.g., evaluating applicants with ‘‘blond hair, blue eyes’’ as more attractive and those with ‘‘dark skin, dark hair’’ as less attractive candidates) violates the company’s core value of diversity. Another HR practice Starbucks adopts in the U.S. is to give every partner stock options; it is called the ‘‘bean stock.’’ However, it is not feasible to do this in many other countries due to restrictions in government policies. To

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show the caring value of Starbucks, the company developed a menu of benefits that varies from country to country and allows employees to choose what they value the most in different cultural contexts.

Factors Influencing Integration of Organizational and Societal Cultures The above examples illustrate how a global company decides not to change its organizational culture to fit the societal culture of a host country. Instead, it achieves integration through localization of HR practices. This integration maintains the core values of the organization but uses local methods to ensure adherence to the core organizational values. This integration means appropriate adaptation of HR practices to the local norms, values, customs, and laws. Most importantly, such an adaptation is guided by the values of the organization. Without the guidance of organizational values, HRM may adopt local practices that reflect values in contradiction to the core organizational values. This integration also suggests that HRM practices in a cross-cultural context perform a very important substantive role in addition to the symbolic role described by Aumann and Ostroff. Two major factors influence the level of integration between organizational culture and societal culture. They are the strength of the firm’s organizational culture and the cultural distance between the firm’s home country and the host country. We expect that firms with a strong organizational culture are less likely to change their company’s core values to fit the values of the local culture. Further, the larger the cultural distance between the home and host countries, the more challenging will be the integration process. In both cases, HRM plays a critical role in this integration. Taken together, we propose three propositions to summarize the role of HRM in the integration of organizational culture and societal culture. Proposition 1. Organizational culture determines HRM practices, such that its recruiting system, training and development system, and reward system reflect the core values of the organization. Proposition 2. The level of integration between organizational culture and societal culture moderates the relationship between organizational culture and the actual HRM practices, such that the stronger the integration, the more likely that the HRM practices will reflect both the company culture and the local societal culture.

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Proposition 3. The stronger the multinational firm’s organizational culture and the larger the cultural distance between the firm’s parent country and the host country, the greater the local adaptation required of the firm’s human resource management system. Integration of Organizational Culture and Individual Employee Values To obtain positive job attitudes, performance, and commitment from employees, previous literature suggests the importance of finding a ‘‘fit’’ between the employees’ characteristics and organizational values in recruiting (e.g., Chatman, 1991; Edwards, 1991; Kristof, 1996). Again, we will elaborate on the idea of integration rather than just on fit. Here integration is achieved mainly through two HR practices: (a) recruiting and selection and (b) training and development. And again, organizational culture is our focal point. In other words, HRM is the mechanism to achieve the integration between organizational culture and local employee values. A Typology of Culture Strength When considering the integration of organizational culture and employee values, we need to first discuss the idea of ‘‘strength’’ in culture, a relevant idea for both organizational and societal culture. As discussed by Aunmann and Ostroff, cultures can be distinguished along the strong–weak and loose– tight dimensions (Triandis, 1989). However, the authors use these terms interchangeably sometimes, but deliberately distinguish them at other times. To facilitate an analysis of integration at the individual level, we propose a more refined definition of a strong culture. To make the distinction clear, we must distinguish between the two different aspects of ‘‘cultural strength,’’ the overall level (on the average, how strongly people feel about the values) and the breadth of the views (variance in the feelings about the values, or loose–tight in Triandis’ terms). We define a ‘‘strong culture’’ as having both a high overall level of the values and a narrow breadth (low variance) on these values. A ‘‘weak culture’’ has both an overall low level of the cultural values and a narrow breadth (low variance) (Weiner, 1988). In the former, there is consensus within a specific cultural context (an organization or a society) about the important values held strongly by all the members of that culture. In the latter, there is consensus that there are no strongly held values among all the members in that culture. In organizations with weak cultures, behaviors are guided by bureaucratic rules and procedures rather than by values.

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There are two additional types of culture when differentiating level from variance in the assessment of the prevalent cultural values. A high overall level with a wide breadth of views fits the ‘‘differentiated culture’’ definition, according to Martin’s model (1992). In this context, there are multiple subgroups that share strong values within each subgroup, but there is a variety of different values held between the subgroups. This may occur in different divisions or functions in a large corporation, or between different ethnic groups within a heterogeneous society as well as between generations in a changing society. Lastly, a low overall level along with a wide breadth of views on the cultural values describes a fourth type of culture, similar to the ‘‘fragmented culture’’ in Martin’s model. In such a culture, the people who hold some values more strongly than other values or which groups hold what values cannot be easily isolated or identified. This typology of culture strength is presented in Table 1. We use this typology to analyze the role of HRM in the integration of organizational culture and individual values in the context of differences in the strength of the culture. As pointed out by Aumann and Ostroff, ‘‘although individuals within a given culture are likely to share a common system of core values, significant and meaningful variation may exist at the individual level y further, the cross-generational transmission of culture is by no means perfect and over time, individuals may acquire variations on the cultural meanings that were held by previous generations.’’ We assume that organizations should be able to recruit and attract people who hold values that are consistent with theirs’ in any cultural setting. We see this ‘‘finding who fits’’ action as part of the integration process between organizational and individual values. Using the typology of culture summarized in Table 1, we can predict that organizations will be more able to gain access to individuals who have values similar to the organization in contexts with a ‘‘fragmented culture’’ or

Table 1.

A Typology of Culture Strength. Average score (mean) of cultural values High

Variance in Scores (SD) of cultural values

Low

High

Low

Differentiated culture

Fragmented culture

Strong culture

Weak culture

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‘‘differentiated culture’’ than in contexts that have either strong or weak cultures when they operate in a context with a large cultural distance. Integrating Organizational Culture and Employee Values at IKEA and Starbucks We use two examples to explain and illustrate how multinational companies may integrate organizational culture and local employee values. IKEA, a Swedish company, values equality even more so than the average U.S. company, which reflects the low power distance value of the Swedish culture, and to minimize symbols of status differences they do not give job titles to employees. Moreover, it does not give employees clear job descriptions, which reflects the low uncertainty avoidance (Hofstede, 1980, 1991) tendency of the Swedish culture. When IKEA opened its first store in Seattle, many recruits quit after a few months because there was a large gap between IKEA’s values and the typical cultural orientations and expectations of U.S. employees. The high turnover rate caused the company to rethink its recruiting strategy. Instead of recruiting the best qualified people in the market, the recruiters deliberately emphasized IKEA’s values and provided realistic job previews and let those who did not identify with the IKEA values self-select out of the company. Due to the large variability in individual values within the U.S., IKEA was able to recruit enough people who could identify with its values. Today, not only is the employee turnover rate low, many employees become real advocates of IKEA’s values and even recommend that their friends and relatives join the company. Consistent with this observation, in a research study of six large accounting firms, Chatman (1991) showed that employee commitment and retention a year later could be directly predicted from the degree of value congruence between the recruits and the company. In addition to recruiting individuals who share company’s organizational values (e.g., in countries with a large cultural distance), the organization can also use training and development to integrate organizational and employee values by socializing the local employees to the company’s values. Researchers have suggested that individual value systems are shaped by the cultural context in which the person was raised and socialized (Bandura, 1986; Erez, 1994). Socialization refers to the process that adapts employees to the organization’s culture (e.g., Cable & Parsons, 2001; Chao, O’Leary-Kelly, Wolf, Klein, & Gardner, 1994). Most companies provide training to new employees to indoctrinate them into their ‘‘way,’’ some more extensively and deliberately than others. For example, all new employees at Starbucks receive at least 24 hours of training within the first three months

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of their employment (Filipczak, 1995). Classes are offered on every necessary topic to turn new employees into brewing consultants. They learn the Starbucks philosophy, the company jargon, and even how to help customers make decisions about beans, grind, and espresso machines. The result is employees who understand and identify with Starbucks’ culture. They project an enthusiastic attitude and engage in knowledgeable interactions with customers. Starbucks brings new core members of an overseas store to the headquarters (Seattle) for three months of intensive training. During the three months, these overseas employees learn Starbucks’ coffee theory, techniques of brewing coffee, and skills of team building. They observe and experience the local (Seattle) coffee culture and are provided opportunities to run a local Seattle store before managing the new store in their home country. Starbucks attributes their consistent and outstanding services across the world to this strong training and socialization program.

Factors Influencing Integration of Organizational Culture and Employee Values Based on the typology of culture, we predict that this integration process will be smoother in a ‘‘weak culture’’ or ‘‘fragmented culture’’ than in a ‘‘strong culture’’ or a ‘‘differentiated culture.’’ This is because individuals in weak or fragmented cultures tend to be more flexible since there are no strong societal values restricting their behaviors, whereas individuals in strong or differentiated cultures tend to be more resistant to change since they are programmed to behave consistently with the society’s or their social group’s deeply engrained cultural values. In sum, the integration process of identifying individuals who fit and socializing new recruits will facilitate the extent to which employees accept or identify with the organization’s values. Based on this P–O fit paradigm (e.g., Edwards, 1991; Kristof, 1996), we offer the following propositions to summarize the role of HRM in the integration of organizational culture and employee values. Proposition 4. Global firms rely on human resource management to integrate local employee values and organizational culture and this integration is more difficult when the host country has a strong national culture and there is a large cultural distance between the home and the host countries.

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Proposition 5. The integration of organizational culture and employee values (i.e., the extent to which employees identify or accept organizational values) strengthens the relationship between HR practices and organizational outcomes such as positive job attitudes and high employee performance, resulting in overall organizational effectiveness.

RETURNING TO AUMANN AND OSTROFF’S FRAMEWORK We intend to offer a parsimonious analysis of HRM in cross-cultural contexts by focusing on organizational culture as the major driver of HRM practices. This approach treats societal culture and local employee value orientation as exogenous factors that the organization must consider and integrate to achieve organizational effectiveness. Our framework is similar to Aumann and Ostroff’s framework in the sense that it takes the contingency approach and integrates multi-level, multi-discipline issues into a coherent framework. By comparing the two frameworks (Fig. 1 in their chapter and Fig. 1 in our commentary), we explain how we have both augmented and simplified their framework. First, we remove organizational values from the vertical bar on the left side of their framework and put it as the first box preceding HRM practices. Second, our framework does not include the concepts of organizational climate and psychological climate, nor the associated emerging processes. We do not see these as essential for understanding the influence of HR practices on employee responses. Third, we replace fit between individual values and values conveyed through HRM with integration of organizational culture and individual values. This integration is more consistent with the focus on organizational culture and also with past research on P–O fit. Lastly, our framework does not include the idea of collective fit because such a fit can be directly inferred from a collective response to the HRM practices of the firm. In summary, Aumann and Ostroff have offered a comprehensive and elaborate framework that unveils highly complex processes that are grounded in many theoretical perspectives. Their framework advances our understanding of a multifaceted issue. Our simple model is intended to augment their highly complex framework with an eye toward simplifying it as well. By using an organizational perspective, we seek parsimony and focus on ideas that may be useful to practicing managers. The propositions derived from this model involve easily testable relationships and familiar

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concepts that can be measured. The various research methods and statistical analyses proposed by Aumann and Ostroff will be applicable to examine the validity of our simplified framework. To conclude, we are in complete agreement with Aumann and Ostroff that ‘‘the question of selecting culturally sensitive HRM practices, the issues of fit (we prefer integration) between values and HRM practices, and the relationships between culture, the individual and the organization in cross-cultural situations offer a myriad of opportunities for future study.’’ We hope that our commentary has contributed to the discussion of this topic and will generate interest from both academics and practitioners to advance this area of research.

REFERENCES Arthur, J. B. (1994). Effects of human resource systems on manufacturing performance and turnover. Academy of Management Journal, 37, 670–687. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. Barkema, H., Shenkar, O., Vermuelen, F., & Bell, J. H. (1996). Working abroad, working with others: How firms learn to operate international joint ventures. Academy of Management Journal, 40(2), 426–442. Boyacigiller, N. (1990). The role of expatriates in the management of interdependence. Journal of International Business Studies, 21(3), 357–381. Cable, D. M., & Parsons, C. K. (2001). Socialization tactics and person-organization fit. Personnel Psychology, 54, 1–23. Chao, G. T., O’Leary-Kelly, A. M., Wolf, S., Klein, H. J., & Gardner, P. D. (1994). Organizational socialization: Its content and consequences. Journal of Applied Psychology, 79, 730–743. Chatman, J. A. (1991). Matching people and organizations: Selection and socialization in public accounting firms. Administrative Science Quarterly, 36, 459–484. Davidson, W. H. (1980). The location of foreign direct investment activities: Country characteristics and experience effects. Journal of International Business Studies, 11(2), 9–22. Delery, J. E., & Doty, D. H. (1996). Modes of theorizing in strategic human resource management: Tests of universalistic, contingency, and configural predictions. Academy of Management Journal, 39, 802–835. Donaldson, L. (2001). The contingency theory of organizations. Thousand Oaks, CA: Sage. Edwards, J. R. (1991). Person-job fit: A conceptual integration, literature review, and methodological critique. In: C. L. Cooper & I. T. Bobertson (Eds), International review of industrial and organizational psychology (Vol. 6, pp. 283–357). New York: Wiley. Egri, C., & Ralston, D. (2004). Generation cohort and personal values: A comparison of China and the United States. Organization Science, 15(2), 210–220. Erez, M. (1994). Toward a model of cross-cultural industrial and organizational psychology. In: H. C. Triandis, M. D. Dunnette & L. M. Hough (Eds), Handbook of industrial and organizational psychology (Vol. 4, pp. 559–608). Palo Alto, CA: Consulting Psychologist Press.

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INTEGRATING HRM PRACTICES INTO A MULTI-LEVEL MODEL OF CULTURE: CULTURE’S VALUES, DEPTH, AND STRENGTH Miriam Erez ABSTRACT This chapter discusses the strengths and challenges posed by the chapter by Aumann and Ostroff entitled, ‘‘Multi-Level Fit: An Integrative Framework for Understanding HRM Practices in Cross-Cultural Contexts.’’ In addition, this chapter proposes an alternative multi-level model of culture, which consists of structural and dynamic dimensions with culture’s strength as a moderator of the top-down bottom-up dynamic processes. This model assumes that there is a fit between the value system and the HRM practices, as they represent two layers of culture – visible and less visible. Yet, the fit can be interrupted when HRM practices are transferred across cultures. The chapter further discusses when HRM practices are rejected and when they are accepted despite the misfit.

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INTRODUCTION The chapter by Aumann and Ostroff attempts to capture the complexity of the work context and its influence on the relationship between HRM practices and behavioral and attitudinal outcomes both at the individual and collective levels. The conceptual framework is illustrated in their theoretical model as presented in their Fig. 1. The comments on this chapter are divided into three parts: (I) strengths of the conceptual framework; (II) challenges posed by the existing model; and (III) other approaches for understanding the interplay between cultural values and HRM practices in a multi-level work context.

STRENGTHS OF THE CONCEPTUAL FRAMEWORK Taking a Multi-Level Approach – Bottom-Up – Top-Down Processes The authors examine the factors that explain the effects of HRM practices both at the individual and the organizational levels. The authors nicely demonstrate how the psychological climate emerges to become an organizational climate via the aggregations of individual responses into collective responses. Reciprocally, the organizational climate shapes the personal climate. At the individual level, the authors propose that the fit between individual values and values conveyed through HRM moderates the relationships between the psychological climate and individual responses. The same psychological climate leads to positive attitudinal and behavioral responses when there is a high fit between the individual values and the values conveyed through HRM practices. However, this may lead to negative responses when there is a misfit. The authors further demonstrate how the fit aggregates across individuals to become a collective fit, which in parallel to the individual level, moderates the relationship between the organizational culture and the collective attitudinal and behavioral responses. At the organizational level, the same organizational climate may lead to positive collective attitudinal and behavioral responses when there is a high collective fit, but it may lead to negative responses when there is a misfit.

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Culture’s Strength as a Moderator of the Dynamic Reciprocity between Top-Down and Bottom-Up Processes The authors make an important contribution by introducing culture’s strength as a crucial moderator of the relationship between different levels of analysis. They propose that a culture’s strength moderates the relationship between psychological climate at the individual level, and the emergent construct of organizational climate at the collective level. Similarly, cultural strength moderates the relationship between fit at the individual level, and the emergent collective fit at the collective level. A culture’s strength also moderates the top-down relationship between organizational climate and psychological climate, and between the collective fit of values – HRM practices and the fit at the individual level. The authors propose that the relationship between the two levels will be stronger in tight cultures in contrast to loose cultures, where the variety of perceptions of psychological climates, and hence, the variety of fit levels, may not merge into one unified organizational climate, and collective fit. Similarly, the macro-level phenomena will not necessarily affect the individual-level phenomena. Hence, culture’s strength determines the transition from one level to another. Low transition means low isomorphism, and a higher level of complexity, allowing for the co-existence of multiple HRM practices.

Fit between Societal Values and HRM Practices The fit between societal values and HRM practices has been discussed in the research literature previously (Aycan, Kanungo, & Sinha, 1999; Erez, 1994, 1997; Earley & Erez, 1997; Ostroff & Bowen, 2000). Based on the research literature and on their Table 1, the authors present the contingencies between national cultures and HRM practices. Their Table 1 illustrates the content domains of existing HRM practices, classifying them into useful categories of human capital, socialization, rewards and incentives, employee relations, etc. They further summarize the most effective HRM practices for each cultural value, including individualism, power distance, masculinity, uncertainty avoidance, and future time orientation. The table also represents the fit between societal values and HRM practices. This fit has an important role in the theoretical model, as it moderates the relationship between the psychological climate and individual responses, and between organizational climate and collective responses. Organizational culture will

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lead to positive collective responses, similar to the effect of psychological climate on individual responses when there is a high fit, but it may lead to negative responses when there is a misfit. Distinction between Societal Culture, Organizational Culture, Organizational Climate, and Psychological Climate The authors take a multi-level approach to differentiate among the multiple constructs of culture and climate at the societal, organizational, and individual levels. Societal cultural values appear at the macro level of society. These values develop within a certain shared ecological environment and convey the meaning system shared by members of a particular culture. Members of a particular culture are exposed to similar features within their setting, and share their interpretations of the setting with others. This explains why members of different cultures develop different ways of shared perceptions and evaluations of the world around them. These values, through a process of socialization, are represented in the self (Erez & Earley, 1993), and further shape the psychological climate perceptions of the individual. Organizational culture conveys the underlying organizational values and assumptions that lead to the adoption and implementation of certain organizational structures, systems, and practices. Organizational culture is mostly measured at the individual level, following which responses are aggregated to the unit and organizational levels. They represent the higher level entities of an organizational culture, given a reasonable level of homogeneity in responses across the members of the organization. Organizational climate, on the other hand, reflects a collective perception and interpretation of ‘‘what’’ happens in an organization. The construct of an organizational climate emerges by aggregating the individual psychological climate perceptions into collective perceptions of the organizational climate. The distinction between culture and climate as proposed by the model is not only in terms of the level of analysis, but also by virtue of its relationship to HRM practices. According to the conceptual framework (although not clearly presented in the model) cultural values, both at the societal and organizational levels, are the antecedents that shape HRM practices, whereas climate perceptions are the consequences, given in response to HRM practices. Integrative Model The contribution of the proposed model is twofold: first, it takes a multilevel approach to explaining the moderating effect of the fit between values

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and HRM practices both at the individual and organizational levels; second, it integrates culture’s strength into the multi-level fit model. The model proposes that the bottom-up/top-down processes are moderated by a culture’s strength. Hence, the fit between individual values and values conveyed through HRM practices develops into a collective fit when culture strength is high, but not when it is low. Therefore, the moderating effect of the collective fit on the organizational climate–outcomes relationships depends on the culture’s strength. Furthermore, the model proposes how organizational level constructs emerge through aggregations of individual level constructs to become macro level phenomena. Organizational climate arises through aggregation of perceptions of individual psychological climates, and the construct of collective fit emerges through aggregation of the fit between personal values and the values reflected by HRM practices. The fit construct, both at the individual and organizational levels, moderates the relationship between climate perceptions of HRM practices and their effects on attitudinal and behavioral responses. The fit leads to positive effects, whereas misfit may lead to negative effects. The model may also predict which HRM practices will have positive effects on the attitudinal and behavioral outcomes, and under what conditions. Yet, because of the complexity such predictions are difficult to make. The next section challenges some of the causal links in the proposed model.

ISSUES OF CONCERN IN THE PROPOSED MODEL Model Parsimony A good model should be parsimonious. Yet, the present model struggles with three complexities: one stems from the multi-level approach; the second results from the fit approach, which reflects the interrelationships between two constructs – societal values and HRM practices, and individual values versus values reflected by the HRM practices; the third complexity is in introducing two sets of constructs – organizational values and organizational climate, which are interrelated. Furthermore, there are multiple moderation effects in this model. Culture’s strength is shown to moderate the effect of individual fit on collective fit, which further moderates the organizational climate–outcomes relationships, and these relationships are also moderated by the fit between societal values and HRM practices.

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Such complex interactions are difficult to interpret and replicate, challenging the validation of the model. Model Representation of the Theoretical Causal Relationships A good model should represent the theoretical causal relationships in a coherent way. The authors theorize that societal values shape the HRM practices. However, their proposed model does not link societal values and HRM practices. On the left side of the model the authors show the link between societal values, organizational values, and individual values. Individual values further shape the psychological climate. However, there is no parallel causal link at the macro level of the organization. The model shows no indication that societal values affect the HRM practices or the organizational climate. The effect of societal values and organizational values on organizational climate is very indirect, through its effect on individual values, which further affect organizational climate. Moreover, Table 1 clearly shows that cultural values influence HRM practices. Yet, this effect is not reflected in the model. It seems that the missing link between societal values and HRM practices has additional implications: First, the effects of societal and organizational values on HRM practices may also suggest a high correlation between organizational culture and organizational climate because the organizational culture affects HRM practices, which further affects the climate perceptions. This link suggests that perhaps organizational climate becomes redundant in the presence of organizational culture. The only exception could be when culture’s strength moderates the organizational values–HRM practices relationship, consequently, leading to climate perceptions that are different than the organizational values. Second, the construct of ‘‘values conveyed through HRM,’’ as it appears in the model, is not clear. How is this construct measured, and how does it relate to all other constructs of values and climates, including societal values, organizational values, individual values, personal climate, and organizational climate? It seems to overlap some of the other constructs. Based on the theoretical framework, it is reasonable to say that values conveyed through HRM reflect the organizational culture, as the latter shapes HRM practices. Yet in this case, there is a paradox in the logic or the causal relationship: if organizational values shape HRM practices, and they also influence individual values, why should we expect a misfit between the values conveyed through HRM and individual values?

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Third, similarly, the culture–HRM relationship may suggest a positive fit between them, making the moderation of the culture–HRM fit redundant. Fourth, following the above paradox, one could argue for a high fit between personal values and values based on HRM practices, both at the individual and collective levels. In this case, the contribution of the fit constructs to the model may not be significant. Hence, the above analyses challenge the contribution of the distinction between culture and climate, and consequently, the meaning of the fit construct in this context. However, in reality, we know that there are many cases of misfit between cultural values and HRM practices (Erez & Earley, 1993; Erez, 1994, 1997). A legitimate question is, therefore, in what contexts does a low fit between societal values and HRM practices, and organizational values and HRM practices take place? Although this question seems to be beyond the scope of the present chapter, it seems to play a crucial role in understanding the full spectrum of the culture–HRM practices relationship. I suggest that a misfit between culture and HRM practices may occur through interventions. Such interventions take place when there are changes in the business environment, and existing values are no longer useful for the sustainable competitive advantage of the company. Such interventions may also take place when the ownership of the organization changes, as in the case of local and international mergers and acquisitions, when public companies are privatized, and vice versa, and in case of a new board of directors that nominates a new top management team with a different set of values than the existing ones. In sum, changes in the organizational structure, ownership and leadership should be considered to further understand cases of misfit.

Propositions Derived from the Proposed Model Propositions derived from a theoretical model help clarify causal relationships between the model’s variables. In this chapter, there are no specific propositions, and therefore, it is hard to be sure that all the causal links are being considered with no redundancies on the one hand, or missing links, on the other. Using clear propositions could have helped validate the model at the conceptual level, and avoid the problems raised in the above section. Furthermore, the discussion of the content dimensions of the HRM practices and cultural values is separate from the theoretical model. While the authors nicely demonstrate in Table 1 the relationships between certain cultural values and specific HRM practices, they do not clearly introduce the content dimensions of the values and HRM practices into the theoretical

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model. Introducing the content dimensions into the model and its derived propositions could have helped our understanding of how the model is to be tested empirically. Another way to help validate the model is by generating regression equations that serve for testing the model. Such regression equations could have reduced ambiguity and lack of clarity with respect to the causal links among the research variables. The authors admit that the organizational culture consists of multiple values. Yet, in Table 1 they use only one value at a time to explain the congruence with HRM practices. Taking a multiple value approach may lead to different conclusions with respect to the fit model as compared with the case of a single cultural value. For example, certain HRM practices may be congruent with a combination of individualistic values with high power distance, but not with a combination of individualistic values with low power distance. Given the complexity of organizations today, and their multiple value culture, it is not clear whether the contingencies summarized by the authors in Table 1 will remain valid in the context of multiple values.

OTHER APPROACHES FOR UNDERSTANDING THE INTERPLAY BETWEEN CULTURAL VALUES AND HRM PRACTICES IN A MULTI-LEVEL WORK CONTEXT Aumann and Ostroff rightly propose a multi-level approach to understanding the relationships between culture and HRM practices. Yet, they also introduce other sets of constructs: psychological and organizational climates, and values – HRM fit at the individual and the collective levels. They assert that the effects of climate perceptions on behavioral outcomes depend on the moderating effect of the fit between societal values and HRM practices, at the macro level, and individual values and the values conveyed by HRM practices, at the individual level. It seems that these double constructs – culture and climate, lead to a very complex model which, for various reasons, treats culture and climate as two independent constructs that interact with each other. Although on the right side of their model the authors recognize the causal relationships among societal values, organizational values, and individual values, they appear as a top-down process only, whereas climate perceptions have both bottom-up and top-down processes. The lack of symmetry between the dynamics of

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culture and climate processes is problematic, mainly in view of Schein’s (1991) model. According to this model, practices and values are both components of culture, with practices being at the more visible layer of culture than values. Climate perceptions refer to the visible layer of culture, and they are shaped by values. Therefore, climate perceptions and cultural values are interrelated, and have similar dynamics of bottom-up/top-down processes. I propose an alternative multi-level model of culture, which consists of two dimensions – structural and the dynamic (Leung, Bhagat, Buchan, Erez, & Gibson, 2005). The multi-level model of culture is characterized by structural and dynamic dimensions. The structural dimension pertains to the hierarchy of levels of culture nested within one another where the most internal level is that of cultural representation at the individual level, nested within groups, organizations, nations, and up to the global culture. Culture as a shared meaning system can be formed at each one of these levels. The dynamic dimension pertains to the interrelationships among the various levels of culture and the way they impact each other. Through top-down processes of socialization, individuals internalize the shared meaning system of the society to which they belong, and its values are represented in their individual selves. Then, through a bottom-up process of aggregated shared values, higher level entities of culture are formed at the team, organizational, and national levels. At each level, culture can be conceptualized at different layers of visibility, from the most visible layer of observed artifacts and practices, to the deeper layer of values that is testable by social consensus, and down to the deepest layer of basic assumptions that is invisible and taken for granted (Schein, 1992). Aumann and Ostroff introduced the dimension of culture strength into the multi-level model of culture. Culture’s strength influences the flow from one level to another and the extent to which the system is isomorphic. They propose that bottom-up processes of aggregation into a unified higher level entity of culture will occur when culture’s strength is high rather than low. Similarly, the top-down processes of influences of higher levels of culture on nested levels will occur mostly in tight rather than loose cultures. This additional dimension of culture’s strength, proposed by Aumann and Ostroff, makes an important contribution to the understanding of the dynamics of the multi-level model of culture. The multi-level approach assumes that there is a fit between the visible layer of practices and the deeper layer of values. Therefore, the fit between cultural values and HRM practices seems to be built into the system. Yet, there are many cases of misfit where HRM practices do not reflect cultural

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values, and can be negatively perceived and evaluated by the members of a particular culture (Erez & Earley, 1993). The question is under what conditions this misfit is created? I suggest that a misfit between values and HRM practices occurs when HRM practices are transmitted from one culture to another. The process of globalization increases the level of interdependence across cultures, and through this high interdependence, HRM practices from one culture are being transferred to other cultures. The result can be the introduction of HRM practices that may be inconsistent with the local culture. Transfer of HRM practices may occur through international mergers and acquisition, when the acquiring company imposes its set of HRM practices on the acquired company. It occurs when multinational companies implement a unified system of HRM practices in all their subsidiaries. It also occurs when expatriates bring with them managerial practices that were effective in their home culture, but may be incongruent in their host culture. Therefore, context becomes crucial for understanding the potential misfit between values and HRM practices. In such cases, the construct of fit, proposed by Aumann and Ostroff, at both the individual and collective levels becomes relevant for understanding the nature of the attitudinal and behavioral responses. Yet, one can argue that a misfit between HRM practices and cultural values does not always lead to negative outcomes. It depends on the willingness of the organizational members to adopt HRM practices that are incongruent with their cultural values. Berry (1980) proposed a model of acculturation that consists of two dimensions – the level of attraction to the new culture (and its derived HRM practices), and the importance of preserving one’s own values (and the existing HRM practices). This typology conveys four types of acculturation – integration, assimilation, separation, and marginalization (Berry, 1980). Separation and marginalization reflect the cases in which the new cultural values and their derived HRM practices are rejected. Yet, assimilation and integration reflect the cases in which the organization members are attracted to the other value system and are willing to adopt it. In the case of assimilation they adopt the new system including its HRM practices and abandon the old ones. In the case of integration, they integrate the new HRM practices into the old system, creating a loose culture that enables both systems to stay together. This new form is sometime defined as a hybrid culture (Earley & Mosakowski, 2000). The integration type was also found by a recent study that examined the role perceptions of managers in a big multinational company (Berson, Erez, & Adler, 2004). The study demonstrated that managers in different geographical zones shared similar role perceptions with respect to their global roles of strategic

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planning and coping with changes; however, they allowed for variations across cultures with respect to local managerial roles of employee and task orientations (Berson et al., 2004). To summarize, the multi-level model is useful for understanding the interplay between cultural values and HRM practices. The former reflect the less visible layer of culture whereas the latter reflect the visible layer of culture. While we often expect the two layers to be in congruence, cultural strength moderates this relationship allowing for a higher tolerance for HRM practices that deviate from the local values when cultural strength is low. HRM practices that deviate from local cultural values may still be endorsed when employees are willing to adopt another set of values that is attractive to them.

REFERENCES Aycan, Z., Kanungo, R. N., & Sinha, J. B. P. (1999). Organizational culture and human resource management practices: The model of culture fit. Journal of Cross-Cultural Psychology, 30, 501–526. Berry, J. W. (1980). Social and cultural change. In: H. C. Triandis & R. W. Brislin (Eds), Handbook of cross cultural psychology (Vol. 5, pp. 211–280). Boston: Allyn & Bacon. Berson, Y., Erez, M., & Adler, S. (2004). Global values in MNC culture, and their manifestation in managerial roles. Best Paper Award, The Academy of Management Conference. Earley, C. P., & Erez, M. (1997). The transplanted executive: Why you need to understand how workers in other countries see the world differently. New York: Oxford University Press. Earley, P. C., & Mosakowski, E. (2000). Creating hybrid team cultures: An empirical test of transnational team functioning. Academy of Management Journal, 43, 26–49. Erez, M. (1994). Towards a new model of cross-cultural I/O Psychology. In: M. D. Dunnette & L. Hough (Eds), The handbook of industrial and organizational psychology (2nd ed., Vol. 4, pp. 569–607). Palo Alto, CA: Consulting Psychologists Press. Erez, M. (1997). A culture-based model of work motivation. In: P. C. Earley & M. Erez (Eds), New perspectives on international industrial/organizational psychology (pp. 193–242). A volume in the series: Frontiers of industrial and organizational psychology (Series Editor, Sheldon Zedeck). San Francisco, CA: Jossey-Bass Publishers. Erez, M., & Earley, P. C. (1993). Culture, self-identity, and work. NY: Oxford University Press. Leung, K., Bhagat, R., Buchan, N. R., Erez, M., & Gibson, C. B. (2005). Culture and international business: Recent advanced and future directions. Journal of International Business Studies, 36, 357–378. Ostroff, C., & Bowen, D. E. (2000). Moving HR to a higher level: HR practices and organizational effectiveness. In: K. J. Klein & S. W. J. Kozlowski (Eds), Multi-level theory, research and methods in organizations (pp. 211–266). San Francisco, CA: Jossey-Bass. Schein, E. (1992). Organizational culture and leadership: A dynamic view. San Francisco, CA: Jossey-Bass.

MULTI-LEVEL FIT: COMPLEXITY, VALUES, AND CLIMATE Kerstin A. Aumann and Cheri Ostroff ABSTRACT In this response, we address the thoughtful commentaries by Chen and Tsui, and Erez and highlight three overarching themes emerging from their contributions. First, we address the challenge of balancing complexity and parsimony in our model of values, HRM practices and fit in cross-cultural contexts. Second, we provide further explanations of the linkages between societal and organizational values. Third, we address the question of whether culture and climate should be treated as separate constructs in the model. In doing so, we hope to stimulate future progress in multi-level and cross-cultural perspectives of HRM and fit.

INTRODUCTION The comments and suggestions offered by Chen and Tsui, and Erez are thought provoking and inspiring for the development of future directions in explaining the linkages between culture, organizational values, HRM, and employee responses. We appreciate their perspectives and novel ideas, and see them as important stimuli for future theoretical development and

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empirical research related to our model. Erez’s succinct analyses are useful in highlighting and summarizing the key propositions. Moreover, she provides an intriguing alternative and more detailed view of how culture can be theorized and examined. Likewise, Chen and Tsui’s contribution enhanced our model by providing a more detailed explication of the underlying processes, and proposed an alternative model emphasizing the role of organizational culture as an integrating mechanism across cultural differences. Taken together, the authors’ comments highlight the need to further develop the linkages between values and HRM. Both Chen and Tsui, and Erez raise concerns about the complexity of our model, as well as our treatment of climate as a construct distinct from culture linking HRM to employee responses. Below, we first address the issue of complexity versus parsimony with respect to our proposed model. Second, we discuss the linkages between societal values and organizational values. Third, we elaborate on the relationship between culture and climate, and provide additional support considering culture, HRM, and climate as interrelated, but distinct constructs. We conclude by highlighting the purpose of our model as a conceptual framework to inspire further theorizing and research, which ultimately may lead to a more parsimonious version, as the commentators suggest.

COMPLEXITY VERSUS PARSIMONY IN THEORETICAL MODELS Both sets of authors express concerns about the complexity of our model. For example, Erez notes that ‘‘a good model should be parsimonious’’ and suggests that the complexities of the interactions between the constructs and interrelationships at different levels of analysis make our model overly complex and thus difficult to test. The issue of complexity versus parsimony is a challenging one, particularly in the context of explaining complex phenomena in an emerging area of research. Feldman (2004) argues that finding a balance between being overly exhaustive and too simplistic, and determining how many variables and theoretical streams need to be included, is more art than science. He notes that parsimony can be achieved by using as few constructs as necessary to explain a phenomenon. Admittedly, we struggled with finding this balance. Yet, we maintain that the three theoretical bases, as well as the key constructs we included in our model, are all necessary to gain a more nuanced understanding of this very complex area of research.

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In order to develop a theory for a field of research often criticized for a lack of sound theories (e.g., House & Javidan, 2004), we attempted to integrate three streams of literature (multi-level approach to HRM and employee responses, fit, culture–HRM-organizational processes and linkages in a cross-cultural context), each of which is complex in its own right. As one moves from individual-level to multi-level relationships, not only does the theory need to increase in complexity to reflect the processes within a level and between levels, but testing such theories also becomes more complex (Kozlowski & Klein, 2000). When one adds societal-level constructs to a multi-level model of organizational behavior, by necessity, the theory grows even more complex to account for the additional relationships and interactions within and between all of these levels of analysis. Therefore, any cross-cultural model addressing the interplay between societal-, organizational-, and individual-level constructs will mirror the complexities of the range of issues involved in cross-cultural HRM and organizational processes. Thus, we feel the degree of complexity of our model is appropriate, although we concur with the commentators that testing and validating such a model in its entirety would be difficult, if not impossible. As such, we purport that our model should be viewed primarily as a general heuristic to guide future thinking and research in this area. Small pieces of our model, focusing on a limited number of variables, could be reasonably tested in single studies, and then findings across studies could build upon one another to refine the theoretical linkages accordingly. In doing so, more depth in the few constructs of interest and a more parsimonious theoretical model would need to be developed. Fortunately, both Erez and Chen and Tsui provide alternative models that are indeed more parsimonious and more immediately testable in their entirety. Erez develops a multi-level model by combining culture, climate, and HRM into a multi-layered single construct and then explicates how fit among these layers relates to employee and organizational outcomes. Chen and Tsui develop a less complex model by positing a number of moderators and antecedents of culture–HRM linkages, and then combine many of the constructs we had proposed as mediators leading to organizational effectiveness. We find both models interesting and useful for guiding future work in the area. Each model delves more deeply into explicating a particular theoretical construct (e.g., Chen and Tsui focus on organizational values; Erez’s predominant focus is on culture as a multi-layered construct) and then links this key construct to overarching outcome constructs. In sum, we see the utility of our very complex model as providing a sense of the myriad of complex relationships that can be considered when

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attempting to understand cross-cultural fit and HRM across levels of analysis. At the same time, the models provided by the commentators are equally useful in that they provide considerable depth around a core construct and develop a less complex, and more directly testable model, around that core construct. We hope that these models will spur future thinking and development in the complicated area.

THE ROLE OF SOCIETAL AND ORGANIZATIONAL VALUES As Erez points out, our intent was to indicate that societal- and organizational-level values shape the HRM practices an organization adopts, and that, in turn, climate is an outcome of these HRM practices, yet this is not reflected in the visual representation of our model. First, as a minor correction, we note that there should be an arrow connecting the values box to the HRM practices box (consistent with the text) and apologize for this oversight. Second, Erez as well as Chen and Tsui rightly note that we did not fully explicate the linkages between societal values, organizational values, and HRM practices. Both sets of commentators make important contributions by further elaborating the relationships between these variables. To address these issues raised by the commentators, we briefly clarify our views on the relationship between societal and organizational values, as well as their relationship to HRM practices and other variables in our model. In doing so, we explore how the ideas and examples provided by Erez, and Chen and Tsui can be used to inform and enhance future work in this area. As we noted in our original paper, societal values, such as the ones conceptualized and validated by studies like Hofstede (1980, 2001) and the GLOBE project (House, Hanges, Javidan, Dorfman, & Gupta, 2004), are broad-based in nature and influence more specific definitions of values that are conceptualized at lower levels of analysis. That is, societal values are often conceptualized in these popular typologies in terms of broad content categories which then provide the foundation for more narrowly defined content dimensions of domain-specific values, such as organizational or work values (e.g., Ros, Schwartz, & Surkiss, 1999; Schwartz, 1999). As a result, societal values not only shape more specific definitions of organizational values, but they are also likely to limit the range of the specific organizational values acceptable within a societal culture. In very tight cultures, the range of potential organizational values may be quite restricted,

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thereby making organizational values, HRM practices and climate more similar across organizations. In looser cultures, different sets of organizational values may be adopted across organizations. Differing sets of organizational values lead to different HRM systems that can be equifinal in terms of ultimate organizational effectiveness. In cross-cultural management contexts, such as multi-national corporations (MNCs), the issue of societal and organizational values becomes more complex. During international expansion or when an organization operates in a country other than its home base, the societal values of the host country may be ignored or misinterpreted. As Chen and Tsui point out, the organization’s values and HRM practices are likely to be inconsistent with the societal values of the host country culture and thus likely to cause discord and misfit among local employees, particularly when there is a large cultural distance between the two countries. We agree with Chen and Tsui’s argument that organizational values are the key construct and that organizational values should be the starting point for integration between the organization and the local culture. Clearly, their argument that organizational leaders must make careful and informed decisions about what aspects of their organizational values to emphasize in the foreign subsidiary is a valid one. The primary difference in our perspectives is that we place more emphasis on HRM practices as an integrating mechanism while Chen and Tsui, as well as Erez, place greater emphasis earlier in the model on values. From our perspective, leaders’ decisions about adopting certain HRM practices signal the core values underlying the organization’s culture, and these value signals must fit the values of the local culture. Yet, the same organizational values can be reflected in a variety of ways by different HRM practices. Chen and Tsui provide multiple examples of this, highlighting that companies such as Starbucks can maintain consistent organizational values across the societal cultures they operate in by adapting their HRM practices in foreign subsidiaries to ensure they are consistent with the norms, expectations, and values of local employees. Thus, while we agree that organizational values must be considered relative to the culture of the host country, we focused more on HRM practices and their fit with values because multiple configurations of HRM practices can be used to reflect the same values. Further, it is the practices and employees’ perceptions of those practices (i.e., climate) that are more proximal influences on employee attitudes and behaviors and should therefore the primary mechanisms to bridge cultural gaps between the organization and their international workforces.

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The lessons learned from Lincoln Electric’s international expansion (Hastings, 1999) help illustrate how our point of view is slightly different than that proposed by Chen and Tsui. Lincoln Electric’s organizational culture is embedded in the broader context of U.S. societal values (very high individualism and masculinity and relatively low power distance and uncertainty avoidance; Hofstede, 1980, 2001). These broad societal values are consistent with the more specific values characteristic of Lincoln Electric’s organizational culture, such as a strong orientation toward individual performance and rewards. In turn, these organizational values were clearly reflected by the company’s HRM practices, which included an individually based piecework pay system and a profit-sharing bonus system. Given the diversity in individual values in the U.S., the company was able to recruit enough employees whose values fit with the values conveyed by its HRM practices. However, when Lincoln Electric began to expand internationally, they transplanted their HRM practices to these locations without adapting them to fit local values. The outcomes were disastrous and nearly destroyed the company (Hastings, 1999). Our model explains Lincoln Electric’s failure as a result of the mismatch between the values conveyed by the HRM practices and local societal values, which was problematic for employees and led to poor outcomes. Lincoln Electric would have fared better if they had focused on facilitating fit at a macrolevel between their HRM practices and local societal values, as well as at the individual level. Chen and Tsui would argue that Lincoln Electric’s leadership should have first identified which aspects of its organizational culture were incompatible with the local culture, and then identified ways to adapt its HRM practices to be consistent with both its organizational culture and the local societal culture. Chen and Tsui provide a more complete picture of how the process of alignment and fit can be facilitated early in the process. We provide a more complete picture of how and why misalignment may cause problems. Both perspectives seem viable to us, and taken together, may offer a more holistic picture of cross-cultural HRM processes. Chen and Tsui offer another significant contribution by proposing a typology of culture strength. Culture strength and cultural tightness–looseness are conceptually related but separate constructs, and as Chen and Tsui rightly point out, should not be used interchangeably. In our model, we focused on the conceptualization of culture strength as the degree of intracultural variability around the average value orientation in a culture. While this is one way to conceptualize culture strength, other definitions of this relatively young construct exist.

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Chen and Tsui distinguish between two aspects of culture strength, the overall level of how strongly people feel about the values (defined as the mean score of the value) and the breadth or the degree to which individuals within the culture vary in how strongly they hold the value. They propose that strength of the organizational culture moderates the relationship between organizational values and HRM practices. We agree that including overall level in addition to variability in defining strength makes good sense and future work should consider how overall value level interacts with the relationships proposed in our model. It may also be important to further distinguish between the notion of ‘‘level’’ of a value as an average or mean score, and the intensity or valence with which the value is held by members of the culture. For example, a societal culture may have a low overall level of power distance (i.e., a low average score), but that does not indicate the extent to which people within the society have strong feelings about this value orientation. Intensity, however, describes an affective quality of the culture, which has implications for the valence or importance of a value dimension in a societal culture. Thus, a societal culture may score low on power distance, but its members may feel very strongly and passionately about being low on this particular value dimension. As a result, members of this societal culture are likely to have a high valence for HRM practices that convey organizational values consistent with low power distance (e.g., participation in decision making), while a mismatch between societal and organizational values on this dimension is likely to have more profound negative impacts than values with lower valence. Erez alluded to such issues in her discussion of how misfit between HRM practices and cultural values may not always lead to negative outcomes. Some individuals may be able to easily adapt to practices that are incongruent with their cultural values or may have a higher tolerance for the discord produced by this misfit. It may be that this willingness to adapt reflects the intensity aspect of culture strength. Clearly this is an area ripe for future research and its inclusion in our model would yield new insights. For example, individual tolerance or the intensity of the values may directly impact perceptions of fit to the organizational culture and HRM practices.

THE ROLE OF CULTURE AND CLIMATE A third theme emerging from the commentaries concerns the issue of organizational culture and climate. Both Chen and Tsui, and Erez do not include psychological or organizational climate in their models. Erez goes

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on to argue that climate is redundant with culture and should be considered a surface-level artifact of organizational culture. In her view, incorporating climate as an aspect of culture would enhance the ability to develop a parsimonious model. This perspective highlights a long-standing query and controversy in the literature. Are culture and climate the same, different, or interrelated constructs? While it is beyond the scope of this paper to detail the history and nuances of various arguments, we note that there is some consensus in the literature (cf. Dennison, 1996; Ostroff, Kinicki, & Tamkins, 2003; Payne, 2000; Schein, 2000; Schneider, 2000). First, climate is viewed as a more overt contextual variable that can be seen upon entering an organization whereas culture is a more stable embedded contextual social feature of the situation that reflects fundamental values, ideologies, and assumptions. Second, culture helps to explain why different organizations have different climates. Third, culture and climate are related in that the practices and climate result from organizational culture, espoused values and assumptions. Where researchers differ to some degree is whether climate is viewed as a ‘‘piece’’ of culture or as an artifact layer of culture, and thus should be placed in the same ‘‘box’’ in a theoretical model (e.g., Schein, 1992, 2000) or whether climate is viewed distinctly from culture and placed separately in models (e.g., Schneider, 2000). Regardless of whether one combines or separates these constructs in ‘‘boxes’’ in a model, as Schein (2000) argues ‘‘climate and culture, if each is carefully combined, then become two crucial building blocks for organizational description and analysis’’ (p. xxiv). We have retained climate (and HRM practices) as separate constructs because (a) we believe it provides a more nuanced description of the underlying linkages in cross-cultural HRM, (b) some of the factors that facilitate the emergence of such constructs differ, and (c) alignment among them, which should naturally occur if they are the same underlying construct, does not always occur. In our original paper, we drew clear conceptual definitions of both culture and climate in an effort to distinguish between them. Below, we provide a few additional arguments for retaining them as separate variables. Organizational values are purported to influence an organization’s choice of HRM practices, which serve as tools to signal to employees which behaviors and outcomes are valued and rewarded by the organization. Thus, HRM practices are the means by which an organization can influence the attitudes and behavior of its employees. In models of HRM and collective employee attitudes, this link is mediated by climate (e.g., Kopelman, Brief, & Guzzo, 1990). Recently, Gelade and Ivery (2003) provided an empirical

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demonstration of the mediating role of climate. As such, climate appears as a more proximal construct to employee attitudes and behaviors than HRM practices or organizational values. That said, we do acknowledge that not all of the influence of culture or practices will occur through climate, and direct relationships between culture and employee outcomes are also expected. Moreover, culture, HRM practices, and climate should be in alignment with one another. Erez argues that alignment will naturally occur because climate is derived from organizational culture and the fit between them is built into the system. Misalignment, in her view, would occur primarily in situations when HRM practices are transmitted from one culture to another. While we agree that the probability for misalignment increases in cross-cultural situations, our perspective is that misalignment can occur regardless. Owing to problems such as poor communication, lack of involvement of HR professionals in the business planning process, and weak integration between the HR system, management and strategic systems, the HR systems developed and the subsequent climate can differ from what was intended and may be misaligned with other systems of the organization (Lado & Wilson, 1994; Ostroff & Bowen, 2000). Similarly, due to poor emergent processes, a climate can develop that is inconsistent with what was ultimately intended by the HRM practices (Bowen & Ostroff, 2004). Thus, alignment is not necessarily naturally occurring but requires the attention of both management and HR professionals to build-in mechanisms to facilitate alignment. For these reasons, we again argue that culture, HR practices, and climate be treated distinctly in models. Turning to cross-cultural situations, the issue of misalignment becomes even more critical. Chen and Tsui provide an interesting example of what happened when IKEA first began operations in the U.S. to illustrate misalignment between cultural values and practices and to show how cultural integration and alignment can be achieved through altering practices. When IKEA first expanded to the U.S., its American employees (whose values are shaped by a societal culture that is less collectivistic, more uncertainty avoidant, and higher in power distance relative to the Swedish societal culture that has shaped IKEA’s organizational values) perceived the company’s practice of not assigning job titles or providing job descriptions differently than employees in IKEA’s home country. Based on our model, we would interpret this to mean that many of IKEA’s initial American employees likely perceived the psychological climate as negative or detrimental to their organizational well being, while the Swedish employees had perceived the climate more positively. This example illustrates how the same organizational values and HRM practices can result in different climate perceptions,

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which in turn implies a different effect of the practice on employee attitudes and behaviors. Further, this example illustrates how alignment can be created by modifying HRM practices as IKEA was able to correct the misalignment between its organizational culture and climate by changing its recruitment and selection practices. HRM practices can be used to facilitate organizational climate perceptions, which are aligned with key organizational values, thereby aiding in the integration between organizational culture and employee values. We agree with Chen and Tsui’s notion of using HRM practices to facilitate cultural integration, and extend their argument by proposing that this integration process occurs because of employees’ shared perceptions about the organization. Finally, we see many points of similarity in the underlying processes and mechanisms across our model and the models proposed by Erez, and Chen and Tsui. Although we separate out the variables of values, HRM practices, and climate, we highlight the importance of alignment and integration among them. Erez does the same, but does so under the rubric of culture. She argues that the structural dimension of culture contains values at multiple levels of analysis, including surface artifacts such as climate. The dynamic dimension pertains to the interrelationships among these various levels, and various practices help to integrate the levels of culture. Thus, we focus on the same general set of variables, but differ in how we model them, under one general rubric or as separate constructs. We believe that the inclusion of organizational climate as a distinct variable in our model is warranted, in spite of the added complexity, because it increases the opportunities for testing different aspects and nuances of the relationships between culture, practices, climate, and responses.

CONCLUSIONS In conclusion, Erez’s and Chen and Tsui’s thoughtful commentaries challenged us to further elaborate on the relationships between societal and organizational values, as well as culture and climate. They raise a number of very important concerns pertaining to how the cultural aspect of our model should be studied and viewed, and have done so in ways that we find enhance that aspect of our model. Although there is some similarity across their views in that they focus on building a parsimonious model as well as providing more depth in the early pieces of our model pertaining to culture and values, they also differ. Chen and Tsui focus largely on organizational

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culture and a cultural integration perspective, while Erez focuses on cultural integration by elucidating an interesting typology of culture as a multi-level and multi-layered construct. Unfortunately, there is relatively little empirical work to answer the questions they raised about parsimony, the role of culture in the model, and how culture should be viewed. On the other hand, our model can be used as a general heuristic in a field that is open to researchers who wish to explore these issues. For us, one of the most interesting conclusions raised by the questions and comments provided is that a focus on societal, organizational, and individual values is critical for our understanding of how HRM practices can be transported to other cultural contexts. Also, despite the complexity of our model, we did not pay sufficient attention to those linkages. To this end, one of the goals of our culture-practices taxonomy (Table 1 in our previous chapter) is to inspire future research about the values inherent in different sets of HRM practices, their relationships to societal and organizational values, as well as how these practices may be used to bridge cultural differences. Our taxonomy could also be further developed in response to Erez’s point regarding the content dimensions of the values. A more fully developed taxonomy could show the congruence between the content dimensions of societal values and the values conveyed by HRM practices in addition to the congruence between societal values and more narrowly defined content dimensions of organizational values. Regardless, we are grateful to Chen, Tsui, and Erez for providing additional insight into the mechanisms that underlie the relationships between values at different levels of analysis and HRM practices across cultural contexts and for providing myriad opportunities for future theory and research.

REFERENCES Bowen, D. E., & Ostroff, C. (2004). Understanding HRM-firm performance linkages: The role of the ‘‘strength’’ of the HRM system. Academy of Management Review, 29(2), 203–221. Dennison, D. R. (1996). What is the difference between organizational culture and organizational climate? A native’s point of view on a decade of paradigm wars. Academy of Management Review, 21, 619–654. Feldman, J. A. (2004). What are we talking about when we talk about theory? Journal of Management, 30, 565–567. Gelade, G. A., & Ivery, M. (2003). The impact of human resource management and work climate on organizational performance. Personnel Psychology, 56, 383–404. Hastings, D. F. (1999). Lincoln Electric’s harsh lessons from international expansion. Harvard Business Review (May–June), 162–178. Hofstede, G. (1980). Culture’s consequences. Newbury Park, CA: Sage.

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Hofstede, G. (2001). Culture’s consequences (2nd ed.). Thousand Oaks, CA: Sage. House, R. J., Hanges, P. J., Javidan, M., Dorfman, P. W., & Gupta, V. (Eds) (2004). Culture, leadership, and organizations: The GLOBE study of 62 societies. Thousand Oaks, CA: Sage. House, R. J., & Javidan, M. (2004). Overview of globe. In: R. J. House, P. J. Hanges, M. Javidan, P. W. Dorfman & V. Gupta (Eds), Culture, leadership, and organizations: The GLOBE study of 62 societies (pp. 9–28). Thousand Oaks, CA: Sage. Kopelman, R. E., Brief, A. P., & Guzzo, R. A. (1990). The role of climate and culture in productivity. In: B. Schneider (Ed.), Organizational climate and culture (pp. 282–318). San Francisco, CA: Jossey-Bass. Kozlowski, S. W. J., & Klein, K. J. (2000). A multi-level approach to theory and research in organizations: Contextual, temporal, and emergent processes. In: K. J. Klein & S. W. J. Kozlowski (Eds), Multi-level theory, research, and methods in organizations (pp. 2–90). San Francisco, CA: Jossey-Bass. Lado, A. A., & Wilson, M. C. (1994). Human resources systems and sustained competitive advantage: A competency-based perspective. Academy of Management Review, 19, 699–727. Ostroff, C., & Bowen, D. E. (2000). Moving HR to a higher level: HR practices and organizational effectiveness. In: K. J. Klein & S. W. J. Kozlowski (Eds), Multi-level theory, research and methods in organizations (pp. 211–266). San Francisco, CA: Jossey-Bass. Ostroff, C., Kinicki, A. J., & Tamkins, M. M. (2003). Organizational culture and climate. In: W. C. Borman, D. R. Ilgen & R. J. Klimoski (Eds), Handbook of psychology (Vol. 12: I/O Psychology, pp. 565–593). New York: Wiley. Payne, R. L. (2000). Climate and culture: How close can they get? In: N. M. Ashkanasy, C. P. M. Wilderom & M. F. Peterson (Eds), Handbook of organizational culture and climate (pp. 163–176). Thousand Oaks, CA: Sage. Ros, M., Schwartz, S. H., & Surkiss, S. (1999). Basic individual values, work values, and the meaning of work. Applied Psychology, 48, 49–71. Schein, E. (1992). Organizational culture and leadership: A dynamic view. San Francisco, CA: Jossey-Bass. Schein, E. (2000). Sense and nonsense about culture and climate. In: N. M. Ashkanasy, C. P. M. Wilderom & M. F. Peterson (Eds), Handbook of organizational culture and climate (pp. xxiii–xxx). Thousand Oaks, CA: Sage. Schneider, B. (2000). The psychological life of organizations. In: N. M. Ashkanasy, C. P. M. Wilderom & M. F. Peterson (Eds), Handbook of organizational culture and climate (pp. xvii–xxii). Thousand Oaks, CA: Sage. Schwartz, S. H. (1999). A theory of cultural values and some implications for work. Applied Psychology, 48, 23–47.

CONTINUOUS LEARNING IN ORGANIZATIONS: A LIVING SYSTEMS ANALYSIS OF INDIVIDUAL, GROUP, AND ORGANIZATION LEARNING$ Manuel London and Valerie I. Sessa ABSTRACT Students of organizations are beginning to recognize the importance of continuous learning in organizations, but to date the concept is not well understood, particularly in terms of how the learning of individuals is related to the learning that takes place in groups, which is related to the learning that occurs in organizations (and all other combinations). To further our understanding, we offer the idea of continuous learning in organizations from a living system’s perspective. We view individuals, groups, and organizations as living systems nested in a hierarchy. We propose that living systems can learn in three ways: they can adapt, they can generate, and they can transform. Learning triggers from the environment spark learning, and this relationship is moderated by the system’s readiness to learn. Readiness to learn is a function of the permeability of $

This paper is based on, and extends, concepts introduced in Sessa and London (2006).

Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 123–172 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05006-5

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the system’s boundaries, the system’s stage of development, and the system’s meta-systems perspective. Additional research questions are presented to explore learning flow between levels and to determine how the match between one system’s pressure for change and another system’s readiness to learn affects the emergence of adaptive, generative, and transformative learning. In addition, research questions are offered as a means to test these ideas and build grounded theory. Finally, using this model, the chapter presents three case studies and suggests diagnostic questions to analyze and facilitate continuous learning from a multi-level perspective.

INTRODUCTION There is a need for continuous learning at the individual, team, and organization levels in today’s organizations. This need stems from such factors as the rapid pace of change in business, the intensely competitive global environment, greater demands placed on organizations for profit and integrity, the realization that knowledge is a competitive advantage, and an increasing sense of dissatisfaction among both managers and employees with the traditional, hierarchical, command-and-control management model. Although we already know a fair amount of how individuals learn (cf. Knowles, 1975) and there is also a growing body of literature about how groups and organizations learn (cf. Hackman, 2002; Senge, 1990), little is known about continuous learning prompted by the interactions of systems at different levels of analysis (individual, group, and organization). The purpose of this chapter, using living systems as the basis of our ideas, is to offer questions to stimulate research that will provide a basis for a multilevel, grounded theory of continuous learning. It identifies parallel learning constructs and processes at different levels of analysis – that is, they apply similarly at the individual, group, and organization levels, although the concepts are not isomorphic (homologous) across levels (see Kozlowski & Klein, 2000, for a discussion of multi-level theory and research). Specifically, this chapter considers the need for continuous learning and examines how systems theory applies for understanding individual, group, and organization interactions in the learning process. It describes different types of learning (adaptive, generative, and transformative); describes how learning triggers, from the environment, impact learning; considers how a system’s learning readiness moderates the relationship between learning triggers and actual learning; and examines how learning readiness is

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influenced by such system characteristics as boundary permeability, system development, and meta-systems perspective. It also explores the dynamics of systems interrelationships, particularly examining the flow between levels (one system prompting another system’s learning) and the match between one system’s press for change and another system’s readiness to learn. The chapter then suggests how these processes might be used as the basis for diagnostic questions, whose answers can be used by specialists in training and organizational development to facilitate individual, group, and organization learning. The chapter concludes with three cases that demonstrate the applicability of the processes to continuous learning in organizations.

THE NEED FOR CONTINUOUS LEARNING Groups at any organizational level need to engage in continuous learning. Increasingly, groups are being called upon to make important decisions in and for the organization. People are not inherently equipped to work in groups. Instead, group members need to learn to be a group – that is, they need to evolve from a collective of individuals to a unit focused on the same goals and understand the methods for achieving these goals. Groups learn how to structure work, communicate, meet changing demands, make decisions, and put these decisions into action. As the organizational context shifts, groups must be fluid enough to shift as well – restructuring themselves, adapting decisions, and updating plans. Groups and the individuals who make up the groups need to learn continuously or they will find themselves standing still in a constantly changing environment. Organizations need continuous learning for at least the following four reasons: 1. They face the increasing complexity of managing in multicultural and multinational environments and uncertain economic climates. 2. Ambiguity is often high because organizations are complex systems within complex environments that are not predictable. 3. Ways of working are becoming more fluid with the advent of electronic communications technologies, allowing, for instance, geographically dispersed teams and labor markets in different parts of the world. 4. We have moved from a manufacturing age to a service age and are now moving toward a knowledge age, and creating knowledge requires continuous learning.

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To address these issues, organizations are paradoxically called upon to be aligned and changed at the same time. They need to recognize and adapt to current conditions even as they generate new models of operating and possibly transform themselves entirely. This means that they need to learn continuously, and they need to encourage the learning of the groups and individuals who make up the organization. If they fail, organizations will not be able to keep pace with their competitors. The average individual at any organizational level is challenged by, and possibly not even equipped to handle, the complexity, ambiguity, and fluidity of today’s organizations. Individuals must learn and develop continuously to perform well in their current jobs, support their employment security as conditions change, and promote their career advancement. When organizations provide their employees with training, they train employees in the skills that the organization deems necessary. Employees’ traits and attitudes that are unnecessary or that seem to impede work (such as cognitive and emotional development that might lead people in new directions) may be unused, suppressed, and perhaps even thwarted, and hence are gradually weakened. As a consequence, employees may work harder and take longer to succeed, but may feel detached, bored, angry, empty, and out of control (LaBier, 1989). If they do not see the need for continuous learning and take control of their own learning, employees may be left out in the cold by the very organizations they work for and the society in which they live. One way to understand how individuals, groups, and organizations learn continuously is to think of the entities at each level of analysis as open, living systems. Systems theory may offer an explanation of learning within systems, the permeability of systems to influences from other systems, and the flow of learning between systems at different levels.

SYSTEMS THEORY Systems theory is a ‘‘general science of wholeness’’ that applies across different disciplines (Bertalanffy, 1967). A system (whether an individual, a group, or an organization) includes input, process, output, and feedback components, as depicted in Fig. 1. Living systems have three additional characteristics. First, they are selforganizing through their interactions with the environment. That is, living systems maintain and renew themselves (even forming new structures and new patterns) by using energy and resources from the environment. Second, living systems are both closed and open. That is, they have forms or

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Environment: Goals and Obstacles

Input*

Throughput*

Output*

*Adaptive, generative, and transformative learning are possible within each element. This system can be an individual, group, or organization.

Fig. 1.

A Simple Open Systems Model: Adaptive, Generative, and Transformative Learning. Source: Sessa and London (2006).

structures that remain stable as information, materials, or other matter are transformed as they flow through the system. Third, living systems have an organizing activity or process involved in the continual embodiment of the system’s pattern of organization and structure (Capra, 1997). Individuals are living systems (cf. Miller, 1978). Groups and organizations are social systems designed for a specific purpose and, as such, may or may not be thought of as living (see Capra, 2002, pp. 96–128; Senge, Scharmer, Jaworski, & Flowers, 2004, pp. 5–7). Regardless, we can view them as living systems in the metaphorical sense. Individual, group, and organization systems do not exist to learn. However, learning mechanisms are essential to how they function. Living systems engage in intention-bound work (Jaques, 2002). For instance, they select goals and determine how to achieve those goals and overcome obstacles in the process. They also track their own progress and evaluate the outcomes of their effort. Systems learn as part of this goal-oriented production process. For example, people select goals and work methods they find satisfying. Groups have goals marked by quality, quantity, and efficiency. Organizations achieve a vision, which more than likely includes bottom-line financial goals and maybe other goals, such as being number one in their industry or improving social welfare. These systems learn as they work by exploring alternative actions and experimenting with behaviors and activities that produce a variety of outcomes. They learn as they adopt different goals, experiment with new behaviors, seek feedback, and try different ways of interacting.

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Systems learn for themselves; others cannot learn for them, although they can stimulate and facilitate learning. Systems are subject to perturbations from the outside. Outside forces disturb the system’s status quo, thereby stimulating learning. Systems choose the forces or disturbances that get their attention. In addition, each system decides how to respond. Thus outside forces can facilitate or hinder learning, but here again, systems determine how to respond (Capra, 2002). In the end, systems learn when they perceive a need to learn, and then apply that learning when they see an opportunity. Because learning is driven by the system and cannot be wholly predicted, it can lead to unexpected outcomes, such as forming dysfunctional habits of interaction that might prove counterproductive when new goals or tasks arise. The constructs and processes we outline need to be assessed to determine their value to the systems enacting them, and interventions or additional triggers (e.g., facilitation, leadership) may be needed to make this unexpected learning more productive.

Systems as Continuous Learners Systems (whether individuals, groups, or organizations) and the environments in which they live and operate are constantly changing. Systems are continually adapting to the environment and, in some cases, changing the environment. From the system’s perspective, this is a constant process of maintenance, adaptation, and evolution as work progresses, obstacles are overcome, goals are achieved, outcomes are evaluated, and new goals are established (Capra, 1997; Laszlo, 1996). This evolutionary process may mean adaptation (e.g., slight changes in behaviors and work processes); the proactive generation of new, creative behaviors, methods, and/or structures to improve performance; or a radical transformation so that the system takes new forms and produces outcomes that are amazingly different from what the system did before. Individuals may learn new skills or start new careers. Groups may add new members with different skills and expectations, change leaders, formulate new objectives, and take responsibility for different functions. Organizations may change strategies, enter new markets, or restructure jobs and organizational levels. Systems that fail to change, or that change in ways that do not work (i.e., do not recognize changing demands, overcome barriers, or address competitive forces), will fail, perhaps to die or be reborn. In general, systems that thrive in constantly changing environments re-create themselves, albeit not necessarily without resistance from within and outside, and often in unexpected ways.

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Defining Continuous Learning Systems learn in the process of change. They cannot help it. They receive feedback about what works and what does not work. Feedback is critical so that a system knows the effects of – and when to make changes in – inputs, processes, and outcomes. Changes that receive positive feedback are reinforced. Changes that receive no feedback or negative feedback are weakened or avoided in the future. Systems learn what is required to experiment and explore, how best to get meaningful feedback, how to overcome hurdles, and how to gain the commitment and involvement of other systems. They become better at learning, and, as they do so, they become better at making changes that produce outcomes in line with feedback. Systems learn how to learn continuously. This is why we define continuous learning as a deepening and broadening of a system’s capabilities in (re)structuring to meet changing conditions, adding new skills and knowledge, and (re)creating into a more and more sophisticated system through reflection on its own actions and consequences. (Sessa & London, 2006)

Interrelationships among Systems We think of individuals, groups, and organizations as a hierarchy of intertwined systems. Each is a whole system that is part of another system. For example, individuals are members of groups that are entities in and of themselves. Multiple groups, in turn, are part of an organization, which itself is a whole system that functions in a larger environment of other organizational systems. The result is a complex hierarchy of interrelated systems. These systems are nested within one another, as depicted in Fig. 2. Moreover, they interact constantly; as they do, their interrelationships evolve. Individuals may join more than one group. Groups may overlap in membership and responsibility. The input of one group or individual may be the output of another group or individual. Some organizations have rigid hierarchies. Others have flat, organic structures with flexible relationships that are constantly changing as projects change. Sometimes organizations, groups, or individuals form alliances with other systems in the form of partnerships, consortia, joint ventures, coalitions, associations, and so on. The joint effort is not necessarily a wellformed or distinct entity, but it also is a system in and of itself. In general, systems at the same and different levels impose demands on one another – expectations, requirements, or objectives. They may demand

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Nested Individual (I), Group (G), and Organization (O) Systems and Environment (E). Source: Sessa and London (2006).

outputs that meet certain specifications for quality or delivery. They may bring in new members. They may be role models for norms or effective processes, with one system infecting or infiltrating others. Learning at one level of analysis (individual, group, or organization) is dependent on learning at other levels. Group learning depends on individual members’ learning. Yet group learning is more than the sum of individuals within the group. We can talk about group members acquiring new skills and knowledge. Taken together, the individuals’ knowledge increases the group’s capability, especially if each member acquires different skills or, if several members acquire the same skills, their redundancy or speed increases the group’s efficiency. Nevertheless, group learning is something more. Group learning reflects the interrelationships among the members as they apply their individual learning; it includes the group engaging in behaviors such as asking questions, seeking feedback, experimenting, reflecting, and discussing errors. It also includes changes in group ‘‘behavior’’ that may be reflected in changes in language, tools, documents, images, symbols, procedures, regulations, practices, and routines used by the group, all of which are explicit or observable, and rules of thumb, underlying assumptions, and shared worldviews, which are tacit or not easily observable. Both individuals and groups contribute to organizational learning. When one group adopts a particularly effective mode of operation, it can communicate that advance to other groups, thereby serving as a model and

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creating new norms of interaction or production within the organization. Moreover, groups learn modes of interaction with one another. They create communication patterns and paths for getting work done within or outside the formal organizational structure. For instance, an organization may learn how to conduct R&D, transfer new scientific developments to practical applications, and bring those applications to market (the process of technology transfer). At the same time, organizational learning is something more. Organizational learning involves monitoring and obtaining data about the environment (scanning), translating events and understanding consistent with prior conceptualizations of the environment (interpretation), and formulating knowledge about the relationships between actions within the organization and the environment (learning) (Daft & Weick, 1984). Similar to what occurs in groups, learning in this sense would mean some sort of change in the organization’s ‘‘behavior.’’ We can also talk about organizations and groups affecting individual learning (Kozlowski & Salas, 1997). Individuals learn new behaviors as a result of interactions between groups and between members within groups. The systems’ perspective helps us understand both the individual and the collective – how one affects the other, and the internal and external processes that influence and define their interaction (Wilbur, 2000). At the individual level, we can study how people make sense of situations and how they feel about them. We can also study their behaviors and decisions. We can examine how people relate to one another within groups, and how these interactions reflect the culture and norms of the group. The organization imposes practices, policies, structures, and strategies on the groups and its members. The organization is the environment in which the group operates. The environment external to the organization also influences organizational, group, and individual phenomena.

Toward a Multi-Level, Systems Model of Continuous Learning In the remainder of this chapter, we propose mechanisms for systems learning, identify triggers for those mechanisms, suggest how readiness to learn influences whether triggers are acted upon, and describe system characteristics that influence readiness to learn. These variables and their relationships are presented in Fig. 3 as a preliminary model of multi-level continuous learning that serves as a basis for research questions for theory development. We offer these questions throughout the chapter as we develop the model. Because this is a tentative theoretical model, we feel that

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Learning Triggers Environment Embedded systems Host systems

Learning Flow Learning Mechanisms Adaptive Generative Transformative

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Readiness to Learn

System Characteristics Boundary permeability Stage of development Meta-systems perspective

Fig. 3.

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A Multi-Level Systems Model of Continuous Learning.

research questions are more appropriate than propositions. As research is conducted to answer these questions, grounded theory will emerge to create a solid foundation for understanding continuous learning in organizations. Specifically, the model described in this chapter proposes that learning triggers from the environment (including other systems) spark learning. The system’s readiness to learn influences the relationship between learning triggers and learning mechanisms. System characteristics of boundary permeability, level of development, and meta-systems perspective influence the system’s readiness to learn. We begin explicating this model by describing mechanisms for learning.

LEARNING MECHANISMS: ADAPTIVE, GENERATIVE, AND TRANSFORMATIVE LEARNING We propose that systems’ learning can be adaptive, generative, or transformative. This concept describes both the nature of learning and the outcome. Systems can learn to adapt – largely a reactive mechanism. They can learn to generate new ideas for improved functioning – a proactive survival mechanism for survival and growth. They can learn to transform themselves into very different modes of production and new outputs – a proactive mechanism for reinvention. Thus what is produced by the system and how it is produced (the learning process) can be adaptive, generative, or transformative. We consider each possibility in turn and describe how it applies

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to individual, group, and organizational systems. Table 1 provides examples of each type of learning at each level.

Adaptive Learning Adaptive learning is the simplest and the most pervasive means of learning, and the strategy that produces the least amount of change. Systems learn to process and react to new stimuli in their environment, such as perturbations, uncertainties, or barriers to goal accomplishment by making minor adjustments in input, process, or output. In other words, the system recognizes that something is different and that adjustments are necessary. The system may adopt changes that worked in the past when confronted by similar problems. Indeed, this reaction may be an almost automatic response – one made mindlessly rather than after considerable thought or discussion. The system has learned in the past what helps overcome minor barriers, so it simply does the same with little change. The system picks up information about how it should react, perhaps by mimicking other systems. Now, consider how adaptation works at each level of analysis. Individual Adaptive Learning At the individual level, behaviorists have described this type of adaptive learning. It occurs when a change in stimulus produces a behavioral reaction. The stimulus leads to a response that works (is reinforced). People learn to recognize this relationship from their own experience and by observing others (social learning; Bandura, 1986). Individual adaptive learning applies to a variety of phenomena, including how people typically behave, solve problems, collaborate, and make decisions (Kelly, Burton, Kato, & Akamatsu, 2001; Mathews et al., 1989). Group Adaptive Learning Similarly, groups can adapt their membership (input), work processes (member interdependencies), and output (quality and quantity) in response to minor environmental changes. The group may have had similar experiences in the past, so it just repeats what had worked before. Its memory of successful transactions (for instance, who in the group has the knowledge or skills needed to solve a problem) allows the group to adapt quickly. Moreover, the group may model other groups that have adapted by changing procedures or roles. For example, habitual patterns of behaviors allow group members to predict one another’s behaviors (LePine, 2003). A transactive

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Table 1.

Examples of Adaptive, Generative, and Transformative Learning for Individuals, Groups, and Organizations.

Types of Learning Adaptive

Transformative

Groups

Organizations

Regional managers in a pharmaceutical company begin to transfer their best district managers and sales people across regions when their new reward system stipulates that they must do so to receive a bonus In response to a company’s desire to open a branch in a new country, a top-level executive signs up for classes to learn the language and culture of that country

A group restructures itself to new circumstances with little thought or discussion

An individual who moves from being an individual contributor into first-line management completely changes the way she does her job (delegation rather than doing it herself) and how she interacts with peers who are now subordinates

A faculty department in the midst of long-term conflict begins to experiment with ways to overcome old tensions by providing resources and rewards to faculty members who collaborate in research

A company responds to new environmental policies by outlining how it is already ‘‘environmentally friendly’’ without making any other changes A company discovers a new product on the market that its production line can easily accommodate. It learns how to make the product and changes its advertising and marketing A typewriter company acquires first other office equipment companies, then computer and technology companies, and finally closes the doors on providing typewriters

A group takes a training course to learn facilitation skills and quality management tools. The group uses its learning back on the job

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Generative

Individuals

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memory system (TMS) is ‘‘the cooperative division of labor for learning, remembering, and communicating relevant team knowledge’’ (Lewis, 2003, p. 587). It enhances group productivity because the group does not have to invent new procedures each time something unanticipated occurs. Instead, the group draws on the repertoire of successful behaviors. Members have adaptations at the ready to meet uncertainties (Kozlowski, Gully, Nason, & Smith, 1999). Each member fulfills a variety of roles of which other members are aware. The team is able to adjust quickly when unexpected changes occur. This process will be successful as long as the group can make sense of the changes and members can see the changing situation similarly. Indeed, groups that face uncertain situations (e.g., teams of fighter pilots, surgeons, fire fighters, or football players) train for such situations. They practice analyzing changing situations, such as equipment failures or competitive forces. They form protocols of alternative interactions that work in most, but not all, cases, and that leaders can call up at a moment’s notice. In groups, adaptive learning is a matter of mutual sense making, practice, coordination, cooperation, and preparedness. Organization Adaptive Learning At the organizational level, adaptation occurs when the organization alters its norms, traditions, or structures in response to changing conditions. In this case, individuals or groups in the organization sense the need for change. For example, the marketing department may recognize a new competitive force or market potential, or the research and development arm may discover a new technology for production or sales. This event is communicated to other groups in the organization. Deliberations take place within and between groups, and changes in strategies, structures, or products are made in response. This adaptation may become a matter of routine. That is, the organization may learn how to recognize and process these changes and react to them. The organization may even form built-in structures or traditions that handle this task – for instance, market scouts who watch competitors and skunkworks that empower employees to experiment with new ideas for production and products. The nature of the organization does not change much as it adapts, however. That is, the organization retains its principal mission and lines of business (Albrecht, 2003). Argyris and Scho¨n (1978) called this ‘‘single-loop learning.’’ It is the process of detecting and correcting errors and taking advantage of opportunities within the framework of existing organizational policies, procedures, structures, and practices.

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Generative Learning Generative learning entails purposefully adding and applying new skills and competencies for performance improvement and development to be prepared for the future. Individual, group, and organizational systems have the capacity to develop on their own as they consider possible future demands and environmental conditions (Laszlo, 1996). Individual Generative Learning At the individual level, generative learning stems from the internal mental processes of insight, information processing, and motivation for enhanced goal accomplishment. Individuals not only recognize when changing conditions require changing behaviors, but also anticipate these changes. They may even induce changes that, in turn, demand new behaviors. Generative learning is the process of interaction between the person, the person’s behavioral capacity, and the environment. People watch others, emulate them, and build on this knowledge to generate new, improved conditions and behaviors. Moreover, people create their own destiny. They are selfdetermined – able to effect positive conditions as they grow personally and professionally (see the educational model of facilitating adult continuous learning, called andragogy; Knowles, 1975). Individual generative learning is self-determined and goal directed. The goal may be to acquire new skills needed on the job, achieve a career objective, or just to learn for the sake of learning. Individuals are generative learners when they perceive the need (recognize a learning gap) or desire to learn and participate in developmental activities. For instance, they may take the initiative to attend a training program, begin working toward a degree, accept a challenging project, and seek feedback. In this way, people control what they learn across their lifespan. Group Generative Learning Group learning is also purposeful. The team itself or a leader or facilitator may determine the need for the group members to acquire certain skills and knowledge or follow new procedures that require different behaviors. The group goal may change, mandating new work methods and creating a learning gap. The group may decide to experiment by reassigning roles, assuming new responsibilities, or adding new members with skills that the other group members do not have. The goal may be to increase the quantity or quality of production, or it may be to prepare for possible events in an uncertain future.

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Group generative learning can also occur when individual members learn something new and integrate it into the group’s operation. In this way, individual members alter the group’s activities as they educate their fellow group members. This process may occur slowly, depending on the group’s prior success and the other members’ willingness to change. If everything is going well, members may need to be convinced that a new process is worth trying. Group members may need a demonstration, perhaps watching other groups model the new learning. This sort of evolution is a generative process in that the group members recognize the need to change and agree that change is needed. As they learn, they alter the group’s transactive memory. Members come to a new understanding of who knows what in the group. They also learn new patterns of interaction that they may use either now or later when the circumstances warrant. Organization Generative Learning At the organization level, generative learning involves purposefully adding new capabilities to enhance profitability, grow the organization, or prepare it to be sustainable in a turbulent or uncertain environment. To do so, the organization may acquire another organization that has different resources, markets, and production capabilities. Such a merger or acquisition requires focusing on ways to take advantage of the added expertise and capacity, perhaps by restructuring units and meshing different organizational cultures. Alternatively, instead of purchasing and melding pre-existing entities, the organization may develop them using existing resources. Here, individuals and groups within the organization assume new responsibilities, invent new work patterns, or acquire new knowledge and skills. Organizational generative learning may be incremental, in the sense that an organization is always changing, albeit slowly (Weick & Quinlan, 1999). New people are hired and others leave, reporting relationships are adjusted, and training programs are conducted. Alternatively, organizational generative learning may be frame breaking, as when the organization enters a new market, perhaps in a very different culture, or merges with another firm. Such planned, goal-directed changes need to be managed and result in developing the organization (Pettigrew, Woodman, & Cameron, 2001).

