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Complexity leadership in industrial innovation teams: A field study of leading, learning and innovating in heterogeneous teams

ProQuest Dissertations and Theses, 2011
Dissertation
Author: Emilio DeLia
Abstract:
Innovation teams comprised of heterogeneous specialists are prevalent in industrial company innovation systems because these teams are perceived to possess special learning and innovative capabilities. There has been insufficient research on how leadership can help create the dynamics advantageous to learning and innovating in heterogeneous teams. Complexity Leadership Theory endeavors to address this issue (Marion & Uhl-Bien, 2001; Uhl-Bien, Marion, & McKelvey, 2007; Uhl-Bien & Marion, 2009). This study uses the insights from Complexity Leadership Theory and from research on team creativity, team innovation, small group process, group learning and team heterogeneity to develop and test a model of complexity leadership in innovation teams. Complexity leadership, which is examined with regard to the influence shared among the team leaders and the team members, is proposed to be positively linked to innovation team outcomes. The model is tested with quantitative data from a field study of 59 innovation teams from 25 industrial companies and informed as well by qualitative data on 5 teams from 3 companies. Complexity leadership was found to have a positive effect on collaborative learning, innovation enabling behaviors, and perceived performance. The analysis tested the mediation effects of collaborative learning and the existence of a heterogeneity norm on the relationship between complexity leadership and team outcomes. Collaborative learning was found to mediate this relationship and some support was found as well for a mediating effect of the existence of a heterogeneity norm. The expectation that complexity leadership would moderate the effects of job relevant heterogeneity on innovation enabling behaviors and perceived performance received only moderate limited evidence of support.

v TABLE OF CONTENTS

Abstract ................................................................................................................... ii LIST OF TABLES ................................................................................................ vii LIST OF FIGURES ............................................................................................. viii

CHAPTER ONE: INTRODUCTION TO THE STUDY Context and Purpose of the Study ............................................................................1 Theoretical Model ....................................................................................................7 Significance of the Study .........................................................................................8 Organization of the Dissertation ............................................................................10

CHAPTER TWO: LITERATURE REVIEW AND HYPOTHESES Introduction to Complexity Leadership Theory ....................................................11 Complexity Leadership and Innovation Team Outcomes .....................................13 Complexity Leadership and Learning ....................................................................33 Complexity Leadership and Heterogeneity ...........................................................40 Complexity Leadership and Job Relevant Heterogeneity ......................................44 Complexity Leadership and Heterogeneity Norm .................................................51 Chapter Summary ..................................................................................................58

CHAPTER THREE: Methods Introduction ............................................................................................................58 Research Design ....................................................................................................58 Field Setting ...........................................................................................................59 Data Collection and Samples .................................................................................60 Data Analysis .........................................................................................................64 Instrumentation ......................................................................................................68 Hypothesis Testing ................................................................................................84

CHAPTER FOUR: RESULTS Descriptive Statistics and Correlations ..................................................................88 Hypothesis 1 Results ..............................................................................................90 Hypothesis 2 Results ..............................................................................................91 Hypothesis 3 Results ..............................................................................................93 Hypothesis 4 Results ..............................................................................................96 Other Considerations .............................................................................................97

CHAPTER FIVE: DISCUSSION ..........................................................................98 Complexity Leadership and Innovation Team Outcomes ...................................102 Complexity Leadership and Learning ..................................................................104 Complexity Leadership and Heterogeneity .........................................................108 Implications for Management ..............................................................................117 Limitations ...........................................................................................................119 Future Research ...................................................................................................122 Conclusion ...........................................................................................................129

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REFERENCES ....................................................................................................130

VITA ....................................................................................................................172

vii LIST OF TABLES

Table 1: Variables and the Ratio of Teams Meeting r wg and AD Criteria 150

Table 2: Mapping Items in the Complexity Leadership (CL) Scale to Components in Complexity Leadership Theory (CLT) ...........151

Table 3: Factor Coefficients for Complexity Leadership ............................152

Table 4: Factor Coefficients for Collaborative Learning and Heterogeneity Norm ........................................................................153

Table 5: Factor Coefficients for Team Reported Innovation Team Outcomes ..........................................................................................154