Transformative Learning Transformative, or transformational, learning entails a dramatic and fundamental change in the system’s interaction with the environment. Old behaviors

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are abandoned, and commitments are made to new procedures and outcomes. Some systems have a proclivity for inventiveness and readiness to change. Others are more comfortable with the status quo and minor adaptations. Environmental forces may require transformational changes; the alternative could be extinction. Systems develop a new view of themselves through reflective practice and experiential learning. They can watch other systems change, but ultimately learning to transform requires experiencing it first hand. Individual Transformative Learning At the individual level, transformative learning requires a dramatic and fundamental change in the way people see themselves and others (Mezirow, 1991, 1994). During this process, people reconstruct the way they interpret reality. They develop a new understanding of themselves and their relationships. They challenge the validity of their presuppositions, and evolve fresh ways to understand themselves and others. Typically they become more open to new ideas, able to comprehend subtle points, and more adept at integrating their interpretations and ideas and perceiving new connections for more complex understanding. This happens as they learn from their experience (Levine, 2002). It means being more mature – able to be critical of oneself and to see weaknesses that can be overcome and strengths that can be enhanced. Transformational individual learning also occurs as one interacts with others. These interactions bring a richer understanding of oneself and of others (Baker, Jensen, & Kolb, 2002). Individuals develop an increasingly complex narrative of their experiences and the accompanying emotions that lead to new insights. As the world and ways of viewing it change, people experience discomfort. The previous equilibrium of the forces in their lives becomes unbalanced. Individuals become transformed by reframing these experiences into a broader and more complex understanding (Ben-Peretz, 2002). Truths get tested through interactions with others, forcing new ways of thinking. This process is aided by purposeful reflection. The result can be a powerful and lasting change. Group Transformative Learning Group-level transformational learning occurs through experiences and reflections among group members. Group members learn from one another as they talk about their worldviews and demonstrate different ways of

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behaving. Habitual patterns of interaction may be broken when new members enter the group, new goals require new behaviors, or sudden barriers emerge, requiring different skills and interdependencies to overcome (Baker et al., 2002). Group dialogue unearths members’ perceptions and consciousness that, in turn, allow members to explore their day-to-day relationships (Bohm, 1996). What they once took for granted suddenly assumes new meaning. Members explore their individual and collective assumptions, beliefs, ideas, and feelings that control their interactions (Bohm, Factor, & Garrett, 1991). They collectively discuss (reflect on) the team’s objectives, strategies, and processes in relation to the environment, a process called reflexivity (West & Anderson, 1998). They experiment with new ways of working together and, in the process, discover functional relationships and abandon dysfunctional ones. Organizational Transformative Learning In organizations, transformative learning also occurs through experiences and reflective discussion (Chuang & Baum, 2003). Argyris and Scho¨n distinguished between two types of transformative learning, with Nielson (1993) later adding a third type. Double-loop learning (Argyris & Scho¨n, 1978) is detecting and correcting errors by determining why they occurred and then modifying the organization’s norms, policies, and objectives. Tripleloop learning (Nielson, 1993) extends double-loop learning to include questioning all existing norms, policies, and objectives, even beyond those of the organization. Deutero or second-order learning (Argyris & Scho¨n, 1978) occurs when organizational members reflect on previous contexts for learning and recognize conditions when and where previous learning occurred or failed to occur. They learn how to learn by analyzing what went right and what went wrong in the past. Organization transformative learning may also entail individual and group transformations. For instance, a top executive may communicate a vision of a new product, market, or strategy, such as on-line sales. Middle managers may help formulate and implement the new direction, as individuals and their workgroups undergo a transition in which they create new roles and accept new goals. So far, we have suggested learning mechanisms that may apply to individual, group, and organizational systems. Which mechanisms arise in a target system depend on the triggers of learning in external systems moderated by the target system’s readiness for learning. We consider these influences next.

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TRIGGERS FOR LEARNING Triggers may comprise forces (demands), pressures (e.g., competition), or opportunities. They may also be learning mechanisms happening in one system that affect other systems. For example, an individual group member who is engaged in generative learning, perhaps by taking courses to acquire new knowledge, can educate others in the group and lead the group to new intermember relationships. How is learning triggered within living systems? As stated earlier, systems are subject to perturbations from the outside. Outside forces disturb the system’s status quo, thereby triggering learning. However, an outsider can never direct a living system to learn; he or she can only disturb (stimulate) and support the system. The living system determines which disturbances warrant a response (Capra, 2002). Teachers, consultants, facilitators, mentors, and other parties can impose triggers, provide needed resources, and encourage learning in the direction the organization needs, but they cannot make learning occur, predict what will be learned (if anything), or even make the system use what it learns. We do not attempt to delineate or categorize different triggers or pressures for learning because they can encompass any sort of change. Suffice it to say that triggers can vary in clarity, immediacy, and force. This leads to our first research question: Research Question 1: In what ways are individuals, groups, and organizations triggered to learn? Are some triggers more powerful or noticeable than others?

READINESS TO LEARN When and how systems notice triggers in the environment and respond by learning depends on their readiness to change in line with that trigger. Systems choose the triggers that get their attention in the process of engaging in goal-directed work. Research demonstrates that people move through five stages of readiness to learn and change (Prochaska, DiClemente, & Norcross, 1992; Prochaska, Prochaska, & Levesque, 2001): 1. Precontemplation: unawareness or underawareness that goals are not being reached, perhaps because of a mismatch between the individual’s and the organization’s goals 2. Contemplation: awareness of a learning gap but not yet ready to make a commitment to change

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3. Preparation: starting to make small behavioral changes with the intention to make larger changes soon 4. Action: learning to change one’s behaviors 5. Maintenance: maintaining changes The learning mechanisms of individuals – and, by extension, groups and organizations – are triggered in relation to their stage of readiness for that trigger. This suggests two research questions: Research Question 2: How does a system’s readiness to learn moderate the relationship between learning triggers and learning mechanisms? Research Question 3: How can a system’s readiness to learn be stimulated or enhanced?

FACTORS THAT INFLUENCE READINESS TO LEARN Three characteristics influence a system’s readiness to learn: the boundary permeability, the development stage of the system, and a meta-systems perspective. Boundary Permeability Living systems are stable in that they maintain their form over time, although that form evolves continuously. Living systems are also dynamic, open systems in that input flows through them and they are affected by outside forces. To be both open and closed, a system needs boundaries, and these boundaries must be, at least to some degree, permeable. On the one hand, the function of the boundary is to buffer and close off the system from the environment to protect it. On the other, boundaries need to allow resource exchange (see Yan & Louis, 1999). Permeability is the system’s openness to external influences. At the individual level, system permeability is biological (nutrients and air enter the body, and waste exits the body) as well as through the five senses – seeing, hearing, smelling, tasting, and feeling. At collective levels, system permeability is accomplished through boundary spanners, gatekeepers, scouts, ambassadors, sentries, guards, and mechanisms such as communications technology (Ancona & Caldwell, 1988; Katz & Allen, 1985; Tushman & Katz, 1980). Systems (whether individuals, groups, or organizations) vary in the permeability of their boundaries (Alderfer, 1980). Generally, these systems

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struggle to attain and maintain an optimal balance between stability and change. For example, a group may be willing to entertain new ideas for improved task output but will not abandon tried-and-true procedures unless the change agent (a group member, a member of another group, or the organization) can demonstrate the usefulness of a new approach. Openness allows the process of learning to be set in motion. Capra (2002) suggests that boundary permeability varies along three openness dimensions that distinguish systems that learn readily from systems that appear more reticent to learn. First, systems vary in their openness to disturbances. This openness facilitates, and sometimes stimulates, the learning process. Second, systems vary in their openness to, or tolerance for, uncertainty (including confusion, pain, ambiguity, and doubt). This openness allows the system to try different options and engage in learning experiences as a means of reducing ambiguity. Third, systems vary in their openness to novelty. This openness allows the system to generate or transform rather than merely adapt. Openness to Disturbances Individual characteristics may predispose people to be more open to disturbances and thus to be ready to learn. For example, in the case of learning orientation or expansiveness (Dweck, 1999; Dweck & Sorich, 1999; Kaplan, Drath, & Kofodimos, 1991; VandeWalle, 1997), mastery learners may regularly scan the environment for disturbances because they want to learn for the sake of learning as well as for realizing the instrumental benefit. Related characteristics are openness to new ideas and conscientiousness (cf. Avolio, 1999; LePine, Colquitt, & Erez, 2000). Groups also have characteristics that determine their openness to disturbances. Bunderson and Sutcliffe (2002, 2003) applied the concept of an individual’s learning goal orientation to team learning. Teams that are high in learning orientation seek opportunities to develop new skills and knowledge, devote time to learning, enjoy and take on challenging assignments from which they can learn, and are willing to experiment with new ideas. They may establish boundary-spanning roles (scout or ambassador) to garner insight and knowledge from outside the team (Yan & Louis, 1999). Organizations, too, vary in their openness to disturbances. They may have mechanisms or roles for professionals aimed at facilitating change, such as internal organization development consultants who help employees and groups design and adjust to new structures and processes.

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They may have fluid organizational structures (open offices, relatively flat organizational hierarchy, flexible project teams) that foster rapid individual and group change in response to changing goals, customers, and work methods. Tolerance of Uncertainty The experience of instability that occurs when there is a realization that learning is needed, can involve strong negative emotions, including fear, confusion, and self-doubt. If a system needs to learn, that means it has to admit that it does not know something, stop doing what it had been doing, and do something different. The system might look bad as it stumbles trying something new; indeed, it might fail outright. Teams that are high in tolerance of uncertainty have a shared belief among members that the team is safe for interpersonal risk taking (Edmondson, 1999; Edmondson, Bohmer, & Pisano, 2001). Usually, there is a tacit (assumed, unstated) sense of confidence that the team will not embarrass, reject, or punish someone for speaking up. Organizations may be conflict averse and thus not open to uncertainty. In such environments, controversial issues are difficult to raise because organization members fear repercussions. Organizations that tolerate uncertainty discourage – if not prohibit – the use of threats, punishment, or blame. Instead, mistakes or problems are viewed as opportunities for learning. Organizations that learn may offer employees special training in needed interpersonal skills and multiple-rater feedback (performance ratings from peers, subordinates, customers, and supervisors) as a basis for development (Laiken, 2003). Openness to Novelty Systems need to be ready to embrace the new idea or solution once it emerges. In terms of being open to novelty, organizational ‘‘ambidexterity’’ is the capability of the organization to achieve alignment and adapt simultaneously (Gibson & Birkinshaw, 2004). Alignment refers to patterns of activities working toward the same goals. Adaptability is the ability to continuously restructure activities or entities in the organization to meet shifting needs as they emerge. The environment can support ambidexterity by providing discipline, rewards, systems, resources, and trust that collectively induce members to work together to do better. Our next research question points to the need to explore factors that affect permeability between systems’ boundaries, particularly systems at

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different levels of analysis, as well as to discover how these factors affect a particular system’s readiness for learning: Research Question 4: In what ways are the boundaries of individual, group, and organizational systems permeable, and how are these permeability mechanisms (openness to disturbances, tolerance of uncertainty, and openness to novelty) related to systems’ readiness to learn?

System Development Although the previously mentioned characteristics determine the living system’s boundary permeability, making it ready to learn in general, what remains unclear is which triggers for learning and change will affect the system. The degree of system development helps determine this relationship. We can view a system as progressing through a series of observable stages, ranging from simple to complex. The system continues in one state until it encounters a transition point at which the old state no longer suffices, perhaps brought on by growth or some unexpected change, and moves into a new stage of development (see Capra, 1997). Thus where a living system ‘‘is’’ in terms of its development influences what the system is capable of noticing. The world ‘‘looks’’ different to a system in its infancy or in a simpler state than it appears to a more mature and complex system. The learning that occurs is commiserate with the stage of development of the system. Theories of individual development include life stage theories of adulthood from growth, through stabilization, and decline at various ages (e.g., Levinson, Darrow, Klein, Levinson, & McKee, 1978). Another view of development analyzes how people create meaning from the events they experience. Kegan (1982) proposed that people develop through stages of consciousness. In their teenage years, they reach the stage in which they define themselves by their interpersonal relationships. Many people remain in this stage throughout most, if not all, of their work lives. Individuals in the interpersonal stage want to learn what is expected of them in their current or desired role. Those who reach a higher order of consciousness (e.g., the ‘‘institutional stage’’) recognize the relationships they have with others but do not define themselves by these relationships. They generate their own work and directions for learning. At work, they are guided by their own visions and goals for learning and career growth. They respond well to situations that empower them to take control over their own learning, and they help the group and organization develop in the process. Individuals at the highest (known) order of consciousness (the ‘‘interindividual stage’’) see themselves

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both as individuals and as partners in interrelationships, recognizing how they can influence their environment and these relationships and be influenced by them. They realize that what they and others choose to learn creates the situation, and not the other way around. Groups, similar to individuals, also experience stages of development. For example, the integrative, life-cycle model of group development argues that groups move through stages of dependency and inclusion, counterdependency and fighting, trust and structure, engagement, and end-game when members evaluate their work (Wheelan, 1994). The punctuated equilibrium model suggests two primary stages of development: first, forces generate a stable state, which may then be punctuated by periods of sudden change (cf. Gersick, 1989). Similarly, theories of organization development include stages of life and models of a continuous process of development (Cameron & Whetten, 1981). Variations may be adaptive or deliberate attempts to alter the organization’s effectiveness (Van de Ven & Poole, 1995). From this developmental perspective, where a system is in terms of development stage drives its interpretations of learning triggers. This leads to another question: Research Question 5: What is the relationship between a system’s development stage and its readiness to learn?

Meta-Systems Perspective From the living systems point of view, humans, and adults in particular, are unique because we are the only living system that possesses a reflective consciousness (Laszlo, 1996). Not only are we, like many other living systems, aware of our experiences, but we are also aware that we are aware. This reflective consciousness allows individuals – and, by extension, groups and organizations – to understand their environment. That is, we can take the point of view of an individual, the team as a whole, and the organization as a whole. Important meta-systems perspective dimensions include selfconstrual, sensitivity to the environment, and a unified mental model. Self-Construal The way individuals, groups, and organizations define themselves in relation to the systems of which they are a part will affect their responsiveness to, and initiation of, learning and change. This phenomenon has been called self-construal orientation (Hackman, Ellis, Johnson, & Staley, 1999). It is the strength of self-identity – that is, the degree to which

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the individual, group, or organization has a clear conception of himself, herself, or itself. Self-construal can be interpreted in relation to how systems take on the identity of the systems in which they are embedded. This distinction could be based on the extent to which group and organizational members identify with the group and the organization. Do they define themselves as group and/or organization members? Is membership an important part of their existence? Do they take on characteristics (behavioral tendencies and beliefs) of other similarly embedded systems? The longer one system is embedded in another, the more it is likely to assume the characteristics of that larger system and the more likely it can be influenced by that system. For instance, employees learn to identify themselves as members of specific work teams, business units, and their company. A group may act in unison and take collective actions because of, or in relation to, the organization of which it is a part. Of course, group members may have other affiliations that are important to their self-identity within or outside the organization. Within the organization, they may be members of multiple project teams. Outside the organization, they may be members of professional associations, recreation clubs, and, of course, families. The effects of identity will be determined by both its strength and its relevance. For instance, individuals may identify strongly with their families, but this factor may have little effect on their work behavior unless a decision relevant to the family has to be made, such as whether to relocate to take another job. Sensitivity to the Environment Sensitivity to the environment refers to sensitivity to other systems’ needs, expectations, and tensions, especially in interacting with new and different systems. Sensitivity may develop with exposure to differences. A related concept is cultural sensitivity (Albert, 1996; Dunbar, 1996). A person who relocates to another country for a period of time is likely to learn the host country’s customs, values, and perspectives. Sometimes the individual internalizes these new cultural characteristics, adopting them or integrating them with his or her own. At other times, it simply helps to understand others’ behaviors and decisions. This applies to national, organization, and group cultures and individual personalities. People who are sensitive to others’ conditions when they become part of, or interact with, a new system are likely to be influenced by that system. People who perceive these characteristics readily are more likely to be influenced by them and have influence over them. For instance, suppose one

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company acquires another firm. The acquired company needs to mesh with the existing culture of its new parent. If the acquired company has a history of adapting to changes or finding new ways of operating when conditions change, it is likely to be responsive to cues about the new culture and to fit in readily. Unified Mental Model The extent to which individuals, groups, and organizations see one another similarly and accurately may affect their ability to communicate clearly, share ideas about what is important, and agree on goals, behaviors, and actions. This mutual understanding has been called a shared mental model (Fiore, Salas, & Cannon-Bowers, 2001). Members of groups that have a strong unified mental model understand one another’s roles, responsibilities, and capabilities. This coalescence usually emerges over time as the group members become acquainted with one another and demonstrate their ability to contribute to the group task. The more highly unified the mental model, the more the group members are likely to be influenced by one another. That is, the tighter their mutual understanding and shared goals, the more likely they are to respond to one another’s initiatives for learning and change, and the more likely they are to see the situation similarly and initiate learning that others will respond to positively. Moreover, the more unified the mental model applies to two or more systems, the more likely the systems are to respond similarly to forces for learning and be open to a flow of learning from one system to another. The systems will be more likely to be unified on their perceptions of the environment, the forces that impinge on them as individuals and as a group, and the behaviors that are called for as a response. The next research question addresses the need to explore meta-system characteristics as factors that affect a system’s learning readiness: Research Question 6: What is the relationship between systems’ meta-systems perspectives (self-construal, sensitivity to the environment, and unified mental models) and their readiness to learn?

In summary, we have described three characteristics that are likely to influence a system’s readiness to learn: boundary permeability, developmental stage, and meta-systems perspective. These system characteristics can apply to individuals, groups, or organizations, and they can be applied at all three levels to understand readiness to learn. The possibilities are outlined in Table 2. Systems (individuals, groups, or organizations) that are more permeable, are in various developmental

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Table 2. Characteristic

System Characteristics Affecting Readiness for Learning between Systems. Group

Organization

Individuals with a learning orientation regularly scan the environment for disturbances

Teams high in learning orientation seek opportunities to take on challenging assignments and experiment with new ideas

Tolerance of uncertainty

Individuals recognize an uncertain situation and understand the reasons for it

Openness to novelty

Individuals seek new ideas and welcome change

Development stage

Individuals are experiencing major transitions in their lives or careers, such as relocating for a promotion

Group members continue work processes or devise new methods of operation despite uncertain conditions and the likelihood of further change The group welcomes new members; members brainstorm ideas and inquire of each other (and sources outside the group) about new developments in technology, operations, product design, and so on A new group is formed or an existing group is reconstituted, working with new members or taking on new responsibilities of goals

The organization has mechanisms in place for facilitating change such as internal Six Sigma consultants who help departments and groups drive change The organization anticipates changes in environmental conditions and creates contingency plans while maintaining operations Top organization executives put themselves in situations that will expose them to new ideas – for instance, discovering best practices

Boundary permeability Openness to disturbances

The organization is engaged in a major transition such as a merger or reorganization, perhaps as a result of new technology or the introduction of a new product

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Individual

Members identify with the group

Sensitivity to the environment

Individuals are sensitive to differences they perceive in others

Groups accept and welcome different members from other groups, recognize their potential value, and welcome new ideas

Unified mental model

Individuals believe they understand others and share their viewpoints

Group members are in synch; they see one another similarly and share the same goals and viewpoints

Individuals and groups define themselves in relation to the organization – its goals and mission The organization, while having a strong identity and culture, is able to function in different environments and adapt its culture to meet changing norms and expectations Individuals and groups in the organization are bonded by similar perspectives and views of their own, and the organization’s capabilities

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Meta-systems perspective Self-construal Individuals have a strong sense of personal identity

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stages, and have more of a meta-systems perspective are likely to be more open to pressures for learning from other systems in the environment. Together, these characteristics determine a system’s readiness to learn.

LEARNING FLOW BETWEEN NESTED SYSTEMS Up to this point, we have concentrated on how a living system learns continuously without taking into account that the living systems under consideration are, in fact, a nested hierarchy in which each level potentially affects every other level. Here we consider more precisely how continuous learning processes evolve between nested systems. In particular, we address three processes:  Learning flow between levels  Sources of triggers for system learning – a system as host trying to affect embedded systems or an embedded system trying to affect its host; also facilitating factors and barriers to learning  The match between one system’s triggers for learning and another system’s boundary permeability, determining the primary type of learning (adaptive, generative, or transformational) and conditions that promote these different learning types

Learning Flow between Levels Systems learn, not in isolation, but rather by interacting with the environment and with other systems in the environments. Responses to triggers from the environment and other systems in the forms of goals, obstacles, and disturbances affect future behavior and decisions, which may form learned patterns that are repeated because they were successful, or that are avoided because they were unsuccessful. That is how learning flows from system to system. We define learning flow as the extent to which events, actions, or decisions within a system at one level affect other systems at the same or other levels via triggers for learning. The organization may affect individuals or groups by imposing policies, new goals, and strategies for achieving them. Individuals may formulate ideas for learning and encourage others to join with them to effect a change. As such, these other systems may recognize the same need for change and be ready to make it happen. Table 3

Flow of Learning Within and Between Levels.

Focus of Pressure

Source of Pressure Group

Organization

Individual

Individuals are mentored, coached, or received on the job training by knowledgeable others Individuals model the behaviors and attitudes of other individuals

Groups pass on their ways of doing things to new members Group work sparks individual ideas on how to proceed in other work The group’s affect and energy during a meeting influences a member during other work throughout that day The group reflecting on how it is doing inspires individuals to reflect individually

Group

Group members’ knowing about others’ experiences and expertise helps them understand who can contribute to different elements of the group’s task. This leads to patterns of behavior that become enmeshed in group operations – that is, ‘‘how we do things.’’ New

Groups benchmark and model successful other groups

Organization policies, procedures, culture, and so on communicate what is appropriate and inappropriate behavior Learning is a value promulgated by the organization; top executives are champions and role models for continuous learning The organization provides training, job assignments, and other learning opportunities and resources The organization evaluates and rewards learning and development Job requirements affect those, who are hired The organizational climate affects individual work satisfaction Organization policies, procedures, culture, and so on communicate what is appropriate and inappropriate behavior The organization creates workgroups The organization establishes crossfunction and within-function structures and hierarchies

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Individual

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Table 3.

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Table 3. (Continued ) Focus of Pressure

Source of Pressure Individual

Organization The organizational culture affects team culture and morale as well as individual feelings and job satisfaction

Intergroup perceptions, work relationships, and communication patterns affect organization effectiveness Team spirit, friendships, and interpersonal feelings affect the organizational climate (feelings about the organization)

Organizations benchmark other organizations Organizations acquire or merge with other organizations

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Organization

group members disrupt routines and lead to new ways of doing things A motivated individual can energize the group Individuals’ work patterns and preferences affect how they interact with others Individuals request attention to, and resources for, career development Individuals in key positions serve as role models, champions, and sources of resources for learning Individuals create communication patterns and work structures that become routine in the organization Individuals use their knowledge of systems and processes to suggest ideas for continuous improvement Individual job satisfaction affects the organizational climate

Group

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outlines intrasystem and intersystem relationships that may bring about learning, and we ask the following question: Research Question 7: How do systems at one level trigger learning and change in systems at another level?

Sources of Triggers for Learning: Host or Embedded System Earlier, we suggested how systems interact and influence one another as forces for learning between levels – group to individual, individual to group, group to organization, and so on. As such, a system is embedded or nested within another system (e.g., an individual within a group) and/or a host to other systems (e.g., a group to individuals). Thus the trigger for learning may come from the host trying to influence its embedded systems or from one or more embedded systems trying to influence the host. Nested systems are, by definition, dependent on the host. The host exercises control over its embedded systems by controlling resources, establishing policies, offering opportunities (attractive job assignments, status), and otherwise influencing the embedded system’s functions, behaviors, and decisions. Thus hosts are likely to more easily influence their embedded systems than the reverse. However, embedded systems can affect learning within the host; in doing so, they may influence other systems that are embedded in the same host and influence other host systems within the larger systems of which they are a part. This influence is more difficult to achieve than the host affecting the embedded system, and it will take a high level of capability and energy on the part of the embedded system unless the host is weak, perhaps lacking direction, energy, and/or resources. As noted earlier, systems may form loose alliances with other systems. These loose confederations are themselves systems. They are permeable by nature, and as such are open to influences from a variety of sources. However, their ability to exert influence on the systems of which they are a part is not likely to be strong. In a loose confederation, the constituent systems essentially act as the host to the larger system, and the larger system is wholly dependent on its member systems and their interconnections, so it is highly sensitive to its constituent systems. Research that investigates the flow of learning from one system to another should explore the relationships between the systems. Research Question 8: Does the effect of one system on another depend on whether the system is a host trying to trigger learning in an embedded system or an embedded system trying to trigger learning in a host?

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Factors that facilitate learning or present barriers to learning are likely to depend on whether the change agent/educator is a host or an embedded system. When a host provides triggers for learning in an embedded system, facilitating factors may include the clarity of the host’s communication, the host’s openness to input from the embedded system, available resources, and consequences (the link between the embedded system’s actions and valued outcomes). The embedded system’s boundary permeability, development stage, and meta-systems perspective will determine the host’s likely influence over the embedded system. For instance, embedded systems may be in a stage of development that does not recognize the trigger as a need for learning. Additionally, having an embedded system that has a unified model that is consistent with other systems within the host and that identifies itself as a part of the host is likely to increase the host’s success in influencing that embedded system. Barriers to a host’s success include the embedded system’s resistance to learning and change (e.g., the system does not recognize the need for learning or is not ready to make the change) and the system’s learning capabilities. When an embedded system presses for learning in a host system, facilitating factors include the embedded system’s clarity of vision and expression of mutual interest with the host and fellow embedded systems. Barriers may be a lack of power (little control over resources or outcomes that are of concern to the host) and a lack of influence (e.g., ability to convince other embedded systems). Our next research question suggests the need for basic research on a host system affecting an embedded system and the reverse situation: Research Question 9: Are the triggers for learning in the direction of the host to the embedded system more powerful – easier, faster, requiring less energy, producing more positive outcomes, including more positive attitudes – compared to the force for learning in the direction of the embedded system to the host?

Match between External Trigger and System Readiness to Learn From a nested system’s point of view, higher order systems are the environments within which the nested or focal system works. Here we address the match between the environmental pressures on a system to learn and the system’s recognition of the need for readiness to learn. Note that the focal system may be a host or an embedded system. For instance, our focus may be on the pressures imposed by an organization that affect learning in a

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workgroup. In this case, the group is the embedded system, and the organization is the host. Individual group members may be affected by their hosts – both the workgroup, as the more proximal host, and the organization, which is trying to induce the particular learning. The match between one system’s triggers for learning and the other system’s internal readiness or desire to learn will affect the nature of learning that is likely to emerge and that is conducive as an intervention to support learning. Moreover, the match between one system’s triggers for learning and another system’s readiness for learning will determine the type of learning that emerges. Table 4 outlines four conditions depending on the degree of external pressure and the system’s internal readiness to learn, which are explicated here: Box A: External pressure low, focal system readiness low. Encourages stagnation. The system and its host are comfortable with the status quo and are unlikely to recognize or feel a need for learning. This situation can occur for two reasons. First, there may be no current need for learning. For example, the organization’s stock price may be rising steadily despite a downturn in the market; executives receive strong performance appraisals and larger-than-expected bonuses. Alternatively, there may be no perceived need for learning by either system. For example, despite some marketplace indicators that the company’s main product is maturing, the organization, teams within the organization, and individual employees may continue to do the same thing. Box B: External pressure low, focal system readiness high. Encourages generative and transformative learning. This condition may occur for the following reason: The focal system is triggered by something unexpected in the environment. However, the focal system is likely to confront resistance and may not be successful without persistence and

Table 4. Environment’s Need for Learning and Change

Match between System’s Readiness for Change and Environment’s Demand or Need for Change. System Readiness to Learn Not ready

Low

A Status quo; stagnation

High

C Learn to adapt or risk failure

Source: Adapted from Sessa and London (2006).

Ready

B Potential for transformative and generative learning; requires persistence; could be frustrating D Potential for transformative and generative continuous learning

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struggle to overcome barriers to learning. For example, a human resources department, realizing that the organization is losing large numbers of its high potential recruits to its competitors, may join with executives in other functions to transform itself into a strategic human resources system as a way of retaining employees. Box C: External pressure high, focal system readiness low. Encourages adaptive learning only. The focal system’s lack of learning beyond adaptation may lead to minor changes or learning in an unanticipated and possibly dysfunctional direction. This situation may occur for several reasons. First, there may be no need for learning beyond adaptation. For example, the organization may have put into place a balanced scorecard to promote learning at the organizational level and merely asks project teams to provide quarterly reports using a particular form. Second, the focal system may be focused on a different goal or obstacle and is thus not triggered. For example, while the organization is attempting to put a new technology infrastructure system in place, an individual may focus on dealing with a family problem rather than work. Third, the trigger provided may be weak or send mixed messages. For example, an organization might ask an individual to lead a project team but provide few resources to create and run the team. Fourth, the focal system may feel coerced by pressures in the environment. For example, the focal system might be told to make changes despite having little understanding of why changes are needed or might be given little input into the change process. Box D: External pressure high, focal readiness high. Creates conditions that encourage (and are conducive to interventions that promote) generative and transformative continuous learning. This condition may occur for two reasons. First, triggers may be numerous, strong, and aligned. The focal system may receive the same trigger multiple times through multiple channels. For example, an organization might move from the individual to the group as its primary building blocks. In doing so, it might provide team training, change the structure of work, change the reward system, change the pay structure, change how the top management team works together, change the design of its building, and even begin to select and promote people who are seen as extraordinary team players. Second, the focal system may be engaged as a partner in the change effort. For example, the organization, after noticing some troubling trends in its biannual attitude survey, might bring together focus groups and engage action learning teams to address the problem and make recommendations. Research Question 10: How does the match between (1) the triggers from one system for learning and change and (2) another system’s learning readiness affect types of learning that are likely to emerge or that create situations that are conducive to different types of learning?

DIAGNOSIS Our multi-level conceptualization of continuous learning can be used to pose questions to diagnose system changes, identify factors that will facilitate learning to effect these changes, and assess learning outcomes. This section provides three short cases of learning flowing through the levels.

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Table 5 lists examples of diagnostic questions and applies them to the three cases that impose different conditions faced by different focal systems.

Case 1 Acxiom is in the data mining business (based on Kiger, 2002). The firm collects and analyzes large volumes of data such as phone numbers, addresses, and demographic data for use by client companies for marketing and regulatory initiatives. Acxiom’s CEO, Charles Morgan, felt that he was being asked to make more decisions than he could handle. The company’s structure had become highly hierarchical and bureaucratic. Departments were organized around traditional functions, and projects wound their way slowly from one function to another. The company had trouble responding to business opportunities and lacked the flexibility to meet client-specific requirements. The CEO worked with the other managers and executives and the human resources department to develop a more flexible organizational structure that was less rigid and top-driven. The old corporate culture was demolished step by step, and a new one was put in its place. The company flattened its organizational structure to four levels and changed individual roles to reflect what people actually did – not just their titles – so that employees could go to whoever they needed to get a job done, regardless of position in the hierarchy. Acxiom also moved to create a team environment. Instead of organizing fixed, functionally based workgroups, teams were organized around products, technologies, services, or specific client needs. The teams expanded and contracted depending on what was needed, and people became members of multiple teams, playing different roles in different teams (e.g., serving as leader on one team and a functional expert on other teams). The physical layout of offices was changed to an open format with cubicles to maximize face-to-face communications. Applying data mining to its own business, the company created an internal workforce database containing information about jobs and employees’ role experiences and expertise. Training courses emphasized collegial skills such as listening with an open mind. Rather than giving or waiting for directives, individual employees took on more responsibility. Overall, this approach has helped the firm grow, avoid wasted activities and decisionmaking logjams, complete client projects in less time, and reduce employee turnover.

Diagnostic Questions for Understanding Systems Learning.

Diagnostic Questions

Scenarios

Case 1: Transform the structure and culture of a company

In what directions were the forces for learning and change (e.g., individual to group, group to individual)?

The CEO was the initial force for change, although she worked closely with the human resources department to enact the changes. Changes were made at the organization level, cutting across and dismantling existing workgroups and requiring individuals to adopt flexible roles and work patterns Individuals needed to be open to new ideas and ready to adopt continuously changing work roles. They needed to accept a new mental model that was unified across the organization

Was the system that spearheaded the

The prime movers (the CEO and the organization – including its structure and systems) were the

Case 2: Encourage workgroups to engage in generative learning by developing new ideas that will enhance future competitiveness The leaders of the organization created a department to foster learning. The new unit’s director communicated the need to create an incentive system (grants) to encourage participation in R&D initiatives

The organization and its professionals were in the business of teaching others, so they understood learning themselves

The organization, as host, established the new skunkworks unit; the unit was one among

Case 3: Adapt to a new flex-time policy

The human resources policy was introduced by corporate headquarters to be more responsive to employees’ personal needs. Once introduced, employees who wanted to take advantage of the policy influenced their offices managers and fellow team members to participate Office managers needed to recognize the value of the policy to employees and to the agency. Their sensitivity to individual needs and fairness to other employees was key to the policy’s success. The policy was implemented corporate-wide (shared mental model) but at the discretion of the office manager Corporate headquarters ‘‘hosted’’ the policy. However, the ‘‘embedded’’ agencies varied in

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What was happening?

Which system characteristics affected boundary permeability?

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Table 5.

host to individuals, the dissolved workgroups, and the emerging flexible teams

Any individual whose career identity was tied to the function, role, and title he or she occupied previously. These workers needed to unfreeze and work in a less stable environment

Which factors were facilitators of learning (including individual differences)?

The small size of the organization allowed the change to be more ‘‘doable’ across the entire organization. The organization’s early stage of development made it more open to transformation of its culture

whether and how they implemented the policy

Office managers were not all open to adapting their operations to a new policy that added to their workload and could adversely affect interpersonal relations in the office if employees viewed the schedules as unfair or added to their work when others were not in the office

All offices (employees and office managers) had a clear policy to adopt. The method of implementation depended on each office manager’s sensitivity to employees’ needs and ways they could work together to establish

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Which factors were barriers to learning and change?

others embedded in the organization. The director of the unit reached out to employees and managers in other units to engender their involvement The organization has continued to have a stellar reputation since its inception almost 40 years ago, making it difficult to change what had worked well in the past and appears to be working well currently The organization and its market are both mature in terms of development making learning beyond adaptation difficult No one was required to participate. Other units had their individual goals to accomplish, and R&D initiatives could be viewed as a distraction. The need for engaging in the process was not unified across the organization and others were not necessarily sensitive to the need The organization is in the business of learning and development The organization had a history of developments through unsanctioned skunkworks in the past The grants were incentives. Also, the

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change a host or an embedded system?

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Table 5. (Continued ) Diagnostic Questions The organization could use its existing product on itself Individuals’ openness to change, sensitivity to colleagues’ abilities and perspectives, and clients’ goals were key factors The organizational press for a flattened hierarchy and flexible roles were the external forces. Employees’ willingness to accept new patterns of working were internal forces

Did the external forces match an internal need or recognition for learning?

Individuals needed to be ready to understand, accept, and contribute to the new environment. Those who were not were likely to leave. Turnover decreased, suggesting that most employees internalized the learning

What type(s) of learning resulted?

Transformational learning within individuals, groups, and organization. The environment

director’s intervention with managers and supervisors to free up participants’ time helped. Of course, the organization funded and staffed the unit, indicating support from leadership The external forces were the changing competitive environment and shifting client interests that suggested to the leaders that they needed future-oriented innovations. The internal press stemmed from individuals seeing an opportunity to test their ideas in a safe R&D-minded environment outside of their regular job responsibilities The external forces were not strong. The forces within a unit or an individual who wanted to experiment with a new idea were only as strong as the individual’s motivation and ability to win the grant and secure the time away from his or her job Transformative learning for the director, who had to change from an individual contributor engaged

schedules and maintain the work in the office

Office managers were under pressure from headquarters (external) and from their employees (internal) to implement the corporate policy

Pressure from headquarters to implement the policy was low. The atmosphere created opportunities for employees but also raised issues within offices and imposed pressures on the office managers that were not there before Adaptive learning. Office managers learned how to adapt a policy to suit the needs of their employees.

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What were the external and internal forces for learning?

Scenarios

The organization induced the learning through new structures, roles, and training

Which learning interventions were tried, and

The interventions were the methods for culture change. They included training, a flatter structure, a

The grants provided the resources needed for people and departments to engage in R&D.

Employees adapted by working with colleagues who had varying schedules and, in some offices, participating in the schedule decision-making process

Initially, headquarters offered little help to office managers in how to adapt the policy. Some office managers resisted the change. Others saw its value. They also recognized the importance of fair implementation and employees’ participation in decision making for accepting the schedules. Also, some office managers appreciated having discretion in how to implement the policy Allowing office managers discretion, models of successful implementation, and participative

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Was the focal system trying to induce learning? Resist learning? Welcome learning?

in her own research projects to a director motivating and managing peers who did not report to her Generative learning – for those individuals who participated. Projects became models for others to follow, suggesting that over time, others would be encouraged to generate and champion their ideas and indicating the possibility of organizational transformation Adaptive learning at the organizational level occurred as the company accommodated the one project, but was not able to support the projects that were farther from its core Yes, but with modest force and through good examples. Some supervisors and managers may have resisted having their staff members participate. Overall, participation depended on individual motivation

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created a learning organization that was poised for continuous learning and change

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Table 5. (Continued ) Diagnostic Questions

Were outcomes predictable from the answers to these questions?

knowledge-management system, and an open physical layout. Organizational forces for change were strong, and most individuals recognized the need for learning – indeed, they had no choice but to participate or leave. The changes provided employees with more autonomy and more rewarding jobs Yes, the emergence of a unified mental model, the change in individual roles that pervaded the entire organization, and the positive nature of the job change (more empowerment and control over one’s job, more challenging work) probably accounted for the positive result in this professional workforce. If individuals had resisted the change, there may have been more turnover

Because the need was uncertain and individuals varied in their drive, a more powerful force may not have been productive (e.g., a requirement that all people have R&D as part of their jobs)

decision-making – these methods recognized the varying perspectives and the importance of fairness of process and a chance for giving input

Not necessarily. An organization in the education business might be expected to be more open to creating and developing new ideas. Nevertheless, this effort, as conceptualized by the founding director of the skunkworks department, was able to encourage individual initiative. Successful efforts take considerable time to emerge, and many are likely to be revealed as duds along the way. This begs the question of whether there are ways to engender generative learning more quickly throughout the organization

Outcomes varied from office to office depending on attitudes and implementation strategies. Over time, examples from individual offices helped other offices adapt

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were they conducive to the match between external pressure and internal readiness for learning?

Scenarios

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This case describes a transformational organizational change driven initially by a need perceived at the top of the organization and designed and implemented by people throughout the organization. Stable, functional workgroups were dissolved in favor of temporary, flexible teams. Data systems were developed to facilitate work processes. New employees were indoctrinated into the new culture through training and by observing role models. Current employees had to become used to the new, less predictable mode of operating quickly. They needed to give up their allegiance to their functionally based workgroups in which they shared expertise with co-workers and be ready to work with different people at different times – people who would not necessarily understand the value they could add to a project. They needed to demonstrate their value for every project. Thinking of the organization as the focal system, the forces for learning and change went from the organization and the CEO, now called the ‘‘corporate leader,’’ to individuals, cutting through established workgroups. Individuals needed to construe their work identities differently, focusing on their roles and potential contributions to different projects rather than their specific functions. They needed to be sensitive to different client needs and be able to move to fill different roles quickly. The organization established a new mental model, one that over time unified the staff across teams. People who felt uncomfortable with the change, who preferred a professional identity that pigeon-holed them at a particular level and function, would not be comfortable. Those who could not accept the transformation probably left the organization. A facilitating factor was that the entire organization needed to learn a new way of working. Training helped employees understand the purpose of the change and gave them a chance to learn new competencies that were needed to work effectively in the new environment. The corporate leader perceived the need and led the transformation. Other employees needed to get on board quickly. However, they had a chance to shape the change, not just go along for the ride. The changes seemed to have served the organization well and are likely to do so in the future because they created a learning organization – one that can take different shapes as the environment (e.g., technology, client needs) shifts.

Case 2 Executive Development Center (name disguised) is a medium-sized educational institution focused on education of, and research on, mid- to upper-level

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managers and leaders in organizations. Unlike case 1, which involved a transformational, multipronged culture change led by the top that pervaded the entire organization, this case involved an attempt to induce generative learning – to encourage professionals and departments to consider future needs and develop innovations to meet those needs. Leaders in the organization became concerned that the firm was getting too set in its ways and was struggling with ideas on how to continuously rejuvenate itself and its members. Recognizing that many of its innovations had been developed by individual employees on their own time and then presented to the organization, Executive Development Center’s leaders came up with the idea of an organizationally sanctioned skunkworks that would encourage employees to bring new ideas into the organization and develop them, ideally to produce new training and developmental services. Although some of the ideas would probably never lead to anything, a few might enhance and be taken over by the core lines of business, and one or two might evolve into new lines of business, thereby keeping the institution current. Essentially, the intention was to institutionalize generative learning by establishing a skunkworks department – potentially a research and development unit or a way to encourage R&D in other units. The role of director of the skunkworks would be a developmental one, such that whoever was in the role would stay there for only two years or so. The department would have a small budget and a fairly free rein in deciding how to use it. The first director, a researcher, decided that she would not engage in any projects herself (although it was implicitly assumed within the organization that the director was supposed to do the research). Instead, she encouraged the development of interest groups across the organization. She created small grants and encouraged individuals at all levels in the organization to apply, not just the professionals. Also, she encouraged supervisors and managers to help employees (at any level) get company time to work on their projects. She spread word about the grants in the company newsletter and through presentations. Tentatively, organization members began meeting with the director of the skunkworks – often bringing a research idea that they thought she should pursue or that they would like to pursue themselves. However, the director was adamant that the person with the idea should act as its champion because he or she was the one who was ‘‘fired up’’ by the idea. Some ideas fell by the wayside because people were unable or unwilling to pursue the ideas themselves. But soon, several interest groups began coalescing and meeting. For example, one interest group coalesced around organizational sustainability, another around emotional intelligence, and a third around ethics.

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By the end of the director’s two years, ‘‘business’’ was small but steady. Members were getting used to the idea that their creative ideas were important to the organization. And the three original research projects? The organizational sustainability group did its project, but never found a permanent niche within the organization. However, one of its members left the organization to pursue full-time research in the area. The emotional intelligence group, which was more aligned with the institution initially, pursued two research projects and brought in a visiting scientist. Their research was communicated at conferences and through the company press. That group became a permanent new unit. The ethics group never got off the ground for a variety of reasons, but the members of the group were able to move their own knowledge forward and became aware that others were interested in the same topic. When the director began to look around the organization for a replacement, she found numerous candidates from across the organization. One of those candidates was a marketing specialist, a nonprofessional who had participated in a number of the groups. She was interested in moving into the management ranks of the organization, was already a supporter of the skunkworks, and was interested in learning. She was the obvious next choice as director. Her challenge was how to continuously increase interest in learning and development of new ideas and foster more successful, future-oriented innovations. In this case, the force for learning came from within the host organization at the top. Groups and individuals in the groups could respond but were not required to do so. The first director could have identified areas for R&D, conducted the research, and then imposed new products on the firm, perhaps by creating new units that would not disturb existing units. Instead, she tried to educate her colleagues in other units about the need for change and their roles in generative learning. This approach offered a way for them to express their interests and bring about innovations they felt were needed. As such, the organization established a mechanism to support individuals who were ready to learn and experiment. The results were not overwhelming, but two years may not be enough time for a tradition of generative learning to take root. The new director might try different methods. Answers to the diagnostic questions in Table 5 might be of use to her in suggesting interventions to encourage generative learning.

Case 3 In case 3, an insurance agency sought to adapt to a new flex-time policy. A large consumer insurance agency with offices across the country, had

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implemented a policy that allowed employees to vary their work schedules at the discretion of the office manager. Led by the human resources department at company headquarters, the goal was to increase retention of sales and clerical personnel by being more family friendly. The flex-time policy allowed employees to negotiate a schedule that met their personal and family needs if the schedule could be accommodated in the office. Employees could be on a flexible schedule that allowed them to come in anytime between 8 a.m. and 11 a.m. and leave anytime between 4 p.m. and 7 p.m. The schedules could be changed monthly. When the new policy was announced at regional manager meetings, many office managers complained. Some felt the policy was unnecessary in that they accommodated individual needs as they arose. Others felt that the policy would create chaos in their offices. A schedule that suited one person would require imposing an undesirable schedule on others. Employees without young children would feel they were being discriminated against. When the policy was introduced, some office managers let their employees work out the schedules. They established a procedure that allowed all employees to work as a group to establish the schedule and make changes each month. This model was described at subsequent regional meetings so that other office managers could see how it worked. Even so, a few office managers continued to resist the implementation of the policy, and in some cases employees complained to headquarters that corporate policy was not being followed. This case describes adaptive learning and an adaptive change. The organization imposed the change, feeling environmental pressure and seeing an advantage to reduce turnover costs and attract and retain high-quality employees. Office managers needed to adapt to what they perceived to be a loss of control over work schedules and to more chaotic comings and goings of employees that were hard to track, at least initially. They needed to work together with employees, one-by-one or as a team, to establish the schedules, manage the workload, and make sure they were available to customers. The policy introduced issues of fairness, operations management, participative decision making, and control within offices, between office managers and their employees, and between corporate headquarters and local offices. The diagnostic questions reveal differences in perceptions and attitudes about the policy, variability in willingness to adapt, and ideas from offices that were using the policy successfully that could teach other offices how to adapt to the new policy. We could apply the diagnostic questions to numerous other cases – some quite macro (for instance, at the societal level, the U.S. federal government

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trying to transform the social security system) and others quite micro (a small workgroup adapting to a new telephone system). Understanding the triggers for change, readiness to learn, and degree of learning and change required suggests interventions to overcome barriers and facilitate the learning process. This dynamic process evolves over time and offers the potential for embedded systems to learn from one another within and between individuals, groups, and organizations.

LEARNING TRANSFER: FEED FORWARD AND FEEDBACK This chapter has concentrated on stimuli for learning – triggers for learning and readiness to learn – and their effects on a system at another level. A related process focuses on the effects of learning and change in one system on later learning and change in other systems. Learning is a dynamic, intersystem process. Learning in one system, affected by other systems, is likely to stimulate pressure for learning and change in other embedded and host systems. Individuals transfer learning to one another. Groups also educate one another, serving as role models for other groups and in other ways communicating and demonstrating new processes and models of behavior. Organizational learning occurs as learning within individual and group systems is institutionalized in the routines, systems, structures, culture, and strategy (Crossan, Lane, & White, 1999; Vera & Crossan, 2003). This becomes a feed-forward process as well as a feedback process. Learning and change energize others to learn and change, while providing feedback to redirect the system for further learning. Changes are recognized, interpreted, and reapplied, sometimes in modified form. Some changes may be discarded because they do not work in other settings, engender resistance to learning, or produce negative results. Knowledge management occurs as a continually evolving set of behaviors, knowledge, and skills and is applied in other parts of the organization. Unified mental models ensue as systems participate in and process their experiences. The shared understanding guides future reactions (Senge, 1990). Systems have choices when it comes to being influenced by, and learning from, other systems. They can ignore pressures or role models for learning, note these pressures and models and make minor adaptations to current processes, or note them, adapt, and add to or embellish new behaviors, knowledge, and skills. Moreover, they can go further to invent new applications, thereby transforming processes and creating new outcomes.

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This evolution, in turn, can be communicated, demonstrated, and in other ways taught to other systems within or between levels. Challenges of Continuous Learning Systems do not necessarily support other systems’ learning. Consider the following challenges to learning from other systems:  Systems resist change. They may adapt when they need to, but generally they remain comfortable with the status quo unless they perceive a need and are ready to react to, or initiate, change (Bellman, 2000).  Triggers for learning at one level may actually thwart learning at another level. For instance, individuals may learn that the organization does not reward extra effort and so will not go above and beyond expectations.  Organizations often focus on short-term results and do not allow enough time for learning. They may be especially intolerant of decreases in performance – even very short-lived decreases – that may result from learning something new (Albrecht, 2003).  Organizations and groups are likely to be conflict averse. They avoid controversial issues for a variety of reasons – they make people uncomfortable, people do not want to rock the boat, they fear repercussions from their supervisor or others higher up in the organization, and so on. As a consequence, they do not raise issues that others do not want to hear – for instance, we could be doing things better, we should seek feedback, we should consider all options. Over time, potentially unpleasant issues become undiscussable, and innovation (double-loop learning) does not occur.  Feedback is important to guide learning. Assessment is needed (1) to determine whether the right learning has occurred (i.e., whether it is functional) and (2) to identify people, groups, and organizations that are adept at continuous learning or can be made more so through learning interventions.

CONCLUSION This chapter outlined how living systems learn. Using the idea of nested living systems to consider learning in individuals, groups, and organizations allowed us to focus on similarities across the levels in what we know about learning and change. Individuals, groups, and organizations can react to

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changes in the environment by adapting. They can be proactive in their learning and generate what they need to learn. Moreover, they can transform themselves and how they interact with the world. Adapting, generating, or transforming is initiated via triggers from the environment. The system is triggered (or not) due to its readiness to learn. Characteristics that determine a system’s readiness to learn include its boundary permeability, its level of development, and its meta-system perspective. In addition, this chapter addressed the learning flow between systems at different levels, the triggers for learning that stem from the host or embedded system, and the match between the pressure for learning imposed by one system on another and the other system’s readiness to learn. Research is needed to examine the effects of source and degree of pressure for learning and change and readiness on type of learning. The results of such research will help us build a well-grounded, multi-level theory of continuous learning. Systems concepts and processes can also be used to formulate diagnostic questions to analyze instances where transformative, generative, or adaptive learning is needed or can be facilitated.

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CONTINUOUS LEARNING: WHY IS IT STILL AN ISSUE? David V. Day and Brian W. Tate ABSTRACT There has been much discussion and exhortation regarding the need for continuous learning in organizations. We examine why this is still an unresolved issue in most organizations by identifying some of the most prevalent obstacles to continuous learning. General issues are discussed that are associated with the schematic nature of human information processing, as well as the fragile nature of the experiential learning cycle, especially as it pertains to action. We conclude with an emergent multi-level framework that is organized around personal, relational, and structural obstacles to continuous learning in organizations. Removing as many obstacles as possible to individual, team, and organization learning appears to be a promising way to begin to move organizations from the state of relative vulnerability with regard to continuous learning to one of resilience.

INTRODUCTION Ok, everyone who is against continuous learning in organizations raise your hands. Hmmm y well of course there are always a few contrarians out there who would be against anything, including continuous learning, but Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 173–188 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05007-7

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our guess is that most readers of the London and Sessa article wholeheartedly endorse the concept and appreciate their multi-level perspective. So why are we still discussing it? Why are we apparently still in need of theories, models, frameworks, and perspectives on continuous learning? In other words, why are we still talking about it and not just doing it? The logical answer would likely be that continuous learning is more difficult to implement and accomplish than it appears on the surface. The reason why it is difficult may have a lot to do with the obstacles and impediments to learning at an individual level, which are then further complicated as we begin to think about team and organizational level learning. It is a pretty safe assertion that multi-level constructs usually are more complex than those bounded at one particular level. London and Sessa briefly address at the end of their chapter some of the challenges to continuous learning as they see them. We would like to pick up where they left off and elaborate on what we think are the primary obstacles and challenges to continuous learning in organizations. We will take our perspective on these challenges from various literatures, including learning organizations, adult learning, and experiential learning theory (ELT), and the basic principles that define these related approaches to learning.

OBSTACLES TO LEARNING ORGANIZATIONS A point of clarification is that our approach to this commentary on obstacles to continuous learning focuses both on the theoretical as well as the practical implications, but perhaps more on the latter. Also, we will summarize what have been proposed by others as obstacles to learning and extend them to a multi-level framework in the last part of this commentary, organized around the personal, relational, and structural obstacles. Given these caveats, one place to begin with is Peter Senge (1990), who discusses what he calls ‘‘organizational learning disabilities.’’ As he writes, ‘‘Learning disabilities are tragic in children, especially when they go undetected. They are no less tragic in organizations, where they also go largely undetected’’ (p. 18). He has identified seven general learning disabilities in organizations: 1. Focus only on one’s position (i.e., ‘‘I am my position’’ resulting in extreme narrowness of perspective and excessively bounded rationality). 2. Blaming others (i.e., ‘‘the enemy is out there’’). 3. Illusion of taking charge (i.e., being too proactive and contributing to one’s own problems).

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4. Fixation on events (i.e., an emphasis on the immediate or relatively shortterm). 5. Failing to notice gradually building threats to survival (i.e., the parable of the boiled frog). 6. Not experiencing directly the consequences of many important decisions (i.e., the delusion of learning from experience). 7. Skilled incompetence (i.e., refusing to engage in collective inquiry for fear of looking like you don’t know what is going on). Labeling these kinds of things as organizational learning disabilities might be considered a misnomer at best and maybe disrespectful at worst. After all, most individual learning disabilities are thought to have some organic or biological etiology. The kinds of things described by Senge definitely seem more psychological than biological, having to do with issues of ego, attribution, and motivation (among others). This is somewhat ironic given that the focus of his attention is on the learning organization, yet his learning disabilities focus mainly on the individual obstacles. Nevertheless, rather than the exception to the norm in terms of disabilities, these kinds of barriers to learning are likely to be the norm. In short, we take for granted that learning is our default orientation. It is more likely the case that there likely needs to be the right set of psychological and environmental conditions for learning to occur, regardless of level. What do these factors outlined by Senge (1990) tell us about the psychological or environmental issues related to learning? From a psychological perspective, it tells us that we are overlooking a basic principle of human information processing that we prefer to operate as cognitive misers (Fiske & Taylor, 1984). We tend to be miserly in devoting attentional resources to understanding what is going on around us or in terms of other forms of sensemaking. Our preference is to rely on heuristics, schemas, and other types of cognitive shortcuts in our day-to-day functioning unless otherwise prompted or motivated. Unless we are deliberate and intentional about learning, there is little reason to expect it to occur spontaneously. We prefer to operate on a form of autopilot whereas learning requires effortful attention, concentration, and reflection. Research has been consistent in demonstrating that we are cheapskates when it comes to doling out those kinds of valued resources. A typical example of how ‘‘mindless’’ our daily behavior is (Langer, 1989) concerns the notorious example of waiting for an elevator. We push the button to call the elevator and wait. If it doesn’t come relatively soon (like 15 seconds) the most typical action is, of course, is to push the button again – even if we

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see that the button is already lit. This is not the most rational response to enact, but it is the easiest course of action to take. And, of course, if the elevator comes shortly after pushing the call button the second (or third) time our ‘‘irrational’’ behavior is reinforced. There are two notable aspects of this kind of behavior (you can also see it in other situations such as pedestrian crosswalks): (a) we prefer to think about anything else rather than why the elevator might not be coming promptly (that’s no fun), and (b) we generally lack patience although there are demonstrated individual differences in our respective levels of ‘‘time urgency’’ (Conte, Landy, & Mathieu, 1995). Returning to Senge’s list of so-called organizational learning disabilities, it is apparent how most of them boil down (apologies to the frog and its parable) to issues of cognitive miserliness and impatience (time urgency). Focusing on one’s position is our easiest course of action because we know our position well and it is likely a core part of our identity. It takes effort to understand a broader perspective. This reminds us of a poem by Wislawa Szymborka (1997) in which one of the verses asserts sadly, but probably accurately, that out of every 100 people: Those who are just:/quite a few, thirty-five./But if it takes effort to understand:/three.

And it does take effort to understand. Blaming others is easy because of ego defense mechanisms that some say operate in an effortless (i.e., unconscious) capacity. Failing to notice gradually building threats is difficult because it requires that we PAY ATTENTION, which is appropriately termed given our miserly inclinations. Overcoming skilled incompetence requires taking ego risks, which we generally prefer to avoid, but just as important it requires time and effort to learn collectively. Of course it is possible to overcome skilled incompetence if we really feel the need. And that is the rub: We appear to be psychologically ‘‘hard-wired’’ to some degree to avoid feeling that need. We would prefer to make do with what we have already learned rather than devote our valuable and limited resources to new learning. Taking charge and being proactive is one way of dealing with impatience, as are our tendencies to focus on the short-term, which is why learning from experience can be so difficult if you don’t take a longer-term perspective. There is a scientific foundation to all of this. Schema theory has proven to be a ‘‘pervasive and useful framework’’ for studying individual behavior in organizations (Epitropaki & Martin, 2005, p. 659). Schemas can be portrayed as cognitive knowledge structures used by individuals to encode and make sense of information. Schemas, scripts, and heuristics are all forms of knowledge structures that help us adapt to challenging and complex environments, while also ‘‘leaving more time and energy available for

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other, perhaps more interesting or rewarding activities’’ (Macrae, Milne, & Bodenhausen, 1994, p. 37). In short, such cognitive knowledge structures are energy-saving devices that help simplify information processing and response generation. But they do not do much for learning. Research on the encoding flexibility model (Sherman, Lee, Bessenoff, & Frost, 1998) has demonstrated repeatedly that people are more inclined to use cognitive heuristics and schemas when the information environment is demanding. We are flexible information processors and can use a mix of relatively controlled (i.e., effortful) and automatic (i.e., effortless and spontaneous) processing. But the default route appears to be with automatic processing, especially when processing resources are in short supply. Imagine the cognitive demands of organizational life and how this affects the type of processing we tend to engage in on our day-to-day work lives. Recent research has shown that even after a year of daily interactions, employees did not change their original categorization of their respective boss or how they viewed their bosses’ leadership characteristics (Epitropaki & Martin, 2005). Framed somewhat differently, people engaged in very little (if any) continuous learning about a critically important aspect of their work lives – their boss. It has been proposed that one’s relationship with one’s boss is a lens through which the entire work experience is filtered (Gerstner & Day, 1997). If people are unlikely to engage in ongoing learning about something as important to their work experience as their boss, then how likely are they to learn about other aspects of their work? All of this is meant to illustrate that learning is often difficult because it is contrary to some of our very basic socio-cognitive tendencies. And then when you add to that the demand for continuous learning across individual, group, and organizational levels, one can begin to see why continuous learning remains mainly an ideal and not a reality in most organizations. What about the issue of levels? Are there the same tendencies on the part of groups and organizations to engage in ‘‘mindless’’ actions and to demonstrate similar impatient, time-urgent orientations as individuals? Researchers have noted that groups develop fairly consistent ‘‘habitual task routines’’ when it comes to their collective actions (Gersick & Hackman, 1990). Those researchers define a habitual routine as existing ‘‘when a group repeatedly exhibits a functionally similar pattern of behavior in a given stimulus situation without explicitly selecting it over alternative ways of behaving’’ (p. 69). Similar to individual cognitive heuristics, the functional consequences of habitual routines include time and energy saving advantages because they do not need to be actively managed, and can also help to minimize overt competition and disagreement in the group (both of which

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involve the expenditure of cognitive resources). But there are also dysfunctional consequences associated with task routines in groups, including the miscoding of situations and reduced innovation. Whether at an individual, group, or organizational level, habitual routines will likely persist unless there is some explicitly recognized need to change. This can take the form of encountering novel situations that existing routines cannot address efficiently, experiencing failure that motivates a search for new ways of operating, or when reaching a milestone. Gersick and Hackman (1990) note that natural breakpoints in a task provide the impetus for groups to discontinue old patterns of performing and to initiate new ones. Individuals also use milestones as a stimulus for life changes – to whit, the so-called ‘‘mid-life’’ crisis. Nevertheless, some routines are especially tenacious especially if they deal with socioemotional (rather than task) concerns and are relatively well-entrenched. To effectively manage their routines, there needs to be conditions that foster attention on the possible effects of routines as well as motivation to keep those routines that are functional but be willing to break routine when necessary. This is a pretty good recipe for encouraging continuous learning. Individuals, groups, and organizations need to attend to the possible side-effects of relying on heuristics and other routines, and also keep the motivation to maintain the routines that contribute to healthy functioning while discarding those that may be impediments to adaptive, generative, or transformational learning. But as noted before, this is easier said than done. What about the issue of novelty? It has been argued by many (including London and Sessa) that the pervasive experience of unique and novel challenges forces people into a learning mode. That is, new strategies for adaptation, generation, and transformation are required for effective functioning and organizational survival. But what if novelty becomes routine? We know that people habituate to stimuli, and novelty can be susceptible to habituation just like everything else. We are not saying that this is the way it should be; rather it is likely the way it is in practice. Continuous organizational learning requires more than evocations that it is needed. It requires a whole different orientation because it means asking people to behave in ways that are counter to much of their basic human nature.

OBSTACLES TO EXPERIENTIAL LEARNING If there is something that the adult learning literature is in agreement about, it is that adults are contextual-based learners and predisposed to prefer

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learning from experience (e.g., Knowles, Holton, & Swanson, 1998; Merriam & Caffarella, 1999). This perspective has been refined into a more formal framework called experiential learning theory (ELT), which posits at its core that learning is the major determinant of human development (Kolb, 1984). It is considered a philosophy of education based on John Dewey’s call for a ‘‘theory of experience’’ to guide the less traditional approaches to education that he favored (Dewey, 1938). One of the points offered by Dewey (1938) in his treatise on education and experience was that education comes about through experience, but that does not mean that all experiences are educational. Indeed, some experiences may mis-educate the learner by distorting or preventing future experience. A number of more specific educational principles are thought to flow from this basic philosophy (Kolb & Kolb, 2005), and we will discuss a few of those principles that have particular relevance for continuous learning. Several of the ELT principles pertain to ‘‘making space’’ for learning, acting and reflecting, and thinking and feeling. Obviously, this does not refer to a physical space but more of a psychological one. Do people feel connected to others as part of a receptive learning environment, or do they feel alienated, alone, unrecognized, and devalued? As noted previously, learning is difficult because it is often counter to our tendency to operate on cognitive and behavioral autopilot of sorts, and made even more difficult if the psychological climate feels unsafe or hostile to learning. It is already difficult if the organizational environment is merely indifferent to learning but nearly impossible if it is hostile to it. How many times have you heard ‘‘that’s not how we do things around here’’ or ‘‘that’s not the [insert name of your organization here] way?’’ Comments such as those are good data that there is little hospitable space for learning in that organization. Compare this with the experience of one of us who had a boss that never failed to end a meeting with the query ‘‘what have we learned here?’’ It may seem trite or even wearisome after a while, but it was an explicit invitation to share learning with each other. It created a climate in which learning was expected and also created a positive routine of expecting to share group learning on a regular basis. From this perspective it is apparent that an obstacle to learning can occur if no one – especially no one in a formal leadership role – takes the initiative to create a regular and hospitable learning space. There are other things that can help to create an inviting learning space. Many communication experts talk about the need to balance advocacy and inquiry to enhance mutual learning, or what Kolb and Kolb (2005) refer to as ‘‘conversational learning’’ (pp. 207–208). When the predominant approach to conversation is advocating one’s own point of view there is

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restricted opportunity for learning to occur, which was a point also made by Senge (1990). Inquiry is an active approach to eliciting and understanding others’ worldviews. Sometimes it is necessary to focus on advocacy, but oftentimes it becomes the only mode of communication. And when that becomes the case, there is little focus on understanding (i.e., learning) and more emphasis on persuasion. Seeking a workable balance between advocacy and inquiry can help create the kind of hospitable space needed for learning to emerge more spontaneously. A key aspect of ELT is the experiential learning cycle. That is, the most profound forms of learning occur when the learner engages in the recursive cycle or spiral of experiencing, reflecting, thinking, and acting, which cycles back to experiencing. If any of these processes is blocked, then learning as a whole is degraded. There are many obstacles that come to mind when it comes to reflection and deep thinking – mainly in terms of time. Most organization members are subject to the ‘‘tyranny of the urgent’’ (some more often than others) that creates a type of blinders that focuses all attention on the urgent task at hand. Less obvious are the obstacles to action. Kolb and Kolb (2005) explain the importance of action to learning in this way: ‘‘Learning is like breathing; it involves a taking in and processing of experience and a putting out or expression of what is learned’’ (p. 208). Most learners likely appreciate the need for taking in and processing experience for learning to occur, but fewer likely understand that action may be the most important part of the learning cycle. Action helps to bridge the inside world of refection and thinking with the outside world. What kinds of things limit behavior or block actions that are needed to close the experiential learning cycle? There are psychological barriers in which people are too timid to try out new behaviors. People shun curiosity because it may further deepen their uncertainties. This has implications for learning at all levels. As noted by Weick (1993), extreme caution like extreme confidence ‘‘can destroy what organizations most need in changing times, namely curiosity, openness, and complex sensing’’ (p. 641). The difference is that those with extreme confidence do not believe that there is anything else to learn from experience whereas those with extreme caution are afraid of what might happen if they acted on their learning. Extreme risk aversion therefore is antithetical to learning (as is hubris), yet there are countless numbers of individuals, groups, and organizations that are either highly risk-averse or over-confident to a fault. In addition to the psychological or personal obstacles to learning there are also relational and structural barriers as well. The final section of this commentary further delineates

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between the three kinds of obstacles to learning and concludes with practical suggestions as to how to avoid or overcome each general type. Multi-Level Framework on Obstacles to Continuous Learning With the learning process apparently so fragile, it does not take much in the way of obstacles to interrupt or derail the process. As a practical guide to designing ways of overcoming these obstacles to learning, we thought it might be useful to categorize them across three different levels: personal, relational, and structural. By first delineating these obstacles by level, we hope to then be able to highlight the interactive nature of variables across the different levels. Similar to London and Sessa’s argument that the pressure an environment places on a system to learn should be aligned with that system’s readiness to learn, we believe that for learning to occur in organizations there should be an optimal configuration of personal, relational, and structural variables. Recognizing obstacles at these levels is a first step to alleviating them. In the next section, important and practical personal, relational, and structural obstacles to learning will be explored. Personal Obstacles Personal obstacles correspond most closely to those operating at an individual level. They include individual differences and other characteristics that may impede an individual’s ability or readiness to learn. Among the personal obstacles that are most typical in organizational settings are those that affect an individual’s motivation to learn. As mentioned before, a major deterrent to learning is the energy that one has to put forth to learn. In addition, the process of learning may be so threatening or otherwise unattractive to people that the process itself prevents learning from occurring. For someone to devote time and energy needed to learn, there should be enough motivation to overcome the general orientation toward cognitive miserliness that was discussed previously. Looking at learning from an expectancy theory perspective, there should be a high perceived utility of learning. Researchers have found that the extent to which individuals in organizations retain material covered in training is related to their post-training developmental goals and objectives (Kontoghiorghes, 2001). Adult learners in particular are motivated to learn when they feel that learning will be useful for them to satisfy goals (Knowles et al., 1998) or when the utility of learning is otherwise perceived to be high. Among the more specific variables that may reduce the utility of learning is employee age, especially as compared to others in the specific work setting.

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An employee’s age may prove to be an obstacle in that older employees may not participate in learning experiences to the same extent as younger employees. Research supports this seemingly stereotyped view of older workers. In a large, national study a negative relationship was found between employee age and participation in learning exercises at work (Maurer, Weiss, & Barbeite, 2003). The obstacle may not be age, per se, but more of a function of perceived utility as noted above. Older employees may believe that they do not have as much to gain by learning as younger employees. Older employees have less time left in their careers, which means less time to advance their careers and possibly experience less support from the organization to pursue learning. Although prohibited by the Age Discrimination in Employment Act (1967), organizations may choose to invest more into younger employees who may be seen as likely to be employed by the organization for a longer period of time (i.e., return on investment issue). Age has been shown to negatively relate to perceived need for learning, non-work support for learning, and extrinsic (e.g., monetary) benefits for learning (Maurer et al., 2003). An unsupportive environment, combined with a decline in self-efficacy for learning and skill development that possibly accompanies aging (Maurer, 2001), can work in ways that combine to make age a possible obstacle to continuous learning. This suggests that more support is probably warranted for older adults if continuous learning is expected on their part. But as will be discussed in the sections on relational and structural obstacles to learning, support is something across all organizational levels that can help to facilitate learning. Relational Obstacles Obstacles labeled as relational are those that exist between two or more people, rather than within a single person. Such obstacles include ways of interacting that prevent or hinder the learning process. A necessary but insufficient condition for learning to occur between people is effective communication. Relational obstacles negatively affect learning by preventing or distorting information or by causing incorrect information to pass between employees. Among the most important relational obstacles are conflict itself, ineffective conflict resolution skills, miscommunication and language difficulties, and as noted at the personal level as well, a lack of support for acquiring such skills. Although disagreement and conflict can be constructive if handled properly, there is a tendency for people to shut down – or shout down the other party – when conflict emerges. In these ways, the avoidance or mishandling of conflict between people may serve as a hindrance to learning. Ineffective

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conflict resolution means that important issues may not be brought up or that discussion may only precede personal conflict between people. Ineffective conflict resolution may actually lead to a climate of blaming rather than a climate of resolving issues, which as previously mentioned is one of Senge’s seven learning disabilities attributed to organizations (Laiken, 2003; Senge, 1990). Even if blaming does not occur, Vince (2001) found that conflict avoidance among coworkers resulted in poor communication and the ultimate failure of a learning intervention that was introduced in an organization. Ineffective communication is a major obstacle to learning. This typically means that employees are not discussing important information and either cannot or will not learn through interaction. Part of this issue may be due to the quantity of communication and part due to the quality. Although it is often bemoaned that there is not enough communication in most organizations, it is rare indeed to hear that there is ever too much communication. In terms of the quality of communication, both expressing and listening to information is necessary to learn through discussion (Jacobs & Coghlan, 2005); when there is miscommunication, or there is difficulty expressing or listening, learning is difficult to achieve. As important as effective communication is for the sustainability and growth of an organization and its employees, it may be a highly under-appreciated obstacle to learning. There are numerous opportunities for miscommunication to occur in a normal working day through e-mails, presentations, phone-meetings, and even face-to-face meetings. This may be further complicated by potential differences in language skills with those whose native language is not English. Whether one person or many have difficulty expressing or understanding information that is being delivered, a case of basic miscommunication resulting from differences in language skills may lead to the downfall of even the most expensive learning and training interventions. Structural Obstacles Structural obstacles refer to instances in which organizational practices or job characteristics interfere with an employee’s learning. Structural obstacles include such things as organizational norms, aspects of an organization’s culture, and job design issues. This part of our commentary will discuss related issues focusing on employee empowerment, feedback and reinforcement, and coworker and supervisory support in terms of how they may hinder learning. Learning is useful to the extent that what is learned can be translated into action. When employees have control and autonomy over what they do at work, they may have the perception that if they learn something, they can

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test out or use what they have learned, thus enhancing the perceived utility of learning. By being able to practice what is learned, employees are also able to try out new ideas and behaviors in a relatively safe environment. Without practice, there is little likelihood that long-term changes in behavior will take hold. In jobs or organizations where workers are not granted much autonomy, employees may feel stifled and thus lose the motivation to learn. Laiken (2003) observes that an imbalance of freedom and authority in organizations negatively affects employees’ ability to participate in learning activities and to use what is learned through their activities. If they can’t use it, the experiential learning cycle literature predicts that they will lose it. There is empirical evidence that suggests that employees’ perceptions of job involvement and ability to participate in the workings of an organization are positively related to the extent to which employees retain learned information (Kontoghiorghes, 2001). Control over one’s job also has implications for the acting phase of learning by means of individual perceptions of relative power in an organization that can influence the quality of reflection on learning materials. People who perceive themselves as low in power relative to others typically behave with a greater amount of inhibition than those who perceive themselves to be high in power (Keltner, Gruenfeld, & Anderson, 2003). For group reflection, inhibition of those with less power means that important information may not be disseminated. In a qualitative study by Edmondson (2002), it was found that perceptions of large power differences by members of a group made those members unlikely to bring up or argue points in group discussions. Edmondson goes on to say that groups do not engage in effective reflection when group members do not participate in discussions. When employees cannot follow through on what they have learned or when they are uncomfortable in group discussions, the learning process is hindered. To unempowered employees, learning may be a pointless endeavor because they will not be able to translate what is learnt into action. Another obstacle occurs when action is not properly responded to once it occurs. Many empirical studies have supported the case for immediate feedback as more effective for learning than delayed feedback, yet often learningrelated behavior goes unnoticed or unreinforced in organizations. Immediate feedback has been shown to relate to greater performance on post-feedback performance in many studies, both in and outside of work (Dihoff, Brosvic, & Epstein, 2003; Maddox & Ing, 2005; Dihoff, Brosvic, Epstein, & Cook, 2004; Smith & Sussman, 1970; Druskat & Pescosolido, 2002). In cases of delayed or absent feedback, employees may not link actions with rewards. According to Kontoghiorghes (2001), the links between learning efforts and

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performance, and between performance and outcomes, should be clear and strong to enhance the likelihood that employees learn and act on what they have learned. With ineffective reinforcement in organizations, the link between performance and outcomes is neither clear nor strong. Although it is important to highlight the link between performance and outcomes, excessive obsession over outcomes is also an obstacle to learning. By focusing so restrictively on outcomes, organizations may not create an environment where employees reflect on and attempt to understand information, and thus they do not actually have a chance to learn. According to Laiken (2003), organizations that focus too closely on outcomes do not allow their employees time to slow down to reflect. Even meetings, which could be the opportunity for open and shared reflection, are often viewed by employees as mostly valuable time taken away from more important work. No doubt this is for good reason. Next to poor communication, lousy meeting protocol is probably the next most common problem with many organizations and a particular obstacle for those that aspire to enhance continuous learning. With employees overly concerned with their productivity, they may not spend enough time thinking about the process of their work and may be reluctant to participant in meetings and other venues where shared work could be discussed that ultimately hinders the learning process. Retention of learning material has also been found to relate to the extent to which there is social support for using what is learned at work (Kontoghiorghes, 2001; Vince, 2001). Although the relationship seems obvious, there is not always support for learning at every level of an organization. In a case study, Vince (2001) found that although an organization’s top management advocated learning certain desired values during a time of organizational change, those in middle management did not support the learning of those values. As a result, the overall learning process was stymied. Without support, employees may not feel the need to learn, and they probably will not receive the praise and encouragement needed to motivate them to learn. An unsupportive work environment may make employees feel undervalued and, most likely, will deter them from pursuing challenges and opportunities for learning at work.

SUMMARY AND CONCLUSIONS Our commentary to the London and Sessa article on continuous learning in organizations has focused on obstacles that make achieving this objective a difficult one. We began with the general observation that it cannot be

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reasonably assumed that people operate naturally as continuous learners. Indeed there are empirically supported reasons suggesting that learning is not the default mode for people. It is only when they begin to expend cognitive resources to overcome their ‘‘mindless’’ tendencies (Langer, 1989) that learning can take place. There is also reason to believe that learning is difficult for teams. At the core of these reasons is the observation that individuals and teams – and possibly organizations as well – develop habits, routines, heuristics, and schemas that guide behavior in relatively effortless (i.e., mindless) ways. To some extent, this is functional because it allows for quick and generally accurate sensemaking, and conserves cognitive resources. The point was made that at the individual or personal level we are cognitive misers. The research evidence suggests that the same would apply to groups of people as well, regardless of the size of the group. Obstacles to learning can also be understood through examining the principles of experiential learning theory (e.g., Kolb, 1984) and especially the experiential learning cycle. Anything that interferes with the core learning processes of experiencing, reflecting, thinking, and acting will undermine the ability to learn from experience. Often overlooked in this cycle is the need for action to connect the cognitive appraisals of experience with behavior. Actions then create the next experience that starts the learning cycle anew. Anything that interrupts these processes breaks the learning cycle. It was noted that lack of support at any of the levels examined (individual, team, or organization) is a likely contributing cause when learning breaks down. The final section elaborated on what we see as some additional potential obstacles to learning. We organized these around personal, relational, and structural impediments. This is not an exhaustive list by any means. It does highlight a couple of important aspects related to continuous learning. First, some things like support and motivation for learning operate across all levels. Second, there are potentially many things that can derail the learning process. Learning is not necessarily a robust one in organizations. The next steps may be in working from this state of relative vulnerability with regard to continuous learning to one of resilience (Weick, 1993) in which it is embedded across all organizational levels. Removing as many obstacles as possible to individual, team, and organization learning appears to be a promising way to begin.

REFERENCES Conte, J. M., Landy, F. J., & Mathieu, J. E. (1995). Time urgency: Conceptual and construct development. Journal of Applied Psychology, 80, 178–185.

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Dewey, J. (1938). Education and experience. New York: Simon & Schuster. Dihoff, R. E., Brosvic, G. M., & Epstein, M. L. (2003). The role of feedback during academic testing: The delay retention effect revisited. Psychological Record, 53, 533–548. Dihoff, R. E., Brosvic, G. M., Epstein, M. L., & Cook, M. J. (2004). Provision of feedback during preparation for academic testing: Learning is enhanced by immediate but not delayed feedback. Psychological Record, 54, 207–231. Druskat, V. U., & Pescosolido, A. T. (2002). The content of effective teamwork mental models in self-managing teams: Ownership, learning and heedful interrelating. Human Relations, 55, 283–314. Edmondson, A. C. (2002). The local and variegated nature of learning in organizations: A group-level perspective. Organization Science, 13, 128–146. Epitropaki, O., & Martin, R. (2005). From ideal to real: A longitudinal study of the role of implicit leadership theories on leader–member exchanges and employee outcomes. Journal of Applied Psychology, 90, 659–676. Fiske, S. T., & Taylor, S. E. (1984). Social cognition. New York: Random House. Gersick, C. J. G., & Hackman, J. R. (1990). Habitual routines in task-performing groups. Organizational Behavior and Human Decision Processes, 47, 65–97. Gerstner, C. R., & Day, D. V. (1997). Meta-analytic review of leader–member exchange theory: Correlates and construct issues. Journal of Applied Psychology, 82, 827–844. Jacobs, C., & Coghlan, D. (2005). Sound from silence: On listening in organizational learning. Human Relations, 58, 115–138. Keltner, D., Gruenfeld, D. H., & Anderson, C. (2003). Power, approach, and inhibition. Psychological Review, 110, 265–284. Knowles, M. S., Holton, E. F., III, & Swanson, R. A. (1998). The adult learner: The definitive classic in adult education and human resource development (5th ed). Houston, TX: Gulf. Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice-Hall. Kolb, A. Y., & Kolb, D. A. (2005). Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of Management Learning & Education, 4, 193–212. Kontoghiorghes, C. (2001). Factors affecting training effectiveness in the context of the introduction of new technology: A U.S. case study. International Journal of Training & Development, 5, 248–260. Laiken, M. E. (2003). Models of organizational learning: Paradoxes and best practices in the post-industrial workplace. Organization Development Journal, 21, 8–19. Langer, E. J. (1989). Minding matters: The consequences of mindlessness–mindfulness. Advances in Experimental Social Psychology, 22, 137–173. Macrae, C. N., Milne, A. B., & Bodenhausen, G. V. (1994). Stereotypes as energy-saving devices: A peek inside the cognitive toolbox. Journal of Personality and Social Psychology, 66, 37–47. Maddox, W. T., & Ing, A. D. (2005). Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 31, 100–107. Maurer, T. J. (2001). Career-relevant learning and development, worker age, and beliefs about self-efficacy for development. Journal of Management, 27, 123–140.

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Maurer, T. J., Weiss, E. M., & Barbeite, F. G. (2003). A model of involvement in work-related learning and development activity: The effects of individual, situational, motivational, and age variables. Journal of Applied Psychology, 88, 707–724. Merriam, S. B., & Caffarella, R. S. (1999). Learning in adulthood: A comprehensive guide (2nd ed). San Francisco, CA: Jossey-Bass. Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization. New York: Doubleday. Sherman, J. W., Lee, A. Y., Bessenoff, G. R., & Frost, L. A. (1998). Stereotype efficiency reconsidered: Encoding flexibility under cognitive load. Journal of Personality and Social Psychology, 75, 589–606. Smith, K. U., & Sussman, H. M. (1970). Delayed feedback in steering during learning and transfer of learning. Journal of Applied Psychology, 54, 334–342. Szymborka, W. (1997). A word on statistics. The Atlantic Monthly, 279, 68. Vince, R. (2001). Power and emotion in organizational learning. Human Relations, 54, 1325–1351. Weick, K. E. (1993). The collapse of sensemaking in organizations: The Mann Gulch disaster. Administrative Science Quarterly, 38, 628–652.

A MULTI-LEVEL INQUIRY AND ELABORATION: CONTINUOUS LEARNING WITHIN AND ACROSS ORGANIZATIONS, GROUPS, AND INDIVIDUALS Steven E. Markham, Richard L. Groesbeck and Bret R. Swan ABSTRACT In this commentary, we will examine London and Sessa’s (this volume) article on continuous learning in organizations from alternative researcher and practitioner perspectives. Specifically, we review its major points, examine the issues of entity selection and definitions, discuss the need for a ‘‘parts and wholes’’ approach, contrast alternative process models that stress the importance of time, and speculate about different user perspectives and their approaches. On balance, their article will stimulate much needed discussion and research in an important area.

Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 189–199 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05008-9

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INTRODUCTION With hundreds of new articles appearing each year on the topic of organizational learning, there is little doubt of the salience of this topic to researchers and practitioners. Thus, there is a need for debate and discourse about how the concepts of the living systems framework, pioneered by Miller (1978), can be transferred from a general discussion of biological systems to direct applications in organizational studies. London and Sessa (this volume) have provided a service in laying the ground work for this on-going discussion of how to build a levels of analysis framework for continuous learning research. In examining their article, we will focus on a number of issues. First, we will review the salient points offered by London and Sessa. They have elected to operationalize three levels of analysis (individuals, teams, and organizations); more importantly, they decided to look at them from a ‘‘wholes’’ perspective. For these three types of entities, continuous learning is a very broad concept. To address this issue, London and Sessa have identified three types of learning (adaptive, generative, and transformational), each referring to a more and more comprehensive and radical form of change. In terms of processes, they are primarily interested in how various triggers can touch off learning and if an entity is ready for such change. Finally, they develop three mini cases to illustrate their points. In conclusion, they offer ten research questions to guide this area’s future development.

SYSTEMS AS METAPHORS: SYSTEMS AS SCIENTIFIC INQUIRY The application of living systems theory to the study of organizations is both attractive and perilous. On one hand, there is a natural elegance to viewing any system, especially social systems, as a set of nested or embedded entities. On the other hand, the strict application of living systems theory presumes that such systems have the ability for self-replication, selfstructuring, and self-regeneration, much the same way an ecosystem would regenerate after a devastating storm. However, these system characteristics appear to preclude the notions of plan-fullness and self-awareness which are attributes of human systems, but not all biological systems. Thus, we are left in a quandary: should we utilize living systems theory as an extended metaphor to help explain and visualize our theories of organizations across levels of analysis? Or, should living systems theory be applied in a rigorous

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and complete manner for scientific testing? If the research community selects the second choice, then more detailed theoretical and operational analysis will be required. Defining Learning To help realize this goal of operationalizing living systems theory, a number of questions can be raised about basic definitions and assumptions. For example, consider the definition provided by London and Sessa (this volume) for continuous learning: y a deepening and broadening of a system’s capabilities in (re)structuring to meet changing conditions, adding new skills and knowledge, and (re)creating into a more and more sophisticated system through reflection on its own actions and consequences.

When examining this definition, it is difficult to clearly separate learning activities and behaviors from those activities that are merely normal responses from the system to a stimulus, e.g., a ‘‘change.’’ In fact, London and Sessa define learning at different levels of analysis from the standpoint of observable changes in behavior. In contrast, what if ‘‘learning’’ did not result in a directly observable change in behavior because, for example, when the new knowledge was acquired, it did not translate directly into behavior despite being important to future work? Can London and Sessa’s definition be used to delineate different types of learning at different levels of analysis? For instance, if we substituted the phrase ‘‘change management’’ for ‘‘continuous learning,’’ would the above definition still apply? We suspect so, which suggests a great deal of overlap between the two constructs. Further, let us consider what would not be considered continuous learning. To the extent that living systems are always responding to the environment in some form or fashion as long as they are alive, would any adaptation also be a version of continuous learning? Or is there something special above and beyond simple change that is unique to learning? We would like to believe that not all adaptation (e.g., the most trivial adaptation in a stimulus–response context) would be considered learning. As a final part of this thought experiment, consider what would be the opposite of continuous learning? Simple learning? Punctuated learning? Regressing? Or might it be stasis or rest? Thus, to clarify the meaning of continuous learning, we seek to differentiate it from related ideas, such as change (mentioned above), and to distinguish it from its antonyms, if possible. In sum, must learning always result in change; or, is change always the result of learning, thereby introducing a tautological aspect to the definition?

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In terms of definitions, we also wonder about the types of learning processes as given by London and Sessa, i.e., the traditional distinctions between adaptive, generative, and transformation learning. Are these categories really learning processes or mechanisms? Are learning mechanisms more related to the software, hardware, and infrastructure associated with the learning process? Or are these three categories really attempts to define states of learning? In addition, while these three can be attractive, are they unique and mutually exhaustive? Do these categories of learning largely characterize or encompass what is continually occurring at the individual, group and collective levels in organizations? In fact, can we safely assume that adaptive, generative, and transformative learning are always (i.e., continually) being enacted across all relevant levels of analysis? Or are there moments of rest when learning activities and change efforts momentarily cease or even regress (e.g., when key people with critical knowledge change jobs or leave the organization)? Are these learning processes independent or can they occur simultaneously in the same individual or group? From a process point of view, when does adaptive learning become generative or transformative? How does one transition from one to the other? With respect to the last category, is it even desirable to suggest that organizations should engage in a continual process of radical transformation? We suggest that there is room for a concept such as ‘‘appropriate change and learning’’ in which an entity’s efforts to change are appropriately matched with the needs of the situation. This term would avoid a bias toward unnecessary transformative struggles, and it would restore a more balanced view of adaptive learning, which appears to be viewed negatively. A final definition, that of triggers, is also tantalizing in that London and Sessa offer the notion of forces, demands, pressures, or opportunities, all of which represent some type of external perturbation. However, they shy away from a detailed operationalization of this concept. For example, what are specific triggers of learning from the educational and organizational literatures? Are these triggers level specific? Which learning processes ‘‘trigger’’ external versus internal change, and when do behaviors become observable, if at all, that result from continuous learning?

Defining Entities A major contribution of London and Sessa’s article is that it stands as an example of work that explicitly recognizes different types of entities (individuals, teams, and organizations) as a necessary component of a theoretical

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description. (We believe that future organizational research and theory building will need to deal with simultaneous specification of both entities and variables.) While their selection of three levels undoubtedly was necessary to make their initial conceptual task more manageable, we will pursue additional themes with respect to the definition and selection of entities. First, the three types of entities described do not represent an exhaustive listing, nor are they intended to. For example, it would be possible to examine the way in which vertical dyads (superior–subordinate pairings) encourage or discourage learning. Similarly, horizontal dyads might hold a key to understanding a learning process which could ‘‘trigger’’ a change effort for an individual within the dyad. There are also a host of larger collectivities that also might uniquely impact the process of learning at lower levels of analysis. These may include departments, plants, divisions, strategic business units (SBUs), corporations, joint ventures, strategic alliances, and virtual enterprises. All might have different policies and practices with respect to learning (i.e., external stimuli). All of these entities may also be influenced by differing societal or corporate cultures with respect to learning (i.e., internal stimuli). Even traditional teams show surprising variation and should not all be considered the same type of self-managed work group when it comes to issues of formal and informal pedagogical efforts. Thus, much work can be done in the future on the taxonomy and classification of different organizational entities and their attributes, particularly with respect to organizational learning. A second theme emerging from the London and Sessa decision to focus on ‘‘wholes’’ is the necessary exclusion of the ‘‘parts’’ view. Within the literatures pertaining to living systems and to multiple levels of analysis, the ‘‘parts versus wholes’’ distinction is one of the most overlooked and difficult aspects of these theories to elucidate. Nonetheless, Senge, Scharmer, Jaworski, and Flowers (2004) noted that the entire argument and framework of their book, ‘‘Presence: An Exploration of Profound Change in People, Organizations, and Society,’’ begins with the understanding of the nature of wholes, and how parts and wholes are interrelated. Thus, while an exclusive focus on wholes simplifies the magnitude of London and Sessa’s framework, it also foregoes key dynamics and insights that are inherent and crucial to the processes of continuous learning in organizations. For example, rather than call an embedded entity a ‘‘part,’’ which denotes a mechanical flavor (as in a ‘‘part of a machine’’), we could, instead, use the term, ‘‘holon’’ (as in ‘‘a small replica of a larger holograph which contains the same information’’). This term emphasizes both the holon’s separate functional integrity along with its shared identity and the characteristics that

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make it an element of a larger collectivity. Indeed, this is the basis for looking at organizations as holographs; i.e., individuals embedded within the structure that contains it, to a greater or lesser extent, the ability to reproduce the entire pattern of the organization. In turn, this nomenclature could lead to interesting research on the way in which ‘‘parts’’ come together to form a ‘‘whole.’’ With respect to continuous learning in organizations, this distinction would, in turn, allow us to examine questions of (1) how hierarchy compares with heterarchy and (2) which processes are unique to one level or that emerge from differences amongst parts at lower levels. When London and Sessa write, ‘‘ydeliberations take place within and between groups, and changes in strategy structures or products are made in responsey,’’ they implicitly recognize the need to deal with the parts level of analysis as represented by their extended discussion of different types of learning processes within different levels of analysis. In the case of a ‘‘parts’’ condition, a group is not yet considered a ‘‘whole,’’ but there is something about the group convergence process that ultimately should produce a shared mental model. As a precursor to this group convergence process (i.e., this process of moving from parts to wholes) should we assume that groups, throughout their life cycles, are homogeneous or heterogeneous and on what dimensions? These are unanswered questions that will need specification to utilize London and Sessa’s framework for multi-level research with organizational learning.

Defining the Process Fig. 3 in the London and Sessa article visually depicts a process flow model which starts with ‘‘triggers’’ of organizational learning. As such, it represents the process; however, in a key respect, it reflects only certain variables of interest, not the multi-level entities or cross-level effects on organizational learning that they advocate elsewhere in their paper, nor does their visual model represent the way in which such a ‘‘flow of learning’’ would operate in an organization. In other words, because the entities are left out of an explicit visualization, we do not have enough information to create a either a functional, multi-level research model, or a simulation model of organizational learning. (Note that in computer simulation, the problem of tracking all entities, be they production inventory or rain drops in a meteorological forecast, is not a theoretical limitation; it is only constrained by the hardware’s limits (Markham, 2002).) How does this apply in an organizational setting? A flow model might make sense if we are tracking the sequential,

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time-ordered processes that an individual enacts from the time that the trigger is perceived to the ending point when remediation has been put in place. However, as soon as we simultaneously include multiple levels of analysis, then other process models become possible. For example, if we envision individuals nested within groups within departments, much like Fig. 2 in London and Sessa, it might be possible to have (1) a transmission form of learning in which one person triggers other individuals who are located sequentially upstream and downstream relative to the process until a critical mass is reached and the resulting consensus produces changes. Alternatively, using the same initial Fig. 2, it might be the case that (2) an ‘‘infection’’ mechanism better models the way in which learning is triggered and spreads through the organization. Anyone ‘‘exposed’’ to the new idea will ‘‘get it,’’ even if they are located in very distant groups that are not directly part of the work process flow. It is simply a matter of time until enough members ‘‘contract’’ the new realization and a major change results. Thus, learning may not necessarily equal an observable behavior change for a long time, if ever. Finally, it may be that (3) exposure to the new realization and learning is so powerful that it induces immediate change; much like a catalyst or a crystallization process; entities in the system convert as whole units, like water exposed to freezing temperatures, and these whole units almost immediately restructure into the a totally different qualitative state, i.e., ‘‘ice.’’ These analogies are helpful in that they lead us to ask the question, ‘‘At what point does the sharing of learning create a whole-entity condition?’’ (i.e., a condition in which a new realization or new skills has permeated a whole dyad, whole group, whole department, etc.). Along the same lines, how many members of the entity have to share the learning to qualify for a shared mental model (i.e., ‘‘wholes’’) condition to exist? Or is this better asked as, ‘‘What percentage of the unit’s members have to share this new learning in order for it to be considered to have affected the whole group?’’ In contrast to a possible ‘‘wholes’’ condition, it may be that change can be enacted and skills learned using a very different organizational model. For example, it is possible that one benefit of organizing in teams or groups is that the diversity of skills and experiences of its members (a ‘‘parts’’ condition) creates an overall better team or group result. However, to achieve this result, learning may need to take place by only among a few key members of the group. This argument suggests that, while shared mental models might be necessary for effective coordination (Cannon-Bowers & Salas, 2001), they may not be a necessary pre-condition for change. In fact, the heterogeneity of distribution of learning among team or group members

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(i.e., a ‘‘parts’’ condition) could well be a primary drive to group and organizational-level change (Hollan, Hutchins, & Kirsh, 2000; Mohammed & Ringseis, 2001). This ‘‘parts’’ perspective toward group learning also seems to align with prior group work literature and practice. With respect to London and Sessa’s proposition that learning equates to observable changes in behavior, is it the case that when everyone at one level of analysis learns the same thing, triggering the same shared mental model throughout the whole group or organization (i.e., a ‘‘wholes’’ condition)? Or, do learning results differ within each different context within the groups or organizations (i.e., a ‘‘parts’’ condition)? To further illustrate that point, is it also possible that, although everyone at a given level of analysis may be taught the same ‘‘thing,’’ each individual in a group or each group within a department may be edified by realizing what is ‘‘learned’’ differently depending on their specific contexts (e.g., roles, objectives, etc.)? Thus, mental models and behaviors from the same ‘‘learning’’ may differ widely throughout an organization (i.e., a parts condition). Another alternative might be the traditional method of ‘‘command’’ as enacted in a traditional hierarchy. It may well be that the organization’s senior officer needs to ‘‘command’’ the change to make it so. How is this situation to be accounted for from a multi-level perspective? Should this be considered as superior–subordinate dyads between each person in the group and the senior officer, or should this situation be treated as a ‘‘wholes’’ condition? This possibility has been addressed in prior multi-level literature (Dansereau, Alutto, & Yammarino, 1984) and must be accounted for in any theoretical framework addressing continuous learning in organizations. To complete our discussion of London and Sessa’s framework and their resulting process model, an implied variable, time, needs a more explicit description because it is inherent to and addressed in learning literature. For example, are we postulating tabla rosa situations in which the individuals and organizations are new to a problem, and therefore developing their learning abilities? Or are we describing mature individuals, dyads, teams, and/or organizations who are well into their prime after they have been through many cycles of both adaptation and transformation? In either case, we need to distinguish a starting condition from an on-going condition. We should allow for a situation where, at one level of analysis, a tabla rosa condition exists: new learning has been triggered for the first time in a group of new hires where they lack the established knowledge and wisdom about the organization that existing employees may possess. Their initial change cycle has just begun. At the same time in other parts of the organization and/or at another level of analysis, there might be well-established learning

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cycles, as is often the case where current employees may have up-to-date knowledge and skills to bring to the organization. Finally, a research framework should also be able to allow for the specification and investigation of both the frequency of these types of learning and change events (such as radical transformative organizational change) as well as their cycle times.

APPLICATION PERSPECTIVES There are at least five different practitioner perspectives that may be applied with respect to the topic of organizational learning. These include: (1) the TQM specialist for whom continuous learning is an important by-product of the search for systematic variability in production processes, (2) a member of the Society for Organizational Learning who would directly apply this framework in designing interventions within organizations for enhancing learning, (3) a Chief Learning Officer (CLO) or Human Resource manager charged with the responsibility of creating and administering systems to facilitate learning in an organization or company, (4) an organizational modeler who has to mathematically describe and then simulate how changes in learning might lead to changes in organizational outcomes, and (5) a team, group, department, or division manager who has been tasked, among other responsibilities, with identifying and facilitating necessary training and learning for his or her subordinates to maintain and improve current dayto-day operations. The latter perspective is probably the most common one experienced by the majority of managers in industry today. First, the TQM practitioner is keenly interested in learning, but usually at the individual, dyadic or group level. More specifically, any insight into problem identification and the application of one of the many tools in the TQM toolkit would be of interest. From a multi-level viewpoint, the interface between individuals and the group would be of most salience to program success. From this perspective, having many different learning mechanisms or triggers from which to choose would be of benefit. More importantly, having some practical rules of thumb for identifying when (1) enough of a ‘‘shared mental model’’ (i.e., a ‘‘wholes’’ condition) had been established, as well as, (2) enough complimentary, distributed learning (i.e., a ‘‘parts’’ condition) had occurred to spur new ideas and innovations necessary for positive change. Second, for the Society of Organizational Learning (SOL) consultant, an even heavier emphasis would exist for developing and delivering learning

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activities that would spark innovation within the organization. Thus, questions of boundary permeability and transmission between groups and departments would be of high interest, but less attention would probably be paid to the issue of what constitutes a trigger. Rather than focus primarily on individuals, the SOL practitioner would be more naturally attracted to the group level of analysis (or higher), but the definition of what constitutes a group would be more encompassing. For example, one could (1) identify different roles and professional requirements that act as key leverage points for changing behavior within the system, and then (2) develop and determine education and learning triggers to promote positive change in the system. Third, CLO or a Human Resource manager would be very interested in those parts of the framework that would apply to the largest entities because these would reflect (1) policy decisions about which learning activities would be encouraged, (2) budgetary decisions concerning which programs would be funded, and (3) infrastructure decisions about which types of tools and IT support would be made available. Thus, there would be a great deal of compatibility between London and Sessa’s framework and the types of concerns evidenced by a CLO. However, the CLO might want a working example of a number of terms, such as a ‘‘transacted memory system’’ (London & Sessa, this volume). If anything, the CLO might complain that the framework is incomplete because it left out key components of a learning system: e.g., the databases and repositories into which the accumulated knowledge and documentation are kept. In other words, a CLO (or CIO) often sees these repositories as key facilitators of a productive learning environment and would want to map their use and influence at the individual, dyadic, group, and collective levels of analysis. The fourth perspective would be that of the scenario builder or organizational modeler or simulation expert. This is the person charged with the responsibility of creating mathematically based models of various organizational processes that predict problems and can be used for designed experiments. From this perspective, more theoretical specification of this framework will have to be done. Why? To create these graphically intense simulations, all holons in the system, along with their nested memberships would need to be displayed. This ability to work at the atomic level (i.e., the most granular entity) also means that the transference mechanisms of state changes (i.e., using a meteorological analogy, when clouds start shedding excess moisture in the form of rain) must be described so that they can be mapped and visualized. These detailed tasks should be reserved for future research activities. The fifth and final perspective would be that of a training manager of a team, group, department, or division. Although not their primary

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responsibility, an important responsibility of a manager that maintains and improves group operations is to identify and facilitate their subordinates understanding of the required processes and have opportunities for learning experiences. These are by far the most numerous group of people that can benefit from functional, multi-level models of organizational learning that includes both variables and entities of interest.

CONCLUSIONS Overall, the multi-level framework developed by London and Sessa should be commended as the type of work needed to advance organizational theories that will ultimately be useful and satisfying to a wide range of audiences. Their paper helps to facilitate thoughtful discussion in an important area of research, and it points to a variety of opportunities for further specification and conceptualization that need to be addressed before multilevel, theory-based models of organizational learning can be effectively applied to future research and practice.

REFERENCES Cannon-Bowers, J. A., & Salas, E. (2001). Reflections on shared cognition. Journal of Organizational Behavior, 22(2), 195. Dansereau, F., Alutto, J., & Yammarino, F. J. (1984). Theory testing in organizational behavior: The varient approach. Englewood Cliffs, NJ: Prentice-Hall. Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: Toward a new foundation for human–computer interaction research. ACM Transactions on Computer-Human Interaction (TOCHI), 7(2), 174–196. London, M., & Sessa, V. (this volume). Continuous learning in organizations: A living systems analysis of individual, group, and organizational learning. In: F. J. Yammarino & F. Dansereau (Eds), Multi-level issues in social systems. Research in Multi-level Issues (Vol. 5). New York: Elsevier Science. Markham, S. E. (2002). Multi-level simulation analysis issues: Four themes. In: F. Dansereau & F. J. Yammarino (Eds), The many faces of multi-level issues: Research in multi-level modeling (Vol. 1, pp. 387–396). Greenwich, CT: JAI. Miller, J. G. (1978). Living systems. New York City, NY: McGraw-Hill. Mohammed, S., & Ringseis, E. (2001). Cognitive diversity and consensus in group decision making: The role of inputs, processes, and outcomes. Organizational Behavior and Human Decision Processes, 85(2), 310–335. Senge, P., Scharmer, C. O., Jaworski, J., & Flowers, B. S. (2004). Presence: An exploration of profound change in people, organizations, and society. New York, NY: Currency Doubleday.

CONTINUOUS LEARNING ABOUT CONTINUOUS LEARNING: CLARIFYING AND EXPANDING A MULTI-LEVEL, LIVING SYSTEM’S ANALYSIS Valerie I. Sessa and Manuel London ABSTRACT In this response to Day and Tate (this volume) and Markham, Groesbeck, and Swan (this volume), we clarify the concept of continuous learning from a living system’s perspective and address the evolution of adaptive, generative, and transformative learning. Further, we assert that a system’s drive for homeostasis is actually a fluid, continuous learning process that may vary in the rate and direction of change. Environmental triggers, readiness for learning, and feedback provide leverage points for change and learning within and across individual, group, and organizational systems. Future research is needed to identify and study the effects of these leverage points on systems’ adaptive, generative, and transformative learning.

INTRODUCTION Markham, Groesbeck, and Swan (this volume) push us to clarify what we mean by continuous learning and the three types of learning: adaptive, Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 201–213 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05009-0

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generative, and transformative. They are curious to hear more about learning triggers and other leverage points, and they ask us to take time into account in our model. Moreover, they see the dynamics of wholes and parts of systems as the most overlooked and difficult aspects of living systems’ theories. They wonder how planning and self-awareness fit with how systems operate. A theme in their response is the evolution of learning. How do systems transition between adaptive, generative, and transformative learning? How many elements of a system need to be affected for the system to learn? How does the learning process unfold over time within and between systems at the same and multiple levels? Day and Tate (this volume) address why continuous learning is still an issue. They concentrate on the barriers to continuous learning in organizations, and they argue that individuals (and by extension groups and organizations) may not operate naturally as continuous learners. They recognize that the right set of psychological and environmental conditions can promote learning. However, habitual routines will persist unless conditions arise and are explicitly recognized as creating a need for change, and systems need to balance advocacy with inquiry to enhance learning. Even novelty can become routine. Urgency can create blinders that focus all attention on the urgent task. Confidence during times of change can destroy curiosity, openness, and complex sensing. Learning, they suggest, requires an optimal configuration of personal, relational, and structural variables. Our integrative response clarifies how learning is related to change. In particular, we address the meaning of homeostasis as a barrier to learning for evolutionary processes within systems that drive different rates of change and learning over time. Then we suggest leverage points that promote change via environmental triggers, systems’ readiness for change and learning, and feedback.

TOWARD CONCEPTUAL CLARITY Learning, in general, is the process of acquiring new behaviors, skills, knowledge, and worldviews. Learning is a process in which the system is triggered by something it notices in the environment as it is working toward a goal. As a response, the system can automatically react, be purposefully pro-active, or radically re-create itself as needed. The result is some sort of change, from the unconscious and un-noticeable to a complete transformation. Unless the cycle includes feedback, the system has no way of knowing whether to repeat the change under similar circumstances or not. Thus,

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change does not always imply that learning has taken place. Groups and organizations exchange members with old ones leaving and new ones entering with no group or organizational learning necessarily taking place. Learning requires some change, however subtle. It also requires feedback such that the system knows whether to repeat the change, refine it, or revert to previous states and habits. At the individual level, learning is manifest in new behaviors (that are perhaps observable) and new awareness (that is not necessarily observable – for example choosing NOT to use a new skill). At the team level, learning is evident in changing structures, communications between team members, standard operating procedures, and behavioral routines. At the organizational level, learning is demonstrated through changes in such areas as vision, strategy, policies, regulations, structure, culture, and products or services. We suggest that systems actually engage in this learning process regularly and frequently as to seem without interruption (continuously) as they engage in their work (Jaques, 2002). Certainly there are periods of learning stasis when goals can be reached while maintaining the status quo without much learning, and rest when no work takes place, for example, when a group temporarily disbands during a holiday. An opposite of continuous learning might be discontinuous learning where a system learns a bit about one thing, then learns a bit about something else, or sporadic learning where a system learns at irregular intervals. Thus continuous learning implies a regular or steady progression or growth as the result of previous learning experiences or events that continue to deepen and broaden as additional learning takes place. Systems learn for themselves; others cannot learn for them although they can stimulate and facilitate learning. Systems choose the forces or disturbances that get their attention, and they decide how to act as well as how to then respond to feedback. Because learning is driven by the system and cannot be wholly predicted, it can lead to unexpected outcomes that are not in line with what was expected (and thus all involved may believe that the system has ‘‘not learned,’’ when in fact it has). In part, this is what Day and Tate were dealing with in their commentary. Day and Tate argue that individuals have built-in mechanisms that cause them to be both cognitive misers and impatient. Yet we claim that living systems have continuous learning systems built into their being. How can both be happening? Living systems have an innate drive for order. That is, they have a strong tendency to reach some sort of steady state or homeostasis. However, the task of maintaining homeostasis is actually nontrivial.

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Homeostasis is variable (Mrosovsky, 1990). Living systems are constantly interacting with a changing environment and other dynamic systems, and are changing themselves, other systems, and the environment in the process. They must continue to change and learn to maintain order and homeostasis. What are ‘‘homeostasis’’ changes? Order is maintained through continuous action within the changing environment and feedback. Day and Tate maintain that employees in today’s organizations have probably reached a degree of homeostasis in terms of their knowledge, skills, abilities, and behaviors and are satisfied to stay there. A group may reach a steady state of operations within a few meetings. That group may face brief fluctuations due to unexpected demands or changes (Carley, 1991), or it may experience periods of punctuated rapid change (Gersick, 1989). However, the group quickly returns to a steady state or homeostasis. Organizations may take longer to reach homeostasis, but many reach a steady state as well. Evidence suggests that adults do continue to learn over the course of their lifetime. For example, research in the area of adult intellect suggests that there are two types of intelligence (based on perspectives of Hebb, 1941, and Cattell, 1943; see Ackerman, 1996, for a review). One type is physiologically based, including perceptual speed, spatial orientation, and abstract reasoning. Another type of intelligence is educationally and experientially based and is more practically focused. It includes acculturated or tacit knowledge based on such things as occupational and avocational experience as well as interest. While physiological intelligence naturally peaks in early adulthood and then declines over time, the educationally and experientially based intelligence can increase across the lifespan (Ackerman & Rolfhus, 1999; Reeve & Hakel, 2000; Schaie, 1994; Tennant & Pogson, 1995; Torff & Sternberg, 1998). With the proper environment, stimulation, and training, adults can and do learn and develop both kinds of intelligences across their entire lifespan. Even skills associated with the physiologically based intelligence that have been found to decline over time (possibly due to disuse rather than some sort of detriment in human brain functioning) can be improved with training (Schaie, 1994). Homeostasis is useful most of the time. Occasionally, though, a system may need to change to a different state according to its goals. Most living systems are actually not constrained to respond only within standard conditions of homeostasis; they have much broader capabilities. Sometimes the environment and other systems change, and change drastically. Thus, a living system cannot always seek constancy of its internal environment, nor can it always react in ways that prevent change in order to survive. On the

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contrary, sometimes mechanisms actively promote change. This phenomenon is called rheostasis (Mrosovsky, 1990). In response to a trigger from the environment, the system may respond in one of two ways. It may try to maintain constant conditions (homeostasis), or it may continue to change more in the same direction, resulting in a continuing spiral of change (rheostasis). However, in trying different means to ends or goals, living systems tend to settle into a simple pattern of interaction that minimizes conflict with critical factors in the environment. Thus, individuals, groups, and organizations will tend to seek or maintain homeostasis if at all possible and learn only what is needed to maintain homeostasis (Day and Tate’s cognitive miserliness and impatience). The primary learning process for maintaining homeostasis is adaptive learning, although generative learning may also occur. Systems will engage in rheostasis when maintaining homeostasis is no longer sufficient. Learning processes for rheostasis include generative and transformative learning, although adaptive learning is also possible. In response to a trigger, living systems generally try to adapt to maintain homeostasis. If adaptation does not work, the system tries to learn generatively. And only if generative learning does not work, will the system try to transform. Adaptive, generative, and transformative learning form a hierarchy: generative learning requires the ability to adapt to environment; transformative learning requires the openness and forward/mindful thinking of generative learning as well as the ability to adapt. Effective adaptive learning can set the stage for generative learning, which in turn can provide fertile ground for transformative learning. However, sometimes adaptive learning can ensure that generative learning and transformational learning will not be attempted. Or a failure at generative learning can set the stage for learning that anything beyond adaptation is futile. Adaptive, generative, and transformative learning outcomes are not mutually exclusive. Rather, they may blend into each other and occur together. A system can be automatically re-active in meeting immediate needs; purposefully pro-active in adding new behaviors, skills, and knowledge; and radically re-creative as it identifies and tries new interaction patterns that go beyond what is required by existing pressures for adaptation. At any one time in a system’s history, one learning process may be dominant depending on the work the system is doing. Which learning process is dominant may change over time. Often, a system adapts as it works. Most of the time, adaptation is enough. Once in a while, the system may find that in order to do its work, new knowledge, behaviors, or skills are needed. Upon rare occasions, the system may find that in order to do its work, it

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needs to become a different entity. For example, the living system may need to adapt current procedures and products and then may find it needs to produce different products or institute new production methods. The system may design and institute a radical transformation of methods or products and then, more incrementally, adapt it to current conditions and demands.

LEVERAGE POINTS FOR CHANGE: TRIGGERS, READINESS, AND FEEDBACK Given that living systems will tend toward the simplest pattern of interaction around homeostasis, how can living systems be triggered to change and learn? There are places within living systems, leverage points, where a small shift can produce big changes. Our model suggests that there are two main areas for instigating learning and change – by triggering the system to learn and/or by changing the system’s readiness to learn (either directly or via feedback). Here, we draw on the work of Meadows (1999), who identified twelve leverage points to intervene in a living system. We divide these into triggers for learning, directly intervening on the system’s readiness to learn, and feedback. The leverage points in each section are ordered from the weakest to the strongest. The stronger the leverage point, the more influence it has on a system’s learning but the more difficult it is to manipulate to provoke learning. Triggers for learning include the following: (1) constants, parameters, and numbers; (2) the size of buffers and other stabilizing stocks, relative to their flows; (3) the structure and physical arrangement; (4) the rules of the system; and (5) the superordinate goals of the system. Readiness to learn leverage points include the following: (1) the power of systems to self-organize; (2) the mindset, paradigm, or culture out of which the system arises; and (3) the power to transcend paradigms. Feedback mechanisms include the following: (1) the length of delays relative to the rate of system changes; (2) the strength of negative feedback loops, relative to the impacts they are trying to correct against; (3) the gain around driving positive feedback loops; and (4) the structure of information flows. However, it must be remembered that although leverage points can stimulate the system to learn, only the system determines what is learned and how it is used. Triggers for Learning Again, the leverage points that are direct triggers for learning are changing the acceptable ranges within which the system acts, manipulating the

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system’s buffers, changing the structure and the physical arrangement of the system, changing the rules of the system, and changing the superordinate goals of the system. Constants, parameters, and numbers are the acceptable ranges that the system acts within. Some of these are unchangeable, but some are intervention points. Most systems have evolved or are designed to fit well within the critical parameter ranges. Unless the changes in parameters are large enough that they kick off more powerful leverage points, these leverage points rarely change behavior or stimulate learning. For example, organizations and groups cycle people through – firing members and bringing in new ones. But if these new members are working with the same processes, in the same system, according to the same information, goals, and rules, the system isn’t going to change or learn much beyond adaptation. One way to stabilize a system is by increasing the capacity of its buffers. Some organizations hold inventory (merchandise, parts, etc.) instead of calling for new stock just as customers walk out with the old stock. But if a buffer is too big, the system becomes inflexible. Plus big buffers are a cost to maintain. Organizations may move to a ‘‘just-in-time’’ inventory with the idea that small inventories allow a more flexible response to shifting demand. The structure and physical arrangement of groups and organizations have an enormous effect on how they operate. Proper design in the first place is crucial to optimal operation of the system. After the structure is put in place, the only way to fix it is to rebuild (or reorganize) it. The other option is in understanding the limitations of the structure and refraining from putting strain on its capacity. Reorganizations, restructuring, mergers, acquisitions, moving from face-to-face to geographically dispersed teams, and changing teams from fixed and functionally based to being organized around products, technologies, services, or client needs are all strategies in use by organizations that can stimulate learning. The rules of the system define its scope and boundaries. We have constitutions to spell out what is appropriate in our governments. We also have laws, punishments, taboos, incentives, and norms in our societies. In organizations, management is concerned with predicting, ordering, controlling, coordinating, and directing the other activities in the organization so that the total organization continuously adjusts to its environment. Activities of management are concerned with such things as the formulation of rules, procedures, and policies. Hierarchical management structures and systems may emphasize control and surveillance and maintain decisionmaking at the highest levels in the organization. This enhances the organization’s stability and predictability. However, it limits discovery, flow, and

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flexibility – all needed for generative and reflective learning of individuals and groups. Instead employees become strong adaptive learners as they comply with the latest edict. To understand the deepest functioning of systems, we need to pay attention to the rules, who is making them, and who has power over them (Meadows, 1999). The superordinate goals of the system include survival, resilience, differentiation, and evolution. These are higher order goals than the system’s immediate goals for accomplishing work (Jaques, 2000). For example, the goal of most organizations (and actually most living systems) is not just to increase shareholder wealth but to bring more and more of the world under control (customers, suppliers, and regulators) so that its operations are more and more shielded from uncertainty and it can more easily maintain homeostasis. For example, an organization that shifts its goal from being a notfor-profit entity to a for-profit entity, or from being a privately held venture to a public company, will trigger learning.

Readiness to Learn The leverage points that directly affect readiness to learn include the power of the system to self-organize, the mindset or culture from which the system emanates, and the power to transcend paradigms. Systems have the power to self-organize and regulate their own learning. This is the ability of the system to change how, where, and what the system can add onto, subtract from itself, or change such as adding and changing its structure, adding new feedback loops, making its own rules, etc. Selfdirection allows the system to evolve and survive. Organizations that insist that all groups and individuals adhere to a single culture or way of being shut down or limit groups’ and individuals’ capabilities in self-organizing and learning. Encouraging diversity, experimentation, and innovation allows systems to self-organize as needed and stimulate their own learning. The mindset, paradigm, or culture from which a system emanates is one of the most powerful leverage points for learning and one of the most difficult to affect. This includes having a performance orientation versus a mastery orientation at the individual level (Dweck, 1986; Senge, 1990), a learning orientation at the group level (Bunderson & Sutcliffe, 2003), and Model I and Model II beliefs at the organizational level (Argyris & Scho¨n, 1978). An example of this is assumptions about ‘‘workers’’ in the organization, particularly assumptions held by top management. In the early 1900s, the

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common belief was that the fundamental motivation of all hired workers was to do the least amount of work possible (see Albrecht, 2003). And, that workers were in general incompetent (Handy, 1995). These beliefs were codified into and permeated the organization – seen in strategies, structures, layers of management, pyramids of power, language, subsystems, practices, policies, controls and directives, rules and procedures, etc. (Handy, 1995). For example, organizations were structured so that workers were treated as interchangeable parts of a production machine. Albrecht (2003) suggests that many organizations still retain these beliefs as codified into and permeated through the company. This situation is very different when employees are seen as competent adults who want to master their jobs, continuously update their skills, develop valuable new skills, and disseminate their information to others within the organization. While continuous learning beyond adaptation may be difficult in the first instance, it is much more likely in the second. The power to transcend paradigms is the strongest leverage point of all. It is to understand that there are multiple paradigms (including the realization that that understanding is itself its own paradigm). Here, the system understands that no paradigm is ‘‘right’’ or ‘‘true,’’ thus allowing it to choose whatever one will help achieve its purpose. At the individual level, this would be similar to what Buddhists call enlightenment. We know of no examples of this at the group or organizational levels.

Feedback The leverage points that impact the system’s readiness to learn via feedback mechanisms include the length of the delays of feedback relative to the rate of system changes, the strength of the negative feedback loops, the gain around driving positive feedback loops, and adding new feedback loops. The length of the delays of feedback relative to the rate of system can change. If the delays are too long in relation to the changes the system is making, the change is unpredictable and may be dampened, sustained, or explosive. For example, if an employee is trying to change his or her behavior to be more in line with the goals of the organization, but only receives performance feedback once a year during performance appraisal time, he or she may react by trying something and not repeating it, engaging in a new behavior (but not knowing if it is right or not), or doing more and more of that behavior assuming that it is working. If the delays are too short in relation to the changes the system is making, it will over react. For example,

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an organization puts in place a new technological infrastructure but tries to respond to the immediate and negative feedback from employees without allowing enough time for adjustment. Individuals, groups, and the organization as a whole can only change and learn so fast – not instantaneously. As the organization tries to address the feedback by immediately tweaking the technological infrastructure (with individuals and groups now having to learn the newest processes and complaining about them), individuals, groups, and the organization may move into a positive feedback spiral. Negative feedback loops bring the system into alignment with its goal, and allow the system to self-correct. At the individual level, performance management systems are negative feedback loops (see Vancouver, 1997). These feedback loops allow the system to remain in homeostasis. Individuals monitor their work by comparing their goal for a particular task with perceptions of task progress. If a discrepancy exists between where the individual is and where he or she wants to be, the individual will attempt to reduce the discrepancy by either altering the goal or applying resources to task-related behavior (Austin & Vancouver, 1996). A living system usually has numerous negative feedback loops at the same time so that it can selfcorrect under different conditions and impacts. Individuals act on and monitor multiple goals at once, react to feedback or consequences of their actions, and assess strategies and resources needed to achieve the various goals, all within the opportunities and constraints provided by the environment (see Naylor, Pritchard, & Ilgen, 1980). The stronger the negative feedback loops, the stronger the drive to remain in homeostasis. Positive feedback is self-reinforcing rather than self-correcting and propels the system into rheostasis – the more it works, the more it gains power to work some more. Thus, while positive feedback loops are sources of growth, unchecked, they are also sources of explosion, erosion, and collapse. There are fewer positive feedback loops operating at one time within systems than negative feedback loops. The idea for this leverage point is to slow or reduce the gain around a positive feedback loop rather than letting it run or trying to stop it. We mentioned one example of a positive feedback loop above, with the organization attempting to respond to individual and group feedback about a new structure. Another example is learning itself. Teams that are high in learning orientation seek opportunities to develop new skills and knowledge and devote time to learning, enjoy and take on challenging assignments from which they can learn, and are willing to experiment with new ideas. As they receive feedback on their practices and become more ready to learn, they may want to engage in learning for its own sake. Continuous improvement can become a group habit. Groups need to recognize

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that there is a time and place for generative and transformative learning. More generative and transformative learning is not necessarily a good focus for a team, particularly one that already has the knowledge and skills it needs to perform (Bunderson & Sutcliffe, 2003). Delivering information to a place it was not going before causes individual, groups, and organizations to behave differently. Many feedback loops in today’s organizations are completely missing, and missing feedback is a common cause of system malfunction. For example, the first author once coached a human resources professional who had been asked to fire a midlevel manager for poor performance. The manager had received good or outstanding performance appraisals during his many year career at that organization. His bosses had never delivered accurate feedback to the manager who had no idea that he had any flaws that needed to be addressed in any way until it was too late. The human resources professional and the current manager developed a performance management plan to try to bring the midlevel manager into alignment during the following six to nine months before moving forward with any drastic measures. Adding or restoring feedback to the right individuals, groups, or organizations that need it, is a powerful intervention.

CONCLUSION In this response, we have further clarified what we mean by continuous learning and types of learning. We argued that although living systems strive toward homeostasis and the easiest path toward that order, that they are never the less seldom ‘‘still’’ when it comes to change and learning. Homeostasis is, itself, a dynamic and ever changing state. We suggested 12 leverage points that help propel systems into a learning process, either directly through triggers for learning, or through their readiness to learn. We need to understand these and other levers that can be manipulated to promote learning. These can be explored in longitudinal, field experiments on interventions to stimulate and encourage learning, understand the relations between systems components and relationships between systems within and between levels, and how adaptive, generative, and transformative learning evolve. Final Comments The development of our continuous learning model is a work in progress. As colleagues and co-authors, we have been continuously learning and developing

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our ideas. At the individual level, we are learning generatively about individual, group, and organizational learning as well as diving deep into living systems’ theory. Perhaps in the process, we are transforming ourselves individually in the way we look at the world, seeing events more integrally rather than separately. As a dyad, we are adapting into a unit by developing a transactive memory system. We are learning generatively by trying out new methods of inquiry, purposefully switching roles of authorship, and trying out our ideas in a number of different outlets for publication and presentation. We hope that the present forum will transform the way the larger network of our colleagues think about learning, just as it has given us feedback to expand our own thinking. This has been a valuable opportunity (trigger) for us to work together (process) to expound on our theory (outcome), receive real-time feedback from esteemed colleagues, and refine our own learning, in the process affecting our own readiness to learn and thereby allowing us to move through and assess our own learning cycle. We look forward to basic and applied research on adaptive, generative, and transformative learning within and between multiple levels of analysis.

REFERENCES Ackerman, P. L. (1996). A theory of adult intellectual development: Process, personality, interests, and knowledge. Intelligence, 22, 227–257. Ackerman, P. L., & Rolfhus, E. L. (1999). The locus of adult intelligence: Knowledge, abilities, and nonability traits. Psychology and Aging, 14, 314–330. Albrecht, K. (2003). The power of minds at work: Organizational intelligence in action. New York: Amacom. Argyris, C., & Scho¨n, D. A. (1978). Organizational learning: A theory of action perspective. San Francisco, CA: Jossey-Bass. Austin, J. T., & Vancouver, J. B. (1996). Goal constructs in psychology: Structure, process, and content. Psychological Bulletin, 29, 338–375. Bunderson, J. S., & Sutcliffe, K. M. (2003). Management team learning orientation and business unit performance. Journal of Applied Psychology, 88(3), 552–560. Carley, K. (1991). A theory of group stability. American Sociological Review, 56, 331–354. Cattell, R. B. (1943). The measurement of adult intelligence. Psychological Bulletin, 40, 153–193. Day, D., & Tate B. (this volume). Continuous learning: Why is it still an issue. In: F. J. Yammarino & F. Dansereau (Eds), Multi-level issues in social systems. Research in multi-level issues (Vol. 5). Oxford, UK: Elsevier. Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41, 1040–1048. Gersick, C. J. G. (1989). Marking time: Predictable transitions in task groups. Academy of Management Journal, 32, 274–309.

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Handy, C. (1995). Managing the dream. In: S. Chawla & J. Renesch (Eds), Learning organizations: Developing cultures for tomorrow’s workplace. Portland, OR: Productivity Press. Hebb, D. O. (1941). Clinical evidence concerning the nature of normal adult test performance. Psychological Bulletin, 38, 593. Jaques, E. (2002). The life and behavior of living organisms: A general theory. Westport, CT: Praeger. Markham, S., Groesbeck, R., & Swan, B. (this volume). A multi-level inquiry and elaboration: Continuous learning within and across organizations, groups, and individuals. In: F. J. Yammarino & F. Dansereau (Eds), Multi-level issues in social systems. Research in multi-level issues (Vol. 5). Oxford, UK: Elsevier. Meadows, D. (1999). Leverage points: Places to intervene in a system. Hartland, VT: The Sustainability Institute. Mrosovsky, N. (1990). Rheostasis : The physiology of change. Oxford, UK: Oxford University Press. Naylor, J. C., Pritchard, R. D., & Ilgen, D. R. (1980). A theory of behavior in organizations. New York: Academic Press. Reeve, C. L., & Hakel, M. D. (2000). Toward an understanding of adult intellectual development: Investigating within-individual convergence of interest and knowledge profiles. Journal of Applied Psychology, 85, 897–908. Schaie, K. W. (1994). The course of adult intellectual development. American Psychologist, 49, 304–313. Senge, P. M. (1990). The Fifth discipline: The art and practice of the learning organization. New York: Doubleday. Tennant, M., & Pogson, P. (1995). Learning and change in the adult years: A developmental perspective. San Francisco, CA: Jossey-Bass Publishers. Torff, B., & Sternberg, R. J. (1998). Changing mind, changing world: Practical intelligence and tacit knowledge in adult learning. In: M. C. Smith & T. Pourchot (Eds), Adult learning and development: Perspectives from educational psychology (pp. 109–126). Mahwah, NJ: Lawrence Erlbaum. Vancouver, J. B. (1997). The application of HLM to the analysis of the dynamic interaction of environment, person, and behavior. Journal of Management, 23, 795–819.

THE IMPORTANCE OF THE COMMON FAMILY BACKGROUND FOR THE SIMILARITY OF DIVORCE RISKS OF SIBLINGS: A MULTI-LEVEL EVENT HISTORY ANALYSIS$ Jaap Dronkers and Joop Hox ABSTRACT This study examines the effects of a family’s and individual children’s characteristics on the probability of having a divorce. Current research shows a clear indication of increased divorce risks if an individual’s parents or siblings have experienced a divorce. Explanations include both shared family characteristics (including genetic effects) and common characteristics of the individual children involved. This study analyzes the effects of shared family background characteristics on the divorce risk of individuals. By analyzing siblings within families and including individual $

An earlier version of this paper was presented at the European Research Conference, ‘‘European Societies or European Society? Inequality and Social Exclusion in Europe: The Role of the Family and Social Networks,’’ Castelvecchio di Pascoli, Italy, April 3–7, 1998.

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children’s characteristics in the analysis, it is possible to separate individual-level and family-level effects. In addition to employing a multi-level structure of individual siblings nested within families, the data cited here are censored. For all individuals, the length of the marriage and the divorce status are known, but the divorce status is interpreted differently for individuals who have or have not experienced divorce. For divorced individuals, the final divorce status is known; for individuals who have not experienced divorce, the final marriage status is unknown or censored. The proper analysis model for such data is event history (also called survival) analysis. This study therefore employs a multi-level event history model. Our results show that there is a similarity in the divorce risks of siblings from the same family, which is not explained away by the available child and family characteristics. This finding suggests that shared genetic and social heritage play an important role in the intergenerational transmission of divorce risks.

INTRODUCTION There is a clear intergenerational transmission of divorce risks in many different societies: Children of divorced parents divorce significantly more often compared to children of nondivorced parents.1 Several mechanisms within the family and common characteristics of the involved children can explain the existence of the relationship between parental and children’s divorce risks as follows (McLanahan & Bumpass, 1988; Amato & Booth, 1991; Amato, 2001). 1. Stress. The stress that accompanies parental divorce or separation is a ‘‘push’’ factor that induces children to leave the parental home early, to get married early, and to have their own children at a young age more frequently. Leaving one’s parental home, getting married, and having children at a young age are themselves events that increase the odds of divorce or separation. Thus it explains a positive relationship between parental and children’s divorce risks. 2. Socialization in the parental family. The conditions of socialization in the parental home lead to the children developing certain attitudes and ways of behaving, which subsequently render them less capable to maintain their relationships, or teach them to leave an unsatisfactory relationship at an earlier stage. Experiencing a parental divorce or separation

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socializes the children in this respect and can increase the odds of their divorce or separation. Thus it explains a positive relationship between parental and children’s divorce risks. 3. Economic deprivation. The economic circumstances that follow a parental divorce affect the lives of the children in a negative way (with respect to educational and occupational attainment). Reduced economic circumstances can increase the risk of these children of divorced parents getting divorced themselves. Thus it explains a positive relationship between parental and children’s divorce risks. 4. Stigmatization (Spruijt, 1993). The stigma of parental divorce will affect the life opportunities of their children in a negative way and thus decrease their chances of maintaining their own marriages. It might also explain a positive relationship between parental and children’s divorce risks. This stigmatization mechanism should be less important for younger cohorts, due to the normalization of divorce in modern societies. 5. Genetic heritage. Parents and children share particular heritable personality traits, which can promote or hinder divorce in both the parents and their children (McGue & Lykken, 1992; Cramer, 1993). This factor might also explain a positive relationship between parental and children’s divorce risks. It is not yet clear whether this transmission can be explained by the common background of parents and children (like personality traits), which cannot be measured by identifying age at marriage, age of first child, educational attainment, occupational level, and birth cohort. It is necessary to estimate the importance of a common background for intergenerational transmission of divorce risks relative to the importance of the measured characteristics of the individuals. If the common background of parents and children remains important after controlling these individual characteristics, socialization by parents and siblings and the common genetic and social heritage of siblings are also relevant explanations of the intergenerational transmission of divorce risks. Divorce or separation is, in that case, not only an individual decision taken by individuals in solitude, but also a consequence of more general social and genetic characteristics of the involved persons. We can partly estimate the importance of the unmeasured common background of parents and children by analyzing the similarity in the divorce risks of siblings and by trying to explain this similarity in terms of the measured characteristics of parents and their offspring. Analyzing similarities between the behavior patterns of siblings in relation to parental

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characteristics, such as divorce or separation, is a good means of investigating the relative importance of families and of individuals within these families, as has been shown by many sibling studies (de Graaf & Huinink, 1992; van Eijck, 1996; Sieben, 2001). Another important advantage of sibling studies is that we do not need to measure all relevant characteristics of parents and children before reliably estimating the relative importance of family and children for the explanation of divorce. Until now, such an analysis of the similarity of divorce or separation risks of siblings has not been made. This chapter is the first analysis that connects two traditions: sibling studies within the social stratification research and effects of divorce studies within family research. Amato (2001; Amato & Booth, 1991) does not mention a single sibling analysis in his well-known overviews.2 There is no clear explanation why this combination has not been made earlier. A possible explanation might be the politically sensitive aspect of analyzing a common family background of divorce or separation, which might deter scholars from tackling this topic. Another possible explanation is the lack of useful data that cover both parents and all siblings from the general population sample. However, the Australian data of the National Social Science Family Survey of Australia of 1989–1990 represent one of the few surveys in the world to cover both parents and all siblings, and provide enough information on their marriage and divorce. The analysis model is a multi-level model with children (siblings) nested within families, and variables at both the sibling level and the family level. The analysis model we use is explained later in this chapter.

LITERATURE REVIEW There is only one study of the effects of parental divorce on siblings (O’Connor, Plomin, Caspi, & DeFries, 2000), and that is a comparison between biological children and adopted children of divorced and nondivorced parents. McGue and Lykken (1992) and Jockin, McGue, and Lykken (1996) studied the effects of family factors on divorce by analyzing twins and nontwin siblings. The former researchers concluded that the difference in divorce risks between monozygotic and dizygotic twins indicates a strong influence of genetic factors in the event of divorce. The family background of both spouses contributes independently to the couples’ divorce risk, suggesting that divorce might be largely the result of characteristics that the two spouses bring to the union rather than the result of

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interaction effects. McGue and Lykken’s (1992) findings also suggest that the adjustment difficulties seen with some children with divorced parents may be due to an interaction between genetic and environmental factors rather than environmental influences alone. Jockin et al. (1996) concluded that in women and men, respectively, 30% and 42% of the heritability of divorce risks consist of genetic factors affecting personality, and personality and divorce risk is correlated largely as a result of these common genetic influences. An important advantage of sibling analyses is the potential to distinguish between the measured and unmeasured effects of family characteristics on individuals and to estimate the importance of the latter. These advantages can be important for two reasons: 1. The noninclusion of unmeasured family characteristics might lead to a misspecification of the effects of the measured family characteristics, because the measured and unmeasured characteristics are positively or negatively related with each other. 2. The measurement variances of both measured and unmeasured family characteristics open up the possibility of estimating the amounts of unexplained variances both at the individual and family levels. These give an indication of the remaining variance to be explained by the nonmeasured variables. Sieben (2001) gives a summary of her comparison of the outcomes of sibling analyses and the outcomes of conventional analyses without siblings, applied to the measurement of family background on mobility across countries and cohorts. That summary is quite representative of the outcomes of other sibling studies on other populations: First of all, the effects of measured aspects of family background are quite similar for the two kinds of analysis y. Second, the percentage of explained variance does not differ very much between the two analyses. About 25% of the total variance in educational attainment is explained by including parents’ educational attainment, father’s occupational status, and the number of siblings in the models. The percentage of total variance in occupational status that can be explained by these measured effects of family background and one’s educational attainment turns out to be about 40% y. About 50% of the variance in educational attainment and 40% of the variance in occupational status can be attributed to the family. Measured effects of family background account for about 45% of total family impact on educational attainment, whereas 75% of total family background on occupational status is represented by these measured effects of family background and the family factor for educational attainment (Sieben, 2001, pp. 162–165).

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This summary indicates that sibling analysis has certain advantages, especially if one expects a large amount of unexplained family variance due to the practice of measuring the processes that produce certain outcomes. Divorce risk is such an outcome, and one about which many assume that the various processes leading to a divorce are far more nuanced than the crude individual indicators used to measure these processes. Sibling analysis is a useful tool to measure the importance of the family variance. The basis of this family factor is the similarity in divorce risks of children from the same family and thus the effects of the divorce rate of the brothers and sisters of a family on the divorce risk of ego.

RESEARCH QUESTIONS A higher similarity in the divorce risks of siblings, indicated by a positive effect of the divorce rate of brothers and sisters on the divorce risk of ego, can be explained with reference to five factors. These factors provide indicators for the measured characteristics of parents and siblings that need to be included in the analyses: 1. Intergenerational transmission of divorce risks. A consequence of a significant intergenerational transmission of divorce risks is a similarity in the divorce risks of siblings from the same family, because a parental divorce increases the divorce risks of all the parents’ offspring. If the intergenerational transmission mechanism is an important explanation of the amount of similarity in divorce risks at the family level, the effect of the divorce rate of brothers and sisters on the divorce risk of ego should decrease significantly after controlling for parental divorce. 2. Socialization in the parental family. Because siblings receive more or less equal socialization from their parents, there is a high likelihood that they will develop common attitudes toward marriage and divorce, common behavior patterns, and common solution repertoires for marital problems. This common heritage renders them more or less able to maintain their relationships, teaching them either to leave an unsatisfactory relationship at an early stage or to make an extra effort to maintain and improve it. This leads to a similarity in the divorce risks of siblings. If age at first marriage is a good proxy for socialization of attitudes and behavior toward marriage in the parental family, the effect of the divorce rate of brothers and sisters on the divorce risk of ego should decrease significantly after controlling for age at first marriage.

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3. Socialization by siblings. Siblings influence one another through imitation and interaction while living together in the parental home, and this influence continues after their departure. They have more attitudes, behavior patterns, and problem–solution repertoires in common than comparable individuals raised in different families. Furthermore, siblings can learn from the success or failure of one another’s marriages. This learning will lead to similar divorce risks among siblings. If the level of divorce among the other married siblings is a good proxy for socialization of attitudes and behavior toward marriage by the other siblings, the effect of the divorce rate of brothers and sisters on the divorce risk of ego should explain significantly the effect of parental divorce and the common individual characteristics such as education. 4. Common socioeconomic background of parents (family size, father’s occupation, father’s and mother’s education, siblings’ education and occupations). The common socioeconomic background of their parents renders siblings more similar in their educational level and the number of children they produce than nonsiblings. If level of education and number of children influence the divorce risks of siblings, these factors will be more similar than the divorce risks of comparable individuals raised in different families. If their common socioeconomic background is an important explanation of the similarity in the divorce risks of siblings, the effect of the divorce rate of brothers and sisters on the divorce risk of ego should decrease after controlling for the socioeconomic background of the siblings and parents and the number of children in the parental family. 5. Common genetic and social heritage. Siblings share a number of genetically and socially transmissible personality traits that can either enhance or reduce their prospects of divorce, giving rise to similar divorce risks. If this explanation is correct, the effect of the divorce rate of brothers and sisters on the divorce risk of ego should hardly change by the controlling for other individual and parental characteristics. Thus, the similarity in the divorce risks of siblings (indicated by the effect of the divorce rate of brothers and sisters on the divorce risk of ego) can be explained by measured common characteristics of the parents (parental divorce, family size, father’s occupation, father’s and mother’s education, parental migrant background) and by overlaps in measured characteristics of the siblings (year of birth, age at marriage, education, occupation, number of children). However, it remains possible that the similarity in the divorce risks of siblings cannot be fully explained by these measured common parental characteristics and the overlap in siblings’ measured

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characteristics. If this is the case, we will still find, after controlling for the measured characteristics of parents and siblings, a significant effect of the divorce rate of brothers and sisters on the divorce risk of ego. The aim of this chapter is to establish whether there is a significant similarity in the divorce risks of siblings and, if so, to what degree this similarity can be explained by measured characteristics of parents and children. The main research questions posed by this chapter are as follows: 1. Is there a similarity in the divorce risks of children from the same family (siblings) in Australia? 2. Can jointly measured parental characteristics and the overlap in measured characteristics of their children explain this similarity in the divorce risks of the children and, if not, how much is left?

HYPOTHESES ON THE EFFECT OF MEASURED CHARACTERISTICS A positive relationship between the divorce risks of children from the same family can also be explained by the measured parental characteristics (parental divorce, family size, father’s occupation, father’s and mother’s education, migration, working mothers) and the measured characteristics of the children (age of marriage, age at birth of first child, political choice, occupation, education, children, gender, year of birth, percentage of married siblings who have divorced). To guide our analysis of the similarities in the divorce risks of children, we formulate a number of hypotheses on these effects of the parents’ and children’s characteristics on the divorce risk of the latter (Amato & Booth, 1991; Amato, 2001). Although testing these hypotheses is not the aim of our analyses, because these variables are already often applied in divorce research, the results can be interesting in relation to other Australian research on the effects of divorce on children (Pryor & Rodgers, 2001). We have five hypotheses formulated on the family characteristics, which might affect the divorce risks of all children within that family:
The parental divorce can hamper the socialization of the children in learning how to maintain an intimate relationship in a difficult time and thus increase the divorce risk of the children. The parental divorce might also teach the children that divorce is an acceptable way out of an unsatisfactory marriage and thus also increase the divorce risk of the children.

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A large parental family size can be an indication of traditional parental values and attitudes toward family and divorce, which may influence the values and attitudes of children toward family and divorce and thus decrease the divorce risks of children in large families.
A high occupational level of the father can provide more financial and social capital to the children by giving them more possibilities to end an unsatisfactory marriage and thus increase the divorce risks of children with fathers having higher occupational levels.
Regarding the migration history of the children, we have two contradictory hypotheses. The first hypothesis is that migration entails a drastic change in the living conditions, which can increase uncertainty about values, norms, and preferences (the anomie of Durkheim). This uncertainty can destabilize marriage and thus increase the divorce risk of children who are not born in Australia. The second hypothesis assumes that migration requires all resources of the migrants to make their migration successful. This need of resources does not allow the migrants to spend the scarce resources for less pressing problems such as solving an unsatisfactory marriage and thus decreases the divorce risk of children who are not born in Australia.
If the mother of the child works while her children are still young, it might indicate a less adequate socialization of her children or less traditional values in the parental family. Both factors can increase the divorce risk of children in families where the mother worked while her children were still young. We have also hypotheses formulated on the sibling characteristics that might affect the divorce risks of individual children:
The percentage of divorced siblings reflects cultural values and attitudes, not in the least toward family and divorce, which might be shared by children from the same family. As a consequence, children with more divorced siblings might divorce more quickly than children with fewer divorced siblings.
The political choice of children often reflects cultural values and attitudes, not in the least toward family and divorce: Left-leaning voters more often have less traditional values and attitudes and are less traditionally religious. As a consequence, they might divorce more quickly than right-wing voters. Another explanation of the left-leaning voting of divorced people is that they depend more heavily on social welfare than nondivorced people and are thus more inclined to vote left.

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A high occupational level of children can provide them with more financial and social capital and thus gives them more possibilities to end an unsatisfactory marriage and consequently a higher divorce risk.
Having children increases the negative consequences of divorce, especially for women because they get the care for the children in the vast majority of the cases. Thus, women with children (who in the majority of the cases initiate the divorce) will be reluctant to seek that divorce. Thus, having children might decrease the divorce risks. Conversely, having children indicates a higher prior investment in the relationship by both spouses and thus a higher loss for both spouses in breaking up their marriage.
Children born at the beginning of the twentieth century have lower odds to be divorced than children born around the middle of the twentieth century because of the general increase in the divorce rates in the last century. The youngest children born in the 1960s will have a lower divorce risk than children born earlier because they had not yet the opportunity to divorce.

METHOD: DATA Our data come from the National Social Science Family Survey of Australia of 1989–1990. Through this national representative survey on the adult Australian population, detailed information was collected, including data on the educational attainment, social context, and economic background of respondents, such as parental education, occupational status of the father, parental family size, family form, and other relevant characteristics of 4,513 men and women in Australia. Each respondent also answered all of these questions about his or her parents and siblings. The respondents gave information about a maximum of three siblings, even if there were more siblings in the family. As a consequence of this data selection procedure, information is not available about all siblings, but only about the three selected by the respondent. All siblings were recoded in the same way as the respondents, and they were combined in a new file with respondents or siblings as the unit of analysis. In that new file, respondents and siblings from the same family had the same values for their parental characteristics but different values for their child characteristics. We selected only those respondents or siblings for whom one of them was married or had been married (remaining N families ¼ 3,797 with a total of 10,808 married or divorced children). Thus families with only one married respondent or sibling were not excluded. As a consequence of missing values in core variables – year of marriage, family size, age at first marriage, years of education, year of birth, and

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gender – we lost an additional 4,504 children. The duration of the marriage was the variable with the highest number of missing values (4,189) due to the fact that many respondents did not know the marriage duration of their siblings. The missing values on the variables of father’s occupational status, mother’s educational level, father’s educational level, left-oriented political choice, and occupational status were replaced by the average score of these variables. After this replacement of the missing values on these noncore variables, we had a sample of 3,221 families with a total of 6,305 respondents and siblings. We use the following variables at the children level or the family level. The means and standard deviations of these variables are shown in Table 1. Children Level 1. Divorce of child (respondent or sibling) (dependent variable; used in the construction of the marriage-year file). 2. Length of marriage: from zero to year of divorce or death or to year of interview (time variant). 3. Age at first marriage of child. 4. Child born before first marriage of child: none, yes. 5. Year of marriage at birth of first child of child3 (time variant). 6. Left-oriented political choice of child: Labor Party; none, other; Liberal or Nationalist. 7. Occupational status of child: Kelley’s Worldwide Status Scores, ranging from 0 to 100, which are conceptually similar to Duncan’s SEI scores (Kelley, 1990, pp. 344–346). 8. Years of education of child. 9. Gender of child: male, female. 10. Year of birth of child. 11. Percentage of divorced children in parental family, excluding ego. The percentage is 100% if all other married children (ego not included) of the parental family are divorced. It is 0% if none of the other children of the parental family are divorced. Family Level 1. Family size: total number of brothers and sisters. 2. Father’s occupational status: Kelley’s Worldwide Status Scores, ranging from 0 to 100, which are conceptually similar to Duncan’s SEI scores (Kelley, 1990, pp. 344–346).

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Table 1.

Descriptive Statistics of the Variables.

Children level Length of marriage Percentage divorced Age by first marriage Percentage with child born before first marriage Percentage with children Left political orientation Occupational status Years of education Percentage female Year of birth Percentage of married and divorced siblings, exclusive ego Family level Family size Father’s occupational status Percentage migrant from an English-speaking country Percentage migrant from Mediterranean countries Percentage migrant from Eastern Europe Percentage migrant from Third World Percentage migrant from North and West Europe Mother’s educational level Father’s educational level Percentage with parents divorced: respondent with only mother at age 15 Percentage with father dead: respondent with only mother at age 15 Percentage with stepparent at age 15 Percentage whose mother worked full-time at age 6 and 10 Percentage whose mother worked part-time at age 6 and 10 Percentage whose mother worked full-time at age 6 and part-time at age 10 Percentage whose mother was at home at the age 6 and worked at age 10 Percentage whose mother was at work at age 6 and home at age 10

N

Mean

Standard Deviation

6,305 6,305 6,305 6,305 6,305 6,305 6,305 6,305 6,305 6,305 6,305

20.50 0.14 23.60 0.03 0.86 51.90 50.40 10.60 0.52 42.80 11.50

14.30 0.35 4.60 0.17 0.34 45.00 23.10 2.90 0.50 14.50 27.20

6,305 6,305 6,305

4.40 37.20 0.12

2.10 25.60 0.33

6,305 6,305 6,305 6,305 6,305 6,305 6,305

0.04 0.01 0.04 0.02 8.70 8.90 0.03

0.19 0.12 0.20 0.14 3.20 3.30 0.16

6,305

0.04

0.20

6,305 6,305

0.05 0.08

0.22 0.02

6,305

0.08

0.02

6,305

0.09

0.08

6,305

0.08

0.03

6,305

0.09

0.01

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3–7. Native country of respondent: five dummy variables: English-speaking nations outside Australia; Mediterranean countries; Eastern Europe; northwest Europe; other nations outside Australia (see Borgers, Dronkers, Rollenberg, Evans, & Kelley, 1995). Reference category: born in Australia. 8. Mother’s educational level: none; some primary education; primary education completed; left education at age 14 or 15; more secondary education; secondary education completed; some tertiary education; university. 9. Father’s educational level: see mother’s educational level. 10–12. Family form of the respondent at the age of 15 years of the respondent: three dummy variables: only with mother after divorce; only with mother after death of father; stepfamily created by remarriage after death or divorce. Reference category: living with both natural parents. 13–17. Mother working outside home while respondent at the age of 6 and 10: five dummy variables: working full-time at ages of 6 and 10; working part-time at ages 6 and 10; working full-time at age 6 and part-time at age 10; at home at age 6 and working at age 10; working full- or part-time at age 6 and at home at age 10. Reference category: at home at ages of 6 and 10. Table 1 shows that 14% of all children in this national representative survey have been divorced, while 11.5% of their siblings have also been divorced. Their parents divorced less quickly: Only 3% of the parents were divorced at the moment that the respondents were 15 years old. Death of a parent was more common: 4% of the respondents had a dead father at the age of 15. We do not know the cause of living in a stepfamily (5%), but probably death of one of the parents will be the main cause.

ANALYSIS MODEL We use multi-level analysis (MLA) to distinguish between the individual level (respondent and siblings) and the family level (parents). This MLA is more appropriate for sibling analysis than the more commonly used LISREL (Hauser & Wong, 1989), because MLA can distinguish more reliably between the different analytical levels and thus can avoid the statistical pitfalls associated with the nested nature of data on individuals in families (Hox, 2002). Divorce risk is a right-censored characteristic in this Australian sample, because the respondents and a majority of their siblings are not yet dead. Thus the correct view of nondivorced respondents and siblings is that the final outcome (divorce or not) is not yet established. The recommended

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mode of analysis for right-censored observations is event history analysis, also known as survival analysis (Singer & Willet, 1993). Thus our analysis is a combination of multi-level and event history analysis. The combination of marriage-year/child is the lowest level, the respondent and sibling level is the next level, and the family level is the highest level. To analyze these data, the data file is restructured into a three-level data structure: families, siblings nested within families, and marriage-years nested within siblings. The series of marriage-years ends either with an observed divorce or as a right-censored variable. The lowest level can be conceptualized as a series of independent trials in which the event does or does not occur. This results in a discrete-time analysis; in each interval t, we observe a binary response variable that indicates whether divorce occurred. This representation allows us to use models for binary response variables in a multi-level context (Hox, 2002). We make the usual assumption that the censoring is noninformative, meaning that the censoring mechanism is not related to the time-to-event. The hazard function h(t) is the probability of the event occurring in interval t conditional upon no earlier occurrence. In our case, the time variable t is the length of the marriage at time t. The hazard is modeled using a logistic regression of the following form:
logit hij ðtÞ ¼ aðtÞ þ bxij ðtÞ þ bzj þ u0j

(1)

In this equation, a(t) is the baseline hazard at marriage-year t, xij represents the sibling-level predictors, and zj represents the family level predictors. The value of u0j incorporates the family level residual errors; since this is a logit model for binary outcomes, there is no sibling-level error term (cf. Hox, 2002). The regression coefficient a for the effect of marriage length may or may not vary across individuals or families; in our case, there was a between-family variation that disappeared when all available predictors were included in the model. The regression coefficients b for the sibling-level predictors may or may not vary across families. In our case, there was no higher-level variation of these regression coefficients; consequently, in Eq. (1) they carry no subscript for families. The model was estimated using MLwiN (Rasbash et al., 2000). The estimation method used was Marginal Quasi Likelihood estimation for binary data (Goldstein, 2003; Hox, 2002) with asymptotic standard errors for significance tests of separate parameters. Fig. 1 presents the multi-level event history model in graphical form. It contains three levels: families, siblings (children), and the repeated

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Fig. 1.

231

Graphical Representation of Multi-Level Event History Model.

information over time. The outcome variable is at the time level: Either a divorce is observed or the event is censored. At the time level, only one explanatory variable is used: the length of the marriage. This explanatory variable is essential because it represents the length of time that the couple has been ‘‘at risk.’’ There are also explanatory variables at the sibling level and the family level, which were listed earlier in this chapter. Note that Fig. 1 includes two residual error terms: one at the sibling level and one at the family level. It is this more complicated error structure that makes the use of multi-level modeling necessary.

RESULTS Before we can start the multi-level analyses of the importance of the family for the divorce risks of children, we must show the relationship between the divorce risk of a child and the percentages of married sibling who have divorced. Table 2 shows that such a relation does, indeed, exist: The higher the percentage of divorced siblings, the more often the child is also divorced

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Table 2. Relationship between the Divorce Risk of a Married Child and the Percentage Married and Divorced Siblings (Exclusive Ego). Not Divorced Child (%) Divorced Child (%) Total N ( ¼ 100%) 0% divorced siblings 33.3% divorced siblings 50% divorced siblings 66.7% divorced siblings 100% divorced siblings

86.5 86.6 82.7 78.2 80.1

13.5 13.4 17.3 21.8 19.9

5,162 305 387 55 396

w2 ¼ 18.77; df ¼ 4; po0.005; g ¼ 0.15.

(13.5%, if no married sibling is divorced; 21.8%, if 66.7% of siblings are divorced). Having a marriage-year-person file we have to control for year of marriage in all equations. The parameter of this variable has the usual direction: The longer a union exists, the lower the risk of divorce. Divorce risks are especially high in the early years of the union. Table 3 presents the regression coefficients and associated standard errors for several consecutive models. Unless otherwise indicated, all effects are significant at the 0.05 level. Model 1 of Table 3 is a repetition of Table 2: It shows that having a higher percentage of divorced siblings increases significantly the divorce risks of ego. Model 2 shows the positive effect of having divorced parents on the divorce risk of their children: Children of divorced parents also divorce more. Model 3 (the combined effect of divorced parents and percentage of divorced siblings) shows that both family characteristics are highly independent in their effect on divorce risks. The strength of their parameters hardly changes by controlling for the other characteristic, and both remain highly significant (model 4). Their parameters also hardly change after adding the individual characteristics of the children to the equation. Although some of these individual characteristics are significant (age at first marriage; having children; years of education; year of birth), they are not responsible for the effects of percentage of divorced siblings or divorced parents. The latter effects are thus not spurious. The same holds true for the family characteristics that are added to the equation in model 5. The vast majority of these family characteristics have no significant effects on the divorce risk; only having a mother who works full-time decreases the divorce risk of children significantly. These results show that there is a similarity in the divorce risks of children from the same family in Australia and that this similarity cannot be

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Table 3. Divorce-Risks of Ego Explained by Parental Divorce, Divorce of Siblings, and Individual and Family Characteristics (Logistic Regression on Marriage-Year-Person File within Multi-Level). Model 1 Marriage-year level Length of marriage  10 (time varying) Children level Percentage divorced siblings  10 Age by first marriage Child born before first marriage Children Left political orientation  10 Occupational status  10 Years of education Female Year of birth Family level Parents divorced: only mother at age 15 Family size Father’s occupational status  10 Migrant from an English-speaking country Migrant from Mediterranean country Migrant from Eastern Europe Migrant from Third World Migrant from North and West Europe Mother’s educational level  10 Father’s educational level  10

2

3

4

5

0.33 (0.03) 0.34 (0.03) 0.34 (0.03) 0.04 (0.04) 0.04 (0.04)

0.05 (0.01)

0.05 (0.01)

0.04 (0.01)

0.04 (0.01)

0.03 (0.01) 0.03 (0.01) 0.21 (0.22) 0.20 (0.22) 0.21 (0.08) 0.20 (0.08) 0.00 (0.01) 0.00 (0.01) 0.01 (0.02) 0.01 (0.02) 0.04 (0.01) 0.04 (0.02) 0.05 (0.07) 0.04 (0.07) 0.04 (0.00) 0.04 (0.00) 0.56 (0.17)

0.53 (0.17)

0.50 (0.18)

0.43 (0.17) 0.01 (0.02) 0.02 (0.02) 0.05 (0.10)

0.19 (0.21)

0.15 (0.27) 0.11 (0.18) 0.21 (0.26) 0.08 (0.14) 0.07 (0.13)

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Table 3. (Continued ) Model 1

2

Father dead at age 15 Respondent with stepparent at age 15 Mother worked fulltime at age 6 and 10 Mother worked parttime at age 6 and 10 Mother worked fulltime age 6 and parttime 10 Mother was at home at age 6 and worked at 10 Mother was at work at age 6 and home at age 10

3

4

5 0.14 (0.19) 0.06 (0.16) 0.03 (0.01) 0.02 (0.02) 0.08 (0.06)

0.01 (0.01)

0.04 (0.03)

Note: Values in bold are significant at the 0.05 level.

explained by the overlap in measured characteristics of the siblings or by their jointly measured parental characteristics. Table 3 shows some other interesting results. Even after controlling for individual and family characteristics, year of birth has a significant parameter: The younger the person, the higher his or her divorce risk. Clearly, there is a secular trend in Australia of increasing divorce risks that cannot be explained by changes in individual and family characteristics such as educational level or mothers entering the workforce. Having children decreases the divorce risk, either because of a larger reluctance to divorce with children or because of the higher investment in the union made by both parties in creating children. Occupational status has no significant effect on the divorce risk, but educational level has. The higher the educational level, the higher the divorce risk. This relationship suggests that cultural knowledge and resources open up more possibilities to end an unsatisfactory marriage and provide more financial and social capital, as indicated by occupational status. Parental occupational status or educational levels are irrelevant for the divorce risk of their children.

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Political choice is not related to divorce risk. This finding contradicts the notion that divorce risks are strongly related to values and attitudes toward society. Being a migrant does not increase the divorce risk significantly compared with the risk for native Australians. The possible increase in uncertainty about values, norms, and preferences seems to be neutralized by the need to use all scarce resources for the success of the migration and thus not for ending an unsatisfactory marriage. Contrary to what might be expected, having a working mother has no significant positive effect on the divorce risks of her children. Indeed, the only significant effect runs against the expectation: Having a full-time working mother decreases the divorce risk of her children significantly. A possible explanation is that the power balance in the parental family is more equal, thanks to the mother’s full-time work. This power sharing teaches the children that a more equal division of labor between husband and wife is possible. That knowledge increases the chances that the power balance will less unequal in the children’s own marriages and thus that their unions will be less prone to conflicts related to the gender division of labor and thus to disruption. The parameters of the other individual characteristics are obvious, given the research literature on divorce: Marrying young increases the risk of divorce. But having a child born before one’s first marriage is not significant after controlling for the other individual characteristics, nor does family size have a significant effect on divorce risk.

CONCLUSION The two main research questions of this chapter can be answered positively. There is a similarity in the divorce risks of children from the same family in Australia, and measured parental characteristics (parental divorce, common socioeconomic background of the parents) and the overlap in measured characteristics of their children (educational level, age at first marriage) cannot explain this similarity in the divorce risks. Nevertheless, it is possible that unmeasured family characteristics such as common socialization can explain this similarity in divorce risks. However, we find it difficult to propose unmeasured characteristics of socialization in the parental family or by the siblings that are not related to the measured characteristics that we already included in the equation (educational level, age at marriage, parental divorce, and political choice). Such an unmeasured characteristic should

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be unrelated to the already included characteristics; otherwise, adding an unmeasured but related characteristic probably cannot explain the similarity of divorce risks within families. This suggests that the similarity in divorce risks also might be the consequence of the common genetic and social heritage of the siblings. More often than nonsiblings, they share the same genetically and socially transmissible personality traits (like the ‘‘big five’’) that are related to divorce risk and thus to more similar divorce risks. Our results also suggest that an interaction between genetic and environmental factors, rather than environmental influences alone (as is assumed in many theories of divorce), might be responsible for divorce risk differences in the population. The model used in this chapter is a general model for multi-level event history. It would also apply to situations where individuals in organizations are followed in a panel design, with outcome variables such as career changes or events such as leaving the organization, to be predicted by both individual-level and organizational-level explanatory variables. The specific analysis model used requires multi-level software for binary outcomes and at least three available levels, a description that applies to most modern multi-level software. Hedeker, Siddiqui, and Hu (2000) have published software for multi-level grouped-time survival analysis that can also be used to analyze our model. Goldstein (2003) discusses several approaches to multi-level event history analysis that can be analyzed using MLwiN in combination with special macros.

NOTES 1. ‘‘Divorce’’ also includes separation of a cohabitating couple with children. From the point of view of the children, the exact juridical bond between their parents (marriage or cohabitation) is not highly relevant for their experience of a parental breakup (divorce or separation). 2. Amato confirmed in a personal communication that such a sibling analysis does not exist. The analyses that come close are twin analyses. 3. If the respondent or sibling had a child before the first marriage, this variable was coded zero.

ACKNOWLEDGMENT We thank J. Kelley for making available the Australian data set.

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REFERENCES Amato, P. R. (2001). Children of divorce in the 1990s: An update of the Amato and Keith (1991) meta-analysis. Journal of Family Psychology, 15, 355–370. Amato, P. R., & Booth, A. (1991). Consequences of parental divorce and marital unhappiness for adult well-being. Social Forces, 69, 905–914. Borgers, N., Dronkers, J., Rollenberg, L., Evans, M. D. R., & Kelley, J. (1995). Educational resemblance between Australian siblings: Gender, generations, migration, family forms, and mothers work. Paper presented at the Annual Conference of the American Sociological Association, 19–23 August 1995, Washington, DC (Available at www.iue.it/ Personal/Dronkers/English/ozziesib.pdf). Cramer, D. (1993). Personality and marital dissolution. Personality and Individual Differences, 14, 605–607. de Graaf, P. M., & Huinink, J. J. (1992). Trends in measured and unmeasured effects of family background on educational attainment and occupational achievement in the Federal Republic of Germany. Social Science Research, 21, 84–112. Goldstein, H. (2003). Multi-level statistical models. London: Arnold. Hauser, R. M., & Wong, R. S.-K. (1989). Sibling resemblance and intersibling effects in educational attainment. Sociology of Education, 62, 149–171. Hedeker, D., Siddiqui, O., & Hu, F. B. (2000). Random-effects regression analysis of correlated group-time survival data. Statistical Methods in Medical Research, 9, 161–179. Hox, J. J. (2002). Multi-level analysis. Techniques and applications. Mahwah, NJ: Lawrence Erlbaum Associates. Jockin, V., McGue, M., & Lykken, D. T. (1996). Personality and divorce: A genetic analysis. Journal of Personality and Social Psychology, 71, 288–299. Kelley, J. (1990). The failure of a paradigm: Log-linear models of social mobility. In: J. Clarke, S. Modgil & C. Modgil (Eds), John Goldthorpe: Consensus and controversy (pp. 319–346). London: Falmer Press. McGue, M., & Lykken, D. T. (1992). Genetic influence on risk of divorce. Psychological Science, 3, 368–372. McLanahan, S., & Bumpass, L. (1988). Intergenerational consequences of family disruption. American Journal of Sociology, 94, 130–152. O’Connor, T. G., Plomin, R., Caspi, A., & DeFries, J. C. (2000). Are associations between parental divorce and children’s adjustment genetically mediated? An adoption study. Developmental Psychology, 36, 429–437. Pryor, J., & Rodgers, B. (2001). Children in changing families. Life after parental separation. Oxford: Blackwell. Rasbash, J., Browne, W., Goldstein, H., Yang, M., Plewis, I., Healy, M., et al. (2000). A user’s guide to MLwiN. London: Multi-level Models Project, University of London. Sieben, I. (2001). Sibling similarities and social stratification. The impact of family background across countries and cohorts. Unpublished doctoral dissertation. Catholic University of Nijmegen. Singer, J. D., & Willet, J. B. (1993). It’s about time: Using discrete-time survival analysis to study duration and the timing of events. Journal of Educational Statistics, 18, 155–195.

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Spruijt, E. (1993). Volwassen worden in een kerngezin, eenoudergezin of stiefgezin. In: M. du Bois-Reymond, & J. de Jong Gierveld (Eds), Volwassen worden. Generaties toen en nu: Transities in de levensloop (pp. 73–92). [XYZ.] Houten/Zaventem: Bohn Stafleu Van Loghum. van Eijck, K. (1996). Family and opportunity. A sibling analysis of the impact of family background on education, occupation, and consumption. Tilburg, The Netherlands: Tilburg University Press.

SIBLING EFFECTS ON DIVORCE: COMMON FAMILY BACKGROUND, COMMON GENETIC HERITAGE, OR CONTINUING INTERACTION AMONG ADULT SIBLINGS Michael P. Farrell ABSTRACT This article highlights some of Dronkers and Hox’s significant findings about family background and sibling effects on divorce. It proposes that in addition to siblings’ common family background and genetic heritage, their interaction over the life course may influence their attitudes toward marriage and divorce. The influence of sibling modeling and interaction over the life course may vary, depending on the gender and birth order of siblings.

CONTRIBUTIONS OF DRONKERS AND HOX’S CHAPTER Dronkers and Hox’s study (this volume) is highly original both in the research questions they address and in their methodological approach for Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 239–242 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05011-9

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answering these questions. Although it is now quite common to test theories of genetic and ‘‘shared environment’’ effects in studies of problem behavior in adolescence (e.g., Rowe, Rodgers, & Meseck-Bushey, 1992; Taylor, McGue, Iacono, & Lykken, 2000), meta-analysis of the divorce literature suggests that no one has examined whether these factors are important determinants of divorce (Amato, 2001). Indeed, there has been virtually no research on the effects of one sibling’s divorce on another’s likelihood of divorcing. If both genetic and shared family environment factors affect the likelihood of delinquency and substance abuse in adolescent siblings, might these factors also affect the likelihood of divorce among adult siblings? In part the gap in research on this question is a consequence of the lack of adequate data. There are few if any data sets available that assess marital outcomes for siblings. Dronkers and Hox found the best data set available, and they have carried out a ground-breaking study sorting out the net impact of sibling divorces on respondents’ likelihood divorce, after taking into account shared family environment and individual characteristics that might account for divorce. In sorting out these effects, they make use of some of the most advanced methods of multi-level analysis, an appropriate technique to use when individuals are nested within groups as siblings are nested within families. Using multi-level analysis, they find that the percentage of divorced siblings in an adult respondent’s family is related to his or her likelihood of divorce. This effect remains significant even after controlling the respondent’s length of marriage and a host of parental family level variables that might affect likelihood of divorce. For example, after controlling for parental divorce, education, and migration, they still find that the higher the percentage of divorced siblings, the more likely it is that a person will get divorced. Thus they conclude that the sibling effect is not accounted for by a shared family environment. One mark of original research is that it raises new questions, opening up whole new areas of research. Dronkers and Hox set out to sort out the relative effects of shared family environment, individual life history, and genetic factors on divorce. But by focusing attention on the effects of sibling divorce rate on a target person’s likelihood of divorce, and by demonstrating that sibling effects persist after controlling for alternative factors that might make these effects spurious, they suggest a new set of questions. Are these sibling effects the consequence of shared genetic heritage, or are they the consequence of sibling interaction in late life? We know from studies of social exchange and social support across the life course that siblings often interact and influence one another throughout their lives (Cicirelli, 1995),

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and it is possible that siblings’ attitudes toward marriage and their choices to divorce may affect a target person’s likelihood of getting divorced. Thus, the relationship between sibling divorces may not be solely a consequence of their shared social or genetic background; rather, it may be a consequence of their continued interaction, shaping one another’s values and decisions. The finding of a secular trend in divorces suggests that the stigmatization of divorce is declining. The most difficult stigmatization to endure is often that which occurs within family boundaries. The secularization of attitudes toward divorce that may be most consequential in individual decision-making is that which evolves within sibling networks. As siblings exchange opinions and observe the consequences of divorce of a sibling, they most likely influence one another’s attitudes towards remaining married or divorcing.