Table 6: Factor Coefficients for Stakeholder Reported Innovation Team Outcomes Performance ........................................................155

Table 7: Factor Coefficients of Five Factor Structure Produced by Oblique Rotation ..............................................................................156

Table 8: Means, Standard Deviations, Correlations of Variables and Coefficient Alphas ............................................................................157

Table 9: Results of Standardized Regression Analysis of Complexity Leadership’s Effects ........................................................................158

Table 10: Results of Standardized Regression Analysis of ................................. Collaborative Learning’s Effects ....................................................159

Table 11: Collaborative Learning as Mediator of Complexity Leadership’s Influence on Innovation Team Outcomes ..............160

Table 12: Results of Standardized Regression Analysis of Vertical Complexity Leadership’s Moderation of Job Relevant Heterogeneity ...................................................................................161

Table 13: Results of Standardized Regression Analysis of Shared Complexity Leadership’s Moderation of Job Relevant Heterogeneity ...................................................................................162

Table 14: Heterogeneity Norm as Mediator of Complexity Leadership’s Influence on Innovation Team Outcomes ......................................163

viii LIST OF FIGURES

Figure 1: Model of Complexity Leadership in Innovation Teams ...............164

Figure 2: Study Model and Hypotheses ..........................................................165

Figure 3: Moderation of the Effect of Function Heterogeneity on Stakeholder Perceived Performance by Vertical Complexity Leadership ........................................................................................166

Figure 4: Moderation of the Effect of Function Heterogeneity on Stakeholder Perceived Performance by Shared Complexity Leadership ........................................................................................167

Figure 5: Moderation of the Effect of Education Heterogeneity on Stakeholder Innovation Enabling Behaviors by Shared Complexity Leadership ...................................................................168

Figure 6: Moderation of the Effect of Industry Tenure Heterogeneity on Stakeholder Innovation Enabling Behaviors by Shared Complexity Leadership ...................................................................169

Figure 7: Moderation of the Effect of Industry Tenure Heterogeneity on Stakeholder Perceived Performance by Shared Complexity Leadership ........................................................................................170

Figure 8: Summary of Model Findings ..........................................................171

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CHAPTER ONE INTRODUCTION TO THE STUDY Context and Purpose of the Study Dynamic overarching trends, such as globalization, technological revolution and morphing industry and company demarcations, are compelling industrial organizations to focus on increasing learning and innovating (Eisenhardt, 1989; Jennings & Haughton, 2000; Prusak, 1996). Innovations in new products and in new ways of doing business become imperatives for companies to survive and prosper. In this complex environment, leveraging knowledge assets becomes more critical for success than management of physical assets (Boisot, 1998). The success of an industrial company now rests on its organizational intelligence, that is, its capacity to learn new knowledge (Cohen & Levinthal, 1990) and its creative use of knowledge (McKelvey, 2001; Quinn, Anderson, & Finkelstein, 2002).

Organizational arrangements, such as distributed knowledge networks (Miles, Snow, Matthews, & Miles, 1999) and innovation teams (Farris & Cordero, 2002; Mumford, 2000), which enable the leveraging of intellectual assets, have become commonplace. Within these new arrangements, both the work and the social relationships among people performing the work become more interdependent and more fluid (Zammuto, Griffith, Majchrzak, Dougherty & Faraj, 2007). Leadership must also change to fit the knowledge work of these new arrangements. To avoid limiting knowledge flows and knowledge creation, leadership can no longer operate as the directing mind from the top, nor act as the only bridge across organizational boundaries and knowledge barriers. Historically, conceptions of leadership were necessarily tied to prevalent bureaucratic hierarchical structures, favored stability rather than adaptability, and were oriented to lead for efficiency and control, appropriate to manufacturing (Jones, 2000). A unifying paradigm for recent leadership conceptions is the leader to follower(s) dynamic which emphasizes the symbolic, motivational and inspirational actions of leaders with followers. While their means vary, different