QUESTIONS FOR FUTURE RESEARCH If future researchers examine the effects of divorced siblings’ interaction on nondivorced sibling’s likelihood of divorcing, then they may want to examine other questions raised by Dronkers and Hox’s work. Specifically, does the gender and birth order of the siblings and the target respondent affect the relationship between sibling divorce and target respondent’s divorce? If modeling is a factor that accounts for the effects of siblings on one another, it may be important to take into account the birth order and gender of the divorced sibling. Modeling theory suggests that same gender siblings are more likely to influence one another than those of different genders; and older siblings are more likely to influence younger siblings, than vice versa (Bandura, 1986). The birth-order effect may be less important as sibling’s age, but the gender effect may be stronger. In addition to testing modeling theory, future research should also take into account empirical findings on gender differences in communication among adult siblings. Research on social support over the life course shows that adult sisters are more likely to maintain contact and rely on one another as confidents than adult brothers (Cicirelli, 1995). If adult siblings affect one another, it may be that, because of their continuous interaction, sisters are more likely to affect one another than brothers. This question can be examined in future research by including interaction effects in the analysis. It would be an easy step to examine whether the effects of percent of sibling divorces varies, depending upon the gender of the respondent and the sibling. The questions raised in this commentary do not detract from the importance of Dronkers and Hox’s work. Rather, they reflect the fact that this

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study, and future studies like it that include multi-level thinking and analyses, are likely to provide seminal work in the divorce literature.

REFERENCES Amato, P. R. (2001). Children of divorce in the 1990s: An update of the Amato and Keith (1991) meta-analysis. Journal of Family Psychology, 15, 355–370. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: PrenticeHall. Cicirelli, V. G. (1995). Sibling relations across the life span. New York: Plenum Press. Dronkers, J., & Hox, J. (this volume). The importance of the common family background for the similarity of divorce risks of siblings: A multi-level event history analysis. In: F. J. Yammarino & F. Dansereau (Eds), Multi-level issues in social systems. Research in Multi-Level Issues (Vol. 5). Oxford, UK: Elsevier. Rowe, D. C., Rodgers, J. L., & Meseck-Bushey, S. (1992). Sibling delinquency and the family environment: Shared and unshared influences. Child Development, 63, 59–67. Taylor, J., McGue, M., Iacono, W. G., & Lykken, D. T. (2000). A behavioral genetic analysis of the relationship between the socialization scale and self-reported delinquency. Journal of Personality, 68(1), 29–50.

MULTI-LEVEL EVENT HISTORY ANALYSIS FOR A SIBLING DESIGN: THE CHOICE OF PREDICTOR VARIABLES Tom A.B. Snijders ABSTRACT The chapter by Dronkers and Hox presents an interesting multi-level event history analysis of divorce risks. The sibling design gives excellent opportunities for studying the similarity between brothers and sisters in the risks of divorce. Various discussion points are raised, all of which bear in some way upon the choice of predictor variables in the multi-level logistic regression. Questions are posed about the level of detail of modeling time trends; about the fact that sampling weights are a function of number of siblings; and about the inclusion in the fixed part of the model of the fraction of previously divorced siblings, which is correlated with the family-level random intercept.

INTRODUCTION Sibling designs provide an elegant way to estimate and test effects of family background on behavior. The chapter by Dronkers and Hox (this volume) Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 243–251 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05012-0

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does this by combining the elegant design with an elegant method of analysis. The data set used, the Australian National Social Science Family Survey, contains information about being divorced and about the year date of a possible divorce – this is retrospective information which allows an event history analysis of risks of divorce, in particular, of effects of the divorce of a sibling on later getting a divorce oneself. As indicated by the subtitle, the analysis has a multi-level nature because of the nesting of siblings within families (the word ‘‘nest’’ is particularly appropriate here), so that unexplained effects both on the individual level and at the family level can be taken into account. The longitudinal multi-level nature of the data permits the authors to employ a device of a kind that would be frowned upon in a cross-sectional multi-level design: the dependent variable is used as an independent variable for what happens in a later year to other individuals belonging to the same family. One could speak of a cross-lagged autoregression model where the crossing is done within families. My comments focus on the details of the choice of predictor, or ‘‘independent’’ variables.

EFFECTS OF UNMEASURED OR UNRELIABLY MEASURED CHARACTERISTICS The chapter starts by proposing a number of theoretical mechanisms that could explain family effects on divorce risks operating similarly on all siblings; and learning as a direct effect of divorce of siblings on the risk of a future divorce of ego. Let us use U as a generic symbol to indicate variables representing such mechanisms similarly affecting the divorce risk DR of all brothers and sisters in the same family. Examples of U are relevant elements of common socialization, socio-economic background, and genetic heritage. When some such variable U is represented incompletely, or not at all, by measured variables, then earlier divorce of siblings in the role of independent variable will take up some of the effect of U. In their discussion of theoretical mechanisms the authors mention as a condition for this to occur, that the level of divorce by married siblings is a good proxy for U. In the mathematical analysis given below, it will, however, be shown that, precisely because one is dealing here with the dependent variable although measured at another instance for another family member, this condition is superfluous and the existence of the effect of U on DR is already sufficient for earlier divorce of siblings to take up some of the effects of U when U is omitted from the fixed effects or measured unreliably.

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In the fifth class of mechanisms discussed, genetic and social heritage, the authors conclude that if such mechanisms explain divorce risks, then ‘‘the effect of divorce risk of ego should hardly change by the controlling for other individual and parental characteristics.’’ This will be true only if genetic and social heritage is the only mechanism explaining divorce risks, and is not at all represented by the other individual and parental characteristics under consideration – which seems unlikely for most meaningful sets of characteristics.

SIBLING DESIGNS AND SAMPLING WEIGHTS The design of the family survey allows the researchers to recode the data for all mentioned siblings, so they can be used in the same way as data for respondents themselves. Thus, a survey respondent with k siblings will lead to k+1 data points. This is great as a way to use the data more efficiently; however it gives a higher sampling probability to individuals born into families with more children. This probability will be proportional to the total number of siblings in the family, capped at four because individuals were allowed to give information about three siblings at most. Since the authors do not mention using any kind of sampling weights, I assume this has been omitted, which implies that the results are biased toward the larger families. A weighted analysis might have been appropriate. Weighting in multi-level models is rather complicated (Pfeffermann, Skinner, Holmes, Goldstein, & Rasbash, 1998) and in this case – like in many others – it seems easier and more enlightening to control for the family size, rather than using weights depending on the family size (cf. Winship & Radbill, 1994). Perhaps the number of siblings affects divorce risks, or perhaps it modifies effects of other variables on divorce risks; if this is the case, it will be enlightening to know how number of siblings affects divorce risks, and knowing this will be more important than reporting results that are valid when averaging out family size in a correct way, which would be the result of a weighted analysis.

TIME VARIABLES There must be more in the duration, cohort, and period effects than the linear effects of duration and birth year on the logit scale, used in this chapter in the event history analyses as presented in Table 3. In the first place, there has been a legal change affecting the divorce rate. The divorce

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rate in Australia rose slowly from 1900 to 1975; with the Family Law Act, 1975 the number of divorces rose sharply in 1976, stabilizing again after 1979 at a much higher rate than before 1976 (Australian Bureau of Statistics, 2003). Given this legal change, a nonlinear effect of the year date would have been appropriate, e.g., with the pre-1975 period as the reference level, with dummies for each of the years 1976–1979, and with one dummy variable for the period after 1979. Next to this, the effects of marriage duration (up to the year under consideration) and birth cohort might be nonlinear. Models 1–3 in Table 3 show a significantly negative effect of marriage duration, but it seems implausible to me that the odds of marriage are maximal immediately after the wedding. It is more likely that they go up first, reach a maximum some years after marriage, and then may go down again. I would have been interested to see graphs of how marriage duration impinges on divorce risks – what are the relatively less or more risky years? This could be investigated by using dummy variables also for the years of marriage duration, perhaps after some grouping; or by using spline functions as in Section 12.2 of Snijders and Bosker (1999). For the variable defined by birth cohort, more gradual changes are expected, and a spline model with just a few parameters would presumably be sufficient. The dummies for year date are not linearly dependent on these proposed nonlinear effects for duration and cohort, so this rather detailed modeling of time variables should be possible without falling prey to the age– period-cohort problem. This detailed use of time variables is not only important to satisfy the scientific or human curiosity about the dependence of divorce risks on, for example, marriage duration. Siblings in most families are relatively homogeneous in birth year. Therefore controlling inadequately for birth year may introduce a distortion that would then be partially taken up by the random family level intercept, or by the effect of having a divorced sibling, and this could lead to an overestimate of the importance of the interesting mechanisms 1–5 treated in the theoretical part of the chapter.

HOW LARGE IS THE BETWEEN-FAMILY VARIATION? Table 3 presents many interesting coefficients. However, the chapter does not give the intercept variance, i.e., the part of the variance between families that is unexplained by the variables in the model. The MQL method used in these analyses is known to yield downward biased estimates of the intercept

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variance (Rodrı´ guez & Goldman, 1995), but nevertheless it would be interesting to know the estimated family level variance; or the outcome of a variance estimator that is better than the variance obtained from the MQL procedure.

OMITTED LEVEL-TWO VARIABLES IN MULTI-LEVEL LOGISTIC REGRESSION The paper uses the intuitively plausible assumption that family level variables that have an effect on divorce risk, but that are omitted from the analysis, will lead to an effect of the number of divorced siblings on the divorce risk of a given individual (‘‘ego’’). A mathematical analysis can illuminate this and give a more detailed insight. For the sake of simplicity I first consider the case of a two-child family, that is, of individuals with one sibling, neglecting the event history nature of the analysis. Later some comments are made that apply more specifically to the event-history situation. The two siblings are denoted i ¼ 1, 2, and the event that sibling i gets a divorce is represented by the dummy variable Yi. An index for the family such as j is omitted to keep the notation relatively simple. The events Y1 and Y2 are assumed to follow logistic regression models with a total linear predictor ai+U for sibling i, where ai is the total result of fixed effects of measured variables (the ‘‘linear fixed predictor’’), while U is a family level variable independent of the sibling and therefore without the index i, and that is unmeasured (latent) or for other reasons omitted from the analysis. Thus, the probability of divorce conditional on U is determined by logit PfY i ¼ 1jUg ¼ ai þ U

for i ¼ 1; 2

where the logit function is defined as usual by logit(p) ¼ ln(p/(1
p)). Denote the logistic function, which is the inverse of the logit, by f ðxÞ ¼ expðxÞ=ð1
expðxÞÞ The marginal probabilities of divorce, without the conditioning on U, are given by PfY i ¼ 1g ¼ E f ðai þ UÞ where the symbol E denotes mathematical expectation, averaging over the probability distribution of U. It is not a restriction to assume that U has expected value 0 (this expected value can be absorbed into ai); its variance is

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denoted var(U) ¼ s2. Thus s2 is a measure for the total importance of the omitted family-level effects that influence both siblings similarly. An approximation will be presented which is valid for relatively small values of s2. Suppose that we analyze Y1 by logistic regression, taking into account whether the sibling was divorced, i.e., we know the outcome of Y2; and suppose that we do not also use Y2 as an outcome, so that a uni-level logistic regression analysis is appropriate. The contribution of Y2 in the logistic regression for Y1 is the logarithm of the odds ratio, which can be written as PfY 1 ¼ 1jY 2 ¼ 1g PfY 1 ¼ 0jY 2 ¼ 0g PfY 1 ¼ 0jY 2 ¼ 1g PfY 1 ¼ 1jY 2 ¼ 0g covðY 1 ; Y 2 Þ ¼1þ EY 1 ð1
Y 2 Þ EY 2 ð1
Y 1 Þ

odds ratio ¼

Since log(1+x) E x, the log odds ratio, which is just the coefficient of Y2 in the logistic regression, is approximately log odds ratio 

covðY 1 ; Y 2 Þ EY 1 ð1
Y 2 Þ EY 2 ð1
Y 1 Þ

(1)

The numerator and denominator of this expression will be considered in turn. First, the numerator cov(Y1,Y2) is written as EY 1 Y 2
EY 1 EY 2 ¼ Eðf ða1 þ UÞf ða2 þ UÞÞ
ðEf ða1 þ UÞÞ ðEf ða2 þ UÞÞ (2) and a second-order Taylor series is applied to the function f. The Taylor series approximation is 1 f ða þ UÞ  f ðaÞ þ Uf 0 ðaÞ þ U 2 f 00 ðaÞ 2 where f0 (a) and f00 (a) are the first-order and second-order derivatives. In the expansion of the right-hand side of Eq. (2), the term that is constant in U yields f(a1) f(a2)
f(a1) f(a2) ¼ 0. All terms linear in U give zero contributions to the expected values since EU ¼ 0. Therefore, the main approximation to Eq. (2) is given by only the quadratic terms in U. Denoting f(ai) ¼ fi for i ¼ 1, 2, and similarly defining fi0 and fi00 , the quadratic terms yield  1  1 EU 2 f 1 f 002 þ 2f 01 f 02 þ f 001 f 2
EU 2 f 1 f 002 þ f 001 f 2 ¼ f 01 f 02 s2 2 2 Since for the logistic function we have fi0 ¼ fi (1
fi), the result is covðY 1 ; Y 2 Þ  f 1 f 2 ð1
f 1 Þð1
f 2 Þ s2

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Second, the denominator of formula (1) is considered. It can be approximated simply by EY 1 ð1
Y 2 Þ EY 2 ð1
Y 1 Þ  f 1 ð1
f 2 Þ f 2 ð1
f 1 Þ Combining numerator and denominator, the approximation (1) to the log odds ratio gets the particularly simple expression log odds ratio  12 s2

(3)

In sum, the contribution of the sibling’s divorce to the logistic regression of ego’s divorce is a fixed effect equal to half the variance of the total effect, called U here, of the variables affecting the siblings jointly and not represented by other variables in the logistic regression. It is important to know that this effect does not depend on the values a1 or a2, which determine the propensities for the two individuals to get a divorce. (The derivation shows that this is because of the properties of the logistic link function; for other link functions the dependence on a1 and a2 would not vanish.) Thus, this model does not imply an interaction effect between the sibling’s divorce and characteristics of ego or of the sibling. In addition, twice the size of the coefficient of Y2 in the logistic regression provides an estimate for the variance of the family level effects.

WHAT ABOUT THE CORRELATION BETWEEN PREDICTOR AND RANDOM INTERCEPT? The preceding gives some insight, but is only of indirect relevance to the event history analysis presented in this chapter. The model employed in this chapter is logit ðPfindividual i gets a divorce in the current year tji was not divorced earliergÞ

¼ logit ðPfY tij ¼ 1jY t0 ij ¼ 0 for all t0 otgÞ ¼ atij þ gStj þ U j

where Ytij is the indicator variable of the divorce of individual i in family j in year t, Stj the fraction of married siblings divorced before year t, g the regression coefficient of the latter variable, atij the total result of all the other predictor variables in the fixed part of the model, and Uj is the random intercept for family j. There is one problem with this model: the predictor variable Stj is itself a function of the vector of dependent variables, and therefore there is a correlation between the predictor Stj and the random

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intercept Uj – indeed this predictor is meant to reflect the family effects which are represented by Uj. This is precisely the aim of the authors when they state that similarity in the divorce risks of siblings is indicated by a positive effect of the divorce rate of brothers and sisters on the divorce risk of ego. However, this violates the usual assumption in hierarchical linear modeling (which is also made in OLS linear regression modeling, for that matter) that predictor variables are uncorrelated with the random disturbance terms. A closer analysis of the consequences of this violation of assumptions would be interesting. Apart from this point, there is another reason for discussing the choice of the fraction of previous divorces by siblings as a predictor variable. If the siblings have had a larger number of married years, they have been ‘‘at risk’’ for divorce during a longer period; a quick divorce after just a few married years should carry more weight than a divorce after many years. Therefore, a negative interaction effect could be expected between the fraction of divorced siblings and the total number of married sibling-years.

CONCLUSION The chapter by Dronkers and Hox (this volume) provides a very fruitful step forward in using multi-level event history methods, for data collected in a sibling design, to study family effects on divorce risks. Some questions were raised, mainly about the specification of the fixed part of the model. Given the large size of typical data sets in this type of applications, there is room for a more extensive set of control variables in the fixed part, which may lead to more detailed and more reliable conclusions. The questions about the correlation between predictor and random intercept may be related to questions of endogeneity as studied often in the econometrics literature, and hopefully will lead to new research about how best to distinguish, between, on one hand, correlation between siblings, caused by characteristics of the family of origin that are partially unobserved, and on the other, direct causal effects from the life course of one person on what happens later to a sibling of this person.

REFERENCES Australian Bureau of Statistics. (2003). Marriages and divorces Australia 2002. Canberra, ACT: Australian Bureau of Statistics, Catalogue no. 3310.0.

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Dronkers, J., & Hox, J. (this volume). The importance of the common family background for the similarity of divorce risks of siblings: A multi-level event history analysis. In: F. J. Yammarino & F. Dansereau (Eds), Multi-level issues in social systems. Research in multi-level issues (Vol. 5). Oxford, UK: Elsevier. Pfeffermann, D., Skinner, C. J., Holmes, D. J., Goldstein, H., & Rasbash, J. (1998). Weighting for unequal selection probabilities in multi-level models. Journal of the Royal Statistical Society, Series B, 60, 123–140. Rodrı´ guez, G., & Goldman, N. (1995). An assessment of estimation procedures for multi-level models with binary responses. Journal of the Royal Statistical Society, Series A, 158, 73–90. Snijders, T. A. B., & Bosker, R. J. (1999). Multi-level analysis. An introduction to basic and advanced multi-level modeling. London: Sage. Winship, C., & Radbill, L. (1994). Sampling weights and regression analysis. Sociological Methods and Research, 23, 230–257.

ADDITIONAL THOUGHTS ABOUT THE IMPORTANCE OF COMMON FAMILY BACKGROUND FOR THE SIMILARITY OF DIVORCE RISKS OF SIBLINGS Jaap Dronkers and Joop Hox ABSTRACT In this article, we further discuss the substantive and statistical issues raised in the articles by Farrell and Snijders. We point out where we agree and disagree with the two commentators. In addition to responding to a variety of issues that were raised, we point out where we believe additional studies may be particularly fruitful. We conclude that we believe that combining multi-level analyses with event-history analysis is potentially a very useful approach for future research.

INTRODUCTION We thank the editors for eliciting this discussion, and our critics for their thoughtful reactions. Since Farrell’s discussion is content oriented, and Snijders’s is statistically oriented, we will reply to their comments in separate sections. Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 253–255 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05013-2

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CONTENT-ORIENTED ISSUES We fully agree, as suggested by Farrell, that the relationship between sibling divorces can also be a consequence of their continuing interaction during their lifetime, in addition to their shared social and genetic background. And indeed, we find a secular trend in divorce, related with year of birth. But we are not sure whether we can take this secular trend as a support for a declining stigmatization of divorce. The interaction between percentage of divorced siblings and year of birth is not significant, as we found in an additional test. It should be significant if there is a decline in stigmatization with family boundaries, as suggested by Farrell in his article. This nonsignificant effect at least suggests that there is no change in the relationship between sibling divorces over time. We fully agree, however, with Farrell’s remarks on gender and birth order. As a next step, we could try to model these additional effects. However, in our data set the absolute number of divorcees among the siblings, as well as the number of siblings per family is too small to obtain reliable estimates of these more refined effects. It would be interesting to investigate data where larger families are studied, with more emphasis on their mutual relationships.

STATISTICALLY ORIENTED ISSUES Snijders correctly points out that asking target respondents to report on their siblings will give larger families a higher probability of being drawn into the sample. However, weighting in multi-level analysis is not only more difficult statistically, as he concedes, but also more complex conceptually. It is true that larger families will tend to be over-represented, but only at the family level. At the sibling level, the proportion of siblings having a family of a specific size is unbiased. One could argue that divorce is an individually experienced event, although it can be influenced by family-level characteristics. This would make the individual level most appropriate, and weighting unnecessary. We do not take this position. Our analysis aims to demonstrate the effect on individual divorce of common family background among which prominent effect is having a divorced sibling. Our main focus is estimating such effects in our multi-level model, not providing unbiased population estimates for the effect sizes. An unweighted analysis is adequate for that goal (cf. the discussion in Groves, 1989). We agree with Snijders, however, that it is important to have a satisfactory model for time as indicated by the birth year. In addition to the linear

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effect (on the logits), we have also applied polynomial models following Goldstein (1995). Some higher order polynomials turn out to be significant, but including them in the model leads to estimation problems (mostly nonconvergence). We conclude that our data do not permit a more elaborate modeling of the time effect. Similarly, given the relatively low overall propensity for divorce and the small family sizes we have refrained from attempts to estimate the family-level variance, as we feel this will not be accurately estimated by our data. Snijders also presents a mathematical analysis to demonstrate that the effect of omitted causative variables at the family level will show up in an increased effect of a sibling’s divorce on an individual’s own divorce risk. The problem here is that it is likely that there is always some degree of misspecification in a model, and the relevant question is whether the effects are plausibly explained, assuming only omitted variables. As we report in our paper, we have explored models with other explanatory variables, but we found no alternate models that showed a superior prediction, and also no predictor sets that eliminate the family effect. But we agree that our analysis relies on subtraction; given our data, we estimate the family effect as the effect that is unexplainable by relevant alternative variables. A much stronger argument could be made with data that include strong indicators for genetic resemblance, such as twin data or extended pedigree data. Since existing multi-level software can be used to deal with such data, the problem at hand is not the model but the availability of a rich data set.

CONCLUSION As we mentioned previously, we believe that we have shown that the multilevel analysis that we used in our original essay offers an opportunity to investigate this as well as other very interesting research questions.

REFERENCES Goldstein, H. (1995). Multi-level statistical models. London: Edward Arnold/New York: Halsted. Groves, R. M. (1989). Survey cost and survey error. New York: Wiley.

INDUSTRY–UNIVERSITY INTELLECTUAL PROPERTY DYNAMICS AS A MULTI-LEVEL PHENOMENON Mariann Jelinek ABSTRACT U.S. industry–university (I–U) relations around intellectual property (IP) have become increasingly contentious since the Bayh-Dole Act of 1980, while especially lucrative patents and licenses resulting from biomedical and pharmaceutical discoveries capture the headlines. Some assert that I–U relations around IP are in crisis, others suggest that no such problem exists, and still others bemoan the ‘‘increasing commercialization’’ of U.S. education. This chapter develops a multi-level model of I–U IP dynamics, drawing on pluralistic, multi-theory perspectives, field interviews, and secondary data. The model includes three levels: the institutional (economy) level, I–U (sector) level, and the organizational level. These levels jointly affect the immediate context of any deal. The chapter closes with a discussion of this model’s implications for further research and some theoretical speculations.

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INTRODUCTION This chapter develops the background and rationale for a multi-level model of industry–university (I–U) dynamics centered on intellectual property (IP) that includes the following levels: the institutional (economy) level, the I–U (sector) level, and the organizational level (the particular organizations involved) affecting any particular IP deal. Because the institutional level of rules, regulations, and judicial precedent dominates by framing the ‘‘rules of the game,’’ this chapter begins with a historical overview. Next, it discusses the I–U (sector) level. Because industries differ, this discussion addresses differences by industry as well as differences among universities and between industry and universities. The level of the deal comes next, with a look at both the particularities of individual firms or universities and the individuals involved. These levels jointly interact, as individuals make decisions in the context of their professional surroundings and on the basis of their (and their organization’s) interpretations of the rules, laws, requirements, and procedures that shape any particular deal. The sum total of many such interactions helps us to understand how successful IP deals, no deals, or unsuccessful deals come to pass, affecting both the aggregate outcomes reported and the way people think about the IP process. As such, this model offers a much more detailed and actionable approach to I–U IP and suggests directions for further research.

U.S. INDUSTRY–UNIVERSITY IP: IS THERE A PROBLEM? U.S. industry–university (I–U) relations around intellectual property (IP) have become increasingly contentious since the Bayh-Dole Act took effect in 1981, while especially lucrative patents, licenses, and large revenues have increasingly resulted from biomedical and pharmaceutical discoveries. Disclosures, patents, licenses, revenues, and the number of U.S. universities undertaking funded research have all increased since the enactment of Bayh-Dole. Some claim that contention arises because industry is seeking ‘‘something for nothing, the way (they think) it used to be,’’ when universities freely distributed their discoveries in the (imagined) golden days of yore. Others, based on the industry side, attribute the problem to universities that are woefully ignorant of critical factors of risk, cost, or industry structure and that seek ‘‘completely unreasonable’’ payments for nascent technologies.

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The Patent and Trademark Law Amendment Act of 1980, better known as Bayh-Dole (in recognition of the act’s co-sponsors), simplified commercialization by allowing universities and small businesses to claim ownership of discoveries made in the course of federally funded research and by standardizing policies across the government. Bayh-Dole is often depicted as a singularly important driver – of massive technology transfer by its supporters or, by its foes, of ‘‘the commercialization of higher education’’ (Bok, 2003), inducing faculty to sell their integrity for the contemporary equivalent of a mess of pottage. Some say that I–U IP relationships are in crisis; others claim that increasing revenues and license agreements corroborate the value of Bayh-Dole as well as the success of I–U relations around IP. Discussion almost always assumes a ‘‘linear model,’’ in which university discovery leads to industry development and commercialization, and a one-way flow of information, from universities to industry (Bush, 1950). Much of the burgeoning body of literature on I–U IP has centered on the quantitative analysis of questionnaire data – often the convenient data collected by the Association of University Technology Managers (AUTM) in its annual members’ survey. The AUTM material has the advantage of being readily available, reflecting good response rates by surveyed members, and comprising a lengthy data series. Its key disadvantages are those we would expect from questionnaire data: limited depth and insight on complex questions, potential misunderstanding. These normal hazards are exacerbated by the data’s limitation to the perceptions of AUTMs overwhelmingly university-based members. Other questionnaire samples have addressed industry sources (e.g., Link & Rees, 1991; Thursby & Thursby, 2003), but again these data focus on outcomes, rather than process, and they assume simple cause–effect relationships. A closer look at IP dynamics reveals a much more complex picture of multi-party (and multi-level) relationships, dramatic differences in industry context, and significant misperceptions among the parties. Virtually every one of the stereotyped assertions – about the role of Bayh-Dole, about the intent of industry in IP relationships, about the interests and intents of universities, about the motivation of faculty researchers, and more – falls apart upon closer examination. Given the vast array of universities and colleges involved in research (hundreds) and liaisons with industrial firms (thousands), and consequently the many thousands of individual faculty members, technology transfer officers (TTOs), and industry managers involved in IP, there may well be cases of unreasonable demands made or selfinterested positions taken in some negotiations. But within this vast array,

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most relationships are informed by something else: discernible patterns, structured assumption sets, and divergent cultural norms that create probabilistic paths of interaction, at least much of the time. Teasing out what those patterns, assumptions, norms, and paths might be will surely help to assess the I–U relationship. Industry–university IP dynamics and technology commercialization efforts are also affected by contexts, events, individuals, and personalities that are far less predictable. Unintended consequences, unanticipated exogenous factors, the serendipity of scientific success (or failure) – including discoveries by others, in other fields entirely – and the chemistry of human interactions all play important roles. Yet amidst this unpredictability, some courses of action and approaches seem more likely to foster successful IP relationships. Others approaches, although perfectly sensible in light of common assumptions or widely held beliefs, are both uncorroborated in fact and far more likely to foster contentious interactions or failed relationships. From the perspective afforded by economic data, we can discern some overall characteristics at the institutional level as well as some idea of the locus of the action. A cottage industry has sprung up of scholars studying patent data (Jaffe & Trajtenberg, 2002), arguing that patents and citations offer ‘‘a window on the knowledge economy’’ (Penner-Hahn & Shaver, 2005), using patents to assess a firm’s existing capabilities, and determining the likelihood of benefiting from outsourcing or internationalizing R&D (e.g., Jaffe, Trajtenberg, & Henderson, 1993, examining geographic localization of knowledge spillovers via patent citations). As with analysis of the AUTM data, some of the patent work is thoughtful and interesting. However, such a ‘‘view from 50,000 feet’’ provides little detailed insight into what works or does not work at the implementation level, in part because the data examined – such as the number of patents filed, granted, or subsequently licensed; the number of licenses signed; or the revenue they produce – are silent on the deals not done, the failures of relationships, or, for that matter, the state of relationships among the parties. Moreover, institutional-level analyses typically focus almost exclusively on what are seen as structural dimensions. Yet, as a substantial body of literature asserts, Action and structure y are mutually implicated. Action is legitimized (sanctioned), ratified (made sense of), and empowered (given sufficient resources to be able to be carried out) by existing social structures. Thus legitimation, signification, and domination are necessary preconditions of action, and structural dimensions of social systems. Action helps shape social structures by reproducing them or transforming them, as the case may be (de Ronde, 2003, p. 155).

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To gain insight into these matters, a much different approach to the impact of institutional dimensions and much finer-grained data are required on other levels than the typical questionnaire or patent analysis affords. Inquiry into the licensing activity at a number of universities and within a number of industries informs the next level of the research. From this perspective, I–U dynamics around IP quickly indicate striking differences between, say, chemical industry relationships with universities and biomedical industry relationships with universities, or those in the software or engineered materials industries. The short version of the story here is that the type of industry matters. Industry partners enter negotiations with important differences in viewpoint, perspectives, and expectations, depending on which industry they come from. These differences profoundly affect how industry parties will react to their university counterparts’ demands for IP payments, ownership control, and other terms. But what are the differences that distinguish one industry from another, creating those viewpoints? Which industry factors are relevant to IP negotiations, dynamics, and relationships? And what about the universities? A possible university analogy to ‘‘industry-level differences’’ may be the public–private split, with public universities typically being constrained by various state laws as well as by tax regulations related to their not-for-profit status. One level down, firm- and university-specific differences also play a role. Size, prior research and patent experience, and focus of expertise can be anticipated as factors for both university and industry partners. Particular firms successfully license again and again, sponsor research repeatedly, and express enormous satisfaction with their IP relationships. Others, by contrast, are frustrated and vituperative at what they perceive as unfair treatment. Firms differ in how much and what kind of research they do inhouse – ranging from much to none, and from very fundamental to very applied and developmental, respectively. Firms and universities also differ in local culture, past history, and experience. Likewise, universities differ not just in their level of research, disclosures, and patent applications and awards, but also in their licensing activity, the relative approval or disapproval of patenting and licensing activity within their culture, their approbation (or not) of industrial relationships, and their reputation among industrial firms. But with what results? Which of these factors affect the I–U dynamics of IP? How and why do some I–U relationships work well, whereas others seem to fail? Is there a problem or not? If there is a problem, what seems to cause difficulties (or to mend them)? What makes relationships around IP go well? We cannot find answers to these questions in economic data or from summaries of IP licenses, patent data, revenues, or deals done. Furthermore,

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much of the research to date, because it has been based on quantitative analysis of readily available, quantitative economic or questionnaire data, has examined only the positive portion of the distribution – the deals done, the licenses signed. Nowhere can one find good data on deals that did not happen, relationships that soured, or licenses opportunities that were abandoned. To explore these issues even in a preliminary fashion, the finergrained data of participant accounts of how specific relationships fared and what went well (or ill) in their course are required, along with close analysis and comparisons. Drawing on a range of data – from economic data on research funding and patents, to industry- and university-level data on licensing activity, down to specific accounts of individual deals and relationships – this chapter will sketch a multi-level model of I–U IP dynamics. The theoretical bases will be varied, as befits the varied analytical levels of the data. An important source of insight will be the accounts of individuals and their rationales for action (their own and others’). In agreement with structurationists and social constructionists (Berger & Luckman, 1966; Giddens, 1993; Morgeson & Hofmann, 1999) and evolutionary economists and economic historians (Rosenberg, 1982, 2000; North, 2005), this chapter will argue that social structure and interpretation of data are critical to I–U negotiations around IP; that path dependency – at the institutional level, that of the firm or university, and that of the individual involved in the deal – is a central shaping force; and that the individual is the primary focal point for entry of these influences into the deal. The larger contextual levels frame action at the level of the individuals who are negotiating a deal, and that influence enters the deal through the deal makers’ sense making, interpretation, legitimizing, and sanctioning of their actions. Only individuals act, think, or comprehend (Silverman, 1971), yet individuals act within context. Their interactions are framed by the varying cognitions, expectations, knowledge, and so on – the organizational, occupational, and firm or university cultures – within which they reside. However, human actors are not merely passive recipients of influence: As structurationists remind us, individuals may act to replicate existing social structures – including the policies, strategic intent, and past practices of a given university or firm – or they may act to transform them (Giddens, 1993; de Ronde, 2003). Thus, although the entry point is micro, the factors involved are nevertheless macro, reaching well beyond the individuals in question (cf., Jelinek & Wilson, 2005); the data required to bridge these levels are vastly more detailed than questionnaires or quantitative summaries can address.

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Because one aim of this research is to explain how these levels interact, special attention will be paid to factors that link individuals’ and organizations’ shared cognitions and sense making, and to differences that distinguish industry from university partners. Thus economic factors and legislation that appear to mandate particular stances will be noted, along with the contextual factors that much more immediately affect particular industries and firms, individual universities, and those involved in a given deal. Of special interest will be the dynamics that link conditions and circumstances at one level with assumptions, rationales, and interpretations that drive action at another level. At the end of the trail, I–U IP dynamics necessarily play out deal-by-deal and relationship-by-relationship in specific individuals’ interactions, as particular firms and universities negotiate about particular technologies. The understandings, sense making, and rationales of those individuals, and thus the behavior of the parties to any negotiation, will be affected by their particular firm’s or university’s culture and competitive circumstances (as well as the larger economic and institutional frameworks mentioned earlier). The assumptions and beliefs of individuals, shaped in part by exogenous factors of economics, policy, law, and competition, loom large. What participants see as driving forces for why deals and relationships go well (or ill) has everything to do with the cognitive contexts that frame individuals’ actions as well as the structural differences enacted on either side of a deal. This chapter will illuminate these factors by means of field interviews with these perpetrators, the ultimate creators of I–U IP dynamics.

EVOLUTION OF THE U.S. INSTITUTIONAL CONTEXT: ECONOMICS, HISTORY, AND INNOVATION Intellectual property is all about innovation and its economic rewards. Innovation is notoriously hard to pin down, measure effectively, or link to economic outcomes. Economists and economic historians have struggled to devise a framework that takes technology into account (North, 1981), although important voices identified technology’s primacy long ago. Schumpeter famously identified capitalism’s central mechanism as ‘‘gales of creative destruction’’ in which new products, processes, and technology swept away older, poorer methods (Schumpeter, 1950 [1940]). Moses Abramovitz declared that the size of the unexplained residual in aggregate productivity growth after accounting for capital accumulation, generally taken to be

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some form of ‘‘technical progress,’’ could be interpreted as ‘‘a measure of our ignorance about the causes of economic growth’’ (Abramovitz, 1956). Simon Kuznets suggested that economies grow by means of ‘‘the extended application of science to the problems of economic production’’ (Kuznets, 1966, p. 9), resulting in structural shifts through which formal education and technology became more important as they contributed to reducing reliance on natural resources and enhancing the effectiveness of production (Kuznets, 1959). But while Kuznets’s approach recognized productivity improvements, it was left to others to account for such important benefits as the new types of goods and services that technology makes possible, or greater choice. Thus neoclassical economics, even until fairly recently, significantly understated the effects of technology on welfare (Rosenberg, 1982, 1994; Rosenberg & Birdzell, 1986; Mowery & Rosenberg, 1998; North, 2005). In economists’ terms, ‘‘technology tends to be capital-augmenting’’ (Rosenberg, Landau, & Mowery, 1992, p. 13), suggesting important revisions of neoclassical approaches to measuring economic growth when technology’s role is recognized, a task undertaken by evolutionary economics (North, 1990, 2005). A key insight: it is not just the supply of R&D (typically measured in dollars) that determines economic growth and competitive strength, but also the success of commercial application of discoveries, a translation fraught with difficulty, uncertainty, and risk. That is, research findings must be converted into innovations that succeed in the marketplace to produce economic benefits – and achieving success is difficult. These perspectives frame the technology transfer process, and thus I–U IP relationships. Regardless of economists’ success or failure at measuring it precisely, the impact of technology on economic growth has been assumed from the beginning in American history. From Jefferson’s insertion of patent and copyright language into the U.S. Constitution to the Morrill Act of 1862 establishing Land Grant colleges to 1980’s Bayh-Dole Act, U.S. politicians have viewed science, technology, and various innovations in arts and sciences in terms of economic growth and social benefit. That broad assumption provided the rationale for supporting education with federal funds and for granting innovators temporary, limited monopoly rights under patents, despite Jefferson’s grave misgivings about monopoly (Lessig, 2002). Schumpeter’s restatement of the importance of monopoly incentives has been widely embraced by economists, although some recent arguments have been raised against it (e.g., Boldrin & Levine, 2004). Technology’s economic impact and universities’ roles in fostering development of useful technologies have long been a distinctive central focus of academe in the United States.

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The locus of Land Grant colleges in every state both spread the gospel of education, science, and innovation as drivers of economic growth and directed the colleges’ attention to technology and applied sciences of interest to their locales. Because much of the schools’ ongoing funding came from state legislatures, practical applications, and the needs of nearby agriculture and industry were paramount in both their educational endeavors and their research. In stark contrast to, say, the German university system, U.S. research universities were involved with useful outputs and solving industrial problems from very early times (Rosenberg et al., 1992; Mowery & Rosenberg, 1998). In the German system, technical schools were distinguished from universities and were (and are) generally separate from them. Virtually from its beginnings, U.S. industry has drawn strength from rapid commercialization of innovative ideas (Mowery & Rosenberg, 1998). Increasingly since the late nineteenth century, that innovation has been rooted in formal science and technology. In the United States, perhaps more than anywhere else, the flow of new science and technology into commercial applications has relied upon close relations between industry and universities to achieve tremendous benefits in economic growth. Pierre S. Du Pont, scion of the gunpowder family and founder at the turn of the twentieth century of the Du Pont Chemical Corporation, was a Massachusetts Institute of Technology (MIT) graduate, and both his technical training and his appreciation of accounting provided key underpinnings for the success of his company (Chandler & Salsbury, 1974). MIT, in particular (a recipient of Morrill Act and state funding before the 1919 withdrawal of support by the Massachusetts legislature), has long valued its close relationships with industry. It is also among the most prolific of universities at invention, patenting, and licensing. Not surprisingly, given that many Land Grant colleges emphasized agriculture, the first major areas of licensing and patenting activity at U.S. universities were in biology and agriculture – hybrid seeds, for example. These categories remained generally important until they were replaced by biomedical and pharmaceutical patents in terms of license revenue. Although discoveries in engineering, polymer science, computer science, and other areas are more numerous, they are rarely as lucrative as their counterparts in bioscience (Mowery, Nelson, Sampat, & Ziedonis, 2004). Moreover, the responsive development of new academic areas – particularly engineering specialties like aeronautical engineering, chemical engineering, computer engineering, and polymer science – sustains an important, continuing, and long-standing link between U.S. universities and industry, and U.S. economic development as a whole (Mowery & Rosenberg, 1998; Mowery et al., 2004).

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In short, despite much contemporary anxiety about ‘‘entrepreneurial science’’ as ‘‘a second academic revolution’’ eroding academic independence (Etzkowitz, Webster, & Healy, 1998; Bok, 2003), close I–U relationships were established well before World War II. Indeed, Professor Frederick Cottrell of the University of California at Berkeley, a patent holder himself, founded Cal’s Research Corporation in 1912 to manage licensing of the university’s IP. Cottrell was concerned about different danger – namely, that too many universities engaged in potentially valuable commercial research might lead to secrecy that could constrain scientific progress (Mowery, 2003). These concerns were far more visible in times when industrial support for university research was greater – that is, before the enormous federal investments of the 1940s and the Cold War era. That federal run-up in support increased the total amount of externally funded university research substantially, even as it diminished the fraction of support coming from industry. Industry’s investment in extramural research at universities has increased in recent years, but only to about 15% of the total funding. This percentage scarcely seems high enough to jeopardize intellectual independence, particularly given that most university departments receive no industry funding and that the vast majority of academic R&D is still supported by federal funds. Nevertheless, these concerns received little or no attention in discussions surrounding passage of the Bayh-Dole Act, widely identified as the crucial driver for contemporary university IP activities. Instead, attention was focused exclusively on assumptions that clarifying ownership rights for IP would encourage licensing and commercialization, thereby throwing open to use the wealth of federally funded university discoveries thought to be languishing for want of clear IP rules. (Fig. 1 depicts the sources of university R&D funding in the United States during 1972–2002.)

THE U.S. CONTEMPORARY INSTITUTIONAL LEVEL: BAYH-DOLE AND UNIVERSITY PATENTING U.S. universities’ potential for creating valuable innovations and the felt need to facilitate the transfer of federally funded discoveries from universities to the marketplace were among the rationales for the Bayh-Dole legislation, which took effect in 1981: The ‘‘taxpayer benefit’’ argument was paramount. With the growth of federal funding for university research, opportunities for commercializing discoveries increased. As Mowery et al. (2004) suggest, the universities themselves were among those pushing for a

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Fig. 1. Sources of R&D Funding, 1972–2002 (Billions of Constant 1986 Dollars). Source: National Science Board, Science and Engineering Indicators, 2004.

simplification of the law to clarify ownership: although many individual universities had already negotiated agency-specific IP ownership agreements (IPAs) well before Bayh-Dole, the terms of these agreements varied by agency. It was widely asserted that both exclusive licenses and strong IP protection would be facilitated by the promulgation of government-wide common ownership regulations. Both were seen as essential to attract the industrial capital needed to bring nascent science and technology to market. Particular agencies’ IPAs (especially those of the Department of Health Education and Welfare) might preclude exclusive licenses. Without exclusive licenses, the argument went, industry would not bear the risk entailed in development geared toward commercializing science. These and other arguments ultimately prevailed. Eventually, with little debate, Bayh-Dole became law, creating a uniform policy on ownership of discoveries resulting from federally funded research, and expressing Congress’s support for exclusive licenses. So what? How have the Bayh-Dole Act and its subsequent amendments – which extended ownership from universities and small businesses to large

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businesses, required licensing revenues to be shared with inventors, and limited the duration of exclusive licenses to large businesses – affected universities’ patenting and licensing behavior? Economist magazine went so far as to call Bayh-Dole ‘‘Possibly the most inspired piece of legislation to be enacted in America over the past half-century,’’ because it ‘‘unlocked all the inventions and discoveries that had been made in laboratories throughout the United States with the help of taxpayers’ money,’’ so that ‘‘Overnight, universities across America became hotbeds of innovation, as entrepreneurial professors took their inventions (and graduate students) off campus to set up companies of their own’’ (Innovation’s Golden Goose, 2002). Bayh-Dole’s effects are most often illustrated with reference to such easily quantifiable results as the number of universities carrying out federally funded research, numbers of patents and licenses executed, and licensing revenues. The following data are typical: 1984: 25–30 universities were involved in technology transfer 1974–1984: 84 universities filed for 4,105 patents (2,944 were granted) 1974–1984: 1,058 licenses were granted by universities

1999: 200+ universities were involved in technology transfer 2001: 190 universities received 3,200 patents 2001: 3,200 licenses and options were granted by universities

This formulation assumes that technology transfer via patents and licenses is the only means by which university discoveries enter into commercial use, and ignores what have always been the major paths for disseminating university science and technology: publication, conference presentations, consulting, and especially students taking internships or graduating to carry new knowledge into the workplace. So long as we assume a simple cause–effect relationship and ascribe the effects as due solely to Bayh-Dole, and so long as we truncate our data helpfully, the act does seem a resounding success. Certainly, many more universities are now involved in patenting and licensing; there are more patent applications (and many more patents received) by U.S. universities, which receive revenues in the millions of dollars; and universities’ overall licensing activity has expanded enormously. But a simple cause–effect assumption does not seem warranted. Data on the output of U.S. universities regarding patents and licenses before and after Bayh-Dole do not immediately corroborate the Economist’s

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assertion that Bayh-Dole was the major factor driving these trends – and the act certainly did not function as an ‘‘overnight’’ wonder. If it did, we would expect Bayh-Dole to be immediately followed by dramatic increases in licenses granted and possibly in licensing revenues. Instead, the National Science Foundation’s Science Indicators data show a steady increase in university patenting and licensing activity from the 1970s through the 1990s – that is, both before and after Bayh-Dole – with no sharply evident break. Where activity seems to accelerate, it is well after 1981. What is more noticeable is the increase in the patent productivity of the ‘‘top 100’’ research universities, as Fig. 2 indicates. The total license revenue numbers are not small: Demonstrating the economic power of academic research, particularly in the life sciences, U.S. and Canadian universities, teaching hospitals, and research institutions generated nearly $1.1 billion in royalties and fees from discoveries licensed to commercial companies in FY 2001. U.S. universities alone took in more than $827 million from 7,715 licenses. They also received 3,179 new patents and spun-off 402 companies, according to the latest survey from the Association of University Technology Managers (AUTM).

Fig. 2.

Patents at U.S. Universities, 1981–2001. Source: National Science Board, Science and Engineering Indicators, 2003.

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Columbia University led the pack at $129.9 million, with the vast majority coming from pharmaceutical patents. Indeed, most of the $511 million amassed by the top-10 grossing U.S. universities was derived from life sciences–related discoveries. Across the board, the 143 U.S. universities responding to the survey averaged $5.8 million each in licensing revenue. (The Scientist, www.the-scientist.com: Licenses worth a billion, 2003, May 27)

Yet while ‘‘licenses worth a billion’’ sounds enormously lucrative, this total is spread over some 3,200 U.S. colleges and universities (more, if we include Canadian universities as well as the teaching hospitals and other research entities on both sides of the border). The top research universities are themselves multibillion-dollar enterprises, with research budgets ranging from hundreds of millions to billions of dollars. The average receipts of the respondents to AUTMs 2002 survey, which are generally ‘‘researchintensive’’ institutions, amounted to only $5.8 million. For comparison, the University of California system (10 campuses) ranked first in research expenditures in 2003 at $2.6 billion, but the system’s total operating budget was much larger. Indeed, ‘‘[most] technologies earn small (and frequently zero) income’’ (Thursby & Thursby, 2003), and most universities receive only miniscule royalty returns compared to their research expenditures, let alone their institutional budgets. Relatively speaking, royalty revenues are peanuts. The original Bayh-Dole legislation was only one portion of a much broader evolution of IP policy. The Court of Appeals for the Federal Circuit (CAFC) was established as the court of final appeal for patent cases in the federal judiciary shortly after Bayh-Dole’s enactment. This court has been a strong supporter of patent rights, raising the importance of patenting (and thus the potential of licensing as well as litigating). The sheer number of patents issued has increased, at the same time that resources have been withdrawn from the U.S. Patent and Trademark Office (USPTO), ‘‘widening the gap between compensation for examiners in the private and public sectors’’ (Jaffe & Lerner, 2004, p. 149), and resulting in a decline in ‘‘patent quality’’ as undertrained, inexperienced, and overwhelmed examiners struggle to cope with the surge in applications. More and ‘‘poorer-quality’’ patent output coupled with a CAFC that has been unduly deferential toward patents as issued – particularly in light of the cursory examination of claims by the USPTO – has given rise to a much more litigious, contentious patent atmosphere (Jaffe & Lerner, 2004). An important U.S. Supreme Court decision (Diamond v. Chakrabarty, 1980) upheld a broad patent in biotechnology and cleared the way for patenting of organisms, molecules, and research techniques in that field, a major arena for patenting activity. Research funding changes, the shift in

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focus toward higher value biotechnology as that industry (and its lucrative worldwide potential markets for remedies) matured, and intensified application of science and technology to commercial uses more generally also affected IP. The origins and effects of Bayh-Dole must be viewed in the context of this larger shift in U.S. policy toward intellectual property rights, and the effects of the Act are easily confounded with those of other intellectual property initiatives (Mowery et al., 2004, p. 93).

For example, the focus of federal funding has shifted increasingly toward biotechnology, genomics, and related areas. The ‘‘War on Cancer’’ doubled National Institutes of Health (NIH) research funding and shifted the fraction of total federal research support earmarked for universities definitively toward bioscience: by 2001, NIH accounted for fully 60.5% of U.S. academic research funding. With such enhanced resources, it would be surprising if bioscience discoveries had not accelerated, and more surprising still if some discoveries were not developed for commercial application. The importance of bioscience patents for universities’ royalty revenue – the source of virtually all of the top patents and the overwhelming majority of all revenue dollars – makes it clear that bioscience discovery pays. In short, even a brief inspection of IP dynamics at an institutional level reveals complexity. Historical context, legal and judicial issues, federal funding shifts, and changes in the relative importance of industry versus government sources for university R&D funding over time in the U.S. all affect IP. These data suggest exercising caution: embracing Bayh-Dole as the singular driver of increased university patenting is likely to mislead, as a more complex dynamic is at work than simple economic rationality of incentives, licensing exclusivity, and industry investment.

SECTORAL-LEVEL (I–U) PERSPECTIVES The Nature of University Discovery Beneath the institution-level perspectives of economic and legal factors, still more complexity resides at the sectoral level. An important context for understanding I–U IP is the nature of discovery, the relative stage of development of university discoveries, and the relation of these to commercial success. Here the links between institutional-level factors and universities are especially clear. Naturally enough, university laboratories are typically

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concerned with fundamental science: Indeed, much federal funding (from NIH or NSF, for instance) mandates awards to proposals for fundamental research, rather than applied development. The proportion of academic research devoted to basic research has remained very high over more than 30 years: In 1970, 76.7% of academic research funds went for basic research, and that figure remained at 74.1% in 2002. Applied research accounted for 18.7% of such funding in 1970 and 22.2% in 2002, while development consumed 4.6% of these funds in 1970 and 3.7% in 2002 (NSF Science Indicators). As a consequence, and not unexpectedly, most university discoveries are at an ‘‘early stage’’ relative to their readiness for the commercial marketplace (see Table 1). These facts have consequences. The data in Table 1 refer to inventions that were licensed – but, of course, many inventions are neither patented nor licensed. Different universities have different propensities to patent; since patenting is expensive, universities are constrained to be selective. Stanford, for example, seeks patents for about 40% of disclosures, while Harvard applies for roughly 55% of its disclosures, receiving patents on 23%. A range of universities are reported to patent less than 50% of disclosures (Pressman, 2000; Ku, 2001). Many patents are never licensed, and most that are have only one bidder: one survey reported that only 22% of technologies had multiple bidders (Jensen & Thursby, 2001). It is the exceptions that garner all the attention, however, and they merit a closer look on their own.

Table 1.

Stage of Development of Licensed Inventions by U.S. Universities, 1998.

Stage of Development Proof of concept but no prototype Prototype available but only lab scale Some animal data available Some clinical data available Manufacturing feasibility known Ready for practical commercial use

Invention Disclosures (%) 45.1 37.2 26.7 9.5 15.3 12.3

Note: Survey of patenting and licensing offices at 62 U.S. research universities. Sum of shares exceeds 100% because some respondents indicated more than one stage of development. Source: Thursby, J., Jensen, R., & Thursby, M. (2001). Objectives, characteristics and outcomes of university licensing: A survey of major U.S. universities. Journal of Technology Transfer, 26, 59–72. Reproduced from National Science Foundation, Science and Engineering Indicators, 2004.

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Where the Money Is For all U.S. universities, Mowery et al. (2004) report that the ‘‘top five’’ (and in many instances the ‘‘top two’’) licenses earn the lion’s share of the revenue (90% or more). Those rare ‘‘home runs’’ are overwhelmingly found in pharmaceutical and biomedical areas today. It is not difficult to see why: in the United States, third-party payer systems often cover these items for consumers, and they are typically prescribed as essential for health or even continued life. The most lucrative patents, even in biotechnology, have been widely licensed because they have driven new industries, which clearly contributed to their superior revenues. The Cohen–Boyer DNA patent jointly owned by Stanford and Berkeley and the Axel co-transformation patent owned by Columbia each brought in hundreds of millions of dollars over their terms. The pattern of extreme outliers – among thousands of patents, a small fraction make money, and only a handful are truly lucrative – is striking, and it should guide our thinking about I–U relationships around IP. This assertion is underlined by a look at university royalty revenues, even among the well-regarded research institutions. The University of Michigan’s royalty revenues of $12 million may be set against the institution’s budget for 2004, which totaled more than $4 billion for the Ann Arbor campus alone. By comparison, Berkeley’s 10 campuses produced licensing revenues of $261 million in 2000, which represented a quarter of all U.S. universities’ royalty income; in 2002, however, royalty income was only $88 million. These figures must be set against a University of California (UC) system budget in excess of $19 billion for 2005–2006, according to the governor’s budget request (http://govbud.dof.ca.gov/StateAgencyBudgets/6015/6440/ department.html). In an era of budgetary stringency – the UC system’s shortfall over five years is on the order of $5 billion – royalty income would certainly be desirable. Research faculty and graduate students are making on average three discoveries per day (Dynes, 2005) and patents are being filed – but revenues have not kept pace, and totals are irregular. The UC system’s situation is not unusual; across the country, state funding for public higher education has declined. Thus universities would surely be interested in generating revenue, including royalty revenue, if possible. In a survey of university TTOs, 70.5% of respondents indicated that royalties and license fees generated were ‘‘extremely important’’ and 60.7% reported that the number of inventions commercialized (seen as a measure of compliance with Bayh-Dole) was ‘‘extremely important’’ (Thursby & Thursby, 2003), so presumably these officers are endeavoring to license and commercialize technology.

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Yet while universities have patented their discoveries abundantly, royalty revenues have not risen proportionally, and virtually nowhere do these revenues constitute a substantial portion of operating budgets or even research expenditures. When these universities are deriving relatively small amounts of revenues from these sources, and when those royalties overwhelmingly come from a very few, very lucrative and important patents, we are compelled to conclude that highly profitable ‘‘hits’’ are exceedingly rare, even for the scientifically accomplished, and even for those who patent frequently, and whose science and technology are excellent. These facts must form the backdrop to any examination of I–U IP negotiations and their dynamics. Cultural Differences and IP Dynamics I–U relationships around IP occur at the interface between two cultures: the university’s realm of research, discovery, education, and publication, and the industry partner’s realm of costs, prices, production yields, dollars, and markets. Interviews with university researchers, technology transfer officers, and their industry counterparts reveal significantly different perspectives and cultural norms. While such differences are predictable, they are also critically important in framing how IP relationships and dynamics are (differentially) perceived by the parties. The following discussion draws on data from a series of interviews conducted in 2003–2005 with NSF funding to investigate I–U relationships as they are affected by IP. University participants (33 of the 82 interviews) represented both ‘‘top 100’’ research universities on NSF’s list of those receiving most federal R&D funds and other universities below the top 100. Industry participants (49 of the 82 interviews) came from a wide range of industries, including chemicals, petroleum, building products, electronic instruments, financial services, and biomedical devices, among others. To preserve anonymity, individual interview sources will be identified only as ‘‘IP Project Industry Interview’’ or ‘‘IP Project University Interview.’’ No claim is made for statistical representation of the entire economy; instead, these qualitative data are used to illustrate cultural perspectives, deeply felt reactions, and salient assumptions, which diverge in interesting patterns. Academic Freedom Among 33 university representatives, most of whom had titles such as Technology Transfer Manager, Provost, or Vice President for Research,

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‘‘top 100’’ research universities were well represented, along with smaller research-oriented institutions. The sample reflected both well-established technology transfer operations, with literally thousands of patents in active licensure, and much newer, less experienced, smaller offices. Despite this diversity – but perhaps due to the virtually universal affiliation with the AUTM – these sources were surprisingly consistent in their assumptions and viewpoints. ‘‘Academic freedom,’’ identified in the same terms by more than one respondent as ‘‘the Holy Grail,’’ was of primary importance to them in negotiations. The importance of academic freedom rested on the necessity to universities of freely shared research findings, so as to foster scientific advancement. Acceptance of this key element of the academic culture was widely referenced – more than money – as the sine qua non for a successful I–U relationship. The consensus was repeatedly stated as ‘‘We’ll negotiate on virtually anything else, but we cannot give on this.’’ University sources also referred to their not-for profit status and, through it, to their access to IP needed for fundamental research: [We tell industry partners,] ‘‘We can’t have you approve our publications, because otherwise it’s a work for hire y and we lose our fundamental research exception.’’ (IP Study University interview)

Underlying rationales offered by university sources for this perspective were also consistent. First, the core missions of any university are research and education, the associated free flow of ideas makes universities ‘‘leaky’’ by design, and no deal is important enough to warrant subverting these characteristics. Examples were given of large-ticket research agreements where specific language protected academic freedom – often discussed in terms of faculty needs for publication to advance to tenure – while also providing industry with access to faculty and their research findings. Closely related to academic freedom (and a result of it) is the nature of the university environment. Because the university’s cultural norm is a free flow of ideas, sharing of research agendas and data is not unusual. The failure to share is depicted as scandalous and culturally inappropriate, although it does occur; Blumenthal, Campbell, Anderson, and Causino (1997), however, found denial of requests appeared more closely related to assuring publication priority than to any desire for commercial secrecy. Moreover, students working in labs are not precisely employees (although sometimes they are), and students are a major traditional conduit for the spread of university discoveries and techniques as they graduate, most often to jobs in industry. Universities saw a central benefit for industry of this arrangement in the ‘‘early look’’ at prospective technologies through new

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hires. Of course, others not hired may also have acquired knowledge in the course of their education, work in the lab, research assistantships, or dissertation activities. Thus any new hire is unlikely to be the exclusive possessor of new information. In short, the fundamental nature of the university makes academic freedom a necessity, even as it precludes universities from guaranteeing protection of information. ‘‘Academic freedom’’ as an overarching value that empowers good research was the preeminent legitimating factor in universities’ views. Industry Norms and University Reference Points An example of just how different in tone and focus some industry participants’ views are can be gleaned from one industry source’s description of difficulties in dealing with universities. The differences in perspective are stark. Industry sources consistently cited fairness as a value, comparing university researchers to in-house researchers. Here is one industry source, responding to universities’ desire for royalties: We hire people here. We hire them to an event, they get a salary, they get benefits, and they get a thousand dollars every time they file a patent application as a reward. Okay? We’re not gonna pay a university researcher better than we pay our people. We can’t. That is just a poor piece of company policy. So, generally speaking, we will y cut a deal where y we can get it through our management, and they’ll approve it – that we’ll pay a university researcher a thousand dollars. It’s simply equivalent to what we give our people. Now, other companies only give their people a buck: a silver dollar, you know, which may cause a problem for your academic. But for us? I can argue legitimately that we should reward the [university] researcher, too. But why? They’re getting the research money to do the job – they’re getting, you know, research money to support their research, to pay for their graduate students: Guys, that’s just the way it is in today’s environment. I can usually get that past management, and they’ll agree to it. Royalties? We don’t give our own employees royalties. Period. (IP Study Industry Interview)

Another source in another industry described a formerly successful I–U relationship that came to a halt when the university reinterpreted the ground rules and renounced a previous master agreement with the company: Based on the content of the discussions that we’ve had, the contracts office at [Major Research Z] has decided to read ‘‘may’’ as if it was ‘‘shall’’ in the Bayh-Dole legislation, where the legislation says that the university ‘‘may’’ own the IP and charge royalties for it, blah, blah, blah. It doesn’t say ‘‘shall.’’ And one way to look at our position is, if we’re paying for [university research], we ought to have royalty-free rights to use the results. Another way to look at it, however, and one that I think is more in keeping with our values, is to look to [our internal research employees] y Considering the size of our research enterprise and the number of world-class people we have working in the

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research, it is unreasonable to put the external researchers on a different footing from the internal ones. (IP Project Industry Interview)

In this instance, the university’s move to withdraw from its prior agreements was perceived as unfair as well as inappropriate. Universities do not speak of industry norms or of fairness. Instead, they routinely cite Bayh-Dole’s amendments, which specify royalty-sharing arrangements with university researchers, to rationalize and legitimate their demands for royalties. By contrast, industry sources legitimate the company’s stance by referencing industry practices, where research employees routinely sign over all IP to their employer, performing work for hire. In the preceding case, the industry source ignored Bayh-Dole, referencing instead industry practice. Other industry sources insisted that Bayh-Dole should properly apply only to federally funded research, not to industry-sponsored research. University sources noted that virtually all equipment and buildings needed to do the industry-sponsored research were federally funded, and insisted on applying Bayh-Dole. The industry criterion is ‘‘fairness,’’ because internally ‘‘employees are employees,’’ and ‘‘researchers are researchers,’’ in the industry view, whether employed by industry or by universities. By contrast, university sources assume the long-standing special status of faculty researchers in the United States, who function as quasi-independent professionals (rather than as mere ‘‘employees’’). Thus the university perspective draws on the institutional-level parameters of Bayh-Dole to contextualize its sense making, a framing that differs significantly from the industry focus on industry- and firm-level norms (focused more on industry practices and comparative justice, in light of industry practice). Risk and Worst Cases Another central dimension of the industry approach concerns risk mitigation. In a telling account, an industry source complained about the potential for government takeover, the exercise of ‘‘march-in rights.’’ These give the government statutory power (according to the statute), y [to] require the contractor, an assignee or exclusive licensee of a subject invention to grant a nonexclusive, partially exclusive, or exclusive license in any field of use to a responsible applicant or applicants, upon terms that are reasonable under the circumstances y (35 USC 203)

The code goes on to specify in great detail the circumstances under which such rights might be exercised, and the procedures and protections afforded to licensees. In the following quote, the industry source’s interpretation of

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the code is striking in the strategic implications drawn and their stated impact on firm commitments and behavior: The one I’m dealing with [a government-sponsored university lab] at the moment is ungodly hard, and they won’t negotiate. It’s a ‘‘take it or leave it’’ deal. The government is just not kind that way. But, I have no choice, they own the [necessary experimental equipment]. I want to use the [necessary experimental equipment], but it has limited what research I want to do there, because I’m not going to do anything where I might possibly, remotely come up with something commercially interesting, because I don’t want the government to have march-in rights. Researcher: Have they ever exercised march-in rights? Not on us. Not that I know of. That’s not the point. Again, it’s one of those fears as to ‘‘what could happen’’ things. If you go and tell a president of this company, ‘‘Well, the government could actually come in and, you know, take your patent and tell you you couldn’t use it, and they get to decide what the royalties that they’re going to pay you.’’ Oof! (IP Study Industry Interview)

This source’s concerns regarding risk management, worst-case scenarios, and IP rights are mirrored in the comments of another industry source, in another firm and industry. Here concern also centered on control of risk, and the potential for unwittingly and unwillingly funding a rival’s success, due to university hold-up in license negotiations: The nightmare scenario is that we’ll pay for some critical research – something we really need – then can’t come to terms with the university. The university then licenses this to our fiercest competitor, and we’ve paid for it! We’ve paid to equip our competitor with the stake to go through our heart! (IP Study Industry Interview)

Both the march-in account and the nightmare scenario were notable for their passionate intensity. As with government march-in, no source (industry or university) mentioned any specific instance of the nightmare scenario coming to pass, but several mentioned it as a concern. Still another source commented: The essential question of the competition is, If I sponsor this research, what assurance do I have that if there’s an invention that I’m interested in y or need, I’m gonna be able to lock it up and exclude my competitors? (IP Study Industry Interview)

Evident here is a focus on risk, on a downside ‘‘what if,’’ and on competition quite absent in the typical university response, intertwined with a profound concern that competitors will be able to take rapid advantage of any discovery not protected by exclusive rights. It is significant that the focus is on ‘‘the worst case,’’ rather than on what usually happens or even on what has ever happened.

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Also visible here is a strong expressed preference for exclusivity. Numerous industry sources articulated preferences for exclusive rights, concerns about the cost of exclusive rights, and dissatisfaction with universities’ reluctance (in some cases) to grant exclusivity. The value and importance of IP exclusivity are assumed. This logic is precisely the Schumpeterian logic of monopoly incentives, deemed essential to induce investment in risky development. It was cited as the rationale for Bayh-Dole in the first place, and its logic is widely embraced by mainstream economists, although it has also come under question by some (e.g., Boldrin & Levine, 2004). This is not the logic of universities. Some state universities are constrained under state law from granting exclusive rights. Moreover, the most lucrative university patents have been those, like Cohen–Boyer and the Axel co-transformation patents that have been widely licensed to launch new industries. Firms benefit the public good by making profits, and they cherish exclusivity as a means to assure them. Universities benefit the public good by encouraging the widespread use of their scientific and technical discoveries, enhancing the capabilities of the economy as a whole, or creating new industries. Thus the parties appear to have diametrically opposed perspectives on exclusivity, driven in part by different legal perspectives, in part by self-interest, and in part by different understandings of the common good. Firm Size The industry sources cited earlier represent large firms fixated on worst-case scenarios and their need for legal protection. A small start-up firm took a very different stance when faced with difficulties in negotiating rights to an invention that initially appeared central to the firm. This spin-off was based on the technology in question – yet the firm walked away: It turned out to be a good decision because, in retrospect y we didn’t need it y You know, if you do need it, y you’re the inventor, you can figure out ways to go around it, usually, other ways. It’s just so many ways – that’s why there’s very few patents that are just y so good, and well written y and broad, and so forth [that they are really worth a lot]. (IP Study Industry Interview)

This small-firm source was adamant about not paying ‘‘too much,’’ but confident that knowledge could be accessed. The speaker, as the inventor of the university technology in question, obviously enjoys an advantageous negotiating stance in any case as well as the technical expertise for inventing alternatives. This inventor estimated that only perhaps 1% of all patents were worth very much, and went on to compare the very different

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approaches of two different research-intensive universities the company had dealt with around technology: The University of [X] y I think they understood that. [Major Research University Y] just didn’t understand that, I think. [Major Research University Y] thinks they’re – you know – God’s gift to technology. Which just may be true, and as for the students and faculty, they’re great. But the licensing office is just horrible. And they just y wanted y far too much in the beginning for what the patents were worth. And I don’t think they realized that. So, in the end, actually they got nothing because we decided to turn them down. And they shopped it around, shopped it around, and they’re still shopping it around, five years later. So they got nothing out of it, where in the beginning, they could have y could have actually gotten something. So, for us the most important criterion actually was how cheap can we get the patents y in terms of fees and royalties? [That] really was for us the bottom line. (IP Study Industry Interview)

Systematic differences among firms based on size were corroborated by a university source: I prefer not to work with a [Major Firm A]; [Major Firm B] does the same thing. [Major Firm C], if they fund you, y will tell you that they will not pay you a single dime in a royalty, that they will own everything. And I’ve basically confronted them and said, ‘‘Look, you make a billion dollars, y I don’t mind you’re getting your research money back before you pay me a dime, but I’d like the dime.’’ ‘‘Nah, unheard of.’’ So the larger company is just very difficult to work with. (IP Study Industry Interview)

Other industry comments emphasize cost considerations, both in terms of total costs and in terms of cash flow, again with some interesting differences by firm size. Some smaller-firm sources were emphatic in asserting that particular universities’ demands for guaranteed expenditures on development, up-front payments, or fees keyed to milestones placed small firms at a disadvantage. For these small firms, subsequent royalty streams based on profits made sense, while earlier payments (even small ones) might sink the firm. By contrast, large-firm sources were equally emphatic that they preferred one-time payments, not running royalties, so that they could forecast costs with greater certainty. These differences are comprehensible from the perspective of product life cycle investment and cash flow: Start-up firms lack revenues until their first products hit the market, and all necessary development work must be somehow financed before that point. Start-ups are notoriously short of cash and, without other products to subsidize development, may find it onerous to make up-front fees and progress payments based on arbitrary milestones like permit filings, prototype completion, or even initial product launch, rather than to remit a cash flow from profits. (In response, some universities

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have begun accepting equity in start-ups, so that they can share in the firm’s eventual success and profit by selling those shares upon the firm’s IPO.) Countervailing Views, Particular Parties In the industry quotes presented so far, some depictions of different parties’ views of particular others stand out: ‘‘[Major Research University Y] thinks they’re – you know – God’s gift to technology.’’ The same university was critiqued by another source in a different industry: The bigger, more prestigious universities usually have a heavier hand in the contract negotiations around the intellectual property provisions. They are usually less flexible, and they usually lose the contracts because of it. [Major Research University Y] has some absolutely hideous provisions, and they make no allowance for the fact that I can’t calculate royalty. (IP Study Industry Interview)

Yet another source – this time an academic – noted that a particular TTO at ‘‘Major Research University Y’’ had previously ‘‘wreaked havoc at my university’’ and was responsible for lost deals because of an inflexibly rigid attitude toward licensing. This comment highlights another facet of the context: the free flow of personnel, including TTO personnel, between universities. While there is some flow of personnel into university TTOs from industry, one officer, himself a former industry manager, attributed universities’ harder-nosed negotiating tactics to such a background. The comparison cited earlier between ‘‘The University of X’’ and ‘‘Major Research University Y’’ appears to corroborate one assertion: that some universities at least lose deals because of inflexible demands perceived as unreasonable by putative industry partners. Rumors circulate about an industry ‘‘blacklist’’ of ‘‘unreasonable’’ universities, although no industry corroboration of any organized industry resistance was uncovered. Among industry sources mentioning such a list, the rationale for it was an assertion that universities’ unreasonable demands were driven by ‘‘greed,’’ generalized to ‘‘universities’’ from one or a few experiences with particular universities. Indeed, some survey data from AUTM do confirm that royalty revenues and number of contracts signed are both ‘‘very important’’ metrics for technology transfer officers. Outsourcing Options Numerous industry sources did mention exploring university research options abroad – in Russia or China, for example – where constraints on IP ownership around industry-funded research are essentially nonexistent. Some industry sources tied their interest in finding offshore research to IP

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issues; others noted the much lower cost of top-flight researchers in, say, Moscow. Those researchers and their universities, hungry for research funding and in-kind contributions such as computers, are eager to facilitate industry ownership. There are risks in pursuing this strategy, however, ranging from translation difficulties and distance impediments to communication, to IP leakage to other-country industries, to the languishing of results in the back-home company for want of an internal champion. Culture and Criteria Disagreeing with industry sources’ allegations of undue financial motivation at universities, major research university representatives uniformly acknowledged that large amounts of royalty revenue were improbable. Universities must always value continued amicable relationships with industry more highly than likely royalties from any given deal, in the view of major research university representatives. These officers depicted their universities’ interests as best served by moving early-stage science and technology out to those who could develop it; by maintaining good relationships with industry partners to assure access to research support, data, and real-world problems; and by continuing to collaborate. University technology transfer officers noted that concerns about IP ownership had ‘‘metastasized’’ among research sponsors, all of whom seemed imbued with the conviction that blockbuster commercial success would roll forth from their sponsored research. Several university sources expressed skepticism about the commercial value of most early-stage research, noting that even Nobel Prize – level scientific success does not automatically (or quickly) translate to marketplace success and profits. Still, as one major research university source expressed it, We get more and more requests from everybody now, not just industry sponsors and not just federal sponsors, but even the foundations and associations that are giving us money start talking about who owns the copyright and whether if anything’s discovered y do they get royalty? So it’s just really, everybody thinks there’s a little something – that they’re going to find the [next] Xerox machine and make it big. So this IP stuff is not just when it comes to industry sponsors. (IP Study University Interview)

The skepticism voiced here about the profit potential of early-stage research was widely repeated, with multiple respondents both in industry and in universities laughingly noting that ‘‘what’s not yet invented’’ has ‘‘infinite potential’’ – often pointing to their counterparts as uninformed on this central fact of IP life.

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A major research university officer noted that universities and industry partners simply begin from different assumption sets and goals – clear evidence of cultural divergence at the sectoral level: We do things differently; we have different basic appreciations of why we’re entering into something. And again, I’m going to be very simplistic: You know, it’s not always this way, but for universities it’s really they want the research money to do the research. Basic, fundamental research. I don’t think any investigator starts out by saying, ‘‘I want some research money because I know I’m going to invent something and I want to become a millionaire.’’ Now, that happens along the way, but that’s not the beginning of it. Well, the company says, ‘‘I want to pay for this research because I think you’re going to invent something and it’s going to make us a million dollars.’’ So we start out with a different reason even to do research. Universities are very bound on protecting faculty freedom, academic freedom – are very bound on absolutely requiring everything be open and publishable – where industry wants everything to be proprietary and quiet. So, we’ve got just, at a conceptual level, real differences. (IP Study University Interview)

Nevertheless, despite the conceptual differences and divergent aims, university technology officers were nearly unanimous in asserting the value of industry-sponsored research, ongoing industry relationships for problem and data access, student internships and subsequent hiring, and a broad range of other benefits. These benefits were deemed far superior to any potential financial benefit. Several described the paltry returns on research expenditures that royalties represented; others pointed to TTO costs. As one put it, ‘‘Everything you read says it’s more costly to run one of these offices than any royalty stream that you get.’’ Technology officers with such understandings in mind will surely negotiate very differently from those who may believe that every deal represents a potential goldmine of revenues. Public U.S. universities are, by and large, controlled by the states rather than by the federal government, and no broad restriction on professional mobility exists. By contrast, the German professorate consists largely of federal government employees, who risk their pensions and tenure should they seek to start private firms. At the level of the organization, good data suggest that some universities are more consistently supportive of faculty doing applied (developmental) research or starting firms (e.g., Shane, 2004), with institutional rules and procedures that permit easy egress from and return to the university. Other universities appear to frown on faculty involvement in commercial ventures (Feldman & Desrochers, 2002; Feldman, Feller, Bercovitz, & Burton, 2002). In a close parallel, some industrial firms have long, distinguished records of collaborating with universities in general, or with particular educational institutions. As mentioned earlier, Pierre S. Du Pont’s relationship with MIT goes back a century, when Du Pont and his cousins, MIT graduates all,

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brought professional chemists into the private firm (Chandler & Salsbury, 1974). Numerous industries enjoyed similarly close relationships going back to the 1910s, 1920s, and 1930s, including those focusing on electric power, autos, aircraft manufacture, and the internal combustion engine, as well as the more widely acknowledged and later-emerging electronics and computer industries (Mowery & Rosenberg, 1998).

THE LEVEL OF THE DEAL At the level of the deal, all of the contextual factors mentioned so far can be expected to intervene, along with some much more individual factors: the negotiator’s personal characteristics and experiences. The TTO whose rigidity and insistence on revenue reportedly ‘‘wreaked havoc’’ or a specific industry representative who scorned universities’ early-stage research will clearly negotiate differently from other representatives who were reported to be ‘‘very reasonable,’’ ‘‘easy to work with,’’ and ‘‘most professional.’’ Nevertheless, at the level of the deal, it is important to note that individuals have very different takes on other individuals; thus the same person said to ‘‘wreak havoc’’ by one respondent might be reported by others to be ‘‘enormously helpful.’’ Both facets of individual differences are relevant. At the level of the deal, various parties reference higher levels: Universities cite Bayh-Dole, industry negotiators weigh their compensation to faculty researchers against what they pay to their in-house research employees, and all parties are concerned with legal IP rights, contracts, and their economic and financial implications. Participants also reflect the more immediate context of their home organization. Industry partners with a long history of successful university research and numerous collaborations tend to direct their negotiators to be much more straightforward at the outset about explicit goals, milestones, contact, and feedback arrangements. Others with less experience appear to ‘‘hope it will turn out okay,’’ and they often find daunting the sheer ambiguity of a genuine research effort, in which no one knows the outcome in advance. In search of eventual profits, more experienced industry partners often come to sponsored research with very definitive expectations for output from any given project, typically with a stringent timeline for go/no go decisions back home – for example, to feed research results into the firm’s ‘‘stage-gate’’ procedures for innovation management (Cooper, 1990; Koen et al., 2001). Research can be targeted to ‘‘take the fuzziness out of the ‘fuzzy front end’’’ of innovation (Reinertsen, 1999).

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There are also other reasons for pursuing carefully defined research: the more carefully scoped out the project, the easier it is to ensure that the firm gets the information it needs without surrendering enough of the work to compromise its internal patent work. After participating in more than 200 projects over 10 years, one highly experienced industry source expressed confidence that IP could be effectively managed, noting that it was not ‘‘one size fits all’’: There are two main distinctions. You can work with a friend, a junior professor, someone with a lot of smarts and enthusiasm and time and interest, who doesn’t have a lot of focus – doesn’t have a lot of specific knowledge there, okay? So, the IP that’s created belongs to the team, and it’s very easy to say, ‘‘Okay, this is ours together’’ or ‘‘[Industry partner], you gave us these technical reports; that’s still yours, no problem there.’’ However, working with an established researcher in the field, you have to be a little more sensitive to the IP, in that there would typically be a lot of publications, maybe even patents, software tools, and [prior] license agreements in place. Some of that would just be shared so that the research can continue. But [about] others you need to be sensitive y And from our side, y we want to make sure that we don’t leak more knowledge into the project than is necessary y (IP Project Industry Interview)

The importance of individual-to-individual, deal-level communication is visible in another industry source’s list of dimensions of success. Here, the discourse shifts between description of a particular successful project, where personal relationship was critical, and generalized dimensions: y What made [a particular project] work was probably proximity, [willingness to work with a] junior professor at the time, having a [company] master agreement in place [with the university]. And in terms of intellectual property, I’ve told all of my [subordinates responsible for projects] that an NDA [nondisclosure agreement] is only as good as the personal relationship behind it. It really is. I mean, there’s some places where you know that you don’t even have to sign it or talk about it, and the right thing will be done because you know the person is loyal. But the lawyers make you sign it, you know [laughs], once up-front. (IP Project Industry Interview)

The more experienced industry sources insisted on the importance of timely communication, and of getting all parties ‘‘on the same page’’ from the outset. While many mentioned communication, one source was especially clear on how this factor enabled effective management of the potentially conflicting needs of the university partner for publication and the industry partner for proprietary advantage: It’s really communicating if there are any sensitive areas that shouldn’t be published. At the beginning of the year for each project, we talk about ‘‘Would there be any sensitive areas?’’ I’ve found that really helpful. This is just to be up-front. Most of the time we can find a really interesting, highly publishable research project that a student can get a

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Ph.D. on, without giving away the shop. But, you gotta do that up-front. (IP Project Industry Interview)

The confidence evident in such a response is rooted in prior experience and successes, where the company achieved its desired results and the university researchers were able to publish. One of the most successful industry sources was continually involved both in research and in publication, contributing substantially to papers jointly published with university colleagues. ‘‘We improve those papers substantially,’’ this source noted. Another industry source commented that university personnel often stereotyped firms as doing little research: If it’s a university we haven’t worked with a lot, they will make the assumption that we don’t do a lot [of research]. Now, the better university licensing offices will do their homework and, after doing their homework, their behavior will change because they’ll realize we generate a lot of patents. They’ll say, ‘‘Well, okay, so we’re getting 30 a year,’’ or ‘‘We’re getting 40 a year,’’ or ‘‘We’re getting 100 a year – and these guys are getting almost 400. Hmm y ’’ There’s something about the throw weight in patents that actually makes sense. (IP Project Industry Interview)

This firm’s highly productive research department did not especially need university collaborations to accomplish research, but did want to maintain contacts for other reasons: We retain universities for sponsored research in order to keep a pipeline of graduates coming into the research organization and to conduct research that makes more sense to do in the university context than it does in our context. And because of the size and scope of our research enterprise, that is just not a really big space. It really isn’t. (IP Project Industry Interview)

By contrast, because they had outsourced or simply eliminated their research departments altogether, many other firms were less capable, according to this source: y A lot of American industries kind of cratered when it comes to industrial research and aren’t necessarily doing the degree of primary research that we’re doing in house. [As a result], they’re absolutely more dependent upon [research universities]. (IP Project Industry Interview)

Deals that worked well often did so because the parties were initially clear about their expectations, promptly clarified any misunderstandings, and periodically revisited their understandings about results. Deals that fared poorly were characterized by a lack of timely response, a failure to clarify deliverables at the outset, or, more disastrously, a breech of trust. Of particular importance in this regard were ‘‘surprises’’ – on both sides – about a partner’s behavior. One instance involved a university researcher

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who ‘‘neglected to mention’’ that his prior relevant work existed, until the research required access to it, whereupon the university sought additional license fees. Another instance involved a researcher’s unilateral filing of patent applications for joint work, without acknowledging the company’s claim. Still another instance involved a professor who urged the university technology transfer officer to go easy: [Firm E] was another disaster, basically. This is one of those [instances] where a professor came walking in here [to the Tech Licensing Office] about 12 years ago and said, ‘‘I want you to license this technology with them for $5,000 [a year] because they promised me future research dollars.’’ Okay, well, turns out they filed a patent application and left him off, you know? They’re banking millions of dollars on it, and we get $5,000 a year, and they don’t fund [his research]. So that’s a typical thing [from faculty]: ‘‘You need to fund these people; you need to give them what they want because they’re going to come back and fund my lab.’’ How many times do you have to do that? (IP Project University Interview)

Many instances of dissatisfaction arose out of indifference or lack of participation by the company in the research project. Without an internal sponsor to receive the results, early-stage university research may well languish for want of needed further development. At the level of the deal, then, the institution-level factors, sectoral factors, and organization-specific factors will frame the deal, as will individuals’ own prior experiences. These contexts, in turn, shape individual negotiators’ goals, expectations, and beliefs. An experienced firm’s contexts will signal that success in I–U negotiations is routine, while a personally experienced industry officer can mentor subordinates to promulgate previously successful methods, such as clarifying misunderstandings and engaging in frequent communication. Firms’ contexts (e.g., their particular industry; history of success; definition of ‘‘fairness’’) and universities’ contexts (e.g., academic freedom; history of prior industry-sponsored research; view of industry as a partner or an adversary) frame the deal. So what? How do the pieces fit together in multi-level fashion? For this we now turn to structuration theory.

A MULTI-LEVEL MODEL OF IP DYNAMICS: STRUCTURATION PERSPECTIVES Researchers have adapted Giddens’ structuration theory (DeSanctis & Poole, 1994; Giddens, 1984, 1993), which we may define as ‘‘the production and reproduction of the social systems through members’ use of rules and resources in interaction,’’ to interactions with information technology.

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Adaptive structuration theory made much of the social aspects of the use of information technology, and of perceptions of IT in affecting how the technology was used and thus its impact (DeSanctis & Poole, 1994). In a similar fashion, one can adapt structuration to IP dynamics, looking to the social aspects of how IP is understood, interpreted, and made sense of by various parties – most particularly, by industry participants on the one hand, and university representatives on the other hand. The contexts of multiple levels – institutions, sector (university vs. industry, within industries, and within particular firms and universities), and the level of the deal, where particular individuals interact – frame negotiations about any specific IP. Moreover, while not a central focus of this chapter, structuration theory’s ‘‘double interact’’ points to how structures and contexts influence the participants’ evolving understandings over time. (Current debates over the impact of Bayh-Dole, with some advising its repeal and others lauding it as inspired legislation, are examples.) These levels of structure and interaction are mapped in Fig. 3, which depicts levels and structures at different levels that shape I–U IP relationships. As the figure indicates, primary routes of influence are top down and organization in, coming eventually to affect IP processes at the deal level, through the expectations, understandings, and rationales of the parties to the negotiation. The illustrative data from the field interviews can flesh out the social and cognitive aspects of I–U IP from this perspective. As Fig. 3 suggests, various elements at the institutional level (above the topmost horizontal line in the figure) include the Bayh-Dole Act, the USPTO, the CAFC, and historic I–U links in the U.S. national innovation system. These elements constitute the institutional context. They include not only formal laws (e.g., Bayh-Dole, patent, and march-in rights), but also the ways in which the laws and regulations are implemented (USPTO) and judicially interpreted (CAFC). While other institutional factors might be identified (e.g., federal funding priorities), these are the primary elements of the U.S. institutional context. The heavy arrow from the institutional context downward to the sectoral level and the level of the deal signifies the important direct role of the institutional context in influencing any given deal. Bayh-Dole is shown on the right because it looms larger in universities’ contexts. In the middle portion of Fig. 3, the sectoral level consists of the industry context, the university context, and the interaction of the two. Thus, not only does industry context differ from university context, but different industries also differ from one another – and the differences matter. Universities share a context, while differences also arise between universities

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Institutional Context Historic I-U links in U.S. national innovation system

Institutional Level

USPTO Application examination and patents issued Bayh-Dole (1981) Formal legal framework

CAFC (1981) Judicial interpretation

Sectoral Level Industry Context

University Context

Context of the Deal Level of the Deal The Deal

Fig. 3.

A Multi-Level View of I–U IP. Heavier Lines Show Greater Influence; Lighter Lines Show Lesser Influence.

(e.g., public vs. private, large vs. small, and technical vs. comprehensive universities). In the lowest portion of Fig. 3, at the level of the deal, the contexts of the particular firm and the particular university come together to influence IP negotiations. In terms of structuration theory, the structures provided by the institutional context of laws such as the Bayh-Dole Act, the administrative practices provided by the USPTO, and the judicial interpretations of the CAFC all clearly affect IP understanding and interpretations at lower levels, and thus IP. These structures create a shared ground on which all parties

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meet. The structures do change over time, however, and these changes in turn affect individual and aggregate behavior such as major industrial reconfigurations, the initial development of U.S. industrial research, and early-technology acquisition strategies (Mowery & Rosenberg, 1998, p. 19). Subsequent changes in institutional context, including the enactment of Bayh-Dole and the massive post-World War II federal investment in university research, have encouraged widespread outsourcing of industrial research, often to universities. The universities’ purported impact on Bayh-Dole is an example of reverse influence, where aggregate deal experiences over time have affected the institutional level by influencing legislation. More commonly, however, we see institutions shaping deals. Thus the influence arrow down is thickly drawn, while the influence arrow up from the level of the deal is thin. Within and beneath the high-level institutional structures at the sectoral level, we can discern groups or organizations that differ as systems: each with ‘‘observable patterns of relationships and communicative interaction among people creating [sub]structures’’ – their shared rules, procedures, and communication patterns (for instance) that diverge from the rules, procedures, and communication patterns of other groups (Giddens, 1984). Thus the norms and business facts of a given industry – its normal margins; the fraction of total cost contributed by, for example, a catalyst in a commodity plastics business versus the cost of an alloy in a high-tech product – create an interpretive context that colors the expectations of industry partners in an IP negotiation. The overwhelmingly university-based membership of the AUTM does appear to differ from the equally homogenous membership of industry organizations such as the Industrial Research Institute, for example. It is scarcely surprising that this should be so. Yet beyond the obviousness structural and cognitive impacts must be taken into account regarding both industry participants and university participants in IP, as they can be expected to affect IP dynamics. We would expect ‘‘academic freedom’’ and the royalty-sharing provisions of Bayh-Dole to be more salient in AUTM meetings and training sessions, and for university TTOs. We would also expect ‘‘industry norms’’ and shared knowledge about normal margins and profits and the difficulties of bringing new products through the ‘‘fuzzy front end’’ and ‘‘the Valley of Death’’ (Jelinek & Wilson, 2005) to be more salient for industry negotiators, and they are. In addition, the mainline Schumpeterian logic of exclusivity and strong IP protections loom larger for industry participants. Commodity businesses differ from higher margin businesses in consistent ways, which continually reinforce the expectations of their members and

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codify their assumptions into rules, procedures, and taken-for-granted cognitive structures that endure as members interact both with one another and with outsiders. For commodity businesses, such structures often emphasize cost minimization, control, and risk reduction. Similarly, discourse among AUTM members at their annual meetings and in AUTM training courses continually reinforces expectations of members and highlights the critical issues and successful practices of exemplar university technology managers – for instance, in the basic licensing course offered several times a year, or during the panel sessions at meetings. Firms (or university organizations) and their institutional memories will provide immediate contexts of meaning, as well as rules, procedures, and practices embodying them, that will affect their representatives’ behaviors, negotiations, and expectations in interactions with IP partners. Thus de Ronde noted that a larger alliance partner felt free to poach a research recruit from its smaller partner, rationalizing the breech of industry behavioral norms by stating that ‘‘otherwise this person would have left the project entirely in leaving (both) firms.’’ The larger firm did it because it could, just as larger partners bankrolling other alliances insisted on changes of project goals and scope because they could (de Ronde, 2003). Similarly, the professor who ‘‘neglected to mention’’ previous work undoubtedly had a rationale for the omission – and the university in question legitimated the action by asking for additional royalties. The firm that promised future research support as a ploy to access IP at a low price similarly rationalized its action. In all of these cases, the actors did what they could get away with, at the ultimate cost of destroying the trust between the partners. In all of these cases, the organizations ratified the behavior of their agents. The ‘‘structuration’’ interacts that are visible here – existing rules, procedures, and taken-for-granted assumptions are espoused by AUTM members, who in turn reinterpret, restate, and (most importantly) enact them – are very much akin to the structuration noted by DeSanctis and Poole (1994) and those ‘‘double interacts’’ previously defined by Giddens (1979, 1984) and Weick (1977, 1979a, b, 1995). It is because IP participants behave and interact based on their beliefs, expectations, and contextual cognitive framings that the structures of IP dynamics persist. In turn, participants espouse, reaffirm, and institutionalize the understandings they enact, and sometimes change them. Structures at the institutional level are relatively well studied, yet as evolutionary economists and economic historians remind us, what those structures mean and how they shape understandings and actions change over time, particularly as institutional structures interact. Following the

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argument of Mowery et al. (2004), a ‘‘seemingly minor’’ change such as the creation of the CAFC to provide a patent court unintendedly encourages patent litigation, which is made more uncertain as the USPTO does less thorough patent examination. Patent examiners do less thorough examination because turnover is high, due to low wages at the USPTO and the existence of much more lucrative external opportunities in assisting patent litigation before the CAFC. These ‘‘institutional-level’’ changes, in turn, affect individuals’ and firms’ propensity to engage in patent application and to litigate others’ patents, as well as patent examiners’ propensity to change jobs, which in turn shifts the structures themselves, by altering how they are enacted over time. Structures and frameworks of meaning at the level of the industry discernibly affect expectations and behaviors in IP negotiations because they create the contexts defining what is legitimate, acceptable, and expected within a firm to some degree at least – as, for example, where the cost structure of a particular industry constrains negotiating options. Universities’ understandings differ generally from industries’ understandings, with universities emphasizing research, inquiry, teaching, and academic freedom, whereas industries emphasize cost reduction, risk management, and profits. Particular universities’ norms and cultures may foster more or less favorable attitudes toward engaging in collaborative work with industry at all. For example, Johns Hopkins’s preference for ‘‘pure research’’ and disinclination to engage in profit making have historically disinclined faculty there from forming firms (Feldman & Desrochers, 2002), whereas MIT is broadly supportive of university spin-offs (Shane, 2004). Within particular firms, there may be more or less support for engaging in collaboration with universities, for a variety of reasons – a belief in the need for particular basic research, or a desire to access ‘‘a pipeline of students,’’ or no interest at all in university relations because ‘‘we would rather buy our technology outright on the open market.’’ In summary, a structurationist perspective on multi-level IP points to higher-level institutional contexts as providing the primary ground on which both industries and universities pursue IP relationships. At the sectoral level, industry differences form industry partners’ contexts, while the university context frames sense making, legitimation, and resourcing for universities. Thus, in the aggregate industry, partners appeal to fairness, profit, cost, and risk minimization logic. University partners reference Bayh-Dole and academic freedom. At the level of the deal, particular individuals’ behavior, espoused rationales, and attitudes enact the influences of these structures – causing them to persist or, alternatively, changing them over

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time. Individuals’ interactions create deals (successful or unsuccessful) or cause deals not to be made. Over time, the deals done (or not) can affect particular firms or universities, the sectoral level (as when industry groups share stereotypes about universities, or universities share stereotypes about industry), and even the institutional level.

IMPLICATIONS FOR FURTHER RESEARCH The research reported here delves more deeply into IP decisions than much prior work and proposes a model for how phenomena at different levels interact. Nevertheless, many further questions remain to be examined. Using Fig. 3 as a guide, several research questions can be suggested, and some speculative answers offered. 1. Have concerns with IP and licensing driven I–U relations, distracted the parties from the interchanges that still dominate transmission of new science and technology to industry – namely, conferences, publications, and most particularly students? Despite the uproar about I–U relations centered on IP, many informants asserted that traditional channels of new science and technology transmission are more important. This is an empirical question that might be addressed by identifying the source of critical information in some large number of innovations. Especially in light of such recent work as that of Hargadon (2003) and historical accounts of technology evolution (e.g., Rosenberg, 1994, 1982), the whole IP contest may be simply overblown. If so, managers might be better advised to cultivate traditional channels for useful information, and universities might devote fewer scarce resources to licensing. 2. What kinds of data might support a deeper understanding of the impact of industry context on IP? While business models seem a suggestive lead, closer analysis of particular deals and participants’ understandings would be highly desirable. A careful study of innovation examples in different industries might offer useful insight. Prior research suggests not only that industry matters, but also that different industry contexts may warrant quite different strategies on IP and different forms of ‘‘intellectual capitalism’’ (Granstrand, 1999). 3. How much impact do particular organizations’ culture and norms have on particular deals? Do organizational culture and norms override industry

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norms and expectations? Are particular political, industry, or personal contexts especially vulnerable to distortion? Preliminary investigations make it clear that some universities are much more successful than others at patenting and at generating IP deals. While Shane (2004) suggests genuine qualitative differences in scientific output as a prime factor, the lack of predictable and cumulative superiority in financial returns suggests caution. Similarly, certain industrial firms have a much better record in collaborating with universities around IP and profiting from their relationships. Closer inquiry into industry-wide patterns and distinctive results differing from industry norms could illuminate these issues. As to contexts especially vulnerable to distortion, there exist essentially no data on ‘‘deals not done’’ or ‘‘deals gone awry,’’ so this area is wide open for further investigation. 4. Does any particular level exert a dominant force on IP relationships, as, for example, when an especially forceful individual joins a new organization? Under what conditions will organizational norms or industry norms dominate? Everyone operates under the dominant impact of institutional factors: they constitute ‘‘the law of the land’’ and clearly must affect deals contracted under them. However, comparisons of how deals differ under different regimes – more historical studies like those of Mowery et al. (2004), for example, or more cross-national comparisons – seem warranted. The influence of particular individuals is widely rumored, but in light of the nowdefunct ‘‘great leader’’ theories of management, caution seems advisable in attributing outcomes. Nevertheless, different levels’ effects, the particular circumstances under which a given level might be most important, and the potential leverage exercised by individuals are all empirical questions worthy of investigation. Exploration of these and similarly fine-grained questions raises another issue: 5. What evidence should be arrayed, and how, to move discussion of the U.S. innovation system away from simple-minded assertions of Bayh-Dole’s success or failure and toward more inclusive and thoughtful understanding? Without substantially more data about particular deals, for instance, it will be difficult to corroborate causal speculations about the impact of legislation, the state of I–U relations, or differences across industries. Consideration of what data might be required and how to gather those data while

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respecting legitimate proprietary and strategic constraints is itself a worthy research topic. These research questions point to the importance of considering the U.S. innovation system as regards I–U IP relationships in terms of both institutional structural, industry, and organizational contexts and individuals’ cognitive factors. While much work remains to be done, the data presented here strongly corroborate a multi-level view of I–U IP dynamics, along with the importance of hitherto ignored cognitive and social factors.

ACKNOWLEDGMENTS The National Science Foundation provided support for this inquiry under research NSF grant #02-18047. Further support and assistance from the Center for Innovation Management Studies (CIMS) at North Carolina State University and the Industrial Research Institute were critical to the work and are gratefully acknowledged. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

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INDUSTRY–UNIVERSITY RELATIONSHIPS AND THE CONTEXT OF INTELLECTUAL PROPERTY DYNAMICS: THE CASE OF IBM Marietta L. Baba ABSTRACT Jelinek has developed a multi-level model for conceptualizing the contextual influences through which intellectual property (IP) is ‘‘understood, interpreted and made sense of’’ by key parties to IP ‘‘deals.’’ This commentary reflects upon that model through a historical examination of industry–university relationships in one case – specifically, IBM. Since the late 1920s, IBM has encouraged multifaceted relationships with universities. From the start, IBM sought relationships with academia not only because of the market potential represented by university campuses, but also because Thomas Watson Sr. viewed academic customers as potential research collaborators, a novel idea at the time that later proved instrumental in the development of the corporation’s successful research enterprise. IBM’s university relationships have continued to evolve over time, reflecting shifts in the corporation’s business strategy, and changes in larger macroeconomic structures. The case of IBM reveals complex Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 301–319 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05015-6

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interactions among governmental, corporate, and academic actors and their policies at different points in time, providing support for Jelinek’s multi-level approach to framing IP dynamics, and suggesting possible refinements of the model for the future.

OVERVIEW This commentary examines industry–university relationships in historical perspective, focusing on a single case – IBM. The case provides a means to explore Jelinek’s claim that ‘‘local culture, past history, and experience’’ of the firm establishes a background that frames industry–university relationships and the overall context of IP dynamics. A historical view reveals multiple contextual influences on IBM’s long-standing and sophisticated relationships with universities up through the present, providing support for Jelinek’s multi-level model and suggesting additional contextual factors that could be considered in future refinements.

INTRODUCTION Drawing upon adaptive structuration theory (DeSanctis & Poole, 1994), Mariann Jelinek examines the contextual influences through which intellectual property (IP) is ‘‘understood, interpreted and made sense of’’ by key parties, with emphasis upon ways in which respective industry and university actors’ understandings may complement or contrast with one another at three contextual levels; specifically, the institutional, sectoral, and ‘‘deal’’ levels. Importantly, Jelinek’s model postulates interactions across these levels, and the influence of interactions on outcomes. The greatest influence is assigned to the institutional context, with secondary influence deriving from the sectoral level. For the sectoral context, Jelinek argues that both the type of industry or academic institution, as well as the specific characteristics of the firm or university in which IP is situated, will be highly influential in shaping the actors’ perceptions of one another, expectations for IP outcomes, and the overall relationship between the parties, which in turn will affect IP dynamics. Here is Jelinek on the sectoral context: The short version of the story is that industry matters. Industry partners come to a negotiation with important differences in viewpoint, perspectives and expectations, depending on what industry they come from. These differences profoundly affect how industry parties will interact to their university counterparts’ demands for IP payments, ownership control, and other terms y One level down, firm- and university-specific

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differences also play a role. Size, prior research and patent experience, and focus of expertise can be anticipated as factors for both university and industry partners. Particular firms successfully license again and again, sponsor research repeatedly, and express enormous satisfaction with their IP relationships. Others, by contrast, are frustrated and vituperative at what they perceive as unfair treatment. Firms differ in how much and what kind of research they do in-house – from much to none, and from very fundamental to very applied and developmental. Firms and universities also differ in local culture, past history and experience.

One of Jelinek’s key observations is that IP exists within an overall framework of motivational influences that are complex and multi-dimensional. Generally speaking, neither party is engaged with the other strictly for IP purposes; each has other goals in mind. Indeed, IP is not the only or even the primary mechanism by which universities transfer technological innovation to industry. There are many other linkage mechanisms, some potentially more productive than IP (e.g., hiring of graduates, collaborative research, access to data and equipment, and so forth). Thus, it is important to consider the background – why are the firms or universities in question getting together, what else is going on in the background, and what is the relative role of IP in the relationship? IP may or may not be the thing to focus on if technological innovation is the outcome of interest. This commentary reflects upon Jelinek’s argument concerning sectoral influences by considering one firm’s (IBM) relationships with universities and how these have changed over time. In general, the commentary affirms Jelinek’s observation that the ‘‘local culture, past history, and experience’’ of the firm establishes a background that frames industry–university relationships and the overall context of IP dynamics. Throughout its history, IBM has been a leader in forging innovative relationships with American universities (Bashe, Johnson, Palmer, & Pugh, 1986; IBM Research, n.d.). Its current program for encouraging collaborative education and research with universities is robust, multifaceted, and responsive to dynamic changes in the company’s business strategy (e.g., Perelgut, 2004; www.research. ibm.com/ssme/). At the same time, the company also displays strong performance in technological innovation and patenting (Chandler, 2001). For each of the years between 1994 and 2004, IBM was granted more U.S. patents than any other American company. During that period, IBM received 29,021 U.S. patents (3,248 in 2004 alone). The company holds a portfolio of 25,000 active patents in the United States, and over 40,000 patents worldwide. The patent portfolio reflects an annual investment of approximately $5 billion in research, development, and engineering (each year since 1996; www.ibm.com/ibm/licensing/patents/portfolio/shtml).

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Both IBM’s collaborative relationships with universities and its sizable patent portfolio are outgrowths of an enduring corporate legacy of innovation, one that is rooted in the firm’s history and culture; the two aspects of this legacy (i.e., university relations and patenting) reinforce one another in mutually beneficial ways. Yet, viewed within the context of IBM’s ongoing and substantial R&D investment, universities probably must be seen as making relatively modest contributions to IP overall. Still, relationships with universities are vital to IBM, not necessarily because of IP, but because of the many ways in which universities support ‘‘innovation’’ writ large (i.e., something new within the context of IBM’s many markets, which in turn advance the firm’s long-term business interests). This commentary reflects upon the historical and contextual framework surrounding IBM’s interest in university relationships, now and in the past, and the current role of universities in IBM’s transformation to a high technology services company. The final section of the commentary draws upon the discussion of IBM’s historical experience with universities, both to confirm, and to suggest possible revisions for, Jelinek’s multi-level model of I–U IP relations.

IBM’S EARLY RELATIONSHIPS WITH UNIVERSITIES Thomas J. Watson, Sr., the company’s president at the time IBM adopted its current name in 1924, had a reverence for education, and as a result he supported various activities at Columbia and Harvard since the late 1920s, especially donations of punched-card equipment and IBM machines to establish scientific and technical facilities on those campuses (Bashe et al., 1986). Watson was motivated to pursue these activities because of a business philosophy that recognized a duty to society (e.g., IBM benefited from science, and should contribute to science), and a recognition that IBM’s product line traditionally had been enhanced and broadened through suggestions made by customers. The university scientists that used automatic calculators represented one type of customer. Watson was prescient in conceptualizing academics as potential partners in research, even though most industrial firms at the time thought of academia primarily as a means to establish benchmarks for excellence and as a stimulus for creative thought (IBM Research, n.d.). A colorful illustration of Watson Sr.’s vision is depicted in a story told about the head of the Bureau of Collegiate Educational Research at Columbia University, who approached the leaders of ten office supply companies in 1929 to ask for help in automating the analysis and scoring for large-scale testing.

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Only Watson set up an appointment to meet with the Columbia professor, after which Watson hired him as a consultant. Later, Watson ordered three truckloads of equipment to be delivered to the campus, and eventually this became the University Statistical Bureau (IBM Research, n.d.). In 1939, Watson signed an agreement with Harvard to establish an automatically operating assemblage of calculating machines, as proposed by Professor Howard Aiken of Harvard. While Aiken had the concept, IBM carried out the detailed design work, provided the components, engineered the technology, and delivered it to Harvard in 1943. This device, known as the Mark I (or Automatic Sequence Controlled Calculator, ASCC), was the first of four wartime projects that evolved to become the modern computer (Chandler, 2001). According to Chandler (2001), Aiken (who had been inducted into the Navy) took over the project, but Watson and Aiken quarreled, and the Mark series ultimately had little impact on modern computer technology. It did, however, help expose graduate students and academics to the complexities of inventing the computer. Bashe et al. (1986) postulate that it was Aiken’s snub of IBM at the ASCC’s dedication in 1944, and his plan to build an even larger automatic calculating machine without IBM’s involvement, that prompted Watson to invite Wallace Eckert, Director of the Nautical Almanac at the U.S. Naval Observatory, to join IBM and form a new Department of ‘‘Pure Science,’’ which would be charged with the mission of becoming the premiere organization in the country for developing and using automatic calculating machines in science. The ‘‘purity’’ of this department was meant to distinguish it from the advanced engineering activity that Watson referred to as ‘‘science.’’ Continuing Watson’s philosophy of service to education and science while also advancing IBM’s business, the new department’s facilities were to be made available to scientists, universities, and research organizations throughout the world (Bashe et al., 1986). Eckert became the first scientist with a doctorate to be hired at IBM, and Watson encouraged him to maintain his standing as a scientist. Less than 1 year after joining IBM, Eckert became concerned about the number of competing projects being sponsored by the federal government at various universities, and he noted that ‘‘several commercial concerns in addition to IBM have been developing electronic computing machines’’ (Pugh, 1995, p. 133). The Army and Navy were financing independent projects on electronic computing at the University of Pennsylvania, the Institute for Advanced Studies at Princeton, MIT, and several other places, some of which involved significant milestone projects in the history of computing. These developments presented IBM with an IP dilemma. The

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company traditionally had a policy of secrecy and attempted to maintain all required electronic capability internally, which meant that it might fail to keep pace with companies that used government funds to support their own development efforts (Bashe et al., 1986). On the other hand, if IBM were to become involved in some of these government-backed efforts, it might forfeit some of its patent rights. As these issues were debated internally, IBM was dis-invited to participate in an 8-week course at the Moore School of the University of Pennsylvania held in Summer, 1946 on the ‘‘Theory and Techniques for Design of Electronic Digital Computers,’’ jointly sponsored by the Army and Navy. Invitees included representatives from Columbia, Harvard, Princeton, MIT, AT&T, Eastman Kodak, General Electric, National Cash Register, and government agencies. Universities, in the meantime, had their own patent issues to contend with. The Moore School at the University of Pennsylvania, for example, (which was the birth place of one of the most fruitful projects in the early history of computing, the ENIAC; Chandler, 2001) had a policy that required all employees to assign their patent rights to the university as ‘‘works for hire.’’ The intent was to ensure the ‘‘intellectual purity’’ of university work, but the effect was to drive J. Presper Eckert and John W. Mauchly, the two faculty inventors of the ENIAC, out of the university and into their own company (Pugh, 1995). In the meantime, consistent with Watson’s philosophy, Wallace Eckert established IBM’s new laboratory in a renovated fraternity house on the Columbia University campus in Manhattan, where there could be ample access to university scientists. The lab was known as the Watson Scientific Computing Laboratory at Columbia University (IBM Research, n.d.). Besides serving as director of the laboratory, Eckert also was appointed Professor of celestial mechanics at Columbia in 1946, and joint appointments with Columbia became a common practice for scientists at the lab (Bashe et al., 1986). Teaching was not to be used as salary supplementation; appointments were conceptualized as a means to enhance the intellectual life of IBM scientists. Eckert commented: The relations of IBM with the Columbia University are unprecedented and ideal. There are no formal agreements or regulations; the entire arrangement is based on Mr. Watson’s statement that the Laboratory is for Pure Science. We collaborate on matters of mutual interest. The University makes no inquiries concerning our relations with our customers, members of IBM, visitors from elsewhere, or our development work. Our staff is free to use all the facilities of the University, and we try to reciprocate where we can be of use (Eckert, undated; cf., Bashe et al., 1986, p. 530).

Within this open environment, the laboratory contributed to the development of scientific computation techniques, the design of new computers, and

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to the education of people inside and outside IBM in the use of automated computation methods over the next decade. Technical project groups (e.g., the Naval Ordinance Research Calculator project group) trained many people who worked on programming and the development of machine languages (e.g., FORTRAN). The Watson Laboratory developed a ‘‘ThreeWeek Course on Computing’’ in 1947 to teach others how to use punchedcard equipment for sophisticated calculations (Bashe et al., 1986). It is estimated that this course was attended by 1,500 people from over 20 countries, and was influential in IBM’s decision to establish computer instruction centers throughout the country in 1957. At this point, the academic discipline of computer science did not yet exist, although Chesbrough (2005) notes that Columbia offered the first course in computer science (probably automatic computing) during this same period (1946), while Thomas Watson Sr. was serving as a Columbia trustee. World War II brought a significant shift in IBM’s research orientation. Fundamental technological advances in basic science supported by collaboration among government, industry and academic scientists during the war were so impressive that IBM decided to charge its research laboratories with ensuring that IBM would not be caught off-guard by disruptive technologies in the future. The labs were given the mission of anticipating and even creating these breakthroughs (IBM Research, n.d.). Eckert was quoted as saying that the role of the labs was to ‘‘carry out scientific research where the efforts are dictated by the interest in the problem, and not by any external considerations.’’ This sounds more or less comparable to a university (basic) research environment, something that IBM’s scientists were well prepared to emulate, given their close association with a university over the years since the founding of the Department of Pure Science. It should be noted that the 1960s generally represented a low point for industry support of academic research (2.4% in 1966; National Science Foundation, 1985). The federal government was providing the lion’s share of academic research funding at the time (73.5% in 1966), and top academics were less likely to be interested in ‘‘applied’’ research in those days. IBM’s approach to basic research was extraordinarily successful for decades, and highly instrumental in enabling IBM to recruit some of the top talent in many scientific and engineering fields. Yet, the basic research orientation of the laboratories was not without controversy. There were tensions and overlaps between Eckert’s ‘‘pure science’’ approach and the more applied development orientation of the engineering group at Poughkeepsie, New York (Bashe et al., 1986). There was uncertainty regarding how research and development should be managed, and whether it should be

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carried out in the same group, or in two separate groups. If they were to be separate, then how should research be defined, and what should be the relationship between the groups? Two different management philosophies regarding research and development continued to be present at IBM from the 1950s onward, with the basic research approach achieving dominance after World War II and up through the 1960s, until setbacks in the marketplace called it into question. Consequent shifts in the orientation of the research division will be discussed in the next section of the commentary.

IBM RESEARCH AND UNIVERSITY RELATIONS Given the importance of its role, IBM Research became a full-fledged division of the corporation in 1963, and today it is the largest industrial research organization in the world, with approximately 3,000 scientists and engineers based in seven research laboratories and institutes worldwide. IBM scientists have been awarded five Nobel prizes, 20 memberships in the National Academy of Science, and 57 memberships in the National Academy of Engineering. They also have received five U.S. National Medals of Technology, five U.S. National Medals of Science, and four Turing Awards. This intellectual productivity both contributed to, and was supported by, the remarkable achievement of IBM’s products and services. In 1966, the IBM System/360 was launched, a hugely profitable product that set the standard in the computing industry for many years, and established IBM as an excellent technology company. The business model for this product was built upon ‘‘internal innovation, proprietary control over the architecture and all its key elements, and extremely high switching costs for clients’’ (IBM Research, n.d.). Patenting was a core component of this successful business model, and an integral element of IBM’s in-house research effort. At about this time, computer science was emerging as a distinctive academic discipline, with major universities beginning to offer advanced degrees in the new field. IBM offers this interpretation of the relationship between the emergence of the new academic discipline and its business interests: According to a National Academies report, IBM played a key role in the creation of computer science as an academic discipline in the 1950s. At the time, IBM was the industry leader in computing technology and provided systems to universities for research and educational purposes. As the leader in this area, IBM had the most resources to share with universities, and it made good business sense for IBM to help establish an academic field that would contribute to its business growth and value (www. research.ibm.com/ssme/workuniv.shtml).

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We have already seen the way in which IBM established a pattern for interaction with universities such as Columbia, with Watson’s provision of technical equipment and facilities, Eckert’s establishment of the Watson Laboratory on the Columbia campus, and lab scientists intermingling with university faculty, including collaborative teaching and research. IBM’s claim that its contributions of resources to campuses helped a new academic field to grow is not an unreasonable argument. One can detect the pattern through which such activities would bring major benefits to IBM, both in the short and long terms, by providing well-trained graduates for hire, faculty for research and development collaborations and consultancies, and a growing market of users for IBM’s equipment. Indeed, these emergent outputs from the firm’s contributions to universities could very well have helped to create a new discipline (computer science), which then played a role in building a new industry (the computer industry), which in turn IBM dominated for much of the latter half of the 20th century (Chandler, 2001). One could argue that IBM’s university investment and the full range of returns from this investment (of which university-related IP is only one) are integral components of what Chandler (2001) has called the ‘‘learned organizational capabilities’’ through which IBM gained a commanding stance within the marketplace. According to Chandler (2001), learned organizational capabilities – the foundation of an industrial firm’s competitive strength in a market-based economy – are grounded in three types of knowledge, including technical (scientific or professional), functional (product-specific), and managerial (business-related) knowledge. All of these would be strengthened through multifaceted relationships with universities. The ‘‘pure science’’ orientation of IBM Research shifted substantially n the 1980s, toward closer collaboration with the product divisions, following two disappointments related to lost opportunities. Scientists at IBM conducted groundbreaking work in relational databases (Edgar F. Codd) and Reduced Instruction Set Computing (RISC; John Cocke), but IBM was largely unrewarded in the marketplace for these innovations, as it was unable to nurture the core ideas into innovative products. It remained for other firms to pick up the innovations and carry them through to product realization (i.e., Oracle and Sun). These painful discoveries led IBM to realize that its research model, in which scientists focus on ‘‘pure science’’ and then rely on developers to figure out how to spin the science into new products, was no longer working. As a result, the corporation began to devise ways to strengthen links between its research and product divisions. Several formal mechanisms have been developed, beginning in the mid-1980s

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(IBM Research, n.d.):  Joint Programs – Product groups collaborated closely with researchers during the 1980s to define a range of work to be undertaken, including projects contributing to near-term product and market needs, as well as exploratory work meant to support future products or break-through technology (e.g., integration of copper wiring into microchip manufacturing).  Internal Centers for Competency – Researchers and developers worked as a team in the 1980s to tackle difficult technical problems on aggressive timelines.  First-of-a-Kind (FOAK) – These are partnerships between IBM and clients that turn promising research into market-ready products (created in the mid-1990s). The program helps to match researchers with client firms for the exploration of innovative technologies in emerging opportunity areas. Researchers obtain instant client feedback to enhance their projects, and clients gain access to a research team to help solve problems that need solutions.  On Demand Innovation Services (ODIS) – Researcher–consultants partner with consultants from IBM Global Services on client engagements to explore new ways to increase clients’ flexibility and provide market advantages and solutions (initiated in 2002). All of these programs strengthen relationships among research scientists and the IBM product and/or service divisions, and also bring researchers into closer contact with IBM clients, either directly or indirectly. The corporation also has continued its university relations program, which is now global in scope. A priority of the current program is ‘‘collaboration to develop the technical talent pipeline,’’ with an emphasis on creating mutual value with the academic community (Perelgut, 2004). Echoing themes that have been noted previously throughout this commentary, the program focuses on developing skills that are required in IBM’s business (i.e., courseware development, training, certifications, programming contests), pursuing research with selected institutions (shared university research awards, faculty awards, fellowships, centers for advanced studies), accelerating recruitment of top talent (internships, post-docs, co-ops, full-time hiring), and increasing sales. IBM’s long history of university relationships endows it with sensitive and sophisticated views of faculty concerns related to industry–university relationships. In a recent presentation at Concordia University oriented toward a computer science audience, IBM’s University Relations Manager emphasized that in research collaborations ‘‘publication approval is very fast, based only on protecting existing rights and trade secrets’’ (Perelgut,

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2004). Another statement in the presentation stressed that protection of information in IBM sponsored research would utilize ‘‘best of breed’’ policies for collaborative work. It is possible that these approaches have been influenced by the corporate environment created through IBM’s strategic commitment to open standards, which in turn are related to the servicesoriented (or ‘‘client solutions’’) business strategy developed under CEO Lou Gerstner’s leadership in the 1990s (see Gerstner, 2002, pp. 128–135). As noted in IBM’s 2004 Annual Report: The broad adoption of open standards is essential to the computing model for an on demand business and is a significant driver of collaborative innovation across all industries. Without interoperability among all manner of computing platforms, the integration of any client’s internal systems, applications and processes remains a monumental and expensive task. The broad-based acceptance of open standards – rather than closed, proprietary architectures – also allows the computing infrastructure to more easily absorb (and thus to benefit from) new technical innovations. IBM is committed to fostering open standards because they are vital to the On Demand Operating Environment, and because their acceptance will expand growth opportunities across the entire business services and IT industry. There are a number of competitors in the IT industry with significant resources and investments who are committed to closed and proprietary platforms as a way to lock customers into a particular architecture. This competition will result in increased pricing pressure and/or IP claims and proceedings. IBM’s support of open standards is evidenced by the enabling of its products to support open standards such as Linux, and the development of Rational software development tools, which can be used to develop and upgrade any other company’s software products (2004, p. 17).

This statement suggests that a services-oriented business strategy, and the open standards approach that it fosters, could create an even more favorable environment for collaborative university relationships with IBM far into the future. [It should be noted that, historically, IBM has not included computer programs and procedures within its patent policy (Pugh, 1995).]

IBM’S TURN TO SERVICES AND THE EMERGENCE OF ‘‘SERVICES SCIENCE’’ IBM appears to be in the midst of a major expansion of its university interface beyond the traditional scientific and technical fields with which it has been associated (e.g., computer science and engineering), and into the business administration and social science fields that are linked to the rise of a service economy in the 21st century (e.g., business strategy, management science, cognitive science, economics). This shift in IBM’s university relationships is interesting because it points not only to the efforts of a major

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corporation to improve its situation in the marketplace (i.e., by developing the knowledge and skill base from which it may draw technical talent), but also to a much larger and more ambitious goal – to alter the marketplace itself (as happened previously with computer science) by creating a new academic discipline called Services Sciences Management and Engineering (SSME). This is the name that IBM has given to the budding interdisciplinary enterprise that it is working to birth, with the help of business and engineering professors based primarily in the United States and United Kingdom. This phenomenon illustrates the complex nature of industry– university relationships within the context of particular companies, and highlights the ways in which these contexts can overshadow the role of IP, at least in the near term. IBM Global Services, founded in 1991, is the world’s largest information technology services and consulting provider, employing approximately 190,000 professionals in some 160 countries. Global Services includes IBM Business Consulting Services (BCS), which was created in 2002 following the acquisition of PriceWaterhouse Coopers (PWC). PWC was merged with IBM’s existing Business Innovation Services group to form BCS, which employs approximately 60,000 consultants. Beginning in 2001, IBM’s revenue from Global Services, which had been growing over the past decade, began to top that from its largest product division (i.e., Hardware), whose revenue had been experiencing a long-term decline. In 2004, the revenue from Global Services was $46.213 million; this represents more than half of the corporation’s total revenue. The table shown immediately below identifies revenue as a percent of company total from each of IBM’s major divisions since 1998. All data shown in the table was drawn from IBM Annual Reports, which are available on-line. For consistency, the percentage for each year was taken from the Annual Reports for that year, not from those of a subsequent year, where the percentages often had been recalculated (and thus may be different). Service Global services Hardware Software Global financing Enterprise investment/other Total

2004 2003 2002 2001 2000 1999 1998 48.0 32.3 15.7 2.7 1.3 100

47.8 31.7 16.1 3.2 1.2 100

44.8 33.8 16.1 4.0 1.3 100

40.7 38.9 15.1 4.0 1.3 100

37.5 42.7 14.3 3.9 1.6 100

36.7 42.3 14.5 3.6 2.9 100

35.4 43.4 14.5 3.5 3.2 100

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One issue that emerged with Global Services’ growth was the fact that the gross profit margin in Global Services was less than that of IBM’s other divisions, as shown in the table below (IBM Annual Report, 2004, p. 19): Service Global services Hardware Software Global financing Enterprise investment/other

2004

2003

Year-to-Year Change

25.1% 29.6 87.3 60.0 40.3

25.2% 27.8 86.5 55.8 43.4

(0.1) pts. 1.8 0.8 4.2 (3.1)

A growing revenue stream from a huge division with a smaller profit margin was of concern to the entire corporation and its shareholders, and especially to IBM Research, which had been supported by close relationships with the product divisions since the 1980s. The potential paradox opened up new challenges and opportunities for IBM. Two kinds of challenges are noteworthy in the present context: (1) engaging IBM scientists and engineers in services-related research that can drive innovation within Global Services, thereby improving profitability for IBM and its clients; and (2) engaging universities to support IBM in transforming itself into a 21st century high technology services provider. These two challenges are related; for example, the engagement of universities can enable IBM to bring new kinds of technical talent into its labs, and thereby support innovation and services transformation. The first challenge is beyond the scope of this commentary, although, see for discussion Kirkpatrick (2004); ODIS, mentioned previously is a key part of this effort. The remainder of this commentary will focus on the second challenge. As IBM once helped to lay the groundwork for the eventual birth of the computer science discipline in the 1950s and 1960s by providing universities with new computing equipment and systems, and through collaboration in teaching and research, so IBM now hopes to stimulate the emergence of a new interdisciplinary ‘‘services science’’ that likewise could contribute to the company’s business growth and value (www.research.ibm.com/ssme). Here is an IBM statement on the subject: Just as computer science emerged as a new discipline in the 1960s, we believe an academic discipline focused on computing and information services will soon emerge. It will draw on a variety of existing areas – computer science, management science, operations research, and business administration – and will generate specific academic

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programs, degrees and refereed journals. It will be at the forefront of business innovation, and will help us find ways to integrate advances in underlying technology (drawing on the physical sciences), mathematics and computer science and apply them to evolving business opportunities. It’s a research discipline of the future, one which we are committed to help launch, much as we did computer science a few decades ago (IBM Research, n.d., pp. 36–37).

The rationale for this effort is the realization that 80% of the U.S. Gross Domestic Product (GDP) now derives from services, while 60–80% of the GDP in other advanced economies also is service related. Yet, the American higher education establishment still appears to be organized around the ‘‘dominant logic’’ of goods production and commodity exchange that has been inherited largely from neoclassical economics (Vargo & Lusch, 2004). There are few, if any, business or engineering departments organized on the basis of examining services or the service economy. Courses related to services are scattered about in many different departments, so that there is no coherent view of ‘‘services’’ per se (Chesbrough, n.d.). On the other hand, there are departments related to ‘‘industrial engineering,’’ ‘‘manufacturing engineering,’’ and ‘‘marketing,’’ which presumably are dominated by the viewpoint of the tangible goods producer. A focus on services, which capture value through provider–client interactions, would need to focus on intangibles that are produced and consumed simultaneously through a collaborative exchange. As a result of the lag between economic reality and academic organizational structure, there is a critical skills and knowledge gap in higher education training related to services, since the theoretical paradigm and underlying knowledge base required for a services-centered education is quite different from that intended for the world of traditional goods production. There also are a series of intellectual ‘‘grand challenges’’ related to service innovation that are not being addressed as rapidly as their urgency requires (Chesbrough, 2005). These include the problems of productivity improvement and innovation when the focus is an intangible (i.e., most services involve intangible exchanges rather than tangible goods), and the difficulty encountered when attempting to transfer tacit knowledge across cultural boundaries during service encounters (i.e., most service delivery requires the producer and consumer to exchange some degree of tacit knowledge with one another). These problems are likely to become more urgent given the rapid worldwide labor force migration toward services, and the process of globalization. As noted earlier, IBM is answering this challenge by launching a new university-facing effort called ‘‘Services Sciences, Management and Engineering’’ (SSME). This effort will bring IBM into collaborative relationships

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with areas of the academic establishment that have not played an especially important role in the past (i.e., business administration and social science). As SSME contributes to the development of a ‘‘science of services,’’ it also can help to support IBM’s goal of grounding its services more firmly in the full spectrum of sciences, and thereby better systematizing them, in much the same way that software was transformed some 15 years ago (see Milunovich, 2004). Toward this goal, IBM offers to provide its experience and technology in partnership with a variety of diverse academic disciplines to drive the development of new SSME courses and curricula for student training, to collaborate with academics in research and development on projects to solve complex service delivery problems, and to provide a dynamic testing ground for emerging SSME theories and practices. An example of a services problem that IBM is interested in solving with academic support lies in the area of Business Performance Transformation Services (or BPTS), which involves understanding the core value that a company offers, and removing obstacles preventing the company from focusing on its primary business. BPTS has the potential to increase a company’s business process effectiveness by combining in-depth knowledge of a particular industry (e.g., industry dynamics, business models, and expertise in specific domains such as logistics, procurement, or finance) with enabling technologies (e.g., real-time data analytics, optimization tools, massive computing power). IBM claims that it can deliver this combination with economies of scale (IBM News (2005), accessed from: http://www.ibm.com/news/us/en/ 2005/08/2005_08_04.html). BPTS is estimated to represent a $500 billion market opportunity (see Milunovich, 2004). Some additional examples of potential SSME collaborations include development of methods and skills to create reusable assets; sponsoring centers, journals, and conferences; honoring the innovative work of faculty members and students; serving on curriculum and research advisory committees; and offering joint programs with IBM Research, BCS, and Global Services. Some of the academic activities that IBM has been supporting under the rubric of SSME include IBM faculty awards and sponsorships, and IBM sponsored programs and papers, as well as hosting 30 university representatives at a Faculty Summit on SSME in May 2004 (www.research.ibm.com/ ssme/workuniv.shtml). Several universities have hosted their own workshops on services science, and are linked in to IBM’s website (e.g., Oxford; http://researchweb.Watson.ibm.com/ssme/oxfordworkshop.shtml). IBM has set up a steering committee to develop strategies, definitions, and initiatives related to SSME, and a tactical team to coordinate SSME related activities taking place across the company. More recently, IBM has been

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engaged in discussion with university and industry representatives about the possibility of supporting a National Academy of Engineering study on the subject of higher education in the area of services. Generally speaking, academics who have been pioneers in services research for decades welcome the spotlight IBM is shining on service innovation, and the company’s call for more systematic approaches. True to its historical roots, IBM is mining the university as a rich vein of intellectual resources, in pursuit of innovation within the service economy. Certainly, the potential exists for IBM to discover creative people, new ideas, and novel solutions to client problems, as well opportunities for the sale of new products and services, through its expanded relationships with universities. IBM has the historical legacy, knowledge base and cultural traditions that should enable it to succeed with this agenda. Whether SSME proves to be the analog of computer science, ushering in a temporal parallel to the ‘‘electronic century’’ (the services century?) in which IBM is a grand master, remains to be seen. But whatever transpires, this corporation’s academic partners probably will find a welcoming industry context for years to come, with the potential for more innovative IP arrangements as IBM pursues its policy of collaboration and open standards in fields across the spectrum of academic disciplines.

DISCUSSION The long-view provided by the case study highlights larger contextual factors (i.e., above the institutional level) that act upon government, corporations, and academia, motivating changes in both public and private policies that ultimately are influential ‘‘on the ground.’’ For example, World War II introduced momentous technological disturbances via novel collaborations among government, industry, and academia, and this in turn prompted new public and private policies, and institutional vehicles for funding of research, which in turn modified industry–university funding patterns. On the public side, there were government–university research projects in which strict industry control of IP was in question. The federal government also launched the National Science Foundation and other research funding agencies, which had the consequence of reducing university dependency on industry relationships. On the private side, IBM created its own ‘‘internal university’’ where, one assumes, patent protection could be better assured. For many years, IBM successfully pursued a policy of ‘‘proprietary control over the architecture and all its key elements’’ (i.e., a

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product model grounded in the industrial 20th century). More recently, however, we have observed the macro-economic shift from an industrybased to a service-based economy, and IBM’s adoption of an open standards policy to complement its service-oriented business strategy. Thus, when the macro-economic structures shift so that the corporation’s revenue and bottom line are dramatically affected, its business strategies and policies seem to follow suit. It might be postulated that such changes would affect IP deals involving universities. Shifts in larger contexts and policy arenas take a long time to unfold, but it is useful to observe them, since they remind us of an important reality. Industry–university relations involve three sectors – industry, academia, and government. The government is a policy actor in these relationships, albeit at times a ‘‘slow motion’’ one (i.e., there may be long periods of stability), whose changing patterns of influence are best seen in longitudinal perspective. In the context of a given IP deal, the government is not visibly present ‘‘on the ground.’’ But the case study suggests that the government is not only an implicit constructor of the institutional context; it is also a potential actor whose policy stance could change, bringing with it significant consequences for the other actors – as when Eckert suddenly found the government sponsoring competing projects on electronic computing machines. The tendency for the government to intervene and/or change its stance over time, and the significance of such moves when they happen (however rare they may be), could be an another reason why some corporations are nervous about ‘‘march in’’ rights (besides risk aversion), even though such rights have not been exercised on them in the past (i.e., they might yet be acted upon in the future). Organizations and their members ‘‘know’’ that government policies shift over time. Thus, what is public policy ‘‘truth’’ in the institutional context today might not be so tomorrow, and the government cannot be controlled by any contract. These observations suggest that Jelinek’s multi-level model might be modified by inclusion of a larger context involving economic and political forces, and a restructuring of sectors to include the government as a policy actor. A means to represent the temporal dimension may also be useful, since sectoral interactions are dynamic, and understanding them requires a longitudinal perspective. The case of IBM also brings to the fore the role of agency, which could be viewed as a force that operates at multiple levels within the model. Agents act to structure deals, and they also structure corporations; in the case of IBM, an argument could be made that entrepreneurial agents structured industries and disciplines, at least in their formative years. Historical ‘‘deals’’ (that may or may not have anything to do with IP) become the

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institutional and sectoral contexts of today when key agents are involved (e.g., ‘‘deals’’ related to Thomas Watson Sr.’s business philosophy regarding universities, Watson and Aiken’s arguments, Aiken’s snub of IBM, Watson’s decision to compete with Aiken). While agents’ actions always appear to be grounded in the context of a particular deal, ultimately their consequences move up and out over time to (potentially) influence the sectoral and institutional contexts. Thus, Watson Sr. made IBM a pioneer in developing deep, long-lasting, and sophisticated relationships with universities, these relationships helped to shape the company’s future, and IBM was a dominant firm in its industry for decades. The IBM case raises an interesting point regarding agents. Typically, a single corporation or university would not be sufficiently influential to shape an entire sector, or an institutional structure. However, the case of IBM is an exception, and there are other exceptions of this type. Some firms, and possibly some universities, are so large or so influential, that they are able to create their own industries or markets, and in doing so, they can structure the behavior of others in their sector and in other sectors. IBM is not just another firm; it is a dominant firm, a world maker. In such cases, there could be an argument for considering the firm as an agent in its own right, and for representing such a force more explicitly in the model, perhaps at the sector level. Possibly, in cases such as IBM, an extraordinary agent in one sector might influence actors in another sector in a way that has a bearing on IP deals across the entire sector (e.g., this might be a possibility for open standards and Linux). Such potential configurations require additional empirical investigation. Jelinek has argued, and the IBM case affirms, that industry–university relations cannot be understood without a broader conceptualization of forces operating beyond the local players in question. Her multi-level model goes a long way toward providing us with a framework for conceptualizing these forces, their interactions, and the consequences for IP dynamics. Consideration of a temporal dimension, economic and political forces, a government sector, and extraordinary agents, could enable the multi-level model to more fully represent dynamic changes over time.

ACKNOWLEDGMENTS The author expresses her appreciation to Mariann Jelinek, Jeanette Blomberg, Paul Maglio, and Keri Brondo for insightful comments and critique on an earlier version of the manuscript. Thanks also are due to Loretta

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Knutson for invaluable technical support. Any errors are entirely the responsibility of the author.

REFERENCES Bashe, C. J., Johnson, L. R., Palmer, J. H., & Pugh, E. W. (1986). IBM’s early computers. Cambridge, MA: The MIT Press. Chandler, A. D. (2001). Inventing the electronic century: The epic story of the consumer electronics and computer industries. New York: The Free Press. Chesbrough, H. W. (n.d.). A failing grade for the innovation academy. Chesbrough, H. W. (2005). Breakthrough ideas for 2005: Toward a new science of services. Harvard Business Review, 83, 20–21. DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use: Adaptive structuration theory. Organization Science, 5(2), 121–148. Gerstner, L. V., Jr. (2002). Who says even elephants can’t dance. New York: Harper Collins. IBM News. (2005). Business performance transformation brings meaningful change. Accessed from: www.ibm.com/news/us/en/2005/08/2005_08_04.html IBM Research. (n.d.). The evolution of innovation at IBM. Retrieved November 26, 2005, from http://www.research.ibm.com/about/message.shtml Kirkpatrick, D. (2004). Inside Sam’s $100 billion growth machine. Fortune, 149(12), 80–90. Milunovich, S. (2004). Services as a science. Merrill Lynch Report on IBM. June 21, 2004. National Science Foundation. (1985). Academic science and engineering: R&D funds. Fiscal year 1983. In: Surveys of science resource series. Washington, DC: National Science Foundation. Perelgut, S. (2004). IBM university relations: Overview and programs of interest. A presentation delivered at Concordia University on June 7, 2004. Pugh, E. (1995). Building IBM. Cambridge, MA: MIT Press. Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68, 1–17.

INDUSTRY–UNIVERSITY TECHNOLOGY TRANSFER: MOVING THE RESEARCH AGENDA FORWARD Maryann Feldman ABSTRACT Both Marianne Jelinek’s chapter and this commentary examine the legal, economic, and policy environments for university–industry technology transfer and the management of intellectual property. To complement Jelinek’s framework, this commentary offers an alternative conceptual framework that incorporates the role of individual scientists and also acknowledges repeat transactions that form relationships between university and industry partners.

INTRODUCTION If we are going to think creatively about public policies that aim to increase the social benefits from the intellectual property that results from university discoveries, we need to focus our analysis on the larger context. Marianne Jelinek’s chapter provides a framework for moving the research agenda Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 321–331 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05016-8

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forward to considering the systemic effects of these new, still emerging relationships. In the U.S. we tend to focus on the 1980 Bayh–Dole legislation but it is important to keep in mind that all around the world, university– industry collaboration has intensified in recent years (Geuna, 1999). However, transferring technology-transfer is challenging because private firms and research universities have profoundly different missions and often display mutual distrust (Slaughter & Leslie, 1997). Despite these difficulties, relationships between universities and firms are important if we are to realize the economic and social potential of university research. Jelinek’s paper provides a conceptual framework that uses multi-structure theory to study the dynamics of industry–university intellectual property technology transfer relationships. Many times when we study new phenomena we tend to focus on quantifiable outcomes at the expense of considering the more complex dynamics. But certainly the devil lies in the details and in the more subtle contextual factors that condition these outcomes. Certainly, to better understand the changes that are taking place in the transfer of university intellectual property and the larger effects on economic growth and technological change requires considering the motivations and incentives provided to the various actors, additionally, the larger economic, social, and political influences that shape the ability of universities to both create new knowledge and deploy that knowledge in ways that are economically useful to industry. Jelinek’s framework identifies points of influence and specific factors that enhance or inhibit the creation and transfer of academic science. She provides rich interview evidence to illustrate and support the framework. The chapter advances our understanding of the operative rules of the game that guide university and industry technology transfer relationships. To complement Marianne Jelinek’s chapter this commentary will offer another alternative conceptual framework to model industry–university technology transfer relationships. This conceptualization extends the framework to consider the role of individual scientists and also is predicated on the observation that the most effective technology transfer involves multideal relationships that evolve over time. Individual academics have their own ideas about ownership of intellectual property and participating in commercial activity, either embracing the new technology-transfer norms or retaining the older norms that might have been part of their training. Certainly, individual behavior can change and that is one important part of the dynamic. But, individual agents also work within organizations and institutions that are defined by routines, norms, and standard operating procedures. In addition, the governance and evolution of working relationships, and ultimately, the efficiency with which knowledge is absorbed and put into

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economic use is a function of this context. Further, it appears that university relationships with industry are formed through a series of sequential transactions that include not only formal mechanisms, such as sponsored research, licenses, and spin-off firms, but also informal mechanisms such as informal social networks and the hiring of students. As success with transactions accumulate, working relationships are formed and it is in the depth of these relationships that meaningful technology transfer occurs. Scholars have tended to analyze formal technology transfer mechanisms such as sponsored research agreements, patents, licenses, or equity swaps (see, for example, Feldman, Feller, Bercovitz, & Burton, 2002; Mowery & Ziedonis, 1999; Thursby & Kemp, 2002). While enlightening, this focus is narrow as firm–industry interactions combine both formal and informal interactions and are also further influenced by firm strategy and industry characteristics, university policies, as well as the structure of the technology transfer operations and the parameters defined by government policy. University– industry technology transfer is therefore a very complex and very interesting phenomenon to study. Rather than provide a comprehensive review, this commentary draws primarily upon my own conceptualization which has benefited from work with co-authors, primarily Janet Bercovitz, but also Rich Burton, Pierre Desrochers, and Irwin Feller. The next section presents an alternative conceptual framework that incorporates individual scientists and repeat transactions. The intent is to incorporate the level of individual researcher as well as the potential for repeat relationships between university and industry partners to complement the prior chapter. The elements of this framework are developed and discussed in turn. The importance of considering relationships rather than transaction is then further considered. The final section considers policy implementations.

CONCEPTUAL MODEL Feldman and Bercovitz (2006) provide a conceptual model that considers the commercialization of university research as a dyad involving transactions between the university and a commercial firm. The core elements in university–industry relationships are not only transactions that occur through agreements to license university intellectual property, but also through sponsored research support, the hiring of research students, and a menu of special considerations for start-up firms. Commercializing university technology may encompass any combination of these different types of transactions either simultaneously to reinforce the transfer of intellectual

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Fig. 1.

University–Industry Relationship Evolutionary Schema.

property, or sequentially as a relationship develops to further the interests and goals of each party. Fig. 1 diagrams the conceptual framework. It is important to recognize that universities are far from homogeneous and have complex bureaucracies with their own rules, rewards, culture, and incentive structures. In contrast to commercial firms’ relatively straightforward profit motive, universities have complex objective functions that involve a variety of educational and societal objectives, as well as the interests of faculty members, alumni, funding agencies, and the larger scientific community.

FACULTY MEMBERS At the heart of technology transfer is the individual faculty member who is motivated by a set of personal and institutional incentives (Feldman &

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Bercovitz, 2006). All of the outcomes that we care about in technology transfer are predicated on individual faculty members disclosing their discoveries to the technology transfer office. Filing a disclosure is the first step to IP ownership and without scientists disclosing their invention and thus participating in the technology transfer process, there is no potential for commercial activity. The decision to disclose research results should be straightforward, however, Thursby, Jensen, and Thursby (2001) find that only a subset of university research with commercial potential is actually disclosed. The question then becomes what determines who discloses their inventions and why? Feldman and Bercovitz (2006) find that the decision of the individual faculty member to participate in technology transfer through the process of disclosing inventions is strongly influenced by three factors: training effects, leadership effects, and cohort effects. Individuals are more likely to disclose inventions if they trained at institutions at the forefront in terms of technology transfer benchmarking. Individuals who trained at institutions that have long established and relatively successful technology transfer operations are more likely to disclose their inventions. In addition, the longer the time that had elapsed since graduate training, the less likely the faculty member was to actively embrace the new commercialization norm. Moreover, the actions of the chair of the department appear to influence behavior: if the chair is active in technology transfer then other members of the department are also likely to disclose. Most strikingly, technology transfer behavior is mediated by the experience of those in a similar position, in terms of academic rank and departmental affiliation. If an individual can observe others at their academic rank disclosing, then they are more likely to participate in technology transfer.

FIRM-LEVEL CONSIDERATIONS The analysis of university–industry technology transfer is incomplete without a reciprocal understanding of the firm’s perspective. Unfortunately, there are few studies that consider the firm, rather than the university, as the focal actor. Prior research demonstrates significant variation in firms’ use of external resources, organization of inter-firm R&D activity, and objectives in inter-firm R&D strategic partnerships (Sampson, 2003). Though the broad literature on strategic R&D alliances mentions the importance of firm– university alliances, it does not specifically focus on the unique aspects of universities as research partners. As such, we have only limited understanding of how university interactions fit within the firm’s broader R&D

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strategy – and how firm strategy and organizational structure influence both the technology-transfer mechanisms employed by the firm and the ultimate relationship the firm maintains with the university. Bercovitz and Feldman (2005) examine how innovation strategy influences firms’ level of involvement with university-based research. On the basis of a survey of large R&D performing corporations, they investigate the influence of firm innovation strategy and internal R&D structure with the firm’s level and degree of involvement with university research partners and find a strong relationship between firm innovation strategy and firm–university research interactions. Firms spend a greater share of their R&D dollars on universitybased research projects and these projects are more likely to be considered part of an ongoing relationship when the firm pursues an overall innovation strategy more heavily weighted toward exploring new technologies. The link found between firm R&D organization and firm–university research interactions is somewhat weaker. While a more centralized R&D function is related to greater involvement with exploratory university-based research, the depth of these university-based interactions appear to be independent of the firm’s internal R&D organizational structure. The analysis also sheds light on the factors that differentiate university partners from other external partners. Although firms may leverage all types of cross-boundary alliances to gain distinct knowledge, our evidence suggests that universities are preferred partners when there are concerns about the perceived ability to fully appropriate the results. A firm that focuses on exploratory research is engaging in a risky, long term strategy and university partners are perceived as less likely to engage in opportunistic behavior that competes with the firm. This paper suggests the need for a more nuanced understanding of firm strategy and its use of external partners in general and universities specifically.

ACADEMIC CULTURE Understanding historical context provides another instructive, though unfortunately overlooked, perspective on current activity and performance. Feldman and Desrochers (2003, 2004) focus on the evolution of technology transfer activities at Johns Hopkins University. By any number of measures and independent assessments, it is one of the world’s leading institutions of higher education and research. Yet, despite substantial academic achievements, Hopkins provides an example of a university that has had little direct effect on the regional economy in terms of reaping the benefits of the university’s research in terms of spin-off companies and mutual relationships

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(Feldman, 1994) and thus provides an interesting contrast to the well-studied examples of Stanford and MIT. Changing university culture is possible, however it takes time and it requires providing the correct incentives as well as adapting practices and procedures to specific and sometimes idiosyncratic institutional norms and standard operating procedures. Bercovitz, Feldman, Feller, and Burton (2001) consider how the organizational structure of the technology licensing office mediates technology transfer outcomes, finding that technology transfer activities, manifested as licensing university-created knowledge, seeking additional sponsorship of R&D projects or a combination of these two, are shaped by the resources, reporting relationships, autonomy, or incentives of technology licensing offices. The analysis treats the structure of the technology transfer office as an independent variable that accounts, in part, for measured inter-institutional differences in patenting, licensing, and sponsored research activities. This analytical lens permits a sharpened focus for examining variation in the performance of technology transfer offices. To understand how technology transfer actually works, Feldman, Colaianni, and Liu (2005) examine the Cohen–Boyer licensing program, which is considered among the most successful examples of university technology transfer in terms of generating revenue and creating a range of products. The patents were granted in 1980 when procedures for transferring technology to companies were not well established. Stanford was negotiating new ground with their licensing program and the paper demonstrates that they consulted widely in the design and implementation of their program. Stanford’s strategies evolved as the times changed, adopting and learning about procedures and working with companies. In sum, there were five standard and three non-standard licensing agreements – each reflecting learning and adaptation. What is now commonplace in university technology transfer was not immediately apparent. The patent was licensed non-exclusively to 468 companies, of which Amgen alone contributed about 22% of total $254 million licensing revenue and followed by Genentech and Lilly each with 14% of total revenue. The paper demonstrates that even with a successful nonexclusive license the outcome is highly skewed, with 80% of the revenues originating from just 10 companies and a handful of products.

REPEAT TRANSACTIONS AND RELATIONSHIPS As depicted in Fig. 1, the transaction mechanisms do not occur in isolation but are part of a larger system. University–industry relationships are multifaceted,

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complex, and diverse, and feedback loops are common. Commercializing a technology may encompass several different transactions between a university and a company. For example, commercialization can involve multiple licenses and requires that a company fund a sponsored research project for developmental work to usefully apply the licenses. In addition, the company may use a sponsored project to evaluate potential employees and subsequently hire graduate students who worked on the project. The relationship may be so fruitful that the company may ultimately endow a university chair or make another type of philanthropic gift. A walk around any private university, and increasingly many public universities, reveals building and infrastructure provided by private firms and wealthy entrepreneurs. Only by considering the complexity of the industry–university relationships can we obtain a fuller understanding of their nature and impact on the academic enterprise. Organizational learning inevitably indicates evolution in the form of the industry–university relationship. Through the knowledge exchange of a single transaction additional projects and ideas for research may result. We should also expect to see a progression from single transactions to longerterm relationships as trust and joint vision are built. Feedback effects, for example, may include an improvement in contract specification, brought about because the existing specification was found to be poorly suited to support the integrity of contract. Bercovitz and Tyler (2005) examine repeat industry sponsored research agreements with universities and find that these sponsored research agreements have a nested structure – the core interaction between a specific academic investigator and the company falls within the broader relationship between the university and the firm which falls within the university’s cumulative contracting experience. Taking advantage of this nested structure, their study empirically investigates how the prior relationships at each level influence the structure and evolution of these sponsored research agreements, focusing on terms that specify monitoring and those that specify intellectual property rights. The results indicate that university–industry contracts demonstrate both trust-related and learning-related views of contract evolution. Previous sponsored research activity within an investigator– company dyad is negatively related to the level of monitoring and IP contractual detail specified in the current agreement. When gained at the level of the individual, experience with a transaction partner may produce trust and reduce the perceived need to draft explicit formal contracts. However, the evidence indicates that the learning effect dominates the trust effect when repeated relationships are considered at the level of the organization. The greater the number of prior sponsored research agreements

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conducted between the university and a particular firm, the more explicit the IP allocation clause and the monitoring clause. In addition, relationships are likely to encompass different types of transactions, something that we have not considered. For example, a firm’s licensing experience, if positive, is expected to increase the likelihood of the adoption of a sponsored research agreement to extend the licensed technology. Following this, firm involvement with sponsored research or licensing may increase the likelihood of corporate gifts. Alternatively, if initial experience with the university technology-transfer office is negative, firms may engage in opportunistic behavior by contracting directly with faculty members, bypassing the university intellectual property apparatus. These elements may be best understood by framing patent and licensing transactions within the larger relationship framework.

REFLECTIONS ON POLICY While universities have a long recognized role in the system of innovation, this role is changing. The new role of universities as engines of local economic development (Feller, 1990) or magic beanstalks of invention and research (Miner, Easley, Devaughn, & Rura-Polley, 2001) places new demands on them and raises the question about the role of research universities in advanced economies. Universities have demonstrated great adaptability in their response to active technology transfer and their relationships with industry are evolving. One of the traditional strengths of the American system of higher education has been its diversity and decentralization, both in terms of reliance on different funding sources and having different missions. While some universities have been successful, we should question if the examples provided by WARF, Stanford University, and MIT have established an unrealistic de facto standard against which all research universities are judged. Moreover, we should question what the long run implications of a university’s greater assertion of IP rights on technological change and economic growth will be. The basic premise of Jelinek’s chapter and this commentary is that the legal, economic, and policy environments that comprise the system of innovation determine the rate and type of university knowledge production and thereby influence the rate of technological change. Moreover, within a given university there will be additional internal influences that determine the rates and directions of knowledge flow from that institution. This commentary has the modest aim of identifying some of these influences and

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speculating on their effect on knowledge transfer. Certainly, given the limitations of space and time, this paper raises more questions than it answers. But certainly an understanding of the evolution of relationship between universities and industry warrants further attention. If we are going to think creatively about public policies that govern university IP and technology transfer to industry, we need to focus on the larger innovation context. Mariann Jelinek’s paper advances that cause and helps to add structure to the emerging field of inquiry.

REFERENCES Bercovitz, J., & Feldman, M. P. (2005). Fishing upstream: Firm innovation strategy and university research alliances. Paper presented at the 2005 INFORMS conference, San Francisco, CA, November 12. Bercovitz, J., Feldman, M. P., Feller, I., & Burton, R. M. (2001). Organizational structure as a determinant of academic patent and licensing behaviour: An exploratory study of Duke, Johns Hopkins, and Pennsylvania State Universities. Journal of Technology Transfer, 26, 21–35. Bercovitz, J., & Tyler, B. (2005). Effects of prior relationships on contract structure: Agreements between corporate sponsors and academic researcher. Paper presented at the International Society for New Institutional Economics (ISNIE), Lisbon, September. Feldman, M. P. (1994). The university and high-technology start-ups: The case of Johns Hopkins university and Baltimore. The Economic Development Quarterly, 8, 67–77. Feldman, M. P., & Bercovitz, J. (2006). Entrepreneurial universities and technology transfer: A conceptual framework for understanding knowledge-based economic development. Journal of Technology Transfer, 31, 175–188. Feldman, M. P., Colaianni, A., & Liu, K. (2005). Commercializing Cohen–Boyer 1980–1997. Paper presented at the REER meetings, Atlanta, December 3. http://mgt.gatech.edu/ news_room/news/2005/reer/files/sun_commercializing.pdf Feldman, M. P., & Desrochers, P. (2003). The evolving role of research universities in technology transfer: Lessons from the history of Johns Hopkins university. Industry and Innovation, 10, 5–24. Feldman, M. P., & Desrochers, P. (2004). Truth for its own sake: Academic culture and technology transfer at the Johns Hopkins University. Minerva, 24, 105–126. Feldman, M. P., Feller, I., Bercovitz, J. E. L., & Burton, R. M. (2002). Equity and the technology transfer strategies of American research universities. Management Science, 48, 105–121. Feller, I. (1990). Universities as engines of R&D-based economic growth: They think they can. Research Policy, 19, 335–348. Geuna, A. (1999). The economics of knowledge production: Funding and the structure of university research. Northampton, MA: Edward Elgar. Miner, A. S., Easley, D. T., Devaughn, M., & Rura-Polley, T. (2001). The magic Beanstalk vision. In: C. B. Schoonhoven & E. Romanelli (Eds), The entrepreneurial dynamics. Stanford: Stanford University Press.

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Mowery, D. C., & Ziedonis, A. (1999). The effects of the Bayh–Dole Act on U.S. university research and technology transfer: Analyzing data from entrants and incumbents. Paper presented at the Science and Technology Group, NBER Summer Institute. Cambridge, MA: National Bureau of Economic Research. Sampson, R. C. (2003). R&D alliances & firm Performance: The impact of technological diversity and alliance organization on innovation. (September). http://ssrn.com/ abstract=265999 Slaughter, S., & Leslie, L. (1997). Academic capitalism: Politics, policies and the entrepreneurial university. Baltimore: Johns Hopkins University Press. Thursby, J. G., Jensen, R., & Thursby, M. C. (2001). Objectives, characteristics and outcomes of university licensing: A survey of major U.S. universities. Journal of Technology Transfer, 26, 59–72. Thursby, J. G., & Kemp, S. (2002). Growth and productive efficiency of university intellectual property licensing. Research Policy, 31, 109–124.

INDUSTRY–UNIVERSITY INTELLECTUAL PROPERTY IN CONTEXT: FRAMING THE DEAL, AND DEALING WITH THE FRAME(S) Mariann Jelinek ABSTRACT This paper outlines a multi-level conceptual framework of industry– university (I–U) intellectual property (IP) relationships to understand efforts to commercialize university discoveries by considering how the parties to deals make sense of their interactions. Institutional, sectoral, and organizational levels frame interactions around any single deal, shaping participants’ sometimes divergent views. The complex dynamics of interactions between the parties and between and among levels mean that details and nuances will be vital. Commentaries by Maryann Feldman and Marietta Baba provide detailed insights on universities (Feldman) and industry (Baba) that enrich and corroborate the multilevel model. Directions for further research and policy implications in this important emerging area are suggested.

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INTRODUCTION Far too much of the research about industry–university (I–U) intellectual property (IP) has been cast in terms of quantitative analysis of patent data, arguments against or in favor of Bayh-Dole, or ‘‘deals’’ as discrete, standalone dyadic transactions. Quantitative data analysis has the advantage of convenience and sharp results, although it fails to actually explain much about the dynamics of I–U relationships. Many discussions of Bayh-Dole, by contrast, have been remarkably bereft of quantitative evidence (Mowery, Nelson, Sampat, & Ziedonis, 2004) – but these discussions too offer little insight. Focus on individual deals as discrete and unrelated obscures important ongoing dynamics in I–U relationships that have been historically important, implicitly stripping away contexts that shape participants’ understandings, including historical background, details of how innovations actually come to market, and even the role parties to the deals in question: The impoverished view that results fails to address key issues of how I–U IP relationships work, and why they sometimes don’t. Bringing background back in, immediately suggests a wide array of factors that help us to understand I–U relationships around IP in a deeper, richer way. In the U.S., close and productive relationships between industry and universities have long been the norm, fostering economic development and giving rise to new industries (Rosenberg, 1982; Rosenberg, 1994) – so assertions that the relationship is in trouble quickly attract attention. What has changed, and how might the parties’ mutual interests in bringing new technologies to market have diverged? Only historical perspectives can tell us. Moreover, this historical long view also dismisses facile explanations that point to Bayh-Dole as the single factor driving change. Instead, numerous other elements of the institutional, sectoral, and organizational environments are seen to contribute to the thus changing IP environment, and to warrant investigation. The case study reported in Baba’s commentary offers a wonderful illustration of I–U dynamics evolving through IBM’s long history of relationships with universities. The timeline is important: as with the U.S. I–U relations more generally, the long view underlines how industry perspectives on I–U interactions are shaped both by factors endogenous to the firm and those outside it, in the larger institutional/historic context. What’s going on inside the firm matters: its history and its strategy affect its relationships with universities. Maryann Feldman focuses instead on the university side, examining in particular how faculty decisions to disclosure (or not) affect the technology transfer process. Both commentaries corroborate the model,

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extending and enriching it; and both help to frame the broader discussion about IP, pointing to the importance of ongoing, iterative sequences of ‘‘deals’’ over time that create relationships, and of relationship events going well beyond the typical transactional focus. This response will place the Baba and Feldman commentaries against the Jelinek multi-level model, seeking to incorporate their insights as a means to invite further research. Of particular interest here are broader factors of institutional evolution and historic development, on the one hand; and of individual and organizational level context and incentives, on the other. These perspectives suggest further research questions.

THE VIEW FROM THE FIRM Marietta Baba’s historical account of IBM’s university relationships provides rich data on how IBM was shaped by its founder’s philosophy of social responsibility – and also on how an effective firm does well by doing good. IBM ‘‘gave back’’ to its society by supporting university research at the same time that it garnered important research insights and recruited productive employees. Over nearly 80 years, both philosophy and benefits have encouraged IBM to maintain and expand its university relationships. IBM was a major player in the development of computer science and engineering, together with U.S. universities during the decades of the 1950s and 1960s. As Baba points out, IBM continues active with universities (both in the U.S. and abroad), presently supporting the development of ‘‘service sciences, management and engineering’’ (SSME), which the company (and many scholars) argue will be the key feature of the knowledge economy of the 21st century. Just as aeronautical engineering, chemical engineering, and petroleum engineering curricula, research and graduates resulted from close I–U relationships in former times, leading to U.S. dominance in these fields when other countries’ universities were slow to respond (Mowery & Rosenberg, 1998), so Baba’s observations suggest a fresh opportunity for universities to collaborate with industry to create a new academic domain focused on services. The industry–university relationship suggested here is vastly richer, deeper, and less transactional than the typical focus of the IP literature. Because it plays out over time, this viewpoint also allows us to consider the firm’s evolving strategic interests, both its more immediate needs and its longer-term history, enriching the multi-level model in detail. This viewpoint surely comes closer to how we expect managers to make decisions, since it

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reflects organizational culture and norms on the one hand, and contemporary strategy goals as they emerge, on the other. While others have noted the strategic importance of IP and of IP strategies (Grandstrand, 1999), only a longer historic view permits strategic change to be considered. Strategic change may include a reconfiguration of firm resources, including key skills, technologies and science base for products, according to the widely embraced resource-based view of the firm (Collis & Montgomery, 2004; Wernerfelt, 1984) and as Baba’s case study illustrates. IBM’s move from electro-mechanical calculation devices to computers was one such instance, while the firm’s involvement in the development of computer science shows how university relationships changed in response. That shift reflected a major reconfiguration of the firm’s technology and skill set, setting up IBM’s historic dominance in computers (Chandler, 2001). IBM’s current shift from emphasizing proprietary IP toward more open standards (and thus toward shared, nonproprietary IP) offer still another example of how strategic change affects both IP approach and university relationships. IBM’s shift from ‘‘big iron’’ to services again significantly reconfigures the firm – and the firm’s goals in university relationships as well as its stance on proprietary IP, the sorts of research involvements it is likely to support, and its preferences regarding publication. These shifts are especially noteworthy in the context of I–U friction around IP, proprietary information and publication. Industry sources identified universities’ inability to assure confidentiality for research findings that firms wished to retain as proprietary as an important source of I–U relationship difficulty. Baba’s case illustrates several IBM responses over time, among them careful management of research assignments, internally and externally, to manage the problem: IBM’s massive ‘‘internal university’’ research effort brought proprietary efforts in-house. The firm’s contemporary strategic shift by contrast both acknowledges the ‘‘leaky’’ nature of knowledge in universities, and identifies an alternative approach to exploit that nature for strategic advantage. Another important source of friction for some is license cost, while framing fact is the financial impact of university license activities. Across the U.S., arguably the most prolific of national innovation systems during the past half century (Arora & Rosenberg, 1998; Mowery & Rosenberg, 1998; Rosenberg & Nelson, 1994), a huge outpouring of disclosures, patent applications, and patent awards has produced remarkably few truly lucrative licenses (Mowery, 2003; Mowery et al., 2004). While universities both in the U.S. and abroad (Geuna, 1999) have increasingly patented discoveries, and sought to license those patents, most university patents find no customers.

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Those that do typically have only one bidder, and only a vanishingly small fraction produce significant revenues. The very, very few patents that do produce significant revenues are broadly licensed and overwhelmingly in biotechnology and pharmaceuticals. But while these few may produce revenues in the hundreds of millions over the life of the patent, even those revenues are rare enough that the average income of technology licensing across the U.S. is on the order of $5 million for institutions responding to annual surveys conducted by the Association of University Technology Managers (AUTM), with many failing to cover the costs of the Technology Transfer offices (Pressman, 2000). Even research powerhouses such as MIT, Stanford, and the University of California (with a patent portfolio of over 5,000 active patents) see most revenues coming from the top one, or two, or five licenses (Mowery et al., 2004). Against financial outcomes data that documents very limited license revenue from most university discoveries (even as society has reaped enormous rewards in rich economic development), the broad array of other I–U relationships benefits suggested by Baba’s case commentary is striking. Willy Sutton said he robbed banks because ‘‘that’s where the money is,’’ yet the diminished frame of transactions only focuses research attention on ‘‘where the money isn’t.’’ Baba’s commentary suggests that I–U relationships, not IP transactions, are where the benefits reside. Others industry sources with long histories of university relationships make similar observations, even as firms with limited university relationship experience emphasized transactional, cost–price assessments. The framework for evaluation predisposes the outcome: because IP licensing is so small a fraction of the benefits flow, its easy quantification drives a woefully understated assessment, for firms that look only there. Baba’s case suggests that IBM’s major benefits have come not so much from licensing individual blockbuster technologies, as from the carefully woven fabric of ongoing interactions, student hires, and discussions over many years. IBM’s experience is far from unique – among firms that choose to develop broad and deep ongoing university relationships.

INTELLECTUAL PROPERTY FROM INSIDE THE UNIVERSITY If the simplified quantitative frame distorts industry evaluation of I–U dynamics, it can also affect universities. Maryann Feldman draws in legal,

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economic and policy environments from the university perspective in her commentary to focus on the role of individual university scientists in creating an ongoing stream of I–U interactions over time that make a relationship. Abundant evidence documents the nascent state of much university research output: concepts, laboratory results, and more rarely, prototypes (Thursby & Thursby, 2000). Some sources point to the ‘‘fuzzy front end’’ (Koen et al., 2001) firms face in trying to identify prospective innovations to bring to market, while others characterize a ‘‘Valley of Death’’ between university discovery and far-distant market launch and potential profits (Deitch, 2004; Jelinek, 2005; Judson, 2005; Markham, 2001; Wessner, 2003). These documented difficulties lend even more importance to Feldman’s focus on individual scientists’ interactions with firms, and their ongoing relationship potential. Nascent technologies have far to go to cross the valley, and scientist discoverers are most likely to possess the tacit knowledge needed to build the bridge to commercial application (Jensen & Thursby, 2001). Feldman also notes the potential for lack of trust in I–U relationships, citing (Slaughter & Rhoades, 2004): an ongoing stream of multiple deals, sponsored research, and informal relationships over time will necessarily be a more likely foundation for building trust, nuanced mutual understanding, and communication capability than any single deal could. Feldman’s commentary reproduces a model from earlier work that looks to extended interactions between a particular university and firm dyad over time. This elaboration particularizes the more general Jelinek multi-level model to the cultural contexts of the focal firm and university. In this light, Feldman’s focus on disclosure and licensing emphasizes formal technology transfer – even though she would clearly agree that a vast array of transfer occurs by other, less formal means, such as student hires, published papers, faculty consultancies, and symposium discussions like those IBM has sponsored that Baba mentions. Nevertheless, as Feldman indicates, disclosure is an essential first step toward formal licensure. Feldman notes that faculty disclosure and technology transfer behavior are mediated by the experience of surrounding colleagues, a point also underlined by Shane (2002, 2004). Universities where I–U relationships are respected and fostered will be very different places than those where such activities are frowned upon, or even prohibited. Contemporary emphasis on potential conflict of interest (Butts, Irwin, & Killoren, 2004), or wariness about industry control of academic research (Bok, 2003; Slaughter & Rhoades, 2004) can make interactions less likely, more risky for the academic participant, or simply less culturally acceptable, potentially undermining the

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development of the broader, richer I–U relationships that both commentaries advocate. The example of Johns Hopkins University, with outstanding research and yet minimal impact on its regional economy, illustrates an academic culture where industry collaboration and interest in commercialization have traditionally been delegitimated (Feldman, 1994).