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leadership theories explain how leaders influence followers to align their efforts to accomplish something desired by the leader. Different means include, for example, a leader’s dominating personality and heightened prestige with followers (Hogg, 2001), leader task oriented and relationship oriented behaviors toward followers (Fiedler, 1964; Likert, 1961), fair exchanges between leader and followers (Hollander, 1964), development of valued relationships between leader and followers (Graen & Uhl-Bien, 1995), and leader’s inspirational behavior heightening followers’ emotional involvement and commitment (Bass, 1985). The imbalance of influence inherent in the leader to follower paradigm becomes a stumbling block, however, in knowledge based organizations. Knowledge and ideas residing anywhere, at any level are the critical factors for survival and success and must be permitted the possibility of having influence. Influence is no longer understood to be limited to interactions involving leaders but is seen to occur anywhere at anytime in an organization (Yukl, 2009; Yukl & Falbe, 1990) through interactions involving knowledgeable members whatever their level or location. In response to this, new leadership approaches are emerging that better enable learning and innovation to originate and to be propagated throughout the company (Achtenhagen, Melin, Mullern, & Ericson, 2003; Tichy, 2002). This new type of leadership (Ekvall & Arvonen, 1991, 1994) increases knowledge flows so that new ideas are prompted; reduces adherence to plans so that adaptive decisions can be made; and encourages greater cooperation so that organizational change can occur. Leadership is reconceived as a dynamic for adaptation. Recently, a new theory of leadership, complexity leadership (Marion & Uhl-Bien, 2001; Uhl-Bien, Marion, & McKelvey, 2007), has been articulated to describe leadership which enables learning and innovating in complex environments. Complexity leadership shifts from only considering the dynamic of leader and followers; the dynamics of greater interest are the interactions among heterogeneous agents, groupings of agents or across

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agent networks which generate learning and innovation. Leadership is inherent in such interactions. Here agents refer to insiders and outsiders of organizations who are collectively engaged. Leadership does not refer to an influential person as leader but to interchanges wherein influence is held by different people at different times and for different purposes. Complexity leadership is a form of shared leadership (Carson, Tesluk, & Marrone, 2007; Ensley, Hmieleski, & Pearce, 2006; Pearce, 2004; Pearce & Conger, 2002). In complexity leadership, any agent involved in collective action can manifest the influence dynamics which enable learning and innovation. These dynamics are orchestrations of interaction, interdependence, tension and resonance among heterogeneous agents (Lichtenstein, Uhl-Bien, Marion, Seers, Orton & Schreiber, 2006; Uhl-Bien et al., 2007). Because complexity leadership theory (CLT) has only been recently formulated, little empirical study has been done, particularly, of complexity leadership at the operational level of companies, that is, where products are produced or services provided. This study is designed to address that gap and examines whether complexity leadership enhances organizational capacities to learn and to use knowledge creatively for innovation. In addition, because interaction in and among aggregates of heterogeneous agents is a fundamental dynamic of CLT, this study also examines how the dynamics associated with heterogeneity are influenced when complexity leadership is present. The context for this study is an important consideration which will be dealt with in this introduction because complexity leadership can only be understood in its organizational context. Generally, leadership is tightly bound to its context (Osborn, Hunt & Jauch, 2002) and is mistakenly understood to be an exogenous factor acting on an organization. Rather leadership is bound to organization and is correctly viewed as a complementary element in an organizational system. While this idea is not new (Burns & Stalker, 1961; Katz & Kahn, 1978), CLT seeks to extend leadership theory into organizations exhibiting greater dynamism and complexity. Innovation teams in