REFRAMING INDUSTRY–UNIVERSITY RELATIONS Both commentaries corroborate the multi-level model’s emphasis on broad contextual factors and on longer-term perspectives in a larger I–U innovation system. In doing so, they invite us to reframe I–U relations – around IP, but also as iterative IP transaction streams, which simultaneously generate a broad array of other potential streams: of sponsored research projects, internships, consultancies, hiring, consortium memberships, and more. The cognitive shift from purely transactional economics to the interactive, iterative, and evolving relational structure is noteworthy: trust, learning and ‘‘side benefits’’ beyond IP all rise in significance, whereas a transactionbased viewpoint narrows focus to emphasize the dollar outcomes of the stand-alone deal. Feldman’s commentary concludes by pointing to ‘‘the changing role of universities as engines of local economic development (Feller, 1990) or magic beanstalks of invention and research (Miner, Easley, Devaughn, & Rura-Polley, 2001)’’ as a driver for renewed attention to I–U relationships, even as Baba’s details how extended sequences of iterative interaction within a particular firm play out to affect such outcomes. Both underline how limited the transaction-based view is, considering both larger I–U relationships, such as those Baba describes, and their larger social consequences, like economic development. The commentaries also provide examples of how research at different levels might be related across sectors and organizations, enabling us to compare and contrast outcomes. Thus, for instance, Baba’s richly detailed case invites other such case studies of how individual firms’ relationships with universities evolve over time, and how that evolution relates to the firm’s changing strategy. In light of universities’ well-documented importance as a source of new science and technology, firms might well consider their university relationships as levers for technology change, skills evolution, and scientific insight generally looking beyond the highly specific and applied IP transaction focus. If we include a too-often ignored flow of knowledge from industry to universities, including problems, data, and

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industry needs, as well as IP (Mowery et al., 2004), as well as the kinds of symposiums Baba describes, universities taking broader relationship benefits and knowledge flow into account may well consider their relationships with industry as far more valuable (and less threatening). Such mutual reevaluation might go far to mend any strains in U.S. I–U relations. Feldman’s account and the model she cites suggest deeper inquiry into the drivers of collaborative relationships between universities and their industrial surround (taking that to broadly include both manufacturing and service activities). Technology spin-offs, workforce technological development and training, and numerous other forms of support – such as the IBM university symposia that Baba describes – are all potential contributors to regional economic development, and all at least somewhat disjoint from formal IP technology transfer per se. All these additional university-industry relationship venues are far more likely to be affected by sectoral and particularly university culture than institutional-level arrangements (or at least those at the federal level). The issues are of course not limited to the United States. Gary Helriegel (in Kogut, 1993) describes several institutional arrangements in southern Germany whereby shared risk and collaborative workforce training (among other approaches) help to foster economic development.

CONCLUSION Taken together, the commentaries offer strong support for the multi-level model as a useful conceptual framework for structuring burgeoning research on IP. They suggest that deeper inquiries into particular elements of the larger multi-level model will offer additional insight into I–U relations where they are framed against the larger backdrop of levels and of history.

REFERENCES Arora, A., & Rosenberg, N. (1998). Chemicals: A U.S. success story. In: A. Arora, R. Landau & N. Rosenberg (Eds), Chemicals and long-term economic growth: Insights from the chemical industry (p. xii+564). New York: Wiley. Bok, D. (2003). Universities in the marketplace: The commercialization of higher education. Princeton, NJ: Princeton University Press. Butts, S., Irwin, K., & Killoren, R. (2004). Industry–university contracting: Confluence or conflict. San Francisco, CA: Industrial Research Institute.

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Chandler, A. D. J. (2001). Inventing the electronic century: The epic story of the consumer electronics and computer industries. New York: The Free Press. Collis, D. J., & Montgomery, C. A. (2004). Corporate strategy. New York: McGraw-Hill. Deitch, N. L. (2004). The Sarnoff corporation (final report, corporate fellows program). Washington, DC: Secretary of Defense. Feldman, M. (1994). The university and high-technology start-ups: The case of Johns Hopkins University and Baltimore. The Economic Development Quarterly, 8, 67–77. Feller, I. (1990). Universities as engines of R&D-based economic growth: They think they can. Research Policy, 19, 335–348. Geuna, A. (1999). The economics of knowledge production: Funding and the structure of university research. Northampton, MA: Edward Elgar. Grandstrand, O. (1999). The economics and management of intellectual property: Toward intellectual capitalism. Northampton, MA and Cheltenham, UK: Edward Elgar. Jelinek, M. (2005). Crossing death valley together: Cultural dynamics of industry–university IP. In: Southern Management Association Annual Conference, November 9–12, Charleston, SC: Southern Management Association. Jensen, R., & Thursby, M. (2001). Proofs and prototypes for sale: The licensing of university inventions. The American Economic Review, 91(1), 240–259. Judson, M. (2005). Crossing the valley of death between innovation and application: Experiences in subsalt imaging. In: 2005 AAPG International Conference and Exhibition, Paris, September 11–14. Koen, P., Ajamian, G., Burkart, R., Clamen, A., Davidson, J., D’Amore, R., et al. (2001). Providing clarity and a common language to the fuzzy front end. Washington, DC: Industrial Research Institute. Kogut, B. (1993). Country competitiveness: Technology and the organizing of work. New York: Oxford University Press. Markham, S. K. (2001). Product champions: Crossing the valley of death. Unpublished manuscript. Duke University, Raleigh, NC. Miner, A. S., Easley, D. T., Devaughn, M., & Rura-Polley, T. (2001). The magic beanstalk vision. In: C. B. Schoonhoven & E. Romanelli (Eds), The entrepreneurial dynamic. Stanford, CA: Stanford University Press. Mowery, D. C. (2003). US university patenting and licensing: Historical evolution and recent trends. Washington, DC: National Academies of Science: STEP. Mowery, D. C., Nelson, R. R., Sampat, B. N., & Ziedonis, A. A. (2004). Ivory tower and industrial innovation: University–industry technology transfer before and after the Bayh-Dole act. Stanford, CA: Stanford University Press. Mowery, D. C., & Rosenberg, N. (1998). Paths of innovation: Technological change in 20th-Century America. Cambridge, UK: Cambridge University Press. Pressman, L. (2000). AUTM licensing survey: FY 2000. Northbrook, IL: The Association of University Technology Managers. Rosenberg, N. (1982). Inside the black box: Technology and economics. Cambridge and New York: Cambridge University Press. Rosenberg, N. (1994). Exploring the black box: Technology, economics, and history. New York: Cambridge University Press. Rosenberg, N., & Nelson, R. R. (1994). American universities and technical advance in industry. Research Policy, 23, 323–348.

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Shane, S. (2002). Selling university technology: Patterns from MIT. Management Science, 48(1), 122–137. Shane, S. (2004). Academic entrepreneurship: University spinoffs and wealth creation. Northampton, MA and Cheltenham, UK: Edward Elgar. Slaughter, S., & Rhoades, G. (2004). Academic capitalism and the new economy. Baltimore, MD: The Johns Hopkins University Press. Thursby, J., & Thursby, M. (2000). Industry perspectives on licensing university technologies: Sources and problems. Journal of the Association of University Technology Managers, 12, 9–21. Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5, 171–180. Wessner, C. W. (2003). The advanced technology program in the knowledge economy: Briefing for the manufacturing task force. In: Government–industry partnerships for the development of new technologies. Washington, DC: The National Academies Board on Science, Technology and Economics.

THE LAMPE THEORY OF ORGANIZATIONAL LEADERSHIP Kenneth D. Mackenzie ABSTRACT This chapter provides a new theory for organizational leadership in which an organization’s leadership, authority, management, power, and environments (LAMPE) are made coherent and integrated. Organizations work best if their LAMPE is coherent, integrated, and operational. The chapter begins by introducing basic concepts, such as structures, processes, process frameworks, task–role matrices, interdependence uncertainty, and virtual-like organizational arrangements. The LAMPE theory is then built upon this base. Leadership is defined as the processes of initiating, enabling, implementing, and sustaining change in an organization. Authority is defined as the legal right to preempt the outcome of a decision or a process. Management is defined in term of its major processes. Power is the control of interdependence uncertainty. When 29 leadership practices are introduced, it is possible to link them to all five of LAMPE’s constructs. A number of conclusions are derived, in the form of 36 propositions: 5 dealing with leadership, 5 focusing on leadership requirements matching, 4 relating to leadership effectiveness, 5 dealing with leadership capacity, 4 concerning the benefits of distributed leadership, and 13 linking LAMPE to the theory of the organizational hologram.

Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 345–428 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05018-1

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THE PROCESSUAL, MULTI-LEVEL NATURE OF LEADERSHIP PHENOMENA Leadership Issues The imbalance between the interest about leadership and the quality of our understanding of leadership phenomena demands better theory. This chapter addresses some of the numerous unresolved issues. The choice of issues discussed here reflect a belief that the literature about leadership and its science needs, like an overgrown houseplant, to be repotted in better soil and given more nutrients, light, and water. The purpose is to advance a theory that offers a faint glimmer of hope. This chapter addresses 17 issues: 8 about the nature of leadership; 5 about the linkage of leadership, authority, management, and power; and 4 about the consequences of leadership. The position of the author on each issue is stated upfront. That way, the reader will know where this chapter is headed. The basis for these positions will be made clear later. Issue 1: What is leadership? There is no common definition for ‘‘leadership’’; instead, many different ones exist. In this chapter, leadership is defined as the processes of initiating, enabling, implementing, and sustaining change in the management of a group or an organization. Here leadership is not considered to be a group of people, a trait, a style, an attitude, a belief, a communication style, or a power/ego exercise. Instead, it is a set of specific processes about managing change in a group or an organization. Issue 2: When is leadership appropriate? Because many authors on leadership implicitly claim that leadership is good and, therefore, that more of it is better, given the current state of the leadership literature, it is reasonable to question these assertions. The dominant, widely observable fact about leadership in most organizations and groups is that most people do not choose to provide it. Leadership is appropriate when the leadership provided matches the leadership required by the situation. There can be too much leadership or too little of it. Issue 3: Is leadership solely the property of the person? Most books and articles on leadership focus on the attitudes, personalities, values, styles, competence, and other characteristics of the person designated as a leader. But it is obvious that a leader cannot lead anyone unless there are other parties involved – that is, followers. Leadership is not solely the property of the person but also depends upon the leadership issues faced by the group or organization and the specific context in which leadership is exercised.

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Issue 4: Is leadership binary? Is leadership either present or absent? Is it possible to exert partial leadership? Because leadership is a set of processes, those performing these processes can – and should be – distributed widely throughout the organization. Thus leadership is not considered binary. Issue 5: Should leadership be distributed? Or should it be concentrated in the capable hands of a supreme leader? Given an environment of dynamic complexity, organizations are more productive and more efficiently adaptable if the leadership processes are distributed widely throughout the entire organization. Issue 6: What are leadership practices (LPs)? Is there some set of practices, such as those performed by a heart surgeon, that leaders ought to perform or are traits, attitudes, values, style, and other attributes sufficient? The main problem in the extensive literature on leadership is the absence of agreement on the sort of behaviors or processes a leader needs to perform or have performed to be judged as ‘‘leadership.’’ This chapter identifies 29 specific LPs that work best when they are distributed throughout the organization and are not just confined to the behaviors of the leader. Issue 7: How do LPs affect employee effort and impact? The research literature is consistently hopeful that good leaders motivate followers to do more, do it better, and have a positive impact on the performance of the organization. Unfortunately, the linkages between LPs and employee effort and impact are weakened by the lack of specification of the LPs causally linking them to the resulting employee effort and impact. Issue 8: Is leadership multi-level? Of course! It is absurd to treat leadership as a single-level issue, because any leadership necessarily involves others. Leadership processes at a higher organizational level can affect those on lower levels, and vice versa. But leadership also involves multiple levels of work, interdependence, and even the interdependence uncertainties. Unfortunately, the usual statistical methods using variables rather than processes limit rigorous multi-level leadership research (Mackenzie, 2004a, b). This chapter complements the recent pioneering work on a multi-level approach to leadership (Dansereau & Yammarino, 1998a, b). There are five issues related to leadership, authority, power, and management: Issue 9: Is leadership related to power? This issue can get technical quickly unless it is defined in a manner that allows comparison. This chapter argues that leadership is the exercise of power. That is, leadership is definitely linked to power. Issue 10: How is leadership related to management? Aside from the usual aphorisms that substitute for experience and wisdom (e.g., ‘‘leaders do the

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right thing, managers do the thing right’’), the answer depends on the definition of leadership and management. The processual approach taken in this chapter will show exactly how the 29 LPs are linked to the main processes of managing. Issue 11: How is leadership related to authority? Can we expect leadership without the authority to lead? Sure. But does someone with authority use it to lead? Not necessarily. So, what are the relationships between authority and leadership? The answer provided here is a key part of the LAMPE theory of organizational leadership. Issue 12: Are LPs linked to the theory of the organizational hologram? Back in 1991, the author published a book (Mackenzie, 1991) about a theory of the organization as a hologram. That book included no discussion of LPs. Since then, 29 have become apparent. These 29 LPs describe how to establish and maintain an organizational hologram, which produces an organization that is simultaneously capable of being maximally productive, adaptable, and efficiently adaptable. These LPs are essential for achieving coherence and integration in the LAMPE theory of organizational leadership. Issue 13: Are LPs linked to the control of interdependence uncertainty? Of course! Because power is the control of interdependence uncertainty and leadership is the exercise of power, the constituent LPs are linked to the control of interdependence uncertainty. However, this control depends on those LPs becoming widely distributed throughout an organization. Four issues deal with the consequences of leadership: Issue 14: Can individual capacity for leadership be improved? Generally, the leadership literature is consistently bullish on this hope. The controversies swirl around which theory is the key to making this happen. This chapter joins the controversy by arguing that individual capacity for leadership can be improved by distributing the LPs and leadership processes of initiating, enabling, implementing, and sustaining change in the management of a group or an organization. What is interesting in this view is that an individual can engage in LPs and not necessarily be public about it. In addition, an individual’s capacity for leadership can be improved by the behavior of others. Issue 15: Can the leadership of a group or an organization improve? This is probably the least controversial leadership issue in that leadership authors hope that this is, indeed, true. Otherwise, why read the books or attend the seminars and workshops? But, what is interesting here is how the leadership of a group or an organization can be improved. Issue 16: How does improving the quality of leadership improve the organization? This issue involves how one defines the quality of leadership and

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what one means by organizational performance. The definition of leadership quality is the average value of the 29 LPs, with higher scores indicating higher quality. Organizations are improved if they become more productive, adaptable, and efficiently adaptable. The quality of leadership to improve the organization is accompanied by persistent, purposive, and disciplined improvement of the 29 LPs. Issue 17: What happens when LPs are distributed throughout an organization or group? Many benefits become possible, such as being dynamically congruent, holonomic, more productive, more adaptable, and more efficiently adaptable. These 17 issues are by no means either final or exhaustive. The voluminous and extensive literature on leadership cannot be contained and framed within such a limited set of questions. Nevertheless, addressing these issues opens up some different approaches for understanding the multi-level nature of leadership, including the theory of organizational leadership called LAMPE. The thesis of LAMPE is that organizations work best when their leadership, authority, management, power, and environments are coherent, integrated, and operational throughout the organization. Organization of this Chapter This chapter suggests that organizations work best when all elements of LAMPE (leadership, authority, management, power, and environments) are coherent and integrated throughout the organization. The first part of this chapter proceeds logically by defining leadership, authority, management, and power in more processual terms. The next part includes this foundational analysis; it begins by defining six basic concepts and then addresses each of the five constructs of LAMPE. The meaning of coherence for LAMPE is discussed in the third part of this chapter. LPs are introduced, and then the following chain of relationships is examined sequentially: leadership and power, power and authority, authority and management, and management and leadership. From a processual view, the constructs in LAMPE are closely related to one another. A description of each of the 29 LPs is provided as well. The fourth part of the chapter explains how to integrate the concepts in LAMPE. This discussion introduces a set of ideas from which one can derive the 36 propositions contained in this chapter. These concepts include change events, windows, ripple and white-water change events, LAMPE processes, and distributed leadership. LAMPE supports and is supported by

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the theory of the organizational hologram (Mackenzie, 1991). It is the LPs that tie together the elements of LAMPE to produce and integrate operational coherence in an organization. The chapter concludes by summarizing the arguments in favor of the LAMPE theory and highlighting the multi-level nature of leadership. LAMPE may appear more novel than it actually is. It is an outcropping from existing research on leadership, organization theory, and management. However, the transition from general leadership issues to the LAMPE theory of organizational leadership is smoothed by first discussing the nature of leadership as seen from different vantage points. This general and introductory discussion proceeds to more specific questions concerning the distribution of leadership, viewing leadership as a distributable process, and even the manner in which leadership is defined. This exploration demonstrates that, while LAMPE may appear an odd species of a leadership theory, it is at least a mongrel with legitimate forebears. General Observations on the Nature of Leadership History records many examples of individuals who made an enormous difference by determining the conduct of a war, launching new products, creating new organizations, turning around failing enterprises, creating social upheavals, building up the membership and spiritual awakening of religious movements, and many more. The fascinating yet mysterious construct called leadership is both universal and ubiquitous. It is not limited to any specific culture, but the leaders within a culture generally reflect it. Leadership phenomena evolve both good and evil. We have all seen its effects in our families, schools, clubs, and at work. Clearly, there is widespread interest in leadership phenomena. For example, typing the word ‘‘leadership’’ into the Google search engine will return more than 200 million hits, and that number currently increases by more than 1 million every week. Go to any bookstore and you will find dozens of books on leadership. The author has 53 such books in his personal library. Most authors seem to believe that (1) leadership is a good thing and (2) the more of it, the better. Most trade books on leadership offer intriguing personal histories, spell-binding testimonials, and a self-help program to enable the reader to become an enhanced person. Ambitious persons, individuals interested in self-improvement, underachievers, the disappointed in life, and individuals seeking motivation gobble up leadership books. Organizational behavior and management textbook coverage on leadership has reached a commodity status, in that most authors have the same list

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of leadership topics and present them in roughly the same order. These texts differentiate themselves primarily by the choice of examples, use of graphics, and exercises. The usual text includes these topics: leadership traits and Ohio State studies (cf. Yukl, 2002; Schriesheim & Bird, 1979); Michigan studies (cf. Likert, 1979); Blake and Mouton’s leadership grid (1985); Fiedler’s contingency model (Fiedler, 1967; Fiedler & Chemers, 1974); Hersey and Blanchard’s situational theory (Hersey & Blanchard, 1982; Blanchard & Miller, 2003); House’s path–goal theory (1971); transactional and transformational leadership (cf. Burns, 1978); leading in learning organizations (Senge, 1990); charismatic leaders (House & Howell, 1992; Conger, 1989); and leaders as servants (cf. Daft & Lengel, 1998). Because these topics are so well covered in the standard texts, they are not the subject of this chapter. The Research Literature The research literature on leadership is less certain of its own truth and contains lively discussions about competing models, choice of research methods, and the ontology of the subject. There are masterful compendia of these literatures in Stogdill (1974), Bass (1981), Hunt (1991), Dansereau and Yammarino (1998a, b), and Yukl (2002), just to name a few. Different conceptual schema and typologies are proposed that provide frameworks upon which to hang the plethora of findings and issues. Most leadership models emphasize some combination of the motivations, traits, skills, and styles of the persons designated as a leader. They focus on simple face-to-face influence and tend to rely on the use of ‘‘variables,’’ usually encapsulated into a survey instrument. A survey is administered and analyzed using statistical methods. The results are usually underwhelming and collectively confusing. There are few studies in which the actual processes and behaviors of a leader are analyzed in situ. The requirements of statistical models for ‘‘gobs’’ of data mean that most studies have to have samples of more than, say, 20 persons. In terms of sheer numbers, I am saying, then, that of the estimated more than 10,000 leadership studies almost all have been done at the bottom of the organization y Thus today there is increasing interest in the study of leadership at the Chief Executive Officer (CEO) level as well as the other levels in the organization. However, such work has not tended to be very systematic and mostly has not had much of a conceptual base. (Hunt, 1991, p. 2)

Hunt (1991) synthesizes an ecumenical (critical pluralism) multi-level model of leadership. He calls it the extended multiple-organizational-level leadership model. It builds on Jaques’s (1989) stratified systems theory (SST). SST holds that leaders face increasing organizational complexity as they move up

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in an organization and that these moves require more cognitive complexity. Hunt’s model includes analyses of critical tasks, individual capability, the organizational culture, and the external environments. SST has its own conceptual difficulties because of its genesis in the slow-moving bureaucracies of 50 years ago, which had longer product-life cycles than today’s smaller, more entrepreneurial organizations. It is too static and deterministic. Hunt’s model allows a classification and ordering of much of the existing leadership research. It emphasizes the many variables possible and their relationships that have appeared in the research literatures. While it mentions processes, it is not a processual model. At the same time, Hunt’s summary table (1991, p. 298) lists 15 thoughts for leadership researchers to think about. Fourteen of these are consistent with this chapter. (The exception is ‘‘thinking cognitively,’’ which LAMPE theory would replace with ‘‘thinking processually.’’) A series of studies have focused on executive leadership, including the work of Augier and Teece (2005), Barnard (1938, 1948), Blanchard and Miller (2003), Burns (1978), Collins (2001), Conger (1989), Dupuy (1977), Sashkin and Fulmer (1988), House and Howell (1992), and Ready (2004). There are, of course, many more, including biographies of famous leaders. Serious research studies have been undertaken to investigate leadership across different cultures. Hofstede (1980) started the trend with his attempt to depict national cultures in terms of five variables. The massive GLOBE project (House, Hanges, Javidan, Dorfman, & Gupta, 2004) extended the Hofstede framework to examine how leadership is understood in 62 countries. There are also country-pair studies of leadership survey instruments (cf. Javidan & Carl, 2005). Despite this vast outpouring of leadership research, its very nature has been obscured by the reliance on variables methodology, including the choice of variables, the conversion of variables into survey instruments, and the excessive dependence of the researchers on statistical packages to ‘‘analyze’’ the data. It is difficult to believe that a model based on abstract variables and applied across a population could ever capture the processes of leading. Each leader in the sample has different ‘‘followers,’’ tasks, context, history, and so forth. Simple aggregation destroys the natural interdependence of the leadership processes that occur naturally in groups and organizations. In short, the models do not seem to be a close fit with the phenomena they purport to study. Furthermore, the methods to test the models obliterate the natural leadership processes. For example, assumptions of independence and linearity violate the process framework of group and organizational processes (Mackenzie, 2000a, 2004a, b) and ignore the

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dynamics of interdependence. More importantly, analyzing questionnaire responses is not equivalent to studying actual processes. Like all healthy scientific communities, the leadership research literature has its agnostics and heretics. Many quotations about the leadership literature point out this diversity of opinion:
‘‘Leadership has been the subject of an extraordinary amount of dogmatically stated nonsense’’ (Barnard, 1948, p. 80).
‘‘Four years of research on literature has produced a bewildering mess of findings y. It is difficult to know what, if anything, has been convincingly demonstrated by replicated research. The endless accumulation of empirical data has not produced an integrated study of leadership’’ (Stogdill, 1974, p. vii).
‘‘As a scientific concept, leadership is a mess. It is not a good scientific concept. If you look at the scholarly literature on leadership, and if you are a serious scholar, you are likely to feel some combination of embarrassment and revulsion. y I’ve said for many years that power is a terrible concept. I’ve also said that leadership is a terrible concept, so how can I put two terrible concepts together?’’ (James March, reported in Augier & Teece, 2005, pp. 128, 130).
‘‘Reification is a social process which converts an abstraction or mental construct into a supposed real entity. Through reification the social construction of leadership is mystified and accorded an objective existence. y With reification, social progress is viewed or ‘caused’ by or ‘determined’ by a leader, a cadre of leaders, or ‘leadership.’ It is assumed by researchers and practitioners that, because there is a word (‘leader’ or ‘leadership’), there must be an independent objective reality it describes or denotes. Reification functions to trap such labeled individuals within a mode of existence that seems to meet various unconscious emotional needs of members of an organization and of a society’’ (Gemmill & Oakley, 2001, pp. 272–273). One could go on, finding even more damaging quotations pummeling the leadership research community. There is no point, however, because, in truth, just about the same sort of comments could be made about the realities of social science research in general. The curmudgeon’s favorite is by the philosopher, Alisdair MacIntyre: What managerial expertise requires for its vindication is a justified conception of social science as providing a stock of law-like generalizations with strong predictive power. It might therefore seem at first sight that the claims of managerial expertise can be easily sustained. For just this conception of social science has dominated the philosophy of

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social science for two hundred years. According to this conventional account – from the Enlightenment through Comte and Mill to Hempel – the aim of the social sciences is to explain specifically social phenomena by supplying law-like generalizations which do not differ in their logical form from those applicable to natural phenomena in general, precisely the kind of law-like generalization to which the managerial expert would have to appeal. This account however seems to entail – what is certainly not the case – that the social sciences are almost or perhaps completely devoid of achievement. For the salient fact about those sciences is the absence of the discovery of any law-like generalizations whatsoever (MacIntyre, 1984, p. 88).

Often the pessimistic assessment of the stature of leadership research is followed by the author’s brave attempt to fix the problem. It is to the great credit of leadership research is that it keeps on trying to get it right despite the carping negativity of a Greek chorus of cynics. It would be charitable to think of this chapter as yet another in a long sequence of such efforts. It is written with the sure knowledge that it would not be the last one, either. Distributed Leadership The arguments by Hunt (1991) and in Dansereau and Yammarino (1998a, b) that leadership is a multi-level phenomena are persuasive. The problem is that if leadership is a multi-level phenomena, what exactly is a level? If there are more than two levels, does not that imply that there might be more than a single leader? If there is more than one leader in an organization, there is the problem of the distribution of the leaders and their functions throughout an organization. Yukl puts it succinctly: Beliefs about heroic leadership may help to justify large salaries for chief executives, but they also foster unrealistic expectations. The heroic leader is expected to be wiser and more courageous than anyone else in the organization and to know everything that is happening in it. Leaders are seldom able to live up to these expectations. Just as they are given too much credit for success, they are also blamed for failure. According to Bradford and Cohen (1984), shared responsibility for leadership functions and empowerment of subordinates is more effective than heroic leadership, but it is unlikely to occur as long as people expect an individual leader to take full responsibility for the fate of the organization. An alternative perspective that is slowly gaining more adherents is to define leadership as a shared process of enhancing the individual and collective capacity of people to accomplish their work effectively (Heifetz, 1994). According to this perspective, leadership processes cannot be understood apart from the dynamics of the social system in which they are embedded (Dachler, 1984). Instead of a heroic leader who can perform all essential leadership functions, the functions are distributed among different members of the team or organization. Some leadership functions (e.g., making important decisions) may be shared by all members of a group, and some leadership functions may be allocated to individual members (e.g., whoever is most qualified). Different people may perform a particular leadership function at different times. The leadership actions of an

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individual leader are much less important than the collective leadership provided by the members of the organization. The heroic bias has strongly influenced theory development and research on effective leadership, and the result is that some important aspects of leadership have not received as much attention as they deserve. The extent to which leadership can be shared, the conditions facilitating success of shared leadership, and the implications for design of organizations are all important and interesting questions that leadership scholars have only recently begun to investigate (Yukl, 2002, p. 432).

Interest in organizational leadership is evolving. There is intense interest in the psychology and behaviors of outstanding historical processes, the charisma and transformational styles of such leaders. This research has focused on the supervisionary leader–follower relationship, the traits and styles of the leader, and the context of the small group. Viewing leadership from an organizational perspective brings up the question of how to understand leadership from the perspective of the organization. Sashkin and Fulmer (1988) stress the issues involving executive leadership, including the leader’s personality, the situation in which he or she must operate, and the behaviors of such an executive. The need to incorporate the actual organizational processes that constitute leadership processes has been recognized by some researchers. Notable sources include Child and Hosking (1979), Hosking and Morley (1988), and Hosking (2001). The notion that leadership and leader behaviors are, at least in part, the results of organizational arrangements is not new. For example, Dupuy (1977) documents the role and the success of the German General Staff in producing a long line of outstanding military commanders. Collins (2001) describes how the great business leaders in his study are continually working on improving the distribution of leadership in their companies. Others (cf. Maxwell, 1995; Pearce & Conger, 2003; Tichy, 2002) stress the importance of developing leaders in organizations. What is less clear is why and how distributed leadership may be preferred to heroic leadership in a group or an organization. It will not be a surprise to most to learn that the question of the distribution of leadership depends on numerous factors, such as the rate of change. Leadership as a Distributed Process Leadership requires personal involvement in one or more of the four main leadership processes. Leaders, then, are engaged agents (cf. Taylor, 1993). Leaders are not disembodied agents who merely execute computational routines so as to achieve some goal. While leaders usually have purposes, their engagement is personal and dependent on their background. Accordingly, it is doubtful whether leadership can be reified to become the

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‘‘objective’’ calculation of instrumentalities and valences as in an interesting model such as path–goal theory (cf. House, 1971). Instead, it may be profitable to view leadership and its distribution in terms of its processes for managing work in an organization. There is a growing body of literature on shared leadership, as evidenced in Shared Leadership: Reforming the Hows and Whys of Leaders, edited by Pearce and Conger (2003). In this literature, shared leadership is depicted as more of a sharing of the governance of a team than an issue of the distribution of leadership throughout an organization. The shared leadership literature is concerned with empowerment of self-managed teams (cf. Manz & Sims, 1993) and the apparent equalization of power among a team’s membership. O’Toole, Galbraith, and Lawler (2003) discuss the positive and negative aspects of shared leadership. Locke (2003) analyzes what can be shared and what should not be shared (e.g., vision, core values, organizational design, choice of top managers). Locke makes the sensible point that people vary in intelligence, reasoning ability, drive, knowledge, courage, and other characteristics, so how leadership is shared will necessarily vary as well. Locke’s concern is shared by Conger and Pearce (2003) in their discussion of the limits and liabilities of shared leadership. This chapter follows up on four of their seven domains of opportunity for shared leadership research. However, shared leadership does not quite capture the idea of distributed leadership. ‘‘Shared’’ in this chapter on LAMPE means that each part of the organization has LPs that contain the whole. What each unit does and who is in each unit can vary. The idea is to have every unit consistently perform the same LPs. It is in the sense of enfolding rather than equalizing leadership that this chapter differs from the shared leadership research. The ultimate example of distributed (and sequentially shared) leadership is the study by Seifter and Economy (2001) of the famous Orpheus Chamber Orchestra, which has no conductor. Leadership is distributed and shared in this organization, but the Orpheus Chamber Orchestra is anything but leaderless. This 27-person orchestra employs everyone, as appropriate to his or her musical instrument and score, to synchronize musical entrances and smooth passage of themes from section to section, all in tune with the composer’s vision for a piece. Different members take on different roles with different pieces. The flip side of leadership is followership. Kelly (1989), for example, differentiates effective followers from ‘‘yes people,’’ ‘‘sheep,’’ and alienated followers. Effective followers are actively engaged and exhibit independent critical thinking. The main issue in leadership that is both shared and distributed is that in every group within the organization ‘‘leadership is

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distributed among, and stems from, team members’’ (Burke, Fiore, & Salas, 2003, p. 103). The main job of the leader is to do, or get done, whatever needs doing or is not being adequately performed. The leader’s job is to ensure that critical group functions are being carried out. The organization has many leaders distributed throughout. The approach taken in this chapter is to focus on the types and distribution of leadership processes occurring in organizations, especially those in rapidly changing environments. Instead of providing yet another synopsis of the important topics of understanding the behaviors of the individual leaders (cf. Staw, 2004), the focus here is on the multi-level nature of the processes of leadership as they are deployed throughout an organization. Issues in Defining Leadership Authors rarely define leadership carefully. Indeed, no generally accepted operational definition of leadership exists. For example, Rost (1991), after surveying 587 books, chapters, and tapes on leadership, discovered 190 different definitions of it. Some old definitions such as that employed by Mumford (1909) serve well even today. Mumford argued that leaders emerge because they are seen as being able to solve problems in times of stress. In the language used in this chapter, these leaders can control interdependence uncertainty. Tead defines leadership ‘‘as the activity of influencing people to cooperate toward some goal’’ (1935, p. 20). This definition is similar to that espoused by Daft, who defines leadership as ‘‘the ability to influence people towards the attainment of goals’’ (2000, p. 502). Stogdill (1974) argues that great leaders emerge because specific conditions require their ability. Bass (1960) views successful leadership in terms of being able to create transformations in others. The British Prime Minister, Tony Blair, is quoted in the Mail on Sunday (October 2, 1994) as stating that ‘‘The art of leadership is saying no, not yes. It is very easy to say yes.’’ Colin Powell (1995) states that leadership is the art of accomplishing more than the science of management says is possible. Tom Peters, the popular business author, proposes a brand of leadership called ‘‘management by wandering around’’ in which to wander with customers, vendors, and employees is to be in touch with the first vibrations of the new (Peters & Austin, 1985). It is not clear whether leadership is a set of abilities, a list of behaviors, a class of processes, or even a verb or a noun. The lack of a clear construct makes the measurement of leadership problematic. For instance, House et al. define leadership ‘‘as the ability of an individual to influence, motivate, and enable others to contribute to the effectiveness success of the organization of which they are members’’ (2004, p. 15). This definition was

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essential to the successful coordination of the GLOBE project. Most other studies lack a definable measure for leadership that is independent of the reputational assessments of some leader or set of leaders based on the assumption that the chosen are, in fact, leaders exhibiting leadership. Without a clearly defined construct upon which to base a defined measure of leadership, it is difficult to validate or invalidate any measurement of leadership. Lacking standards, anything goes. As a consequence, the selection of those called leaders is based on their reputations or positions. This situation produces loops of circularity: the researcher chooses a person, assesses that person’s ‘‘leadership,’’ and then uses the assessment to justify the measure of itself. A great man (or woman) is selected and his (or her) greatness is used to show his (or her) greatness. Biographical sketches of historical ‘‘great leaders’’ such as Alexander the Great (Cartledge, 2004; Fox, 1997), Julius Caesar (cf. Goldsworthy, 2003; Meier, 1982), Genghis Khan (Weatherford, 2004), and General Douglas MacArthur (cf. Manchester, 1978) are selected by authors who use snippets from their lives to illustrate the author’s ideas about leadership. A variant on this theme is the autobiography of a great man (or woman), in which the author evaluates his or her own leadership. These biographical exemplars are entertaining, inspiring, and capable of yielding hypotheses about leadership. But they are not valid proof of a hypothesis, especially as most ‘‘great’’ people have their detractors who write about them in less flattering prose. For example, General Douglas MacArthur, Alexander the Great, and recently President George W. Bush are both revered and reviled. Their ‘‘greatness’’ is debatable. Hence, the same person can be an example of a great leader or a failed leader. Reputable historians are always very careful about which references and sources they use and how they interpret them. The records are never as detailed and complete as one would desire. Answers lead to more questions. Regarding Alexander the Great, the subject of intense analysis ever since he lived, both the facts and their interpretation are in dispute: Was Alexander a Greek? Was he of average height or ‘‘vertically challenged?’’ How many children did he beget? Just think of the difficulties in gaining an accurate understanding of other great persons for whom sources are less bountiful. The summary by Cartledge is instructive: For Alexander has been handed down to us ultimately as an enigma, thanks above all to the inadequate nature of our sources of evidence. Though the extant evidence is very far from slight in quantity, it is in several aspects seriously deficient in quality. It is mainly non-contemporary, it is partisan (con as well as pro), and it tends to be sensationalist. Whichever of the major aspects of Alexander’s career we study, therefore, we are unable to reach anything firmer than a high probability in explanation, and even that degree of

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probability is a rarity. The very facts themselves – what actually happened – are often unclear (Cartledge, 2004, p. 40).

Some studies of groups of leadership have generated organizational samples by using criteria based on selected performance measures, such as return on investment (ROI), stock price increase compared to the Dow Jones average, profits, or increase in market share. The influential work by Collins (2001) uses the consistent, relative increase in stock prices over a 15-year period to select the best companies for his study. The assumption is that the CEO is the cause of this organizational performance. Organizations meeting the stated criteria are used to select leaders for more intensive interview or data collection. The objective is to uncover special traits, skills, or other characteristics that seem to differentiate these winners from the rest of the herd. The challenge lies in identifying what makes them special as exemplars of good leadership. In other cases, such lists have been used in comparison with lists of persons who do not have as high a reputation for leadership. This approach gives a pair of lists that identify differences between the ‘‘successful’’ leaders and the others. The proposed differences are the basis for conclusions about the traits, styles, or behaviors of leaders. These types of studies are more plausible (but usually less interesting) than the ‘‘great man’’ studies because they include a broader sample, clear criteria for selection, and a ‘‘control’’ group. Such studies can help develop hypotheses and challenge long-held assumptions. The work of Collins (2001) is a good example of the best of this type of leadership research. Unfortunately, such studies are less useful as tests of the author’s thesis because of their built-in circularity and the lack of independent measures of the leadership construct favored by the author. Dupuy (1977), by contrast, takes a special institution and examines a string of leaders who came from it, arguing that the institution (in this case, the German General Staff) makes a major contribution to the understanding of how organizations can affect leadership quality. In addition to problems of circularity, hidden assumptions may present obstacles to researchers. For example, is the person a leader or merely a person in a position in which leadership is often required? Is this person simply ‘‘fronting’’ for others who provide the real leadership? Is the organizational effectiveness due solely to the actions of this designated person? Does a one-to-one correspondence exist between good leadership and good organizational effectiveness? What does the person actually do when he or she is leading? Actual leadership processes remain a mystery, as they are rarely documented and analyzed. The best sources of the actual processes of leadership come from biographies, autobiographies of honest (or repentant) leaders, and historical fiction.

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Suppose that specific processes yield behavior that can be classified as leadership. Do we know what these processes are or should be? If not, how do we colligate and ‘‘code’’ the narrative to ‘‘measure’’ leadership behaviors? The lack of agreement spawns a variety of narratives, leaving the coded record of the processes spotty at best. Given the demands for numbers and statistics, researchers are likely to rely on reputational measures. These measures can be used to compare groups or to compare ‘‘before’’ and ‘‘after’’ scenarios. The problem with reputational measures of leadership is that they cannot incorporate the actual processes of leadership behavior. That is, they confine the assessment of leadership to fixed categories. Persons are asked to evaluate the leadership of others as embodied in outcomes, the behaviors and processes of which are loosely anchored judgments of ambiguously undefined behaviors. In short, the unknowing are asked about the unknowable. This exercise diverts our attention away from the goal of truly understanding the phenomena of leadership (cf. Mackenzie, Rahim, & Golembiewski, 2001, for a discussion on how constructs can distort research). It is reasonable, at this point in the development of a theory of organizational leadership, to explore other frameworks. The focus in this chapter centers on the core leadership processes and key LPs. This processual, multilevel approach requires familiarity with several basic concepts, which are the subject of the next section.

DEFINING LEADERSHIP, AUTHORITY, MANAGEMENT, POWER, AND ENVIRONMENTS (LAMPE) Basic Concepts Organizations work best, especially in fast-changing environments, by having all elements of LAMPE (leadership, authority, management, power, and their environments) be coherent, integrated, and operational throughout the organization. The argument for this thesis rests on six multi-level ideas about structure, processes, process frameworks, interdependence, task–role matrices, and virtual-like organizational arrangements (VLOAs). Structure A structure represents a need-satisfying pattern of interaction among members of a group (Mackenzie, 1976a, b). If there are n members, then each has the potential of n – 1 half-channels (i.e., one-way channels) by which to

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communicate with the other n – 1 members. The member’s decision to exercise the use of his or her half-channels is influenced by self-interest and the influence of the other group members. Because each member has control over his or her half-channels, the only way to have a stable pattern of usage is to reach a consensus in which the pattern of usage is established in the available n(n – 1) half-channels. Let rij be the measure of the use of half-channel from member xi to member xj The choice of measure can range from existence, authority (superior–subordinate), number of messages, volume of transactions, and so on. The matrix, R, has elements, rij. Formally, a structure S ¼ ðX n ; RÞ

(1)

where Xn is the set of n members and R the matrix of rij A R. Members do not necessarily have to be individuals. They could be groups, teams, units, other divisions, or companies. In fact, in some cases the relevant xi may include nonhuman processual agents such as computer intranets or command and control systems. A processual agent is any entity capable of effecting a process. ‘‘There are classes of processual agents including, but not restricted to, entities, processes involving entities, purposes of entities, technologies deployed by entities, and the context, situation, or environment surrounding an entity’’ (Mackenzie, 2001b, p. 174). Recognize that even though a structure is a need-satisfying pattern of interaction, this statement does not imply that all members are happy or satisfied with it. Rather, given the cost of changes, the risk of changes, and the available alternatives, the current structure is merely the one with zero opportunity costs to the members. Process The author’s article in Volume 3 of the Research in Multi-level Issues series (Mackenzie, 2004a) includes an extended section on processes and the process approach to the analysis of multi-level group organizational phenomena. A reasonably complete description of group and organizational processes, their frameworks, and their analytical properties can be found in Mackenzie (2000a). Pettigrew (1997), Ropo and Hunt (1999), and others are calling for more processual research in organizational behavior research. Hunt and Ropo (2003), Jago (1982), Ropo and Hunt (1999), Hosking (2001), Hosking and Morley (1988), and Conger (1989), in particular, suggest some advantages to studying leadership and organizational change via processual models. Many others join in to support reframing the study of organizational behavior from variables to processes, including Bornstein

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and Smith (1996) and the persistent pressure from Northouse (2004) and Hosking. One problem with appeals for taking a process approach is the lack of clarity about how to answer the appeal. There are all sorts of processes, ranging from falling in love to arranging for the merger of two companies. The basic idea is that a process represents a sequence of events governed by a rule or ordering called a process framework. All task processes are processes, but not all processes are task processes. Task processes are a type of process used to describe the work of an organization (Mackenzie, 1986a, p. 47). In the task process of building a new house, the sequences might include arranging financing; gaining title to the land; clearing the land; digging the foundation; framing; roofing; installing electrical, plumbing, and heating/air conditioning; finishing the walls and interior supports; engaging in all sorts of finishing steps; landscaping; construction of decks, patios, walls, and so on; the final ‘‘walk-through’’; and legal acceptance and ownership transfer to the buyer. Some processes involve a sequence of decision elements or considerations. Other processes involve interpersonal influence, politics, and searching for, finding, removing, and preventing process maladaptations. The main idea is simple: group and organizational processes embody movement through time. They are not static, are rarely fixed, and should not be assumed to be random. However, this simple idea of process quickly becomes complex in the study of an organization. First, there can be literally thousands of processes may take place during the same time period. Second, these processes are influenced by the members’ choices. Third, these processes can change due to influences from outside the organization (e.g., vendors, customers, competitors, government rules and regulations, technological shifts, social change, movements in the economy and market). Fourth, these processes can and do change. Fifth, changes in structures and processes are highly interdependent and these interdependencies are multi-level. Furthermore, any group or organization process has five components: 1. The processual agents involved in the process 2. The steps or considerations contained in the process 3. The pairwise linkages between elements of the preceding steps or considerations 4. Links to other processes 5. The task resources and their characteristics-in-use The fifth component can get technical, as it includes task resources that are intrinsic to a specific task within a unit, such as member resources and technological support resources. The notion of task resources’ characteristics-in-use

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are those that are intrinsic to the interdependencies existing across units, boundaries, and time. These five components of group and organizational processes are typically neglected in analyses of group and organizational structures and processes. For example, a common failing of the sort of process models produced by information technology analysts is the proclivity to leave out component 1 (who is doing the work), component 4 (links to other processes), and most of the constituents of component 5 (task resources and their characteristics-in-use). Consequently, because of these deficiencies, it should be no surprise to find that attempts at processual analyses, such as business process reengineering (Champy, 1995), pose implementation problems and have high failure rates. Conversely, attempts to examine structures (especially in their political form as organizational charts) without considering the processes involved tends to backfire because the analyses are woefully incomplete. They leave out what the positions are supposed to be doing. That is, such analyses are silent on the effects of structural change on the organization’s many task processes. Process Frameworks ‘‘Process framework’’ is a term contained in the definition of a group or organizational process given earlier. A process framework consists of the three components (2, 3, 4) of a process. That is, it includes the steps or considerations contained in a process (including the outcomes), the pairwise linkages between all pairs of the considerations, and the links to the other processes of the organization. Formally, if Y is the set of possible outcomes of a process (always shown as the right-hand end of F), F is the graph of the pairwise relationships between the considerations, and C is the set of considerations, then Y ¼ F ðC Þ

(2)

The properties of the considerations C, the properties of the outcomes in Y, the properties of the process framework F, and the uses of process frameworks are summarized in Mackenzie (2000a). Mackenzie (2004a, b) argues that virtually all known group and organizational processes can be modeled by this definition of process and process frameworks. He also argues that known multi-level analyses can (and should be) be represented by Y ¼ F(C) (cf. Mackenzie, 2004b). Interdependence Interdependence is considered to be the common property of all organizations. In fact, without interdependence, there can be no organization. The organization of organizations is interdependence (Mackenzie, 2001b). The

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concept of interdependence is simple to state: two processual agents are said to have interdependence if each can affect the other. That said, there is much to understand because of the multi-level nature of groups and organizations and the multi-level nature of interdependence. There can be task process interdependence, structural interdependence, interdependence created by the multi-level nature of the tasks themselves, and more. Interdependence is necessary to align and link members to members, processes to processes, and members to processes. This is an ongoing fourdimensional challenge because change is continually snapping existing linkages, creating needs for new ones, and making others redundant and obsolete. Many organizations have inadequate interdependence because they have underconnected processes and structures. An underconnected process is one for which no common, integrating, predecessor process exists; it just sits there, isolated and lacking linkages. An underconnected structure includes (1) isolated members for whom there is no defined linkage with the rest of the group or organization, and (2) remote members whose indirect linkages should be direct. The latter case occurs more frequently because of VLOAs (discussed later in this chapter), outsourcing, and the use of temporary employees. Some organizations, however, suffer from excess interdependence (the more likely case). Excess interdependence is that which exceeds the needs for operating an efficient organization. Excess interdependencies imply overconnected structures and processes. An overconnected structure is one characterized by an excess of linkages over those needed to achieve the strategic direction. Overconnection shows up in the form of VLOAs, such as committees, task forces, and teams, and as virtual processes. An overconnected process also includes redundant and/or untimely subprocesses. The overconnected structure is epitomized by low degrees of hierarchy and the presence of excess horizontal coordinating people and groups. If one uses the metaphor of the organizational black space (OBS), which is depicted by the official organizational chart, then the black ink of the boxes, lines, incumbents’ names, and position titles designates the OBS. The white paper on which the organizational chart is printed is the organizational white space (OWS), representing the arenas of behavior lying outside the official-role system. Most organizational changes begin in the OWS. However, overconnected structures include incongruencies between the OBS and the OWS structures; further, these incongruencies hinder the management of the organization. Underconnected processes and structures lack the necessary interdependence to achieve integration. Overconnected processes and structures represent excess interdependence. Managing includes the processes of directing, controlling, and coordinating

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(DCC) as well as those of planning. Underconnected processes and structures that require establishing and maintaining DCC processes are a source of process maladaptations. Overconnected processes and structures that require rationalizing and pruning via planning are another source of process maladaptations. Underconnected processes and structures often result from organizational changes whose processes and structures have not yet been integrated into the OBS. In contrast, overconnected processes and structures are a form of organizational slack (cf. Cyert & March, 1963) that builds up over time when interdependencies are not managed. These overconnected processes and structures need to be pruned back to enhance the welfare of the organization. Maladaptations create unnecessary duplication of task processes, task resources, and their characteristics-in-use, which in turn creates overlapping and redundant positions. Process maladaptations interfere in the timely completion of a task process and create conflict in the coordination of task processes. Process maladaptations that generate dysfunctional structural interdependence have the tendency to cascade downward and outward to other levels (cf. Mackenzie, 2000b) and parties by encroaching on them (Marker, 2002). In Burt’s (1992) terms, a leader can be seen as a person repairing and patching holes as they occur in the fabric of the organizational space. One of the main tasks of leadership is to continually watch for underconnected processes and structures and to create linkages for the achievement of the organization’s strategic direction. Another is to continually evaluate the organization and to prune the overconnected processes and structures in an effort to improve efficiency and accountability. Another important consequence of interdependence is the uncertainty of the interdependencies. Interdependence uncertainty is the uncertainty associated with the interdependence among a set of processual agents. Mackenzie (1986b) argued that interdependence uncertainty is a multi-level class of phenomena. Interdependence uncertainty, for example, may arise at the level of the machine operator in a factory who is operating a punch press. Levels of interdependence uncertainty can be very high, such as when the company is deciding whether to close or expand the factory that employs the machine operator. Most of the higher levels of interdependence uncertainty are invisible to those on the lower levels. Unfortunately, the reverse is generally true, as senior officials lose contact with the organization for which they are responsible. Interdependence uncertainty will play a major role in how power is defined in this chapter and in the achievement of LAMPE coherence. There is a multi-level structure to these interdependencies, as illustrated in Fig. 1.

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Systemic Interdependencies

Immersive Interdependence

Generalized Marinating of Multiple Processual Agents Involved in an Organization

Purposive Interdependence

Prototypical Interdependence Among Context, Purpose, Means, & Outcomes Within An Organization

Compound Congruencies

Role Interdependence

Operational Interdependencies

Processual Plays

Task Process Interdependence

Fig. 1.

Compound Interdependence Among Processual Agents of Different Classes & Context Within An Organization

Interdependence Between Sets Of Tasks & Entities Within An Organization

Integrated Task Processes

Base Phenomena

The Organization of Organizational Interdependence.

Immersive interdependence is a set of the generalized processes of the coadaptation and evolving congruency among sets of processual agents of different classes. Note that immersive interdependence need be neither direct nor deliberate, but rather can occur in a process analogous to soaking meat in a marinade. Also, control of the uncertainty of immersive interdependence involves the processes of ensuring that all members of a group or organization are moving toward common purposes, practices, and outlook. Purposive interdependence is the compound interdependence among the context, the purposes, the means, and the outcomes of a set of processual agents. Control of the uncertainty of purposive interdependence involves the processes of establishing and maintaining the strategic direction at every level of a group or an organization. Compound interdependence is the interdependence among processual agents of different classes and contexts within an organization. Control of

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interdependence of compound interdependencies involves processes to ensure (1) compatibility within and between units, (2) compatibility of the organizational rewards system to performance, (3) results congruency of units within the strategic direction, using organizational forums, and (4) management and integration of new technologies. Role interdependence includes the interdependence between sets of tasks and entities within an organization. Control of the uncertainty of role interdependence is brought about by the coping mechanisms deployed to manage interdependence among entities, task processes, and technologies. It includes practices to define and regulate behavior in a group or an organization. Processual play interdependence is defined by the interdependence among processual agents for an integrated task process. A processual play defines the interdependence among processual agents for an integrated task process. Control of the uncertainty of processual play interdependence includes ensuring results-oriented problem solving and the nurturing and rewarding of innovation in a group or an organization. Task process interdependence is the lowest level of interdependence and is concerned with the interdependencies of the task processes, the task resources and their characteristics-in-use, and structures. It involves the interdependencies among the five components of a group or organizational process. Task–Role Matrix A task–role matrix is a chart or table in which the rows are the processual agents (usually its members or units) of an organization and the columns are the tasks performed by these processual agents. The entry, tij, is defined as follows:
1 if processual agent xi is involved in task j tij ¼ 0 otherwise A generic task–role matrix is illustrated in Fig. 2. Note that tij varies and can be coded as follows: I if the processual agent performs the task S1 if the processual agent is the direct supervisor of the person performing the task directly S2 if the processual agent is the supervisor of S1 S1/P if the supervisor is also engaged directly in performing the task X if the processual agent is involved but only in a ‘‘works with’’ role There can also be S3, S4, and so on.

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x1 x2 x3 . . . xi . . . xn

1 t11 t21 t31 . . . ti1 . . . tn1

2 t12 t22 t32 . . . ti2 . . . tn2

3 t13 t23 t33 . . . ti3 . . . tn3

4 t14 t24 t34 . . . ti4 . . . tn4

5 t15 t25 t35 . . . ti5 . . . tn5

6 t16 t26 t36 . . . ti6 . . . tn6

7 t17 t27 t37 . . . ti7 . . . tn7

8 t18 t28 t38 . . . ti8 . . . tn8

Tasks 9 t19 t29 t39 . . . ti9 . . . tn9

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j t1j t2j t3j . . . tij . . . tnj

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m t1m t2m t3m . . . tim . . . tnm

1 if processual agent xi is involved in task j Where tij = 0 otherwise

Fig. 2.

Task–Role Matrix.

When the entries are of these types, each column shows a chain of command for its task. Each row can be read for its P’s and S’s to generate a position description. In this case, the task–role matrix is called an organizational responsibility grouping (ORG) chart. The ORG chart illustrates the main multi-level structural and process interdependencies. Task–role matrices are used later in this chapter to define leadership windows, issues, and distribution. The concept of windows is key to the LAMPE theory of organizational leadership.

Virtual-Like Organizational Arrangements If one determines who actually works with whom on each task, and if this information is displayed by connecting the position boxes in the organizational chart, the result is networks of slanting lines. Those lines that are not in the immediate supervisor–subordinate link depict the fact that most organizations do not work like the representation in the organizational chart. In some cases, these lines can be connected to a ‘‘box’’ representing a process that is itself the connection of position boxes in the OBS. This box represents a virtual position. A virtual position (cf. Mackenzie, 1986b) can be formally defined by the number and types of entries in the ORG chart. A virtual position (most teams, committees, task forces, cabals, and other groups are virtual positions) acts like a process that gathers people resources. In the domain of tasks, there is a related entity called a virtual process: a person who goes outside his or her normal tasks to gather task

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resources from other parts of the organization. Virtual processes are often referred to as ‘‘work arounds.’’ Both virtual corporations and other VLOAs are possible (Mackenzie, 2001a; Mackenzie & Scott, 2001). VLOAs are natural processes for achieving a balance, fit, and congruency between the goals of an organization and its available information technologies for achieving these goals. All organizations must deal with a permanent and fundamental problem in the presence of change. It is represented by the time of analysis and time of change inequality: T A oT C

(3)

where TC represents the time period during which the problem remains unchanged, and TA represents the time taken to find, formulate, solve, and implement the solution to the problem. TA the time taken for analysis. Note that TA includes solution implementation. Whenever TA exceeds TC, the solution is always late. Ideally, TAoTC, as shown in Eq. (3). Virtual-like organizational arrangements (VLOAs) occur naturally in organizations when TA4TC. Because the organization is ‘‘officially adapting’’ more slowly than needed, existing organizational arrangements become altered. VLOAs are coping mechanisms that evolve right along with the evolving organization. They are important to the understanding of the challenges of organizational leadership because they become the arenas for power struggles (Mackenzie, 1986b). Whenever change is slow and goals are clear and legitimate, VLOAs are highly adaptive mechanisms and usually emerge without the guiding hand of a leader. They are created for new problems without having to alter the OBS. When the problem is solved, the processes can be absorbed into the OBS. When change is more rapid, however, the VLOAs may be created more quickly than they are absorbed. Whenever change is rapid and goals are fuzzy, bureaucracies devolve into interdependent coteries of miscellaneous VLOAs. Leadership Defining leadership creates problems because the word leadership takes on different meanings depending on the purposes of analyses and those performing them. In some cases, leadership is a noun referring to the group of individuals who are seen as directing and controlling an organization (the leadership). Leadership is also used as a substitute for the word management, as in ‘‘our organization needs better leadership.’’ This usage is closer to what this author means than it might appear. Another use of leadership is to label

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certain behaviors by a person designated as a leader. This position is also close to the author’s. The word leadership is also used to denote a set of traits, talents, and attributes that a group of persons, designated as leaders, seem to possess (cf. Howell & Costley, 2005; Knowles & Saxberg, 1971; Bass, 1960; Conger, 1989; Staw, 2004). This personality view is very far from the one in this chapter. A metaphor might be appropriate for describing the notion of leadership in this chapter. Leadership is akin to the cortex of the brain (cf. Diamond & Hopson, 1998, for an introduction to the structure of the brain). It acts to grow, establish, and strengthen proper connections and to prune improper connections. Leadership, in this way, acts as much like dendrite trees, synapses, and axons. The main part of the brain is the execution task processes world. Leadership involves continually repairing and improving the organization’s cortex. Furthermore, leadership always involves coping with uncertainty, and especially the interdependence uncertainty within a group or organization. For example, Dalton states that ‘‘Persons able to deal with confusion come to the fore as leaders, with or without the official title. They become the nucleus for cliques that work on interlocking action centers and as bridges between official and unofficial purposes’’ (1959, p. 68). If there were no change, there would be no need for leadership to manage the independence uncertainty that accompanies it. A leader needs to have a good understanding of the technological, market, and competitive issues to be able to continually repair and improve the organization’s cortex. If he or she is to provide leadership, the leader needs to exploit information asymmetries in which he or she discovers and/or takes advantage of opportunities unseen by others. The leader may act to ensure information asymmetries by limiting access or blocking access to information about interdependence uncertainty. In a sense, leadership resembles entrepreneurship (cf. Shane, 2003). In this chapter, leadership focuses on the management of change in a group or organization. Leadership is defined as the processes of initiating, enabling, implementing, and sustaining change in the management of a group or organization. Change in management means activating the processes of planning, DCC organizational interdependence as well as proactively searching for, removing, or preventing process maladaptations. The four main leadership processes are defined as follows: 1. Initiating change processes start up, redirect, or shut down changes in the interdependencies in an organization. They originate, launch, pioneer, begin, and establish change processes. Sometimes one must initiate change

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processes so as to stop, redirect, or prevent changes that are viewed as interfering with the drive to achieve the strategic direction of the organization. Stopping unwanted, unnecessary, or unwise change is a defensive version of initiating change. This point is mentioned here because many are predisposed to think of change as always being beneficial or progressive (despite the many lessons from history, such as communism and prohibition). 2. Enabling change processes are problem-solving and resource-allocation processes that support and foster internal organizational change. They help change processes succeed by empowering action, authorizing support, and warranting the change processes. The enabling change process involves others in the organization. 3. Implementing change processes encourage and support implementation of organizational change. They help ensure that the changes are actually accomplished. These change processes are vital for effective organizational leadership and involve other members of the organization. 4. Sustaining change processes ensure that an organization’s processes continually adapt to current conditions as the organization undergoes change. They help maintain momentum, nourish, uphold, ensure, and continually provide support over time as change processes unfold. They are also critical to successful organizational leadership and usually involve other members of the organization. The idea that leadership is binary (either one is leading or one is following) is at odds with reality. Leadership, by definition, includes four main processes: initiating, enabling, implementing, and sustaining change in management of a group or an organization. One can do any or all of these four and be engaged in leadership. Leadership also varies over the duration of the process. Furthermore, numerous gradations of leadership exist, ranging from Julius Caesar’s dramatic, history-changing crossing of the Rubricon (cf. Meier, 1982) to someone who helps sustain a committee task. A leader is any processual agent (usually a person or a unit) who is engaged in leadership processes. A leader can be an individual, a group of individuals, or any processual agent. However, both leaders and leadership are continually adapting. Viewed in terms of their processes, both terms should be thought of as if they are verbs (a position supported by Hosking, 2001). Authority Authority is a term that is left over from the nineteenth century and usually confused with the concept of power. Max Weber (1947) used the term

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authority as one would the word power. This usage made more sense in 1910, when most of the population was poorly educated, poor, and without influence or lacking decent prospects of influence, especially in Europe and particularly in Germany. A small group of nobility and landed aristocrats controlled societies. These powerful individuals held the authority in all important institutions. Since that time, social and political changes have occurred that have resulted in more diversity in the distribution of power, authority, wealth, and opportunity. We also have seen the rise of more ‘‘checks and balances’’ to units of government. For example, until the administration of President Lyndon Johnson (1963–1968), most regulatory agencies of the U.S. government were defined vertically along the lines of the industry they regulated. Soon, however, new regulatory agencies were created for constituency groups. This trend led to the establishment of OSHA (for unions), EPA (environmentalists), EEOC (women and minorities), ADA (the disabled), and other agencies. Now the regulations, instead of extending vertically to the industry, extend horizontally, cutting across industries. The cross-hatching regulatory system has created confusion about the authority of the regulators in the minds of the regulatee who faces conflicting demands. If one were to draw one line representing authority and another line representing power, back in 1900 the two were roughly parallel. Today, however, they are moving apart and approaching becoming orthogonal. This view is shown graphically in Fig. 3, where the angle y depicts the growing separation. If y ever exceeds 901, our society is in big trouble, as authority and power will have lost their meaning. In this chapter, authority is defined as the legal right to preempt the outcome of a decision or a process. Authority is a legal right; it is granted by the state or government to an organization. Authority can be delegated to others. It can be defined independently of the interpersonal relationship between persons. Authority is ultimately backed by the willingness of government to enforce it.

Power Power θ

θ

Authority 1900

Fig. 3.

Authority 2000

The Separation of Authority and Power.

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The word authority has rhetorical advantages over the word power. When defending one’s position, calling the attack ‘‘a threat to one’s authority’’ is socially preferable to being portrayed as ‘‘clinging to power.’’ When attacking a rival, it is best to describe him or her as ‘‘grasping for power’’ because this terminology is more damaging than claiming the individual is ‘‘seeking more authority.’’ Authority is legal, and legality confers some measure of legitimacy. In contrast, power is not legal and appears more threatening. Consequently, the word authority is used more often than the word power. Despite the fact that power is usually more desirable than authority, one does not talk about power in polite company without risk. Indeed, it is a social taboo to openly seek power. Pfeffer (1992, p. 13) quotes Rosabeth Kanter, who said that ‘‘Power is America’s last dirty word. It is easier to talk about money – and much easier to talk about sex – than it is to talk about power.’’ Because the acronym LAMPE includes both an ‘‘A’’ referring to authority and a ‘‘P’’ for power, it is necessary to openly discuss power and the two concepts’ interdependencies. But first, let us consider the construct of management. Management Management, as described in standard textbooks on management, does not resemble the reality experienced by this author in his travels in and out of corporations and universities. For example, management is defined something like this: ‘‘the attainment of organizational goals in an efficient and effective manner through planning, organizing, leading, and controlling the organizational resources’’ (Daft, 2000, p. 7). Few organizations are clear about their goals, and consequently most are unclear about how to achieve them ‘‘in an efficient and effective manner.’’ This author’s experience as a university professor leaves him perplexed about the real goals of a modern university, which seem to consist of revenue maximization and security for the faculty and staff to maintain their lifestyles. Most real-world companies likewise have many – and often conflicting – goals, ranging from sales growth, innovation, profits, tax avoidance, and maintenance of family control to revenge and retribution on certain competitors. The purpose in this traditional definition of management is attainment of goals rather than achievement of the strategic direction. It espouses the desiderata of efficiency and effectiveness rather than adaptability, efficient adaptability, and dynamic congruency (cf. Mackenzie, 2003). The definition is strangely static rather than forward-looking and ongoing. Concern for the organization’s external environments and technologies are assumed to be

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given rather than strategically critical. The focus of control is the organization’s resources rather than its people, its processes, and their manifold interdependencies. It implies that the leader is the person or clique at the top of the organization. Although this perspective does mention leading, it remains silent about LPs. The main source of power seems to be authority rather than the proven ability to produce results. No one – least of all the CEO – does actual work in this definition. What seems most strange is the emphasis on managing results rather than managing the processes that produce the results. A preferable definition comes from The Organizational Hologram: Management is the ongoing effort to achieve the organization’s strategic direction in the presence of changing conditions through the processes of: (a) planning, directing, controlling, and coordinating organizational interdependence and (b) proactively searching for, removing, and preventing process maladaptations. (Mackenzie, 1991, pp. 184–185)

This definition of management emphasizes the processes involved in the interdependencies. More traditional definitions of management refer to the management of resources rather than the management of interdependencies. Also, part (b) of the definition is not included (but often implied) in more static and traditional definitions of management. The strategic direction of an organization consists of its philosophy, organizational assumptions, vision, goals, strategies, and tactics as well as its organizational plans and organization-wide guidelines. Strategic direction means much more than just goals. Planning processes are those involved in selecting the strategic direction and in changing the specification, organization, and distribution of tasks. Directing, controlling, and coordinating (DCC) task processes are employed to direct, control, and coordinate execution-level processes within an organization. DCC processes are concerned with getting the organization to successfully complete the execution processes by integrating the task processes with the task resources and their characteristics-in-use.
Directing task processes decide what to do and then see to it that the needed task resources and the characteristics-in-use are available and used.
Controlling task processes ensure that the execution processes are performed properly. Control, in this term, is a verb rather than a noun. Controlling task processes can include quality control, quality assurance, setting standards and monitoring compliance, auditing, budgeting, and more.
Coordinating processes manage interdependencies among the task process frameworks and the task resources and their characteristics-in-use.

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In practice, the processes of DCC usually overlap. For that reason, they are referred to as a set, denoted by DCC. Execution task processes are the routine tasks performed in an organization. They constitute the routine work of an organization and usually account for the majority of tasks performed (cf. Mackenzie & Benoit, 1997). Note that execution task processes are not included in this definition of management. Power Power is an active, verbal term connoting actual ability to get a change implemented. As such, it is analogous to kinetic energy in physics. But what are the sources of power? Mackenzie (1986b) argued that power is the ability to control interdependence uncertainty. Power does not exist without the need to deal with this type of uncertainty. In a perfectly static world, there is no need for power. Power is needed only when interdependence uncertainty exists. Control of interdependence uncertainty takes on three distinct processes:
Control can be exercised to decrease interdependence uncertainty. An example is making a decision and implementing it.
Control can be exercised by increasing interdependence uncertainty by initiating something new.
Control can be achieved by manipulation in which the ‘‘leader’’ plays off one party against another. Manipulative control is the preference of the weak. Power is not granted by the state. It cannot be delegated. Power is always directly or indirectly interpersonal. It ebbs and flows. Unlike authority, power is not a zero-sum exercise in which the more power one person has, the less power another person has. Usually power is a positive-sum game in which the more power one person has, the more power others can have. Power cannot be inherited; rather, it must be earned. Power is dynamic, whereas authority is static. To keep authority, one needs to keep one’s position. To keep power, one must continually perform. Power is inherently contextual, interpersonal, and processual. Environments The environments of an organization are those processual agents and processes outside the boundaries of the organization with which the organization is either directly or indirectly interdependent. The environments can change independently of the internal processes of an organization. In some cases, an organization can affect its environments. Changes in an organization’s

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environments set into motion internal changes as the organization’s leadership, management, distribution of power, and authority dynamically adapt to actual and anticipated environmental changes. The metaphor of a frog and a frog pond will serve to make this relationship clear. The frog represents the organization and the frog pond represents the environments facing the frog. Any changes in the frog pond can affect the frog. Of course, organizations are not frogs. Unlike frogs, organizations can mutate into other more formidable creatures and begin to affect the frog pond with these new processes. Changes in environments create interdependence uncertainty. As noted previously, power is the control of interdependence uncertainty and leadership is the exercise of power. Leadership and management are closely linked via the 29 LPs described later in this chapter. Hence, leadership, management, and power are intimately linked to the processes of adapting to the organization’s environments. Environmental changes can alter the authority within an organization. Internal changes to make leadership, management, and power coherent and integrated can change the authority within an organization. Hence, all five constructs of LAMPE are interdependent. The description of an organization’s environments can be uncertain. For example, these environments include ‘‘competitors, resources, technology, and economic conditions that influence the organization’’ (Daft, 2000, p. 73). A less direct set of interdependent processual agents also exists: ‘‘These include social, demographic, and economic factors that influence all organizations about equally’’ (Daft, 2000, p. 73). The listing of the many possible subenvironments brings up a tough set of issues. First, not all of these subenvironments can be described objectively, even if one has objective factors such as tax tables, interest rates, and specific governmental regulations. Second, even when the facts are believed to be actual facts, they can be interpreted subjectively. For example, if the prime rate goes up by 150 basis points, what will that increase mean to a company’s pricing system or its ability to handle its debt? Other factors are clearly subjective, especially ones dealing with general social and demographic changes. Surely, the environments perceived by a coward differ from those identified by a brave person. Third, the organization often fails to pay attention to many environmental changes. Fourth, through innovation, mergers, acquisitions, trade associations, patents, and copyrights, an organization can partially enact its environments. Finally, the environments of the organization, while exerting powerful forces on the organization, are themselves subject to change by actions of the organization.

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THE COHERENCE OF LAMPE Members of an organization are concerned about its leadership, its leaders, its distribution of authority, its management, and its exercise of power. Inconsistencies between authority and power are causes for concern in analyzing leadership. This lack of coherence may spill over, contributing to the likelihood that the organization is improperly managed and that its leadership and management are inconsistent. Incongruencies create doubt, doubt stimulates speculation, speculation can result in rumors, and rumors beget other problems that confound the ability of the organization to function properly. There is a good story about the King of Pontus, Mithridates Eupator VI, who lost a battle (and ultimately a war) because of a lack of coherence among the five elements of leadership, authority, management, power, and the environments. The famous Roman general and consul, Pompey, led an army to punish and subdue King Mithridates about 63 B.C. The advancing Roman army trapped Mithridates’s army in a narrow peninsula near the border between the Kingdom of Pontus and Armenia. Mithridates ordered his crack cavalry to dismount and hold the front line because they were his best archers and soldiers. Upon repulsing the Romans, the dismounted cavalry raced behind the lines to get their horses in an attempt to counterattack the Romans. But some other Pontic soldiers, not knowing the cavalry’s intentions, interpreted this sudden change as the beginnings of a rout and slaughter. The Romans also interpreted the change as a retreat. Sensing victory, they raced back to the Pontic defensive line to renew their attack. The confusion, shouting, and uproar created a panic in the Pontic camp. All was lost, and most of the Pontic army was killed or enslaved. King Mithridates could not stem the rout but was forced to flee and continue the fight in Armenia. The integration of his leadership, authority, management, power, and environments all collapsed in a few hours. The coherence of LAMPE is more than theoretical coherence. Theoretical coherence can pave the way for logical coherence. This section proposes a theory to establish a theoretical coherence among the five LAMPE constructs of leadership, authority, management, power, and the environments. When this is followed up with specific LPs working throughout the organization, theoretical coherence becomes logical coherence for an organization. In the long run, what is vital for producing an effective and adaptable organization is operational coherence. When operational coherence occurs, the linkages among leadership, authority, management, and power become transparent as they continually adapt to changes in the environments. The

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usual distinctions among these terms are no longer operational issues or problems. Those who manage can lead. The distribution of authority is congruent with the management. Power is widely distributed and is exercised through leadership. The organization is efficiently adaptive to change. Leadership Practices A leadership practice (LP) is a discrete process of changing the management of an organization and is a controlling process for interdependence uncertainty. LPs are the bridge between the leader’s traits and skills (e.g., communication, interpersonal skills, critical thinking, and decision making) and their impact on how employees and their tasks fit together to produce successes and how equitable financial and personal rewards can produce highly motivated and productive employees. There are 29 LPs derived from the theory of the organizational hologram. These practices are all processes; that is, each LP consists of a set of constituent processes. The 29 LPs identified in this chapter are, frankly, rather arbitrary. Originally, a survey instrument called the Organizational Diagnostic Survey (cf. Mackenzie, 2000c, d) was developed by creating ‘‘knobby’’ items directly from the 12 holonomic processes, 6 desired organizational characteristics, and 20 principles in the theory of the organizational hologram. A knobby item is one whose text describes a process that establishes and defines a causal and functional relationship between the process cause and its outcome. These knobby items evolved over time to settle on a total of 147. Each item is a statement of a process, and the respondent is asked to judge its usage according to a scale of 1 (never), 2 (rarely), 3 (sometimes), 4 (often), and 5 (always). About 1992, these 147 items were separated into a set partition of 77 ‘‘piles’’ called key implementing processes. After years of experience, with further developments in the use of knobby analyses, and accompanied by a growing appreciation for issues of leadership, these 77 key implementing processes were reorganized into the present 29 LPs in 2003. The present grouping is serviceable, but there are no compelling theoretical reasons why these existing 29 LPs should be considered the ‘‘final’’ ones. There are, however, practical reasons for using these 29 LPs:
They make sense in terms of control of interdependence uncertainty, management, and leadership, as shown in Tables 1 and 2.
They combine nicely into the 12 holonomic processes. In fact, each of the holonomic processes is measured by a set of items that is the sum of those within each of the holonomic process’s constituent LPs.

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They are ‘‘small’’ enough processes [a process of processes (each item) is a process] to be implementable. The holonomic processes are aggregations of the LPs and, consequently, more challenging to improve.
They map onto an organizational level learning model (cf. Benoit & Mackenzie, 1995; Mackenzie, 1994, 1995).
They allow the use of linear programming to solve for possible recommendations (cf. Mackenzie, 1995).
They are comprehensive, in that they cover issues involved with dynamic congruency (Mackenzie, 2003).
They have ‘‘face validity’’ for practitioners.
They can be used to explain the variance in employee opinion survey (EOS) items (e.g., ‘‘I’m satisfied with my compensation at ACME, Inc.’’).
Combining linear programming calculations to select those LPs that are most capable of improving the organization’s processes and those LPs that do the best job in explaining EOS items, one can derive serviceable recommendations for actions.
The LPs can be used to ‘‘drill down’’ to provide supplementary analyses of survey data. A drill-down is a technique for exploiting the inherent multiple levels in a process by moving from the more general level to a succession of more specific and lower levels. A ‘‘build-up’’ reverses the analysis.
These properties have been combined into a process (U.S. patent pending) and a web-based technology called HALO to perform assessments of organizations and their LPs. These properties were so useful that the LPs were made integral to the theory of the organizational hologram and occupy a layer in the analytical pyramid. Description of the 29 Leadership Practices As you read the descriptions of these LPs, check them against your understanding and intuitions about leadership. LP01. Understanding Environmental Changes is the strategic process that enables the organization to understand and systematically monitor its external environments. This process ensures that the strategic direction makes sense given the actual organizational environment, and it seeks to assess the impacts of environmental changes. When working throughout the organization, it ensures that the employees understand the potential and real impacts of external changes. LP02. Developing and Using the Strategic Direction is the strategic process by which the organization understands and uses its shared strategic direction.

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This important process assesses the dedication of the organization in keeping the strategic direction current, and it insists that decisions are made based on the strategic direction. It ensures that the mission, goals, and strategies driving it are internally consistent and that new technologies selected for the organization are based on the strategic direction. LP03. Ensuring Unit-Level Strategic Direction is the management process that ensures that employees know, understand, and use their unit-level strategic direction. It also ensures that the unit-level strategic direction remains consistent with the organizational-level direction as it evolves and changes. LP04. Using Strategic Long-Range and Tactical Plans is the strategic process by which the organization establishes and deploys its long-range strategic, tactical, and annual operating plans. It ensures that these important components are used to guide decision making throughout the organization, and that the tactical and annual operating plans are implementable with effort. LP05. Updating Organizational Assumptions is the strategic process of discussing, updating, and acting upon changes to the organizational assumptions as the organization evolves. This process ensures that the consistency of the assumptions (about strategies, the actual environments, the strategic direction, and the basis for organizing) is maintained. The organizational assumptions are the basis of how the organization arranges itself; therefore, they need to be reviewed and updated to maintain a good fit between how the organization is organized and what is needed to keep it succeeding. LP06. Linking the Organizational Rewards to Performance simultaneously links the organizational-level rewards system to individual performance, unit performance, and accomplishment of the organization’s strategic direction. Each employee understands that his or her total compensation is linked to the success of all three. Success at one level is not enough; instead, performance at all three levels must be successful. This process ensures consistency between individual rewards and organizational rewards. It also reassures employees that the organization is committed to linking individual performance with personal career and financial growth. LP07. Updating the Organizational Logic is an administrative process of defining the many task processes in the organization. It ensures that the organizational logic is consistent with the organizing assumptions and that the organizational logic is kept up-to-date. It also ensures that all units and individuals understand their roles in the organizational logic and how they relate to other units and individuals. At the job level, individual job logics are derived from the organizational logic. LP08. Updating the Organizational Architecture is an administrative process to ensure that the organization and unit organizational charts accurately

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mirror how employees actually work together. It ensures that the organizational logic is congruent with the organizational charts and that the organization keeps these charts up-to-date. Additionally, this process seeks to align the strategic direction with the means developed to implement it at the organizational, unit, and individual levels. LP09. Ensuring Consistency of Organizational Rewards is the administrative process that enables the organization to professionally manage its organizational-level rewards systems so that they apply to all employees and are updated as conditions in the environments change. This process supports and is consistent with the organizational architecture. LP10. Ensuring Results Consistency with the Strategic Direction establishes a formal set of procedures and processes to provide for the consistent and current measurement of organizational- and unit-level results. The measurement process, which is updated as conditions change, verifies that the results produced are consistent with both the strategic direction and the organization’s operation. LP11. Ensuring Successful Goal Achievement ensures systematic problem identification throughout the organization. Once a problem is identified, a plan – supported by tough and realistic performance standards – is developed to solve the problem. The plan is crafted to match the organization’s specific situation and is aimed at achieving the strategic direction. The problem-solving process involves deciding what to do and how to do it. It fosters successful goal achievement. Additionally, an accountable manager audits and reviews progress toward the goal and ensures that project plans are implemented. LP12. Ensuring Compatible Interests of Results links performance and rewards among individual employees, the employees’ unit, and the organization as a whole. It recognizes that within the organization, individuals rise and fall together; that is, gain and pain are shared as profits increase and decrease, respectively. As part of this process, each employee’s personal goals within the organization are consistent with achieving the strategic direction. LP13. Using Tough and Realistic Standards is the basis for individual rewards. The standards, which are based on the strategic direction, reinforce unit activities and the work performed by individuals. They ensure development at the employee level by supporting the growth of an employee’s skills as the organization continues to evolve. LP14. Ensuring Job Performance Standards involves setting performance standards that must be attained if the employee is to remain employed and receive average total compensation. A formal set of procedures exists to measure job performance, and the outcome of the performance evaluation affects job rewards.

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LP15. Applying Total Compensation Process ensures that individual job performance, unit success, and organization success all play roles in determining employee compensation. Differences in total compensation are based on actual differences in individual performance. This process ensures a good fit among personal goals, strategies, and commitments, and with the organization’s strategic direction. LP16. Integrating Jobs with the Organization ensures that each employee understands how his or her job logic fits into the larger organizational logic from which it was derived. Furthermore, this process ensures that employees are well suited for the jobs they hold based on their skills, education, training and development, and commitment. The job logic is consistent with achieving the organization’s strategic direction and makes sense given the formal organizational architecture. LP17. Ensuring Compatible Interests strives to achieve harmonious interests as a matter of policy among the organization’s employees, units, customers, and suppliers. The organizational rewards systems and human resources policies foster and support working together to enhance unit and organization success. Employees view these policies as fair, reasonable, and ethical. Furthermore, the policies ensure that individual rewards systems are consistent with the organizational rewards systems. They help employees replace a ‘‘lone wolf’’ mentality and recognize that unit- and organizationlevel success depend on working together. LP18. Developing Employees is the administrative process that encourages employees to go beyond their specific job requirements, to exhibit initiative, and to actively prepare themselves for skills and knowledge beyond their current job specifications. Additionally, this process ensures that employees are involved in making plans at their level within the organization, using their knowledge, the strategic direction, and their roles in implementing the plans. LP19. Aligning Employees with the Strategic Direction ensures that, when employees are assigned jobs or promotions, such assignments are based on achieving the strategic direction. Furthermore, how the organization hires, selects, orients, and trains its employees is determined by the evolving strategic direction. Employees are hired and trained to go beyond their current job assignments, and their personal goals are aligned with the strategic direction. LP20. Encouraging Best Decision Making fosters decision making compatible with the interests of the organization. It ensures that management uses the best decision-making strategies to lead and direct the organization. This important process educates and trains employees about the principles driving the organization. It asks and enables employees to take responsibility for

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making the best decisions, with participation in the process being supported by the organizational rewards systems. LP21. Ensuring Ethical Decision Making encourages employees to do the right thing in their decision making. This important leadership process discourages employees from engaging in behavior that is legal but unethical. It encourages them to adhere to ethical standards. Most importantly, it rewards them for being ethical. LP22. Using Organizational Forums is the management process that establishes and deploys a systematic series of normal meetings throughout the organization to communicate and share information. Forums are arenas for problem solving and for information sharing as a means to conduct and update operations. Forum participants consistently work to improve the quality of the forums. LP23. Ensuring Healthy Problem Solving is a leadership process driven by three considerations. First, the objective is not to pick winners and losers. Second, doing what is right is more important than identifying who is right. Third, leadership ensures that the problem-solving process is not a politically driven process. Healthy problem solving is practiced to resolve conflict. When conflict cannot be resolved at one level, the organization’s conflict resolution process brings the unresolved issues to the next higher level for review and resolution. The objective of the process is to make the best decision. LP24. Ensuring Results-Oriented Problem Solving is a general problemsolving process described by seven characteristics: (1) decisions are made at the lowest possible level; (2) the best available resources are brought to bear on problem solving; (3) these resources are utilized; (4) there is a clear identification of who is the accountable manager and who is in support; (5) problem solving is healthy; (6) rewards for problem solving are clearly defined; and (7) the result is an implementable best decision. In LP24, the organization supports and consistently applies results-oriented problem solving. In addition, the focus of problem solving is to make something happen that will have a positive impact on the organization’s success. LP25. Nurturing and Rewarding Innovation is a problem-solving process for encouraging and rewarding all employees to seek opportunities that benefit the organization and for encouraging innovation. Typically, this process is applied to group projects that cut across internal organizational boundaries. Employees are encouraged to seek opportunities for innovation and to believe that this is their right and responsibility. For such projects, the organization establishes a one-time reward for the project team based on anticipated results, which is distributed according to each employee’s contribution.