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industrial companies have been chosen as the context for this study because they mirror the organizational system context of complexity leadership, complex adaptive systems (CAS). CAS, which are basic units of analysis in complexity science, describe organizational systems which exhibit high levels of adaptability, innovativeness, complexity and dynamism and, consequently, posses better fitness to more complex and dynamic environments (Uhl-Bien et al., 2007). CAS provide an ambidextrous organizational system paradigm (Schneider & Somers, 2006) comprised of both mechanistic elements needed for exploitation and organic elements conducive to exploration (He & Wong, 2004; March, 1991; Van den Bosch, Volberda, & de Boer, 1999). The ability of CAS to evolve depends upon their mix of dynamism and order (Kaufman, 1995). Too much dynamism and the system can tip into chaos. Chaos is a state of disturbance in which a system cannot maintain any pattern of behavior and can happen when a system continually overreacts to even small environmental changes. For example, overly organic organizations may readily create adaptive responses but lack structures that can stabilize a useful adaptation into routines and repeatable behavior patterns. Contrarily, organizations comprised of many ordered elements, interrelated within ordered patterns, are frozen so that only small system changes can occur even when confronted by large forces. Mechanistic systems are best at routinizing behaviors but lack structures to adapt routines when conditions change. The blend of dynamism and order make CAS particularly capable of successful adaptation (Kaufman, 1991).Through dynamism, a repertoire of responses is created and order provides buffering which allows for an accumulation of potentially useful adaptations available to respond to environmental pressures. Ambidexterity, that is blends of ordered and dynamic elements, is constituent of CAS. Industrial company innovation teams exhibit the qualities of CAS and are the organizational context for this study. Industrial companies in their quest to improve their ability to innovate have adopted the use of project teams as a principle organizational

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arrangement within their innovation systems (Farris & Cordero, 2002; Mumford, 2000). Hurdles inherent in the ordered organization of industrial companies require special systemic arrangements advantageous to innovation. Formal hierarchical structures tend to impede the dissemination of knowledge helpful to innovation and the sharing of innovative ideas and outcomes throughout the organization (Dougherty & Hardy, 1996). People and knowledge which need to be linked are not; criteria within different power bases are inconsistent, stopping collective innovative actions; and resources required for new development stay bound up in old routines (Dougherty & Hardy, 1996). Innovation teams possess the dynamism to expand the sources of new ideas, to facilitate communication across disciplines, functions, and organizations, and to speed the development of innovation from initiation to commercial success (Guimera, Uzzi, Spiro, & Amaral, 2005; Reagans, Zuckerman, & McEvily, 2004). Innovation teams overcome the de-contextualization of knowledge which occurs when knowledge pools are held in separate groups (Bechky, 2003) by bringing people possessing the different pools of knowledge together for mutual learning. Teams are particularly adept at learning and are the fundamental structure used for learning in organizations (Edmondson, 2002; Senge, 1994). Innovation teams provide the social contexts for interchange, learning and co- creation which expand opportunities for collective creativity (Hargadon & Bechky, 2006). Innovation teams are important vehicles for industrial companies to realize ambidexterity when the teams are constituted with members representing ordered functions and knowledge disciplines who are brought together for creative interaction. Innovation teams have been researched extensively and the findings reveal characteristics and dynamics reflective of CAS. Heterogeneity of members is the most consistent distinguishing attribute of innovations teams (Brown & Eisenhardt, 1995) and mirrors the heterogeneity among agents in CAS. According to theory, CAS are fit to their environment only when their internal complexity equals the complexity on their environment (Schneider & Somers, 2006). Similarly, innovation teams are populated

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with specialists possessing varied knowledge reflecting the complexity associated with the targeted innovation (Clark & Fujimoto, 1990; Quinn, 1985; Takeuchi & Nonaka, 1986) and with the different functional skills and competencies which must be synergized for both problem solving and implementation (Cooper, 1979; Cooper & Kleinschmidt, 1987; 1993). CAS are characterized by self organization, that is, structure and interconnections are fluid and changeable and are not simply determined by the environment but dynamically emerge from interaction of system elements. Innovation teams operate autonomously within the larger system of the company (Clark & Fujimoto, 1990; Quinn, 1985; Takeuchi & Nonaka, 1986) and invent routines that do not follow usual roles and relationships (Dougherty, 1990 & 1992; Dougherty & Corse, 1997). Interaction and interdependency among agents are central to the functioning of CAS and are productive of creativity and learning (Uhl-Bien et al., 2007). In their meta-analysis of 104 quantitative studies of team innovation, Hülsheger, Anderson and Salgado (2009) found that team innovativeness increased both when members engage in interactive communications among themselves and with outsiders and when individual member achievement is interdependent with achievement of other team members. Innovation performance improves when teams increase internal interaction (Dougherty, 1990; 1992; Dougherty & Corse, 1997) and external interaction (Ancona & Caldwell, 1990; 1992b). Because they typify CAS, innovation teams are an appropriate context to study complexity leadership. Complexity leadership fits the new dynamic and complex organization systems of knowledge era industrial companies. CLT addresses leadership of ambidextrous organizational systems, such as CAS, by articulating three focuses of complexity leadership (Uhl-Bien et al., 2007). The first focus is on the management of ordered elements accomplished through planning, organizing and controlling (Fayol, 1949). The second focus addresses leading the dynamic elements arising from adaptive, creative, and learning interactions. The third focus attends to the enabling of the conditions which