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Following successful implementation of the solution, additional rewards, based on actual results, are publicly distributed to members of the team. LP26. Ensuring Quality consists of processes to ensure that the organization’s products and services meet customers’ quality standards. The organization actively supports its products and services to meet customers’ needs; makes every effort to ensure that customers properly use its products and services; avoids providing products and services that fail to meet customer requirements; works to eliminate and/or prevent situations in which employees fail to meet the quality standards set for them; and seeks to improve the quality of its administrative task processes. LP27. Ensuring Improvements in Technology is the forward-looking strategic process of keeping abreast of relevant new developments in process and production technologies and in information systems. It consists of processes to select new technologies that meet customers’ requirements and that improve the organization’s competitive position. LP28. Managing New Technologies is the process of integrating new technologies (process and product technologies and information systems) with changes in the organization’s means and in its applications. The organization listens to problems and searches for opportunities to enforce consistency among its organizational means, its technologies, and their application. In addition, the organization regularly monitors the results produced by the introduction and application of the new technologies. LP29. New Technologies Integration is the planning process in which advances in new process and product technologies and information systems are integrated throughout the organization. In new technologies integration, the development of the organizational means is integrated with how the organization manages its applications and how it applies these new technologies. Furthermore, new applications are integrated with changes in the organizational means and the new technologies. Leadership and Power Power was defined earlier in this chapter as the control of interdependence uncertainty. The organization of interdependence uncertainty was presented in Fig. 1. Six levels of interdependence exist, given here in descending order: immersive, purposive, compound, role, processual play, and task–role. Each of the 29 LPs deals with one of those levels of interdependence. Thus, if the LP is active, it acts to control this source of interdependence. Linking the LPs to the levels of interdependence, therefore, establishes the linkages among leadership and power.

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The allocation of the LPs to the levels of interdependence is provided in Table 1. Each check mark in Table 1 refers to the main effect of the LP in the level of interdependence it controls. However, each of these LPs can be involved in other levels of interdependence. For example, LP22 (using organizational interdependence) can be used to define roles and help manage processual plays. Of course, its main effect is to keep the purposes of the organization front and center in the operation of a unit. Table 1.

Leadership Practices and their Main Effects on the Control of Interdependence Uncertainty.

Leadership Practice

Level of Interdependence Immersive

LP01. Understanding environment changes LP02. Developing and using the strategic direction LP03. Ensuring unit-level strategic direction LP04. Using strategic long-range and tactical plans LP05. Updating organizational assumptions LP06. Linking organizational rewards to performance LP07. Updating the organizational logic LP08. Updating the organizational architecture LP09. Ensuring consistency of organizational rewards LP10. Ensuring results consistency with strategic direction LP11. Ensuring successful goal achievement LP12. Ensuring compatible interests of results LP13. Using tough and realistic standards LP14. Ensuring job performance standards LP15. Applying total compensation process LP16. Integrating job with the organization

Purposive

Compound

Role

O O O O O O O O O O O O O O O O

Processual Plays

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Table 1. (Continued ) Leadership Practice

Level of Interdependence Immersive

LP17. Ensuring compatible interests LP18. Developing employees LP19. Aligning employees with the strategic direction LP20. Encouraging best decision making LP21. Ensuring ethical decision making LP22. Using organizational forums LP23. Ensuring healthy problem solving LP24. Ensuring results-oriented problem solving LP25. Nurturing and rewarding innovation LP26. Ensuring quality LP27. Ensuring improvements in technology LP28. Managing new technologies LP29. New technology integration

Purposive

Compound

Role

Processual Plays

O O O O O O O O O O O O O

Pfeffer and Salancik (cf. Salancik & Pfeffer, 2004; Pfeffer, 1981, 1992) developed a strategic contingencies model for power. According to this model, power accrues to those individuals and departments that successfully cope with critical organizational problems. In doing so, they control major uncertainties and thus have power. Strategic contingencies expose interdependence uncertainty, and the control of strategic contingencies yields power. Such power is a double-edged sword: it can help an organization become and remain more aligned with its environments, or it can be used to ignore problems and thus cause the organization to become and remain misaligned with its environments. Pfeffer (1992) writes about managing with power. He argues that power is needed to get something accomplished and that the process of managing involves varying interests and issues, gaining and using power, and fully understanding the internal workings of the organization’s interdependencies. Timing, tasks, use of structure, commitments, and interpersonal influence are all part of the process of managing with power. Leadership is facilitated by power. Salancik and Pfeffer go on to argue that ‘‘because power derives from activities rather than individuals, an individual’s or

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subgroup’s power is never absolute and derives ultimately from the context of the situation’’ (Salancik & Pfeffer, 2004, p. 373). One lever that leaders can push is the use of rewards and sanctions. Power and influence can be enhanced though the exchange of incentives for cooperation. This relationship has led to transactional models of leadership, which extend calculative views of power and influence because it involves the exchange of goods and services or processes. For instance, Cohen and Bradford (2004, p. 388) provide a table of ‘‘commonly traded organizational currencies’’ such as inspiration-, task-, position-, relationship-, and personal-related ‘‘currencies.’’ However, it is not the reward or sanction per se that is important. Rather, it is the control of the uncertainty related to the distribution or application of such incentives. For example, an employee who desires a promotion and knows that her boss must promote her regardless of what he thinks about her performance is in a situation where the boss has little power. By contrast, if her promotion depends on the decision of the boss and this decision is based on performance, then the boss has some control over the interdependence uncertainty of the employee’s engagement in improving performance. This discussion leads to the first proposition of this chapter: Proposition 1. Leadership is the attempt to exercise power in a group or organization. To exercise power is to try to control it. Three main types of control are possible: 1. Reduction of interdependence uncertainty by enabling change processes. 2. Creation of interdependence uncertainty by initiating and implementing change processes. 3. Adjustment and adaptation of existing interdependence uncertainty by sustaining change processes. When this exercise is successful, the leader has exerted leadership. When it is not successful, the leader has failed to exert leadership. Power and Authority Power and authority were defined in the second part of this chapter. Power is active and is defined as the control of interdependence uncertainty. Authority is the legal right to preempt the outcome of a decision or a process. Power and authority, although related, are not the same. Fig. 4 illustrates some combinations of high and low authority and power. As shown in the

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High

Lame Duck

King of the Hill

Low

Organizational Gnome

Succeeding Insurgent

Authority

Low

High Power

Fig. 4.

Relationships between Authority and Power.

figure, one can have inconsistent or consistent amounts of power and authority. This part of LAMPE is coherent when the degree of power and authority match each other (low, low or high, high). Keep in mind that power is inherently processual, shifting, contextual, situational, and structural. It cannot be delegated, often remains invisible to the actors, and is always a property of interpersonal relationships. An individual can have authority without necessarily being involved in some interpersonal relationship. A physics metaphor is apt: power is like kinetic energy; authority is like potential energy. The relationship between power and authority involves their dynamic interdependencies. Authority can be used to get more authority and power. Power can be used to get authority and more power. Power and authority can spiral upward together or they can reverse into a nose dive. Power requires action. Failure and success tend to unravel or enhance power more quickly than authority because changes in authority require others to act. One can gain and lose authority independent of one’s behaviors. Although authority is a legal right to preempt the outcome of a decision or a process, this legal right requires enforcement and/or the threat of enforcement to be effective. The failure to back up a decision made with legal authority weakens the authority and the power of the person making the decision. Failures of a personal nature, such as becoming involved in a politically incorrect scandal or panicking in a crisis, can lead to a loss of authority and thus a loss of power. Unsuccessful initiatives or recurring failure to deal effectively with change will reduce an individual’s power and, eventually, that person’ authority. Dynamically, both power and authority are interdependent and linked. For LAMPE to be logically coherent, authority and power should be matched throughout the organization. To remain coherent, shifts in either power or authority require adjustments.

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Authority and Management Authority, as the legal right to make a decision or preempt the outcome of a process, is vested in various positions throughout the organization. In principle, managers should have a legal basis for their authority. Of course, some may overreach and try to manage outside their authority. Others may be too timid to exercise the authority they have because they fear risking their authority if adverse consequences ensue. When management and authority are coherent, the distribution of authority matches the distribution of management processes to the positions. When the processes of management are backed by the parallel distribution of authority, then decisions are legitimate, proper, and consistent. Both authority and management change. However, they do not change in the same way, for the same reasons, or at the same rate. Authority changes when there is a new controlling regulation, a binding legal decision, a decision by the board of directors, or delegation or recentralization of tasks. Keep in mind that authority can change without being noticed by the organization. Such changes tend to be ‘‘lumpy’’ in that they occur in bursts and can be huge or tiny. However, from a process point of view, management is continually changing as the organization operates. The authority–role system describes who is supposed to be doing what with whom. The authority–role system is the OWS. Management deals with the actual-role system of who is actually doing what with whom. Because of change, these systems are usually inconsistent, resulting in what Lippitt and Mackenzie (1976) referred to as authority–task gaps. To achieve coherence between authority and management, the challenge is to reduce authority–task gaps as they occur or to prevent them from becoming authority task problems. This endeavor can be difficult because of the legal notion of authority. For example, parties outside the organization, such as shareholders and regulators, can subject any organization to abrupt and sometimes far-reaching authority changes. The best way to manage these authority changes is to anticipate them whenever possible and, when they occur, to adapt management swiftly via the 29 LPs. Management and Leadership Management was defined earlier in this chapter in terms of four task processes – planning, directing, controlling, and coordinating (DCC) – and handling process maladaptations. Process maladaptations encompass excessive and unnecessary interdependence uncertainty. They include both overconnected and underconnected tasks and structures as well as redundant and

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untimely task processes. Handling process maladaptations refers to proactively searching for, removing, and preventing them. Four main leadership processes have been identified: initiating, enabling, implementing, and sustaining change. Table 2 shows these leadership processes as rows, and the three groupings of management processes as columns. The entries are the LPs corresponding to pairs of leadership and management processes. Table 2.

Relationships among Leadership and Management Processes for the 29 Leadership Practices.

Leadership Process

Management Processes Planning

Initiating change

Enabling change

Implementing change

Sustaining change

LP11. Ensuring successful goal achievement LP25. Nurturing and rewarding innovation LP24. Ensuring results-oriented problem solving LP28. Managing new technologies LP29. New technology integration

LP01. Understanding environment changes LP02. Developing and using the strategic direction

DCC

Handling Process Maladaptations

LP27. Ensuring improvements in technology

LP05. Updating organizational assumptions

LP19. Aligning employees with the strategic direction LP20. Encouraging best decision making LP22. Using organizational forums LP04. Using strategic long-range and tactical plans LP06. Linking organizational rewards to performance LP10. Ensuring results consistency with strategic direction LP07. Updating the organizational logic LP08. Updating the organizational architecture LP09. Ensuring consistency of organizational rewards LP12. Ensuring compatible interests of results LP13. Using tough and realistic standards LP15. Applying total compensation process LP16. Integrating job with the organization

LP18. Developing employees LP21. Ensuring ethical decision making LP03. Ensuring unitlevel strategic direction

LP14. Ensuring job performance standards LP17. Ensuring compatible interests LP23. Ensuring healthy problem solving LP26. Ensuring quality

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Leadership and management appear closely related when viewed in terms of their processes. By comparison, the relationship between leadership and management becomes unclear when we use the more traditional, static notion of management. Note that leadership and management, when viewed as processes, are intimately linked and theoretically coherent using the 29 LPs. This view is in apparent conflict with the solid work of Hunt (1991), in which he differentiates leadership and management processes. Perhaps this contradiction, although traditional in the leadership literature, is more imaginary than real when we view management and leadership as processes. The distinctions drawn between management and leadership may reflect different emphases on which portions of Table 2 are included in the analysis.

INTEGRATING LAMPE The thesis presented in this chapter is that an organization works best when there is coherence among its processes of leadership, its distribution of authority, its processes of management, the exercise of power throughout the organization, and the proactive adaptation to changes in its environments. Coherence is both theoretically and logically possible. There is an inner coherence between leadership and management if one views each construct in terms of its processes and links via the 29 LPs. There is a strong theoretical and logical link between leadership and power, as both are related to the control of interdependence uncertainty as seen in Proposition 1, which states that leadership is the attempt to exercise power in a group or an organization. The internal wildcard centers on authority, which is a less naturally occurring group process, unlike leadership, management, and power. Authority can be changed by outsiders, and these changes can occur independently of the organization’s internal interdependencies. The achievement of coherence in LAMPE depends on the ability of the group or organization to adapt to such exogenous changes. The internal changes in authority can be used to mirror the de facto solution of who actually leads and manages the group or organization. That is, authority is granted to those who have already gained control of the relevant interdependence uncertainty. There is also the external wildcard – namely, changes in the organization’s environments that are not fully under its control. Sudden shifts in technologies, competitive position, supplier support, or other factors will set change processes into motion. These changes require adjustments to ensure coherence in LAMPE.

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Another problem is that, operationally, LAMPE coherence may not exist even though it could in principle. Achieving LAMPE coherence requires effort, discipline, and consistent purpose. Thus there is a need to understand the conditions under which LAMPE could be coherent and the advantages of integrating LAMPE into a state of coherence. This section reaches two conclusions: (1) in many cases, leadership is not appropriate and (2) distributed leadership works better than concentrated leadership in a ‘‘highvelocity environment’’ or in a dynamically complex milieu. When Leadership Is Appropriate Despite the abundance of books, articles, and videos on the value of leadership and the need for leadership, probably the dominant feature of groups and organizations is that most people, most of the time, decline to exercise leadership. For example, people fail to take initiative or to speak up at meetings, even when they have relevant experience and expertise. People in an ‘‘audience’’ role, as in a pew at a church service, rarely get up and take over the services. Persons in subordinate roles may decline to show leadership because of fear or good common sense. Sometimes, persons believe that speaking up, acting out, and trying to change something is a waste of their time because they believe that when all is said and done, there will be no significant change. As a consequence, a major problem in LAMPE is to explain the appropriate response to a leadership issue. Chester Barnard wrote: The practical difficulties in the operation of organizations seldom lie in the excessive desire of individuals to assume responsibility for the organization actions of themselves or others, but rather lie in the reluctance to take responsibility for their actions in organizations (Barnard, 1938, pp. 170–171).

A leader’s behavior is appropriate if it serves to control interdependence uncertainty. A leader’s behavior is inappropriate if it (1) is unnecessary to control interdependence uncertainty or (2) serves to reduce control of interdependence uncertainty. Appropriate leadership issues include (but are not limited to) the following:
Events or situations requiring handling of possible or actual process maladaptations.
Events or situations that require a reduction in interdependence uncertainty to achieve or maintain the strategic direction.
Events or situations that call for a temporary increase in interdependence uncertainty to initiate and implement needed change.

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Events or situations that call for needed adjustments and adaptations by engaging in enabling and sustaining change processes. A leadership issue is an event or the recognition of a situation in which leadership is appropriate to resolve it. Not every leadership issue is accompanied by leadership, and sometimes leadership is provided when it is unnecessary, as shown in Fig. 5. There are two cases when the leader’s behavior is appropriate: (1) provide leadership when there is a leadership issue and when leadership is recommended and (2) do not provide it when there is not an issue and it is not recommended. The two conditions where leadership is inappropriate are (1) providing leadership when it is not recommended and (2) failing to provide leadership when there is a leadership issue. The decision about the appropriateness of leadership involves an understanding of the organization’s propensity to change. Another typology is illustrated in Fig. 6 for the judgment of the organization’s propensity for change. Fig. 6 includes three responses: (1) the organization will embrace change; (2) there will be a problematic response to change; and (3) the organization will avoid change. The dimensions are (1) the propensity for change by the accountable manager and (2) the propensity for change by others affected by the change. Note that the judgments are change-event specific. An organization may embrace some changes, such as acquiring new IT equipment, and avoid other changes, such as implementing a new system for incentive compensation. The next question then arises: Is leadership recommended? For simplicity, assume that the answer is either yes or no. Fig. 7 presents a process framework for making this choice. The right-hand side of Fig. 7 is either (1) yes, leadership is recommended or (2) no, leadership is not recommended.

Yes Is Leadership Recommended? (from Figure 7) No

Inappropriate Response (Unnecessary)

Appropriate Response

Appropriate Response

Inappropriate Response (Avoidance)

No

Yes

Does a Leadership Issue Exist?

Fig. 5.

Typology for Appropriate/Inappropriate Leader Behavior.

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Embrace Propensity for Change by Others Affected by the Change

Avoid

Problematic Response to Change

Organization Embraces Change

Organization Avoids Change

Problematic Response to Change

Avoid

Embrace

Propensity for Change by the Accountable Manager

Fig. 6.

Determining the Organization’s Propensity for Change.

Fig. 5 provides a typology for the appropriateness of leadership being provided. This appropriateness depends in part on the organization’s propensity for change (Fig. 6) and the recommendation to provide or not provide leadership (Fig. 7). These models show that providing leadership is appropriate in some cases and inappropriate in other cases. Note that Figs. 5–7 constitute another multi-level analysis. Change Events and Windows A change event is an event that initiates a change in the tasks and structures of a group or an organization. Every change event involves combinations of changes in the participants (the rows), the tasks (the columns), and the entries (tij) in a task–role matrix (cf. Fig. 2). The change event participants in a change event are those persons or units that would be affected by the change event, including their direct and indirect subordinates. The change event tasks in a change event are those tasks affected by the change event, including all of their subtasks, down to the lowest level of aggregation. Thus, for every change event, there is a set of change event participants and tasks. These two sets form a rectangle in the task–role matrix called a window. A window is illustrated in Fig. 8. A window is any rectangular section of a task–role matrix defined by a set of entities (the set of rows) and the set of tasks (the set of columns). A change event window is the portion of a task–role matrix defined by those entities and those tasks involved in the change. A change event involves at least one processual agent and one task. The supervisor’s window is the window defined by the supervisor and his or her direct reports as rows and the tasks as columns.

Are These Risks Unavoidable? Do These Changes Pose a Significant Risk to the Processual Agent?

Y

Embrace

Y

Yes N

Y Do These Changes Present a Leadership Opportunity?

N

Organization’s Propensity for Change (cf. Fig. 6)

Problematic Yes

N

Y

Avoid No

Does the Processual Agent Perceive an Emergency Requiring Change?

No N

Embrace Yes

Organization’s Propensity for Change (cf. Fig. 6)

Y Do These Changes Present a Leadership Opportunity?

Problematic

No

Avoid

The LAMPE Theory of Organizational Leadership

128 Yes

No N No Y Yes

Do These Changes Present a Leadership Opportunity? Does the Processual Agent Believe That an Emergency Is Likely to Arise?

No Y

N

N No

395

Fig. 7. Determining Leadership Recommendations.

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KENNETH D. MACKENZIE Tasks

Entities Involved in the Tasks

x1 x2 x3 x4 x5 . xi . . . xn

1 r11 r21 r31 r41 r51 . ri1 . . . rn1

2 r12 r22 r32 r42 r52 . ri2 . . . rn2

3 r13 r23 r33 r43 r53 . ri3 . . . rn3

4 r14 r24 r34 r44 r54 . ri4 . . . rn4

5 r15 r25 r35 r45 r55 . ri5 . . . rn5

6 r16 r26 r36 r46 r56 . ri6 . . . rn6

7 r17 r27 r37 r47 r57 . ri7 . . . rn7

8 r18 r28 r38 r48 r58 . ri8 . . . rn8

9 r19 r29 r39 . . . ri9 . . . rn9

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

j r1j r2j r3j . . . rij . . . rnj

. . . . . . . . . . . .

. . . . . . . . . . . .

. . . . . . . . . . . .

m r1m r2m r3m . . . rim . . . rnm

The window defined by x2, x3, x4 and x5 and tasks 3, 4, 5, 6, 7, and 6 has a size of 4 × 6 = 24

Fig. 8.

A Window.

One important technical point about windows: because almost all organizations are unclear about their task–role matrices, and because the mechanics of establishing them has left many individuals and tasks scattered throughout the task–role matrix, the window may look more like a gerrymandered Texas congressional political district than a rectangle. Such matrices can be altered by interchanging their rows and columns. In particular, the sets of participants and their tasks can be rearranged to form a window that looks rectangular. Lacking an organizational task–role matrix, any person in the organization can construct one just for his or her unit. Both contained and uncontained change events are possible. A change event is contained if its window is a proper subset of the supervisor’s window. It is partially contained if there is an overlap but not inclusion between the supervisor’s window and the change event. Two special (and interesting) cases of uncontained change events may be noted. The first occurs when the change event’s window includes that of the supervisor. In this case, the supervisor is directly involved in the change event but the change event encompasses people and tasks that do not belong in the supervisor’s window. The second uncontained change event is an independent change event in which the change event and the supervisor’s window have no intersection. These four cases of change events are shown in Fig. 9. Structurally, uncontained change creates untimely and redundant behaviors and sets up cousin (lateral) and uncle/nephew relationships (vertical but outside the supervisor–subordinate hierarchy), thereby reducing the process-based measure for the degree of hierarchy (and thus the efficiency) in a group or organization (cf. Mackenzie, 1976a, pp. 98–118). These process and structural effects may require a redesign of the organization to

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Tasks Supervisor’s Window

Uncontained Change Event

Supervisor’s Window

Supervisor’s Window

xn Supervisor’s Window

Contained Change Event

(a)

(b)

Fig. 9.

Independent Change Event

Partially Contained Change Event

(c)

(d)

Contained and Uncontained Change Events.

remedy them. Uncontained task change will require changes in the organizational logic. The organizational logic shows the structures of the organization’s many tasks (cf. Mackenzie, 1986a, 2004a). Because there is a hierarchy of aggregation and a hierarchy of types of process frameworks, organizational logics are multi-level. Changes in the organizational logic usually precede changes in the organizational design; otherwise, the organization’s functions (the organizational logic) will be incongruent with the organizational form (the organizational chart). The size of structural change, N, is the number of entities included in the change event window’s rows. The size of the task change, M, is the number of tasks included in the change event window’s columns. Consequently, the size of a change event, S, is the product of these two measures: S ¼ NM The size of a change event is usually ill defined at the beginning of the change process because it is unclear which people and tasks will be involved. Some change events are resolved swiftly; others can linger for years. For example, the reorganization of intelligence services and security agencies in the U.S. government, forming the Department of Homeland Security, is still evolving years after the precipitating event (i.e., the terrorist attacks of September 11, 2001). In such cases, the change event seems to metastasize into something unexpected.

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The basic case is a ripple effects change – that is, a change event creates more structural changes and task changes, which in turn create even more structural and task changes in a series of iterations, which continue until these changes cease to create more change. A ripple effects change refers to the sequential changes that ensue after a specific change event. Containing a change event minimizes its possible ripple effects. Marker’s (2002) process model of encroaching processes can be adapted to analyze ripple effect changes. If the successive iterations in a ripple effect change become progressively smaller, then the size of a ripple effect change will approach a limited asymptotic value. This would be the case for a ripple effect stopping. Ripple effects can be stopped by decisive action. In some instances, these effects can be halted in two ways: by having some agreed-upon organizational sanctuaries that are removed from the change event by agreement among the processual agents or by setting up a series of firewalls to contain the ripple effects. White, Hodgson, and Crainer (1996) introduced the evocative notion of white-water leadership to explain this phenomenon. They argued that white-water leadership is becoming more likely. To provide it, they suggested, one needs to embrace the necessity of coping with interdependence uncertainty. Before speaking of white-water leadership, it is necessary to define whitewater changes, because one of the objectives of stopping the effects of ripple event changes is preventing these changes. White-water changes are concurrent and sequential multiple change events (1) whose ripple effects have interdependent structural and task changes and (2) whose sizes of structural and task changes become additive and whose sizes of change events become multiplicative as a result of escalating interdependence created by interdependent ripple effects.

Leadership Requirements Matching A great deal of skill is required to achieve a dynamic balance between the leadership available and provided in an organization and the leadership required by the organization. Due to the presence of change, this balancing act is ongoing and adaptive. One key feature of the LAMPE theory of organizational leadership is leadership requirements matching (LRM). The magnitude of required leadership is the size of the change event window required to contain it. If N members are involved and they must complete M tasks, then the magnitude of required leadership is NM. Contained changes lie within the supervisor’s window, so it is less likely that these

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contained changes will generate ripple change effects. This leads to Proposition 2: Proposition 2. The magnitude of required leadership is minimal in the case of contained changes in a pure hierarchy. In a pure hierarchy, the structures and the processes are coherent. Because there are no process or structural maladaptations, the magnitude of the change is within the window of some superior in the hierarchy. A pure hierarchy has the least number of links among entities and tasks, which reduces both N and M. Hence, leadership is restricted to managing the change within the involved units, which reduces the likelihood of ripple change effects. Reducing ripple change effects, in turn, reduces the likelihood of white-water change events. This leads to Proposition 3: Proposition 3. The magnitude of required leadership is greater for uncontained than it is for contained change events. Contained change events lie within the purview of a supervisor’s window and have a size that is less than or equal to NM. Uncontained change events do not fall within the purview of a supervisor’s window, so their size is greater than the supervisor’s window, NM. Therefore, the magnitude of required leadership is greater for uncontained change events than for contained change events. Proposition 4. The magnitude of required leadership for white-water change exceeds that for a ripple event change. A white-water change event consists of simultaneous and sequentially occurring ripple change events. Ripple change events are uncontained and involve the expansion of the members involved and the tasks involved. As more ripple events occur, the size of N and M continues to increase. Eventually, unless resolved, white-water change events can metastasize to encompass all members and all tasks, thereby reaching the largest maximum change event. In cases where white-water changes manage to become contained within some set of supervisors and for a set of tasks, the resulting size is always larger than the original change event. It is also larger than the union of the ripple change events. As a consequence, the magnitude of required leadership is always larger in the case of white-water changes than it is for its constituent ripple event changes. Proposition 5. The magnitude of required leadership is greater for ripple event changes than it is for contained change events.

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This is a direct conclusion from the definition of a ripple change event and Proposition 2. Leadership requirements-matching occurs when the leadership provided matches the leadership required. LRM is dynamic. It can be selective, in that it can occur for some tasks while failing in others, and for some groups but not others. Consequently, it is important to distinguish between LRM for a specific leader and LRM for the entire organization. Organizational LRM occurs when, throughout the organization, the leadership provided matches the leadership required. Later in this chapter, it is argued that the achievement of organizational LRM occurs whenever the 29 LPs are working throughout the organization. The leadership provided is defined as the window of the supervisor’s effort to handle a change event. Deficient leadership occurs when the leadership provided is less than the leadership required. Such leadership is a symptom and a consequence of an inadequate organizational design. Deficient leadership arises when the change event is not contained within the supervisor’s change event window. Conversely, excess leadership arises when the leadership provided exceeds that which is required. This type of leadership spreads the change event into something larger than was required. Excess leadership involves a leader who seizes upon the opportunity provided by a change event to enlarge his or her supervisor’s window. It also occurs when the change event is made larger than required so that it can be applied to the entire supervisor’s window. In this circumstance, change is made for change’s sake, regardless of the requirements. In short, excess leadership includes provided leadership when it is not needed. (In everyday language, a person who repeatedly engages in excess leadership is a jerk.) Fig. 10 defines an LRM typology. Note that LRM occurs in two cases: Leadership is neither required nor provided, and leadership is both required and provided. The other two cases are deficient

Yes

Deficient Leadership

No

LRM

Leadership Required?

LRM

Excess Leadership

No Yes Leadership Provided?

Fig. 10.

A Typology of Leadership Requirements Matching.

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leadership (leadership is not provided when it is needed) and excess leadership (leadership is provided but not needed). Proposition 6. An organization controls the interdependence uncertainty when it has organizational LRM. Leadership is the exercise of control of interdependence uncertainty. If organizational LRM is present, then throughout the organization, the leadership provided matches the leadership required. Consequently, the requisite leadership is being supplied to control the interdependence uncertainty as it is occurring. Proposition 7. When there is organizational LRM, the organization has the power to handle its change events. Change events create interdependence uncertainty. Power is the control of interdependence uncertainty. By Proposition 6, if organizational LRM is present, then the organization controls its interdependence uncertainty. Therefore, when it practices organizational LRM, the organization has the power to handle its change events. Leadership Effectiveness One of the recurring goals in the leadership literature is that an effective leader will cause the achievement of the criterion of an effective organization. The hope is that the emergence of a more effective leader will necessarily improve the effectiveness of the group or organization. Plenty of anecdotal stories describe leaders who were judged to be great because they were seen as the catalysts and causes of more effective organizations. Of course, the usual circularity issue occurs here to confound cause and effects. Leadership effectiveness of an organization is often determined by the criterion of the degree to which the organization reaches some stated goal. Such criteria create logical problems because (1) there are usually many criteria because of different goals, (2) not all share the same goals (of whose goals are we speaking?), and (3) whatever the goal, it can be reached or not reached for all sorts of reasons outside of the leader’s control. In other words, a CEOs rating as a leader may be achieved in spite of the CEOs efforts. The approach taken in this chapter is to concentrate on leadership effectiveness as an internal measure rather than on organizational effectiveness, as the latter is usually based on a measure of the organization’s outputs, such as profits, ROI, or sales growth. Furthermore, the emphasis here is placed on the extent to which the organization’s leadership is

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improving and deploying throughout the whole organization. Accordingly, leadership effectiveness will depend on leadership quality. The leadership quality of an organization is the average LP score taken from the HALO survey. The HALO survey is a web-based tool for assessing the 29 LPs in the organization (cf. Mackenzie, 2000c, d). One of the features in HALO is the capacity to measure each of the 29 LPs based on the judgments of members of an organization and then to use linear programming to solve for the potential improvement value of each of the LPs. The maximum improvement in the jth LP is Dxj and the result of the improvement is xj*, where xj ¼ x0j þ Dxj

(4)

In this expression, xj0 is the average score on the jth LP by the members of the organization taking the HALO survey. The value of xj0 ranges from 1 to 5. The idea of calculating possible improvement values is to incorporate, as constraints, the limits of possible short-term improvements and to estimate potential improvements in the organization by improving each of the LPs. This procedure allows realistic performance expectations for the effects of interventions designed to improve the LPs. The maximum leadership quality, denoted Q*(L), is given by Q ðLÞ ¼

29 x X j i¼1

29

(5)

Eq. (5) states that the maximum quality of leadership is the average of the maximum LP value after making improvements in the 29 LPs for that organization at the time the study was made. The leadership effectiveness, L(E), of an organization is the relative improvement in its leadership quality. Let Qt+Dt(L) and Qt(L) be the leadership quality at times t+Dt and t, respectively. LðEÞ ¼

QtþDt ðLÞ  Qt ðLÞ Qt ðLÞ  Qt ðLÞ

(6)

The denominator in the expression for L(E) is the maximum possible improvement, and the numerator is the actual improvement after a time period of length Dt, so 0 p L(E) p 1. Having defined the measures of leadership effectiveness based on the relative improvement of the quality of the organization’s leadership, it is now possible to examine organizational effectiveness.

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Organizational effectiveness, O(E), is the relative improvement in a criterion measuring performance of an organization with respect to a specified time period. Let Mt+Dt(O) and Mt(O) be the measure of the performance of an organization at times t+Dt and t, respectively. Let O(E) be the measure of organizational effectiveness. Then OðEÞ ¼

M tþDt ðOÞ  M t ðOÞ M t ðOÞ

(7)

An important hypothesis is that organizational effectiveness tends to improve when the organization’s leadership quality improves. This hypothesis can be tested using longitudinal data. Note that there is an infinite number of possible organizational effectiveness measures. The variety of measures of organizational performance means that the hypothesis is surely true in some cases and not true for others. Consequently, this chapter sets aside organizational effectiveness issues. Nevertheless, it would be interesting to ‘‘test’’ the hypothesis, as illustrated in Fig. 11. Proposition 8. As the quality of leadership increases, its interdependence uncertainty become more manageable. As the LPs improve, so does the quality of leadership measure. As the average LP increases, so do the leadership processes and the management processes, as shown in Table 2. Finally, as the LPs improve, so does the control of interdependence uncertainty shown in Table 1. This makes the interdependence uncertainty more manageable.

Organizational Effectiveness, O(E)

1.0

(?)

0 1.0

Fig. 11.

2.0 3.0 4.0 Leadership Quality, Q(L)

5.0

The Hypothesis that Leadership Quality Causes Organizational Effectiveness.

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Proposition 9. As organizational LRM improves, so does the quality of leadership. Proposition 9 follows from Proposition 8. When there is organizational LRM, the organization controls its interdependence uncertainty. As the interdependence uncertainty becomes better controlled, the LPs improve. As the LPs improve, by Eq. (5), Q*(L) improves. Proposition 10. As the organizational leadership approaches organizational LRM, the quality of leadership approaches its maximum value. The maximum value of each LP and Q(L), which is the average of the LPs, is 5.0. As each LP approaches 5.0, the criteria for the processes of both management (see Table 2) and leadership (see Table 1) are met. As a result, there is a logical match between the leadership required and the leadership provided. Hence, as the organizational leadership approaches the maximum, the LPs approach 5.0, and Q*(L) approaches 5.0. Therefore, as the organizational leadership approaches LRM, Q*(L) approaches its maximum value. (In most cases, the maximum value of Q*(L) is less than 5.0.) As previously noted, there is a downside to exerting leadership in the absence of a leadership issue – namely, excess leadership. One way for a leader to avoid excess leadership is to act on only those issues for which leadership would be appropriate. Another is to have the personal discipline not to engage in inappropriate leadership even when doing so is possible. This leads to Proposition 11: Proposition 11. Leadership in the absence of a leadership issue may result in a loss of power. Leadership in the absence of a leadership issue is clearly excess leadership. Initiating change where none is required provokes reactions and defensive behavior that can create ripple event changes. As these uncontained changes occur, the leader begins losing his or her control over the interdependence uncertainty, which represents a loss of power. One way to avoid the loss of power is to establish priorities for problem finding or selecting the issue on which to exert leadership. The priority of a leadership issue depends on two factors: (1) the potential control of interdependence uncertainty if leadership is applied and (2) the likelihood that this leadership would be effective. These two factors are multiplicative, such that the priority represents a balance between risk (the likelihood of interdependence uncertainty) and reward (control of interdependence uncertainty). Setting priorities is critical for effective leadership because only so

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much time is available to contain change events, some leadership issues are more important than others, and wasting time by engaging in excess leadership can result in a loss of power. Clearly, not all leadership issues have the same priority. There is also the issue of timing: Proposition 12. (timing proposition). Leadership takes time and requires good timing to be effective. All processes take time. Leadership is a process of processes. Eq. (3) provides the criterion for good timing: TAoTC, where TA represents the time taken for a leader to recognize, formulate, solve, and implement leadership on an issue and TC represent the time period for which action is appropriate. Any leadership for which TA4TC will be inappropriate and, therefore, ineffective. Failure to satisfy the TAoTC criterion can occur if the leader is too slow in paying attention to the leadership issue; dithers on deciding what actions to take; employs methods that are too slow; is too risk adverse; or pursues methods that create more leadership issues than they resolve. Thus effective leadership requires good timing (defined as TAoTC). Proposition 13. Effective leadership requires the discipline of setting priorities for handling leadership issues. High-priority leadership issues involve potential control of major interdependence uncertainty. There is a limited amount of time for analysis. Given the need to satisfy Eq. (3), TA is limited to being less than TC. If there is not enough time (TA4TC), then the leadership will not be effective. By setting priorities such that both the risk and the reward are considered, the discipline involved in identifying priorities creates an LRM match on more leadership issues. By Proposition 10, as LRM improves, so does the quality of leadership. As the quality of leadership improves, L(E) improves, because the numerator in Eq. (6) becomes larger. Consequently, setting priorities improves leadership effectiveness. Of course, for any situation, the personal characteristics of the leader affect the leadership effectiveness of any selected action, because leaders vary in their TA values. This chapter does not explore the important issue of individual differences or factors influencing the values of TA and TC. A dynamically complex milieu is a situation that entails high levels of interdependence uncertainty, organizational vulnerability, and risk. It has five characteristics: 1. Many different stakeholders have vital interests and the potential for enforcement.

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2. Interdependence uncertainty is high for the development, production, and marketing of new products and services. 3. The organization has only partial control over the interdependence uncertainty involved in new products and services. 4. The major decisions of the organization have multiple aspects, each of which must be factored into making decisions. 5. The major decisions of the organization involve a concatenation of subordinate decisions, each of which affects the interdependence uncertainty (Mackenzie, 1991, p. 188). Proposition 14. In a dynamically complex milieu, effective management always involves leadership, and effective leadership always involves effective management. Under this chapter’s definition of management and the links of the 29 LPs to management (Table 2), effective management requires effective leadership. In turn, effective leadership (high LPs) involves the management processes of planning, DCC, and handling process maladaptations. Leadership Capacity A person’s capacity for leadership has limits. The word capacity refers to the reach of the leader in terms of the size of the window and the proportion of the time available for the leader to handle his or her leadership requirements. In this usage, capacity does not refer to the individual’s talent, personality, or energy level. Instead, it means capacity in the sense of the capacity of a container. A supervisor’s leadership capacity, CL, is C L ¼ aL N L M L

(8)

where aL is the proportion of time available for the supervisor to handle the leadership requirements, NL the number of participants under the leader, and ML the number of tasks for which the leader is responsible. The product, NLML, the size of the supervisor’s window. Managing performance and the changes in the management of organizational tasks eventually define the execution of the organization’s task processes. In reality, these management processes are often separated from the actual execution task processes. Leadership does not involve the execution-level task processes. Rather, it focuses on the management tasks and those dealing with change in the management of the organization.

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The estimation of the size of a change event is always problematic because of possible ripple and white-water changes, unintended consequences, and so on. It is probably prudent to assume that the actual change window will be larger than the one initially suggested by a change event. Proposition 15. The larger the change event, the greater the leadership requirements to contain it. Larger changes have larger change event windows and must involve more supervisors if they are to be contained. If the change event is not contained, it will evolve into ripple change effects and possibly into white-water changes (Propositions 4 and 5). In either case, the containing supervisor’s window will be larger than the change event window. The larger the change event window, the larger the required supervisor’s window. Furthermore, the larger the change, the more time it will take to contain. Given the definition of leadership capacity, CL, embodied in Eq. (8), if both aL and NLML increase, then CL increases. The next issue is: How does leadership capacity affect leadership requirements matching or LRM? There are three basic strategies to achieve LRM: S1. Disconnect the top-level administrative elite from day-to-day operations. S2. Have the leader and his or her personal staff perform the planning and handling of process maladaptations for the organization. S3. Distribute these leadership processes throughout the organization. Strategy S1, which calls for uncoupling the administrative elite, the administration and maintenance services, and the productive processes of an organization, can reduce the change event size. S1 is not uncommon in universities and government organizations. For example, the author had a dean of the school of business for five years. He never met with the dean to discuss his research, teaching, and administration. The dean appeared obsessed with raising funds and was very busy handling ‘‘stuff.’’ Nor did the author ever met with the chancellor, executive vice chancellor, or provost. What he did – as long as he created no serious problems, demanded no additional funding, or was not involved with the dean on any of the many committees and teams – was left up to him. He was an isolate, virtually uncoupled from the administrative elite. He went about his research and teaching mostly independently of those who were there to ‘‘manage’’ him. This strategy of uncoupling is apparently widespread in academia; it seems to work rather well in reducing white-water change and limits the

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change event window by localizing it. The loose coupling prevents change events from growing by creating de facto sanctuaries. Of course, these sanctuaries also limit the flow of information to the downward flow of policies and the upward flow of completed forms. This constraint reduces organizational learning and is ultimately maladaptive. Strategy S1 to achieve LRM is considered the default strategy. Traditional approaches to management hold, in principle, that DCC and execution processes should be integrated and distributed throughout an organization by means of the division of labor and the hierarchy of authority. In fact, the main role of the DCC processes is seen as being to ensure the proper functioning of the execution processes, which constitute the majority of processes in going concerns (cf. Mackenzie & Benoit, 1997). Strategy S2 (the leader and his or her personal staff perform the planning, DCC, and handling of process maladaptations) is chosen for managing routine internal change and for handling imposed changes. When an organization assumes strategy S2, it makes the assumption that the status quo has sufficient capacity to achieve LRM. In a traditional organization, it is assumed that a sufficient number of supervisors are available to direct, control, and coordinate the execution processes and to handle process maladaptations as they occur. The assumption of sufficient leadership is even made when companies reduce management personnel and become reactive (rather than proactive) in dealing with change. This assumption of sufficient leadership tends to be incorrect for organizations operating in a dynamically complex milieu. Let us assume that, as dynamic complexity increases, the time constant during which the problem does not change, TC, becomes smaller. When TC decreases, so must TA, the time taken for analysis, to maintain TAoTC. However, the DCC and execution processes are taking more time. Thus, just when the demand for planning and handling process maladaptations increases, the capacity to provide it has decreased because aL has become smaller. Let us also assume that increases in LRM create demand for more planning and handling of process maladaptations due to the need to contain change events and to deal with ripple change and white-water change events. This leads to Proposition 16: Proposition 16. Increases in LRM increase the need for more leadership capacity in an organization. As LRM increases, strategy S2 (the existing staff and leader have sufficient capacity) begins to break down because the individual capacity is declining

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due to the shortage of time for leadership and the increase in time needed for organizational maintenance (aL becomes smaller). By Eq. (8), as aL decreases, so does CL. To counteract this decline, the need for more leadership capacity grows as LRM increases. Proposition 17. The larger the change event, the greater the need for more leadership capacity. Proposition 17 follows from Proposition 15 (the larger the change event, the greater the leadership requirements to contain it) and Proposition 16 (increases in LRM increase the need for leadership capacity). Proposition 18. In a dynamically complex milieu, increases in LRM create demand for more management. As the dynamic complexity increases, more large change events occur. Hence, by Proposition 17, more leadership capacity is needed. Management involves planning and handling process maladaptations as well as DCC tasks. Proposition 19. In a dynamically complex milieu, increases in LRM create demand for more leadership capacity. The greater the demands for management, the greater the use of the LPs (see Table 2). The more often the LPs are done, the greater the LRM. The greater the LRM, the greater the need for leadership capacity. This leaves organizations with the uncertainty of whether, in a dynamically complex milieu, the existing leadership is enough to handle the load. Strategy S1, the default strategy of uncoupling, allows the organization merely to drift in the midst of pressures of change. Strategy S2, which assumes that the existing leader and personal staff will be sufficient, will break down as overloads occur and change events are less contained. Distributed Leadership Strategy S3 (distribute the leadership processes) is preferred for change events arising out of the unintended consequences of unmanaged change and proactive efforts to handle process maladaptations. Leaders cannot spend all of their time in leadership processes; they must also engage in executionlevel processes. As a consequence, a person in a leadership position can handle only a limited amount of change in management issues without help from others. Ripple effect changes and white-water changes both increase the size of a change event and usually lead to deficient leadership.

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Leadership is distributed if its processes involve other processual agents, each appropriate to his or her level and role in the organization. Leadership is nondistributed if either it is (1) not provided or (2) delegated to a single individual, usually the CEO. A ripple effect leadership process occurs when leadership on one part of a change event stimulates leadership by others in the same change event window. Ripple effect leadership processes are virtuous leadership processes. They improve CL by increasing aL, NL, and ML. Ripple effects leadership processes resemble Tichy’s (2002, pp. 52–54) virtuous teaching cycles, which he observed in special operations forces in the military. This leads to Proposition 20: Proposition 20. Ripple effect leadership processes improve the distribution of leadership by increasing the number of processual agents engaged. Proposition 21. Distribution of leadership increases organizational leadership capacity. Distribution of leadership increases aL by bringing in more help and increases NL and ML for the group around which the leadership tasks have been distributed. Hence, CL increases. Proposition 22. As the LPs become more fully distributed throughout a group or an organization, the ability to achieve organizational LRM increases. Proposition 22 is just a corollary of Proposition 21. Proposition 23. Strategy 3 (distribution of leadership processes) is preferred to the other strategies in a dynamically complex milieu. Strategy S1, the default strategy, is to ‘‘solve’’ the problem of LRM by uncoupling the organization. But this approach does not solve problems so much as it sidesteps them. The changes coming from a dynamically complex milieu continue to demand attention, and S1 eventually will break down. Strategy S2, which calls for using the existing leader and personal staff, runs out of leadership capacity. This leaves S3, which serves to increase organizational leadership capacity and organizational LRM. The possibility of improving the capacity of organizational leadership by distributing it opens up the links between distributed leadership and the theory of the organizational hologram. The organizational hologram is strongly related to LAMPE, as discussed later in this chapter.

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Distributed Leadership and Substitutes for Leadership The LAMPE theory of organizational leadership is not inconsistent with the notion of substitutes for leadership from Kerr and Jermier (1978), who argued that in some situations, the importance of ‘‘a relationship-oriented, people-centered leadership style’’ (Tosi & Banning, 1998, p. 272) is weak. Kerr and Jermier (1978) proposed that certain features of a solution can induce the same results (pro and con) as a leader: ability/experience, professional orientation, indifference to rewards, need for independence, task feedback, routine tasks, intrinsically satisfying tasks, organizational formalization, organizational inflexibility, advisory/staff support, cohesive group, rewards outside the leader’s control, and spatial distance. One might add the effects of large gross margin dollars and revenues, strong market position, and patent and copyright protection as possible substitutes for leadership. When a company is highly profitable, almost any type of leader can appear effective. By contrast, a company facing razor-thin profit margins cannot make many mistakes. In such cases, it requires effective leadership just to survive. The idea of substitutes for leadership is straightforward: situational factors, other than the leader, can cause or evoke the same responses in subordinates as one would expect from interpersonal leadership. It is also possible that groups and units can perform well in spite of their designated managers. Many technical issues arise in testing substitutes for leadership. Podsakoff and MacKenzie (1998) examine the relative impact of individual group-level effects of specified leader ‘‘behaviors’’ and substitutes for leadership on employee attitudes, role perceptions, and ‘‘in-role’’ and ‘‘extra-role’’ performance. The results are a mix. The direct support for substitutes for leadership is weak, but these substitutes for leadership variables definitely correlate significantly with subordinate criterion variables. These results are both complex and interesting (cf. Tosi & Banning, 1998). It is possible that the working of the 29 LPs throughout an organization would reduce the effects of a single, designated manager because more leadership processes would be working throughout the entire organization rather than being directly transmitted via a single person. Although the processes of LAMPE are not equivalent with the scales of Podsakoff and MacKenzie (1998) and, therefore, render comparison problematic, it seems reasonable that a lighted organizational LAMPE would be a beacon of leadership substitutes. However, in this case, it is not that leadership is being substituted by the 29 LPs as much as it is being amplified by them.

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The literature describes a pair of closely connected substitutes for leadership – namely, self-management and self-leadership (cf. Manz & Sims, 1980, 1989). Neck (1998) compares self-management and self-leadership. Self-leadership is described as a broader view because ‘‘it includes strategies for self-management as well as for managing the nature of the motivational value of the task and the patterns of one’s thinking. Consequently, the processes of self-leadership prescribe a more active and comprehensive role for members in a work system and represent a much more advanced form of self-influence’’ (Neck, 1998, pp. 222–223). The logical status of self-management at levels above the self is problematic. However, if described in terms of an identity (the self ‘‘being’’ a person or work unit, a division, or the company), there is no reason why ‘‘self’’-management cannot occur at each level. One approach is to have the 29 LPs be consciously used and improved by the ‘‘self.’’ In this way, the basic insight of self-management can be wrestled out of the limitations created by its embrace of social learning theory so that it becomes a core concept in the means for distributing leaders in an organization. If each level and every processual agent at each level is ‘‘self-managing’’ itself and its interdependencies with other ‘‘selves,’’ the organizational LRM, leadership effectiveness, and leadership quality improve.

LAMPE and the Organizational Hologram Introduction to the Organizational Hologram The theory of the organizational hologram is a theory for managing the processes of adaptation and change in organizations that operate in highvelocity environments (cf. Mackenzie, 2000b). Its core is the idea of stabilizing an organization’s instabilities by ensuring that certain processes of adaptation and change are working throughout an organization. This is based on the notion that in a hologram each part contains the whole. An organization can mimic this property by having 12 processes, called holonomic processes of adaptation and change, working everywhere in the organization. The theory of the organizational hologram offers the solution for having an organization be simultaneously maximally productive, adaptable, and efficiently adaptable. In addition, the holonomic organization achieves dynamic congruency (Mackenzie, 2003). The theory of the organizational hologram has been summarized elsewhere (Mackenzie, 2004a, pp. 393–407). The techniques known as knobby analyses, linear programming, and causal-chain analyses are used to exploit

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the multi-level characteristics of that theory. These methods are now being applied to assessments of organizations and their LPs. This theory has been applied in numerous organizational design projects, and some of its ideas have been incorporated into a new technology for assessing organizations and their LPs. The theory features an analytical pyramid. At its base are assumptions and definitions; at the apex are four macroorganizational principles. The work published earlier (Mackenzie, 1991) has an analytical pyramid that is missing one step that has been included in the new version. Sandwiched between the 20 principles in the theory and the 12 holonomic processes are the 29 LPs described in this chapter (see Tables 1 and 2). The analytical pyramid is illustrated in Fig. 12.

4 Macro-Organizational Level Principles Developed out of the Theory The Holonomic Cube which Combines The 12 Holonomic Processes & the 6 Desired Organizational Characteristics 6 Desired Organizational Characteristics Derived from the Holonomic Processes and Principles 12 Holonomic Processes Developed out of the Principles, Propositions, Definitions, and Assumptions 29 Leadership Practices Developed as Useful Measures for Improving an Organization’s Holonomic Processes 20 Principles Developed as Major Conclusions From the Propositions, Definitions, And Assumptions

35 Propositions Developed as Intermediate Conclusions

Definitions and Assumptions Used in the Theory

Fig. 12.

The Analytical Pyramid.

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The linkages of the LPs within the theory of the organizational hologram are strong. The linkages of the LPs to the HALO technology are tight. What is left is linking the LAMPE theory of leadership to the organizational hologram. Propositions Linking LAMPE to the Organizational Hologram Thirteen propositions link LAMPE to the organizational hologram. Proposition 24. The more holonomically an organization operates, the more it distributes leadership processes. The more an organization is holonomic, the more distributed are its processes of planning, DCC, and execution as well as change in management processes. In a holonomic organization, every LP works throughout the organization. This outcome can be achieved only if the LPs are distributed; otherwise, the parts could not contain the whole, the main characteristic of a hologram. Proposition 25. The greater the leadership quality in an organization, the more distributed its leadership processes. Leadership quality in an organization is the average LP score taken from the HALO survey. As the leadership quality increases, so do the LP scores. But as the LP scores improve, they become more holonomic because the holonomic processes are made up of their constituent LPs. Hence, by Proposition 24, the more distributed are the LPs. Proposition 26. Distributing LPs results in (1) creating more change events and (2) increasing the number of persons engaged in leadership processes. Leadership is distributed if its processes involve other processual agents. Consequently, distributing leadership increases the number of other processual agents, each appropriate to his or her level and role in the organization, who are involved in leadership. But as more processual agents become engaged in the leadership processes of initiating, enabling, implementing, and sustaining change in management, more change events will be created internally. Proposition 27. Change events in a holonomically run organization have these properties: (1) the change effects tend to be much smaller; (2) the change effects are more containable; (3) there are fewer ripple event changes; and (4) there are fewer white-water changes. The more holonomically run an organization, the more it distributes its leadership processes (Proposition 24). By Proposition 22, this wider distribution

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increases its ability to achieve organizational LRM. As organizational LRM improves, so does the organization’s power to handle its change events. By Proposition 26, distributing the LPs will increase both the number of change events and the number of persons engaged in its leadership processes. Because the organization has the power to handle its change events and the organization is holonomic, change events tend to be contained as they occur. As a result, fewer ripple effect changes occur. According to the definition of whitewater change, if there are fewer ripple effect changes, there will be fewer whitewater changes. Proposition 28. As the quality of leadership increases, (1) aL increases (where aL is the proportion of time the leader is available for handling leadership requirements) and (2) aL increases throughout the organization due to experience. As the quality of leadership improves, the more manageable its interdependence uncertainty becomes (Proposition 8). Also, as the quality of leadership improves, so does the average LP. This trend increases its organizational LRM. Increases in LRM, in turn, increase the need for more leadership capacity (Proposition 16). By Proposition 25, the organization’s leadership is more distributed, which, by Proposition 26, increases the number of persons engaged in leadership processes. The more persons involved, the greater the aL, because each contributes more time, which then increases the total time available. But, as aL increases throughout the organization, more persons become involved in leadership processes. This growth, over time, will reduce the time spent managing uncontained change events and improving the efficiency of handling the ones that occur. Proposition 29. As the quality of leadership increases, there are more ripple effect leadership processes. As the quality of leadership improves, its interdependence uncertainty becomes more manageable (Proposition 8). This development improves its organizational LRM, which further improves the quality of leadership. As the quality of leadership improves, the need for more organizational leadership capacity grows (Proposition 16). Leadership capacity goes up if the number of processual agents, NL, increases and if the number of involved tasks, ML, increases. By Proposition 28, as the quality of leadership improves, so does aL. Hence, because CL ¼ aLNLML, the capacity of leadership increases and leadership becomes more widely distributed. Then, by Proposition 26, there are more change events and more persons involved in

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leadership processes, which increases the presence of ripple effect leadership processes. Proposition 30. Improving the quality of leadership allows more selectivity in how the individual chooses to be involved in leadership processes. By Proposition 25, as the quality of leadership in an organization improves, the more distributed are its leadership processes of initiating, enabling, implementing, and sustaining change in the organization. By Proposition 26, the more widely these leadership processes are distributed, the more change events occur and the more processual agents are engaged with these change events. Hence, the number of persons (NL) and the number of tasks (ML) increase. By Proposition 28, aL increases. Hence, by Eq. (8), the organizational capacity for leadership increases. The increased capacity limits personal time liability (risk) because LRM is present and because the work is distributed. This reduced risk of involvement encourages more persons to become involved in the appropriate leadership being recommended (cf. Fig. 5). Furthermore, as individuals gain experience with the leadership processes, they learn how to perform LPs (Table 2), which in turn increases their efficiency and fosters selectivity in how the individual chooses to be involved in the leadership processes. Proposition 31. As the quality of leadership increases, so does the organizational capacity for leadership. The supervisor’s leadership capacity, C(0) L , for the entire organization is a source of the CL values for the N(0) individual leaders. As the 29 LPs are L practiced more consistently, the quality of leadership improves. As the quality of leadership improves, so does N(0) L . Furthermore, by Proposition 26, ML(0) also increases; by Proposition 28, aL increases. Hence, by Eq. (8), organizational CL gets larger as the quality of leadership improves. Proposition 32. As dynamic complexity increases, so do the advantages of distributed leadership. Dynamic complexity occurs in a dynamically complex milieu. In such a milieu, effective management always involves leadership (Proposition 14). This requirement increases the need for organizational LRM, which in turn increases the need for more organizational leadership capacity within the organization (Proposition 19). Distribution of leadership increases organizational leadership capacity (Proposition 21), which in turn makes changes more containable and relieves the number of ripple change events and whitewater changes (Proposition 27). This trend improves the quality of leadership

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(Proposition 9). By Proposition 29, there are more ripple effect leadership processes; by Proposition 31, the organizational capacity for leadership improves. Hence, the advantages of distributed leadership are amplified as dynamic complexity increases. Proposition 33. LAMPE applies if, and only if, the organization is holonomic. An organization is holonomic when all 29 LPs (and, hence, the 12 holonomic practices) are working. Therefore, management and leadership are working (see Tables 1 and 2), and, by extension, then power is working. So is authority. Therefore, as the organization become more holonomic, the 29 LPs are present. And if these LPs are at work, then the organization must be holonomic. Propositions 34, 35, and 36 flow from the arguments in The Organizational Hologram (Mackenzie, 1991). Full arguments in that volume sustain the conclusions that are used here. The coherence and integration of LAMPE are unstable in a dynamically complex milieu. Consequently, integrating LAMPE is a continual process of adapting to become and remain more fully holonomic. LAMPE applies when it becomes coherent, integrated, and distributed throughout an organization. When dynamic complexity is present, both organizational-level congruency and dynamic bonding congruency obtain. As a consequence, there is a fit and coherence among the organization’s environments, its strategic direction, its implementation of organizational technology, and its organizational results. Furthermore, there is strong bonding between the members and the organization, between the members and their positions, and between the positions and the organization. Furthermore, organizational-level congruency is a necessary but not sufficient condition for attaining and maintaining dynamic bonding congruency (Mackenzie, 1991, Proposition 2, p. 174). Dynamic bonding congruency is a necessary but not sufficient condition for attaining and maintaining organizational-level congruency (Mackenzie, 1991, Proposition 1, p. 173). This relationship is summarized in this proposition (Mackenzie, 1991, Proposition 3, p. 176): dynamic congruency exists only when both organizational-level congruency and dynamic bonding congruency occur simultaneously. Proposition 34. If there is dynamic congruency, LAMPE applies in an organization. When the organization is characterized by dynamic congruency, both organizational-level and dynamic bonding congruency are present. An organization

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is maximally productive if, and only if, it has dynamic congruency (Mackenzie, 1991, Proposition 11, p. 192). The attainment of efficient adaptability is the result of managing to achieve and maintain dynamic congruency (Mackenzie, 1991, Proposition 12, p. 193). It occurs when LPs and authority are coherent and integrated throughout the organization. When LPs are coherent and integrated, so are its leadership processes, management, and power, as shown in Tables 1 and 2. Thus LAMPE applies when dynamic congruency is achieved. Organizations are said to work best if they are simultaneously maximally productive, adaptable, and efficiently adaptable. This leads to Proposition 35: Proposition 35. An organization works best if, and only if, it is holonomic. An organization becomes holonomic when all 12 of the holonomic processes of adaptation and change are working throughout that organization. The achievement of the 12 holonomic processes leads to maximum productivity (Mackenzie, 1991, Proposition 34, p. 419) and efficient adaptability of an organization (Mackenzie, 1991, Proposition 35, p. 420). Therefore, an organization works best if it is holonomic. When it is holonomic, the organization is maximally productive, adaptable, and efficiently adaptable and, hence, works best. If LAMPE applies, the organization is holonomic (Proposition 33). If it is holonomic, it is dynamically congruent, and LAMPE applies by Proposition 34. If LAMPE applies, then it is coherent, integrated, and distributed throughout the organization. In other words, its LPs and leadership processes are coherent, integrated, and distributed. Consequently, because the 12 holonomic processes are made up of the 29 LPs, the organization is holonomic. This means, by definition of ‘‘works best,’’ that the organization is maximally productive, adaptable, and efficiently adaptable. Hence, the organization is holonomic and, by Proposition 35, works best. Proposition 36. An organization works best if LAMPE applies. Proposition 36 follows from Propositions 33 and 35. Lighting the LAMPE Given the tight linkage between the attainment of LAMPE and the organizational hologram, the main method of lighting the LAMPE is to first assess the state of the 29 LPs in an organization. This can be done by a web-based survey system called HALO (‘‘Holonomic Assessment of the Leadership of

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an Organization’’). The Organizational White Paper generated by HALO from the responses of members of an organization presents the values of all of the major 53 holonomic properties (6 desired organizational characteristics, 12 holonomic processes of adaptation and change, 29 LPs, 3 organizational-level congruency conditions, and 3 dynamic bonding congruency measures). HALO generates results, conclusions, and recommendations based on the defined interdependence. HALO performs intervention analyses using linear programming as well as causal-chain analyses to select those LPs for intervention (Mackenzie, 2000c, d, 2004a). The diagnoses from HALO help to find and formulate the problems that must be solved so that the organization can light the LAMPE. The next step is to describe which recommendations will be implemented and the means for accomplishing this implementation. In some cases, the best means is to redesign the organization (cf. Mackenzie, 1986a, for one technology for performing organizational designs). This endeavor requires documenting the actual structures and task processes and verifying the state of dynamic congruency; this documentation is called an organizational audit. The organizational audit is followed by a process for coming up with a solution, a stage called the organizational design stage. The organizational blueprint arising out of the design stage is then subjected to refinement and improvements during the implementation planning stage. After completing this planning, the organization is ‘‘repotted’’ into its new organizational design. The effect of the intervention can be investigated using HALO at a later date. Thus HALO is a method for ensuring longitudinal status of leadership and organizations (cf. Ropo & Hunt, 1999; Hunt & Ropo, 2003).

SUMMARY AND CONCLUSIONS This chapter began with stating its position on 17 issues related to the nature of leadership; the links among the constructs of leadership, authority, management, power, and environments (LAMPE); and some consequences of leadership. The common thread linking the answers to these issues is the LAMPE theory of organizational leadership, where LAMPE refers to the five constructs of leadership, authority, management, power, and environments. This theory states that organizations work best when the components of LAMPE are coherent, integrated, and operational throughout an organization. The leadership challenge is to achieve and maintain LAMPE amidst continual change. In this theory, leadership is defined as the processes of initiating, enabling, implementing, and sustaining change in the management of an organization

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or group. Under this definition, some individuals might perform only some of the leadership processes, while others might work on all four. This allows leadership to be distributed rather than concentrated in the hands of a single person. Thus leadership need not be binary; it can be the property of a group or organization as well as that of an individual. The leadership processes need not be restricted to only human actors. Leadership is inherently multi-level for a host of reasons: (1) the nature of hierarchy of leaders and followers in organizations; (2) the multi-level nature of the tasks involved; (3) the multi-level requirements of interdependence and interdependence uncertainty; and (4) the natural nesting of leadership processes and their practices to form aggregations of organizational properties. The LAMPE theory is based on the definitions of the five constructs involved. Three of these constructs (leadership, management, and power) are processual in nature and are interdependent. For example, management is defined in terms of the processes of planning, DCC of task processes, and handling process maladaptations. Leadership is defined via 29 specific LPs. These 29 LPs are combined in Table 2 with these processes of management. At the processual level, leadership and management are the same. Power is defined as the control of interdependence uncertainty, and leadership is defined as the exercise of power. The 29 LPs can be linked (as in Table 1) directly to the control of sources of interdependence uncertainty. Thus the logical and theoretical links among leadership, management, and power are made via the common base of the LPs. Clearly, the description of the 29 LPs becomes vital in understanding the LAMPE theory of organizational leadership. There are a pair of wildcards in this mix. The first is authority, which is defined as the legal right to preempt the outcome of a decision or a process. The second is the external environments facing the organization. Both authority and the organization’s environments can change independently of the internal processes of an organization. Thus there are changes beyond the internal control of management to which the organization must respond to maintain the coherence and integration of LAMPE. The processes of leadership and the exercise of power can and need to adapt to relevant changes in authority and external environments. In some cases, the internal processes can be used to affect the relevant authority and external environments. The existence of the two wildcards, however, makes LAMPE problematic unless the organization exhibits persistence, discipline, and purpose in its pursuit of LAMPE coherence and integration. The chain of arguments linking, in succession, (1) leadership and power, (2) power and authority, (3) authority and management, (4) management

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and leadership, and (5) these to environments establishes what is needed to have coherence in LAMPE. Integrating LAMPE, however, brings up challenges for LAMPE theory. For example, when is leadership appropriate? In fact, leadership is not always appropriate. Furthermore, inappropriate leadership can result in a loss of power. The analysis presents a process framework for deciding the propensity of the organization to change (Fig. 6). This framework is included in Fig. 7 to determine whether leadership is appropriate for the particular situation. The answer from Fig. 7 is then used in Fig. 5 to judge the appropriateness of leader behavior. Incidentally, these nested figures are another example of a multi-level analysis. An analytical device called a window was developed to set up a logic for answering the tough questions of the consequences of leadership. A window is a rectangle, or a section, within a task–role matrix (cf. Figs. 2 and 8). It represents that portion of the organization’s task–role matrix for a set of persons and a set of organizational tasks. For example, the supervisor’s window includes the supervisor, the supervisor’s direct and indirect reports, and all of the tasks for which the supervisor’s unit is responsible. A change event defines a window. Fig. 9 illustrates the possibilities of a match and a mismatch between a supervisor’s window and change event windows. Some change events are fully contained, some partially contained, and some not at all contained. There is also the common case where the change event is larger than the supervisor’s window. Understanding this relationship calls for a new concept called LRM. LRM plays a critical role in leadership effectiveness and leadership capacity. Increasing organizational LRM improves the leadership quality, which in turn improves the organization. Not all changes are alike. Some are contained, but others are not. Some of the uncontained changes evolve into ripple effect changes, and some of the ripple effect changes metastasize into white-water changes. It turns out that distributing leadership throughout the organization is a means both for limiting change and for increasing the organizational leadership capacity. The LAMPE theory may be linked to the theory of the organizational hologram. This results in 13 propositions: Proposition 24. The more holonomically an organization operates, the more it distributes leadership processes. Proposition 25. The greater the leadership quality in an organization, the more distributed its leadership processes. Proposition 26. Distributing LPs results in (1) creating more change events and (2) increasing the number of persons engaged in leadership processes.

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Proposition 27. Change events in a holonomically run organization have these properties: (1) the change effects tend to be much smaller, (2) the change effects are more containable, (3) there are fewer ripple event changes, and (4) there are fewer white-water changes. Proposition 28. As the quality of leadership increases, (1) aL increases (where aL is the proportion of time the leader is available for handling leadership requirements) and (2) aL increases throughout the organization due to experience (it gets better at LPs). Proposition 29. As the quality of leadership increases, there are more ripple effect leadership processes. Proposition 30. Improving the quality of leadership allows more selectivity in how the individual chooses to be involved in leadership processes. Proposition 31. As the quality of leadership increases, so does the organizational capacity for leadership. Proposition 32. As dynamic complexity increases, so do the advantages of distributed leadership. Proposition 33. LAMPE applies if, and only if, the organization is holonomic. Proposition 34. If there is dynamic congruency, LAMPE applies in an organization. Proposition 35. An organization works best if, and only if, it is holonomic. Proposition 36. An organization works best if LAMPE applies. Proposition 36 suggests that the links between LAMPE and the hologram are so tight that organizational leadership might be redefined in terms of the degree to which an organization acts as an organizational hologram. This possibility may seem far afield from the usual formulations of leadership. The organizational hologram involves a different ontological stance, is based on the necessity of continual change, and focuses on the problem of how to manage an organization in dynamically complex milieux. But why not view leadership in terms of the processes of initiating, enabling, implementing, and sustaining change in the management of an organization? If the organization becomes holonomic, it becomes more productive, adaptable, efficiently adaptable, and dynamically congruent. Surely, these desiderata could be embraced by leaders and support the goals of existing leadership approaches. The LAMPE theory of organizational leadership does not use the same tools and is less psychologically based than the other leadership theories. LAMPE does not ask its questions in the same manner as other leadership approaches. Instead, it emphasizes the observable phenomena of tasks,

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structures, and processes. It emphasizes how rather than why (in the teleological sense of this word). It attempts to create laws in the form of process frameworks that foster deductive nomological reasoning without the intercession of assumptions about motivations or other factors that might muddy the waters. LAMPE wrestles with the same problems of how to distribute leadership in an organization, but it offers a different lens through which to study organizational leadership phenomena.

ACKNOWLEDGMENTS The author wishes to acknowledge the encouragement and persistence of Ron R. Roberts in directing his attention to the possibility that the theory of the organizational hologram was really a theory of organizational leadership. The LAMPE theory of organizational leadership would not exist without the push from Ron. Don F. Utter provided needed critiques of drafts of this chapter and deserves to be thanked for his efforts. Don mentors me in all things philosophical and stimulates me by his friendly, yet probing inquiries. Special thanks go to Susan M. Treber for her editorial assistance in preparing this chapter for publication.

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Seifter, H., & Economy, P. (2001). Leadership ensemble: Lessons in Collaborative management from the world’s only conductorless orchestra. New York: Times Books. Senge, P. M. (1990). The leader’s new work: Building learning organizations. Sloan Management Review, 3, 7–22. Shane, S. A. (2003). A general theory of entrepreneurship: The individual–opportunity nexus. Northampton, MA: Edward Elgar. Staw, B. M. (2004). Psychological dimensions of organizational behavior (3rd ed.). Upper Saddle River, NJ: Pearson Prentice-Hall. Stogdill, R. M. (1974). Handbook of leadership: A survey of theory and research. New York: Free Press. Taylor, C. (1993). Engaged agency and background in Heidegger. In: C. Guignon (Ed.), The Cambridge companion to Heidegger (pp. 317–336). Cambridge, UK: Cambridge University Press. Tichy, N. M. (2002). The cycle of leadership: How great leaders teach their companies to win. New York: HarperCollins. Tosi, H. L., & Banning, K. (1998). A need to reconceptualize ‘‘substitutes for leadership.’’ In: F. Dansereau & F. J. Yammarino (Eds), Leadership: The multiple-level analysis. Part B: Contemporary and alternative (pp. 271–276). Stamford, CT: JAI Press. Weatherford, J. (2004). Genghis Khan and the making of the modern world. New York: Crown. Weber, M. (1947). The theory of social and economic organization (Trans. by A. M. Henderson & T. Parsons). Gleneve, IL: Free Press. White, R. P., Hodgson, P., & Crainer, S. (1996). The future of leadership: Riding the corporate rapids into the 21st century. Lanham, MD: Pitman. Yukl, G. A. (2002). Leadership in organizations (5th ed.). Upper Saddle River, NJ: PrenticeHall.

‘‘BREAKING THE FRAME’’ EVEN FARTHER: COMPLEXITY SCIENCE AND LAMPE THEORY Mary Uhl-Bien and Russ Marion ABSTRACT Mackenzie’s LAMPE theory provides a new view of leadership that is multi-level, processual, and reflective of leadership as it actually occurs in practice. While we see this approach as representing a much needed frameshift for leadership research, we believe Mackenzie may be able to ‘‘break the frame’’ even farther by incorporating elements of complexity science into his thinking. We suggest how complexity science might help Mackenzie flesh out his ideas about distributed leadership, as well as consider leadership that is not only about alignment and control but also about enabling and releasing informal, interactive dynamics within the organization.

INTRODUCTION Science is driven by the desire to find answers to questions that intrigue and mystify us. For some, the pursuit of scientific knowledge becomes a life’s journey of exploration and investigation, with twists and turns – and hopefully, discoveries – along the way. In reading Kenneth Mackenzie’s LAMPE theory of organizational leadership it is clear that LAMPE is a product of just such a journey. As Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 429–442 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05019-3

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Mackenzie describes in a commentary in a previous volume of this series (Mackenzie, 2004, p. 445), this work has grown out of four decades of ‘‘indwelling’’ phenomena, i.e., wallowing about the phenomena until reasonably satisfied that one understands it a little, rather than indwelling the research literature about phenomena. In other words, it is a product of a lifetime of learning gained from ‘‘involved actions’’ and, therefore, intended to provide useful, rather than strange knowledge: I felt (and still feel) that our organizational behavior literature consisted mainly of strange explicit knowledge. Many who write about managers and organizational behavior have never experienced it directly and tend to indwell the literature about managing. The result is strange knowledge. As I explained years ago, ‘Knowledge is considered strange by an organization to the degree to which it is not recognized as solving a real problem. Strange knowledge is not useful to an organization y .’ Detached persons are prone to generate strange knowledge because of their partial and distorted version of reality about organizations. Philosophically, strange knowledge is unintelligible to the engaged agent. (Mackenzie, 2004, pp. 445–446)

The result of Mackenzie’s pursuit of useful (rather than strange) knowledge is rich insight into the nature and study of ‘‘real-world’’ organizational leadership. Rather than a model built from the literature, which quickly becomes a convoluted mix of variables – that may or may not represent what happens in leadership practice – the LAMPE model was built from Mackenzie’s reflections of leadership as he has witnessed it occurring in organizations. Because of this, he is able to overcome many of the limitations that scholars face when they try to build multi-level leadership frameworks based on research findings. The latter often result in cumbersome models that are compilations of the most popular variables, with little relevance to how leadership actually works in organizations. While we believe that Mackenzie makes major steps forward in offering a different lens for studying organizational leadership – one that represents a ‘‘frameshift’’ regarding what leadership is and how it should be studied – we wonder whether his frameshift has gone far enough. Mackenzie is very progressive in a lot of his ideas, but his actual model appears to be grounded in general systems views of organizations and top–down thinking that may be holding him back from fully addressing the problems with leadership research that he so eloquently conveys at the beginning of his chapter. We suggest that the concepts of which he speaks, for example, distributed leadership and processual dynamics, may be more appropriately described by a different branch of systems thinking – the ‘‘new science’’ of complexity (Marion, 1999).

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In the commentary below, we describe how Mackenzie’s LAMPE theory might benefit from an infusion of complexity science thinking. We see parallel between where Mackenzie appears to be going and what complexity suggests, but are not sure that the traditional worldview of general systems theory and managerial control can fully get him there. Below, we describe the theoretical underpinnings of complexity and how it is being applied to leadership. We then discuss the implications of this for LAMPE. We conclude with a summary of how we believe complexity science can inform Mackenzie’s LAMPE theory.

COMPLEXITY SCIENCE AND LEADERSHIP Complexity science represents a growing body of interdisciplinary knowledge about the structure, behavior, and dynamics of change in complex adaptive systems (CASs) – open evolutionary systems in which the components are strongly interrelated, self-organizing, and dynamic (Sanders, 2005): Complexity science is moving us away from a linear, mechanistic view of the world, to one based on nonlinear dynamics, evolutionary development and systems thinking. It represents a dramatic new way of looking at things; not just looking at more things at once. Insights from complex systems research provide a new y framework for thinking about, understanding and influencing the dynamics of complex systems, issues and emerging situations. (Sanders, 2005)

Complexity science describes the dynamics of CASs, or aggregations of interactive, interdependent agents. Agents in a CAS tend to ‘‘resonate’’ in sync – to have common interests and goals, for example – because they have a history of interaction and sharing of worldviews. They are driven by external pressures (from the environment or from other CAS or agents, e.g., leaders) and from internal pressures generated as they struggle with interdependency and resulting conflicting constraints (e.g., when the needs of one agent conflict with those of another). Tension, when spread across a network of interactive and interdependent agents, motivates systemic elaboration in the form of learning, innovation, and adaptability. Importantly, such elaborations are products of interactions rather than the specific acts of individuals; a point we will develop further below. A complexity view of leadership suggests a form of ‘‘distributed’’ leadership that does not lie in a person but rather in an interactive dynamic. It is not limited to a formal managerial role, but rather emerges when moderately coupled heterogeneous agents interact (Marion & Uhl-Bien, 2001,

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2003; cf. Homans, 1950; Roethlisberger & Dixon, 1939). Leadership is embedded in a system’s ‘‘context’’ and cannot be separated from it; therefore, leadership study should involve examining the conditions and dynamic processes in which these interactions occur. A complexity perspective offers a theoretical foundation for considering a form of leadership beyond traditional top–down relationships between leaders and followers – where the leader sets directives and the followers carry out the directives. It provides a glimpse into leadership that takes place in the informal systems of an organization – informal leadership that generates ideational energy and produces change in the organization. As noted by Heckscher (1994): ‘‘There is a growing sense that effective organization change has its own dynamic, a process that cannot simply follow strategic shifts and that is longer and subtler than can be managed by any single leader. It is generated by the insights of many people trying to improve the whole, and it accumulates, as it were, over long periods’’ (p. 24). Complexity allows us to understand leadership and change as processual (to use Mackenzie’s terminology but not his definition) – to see it as a dynamic that emerges from interaction instead of simply a function or act performed by formally defined superiors. ‘‘Leaders’’ in the formal system can influence (either enable or suppress) the conditions within which the process occurs, but they are not, in their formal capacity, the direct source of change (e.g., learning, adaptability, and innovation). In a specific application of complexity thinking to leadership, Uhl-Bien, Marion, and McKelvey (2004) develop the ideas described above into a framework that includes the following three general categories of leadership activity: (a) managerial leadership, (b) adaptive leadership, and (c) enabling leadership. Managerial leadership is leadership that occurs in formal roles/ positions (i.e., by managers), and has been the focus of the vast majority of traditional leadership research. This form of leadership focuses primarily on providing direction, ensuring alignment, and motivating worker efforts toward organizationally specified goals, i.e., the efficiency and control functions of the organization. Adaptive leadership occurs in the informal systems of the organization (not only in formal roles) and flows in any direction (e.g., up, down, lateral). This form of leadership has been largely ignored in the organizational literature, though recent work is beginning to recognize this oversight, calling for further exploration of leadership that occurs in the ‘‘collective’’ (cf. Drath, 2001; Gronn, 2002; Pearce & Conger, 2003). Adaptive leadership is an informal change dynamic that emerges from the interaction of agents – it is the proximal source of change and learning in an organization, and

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manifests when divergent, interacting agents spontaneously create a nonlinear outcome (new knowledge or structures). Contrary to managerial leadership, which can be seen as the ‘‘motor functions’’ of the firm (more centralized or top–down), adaptive leadership is more like the ‘‘intellectual functions’’ of the firm (a neural-like network). In organizations, the managerial and adaptive leadership functions are ‘‘entangled,’’ meaning that both operate together and must interchange effectively to produce appropriate amounts of innovation and control. The third category, enabling leadership, operates in that interface between managerial and adaptive leadership. It has multiple functions: (a) it helps create the conditions that foster adaptive leadership and keep it productive while (b) helping disseminate the innovative products of the informal dynamic within the formal managerial structures to generate beneficial outcomes for the firm (i.e., the innovation-to-organization interface, Dougherty & Hardy, 1996). Because of the nature of this role, enabling leadership is most likely performed by managerial leaders. With this as a backdrop, we now examine Mackenzie’s ideas more closely relative to complexity thinking.

IMPLICATIONS OF COMPLEXITY SCIENCE FOR LAMPE Mackenzie appears to purport many of the ideas described above in the early part of his paper. According to Mackenzie, we should view organizations as evolving, complex systems of processes and technologies. When given an environment of dynamic complexity, these systems are most productive and efficiently adaptable if the leadership processes are distributed widely throughout the entire organization. In shifting leadership from a focus on individuals in formal roles, leadership becomes a distributable process in which many individuals can engage (and, in Mackenzie’s words, not necessarily be public about it, cf. Gronn, 1999). Moreover, since organizations are complex systems, they are not entities that can be studied context free (Mackenzie, 2004, p. 449; cf. Osborn, Hunt, & Jauch, 2002). Therefore, like others who are calling for examination of informal leadership, Mackenzie recognizes the importance of leadership not being restricted to managerial roles (e.g., managerial leadership) but also occurring in informal systems (e.g., adaptive leadership). A problem we saw with his model, however, was that as he progressed into development of his framework he seemed to fall back into a focus on managerial leadership and on

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language that describes leadership as managing and directing. He did not, consequently, address the dynamics of actual informal leadership – the distributable leadership as he called it – but rather described a more traditional perspective of leadership activity. The traditional worldview that Mackenzie appears to adopt sees leadership as a function that coordinates the behavior of the system around predetermined outcomes and preferences (Bluedorn, 2002). As described by Marion (in press), a traditional worldview focuses on planning, directing, and controlling; it is an epistemology based on acts of leaders. It focuses on unity of vision, planned outcomes, and motivation by central structures (CEOs, bureaucratic rules, etc.). This worldview is designed for organizations that want to enable workers to efficiently produce useful outcomes on a large scale. A complexity science worldview, in contrast, addresses organizations that seek innovation, adaptability, and responsiveness, organizations whose principal outcome is based on the production of knowledge. Complexity describes leadership functions that are nondirective. Leadership itself does not reside only in an individual; rather it is also to be found in the interaction between or among individuals. Thus its epistemology is based on interactive dynamics (which are entangled with managerial leadership). Complexity focuses on heterogeneity of vision in which emergent outcomes are allowed and welcomed. In this view, organizations are designed to be learning systems, and managers’ roles are to enable a network of agents to dynamically search for fitness and optimize its ability to adapt to environmental exigencies. LAMPE appears to have elements that are quite consistent with the complexity worldview. For example, in LAMPE, members do not have to be individuals – they could be groups, teams, units, other divisions, companies, etc. In fact, in some cases the relevant input may be nonhuman processual agents (according to Mackenzie a processual agent is any entity capable of effecting a process). Complexity science refers to this as aggregates and heterogeneous agents (Marion, 1999). Leadership occurs when agents interact in ways that result in nonlinear change in a flow of events (Uhl-Bien et al., 2004). Mackenzie (this volume) says that ‘‘a structure represents a needsatisfying pattern of interaction among members of a group.’’ Complexity science is likewise grounded in interaction, and envisions systems composed of agents with local, needs-based orientations. That is, agents in any given system are incapable of understanding or visualizing the systemic whole but rather relate to demands in their local environment (Cilliers, 1998). Within that context, they are motivated to satisfy their immediate needs – such as

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needs related to the tasks for which they are responsible or needs related to personal drives (Marion & Uhl-Bien, 2001). Interacting needs underlie the interactive leadership dynamic. In LAMPE, group and organizational processes embody movement through time – they are not static, rarely fixed, and should not be assumed to be random (Mackenzie, this volume). Complexity likewise describes complex organizations as dynamic systems that move through time. In an earlier work, Mackenzie (2004) clarifies this argument succinctly, arguing the illogic of reducing human dynamics to statistical points. For example, he writes of his disenchantment with statistical analysis, arguing that it makes little sense to reduce a ‘‘pattern of information into a point estimate’’ (Mackenzie, 2004, p. 447). I think we should view organizations as evolving, complex systems of processes and technologies. They are not just entities capable of being studied by context free independent and dependent variables, the study of which requires neither intuition nor involvement, and is, ultimately, merely computational. (Mackenzie, 2004, p. 449)

Human behavior is a dynamic and should be analyzed as such; complexity science is in complete agreement with Mackenzie on this point. However, there are also key differences between LAMPE and a complexity worldview. One of these differences involves the issue of interdependence. Like complexity, LAMPE theory is grounded in the idea of interdependence – according to Mackenzie (this volume), ‘‘the organization of organizations’’ is interdependence. Mackenzie envisions interdependency as ‘‘necessary in order to align and link members to members, processes to processes, and members to processes.’’ Complexity is also centered around interdependent relationships among agents, but in a very different sense. In complexity, interdependency is a key source of motivational tension, which drives systems away from stasis [a direct contradiction of the homeostatic perspectives of earlier systems theory; see Buckley, 1967; and of Mackenzie’s (this volume) notion of common purpose]. It forces phase transitions that dissipate that tension (Haken, 1983; Prigogine, 1997). Therefore, whereas in LAMPE leaders manage interdependency to assure that ‘‘all members of a group or organization are moving toward common purposes, practices, and outlook’’ (Mackenzie, this volume), complexity science envisions interdependency as pressure that generates adaptive leadership and emergent change. Mackenzie (this volume) envisions interdependency as something to be manipulated and controlled by managers, but complexity sees it as the informally derived process that underlies change. Mackenzie’s perspective

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revolves around the acts of leaders; complexity’s perspective revolves around interactive interdependency. Another difference is in what is meant by processual. As noted above, Mackenzie (2004) argues convincingly that human behavior is processual and against reducing it to a statistical point. Complexity theory is in agreement with this, but would disagree with how he operationalizes processual behavior. According to Mackenzie (this volume), a process represents a sequence of events governed by a rule or ordering called a process framework (e.g., task processes for building a house include many steps in sequence). He continues by noting that: Management is the on-going effort to achieve the organization’s strategic direction in the presence of changing conditions through the processes of: (a) planning, directing, controlling, and coordinating organizational interdependence and (b) proactively searching for, removing, and preventing process maladaptations. (Mackenzie, this volume)

Mackenzie (this volume) defines ‘‘leadership practice’’ as ‘‘a discrete process of changing the management of an organization and is a controlling process for interdependence uncertainty’’ (this volume). These processual practices include:      

LP12. LP13. LP14. LP15. LP16. LP17.