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catalyze learning, creative and adaptive interactions and to the managing of the entanglements between the ordered and dynamic elements in the system. Complexity leadership relates to the more subtle and less controlling type of leadership which has been observed on innovation teams (Cooper & Kleinscmidt, 1987; Zirger & Maidique, 1990). Subtle leadership facilitates self organization which allows for members’ roles to shift as developments occur and reconciles the freedom required for creative processes and the alignment necessary to ensure the innovation advances organizational purposes (Dougherty, 1992). Complexity leadership facilitates the dynamics conducive to innovating and to learning, which in turn also aids innovation. The potential creative value of the heterogeneity of the team is actualized in leadership facilitated interactions. This study is designed to examine complexity leadership in its theorized optimal context, CAS, and to test whether its theorized influences are present. Theoretical Model The theoretical model examined in this dissertation is presented in Figure 1. In this model, complexity leadership manifested by the designated team leader and the members of the team (i.e., vertical leadership and shared leadership: Pearce & Conger, 2002; Ensley et al., 2006; Carson et al., 2007) positively influences innovation team outcomes, that is, innovation enabling behaviors and perceived performance. Complexity leadership enables the dynamics of interaction and interdependency conducive to team innovation and team learning (Uhl-Bien et al., 2007). Team learning is collaborative learning, because members learn interdependently from one another. Collaborative learning increases individual and group learning (Johnson, Johnson, & Anderson, 1978) and involves the open sharing of the deeper principles, assumptions, and metaphors associated with knowledge held by members (Cronin & Weingart, 2007; Roschelle, 1992). Collaborative learning entails purposeful attentive interactions, members teaching and learning, and divergent and convergent

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thinking (Janz & Prasarnphanich, 2003). Collaborative learning mediates the relationship between complexity leadership and innovation team outcomes. Job relevant heterogeneity, attributes such as, job role, function, educational background and tenure, is reflective of the knowledge based heterogeneity of CLT and has been shown to have a generally positive but inconsistent influence over innovation performance (Hülsheger et al., 2009) with a positive or negative relationship depending upon how attitudes and dynamics moderate the relationship (van Kippenberg, De Dreu & Homan, 2004). Complexity leadership catalyzes dynamics which engage member differences and increase likelihood that differences will have greater beneficial influence on creative and innovative performance (Uhl-Bien et al., 2007). Complexity leadership moderates the relationship between job relevant heterogeneity and innovation team outcomes. Complexity leadership theory anticipates in CAS a heterogeneity norm which leverages differences among agents and has far reaching effects on dynamics (Marion & Uhl-Bien, 2001). In innovation teams, the heterogeneity norm is a team pattern of respecting, engaging and leveraging job relevant heterogeneity. Norms which align member interaction behavior are established early in team formation, and once the heterogeneity norm is formed, less leadership intervention is required regarding heterogeneity (Jassawalla & Sashittal, 2002; Taggar & Ellis, 2007). The heterogeneity norm mediates the relationship between complexity leadership and innovation team outcomes. ----- Insert Figure 1 Here ----- Significance of the Study This study adds to research in leadership in several ways. Its primary contribution is toward understanding complexity leadership at the operational level of organizations,