Ensuring Compatible Interests of Results Using Tough and Realistic Standards Ensuring Job Performance Standards Applying Total Compensation Process Integrating Jobs with the Organization Ensuring Compatible Interests

It seems that by processual he prescriptively refers to specific leader acts that are performed by individuals within an organization. Complexity defines process at a higher level of abstraction that underscores the nature of interactive dynamics conducive to learning, innovation, and adaptability. Complex processes are the ‘‘how’’ more that the ‘‘what’’ of a dynamic. They focus on informal emergence rather than managerial control. Complex processes are nonprescriptive and refer to categories of behaviors rather than specific leader acts. Complex processes include interaction, interdependency, action rules, catalysts, adaptive tension, and nonlinear emergence (Marion, 1999). Mackenzie’s (this volume) perspective of change and leadership is also different from the view one would get from complexity science. He defines leadership as ‘‘the processes of initiating, enabling, implementing, and

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sustaining change in the management of a group or organization’’ (MacKenzie, this volume). He continues: ‘‘Change in management means activating the processes of planning, directing, controlling, and coordinating organizational interdependence as well as proactively searching for, removing, or preventing process maladaptations’’ (MacKenzie, this volume). Mackenzie then defines four change processes:  Initiating change processes startup, redirect, or shut down changes in the interdependencies in an organization. They originate, launch, pioneer, begin, and establish change processes.  Enabling change processes are problem-solving and resource-allocation processes that support and foster internal organizational change. They help change processes succeed by empowering action, authorizing support, and warranting the change processes. The enabling change process involves others in the organization.  Implementing change processes encourage and support implementation of organizational change. They help ensure that the changes are actually accomplished.  Sustaining change processes ensure an organization’s processes are continually adapting to current conditions as the organization undergoes change. They help maintain momentum, nourish, uphold, ensure, and continually provide support, over time, as change processes unfold. These are also critical to successful organizational leadership and usually involve other members. There are two important differences between this and complexity. First, from Mackenzie’s perspective, change is a deliberate, planned action, while in the complexity perspective it is allowed to emerge from interdependent interaction. Second, and even more significantly, Mackenzie describes change as a process controlled by leaders, while complexity sees it as a product of an informal dynamic. Mackenzie (this volume) goes on to define a leader as: y any Processual Agent (usually a person or a unit) who is engaged in leadership processes. A leader can be an individual, a group of individuals, or any Processual Agent. However, both leaders and leadership are continually adapting. Viewed in terms of their processes, both words should be thought of as if they are verbs (a position supported by Hosking, 2001).

This implies a more informal-based perspective of leadership that is similar to that proposed by complexity theory, but Mackenzie’s subsequent propositions belie this. For example, Mackenzie (this volume) proposes that

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(among other things):  Proposition 8. As the quality of leadership increases, its interdependence uncertainty becomes more manageable.  Proposition 15. The larger the change event, the greater the leadership requirements to contain it.  Proposition 18. In a dynamically complex milieu, increases in LRM create demand for more management. In each case, it seems that leadership is perceived as a top–down process, a function of positional power and authority over change events, and the distributed, informal leadership dynamic does not seem to be addressed. Thus although his definition of Processual Agent suggests processes, it appears that he is still thinking in terms of the traditional ascription of leadership as individuals in positions of power rather than leadership in processes. While positional power and processes are not incompatible, complexity theory would call for a focus on leadership that is processual (e.g., interactive; see above) and dynamic, rather than leadership that is simply an exercise of power and control (e.g., managerial leadership). LAMPE concludes from all this that change is enhanced when leadership is distributed. Complexity agrees, and ironically both complexity and LAMPE understand that distributed leadership ultimately results in increased change. Mackenzie’s (this volume) proposition 26 states that ‘‘Distributing leadership practices results in: (1) creating more change events, and (2) increasing the number of persons engaged in leadership processes.’’ The underlying differences are significant, however, for Mackenzie sees change as a function of planning and authority while complexity sees it as more of an emergent process. Finally, while Mackenzie (2004) criticizes traditional methods, the operationalization of his model appears to do what he criticizes. According to Mackenzie (2004; this volume), because organizations are complex systems, they are not entities that can be studied context free (cf. Osborn et al., 2002). Therefore, research on leadership that does not take into account the contexts in which natural leadership processes occur is inadequate (and unacceptable). Furthermore, to generate useful knowledge, leadership research must acknowledge the dynamics and specific context through which leadership is exercised: It is difficult to believe that a model based on abstract variables and applied across a population could ever capture the processes of leading. Each leader in the sample has different ‘‘followers,’’ tasks, contexts, history, etc. Simple aggregation destroys the natural interdependence of the leadership processes that occur naturally in groups and organizations. Basically, the models do not seem to be a close fit with the phenomena

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they purport to study. Further, the methods to test the models obliterate the natural leadership processes. For example, assumptions of independence and linearity violate the process framework of group and organizational processesyand ignore the dynamics of interdependence. But more importantly, analyzing questionnaire responses is not equivalent to studying actual processes. (Mackenzie, this volume)

We find it interesting, therefore, that despite Mackenzie’s (this volume) criticism of ‘‘the reliance on variables methodology’’ and the ‘‘conversion of variables into survey instruments’’ (as well as excessive dependence on statistical packages to ‘‘analyze’’ the data) he chose to end his chapter with a discussion of a survey and variables. We weren’t sure how this captured the history and rich dynamics of the natural leadership processes that he purports. Moreover, while we agree that it is important to consider leadership practices, Mackenzie’s (this volume) advocacy of methodology that identifies leadership practice variables and that causally links those variables to resulting employee effort and impact seems very prescriptive when one considers the dynamic nature of leadership in complex systems (Cilliers, 1998). We suggest that unless there is a more comprehensive theoretical foundation for explaining the dynamics Mackenzie describes, we end up just looking ‘‘at more things at once’’ (see above, Sanders, 2005) rather than ‘‘a dramatic new way of looking at things’’ (Sanders, 2005). We believe complexity theory provides that new framework for thinking about the processes that Mackenzie’s describes. Complexity theory focuses on understanding the dynamics of the leadership process rather than focusing on specific managerial leadership behaviors. It helps us break away from traditional perspectives and to understand leadership in genuinely different ways.

CONCLUSION In sum, we suggest that LAMPE theory may benefit from an infusion of complexity theory. Mackenzie clearly provides insights that are consistent with complexity science, but ultimately, as we stated earlier, he falls back on traditional assumptions about leaders and leadership. Complexity theory helps us see beyond this limitation and to understand the full importance of what Mackenzie has glimpsed. A complexity science perspective of leadership is grounded in (at least) two assumptions that are diametrically different from traditional approaches. First, traditional worldviews perceive the leadership role as managing or controlling activities (preferences) to organize subordinates around a central

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goal. Selznick (1949) called these preferences, irrational forces; Barnard (1938) argued that leaders bring worker preferences in alignment with formal organizational goals; more recently, Berson and Avolio (2004) argue that leaders are responsible for ‘‘convincing their constituents to effectively implement y [organizational] goals’’ (p. 626). That is, the traditional perspective is about suppressing informal dynamics to achieve a given outcome. Complexity is grounded in releasing informal dynamics rather than suppressing them. It is less about controlling and planning and more about enabling. Second, traditional perspectives tend to focus on the acts of leaders while complexity adds a focus on leadership as dynamic processes. Mackenzie seems to agree with the complexity perspective when he talks about processual behaviors but he still couches processes in terms of the acts of leaders. Complexity describes a form of leadership that occurs when agents (individuals or groups) interact; that is, processual leadership exists in the ‘‘white spaces’’ between people and not in the individual per se (cf. Bradbury & Lichtenstein, 2000; Drath, 2001). For example, Mackenzie (2004) writes of his disenchantment with statistical analysis, arguing that it makes little sense to reduce a ‘‘pattern of information into a point estimate’’ (p. 447). The difficulty is, in part, related to the characteristics of causality in nature. Karl Popper (1989) observed that any given event has an indefinite number of causes and it is impossible to know them all; at best we can only choose those we deem most pertinent, and this ‘‘choosing’’ is subject to vigorous debate among theorists. This issue of ‘‘causes’’ relates to what Mackenzie (2004) is arguing: The search for point estimates in complex, dynamic systems draws from a massive pool of possible variables, and the choices (models) one makes represent, unavoidably, only a caricature of reality. Attempts to approach reality with overly complex models run up against a second problem: that of what Mackenzie (2004) called strange knowledge – models that are unintelligible to the user. Strange knowledge also manifests when theorists develop models whose components cannot be easily manipulated by users, when their choices have little face validity to users, or when the models themselves are too simplistic. Contributing to this dilemma is the fact that model-makers don’t always agree on the appropriate mix of ‘‘causes.’’ They frustrate users when they (correctly) debunk models that have high face validity. Mackenzie’s solution is processual research. As he put it, ‘‘Maybe I should explain how the changes occur rather than why they occur!’’ (Mackenzie, 2004, p. 449). David Hume, the father of the ‘‘point estimate,’’ or variable, approach felt that it would be impossible to understand the processes of causality (see discussion by Maxwell, 2004). Mackenzie

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(and Popper, 1989) suggests that the opposite is true: It is impossible to assemble a model of point estimates that represents reality because (in part) of the scope of the choices. Thus the viable alternative is to investigate the processes of causality. Such investigation would take into consideration the rich complex of interactions and relationships that comprise complex systems (Cilliers, 1998). As Mackenzie suggested, if we can find patterns in those processes, then we can better understand leadership and from that can begin to forge knowledge that resonates with users, knowledge that they can apply knowing that it will produce results. Complexity science helps us to do this by offering a theoretical framework for understanding the functioning of interactive dynamics (i.e., processes) in complex systems. It addresses the issues of distributed leadership that were not as clearly expressed in Mackenzie’s LAMPE model, and reframes the role of managerial leadership from aligning and controlling, to enabling and releasing informal dynamics (Uhl-Bien et al., 2004). Complexity focuses on dynamics more than acts, understanding more than prediction (Cilliers, 1998). It draws attention to informal interactions rather than focusing only on managerial coordination and planning. Mackenzie describes several key premises also addressed by complexity science. We suggest that complexity science makes sense of, and adds to, Mackenzie’s premises and their broader implications. While Mackenzie’s search to better understand leadership is certainly on a productive path, we propose that a comprehensive theory of interactive dynamics (i.e., complexity science) can help him better forge this path.

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Drath, W. (2001). The deep blue sea: Rethinking the source of leadership. San Francisco, CA: Jossey-Bass & Center for Creative Leadership. Gronn, P. (1999). A realist view of leadership. Educational leaders for the new millenium – leaders with soul, ELO-AusAsia On-line Conference. Gronn, P. (2002). Distributed leadership as a unit of analysis. Leadership Quarterly, 13, 423–451. Haken, H. (1983). Synergetics, an introduction. Berlin: Springer. Heckscher, C. (1994). Defining the post-bureaucratic type. In: C. Heckscher & A. Donnellon (Eds), The post-bureaucratic organization: New perspectives on organizational change (pp. 14–62). Thousand Oaks, OR: Sage. Homans, G. C. (1950). The human group. New York: Harcourt. Hosking, D. M. (2001). Organizing, leadership, and skillful process. In: K. Grint (Ed.), Leadership: Classical, contemporary, and critical approaches (pp. 293–318). New York: Oxford University Press. Mackenzie, K. D. (2004). Making strange knowledge more intelligible. In: F. J. Yammarino & F. Dansereau (Eds), Multi-level issues in organizational behavior and processes. Research in multi-level issues (Vol. 3, pp. 443–453). Oxford: Elsevier. MacKenzie, K. D. (this volume). The lampe theory of organizational leadership. In: F. J. Yammarino & F. Dansereau (Eds), Multi-level issues in social systems. Research in multi-level issues (Vol. 5). Oxford, UK: Elsevier. Marion, R. (1999). The edge of organization: Chaos and complexity theories of formal social organization. Newbury Park, CA: Sage. Marion, R. (in press). Complexity in organizations: A paradigm shift. In: A. Sengupta & M. Nashed (Eds), Proceedings of the international workshop: Math and physics of complex and nonlinear systems. Berlin: Springer. Marion, R., & Uhl-Bien, M. (2001). Leadership in complex organizations. Leadership Quarterly, 12, 389–418. Marion, R., & Uhl-Bien, M. (2003). Complexity theory and al-Qaeda: Examining complex leadership. Emergence: A Journal of Complexity Issues in Organizations and Management, 5, 56–78. Maxwell, J. A. (2004). Causal explanation, qualitative research, and scientific inquiry in education. Educational Researcher, 33(2), 3–11. Osborn, R. N., Hunt, J. G., & Jauch, G. (2002). Toward a contextual theory of leadership. Leadership Quarterly, 13, 797–837. Pearce, C. L., & Conger, J. (2003). Shared leadership: Reframing the hows and whys of leadership. Thousand Oaks, CA: Sage. Popper, K. R. (1989). Conjectures and refutations: The growth of scientific knowledge. New York: Routledge. Prigogine, I. (1997). The end of certainty. New York: The Free Press. Roethlisberger, F. J., & Dixon, W. J. (1939). Management and the worker. Cambridge, MA: Harvard University Press. Sanders, T. I. (2005). Introduction of Michael Crichton. Smithsonian Associates, Lisner Auditorium, Washington, DC, November 6. Selznick, P. (1949). TVA and the grass roots. Berkeley, CA: University of California Press. Uhl-Bien, M., Marion, R., & McKelvey, B. (2004). Complex leadership: Shifting leadership from the industrial age to the knowledge era. Paper presented at the Academy of Management, New Orleans.

SOME IDEAS ABOUT TESTING PROCESSUAL THEORIES Kenneth D. Mackenzie ABSTRACT This article begins by examining the recommendations of Uhl-Bien and Marion that the LAMPE theory of organizational leadership could be enhanced and improved if it were to incorporate elements of complexity and complexity leadership theory. Their advice should be reversed: complexity leadership theory should incorporate the theory, methods, and models already tested in the construct of the LAMPE theory. The reasons for this conclusion are based on a general discussion of the conditions under which a processual theory can be tested and the testing procedure be made rigorous. According to this approach, complexity leadership theory cannot be most rigorous and the LAMPE organizational leadership theory might be.

INTRODUCTION Uhl-Bien and Marion (this volume) propose that there is a new, rigorous, rich, and comprehensive theory called complexity theory. Complexity theory is suggested as the benchmark against which other theories can be judged. Furthermore, ‘‘progressive’’ researchers ought to embrace complexity theory Multi-Level Issues in Social Systems Research in Multi-Level Issues, Volume 5, 443–475 Copyright r 2006 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-9144/doi:10.1016/S1475-9144(06)05020-X

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to become relevant to this ‘‘new age.’’ The existence of such a grand theory is needed. Unfortunately, their complexity theory does not yet meet the requirements of such a theory. There is a possible offshoot of complexity theory called complexity leadership theory (Marion & Uhl-Bien, 2001; Uhl-Bien, Marion, & McKelvey, 2004). Complexity theory (cf., Kauffman, 1993; Holland, 1995, 1998; Marion, 1999) is used to legitimize and motivate its interpretation as ‘‘complexity leadership theory.’’ They use this melange of complexity theory and a metaphorically derivative called complexity leadership theory to judge how my LAMPE theory of organizational leadership (Mackenzie, this volume) stacks up against their standard. Apparently, LAMPE is on the right track. But LAMPE is too traditional, linear, and into control. This raises two obvious possibilities: (1) Either LAMPE has not gone far enough, or (2) Uhl-Bien and Marion (this volume) have gone too far. While the LAMPE theory is reasonably well-grounded, it is likely that it has not gone far enough (a reasonable assumption given the ‘‘greenness’’ of the theory). The possibility that Uhl-Bien and Marion (this volume) have gone too far is based on my judgment that complexity leadership theory is not firmly grounded. This gives rise to the next question, should LAMPE join up with complexity theory to be more ‘‘progressive’’ or should complexity leadership theory allow itself to become better grounded by paying close attention to the actual LAMPE theory and the methods, theories, and models upon which it is constructed? I believe that the complexity leadership theory is based on metaphors appropriate more to biological and physical systems than to human organizations. I see three main problems: (1) Complexity theory is not grounded in group and organizational phenomena; (2) complexity leadership theory is only metaphorically grounded in complexity theory; and (3) complexity leadership theory is deficient as a model of organizational reality (there is no organization). Thus, despite some interesting ideas in complexity theory, I cannot see a good reason to join LAMPE with either complexity theory or complexity leadership theory. On the other hand, I can see plenty of reasons why complexity leadership theory ought to consider reversing course by becoming better grounded in the phenomena and embracing LAMPE as its starting point. In short, I believe that while complexity leadership theory has managed to argue for several interesting propositions, its status as an organizational leadership theory is that it is underdeveloped. It needs to be emphasized that criticisms of complexity leadership theory are derivative of criticisms that can be leveled at most organization and leadership theories. Most of these theories have their good points but most

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are also incomplete. The set of such theories resembles the fleet of balloons in the annual Albuquerque Balloon Festival. There, balloons take on fantastic shapes (e.g., a castle, a ristra of red chilis). The colorful variety is analogous to the large set of existing organizational and leadership theories. These large balloons are hard to construct, difficult to launch, hard to control, use copious and continuous supplies of hot air to remain in the air, require a chase team, are fun to watch, even more fun to fly, and fly safely only under special conditions. But, they change nothing on the surface. I believe that complexity leadership theory is unmoored, too abstract, and only tangentially related to organizational leadership. However, I find some of the properties and mechanisms of complexity leadership theory to be intuitively appealing (e.g., aggregation and tags), problematic (e.g., diversity and flows), and, frankly, strange knowledge (what’s produced?). Theories and models are easier to create than to test. Testing is hard, frustrating, and tedious. The LAMPE theory of organizational leadership is no exception. Certainly, it is fair to doubt the testability of complexity leadership theory as it is currently postulated. But, is the LAMPE theory testable? Given the plethora of theories and models about leadership and the inability to achieve consensus, it makes sense to step back and examine how we decide if what we think is valid, is, in fact, valid. That is, how does one test a multi-level processual theory? The reasons why, years ago, I chose to eschew chaos and complexity ‘‘theories’’ may help in understanding why it might be helpful to rethink how one tests theories such as LAMPE and complexity leadership theory in particular and multi-level process theories in general. In the next section, I shall briefly state my misgivings about complexity leadership theory. These misgivings lie at the heart of the need to rethink the validity of our theories and models.

CRITIQUE OF COMPLEXITY THEORY AS AN ORGANIZATION THEORY Chaos and complexity theory offer an intriguing class of mathematical models for understanding the presence of order. Instead of relying on random selection as the sole basis for evolution, pioneers such as Waldrop (1992), Kauffman (1993), Holland (1995, 1998), and others associated with the Santa Fe Institute, applied statistical mechanics, dynamic systems modeling, and genetic algorithms (cf., Holland, 1995), to provide a supplementary class of explanations for the existence of the complexity of living organisms

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and physical systems. These modeling techniques offer a plausible new explanation for order in immensely complex biological and physical systems. They are rich mathematically and provide ‘‘act as if’’ theories. While it is unclear that such theories are capable of providing empirically verifiable and rejectable testing (much like the micro-economics theory of the firm which has never been able to prove that firms are profit maximizing), they can point to phenomena and argue that the results are ‘‘as if’’ their models were correct. This is an old issue in the philosophy of science. For instance, Cyert and March (1963, Appendix A, pp. 298–311) point out problems with Friedman’s (1953) positive approach to economics. The main issue is whether or not the validity of the assumptions one makes to derive a result are important as long as the predictions hold. If you maintain that the objects of a theory require ‘‘bridging principles’’ that link theoretical concepts to observables, then one concludes that the assumptions do matter. However, ‘‘act as if’’ theories are indirect and possibly preferred whenever direct observations are impossible or unethical. In the case of complex biological systems such as enzymes, or the human genome, and physical systems such as weather, gases, etc., ‘‘act as if’’ theories can open up new, fruitful lines of inquiry and have a proven track record (e.g., thermodynamics). I remember being excited about chaos theory when I first ran across it in the 1980s. It seemed to me (and it still does) that those at the Santa Fe Institute (cf., Holland, 1995, for a review) had, indeed, offered an interesting, viable, and enlightening supplement to the usual models of evolution. Their ideas provide an answer to persistent critics of evolution who propose ‘‘alternatives’’ such as intelligent design and creationism. These are important issues which make chaos and complexity theories worth pursuing. They certainly have attracted the interest of the public, and of popular authors such as Gleick (1987), Holland (1995, 1998), Marion (1999), Waldrop (1992), and Wheatley (1999), who have published fascinating introductions and interpretations of chaos and complexity theory. But what, precisely, is the relevance of these theories to the evolution, change, and functioning of human groups and organizations? Unlike trying to explain the origin of the cosmos or life and its evolution, it is actually possible to conduct experiments of little organizations forming and adapting to various challenges. These are relatively simple systems which can be modeled and tested empirically. One can, in fact, intelligently design human organizations. Thus, there seems to be no need for assumptions based on phase and parameter spaces, random NM Boolean networks, and artificial conclusions arrived at after exhaustive computer simulation of complex models.

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I spent over a decade of my life performing such experiments, which were summarized in the two volume set entitled A Theory of Group Structures (Mackenzie, 1976a, b). In these experiments, one could actually observe the exact sequences of interaction by which groups organized themselves, selected emergent leaders, corrected errors, solved problems, engaged in power struggles, etc. Using the methods of strong inference (cf., Platt, 1964; Mackenzie & House, 1978; Mackenzie & Rahim, 2003), a theory of group structures evolved and was critically tested and found not yet false. The theory of group structures, at least for the instances of the experiments, explained how structures formed and changed, explained the rate by which structures evolved, and provided detailed algebras of influence attempts. We also investigated the effects of incentives on both problem-solving processes and structures (Luzi & Mackenzie, 1982), and applied diffusion models to explain the direction and speed of adoptation of a new structure. Mackenzie and Bello (1981) ran an experiment to explain the leadership effectiveness ratings of every group member as the group emerged. My next adventures into intelligent design, as opposed to random ordering and invoking mysterious energies, came in the efforts to actually design organizations (Mackenzie, 1986, 1991). These experiences in studying the processes of structural change, leadership emergence, and in designing organizations leaves me wondering if it is either necessary or helpful to invoke complexity theory to explain organizations in general and to improve theories of organizational leadership in particular. Why introduce extra, artificial assumptions when they may be unnecessary and when they produce results that are less precise, operational, and incapable of falsification? This earlier work on a theory of group structures provides a working theory which comes with its own theory, models, technologies, and a history of careful testing. It is this earlier work which led to the theory of the organizational hologram and the LAMPE theory of organizational leadership. If this body of research is deemed to be invalid, incomplete, and in error, the burden of proof is on those critics to explain why. They need to propose alternative models and theories to explain why the existing theory is in need of improvement and exactly how the new replacement plans to accomplish this. Furthermore, they should be able to explain what the existing theories and models explain. Otherwise, the pretty balloon has no effect on the grounded prior research. One of the major tools in complexity theory is the use of dynamical systems modeling. Dynamical systems modeling involves the use of systems of equations describing the states and changes in an interdependent system of variables and parameters. These are usually in the form of a set of

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differential equations. Such models were popularized by Jay Forrester (1969). A difference equation version was used by Roger Hall (1984) in his study of the survival of the magazine called The Saturday Evening Post. Control theorists of many stripes use dynamical systems modeling because it allows them to study the effects of simulated changes in a system. These types of models are hardy perennials. They are successful in many fields (such as electrical engineering), they have an installed base of useable software, and they appeal to the engineer in us. But, they require the belief that organizations are closed systems describable by a set of differential equations. Moreover, differential equations require continuous variables. It is doubtful that organizational data are continuous, that variables should replace processes, that the relationships among processes are fixed and given, and that organizations are closed systems. The usual measure, for example, of an organizational variable is a crude, ordinal, Likert scale of 5–9 values. Differential equations are an acquired taste. It is, however, usually easier to write down the differential equations than it is to solve them. Most solutions represent an equilibrium state. But, to arrive at a solution, one usually has to alter the form of the differential equation to enable its solution. We often just chop off extra terms in an equation. Unfortunately, when one does this, the solution is often much more restricted than that generated by a full solution. Hence, the need for computer simulation. What Kauffman (1993) and others have done is to show fascinating classes of equilibria called strange attractors and to explore the conditions under which these attractors are stable or chaotic. They provide arguments that there could be an edge to the inherent chaos ‘‘nearby’’ the stable equilibrium which is in flux and called ‘‘the edge of chaos.’’ It is in this in-between realm where they believe order evolves. However, is any of this intellectual machinery helpful in running an organization? It is too abstract to capture the context and specifics of a situation confronting a leader or an organization. It ignores the vast cumulation of the history of organizations which have managed to survive and even thrive in the face of adversity. We know much about organizing to cope with massive interdependence uncertainty (cf., Mackenzie, 1991) but whether or not this is an ‘‘edge of chaos’’ is only an unsubstantiated claim. The challenge is to distill these leadership lessons without eliminating the high human drama of leaders leading in the midst of high levels of uncertainty. Military history is chock-full of documented examples of leaders who, through their organizations, successfully manage change by intelligently

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redesigning organizations to meet the severe challenges of combat. For instance, the U.S. Marine Corps has had its full share of trials and tribulations as it fought for a role and for budgets within the Department of Defense. One notable example is the creation, deployment, and deactivation of the famed Marine Raider battalions in the early part of our involvement in WW II (Alexander, 2001; Smith, 2001; Warren, 2005).

REVERSING THE ADVICE Is it necessary to invoke a border between chaos and order and to represent it as an abstract statistical and mathematical creature? Whatever the merits of chaos and complexity theory, there is doubt that we actually know what these ideas mean in a real organization. Marion (1999) seems to have less doubt. He treats the edge of chaos as if it exists, is knowable, and is useful in analyzing an organization and its leadership. Basically, in my opinion, there is a large disconnect between the mathematical and statistical models of Kauffman and the metaphors based on it employed by Marion (1999), Marion and Uhl-Bien (2001, 2003), and Uhl-Bien et al. (2004). Organizations are characterized by their small samples, coarse data, direct observability, and our ability to intervene and intelligently design them to achieve a variety of goals. Thus, it is unlikely that chaos theory can contribute much of value in studying and testing theories and models of organizations. They add unnecessary assumptions, impose unrealistic conditions, and do not engage in the realities of the organizations they claim to model. Furthermore, the linkage between complexity leadership theory and the chaos and complexity models such as those of Kauffman (1993) is more metaphysical than scientific. Consequently, the phenomological underpinnings of complexity leadership theory seem weak and based on metaphors that are neither anchored to the phenomena nor to the statistical and mathematical models of the parent disciplines. For now, I’m convinced that there is neither theoretical nor practical advantage in taking their advice to incorporate complexity theory. (However, exploring the links between Boolean networks and process frameworks appears fruitful.) Actually, reversing Uhl-Bien’s and Marion’s advice may make more sense. It would help Marion and Uhl-Bien to take a second look at my theories, methods, and models to shore up their intuitions based on the metaphors of complexity theory. I believe that they are progressing toward a derivation of their conclusions based on more than metaphors (cf., Uhl-Bien et al., 2004). I think that they ought to move toward incorporating the theory of the organizational hologram

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(Mackenzie, 1991) and jettison unworkable and untestable metaphors based on statistical mechanics and dynamical systems modeling. They would find that the issue of interdependence (Mackenzie, 2001) is far more sophisticated, multi-level, and concrete than an arid set of differential equations or airy abstraction of metaphors. They might want to incorporate organizational goals or purposes by examining Mackenzie (1998) on the prime directive. The work on dynamic congruency (Mackenzie, 1986, 1991, 2003) would yield links between a comprehensive model of organizations and their processes, for achieving organizational-level congruency and processes for bonding people, positions, and organizations. The actual methods of processes can be found in many of these articles but Mackenzie (2000a) is recommended. The multi-level notion of organizational processes, including leadership, is covered in Mackenzie (2004a, b). The links to organizational level learning are well-specified (cf., Mackenzie, 1994, 1995; Benoit & Mackenzie, 1995). There is a specific technology to survey organizations to obtain measures of the processes of 29 leadership practices (which are processes, not variables), and to estimate organizational level learning, intelligence, dynamic congruency, and various process and structural maladaptations (Mackenzie, 2000c, d). However, the main benefit to reversing their advice would be to understand that the LAMPE theory of organizational leadership, the organizational-level learning model, and the knobby analysis technology is based on the theory of the organizational hologram (Mackenzie, 1991, 2000b). The charge by Uhl-Bien and Marion that the LAMPE theory is a top–down, control-oriented, general systems model, and traditional in its view of organizations is wide off the mark. The LAMPE theory of organizational leadership is based on the theory of the organizational hologram (Mackenzie, 1991). The central idea of the organizational hologram is that there exist processes, called holonomic processes, that need to be present everywhere in the organization if it is to become simultaneously productive, adaptable, and efficiently adaptable. The idea of a hologram is that every part contains the whole. It is clearly not top–down, bottom up, and sideways. It is neither control minded nor a general system model. Whether it is traditional or not is arguable. For example, the philosophies and practices embodied in the doctrines of res publica and mos maiorum in the Roman Republic allowed a sort of holographic organization to flourish. It may be that culture is a holonomic concept (cf., Mackenzie, 1991, pp. 421–425). Wagner (2001) argues that world perspectives of New Guinea peoples are holonomic. The concept of the Trinity in Christianity is basically that of a hologram. Bradley (1987) demonstrated the holonomic character of charismatic leaders

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in communes. It is possible that holograms are more natural than bureaucracies as the basis for organizing and understanding our place in society. Of course, since the subject of my article was leadership and the processes involved, it may have appeared to be more control-oriented than it is. But real leaders use power and authority to influence outcomes. They also manage. They must deal with shifting environments, the complexities of actual work, and real people. In LAMPE, the main idea is that organizations work best when their leadership, authority, management, power, and environments are coherent, integrated, and operational. Holograms are not linear models. The multi-level, non-linear, knobby analyses technologies are a working solution to examining the deployment of processes of leadership practices throughout an organization.

TESTING PROCESSUAL MODELS AND THEORIES In previous papers (Mackenzie, 2000a, 2003, 2004a, b), I’ve argued the following: (1) All group and organizational phenomena are inherently processual in their natures, (2) variables which are, at best, a measure of a process, are not processes; (3) there exist an immense variety of group and organizational processes which have a common analytical representation; (4) multi-level phenomena share the same abstract analytical representation; and (5) one cannot conduct rigorous empirical research without using this analytical form. That is, there seems to be a common representation of group and organizational processes at one or multiple levels. These ideas can be extended to develop methods for testing processual inquiries. These extensions further demonstrate the intellectual and theoretical inadequacies of complexity leadership theory and, hopefully, offer a workable roadmap of what will be necessary to improve the studies of complexity leadership theory. What is presented in the appendix is a set of related ideas designed to pin down what is necessary to state and test processual theories and models. This extension, in the appendix, begins with the introduction of a new idea based on an analytical tool called the process skeleton. Then I define a procedure of enhancing process skeletons to create process functions. This procedure allows one to construct theoretical analytic functions based on the underlying process skeleton. In particular, each path in a process skeleton can have its own outcome evaluation function, and the set of these can be assembled into an overall process function. This analysis is followed by a section on process dependences. This sets up machinery for dealing with the

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tough issues of data aggregation. I analyze issues of testing process frameworks and the introduction of the enhancement/reduction model for testing a process framework. Finally, these ideas are placed in the realm of multi-level processes. If these ideas are close to being on target, then it is questionable to conclude that decades of statistical studies on group and organizational phenomena have been worth the effort. It is also clear that complexity leadership theory, as it is currently formulated, is least rigorous.

CONCLUSION There is no shortage of ideas, theories, models, and measures of leaders and leadership. Most have an intuitive and plausible core idea. The presence of competing theories about such vital subjects such as leadership creates a need for better theory. But how does one know when one theory of leadership is better than another? What needs to be done to sort out the better from the less good? This article contains some new ideas about testing processual theories. Because both complexity leadership theory and LAMPE theory are processual, the challenge was to develop some new ideas about testing processual theories.

ACKNOWLEDGMENTS The author wishes to thank Mary Uhl-Bien for sharing some reprints about her research on complexity leadership theory. As has been the case for many years, Donald F. Utter continues to offer serious commentary on my work. In this case he helped clarify the ideas about process skeletons and their frameworks. He also keeps my ‘‘feet to the fire’’ concerning all matters philosophical. Susan M. Treber’s perceptive copy editing certainly improved this article. Any existing flaws are due solely to this author.

REFERENCES Alexander, J. H. (2001). Edson’s raiders: The 1st Marine raider battalion in World War II. Annapolis, MD: Naval Institute Press. Benoit, C. A., & Mackenzie, K. D. (1995). The science of an organization, part II: Realizing a new model of organizational learning. Human Systems Management, 14(2), 119–132. Bradley, R. T. (1987). Charisma and social structure: A study of love and power, wholeness and transformation. New York: Paragon House Pubs.

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Cyert, R. M., & March, J. G. (1963). A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice-Hall. Forrester, J. W. (1969). Urban dynamics. Cambridge, MA: MIT Press. Friedman, M. (1953). The methodology of positive economics. In: M. Friedman (Ed.), Essays in positive economics (pp. 3–46). Chicago, IL: University of Chicago Press. Gleick, J. (1987). Chaos: Making a new science. New York: Penguin Books. Hall, R. I. (1984). The natural logic of management policy making: Its implications for the survival of an organization. Management Science, 30(8), 905–927. Holland, J. H. (1995). Hidden order. Reading, MA: Addison-Wesley. Holland, J. H. (1998). Emergence: From chaos to order. Reading, MA: Helix Books. Kauffman, S. A. (1993). The origins of order: Self-organization and selection in evolution. New York: Oxford University Press. Lippitt, M. E., & Mackenzie, K. D. (1976). Authority
task problems. Administrative Science Quarterly, 21(4), 643–660. Luzi, A., & Mackenzie, K. D. (1982). An experimental study of performance information systems. Management Science, 28(3), 243–259. Mackenzie, K. D. (1976a). A theory of group structures, volume I: Basic theory. New York, NY: Gordon and Breach Science Publishers. Mackenzie, K. D. (1976b). A theory of group structures, volume II: Empirical tests. New York, NY: Gordon and Breach Science Publishers. Mackenzie, K. D. (1986). Organizational design: The organizational audit and analysis technology. Norwood, NJ: Ablex Publishing Corporation. Mackenzie, K. D. (1991). The organizational hologram: The effective management of organizational change. Boston, MA: Kluwer Academic Publishers. Mackenzie, K. D. (1994). The science of an organization, part I: A new model of organizational learning. Human Systems Management, 13(4), 249–258. Mackenzie, K. D. (1995). The science of an organization, part III: Organizational problem solving during the ODS process. Human Systems Management, 14(2), 133–148. Mackenzie, K. D. (1998). The prime directive for organizations. International Journal of Organizational Analysis, 6(4), 289–309. Mackenzie, K. D. (2000a). Processes and their frameworks. Management Science, 46(1), 110–125. Mackenzie, K. D. (2000b). A theory for managing fast changing organizations. In: M. A. Rahim, R. T. Golembiewski & K. D. Mackenzie (Eds), Current topics in management (Vol. 5, pp. 17–39). Stamford, CT: JAI Press. Mackenzie, K. D. (2000c). Knobby analyses of knobless survey items, part I: The approach. International Journal of Organizational Analysis, 8(2), 131–154. Mackenzie, K. D. (2000d). Knobby analyses of knobless survey items, part II: An application. International Journal of Organizational Analysis, 8(3), 239–262. Mackenzie, K. D. (2001). The organization of organizations. International Journal of Organizational Analysis, 9(2), 116–148. Mackenzie, K. D. (2003). Dynamic congruency. In: M. A. Rahim, R. T. Golembiewski & K. D. Mackenzie (Eds), Current topics in management (Vol. 8, pp. 19–42). New Brunswick, NJ: Transactions Publishers. Mackenzie, K. D. (2004a). The process approach to multi-level organizational behavior. In: F. Yammarino & F. Dansereau (Eds), Research in multi-level issues: Multi-level issues in organizational behavior and processes (Vol. 3, pp. 347–417). Oxford, UK: Elsevier Science.

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Mackenzie, K. D. (2004b). The levels of levels in organizational phenomena. In: M. A. Rahim, R. T. Golembiewski & K. D. Mackenzie (Eds), Current topics in management (Vol. 9, pp. 225–255). New Brunswick, NJ: Transactions Publishers. Mackenzie, K. D., & Bello, J. A. (1981). Leadership as a task process uncertainty control process. Human Systems Management, 2(3), 199–213. Mackenzie, K. D., & House, R. J. (1978). Paradigm development in the social sciences: A proposed research strategy. Academy of Management Review, 3(1), 7–23. Mackenzie, K. D. & Rahim, M. A. (2003). Strong inference and weak data. In: M. A. Rahim, R. T. Golembiewski & K. D. Mackenzie (Eds), Current topics in management (Vol. 8, pp. 315–339). New Brunswick, NJ: Transactions Publishers. Marion, R. (1999). The edge of organization: Chaos and complexity theories of formal social systems. Thousand Oaks, CA: Sage Pubs. Marion, R., & Uhl-Bien, M. (2001). Leadership in complex organizations. Leadership Quarterly, 12, 389–418. Marion, R., & Uhl-Bien, M. (2003). Complexity theory and al-Queda: Examining complex leadership. Emergence: A Journal of Complexity Issues in Organizations and Management, 5, 56–78. Platt, J. R. (1964). Strong inference. Science, 146(3642), 347–353. Smith, G. W. (2001). Carlson’s raid: The daring Marine assault on Makin. Novata, CA: Presidio Press. Uhl-Bien, M., Marion, R., & McKelvey, B. (2004). Complexity leadership theory: Shifting leadership from the industrial age to the knowledge era. Unpublished. Wagner, R. (2001). An anthropology of the subject: Holographic worldview in New Guinea and its meaning and significance for the world of anthropology. Berkeley, CA: University of California Press. Waldrop, M. M. (1992). Complexity: The emerging science at the edge of order and chaos. New York: Simon & Schuster. Warren, J. A. (2005). American Spartans: The U.S. Marines: A combat history from Iwo Jima to Iraq. New York: Free Press. Wheatley, M. (1999). Leadership and the new science (2nd ed). San Francisco, CA: BerrettKoehler.

APPENDIX: INTRODUCTION Process Technically, any group or organizational process is defined as a timedependent sequence of events governed by a process framework. Such processes have these five components: 1. 2. 3. 4. 5.

The The The The The

processual agents involved in the process steps or considerations in the process pairwise linkages between the elements of 2 links to other processes, and task resources and their characteristics-in-use.

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The idea of a process and its properties is carefully developed in Mackenzie (2000a, 2004a, 2006) and will not be repeated here. A process framework consists of three components (2, 3, and 4) of a process. That is, a process framework includes the steps or considerations contained in a process (including the outcomes), the pairwise linkages between all pairs of the considerations, and the links to other processes of the organization. Process Frameworks Any process framework can be formulated as Y ¼ FðCÞ

(A.1)

where C is the vector of considerations, Y the vector of outcomes, and F the process graph connecting the ciAC to the yjAY. The yjAY are distinct. There are three components to a process framework: (1) the vector of considerations, C; (2) the vector of outcomes, Y; and (3) the process graph, F, which integrates C and Y. The outcomes are shown as the far right-hand side of F, and are part of F. Practically, process frameworks are constructed by an iterative procedure of defining C, Y, and F. Along the way, the vectors C and Y may be modified and F usually changes. The result is a graphical representation of the process graph, F. Often, the outcomes exhibit the property of equifinality, in which the same outcome can be the result of different paths through the process graph, F. Example 1 Let C ¼ (c1, c2, c3, c4) and let Y ¼ (y1, y2, y3, y4, y5) The process graph, F, includes assumptions connecting the considerations to each other and to the outcome. Example 1’s process graph, F, is represented by Fig. A1. Note that the process graph of Example 1 has eight paths, as indicated by the path column to the right of F. Furthermore, outcomes on paths 4 and 6 are the same outcome (y4) and the outcomes on paths 3, 5, and 7 are the same outcome (y3). Process Skeletons It is possible to transform the process graph, F, into a matrix of n rows and n+1 columns in which the n rows refer to the cj’s (c1, c2, y cn) and

456

KENNETH D. MACKENZIE Path y1 c3 c2

1 0

1

c4

0 F=

c1

1 c4

0

c3

1

c4

1

1

y 2 y1

2

0

y 3 y2

3

1

y4

4

0

y3

5

1

y4

6

0

y3

7

y5

8

0

Fig. A1.

A Process Graph, F.

the (n+1)st column is the outcomes linking the cj A C to the members of Y. In Example 1, this would be a matrix of four rows and five columns. This conversion of sequences of linkages to pairwise linkages loses the information contained in linkage chains longer than a single link. The loss of information in the matrix form creates ambiguities where there is equifinality (more than one path to the same outcome) and the possibility that any specific cj can occur on multiple paths which, in combination with the other cjs in the same path, can produce different outcomes. Thus, the linkage between a cj and a yi is path dependent. In the example, if c4 ¼ 1, then on path 2, the outcome is y2, but on path 4, the outcome is y4. If c2 ¼ 1, then on path 2 the outcome is y2 and it is y3 on path 3. On path 3, if c2 ¼ 1, the outcome is y3, but on path 5, the outcome is also y3 when c2 ¼ 0. These relationships in process frameworks can be rendered unambiguous by rearranging the information in a process framework as a process skeleton. This is accomplished by converting the process graph, F, into a new matrix form called a process skeleton. In a process skeleton, denoted as PS, the rows are the paths through the process graph, F, the columns are the considerations cjAC, and the state of the outcome, yjAY, is the last column in a process skeleton and the other entries in PS are pkj (the jth consideration

Some Ideas about Testing Processual Theories

on the kth path), where 8 1 > < pkj ¼ 0 > :


457

if cj ¼ 1 on path k if cj ¼ 0 on path k if cj is undefined on path k

and yk is the outcome in Y for path k. The yk is the state of the outcome, defined by the set of the cj in path k having a defined entry in a process skeleton, PS. Please note that any path is a vector of its considerations and outcome. The binary nature of the pkj and the cjAC can be extended to multiple values if needed and multiple values can be converted into a sequence of binary values. The process skeleton of Example 1 is shown in Table A1. Process skeletons allow for more intuitive examination of a process framework because one can examine each path individually. Once defined, assuming that the model is correct, there is no ambiguity about the values of the considerations leading to the path’s outcomes. This allows processual models to be rejected by empirical research. For expository purposes, it is most convenient to represent a process framework as Y ¼ F(C), where F is the matrix form of the process graph. However, for empirical research, the process skeleton, PS, is recommended because the presence of possible ambiguity in F is removed. This clarity fosters the use of strong inference to verify that when the considerations in a specific path are defined, the path’s outcome either does or does not obtain. Another advantage of the process skeleton is that each path represents a separate case of a more comprehensive model as represented by the process Table A1.

Considerations, C

Path

PS

1 2 3 4 5 6 7 8

The Process Skeleton of Example 1. Path Outcome in Y

c1

c2

c3

c4

1 1 1 1 1 0 0 0

1 1 1 0 0 – – –

1 0 0 – – 1 1 0

– 1 0 1 0 1 0 –

y1 y2 y3 y4 y3 y4 y3 y5

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KENNETH D. MACKENZIE

skeleton. Thus, the process skeleton can be thought of as a model of models (one for each path), or as a device for representing a class of models (each path or case) in a comprehensive and integrated form. A process framework path is denoted by pfp, where pfp is a vector of the considerations in its path and the outcome of that path. In the case of Example 1, the pfp of path 5, denoted pfp5, is pfp5 ¼ ð1; 0; 2 2 2; 0; y3 Þ

ENHANCING PROCESS SKELETONS TO PROCESS FUNCTIONS This section begins with an axiom that links each consideration to some defining variable. Three other axioms are introduced as the analysis proceeds to objective function based on process skeletons. Axiom 1. There is a defining variable for every consideration in a process framework. More precisely, for every consideration cjAC in a process framework there exists an underlying defining variable xjAX, where X is the set of defining variables. The adjective ‘‘defining’’ is used in place of ‘‘causal’’ because some of the underlying variables are nominal (by states such as legal/illegal) and hence, descriptive, rather than causal. Some considerations are ordinal with values showing relative ‘‘rank’’ or level (e.g., general, captain, lieutenant, sergeant, private). Some considerations are cardinal in that they represent a continuous variable. A good example is the adjusted average rating (AAR) of leadership effectiveness in Mackenzie and Bello (1981). In every case, the underlying variable is defining but in the case of ordinal and cardinal variables, they could be causal. The considerations in a PS are always defined with respect to a threshold value of the underlying defining variable. The threshold values of a consideration, cjAC, is defined with respect to its underlying defining variable, xjAX, such that: ( 1 if xj  x0j cj ¼ 0 otherwise where x0j is the threshold value of xjAX for the jth consideration, cjAC.

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459

Examples of Threshold Values of Considerations with Different Cardinality Nominal Numbers Let a c6 be defined as the existence of a recurring authority task problem (cf. Lippitt & Mackenzie, 1976). Then,
1 if there is a recurring authority task problem c6 ¼ 0 otherwise: So, the x6 is a binary variable whose value is unity or zero. Ordinal Numbers Most social science data obtained from survey instruments involve Likerttype scales which are ordinal. For example, a statement is made such as ‘‘I am satisfied with my compensation.’’ The choices are 5 strongly agree, 4 agree, 3 neutral, 2 disagree, and 1 strongly disagrees. The 5, 4, 3, 2, and 1 form an ordinal scale. In the Lippitt–Mackenzie example, c7 is the acceptability of a solution. The Likert scale could be 5, accepted by all; 4, accepted by most; 3, group is evenly split; 2, the majority would not accept the solution; and 1 all would be opposed to the solution. Then the threshold, for the x7 (degree of acceptance) could be used to define c7 as
c7 ¼

1 0

if x7  4 otherwise

Cardinal Numbers Let c10 be the consideration ‘‘Is it feasible to have a consultant?’’ Next, let x10 be the budget available for hiring a consultant whose cost is $50,000. Then x010 ¼ $50,000. Then,
1 if x10  $50; 000 c10 ¼ 0 if not The defining variable is cardinal in terms of dollars of budget allocated for the hiring of a consultant. Axiom 2. There is a defining variable for every outcome in a process framework.

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More exactly, Axiom 2 states that for every outcome, yjAY, in a process framework, there is a defining variable, zjAZ, where Z is the set of defining outcome variables. The threshold values of an outcome, yiAY, is defined with respect to its underlying defining variable, ziAZ, such that: ( yi ¼

if zi  z0i otherwise

1 0

where zi0 is the threshold value of zi. Examples of Threshold Values of an Outcome with Different Cardinality Nominal Scales Lippitt and Mackenzie (1976) used seven outcomes in its nominal scale for its process framework outcomes. These were: y1 pass the buck upwards; y2 do nothing; y3 hire a consultant; y4 appoint a committee; y5 form a task force or team; y6 refer the problem to an existing standing committee; and y7 solve or direct implementation of your solution. Ordinal Scales Mackenzie and Bello (1981) used this ordinal scale for the outcome in which leadership effectiveness was rated on this 9-point Likert scale: None At all

1

A Little

2

A Moderate Amount

3

4

5

A Great A Very Amount Great Amount

6

7

8

9

For this study, a leader was judged as effective if his or her average rating is greater than or equal to 6 and ineffective if less than 6. Cardinal Scales Mackenzie and Bello (1981) also used a cardinal scale, which employed a rater/ratee matrix of the ratings using the ordinal Likert scale in the preceding example. The matrix was used to define a variable, z, called the AAR which varied from a low of 1 to a high of 9. The outcomes of the process

Some Ideas about Testing Processual Theories

framework were:


y¼

1 0

461

if z  6 if zo6

where the threshold value is z0 ¼ 6. Process Framework Functions Process skeletons are called skeletons because the defining variable for each of their considerations is represented as a binary-valued consideration. Process skeletons can be enhanced by surfacing and incorporating the defining variables. The result is a theoretical analytic process model. Model enhancement is a procedure for converting a process skeleton into a theoretical analytic process model. It is possible to create an enhanced model for every path and for the entire process skeleton. A process framework function is the theoretical analytic process model for the process skeleton. These ideas will become vital in understanding issues for testing the validity of processual theories and for analyzing why most statistical analyses fail to actually test the underlying theory that is purported to be under ‘‘test.’’ The procedure for model enhancement begins with the axiom: Axiom 3. The defining variable of a consideration in a process skeleton is included in a process framework function if it exceeds its threshold value (cj ¼ 1). If cj is undefined, then the consideration is not considered to be defining in creating the outcome. If cj ¼ 0, that usually means that the defining variable, xj, is not considered, is absent, or is insufficient to effect a different outcome. Let the outcome evaluation of the kth path in the PS be denoted as z(pfpk). Then, the outcome evaluation of the kth path is PS is proportional to the product of the defining functions of its non-zero considerations. For the sake of clarity, denote by Jk the number of non-zero considerations in path k in PS. Then, the value of z(pfpk) is given by 8 P ak xj > > > > < k 2 JK zðpfpk Þ ¼ or > > > > : b if there are non-zero considerations in path k: 0

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What is the meaning of the a’s? The ai are used to create uniform scales for the dimensions and quantities of variables. The ai coefficients create dimensionless numbers between 0 and 1 out of the xj. If, for example, x1 is in [dollars], then a1 has the dimension of [dollars]
1. If x2 is in [meters], then a2 has the dimension of [meters]
1, etc. If x1 x2 is in units of [dollars][meters], then a3 has dimension of [dollars]
1[meters]
1, etc. Axiom 4. The theoretical analytic process model, Z(PS), combines the outcome evaluation of the paths in PS. One such theoretical analytic process model is: m X ZðPSÞ ¼ b0 þ bi zðpfpk Þ k¼1

where there are m paths in PS. Please note that the weights in the above equation of Z(PS) is a linear combination of the path evaluations in PS. These coefficients, b0, b1, y, bm, can be interpreted as ‘‘weights’’ assigned to each path outcome. The basis for these weights is not specified here. But in some cases they could come from regression coefficients in a very carefully controlled study which keeps each case or path separate in the analysis. It needs to be noted that such linear weights of the outcome evaluation for the paths is but one of many possible analytical theoretical process models. For example, one could use time, money, space, etc. to serve for Z(PS). Process framework enhancements follow an eight-step procedure. These steps include:  Step 1: Understand and formulate the process. (This is the most difficult step.)  Step 2: Produce the process framework by defining C, Y, and F. (This is also difficult but becomes easier to accomplish as one gains experience.)  Step 3: Label each path in F.  Step 4: Specify the process skeleton, PS.  Step 5: Specify the defining function for every consideration.  Step 6: Apply these variable linking rules for linking the values of the considerations: a. Any path, pfp, in F with no non-zero values of its considerations represents a constant. b. Any path, pfp, with a single non-zero value of a consideration represents the existence of its defining variable. c. Any path, pfp, with two or more non-zero values of its considerations represents the product of their defining variables.

Some Ideas about Testing Processual Theories

463

 Step 7: Define the outcome evaluation z(pfpk) for each of the k paths in PS.  Step 8: Define the overall analytical theoretical process model, Z(PS). Special notes: Given the possibility of equifinality, the same outcome can sometimes be reached via different paths. Since each path has its own outcome evaluation, z(pfpk), the consequence of equifinality is a set of simultaneous equations in the defining variables. These can be used analytically to combine and simplify the theoretical analytic process model, Z(PS). Furthermore, not all Z(PS) need be linear in their variables. Depending on how one decides to evaluate the considerations and outcomes on the paths in a PS, it is possible to derive almost any theoretical analytic process model of the defining variables of its non-zero considerations. For example, there is no inherent reason why xi cannot be sin2xi or logexi, etc. However, variable powers above unity are suspect because that implies that the same consideration occurred multiple times in the path whose outcome is being evaluated. For example, if there is an xi3 term, then the same consideration occurs three times in the same path. Ordinarily, one assumes that most humans do not ‘‘loop’’ in representing behaviors unless they are obsessive/compulsive or irremediably stupid. (Incidentally, models based on control theory usually contain such loops as the modeler assumes that human processes are isomorphic with those of circuits, a highly dubious belief.) Example 2 Let the process graph be the one shown in Fig. A2 and its process skeleton shown in Table A2. In Step 5, we specify the underlying defining process for the considerations. Let x1 be the defining variable for c1, x2 be the defining variable for c2, etc. In Step 6, we apply the variable linking rules to the path: Path 1 becomes x1x2; path 2 becomes x1; path 3 becomes x2; and path 4 becomes constant. In Step 7, for a linear model, the outcome evaluation for each path becomes: z(pfp1) ¼ a3x1x2; z(pfp2) ¼ a1x1; z(pfp3) ¼ a2x2; and z(pfp4) ¼ ao (a constant), for paths 1, 2, 3, and 4, respectively. In Step 8, the overall theoretical analytic process model, Z(PS) is defined as ZðPSÞ ¼ b0 þ b1 xj1 þ b2 xj2 þ b3 xj1 xj2

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KENNETH D. MACKENZIE

1 1 F=

c1

c2

0 c3

Fig. A2.

Table A2.

PS

Outcomes (1,1) y3

0

(1,0) y1

1

(0,1) y2

0

(0,0) y4

The Process Graph of Example 2.

The Process Skeleton of Example 2.

Path

c1

c2

y

1 2 3 4

1 1 0 0

1 0 1 0

y3 y1 y2 y4

where x|1 ¼ a1x1, x|2 ¼ a2x2. Please note that Z(PS), while customarily linear in the social sciences, need not be, as shown in the last term in the equation above (b3 x|1 x|2). Example 3 Let the process graph, F, be as shown in Fig. A3 and its process skeleton as shown in Table A3. This translates into the process function of ZðYÞ ¼ b0 þ b1 xj1 þ b2 xj2 þ b3 xj3 A strictly linear combination of variables depends on a special process graph, F, in which each consideration with unit value is (1) contained on only one path, and (2) the only non-zero link to an outcome on the same path. The C portion of PS can be arranged so that it is a matrix with 1’s on the diagonals and the off-diagonal cells are either zero or undefined. This is a hunch. I think that the process skeleton resembles a decision matrix whose outcomes are the paths’ outcomes [z(pfpk)] and whose overall objective function, Z(PS), is a combination of the outcomes of each path.

Some Ideas about Testing Processual Theories

465 y1

1 F=

c1

y2

0

1 c2 0

1

y3

0

y4

c3

Fig. A3.

The Process Graph of Example 3.

Table A3.

PS

The PS for Example 3.

Path

c1

c2

c3

y

1 2 3 4

1 0 0 0

– 1 0 0

– – 1 0

y1 y2 y3 y4

AGGREGATION OF PROCESSES AND THEIR DATA Process Addition Let P and Q be any two processes, where the process frameworks are, respectively, YP ¼ FP ðCP Þ and YQ ¼ FQ ðCQ Þ The process addition of processes P and Q is denoted by PQ where, YPQ ¼ YP [ YQ FPQ ¼ FP [ FQ CPQ ¼ CP [ CQ Any pair of processes, P and Q, can be added. To do this, just apply the definition of process addition to every possible case of dependent and parallel processes. Two processes, P and Q, are identical if their process skeletons are identical. This is stronger than saying the CP ¼ CQ because it also means that, for any path, the outcomes are the same for both processes.

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The process junction between processes P and Q is denoted as CP \ CQ. Process junctions are independent of their order of dependence. If a nonempty process junction exists, then there is at least one element of their process skeletons with a defined consideration. If two processes are identical, then their process junction is CJ ¼ CP ¼ CQ. Let P, Q, R, S, etc. be identical processes. Then the addition of any set of them is identical to any of them because if P ¼ Q, then their process skeletons are equal. Let P and Q be any pair of processes whose process skeletons are PSP and PSQ, respectively. P and Q are identical if FP ¼ FQ and, if they are equal, then their process skeletons are identical. This can be summarized as Theorem 1. Theorem 1. Two processes are identical if and only if their process skeletons are equal. Argument: If processes P and Q are identical, then FP ¼ FQ and, therefore, PSP ¼ PSQ. However, if the process skeleton PSP and PSQ are equal, then FP ¼ FQ and, hence, P ¼ Q. Aggregation of Process Framework Data A process framework datum, pfd, is the vector of the states of the considerations, cjAC, and the outcome yjAY, on a path in the PS. Please note that a pfd is defined by the subject of a study, not the theorist who designed process frameworks. Consideration vectors C1 and C2 are comparable if the order and the number of components is identical. Data aggregation requires the addition of comparable process framework data. Comparability of considerations is important for testing a process model. For if the vector of considerations for each pfd are comparable, then the data flow from a common context. It would seem reasonable to suppose that data are comparable if their pfds are comparable and not so, otherwise. When data are not comparable because their underlying pfds are not comparable, it is a mystery what their aggregation means. The next theorem speaks to the case in which the pfds are comparable. Theorem 2. Process framework data can be aggregated as if they flow out of the same process framework if and only if they share the same process framework. Argument: pfd is a vector of the states of its considerations and an outcome. If the pfds flow out of the same process framework, they can be

Some Ideas about Testing Processual Theories

467

added, without changing a PS, because they are comparable. This means that their order and the number of considerations are identical. But, if this is the case, then they share the same process framework. Conversely, if process framework data share the same process framework, then by Theorem 1 they must be comparable. This means that these process framework’s data can be aggregated. An Example of Multiple Processes Occurring within a Small Group A subject of inquiry can involve multiple processes taking place within a group and across groups. For example, in assessing leadership effectiveness, a group could be wrestling over how it is to be organized; how it is to correct errors; improve its performance; how it is to handle an attempt to change the leader, etc. Different groups could be working on different tasks or under different controlling environmental conditions such as in the case of a natural disaster. These different processes have different process frameworks. Hence, the task of aggregating process framework data within a group and across groups is highly problematic, at best. For example, Mackenzie and Bello (1981) published a paper on a laboratory study of leadership effectiveness with four different process frameworks to capture eight scenarios in which groups of laboratory subjects solved simple problems and, at the end of each, rated each others leadership effectiveness. The scale produced two outcomes: Effective and non-effective, as shown in Section 5.2.3. There are 17 considerations and only two outcomes: (1) y1, the person is judged to be an effective leader; and (2) y2, the person is judged not to be an effective leader by the group. The four process frameworks were: (1) the group is selecting its structure; (2) the group is implementing its new structure; (3) the group is correcting the group’s error; and (4) a member helps the group change its structure. The four process frameworks were for the same sets of groups, but each of the models depicts a different process which affects the group’s ratings of individual leadership effectiveness. There were eight different group situations. Even though the 17 considerations were defined over the set of four process frameworks, the process frameworks were not comparable. They did not have the same process framework and by Theorem 2, the pfds cannot be aggregated because each involved a specified vector of considerations and no process framework involved the same considerations as any other model. Also, it should be noted that the rating of a person’s leadership effectiveness could (and did) change over the course of the experiment. Thus, a person’s rating could rise, fall, and stay the same over the course of an experiment.

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TESTING PROCESS FRAMEWORKS Given that group and organizational phenomena are inherently processual in their nature, it would seem reasonable to work toward developing research methods that are in greater conformity with the research phenomena. Because processes and multi-level phenomena can be represented by process frameworks, the basis for conducting processual inquiries must center on using process frameworks. A processual inquiry is an investigation into a phenomenon in terms of its process framework and context. This means specifying the outcomes, the considerations, the process graph, and the context surrounding the object of the inquiry. Furthermore, processual inquiries delimit the processes and those involved in performing the processes by becoming specific. In particular, as we have seen from the Mackenzie and Bello experiments, the same group can engage in different processes within a brief time period (2 h maximum in their experiment). Different groups can engage in different processes at the same time and in the same processes at different times. Luzi and Mackenzie (1982) showed that incentives for different objectives trigger different processes in groups. Thus, it is difficult to understand how one can hope to successfully aggregate intergroup data where there are different contexts, purposes, members, processes, etc. In particular, a theory of a specific set of dyadic relationships between, say, a boss and a subordinate, does not readily admit to crude aggregation of data if different processes are working in different dyads. Accordingly, we need to pick our way through the data aggregation problem with more sensitivity than is the usual practice in the organization sciences. Theorem 3. Process framework data aggregation is undefined if the data are not comparable. Argument: If there are data from one consideration vector C1, and data from another consideration vector C2, unless they are comparable, the order and number of components are different in the two vectors. Adding them would yield a combined pfd of which either would be incomplete. The combined pfd represents a path in some new process skeleton, and the subsets of the pfds for the incomparable data are from other process skeletons. Hence, one would be combining data from different processes and from different paths of a combined new process. In a sense, aggregation data from different processes is akin to adding apples and oranges. pfd is defined by the subject and not by the researcher.

Some Ideas about Testing Processual Theories

469

A pfd represents the subject’s judgments about the state of the considerations and the outcome as he or she sees it. pfp is the theoretical values of its considerations, as defined by the theory, leading to a specific outcome. A pfp is defined by the researcher’s process framework for the process. A scenario for testing a process skeleton consists of a situation in which the subject can judge the values of the considerations and reach a conclusion about the outcome. Each pfp has its own scenario. Thus, if a process framework has n paths, it requires n scenarios to test it fully. Given a scenario related to the process framework to be tested, the role for the subject is to define the pfd and the outcome as he or she sees it. Think of each path as a case of a more encompassing process framework. For each case there is a specified pfp. Note that the pfp and the subject’s pfd are case specific. That is, each case has its own unique pfp and the relevance of pfd is case – or path dependent. Statistical data, on the other hand, are assumed to be path or case independent. That is, all cases are assumed to involve all of the same variables in the same order. The relevance of any variable is assumed to exist across all cases. A statistical datum is the value of a specific variable. Statistical variable data are defined independently of possible cases or paths and claim to be more ‘‘general.’’ The relevance of any statistical variable is assessed to be independent of the underlying possible paths in some process skeleton. In short, aggregation of statistical data implies that there exists a common process from which every datum is a pfd. It is conceivable that this is indeed possible in the special case in which the data come out of a controlled experiment in which the pfp are fixed and the pfd are always comparable. For example, there were eight cases in the Mackenzie and Bello (1981) experiments for the eight situations. There were 660 pfds across 22 experimental groups organized by the case pfds. However, the comparability of data are rarely established and, hence, by Theorem 3, it is unlikely that the data can be aggregated. A test of a process framework consists of matching a subject’s pfd with the pfp in its PS. A test is said to be a hit if the subject’s pfd ¼ pfp. A test is said to be a miss if the subject’s pfd6¼pfp. Theorem 4. Any test is a miss when data come from incomparable processes. Argument: If the processes are incomparable, then they have different sets of considerations and by Theorem 3, the process frameworks cannot be aggregated. If they have different considerations, they have different pfps. But, if they have different pfps, then the pfds will be unequal to the pfps.

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Hence, by definition of a miss, data from incomparable processes will result in a miss. A test which cannot produce a hit is not a test. This leads to the obvious implications as summarized in Theorem 5. Theorem 5. A test of a process framework requires comparable pfd sets. Argument: If the data sets are not comparable then any test is a miss. However, if the data sets are comparable, then they can be compared, case by case (or path by path) in a PS. It is then possible for at least some of the tests to be hits. A miss is said to be confirmed if the original call is not in error. That is, the pfd and the outcome for the subject are verified by checking the data. Any confirmed miss is a counterexample to the PS. Finding a confirmed miss is the object of the test of a PS. Any confirmed miss requires a modification of the PS to get rid of it while not eliminating prior hits. A confirmed hit adds no new information to what was already known.

THE ENHANCEMENT/REDUCTION MODEL FOR TESTING A PROCESS FRAMEWORK Model reduction is the procedure for converting a theoretical analytic process model into its process skeleton. Model reduction fosters empirical research because it clarifies the cases. However, the specifications of the process graph, F, can be ambiguous. For example, x1x2 ¼ x2x1, but x1x26¼x2-x1. The problem of the ambiguity of the ordering between considerations may be possible to reconcile logically. In some cases, for example, there is no ordering problem because the model tested in a linear combination of considerations. In the case that there are multiplicative combinations of considerations (e.g., x1 x2 x3), the existence of combinations such as x1 x2, x2 x3, and x1 x3 will provide clues. For example, if there is a x1 x2 and a x2 x3 term in the model, then probably x1 and x2 precede x3. Some of these ambiguities can be resolved by constructing the process graph, F, while thinking about the process itself. Furthermore, junction points provide clues about ordering. Junction points define ordering and the absence of a junction between considerations means that they are parallel and there is no a priori ordering between them.

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471

Model enhancement is the procedure for converting a process skeleton into a theoretical analytic process model. Section 5.3 on process functions describes an eight-step procedure for doing model enhancement. The model tested is the actual model used to test hypotheses in the analysis of data for a study. Strong inference involves testing the process skeleton, pfd by pfd. A strong inference procedure can result in a test of a process framework. Unfortunately, the model tested in a group and organizational study is usually one that comes from the existing set of models in the researcher’s statistical package. Statistical inference on a process skeleton requires its enhancement prior to being ‘‘tested.’’ Usually, the enhanced ‘‘theoretical analytic process model’’ is not the model tested. Hence, the original theoretical analytic process model remains ‘‘untested.’’ The relationships among the process skeleton, the theoretical analytic process model, and the model tested are illustrated in Fig. A4. Statistical inference on a theoretical analytic process model involves converting it into one that can be tested using available statistical procedures. But, the theoretical analytic process model may no longer be the model tested. If so, then the reduction of both the theoretical model and the model ‘‘tested’’ yield incompatible process skeletons. Therefore, by Theorems 4 and 5, the theoretical analytic process model was never actually tested. Given a process skeleton, assume that it has been enhanced into a theoretical analytic process model. The reduction of this theoretical analytic process model to the process skeleton should yield the original process skeleton. However, converting a theoretical analytic model to the model tested creates problems because the form of the model tested is often restricted by requirements imposed by the use of statistical methods. The reduction of the model tested to the process skeleton may not be identical to

Theoretical Analytic Process Model Enhancement

Conversion Reduction

Model Tested

Process Skeleton Reduction of Model Tested

Fig. A4.

The Enhancement/Reduction Model.

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the reduction of the theoretical analytic process model to its process skeleton. When these two reductions fail to match, by Theorem 5, the theoretical analytic process model has not actually been tested. Please note that if the theoretical model is a process framework, then the reduction to a process skeleton followed by the expansion to the original process framework would result in an identical theoretical model. Consequently, there is a little risk of ‘‘model shifting’’ in this case. On the other hand, whenever strong inference is used, the Process Skeleton ¼ Model Tested. Weak (Statistical) Inference means that reduction of theoretical process model may or may not be reduction of model tested. The fixed theoretical model’s dilemma: There is no way to know if the choice of variables is ever ‘‘correct,’’ for at least four reasons: (1) It may include variables (considerations) that are not relevant; (2) it may exclude those which are those of context; (3) it is too clumsy to test if there are dependences; and (4) different processes can be working in different groups at the same time and in the same group at different times. So, how can one aggregate data to form a statistically adequate sample? The previous five theorems imply that one must aggregate dyadic comparable process framework data dyad by dyad. Forming a group of individuals irrespective of their different process frameworks manages to ignore the specific dyadic relationships embedded in their respective Fs. To illustrate the research issues, let Lj represent the leader of the jth subordinate who has his or her leadership defined with respect to the subordinate (e.g., their relationship can be familial, professional, personal, official, etc.). The ideal case obtains when all Fs are equal for all dyads as shown in Fig. A5a. If the dyadic relationships vary over time or vary with the specific context of each dyad; if different dyads are at different stages of the same process; if different dyads are engaged in other processes at the same time; etc., then one arrives at the ‘‘dog’s breakfast’’ or mess illustrated in Fig. A5b where the Fs need not be identical. Note that a person can be subordinate in rank to another but not necessarily functionally subordinate in the sense that the person identified by ‘‘leader’’ is actually the boss. The technique of forming a new ‘‘dyadic’’ relationship between the set of all designated leaders L ¼ (L1, L2, y, Ln) and all designated subordinates G ¼ (Subordinate 1, Subordinate 2, y, Subordinate n), shown in Fig. A5c, creates real problems in the nature of F, unless the Fi are identical. The need for more degrees of freedom dooms most studies because it usually requires the aggregation of incomparable data. Thus, aggregating data from different dyads to test specific dyadic theories produces processually meaningless test results. In the language of the Fig. A4 (the Enhancement/Reduction

Some Ideas about Testing Processual Theories

L1

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Fig. A5.

(a) The Ideal of Comparable Dyads. (b) The Mess of Non-Comparable Dyads. (c) The Improbable Ideal Case.

Model), the conversion of the theoretical analytic process model to the model tested can have the consequence that model tested’s reduced process skeleton will not be the same process skeleton as the original theoretical analytic process.

MULTI-LEVEL PROCESSUAL TESTING A previous paper in this series of volumes (Mackenzie, 2004a) explored the notion of level. There were basically two classes of levels: the first class are the causal in which a higher level is deemed to be a cause or precursor to another level. The second class are inclusional levels, in which one level is included in another. Mackenzie (2004a) identified five types of causal levels and five types of inclusional levels. Later, in Mackenzie (2004b) the inclusional class was reduced to three types: (1) structural inclusion; (2) levels of

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task aggregation; and (3) levels of interdependence. This paring down was done to emphasize the types of levels for which there can be defined clarity. Examples of all types of process levels are given in both papers. Because leadership processes are inherently multi-level, as are most group and organizational processes, the question of rigorous multi-level research occurs. In Mackenzie (2004a), rigorous multi-level research meets these criteria: (1) The constructs on each level are clearly defined, and (2) There are well-specified linkages among the constructs between each pair of levels. This suggests a classification schema for multi-level research rigor: Most rigorous meets both criteria. Moderately rigorous meets the first but not the second criterion. Weakly rigorous has defined constructs on the lower level but not on the higher, and the second criterion is not met. Least rigorous fails to meet either criteria. This leads to the following speculative theorems (Mackenzie, 2004b). Let Cc ¼ (c1, c2, y,cm) be the state of m constructs on the lower level of a study of bi-level group or organizational phenomena. Let Y ¼ (y1, y2, y, yp) be the state of p constructs on the upper level of the study. Let CL ¼ (cm+1, y cn) be the n–m considerations in F, linking the constructs in Cc with those in Y. Let C ¼ Cc [ CL. With this notation, one has the means of expressing two speculative general conclusions:

Bi-Level Linking Representation Theorem. Given Cc, CL, and Y, the states of constructs on a lower level are linked to those on a higher scale by this equation: Y ¼ FðCÞ Note that this is the same general representation as the process framework, even though the meanings of C, F, and Y need to be reinterpreted. Bi-Level Rigorous Research Theorem. Rigorous multi-level process research can obtain only if there exists a bi-level representation theorem for the constructs used in the study. Note that this means, because of the Bi-Level Linking Representation Theorem, that rigorous multi-level research requires that Y ¼ F (C).

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Thus, these two theorems, taken together, can provide the analytical standard for conducting bi-level research. Mackenzie (2004b) explores some ramifications of these ideas. But, consider the summary:  Group and organizational processes are representable by their process frameworks.  Group and organizational processes are inherently multi-level.  Multi-level processes have the same analytical representation as a new type of process framework. Therefore, processual inquiries at one or more levels require representation in the form of process frameworks. Research ideas not representable as process frameworks are simply unrigorous. Furthermore, any process framework can be rewritten as a process skeleton and process skeletons can be used to compare the model tested and the theoretical analytic process model. When these reduce to different process skeletons, no rigorous test has occurred. In its current state of development, complexity leadership theory, is not well enough defined to use process frameworks. Their constructs are not well defined and their concept of level is not defined. Therefore, complexity leadership theory cannot meet either criteria of rigorous multi-level research. At best, complexity leadership theory is least rigorous, whereas, LAMPE theory of organizational leadership is potentially most rigorous.

ABOUT THE AUTHORS Kerstin Aumann is a doctoral student in the social-organizational psychology program at Teachers College, Columbia University. She received her B.S. at Northwestern University, after which she spent three years working in the Change Communication Specialty Group at Burson-Marsteller, a global communications firm. Her research interests include international human resources management, cross-cultural organizational behavior, and cultural diversity. Marietta L. Baba is dean of the College of Social Science and professor of Anthropology at Michigan State University. She also holds an appointment as adjunct professor of Management in the Eli Broad College of Business. Dr. Baba’s research interests are in the areas of technological innovation, organizational and work group cultures, and their interactions. Xiao-Ping Chen (Ph.D., UIUC) is a professor in the Department of Management and Organization at University of Washington. Her current research interests include decision-making, group dynamics, and cross-cultural management. Her research has appeared in journals such as Academy of Management Review, Academy of Management Journal, and Organizational Behavior and Human Decision Processes. David V. Day is professor of Psychology and director of Graduate Training at the Pennsylvania State University. Effective July 2006, his new position will be professor of Organizational Behavior in the Lee Kong Chian School of Business, Singapore Management University. His research interests are focused on leadership and leadership development. Jaap Dronkers is professor of Social Stratification and Inequality at the European University Institute. His research focuses on unequal educational and occupational attainment, stratification and mobility research, on changes in educational opportunities, and on the effect of parental divorce on their children. More information is available on his homepage at: http://www.iue. it/Personal/Dronkers 479

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Miriam Erez is the Mendes France Professor of Management and Economics in the William Davidson Faculty of Industrial Engineering & Management at the Technion, Israel. Her research focuses on three major areas: work motivation, innovation, and cross-cultural organizational behavior. Erez is fellow of SIOP and of the Academy of Management. In 2002, she received the IAAP Award for Distinguished Scientific Contributions to the International Advancement of Applied Psychology, and in 2005, Erez received the Israel Prize in Management Sciences. Michael P. Farrell is professor and chair of the Department of Sociology at the State University of New York at Buffalo. He has published three books and over 50 articles and book chapters in areas such as Social Psychology, Sociology of the Family, Sociology of Small Groups, Friendship Groups, and Adolescence. Maryann Feldman is the Jeffery S. Skoll Chair in Technical Innovation and Entrepreneurship and professor of Business Economics at the Rotman School of Management, University of Toronto. Her research and teaching interests focus on the areas of innovation, the commercialization of academic research and the factors that promote technological change and economic growth. A large part of Dr. Feldman’s work concerns the geography of innovation – investigating the reasons why innovation clusters spatially and the mechanisms that support and sustain industrial clusters. Richard Groesbeck is a research assistant professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech (or VPI&SU). He earned his PhD in Industrial Engineering at Virginia Tech, an MBA from Case Western Reserve University, and a BS in Engineering from Brigham Young University. Prior to joining the faculty at Virginia Tech, he worked in industry for 20 years in engineering and operations management positions. Joop Hox is professor of Social Science Methodology at Utrecht University. His research interests focus on data quality in social surveys and multi-level modeling. In survey methodology, he has written articles on nonresponse problems and the effects of data collection mode and interviewers on data quality. In multi-level modeling, he has published widely, including an introductory monograph. More information is available on his homepage at: http://www.fss.uu.nl/ms/jh

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Mariann Jelinek is Richard C. Kraemer Professor of Business Strategy in the Mason School of Business at the College of William and Mary in Williamsburg, Virginia. From June 1999 to August 2001, she served as director, Innovation and Organizational Change Program at the National Science Foundation, where she directed solicitation, assessment, and awards for research projects on topics of organizational innovation. She has published five books and more than 40 papers. She has taught strategy at the undergraduate, MBA, executive and doctoral levels, including video and distance formats. Her current research interests include IP, organizational cognition, and other factors affecting innovation. Manuel London is professor and director of the Center for Human Resource Management in the Harriman School for Management at the State University of New York at Stony Brook. His research interests focus on leadership development and how people learn about themselves and others in organizations. Ken Mackenzie went to the University of California at Berkeley where he received a BA in mathematics and a Ph.D. in Business Administration. He is the Emeritus Edmund P. Learned Distinguished Professor at the University of Kansas. He has taught at U.C. Berkeley, Carnegie-Mellon, University of Pennsylvania (Wharton), University of Waterloo, and KU. He has written and edited 19 books and over 100 articles. He is listed in Who’s Who in the America, Who’s Who in Finance and Industry, and is a Fellow of the American Association for the Advancement of Science. Russ Marion is in the Department of Educational Leadership at Clemson University. He is author of The Edge of Organization (1999), Leadership in Education (2001), and ‘‘Leadership in Complex Organizations’’ (The Leadership Quarterly, 2001). He is currently co-editor of forthcoming issues on Complexity Leadership for The Leadership Quarterly and Leadership Horizons: The Series. He has presented on complexity leadership at the Indian Institute of Technology; the Institute for Management Development in Switzerland; and in workshops on destructing complex movements at the US Department of Defense. Steven E. Markham is the Digges Professor of Entrepreneurship and the director of the AT&T Center for Scientific Visualization of Organizations in the Department of Management at Virginia Tech. He received his

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Ph.D. from the University of Buffalo in 1978. He has published more than 120 articles and chapters about improving performance at multiple levels of analysis. Cheri Ostroff is currently a professor of Psychology in the Department of Psychology at the University of Maryland. Her research interests include levels of analysis issues, person–environment fit, and human resource management systems. She has received early career awards for her research from APA and SIOP, and currently serves on the editorial board of several journals. Valerie I. Sessa is assistant professor of Industrial and Organizational Psychology at Montclair State University in New Jersey. Previously, she was a research scientist at the Center for Creative Leadership in Greensboro, North Carolina. Her research interests are in the areas of executive assessment and selection, continuous learning, and group development. Tom A.B. Snijders is a professor of Methodology and Statistics in the Faculty of Behavioral and Social Sciences at the University of Gro¨ningen, The Netherlands. His main research interests are: multi-level analysis, social network analysis, mathematical sociology, item response theory, and social science statistics. He has published four books and over 100 articles in scientific journals and books. Bret Swan is an assistant professor of Information Systems at Brigham Young University (BYU) in Hawaii. He will soon complete a Ph.D. in Management Systems Engineering from Virginia Tech. He holds an MS in Manufacturing Engineering and a B.S. in Mechanical Engineering from BYU. He has more than 15 years of research and consulting experience in knowledge management, business process management, BPM/Workflow modeling and analysis, distributed cognition, and information visualization. Brian W. Tate is a graduate student in Industrial/Organizational Psychology at the Pennsylvania State University. He received his Bachelor of Science degree from Virginia Commonwealth University. His research interests include leadership, selection issues such as adverse impact, and individual difference personality variables. Anne S. Tsui (Ph.D., UCLA) is the Motorola Professor of International Management at Arizona State University. She was the 14th Editor of the

About the Authors

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Academy of Management Journal and a fellow of the Academy. She is the author of a number of award-winning papers and one of most cited researchers in management (21st of the top 100 in 1981–2001). Mary Uhl-Bien is an associate professor in the Department of Management at the University of Central Florida. Her research interests include complexity leadership theory, relational leadership, and leader-member exchange. Her work has appeared in journals such as Academy of Management Journal, Journal of Applied Psychology, Journal of Management, Human Relations, and The Leadership Quarterly. She is on the editorial boards of the Leadership Quarterly and the Academy of Management Journal, and is Senior Editor of The Leadership Horizons Series.