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that is the level of producing products or providing services (Uhl-Bien et al., 2007). Because of the relative newness of complexity leadership theory, few studies have been done to test or demonstrate that the theorized leadership is capable of influencing the learning and innovation performance of CAS. Consequently, little field testing of the theory has been done. This early stage of theory development provides a rich and open field for this study to explore. Complexity leadership theorists suggest research strategies that address the difficulties associated with capturing the complex dynamics of leadership in CAS and that emphasize qualitative ethnographic approaches and the use of simulation and other mathematical modeling as prime research approaches (Hazy, 2007; Marion & Uhl-Bien, 2001; Uhl-Bien et al., 2007). This study, which is primarily quantitative field research supported by a qualitative study, is designed to provide a bridge between the existing research of leadership and complexity leadership. This study’s theory development and research methods rely heavily on existing research literatures of team creativity, innovation, small team process, team learning, team leadership, and team diversity. This study’s goal is to increase the potential synergies between complexity leadership and other research disciplines. The second contribution of this study will be to our understanding of leadership in innovation teams. A gap exists in our descriptions of innovation team leadership with little research delineating leadership’s role in the internal team dynamics (Brown & Eisenhardt, 1995). This observed gap is reinforced by Hülsheger et al.’s (2009) meta- analysis of 104 quantitative studies of innovation teams. Leadership was not identified in their review of the extensive empirical team innovation literature as one of the team level variables influencing innovation performance. This current study will provide new insights into leadership of innovation teams that will have value not only to researchers but also to industrial companies, especially given the dominate role innovation teams have in their innovation systems. When complexity leadership is shown to have the influences on learning and innovating purported, new direction will be provided to

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industrial companies for leadership training and development of innovation team leaders and members. Additional value will be provided by this study to research on heterogeneity, which has been developed in the diversity literature. In this literature, the contradictory effects, both positive and negative, of diversity on team performance have led to the theorizing of moderating factors. Because diversity on teams, for example, job relevant heterogeneity, has been observed to have different, even opposite, effects in different studies, there is a need to separate the effects of diversity from the dynamics that may emerge from diversity. Intervening moderating variables, such as a positive or negative bias toward diversity, have been proposed to influence the relationship between diversity and performance (van Knippenberg et al., 2004). This study’s heterogeneity norm reflects a positive bias toward heterogeneity and, because positive bias is an understudied phenomenon (van Knippenberg & Schippers, 2007), this research will shed new light on potential moderating factors associated with heterogeneity. Organization of the Dissertation The study upon which this dissertation is based is presented according to the following outline. Chapter Two provides the literature review for the hypotheses tested in the study. The literature review uses empirical and theoretical research to support the proposed hypotheses summarized in the theoretical model shown in Figure 1. Chapter Three outlines the methods used to test the hypotheses presented in Chapter Two. The methods include the research design, field setting, participants and samples, measures used to operationalize the constructs in this study, data collection and data analysis. In Chapter Four, the results of the empirical hypothesis tests for this study are presented. A concluding discussion of the study’s findings, strengths and limitations, and directions for future research is presented in Chapter Five.

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CHAPTER TWO LITERATURE REVIEW AND HYPOTHESES Introduction to Complexity Leadership Theory Complexity leadership theory (CLT) asks that the paradigm that has held sway over leadership theory and research for some time be reshaped in light of new understanding of how complex systems learn, adapt and innovate to maintain fitness within complex environments. A shift of paradigm is called for because the context of leadership both inside companies and in the environments experienced by companies has greater complexity, that is, greater dynamism and greater number of disparate system elements needing to be interwoven. While traditional hierarchical views of leadership were relevant in bureaucratic organizations seeking consistency and predictability, the upheavals arising from the knowledge explosion, continual technology revolutions, globalization, and other complexity creating factors, require leadership that is primarily an enabler of change and adaptation in organizational systems. Conceptions of organizations and leadership must change simultaneously in order to grasp the organizational imperatives required now. Within CLT, organizational systems are not bounded entities but viewed as complex adaptive systems (CAS) made up of heterogeneous agents or elements which interact and mutually affect each other, and from this dynamic, novelties arise which change the system as a whole ( Marion & Uhl-Bien, 2001). Previously, leadership would have been envisioned to be the force organizing and controlling these dynamics with the aim of producing organizational goals. Research in the leadership of innovation supports the need to change this view. Traditional top-down leadership providing direction and control limits flexibility and experimentation and

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reduces innovativeness (Amabile, 1997, 1998). CLT theorizes leadership as an adaptive dynamic, enabling the dynamics of system agents to interact through self organizing and to create emergent learning, adaptation and innovation. Complexity leadership is leadership of aggregates (Marion & Uhl-Bien, 2001). Complexity leadership differs from recent leadership theories which focus on the relationship between leaders and followers and relies heavily on dyadic, that is, leader and follower, influence constructs and research techniques. The aggregates, to which complexity leadership is attuned, are groups of interacting actors who operate with a sense of commonality and, at a higher level, meta-aggregates of interacting groups linked by some form of interdependence. The important feature of these aggregates and meta- aggregates is their propensity for emergence, that is, pattern and order not predicted by previous conditions arising from the multiplicity of interactions within the complex system. Emergence happens in aggregates as conflicting constraints and preferences clash and are resolved when correlation is created. One task of complexity leadership is to enable dynamics which enhance emergence of learning and innovation in aggregates. A second task is to enable the diffusion of the learning and innovation which has emerged into meta-aggregates. The shift in complexity leadership from dyadic concerns to aggregate concerns is central to the handling of complexity. As bureaucratic management was formulated to lead predictable and controlled organizations, complexity leadership leads complex organizations in complex environments and must not only attend to the challenges of managing ordered organizational elements but also to challenges of creating learning, innovation and adaptation in dynamic system elements. A

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more detailed description of the functions of complexity leadership given in the next section will clarify the relationship between complexity leadership and innovation. Complexity Leadership and Innovation Team Outcomes A potent source of change and adaptation in contemporary companies is innovation which is purposively pursued through organized innovation systems. Innovation can relate to products and services, or processes and practices (Slappendel, 1996). Innovation is the generation of new knowledge with special attention to its usefulness and, subsequently the practical application or implementation of the knowledge (Camison-Zornoza, Lapierdra-Alcami, Segarra-Cipres & Boronat-Navarro, 2004). Innovation is creativity with an organizational purpose because innovation by definition entails the application or implementation of the results of creativity. In industrial companies, innovation is a team activity and the role of leadership in shaping team dynamics for innovation is essential (Jassawalla & Sashittal, 2002). Within CLT, complexity leadership is a complex dynamic comprised of three functions: administrative leadership, adaptive leadership and enabling leadership (Uhl- Bien et al., 2007). Together, these three functions permit organizations to take advantage of the learning and innovation potential of CAS, such as the organizational systems of innovations teams, within the typically bureaucratic contexts of industrial companies. The following builds on the theory of complexity leadership, its functions relative to team innovation, and how existing research literature on group creativity, innovation teams, group conflict, and group leadership can be used to explain the relationships between complexity leadership with innovation and learning. Some of these cited studies

Full document contains 182 pages
Abstract: Innovation teams comprised of heterogeneous specialists are prevalent in industrial company innovation systems because these teams are perceived to possess special learning and innovative capabilities. There has been insufficient research on how leadership can help create the dynamics advantageous to learning and innovating in heterogeneous teams. Complexity Leadership Theory endeavors to address this issue (Marion & Uhl-Bien, 2001; Uhl-Bien, Marion, & McKelvey, 2007; Uhl-Bien & Marion, 2009). This study uses the insights from Complexity Leadership Theory and from research on team creativity, team innovation, small group process, group learning and team heterogeneity to develop and test a model of complexity leadership in innovation teams. Complexity leadership, which is examined with regard to the influence shared among the team leaders and the team members, is proposed to be positively linked to innovation team outcomes. The model is tested with quantitative data from a field study of 59 innovation teams from 25 industrial companies and informed as well by qualitative data on 5 teams from 3 companies. Complexity leadership was found to have a positive effect on collaborative learning, innovation enabling behaviors, and perceived performance. The analysis tested the mediation effects of collaborative learning and the existence of a heterogeneity norm on the relationship between complexity leadership and team outcomes. Collaborative learning was found to mediate this relationship and some support was found as well for a mediating effect of the existence of a heterogeneity norm. The expectation that complexity leadership would moderate the effects of job relevant heterogeneity on innovation enabling behaviors and perceived performance received only moderate limited evidence of support.